Datasets:
codeparrot
/
github-jupyter

Dataset card Data Studio Files Community
2
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pelson/python-stratify
INTRO.ipynb
1
248405
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Stratify\n", "\n", "## Vertical interpolation for numerical weather prediction (NWP) model data\n", "\n", "Whilst this is not the only use for the ``stratify`` package, NWP vertical interpolation was the motivating usecase for the package's creation.\n", "\n", "In its simplest form, vertical interpolation ammounts to a 1d interpolation at each grid-point. Whilst some more sophistication exists for a number of interpolators, ``stratify`` can be seen as an optimisation to vectorize these interpolations beyond naïve nested for-loops.\n", "\n", "#### Data setup\n", "In order to setup the problem, let's manufacture some reasonably realistic NWP data.\n", "\n", "First, let's randomly generate some orography (or, if this were an ocean model, bathymetry) that we can use for our model:" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import numpy as np\n", "\n", "nx, ny = 6, 3\n", "\n", "np.random.seed(0)\n", "orography = np.random.normal(1000, 600, size=(ny, nx)) - 400\n", "sea_level_temp = np.random.normal(290, 5, size=(ny, nx))" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.figure.Figure at 0x7fbc206f6a20>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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RW4yDHnr7YphSTD8leoufebopRaxTihiCcltv3zJ6orcvnimN+TJQnyKWxryL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\nV+pev5q3y+bkXP9fxbbZHfh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"text/plain": [ "<matplotlib.figure.Figure at 0x7fbc206f69e8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Now visualise:\n", "\n", "import matplotlib.pyplot as plt\n", "\n", "plt.set_cmap('viridis')\n", "fig = plt.figure(figsize=(8, 4))\n", "\n", "plt.subplot(1, 2, 1)\n", "plt.pcolormesh(orography)\n", "cbar = plt.colorbar(orientation='horizontal',\n", " label='Orography (m)')\n", "# Reduce the maximum number of ticks to 5.\n", "cbar.ax.xaxis.get_major_locator().nbins = 5\n", "\n", "plt.subplot(1, 2, 2)\n", "plt.pcolormesh(sea_level_temp)\n", "cbar = plt.colorbar(orientation='horizontal',\n", " label='Sea level temperature (K)')\n", "# Reduce the maximum number of ticks to 5.\n", "cbar.ax.xaxis.get_major_locator().nbins = 5\n", "\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Next, let's define a vertical coordinate system that minimises missing data values, and gives good resolution at the (orographic) surface.\n", "\n", "To achieve this we invent a scheme where the \"bottom\" of the model closely follows the orography/bathymetry, and as we reach the \"top\" of the model we get levels of approximately constant height." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [], "source": [ "nz = 9\n", "\n", "model_levels = np.arange(nz)\n", "\n", "model_top = 5000 # m\n", "\n", "# The proportion of orographic influence on the model altitude. In this case,\n", "# we define this as a log progression from full influence to no influence.\n", "sigma = 1.1 - np.logspace(-1, np.log10(1.1), nz)\n", "\n", "# Broadcast sigma so that when we multiply the orography we get a 3D array of z, y, x.\n", "sigma = sigma[:, np.newaxis, np.newaxis]\n", "\n", "# Combine sigma with the orography and model top value to\n", "# produce 3d (z, y, x) altitude data for our \"model levels\".\n", "altitude = (orography * sigma) + (model_top * (1 - sigma))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Our new 3d array now represents altitude (height above *sea* surface) at each of our \"model levels\".\n", "Let's look at a cross-section of the data to see how these levels:" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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HB4cG648dOxZ/f39WrVrFDTfcwKpVqwgICGDUqFGNOnZLWL16dZ1zrS8Jq4/d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\nIpdc6pDF6ZzNJRIRpVQAEMfHXSULlVLJQKvWuhr4CfCSUuo0cB74LnA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"text/plain": [ "<matplotlib.figure.Figure at 0x7fbc0c0130b8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.fill_between(np.arange(6), np.zeros(6), orography[1, :],\n", " color='green', linewidth=2, label='Orography')\n", "\n", "plt.plot(np.zeros(nx),\n", " color='blue', linewidth=1.2,\n", " label='Sea level')\n", "\n", "for i in range(9):\n", " plt.plot(altitude[i, 1, :], color='gray', linestyle='--',\n", " label='Model levels' if i == 0 else None)\n", "\n", "plt.ylabel('altitude / m')\n", "plt.margins(0.1)\n", "plt.legend()\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To recap, we now have a model vertical coordinate system that maximises the number grid-point locations close to the orography. In addition, we have a 3d array of \"altitudes\" so that we can relate any phenomenon measured on this grid to useful vertical coordinate information." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's now define the temperature at each of our x, y, z points. We use the [International Standard Atmosphere lapse rate](https://en.wikipedia.org/wiki/International_Standard_Atmosphere) of $ -6.5\\ ^{\\circ}C\\ /\\ km $ combined with our sea level standard temperature as an appoximate model for our temperature profile." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [], "source": [ "lapse = -6.5 / 1000 # degC / m\n", "temperature = sea_level_temp + lapse * altitude" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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Neg0o7vOMmrQHoybvUVUD+vTcWFX+jnx5xntzre+gA5/Jtb7PL31nbnXt3XdJ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\nWZPwel4+u+fr+4BUHYqEj6cUcfd9ZraU6HGVbm2/6x4GDKs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"text/plain": [ "<matplotlib.figure.Figure at 0x7fbc0c004470>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from matplotlib.colors import LogNorm\n", "\n", "fig = plt.figure(figsize=(6, 6))\n", "norm = plt.Normalize(vmin=temperature.min(), vmax=temperature.max())\n", "\n", "for i in range(nz):\n", " plt.subplot(3, 3, i + 1)\n", " qm = plt.pcolormesh(temperature[i], cmap='viridis', norm=norm)\n", "\n", "plt.subplots_adjust(right=0.84, wspace=0.3, hspace=0.3)\n", "cax = plt.axes([0.85, 0.1, 0.03, 0.8])\n", "plt.colorbar(cax=cax)\n", "plt.suptitle('Temperature (K) at each \"model level\"')\n", "\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Restratification / vertical interpolation\n", "\n", "Our data is in the form:\n", "\n", " * 1d \"model level\" vertical coordinate (z axis)\n", " * 2 x 1d horizontal coordinates (x, y)\n", " * 3d \"altitude\" variable (x, y, z)\n", " * 3d \"temperature\" variable (x, y, z)\n", " \n", "Suppose we now want to change the vertical coordinate system of our variables so that they are on levels of constant altitude, not levels of constant \"model levels\":" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": true }, "outputs": [], "source": [ "target_altitudes = np.linspace(700, 5500, 5) # m" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If we visualise this, we can see that we need to consider the behaviour for a number of situations, including what should happen when we are sampling *below the orography*, and when we are *above the model top*." ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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LHGP//v1148aNTcoyMjL0Rx99pBs3bqxtbW111apV9UsvvaQTExONMZ07d9ZN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\nH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"text/plain": [ "<matplotlib.figure.Figure at 0x7fbc0b4771d0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(7, 5))\n", "plt.fill_between(np.arange(6), np.zeros(6), orography[1, :],\n", " color='green', linewidth=2, label='Orography')\n", "\n", "for i in range(9):\n", " plt.plot(altitude[i, 1, :],\n", " color='gray', lw=1.2,\n", " label=None if i > 0 else 'Source levels \\n(model levels)')\n", "for i, target in enumerate(target_altitudes):\n", " plt.plot(np.repeat(target, 6),\n", " color='gray', linestyle='--', lw=1.4, alpha=0.6,\n", " label=None if i > 0 else 'Target levels \\n(altitude)')\n", "\n", "plt.ylabel('height / m')\n", "plt.margins(top=0.1)\n", "plt.legend()\n", "plt.savefig('summary.png')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The default behaviour depends on the scheme, but for linear interpolation we recieve NaNs both below the orography and above the model top:" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import stratify\n", "\n", "target_nz = 20\n", "target_altitudes = np.linspace(400, 5200, target_nz) # m\n", "\n", "new_temperature = stratify.interpolate(target_altitudes, altitude, temperature,\n", " axis=0)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "With some work, we can visualise this result to compare a cross-section before and after. In particular this will allow us to see precisely what the interpolator has done at the extremes of our target levels:" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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iqqsLST7tAmy2aL/J4U5S4kiOFW5m3e5XiQrtgxAGiisOEBGeRkryOL9HGQBM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\nPIgENC9D/D8aho5N0h0TQGgO9DZqz0zcx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"text/plain": [ "<matplotlib.figure.Figure at 0x7fbc0b440550>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ax1 = plt.subplot(1, 2, 1)\n", "plt.fill_between(np.arange(6), np.zeros(6), orography[1, :],\n", " color='green', linewidth=2, label='Orography')\n", "cs = plt.contourf(np.tile(np.arange(6), nz).reshape(nz, 6),\n", " altitude[:, 1],\n", " temperature[:, 1])\n", "plt.scatter(np.tile(np.arange(6), nz).reshape(nz, 6),\n", " altitude[:, 1],\n", " c=temperature[:, 1])\n", "\n", "plt.subplot(1, 2, 2, sharey=ax1)\n", "plt.fill_between(np.arange(6), np.zeros(6), orography[1, :],\n", " color='green', linewidth=2, label='Orography')\n", "plt.contourf(np.arange(6), target_altitudes,\n", " np.ma.masked_invalid(new_temperature[:, 1]),\n", " cmap=cs.cmap, norm=cs.norm)\n", "plt.scatter(np.tile(np.arange(nx), target_nz).reshape(target_nz, nx),\n", " np.repeat(target_altitudes, nx).reshape(target_nz, nx),\n", " c=new_temperature[:, 1])\n", "plt.scatter(np.tile(np.arange(nx), target_nz).reshape(target_nz, nx),\n", " np.repeat(target_altitudes, nx).reshape(target_nz, nx),\n", " s=np.isnan(new_temperature[:, 1]) * 15, marker='x')\n", "\n", "plt.suptitle('Temperature cross-section before and after restratification')\n", "plt.show()" ] } ], "metadata": { "kernelspec": { "display_name": "Python [conda env:pre-proc]", "language": "python", "name": "conda-env-pre-proc-py" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 1 }
bsd-3-clause
John-Keating/ThinkStats2
code/chap04ex.ipynb
1
54021
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "Exercise from Think Stats, 2nd Edition (thinkstats2.com)<br>\n", "Allen Downey\n", "\n", "Read the pregnancy file." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "nsfg.py:42: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame\n", "\n", "See the the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n", " df.birthwgt_lb[df.birthwgt_lb > 20] = np.nan\n" ] } ], "source": [ "%matplotlib inline\n", "\n", "import nsfg\n", "preg = nsfg.ReadFemPreg()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Select live births, then make a CDF of <tt>totalwgt_lb</tt>. " ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import thinkstats2 as ts\n", "\n", "live = preg[preg.outcome == 1]\n", "\n", "wgt_cdf = ts.Cdf(live.totalwgt_lb, label = 'weight')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Display the CDF." ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x10b5fc650>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "<matplotlib.figure.Figure at 0x10b48f790>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import thinkplot as tp\n", "\n", "tp.Cdf(wgt_cdf, label = 'weight')\n", "tp.Show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Find out how much you weighed at birth, if you can, and compute CDF(x). " ] }, { "cell_type": "code", "execution_count": 44, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "0.81422881168400085" ] }, "execution_count": 44, "metadata": {}, "output_type": "execute_result" } ], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If you are a first child, look up your birthweight in the CDF of first children; otherwise use the CDF of other children." ] }, { "cell_type": "code", "execution_count": 59, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "0.79657754010695192" ] }, "execution_count": 59, "metadata": {}, "output_type": "execute_result" } ], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Compute the percentile rank of your birthweight" ] }, { "cell_type": "code", "execution_count": 46, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "81.422881168400082" ] }, "execution_count": 46, "metadata": {}, "output_type": "execute_result" } ], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Compute the median birth weight by looking up the value associated with p=0.5." ] }, { "cell_type": "code", "execution_count": 45, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "7.375" ] }, "execution_count": 45, "metadata": {}, "output_type": "execute_result" } ], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Compute the interquartile range (IQR) by computing percentiles corresponding to 25 and 75. " ] }, { "cell_type": "code", "execution_count": 47, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(6.5, 8.125)" ] }, "execution_count": 47, "metadata": {}, "output_type": "execute_result" } ], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Make a random selection from <tt>cdf</tt>." ] }, { "cell_type": "code", "execution_count": 48, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "7.0" ] }, "execution_count": 48, "metadata": {}, "output_type": "execute_result" } ], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Draw a random sample from <tt>cdf</tt>." ] }, { "cell_type": "code", "execution_count": 49, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[6.25, 5.1875, 8.1875, 6.5, 7.9375, 6.6875, 5.75, 6.5625, 7.8125, 5.25]" ] }, "execution_count": 49, "metadata": {}, "output_type": "execute_result" } ], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Draw a random sample from <tt>cdf</tt>, then compute the percentile rank for each value, and plot the distribution of the percentile ranks." ] }, { "cell_type": "code", "execution_count": 50, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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fAwcOLPlccVs0dXV1Gh8f18TEhK5fv67+/n41NzdHHPPee+9px44dev3111VRUbHkwVgZ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\nAFgUAQ8AFkXAA4BF/T8RiQJEMgtbWgAAAABJRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x62826d0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Generate 1000 random values using <tt>random.random()</tt> and plot their PMF." ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import random\n", "random.random?" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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AdHdfaWY3RdunufscMxttZmVANXBDqG3U9f3AU2Y2kcbvIq6OyqcB083sbRpD7OfuvuII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\nQSEiIkEKChERCVJQiIhIkIJCRESC/gcIxG+i9f/kaAAAAABJRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x103a08b10>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "<matplotlib.figure.Figure at 0x1063ba3d0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import random\n", "\n", "thousand = [random.random() for x in range(1000)]\n", "thousand_pmf = ts.Pmf(thousand, label = 'rando')\n", "tp.Pmf(thousand_pmf, linewidth=0.1)\n", "tp.Show()" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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OR8SH2zlfuzT5+1GJiF9GxO6IqLZ5xLZp4vdjVkRsj4gna2vx8Q6M2RYR8fWIeCEifjXG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\nAAAAAElFTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x10b52af90>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "<matplotlib.figure.Figure at 0x10b4b6890>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "t_hist = ts.Hist(thousand)\n", "tp.Hist(t_hist, label = \"rando\")\n", "tp.Show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Assuming that the PMF doesn't work very well, try plotting the CDF instead." ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x10b315c90>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "<matplotlib.figure.Figure at 0x10b315750>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "thousand_cdf = ts.Cdf(thousand, label='rando')\n", "tp.Cdf(thousand_cdf)\n", "tp.Show()" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import scipy.stats\n", "scipy.stats?" ] }, { "cell_type": "code", "execution_count": 64, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "0.5" ] }, "execution_count": 64, "metadata": {}, "output_type": "execute_result" } ], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.10" } }, "nbformat": 4, "nbformat_minor": 0 }
gpl-3.0
evgeniiegorov/evgeniiegorov.github.io
_posts/Seminar+5+Trees+Bagging+%28with+Solutions%29.ipynb
1
514575
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Seminar 5: Tree and Bootstrap Aggregation\n", "Course: MA06018, Machine Learning by professor Evgeny Burnaev <br\\>\n", "Author: Evgenii Egorov" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Table of contents:\n", "* [Tree](#tree)\n", " * [Simple Stamp](#stamp)\n", " * [Limitations of trees](#lim)\n", "* [Bagging](#bag)\n", " * [Bootstrap](#bootbag)\n", " * [Random Forest](#rf)" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<a id='tree'></a>\n", "# Tree" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's play with a toy example and write our own decion-regression stamp. First, consider following toy dataset: " ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "image/png": 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Orpydifd5GvT5XNzW1c84Kzfw9jLtv9f7svTQw0k+VFUnk9yR5Et7BV0AYPhGPbx2kkxy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\nH8casImCOXsZVkgb9BhPe3G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"text/plain": [ "<matplotlib.figure.Figure at 0x10f6a8128>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "X_train = np.linspace(0, 1, 100)\n", "X_test = np.linspace(0, 1, 1000)\n", "\n", "@np.vectorize\n", "def target(x):\n", " return x > 0.5\n", "\n", "Y_train = target(X_train) + np.random.randn(*X_train.shape) * 0.1\n", "Y_test = target(X_test) + np.random.randn(*X_test.shape) * 0.1\n", "\n", "plt.figure(figsize = (16, 9));\n", "plt.scatter(X_train, Y_train, s=50);\n", "plt.title('Train dataset');\n", "plt.xlabel('X');\n", "plt.ylabel('Y');" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<a id='stamp'></a>\n", "### Task 1" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To define tree (even that simple), we need to define following functions:\n", "\n", "*** Loss function *** \n", "For regression it can be MSE, MAE .etc We will use MSE\n", "\n", "$$\n", "\\begin{aligned}\n", "& y \\in \\mathbb{R}^N \\\\\n", "& \\text{MSE}(\\hat{y}, y) = \\dfrac{1}{N}\\|\\hat{y} - y\\|_2^2\n", "\\end{aligned}\n", "$$\n", "\n", "Note, that for MSE optimal prediction will be just mean value of the target.\n", "\n", "*** Gain function *** \n", "We need to select over different splitting by comparing them with their gain value. It is also reasonable to take into account number of points at the area of belongs to the split.\n", "\n", "$$\n", "\\begin{aligned}\n", "& R_i := \\text{region i; c = current, l = left, r = right} \\\\\n", "& Gain(R_c, R_l, R_r) = Loss(R_c) - \\left(\\frac{|R_l|}{|R_c|}Loss(R_l) + \\frac{|R_r|}{|R_c|}Loss(R_r)\\right)\n", "\\end{aligned}\n", "$$\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Also for efficiency, we should not try all the x values, but just according to the histogram" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<img src=\"stamp.jpg\" alt=\"Stamp Algo\" style=\"height: 700px;\"/>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Also don't forget return left and right leaf predictions" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Implement algorithm and please, put your loss rounded to the 3 decimals at the form: https://goo.gl/forms/AshZ8gyirm0Zftz53\n" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def loss_mse(predict, true):\n", " return np.mean((predict - true) ** 2)\n", "\n", "def stamp_fit(x, y):\n", " root_prediction = np.mean(y)\n", " root_loss = loss_mse(root_prediction, y)\n", " gain = []\n", " _, thresholds = np.histogram(x)\n", " thresholds = thresholds[1:-1]\n", " for i in thresholds:\n", " left_predict = np.mean(y[x < i])\n", " left_weight = np.sum(x < i) / x.shape[0]\n", " \n", " right_predict = np.mean(y[x >= i])\n", " right_weight = np.sum(x >= i) / x.shape[0]\n", " \n", " loss = left_weight * loss_mse(left_predict, y[x < i]) + right_weight * loss_mse(right_predict, y[x >= i])\n", " gain.append(root_loss - loss)\n", " \n", " threshold = thresholds[np.argmax(gain)]\n", " left_predict = np.mean(y[x < threshold])\n", " right_predict = np.mean(y[x >= threshold])\n", " \n", " return threshold, left_predict, right_predict\n", "\n", "@np.vectorize\n", "def stamp_predict(x, threshold, predict_l, predict_r):\n", " prediction = predict_l if x < threshold else predict_r\n", " return prediction" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "predict_params = stamp_fit(X_train, Y_train)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "prediction = stamp_predict(X_test, *predict_params)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.010699235982691863" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "loss_mse(prediction, Y_test)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "image/png": 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Nq9JBnwdCCCGEDAj60h13IMeNhonfc/ZHQmLt1has2doyqGI8w7gil3u8YzHfi3J19R7s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\nyjX4NIB7hRB/BLAewA/yMzSiNLdzxoIWDnoAZcIsQ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"text/plain": [ "<matplotlib.figure.Figure at 0x112f67710>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(figsize = (16, 9));\n", "plt.scatter(X_test, Y_test, s=50);\n", "plt.plot(X_test, prediction, 'r');\n", "plt.title('Test dataset');\n", "plt.xlabel('X');\n", "plt.ylabel('Y');" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<a id='lim'></a>\n", "## Limitations " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now let's discuss some limitations of decision trees. Consider another toy example. Our target is distance between the origin $(0;0)$ and data point $(x_1, x_2)$" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from sklearn.tree import DecisionTreeRegressor" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def get_grid(data):\n", " x_min, x_max = data[:, 0].min() - 1, data[:, 0].max() + 1\n", " y_min, y_max = data[:, 1].min() - 1, data[:, 1].max() + 1\n", " return np.meshgrid(np.arange(x_min, x_max, 0.01),\n", " np.arange(y_min, y_max, 0.01))" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "image/png": 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sR5Lq0+39vp05MFoqJ/aRHnO83KNgykf1IZJjX3P/kHU0IQsOtMKcIayNVgp+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\nAL9Ci8fAmv25e5M9khbmjPD+uKc62g03P+vC+dS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"text/plain": [ "<matplotlib.figure.Figure at 0x1144a80b8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "data_x = np.random.normal(size=(100, 2))\n", "data_y = (data_x[:, 0] ** 2 + data_x[:, 1] ** 2) ** 0.5\n", "plt.figure(figsize=(8, 8));\n", "plt.scatter(data_x[:, 0], data_x[:, 1], c=data_y, s=100, cmap='spring');" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Sensitivity with respect to the subsample" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's see how predictions and structure of tree change, if we fit them at the random $90\\%$ subset if the data." ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "image/png": 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0GP5cCy89B6GhsC8J/vVviGsOHTpYHV2j5qzVxC51RjsilijKhkO/mwKe4f1M\noqc6Ot1mLuyXXG4y+NHDYch74FaDYYstzoAtH0HsEHD3hJICs5pBs44mEQTmyWjXK+GrRvbFDbBs\nCYweaRJBAIGBcPml8MbbSga5GPUT0qgVHIbDq8E7HMJ6Vn/ESs9HYdML8PNEM104dhwMfK1mI17C\nx8DG/8LQx82T44JM2DobEkabRBCYkZZdr4BljzW+ZNCvC+G8iSYRBBAeDhddCL8sVDLIhaiPkEYv\nfz9krQOfeAjsUr332GzQ5S3Y+Tj8MMrMPIg6Hzo8UH5Mi8zTt9NuCGyaBb3uMNs5qbDne+h1s0kE\nAXh4Q+dLIXEOjBpTo49muV+WwCVTTCIIoEVzOHsCLFmiZFAtNbIxYtJoZHrXbfv5HqY4Z/ZawA0C\nute8po53MRz8Abb8HYK6Q1EGFGdDj5ngl1CdBiD2HvM6phg4WoMY2l0IB1bBnHMhoiccXAV+UeVL\nSh5TmFk+zcARaRth7TtmWfvQttB9OoR3cry96iouhgCfyvu8vaCoHp/Oi4jrKi2Eo+vA3Q8CHfzO\n2/kxbHoJQvpAXqJZwWXwm+VTvU7Fwxe6P2hejkq4FdZfB3MuhJD2cOB38EkAn5DKxxVkmvM5KnkF\nbJgJWckQ0R163gBB8Y63V13FxSfWfvDyNvtFROpacS5krgevZhDQtubvt9th93OQ/BGE9IPsTeDX\nFrq8YUpKnI5nKHR8zrwcNeZW+PQemLccAuIg9Q8IjQHP467BCzPBy6fqNqpjy0JYORtyMqBlH5hw\nGQTWQwmLqvoJb28oqu2Sn6JkkDROOZtgy3SzShd2s+Rix7fBrwbZ4aIjsOUB6PUhBHY1+xJnmORQ\n74/rJOwTuHnC8GcgfbNJ1HS91hR++/ZCSFwELUaYJep/fwban3/a5qqUsQMW3AojroWE6bB7FSy4\nGSa8ByGtnPpxTtB/ELz8HIwZBWFhUFICX3wN/Qec/r0iIrVxeDmsuwe8o6DoKHiGQKe3zZD86srd\nAZtfh35zwbe5uejf/hj8+Rx0fL7uYq/IIwB6fgKH10D+Puj2IJTmweZLIW4wRPU0CZxVr0CnSxw7\nR/Lv8OtD5oYiqh1sWQzfXwsTP6v7WnX9B8GcL2BAf/D3g4ICmPMNDBxat+cVEUn5FrY8Cv6tIC/Z\nJIO6v2qmWFVXxjI4NBcG/WwSO6XFsPF2SHwdWt1dd7FXFBgO174LiX+ZRM25t8KhnTDveZPcD20D\n6dth/btw9r2OnWPdD7BsJoy+HUJiYe238PbdcPtb4FnHxZwH9Yev5kDHDuDpacpOfP8DXDGtbs/r\nApQMksbHXgJbboD2t0PsJHMB/KPrAAAgAElEQVRxnvQ5bL0Jev5c/eH36b9CyIDyRBBA82mw+0Uo\nygTPILMvdw/sec08NfBrCQk3QnBP536msE7mdcyoZ2H5v2Hpo2AvhraToMd1jrW96SMYcDH0Oq/s\nXPGQdxQ2fwKDavG0ujratYdxE+DOe8xUsaT9EJ8A519Ut+cVEddWnA1r7oSeL0Ozgaaf2PEKbLsf\nuv23+u0c/gkizzGJIDD9S8JNsOK4IfbZGyDxDcjdDgGdocUt4O/EVVtsNgjqa17HDHoQljwIxfmm\n+GiXy6Gdg9NvN8yC0bdA57LPNWQaHE2F7V9D92tqHf4pDRgE27fCLbebfmLnLujVB0adYvEFEZHa\nyttvEkH9P4TADmVJnH/Ctiehy7+r307ajxA71SSCwBRnTrgBNt9fORl0ZAUkvgMF+yGoDyTcAj5O\nXKbezR1aVugjAsNh8FT4cTrYMf3I8GugVT/H2l/xIZz9f9Ciu9keczt8tgc2LoWeo0751lo790zY\nuRtuvBVaJcC2HTB2LPTqVbfndQFKBknjk7XBjKiJmWi2bTZoPhl2vQm5W8C/mlMB3HygJLvyvtJ8\nwFZeZb/gIKy+BGIug47XQtYaWHc9dH/n5Akhux2Kj4CbH7g7OF0uug+c/wXkHQYv/9oN/c9Jhqjj\nnrBGtYO93zjeZk2cex4MHwm7dkB4BLSoh2kHIuLa0pZCcDeTCALTT7S+AfYMKBslFHzq9x9TVT9R\nkm2KQR+TsxXWT4P42yH+VshYAusugZ5fge9Jvu/sdihKh9LQmtWZq6jlaIg/A/IPg3dIeV0IR2Tv\nh6jjpkdEtYV9iY63WV02G1xxNUw4BxL3whVxEF2D0VsiIo44OB+ixptEEJhr/3Z3wtLxNUsGuVfR\nTxRnm/7jmIxlsOlOaPWAKRp96Dv46yLoO+/k/ZG9xMxiKC12fPWv3pOgx9mQewT8QsC9Frf+R1JO\n7Cci20JGquNtVpenB9x7GyQlQ0oqxN1kZhxIrdWwyIpIQ+AOlBy3z26+NKuqG1SwH7bfA6uGwfqL\n4PCPZn/YcMjdDSlfmKeqJbmw/QlTrNO9rD7P/o+h2ThIuMM87Y2ZCgl/g8S3qw7t6BpYORGWj4Bl\n/WH746ZoqCNsNvALr10iCCC8B2xeWHnflkVmf30JDjZPepUIEpH6YHM3oyorKVuFrKrRoznbYNMt\n8Mdwk9g58rvZH3EWpP0ChxeVJXCOmO/16ApLwSfNgObTIe4qc5Hf4gaIvhiS3686tvSF8OdI81rT\nB5JfNm07ws0d/CJrlwgCM41gy6LybXupmSoW3r127dZEeAT07qtEkIjUE3dz71CRvcTsr8rR1bDu\nWlgxAjbcZGoDgSn4vP9jyFxrtgsOwM5nILrCKPi9b0Cbf0L0ZAjoAq3ug+B+kPpF1edK/QJWDIM/\nxsArF8HqWjzAdfcwo4RqkwgCiOsCWxeXbxcXwo5lEF+D1TFrq3ks9OutRJATaWSQnFppMaz/CHb9\nYC4OW4+H7leYkTlWCewMuEPixxA/FbDDnvfBPRh821c+tjjLJIAizodOMyFvJ+x+GCiB2LHQ/V3Y\nfC/s+I8pNNpsBHR8ovz9eYkQPOS48/eAlI9OjKvoiBk11P5hiDwLCg/DprthzyvQ+i4n/yXUQOep\n8P1V8MVD0LI37P4TDu+HCX+3LiYRaToK82DZh7BtqVmtpMdZ0GdSzVbMcrbwobDxH3DwZ4gcbfqy\n7S9ByCDwCKp8bP5+WDcV4m6E+Hsh6y/YfItZ4SWoD3R+DbY9CCU5UJIPkRMh4c4K798LkcfVdAvs\nDqmfnxhX7i7Yejd0eQlCB0N+Eqy/GTyaQeRlzv97qK6eN8K8a+BIKkS3g61LoMgGrc+0LiYRaTry\njsDqdyBphSl+3+ViaDvO2piixsOuVyBjCoT2Miv6bn26fOZBRZkbYMN0SHgAWvaHjIWwdhr0+BJ8\nu0CbR2HdTebY0lyIuRKiroCSsn4wb6/pFyoK7GFKUZQc9yD7yApTkLrbmxDUDbI3w6/TITgK2lhY\nc3PkTTD7PkjdamoGbZgPsa2gdT0+XBanUzJITm3Zk5CTCGPuMhf2y9+FJXvgjH9ZF5PNDTq8A9tu\ngt3vmCSVZzPo8OaJNx9pc8xKYwllxdL82prh/YnPmWRQYCfo9y0UpJqK/57Hrc4S1MOMJIqaXN72\n4flVTxE7OA/CBkPUOWbbOwI6PAqrplibDPIJgXM+gh3fwr4dED4EBp5rVsQREakNux0+/wd4BsD4\nh6AwB359A7LT4AwH65w5g7sv9HoD1t0NW540iRz/ttD5JfAorXzsgY8g4jyIm262fVubKcNJ70D3\nXhA+EJr9BAUpJpHkcaywaFk7x/qJ0ArLoKf9CME9TjzXoc8h5iIIK3vI4NsC2j4A25+xNhkU2Bwm\nfQbb55jabi3Ph1bjaz/iSESkpBjm3ggtusHExyDzACx6GYqyodMF1sXlEw1dnoK/bjFTtQoPQ9gA\naFdFgeU9M6H5rRB1sdn2bQVFaZD6X2j3EMScbZJLBangFVY2w8Be9qK8n2he1s/YSyFtAcROPrGf\nOPiJqU8a1M1sB3Qy05BXz7U2GRTbCa6eAWvnQUaSqS3Xp5+1D36k1pQMkpPLPQS7f4bpX5i6NQCT\nnoS3z4fsFAiwcCi3XxvoMR/ytgNu4Num6i+j/H3gf9zwRf9OZv8xNhv4nOSzxEyGlM9h43XQbKyp\nGZS+CPr878Rji7PLi8cd4xl24jxiK3j6O77KjIjIyaRug/R9cM3/zJQlgIlPwvuXwZDLT1zWtj6F\n9oLhP0H2NnNh7hcPhVUM/8/bB8EjK+/z72ySRMfY3MAnrurzxF8HqybDlqMQPBAyFkPuVujwjxOP\nLckG7+MKhnqGmmSV1XxCodtVVkchIk1N4q/g4w+j7zHX3BFtwT8MvnnI2mQQmJGj4cNNP+EVdvL7\ngbwkCJ9aeZ9/Zzg8p3zbzaN8sYHjtboT/rrUjAb1L6sZRBFEnn3isSVV3U+EQk5utT9WnQmKgmFX\nl2+7FVgXiziFagbJyWUfgKDo8kQQmAv7kBaQVUfFwkryYceTsGwoLBsOO581wzarYrOBX3sz2udk\nWenAPnD4h8p1e9K+M0P/q8PDH/p8ZjqKrJXgGwf9v6n6yz58NBz4DvJTyvclzoDwel4RpbQEkpbB\npo/hwBrHa1EAFBbAnt2Qmem8+ESk6TiSYi7s3SokWQIjTF+Re6RuzlmYAZv+CYuHwJJxsGvGiXUf\njrG5QWBHkwg6meA+pl+o+F2ZNheCe1cvHu8I6DfHFCHN+gNCekPfL6suCho+GlI+MytWgjnnvvcg\npJ77iZIi2PMzbPoE0rfWrq38HEjeAfkN4MGHiDQ8Wfshsl3la/XIduZewl568vdVl9124is3Bdbd\nBYsGwvJzIGm2GcxZ1bE2L7OysHds1b+328z3etrcCue0l91PVHM1K/+20G+uSThlrYKos6HHh5UX\nIzim2RhI+i+Ult3/lBZC8izoOPjEY+tSYR5sXACrvoT0pNq1lXPU9BOF+c6JTZxGI4Pk5EJbQ2Yq\nZOyD0BZm39EUyNgLzdqe+r2O2ngX2IDe7wGlsP052PIAdHnBsfbCRsOBT2D9ZIg4F3J3QvqP0OXj\n6rfh7gfNrzj9cf5tzLDOP86CsKHmaXNpHvQ8SRHRulCUAz/eYjqQyG6w+VMI6wgjnqj5SgSLfoGP\nPoDAMDiaBoOHwdVXV77pExHXFtsR5j0LeUfBtyz5kbyhLAkT7vzz2Uth9XUQ1AX6fWJWBtv6uPnZ\n4e7Tv78qsZMh9SvYdDmEjoGs1ZC9quoRoCfjGQIJ009/XOhQaDYaVow204qztpp6d+1rsHJNbeUe\ngh+mg18YhLSC9bPMymT97635cP/FH8OSzyA4Ao4egiEXwsgrNG1ARMpFdodFs6G4wNSVA9i+GKK6\nVL3wS22V5MGqqRAzCdrfA7lJsPmf5sFwCwen47a8Dv64GLbcBMEDTM2gogMQ95/qt+EdBa3uOP1x\n0Rea1cd+GwUh/SHrT4htC72qqGVUV9L2wCd3QVh7s5jNkpkwaCoMvLRm7ZSWwHdvweofITgcMg/D\n+GthwDl1ErbUnJJB9c1uh0W/w8/LoNQOIwfCmCEN88LJ0w/63gyf3Qo9zjNf2Gu/gt43gFfg6d9f\nU3mJcHQlDF9Sninv/iIsGQ75qWZub03Z3KHTO3D4ezj6uxnm33MeeEUBx6804wTx15ri0RkrwCvc\nXOzb6jF5suF9CIyC0f82/6ZKCuGbG2H3fGhTxVDUk0nbDIs/getehMiWkJcNH/8T5n0H59RjZyTi\nikqLYc13sGORuXDueg60H2p1VFULjjZL1358PXSfaGoGrfsGxv/N8aVwT+XISlPPp9Oj5f1m95dg\n+VnQ9lZwr+Ip6+m4+0HvT+DAN5C5HkK6QadHqr/8fE3YbNDuQYi7xKw+GXYVBPSv32uAVa9AwjAY\nVHZTUnAbfHk5JIyG6GqOmgXYvNxc4N/2DoREQmYazLwfIhKg24i6iV1EjMIC+PF72LAGAgJg9ATo\n1MXqqKoW1c0khD6eDl3PMjWDNv4A456tm/Md+MGMxGlbVujfJxa6Pg3r7nQ8GeQVDn3nQMqXkLsN\nIsZC9HmmPp2zuXlAl5chaxPkbIYJ50JMB+ef51TmvwA9roKuZTWS+t4An18KHYZD6EmmTFdlxbew\nfyvc/QH4BcGhffDePRDTun5XIZOTUjKovr3/Bfz2F1x6Lri5wWffwY69cJOFhSNPpfNFEN4Jdv5g\ntkc9BVF1tNRsfqqZflVxyKS7r0kCFR5wLBkEYPOA8HPNqz74xEDM+ac/DmB/haRaVbUsQms4nHLv\nChh2e/mNhbsXdJoEO1dA8EWnfm9FmxZA33NNIgjANwBGXw0/vFS7ZFB+HmQcgfBw8LRwRTqRhmze\nf6BoH1w2HnLz4YNX4Mh+6H+x1ZFV7YzrzEqFW5eY6WGXvQCRrevmXAUpZhRmxeSJd5R5WFGc5Vgy\nCMDdB2KnmFd98GttXtkWFGjevxzOn1W+7R0AbSfA/mU1SwatWQDDLzGJIICgcBhxKaz5qXbJoJwc\nyMqEiEhw10hUkROUlsKz/4Ygb7hkHKSlw5svwtSrYMCQ07693tlsMPJR2LMEkn4Dn2DzHRR0kvo6\ntVWQavqJivzbmP214REELa6qXRs1EdjZvGJqOUWrpkqKIGkdjHutfJ9/JCQMh90ra5YM+usnGHet\nSQQBRLSAgRPhr59rlwzKzIS8PIiIMPfT4jAlg+pTVg7M+QneewpCy5449u4C0+6BKWdDs5BTv98q\nkV3Nq64FdoacPZCzC/zLbiSytkL+AfCv54x4Y+UVDDkHK+/LOXTiKmmnYy8G9+OSNe4e5gLEEXY7\nfPU5zJsL/v5QkA8XT4WRYxxrT6SpStsLe1bCR8+Cd1mioEtbuPkx6H1e+RD7hsRmg1Z9zKuuBfeD\nbY9BwWHwbmb2pS0xdRi8mtX9+RsSdwe/j73L+onACoVScw9BUELN2iwtPTFZ4+5ppgU4oqQEPpgF\nSxeDn79p/+rrzGo1IlJu43rIy4IX7i2/EW4eA8/PapjJIDAJ+1ZnmFddC+0HG+6FNreDR9nKuSnf\nQIi+S6rF5lZW9+8wBFZ4EJ9zEHz71qwte+mJo4Rr00/k5cOrM2DVGvDxAW9vuOkm6NjRsfZEyaB6\nlXwAoiPKE0EAAf6QEAdJqQ03GVQdxwpv1maou0cAtPs7rLwUYi4wBUFTvob2D5mntnJ68ZfB749B\nUBxEdIZ9v8GG/8GAWTVrJ+YsWHk79BpnakEUFcLiD2DQQMfiWroEVv4OTz0PzZpB0j548nGIiYOO\nnRxrU6QpStsDHdqWJ4IAYqPAxwuyD0NI7Enf2uA5o5/wjYP4afD7eRB7ARQdgdR50PPFhjnduiHq\nfBEsfRpGPw7BLWDnAtizGC74tGbtdBth6gV1GAi+gaaA9K+fwcBJjsX17dewPwleeA0CA2HrFnj+\nafhXC4hycGSwSFO0fx9071B5RET3DpCSYm6yG3NtR3sppnhoLQT3gdAB8Pv5EHWOWcErbTH0nuWM\nCOvfgtOMtM32gmZ5NW837BTvaXMRLP4XjPgH+IbBpi8gIxG8JsDeCvdk3Q6evA0w/cSiD6H5Y+Dp\nbWoGrZgDF95T83gB3v0QSt3g1bfA2wdWrYRnn4WXXwY/P8fadHFKBtWnuChIPQRpGRBetmRgZhbs\nSYJ4C5dpr43CHPjtJdj9HWCHhHHQ917AwZpCsVMgqCcc/A5wMyt5HT/UU04uepS5OZr/f5CXAkFt\nocdTEFTDkVVhveGsCfDKtWZe76F90LkLnHueY3EtWQgXTjGJIIDmLWDCOfDrIiWDRCqKbA0LtkNe\nAfiWjQJKTIaCIgiog4LM9SH7MCx5HPYtNNO42kyE+L87nuRvc5tZ4fHgT2b68OCvwbeaSbLUAMfO\nWVcCCuv/nJ0uguJ8+PZGyD8CEV1g3IumSGhNdBsJ+7fBc1dAdGtI3QU9x0BPB1dGW7wQ7rjLJIIA\nOnSEIcNg+VI4f7JjbYo0RS0S4KMFZjTdsdF5f22EuLjGmwjK3AMrn4GU38ArCBKmQoebHau7abNB\n5/9A+nJIXwoBHaDd/a43erQ2ut8K616Dz6dCUR5E94ORb4FHDfvtoRdC6m54ZipExkPKLhg+Bdr0\nrHlMJaWwZBm8+Dr4lNVq6tvf3GOsXAkjVKvOEUoG1acAf5h8Ftz3pJkW5u4Gs7+Hc0ZVHi3UmCz8\nB3gEw/nzTAe05nVYcg90qeYTxvTlsOsFyN4C/u2h9Z3QbBgEtK/buJuyFhdA8/PNVC+3WtTlOWci\njBgJe3eb2g21eTJbVGQy+BX5eJv9IlIurAW0HQp3PQkXjTU1gz6eB8OuAQ8L6svUlt0Onz4A4YPg\nooVQnAsrn4ZND0O3p6rXxoF5sOd1yNsLQT2g/d0Q3MO83Oynf79UZrNB92nQ7QozAtfRQt82G0y4\nEYZMhkOJEN7CjCR1VFX9hLf6CZETdO4KoeHw9+fgnDPgUDp8Ng+uqsaKhg1RUT4suAE6XQYjnoPc\ng/Dbo7DVDh1vP/37i4HkT2Dfe1BwEEIHQZv7IWSoeR3j4Mwkl+TmCT3vhB531K6fcPeAix+A9BTz\nimkD/g7e89pLobgEvI67FvL2UT9RC6q4VN+mngvXXQwr15lC0pdPgmtqUNi3IclOgQNrYeA/wLcZ\neIdAv/shcy9kbz/9+7M2wvrbIWI8DJwPLW8wS8sfXVv9GEpyIelN2HAFbLsLsmrwXmfJ3w973oJd\nL0P21vo/f1Vsttolgo4JDISu3Ws/RL/fQJj7dfmXdXY2zP8e+js47UykKTvzXuh8CXy9Hn5JhDH3\nQ+9qFqVvaJI2QGER9LrTrELpFwWDHoXUH81y8KeTttDUCIq7DAb+ZFZvWX095O6rfgyF6bDtRfj9\nGki5Fwqq0T85W8F2OPQ8pL0MhXvq//xVsdmcs+JbUDi06V27RBCY/uDrL8prSRw+DEsWQb8BtQ5R\npEmx2eDOB6D7QJj3G2xKhjvuhz6N9P/K3kUQ0gY6TzMjT4LiYfCjsOej8unFp5L8KeybCa1ug0E/\nmdpAf10GxZnVjyE/Gbb9G1ZfaX7mJzv8cRyWtR52vQiJM0xSqyFwVj8RFgNtezueCALw8IC+vWDO\nl+X/LvYlwprV0Lt37WN0URoZVN9sNhjc27wau/yj4BNmVqw6xs0d/CKhKOP079/6MNgLIPl/sOdV\naHEltLwZkt6H4OdP//7SIthwObhFQsi15gJ709XQ/kUIHe7wx6qRw4tg490QOBHc/CFpGrS62XwW\nKTd+AuzaAXfcBC1bw45tZtSRCoOKnMjmBt3Gm1djl3sE/KMr1/Px9AcPXyjKOvXy7Xa7SQSVZJsn\nvjufgTZ3mdUa9/8P2t19+vMXZcFvl4DvIAi6EXLXwZ6JkDAbfOphYQSAjI/h4OMQfCFQArvPhJhn\nIKieVrhsLCZfDC88A3+7DWJjYft2Mz2sVR2tTCfSmHl6wtgJ5tXY5Zf1ExX5RUJRNlAKnGKqWGmh\nmWFQWgC7X4btT0CHRyCkDxyYC3FTq3H+ZPjzQgi6AIJvgpwlZrvvF2ZZ+vqw+2XY/wkETYaS3bB3\nAnR7A0L618/5G4sbroLHnoE//4CQMNizC669FsLCrI6s0VIySBwX1sZcaB9YBVFlq8gc3gxZ+yCo\n26nfe/hXyEuC/nPBLwEKDsCaqyFsWPWz4Rk/mT4i4b3yGw2veNj7XP0kg+wlsOWfEPce+Jet3hB2\nHewaaQrW1WRucmE6HNkBPvHgU4MlGxsLDw+47W+wfz+kJsM110N4LZ8ii0jDF98Dvn3S1IMIamn2\nJS02Cwb4nua7LmW2eSI56BfwjoDcXbD6Cog8CwoOmWPyKoyATAo6sY0j/wPP7hD7gtkOHAPugZD2\nIjSfUdtPd3olWXDgYWj1PXi3NfuCp0DiZRAwjhoVSi1Ihbzd4NcWvJrg96efHzz4TzM1+XA63Hgb\nBDfSKfQiUn1xA2HVe9DjFjPTAGD7FxDe//Q1g7a9ZWqL9ngbPALN7IK110LEmVB4qHrnT3wPgi6E\n6IfNduDosv2zoP2DDn2kGsnbB/tmQZul4FH23Z45F7Y+Av2/q1lb+XuhIAX8O5kyHk1Ns1B44d+w\nfreZZdCxowpH15KSQeI4N08Y/k/45W8QNwRsHpC0CAY+DO6+p35v6tfQ6haTCALwjjLDO7c/CXFT\nqnf+nK3gP7TyE2f/YZB0k0Mf5/8ryTc3Iel/gm+0eapwLM6K8pLM0rf+FZbx9GwOfr3h6F8QUc1l\n03e/Bolvg39HyN0OzcZD2yfM32dTExdnXiLiGnyDYOyt8P00iB8FRTmQuhJ6vnb61b9SvoK295tE\nEIBfa2hxjZkO0O6u6p2/cDMEDq28L2AYpL9f889SUclRyPgv5K0H7zYQehV4Rp14XP5a8O5QnggC\n8O0JHmFQuA2oRnF/e6kZIXXga/DrADlbIHYadLiq6a2gZrOZ0aMtNRpIxGWEtoQOl8LcyRA/BnIP\nQNoWGPTe6d+7bw50e8skgsDUkoueBClzIG7miYsGZFRxf5K+G6KPq7fkPxwOzICSWiTeS1IhdwYU\nbwfPHuB3DbhVWDk6qMD8PLoO/EeWJ4IAAs+C5NshqQSqs9h0aQFsvwuOLgefVpC7DRLugZirHI+/\nobLZoL1qyzqLagZJ7bQYDBO/gsheEN4Vzv0CEqqRBLEXgdtxBcDcvKA0H5pXc4qVfwfIWVp5PnHO\nr+Bbw5WzKiothNXTYN9SKDkHjgTAn5Mha8OJx3qGQmk2lFSYEmcvNbUhDsyFv66Bnc9BYdrJz5f+\nK6R8Dn1+hu6fQ7/lpkhq8n8d/wwiIg1Jjwlw9qcQ2h5iBsGkORBWjSmi9mKwHddPuHuZ0ULRZ1fv\n3F6dIHtp5X3Zv4J3x+q9vyolR2H3WWbhA89zIC8Tdo2FwsQTj/WIhqJE0+cdU5oDRSmQ9iZsmQbJ\nr5oRRCeTPBsy10LfpdB9NvRdBIfnw96Fjn8GEZGGpPt0GPcuBCVAwlgYNQ8Cq7GasL3YrFJZkc0D\nPJqDXzXrUnp0rrqfcO9SvfdXpTgR0kaA/TD4nQXFGyFtFJRWUUbDIxoKd1a+nylONdsHH4RtV8KB\nmSbhczJJr5p7qH7LoccX0Os7SHrNmlqq0qgoGSS159sM2k+BDhebOb7VEXkWJM40y6CDKQS9+3VT\nM8ijmkv/ho4xK8nsuwYy50PaW5B8j8mEO+rgD1DkBWGzwf8SCH4EAh6CHS+eeKxnEERNhP03Qv5m\ncyOwfzoUZ0BJS/C/nv/H3n3HV13djx9/3nuzExL2XiIqihvc4sYtaqvWra1W22q1Wq1t1VZtnV9X\n66qjdWLVat27Ttx7IaCCokzZJGTfe39/nPhLAgGScEMYn9fj8XnAOTnj/cH4eZ/zPu/zfltYxrs/\nDtfAmmLmk/Q6jpy6E+VEIf1/zezHW/8Oi1O7iM8u4tmteXYrPr2A2rLMjR8RERGxPIp6M+RI1vsR\nuc10Xe++D5P/Ebw1oXpOcOff4LzmB8jvcBQVnzHlDEr/x8yrmXUN3c5o3XsQvIqytqDzbRQcSqcr\nKTiK2dcv2TZ3MHmbhBPe6q/DYcG3RyBF7noUH8/CcYw7LCzkm+L7J+n7K7LqrsFld6H3z/nq+da/\nw+JUzOXlP3HnTozek3evJxllZ4mIiFiJlAxiw6MZdABZhc3r03svJl5HqjaUK75j2kP0urb5npOF\npzD/IaZfSOmLTL+A+Y9Q+KtWvQZYdA0Fh9PpqqAnutxKzpaUN+HtVDAiGLVm/omaacHjdPKh4ZA8\nb3M6HMnc5/nqxKUH1J79BP1PJ16XkTGvPz2OYM4TrX+HxSmbwqun88DWPLoX4+4INyQiVmvWwHso\nEasEywv+33UvFnzEG7vSYWPKPqfrSPoc3XTfL5YSf6f4MRbdxvd3hkDSnR9g4TBakECgEfO/ImfP\nxgokf29mXta0DLFriV3B5KNCMOxEdzqfQrezw8877Mm005nwIF2bCHYaSwiBjxqQToYAssuiZiHf\njwmnH913CsFYl8ZHvyM/mx+NRox3b+SD37L1zcueIyIiIqI96XsMpZ/z+giKNggemn2PpduuzR8j\nUUyfZ1hwM9/fSnwQXZ+hen2qWylX2WfkLxb8OW9v5v+ehblLti++g4V/4etRiBMroscldDwi/Lxo\nb747hLlP0PWQJfvHEmGj0IiU5Z7nlc9h6tvkFtFnOxJLMaCl0zx7Oj0359CHw1W+N6/kravY4ffL\nniMiIiKiPRlyGq+ezes7kz+AsnF0+wP5LUgQkOhHlxdZdCNlt5A1NJQTfVsvV81HdLy4cV3+npQ3\nOOz9//udBH0eZPZFTNqdeIewp+jzzxD+Ajrsw9c7U/omxds3MWE87B8akk5avp6Ywpz3yOtO122W\nHqOptoIXTmDDH7Prn2e7HvYAACAASURBVCibwZi/8upCdjl82XNErNJExqCI9iEWY/Dv6Xc8ZV9Q\nsA75/Vo+TryIDr8JTybIWo/K5xrXVb0d6psilk3JueGB73ejcJfGbQp3oXQplvkeB/L5b0OcoLz+\nwVPq22vo3sSG4AdmvsKHv6PzluFa22cXsdWNdNpsybblU5n7Pkc+VZ/1bafz+fd+LPqWwv5Lnyci\nIiKiPYlnM/RKyieH67NFQ8jtTqyFJ5GJLnSuCwJatZxgpM0haz2q36GgwXe6+m2yl6In4kV0vDw8\nMH39xnoiFgvlRePo2kT/ngcy+XpKtg1eQVXTmXIzOy9D7014lLeuodv2VM3mjSvZ53pKmvjmz/yI\nZBXbnFl3ENKFXS7i/lFs9WtymnlCHxEREbGyySqk/31Ujqd2ejBqJzq1fJxEP4ovzaBc61H1DrkN\nrqpVvUvW4KW070bP68Lf0zV82YeCBvHuYlmhXDGuaWNQt4NCAp0hN4UbFuVfMPM+ht6zdBm/uJFJ\nd9J9R8q+Dsajbf9FbhOH31NepNMgNv9pKOcWs/MFPPuLyBi0mhMZgyLal9ye4VlVKDiEsuuY91sK\nDqN2PAv+Sudbmtc/e8MQx6igQTyM8teWHp+i09b0P4GP9ie3D5VT6HEoPZeSCrO2go/OYZub6bR5\nqJv+XDAO7fr0kh5F1XMo6FZvCCKcDhf2CBuEyBgUERGxqlMwoOkg/u1F0QnM3IVYMXl7BUNQ6TV0\nfax5/bM3DHqh5NBQTqdDudtS4iD1OJCyL3lvF/L6Bj3R95f027Hp9otm8fa17PggRQND3aQ7eO1S\n9rtpyfYVc+jQt7FHbG5JiMNRUxYZgyIiIlZ98oZgBWLBZZrC05m7H2Lkbkfls1Q8QtdXmtE5i+yB\nVLxJQZ3hJ52k/A267dl0lz6/oOo73tuenN5Uz2DguSGrWFOUfcLk+9j1qWD8Saf5/DLGXcnmTRjF\nKuZQvJinVIc+LJoX+q5pyQzWIiJjUEREQ+JFdHuG0muDy39WH7rcFT7ky6P6Y+K9mXsT6WoKtmHR\n/yh7kXWeW3q/fsfT6xAWfhsMQtmdl9523kcUrlNvCIKeIxl7WbDqLx5sr8MGlM9m9ji61imEORMo\nnUbJRst/p4iIiIiIxiR60f3ZOj1xdvAI6voYOcu5lpBOU/0WWRsy41xqZ5O7Pgv/G060u4xqul8s\nxuCz6VW32M8bUBc/aCnJCaa+SfcR9YYgGHgk46+mpoLsxa4V99giuPuXTqdDr1D33RhyOzQ/DmBE\nRERERD3ZQ+n8BIuupeIhsjeny3Mkei+7XzpJ+SvkbcuUk+h6Ftm9mX8nOV2XzI75A/FsBl9B/7OD\nISh/8LIzO8/9H31H1XsBxWIM+imvHNh0+57b8MqdDPtF8AoieKCus0VkCFrNaR9jUE2CqR3aZeqI\n1ZT48oIQZXKurnT+62KVy5g/nWbeWVQ8E+4DJ/ox/27KXyd/SwY+3ThdZFNkFVG0yfJlyyqkejEr\nfKomBIlePPB2bXkIFD30Tzx1Cuvsjhhfv8AmfyaRt/z5IiIiIiKWJGsgnZpILLA00jXMOZbaL8jd\nOaQXnnNruB5dvC3r/pn4MhbukN0xPMsjuyDoiYbUlIZYEPHFln3VZUGXDPsljx7LoJEhZtC3Y9j9\nimiRHxEREdFasjem421L/3nZYtkyU6XM+REqyBlOOsacm8jvQ8dd6X7s8r/JOd3CszwSHaie0riu\neh7ZTSTxqV5IQQ8G7Md/j2CdPULMoJkfc9Ily58rYpUm8gyKiFhRKl+icgy93qoL+pZm3u/IqqXH\nBZmdq+MmwYjz5Q2s+/OwwRh3VfAUyq/LSJaq4fMr+O6/YeGf24WNL6RyBtKM+BUFKxAULyIiIiKi\nZZTfS3oBvd4MsebSKWYfQ8dN6X1KZufqtyNvXhWuAPQ7JCQc+PQCBu9bH0S6ppzXLuPbl8P14uL+\njDiX+d+QlcdWp1GwlMQNERERERGZp+xvZPcLmcdiMdKXMnM/uh5Ol4MyO1e3UXx8Pd13oeceVM7k\ns7/S/yf1bSrn8Naf+f79UO66CcPOY8EEug9i+IX0rMisXBErncgYFNE2pGqZ+yrVs+m0LfkZjE1T\nOz38mdUrc2OuCJXPU3hEMAQRPuAdfs6sZQSBbi55i2eQwYjreO8Cnq0LStdzBNtdTGFVKH9yAxVf\nMupxcjsz9WXevoB9/0v+D4v7qhWXLSIiImJFSFYx6+Xgwdh1BLEMfdPTaWqnEMtbvlfmyqLyeYqO\nD4YgggGm6ATmXZF5Y1BWHntfx5hL+PxyxFh3L7Y9o77N65eTxIHPkVXA14+EusMeIauJjGgRERER\n7UFNGTNfRjpkz80pWbHxcuoybqVT1H4bvDQTzfC4bAtqFovzWfU8nS6p9/6J5VJ0LPNfzLwxKKcH\nw68PSWg++n3QSQOOYPAJ9W1e/x2dN2LHK4Nn6ef/4tPr2fv+Bh5KkTFodScyBkVknvKZvPNLcoop\n6M/Ey+n3UwaeumLj1n7HnJOomRDK2UPocjNZrchClkninUhOb1yXnN52yqWgFzvdHBRkLB4W8mHS\n8MfEh9ntVvLqDD99dw1ZAL59hg2OahuZIiIiIlpC6Ze89zMKB5HTiXGXsN659Dp0xcatGsv3vyI5\nM6Tmzdue7jfUf49nFyy97w/XkZPLScXbaL7mZifrTs6MxlXJaWS1IutNc+g8mAP/RVVp8AbKanAt\nuLaCb17koOfIqTvEWPfHfPMUU95g4K5tI1NERERES5jzLu+eRsctwnr3078y7MoQE21FqBzDnNNJ\nV5BeRP7+dLk6HCC0J/FOJBfTE7XTyG2j/USX4ez0aMhknChsnGymbAoLJrHrzfXXizc+ia8fZ964\nYCSKWCNowYonIqKZvHs1PfdkuwfY7EpGPMXUeygd1/oxU8ngUl+wOwPHM3AcBbsy+9hwCtyeFB5B\n+UOUPxICv1WPY94f6PSztp03u6iBIagBtRX1C/z/37ZDqI+IiIhYFfjsXNY9ha3vZPNr2f5BvryY\n6lmtHzNVwfSfUHIyAz4PT1ZvZp2eOblbzcksvI7KV4LOqnqH+ZfT85i2nTa3Q2NDEMFzV3rJ4KI5\nxZGeiIiIWDVIJ/ngHDa9kmE3s+VNbPkPPvg9yerWj1v7PbOOo+ulDPiMAZ+QXsi8izIne2spOJH5\nF1L1YdATFc9T9k+6LyXDcCaIxcKBTGKx+EW1FWGPEUss1jbaT6xpRMagiMwzbQwDj6sv53al537M\neanlY5WO4/0jmdI1uP13+g2xrOBq3+kMUrPxOdmp9nvy+9DzLhb+je96MOtgOh1Bxzb8eC+Lvrvw\n+e31RrLyGUx+mj67tI88EREREQ2pWkjpF/RtcJW2cB26bMfc11s+3vw3ee8Avu5DogvFR4ZT5Hg+\nXS6g/CWSCzImfuvYivTNzPod33Xj+5NJXUzJTitflJwOdN2IL++rr5v/JTPfoW8zMmdGREREtDUL\nvyKeS7cG38jOw8nvzfxPWj7egsf5agemrk/+DhSODMaNeDFdL2HR6MzJ3lry96fot+Gg+7tuzPsz\nHf9B/pCVL0tJXXbiKS/U1814O9z+6LLpypcnos2IrolFZJ7sIqrn1qcrhKrZFPZsnAVredTM56Pj\n6fwHSv7M9NPCAv8HYnFi+eEqQHNIV7Pg7pDuPdGJjseRt02zX2uZ5G9HvxfCHLLJTWVm3NawxZm8\n9EuePpSifmGBv8kv6Di4ZePMbCKjQERERMSK8sMJZG1pOJH8garZwdjfEj1RPomxp9D9aopTzL+r\n8c9jOXV6o6Z546VLqb2Z9Bj0JfEr4s3I9NgsDiJ9IKpJ5yCGqRkau4WMOI9nfs23z4YrCLM+ZPs/\nkNdG19YiIiIiWkJ2YQh+n6oJadMJcX6q5gQ90RIWvcXMc+l1E5WfUflF45/HC5u/l4DkLBbeTNXH\nZA+m+GSyB7ZMpqVReBwFx6I6xAwCpZkZuyXE4mx/Ka+ezhf/Dv/m88aF+EE/JCKIWCOIPIMiMs/6\nhzD2AiqmBzfPT/7A9//j62t5a3dmPde8cWY+Tv4IOh5L3pbhQ132RP3Pyx4PdTnNWKin00w/lnlP\nU/szqrZh2k8pfaxVr7hUYjntn4o3rwt738ew3zNgL/Z7mCFtfBUhIiIiorlk5dF7f8b+OSzsk5W8\n93MWjmXcObx7APPfad5Y0x+g+Gg67E/hrmFxXvle/c8X3EbOUBJdlz9WuorKPUl/QPznxAZSM5LU\nG614yaURQ27dn+1ISX8OfZDhP2eDfTnsUQbv1b4yRURERPxAQV9KNmT8JdSUUr2AN39C9RzeOJ4x\nR7Hwy+aNNe8OOp9JwQiK9mXRk1RPDD9Lp5l3JQUHNG+s5Fym78mi+SR/RUVxKNd81Zq3bJpYrIEh\nqB3puimjnmaDo1nvMEY9Q4+t21uqiAwTeQZFZJ6NT2B+itf2D4v8nM4Mf4jCDZj/NmNPI68PHYYu\ne5ya+WT1CX+PJeh9G1OPYe6lxDpQ8x1ZDzG1GSeZqTEha0D2R/UnCrFNmfVL5h/VfgacXm1k7Y/F\n6TG8bcaOiIiIWFE2PI9xF/PK7uHkt3Bdtnk26IZZz/Hpr9j6CXJ7LnucmnlkbRn+nuhArxuYfijZ\ng8JhRLqcXvcte4wfSD6EYrL+XacTRhHrQ+2F5Dy7Im+7ahLPpu/27S1FRERERNMMu5JPLuKlHUij\nZBjbvUp2x3AQ8OYJ7N6Mb3NyDtl1+4mcAXT7M1N2JXdoMO7EiuneTD1RejtGkPWPuooDqC1g/rV0\nu74VL7mKk5UXEtFErLFEnkGrMlWVvDWGV55n3pz2lqb5xBOsezY7vkvH4ax3HkVDwuK607b0PY5p\nDyzZL51k8m28tQ9v7EH5dyx8kOT88PP8LSk5hmQX0heSPZF4My3U6U+wFbXnUb07NSeR7kL6a1Ga\n9YiIiNWWilI+eYaPn6a8vePitIBEHhv/hd3fDnHXNriY/H7BkN19b7rtxYxHl+yXrOTLaxizJ6/t\nV3f9dzSpyvDzwpEU7k2iN10vpt+bwTDUHFKfENuS2tOp3o2aM4gNqdMfEREREasp8+bx8v94YwyV\nq1Hw35yODL+avV4Le4uNriC3WzBk9zkq7C1mvrhkv+R8ZvyRr7Zm0sgQF2jenWGfASWHk7cFiSF0\nuYme/yPRrXkyVY/FBtScWLefOJfYMKo/z9hrR0SsTCLPoFWVyV9z9V8YMIjCQh64i8OPZ8Tu7S1Z\n84lnkywnu0vj+pyuLJq4ZPsvLmb+BHpeRTyPWdeGO8Pf7EDBvlR/Q/XXZL1AbEALhelP+nwcTexc\n0u9Ruxt6CC77TZCeReoRpIiPItarhXNGREREtCFfv8cjf2bIxojx4vUccD6Dt21vyZpPIpfkInKa\n0BO1TXhOfvYbahP0uTkYgmZeQryCyTtSMJLKj0hV0fuR+nTyzSXWndRfiZ1O7HzS/6NmP6y3jE7f\n4nHk42BEMXciIiJWIV57hbv/xSZbUFHB6Dv47R8Y1MI4ku1JPJtkFVmLfV9zuoYrZA1JJ5l8GPkb\nM+BOamcx/c/hIGHybiF4dPkY4r3ocnnL08nHS0heHvYSsSNJP0jtiSGsxdKo+ZKKF4h3pmC/EKMo\nImIVITIGrYqk09x+A4cdww51rnkzpnLROWw2nOKS9pWvJXTZhSl30nGrcOKbrGTqvfQ/sXG7moXM\n+C/rv01W51DX7x9MGM6QCxhXQWwkWaNa/uGG1BfYi3idC2dsD1L5+E/TV8RSz1JzDPZGAueTdQuJ\ng1o+94pQvYCvH2bh13QawsBRZEVKJCJirSdZyxMX8+uz2bAubtoXn/P3SzjlQbJylt1/VaLbzky5\ng8Hnhe9xzfygD4Ze27jdokks/Jj13yNe9379/8WErdnwb0yfQsddglGopQFGIfU1fkn8L6Ec251k\nBb6nNk4s3bh98k6chQNRhnPwCHZo+dwrQvksxj9K2Qx6bc6gvaIAnxEREZSVctc/OfdS+vQLdW+N\n4dYbueSq9o9x2VziOXTZmqn30P+EUFcxhdkvMvQUpjVou2gMUvS5su79htD/ViYeQM8bKJtIx33J\n26lxUprmUjuV2OXETw7l2B4kp5Ldv+n2868KwabTBxF7lXkX0vNhstdv+dwrQuVkZt5PzVw67Uzn\nKEZcRCAyBq2KlC5k5nS2a5BOsWefcPr7+Sdsuwzr86rCG3VKJ30ByYN4c1+KN2PeG6R34auTmZio\nb58aT6xrvSGIkD4+awgT+pC9T4PBF1uQN4uxwZjUkNhupG5dcpGfriH5c/wXO9dVvk/t3tTuK5wA\nrwSqv+f5wygaRvE2fPsSEx9gt7vI7rByZIiIiFg1+X5i8BrdsEEA/fU3olMXZkygb6YyYK0E1v8t\n7xzPewdTOJg5r9D7sBAfoiGVU8ldr94QRPD+yepKXm86jloxOdJfEP9947r4SFI3NNF2Lukz8SZ+\nSPv7KE7AOCstQPT8r3n8ZLrsQ/4wPnmc8U+w33X1GXgiIiLWTsaNZfAG9YYg2HoHRt/G3Ll06bL0\nvqsam54fYgTN+R85PYKeGHIaBX0at6uZQt5GjQ1dOYNIlVOwNfG9V0yO2onEdmlcF9uH5PtLtq35\nss4Q9Al6hO1L+m/MPodeD6+YHC1hwduMP5keh1C4Pt9dx5yn6XzZ6mMQjGgzophBqyI5OcE7qGKx\ne73z51G0mhkBYoUkniP1D+buRuxhsu4IAaEbtRtEahEVDWIz1Myg8kPii20IWiXHpni+cV36OWKb\nNdF4LErUG4JgGAbh3RWXpblMvYWOe7DB3+l1FBveSu4GwSAUERGxdpNXxKJSamvr61JJSheQV9x+\ncrWG3C7s+AiDzghepMPuD3HnFqfD0KAjahocA1eMJbmA/JZeHW6C2KZBLzRkaXoiPQbbqDcEwSgs\nxOQVl6W5vHszvX/BuhfT+xiG3kdVmq+biKMRERGxdlFYyIJ5jeuqKoPeyGuFl317UjSQ3Z5m8DH0\n2oZdH2XQUUu2yx9O2YskF9bXlT4fAkdn4npW9tAl9YTnyNl4ybYVL5I+UAhJ8QMnU/1qffyilcE3\nlzD4rww6lz4/ZfOHKP2QeR+uPBkiVlky4hkUi8X2xt+E+zS3pdPpyzIx7lpLXn6w3N9+I8eeFMrP\nPxkW/huuRqe9PxCLERtBfBkeTbEcElcy6Ug6H0E8nzn3kDgnxHFoDekk6Y+RR+J4kreR+imxg0i/\nS/pW4k0tmDtjlhBY+od4QknBD7UZ6YkzRemHDGiwIYrFwunv7IdWngwRERki0hMZplMfuq3Lvbdz\nyJHh+/Df++jYh64ZMIysbGIJuuy87DY5nRl4KpP2peMxIWbQvHtY9zzirUzDm66u0xOdyT6Nyp1C\ndrPYbqSfJ/0Y8beakLcrpghHvT+crJZikXCYsJL4/lM2/lMDueJ02osZn7JudA0gYvUi0hMZZshG\n1Nbw2APsdWAwBN37T4ZtHQxFqxuJHHrtsew2eUMoHsVX+9Dp8BAzaP6DIc5ca71gUuXUfE6iJ53O\nZsaPSM0mtkWIGZT4kqIbm5C3C7GXFrvQMI1YiZXmj5FOUfYhXRvog3genXZj3kd03nLlyBGxyrLC\nxqBYLJbADRgprIzejcVij6XT6Sis+opw9Incdwe/+2Ww4G+4CWf9mURiuV1XWxKHE9uE+feiisS/\niW/XurFS71FzFHJCauFYd3JGU/MkqduIrUv8jfDn4sT6Y1v8EpcL/5ucLwQR3ah18rSG3P4s+pSO\nDYLBLvqUon5L7xMRsQoS6Yk24sALeP4aTvtZKG+wAwf/pV1FanMGnEiXLZjxDPEs+t8TMsq0huSz\nISNMrFtIGBDflNwnqb43XCGObRoMQU0mD9gO2fgj/iAYgU630oNId+hL2WfkNYhXUf4pPVZyPIqI\niBUk0hNtQDzB2edxx62cekwobz+Co45vb8kyyxeLXXdL/yMkAJj1JLoTe4tpzcwquTipu0idFfYG\n6W/D1eHE82Evkf4nsW2J/Z2ZTSQsSB8Wsh+7RthTTMMJxH7JjBbc9OjdRDKF5hKLk9uXsrF0qPNy\nTadZ9Bl9jm/9uBFrDJnwDNoaX6XT6UkQi8XuE6IpRh/vFSEnl2NP5qgTSSbD1bG1gfhQ4hev2Bjp\naqoPIfsqEj8OVvHk5SFNcPzl5o2RuJvk2RggmPQPxX9WTK6W0udExh4VvKSKt2bey8x8gN1Hr1w5\nIiJWnEhPtAX5xYz6M/vVhPLaEjS44xbhgapWLmPSs6k5lpyHiO8YYsXV/oba/yN+9/L7x+IkniD5\nG3RDDn4qHCCsRLY4jpf/FLyZCocw6zFK32G9M1auHBERK06kJ9qCrt0464/U1BCPr9mHyj8Qi9XF\nCR253KbLJP05qd+R80LYn6QrqDma1P1kXbv8/rECsp+h9gzSZ6Mj8V+SOH/F5Gop/X7N+NMYdB65\nvZk+mtoyeizHGzdirSATxqA++K5BeYpwkb4RsVjsJJwEunTLwLRrCYnE2vHhziSpV4n1C4Yg6hbt\nZ1P7N9JTifVZdn+IFeNW3FxX0Q7htYqGstNNfH4r02+j4xB2+VfkGRSxOtJyPVHcY/EfRyyNtcUI\nlEmSjxHfIxiCqEtY8Feq+hO/Y8m4dk0R6yUcEqSEq2LtEIiz3/bsegEf38O06fTcnFG3kbuaxY2K\niGiNnui9Er3wVneyIz3RYlL3kzg2GIIglk/WBVT/GM30wo1tQPZTdTGC4u0TsLnHESQ6MOVWaubQ\ncWc2vi9KMhCBzBiDmvqtXiLdUzqdvgW3QGydwa1JBxUR0QKa+rVszQe4nWOsd96EHf/evjI0l1SK\nD1/hk9fJzmGbPVlv8/aWKmLVoOV6oteQSE9EtDGL/1r+UG7pr14764m+24ZndaC2hpde5JOPQ0KM\nkSMZNLi9pYpYNWi5ntikf6QnItqYpvREK37tmnPA0JZ03T88jahosmm7U1nO608zcSydurHT/gxc\nzRIorUZkYgUzBQ1dFfoKlyIjItqH+E6kJ5N8NJTTKZJXExvSPK+giNbx0A288l822Y51NuLfV/PG\nk+0tVcSqQaQnIlYtEgeQep7UG6GcrqX2TyQOJpaR3BoRi5NOc9X/8fq7DN2dknW44jI+eK+9JYtY\nNYj0RHNJx9rvWZuIH0byLlLjQzldSe2FxH/SvnKtydRUc93v+WYCw/egsIRrz+bLie0t2RpLJlY8\n72K9WCy2DqbicByZgXEjIlpHLIec/1B9JLV/Rjk6hrra5XWOaBWzpvHJa5x3B7n5oW7dTbjhHLbe\nk6zIFXUtJ9ITEasWsW5k3071YeG6V/p74huSO5rq9haumZSuZrEEv/6ImbM566b66++9BnLfrWw5\nvF1Fi1gliPRExKpFbCjxS6jemdigcNAc32Xlx/xZm/jgVfI7cNy54UrdZjtS3IXRD3PBWe0t3RrJ\nChuD0ul0bSwWOxXPCqkg/5VOp8eusGQREStCfGtyJ5D+AHnENm6fe7prC9MmMXCjekMQ9BwQrost\nmEOXnu0nW0S7E+mJiFWSxL7EJ9bpiS5krdfeEq3ZTJ8Yrg43jIO4wTBuOZ9UMmQ6ilhrifRExCpJ\n/KfEDsUnxHsTG9DeEq3ZTJnEkOGN92xDhvN8MxI7RLSKjPhCp9Ppp/BUJsaKaEBFBa/+j4lf0K0H\nu+1Nl67tLVXziK8K17jjpDej9iZqT0U26WOI/XTZhqH0ZNLPoTP2R/7S20YEevbn2wnBvTO77rR6\n9jSqKynu3L6yRawSRHqijSifzwePMnsiXddlywMpaCLFbUTTxHKxabhKXPkMOuIXxA5adr/0ONKv\nCrda9pah5dSaTfcBfPpciC8Xr4tSMOkzevSODEERiPREmzFnNi88w/czWXd9dtmD/Ght22xiRaTX\nJ3kF6VfQm8RpwUtoWaTeI/0+scHEdg0JbSKWTc/+fPY2u/64vm7iJ/Tr234yreFEv5UtpaqKN17h\n2ceY+m0bzlPJpefyxedsvlXYZF94NtOmtN2cawLpudT8g5pLSL5P9UkknyT7XLJOx/Wkl+HembqR\n5DDSrwnxCTfA+AwLOYvYMShmzNZ8eTmp1eVewlLo0Z/1NuOW8/jsLd5/iVv/xO4/qTcORUSsLVSU\n8tETvPsg89sw5EXZHO44ieRkdtgi/HnHSZTNbbs51wTS06i9ltrLSY6j5kBSn5J9CVnHkTqb1M1L\n75/8I8ndSb+DS7EZpmdWxsrJfHEC7w7mtR355uYQ/251Zt1hFORy518Z/x5vPc09l3FoFH8jYi1k\n1kLuHROeWQvbbp5pU/jz76iqYbOtmTCOi8+jsrLt5lwTSH9F6gpSV5OaQHJXYgvJuZLsA6g9htRS\n7JbpJDXHU/sTUh9Qeza1u5IuzayM5Z8x4XAm9OP9XZhxX2bHbw7J2Io/DRm+SzhMvu8avviQVx/h\n0Vs4YjkHNBGtJjIGtYQZUzn3FMa+QHIiV/2Jh0e3zVyvvUznrpxyDtvtzBEnsNcoHvtP28y3OpOu\noHY01adSMQSvkVhA1cEknyL3ERIjydqfvMdI30i6CcWb/pbUn0i8T+JOsp4ldjZ+1QJhvsaHlh6c\nKE1sXzoWssFbDHiC+RP5opkpKldljjiLzUaED/cHL7PfTxtb9iMi1gamjuXWI6l4gfwPuOvnfPho\n28z17oNsviUnnBr0xAmnsvkWvBvpiSVILmThnVSfQNUmJD4jMZ2aEaSnk/tvEjuRdSh5/yb916aN\nL+k3Sd9X1/+fZI0J33R/aKYgaUzAp0JK+iZIVTD+cHKGscGn9LuXGc8z5aZWvfoqQzzOOX9ko3V4\n5T4mvcMpv2a77dtbsoiIlcsLn7LvJbw/OTz7XsKLn7bNXI/8hz1HhX3EdjuHfUWnrrz+StvMtzqT\n/p7UDSR/QnJrsieTNYHU1sR7kXsDie3DoUHO9SQvbnqc1EOkx5MYS+KWsK/Qh+SVzZQjFQ4o0uND\n4P2mqPmeL46ilQ9nlwAAIABJREFU6CA2+Jxe1zHlZr5/uFWvvsqQm8dv/o+Sjjw3mu8m8IuLGDqk\nvSVbY4n8mlvC/f/k+F04bvdQPnV/Dr6E4TvSL8N3SL+ZyKaL3ZncbDhjXsjsPKs76elU7U7WOuQO\nJd2FWJKCi8jagvLriDVwhY31JNYD32HoYmM9S2y/xveB4z8neZaQfnFZLrVziR2Bj4h1JlWGO7Hb\nYu3eIKuMXheF/7ZZ3eh9A19tRf8/kLUap05MJNjxgPBERKyNpNM8exkXHMqeW4a67/bkkMtZfycK\nO2V2vpnjGbVv47rNh/PY05mdZ3WnegLTfkT2NuT1obKIWDGFlxPvSvX4xu77sc0wF4uw2Dc59TSx\no8J3/gfip5LcqhmCfEvsEEwL19PS2UFHFWzUuNm8Z8kZTLffhHKihN7X880o+v5q9Y5/l5fHqIPD\nExGxNlJdw3n3ceMpbLpOqPv4a07/B68MISfDCTe+mcR+h9aXY7Gwv/j6y8zOs7qTHkPyEPJGEu9E\nRTaJgeSfRmkl6UGN2ye2o/qLpsdKPU38hPr9RyxO/BRSZ+LCZcuR+oTkEcSqhWwGvai6gdzFrknN\n/g8d9qHzMaFcsBU9L2XaxXRfzb+vhcXsf9xilfPbRZS1gcgzqLmk03zyCYfuWF/XqYg9NuezjzI/\nX6/eTFzsetJXE+jZO/Nzrc7UXETevnT5L8V/odvbpCZS/QTZ25P6nPSc+vapL0jPxqAmBuuEGYvV\nzUYesoWsZJdjdxyGl+ubxU4hvz89x9HzHbrcWNdm8Y/X92T3b7yYT3QknkdtG7oJR0REtD1lc1g0\nl5Fb1Nf168qW6/FtG+iJzv2DXmjIl+NDfUQ9s8+j8Aw63U3JZUFPVD9M7QfkjCT5ckgZ/AOpl4VY\nQEVLjhXraEk9MUPQHwQj0vnEdsNx+KBB3yMo2oeeY+nxISVnMeFnIa19Q2pmkz2wcV12P2rnCp5F\nERERqy2fT6F7Sb0hCDZbh24ljJua+fl69l5ST3w1Luwz1hRq4yv4xEidQqfr6HQLJVfT7TUqLiM1\njZx9qf1vY2/R5FMYRnViySfdkfTMxjKmZ5DuVNdmOtVnUbMH1adQPamuPk3tjyk+kx4f0WMshfsz\n8ZQl37lmFtmLOSLkDAj1EREtIDIGNZdYjOIOzJjXuH76PDq0gTfHTrsz/jPuv52vxvPCkzx0N/uv\nJtdu8mtXzpN+mYJj6ueN5ZL/E2perov635PKEdTcTM21VO5Cwfl0itGxsv4pqaLjSIwndR3p6hBX\nInUyucfSuYqsfch+jaLTKNg5nA4XjabzAtKPUvxnYnUnOrm7kLs9RQ/RpaL+6bQllW9T3SDeVNkL\nJIrIXYMUc0TE2khOATVJFpbX16XTTJ9Lfknm5xt+KC89x+P/CYkGHv8PLz/PVocuv+/aRMUY8hvo\niXhH8g4MeiKxBZJU7ULt7dRcStVhdLqIHuV0X1T/dCun68F4jNR9IS5EeiKpX9Php/SYSWIHciZT\neCb5Q4ntRZfn6PY5sS8pOiOcEsdiFPyEWFdK320sb/EOlD5FbYP1xvx/U7xdFIA0ImJ1p1NRiBFU\nk6yvq0kyawGdCjM/36gfhf3DC0+G/cT9tzPhM3bePfNztRclVSv2dPgW08ndr37MRB9ydqLmdbJ3\nC6Ekqval9m6qz6P6DAr/1PR4RceRvoHUc2ENkP6I9LkUnECHycS2JTdN4e/I605sR4o+pfAVsoop\nPCLoiFicotOp+jY8DSkewcKHwrXiH5g3mpIdRUS0hOiaWEvYfV/OH83FR9OrMw++xtjvOHK7zM9V\nVMy5l/DUI4y+je49OfN81hm8YuNO/Y4JY+nYiU2HkdVGvwI5yeW3yQTxbiS/JrvBXdLkxHAFoPwv\nSJF/KTWPC7bP+ZScgKbky6brI8w/k5rfIY+Coym5gKrnSc+j6On6xXh8fSpOpcNewmntYov0WJys\nGnIbnvp2JnU+E/em5MAQu6jsf2xyw+rt+h8REUFuAUN344+jOf9QOuRz2/OUxxmweebn69yXo67j\n7Xt5+za6rcvR19OpT+vHTKeZNo7pE8L46wxb/Q0QiW4kvyG+cX1d7USyBrDoNOKDyT2Z2mcQI96B\nvP2WMlYPOt/P/LNJ/ixkmSn8VXjK7yK+DoW3hLbZexDrQukVlFwbxl6CuCViBxVsSLdDmbQrxaOo\nnUr522x8zwr/U0RERLQzA7qxYR8u/jen1wXF/dsjDO1H/26Zn2/wBpx1Lk89xusvhvKfLqFoBQ6y\n02kmf8Tsb+gxmL4bt+8adkX3HOlCympJzQ76gvCOyYlkpSg7iayRZO1C8hnUkhhCwTBN7yc2JHET\npWcE3RPrTOE5FBxA2V/I2Y+Cy0LT7F0Ro/Zq8n5C1eL6NhaexWPYlezK3EeZuFu4LlY9juqv2OSB\nFfu3iFjriIxBLWHfH5NKc8w1LCxlk0357QXktVF6xs5dOfrEzI13/90hMPXgbZn7Bvffw+8vpFMb\npP6uWEm/Wtm/ZsE5xIvJ2pTKhym/VwjUXETRCyTWI+dAqu4k/d1yZBtK4bMhKLVsYlkhXFDlFyR2\nbLwpytqR5JeU55O9D6WXhqtqsQRVb1H1Kjk3UL7Y/e/YKRSOpOJx+tey8VnkdAtXgyMiIlZvdv8N\nr97MqIuprmbD7TnkqrYzqHQdwH7NDV68HFJJHr+E7z5l0FYhI1pWDkdcSW4bnFivLEp+wYJfUnI9\nif4sup3qV6l+jVgnil4PG4Dcw6n4a/DwrFyWntie4tdJlyO37puPqglk7dS4afYIKs6ndjCx/pTd\nRNEpYeNU8Rip6RRtveQU/X5P5/1Y8AqF/enxF1IZjjkVERHRPlxzHJc+wp7nhvJ+w0JdWzFoPU79\nbWbGqqni/j+ErJV9N+HtB+k6kEMuJNFO28oV3nMUk3Us839OyRVBL5RdRfJbFp1OvB9FrxIvwIks\nOpnYyKXPG4MDKNhfCDGRH9YAFUIMu9xDGrfP3ony88gaQXIWFY+Sf2AwSC36Bzk9yV3sSlgszjp/\no+xt5rxHzwPosi+JvBX8t4hY24iMQS0hHmfUYeFJpUJ5deGr8bz5Oj+/m/y604CX/sEDd3Py6Zmb\nJ1nFl49QORY9yT6JxEbL7dZqcn6ECub9htRksral6Png+l9+AouOIucn4Wc1j1D4SPPGjS1m4Ets\nSvWdIbZDrO5/m9oXiW8YNgL5f6P8CGZuGgKSJqdQ8C/iXZoeP7E+id/SN8PpiCMiItqXrBx2+zW7\nnhrKq5PH3/iXmfMdv7ibrNywEH30L7x5L7v8PHPzVJcy6R7mf0xBP/ofTUHf5fdrLR1/FRbO84+i\ndlZw+S9+Jyzwyw6i/CdkH0jyE2pfocP/mjdurKBxObEp1Q+Qe3r9f/eaF0I9FN7JokMp/yexPFIL\nGXIr8aUEjC3cJDxFdScF0YFBRMSaQXEBlx7JJUeE8uqkJ979b9APJ/6LeIJkLf8+k4+eYtiozM1T\nOZtv7qX0SzqszzpHkruUNXUmyLmM6ouZvR/pRSEDcf7Y8K2u2J3yo8jajdpXQ2zS/MuXP2YshsUO\nUuKbBL2Q0yDsR/ULwXM1lk3+A8w/nIWXoTokGtjolqZ/R2IxOmxLbCd6ltVVrqSbGRFrDJExqLWs\nToYg+OQDNhpZbwiCYT/iXz/L3BypJC+cGj5cQw9i0XdM2oOt/k7X5mRaaQHjujYoHFT3NKSc9N9Y\n9D8WvUJWD0peJKuXkCGmhaSHM6U3iw4k52ekplB1DT2upMMiFOBRqsaTmk/uFsRz6ThneSNHRESs\niaxOi/sfmPg2m+8XFvqEdxh+MM/+LXPGoJpFPHM8hevTbx/mfcabh7DNPWRvkJk5FicWo+MvwzN3\nMUN/0aPUPEzN2yQ2p+D/iLfSWzbnEKpuYtEx5BxKclzIAlP03/DzxCA6vEfyI1STGEbRghV6tYiI\niNWY1VFPfPU22x4RDEEEb6AtDmTcC5kzBpV/z5hj6b4zffZj1hu8eigj7ievDa7SEQwxuReEZ3Hy\nX6X2/nBgkNib3CPC7YPWkH0y5Tuy6Ndk70Xt61TfR8FL4eeJYRSOJ/U+solvTl60l4hoOyJj0NpC\nYQe+XcwLpWwOBa38mDXF1DFUlbPTA/VXIgoHMO5aRozO3DzNJRanaM/wrPBYMfrcyYLRLLqHrM50\nvZv8LRu3yx3SdP+IiIiIVZ28DkEvNKR0TqjPFBMfo2AAW10dyn32IacTk25gy6taPl7VCi5jYtnk\nHBaeFSWWT4dnqLqV6ruC51GH50Nsif/fJkbWFksfIyIiImJVJr8JPVE2K7N6Ytxoeo1kkz+Gcp+9\n+fgivr6bDc/M3DzNJVZA9k8zM1a8GwVjqLmRyn8RHxLK8QZZQGMJEk1cH46IaANWM/eWiFaz7Qi+\neoPPngsePHO/47mr2WPvzM0xdzzdF4ur02NnFozL3BztSTyXTj+j7z30+vuShqCIiIiI1ZnN9uP9\nR/jqrXBFbMYXvHQzwxb3vFwB5o4Pp70N6bnTmqMnYsXk/TZkkiy4trEhKCIiImJ1Z9goxtzO1LFB\nT3zzAW/el9krYnPG02NE47qeOzP/88zN0Z7EuwcPpIJHyLussSEoImIlE3kGrS2UdOTMP3LXv3ji\nMvIK2OsARi4lY0qr5hjItIeCcvjB9XXu+xStk7k52puqL5j/z5DdJX9bOh4XMs9ERERErO50H8So\nc3nhRh48l4KO7HgcQ3Zeft/mUrwOs99nYANPnNnvUzQoc3Msc/6qtp+j6n1Kbyc1j/zdKTqaWE7b\nzxsRERHR1gzail1/zsMXBg+hkh7scwZ9MhgftONA5nxA9wYGodnv0WHdptuXrITveqapfIny0aQr\nyR9F/iGrf+bOiNWSyBi0NjFofS64LGS4ycrKfNyjfrvz2e18eA79fkT5d4z7O5tfmNl52ouK95l6\nJB1OJn8nyh+i9BH6PRm8hiIiIiJWd9bdhkFbU1sdgmFnOqbFegcz4XA+vZTeI0PMoC9vZptbMjtP\ne1H+JHPPpPjXJHpTdicVz9LtvtUzPkhERETE4my6J5uMbDs9seHRPHVcSNjSbVu+f4PvHmbH+zI7\nT3tRditlf6PkN+EKWukNVL9Jx2vaW7KItZDIGLQ2ktNGJ5SJbPa8lbF3Mf7v5HZl+NWZDx7dXsy5\ngk4XUHRMKBeMYuaBzP4r2QPI34q8zZY/TjpFxZckiihqo0B4EREREa0lFiO7jQzcuSXscycf3MPY\nKynoz7a3U7Jh28y3spl/EV1uJn+XUC4YxbTtWHA5ia7k7Uz2essfJ1VN5VdkdUXHtpQ4IiIiouW0\npZ7o0I8dRzPxn4z9P4rXZ4fRFLZh1smVRbqK0kvo+TTZ64e6gv2ZsmlIMhAvJHdPsprxrskKqiaR\nHIDI+zSidUTGoIjMklPMFqey7hroKVP1GZ2uqy+n5lH7NRXzSJcz73oKd6X71Us/JSl9j0mnh78n\nSynehKHXkB0t9iMiItYSCrqz8R/bW4rMk66h5stg8PmB2imk5lD1KjmDmHk5RSfS8ZyljzPvOb75\nQzgwqJlNl13Y8FJLpCiOiIiIWFMpGshmf2lvKTJPciqxwnpDENR8hWpqXyLRhVkXUnwRhcctfZxZ\no5l6KdndqZpO+Y8ZfG4IPh0R0QIiY1BERHPJWZ+q18k6NJQXXEbhzvS4Jhh/Uov4dq+Qzr5o5JL9\nk4v48gTW+T86jgwbh28vYvx5bHJ962RKVvH1f/n+bQq6st6hdBzc+neMiFhVqI0zq6C9pYiIaAFZ\nZA2k6i3ytgtV8/5Ap5Pp8ttQrp3F5F3CSXDO0CWHqJrKpDNZ/046DCNZzqTfMOlqBpzfOrGqS5n4\nH+Z+Gk7W1zucwj6tGysiIiIiovUkepEupeYbsgeGOKtzf02P/6O4bn9RPZFv9yRvv+BRujhlHzDt\nKjZ8mPzB1M7jy58x5S76tTLrWdVsvh5N2Zd0WJ+NDyGvS2vfMmI1IjIGRbQN8/PaW4LM0+Vspv2M\n5GyyN2TRf+n3cL0XULyQ4sMpf6FpY9CClyjclE51qe5jOfT7PR9uERb8WhiIOp1kzC/C9byBB1D2\nLS+cwIir6D58hV41IiIiIqKFxGJ0PJfZJ1JyDlm9qXie3jfWt8nqRtEoKl5o2hg09wk67x8MQZAo\noN8fGXdQ64xBNYt48VhK1qPfXsz7nOePZLfbKV5JQbsjIiIi2pK82vaWoAVk0/E3fH8oHX9Hupaa\nSXT4cX2TnHXJ3570i+T9aMkh5j5M9+ODIQiyOtHnLKZc0jpjUOVMXvsJvUewzl7MeItnj2LPe8hv\nwhgVsUYRGYMiWkYqycJvw3WwglXUYlw1nqpPyFmP3M0zF9iuYHv6/pt5t1DxeDD+1M4gt0Gsi9oZ\nxJdy5StdSyy7cd0P7pzpVMvlmf4KtYvY9d76DAQdBvLxdYy8s+XjRURERGSCZA0Lp5DfmbyS9pZm\nSdJpqj8IV7pyNicng+nfC39MvCtl/yI5l3gRyRkkGhj7a2eQu8lSZKsNQVMbEssOxv/W8M2jFA1g\nuytCuf/e5HTk81vY9rLWjRkRERGxotRUUjaNwu7kFLW3NEuSTlH5ZsgenLct2RlM/97p18ErqPTf\npMqQJrWQRIP9Q+0M4p2WIltyKXqilUaxSXfSd3e2rLu+3G8v3r+YCaPZ/PTWjRmx2hDlsItoPtPe\n5YGDeeZ0HjyEF35PTXlm56gt44tzeW0TXhvKhN9Tu7B5fdMpZpzG1EPCaez0E5l2NKkMppzM24Je\nN9H/Mbr+jlnnUfV5mLv0CUr/Q8kRTfct2ZXSd0LcIMKGZNr1dNyWrFYownnj6blD41SUvUaE+oiI\niIj2YOLz3Ls/z57JfQfy2mWkMnxqWzWb93/Lk5vx+uZMvKT53/l0JbN+wpwTqHmO7w9kzmmtM8gv\njfyd6XYnPR+nwynMOIOayaSrmX8nle9QeGDTfTvvw9zHqPiqTt4kU6+i+76tk2XeeHrt2Liu1wjm\njWvdeBEREREryqf3M3o/nj2b0Qfw7s1hTZxJaiYx43Am9WDyRsy7uvnf+eQ8pu7JnN9R+TRTd2Nu\nho3nRQfQ6176PEbRIcz4TTAApcqZc1UwEuXv1HTfTvvz/V1UzwjlVCXT/k73/Vony4KxTeuJuZGe\nWBuIPIMimkflfP53Drv9lX7bUVvJqxfz9rUM/1Pm5hl/Blkd2OqF4NHz9ZV8/ms2XczTJaeJU9L5\nD1HzOeu/SbwgWMgnH0fZLXT91bLnjbdCCeX+iNg8ph5G7WzyhjLgVor6oInNT1YJ6/49xA3K7U/N\nHGI1xHN45wC6H0avYxsbd5ZF8TpMvD8o0B+8n2Z9EOrXNmqr+fQtFsxi0Mb036C9JYqIWPuY/w2v\nX8E+f6fbhlSVBr3x8d0MOSkzc6RTvPVzindg27dCIP6vLuSrCyi4Yfn9F95IIs2g18PJamoRkw6m\n/FEKD86MjA0pOZMFNUzeg1QpudvQ/SHiS/GYyhtEv/P5/EDyN6DqGxJ5xPDxQfQ6nu4HNX/+4nWY\n/SHrHlJfN/uDtfOKWHklL3zEwkVstxGDerW3RBERax9T3uGTezjoDkr6sWgWT59G/joMaqUxI3ex\nNXeqnG8PpO9x9LmGymlMOIfSWrqdufzxpv+Fgo3pfUVYX9fOZuJelOxCQRuEYejzV2ZexDfbhoON\nol0ZcB85aU3uJ4q3p9tRfLo7BRtSPoGcTkyfyLwx9DspJB5oLkXrhv1Drx3q62Z/QMlaqCdKS3n7\nbWpqGD6cbmt+1ufIMyiieXzzEn22DoYgyMpj29/w5ZPh6lgmqJzCwg9Y/1Jyu5PTjfUvZtE4yict\nv3/pc3T+aTAEERb6/4+9+w6Pqtr6OP6d9J6QCqGF0HvvAiIgqAiKgqgoCAhiV9Qr9is27NeCiF1R\nbFhRARWkCtJ77xAIgZDek3n/2PCGYICUSc4k+X2eZ557z2bOOStgzp5Ze++1Q8dB0pzSx2a3w8lZ\nsHeEeZ2cZdrDxkLTtdBiDzSaC37dzn+doEugzQqo9TB4hEJge2jxNjR8DOK+h/0vFz2mmn0gOxlW\nPAax/8Dub2HlU9DithL/mBVSwnF4eTxs/h68dsEXk2HWG44fZRKR89s1BxpfaRJBAJ7+0PEO2PaD\n434f49dAbg5EP2J2YfSqDY1fgWOzzTT7C0mfA6Hj86fYu/hC8EhI/6X0sdmzIfk9OHY1xN0IaXPM\nUuCgR6DWbqgTA9V/AY8W579O2FBosxxq3gOuvhDaG1pOh+i74eD/4OjnRY+p3hA4tgrWToG41bD9\nU9j0NjQdW7qftaLZHQNXPQl/roH9sTDuNfjgN6ujEql6dv4KrW8yiSAA3zBoNxZ2fuu4eyT/Cn6N\noM448wz1bQhNXoKT7xfx/HlmEPn0QKtbKAQNg2QHfJ/IS4Xjb8D+a+DQOEhbDi5eUOM5aLwTmu6D\nOjPAo/b5r1PjLmixGMLHmBqkEYOg9UdQ60azquLEgqLHFD0K9syCTe+YpNDGqbD3B2h8Q2l+0opn\nyxa4/17Ytxbid8DDD8Ffxfh7rKA0M0iKJi8bXM/aLt7N09SG+HkIXPxa6TPI2fEmAeRyxn1sbuBZ\n3fwZF7i+iy/kJhRsyz1pajaU1tFnIHkhhN9pjo+9BRlboMbjprOweRT9Wi5eZsvgnOPQ/Kv8ukGt\n3oEV/aH2XeDq/e/zjq+Gre9D8l6o1hyaTYBeH8GOj2Hjm2Y3sYumVL3i0XM/hstawz2nRsvvHgzD\np8DOddCoraWhiVQpudngetaz0M3TzCydczP0esVsK18aWfHgVatgLTi3ANNv5CWBS8D5zy+0n0gA\nmwP6iRPjwX4UwsdBbiLEPgy5B8B/3KkZn8XoJ1z9zQ6UnuHQ6EnT5tcIWoTAhglQ/cbCz4tZCNs+\ngfRYCGsPzSdA389g20ew/jXwqw29pkM1B9ZJqgimfAVjL4dhvczx+IEwdDL0bQd1I6yNTaQqOVc/\nkbgb5t9hPseWtoZQzgnTT5zJqybkxJvZpReage/ia74/cMZM+9wEcC3ls8KeCweuB7dgCBsPWYfg\n0K1QYwr4X37q+0AxtoZ3D4GswxDSzQwWAPhGm+scmAYhvQuJwQ6x38GRGZCTAOE9odEd0O0L2P0+\nxLxmdhPr9wn41fr3+ZVVXh5MnwaTb4Yep2r6jeoHN70IHTqCnxPWtXIQJYOkaOpeDKvegfhdENzA\nPExXvwf1+kCNDrDoARg4q3TFmn0bQ9ZxMzsooJ1pS95kZgz5XWAkFaDa9XBgDPh0Ap92kLkTYl+E\n6o+VPCYwSZsTn0GT5eYBDuDXA7Z1hbAJZsSguLIOgm+T/EQQgGcEuHibxJfrWdv+ntgAy+6FVvdD\nSBvzgX/haOjzJbQ41QG4O2iGVkWzbTU8PjH/2McLBnaAHauVDBIpT9F9YO5EaHIV+FU3tYLWfABN\nrwEXd1j+FFwy9YKXOa/gDrDuEUg/AN6nCnqe+MMkhM5+bhbGdxTETgavpuDZANJWw/F3IeyL0sWV\ntRky/zb9xOkBDZ92sPtq8BtVvAGD0zIPgn/Tgm2+TSAzxvTBeWd9oTn8J6x9Dto8bJaBHZgN80fB\npd9D60kF3+vAEklOLzsX1u6Et+/KbwsJgItbwfKtSgaJlKfoPrDqXYjqbTYYyMmAdZ9Cu1vh+HZY\n8xp0KebOiSHpBY+9OsOmNyD7fnA/VYQ55isI6g6hRagvl34dHH0car8HbjUgZT4k/QBtfwav9Auf\nfy7xfwLpUPf9/ISUZz2IfdL8fZwznvN8Xc88AAFn9RN+TSHjYOHvP/wpxH4GbR4CrzDYNROW3wI9\nvoXWz+S/z9+B9VYrgrg4yM6Ei874vhlVHZpHwbZtZslYJaVkkBSNbzh0/w/8NBYCakFWKngFwaWv\nglc12PApJOyCag1Lfg8XT7NEbONoCOlrHpTH50Gj50zNhAvx6QjVn4SDY00xNWwQfh8ElLDw5mmZ\ne8Gzfn4iCMAtBLwamD8rSTLIty3se9iMcnucum7iapMc8qz+7/fv+Bia3wFRp2paNK4HGXGw52to\neW/x71+Z+AfAkZMQeca/Q8xJk1yUis3uoJ0ApXyENodWI+CbYVCtPqTGQnAjaHdqZsyMfpCVZHaj\nLCnPYGj6AKwdDKGXQ26KqZHQbBqkFOG/F9/BkBsDewabhIqLD1R7FjzblzwmgOxtppbEmTNbvRqa\nZ3puHLgVIVF1Nv8OZpeX+g/l94Fxv4F/28JHtre9D+2fgBqnZr+0uAeS98OBX6D+dcW/f2XhagNf\nbziWADXP6CeOnoQuTc99nog4Xr3eELsRZg6CkIaQsB/qXATNhkL6Sfjm2uIng87m2xgihsOqARB6\nmUmgJ6+HFjOKdn7NMWbWzK6LARu4h0KTN/8926i4UneCT9eCz2+/7rB3R8H6n8Xh1wFiPzBbyp8e\nYI79FQIK6dPsdjj4DvSaBoGNTFv7J2H+DXBsCUT0Kv79KwtfX1NXLjUD/E6tzrDbTT8RUIrPLBWA\nkkFSdPUHgHc4LHgULn0FQpuaB5fd7ridWEL7gf9cOP6buW7UfeBZjCKPQddA4FWQewJcq/17K/eS\n8KwPWXsgOw7cTxUSy46DzN3mz0p0zZoQPhJWDYHI60wR1JhvoP6zBWcLnZZ6CIJGFmwLamK2l6/q\nul0Fz34Jz98C0dXht39gwQaYeIGi4SLieC1HgEcgbJ4JA96C4FPPyJwMx9UNihoOYV3h6ALI9oUG\nT4J7MKQU8fyACeA/FvJOgktI4c/c4nJvDgmPQF66meEJkL7V/MyuJVwa59sO/DrB6iFQfYgpghr7\nEzR9r/D3px6GoLOSG0FNTP9Rlbm4wNBe8OQn8MRNEBYE3/wF+45Cz1ZWRydStdhs0PUecPeGoxtg\n8EcQcKo+jj2vdCsMzhT1gBkwSFgEvs2h0WtF37nX5gJRD0KduyEn2SzHckRcfk0hdsqpreFP9TvJ\nf5ki0CVYhZAZAAAgAElEQVS9ftDlEPsVrBludp1M2WHqBbWZ+e/35mWZQeiABvltNpvpJ9KqeD/h\n5wcdO8JTn8FDQ8HbE977zfx32rAUEx0qACWDpHhqtDG/GCe2mWSQ3Q6bvwJ3PwhqcOHzi8IzAmqO\nKvn5NldwK2VdijO5BUPIGNhzDYSdSjDETYWQ0QVnCxVXrQchogvEzTU1glp+BT7neOCEtIFD8yCk\ntTm2281xeOeS37+y6DzATO28YxrEn4CGzWDMMxBQin8bESm56L7wz/8g5YhJBuXlwurpUL1D6WYF\nncm3LtQfBUmeF3pnQe2OnNVwrOQxuJ45COIP/3SFvddC8FhTv+j4G9DobqideO5rJJxn1qvNBvVe\nhcy5cGIRuIZA65/PPTod0tr0Cw1HmOO8HDj8BzQdX+wfrdK5bSC89wuMeil/N7Fp94JXCZbviUjp\nNRkMm76CjESTDMrNgpVvQdRljruHXzPzKikXT/AoZh9zPkE9wONd2DcCgm80JSPipkLjF0sRozvU\nmgku30DiSvCOgg6/mE1qzubqCX4NIWaB2YQGICcNjiyCjteXPIbKYuw4+GIGXP00ZOdA5w7w0MOO\nS1A6KSWDpHhsLtDv5VPbBX9yatTTxxQGrcy/LBEPgndzSPjeHFf/T+mXnwFU62xeAFnnGZ1uMhbm\nj4CM4+YD/5GFkHkS6pXBVsgVjc0GPa4yr5JOsxURx3H3Nv3EgkdhhTdkpZjCxT2LsVtiRdTxadj/\nExz81vwddHoavEs57d7mAqF9zOt8fQSYZWELR0PSHlMz6OBvZlld5MWli6EycHWB2640L/UTItbz\ni4Bej8O8+8A7BNKOQ1hr6F6CJWKBGY6Pr6x0ewf2fw3HPjfP5y5TwbsdhW4hf9r5agaBWQURMci8\nLiT6cVh1u9ld0ifc7BoW1gMCS5E0qyw8PGDUaBh5izmuIv2EkkFSfNWi4dqvTTFpG+DdxLG/MHY7\npG43H4J9GjrHL6PNBoFXmJcVvMOh3zew70dI3AU1+0HdgUWrpVSVOMN/KyIC1dvAdT9C/E5w8wGv\nEi6pPRd7LiRth/Qg8Kl34feXB5urqesWdUaSvgi73TtMYAPoNwv2fQ/Je6DBjVCrn2OWwVUm6idE\nnEO9i6FONzixEwgDvxLUVjuf3CxI3m5qc/o4+Nol5eoJ0TeZ12nlWau5Whdo+wMc/RayD0OTByC8\nCtcKKkwV6yOUDJKSsdlM4TeADAf+0qTuhK23nyoAbTfb6zZ7x0x7rOo8AqHRzVZHISJSNC5uZjkx\ngCMHbk+sgrUPmuW12cngXQ+avgVU7iKPReIdBk3HWR2FiEjRuHpAeHNIc0CNzzMd/QM2PAGeoZB5\nzOxE2WYKuPk69j4VkXcdqHc/+GdZHYk4ASWDpGx4lGCbc3sebB0P0bdA3aGmbe9nsHUCdP65YKa2\nRlErhTqAzUFFTwuTU8iOMCIiUricNFh1F7R5wex8kpcDW1+FHQ9D0DdWRyciIlZLPwLrH4XO70G1\nVmaG0PpHYOsr0PIJq6MTcSr6JirOIfMYbH4QchIhcSOk7j1VPPMmyEkyM4bOlpsG+9+HtWNg88OQ\ntKn84xYRkfKRuh9W3wMuHnB8KaQfNbOPGt8FCcsht5A1WbnxED8FYofC8YcgZV+5hy0iIuUkcSus\nuRfc/SF2AWQlmNlHTSdCzOzCz0k/CltehBW3wpaXID22fGMWsZBmBon1MuNgxWBTB6fjFIjfAEtv\nhG6fgl8DTGGis2bn5OXAmlHgHgQ1h0HaAVg7Glq8BiHdyy92ux1Sd0FeJvg3VW0GEZGykLwTlo6A\n+sOh8Ug4uhiWXAc9vgH3wMLPyUuCI5dBcCuoO8wMNCy8ES76CAIblV/s9jxI2Q64gF+jgrNcB+4o\nvzhERCqzuGUmEdRkHAQ2hoO/wtIboMe3gIt5Fp8t/QgsGQbVr4BaI+DEUlgyFC76Brwjyi/2vBxI\n3gJu/uDrJHXwpEpQMkislbobVg6HGt2h7aOmLaKb2Spx53QIbm92K/M964P78T9NAdE275hC0wA+\ntWB3OSaDMmJg3V2QFWdqV+RlQav/QWCr8rm/iEhVEL8G/rnNFERudqdpC+9qpv7vnQHYILADuJ5V\nMyjpCwhoCC2nnDqnj9nafvs06PRq+cSevAM23GX6K3suuPpCm7fAJ6p87i8iUhUc/QPWT4LWD0Pd\nq0xbRDdYegcc+gFOboTIQnYB3vMJVL8SGk8yx2GXADbY+wk0e6h8Yo9fAZvuN4mgnCRT06f1W4Vv\nDy/iYEoGiXXsdth0LwTUgYizEjhhnWDHh5CwCVpP+3dl9+TtENw9PxEEJgm0qYgP7sQNcPhLyE6E\n0IuhxtVmuUFxbHwIwnpD9O0mjti5sO426LEAXDyLdy0r5OVAzGJI3g/BzSC8Y5WroC8iTi4vC1bf\nDT4RENal4J+Fd4I1T4NXXWg2DeLOOjd7M9TqVrAt+CI4/FXR7h23HPb/YGKo2R8iLy3eM9KeB+sn\nQL0JEHmNaTvwKay/G7r8WDGet9lZsG0pJMVByyZQtxxnVImI86mbkP//3QqZaXOm+vFlG8tpP4TA\nhklmOVhox4J/Ft4JtrwKIW2h88vgflZN05RtUPvWgm0hF8Ghjy/889ntcOQPODwHbG5QZxCEn/V9\n5kLXyE6FDXdAy1cgtKcZNNjxMmx5FNq8e/5znUVmGmxeBOkp0LADhEdZHZEUg2oGiXXSD0DWcYjs\nCbHLCv5Z7BIIbAdd54JvIVsS+zWCkyvMg/i0+OXg2/DC9z02D9aNM9sRh18KMbNg4z0Fr3UhmcdM\nB1JvfH5CKqI/eNc2GX5nl5UMf9wEu6aDxwFYNxmW3A152VZHJiKSL34N+NSA0LZwbGnBPzu6GEIu\nhzbfgWch0/ndm8KJs57HJ/+GgAYXvu/uL2D14+DbBgJ7wZZpsHFKMWPfBDYPqHmtSfzYbFDnJshO\nMLNinV1SHLw9Blb/ConH4P3n4cu3i9dXioiUtZhFENEFqjWDY2d8n7DbTT/ReAxcNA3c/f59bkAD\n8/3hTPFF7Cc2vwpb3oLAHuDXAdb+F3Z9UrzYY5dAQCuTCAJTbqLB3RC/DHLKcbOckordC6+Pgq3L\n4Phh+GAi/PWF1VFJMWhmkFjH5m5mp0QPMYmJ1Y9Djd5wfDXs+x46flNw5s+ZwvrAvumw4W4z4pp2\nAPZOheYvnf+edjvsfAlavgbBXU1bxGWwbAAkroWgdqX9oUp5fjnZ9jHUqAHXTjJfUHJz4MMHYN+v\nED3Y6uhERAybG+RmQpNR8OdNZmlYaHuImQ/H/oY2P597ho3/jRDzAWx+AiL6QtJmOPAhdP/g/PfM\nzYCtb0LHb/NrN4T3h6W9ocHN4BNZjPjPjs1Ghekn/vgAmveCfmPNcZ/RMG087FgPjdtYG5uIyGku\nbqZvaD4BFt0OGXEQ0MjM7Mw8AQ1vPve5DW+GBTeYZ3VwVzixDI7+BL0uMIM0/Rjs/wa6zzf1S8Ek\ndFZcBVFDwc2n6PFXhFmi5zL7Tbj4Jug4yBxffLMZRGjRE0JrWRubFIlmBol1vCPBtwHs/BL6fAI+\nYbB1qnl4d5oFvtHnPtfFHdp/aoo27/8Iktab6ZSnM+vnkpcB6Yeg2hnLDVw8ILibKdxWVJ7hZnbS\nvvfyC9Id+wPS9kNw56Jfxyqxy6DjwPwOyNUNOgyA2KXnP09EpDwFt4WcdDi6HPrMAFsubH4d4v4x\nRUHPV1PBNRBqzIG0cNj+IcQdgx6fQlDT898zLcYUpT6ziKd7AAS2hsTtxYi9uUksxXxvBiLsdjg4\nw1yrsBmvzmbXKtNPnObpA636wra11sUkInK2yF5wYgNkHIdLPoKMWNj0itklrNdn4Op17nN9a0Lv\nL8AlFfZPA9d06DXTzEg9n+Sd4N8sPxEEZnWAZxikHix67BEXQeJ6k4QCs0xs91vme4pbITOZnElu\nNuzfBG0vy2/zD4Ym3WD3auvikmLRzCCxVovXTN2g3d+YEWD3atB+ZtGKa7r5Qb07zKuoXLzMgzpp\ng/lgD+bBm7ja7CRQrNhfhvW3w6GvTQHp3DRoM7Vi1AvyrGam/Z/p5DHwqGZNPCIihbG5Qsd3YM1E\ns1QrLwf86pkC0J6hkHmB893CIPiJ/OOAImwZ7B1hlnJlHAGvU18IctMhaRP4Type7G2mwvo7Ye80\nsOeYZWNtplaMkWDfIEg4BkHV89sSY6GORntFxIm4+0H31+Cfx8wAbXYKhLSHjs8XvjTsbL41oc0j\n+ce5RXg++0VB8lbISQU3X9OWGWcSUcWZPeruC63ehE0TzXegnETwioRWbxf9GlZxcTWDBMknoNoZ\n/URCLDTqeO7zxKkoGSTW8qoOHb6E9MOmSKdPVNl+SLbZoP69ZneX+veaGT4HPwOPkIKzhYrCOxI6\nfw8pO8zW8gHNK87W8g2uh3n/hcBwqN0cdiyHFT9C7wssnxApLz7Z0D7G6ijEEXYFw9m7v0cUpxZC\nELR/HxIOmVmhTX2BNGBv8WNZXPfC73HzhQYjYe1oiL4LXLxh/7sQ0QP8inD+mfybQPd5p2aeupjZ\nrBUhEQTQ+Sr45Q24ZhKE1oYNf8L2v+GaqVZHJiJSUFg7uHw2JO8DWyB4h5ft/XxqQmQ/WHsLRI03\nNTf3vgnRN4K7f/GuFdwVLloIyZvB1Q/8ilCvyBnYXKDTIPh+CgyeCP4h8M+PEB8DjbtaHZ0UUamS\nQTabbSjwFNAU6GS321c5Iiipgrxrlt+9IoeYpQWHZkB6jBn5rTuhZNey2cC/sWPjKw+RPSDrfpj1\nGiQegNAm0HUKBFWQDkgqDPUT4hA2G1Srfeoguezv1/g2k/jZ9yVkxoNfbfMhvyRsLhDQwrHxlYf2\nV0BOFnz+KCQdh+jmcPvTEKAZpOJY6ifEIWwuEBANOeU0MNvmSdj3LRx838wQCmwMta8s2bVc3CGw\nAtZi6zMSFsyA9++G9CRo2AlueQncPayOTIqotDODNgFDgAqy952Um8AMqyM4P59msP84uLlCQHXY\n8TgENYbOL517i3m73WT+XdwdN7Ib7+2Y65RE1OXmZbdXnJFqqYjUT0jFY7NBSAdTSNorAjyD4Z+7\nzEhwy0nnfmba7WDPNrXoKjqbDboMMS+7HYIvtCZPpMTUT0jFY3M1y9G2vwOBTczW9otHQMMx0HD0\nuc+z55llw5Whn3BxNQmhPiP1faKCKlUyyG63bwWw6R/e+dntMH8O/PkbJCdBy7YwdARUC3HsfXKz\nYc/PEPOPqc0TPQz86134vPK2/SPwqQUdXjIPrrwsWDwSDv4GdQvJ6h+YDZvfgrQj4FcHWt4HkZeU\nf9xlQb+/UobUT1Qgubkw91tY9gdkZ0GbrjB4BPg4uIhlVhps/BmObIHASGh9FQQUsjW81ba8ZpI/\nze83x9kTYcE1pi30rHoIdjvsmgHbP4DMk2ZwofV/wKNb+cddFvT7K2VI/UQFkpkJP34BK5eAiw26\n9oRBw8DDwYmN5CSYPxcOHYTadeCSS8EvwLH3cIQNk6HxeIi+3hyn3wnzB5t+wrd2wffac2HLVNg9\nE7JTIbTdqVpFLcs97DKh398KSbuJVRVzfoK/focb74JJr5p1nS88ATnZjrtHXi78dRccmg3N2kKw\nDRaOgONrHHcPR4ldAvWuz39wuXhA3Wvh6JJC3vs3bHzNFKIbsg7aPApr/gvxG8s3ZhGRsvTtB7Bj\nI4yfBBOfNwmhd54xiQ5HyUqHL2+HQ+shuhvkZMCMsRC/33H3cJTYJVBveP6xux/UHmjaz7b/B9g7\nC3q8B0PWQOMxsOweUw9PRKSyeO8VOHEc7nka7ngSDhyEjxxcRywxAZ58CHYlQlhv2HnSHCcmOPY+\npZWbBSfWQtQ1+W3e4VC9NxwrZHfere9C3Gro8zVcvRJqDYDF4yCnODX0RBzrgjODbDbbH0D1Qv7o\nUbvd/mNRb2Sz2cYB4wAICSvqaeIIdjvM+RHueQYi65i2q0fBvh2wdhV0dFCRr5ilkB0PN71npg0C\nhEbByteh56eOuYejeARBxtGCbRlHTfvZ9nwJze+E0LbmOKILNLoF9n4DwZUkmy9SCmXSTwSrnyhX\n6WmwfD5Mfhf8TlV7HnEnPHkb7N8JUY0cc58tc8A/HAY9a5LxzfqDTzAs/wQuf+LC55cnj0BIP2tn\nmPSj4F9IbbXdM6H1wxDY0BzX6m8GQmK+g/p3lU+8Ik6sTPqJSNWvKlexR2DPdnjuQ3BzN21jHoRH\nRsPJeKgW7Jj7zP0Z6naHPhPNcbP+8MdLMG+2WdXgLFxcwc0bMk4U3Io+/ShE9Pr3+3fPhItngO+p\nHRnrXwexS+HYXIi85t/vP+3KHeZ/g84owZHpJBvWZDlJHFJiF0wG2e32vo64kd1unw5MB7DVbWgn\nQxuZlZu8XEhKhIhTH2jz8uBELASHw7GT/P+/RUAm9C7B7iynfbkM6nfOTwQBRHeHeS/AgF3Fv97W\nMvwymDMYfn/VLBWr1hJiF8Hez+G6NyA0seB7l8WD11mxeIVBxgpoHlc28YWmlc11RcpAmfUTUn5S\nk8DTKz8RlJsDJ45BWHU4eQKiHHSfo9tMv3DmdPLo7rD5NwfdwIGib4D1k6HDi2Yb4UOz4chf0Oy+\nf783KxG8C+knkuLLI1IRp1cm/UTLOuonylN8PIRWz08EZWdBQjwEBkHiScclg/bsgeY3FWyL7g5b\nv3DM9R3F5gpRw2DNo9B2MniFwp6ZkLIfql9c8L12O2Ql/XuXM+8wyHayGU9SpSgjUxW4uEJ0YzPq\ne2gvrPgLXF0hIx3SU6HnAMdUfa8eCRsWFCwgFrMJImqV/tqOVr87pCfC3xMh+QgE14crnoDQ+v9+\nb3Rn2PslRHQzOxXkZcP+r6FDIVn/iiov1yQJT3fwIlK1BIeZfmHDSlj5F2xcBZ6ekJoC4ZHQujO4\nOGBleXAd0y+0HpzfdmSTaXc29a6H3ExYcovZUSy4DXSbZj7wn636RbD7S1P/wWYzO8vs/xEaPlr+\ncZeV3FzADq766ChSJUVFw9FDsGsL/PaV+V8vH0hLgU3rIKqQz9AlUaOG6SfqdclvO7IJIiPPfY5V\nmt4D29409eRyUiCsG3T/AFw9C77PZoPq3c3soManiktnHIdDv0O7z8s/7rKSmwPYzOcJqRBKu7X8\n1cCbQBjwi81mW2e32/s7JDJxrKFj4PXHIc8OEx42NX3S0+DD1+DHz+DaMaW/R9uuMHsWzHseWgyE\nhMOw5B0YcWvpr10WWlxuXnk5595BDKDdUNj3IPw5BELawfEVEF4T2l5WfrGWldwcmP0pLJ0DWRlQ\nvwUMnQDVa1/4XJEiUD9RQbi4wrVj4f0p5sv+/ZMhqiEknYS3n4W/ZsMlg0p/nxZXwGejYeFUaNgT\nYrfDsg/h6imlv7aj2WzQ8BZoMMoU/jxfP9HsTlg0Gv4aYbYXPrIQavSCkJ7lFm6ZyUyHuVNhwx9m\n0KBZRxg2AWxO+MVMKiT1ExWEtzcMvA7+9xj4BsBjr5tVB3FH4X9PQo1a0L5z6e8z4EqY/Ai4eUCd\nDrB/JWz8EZ54rvTXdjQXNzNbtOm9QJ6ZLXQurR+GRWNMLVLfGhCzABqMAL+G5RZumUk5Cb+8CVuX\nmYHzlhfD5beDl4M3oBCHK9Uwn91u/95ut9ey2+2edrs9Qg9uJ5aebpYAtOsKzduZD7k+vjB8LPz9\np2Pu4e4ODz0DNT1h6WtwaB7ceje0d1BNorJyvg/4AO5eMOx/0PcuqBMJV98P1z9TOUZHZ38Kh/bA\npLfhpVnQqgtMfQyytIWwOIb6iQokLQUCg+GSK0wiCCCgGgwZ5bh+wicIrn8HstPhz1fh0DoY8jJE\ntnDM9cuCzXbhfsKzGvT5BhqPhYD60O0taPdE5dhd5aeXgTT478fwwpdQozZMe8qxhcWlSlM/UYGk\np0JoBFw5PL/8RFh1uGI4LJnvmHtE1IBHnoacnbBoCuTtgkcmQ3iNC59rFZvt/IkgMLsR958N0dea\nregv/gyaji+f+MqS3Q5fPAFhIfD8TJj8KXjYYZYTDvLIv1SCb7NSJIf2moeop1fBdg8vx+4o5ucP\n14503PWchc0F6nYwL78sq6NxjLxcMyNo0ttQ7VSti16DYPNK2Lgc2leiZXAicmGH9kK1EPA4a3q7\npydkO7CfCIiAvhMddz1n4eIGkb2tjsKxUhNg10rz4f7054crbob1y+DgVqjTzNr4RKR8HdoLPn7m\n+8OZHN1PRNaG8ZWw+L6rp9lgoDI5ugdS4mHw6Pzl5MNuh8duhsQ4CNSGIM5MW8tXFTVqQ0oKrFoC\nx2JMm90OP880y7uk6snLM0vD/M7aQS0w2MwQEJGqpUZtkyRePA+SThW0zM2F2V+aWaVS9WSmmi99\nZyYIbTYICIG0JOviEhFr1Khtngd//mRqjwJkZsJv30KHLuc/Vyqn9CQzi/jMuoLuHuDjD+nJ1sUl\nRaKZQVVFs7Yw5xtwd4Nn7oM69eHYEfDyhgdesDo6sYKbu6kRtOw3MyMI4ORx2LgC+g+3NjYRKX+d\nLob5P5kZno/dZpaKHdoDEbVhwFCroxMrBNUwS6U3LodWpxKCMXvh0C64rqW1sYlI+bvkcnjmATNw\nOGmM+T6xdye0aA09+1gdnVihVlM4dhgO7IA6jUzbtjWQkwPhda2NTS5IyaCqwsUV7vovLPgZNq00\no72XDYOel1WOmgZSMkMnmBpBm1eajn3jCrh0GIQ68bpsESkbXt7wwBT480ez5CkvD4ZPgA49rI6s\n8sp19gnaLnDlf+CLx2HFn2ZGwJaVMOwO8PK1OjgRKW9BIfDIizDvR3CxgasL3PkgtGhjdWRiFQ8v\nuGoivPUYNG1vNqfZuRGGP2G+f4pTUzKoKvH0MqO7GuGV06rXhsemm1HftBQzI0iJIJGqyy8ABt9k\ndRTiTOq0hDtnwOEFpsbg1WMgIBi0SkykagoOMxvQuOVZHYk4i2YXQZ3msG2ZSQANfBC8/a2OSopA\nySCRqs7DU8WiRUTk3Lz8oLOWgIiIyDn4VYMOV1gdhRSTs89PFhERERERERERB1IySERERERERESk\nCtEyMRERcT6ueRCQaXUUIiIiIiKVkpJBUrXl5cK+FRC7HYLrQoMe4OpudVQiIuIs8rLgyHxI3gNB\nzSCiB9i0Q4qIiJySkw4xcyH9CIS0h5CO2q1ZKgQtE5OqKycLvpsIS6dDTiasmwWfj4WMZKsjExER\nZ5CVBIuGw6FPwT8Rdv0P/r4VcrOsjkxERJxB2hFYcCXEzwa/eNj4OKx9GOx2qyMTuSAlg6Tq2vIb\n2PPgxg+gx20w7C0IbwirvrA6MhERcQa7PoLqUTD8LdNPjHgPPHLgwPdWRyYiIs5g2+vQoh9c8xL0\nvB1GfgIpm+HYEqsjE7kgJYOk6jqwCppfBi6npvvbbNDiCtMuIiJy4m9oOTB/ur+LK7S4DI7/bW1c\nIiLiHI4vN/3Eae6e0KwvxKmfEOenmkFSdfkEQ0JMwbaEGNNeGomx8Od7sHsV+AZB52uh3RVaOywi\nUtF4hkDiYajdNr8tIQY8Q0t33eQdsPdlWLIagqpD9xugWa/SXVNEpDzZ9bkWMP1EQgwERua3nYwB\nz8alu27CKtj7GizZAuFRcOUN0LB16a4pchYlg6Tqan0VfHWHKRwd3Q1iNpr6QZc9XvJrZmfCJ/dB\nq37QdxwkHIVfXoPcbOh0ddGv46d6FCIilqt3Myx+EPxCoWZr2LMM1s6C7p+W/JqZcbD2Jrj4Rmh+\nBxzdBbNfBVc3aNzdcbGLiEjZqzcSfn8ZLn8UQqJh6zzYvRR6/6fk10zZDhtvg8smQPRDsH89fPwC\njP8v1GnkuNilylMySKqukHow6FlY/C7MfQaq1YVL7oc6HUp+za2LIKQ2XHyLOQ4Ih6smwddP5CeD\n0pMg6TgE1zRTSUVExDmFdYaWj8P8qZC0F6o1hY6vQ0ADIK9k14z5Fpp2h85DzHGDEBhwJ/z9dX4y\nKCUe0hIgpI5JEomIiHOqMxjysmH2c5AWC6HtoOsHZsZQSfuJQ59B12ug1aXmuHlvSDkBC3+Cmx4w\nbQnHIDMDwmqBiyq/SMnoE4ac26GD8OtsOHoEouvDFVdCtVIuoXI2tdrC9dMcd72kOAitU7AttI5p\nz8uFeVNh/RzwD4HUROg7Htpe7rj7i4iUp107YM6vcDIemjaHywaCr6/VUTlWZD/zcpTMIxB9jn4i\nOxN+fgl2LgffQMjKgIEToVE3x91fRKQ8bVoLf82BtFRo2R76XgHuHlZH5VhR15qXo2QdgbA+BdtC\n68LOxZCaBJ+8DId2gIeXqWU3/D9Qt6nj7i9VhtKIUrgD++GZJyEiAq4ZanbdevIRSEq0OjLnFtUa\nti2GrLT8tg2/Q1Qb+Oc7OLodHvwUJn4A41+BBR9AzDbr4hURKalNG+DlKdCwMVx1LcQdg8lPQJaW\nuZ5XUGdYvwByc/LbNvwOddvA/PchLwMe+QIe/BhufAx+fAGSjlkWrohIiS2dDx+/De3bw5WDYOcm\neOM5bbt+IQFdYN0f+X9PdjtsmAcNW8JXb0JwJEyaCQ99BpfdCp88CVnp1sYsFZJmBknhfv4Brrza\nzAYCaN4S0jNg/h9w1TXlE0NYavncx5EaNIRG7WH6OGjRx9QM2rUCxrwAs16By8eDb4B5b0Rd6DIQ\ntsyD+vWtjVtEpLhmfQ23jIXOXc1xi5bwwmT4Zzlc1NPa2JxZeH+I/R6m3wHNu0Psbji8DW55E94b\nB7e/AR7e5r1RLcx7Ni+ErkOtjVtEpDjsdvhhJtwz0Xw+BmjVGh6aCDu2QOPm1sbnzGrdAOt+hQ/v\nN98r9q2DjHgYchM8Nw4e/Rrc3M17W1wEK2bD9lXQsoe1cUuFo2SQFO7QQbjsioJtTZvB+rXWxFNR\n2Gxw9T2wZz3sXA21ouHKseAXZJaJnX5wn+bmbtpFRCqaw4dMv3CazWaWih06aF1MFYGLG7R6F+L+\ngrSqCfQAABt9SURBVKyFUK89DHoIPH0L7ydc1U+ISAWUlQmJiVC/QX6biys0bgIxB5UMOh83X2j3\nFcTOgcx/oE0/6NbFbEgD5u/xTK5ukKt+QopPySApXN16sGE9RJ/xAN+wzrTL+dlsUL+NeeXlwYqf\nYfXvkJ4C37wEd7wJ3n6QdAKWz4ahD1odsYhI8dWNMv3E6VlAdrvpJ/oNsDSsCsHmCuF9YEBdyMmG\nv76B9YvB0ws+nwzjXgI3Dzh2ADYuhLtfg8AMq6MWESk6D08IDoFtW/MHDnKyYfMm6NbX2tgqAhcP\nqDEILmoDGanw2yewZSV4+cLM52DEE6Zw9L5NsH8LXP+w1RFLBaRkkBRu8NXw38chPd1M7VyzCnbv\ngpFjSnfdvFywA66uF3zrBaUkwKo5EB8DtZtBm0ucryDd3A9h73oYOMp0ivO+gBdvghoNIGYX9BoG\n0a2sjlJEpPiuHQ6vvgjH46BGJCxZBDm50LFT6a6bm2OS6mePfJbEyWOwYi4kxUOjtpB3vZmZ40xm\nvgzpaTDkdvOz//wBPHe9WUp8dC8MGgdhkVZHKSJSPDYbXDMC3nwNBg6CoGrwxzyoVRfqNy7dtXOy\nTR/hiF20kvbAge8hJwWq94bQniZ2Z5GXB58/DGHhMPx+SEmE76bCc8MhuAacOAzX/we8K9nmDVIu\nnOwTkTiNGpEw+XmY9xssXGB2E7vhZvDzK9n10tPgy/dh5WLIs0ObTnD9OAgMOvc5HueZ7njiCLz1\nIDRpD7Ubwsb5sPo3mPCC8ySEMlLNGt5J74N/NdM25imYfDN06AUtHjbLx9C0ThGpgBo3gcf/C/Pm\nwO6d0KQ59On372VORZVyHBa8Arv+Nh/ym/aFi+8BD5+SXe/gDnjvcWjbG2pEwV+zIG8FdHgDbE6y\nf8bxGNi5Hh7/NL/vGvcsPDsKeg02CSwvfcAXkQqqY3cIDoW/5kLaNuhyMXS/pOTJliOHYcZ02L7J\nDLL2uhT8nwCXEvY7RxbC2knQtT/4BcCy5yF2MbR4rGTXKwv7N0BWKtzwYP7f261PwxsT4dIRUK8l\nuHtaG6NUWEoGybmFhcONIx1zrQ//Bz6+8OIH4OYGP38Fbz8Hk6aUrEP4fSZ07g8DbjLH3a6A6Y/C\n6j+hy2WOibm0kuJNsejTiSAwa3ojo02b33kSYWc7dgS+/gS2bAD/QOg3EPpc7lwjFyJS9dSqDaNv\nLf117Hb48UFo0wru+gyys+HLD+H35+CKZ0p2zdkfwhWjTV8B0PVyeOkuOPY3RHQvfcyOcOIoVK9b\ncBDD1x8CQyA0sniJoEM74ZcPYd82CImAS66Ddr0dH7OISHHUb1z6mUAA2VnwylPQbxDc9TgkJ8An\nb0Pe69CqBCUX7HbY9DyM/I9JvAN0GQDP3Ap1rwd/J9ncJf4w1Kpf8DN/WC1TP6h2k+IlgnathXmf\nwJG9UL0O9B0JjTs4PmapMJxkaEwqtYQTsGMzjLjNZN29fOCakZCcCAf3luya+7ZCq4vyj202aNkd\n9jvRNu3B1SErAw7vzm9LSTAx1m5Y9OtkpMOLT0C9hjBlGoy/Hxb9Dn/84viYRUSscHQL5KbBkBHg\n5Q3+AXDzbbBvJaSeLNk195/VT7i5Q6vOcHKdY2J2hJr1TR+ReCK/LfaA6StCaxb9OglxMP0x6NoV\nXnkPRoyB3z6CjcscH7OIiBXWrYSISOg7CNzdITgMRt4Je74rWZH9rETIjIeGbfLbvHyhYVuI3+C4\nuEurVjPYvgYy0vLbdqyFgJDiDRgc3gVfPAeXD4RX34dBV8PXL8KBrY6PWSoMzQySspeWamYFnTny\n6eICgdUgNblk1wypbj5AR55R0PrQLgipUbpYHcnN3ewkNv0x6DbQTGdd9gv0GGwe4EW1ahnUrgdX\nXGOO/QNgzN3w1gtmhpCISEWXkQyBwQVHPj08wdMbslLAt9q5zz2X4Brmw2+D1vltB/ZAQLvSx+so\ntT3h8qvhzfvNDNfcHFg6G4beBBF2oIhFoxf9Cp27myUTAE1bwg2jYd4s6OmgnzfFSZZgi0jVlJIC\nQcEF2wICIScT7DlAMevMufuCzQ2OH8mvy5aXBzF7oLGDVkY4Qtcg2NYR3rwPOl8GKSdh+RwYfydE\nphb9Or/MgssHQ8du5rhdFzgRB2u+g873lCy2/YElO0+chmYGSdmrXhPsebBpTX7b/t1w5BBEl3Da\naO+h8PP7sOoPOLoffv8CtqzIXw7gLDr0Met6M1Ig/igMuyd/aVtRJZyEiLOSXOE1TLuISGUQ2RIO\n7YWD+/Lb1q80u6kEFWOGzJn6Xgdfvmpmx8TshR/fhdhjUPNSh4TsMAOvgVvvgrSjkB0P90yCXv2K\nd43Ek2bE/EwRkeonRKTyaNEGNqyC+Lj8tkXzILw1uJagZo6LOzS8BT58BravNkttP38FCIIQJ1s6\nNWocXH8jJO4Bz0x4bDK0aV+8aySon5B/08wgKXsurjDqbpg2BRq3AFd3kxgaeafZRrckGrSCmx+B\n+V+b+kF1m8IdLxesz+MsajU0r5Jq1gqmvgSDrwPvU4VUl/5pRn5FRCoDT1/o8wC8+Bi0bA+ZmbBz\nCwx6vuTFntv1NrUUFn0PySfNUoDun4FrCfudstSkhXmVVNOW8NsP0Lt/fgHvJfPVT4hI5REWAQOH\nwtP3QetOJgl+eD90+bDk12w4FjxC4LuZkJ0KEZdApxecryanzQZtO5hXSTVrafqFNp3MCg273Rw3\nK0XfIxWekkFSPpq0hGenwboVkJsL1405/05iRdGglXlVdvUaQpuO8OR9ZleGuFjzJemBp62OTETE\ncZr0hVptYM8ys/17jyfBy79012zZzbxOWxxWuus5q3ZdYPlieGoitO1s6vEdOQyTnrU6MhERx+k/\nCNp2gk1rwdfPJDYWRJf8ejYbRA0xr9NynSwR5CiX9IdVy+G5SdC8FWzdZIpQ33q71ZGJhZQMkvLj\n6wfd+1gdRcVjs8GNt0LnnrBlPTRtBaMmgI+f1ZGJiDiWXyi0GmR1FBWPqyvc8RBsXg+7t0OHbmbw\nwFPbDYtIJRNeHS5xkp2DKxIvL3hkMqxdZZZk978C2ncyuzxLlaV/fZGKwGaDhk3MS0RE5GwuLtCy\nrXmJiIiczc0NOnYxLxFUQFpEREREREREpEpRMkhEREREREREpApRMkhEREREREREpApRMkhERERE\nREREpApRMkhEREREREREpApRMkhEREREREREpAqxZmv5gEzot8eSW0slEZlsdQTOzSvH6gikpG6x\nOgARKTej1lkdgYiIiFRRmhkkIiIiIiIiIlKFWDMzSERERERERKQ0vpxldQRla0YrqyOQSqxUM4Ns\nNttLNpttm81m22Cz2b632WxBjgpMREQqPvUTIiJyPuonRESsUdplYr8DLex2eytgBzCp9CGJiEgl\non5CRETOR/2EiIgFSrVMzG63zzvjcDlwbenCERGRykT9hIiInI/6iQpkwC6rIxARB3JkzaDRwFcO\nvJ6IVDUHE+Gtf2BrHLStAXd0hHA/q6MSx1E/ISKlsy0O3l4JBxLhojpwWwfw97Q6KnEc9RMiUjq7\nd8P8eZCSCM3bQu9Lw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"text/plain": [ "<matplotlib.figure.Figure at 0x1a1db02b70>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(20, 6))\n", "for i in range(3):\n", " clf = DecisionTreeRegressor(random_state=42)\n", "\n", " indecies = np.random.randint(data_x.shape[0], size=int(data_x.shape[0] * 0.9))\n", " clf.fit(data_x[indecies], data_y[indecies])\n", " xx, yy = get_grid(data_x)\n", " predicted = clf.predict(np.c_[xx.ravel(), yy.ravel()]).reshape(xx.shape)\n", "\n", " plt.subplot2grid((1, 3), (0, i))\n", " plt.pcolormesh(xx, yy, predicted, cmap='winter')\n", " plt.scatter(data_x[:, 0], data_x[:, 1], c=data_y, s=30, cmap='winter', edgecolor='k')\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Sensitivity with respect to the hyper parameters" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "image/png": 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dhPOTfFI47DJup2Nw1QzDOpauToIxjqcqd8TVaAbAoAw8W7UzkcYz+V6WFStd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npOmMCC227Hz0/vlci5osoGEFVWu4TnstbS5/fXiK7kYuKDLYlLSObxiTM4kBTHsIZqAHoj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Of5Nr4Q+ApeejuD9qCq3L1SbDbGD87u/5o+VLNCmf91CYKceX4qnzYGWLD9ApHdlmI113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\nDtBtRcECuacVOHt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"text/plain": [ "<matplotlib.figure.Figure at 0x1a1f0568d0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(14, 14))\n", "for i, max_depth in enumerate([2, 4, None]):\n", " for j, min_samples_leaf in enumerate([15, 5, 1]):\n", " clf = DecisionTreeRegressor(max_depth=max_depth, min_samples_leaf=min_samples_leaf)\n", " clf.fit(data_x, data_y)\n", " xx, yy = get_grid(data_x)\n", " predicted = clf.predict(np.c_[xx.ravel(), yy.ravel()]).reshape(xx.shape)\n", " \n", " plt.subplot2grid((3, 3), (i, j))\n", " plt.pcolormesh(xx, yy, predicted, cmap='spring')\n", " plt.scatter(data_x[:, 0], data_x[:, 1], c=data_y, s=30, cmap='spring', edgecolor='k')\n", " plt.title('max_depth=' + str(max_depth) + ', min_samples_leaf: ' + str(min_samples_leaf))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To overcome this disadvantages, we will consider *** bagging or bootstrap aggregation ***" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<a id='bootbag'></a>\n", "# Bagging" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<a id='bootbag'></a>\n", "## Bootstrap" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Usually, we apply the following approach to the ML problem:\n", "\n", "1. We have some finite sample $X=\\{x_i\\}_{i=1}^{N}$, $x_i\\in\\mathbb{R}^{d}$ from unknown complex distirubution $F$\n", "2. Inference some machine learning algorithm $T = T(x_1,\\dots,x_N)$\n", "\n", "However, if we want to study statistical proporities of the algorithm, we are in the trouble. For variance:\n", "\n", "$$\n", "\\mathbb{V}T = \\int_{\\text{range } x}(T(x))^2dF(x) - \\left(\\int_{\\text{range } x}T(x)dF(x)\\right)^2\n", "$$\n", "\n", "** Troubles: **\n", " * We do not have the true distribution $F(x)$ \n", " * We can not analytically integrate over complex ml-algorithm $T$ as tree, or even median\n", " \n", "** Solutions: **\n", " * Model $F(y)$ with emperical density $p_e(y)$:\n", " \n", " $$ p_{e}(y) = \\sum\\limits_{i=1}^{N}\\frac{1}{N}\\delta(y-x_i) $$\n", " \n", " * Esitemate any integral of the form $\\int f(T(x))dF(x)\\approx \\int f(T(x))dF_{e}(x)$ via Mone-Carlo:\n", " \n", " $$\n", " \\int f(T(x))dF(x)\\approx \\int f(T(x))dF_{e}(x) \\approx \\frac{1}{B}\\sum\\limits_{j=1}^{B}f(T_j),\\text{where } T_j = T(X^j), X^j\\sim F_e\n", " $$\n", " \n", " \n", "Note, that ** sampling ** from $p_e(y)$ is just selection with repetition from $X=\\{x_i\\}_{i=1}^{N}$. So it is the cheap and simple procedure. \n", "\n", "Let's play with model example and estimate variance of the algorithm:\n", "\n", "\n", "$$\n", "\\begin{aligned}\n", "& x_i \\in \\mathbb{R} \\\\\n", "& T(X) = \\text{median }X\n", "\\end{aligned}\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "![Scheme of bootstrap aggregation](bootstrap.jpg)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Task 1" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For this example let's make simultated data from Cauchy distribution" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def median(X):\n", " return np.median(X)\n", "\n", "def make_sample_cauchy(n_samples):\n", " sample = np.random.standard_cauchy(size=n_samples)\n", " return sample" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x1a1daefeb8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "X = make_sample_cauchy(int(1e2))\n", "plt.hist(X, bins=int(1e1));" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "So, our model ** median ** will be:" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.079852983102022893" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "med = median(X)\n", "med" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Exact variance formula for sample cauchy median is following:\n", "\n", "$$\n", "\\mathbb{V}\\text{med($X_n$)} = \\dfrac{2n!}{(k!)^2\\pi^n}\\int\\limits_{0}^{\\pi/2}x^k(\\pi-x)^k(\\text{cot}x)^2dx\n", "$$\n", "\n", "So hard! We will find it by bootstrap method." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now, please apply boostrap algorithm to calculate its variance. \n", "\n", "First, you need to write bootstrap sampler. It will be usefull https://docs.scipy.org/doc/numpy-1.13.0/reference/generated/numpy.random.choice.html#numpy.random.choice" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def make_sample_bootstrap(X):\n", " size = X.shape[0]\n", " idx_range = range(size)\n", " new_idx = np.random.choice(idx_range, size, replace=True)\n", " return X[new_idx]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Second, for $K$ bootstrap samples your shoud estimate its median.\n", "\n", "1. Make K=500 samples\n", "2. For each samples estimate median ont it\n", "3. save in median_boot_samples array" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": true }, "outputs": [], "source": [ "K = 500\n", "median_boot_samples = []\n", "for i in range(K):\n", " boot_sample = make_sample_bootstrap(X)\n", " meadian_boot_sample = median(boot_sample)\n", " median_boot_samples.append(meadian_boot_sample)\n", "median_boot_samples = np.array(median_boot_samples)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we can obtain mean and variance from *** median_boot_samples *** as we are usually done it in statistics" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.0927211126765 0.173272821996\n" ] } ], "source": [ "mean = np.mean(median_boot_samples)\n", "std = np.std(median_boot_samples)\n", "print(mean, std)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Please, put your estimation of std rounded to the 3 decimals at the form:\n", "https://goo.gl/forms/Qgs4O7U1Yvs5csnM2" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x1a20d7f2b0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.hist(median_boot_samples, bins=int(50));" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<a id='rf'></a>\n", "# Tree + Bootstrap = Random Forest" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We want to make many different trees and then aggregate score. So, we need to specify what is different and how to aggregate.\n", "\n", "*** How to aggregate ***\n", "\n", "For base algorithms $b_1(x),\\dots, b_N(x)$\n", "\n", "* For classification task => majority vote: $a(x) = \\text{arg}\\max_{y}\\sum_{i=1}^N[b_i(x) = y]$ \n", "* For regression task => $a(x) = \\frac{1}{N}\\sum_{i=1}^{N}b_i(x)$\n", "\n", "*** Different trees *** \n", "\n", "* Note, that more different trees, than less covariance their predictions have. Hence then we get more gain from aggregation.\n", "* One source of the difference: bootstrap sample, as we consider above\n", "* Another one: select random subset of features for each $b_i(x)$ fitting" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let' see how it works on our toy task" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "image/png": 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Tz9fRwDfS7mN7kPlh00XM+OM0pgyIxyejkhDgpcbZG/mYs/EKmk7bhHVvdkCT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\ngenhqYQ9Pp3x839h96m0SmvjP4+GYnZJLkQPf0sTJiQkXvN/hl6FA9j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DeqHGCjkXgBmn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\nMRLgy9KUfR6ieX+N1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"text/plain": [ "<matplotlib.figure.Figure at 0x1a20eec4e0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from sklearn.ensemble import RandomForestRegressor\n", "clf = RandomForestRegressor(n_estimators=100)\n", "clf.fit(data_x, data_y)\n", "\n", "xx, yy = get_grid(data_x)\n", "\n", "predicted = clf.predict(np.c_[xx.ravel(), yy.ravel()]).reshape(xx.shape)\n", "\n", "plt.figure(figsize=(8, 8));\n", "plt.pcolormesh(xx, yy, predicted, cmap='spring');\n", "plt.scatter(data_x[:, 0], data_x[:, 1], c=data_y, s=100, cmap='spring', edgecolor='k');" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You can note, that all boundaries become much more smoother. Now we will compare methods on the Boston Dataset" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from sklearn.datasets import load_boston" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": true }, "outputs": [], "source": [ "data = load_boston()\n", "X = data.data\n", "y = data.target" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Task 1" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Get cross validation score for variety of algorithms: BaggingRegressor and RandomForestRegressor with different parameters." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For example, for simple decision tree:" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.733146956973\n" ] } ], "source": [ "from sklearn.model_selection import KFold, cross_val_score\n", "cv = KFold(shuffle=True, random_state=1011)\n", "regr = DecisionTreeRegressor()\n", "print(cross_val_score(regr, X, y, cv=cv,\n", " scoring='r2').mean())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Find best parameter with CV. Please put score at the https://goo.gl/forms/XZ7xHR54Fjk5cBy92" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from sklearn.ensemble import BaggingRegressor\n", "from sklearn.ensemble import RandomForestRegressor" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# usuall cv code" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.3" } }, "nbformat": 4, "nbformat_minor": 2 }
mit
JasonSanchez/w261
week7/MIDS-W261-HW-07-TEMPLATE.ipynb
1
14801
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## MIDS UC Berkeley, Machine Learning at Scale \n", " \n", "__W261-1__ Summer 2016 \n", "__Week 7__: SSSP \n", "\n", "__Name__ \n", "[email protected] \n", "\n", "July 1, 2016 \n", "\n", "***" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<h1 style=\"color:#021353;\">General Description</h1>\n", "<div style=\"margin:10px;border-left:5px solid #eee;\">\n", "<pre style=\"font-family:sans-serif;background-color:transparent\">\n", "In this assignment you will explore networks and develop MRJob code for \n", "finding shortest path graph distances. To build up to large data \n", "you will develop your code on some very simple, toy networks.\n", "After this you will take your developed code forward and modify it and \n", "apply it to two larger datasets (performing EDA along the way).\n", "\n", "<h3>Undirected toy network dataset</h3>\n", "\n", "\n", "In an undirected network all links are symmetric, \n", "i.e., for a pair of nodes 'A' and 'B,' both of the links:\n", "\n", "A -> B and B -> A\n", "\n", "will exist. \n", "\n", "The toy data are available in a sparse (stripes) representation:\n", "\n", "(node) \\t (dictionary of links)\n", "\n", "on AWS/Dropbox via the url:\n", "\n", "s3://ucb-mids-mls-networks/undirected_toy.txt\n", "On under the Data Subfolder for HW7 on Dropbox with the same file name. \n", "The Data folder is in: https://db.tt/Kxu48mL1)\n", "\n", "In the dictionary, target nodes are keys, link weights are values \n", "(here, all weights are 1, i.e., the network is unweighted).\n", "\n", "\n", "<h3>Directed toy network dataset</h3>\n", "\n", "In a directed network all links are not necessarily symmetric, \n", "i.e., for a pair of nodes 'A' and 'B,' it is possible for only one of:\n", "\n", "A -> B or B -> A\n", "\n", "to exist. \n", "\n", "These toy data are available in a sparse (stripes) representation:\n", "\n", "(node) \\t (dictionary of links)\n", "\n", "on AWS/Dropbox via the url:\n", "\n", "s3://ucb-mids-mls-networks/directed_toy.txt\n", "Or under the Data Subfolder for HW7 on Dropbox with the same file name\n", "(On Dropbox https://www.dropbox.com/sh/2c0k5adwz36lkcw/AAAAKsjQfF9uHfv-X9mCqr9wa?dl=0)\n", "\n", "In the dictionary, target nodes are keys, link weights are values \n", "(here, all weights are 1, i.e., the network is unweighted).\n", "</pre>\n", "</div>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<h1 style=\"color:#021353;\">HW 7.0: Shortest path graph distances (toy networks)</h1>\n", "<div style=\"margin:10px;border-left:5px solid #eee;\">\n", "<pre style=\"font-family:sans-serif;background-color:transparent\">\n", "In this part of your assignment you will develop the base of your code for the week.\n", "\n", "Write MRJob classes to find shortest path graph distances, as described in the lectures. In addition to finding the distances, your code should also output a distance-minimizing path between the source and target.\n", "Work locally for this part of the assignment, and use both of the undirected and directed toy networks.\n", "\n", "To proof you code's function, run the following jobs\n", "\n", "- shortest path in the undirected network from node 1 to node 4\n", "Solution: 1,5,4. NOTE: There is another shortest path also (HINT: 1->5->4)! Either will suffice (you will find this also in the remaining problems. E.g., 7.2 and 7.4.\n", " \n", "\n", "- shortest path in the directed network from node 1 to node 5\n", "Solution: 1,2,4,5\n", "\n", "and report your output---make sure it is correct!\n", "\n", "<h3>Main dataset 1: NLTK synonyms</h3>\n", "\n", "In the next part of this assignment you will explore a network derived from the NLTK synonym database used for evaluation in HW 5. At a high level, this network is undirected, defined so that there exists link between two nodes/words if the pair or words are a synonym. These data may be found at the location:\n", "\n", "<a href=\"s3://ucb-mids-mls-networks/synNet/synNet.txt\">s3://ucb-mids-mls-networks/synNet/synNet.txt</a>\n", "<a href=\"s3://ucb-mids-mls-networks/synNet/indices.txt\">s3://ucb-mids-mls-networks/synNet/indices.txt</a>\n", "On under the Data Subfolder for HW7 on Dropbox with the same file names\n", "\n", "where synNet.txt contains a sparse representation of the network:\n", "\n", "(index) \\t (dictionary of links)\n", "\n", "in indexed form, and indices.txt contains a lookup list\n", "\n", "(word) \\t (index)\n", "\n", "of indices and words. This network is small enough for you to explore and run\n", "scripts locally, but will also be good for a systems test (for later) on AWS.\n", "\n", "In the dictionary, target nodes are keys, link weights are values \n", "(here, all weights are 1, i.e., the network is unweighted).\n", "</pre>\n", "</div>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<h1 style=\"color:#021353;\">HW 7.1: Exploratory data analysis (NLTK synonyms)</h1>\n", "<div style=\"margin:10px;border-left:5px solid #eee;\">\n", "<pre style=\"font-family:sans-serif;background-color:transparent\">\n", "Using MRJob, explore the synonyms network data.\n", "Consider plotting the degree distribution (does it follow a power law?),\n", "and determine some of the key features, like:\n", "\n", "number of nodes, \n", "number links,\n", "or the average degree (i.e., the average number of links per node),\n", "etc...\n", "\n", "As you develop your code, please be sure to run it locally first (though on the whole dataset). \n", "Once you have gotten you code to run locally, deploy it on AWS as a systems test\n", "in preparation for our next dataset (which will require AWS).\n", "</pre>\n", "</div>\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<h1 style=\"color:#021353;\">HW 7.2: Shortest path graph distances (NLTK synonyms)</h1>\n", "<div style=\"margin:10px;border-left:5px solid #eee;\">\n", "<pre style=\"font-family:sans-serif;background-color:transparent\">\n", "Write (reuse your code from 7.0) an MRJob class to find shortest path graph distances, \n", "and apply it to the NLTK synonyms network dataset. \n", "\n", "Proof your code's function by running the job:\n", "\n", "- shortest path starting at \"walk\" (index=7827) and ending at \"make\" (index=536),\n", "\n", "and showing you code's output. Once again, your output should include the path and the distance.\n", "\n", "As you develop your code, please be sure to run it locally first (though on the whole dataset). \n", "Once you have gotten you code to run locally, deploy it on AWS as a systems test\n", "in preparation for our next dataset (which will require AWS).\n", "\n", "=====================================\n", "<strong>NOTE: Dataset 2 English Wikipedia hyperlink network.data </strong>\n", "The dataset is available via Dropbox at:\n", "\n", "https://www.dropbox.com/sh/2c0k5adwz36lkcw/AAAAKsjQfF9uHfv-X9mCqr9wa?dl=0\n", "\n", "on S3 at \n", "<a href=\"s3://ucb-mids-mls-networks/wikipedia/\">s3://ucb-mids-mls-networks/wikipedia/</a>\n", "<a href=\"s3://ucb-mids-mls-networks/wikipedia/all-pages-indexed-out.txt\">s3://ucb-mids-mls-networks/wikipedia/all-pages-indexed-out.txt</a> # Graph\n", "<a href=\"s3://ucb-mids-mls-networks/wikipedia/indices.txt\">s3://ucb-mids-mls-networks/wikipedia/indices.txt</a> # Page titles and page Ids\n", "\n", "For the remainder of this assignment you will explore the English Wikipedia hyperlink network.\n", "\n", "The dataset is built from the Sept. 2015 XML snapshot of English Wikipedia.\n", "For this directed network, a link between articles: \n", "\n", "A -> B\n", "\n", "is defined by the existence of a hyperlink in A pointing to B.\n", "This network also exists in the indexed format:\n", "\n", "Data: <a href=\"s3://ucb-mids-mls-networks/wikipedia/all-pages-indexed-out.txt\">s3://ucb-mids-mls-networks/wikipedia/all-pages-indexed-out.txt</a>\n", "Data: <a href=\"s3://ucb-mids-mls-networks/wikipedia/all-pages-indexed-in.txt\">s3://ucb-mids-mls-networks/wikipedia/all-pages-indexed-in.txt</a>\n", "Data: <a href=\"s3://ucb-mids-mls-networks/wikipedia/indices.txt\">s3://ucb-mids-mls-networks/wikipedia/indices.txt</a>\n", "\n", "but has an index with more detailed data:\n", "\n", "(article name) \\t (index) \\t (in degree) \\t (out degree)\n", "\n", "In the dictionary, target nodes are keys, link weights are values .\n", "Here, a weight indicates the number of time a page links to another.\n", "However, for the sake of this assignment, treat this an unweighted network,\n", "and set all weights to 1 upon data input.\n", "\n", "</pre>\n", "</div>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<h1 style=\"color:#021353;\">HW 7.3: Exploratory data analysis (Wikipedia)</h1>\n", "<div style=\"margin:10px;border-left:5px solid #eee;\">\n", "<pre style=\"font-family:sans-serif;background-color:transparent\">\n", "Using MRJob, explore the Wikipedia network data on the AWS cloud. Reuse your code from HW 7.1---does is scale well? \n", "\n", "Be cautioned that Wikipedia is a directed network, where links are not symmetric. \n", "So, even though a node may be linked to, it will not appear as a primary record itself if it has no out-links. \n", "\n", "This means that you may have to ADJUST your code (depending on its design). \n", "\n", "To be sure of your code's functionality in this context, run a systems test on the directed_toy.txt network.\n", "</pre>\n", "</div>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<h1 style=\"color:#021353;\">HW 7.4: Shortest path graph distances (Wikipedia)</h1>\n", "<div style=\"margin:10px;border-left:5px solid #eee;\">\n", "<pre style=\"font-family:sans-serif;background-color:transparent\">\n", "Using MRJob, find shortest path graph distances in the Wikipedia network on the AWS cloud.\n", "Reuse your code from 7.2, but once again be warned of Wikipedia being a directed network.\n", "To be sure of your code's functionality in this context, run a systems test on the directed_toy.txt network.\n", "\n", "When running your code on the Wikipedia network, proof its function by running the job:\n", "\n", "- shortest path from \"Ireland\" (index=6176135) to \"University of California, Berkeley\" (index=13466359),\n", "\n", "and show your code's output. Show the shortest path in terms of just page IDS but also in terms of the name of page (show of your MapReduce join skills!!)\n", "\n", "Once your code is running, find some other shortest paths and report your results.\n", "</pre>\n", "</div>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<h1 style=\"color:#021353;\">HW 7.5: Conceptual exercise: Largest single-source network distances</h1>\n", "<div style=\"margin:10px;border-left:5px solid #eee;\">\n", "<pre style=\"font-family:sans-serif;background-color:transparent\">\n", "Suppose you wanted to find the largest network distance from a single source,\n", "i.e., a node that is the furthest (but still reachable) from a single source.\n", "\n", "How would you implement this task? \n", "How is this different from finding the shortest path graph distances?\n", "\n", "Is this task more difficult to implement than the shortest path distance?\n", "\n", "As you respond, please comment on program structure, runtimes, iterations, general system requirements, etc...\n", "</pre>\n", "</div>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<h1 style=\"color:#021353;\">HW 7.5.1: </h1>\n", "<div style=\"margin:10px;border-left:5px solid #eee;\">\n", "<pre style=\"font-family:sans-serif;background-color:transparent\">\n", "Can we utilize combiners in the HW 7 to perform the shortest path implementation?\n", "Does order inversion help with the HW 7 shortest path implementation?\n", "</pre>\n", "</div>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<h1 style=\"color:#021353;\">HW 7.5.2: OPTIONAL </h1>\n", "<div style=\"margin:10px;border-left:5px solid #eee;\">\n", "<pre style=\"font-family:sans-serif;background-color:transparent\">\n", "Implement combiners in the context of HW 7.5 and contrast the performance of this implementation versus the implementation with no combiners. \n", "\n", "Please report the cluster configuration and runtimes in tabular format for both experiments and comment on your findings.\n", "</pre>\n", "</div>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<h1 style=\"color:#021353;\">HW 7.6: Computational exercise: Largest single-source network distances: OPTIONAL </h1>\n", "<div style=\"margin:10px;border-left:5px solid #eee;\">\n", "<pre style=\"font-family:sans-serif;background-color:transparent\">\n", "Using MRJob, write a code to find the largest graph distance and distance-maximizing nodes from a single-source.\n", "Test your code first on the toy networks and synonyms network to proof its function.\n", "</pre>\n", "</div>" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "==================END HW 7==================" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.12" } }, "nbformat": 4, "nbformat_minor": 0 }
mit
feststelltaste/software-analytics
notebooks/Generating Synthetic Data based on a Git Log.ipynb
1
116296
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Context\n", "\n", "Often, it isn't possible to get the real data where we applied our analysis. In these cases, we can generate similar dataset that contain similar phenomena based on real data. This notebook shows an example about how we can do it." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Get base data\n", "The data, we want to derive another dataset. It's just there to get some realistic file names" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>additions</th>\n", " <th>deletions</th>\n", " <th>file</th>\n", " <th>sha</th>\n", " <th>timestamp</th>\n", " <th>author</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>1</th>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>pom.xml</td>\n", " <td>f96d80e</td>\n", " <td>2018-06-12 08:32:28</td>\n", " <td>Dirk Mahler</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>5</td>\n", " <td>5</td>\n", " <td>jqassistant/layer.adoc</td>\n", " <td>d6e9509</td>\n", " <td>2018-05-30 14:59:44</td>\n", " <td>Dirk Mahler</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>jqassistant/layer.adoc</td>\n", " <td>87b88d9</td>\n", " <td>2018-05-18 22:43:32</td>\n", " <td>Dirk Mahler</td>\n", " </tr>\n", " <tr>\n", " <th>7</th>\n", " <td>4</td>\n", " <td>0</td>\n", " <td>pom.xml</td>\n", " <td>ebb50e0</td>\n", " <td>2018-05-17 20:51:14</td>\n", " <td>Dirk Mahler</td>\n", " </tr>\n", " <tr>\n", " <th>9</th>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>jqassistant/index.adoc</td>\n", " <td>b9b6dcf</td>\n", " <td>2018-05-16 21:32:29</td>\n", " <td>Dirk Mahler</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " additions deletions file sha timestamp \\\n", "1 1 1 pom.xml f96d80e 2018-06-12 08:32:28 \n", "3 5 5 jqassistant/layer.adoc d6e9509 2018-05-30 14:59:44 \n", "5 1 1 jqassistant/layer.adoc 87b88d9 2018-05-18 22:43:32 \n", "7 4 0 pom.xml ebb50e0 2018-05-17 20:51:14 \n", "9 1 1 jqassistant/index.adoc b9b6dcf 2018-05-16 21:32:29 \n", "\n", " author \n", "1 Dirk Mahler \n", "3 Dirk Mahler \n", "5 Dirk Mahler \n", "7 Dirk Mahler \n", "9 Dirk Mahler " ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from lib.ozapfdis import git_tc\n", "\n", "log = git_tc.log_numstat(\"C:/dev/repos/buschmais-spring-petclinic\")\n", "log.head()" ] }, { "cell_type": "code", "execution_count": 2, "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>additions</th>\n", " <th>deletions</th>\n", " <th>file</th>\n", " <th>sha</th>\n", " <th>timestamp</th>\n", " <th>author</th>\n", " <th>type</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>234</th>\n", " <td>4</td>\n", " <td>5</td>\n", " <td>src/test/java/org/springframework/samples/petc...</td>\n", " <td>e525415</td>\n", " <td>2016-08-19 16:54:56</td>\n", " <td>Antoine Rey</td>\n", " <td>other</td>\n", " </tr>\n", " <tr>\n", " <th>235</th>\n", " <td>25</td>\n", " <td>7</td>\n", " <td>src/test/java/org/springframework/samples/petc...</td>\n", " <td>e525415</td>\n", " <td>2016-08-19 16:54:56</td>\n", " <td>Antoine Rey</td>\n", " <td>other</td>\n", " </tr>\n", " <tr>\n", " <th>236</th>\n", " <td>21</td>\n", " <td>9</td>\n", " <td>src/test/java/org/springframework/samples/petc...</td>\n", " <td>e525415</td>\n", " <td>2016-08-19 16:54:56</td>\n", " <td>Antoine Rey</td>\n", " <td>other</td>\n", " </tr>\n", " <tr>\n", " <th>237</th>\n", " <td>23</td>\n", " <td>3</td>\n", " <td>src/test/java/org/springframework/samples/petc...</td>\n", " <td>e525415</td>\n", " <td>2016-08-19 16:54:56</td>\n", " <td>Antoine Rey</td>\n", " <td>other</td>\n", " </tr>\n", " <tr>\n", " <th>238</th>\n", " <td>10</td>\n", " <td>6</td>\n", " <td>src/test/java/org/springframework/samples/petc...</td>\n", " <td>e525415</td>\n", " <td>2016-08-19 16:54:56</td>\n", " <td>Antoine Rey</td>\n", " <td>other</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " additions deletions file \\\n", "234 4 5 src/test/java/org/springframework/samples/petc... \n", "235 25 7 src/test/java/org/springframework/samples/petc... \n", "236 21 9 src/test/java/org/springframework/samples/petc... \n", "237 23 3 src/test/java/org/springframework/samples/petc... \n", "238 10 6 src/test/java/org/springframework/samples/petc... \n", "\n", " sha timestamp author type \n", "234 e525415 2016-08-19 16:54:56 Antoine Rey other \n", "235 e525415 2016-08-19 16:54:56 Antoine Rey other \n", "236 e525415 2016-08-19 16:54:56 Antoine Rey other \n", "237 e525415 2016-08-19 16:54:56 Antoine Rey other \n", "238 e525415 2016-08-19 16:54:56 Antoine Rey other " ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "log = log[log.file.str.contains(\".java\")]\n", "log.loc[log.file.str.contains(\"/jdbc/\"), 'type'] = \"jdbc\"\n", "log.loc[log.file.str.contains(\"/jpa/\"), 'type'] = \"jpa\"\n", "log.loc[log.type.isna(), 'type'] = \"other\"\n", "log.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Create synthetic dataset 1\n", "For the first technology, where \"JDBC\" was used." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Create committed lines" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>lines</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>118</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>50</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>78</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>142</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>123</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " lines\n", "0 118\n", "1 50\n", "2 78\n", "3 142\n", "4 123" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import numpy as np\n", "import pandas as pd\n", "\n", "np.random.seed(0)\n", "# adding period\n", "added_lines = [int(np.random.normal(30,50)) for i in range(0,600)]\n", "# deleting period\n", "added_lines.extend([int(np.random.normal(-50,100)) for i in range(0,200)])\n", "added_lines.extend([int(np.random.normal(-2,20)) for i in range(0,200)])\n", "added_lines.extend([int(np.random.normal(-3,10)) for i in range(0,200)])\n", "df_jdbc = pd.DataFrame()\n", "df_jdbc['lines'] = added_lines\n", "df_jdbc.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Add timestamp" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0 00:00:00\n", "1 00:00:01\n", "2 00:00:02\n", "3 00:00:03\n", "4 00:00:04\n", "dtype: timedelta64[ns]" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "times = pd.timedelta_range(\"00:00:00\",\"23:59:59\", freq=\"s\")\n", "times = pd.Series(times)\n", "times.head()" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0 2013-05-15 03:35:33\n", "1 2013-05-16 02:15:44\n", "2 2013-05-17 15:12:26\n", "3 2013-05-20 00:16:06\n", "4 2013-05-21 17:43:53\n", "dtype: datetime64[ns]" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dates = pd.date_range('2013-05-15', '2017-07-23')\n", "dates = pd.to_datetime(dates)\n", "dates = dates[~dates.dayofweek.isin([5,6])]\n", "dates = pd.Series(dates)\n", "dates = dates.add(times.sample(len(dates), replace=True).values)\n", "dates.head()" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>lines</th>\n", " <th>timestamp</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>118</td>\n", " <td>2013-05-15 03:35:33</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>50</td>\n", " <td>2013-05-16 02:15:44</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>78</td>\n", " <td>2013-05-17 15:12:26</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>142</td>\n", " <td>2013-05-24 05:52:31</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>123</td>\n", " <td>2013-05-28 08:15:35</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " lines timestamp\n", "0 118 2013-05-15 03:35:33\n", "1 50 2013-05-16 02:15:44\n", "2 78 2013-05-17 15:12:26\n", "3 142 2013-05-24 05:52:31\n", "4 123 2013-05-28 08:15:35" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_jdbc['timestamp'] = dates.sample(len(df_jdbc), replace=True).sort_values().reset_index(drop=True)\n", "df_jdbc = df_jdbc.sort_index()\n", "df_jdbc.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Treat first commit separetely\n", "Set a fixed value because we have to start with some code at the beginning" ] }, { "cell_type": "code", "execution_count": 7, "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>lines</th>\n", " <th>timestamp</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>250</td>\n", " <td>2013-05-15 03:35:33</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>50</td>\n", " <td>2013-05-16 02:15:44</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>78</td>\n", " <td>2013-05-17 15:12:26</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>142</td>\n", " <td>2013-05-24 05:52:31</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>123</td>\n", " <td>2013-05-28 08:15:35</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " lines timestamp\n", "0 250 2013-05-15 03:35:33\n", "1 50 2013-05-16 02:15:44\n", "2 78 2013-05-17 15:12:26\n", "3 142 2013-05-24 05:52:31\n", "4 123 2013-05-28 08:15:35" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_jdbc.loc[0, 'lines'] = 250\n", "df_jdbc.head()" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "df_jdbc = df_jdbc" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Add file names\n", "Sample file names including their paths from an existing dataset" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "df_jdbc['file'] = log[log['type'] == 'jdbc']['file'].sample(len(df_jdbc), replace=True).values" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Check dataset" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x1e7b24efe10>" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "<matplotlib.figure.Figure at 0x1e7b1eb77b8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%matplotlib inline\n", "df_jdbc.lines.hist()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Sum up the data and check if it was created as wanted." ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x1e7b25926d8>" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "<matplotlib.figure.Figure at 0x1e7b2349518>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "df_jdbc_timed = df_jdbc.set_index('timestamp')\n", "df_jdbc_timed['count'] = df_jdbc_timed.lines.cumsum()\n", "df_jdbc_timed['count'].plot()" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Timestamp('2017-07-21 19:02:47')" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "last_non_zero_timestamp = df_jdbc_timed[df_jdbc_timed['count'] >= 0].index.max()\n", "last_non_zero_timestamp" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>lines</th>\n", " <th>timestamp</th>\n", " <th>file</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>250</td>\n", " <td>2013-05-15 03:35:33</td>\n", " <td>src/main/java/org/springframework/samples/petc...</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>50</td>\n", " <td>2013-05-16 02:15:44</td>\n", " <td>src/main/java/org/springframework/samples/petc...</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>78</td>\n", " <td>2013-05-17 15:12:26</td>\n", " <td>src/main/java/org/springframework/samples/petc...</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>142</td>\n", " <td>2013-05-24 05:52:31</td>\n", " <td>src/main/java/org/springframework/samples/petc...</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>123</td>\n", " <td>2013-05-28 08:15:35</td>\n", " <td>src/main/java/org/springframework/samples/petc...</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " lines timestamp \\\n", "0 250 2013-05-15 03:35:33 \n", "1 50 2013-05-16 02:15:44 \n", "2 78 2013-05-17 15:12:26 \n", "3 142 2013-05-24 05:52:31 \n", "4 123 2013-05-28 08:15:35 \n", "\n", " file \n", "0 src/main/java/org/springframework/samples/petc... \n", "1 src/main/java/org/springframework/samples/petc... \n", "2 src/main/java/org/springframework/samples/petc... \n", "3 src/main/java/org/springframework/samples/petc... \n", "4 src/main/java/org/springframework/samples/petc... " ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_jdbc = df_jdbc[df_jdbc.timestamp <= last_non_zero_timestamp]\n", "df_jdbc.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Create synthetic dataset 2" ] }, { "cell_type": "code", "execution_count": 14, "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>lines</th>\n", " <th>timestamp</th>\n", " <th>file</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>150</td>\n", " <td>2015-05-17 15:12:26</td>\n", " <td>src/main/java/org/springframework/samples/petc...</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>86</td>\n", " <td>2015-05-20 00:16:06</td>\n", " <td>src/main/java/org/springframework/samples/petc...</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>-27</td>\n", " <td>2015-05-24 05:52:31</td>\n", " <td>src/main/java/org/springframework/samples/petc...</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>14</td>\n", " <td>2015-06-04 21:09:15</td>\n", " <td>src/main/java/org/springframework/samples/petc...</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>66</td>\n", " <td>2015-06-06 19:22:39</td>\n", " <td>src/main/java/org/springframework/samples/petc...</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " lines timestamp \\\n", "0 150 2015-05-17 15:12:26 \n", "1 86 2015-05-20 00:16:06 \n", "2 -27 2015-05-24 05:52:31 \n", "3 14 2015-06-04 21:09:15 \n", "4 66 2015-06-06 19:22:39 \n", "\n", " file \n", "0 src/main/java/org/springframework/samples/petc... \n", "1 src/main/java/org/springframework/samples/petc... \n", "2 src/main/java/org/springframework/samples/petc... \n", "3 src/main/java/org/springframework/samples/petc... \n", "4 src/main/java/org/springframework/samples/petc... " ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_jpa = pd.DataFrame([int(np.random.normal(20,50)) for i in range(0,600)], columns=['lines'])\n", "df_jpa.loc[0,'lines'] = 150\n", "df_jpa['timestamp'] = pd.DateOffset(years=2) + dates.sample(len(df_jpa), replace=True).sort_values().reset_index(drop=True)\n", "df_jpa = df_jpa.sort_index()\n", "df_jpa['file'] = log[log['type'] == 'jpa']['file'].sample(len(df_jpa), replace=True).values\n", "df_jpa.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Check dataset" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x1e7b2613898>" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "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\n", "text/plain": [ "<matplotlib.figure.Figure at 0x1e7b26b3a90>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "df_jpa.lines.hist()" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x1e7b372c6d8>" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "<matplotlib.figure.Figure at 0x1e7b261b940>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "df_jpa_timed = df_jpa.set_index('timestamp')\n", "df_jpa_timed['count'] = df_jpa_timed.lines.cumsum()\n", "df_jpa_timed['count'].plot()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Add some noise" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0 2013-05-15 17:36:46\n", "1 2013-05-16 22:05:34\n", "2 2013-05-17 19:27:07\n", "3 2013-05-20 06:28:34\n", "4 2013-05-21 03:46:00\n", "dtype: datetime64[ns]" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dates_other = pd.date_range(df_jdbc.timestamp.min(), df_jpa.timestamp.max())\n", "dates_other = pd.to_datetime(dates_other)\n", "dates_other = dates_other[~dates_other.dayofweek.isin([5,6])]\n", "dates_other = pd.Series(dates_other)\n", "dates_other = dates_other.add(times.sample(len(dates_other), replace=True).values)\n", "dates_other.head()" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>lines</th>\n", " <th>timestamp</th>\n", " <th>file</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>38</td>\n", " <td>2013-05-15 17:36:46</td>\n", " <td>src/test/java/org/springframework/samples/petc...</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>74</td>\n", " <td>2013-05-15 17:36:46</td>\n", " <td>src/main/java/org/springframework/samples/petc...</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>143</td>\n", " <td>2013-05-15 17:36:46</td>\n", " <td>src/main/java/org/springframework/samples/petc...</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>-54</td>\n", " <td>2013-05-15 17:36:46</td>\n", " <td>src/test/java/org/springframework/samples/petc...</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>-46</td>\n", " <td>2013-05-15 17:36:46</td>\n", " <td>src/main/java/org/springframework/samples/petc...</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " lines timestamp \\\n", "0 38 2013-05-15 17:36:46 \n", "1 74 2013-05-15 17:36:46 \n", "2 143 2013-05-15 17:36:46 \n", "3 -54 2013-05-15 17:36:46 \n", "4 -46 2013-05-15 17:36:46 \n", "\n", " file \n", "0 src/test/java/org/springframework/samples/petc... \n", "1 src/main/java/org/springframework/samples/petc... \n", "2 src/main/java/org/springframework/samples/petc... \n", "3 src/test/java/org/springframework/samples/petc... \n", "4 src/main/java/org/springframework/samples/petc... " ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_other = pd.DataFrame([int(np.random.normal(5,100)) for i in range(0,40000)], columns=['lines'])\n", "df_other['timestamp'] = dates_other.sample(len(df_other), replace=True).sort_values().reset_index(drop=True)\n", "df_other = df_other.sort_index()\n", "df_other['file'] = log[log['type'] == 'other']['file'].sample(len(df_other), replace=True).values\n", "df_other.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Check dataset" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x1e7b380af28>" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "<matplotlib.figure.Figure at 0x1e7b3817d68>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "df_other.lines.hist()" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x1e7b387e6a0>" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "<matplotlib.figure.Figure at 0x1e7b38176d8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "df_other_timed = df_other.set_index('timestamp')\n", "df_other_timed['count'] = df_other_timed.lines.cumsum()\n", "df_other_timed['count'].plot()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Concatenate all datasets" ] }, { "cell_type": "code", "execution_count": 21, "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>additions</th>\n", " <th>deletions</th>\n", " <th>file</th>\n", " <th>timestamp</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>41799</th>\n", " <td>56</td>\n", " <td>0</td>\n", " <td>src/main/java/org/springframework/samples/petc...</td>\n", " <td>2019-07-19 19:16:44</td>\n", " </tr>\n", " <tr>\n", " <th>41798</th>\n", " <td>0</td>\n", " <td>62</td>\n", " <td>src/main/java/org/springframework/samples/petc...</td>\n", " <td>2019-07-19 19:16:44</td>\n", " </tr>\n", " <tr>\n", " <th>41770</th>\n", " <td>0</td>\n", " <td>117</td>\n", " <td>src/main/java/org/springframework/samples/petc...</td>\n", " <td>2019-07-19 06:09:16</td>\n", " </tr>\n", " <tr>\n", " <th>41777</th>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>src/main/java/org/springframework/samples/petc...</td>\n", " <td>2019-07-19 06:09:16</td>\n", " </tr>\n", " <tr>\n", " <th>41776</th>\n", " <td>0</td>\n", " <td>46</td>\n", " <td>src/main/java/org/springframework/samples/petc...</td>\n", " <td>2019-07-19 06:09:16</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " additions deletions \\\n", "41799 56 0 \n", "41798 0 62 \n", "41770 0 117 \n", "41777 0 0 \n", "41776 0 46 \n", "\n", " file timestamp \n", "41799 src/main/java/org/springframework/samples/petc... 2019-07-19 19:16:44 \n", "41798 src/main/java/org/springframework/samples/petc... 2019-07-19 19:16:44 \n", "41770 src/main/java/org/springframework/samples/petc... 2019-07-19 06:09:16 \n", "41777 src/main/java/org/springframework/samples/petc... 2019-07-19 06:09:16 \n", "41776 src/main/java/org/springframework/samples/petc... 2019-07-19 06:09:16 " ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df = pd.concat([df_jpa, df_jdbc, df_other], ignore_index=True).sort_values(by='timestamp')\n", "df.loc[df.lines > 0, 'additions'] = df.lines\n", "df.loc[df.lines < 0, 'deletions'] = df.lines * -1\n", "df = df.fillna(0).reset_index(drop=True)\n", "df = df[['additions', 'deletions', 'file', 'timestamp']]\n", "df.loc[(df.deletions > 0) & (df.loc[0].timestamp == df.timestamp),'additions'] = df.deletions\n", "df.loc[df.loc[0].timestamp == df.timestamp,'deletions'] = 0\n", "df['additions'] = df.additions.astype(int)\n", "df['deletions'] = df.deletions.astype(int)\n", "df = df.sort_values(by='timestamp', ascending=False)\n", "df.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "## Truncate data until fixed date" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>additions</th>\n", " <th>deletions</th>\n", " <th>file</th>\n", " <th>timestamp</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>31486</th>\n", " <td>19</td>\n", " <td>0</td>\n", " <td>src/main/java/org/springframework/samples/petc...</td>\n", " <td>2017-12-31 19:41:29</td>\n", " </tr>\n", " <tr>\n", " <th>31485</th>\n", " <td>55</td>\n", " <td>0</td>\n", " <td>src/main/java/org/springframework/samples/petc...</td>\n", " <td>2017-12-30 12:48:20</td>\n", " </tr>\n", " <tr>\n", " <th>31484</th>\n", " <td>29</td>\n", " <td>0</td>\n", " <td>src/main/java/org/springframework/samples/petc...</td>\n", " <td>2017-12-30 12:48:20</td>\n", " </tr>\n", " <tr>\n", " <th>31461</th>\n", " <td>0</td>\n", " <td>99</td>\n", " <td>src/main/java/org/springframework/samples/petc...</td>\n", " <td>2017-12-30 00:38:54</td>\n", " </tr>\n", " <tr>\n", " <th>31467</th>\n", " <td>19</td>\n", " <td>0</td>\n", " <td>src/main/java/org/springframework/samples/petc...</td>\n", " <td>2017-12-30 00:38:54</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " additions deletions \\\n", "31486 19 0 \n", "31485 55 0 \n", "31484 29 0 \n", "31461 0 99 \n", "31467 19 0 \n", "\n", " file timestamp \n", "31486 src/main/java/org/springframework/samples/petc... 2017-12-31 19:41:29 \n", "31485 src/main/java/org/springframework/samples/petc... 2017-12-30 12:48:20 \n", "31484 src/main/java/org/springframework/samples/petc... 2017-12-30 12:48:20 \n", "31461 src/main/java/org/springframework/samples/petc... 2017-12-30 00:38:54 \n", "31467 src/main/java/org/springframework/samples/petc... 2017-12-30 00:38:54 " ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df = df[df.timestamp < pd.Timestamp('2018-01-01')]\n", "df.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Export the data" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [], "source": [ "df.to_csv(\"datasets/git_log_refactoring.gz\", index=None, compression='gzip')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Check loaded data" ] }, { "cell_type": "code", "execution_count": 24, "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>additions</th>\n", " <th>deletions</th>\n", " <th>file</th>\n", " <th>timestamp</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>19</td>\n", " <td>0</td>\n", " <td>src/main/java/org/springframework/samples/petc...</td>\n", " <td>2017-12-31 19:41:29</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>55</td>\n", " <td>0</td>\n", " <td>src/main/java/org/springframework/samples/petc...</td>\n", " <td>2017-12-30 12:48:20</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>29</td>\n", " <td>0</td>\n", " <td>src/main/java/org/springframework/samples/petc...</td>\n", " <td>2017-12-30 12:48:20</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>0</td>\n", " <td>99</td>\n", " <td>src/main/java/org/springframework/samples/petc...</td>\n", " <td>2017-12-30 00:38:54</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>19</td>\n", " <td>0</td>\n", " <td>src/main/java/org/springframework/samples/petc...</td>\n", " <td>2017-12-30 00:38:54</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " additions deletions file \\\n", "0 19 0 src/main/java/org/springframework/samples/petc... \n", "1 55 0 src/main/java/org/springframework/samples/petc... \n", "2 29 0 src/main/java/org/springframework/samples/petc... \n", "3 0 99 src/main/java/org/springframework/samples/petc... \n", "4 19 0 src/main/java/org/springframework/samples/petc... \n", "\n", " timestamp \n", "0 2017-12-31 19:41:29 \n", "1 2017-12-30 12:48:20 \n", "2 2017-12-30 12:48:20 \n", "3 2017-12-30 00:38:54 \n", "4 2017-12-30 00:38:54 " ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_loaded = pd.read_csv(\"datasets/git_log_refactoring.gz\")\n", "df_loaded.head()" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "<class 'pandas.core.frame.DataFrame'>\n", "RangeIndex: 31487 entries, 0 to 31486\n", "Data columns (total 4 columns):\n", "additions 31487 non-null int64\n", "deletions 31487 non-null int64\n", "file 31487 non-null object\n", "timestamp 31487 non-null object\n", "dtypes: int64(2), object(2)\n", "memory usage: 984.0+ KB\n" ] } ], "source": [ "df_loaded.info()" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.3" } }, "nbformat": 4, "nbformat_minor": 2 }
gpl-3.0
atulsingh0/MachineLearning
ML_UoW/Course00_MLFoundation/03_Classification_Analyzing_Product_Sentiment.ipynb
1
534724
{ "cells": [ { "cell_type": "code", "execution_count": 30, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# import\n", "import graphlab as gl\n", "import pandas as pd" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# reading the data\n", "data = gl.SFrame(\"data/amazon_baby.gl/\")" ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\"><table frame=\"box\" rules=\"cols\">\n", " <tr>\n", " <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">name</th>\n", " <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">review</th>\n", " <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">rating</th>\n", " </tr>\n", " <tr>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Planetwise Flannel Wipes</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">These flannel wipes are<br>OK, but in my opinion ...</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">3.0</td>\n", " </tr>\n", " <tr>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Planetwise Wipe Pouch</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">it came early and was not<br>disappointed. i love ...</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">5.0</td>\n", " </tr>\n", " <tr>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Annas Dream Full Quilt<br>with 2 Shams ...</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Very soft and comfortable<br>and warmer than it ...</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">5.0</td>\n", " </tr>\n", " <tr>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Stop Pacifier Sucking<br>without tears with ...</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">This is a product well<br>worth the purchase. I ...</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">5.0</td>\n", " </tr>\n", " <tr>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Stop Pacifier Sucking<br>without tears with ...</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">All of my kids have cried<br>non-stop when I tried to ...</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">5.0</td>\n", " </tr>\n", " <tr>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Stop Pacifier Sucking<br>without tears with ...</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">When the Binky Fairy came<br>to our house, we didn't ...</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">5.0</td>\n", " </tr>\n", " <tr>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">A Tale of Baby's Days<br>with Peter Rabbit ...</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Lovely book, it's bound<br>tightly so you may no ...</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">4.0</td>\n", " </tr>\n", " <tr>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Baby Tracker&amp;reg; - Daily<br>Childcare Journal, ...</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Perfect for new parents.<br>We were able to keep ...</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">5.0</td>\n", " </tr>\n", " <tr>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Baby Tracker&amp;reg; - Daily<br>Childcare Journal, ...</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">A friend of mine pinned<br>this product on Pinte ...</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">5.0</td>\n", " </tr>\n", " <tr>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Baby Tracker&amp;reg; - Daily<br>Childcare Journal, ...</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">This has been an easy way<br>for my nanny to record ...</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">4.0</td>\n", " </tr>\n", "</table>\n", "[10 rows x 3 columns]<br/>\n", "</div>" ], "text/plain": [ "Columns:\n", "\tname\tstr\n", "\treview\tstr\n", "\trating\tfloat\n", "\n", "Rows: 10\n", "\n", "Data:\n", "+-------------------------------+-------------------------------+--------+\n", "| name | review | rating |\n", "+-------------------------------+-------------------------------+--------+\n", "| Planetwise Flannel Wipes | These flannel wipes are OK... | 3.0 |\n", "| Planetwise Wipe Pouch | it came early and was not ... | 5.0 |\n", "| Annas Dream Full Quilt wit... | Very soft and comfortable ... | 5.0 |\n", "| Stop Pacifier Sucking with... | This is a product well wor... | 5.0 |\n", "| Stop Pacifier Sucking with... | All of my kids have cried ... | 5.0 |\n", "| Stop Pacifier Sucking with... | When the Binky Fairy came ... | 5.0 |\n", "| A Tale of Baby's Days with... | Lovely book, it's bound ti... | 4.0 |\n", "| Baby Tracker&reg; - Daily ... | Perfect for new parents. W... | 5.0 |\n", "| Baby Tracker&reg; - Daily ... | A friend of mine pinned th... | 5.0 |\n", "| Baby Tracker&reg; - Daily ... | This has been an easy way ... | 4.0 |\n", "+-------------------------------+-------------------------------+--------+\n", "[10 rows x 3 columns]" ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data.head()" ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Build a word count vector\n", "data['word_count'] = gl.text_analytics.count_words(data['review'])" ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\"><table frame=\"box\" rules=\"cols\">\n", " <tr>\n", " <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">name</th>\n", " <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">review</th>\n", " <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">rating</th>\n", " <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">word_count</th>\n", " </tr>\n", " <tr>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Planetwise Flannel Wipes</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">These flannel wipes are<br>OK, but in my opinion ...</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">3.0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">{'and': 5L, 'stink': 1L,<br>'because': 1L, 'order ...</td>\n", " </tr>\n", " <tr>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Planetwise Wipe Pouch</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">it came early and was not<br>disappointed. i love ...</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">5.0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">{'and': 3L, 'love': 1L,<br>'it': 2L, 'highly': 1L, ...</td>\n", " </tr>\n", " <tr>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Annas Dream Full Quilt<br>with 2 Shams ...</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Very soft and comfortable<br>and warmer than it ...</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">5.0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">{'and': 2L, 'quilt': 1L,<br>'it': 1L, 'comfortable': ...</td>\n", " </tr>\n", " <tr>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Stop Pacifier Sucking<br>without tears with ...</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">This is a product well<br>worth the purchase. 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Jane - Black\": {\"frequency\": 33, \"value\": \"Chewbeads Necklace ...\"}, \"North States Supergate Auto-Close Metal Gate\": {\"frequency\": 30, \"value\": \"North States ...\"}, \"NUK Toddler Tooth and Gum Cleanser, 1.4 Ounce, (Colors May Vary)\": {\"frequency\": 22, \"value\": \"NUK Toddler Tooth ...\"}, \"Medela New Pump in Style Original Breast Pump\": {\"frequency\": 21, \"value\": \"Medela New Pump in ...\"}, \"Motorola Digital Video Baby Monitor with 1.5 Inch Color LCD Screen\": {\"frequency\": 23, \"value\": \"Motorola Digital ...\"}, \"Thermos FUNtainer Bottle, Disney Cars, 12 Ounce\": {\"frequency\": 21, \"value\": \"Thermos FUNtainer ...\"}, \"Tiny Love Classic Mobile\": {\"frequency\": 38, \"value\": \"Tiny Love Classic ...\"}, \"Clevamama Splash and Wrap Baby Bath Towel (Cream)\": {\"frequency\": 25, \"value\": \"Clevamama Splash ...\"}, \"Baby Einstein Sea Dreams Soother\": {\"frequency\": 49, \"value\": \"Baby Einstein Sea ...\"}, \"Classic Connect Graco SnugRide Classic Connect Infant Car Seat Base, Silver\": {\"frequency\": 56, \"value\": \"Classic Connect ...\"}, \"Jolly Jumper Weathershield for Infant Car Seat\": {\"frequency\": 22, \"value\": \"Jolly Jumper ...\"}, \"Bunnies by the Bay Buddy Blanket, Skipit\": {\"frequency\": 24, \"value\": \"Bunnies by the Bay ...\"}, \"Inglesina 2011 Fast Table Chair, Marina\": {\"frequency\": 30, \"value\": \"Inglesina 2011 ...\"}, \"Philips AVENT BPA Free Freeflow Pacifier, 6-18 Months, Colors and Designs May Vary, 2-Count\": {\"frequency\": 21, \"value\": \"Philips AVENT BPA ...\"}, \"Mommy's Helper Step Up Non-Slip Stepstool Froggie Collection, Green\": {\"frequency\": 27, \"value\": \"Mommy's Helper ...\"}, \"Brica Baby In-Sight Mirror, Gray\": {\"frequency\": 80, \"value\": \"Brica Baby In- ...\"}, \"Vulli Chan Pie Gnon Natural Rubber Teether - 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19, \"value\": \"Graco SnugRide ...\"}, \"Fisher-Price Luv U Zoo Jumperoo\": {\"frequency\": 88, \"value\": \"Fisher-Price Luv U ...\"}, \"BRICA Seat Guardian Car Seat Protector\": {\"frequency\": 42, \"value\": \"BRICA Seat ...\"}, \"Dr. Brown's Drying Rack\": {\"frequency\": 22, \"value\": \"Dr. Brown's Drying ...\"}, \"Fisher-Price Precious Planet Blue Sky Jumperoo\": {\"frequency\": 37, \"value\": \"Fisher-Price ...\"}, \"Dreambaby Extra Tall Swing Close Gate with Extensions, White\": {\"frequency\": 83, \"value\": \"Dreambaby Extra ...\"}, \"Graco Stanton Convertible Crib, Classic Cherry\": {\"frequency\": 24, \"value\": \"Graco Stanton ...\"}, \"Safety 1st Magnetic Locking System\": {\"frequency\": 22, \"value\": \"Safety 1st ...\"}, \"Summer Infant SwaddlePod, Ivory, Newborn\": {\"frequency\": 25, \"value\": \"Summer Infant ...\"}, \"Britax 2 Pack EZ-Cling Sun Shades, Black\": {\"frequency\": 173, \"value\": \"Britax 2 Pack EZ- ...\"}, \"Peace of Mind Two 900 Mhz Baby Receivers, 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Blue\": {\"frequency\": 26, \"value\": \"Thudguard Baby ...\"}, \"New Mommy Advice Cards -24ct- Party Supplies\": {\"frequency\": 18, \"value\": \"New Mommy Advice ...\"}, \"Bibimals Baby Bibs (Safari Pack) Button Latch Better for Long Hair - Funny Cool Cute 2 Pack of Bibs with Food Catcher Pocket Made From Waterproof Washable Silicone Plastic, Best for Use with Girl or Boy Infants and Babies - Your Baby Will Love These Silly Animal Face Bibs, Great Baby Shower Gift, Lifetime Guarantee - [Add These Bibs to Your Baby Registry Today]\": {\"frequency\": 18, \"value\": \"Bibimals Baby Bibs ...\"}, \"bumGenius Freetime All-In-One One-Size Snap Closure Cloth Diaper - White\": {\"frequency\": 23, \"value\": \"bumGenius Freetime ...\"}, \"Lamaze Early Development Toy, Marina the Mermaid\": {\"frequency\": 28, \"value\": \"Lamaze Early ...\"}, \"Bestever Baby Mat, Pink Bear\": {\"frequency\": 62, \"value\": \"Bestever Baby Mat, ...\"}, \"Graco Contempo Highchair, Forecaster\": {\"frequency\": 26, \"value\": \"Graco Contempo ...\"}, \"Think King Mighty Buggy Hook for Stroller, Wheelchair, Rollator, Walker, 2 Pack\": {\"frequency\": 39, \"value\": \"Think King Mighty ...\"}, \"Kair Air Cushioned Bath Visor, Blue\": {\"frequency\": 38, \"value\": \"Kair Air Cushioned ...\"}, \"The HERO Pocket Cloth Diaper (English Periwinkle) by Coqu&iacute; Baby\": {\"frequency\": 18, \"value\": \"The HERO Pocket ...\"}, \"The First Years Jet Stroller, Red/Black\": {\"frequency\": 284, \"value\": \"The First Years ...\"}, \"Ju-Ju-Be Paci Pod Pacifier Holder, Lilac Lace\": {\"frequency\": 27, \"value\": \"Ju-Ju-Be Paci Pod ...\"}, \"Kidco Y Spindle\": {\"frequency\": 33, \"value\": \"Kidco Y Spindle\"}, \"Lollaland Lollacup, Good Green\": {\"frequency\": 67, \"value\": \"Lollaland ...\"}, \"Medela Swing Breastpump\": {\"frequency\": 72, \"value\": \"Medela Swing ...\"}, \"Munchkin Gone Fishin' Bath Toy\": {\"frequency\": 26, \"value\": \"Munchkin Gone ...\"}, \"Philips AVENT Twin Pack Nipplette\": {\"frequency\": 28, \"value\": \"Philips AVENT Twin ...\"}, \"My Brest Friend Deluxe Pillow, Blue\": {\"frequency\": 19, \"value\": \"My Brest Friend ...\"}, \"Contours Options LT Tandem Stroller, Valencia Gold\": {\"frequency\": 29, \"value\": \"Contours Options ...\"}, \"Bright Starts Comfort and Harmony Bouncer, Vintage Garden\": {\"frequency\": 18, \"value\": \"Bright Starts ...\"}, \"Carters Keep Me Dry Water Resistant Flannel Crib Pad, White\": {\"frequency\": 28, \"value\": \"Carters Keep Me ...\"}, \"iBaby M3 Baby monitor for iPhone\": {\"frequency\": 30, \"value\": \"iBaby M3 Baby ...\"}, \"Safety 1st Exchangeable Tip 3 in 1 Thermometer\": {\"frequency\": 19, \"value\": \"Safety 1st ...\"}, \"Bright Starts Around We Go Activity Station, Tropical Fun\": {\"frequency\": 43, \"value\": \"Bright Starts ...\"}, \"WubbaNub Elephant\": {\"frequency\": 19, \"value\": \"WubbaNub Elephant\"}, \"Medela One-Piece Breastshield w/ Valve and Membrane\": {\"frequency\": 20, \"value\": \"Medela One-Piece ...\"}, \"South Shore Angel 4 Drawer Chest, Espresso\": {\"frequency\": 18, \"value\": \"South Shore Angel ...\"}, \"BRICA Super Scoop Bath Toy Organizer\": {\"frequency\": 111, \"value\": \"BRICA Super Scoop ...\"}, \"One Step Ahead Secure Transitions Inflatable Baby Tub\": {\"frequency\": 38, \"value\": \"One Step Ahead ...\"}, \"Britax Parkway SGL Booster Seat, Cardinal\": {\"frequency\": 62, \"value\": \"Britax Parkway SGL ...\"}, \"Medela Freestyle Spare Parts Kit\": {\"frequency\": 19, \"value\": \"Medela Freestyle ...\"}, \"Woolzies 3 XL Wool Dryer Balls ,Natural Fabric Softener\": {\"frequency\": 33, \"value\": \"Woolzies 3 XL Wool ...\"}, \"American Baby Company Waterproof Quilted Cotton Portable/Mini Crib Mattress Pad Cover, White\": {\"frequency\": 83, \"value\": \"American Baby ...\"}, \"Munchkin Deluxe Dishwasher Basket, Colors May Vary\": {\"frequency\": 63, \"value\": \"Munchkin Deluxe ...\"}, \"Fisher-Price Space Saver High Chair, Pink\": {\"frequency\": 79, \"value\": \"Fisher-Price Space ...\"}, \"Levana Lila Digital Baby Video Monitor with Night Vision and Talk to Baby Intercom 32000 (White)\": {\"frequency\": 18, \"value\": \"Levana Lila ...\"}, \"Boon Benders Adaptable Utensils, Blue Raspberry/Tangerine\": {\"frequency\": 20, \"value\": \"Boon Benders ...\"}, \"Contours Options 3 Wheel Stroller, Berkley\": {\"frequency\": 24, \"value\": \"Contours Options 3 ...\"}, \"Mam Nipples Slow Flow, 0+ months, 2 pack\": {\"frequency\": 27, \"value\": \"Mam Nipples Slow ...\"}, \"Munchkin Baby Care Cart\": {\"frequency\": 20, \"value\": \"Munchkin Baby Care ...\"}, \"Baby Safe Disposable Feeder (Pack of One)\": {\"frequency\": 33, \"value\": \"Baby Safe ...\"}, \"Cloud b Sleep Sheep On The Go Travel Sound Machine with Four Soothing Sounds\": {\"frequency\": 105, \"value\": \"Cloud b Sleep ...\"}, \"Stroller Hook - 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Standard Size (24mm)\": {\"frequency\": 22, \"value\": \"Medela Contact ...\"}, \"EZ-Freeze Cereal on the Go (Colors May Vary)\": {\"frequency\": 27, \"value\": \"EZ-Freeze Cereal ...\"}, \"Friendly Toys, Little Playzone with Electronic Sound and Lights\": {\"frequency\": 79, \"value\": \"Friendly Toys, ...\"}, \"Safety 1st Easy Saver Diaper Pail\": {\"frequency\": 24, \"value\": \"Safety 1st Easy ...\"}, \"Combi Flare Lightweight Stroller in Mandarin\": {\"frequency\": 29, \"value\": \"Combi Flare ...\"}, \"Fisher-Price Healthy Care Booster Seat, Green/Blue\": {\"frequency\": 67, \"value\": \"Fisher-Price ...\"}, \"green sprouts Wooden Brush and Comb Set, Natural\": {\"frequency\": 24, \"value\": \"green sprouts ...\"}, \"OXO Tot On-the-Go Feeding Spoon, Green\": {\"frequency\": 20, \"value\": \"OXO Tot On-the-Go ...\"}, \"Evenflo Top of Stair Gate\": {\"frequency\": 50, \"value\": \"Evenflo Top of ...\"}, \"Fisher-Price Cradle 'N Swing, My Little Snugabunny\": {\"frequency\": 278, \"value\": \"Fisher-Price ...\"}, \"Summer Infant Complete Coverage Color Video Monitor Set with 7&quot; LCD Screen and 1.8&quot; Handheld Unit\": {\"frequency\": 35, \"value\": \"Summer Infant ...\"}, \"Munchkin Caterpillar Spillers Stacking Cups\": {\"frequency\": 27, \"value\": \"Munchkin ...\"}, \"HALO Early Walker SleepSack Lightweight Knit Wearable Blanket, Blue, Large\": {\"frequency\": 29, \"value\": \"HALO Early Walker ...\"}, \"KidCo S353 Door Lever Lock White\": {\"frequency\": 18, \"value\": \"KidCo S353 Door ...\"}, \"Lily's Home Starry Night Projector and Sound Shooter. With 6 Lullabies and 4 Nature Sounds. Large LCD Alarm Clock\": {\"frequency\": 31, \"value\": \"Lily's Home Starry ...\"}, \"Sassy Baby Food Nurser, Colors May Vary\": {\"frequency\": 36, \"value\": \"Sassy Baby Food ...\"}, \"Boppy Cottony Cute 2-Sided Slipcover, Polka Stripe Green\": {\"frequency\": 23, \"value\": \"Boppy Cottony Cute ...\"}, \"Infant Bucket Seat Liner Color: Pink\": {\"frequency\": 21, \"value\": \"Infant Bucket Seat ...\"}, \"Safety 1st Whale and Baby Spout Guard\": {\"frequency\": 27, \"value\": \"Safety 1st Whale ...\"}, \"NUK Ultra Thin Breast Pads, Pack of 2, White, 120-Count\": {\"frequency\": 39, \"value\": \"NUK Ultra Thin ...\"}, \"Carters Super Soft Dot Changing Pad Cover, Chocolate\": {\"frequency\": 47, \"value\": \"Carters Super Soft ...\"}, \"Babe Ease Original Clean Shopper, Blue Zoo\": {\"frequency\": 20, \"value\": \"Babe Ease Original ...\"}, \"FunBites Hearts - Cuts kids' food into fun-shaped bite-sized pieces . . . Great for picky eaters and bento!\": {\"frequency\": 20, \"value\": \"FunBites Hearts - ...\"}, \"Britax Car Seat Travel Cart, Black\": {\"frequency\": 28, \"value\": \"Britax Car Seat ...\"}, \"Dr. Brown's Microwave Steam Sterilizer\": {\"frequency\": 24, \"value\": \"Dr. Brown's ...\"}, \"Baby Brezza Temperature Control Kettle, White/Grey\": {\"frequency\": 30, \"value\": \"Baby Brezza ...\"}, \"BRICA Day &amp; Night Light Musical Auto Mirror for in Car Safety, Grey\": {\"frequency\": 20, \"value\": \"BRICA Day &amp; ...\"}, \"Steribottle Ready to Use Disposable Baby Bottles, 10-Count\": {\"frequency\": 23, \"value\": \"Steribottle Ready ...\"}, \"Bumbo Floor Seat, Aqua\": {\"frequency\": 51, \"value\": \"Bumbo Floor Seat, ...\"}, \"Joovy Nook Highchair, White Leatherette\": {\"frequency\": 22, \"value\": \"Joovy Nook ...\"}, \"Diaper Dekor Plus 2-Pack Refill Biodegradable\": {\"frequency\": 20, \"value\": \"Diaper Dekor Plus ...\"}, \"Safety 1st Wide Doorways Fabric Gate, Natural\": {\"frequency\": 20, \"value\": \"Safety 1st Wide ...\"}, \"Lansinoh mOmma Feeding Bottle, 5 Ounce\": {\"frequency\": 66, \"value\": \"Lansinoh mOmma ...\"}, \"Woombie Air Ventilated Baby Swaddle ~ Choose Size/Color (Big Baby 14-19 lbs, Love Print)\": {\"frequency\": 21, \"value\": \"Woombie Air ...\"}, \"Regalo Top of Stair Gate, White\": {\"frequency\": 22, \"value\": \"Regalo Top of ...\"}, \"Prince Lionheart Soft Booster Seat in Green\": {\"frequency\": 81, \"value\": \"Prince Lionheart ...\"}, \"Playtex Embrace Breast Pump System\": {\"frequency\": 22, \"value\": \"Playtex Embrace ...\"}, \"Spasilk 100% Cotton Hooded Terry Bath Towel with 4 Washcloths, Beige\": {\"frequency\": 26, \"value\": \"Spasilk 100% ...\"}, \"WubbaNub Pink Bear\": {\"frequency\": 18, \"value\": \"WubbaNub Pink Bear\"}, \"The First Years Clean Air Diaper Disposal System\": {\"frequency\": 29, \"value\": \"The First Years ...\"}, \"Aden and Anais UpAwaySwddleBlnkts\": {\"frequency\": 188, \"value\": \"Aden and Anais ...\"}, \"Leachco Back 'N Belly Contoured Body Pillow, Ivory\": {\"frequency\": 283, \"value\": \"Leachco Back 'N ...\"}, \"The First Years 2 Pack GumDrop Newborn Pacifier, Blue/Green\": {\"frequency\": 20, \"value\": \"The First Years 2 ...\"}, \"Safety 1st Alpha Omega Elite Convertible Car Seat, Lamont\": {\"frequency\": 46, \"value\": \"Safety 1st Alpha ...\"}, \"Fisher-Price Deluxe Jumperoo\": {\"frequency\": 77, \"value\": \"Fisher-Price ...\"}, \"BabyPlus Prenatal Education System\": {\"frequency\": 41, \"value\": \"BabyPlus Prenatal ...\"}, \"Lorex BB2411 2.4&quot; Sweet Peek Video Baby Monitor with IR Night Vision and Zoom, White\": {\"frequency\": 29, \"value\": \"Lorex BB2411 ...\"}, \"Mommy's Helper Kid Keeper\": {\"frequency\": 21, \"value\": \"Mommy's Helper Kid ...\"}, \"Summer Infant SwaddleMe Adjustable Infant Wrap, 3-Pack, Mom &amp; Baby\": {\"frequency\": 77, \"value\": \"Summer Infant ...\"}, \"KidCo Angle-Mount Safeway Gate\": {\"frequency\": 24, \"value\": \"KidCo Angle-Mount ...\"}, \"BABYBJORN Soft Bib 2 Pack - Red/Blue\": {\"frequency\": 129, \"value\": \"BABYBJORN Soft Bib ...\"}, \"Foscam FBM3501 Digital Video Baby Monitor - 2.4 Ghz with Pan/Tilt, Nightvision and Two-Way Audio/Video Camera with 3.5-Inch LCD (White/Gray)\": {\"frequency\": 66, \"value\": \"Foscam FBM3501 ...\"}, \"Metal Wall Decor Butterfly Sculpture 29x15\": {\"frequency\": 20, \"value\": \"Metal Wall Decor ...\"}, \"Jeep Liberty Renegade Walker, Storm\": {\"frequency\": 22, \"value\": \"Jeep Liberty ...\"}, \"JJ Cole Satchel Diaper Bag, Green Arbor\": {\"frequency\": 51, \"value\": \"JJ Cole Satchel ...\"}, \"Diaper Dekor Plus Diaper Disposal System\": {\"frequency\": 126, \"value\": \"Diaper Dekor Plus ...\"}, \"Prince Lionheart Fireplace Guard with Two Corners\": {\"frequency\": 20, \"value\": \"Prince Lionheart ...\"}, \"Sassy Developmental Sensory Ball Set - Inspires Touch\": {\"frequency\": 28, \"value\": \"Sassy ...\"}, \"Ameda Purely Yours Breast Pump\": {\"frequency\": 68, \"value\": \"Ameda Purely Yours ...\"}, \"Thirsties Duo Wrap Diaper Cover with Hook and Loop, Aqua, Size 1\": {\"frequency\": 18, \"value\": \"Thirsties Duo Wrap ...\"}, \"Plug 'N Outlet Cover\": {\"frequency\": 19, \"value\": \"Plug 'N Outlet ...\"}, \"Safety 1st 2 Count Side By Side Cabinet Lock\": {\"frequency\": 30, \"value\": \"Safety 1st 2 Count ...\"}, \"RECARO ProSPORT Combination Harness To Booster Car Seat, Blue Opal\": {\"frequency\": 77, \"value\": \"RECARO ProSPORT ...\"}, \"Munchkin Feeding Set, 15 Pack\": {\"frequency\": 36, \"value\": \"Munchkin Feeding ...\"}, \"The Shrunks Indoor Toddler Inflatable Travel Bed\": {\"frequency\": 93, \"value\": \"The Shrunks Indoor ...\"}, \"Prince Lionheart Corner Guards, Chocolate Brown\": {\"frequency\": 56, \"value\": \"Prince Lionheart ...\"}, \"Regalo Easy Step Extra Wide Walk Thru Gate, White\": {\"frequency\": 18, \"value\": \"Regalo Easy Step ...\"}, \"Davinci Jenny Lind 3-in-1 Convertible Crib, Cherry\": {\"frequency\": 43, \"value\": \"Davinci Jenny Lind ...\"}, \"9V Auto Adapter Car Vehicle Lighter adapter for Medela Pump-in-Style Replaces Part # 67174 Retail Packaging\": {\"frequency\": 22, \"value\": \"9V Auto Adapter ...\"}, \"Evenflo Exersaucer Triple Fun Active Learning Center, Life in The Amazon\": {\"frequency\": 30, \"value\": \"Evenflo Exersaucer ...\"}, \"Tiny Love Symphony in Motion Farm Animal Mobile (Styles May Vary)\": {\"frequency\": 24, \"value\": \"Tiny Love Symphony ...\"}, \"Fisher-Price My Little Snugabunny Newborn Rock n' Play Sleeper\": {\"frequency\": 68, \"value\": \"Fisher-Price My ...\"}, \"The Art of CureTM *SAFETY KNOTTED* Raw Butterscotch - 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White\": {\"frequency\": 35, \"value\": \"KidCo BabySteps ...\"}, \"Natursutten BPA-Free Natural Rubber Pacifier, Orthodontic, 0-6 Months\": {\"frequency\": 34, \"value\": \"Natursutten BPA- ...\"}, \"Jeep Cherokee Sport Stroller, Brick Red\": {\"frequency\": 92, \"value\": \"Jeep Cherokee ...\"}, \"Mother's Touch Deluxe Baby Bather\": {\"frequency\": 20, \"value\": \"Mother's Touch ...\"}, \"Evenflo Snugli Comfort Vent Carrier\": {\"frequency\": 22, \"value\": \"Evenflo Snugli ...\"}, \"Graco DuoDiner LX Highchair, Metropolis\": {\"frequency\": 22, \"value\": \"Graco DuoDiner LX ...\"}, \"Regalo Easy Step Extra Tall Walk Thru Gate - White\": {\"frequency\": 101, \"value\": \"Regalo Easy Step ...\"}, \"Dream On Me 4 in 1 Aden Convertible Mini Crib, Natural\": {\"frequency\": 25, \"value\": \"Dream On Me 4 in 1 ...\"}, \"Luvable Friends 6 Pack Washcloths, Blue\": {\"frequency\": 27, \"value\": \"Luvable Friends 6 ...\"}, \"South Shore Savannah Collection 4-Drawer Chest, White\": {\"frequency\": 22, \"value\": \"South Shore ...\"}, \"Skip Hop Zoo Safety Harness, Monkey\": {\"frequency\": 32, \"value\": \"Skip Hop Zoo ...\"}, \"Snap 'N Go Infant Car Seat Carrier\": {\"frequency\": 46, \"value\": \"Snap 'N Go Infant ...\"}, \"DEX Products Sound Sleeper SS-01\": {\"frequency\": 103, \"value\": \"DEX Products Sound ...\"}, \"Prince Lionheart Faucet Extender, Gumball Green\": {\"frequency\": 31, \"value\": \"Prince Lionheart ...\"}, \"OXO Tot Bottle Brush with Nipple Cleaner, Orange\": {\"frequency\": 25, \"value\": \"OXO Tot Bottle ...\"}, \"Philips AVENT Range BPA-Free Front Teeth Teether, Classic\": {\"frequency\": 24, \"value\": \"Philips AVENT ...\"}, \"Combi Activity Walker Black\": {\"frequency\": 18, \"value\": \"Combi Activity ...\"}, \"Status Veneto Glider and Nursing Ottoman, White/Beige\": {\"frequency\": 24, \"value\": \"Status Veneto ...\"}, \"Odorless Diaper Pail by Safety 1st\": {\"frequency\": 28, \"value\": \"Odorless Diaper ...\"}, \"Prince Lionheart Ultimate Wipes Warmer\": {\"frequency\": 152, \"value\": \"Prince Lionheart ...\"}, \"Munchkin Arm and Hammer Bag Refill, 36 Bags\": {\"frequency\": 25, \"value\": \"Munchkin Arm and ...\"}, \"BABYBJORN Safe Step - Blue\": {\"frequency\": 85, \"value\": \"BABYBJORN Safe ...\"}, \"Fisher-Price Space Saver Swing and Seat, Discover'N Grow\": {\"frequency\": 57, \"value\": \"Fisher-Price Space ...\"}, \"Baby Einstein Octoplush\": {\"frequency\": 57, \"value\": \"Baby Einstein ...\"}, \"FISHER PRICE SINGING STAR GYM\": {\"frequency\": 37, \"value\": \"FISHER PRICE ...\"}, \"Munchkin 3 Piece Silly Sandwich Cutter Set\": {\"frequency\": 22, \"value\": \"Munchkin 3 Piece ...\"}, \"Britax Frontier Booster Car Seat\": {\"frequency\": 22, \"value\": \"Britax Frontier ...\"}, \"Summer Infant Best View Handheld Color Video Monitor with 2.5&quot; Screen\": {\"frequency\": 57, \"value\": \"Summer Infant Best ...\"}, \"Medela 9 Volt Vehicle Lighter Adaptor\": {\"frequency\": 22, \"value\": \"Medela 9 Volt ...\"}, \"Safety 1st Magnetic Locking System Complete\": {\"frequency\": 81, \"value\": \"Safety 1st ...\"}, \"BABYBJORN Baby Carrier Miracle, Black/Silver, Cotton Mix\": {\"frequency\": 50, \"value\": \"BABYBJORN Baby ...\"}, \"Safety 1st Crystal Clear Audio Monitor, White\": {\"frequency\": 21, \"value\": \"Safety 1st Crystal ...\"}, \"Graco SnugRide 32 Infant Car Seat, Zurich\": {\"frequency\": 25, \"value\": \"Graco SnugRide 32 ...\"}, \"Baby K'tan Breeze Baby Carrier, White, Large\": {\"frequency\": 29, \"value\": \"Baby K'tan Breeze ...\"}, \"Cloud B Tranquil Turtle - 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Gibson ...\"}, \"Graco Nautilus 3-in-1 Car Seat, Matrix\": {\"frequency\": 419, \"value\": \"Graco Nautilus ...\"}, \"Ameda Purely Yours Ultra Breast Pump\": {\"frequency\": 21, \"value\": \"Ameda Purely Yours ...\"}, \"Baby Banana Bendable Training Toothbrush, Infant\": {\"frequency\": 158, \"value\": \"Baby Banana ...\"}, \"Keekaroo Height Right High Chair with Tray, Natural\": {\"frequency\": 39, \"value\": \"Keekaroo Height ...\"}, \"Regalo Easy Diner Portable Hook-On High Chair\": {\"frequency\": 114, \"value\": \"Regalo Easy Diner ...\"}, \"Britax Advocate 70 CS Convertible Car Seat, Zebra\": {\"frequency\": 22, \"value\": \"Britax Advocate 70 ...\"}, \"Munchkin Arm &amp; Hammer Disposable Changing Pad - 10 Pack\": {\"frequency\": 40, \"value\": \"Munchkin Arm &amp; ...\"}, \"Munchkin Warm Glow Wipe Warmer and Diaper Bag Dispenser Set (Colors may vary)\": {\"frequency\": 19, \"value\": \"Munchkin Warm Glow ...\"}, \"ERGObaby Original Baby Carrier, Camel\": {\"frequency\": 134, \"value\": \"ERGObaby Original ...\"}, \"BRICA Bath Kneeler\": {\"frequency\": 19, \"value\": \"BRICA Bath Kneeler\"}, \"Celebration Candles 1-21 Year Numbered Birthday Candle, Pink\": {\"frequency\": 21, \"value\": \"Celebration ...\"}, \"Infantino Squeeze Station\": {\"frequency\": 28, \"value\": \"Infantino Squeeze ...\"}}, \"size\": 183531}, \"selected_variable\": {\"name\": [\"<SArray>\"], \"dtype\": \"str\", \"view_component\": \"Categorical\", \"view_file\": \"sarray\", \"descriptives\": {\"rows\": 183531}, \"type\": \"SArray\", \"view_components\": [\"Categorical\"]}, \"histogram\": null}, e);\n", " });\n", " })();\n", " " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "data['name'].show()" ] }, { "cell_type": "code", "execution_count": 37, "metadata": { "collapsed": true }, "outputs": [], "source": [ "giraffe_reviews = data[data['name'] == 'Vulli Sophie the Giraffe Teether']" ] }, { "cell_type": "code", "execution_count": 38, "metadata": { "collapsed": false }, "outputs": [ { "data": { "application/javascript": [ "$(\"head\").append($(\"<link/>\").attr({\n", " rel: \"stylesheet\",\n", " type: \"text/css\",\n", " href: \"//cdnjs.cloudflare.com/ajax/libs/font-awesome/4.1.0/css/font-awesome.min.css\"\n", "}));\n", "$(\"head\").append($(\"<link/>\").attr({\n", " rel: \"stylesheet\",\n", " type: \"text/css\",\n", " href: \"https://static.turi.com/products/graphlab-create/2.1/canvas/css/canvas.css\"\n", "}));\n", "\n", " (function(){\n", "\n", " var e = null;\n", " if (typeof element == 'undefined') {\n", " var scripts = document.getElementsByTagName('script');\n", " var thisScriptTag = scripts[scripts.length-1];\n", " var parentDiv = thisScriptTag.parentNode;\n", " e = document.createElement('div');\n", " parentDiv.appendChild(e);\n", " } else {\n", " e = element[0];\n", " }\n", "\n", " if (typeof requirejs !== 'undefined') {\n", " // disable load timeout; 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ipython_app.js is large and can take a while to load.\n", " requirejs.config({waitSeconds: 0});\n", " }\n", "\n", " require(['https://static.turi.com/products/graphlab-create/2.1/canvas/js/ipython_app.js'], function(IPythonApp){\n", " var app = new IPythonApp();\n", " app.attachView('sarray','Categorical', {\"ipython\": true, \"sketch\": {\"std\": 1.2850135559617413, \"complete\": true, \"min\": 1.0, \"max\": 5.0, \"quantile\": [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 4.0, 4.0, 4.0, 4.0, 4.0, 4.0, 4.0, 4.0, 4.0, 4.0, 4.0, 4.0, 4.0, 4.0, 4.0, 4.0, 4.0, 4.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0], \"median\": 5.0, \"numeric\": true, \"num_unique\": 5, \"num_undefined\": 0, \"var\": 1.6512598390054394, \"progress\": 1.0, \"size\": 183531, \"frequent_items\": {\"1.0\": {\"frequency\": 15183, \"value\": 1.0}, \"2.0\": {\"frequency\": 11310, \"value\": 2.0}, \"3.0\": {\"frequency\": 16779, \"value\": 3.0}, \"4.0\": {\"frequency\": 33205, \"value\": 4.0}, \"5.0\": {\"frequency\": 107054, \"value\": 5.0}}, \"mean\": 4.1204483166331505}, \"selected_variable\": {\"name\": [\"<SArray>\"], \"dtype\": \"float\", \"view_component\": \"Categorical\", \"view_file\": \"sarray\", \"descriptives\": {\"rows\": 183531}, \"type\": \"SArray\", \"view_components\": [\"Numeric\", \"Categorical\"]}, \"histogram\": {\"progress\": 1.0, \"histogram\": {\"max\": 5.024, \"bins\": [15183, 0, 0, 11310, 0, 16779, 0, 0, 33205, 0, 0, 107054], \"min\": 0.992}, \"min\": 1.0, \"complete\": 1, \"max\": 5.0}}, e);\n", " });\n", " })();\n", " " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Build a sentiment classifier\n", "data['rating'].show(view=\"Categorical\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "** Defining Positive and Negative Sentense ** \n", "Ignore 0 and 3 star ratings \n", "1 and 2 are treated as Negative \n", "4 and 4 are treated as Positive\n" ] }, { "cell_type": "code", "execution_count": 40, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# ignoring the 3 star rating\n", "data2 = data[data['rating'] != 3 ]" ] }, { "cell_type": "code", "execution_count": 41, "metadata": { "collapsed": true }, "outputs": [], "source": [ "data2['sentiment'] = data2['rating'] > 3" ] }, { "cell_type": "code", "execution_count": 42, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\"><table frame=\"box\" rules=\"cols\">\n", " <tr>\n", " <th style=\"padding-left: 1em; 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padding-right: 1em; text-align: center; vertical-align: top\">This has been an easy way<br>for my nanny to record ...</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">4.0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">{'journal.': 1L, 'all':<br>1L, 'standarad': 1L, ...</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n", " </tr>\n", " <tr>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Baby Tracker&amp;reg; - Daily<br>Childcare Journal, ...</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">I love this journal and<br>our nanny uses it ...</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">4.0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">{'all': 1L, 'forget': 1L,<br>'just': 1L, \"daughter ...</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n", " </tr>\n", "</table>\n", "[10 rows x 5 columns]<br/>\n", "</div>" ], "text/plain": [ "Columns:\n", "\tname\tstr\n", "\treview\tstr\n", "\trating\tfloat\n", "\tword_count\tdict\n", "\tsentiment\tint\n", "\n", "Rows: 10\n", "\n", "Data:\n", "+-------------------------------+-------------------------------+--------+\n", "| name | review | rating |\n", "+-------------------------------+-------------------------------+--------+\n", "| Planetwise Wipe Pouch | it came early and was not ... | 5.0 |\n", "| Annas Dream Full Quilt wit... | Very soft and comfortable ... | 5.0 |\n", "| Stop Pacifier Sucking with... | This is a product well wor... | 5.0 |\n", "| Stop Pacifier Sucking with... | All of my kids have cried ... | 5.0 |\n", "| Stop Pacifier Sucking with... | When the Binky Fairy came ... | 5.0 |\n", "| A Tale of Baby's Days with... | Lovely book, it's bound ti... | 4.0 |\n", "| Baby Tracker&reg; - Daily ... | Perfect for new parents. W... | 5.0 |\n", "| Baby Tracker&reg; - Daily ... | A friend of mine pinned th... | 5.0 |\n", "| Baby Tracker&reg; - Daily ... | This has been an easy way ... | 4.0 |\n", "| Baby Tracker&reg; - Daily ... | I love this journal and ou... | 4.0 |\n", "+-------------------------------+-------------------------------+--------+\n", "+-------------------------------+-----------+\n", "| word_count | sentiment |\n", "+-------------------------------+-----------+\n", "| {'and': 3L, 'love': 1L, 'i... | 1 |\n", "| {'and': 2L, 'quilt': 1L, '... | 1 |\n", "| {'ingenious': 1L, 'and': 3... | 1 |\n", "| {'and': 2L, 'parents!!': 1... | 1 |\n", "| {'and': 2L, 'cute': 1L, 'h... | 1 |\n", "| {'shop': 1L, 'be': 1L, 'is... | 1 |\n", "| {'feeding,': 1L, 'and': 2L... | 1 |\n", "| {'and': 1L, 'help': 1L, 'g... | 1 |\n", "| {'journal.': 1L, 'all': 1L... | 1 |\n", "| {'all': 1L, 'forget': 1L, ... | 1 |\n", "+-------------------------------+-----------+\n", "[10 rows x 5 columns]" ] }, "execution_count": 42, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data2.head()" ] }, { "cell_type": "code", "execution_count": 43, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# training the classifier model\n", "# first, spliting the data into train and test datasets\n", "\n", "train_data, test_data = data2.random_split(0.8, seed=0)" ] }, { "cell_type": "code", "execution_count": 44, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<pre>WARNING: The number of feature dimensions in this problem is very large in comparison with the number of examples. Unless an appropriate regularization value is set, this model may not provide accurate predictions for a validation/test set.</pre>" ], "text/plain": [ "WARNING: The number of feature dimensions in this problem is very large in comparison with the number of examples. Unless an appropriate regularization value is set, this model may not provide accurate predictions for a validation/test set." ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "<pre>Logistic regression:</pre>" ], "text/plain": [ "Logistic regression:" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "<pre>--------------------------------------------------------</pre>" ], "text/plain": [ "--------------------------------------------------------" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "<pre>Number of examples : 133448</pre>" ], "text/plain": [ "Number of examples : 133448" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "<pre>Number of classes : 2</pre>" ], "text/plain": [ "Number of classes : 2" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "<pre>Number of feature columns : 1</pre>" ], "text/plain": [ "Number of feature columns : 1" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "<pre>Number of unpacked features : 219217</pre>" ], "text/plain": [ "Number of unpacked features : 219217" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "<pre>Number of coefficients : 219218</pre>" ], "text/plain": [ "Number of coefficients : 219218" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "<pre>Starting L-BFGS</pre>" ], "text/plain": [ "Starting L-BFGS" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "<pre>--------------------------------------------------------</pre>" ], "text/plain": [ "--------------------------------------------------------" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "<pre>+-----------+----------+-----------+--------------+-------------------+---------------------+</pre>" ], "text/plain": [ "+-----------+----------+-----------+--------------+-------------------+---------------------+" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "<pre>| Iteration | Passes | Step size | Elapsed Time | Training-accuracy | Validation-accuracy |</pre>" ], "text/plain": [ "| Iteration | Passes | Step size | Elapsed Time | Training-accuracy | Validation-accuracy |" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "<pre>+-----------+----------+-----------+--------------+-------------------+---------------------+</pre>" ], "text/plain": [ "+-----------+----------+-----------+--------------+-------------------+---------------------+" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "<pre>| 1 | 5 | 0.000002 | 1.996329 | 0.841481 | 0.839989 |</pre>" ], "text/plain": [ "| 1 | 5 | 0.000002 | 1.996329 | 0.841481 | 0.839989 |" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "<pre>| 2 | 9 | 3.000000 | 3.856565 | 0.947425 | 0.894877 |</pre>" ], "text/plain": [ "| 2 | 9 | 3.000000 | 3.856565 | 0.947425 | 0.894877 |" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "<pre>| 3 | 10 | 3.000000 | 4.627077 | 0.923768 | 0.866232 |</pre>" ], "text/plain": [ "| 3 | 10 | 3.000000 | 4.627077 | 0.923768 | 0.866232 |" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "<pre>| 4 | 11 | 3.000000 | 5.447621 | 0.971779 | 0.912743 |</pre>" ], "text/plain": [ "| 4 | 11 | 3.000000 | 5.447621 | 0.971779 | 0.912743 |" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "<pre>| 5 | 12 | 3.000000 | 6.186113 | 0.975511 | 0.908900 |</pre>" ], "text/plain": [ "| 5 | 12 | 3.000000 | 6.186113 | 0.975511 | 0.908900 |" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "<pre>| 6 | 13 | 3.000000 | 6.923601 | 0.899991 | 0.825967 |</pre>" ], "text/plain": [ "| 6 | 13 | 3.000000 | 6.923601 | 0.899991 | 0.825967 |" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "<pre>| 7 | 15 | 1.000000 | 8.139408 | 0.984548 | 0.921451 |</pre>" ], "text/plain": [ "| 7 | 15 | 1.000000 | 8.139408 | 0.984548 | 0.921451 |" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "<pre>| 8 | 16 | 1.000000 | 8.869896 | 0.985118 | 0.921871 |</pre>" ], "text/plain": [ "| 8 | 16 | 1.000000 | 8.869896 | 0.985118 | 0.921871 |" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "<pre>| 9 | 17 | 1.000000 | 9.558356 | 0.987066 | 0.919709 |</pre>" ], "text/plain": [ "| 9 | 17 | 1.000000 | 9.558356 | 0.987066 | 0.919709 |" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "<pre>| 10 | 18 | 1.000000 | 10.308854 | 0.988715 | 0.916256 |</pre>" ], "text/plain": [ "| 10 | 18 | 1.000000 | 10.308854 | 0.988715 | 0.916256 |" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "<pre>+-----------+----------+-----------+--------------+-------------------+---------------------+</pre>" ], "text/plain": [ "+-----------+----------+-----------+--------------+-------------------+---------------------+" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "<pre>TERMINATED: Iteration limit reached.</pre>" ], "text/plain": [ "TERMINATED: Iteration limit reached." ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "<pre>This model may not be optimal. To improve it, consider increasing `max_iterations`.</pre>" ], "text/plain": [ "This model may not be optimal. To improve it, consider increasing `max_iterations`." ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "clf = gl.logistic_classifier.create(train_data, \n", " target='sentiment', \n", " features=['word_count'],\n", " validation_set=test_data)" ] }, { "cell_type": "code", "execution_count": 45, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "{'roc_curve': Columns:\n", " \tthreshold\tfloat\n", " \tfpr\tfloat\n", " \ttpr\tfloat\n", " \tp\tint\n", " \tn\tint\n", " \n", " Rows: 100001\n", " \n", " Data:\n", " +-----------+----------------+----------------+-------+------+\n", " | threshold | fpr | tpr | p | n |\n", " +-----------+----------------+----------------+-------+------+\n", " | 0.0 | 1.0 | 1.0 | 27976 | 5328 |\n", " | 1e-05 | 0.909346846847 | 0.998856162425 | 27976 | 5328 |\n", " | 2e-05 | 0.896021021021 | 0.998748927652 | 27976 | 5328 |\n", " | 3e-05 | 0.886448948949 | 0.998462968259 | 27976 | 5328 |\n", " | 4e-05 | 0.879692192192 | 0.998284243637 | 27976 | 5328 |\n", " | 5e-05 | 0.875187687688 | 0.998212753789 | 27976 | 5328 |\n", " | 6e-05 | 0.872184684685 | 0.998177008865 | 27976 | 5328 |\n", " | 7e-05 | 0.868618618619 | 0.998034029168 | 27976 | 5328 |\n", " | 8e-05 | 0.864677177177 | 0.997998284244 | 27976 | 5328 |\n", " | 9e-05 | 0.860735735736 | 0.997962539319 | 27976 | 5328 |\n", " +-----------+----------------+----------------+-------+------+\n", " [100001 rows x 5 columns]\n", " Note: Only the head of the SFrame is printed.\n", " You can use print_rows(num_rows=m, num_columns=n) to print more rows and columns.}" ] }, "execution_count": 45, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Evaluate the sentiment model\n", "clf.evaluate(test_data, metric='roc_curve')" ] }, { "cell_type": "code", "execution_count": 46, "metadata": { "collapsed": false, "scrolled": false }, "outputs": [ { "data": { "application/javascript": [ "$(\"head\").append($(\"<link/>\").attr({\n", " rel: 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"outputs": [], "source": [ "giraffe_reviews['predicted_sentment'] = clf.predict(giraffe_reviews, output_type='probability')" ] }, { "cell_type": "code", "execution_count": 48, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\"><table frame=\"box\" rules=\"cols\">\n", " <tr>\n", " <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">name</th>\n", " <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">review</th>\n", " <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">rating</th>\n", " <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">word_count</th>\n", " <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">predicted_sentment</th>\n", " </tr>\n", " <tr>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Vulli Sophie the Giraffe<br>Teether ...</td>\n", 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style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">{'and': 1L, 'right': 1L,<br>'help': 1L, 'just': 1L, ...</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0.999320678306</td>\n", " </tr>\n", " <tr>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Vulli Sophie the Giraffe<br>Teether ...</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">There really should be a<br>large warning on the ...</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1.0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">{'and': 2L, 'all': 1L,<br>'latex.': 1L, 'being': ...</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0.013558811687</td>\n", " </tr>\n", " <tr>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Vulli Sophie the Giraffe<br>Teether ...</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">All the moms in my moms'<br>group got Sophie for ...</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">5.0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">{'and': 2L, 'one!': 1L,<br>'all': 1L, 'love': 1L, ...</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0.995769474148</td>\n", " </tr>\n", " <tr>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Vulli Sophie the Giraffe<br>Teether ...</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">I was a little skeptical<br>on whether Sophie was ...</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">5.0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">{'and': 3L, 'all': 1L,<br>'old': 1L, 'her.': 1L, ...</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0.662374415673</td>\n", " </tr>\n", " <tr>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Vulli Sophie the Giraffe<br>Teether ...</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">I have been reading about<br>Sophie and was going ...</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">5.0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">{'and': 6L, 'seven': 1L,<br>'already': 1L, 'love': ...</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0.999997148186</td>\n", " </tr>\n", " <tr>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Vulli Sophie the Giraffe<br>Teether ...</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">My neice loves her sophie<br>and has spent hours ...</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">5.0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">{'and': 4L, 'drooling,':<br>1L, 'love': 1L, 'her.': ...</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0.989190989536</td>\n", " </tr>\n", " <tr>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Vulli Sophie the Giraffe<br>Teether ...</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">What a friendly face!<br>And those mesmerizing ...</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">5.0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">{'and': 3L, 'chew': 1L,<br>\"don't\": 1L, 'is': 1L, ...</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0.999563518413</td>\n", " </tr>\n", " <tr>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Vulli Sophie the Giraffe<br>Teether ...</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">We got this just for my<br>son to chew on instea ...</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">5.0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">{'chew': 2L, 'because':<br>1L, 'just': 2L, 'what': ...</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0.970160542725</td>\n", " </tr>\n", " <tr>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Vulli Sophie the Giraffe<br>Teether ...</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">My baby seems to like<br>this toy, but I could ...</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">3.0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">{'and': 2L, 'already':<br>1L, 'in': 1L, 'some': ...</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0.195367644588</td>\n", " </tr>\n", "</table>\n", "[10 rows x 5 columns]<br/>\n", "</div>" ], "text/plain": [ "Columns:\n", "\tname\tstr\n", "\treview\tstr\n", "\trating\tfloat\n", "\tword_count\tdict\n", "\tpredicted_sentment\tfloat\n", "\n", "Rows: 10\n", "\n", "Data:\n", "+-------------------------------+-------------------------------+--------+\n", "| name | review | rating |\n", "+-------------------------------+-------------------------------+--------+\n", "| Vulli Sophie the Giraffe T... | He likes chewing on all th... | 5.0 |\n", "| Vulli Sophie the Giraffe T... | My son loves this toy and ... | 5.0 |\n", "| Vulli Sophie the Giraffe T... | There really should be a l... | 1.0 |\n", "| Vulli Sophie the Giraffe T... | All the moms in my moms' g... | 5.0 |\n", "| Vulli Sophie the Giraffe T... | I was a little skeptical o... | 5.0 |\n", "| Vulli Sophie the Giraffe T... | I have been reading about ... | 5.0 |\n", "| Vulli Sophie the Giraffe T... | My neice loves her sophie ... | 5.0 |\n", "| Vulli Sophie the Giraffe T... | What a friendly face! And... | 5.0 |\n", "| Vulli Sophie the Giraffe T... | We got this just for my so... | 5.0 |\n", "| Vulli Sophie the Giraffe T... | My baby seems to like this... | 3.0 |\n", "+-------------------------------+-------------------------------+--------+\n", "+-------------------------------+--------------------+\n", "| word_count | predicted_sentment |\n", "+-------------------------------+--------------------+\n", "| {'and': 1L, 'all': 1L, 'be... | 0.999513023521 |\n", "| {'and': 1L, 'right': 1L, '... | 0.999320678306 |\n", "| {'and': 2L, 'all': 1L, 'la... | 0.013558811687 |\n", "| {'and': 2L, 'one!': 1L, 'a... | 0.995769474148 |\n", "| {'and': 3L, 'all': 1L, 'ol... | 0.662374415673 |\n", "| {'and': 6L, 'seven': 1L, '... | 0.999997148186 |\n", "| {'and': 4L, 'drooling,': 1... | 0.989190989536 |\n", "| {'and': 3L, 'chew': 1L, \"d... | 0.999563518413 |\n", "| {'chew': 2L, 'because': 1L... | 0.970160542725 |\n", "| {'and': 2L, 'already': 1L,... | 0.195367644588 |\n", "+-------------------------------+--------------------+\n", "[10 rows x 5 columns]" ] }, "execution_count": 48, "metadata": {}, "output_type": "execute_result" } ], "source": [ "giraffe_reviews.head()" ] }, { "cell_type": "code", "execution_count": 49, "metadata": { "collapsed": false }, "outputs": [], "source": [ "giraffe_reviews = giraffe_reviews.sort('predicted_sentment', ascending=False)" ] }, { "cell_type": "code", "execution_count": 50, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\"><table frame=\"box\" rules=\"cols\">\n", " <tr>\n", " <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">name</th>\n", " <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">review</th>\n", " <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">rating</th>\n", " <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">word_count</th>\n", " <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">predicted_sentment</th>\n", " </tr>\n", " <tr>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Vulli Sophie the Giraffe<br>Teether ...</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Sophie, oh Sophie, your<br>time has come. My ...</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">5.0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">{'giggles': 1L, 'all':<br>1L, \"violet's\": 2L, ...</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1.0</td>\n", " </tr>\n", " <tr>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Vulli Sophie the Giraffe<br>Teether ...</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">I'm not sure why Sophie<br>is such a hit with the ...</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">4.0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">{'peace': 1L, 'month':<br>1L, 'bright': 1L, ...</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0.999999999703</td>\n", " </tr>\n", " <tr>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Vulli Sophie the Giraffe<br>Teether ...</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">I'll be honest...I bought<br>this toy because all the ...</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">4.0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">{'all': 2L, 'pops': 1L,<br>'existence.': 1L, ...</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0.999999999392</td>\n", " </tr>\n", " <tr>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Vulli Sophie the Giraffe<br>Teether ...</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">We got this little<br>giraffe as a gift from a ...</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">5.0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">{'all': 2L, \"don't\": 1L,<br>'(literally).so': 1L, ...</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0.99999999919</td>\n", " </tr>\n", " <tr>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Vulli Sophie the Giraffe<br>Teether ...</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">As a mother of 16month<br>old twins; I bought ...</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">5.0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">{'cute': 1L, 'all': 1L,<br>'reviews.': 2L, 'just': ...</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0.999999998657</td>\n", " </tr>\n", " <tr>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Vulli Sophie the Giraffe<br>Teether ...</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Sophie the Giraffe is the<br>perfect teething toy. ...</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">5.0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">{'just': 2L, 'both': 1L,<br>'month': 1L, 'ears,': ...</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0.999999997108</td>\n", " </tr>\n", " <tr>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Vulli Sophie the Giraffe<br>Teether ...</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Sophie la giraffe is<br>absolutely the best toy ...</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">5.0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">{'and': 5L, 'the': 1L,<br>'all': 1L, 'old': 1L, ...</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0.999999995589</td>\n", " </tr>\n", " <tr>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Vulli Sophie the Giraffe<br>Teether ...</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">My 5-mos old son took to<br>this immediately. The ...</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">5.0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">{'just': 1L, 'shape': 2L,<br>'mutt': 1L, '\"dog': 1L, ...</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0.999999995573</td>\n", " </tr>\n", " <tr>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Vulli Sophie the Giraffe<br>Teether ...</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">My nephews and my four<br>kids all had Sophie in ...</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">5.0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">{'and': 4L, 'chew': 1L,<br>'all': 1L, 'perfect;': ...</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0.999999989527</td>\n", " </tr>\n", " <tr>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Vulli Sophie the Giraffe<br>Teether ...</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Never thought I'd see my<br>son French kissing a ...</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">5.0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">{'giggles': 1L, 'all':<br>1L, 'out,': 1L, 'over': ...</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0.999999985069</td>\n", " </tr>\n", "</table>\n", "[10 rows x 5 columns]<br/>\n", "</div>" ], "text/plain": [ "Columns:\n", "\tname\tstr\n", "\treview\tstr\n", "\trating\tfloat\n", "\tword_count\tdict\n", "\tpredicted_sentment\tfloat\n", "\n", "Rows: 10\n", "\n", "Data:\n", "+-------------------------------+-------------------------------+--------+\n", "| name | review | rating |\n", "+-------------------------------+-------------------------------+--------+\n", "| Vulli Sophie the Giraffe T... | Sophie, oh Sophie, your ti... | 5.0 |\n", "| Vulli Sophie the Giraffe T... | I'm not sure why Sophie is... | 4.0 |\n", "| Vulli Sophie the Giraffe T... | I'll be honest...I bought ... | 4.0 |\n", "| Vulli Sophie the Giraffe T... | We got this little giraffe... | 5.0 |\n", "| Vulli Sophie the Giraffe T... | As a mother of 16month old... | 5.0 |\n", "| Vulli Sophie the Giraffe T... | Sophie the Giraffe is the ... | 5.0 |\n", "| Vulli Sophie the Giraffe T... | Sophie la giraffe is absol... | 5.0 |\n", "| Vulli Sophie the Giraffe T... | My 5-mos old son took to t... | 5.0 |\n", "| Vulli Sophie the Giraffe T... | My nephews and my four kid... | 5.0 |\n", "| Vulli Sophie the Giraffe T... | Never thought I'd see my s... | 5.0 |\n", "+-------------------------------+-------------------------------+--------+\n", "+-------------------------------+--------------------+\n", "| word_count | predicted_sentment |\n", "+-------------------------------+--------------------+\n", "| {'giggles': 1L, 'all': 1L,... | 1.0 |\n", "| {'peace': 1L, 'month': 1L,... | 0.999999999703 |\n", "| {'all': 2L, 'pops': 1L, 'e... | 0.999999999392 |\n", "| {'all': 2L, \"don't\": 1L, '... | 0.99999999919 |\n", "| {'cute': 1L, 'all': 1L, 'r... | 0.999999998657 |\n", "| {'just': 2L, 'both': 1L, '... | 0.999999997108 |\n", "| {'and': 5L, 'the': 1L, 'al... | 0.999999995589 |\n", "| {'just': 1L, 'shape': 2L, ... | 0.999999995573 |\n", "| {'and': 4L, 'chew': 1L, 'a... | 0.999999989527 |\n", "| {'giggles': 1L, 'all': 1L,... | 0.999999985069 |\n", "+-------------------------------+--------------------+\n", "[10 rows x 5 columns]" ] }, "execution_count": 50, "metadata": {}, "output_type": "execute_result" } ], "source": [ "giraffe_reviews.head()" ] }, { "cell_type": "code", "execution_count": 51, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "\"Sophie, oh Sophie, your time has come. My granddaughter, Violet is 5 months old and starting to teeth. What joy little Sophie brings to Violet. Sophie is made of a very pliable rubber that is sturdy but not tough. It is quite easy for Violet to twist Sophie into unheard of positions to get Sophie into her mouth. The little nose and hooves fit perfectly into small mouths, and the drooling has purpose. The paint on Sophie is food quality.Sophie was born in 1961 in France. The maker had wondered why there was nothing available for babies and made Sophie from the finest rubber, phthalate-free on St Sophie's Day, thus the name was born. Since that time millions of Sophie's populate the world. She is soft and for babies little hands easy to grasp. Violet especially loves the bumpy head and horns of Sophie. Sophie has a long neck that easy to grasp and twist. She has lovely, sizable spots that attract Violet's attention. Sophie has happy little squeaks that bring squeals of delight from Violet. She is able to make Sophie squeak and that brings much joy. Sophie's smooth skin is soothing to Violet's little gums. Sophie is 7 inches tall and is the exact correct size for babies to hold and love.As you well know the first thing babies grasp, goes into their mouths- how wonderful to have a toy that stimulates all of the senses and helps with the issue of teething. Sophie is small enough to fit into any size pocket or bag. Sophie is the perfect find for babies from a few months to a year old. How wonderful to hear the giggles and laughs that emanate from babies who find Sophie irresistible. Viva La Sophie!Highly Recommended. prisrob 12-11-09\"" ] }, "execution_count": 51, "metadata": {}, "output_type": "execute_result" } ], "source": [ "giraffe_reviews[0]['review']" ] }, { "cell_type": "code", "execution_count": 52, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "\"I'm not sure why Sophie is such a hit with the little ones, but my 7 month old baby girl is one of her adoring fans. The rubber is softer and more pleasant to handle, and my daughter has enjoyed chewing on her legs and the nubs on her head even before she started teething. She also loves the squeak that Sophie makes when you squeeze her. Not sure what it is but if Sophie is amongst a pile of her other toys, my daughter will more often than not reach for Sophie. And I have the peace of mind of knowing that only edible and safe paints and materials have been used to make Sophie, as opposed to Bright Starts and other baby toys made in China. Now that the research is out on phthalates and other toxic substances in baby toys, I think it's more important than ever to find good quality toys that are also safe for our babies to handle and put in their mouths. Sophie is a must-have for every new mom in my opinion. Even if your kid is one of the few that can take or leave her, it's worth a try. Vulli, the makers of Sophie, also make natural rubber teething rings that my daughter loves as well.\"" ] }, "execution_count": 52, "metadata": {}, "output_type": "execute_result" } ], "source": [ "giraffe_reviews[1]['review']" ] }, { "cell_type": "code", "execution_count": 53, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "\"My son (now 2.5) LOVED his Sophie, and I bought one for every baby shower I've gone to. Now, my daughter (6 months) just today nearly choked on it and I will never give it to her again. Had I not been within hearing range it could have been fatal. The strange sound she was making caught my attention and when I went to her and found the front curved leg shoved well down her throat and her face a purply/blue I panicked. I pulled it out and she vomited all over the carpet before screaming her head off. I can't believe how my opinion of this toy has changed from a must-have to a must-not-use. Please don't disregard any of the choking hazard comments, they are not over exaggerated!\"" ] }, "execution_count": 53, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# show most negative reviews\n", "giraffe_reviews[-1]['review']" ] }, { "cell_type": "code", "execution_count": 54, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "\"This children's toy is nostalgic and very cute. However, there is a distinct rubber smell and a very odd taste, yes I tried it, that my baby did not enjoy. Also, if it is soiled it is extremely difficult to clean as the rubber is a kind of porus material and does not clean well. The final thing is the squeaking device inside which stopped working after the first couple of days. I returned this item feeling I had overpaid for a toy that was defective and did not meet my expectations. Please do not be swayed by the cute packaging and hype surounding it as I was. One more thing, I was given a full refund from Amazon without any problem.\"" ] }, "execution_count": 54, "metadata": {}, "output_type": "execute_result" } ], "source": [ "giraffe_reviews[-2]['review']" ] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python [conda env:gl-env]", "language": "python", "name": "conda-env-gl-env-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": "2.7.11" } }, "nbformat": 4, "nbformat_minor": 1 }
gpl-3.0
arne-cl/alt-mulig
python/exportxml-tuebadz8.0-format-description.ipynb
1
17795
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# A description of ExportXML (Tüba-D/Z Version 8.0)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# root node: ``<exml-doc>``\n", "\n", "The root contains a **``<schema>``** node, which contains descriptions of all the annotations used in the corpus. \n", "In addition, the root contains a **``<body>``** node, which contains all **``<text>``** nodes.\n", "\n", "# ``<text>``\n", "\n", "Each ``<text>`` node contains a complete document (i.e. newspaper article).\n", "\n", "```xml\n", "<text xml:id=\"text_0\" origin=\"T990507.2\">\n", "```\n", "\n", "Here's a list of all the elements that a text can contain as children (sorted by descreasing frequency):\n", "\n", "* [sentence](#&lt;sentence&gt;) (74342)\n", "* [topic](#&lt;topic&gt;) (141)\n", "* [edu-range](#&lt;edu-range&gt;) (5)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# ``<sentence>``\n", "\n", "```xml\n", "<sentence xml:id=\"s1\">\n", "```\n", "\n", "possible direct children of ``<sentence>``:\n", "\n", "* [word](#&lt;word&gt;) (90958)\n", "* [node](#&lt;node&gt;) (83943)\n", "* [ne](#&lt;ne&gt;) (3786)\n", "* [edu](#&lt;edu&gt;) (748)\n", "* [edu-range](#&lt;edu-range&gt;) (85)\n", "\n", "\n", "\n", "\n", "other descendants of ``<sentence>``:\n", "\n", "* [node](#&lt;node&gt;) (1610654)\n", "* [word](#&lt;word&gt;) (1365270)\n", "* [relation](#&lt;relation&gt;) (109218)\n", "* [ne](#&lt;ne&gt;) (66545)\n", "* [secEdge](#&lt;secEdge&gt;) (5448)\n", "* [edu](#&lt;edu&gt;) (1573)\n", "* [connective](#&lt;connective&gt;) (1520)\n", "* [discRel](#&lt;discRel&gt;) (1372)\n", "* [splitRelation](#&lt;splitRelation&gt;) (297)\n", "* [edu-range](#&lt;edu-range&gt;) (155)\n", "\n", "\n", "# ``<word>``\n", "\n", "A ``<word>`` element describes a token.\n", "\n", "```xml\n", "<word xml:id=\"s1_1\" form=\"Veruntreute\" pos=\"VVFIN\" morph=\"3sit\" lemma=\"veruntreuen\" \n", " func=\"HD\" parent=\"s1_500\" deprel=\"ROOT\"/>\n", "```\n", "\n", "A ``<word>`` element might contain additional features as children:\n", "\n", "* [relation](#&lt;relation&gt;) (11918)\n", "* [connective](#&lt;connective&gt;) (1522)\n", "* [splitRelation](#&lt;splitRelation&gt;) (17)\n", "\n", "\n", "# ``<node>``\n", "\n", "A ``<node>`` describes an element of a syntax tree.\n", "The root ``<node>`` element does not have a ``parent`` attribute, \n", "while non-root nodes do:\n", "\n", "```xml\n", "<node xml:id=\"s1_505\" cat=\"SIMPX\" func=\"--\">\n", " <node xml:id=\"s1_501\" cat=\"LK\" func=\"-\" parent=\"s1_505\">\n", "```\n", "\n", "\n", "# ``<ne>``\n", "\n", "```xml\n", " <node name=\"ne\" locality=\"sentence\">\n", " <enum-attr name=\"type\">\n", " <val name=\"PER\" description=\"Person\"/>\n", " <val name=\"ORG\" description=\"Organisation\"/>\n", " <val name=\"GPE\" description=\"Gebietskörperschaft\"/>\n", " <val name=\"LOC\" description=\"Ort\"/>\n", " <val name=\"OTH\" description=\"andere Eigennamen\"/>\n", " </enum-attr>\n", " </node>\n", "```\n", "\n", "describes a named entity (span of one or more nodes or words)\n", "\n", "```xml\n", " <ne xml:id=\"ne_23\" type=\"PER\">\n", " <word xml:id=\"s3_2\" form=\"Ute\" pos=\"NE\" morph=\"nsf\" lemma=\"Ute\" func=\"-\" parent=\"s3_501\" dephead=\"s3_1\" deprel=\"APP\"/>\n", " <word xml:id=\"s3_3\" form=\"Wedemeier\" pos=\"NE\" morph=\"nsf\" lemma=\"Wedemeier\" func=\"-\" parent=\"s3_501\" dephead=\"s3_2\" deprel=\"APP\"/>\n", " </ne>\n", "```\n", "\n", "\n", "# ``<edu>``\n", "\n", "* the ``arg1`` EDU has a [discRel](#&lt;discRel&gt;) child, the ``arg2`` doesn't\n", "\n", "```xml\n", " <edu xml:id=\"edu_55_21_1\">\n", " <discRel relation=\"Explanation-Cause\" marking=\"-|*um zu\" arg2=\"edu_55_21_2\"/>\n", " <word xml:id=\"s905_9\" form=\"und\" pos=\"KON\" lemma=\"und\" func=\"-\" parent=\"s905_526\" dephead=\"s905_3\" deprel=\"KON\"/>\n", " <node xml:id=\"s905_525\" cat=\"FKONJ\" func=\"KONJ\" parent=\"s905_526\" span=\"s905_10..s905_19\">\n", " \n", "...\n", "\n", " <edu xml:id=\"edu_55_21_2\" span=\"s905_14..s905_20\">\n", " <node xml:id=\"s905_524\" cat=\"NF\" func=\"-\" parent=\"s905_525\">\n", "```\n", "\n", "\n", "# ``<edu-range>``\n", "\n", "* can be a child of ``<text>`` or ``<sentence>``\n", "\n", "* ```<edu-range>``` seems to glue together a number of ```<edu>`` elements, \n", " which may be scattered over a number of sentences\n", "* ```<edu-range>``` may or may not contain a ``span`` attribute \n", " (it seems that the ``span`` attribute is present, when ``<edu-range>`` is \n", " a descendent of ``<sentence>``)\n", "\n", "```xml\n", " <edu-range xml:id=\"edus9_3_1-5_0\" span=\"s128_4..s130_7\">\n", " <node xml:id=\"s128_525\" cat=\"SIMPX\" func=\"--\">\n", " <edu xml:id=\"edu_9_3_1\">\n", " <discRel relation=\"Continuation\" marking=\"-\" arg2=\"edu_9_3_2\"/>\n", " <node xml:id=\"s128_506\" cat=\"VF\" func=\"-\" parent=\"s128_525\">\n", " <node xml:id=\"s128_505\" cat=\"NX\" func=\"ON\" parent=\"s128_506\">\n", " <relation type=\"expletive\"/>\n", " <word xml:id=\"s128_4\" form=\"Es\" pos=\"PPER\" morph=\"nsn3\" lemma=\"es\" func=\"HD\" parent=\"s128_505\" dephead=\"s128_5\" deprel=\"SUBJ\"/>\n", " </node>\n", " </node>\n", " \n", "...\n", "\n", " <edu-range xml:id=\"edus37_8_0-8_1\">\n", " <discRel relation=\"Restatement\" marking=\"-\" arg2=\"edu_37_9_0\"/>\n", " <sentence xml:id=\"s660\">\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# ``<topic>``\n", "\n", "``<topic>`` describes the topic of a span (i.e. a sentence, EDU or EDU range)\n", "\n", "A **``<topic>``** element can contain these children:\n", "\n", "* [sentence](#&lt;sentence&gt;) (715)\n", "* [edu-range](#&lt;edu-range&gt;) (140)\n", "* [edu](#&lt;edu&gt;) (26)\n", "\n", "```xml\n", " <topic xml:id=\"topic_9_0\" description=\"Kuli\">\n", " <sentence xml:id=\"s128\">\n", " \n", " ...\n", " \n", " <topic xml:id=\"topic_37_1\" description=\"Die Pläne der AG\">\n", " <edu-range xml:id=\"edus37_8_0-8_1\">\n", " <discRel relation=\"Restatement\" marking=\"-\" arg2=\"edu_37_9_0\"/>\n", " <sentence xml:id=\"s660\">\n", "```" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "# ``<relation>``\n", "\n", "```xml\n", "<edge name=\"relation\" parent=\"word|node\">\n", " <enum-attr name=\"type\">\n", " <val name=\"anaphoric\" description=\"Anaphorisches Pronomen\"/>\n", " <val name=\"cataphoric\" description=\"Kataphorisches Pronomen\"/>\n", " <val name=\"coreferential\" description=\"Diskurs-altes nicht-Pronomen\"/>\n", " </enum-attr>\n", " <node-ref name=\"target\"/>\n", "</edge>\n", "```\n", "\n", "A ``<relation>`` always has a type attribute and inherits its ID from its parent element:\n", "\n", "```xml\n", " <node xml:id=\"s29_501\" cat=\"NX\" func=\"ON\" parent=\"s29_523\">\n", " <relation type=\"expletive\"/>\n", " <word xml:id=\"s29_2\" form=\"es\" pos=\"PPER\" morph=\"nsn3\" lemma=\"es\" func=\"HD\" parent=\"s29_501\" dephead=\"s29_14\" deprel=\"SUBJ\"/>\n", " </node>\n", "```\n", "\n", "In the case of a non-expletive relation, it also has a target attribute:\n", "\n", "```xml\n", " <node xml:id=\"s4_507\" cat=\"NX\" func=\"ON\" parent=\"s4_513\">\n", " <relation type=\"coreferential\" target=\"s1_502\"/>\n", " <node xml:id=\"s4_505\" cat=\"NX\" func=\"HD\" parent=\"s4_507\">\n", " <word xml:id=\"s4_4\" form=\"die\" pos=\"ART\" morph=\"nsf\" lemma=\"die\" func=\"-\" parent=\"s4_505\" dephead=\"s4_5\" deprel=\"DET\"/>\n", " <ne xml:id=\"ne_32\" type=\"ORG\">\n", " <word xml:id=\"s4_5\" form=\"Arbeiterwohlfahrt\" pos=\"NN\" morph=\"nsf\" lemma=\"Arbeiterwohlfahrt\" func=\"HD\" parent=\"s4_505\" dephead=\"s4_3\" deprel=\"SUBJ\"/>\n", " </ne>\n", " </node>\n", " <node xml:id=\"s4_506\" cat=\"NX\" func=\"-\" parent=\"s4_507\">\n", " <ne xml:id=\"ne_33\" type=\"GPE\">\n", " <word xml:id=\"s4_6\" form=\"Bremen\" pos=\"NE\" morph=\"nsn\" lemma=\"Bremen\" func=\"HD\" parent=\"s4_506\" dephead=\"s4_5\" deprel=\"APP\"/>\n", " </ne>\n", " </node>\n", " </node>\n", "```\n", "\n", "\n", "# ``<secEdge>``\n", "\n", "```xml\n", "<edge name=\"secEdge\" parent=\"word|node\">\n", " <enum-attr name=\"cat\">\n", " <val name=\"UNKNOWN\" description=\"unbekanntes sekundäres Kantenlabel\"/>\n", " <val name=\"refcontr\" description=\"Dependenzrelation zw. Kontrollverb u. seinem Komplement\"/>\n", " <val name=\"refint\" description=\"Dependenzrel. zw. phraseninternem Teil u. dessen Modifikator\"/>\n", " <val name=\"refmod\" description=\"Dependenzrelation bei ambiger Modifikation\"/>\n", " <val name=\"refvc\" description=\"Dependenzrelation zw. zwei verbalen Objekten im Verbkomplex\"/>\n", " </enum-attr>\n", " <node-ref name=\"parent\"/>\n", "</edge>\n", "```\n", "\n", "A ``<secEdge>`` element has a ``cat`` and a ``parent`` attribute, but inherits its ID from its parent element. \n", "It describes a secondary edge in a tree-like syntax representation.\n", "\n", "```xml\n", " <node xml:id=\"s10_505\" cat=\"VXINF\" func=\"OV\" parent=\"s10_507\">\n", " <secEdge cat=\"refvc\" parent=\"s10_504\"/>\n", " <word xml:id=\"s10_6\" form=\"worden\" pos=\"VAPP\" lemma=\"werden%passiv\" func=\"HD\" parent=\"s10_505\" dephead=\"s10_7\" deprel=\"AUX\"/>\n", " </node>\n", "```\n", "\n", "# ``<connective>``\n", "\n", "```xml\n", "<edge name=\"connective\" parent=\"word\">\n", " <text-attr name=\"konn\"/>\n", " <enum-attr name=\"rel1\">\n", " <val name=\"Temporal\" description=\"temporal contiguity\"/>\n", " <val name=\"cause\" description=\"strong causal relation\"/>\n", " <val name=\"enable\" description=\"weak causal relation\"/>\n", " <val name=\"evidence\" description=\"argumentative reasoning\"/>\n", " <val name=\"speech_act\" description=\"circumstances for a speech act (causal)\"/>\n", " <val name=\"Result\" description=\"causal relation (underspecified)\"/>\n", " <val name=\"Comparison\" description=\"comparison relation (underspecified)\"/>\n", " <val name=\"parallel\" description=\"parallel\"/>\n", " <val name=\"contrast\" description=\"contrast\"/>\n", " <val name=\"Condition\" description=\"conditional\"/>\n", " <val name=\"NonFactual\" description=\"counterfactual bevor\"/>\n", " <val name=\"Concession\" description=\"concessive relation (underspecified)\"/>\n", " <val name=\"contraexpectation\" description=\"denial-of-expectation\"/>\n", " <val name=\"antithesis\" description=\"antithesis/Bewertungskontrast\"/>\n", " </enum-attr>\n", " <enum-attr name=\"rel2\">\n", " <val name=\"Temporal\" description=\"temporal contiguity\"/>\n", " <val name=\"cause\" description=\"strong causal relation\"/>\n", " <val name=\"enable\" description=\"weak causal relation\"/>\n", " <val name=\"evidence\" description=\"argumentative reasoning\"/>\n", " <val name=\"speech_act\" description=\"circumstances for a speech act (causal)\"/>\n", " <val name=\"Result\" description=\"causal relation (underspecified)\"/>\n", " <val name=\"Comparison\" description=\"comparison relation (underspecified)\"/>\n", " <val name=\"parallel\" description=\"parallel\"/>\n", " <val name=\"contrast\" description=\"contrast\"/>\n", " <val name=\"Condition\" description=\"conditional\"/>\n", " <val name=\"NonFactual\" description=\"counterfactual bevor\"/>\n", " <val name=\"Concession\" description=\"concessive relation (underspecified)\"/>\n", " <val name=\"contraexpectation\" description=\"denial-of-expectation\"/>\n", " <val name=\"antithesis\" description=\"antithesis/Bewertungskontrast\"/>\n", " </enum-attr>\n", "</edge>\n", "```\n", "\n", "A ``<connective>`` is an annotation of a ``<word>``, featuring one or two rel attributes.\n", "\n", "```xml\n", " <word xml:id=\"s29_1\" form=\"Als\" pos=\"KOUS\" lemma=\"als\" func=\"-\" parent=\"s29_500\" dephead=\"s29_14\" deprel=\"KONJ\">\n", " <connective konn=\"als\" rel1=\"Temporal\" rel2=\"enable\"/>\n", " </word>\n", "```\n", "\n", "# ``<discRel>``\n", "\n", "```xml\n", "<edge name=\"discRel\" parent=\"edu|topic|edu-range\">\n", " <enum-attr name=\"relation\">\n", " </enum-attr>\n", " <enum-attr name=\"marking\">\n", " </enum-attr>\n", " <node-ref name=\"arg2\"/>\n", "</edge>\n", "```\n", "\n", "describes the relation between two EDUs. The ID of the other EDU is given in the ``arg2`` attribute. \n", "Note, that ``arg2`` can either reference an EDU (e.g. ``edu_9_3_2`` or an EDU range, e.g. ``edus9_3_1-5_0``).\n", "\n", "```xml\n", " <edu xml:id=\"edu_9_3_0\">\n", " <discRel relation=\"Explanation-Speechact\" marking=\"-\" arg2=\"edus9_3_1-5_0\"/>\n", " <node xml:id=\"s128_504\" cat=\"SIMPX\" func=\"--\">\n", " <node xml:id=\"s128_501\" cat=\"MF\" func=\"-\" parent=\"s128_504\">\n", " <node xml:id=\"s128_500\" cat=\"NX\" func=\"ON\" parent=\"s128_501\">\n", " <word xml:id=\"s128_1\" form=\"Kulisammler\" pos=\"NN\" morph=\"npm\" lemma=\"Kulisammler\" func=\"HD\" parent=\"s128_500\" dephead=\"s128_2\" deprel=\"SUBJ\"/>\n", " </node>\n", " </node>\n", " <node xml:id=\"s128_503\" cat=\"VC\" func=\"-\" parent=\"s128_504\">\n", " <node xml:id=\"s128_502\" cat=\"VXINF\" func=\"HD\" parent=\"s128_503\">\n", " <word xml:id=\"s128_2\" form=\"aufgepaßt\" pos=\"VVPP\" lemma=\"auf#passen\" func=\"HD\" parent=\"s128_502\" deprel=\"ROOT\"/>\n", " </node>\n", " </node>\n", " </node>\n", " <word xml:id=\"s128_3\" form=\":\" pos=\"$.\" lemma=\":\" func=\"--\" deprel=\"ROOT\"/>\n", " </edu>\n", "```\n", "\n", "```xml\n", " <edu xml:id=\"edu_9_3_1\">\n", " <discRel relation=\"Continuation\" marking=\"-\" arg2=\"edu_9_3_2\"/>\n", " <node xml:id=\"s128_506\" cat=\"VF\" func=\"-\" parent=\"s128_525\">\n", " <node xml:id=\"s128_505\" cat=\"NX\" func=\"ON\" parent=\"s128_506\">\n", " <relation type=\"expletive\"/>\n", " <word xml:id=\"s128_4\" form=\"Es\" pos=\"PPER\" morph=\"nsn3\" lemma=\"es\" func=\"HD\" parent=\"s128_505\" dephead=\"s128_5\" deprel=\"SUBJ\"/>\n", " </node>\n", " </node>\n", " ...\n", " </edu>\n", "```\n", "\n", "\n", "# ``<splitRelation>``\n", "\n", "```xml\n", "<edge name=\"splitRelation\" parent=\"word|node\">\n", " <enum-attr name=\"type\">\n", " </enum-attr>\n", " <text-attr name=\"target\"/>\n", "</edge>\n", "```\n", "\n", "A ``<splitRelation>`` annotates its parent element (e.g. as an anaphora). Its parent can be either a ``<word>`` or a ``<node>``. \n", "A ``<splitRelation>`` has a target attribute, which describes the targets (plural! e.g. antecedents) of the\n", "relation.\n", "\n", "```xml\n", " <node xml:id=\"s2527_528\" cat=\"NX\" func=\"-\" parent=\"s2527_529\">\n", " <splitRelation type=\"split_antecedent\" target=\"s2527_504 s2527_521\"/>\n", " <word xml:id=\"s2527_32\" form=\"beider\" pos=\"PIDAT\" morph=\"gpf\" lemma=\"beide\" func=\"-\" parent=\"s2527_528\" dephead=\"s2527_33\" deprel=\"DET\"/>\n", " <word xml:id=\"s2527_33\" form=\"Firmen\" pos=\"NN\" morph=\"gpf\" lemma=\"Firma\" func=\"HD\" parent=\"s2527_528\" dephead=\"s2527_31\" deprel=\"GMOD\"/>\n", " </node>\n", "```\n", "\n", "```xml\n", " <word xml:id=\"s3456_12\" form=\"ihr\" pos=\"PPOSAT\" morph=\"nsm\" lemma=\"ihr\" func=\"-\" parent=\"s3456_507\" dephead=\"s3456_14\" deprel=\"DET\">\n", " <splitRelation type=\"split_antecedent\" target=\"s3456_505 s3456_9\"/>\n", " </word>\n", "```\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.5+" } }, "nbformat": 4, "nbformat_minor": 0 }
gpl-3.0
mspcvsp/cincinnati311Data
ClusterServiceCodes.ipynb
1
579687
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "### Setup Code Environment" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import csv\n", "import re\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import nltk\n", "from sklearn.cluster import KMeans\n", "from sklearn.feature_extraction.text import TfidfVectorizer\n", "from sklearn.metrics.pairwise import cosine_similarity\n", "from collections import defaultdict\n", "import seaborn as sns\n", "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Initialize service code data structures\n", "- Service code / service name map\n", "- Service code histogram" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "h_file = open(\"./serviceCodesCount.tsv\",\"r\")\n", "\n", "code_name_map = {}\n", "code_histogram = {}\n", " \n", "patternobj = re.compile('^([0-9a-z]+)\\s\\|\\s([0-9a-z\\s]+)$')\n", "\n", "for fields in csv.reader(h_file, delimiter=\"\\t\"):\n", " matchobj = patternobj.match(fields[0])\n", " \n", " cur_code = matchobj.group(1)\n", " code_name_map[cur_code] = matchobj.group(2)\n", " code_histogram[cur_code] = float(fields[1])\n", " \n", "h_file.close()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Plot Cincinnati 311 Service Code Statistics\n", "- References\n", " - [Descending Array Sort](http://stackoverflow.com/questions/14875248/python-numpy-sort-array)\n", " - [Change Plot Font Size](http://stackoverflow.com/questions/25328003/how-can-i-change-the-font-size-using-seaborn-facetgrid)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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/fr2+v6bG7AghhBAiZwpUsXPx4kV69+7N0aNH6du3L1OnTmXIkCGEhYXRt29f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\ntDYt5TzOsiwePnzYIhIGsUePH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"text/plain": [ "<matplotlib.figure.Figure at 0x7f5d9ca06810>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "total_count_fraction = code_histogram.values()\n", "total_count_fraction.sort()\n", "\n", "total_count_fraction = total_count_fraction[::-1]\n", "total_count_fraction /= np.sum(total_count_fraction)\n", "total_count_fraction = np.cumsum(total_count_fraction)\n", "\n", "sns.set(font_scale=2)\n", "f,h_ax = plt.subplots(1,2,figsize=(12,6))\n", "h_ax[0].bar(range(0,len(code_histogram.values())),\n", " code_histogram.values())\n", "h_ax[0].set_xlim((0,len(total_count_fraction)))\n", "h_ax[0].set_xlabel('Service Code #')\n", "h_ax[0].set_ylabel('Service Code Count')\n", "h_ax[0].set_title('Cincinnati 311\\nService Code Histogram')\n", "\n", "h_ax[1].plot(total_count_fraction, linewidth=4)\n", "h_ax[1].set_xlim((0,len(total_count_fraction)))\n", "h_ax[1].set_xlabel('Sorted Service Code #')\n", "h_ax[1].set_ylabel('Total Count Fraction')\n", "f.tight_layout()\n", "plt.savefig(\"./cincinatti311Stats.png\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Cluster service code names\n", "- Compute Term Frequency Inverse Document Frequency (TF-IDF) feature vectors\n", "- Apply the K-means algorithm to cluster service code names based on their TF-IDF feature vector\n", "- References:\n", " - Rose, B. [\"Document Clustering in Python\"](http://brandonrose.org/clustering)\n", " - [Text pre-processing to reduce dictionary size](http://nlp.stanford.edu/IR-book/html/htmledition/stemming-and-lemmatization-1.html)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from nltk.stem.snowball import SnowballStemmer\n", "\n", "def tokenize(text):\n", " \"\"\" Extracts unigrams (i.e. words) from a string that contains\n", " a service code name.\n", " \n", " Args:\n", " text: String that stores a service code name\n", " \n", " Returns:\n", " filtered_tokens: List of words contained in a service code name\"\"\"\n", " tokens = [word.lower() for word in nltk.word_tokenize(text)]\n", "\n", " filtered_tokens =\\\n", " filter(lambda elem: re.match('^[a-z]+$', elem) != None,\n", " tokens)\n", " \n", " filtered_tokens =\\\n", " map(lambda elem: re.sub(\"\\s+\",\" \", elem),\n", " filtered_tokens)\n", "\n", " return filtered_tokens\n", "\n", "def tokenize_and_stem(text):\n", " \"\"\" Applies the Snowball stemmer to unigrams (i.e. words) extracted\n", " from a string that contains a service code name.\n", " \n", " Args:\n", " text: String that stores a service code name\n", " \n", " Returns:\n", " filtered_tokens: List of words contained in a service code name\"\"\"\n", " stemmer = SnowballStemmer('english')\n", "\n", " tokens = [word.lower() for word in nltk.word_tokenize(text)]\n", "\n", " filtered_tokens =\\\n", " filter(lambda elem: re.match('^[a-z]+$', elem) != None,\n", " tokens)\n", "\n", " filtered_tokens =\\\n", " map(lambda elem: re.sub(\"\\s+\",\" \", elem),\n", " filtered_tokens)\n", "\n", " filtered_tokens = [stemmer.stem(token) for token in filtered_tokens]\n", "\n", " return filtered_tokens\n", "\n", "def compute_tfidf_features(code_name_map,\n", " tokenizer,\n", " params):\n", " \"\"\" Constructs a Term Frequency Inverse Document Frequency (TF-IDF)\n", " matrix for the Cincinnati 311 service code names.\n", " \n", " Args:\n", " code_name_map: Dictionary that stores the mapping of service\n", " codes to service names\n", " \n", " tokenizer: Function that transforms a string into a list of\n", " words\n", " \n", " params: Dictionary that stores parameters that configure the\n", " TfidfVectorizer class constructor\n", " \n", " - mindocumentcount: Minimum number of term occurrences\n", " in separate service code names\n", " \n", " - maxdocumentfrequency: Maximum document frequency\n", " \n", " Returns:\n", " Tuple that stores a TF-IDF matrix and a TfidfVectorizer class\n", " object.\n", " \n", " Index: Description:\n", " ----- -----------\n", " 0 TF-IDF matrix\n", " 1 TfidfVectorizer class object\"\"\"\n", " token_count = 0\n", " for key in code_name_map.keys():\n", " token_count += len(tokenize(code_name_map[key]))\n", "\n", " num_codes = len(code_name_map.keys())\n", "\n", " min_df = float(params['mindocumentcount']) / num_codes\n", " \n", " tfidf_vectorizer =\\\n", " TfidfVectorizer(max_df=params['maxdocumentfrequency'],\n", " min_df=min_df,\n", " stop_words = 'english',\n", " max_features = token_count,\n", " use_idf=True,\n", " tokenizer=tokenizer,\n", " ngram_range=(1,1))\n", "\n", " tfidf_matrix =\\\n", " tfidf_vectorizer.fit_transform(code_name_map.values())\n", "\n", " return (tfidf_matrix,\n", " tfidf_vectorizer)\n", "\n", "def cluster_311_services(tfidf_matrix,\n", " num_clusters,\n", " random_seed):\n", " \"\"\"Applies the K-means algorithm to cluster Cincinnati 311 service\n", " codes based on their service name Term Frequency Inverse Document\n", " Frequency (TF-IDF) feature vector.\n", " \n", " Args:\n", " tfidf_matrix: Cincinnati 311 service names TF-IDF feature matrix\n", " \n", " num_clusters: K-means algorithm number of clusters input\n", " \n", " random_seed: K-means algorithm random seed input:\n", " \n", " Returns:\n", " clusterid_code_map: Dictionary that stores the mapping of\n", " cluster identifier to Cincinnati 311\n", " service code\n", "\n", " clusterid_name_map: Dictionary that stores the mapping of\n", " cluster identifier to Cincinnati 311\n", " service name\"\"\"\n", " km = KMeans(n_clusters = num_clusters,\n", " random_state=np.random.RandomState(seed=random_seed))\n", "\n", " km.fit(tfidf_matrix)\n", "\n", " clusters = km.labels_.tolist()\n", "\n", " clusterid_code_map = defaultdict(list)\n", " clusterid_name_map = defaultdict(list)\n", "\n", " codes = code_name_map.keys()\n", " names = code_name_map.values()\n", "\n", " for idx in range(0, len(codes)):\n", " clusterid_code_map[clusters[idx]].append(codes[idx])\n", " clusterid_name_map[clusters[idx]].append(names[idx])\n", " \n", " return (clusterid_code_map,\n", " clusterid_name_map)\n", "\n", "def compute_clusterid_totalcounts(clusterid_code_map,\n", " code_histogram):\n", " \"\"\" Computes the total Cincinnati 311 requests / service\n", " names cluster\n", " \n", " Args:\n", " clusterid_code_map: Dictionary that stores the mapping of\n", " cluster identifier to Cincinnati 311\n", " service code\n", "\n", " code_histogram: Dictionary that stores the number of \n", " occurrences for each Cincinnati 311 service \n", " code\n", "\n", " Returns:\n", " clusterid_total_count: Dictionary that stores the total\n", " Cincinnati 311 requests / service\n", " names cluster\"\"\"\n", " clusterid_total_count = defaultdict(int)\n", " \n", " num_clusters = len(clusterid_code_map.keys())\n", "\n", " for cur_cluster_id in range(0, num_clusters):\n", " for cur_code in clusterid_code_map[cur_cluster_id]:\n", " clusterid_total_count[cur_cluster_id] +=\\\n", " code_histogram[cur_code]\n", " \n", " return clusterid_total_count\n", "\n", "def print_cluster_stats(clusterid_name_map,\n", " clusterid_total_count):\n", " \"\"\" Prints the total number of codes and total requests count\n", " for each Cincinnati 311 service names cluster.\n", " \n", " Args:\n", " clusterid_name_map: Dictionary that stores the mapping of\n", " cluster identifier to Cincinnati 311\n", " service name\n", "\n", " clusterid_total_count: Dictionary that stores the total\n", " Cincinnati 311 requests / service\n", " names cluster\n", "\n", " Returns:\n", " None\"\"\"\n", " num_clusters = len(clusterid_total_count.keys())\n", "\n", " for cur_cluster_id in range(0, num_clusters):\n", "\n", " print \"clusterid %d | # of codes: %d | total count: %d\" %\\\n", " (cur_cluster_id,\n", " len(clusterid_name_map[cur_cluster_id]),\n", " clusterid_total_count[cur_cluster_id])\n", "\n", "def eval_maxcount_clusterid(clusterid_code_map,\n", " clusterid_total_count,\n", " code_histogram):\n", " \"\"\" This function performs the following two operations:\n", "\n", " 1.) Plots the requests count for each service name in the\n", " maximum count service names cluster.\n", "\n", " 2. Prints the maximum count service name in the maximum count\n", " service names cluster\n", " \n", " Args:\n", " clusterid_name_map: Dictionary that stores the mapping of\n", " cluster identifier to Cincinnati 311\n", " service name\n", "\n", " clusterid_total_count: Dictionary that stores the total\n", " Cincinnati 311 requests / service\n", " names cluster\n", "\n", " code_histogram: Dictionary that stores the number of \n", " occurrences for each Cincinnati 311 service \n", " code\n", " \n", " Returns:\n", " None\"\"\"\n", " num_clusters = len(clusterid_code_map.keys())\n", "\n", " contains_multiple_codes = np.empty(num_clusters, dtype=bool)\n", "\n", " for idx in range(0, num_clusters):\n", " contains_multiple_codes[idx] = len(clusterid_code_map[idx]) > 1\n", "\n", " filtered_clusterid =\\\n", " np.array(clusterid_total_count.keys())\n", " \n", " filtered_total_counts =\\\n", " np.array(clusterid_total_count.values())\n", "\n", " filtered_clusterid =\\\n", " filtered_clusterid[contains_multiple_codes]\n", "\n", " filtered_total_counts =\\\n", " filtered_total_counts[contains_multiple_codes]\n", "\n", " max_count_idx = np.argmax(filtered_total_counts)\n", "\n", " maxcount_clusterid = filtered_clusterid[max_count_idx]\n", " \n", " cluster_code_counts =\\\n", " np.zeros(len(clusterid_code_map[maxcount_clusterid]))\n", "\n", " for idx in range(0, len(cluster_code_counts)):\n", " key = clusterid_code_map[maxcount_clusterid][idx]\n", " cluster_code_counts[idx] = code_histogram[key]\n", "\n", " plt.bar(range(0,len(cluster_code_counts)),cluster_code_counts)\n", " plt.grid(True)\n", " plt.xlabel('Service Code #')\n", " plt.ylabel('Service Code Count')\n", " plt.title('Cluster #%d Service Code Histogram' %\\\n", " (maxcount_clusterid))\n", "\n", " max_idx = np.argmax(cluster_code_counts)\n", " print \"max count code: %s\" %\\\n", " (clusterid_code_map[maxcount_clusterid][max_idx])\n", " \n", "def add_new_cluster(from_clusterid,\n", " service_code,\n", " clusterid_total_count,\n", " clusterid_code_map,\n", " clusterid_name_map):\n", " \"\"\"Creates a new service name(s) cluster\n", "\n", " Args:\n", " from_clusterid: Integer that refers to a service names\n", " cluster that is being split\n", " \n", " servicecode: String that refers to a 311 service code\n", "\n", " clusterid_code_map: Dictionary that stores the mapping of\n", " cluster identifier to Cincinnati 311\n", " service code\n", "\n", " clusterid_name_map: Dictionary that stores the mapping of\n", " cluster identifier to Cincinnati 311\n", " service name\n", " \n", " Returns:\n", " None - Service names cluster data structures are updated\n", " in place\"\"\"\n", " code_idx =\\\n", " np.argwhere(np.array(clusterid_code_map[from_clusterid]) ==\\\n", " service_code)[0][0]\n", " \n", " service_name = clusterid_name_map[from_clusterid][code_idx]\n", "\n", " next_clusterid = (clusterid_code_map.keys()[-1])+1\n", "\n", " clusterid_code_map[from_clusterid] =\\\n", " filter(lambda elem: elem != service_code,\n", " clusterid_code_map[from_clusterid])\n", " \n", " clusterid_name_map[from_clusterid] =\\\n", " filter(lambda elem: elem != service_name,\n", " clusterid_name_map[from_clusterid])\n", " \n", " clusterid_code_map[next_clusterid] = [service_code]\n", " clusterid_name_map[next_clusterid] = [service_name]\n", "\n", "def print_clustered_servicenames(cur_clusterid,\n", " clusterid_name_map):\n", " \"\"\"Prints the Cincinnati 311 service names(s) for a specific \n", " Cincinnati 311 service names cluster\n", "\n", " Args:\n", " cur_clusterid: Integer that refers to a specific Cincinnati 311\n", " service names cluster\n", " \n", " clusterid_name_map: Dictionary that stores the mapping of\n", " cluster identifier to Cincinnati 311\n", " service name\"\"\"\n", " for cur_name in clusterid_name_map[cur_clusterid]:\n", " print \"%s\" % (cur_name)\n", "\n", "def plot_cluster_stats(clusterid_code_map,\n", " clusterid_total_count):\n", " \"\"\"Plots the following service name(s) cluster statistics:\n", "\n", " - Number of service code(s) / service name(s) cluster\n", " - Total number of requests / service name(s) cluster\n", "\n", " Args:\n", " clusterid_name_map: Dictionary that stores the mapping of\n", " cluster identifier to Cincinnati 311\n", " service name\n", " \n", " clusterid_total_count: Dictionary that stores the total\n", " Cincinnati 311 requests / service\n", " names cluster\n", "\n", " Returns:\n", " None\"\"\"\n", " codes_per_cluster =\\\n", " map(lambda elem: len(elem), clusterid_code_map.values())\n", "\n", " num_clusters = len(codes_per_cluster)\n", "\n", " f,h_ax = plt.subplots(1,2,figsize=(12,6))\n", " h_ax[0].bar(range(0,num_clusters), codes_per_cluster)\n", " h_ax[0].set_xlabel('Service Name(s) cluster id')\n", " h_ax[0].set_ylabel('Number of service codes / cluster')\n", " h_ax[1].bar(range(0,num_clusters), clusterid_total_count.values())\n", " h_ax[1].set_xlabel('Service Name(s) cluster id')\n", " h_ax[1].set_ylabel('Total number of requests')\n", " plt.tight_layout()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Apply a word tokenizer to the service names and construct a TF-IDF feature matrix" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "# of terms: 31\n", "[u'additnal', u'building', u'cart', u'com', u'complaint', u'compliment', u'damage', u'default', u'dote', u'gallon', u'grassweeds', u'haz', u'ins', u'litter', u'missing', u'new', u'priv', u'prop', u'property', u'recycling', u'repair', u'req', u'request', u'res', u'row', u'service', u'sign', u'street', u'tall', u'trash', u'tree']\n" ] } ], "source": [ "params = {'maxdocumentfrequency': 0.25,\n", " 'mindocumentcount': 10}\n", "\n", "(tfidf_matrix,\n", " tfidf_vectorizer) = compute_tfidf_features(code_name_map,\n", " tokenize,\n", " params)\n", "\n", "print \"# of terms: %d\" % (tfidf_matrix.shape[1])\n", "print tfidf_vectorizer.get_feature_names()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Apply the [K-means algorithm](http://scikit-learn.org/stable/modules/generated/sklearn.cluster.KMeans.html) to cluster the Cincinnati 311 service names based on their TF-IDF feature vector" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "clusterid 0 | # of codes: 28 | total count: 28012\n", "clusterid 1 | # of codes: 187 | total count: 148910\n", "clusterid 2 | # of codes: 14 | total count: 993\n", "clusterid 3 | # of codes: 16 | total count: 6438\n", "clusterid 4 | # of codes: 34 | total count: 30743\n", "clusterid 5 | # of codes: 15 | total count: 3025\n", "clusterid 6 | # of codes: 31 | total count: 7614\n", "clusterid 7 | # of codes: 16 | total count: 7260\n", "clusterid 8 | # of codes: 14 | total count: 30238\n", "clusterid 9 | # of codes: 19 | total count: 14557\n", "clusterid 10 | # of codes: 25 | total count: 32466\n", "clusterid 11 | # of codes: 12 | total count: 2864\n", "clusterid 12 | # of codes: 12 | total count: 10177\n", "clusterid 13 | # of codes: 16 | total count: 2527\n", "clusterid 14 | # of codes: 11 | total count: 5902\n", "clusterid 15 | # of codes: 12 | total count: 426\n", "clusterid 16 | # of codes: 14 | total count: 21972\n", "clusterid 17 | # of codes: 11 | total count: 216\n", "clusterid 18 | # of codes: 11 | total count: 1921\n", "clusterid 19 | # of codes: 14 | total count: 9403\n" ] } ], "source": [ "num_clusters = 20\n", "kmeans_seed = 3806933558\n", "\n", "(clusterid_code_map,\n", " clusterid_name_map) = cluster_311_services(tfidf_matrix,\n", " num_clusters,\n", " kmeans_seed)\n", "\n", "clusterid_total_count =\\\n", " compute_clusterid_totalcounts(clusterid_code_map,\n", " code_histogram)\n", " \n", "print_cluster_stats(clusterid_name_map,\n", " clusterid_total_count)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Plot the service code histogram for the maximum size cluster" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "max count code: mtlfrn\n" ] }, { "data": { "image/png": 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MnTtXbnt6ejpsbGxUtllJSYlCR2LZa2dnZytsU8fXX3+NUaNGITo6Gqmpqfjz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"text/plain": [ "<matplotlib.figure.Figure at 0x7f5d9c9f9990>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "eval_maxcount_clusterid(clusterid_code_map,\n", " clusterid_total_count,\n", " code_histogram)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Apply a word tokenizer (with stemming) to the service names and construct a TF-IDF feature matrix" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "# of terms: 33\n", "[u'additn', u'build', u'cart', u'collect', u'com', u'complaint', u'compliment', u'damag', u'default', u'dote', u'gallon', u'grasswe', u'haz', u'inspect', u'litter', u'miss', u'new', u'park', u'priv', u'prop', u'properti', u'recycl', u'repair', u'req', u'request', u'res', u'row', u'servic', u'sign', u'street', u'tall', u'trash', u'tree']\n" ] } ], "source": [ "params = {'maxdocumentfrequency': 0.25,\n", " 'mindocumentcount': 10}\n", "\n", "(tfidf_matrix,\n", " tfidf_vectorizer) = compute_tfidf_features(code_name_map,\n", " tokenize_and_stem,\n", " params)\n", "\n", "print \"# of terms: %d\" % (tfidf_matrix.shape[1])\n", "print tfidf_vectorizer.get_feature_names()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Apply the [K-means algorithm](http://scikit-learn.org/stable/modules/generated/sklearn.cluster.KMeans.html) to cluster the Cincinnati 311 service names based on their TF-IDF feature vector" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "clusterid 0 | # of codes: 16 | total count: 7260\n", "clusterid 1 | # of codes: 163 | total count: 125123\n", "clusterid 2 | # of codes: 40 | total count: 25091\n", "clusterid 3 | # of codes: 14 | total count: 718\n", "clusterid 4 | # of codes: 37 | total count: 32299\n", "clusterid 5 | # of codes: 34 | total count: 29379\n", "clusterid 6 | # of codes: 22 | total count: 25306\n", "clusterid 7 | # of codes: 16 | total count: 6438\n", "clusterid 8 | # of codes: 18 | total count: 13252\n", "clusterid 9 | # of codes: 13 | total count: 11748\n", "clusterid 10 | # of codes: 11 | total count: 216\n", "clusterid 11 | # of codes: 10 | total count: 1091\n", "clusterid 12 | # of codes: 18 | total count: 3053\n", "clusterid 13 | # of codes: 13 | total count: 982\n", "clusterid 14 | # of codes: 12 | total count: 5913\n", "clusterid 15 | # of codes: 10 | total count: 9446\n", "clusterid 16 | # of codes: 15 | total count: 32572\n", "clusterid 17 | # of codes: 17 | total count: 14571\n", "clusterid 18 | # of codes: 19 | total count: 17540\n", "clusterid 19 | # of codes: 14 | total count: 3666\n" ] }, { "data": { "image/png": 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Lnjx5gvDwcBQWFuLLL7+UslpaWvjss8+wYMECTJ48GR4eHlAoFNi/fz/S09Ox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\nMjMzJTs7O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z8iIiJw8uRJfP/992jXrh0mTZqE4OBg5Zji6o43NDQUJiYmSExMxNatW2Fp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\nrkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f5d99aca950>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "num_clusters = 20\n", "kmeans_seed = 3806933558\n", "\n", "(clusterid_code_map,\n", " clusterid_name_map) = cluster_311_services(tfidf_matrix,\n", " num_clusters,\n", " kmeans_seed)\n", "\n", "clusterid_total_count =\\\n", " compute_clusterid_totalcounts(clusterid_code_map,\n", " code_histogram)\n", " \n", "print_cluster_stats(clusterid_name_map,\n", " clusterid_total_count)\n", "\n", "plot_cluster_stats(clusterid_code_map,\n", " clusterid_total_count)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Plot the service code histogram for the maximum size cluster" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "max count code: mtlfrn\n" ] }, { "data": { "image/png": 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yCsOGDTM6BFs7rXlJ5ObmEhcXx9ixY/WWnzlzBjc3t0KPWXp6ukFHYu1jx8XF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\nhBBCCGE+sxub5cuXc/v2baZNm8aRI0eIiYnh008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7Lc7OzqxcubLEiTzzzDPk5eWxa9cu\ng8k1Aa5du0Z8fDzOzs48/fTTBAcHq9MkPCghIYGzZ8/Sq1cvdVBvv379UBSFNWvWGMQmJSWxb98+\n/Pz81DMrvXv3xs7OjpiYGIPn9aSnp7N161bc3Nxo27YtcH9As7OzM5s3byYnJ0eNzc/PZ926ddSq\nVYuuXbuW+H0QQgghROnZzJ49e7Y5gXPmzGHAgAH06NGjyBgHBwdu3brFnj17GDNmTMkSsbGhYcOG\n7Nq1i127dpGfn8/169fZt28f77//Pnfu3GH27NlotVqeeeYZzp8/T2xsLKmpqeTk5LBv3z7++c9/\n4uzszIIFC9S7mOrVq0dmZiaxsbGcP3+ee/fuER8fz+zZs9FoNCxevJg6deqo+Ts4OLBlyxYSEhIA\n+Pnnn/nwww/5v//7PxYtWqRegrKxscHNzY1vvvmG/fv3o9FoOH/+PHPnzuW3335jzpw5tGzZ0qza\nc3LyS/ReWSIHh6pPfB3WUANYRx3WUANIHZaktDWkp6ex5/j9x3z4t25MnTqm77KtKNayL8qT2WNs\n8vPz1QGzxalRo0axTycuTs+ePWnUqBErV64kOjqajIwMHB0dadWqFfPnz1efQAywaNEiVqxYwfbt\n29m+fTu1atWiS5cuTJo0yej27rCwMNzc3Ni0aRPvv/8+1apVo127dkycOBF3d3eD2OHDh1O3bl2i\no6OZM2cONjY2eHt7M3fuXFq1amUQGxAQwKpVq1i2bBkREREoikKzZs347LPP6NSpU6neAyGEEEKU\nnkZRFMWcwMDAQJydnYud3LGwsJBBgwaRl5en3r0kHu1JHwgG1jOg7UmvAayjDmuoAaQOS1LaGi5e\nTCRsRTwA80b74e7etKxTKxFr2RflyewxNoGBgcTHxxMWFsbvv/9usK6goICffvqJ0aNHc/r0aQID\nA8s8USGEEEKIRzH7UtSIESP48ccfiY2NZevWrdja2uLk5ISiKGRkZFBYWIiiKLRt25bXXnutPHMW\nQgghhDDJ7MbG3t6e1atXs27dOr755hsSExNJS0u7vxFbW5o1a0ZQUBBDhw4t86cQCyGEEEKYo0Qd\niI2NDcOHD2f48OHqlApVqlShdu3a2NnZlVeOQgghhBBmKfWpFXt7e4MpFYQQQgghKluRjU1kZCQv\nvPAC3t7e6t/NpdFoeOuttx4/OyGEEEKIEii2sXFwcDBobDQaDebcHS6NjRBCCCEqQ5GNzbx58/D0\n9FT//s9//hONRlMhSQkhhBBClEaRjU3fvn0N/t6vX79yT0YIIYQQ4nGY/YC+bt26ERkZyaVLl8oz\nHyGEEEKIUjO7sUlOTmbp0qV069aNwYMHs379em7fvl2euQkhhBBClIjZjc3OnTsZO3YsTZo04eTJ\nk8yZM4cXXniBN998k7i4OPLzn+zZRoUQQgjx5DP7OTbPPvssEydOZOLEiZw/f57vvvuO7777jr17\n97Jv3z4cHR3p1q0bgYGBtGvXrjxzFkIIIYQwqVQP6NNqtWi1WiZPnszZs2fZtWsXcXFxbN68mW++\n+QYXFxf27t1b1rkKIYQQQhTL7EtRRWnevDlTp05lx44dTJ48mRo1apCamloWuQkhhBBClMhjzVZ5\n+/ZtfvjhB3bv3s2xY8fUcTbPP/98mSQnhBBCCFESJW5s0tLS+P7779VmRqfToSgKzZo1o3fv3vTu\n3VvmkBJCCCFEpTC7sVm3bh1xcXGcOHGCwsJCFEXBzc2NXr160bt3b9zd3cszTyGEEEKIRzK7sZkz\nZw4AdevWpUePHgQGBuLl5VVuiQkhhBBClJTZjU3fvn3529/+Rrt27ahS5bHHHAshhBBClDmzOpT8\n/HwuXLjA5cuXpakRQgghhMUyq0uxt7fn0qVL3Lhxo7zzEUIIIYQoNbNPvwwZMoQtW7Zw/fr18sxH\nCCGEEKLUzB5j065dO7Kzs+nfvz/t2rVDq9Xi5OSERqMxGT9o0KASJ3P9+nUiIyM5cOAAt27dwsnJ\nidatW/Pmm2/SvHlzg9i7d+8SFRXFrl27uHr1Ko6Ojvj5+TFx4kT++te/GsQqikJ0dDRbtmwhOTmZ\nqlWr4uvry/jx4/H09DTKIzY2lnXr1nHx4kU0Gg0tWrRg7NixdOzY0Sh2//79rFq1irNnz1JYWEjT\npk0ZMWIEvXr1KnH9QgghhHg8GkVRFHMCtVotGo0GfXhRDY2iKGg0Gs6dO1eiRFJTUwkODiYnJ4fQ\n0FCee+45kpOT+eKLLygoKGDDhg1otVoACgsLef3110lISFAbrRs3bvD5559TUFDAV199RZMmTdRt\nh4WFERsbS7du3ejSpQtZWVmsXbuWa9eusXr1anx9fdXYyMhIIiMjad++PYGBgeh0OjZu3Mi5c+dY\nsGAB3bt3V2O3bNnC9OnTadasGYMHD8bOzo5t27YRHx/PtGnTGDFihFm137yZWaL3yhLVq+f0xNdh\nDTWAddRhDTWA1GFJSlvDxYuJhK2IB2DeaD/c3ZuWdWolYi37ojyZfcYmKCioyGamLCxatIi0tDSi\noqLo1KmTutzT05ORI0eyfPlyFi5cCMCOHTuIj49n1KhRTJkyRY318/Ojf//+REREsGzZMgB++ukn\nYmNj6dmzJwsWLFBj/f396d69Ox9++CFbt24F4MqVK0RFReHj48MXX3yh1tuzZ0969epFeHg4Xbp0\nwd7enpycHObPn0/jxo3ZsGEDVatWBaBPnz4MGDCAhQsX0rt3b+rWrVtu75kQQgghDJnd2MyfP788\n86Bhw4YEBQUZNDUAHTt2pEqVKly4cEFdtm3bNjQaDSEhIQaxzZs3x8fHhwMHDpCZmYmTk5MaO3z4\ncIPY+vXr4+/vz86dO/ntt9947rnn2LlzJzqdjpCQEIMmzsHBgaCgIJYvX86hQ4d4+eWX2bNnDxkZ\nGYwcOVJtagCqVKnC4MGDef/994mLizPKUQghhBDlx2Lu3Z44cSLz5s0zWn779m0KCwtxdHRUl506\ndQoXFxeTUze0atUKnU7HL7/8osba2NiYHEvj7e0NwMmTJ9XYB5c/HKsoikGsRqMpNvbnn39+ZN1C\nCCGEKDslbmwSEhJ47733CAoK4oUXXuDAgQPquq+//pq7d++WaYIbNmxAo9GoY1syMzPJzs6mQYMG\nJuMbNmyIoiikpKQA98fuODs7Y2NjYxTr4uJiFAuY3LaLiwtw/3KVubH67QohhBCiYpRoEswPPviA\njRs3GgwgvnfvHnD/jqaZM2eyceNGYmJiqF69+mMnd+DAAZYtW4ZWq2XYsGEAZGdnAxS5/Ro1ahjE\nZWdn4+zsbHasra2tySbIVGxReTg4OBjECCGEEKJimH3GZuvWrWzYsIFmzZqxcOFC1q1bx4M3VNWu\nXZuQkBDOnDlDdHT0Yye2Y8cOxo8fT+PGjYmKisLOzu6xtymEEEII62b2GZtNmzbRpEkT9Q6ghy+z\nVK1alRkzZnDmzBm+++47xo0bV+qkFi9ezGeffYanpyfLly+nTp066jr9WJvc3FyTr83Ozkaj0ahx\njo6OxcY+uE1HR0cKCgrQ6XRGZ230sU5OTo/M4+HYRynvW98qijXUYQ01gHXUYQ01gNRhSUpTQ3r6\nf8d31qnjaBHvgyXkYMnMbmx+//13hgwZYnAHkCkdO3Zk1apVpU5o9uzZbNy4kYCAAD7++GOq3t+J\nBwAAIABJREFUVatmsN7R0ZGaNWty7do1k6+/evUqAG5ubgA0atSIxMRECgoKsLW1fWTs+fPnSU1N\npXHjxiZjXV1d1Vi4P9ZG/3o9fdOnj32UJ/2ZBGA9z1Z40msA66jDGmoAqcOSlLaGtLQsgz9X9vtg\nLfuiPJl9KSovL08dZ1KcBx/iV1KLFi1i48aNDBw4kCVLlhg1NXq+vr6kpqaabG4SEhKoVq0aXl5e\nALRu3RqdTqfezfSgY8eOodFoaNOmjRoLcPz48SJj/fz81FhFUYqMBdRYIYQQQlQMsxubRo0aqb+w\ni3P48GEaNmxY4kTi4+NZvnw5PXr04MMPPyw2Njg4WJ0m4UEJCQmcPXuWXr16qYN6+/Xrh6IorFmz\nxiA2KSmJffv24efnp55Z6d27N3Z2dsTExFBYWKjGpqens3XrVtzc3Gjbti0AnTt3xtnZmc2bN5OT\nk6PG5ufns27dOmrVqkXXrl1L/D4IIYQQovTMvhTVpUsXVq9ezYoVKxg1apTR+tzcXBYsWMCJEyd4\n4403SpzIRx99hEajoUOHDuzevdtkTOfOnalatSr+/v74+/uzZs0aMjMz8fPzIyUlhdWrV9OwYUMm\nT56svqZ58+aEhoaydu1axo8fT0BAAOnp6URHR1OjRg1mzJihxtarV4+pU6cyb948QkND6du3L3l5\neaxfv57s7GwWL16sxtrb2zN79mwmTZrE0KFDGTJkCDY2NmzevJnk5GQiIiLUu6OEEEIIUTHMnivq\nzp079O/fn5SUFJydnXFzc+Pnn3/G29sbW1tbTp8+TW5uLq6urmzevJlatWqVKBH9XFTF2bNnj3o2\nqKCggBUrVrB9+3ZSUlKoVasWL774IpMmTTL54L5169axadMmkpOTqVatGu3atWPixIm4u7sbxe7a\ntYvo6GgSExOxsbHB29ubCRMm0KpVK6PYI0eOsGzZMs6cOYOiKDRr1owxY8YYPUG5OE/69VKwnuu+\nT3oNYB11WEMNIHVYEpkrynKU9xgbsxsbgLS0NObOnUtcXBw6nc5gna2tLd26dSMsLEzmRyqhJ/0g\nBev5sD3pNYB11GENNYDUYUmksbEcFjMJJkCdOnX45JNPmDlzJmfOnOHWrVvY2tpSt25dmjdvbjDt\ngRBCCCFERStRY6NXu3ZtOnbsWNa5CCGEEEI8FrMam7S0NO7evavOgfTg8ujoaM6ePUutWrXo0aMH\n/v7+5ZKoEEIIIcSjPPJ27y1btuDv78/27dsNlqelpREcHMzKlSv58ccf+fbbb5kwYQIRERHllqwQ\nQgghRHGKbWx+/fVXZsyYQV5entEdS0uWLOHq1au4u7vz8ccfM3/+fJ5++mmio6P59ddfyzVpIYQQ\nQghTir0UFRMTg6IoREVFGdy+nJ+fz7Zt27C1tWX58uXq9ALt27enW7dufPPNN3h6epZv5kIIIYQQ\nDyn2jM3Jkydp166d0TNZjh8/Tk5ODh06dFCbGoD69evTqVMnk9MMCCGEEEKUt2Ibmxs3bph8KN1P\nP/2ERqOhffv2RuueffZZUlNTyy5DIYQQQggzFdvY5Ofn4+Rk/CCdn3/+Gbg/GeXDqlevTm5ubhml\nJ4QQQghhvmIbm2rVqpGVlWWwTKfT8csvv2Bvb0/z5s2NXpOVlUXVqlXLNkshhBBCCDMU29i4uroa\n3eF09OhRsrKyaNWqFXZ2dkavOX/+vMm5moQQQgghyluxjU2bNm04cuQIR44cAeDu3bssWrQIjUZD\n9+7djeIvXbrEoUOH8PLyKp9shRBCCCGKUezt3sOGDeOrr75i5MiRPPvss9y8eZO0tDQaNmxI//79\nDWITEhJ4//33KSgooE+fPuWatBBCCCGEKcWesXFzc2PhwoU4Ojpy4cIF0tLScHNzY9myZUbjaN5+\n+22SkpLo3bs3HTp0KNekhRBCCCFMeeRcUV26dOHgwYMkJiZSpUoVPDw8qFLFuB/q1KkTTz/9NKNG\njSqXRIUQQgghHsWsSTDt7e1p0aJFsTEyR5QQQgghKtsjJ8EUQgghhHhSSGMjhBBCCKshjY0QQggh\nrIY0NkIIIYSwGtLYCCGEEMJqSGMjhBBCCKshjY0QQgghrIZFNjb37t0jIiKCZs2aMXz4cJMxd+/e\nZfHixXTr1g1PT0/at2/P5MmTSUpKMopVFIXVq1cTGBiIl5cXbdq0YcyYMUYTfOrFxsYSHByMj48P\nvr6+DBs2jEOHDpmM3b9/PyEhIfj6+uLt7c2AAQP49ttvS127EEIIIUrP4hqbxMREgoOD+eabb4qM\nKSwsZMyYMSxfvpy2bdsyb948Ro0aRUJCAoMGDSI5Odkg/r333iMiIoJnnnmGOXPmMGnSJJKSkggJ\nCeHEiRMGsZGRkYSFheHk5MTMmTMJCwsjJyeHUaNGERcXZxC7ZcsWxo0bR05ODtOmTWPWrFk4ODgw\nZcoUoqOjy+w9EUIIIYR5zHrycEW5c+cOwcHBNGvWjNjYWF555RWTcTt27CA+Pp5Ro0YxZcoUdbmf\nnx/9+/cnIiKCZcuWAfDTTz8RGxtLz549WbBggRrr7+9P9+7d+fDDD9m6dSsAV65cISoqCh8fH774\n4gs0Gg0APXv2pFevXoSHh9OlSxfs7e3Jyclh/vz5NG7cmA0bNqhzZ/Xp04cBAwawcOFCevfuTd26\ndcvlvRJCCCGEMYs6Y6PT6Rg+fDgbNmygUaNGRcZt27YNjUZDSEiIwfLmzZvj4+PDgQMHyMzMNIh9\n+JJW/fr18ff358KFC/z2228A7Ny5E51OR0hIiNrUADg4OBAUFMStW7fUS1J79uwhIyODAQMGGEwI\nWqVKFQYPHkx+fr7RGR4hhBBClC+Lamzq1KnDlClTDJoKU06dOoWLiwv169c3WteqVSt0Oh2//PKL\nGmtjY4Onp6dRrLe3NwAnT55UYx9c/nCsoigGsRqNptjYn3/+udg6hBBCCFG2LKqxMUdmZibZ2dk0\naNDA5PqGDRuiKAopKSkApKam4uzsjI2NjVGsi4uLUSxgctsuLi7A/ctV5sbqtyuEEEKIivHENTbZ\n2dkAVK9e3eT6GjVqGMRlZ2eXKNbW1tZkE2Qqtqg8HBwcDGKEEEIIUTGeuMZGCCGEEKIoFnVXlDkc\nHR0ByM3NNbk+OzsbjUajxjk6OhYb++A2HR0dKSgoQKfTGZ210cc6OTk9Mo+HYx+lXj3z4iydNdRh\nDTWAddRhDTWA1GFJSlNDerqj+uc6dRwt4n2whBws2RPZ2NSsWZNr166ZXH/16lUA3NzcAGjUqBGJ\niYkUFBRga2v7yNjz58+TmppK48aNTca6urqqsXB/rI3+9Xr6sTX62Ee5eTPTrDhLVq+e0xNfhzXU\nANZRhzXUAFKHJSltDWlpWQZ/fnAbOp2OpKQ/APjrX58xOYyhrFnLvihPT+SlKF9fX1JTU002NwkJ\nCVSrVg0vLy8AWrdujU6nU+9metCxY8fQaDS0adNGjQU4fvx4kbF+fn5qrKIoRcYCaqwQQgjrk5T0\nBxM/3s7Ej7erDY6ofE9kYxMcHIyiKEZP901ISODs2bP06tVLHdTbr18/FEVhzZo1BrFJSUns27cP\nPz8/9cxK7969sbOzIyYmhsLCQjU2PT2drVu34ubmRtu2bQHo3Lkzzs7ObN68mZycHDU2Pz+fdevW\nUatWLbp27Voe5QshhLAQNWo9RY1aT1V2GuIBFnUp6siRIxw+fBi4P78T3L+9+pNPPlFjRo8ejb+/\nP/7+/qxZs4bMzEz8/PxISUlh9erVNGzYkMmTJ6vxzZs3JzQ0lLVr1zJ+/HgCAgJIT08nOjqaGjVq\nMGPGDDW2Xr16TJ06lXnz5hEaGkrfvn3Jy8tj/fr1ZGdns3jxYjXW3t6e2bNnM2nSJIYOHcqQIUOw\nsbFh8+bNJCcnExERod4dJYQQQoiKYVGNzfHjx1m1apX6d41GQ2pqqsGyIUOG4OTkxKJFi1ixYgXb\nt29n+/bt1KpViy5dujBp0iScnZ0NthsWFoabmxubNm3i/fffp1q1arRr146JEyfi7u5uEDt8+HDq\n1q1LdHQ0c+bMwcbGBm9vb+bOnUurVq0MYgMCAli1ahXLli0jIiICRVFo1qwZn332GZ06dSqHd0gI\nIYQQxdEo+lMjotI86QPBwHoGtD3pNYB11GENNcCTV0dRg2GftDpMKW0NFy8mErYiHoB5o/1wd29q\n1rryYi37ojw9kWNshBBClD0ZDCusgUVdihJCCFG5ZCCseNLJGRshhBBCWA1pbIQQQghhNeRSlBBC\niMdSGU/gFaIocsZGCCHEY5FBx8KSyBkbIYQQj00GHQtLIWdshBBCCGE1pLERQgghhNWQxkYIIYQQ\nVkMaGyGEEEJYDWlshBBCCGE1pLERQgghhNWQxkYIIYQQVkMaGyGEEEJYDWlshBBCCGE1pLERQggh\nhNWQKRWEEKISyQSSQpQtOWMjhBCVSCaQFKJsyRkbIYSoZDKBpBBlRxobIYQQpaK/jHbpUnJlpyKE\nShobIYQQpaK/jJabeQvnxs0qOx0hAGlshBBCPIb7l9GUyk5DCJU0NsKilOYOEbmrRAghhJ40NmXg\nzp07LFmyhL1793Ljxg3+8pe/0KlTJyZOnEi9evUqO70niv7UNsDiv/8Nd/em5fKa/3XSDAohrJU0\nNo8pNzeXkJAQkpKSCAkJoWXLliQlJfH5559z9OhRvv76a2rXrl3ZaZYrnU7Hb7/9RlpaVpn8kizN\nHSKlvavkwV/wdeq0KtU2nkTSDAohrJU0No9p9erV/P7778yaNYvBgweryz08PBg/fjxLly5l+vTp\nlZhh+XuSf0k+mPuX8xz5y19cKjmjiiO3GAshrJE8oO8xbdu2jerVq9O/f3+D5f7+/jRo0ICdO3dW\nUmYVq0atp57YX5RPcu7CkE6n4+LFRC5eTESn01V2OkUyzLOwstMRwqrIGZvHcOfOHZKTk2nTpg12\ndnZG6728vPjhhx9ISkrir3/9a8UnKMQDHrzsVtwv0yd5/M2TcvbwwTynDPrfuQT6OCzpuJTn91g2\naWweQ2pqKgANGjQwub5hw4YApKSkPJGNjSV9kViTynpfzf1lWhbNQWUeO0/K2bfi8pTP3n892ER8\nsukUUPlNqzy/x7JJY/MYsrKyAKhevbrJ9TVq1AAgOzu7xNs294utPL8A9R9eRSlk6mAf3NyaVOiX\nrP50PVT+l7v+fb5/eUODjU0VNaeS7oOyPqvw8M8vjjm/TC9dSlbj9PvAVN3FeVLOnBSlLD5XjzOo\nviI+e8XVqBQWqmcjHnVMldXPLMrjNBEPfm7/7/8cuXMn1+TPLU1eDz+/x9zvq7L+zpYm2Jg0NpVM\n/8Wn5+7elIsXE7l0KZnwlT8AsGLOSKPXlTSuNMv08rLSi/0ZADl3bgCoX4b67ZX051+6lKxuKyXl\nivpzZ4wKwM2tSZF5FvXzzalR/9o///yTtLSsIuNGz1zF3ezbVHWobfBemNoHj6rxQaXdP0UdAxkZ\njkUeU/pawcXoPXuwxtouzwH/3Qem6jaVS1nVmJ7uWOS+MPf4Kc3PfdDomauA4o+94o7z4rZhzr6A\nR3/2Hvfz/XB+cP/zkJt5i/CVZ9WfW9Qx9d/4NECjrjf3WDWnngeVdN/qa3zU8auPg0ftK4qsuyTf\nVyU5th5e9vDvjAe3t2LOyCfuHxHlQaMoijwyspTOnz9PUFAQgYGBfPzxx0br582bx9q1a/niiy9o\n3759JWQohBBC/G+Ru6IeQ+PGjQG4du2ayfVXr141iBNCCCFE+ZLG5jE4OjrStGlTTp8+TX5+vsG6\nwsJCTpw4gYuLC66urpWUoRBCCPG/RRqbx9S/f3/y8vLYtGmTwfJt27Zx69YtBgwYUEmZCSGEEP97\nZIzNY8rPz2fYsGGcOXNGnVIhMTGR6Ohonn76aTZt2kTVqlUrO00hhBDif4I0NmUgOzubyMhIdu/e\nzc2bN3F2diYgIIAJEyZQs2bNyk5PCCGE+J8hjY0QQgghrIaMsRFCCCGE1ZDGphLcuXOH8PBwunTp\nQsuWLXnxxReZMWMGN2/erOzUjFy/fp2ZM2fSqVMnWrZsSfv27Rk/fjxnz541ir179y6LFy+mW7du\neHp60r59eyZPnkxSUlLFJ/4IixcvRqvVEhYWZrBcURRWr15NYGAgXl5etGnThjFjxvDrr79WUqbG\n/vOf/zBs2DB8fX3x8fFhyJAhHDhwwCjOkvfH77//zpQpU3jhhRfU4+rNN9/k+PHjBnGWVMO9e/eI\niIigWbNmDB8+3GRMSfKtjGPNnBoyMzOZP38+/v7+tGzZkrZt2/LGG29w5MgRi6jB3DoetnnzZrRa\nbZHxsbGxBAcH4+Pjg6+vL8OGDePQoUNlmbYRc+s4deoUI0eOpE2bNnh5edGvXz+2bdtmFGepx9S1\na9d4//331d95bdu25bXXXmPfvn3lUoNciqpgubm5DBw4kKSkJHWwcVJSEp9//jnOzs58/fXX1K5d\nu7LTBO7PhRUcHExOTg6hoaE899xzJCcn88UXX1BQUMCGDRvQarXA/dvbX3/9dRISEujfvz/t2rXj\nxo0bfP755xQUFPDVV1/RpEmTSq7ovsTERPr160dBQQFBQUHMmzdPXRcWFkZsbCzdunWjS5cuZGVl\nsXbtWq5du8bq1avx9fWtxMzvfznPmDGDtm3bEhQURHZ2NtHR0aSmprJy5Uo6duwIWPb+OHfuHEOH\nDqV69eqEhobi6urKjRs3iImJ4erVqyxbtozOnTtbVA2JiYlMnTqV1NRUMjMzadOmDWvXrjWIKWm+\nFX2smVNDVlYWwcHBXLlyhSFDhuDt7c2NGzeIjo7m5s2bREVF8dJLL1VaDebW8bBbt27Rs2dPMjIy\nTMZHRkYSGRlJ+/btCQwMRKfTsXHjRs6dO8eCBQvo3r17pdXx448/MnbsWJo2bcrgwYPRaDTExMTw\n22+/MWfOHIM7by3xmLp+/Tp9+/YlNzeX4cOH4+HhQXp6Ol999RUXLlxg1qxZDBkypGxrUESFWrp0\nqaLVapUNGzYYLP/hhx8UDw8PJTw8vJIyM/aPf/xD0Wq1yv79+w2WHzx4UPHw8FAmTZqkLtu6davi\n4eGh/Otf/zKIPXPmjKLVapVx48ZVSM6PUlhYqAwaNEjp27evotVqlWnTpqnrjh07pnh4eCiTJ082\neM21a9cUb29vpU+fPhWdroGbN28q3t7eyuuvv26w/NKlS0rHjh2VuXPnqssseX+MHz9e0Wq1yrFj\nxwyWX758WdFqtUrfvn0VRbGcGm7fvq14eXkpgwYNUq5cuaJ4eHgow4YNM4orSb4VfayZW8PixYsV\nrVarrFu3zmD577//rmi1WmXAgAGVVkNJ6njYpEmTlJdeeknp2LGjUfzly5eVFi1aKIMHD1YKCwvV\n5VlZWUqnTp2Ujh07Knfv3q2UOvLy8pSXXnpJCQwMNMghIyND6dKli/L222+ryyz1mJo3b56i1WqV\n2NhYg+WZmZnK888/r/j5+ZV5DXIpqoJt27aN6tWr079/f4Pl/v7+NGjQgJ07d1ZSZsYaNmxIUFAQ\nnTp1MljesWNHqlSpwoULF9Rl27ZtQ6PREBISYhDbvHlzfHx8OHDgAJmZmRWSd3HWr1/PqVOnCAsL\nQ3noZKW+hodPp9avXx9/f38uXLjAb7/9VpHpGtiyZQt5eXlMmDDBYLmrqys//vgj7733nrrMkveH\n/rLMw//yaty4MU899RSXLl0CLKcGnU7H8OHD2bBhA40aNSoyriT5VvSxZm4NtWvXpmvXrkbfT+7u\n7jRs2NDkZ74iPy/m1vGg/fv3ExcXx+TJk7G3tzdav3PnTnQ6HSEhIWg0/53vysHBgaCgIG7dulXm\nl6TMrWPPnj1cv36dMWPGGOTu5OTEnj17WLx4sbrMUo8p/ee9devWBsv1D7i9ffs2GRkZZVqDNDYV\n6M6dOyQnJ9OiRQvs7OyM1nt5eXH79m2LGAMBMHHiRIPLNHq3b9+msLAQR0dHddmpU6dwcXGhfv36\nRvGtWrVCp9Pxyy+/lGu+j3Lt2jUWLFhAcHAwbdq0MVp/6tQpbGxs8PT0NFrn7e0NwMmTJ8s9z6Ic\nOXIEBwcHNZfCwkKjJ17rWfL+ePrppwGMjvPc3FzS09N59tlnAcupoU6dOkyZMsXgl54pJcm3oo81\nc2sYPnw4ixcvNnr2VmFhIRkZGUaf+Yr+vJhbh15OTg4ffPABHTp0ICgoyGTMqVOngP/m/CBvb28U\nRam0Og4fPoxGo1EvMQPFfuYt8Zgq6vMO97+TnZ2d1ceilFUN0thUoNTUVAAaNGhgcn3Dhg0BSElJ\nqbCcSmPDhg1oNBr1unNmZibZ2dkWX9cHH3xA9erVeffdd02uT01NxdnZGRsbG6N1Li4uKIpSqTX8\n8ccfuLm5cfbsWYYNG4anpydeXl4EBgaya9cuNc7S98f48eNxdHTk3Xff5cSJE6Snp3PhwgX+/ve/\nU1hYyMSJEy2+hoeZk++Dx4+lH2sP27FjB5mZmQZjTZ6EGhYsWMDt27f54IMPiowp7nvZxcUFgCtX\nrpRPgo/wxx9/ULNmTTIzMxk7dqz6mff39ycmJsYg1lL3x2uvvcZTTz1FeHg4hw4dIi0tjaSkJGbN\nmkVqaipTpkxRY8uqBtsyrUAUKyvr/lTz1atXN7m+Ro0awP0H/lmqAwcOsGzZMrRaLcOGDQP+m29x\ndSmKUql1xcXFsW/fPhYtWmTwr84HZWdn4+zsbHKdJeybO3fuYGtry9ixYxk0aBDjxo0jNTWVFStW\n8M4775Cbm0v//v0tfn8899xzbNy4kTfffJOhQ4eqy+vWrcuKFSto3769OrGspdbwMHPe8wfjLP1Y\ne9DZs2eZM2cOLi4uvP322+pyS6/hl19+Yf369UyZMqXYiYizs7OxtbU1+cu0suu4c+cOcL85CAgI\nICQkhNu3b/PFF18QHh5OWlqauk8sdX889dRTfP3117z11lu88cYb6nInJyfmz59Pnz591GVlVYM0\nNsJsO3bsYPr06TRu3JioqCiTl9MsUWZmJuHh4bz88svlcndDRbl37x5Xr15lyZIl+Pv7q8tfeOEF\nunfvzsKFC+nXr18lZmiexMRERo0ahb29PXPmzKFx48bcuHGDDRs28NZbb7Fo0SI8PDwqO03B/cuf\nEyZMoFq1aqxcuZJatWpVdkpmKSgoYPr06Wi1Wl577bXKTqfU7t27R0ZGBm+++SahoaHq8pdffple\nvXrx+eefExoaatH75caNG7zxxhukp6fz3nvvqeNqYmNjmT59Ovn5+WU+p6I0NhVIf6YgNzfX5Hp9\nJ+rk5FRhOZlr8eLFfPbZZ3h6erJ8+XLq1KmjrjOnLo1GU+SZkvIWERFBbm4us2fPLjbO0dHxkfum\nsmqA+/9iuXfvnkFTA/cH1nXo0IG9e/dy8eJF9ZS6pe6PsLAwbt++zQ8//EC9evXU5T169KB79+6E\nhYXx/fffA5Zbw8NK+hmw9GMNYNOmTcyZM4dGjRqxcuVK3NzcDNZbcg0rV67kjz/+4Ouvv6ZKleJH\nXDg6OlJQUIBOpzM6a1PZ38n6sxS9evUyWO7g4EDXrl358ssvOXnyJJ06dbLY/TF37lx+//13tmzZ\nQrNmzdTlPXr0YPDgwcyZM4cuXbrg7OxcZjXIGJsKpD8dqj/N/rCrV68axFmK2bNn89lnnxEQEMCX\nX35p0NTA/QOtZs2aj6zL1dW13HN92LFjx/jmm2/Uf7Vdv36d69evq7nm5eVx/fp1MjIyaNSoEbdu\n3aKgoMBoO1evXkWj0VRKDXqNGjUqcqDeX/7yF+D+5U5L3h+3b9/m9OnTeHp6GjQ1APb29rRr1470\n9HSSkpIstgZTzH3P9c2BpR9rK1asYNasWXh5ebFx40ajpgYst4bk5GSioqLo27cvzs7OBp95/YD7\n69evk5aWBqDe0aMfa/Ogyj7O9LmZukz24GdeH2uJ++PgwYO4uLgYNDV6L7zwAvfu3VMfzFlWNUhj\nU4H0t7edPn3aaGR7YWEhJ06cwMXFxWK+rAEWLVrExo0bGThwIEuWLKFatWom43x9fUlNTTX5xZ6Q\nkEC1atVo1apVeadr5OjRowAsXbqUTp06qf917twZjUbDd999R+fOnZk3bx6tW7dGp9OZHHV/7Ngx\nANq2bVuh+T/I29ubvLw8kpOTjdY9PADSUveH/hb7e/fumVyv/1xoNBqLraEo5uTr5eUFYNHH2tdf\nf82CBQvo3Lkz0dHR6i/Qh1lqDSdOnCA/P59vvvnG6DN/7do19QzHpEmTgP/ehvzwU6/hfh0ajYZ2\n7dpVaA16+juBzp07Z7RO/5nX34VnqftDUZQi7+R6eHlZ1SCNTQXr378/eXl5bNq0yWD5tm3buHXr\nVplfa3wc8fHxLF++nB49evDhhx8WGxscHIyiKERHRxssT0hI4OzZs/Tq1avIgZXlKTAwkKioKKKi\noli+fLnBf4qi0KFDB6KiohgxYgT9+vVDURTWrFljsI2kpCT27duHn59fpTad+vyWLl1qsPzixYsc\nPXoUrVarNjaWuj/+8pe/0KRJE06fPs3ly5cN1mVmZnL48GH1HwCWWkNRSpKvpR5rf/zxB+Hh4bRu\n3ZolS5aYfO6LnqXWoP9Mm/rMOzs789xzz7F8+XL1bpzevXtjZ2dHTEwMhYWF6nbS09PZunUrbm5u\nldbYBAYGYmdnR1RUlEFut27dYvfu3Tg7O6vNvaXuD19fX27dusVPP/1ksLygoIC9e/diY2NT5jXI\nGJsKNnToUL777jsiIiJISUmhZcuWJCYmEh0djVar5fXXX6/sFFUfffQRGo2GDh06sHv3bpMxnTt3\npmrVqvj7++Pv78+aNWvIzMzEz8+PlJQUVq9eTcOGDZk8eXIFZ39fkyZNin3sfv369Q0eQBgaGsra\ntWsZP348AQEBpKenEx0dTY0aNZgxY0ZFpFwkLy8vQkJCWLduHbm5uQQEBHDz5k1Wr16cT76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fISGMf+/ftDksNA27p167T4nJwcbUO/qakpLdbj8fDpp5+GXHfVqlUkJydz+vTpkKfN3G43W7Zs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\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f5d997e93d0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "eval_maxcount_clusterid(clusterid_code_map,\n", " clusterid_total_count,\n", " code_histogram)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Create a separate service name(s) cluster for the 'mtlfrn' service code" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [], "source": [ "add_new_cluster(1,\n", " 'mtlfrn',\n", " clusterid_total_count,\n", " clusterid_code_map,\n", " clusterid_name_map)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Evaluate the service name(s) cluster statistics" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "clusterid 0 | # of codes: 16 | total count: 7260\n", "clusterid 1 | # of codes: 162 | total count: 52932\n", "clusterid 2 | # of codes: 40 | total count: 25091\n", "clusterid 3 | # of codes: 14 | total count: 718\n", "clusterid 4 | # of codes: 37 | total count: 32299\n", "clusterid 5 | # of codes: 34 | total count: 29379\n", "clusterid 6 | # of codes: 22 | total count: 25306\n", "clusterid 7 | # of codes: 16 | total count: 6438\n", "clusterid 8 | # of codes: 18 | total count: 13252\n", "clusterid 9 | # of codes: 13 | total count: 11748\n", "clusterid 10 | # of codes: 11 | total count: 216\n", "clusterid 11 | # of codes: 10 | total count: 1091\n", "clusterid 12 | # of codes: 18 | total count: 3053\n", "clusterid 13 | # of codes: 13 | total count: 982\n", "clusterid 14 | # of codes: 12 | total count: 5913\n", "clusterid 15 | # of codes: 10 | total count: 9446\n", "clusterid 16 | # of codes: 15 | total count: 32572\n", "clusterid 17 | # of codes: 17 | total count: 14571\n", "clusterid 18 | # of codes: 19 | total count: 17540\n", "clusterid 19 | # of codes: 14 | total count: 3666\n", "clusterid 20 | # of codes: 1 | total count: 72191\n" ] } ], "source": [ "clusterid_total_count =\\\n", " compute_clusterid_totalcounts(clusterid_code_map,\n", " code_histogram)\n", " \n", "print_cluster_stats(clusterid_name_map,\n", " clusterid_total_count)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Plot the service code histogram for the maximum size cluster" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "max count code: ydwstaj\n" ] }, { "data": { "image/png": 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DtWvXMG7cOKXjMTExsLGx0XjNMjMzVSYSK9772rVrKse08e2336Jfv37YvXs3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\nFgCApKSkWoupKrZu3QpBEKT7ztnZ2cjNzTX4dn3xxRewsrLCxx9/rHF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"text/plain": [ "<matplotlib.figure.Figure at 0x7f5d995bc150>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "eval_maxcount_clusterid(clusterid_code_map,\n", " clusterid_total_count,\n", " code_histogram)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Create a separate service name(s) cluster for the 'ydwstaj' service code" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [], "source": [ "add_new_cluster(1,\n", " 'ydwstaj',\n", " clusterid_total_count,\n", " clusterid_code_map,\n", " clusterid_name_map)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Evaluate the service name(s) cluster statistics" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "clusterid 0 | # of codes: 16 | total count: 7260\n", "clusterid 1 | # of codes: 161 | total count: 43365\n", "clusterid 2 | # of codes: 40 | total count: 25091\n", "clusterid 3 | # of codes: 14 | total count: 718\n", "clusterid 4 | # of codes: 37 | total count: 32299\n", "clusterid 5 | # of codes: 34 | total count: 29379\n", "clusterid 6 | # of codes: 22 | total count: 25306\n", "clusterid 7 | # of codes: 16 | total count: 6438\n", "clusterid 8 | # of codes: 18 | total count: 13252\n", "clusterid 9 | # of codes: 13 | total count: 11748\n", "clusterid 10 | # of codes: 11 | total count: 216\n", "clusterid 11 | # of codes: 10 | total count: 1091\n", "clusterid 12 | # of codes: 18 | total count: 3053\n", "clusterid 13 | # of codes: 13 | total count: 982\n", "clusterid 14 | # of codes: 12 | total count: 5913\n", "clusterid 15 | # of codes: 10 | total count: 9446\n", "clusterid 16 | # of codes: 15 | total count: 32572\n", "clusterid 17 | # of codes: 17 | total count: 14571\n", "clusterid 18 | # of codes: 19 | total count: 17540\n", "clusterid 19 | # of codes: 14 | total count: 3666\n", "clusterid 20 | # of codes: 1 | total count: 72191\n", "clusterid 21 | # of codes: 1 | total count: 9567\n" ] } ], "source": [ "clusterid_total_count =\\\n", " compute_clusterid_totalcounts(clusterid_code_map,\n", " code_histogram)\n", " \n", "print_cluster_stats(clusterid_name_map,\n", " clusterid_total_count)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Plot the service code histogram for the maximum size cluster" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "max count code: grfiti\n" ] }, { "data": { "image/png": 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7dqV3795m7zCZPHky7du3Jzo6mpSUFH755RccHR3x8PCgT58+9OvXz6Q/lp6z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pGRkfyky6GhIQQHBwtu4aw3ryQ1NRVlZWV8OLItBLcerVaLV69eoaurC5mZmYiLi8PS0hKePHmC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\nG354AAAAAElFTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f5d991e8110>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "eval_maxcount_clusterid(clusterid_code_map,\n", " clusterid_total_count,\n", " code_histogram)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Create a separate service name(s) cluster for the 'grfiti' service code" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [], "source": [ "add_new_cluster(1,\n", " 'grfiti',\n", " clusterid_total_count,\n", " clusterid_code_map,\n", " clusterid_name_map)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Evaluate the service name(s) cluster statistics" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "clusterid 0 | # of codes: 16 | total count: 7260\n", "clusterid 1 | # of codes: 160 | total count: 34976\n", "clusterid 2 | # of codes: 40 | total count: 25091\n", "clusterid 3 | # of codes: 14 | total count: 718\n", "clusterid 4 | # of codes: 37 | total count: 32299\n", "clusterid 5 | # of codes: 34 | total count: 29379\n", "clusterid 6 | # of codes: 22 | total count: 25306\n", "clusterid 7 | # of codes: 16 | total count: 6438\n", "clusterid 8 | # of codes: 18 | total count: 13252\n", "clusterid 9 | # of codes: 13 | total count: 11748\n", "clusterid 10 | # of codes: 11 | total count: 216\n", "clusterid 11 | # of codes: 10 | total count: 1091\n", "clusterid 12 | # of codes: 18 | total count: 3053\n", "clusterid 13 | # of codes: 13 | total count: 982\n", "clusterid 14 | # of codes: 12 | total count: 5913\n", "clusterid 15 | # of codes: 10 | total count: 9446\n", "clusterid 16 | # of codes: 15 | total count: 32572\n", "clusterid 17 | # of codes: 17 | total count: 14571\n", "clusterid 18 | # of codes: 19 | total count: 17540\n", "clusterid 19 | # of codes: 14 | total count: 3666\n", "clusterid 20 | # of codes: 1 | total count: 72191\n", "clusterid 21 | # of codes: 1 | total count: 9567\n", "clusterid 22 | # of codes: 1 | total count: 8389\n" ] } ], "source": [ "clusterid_total_count =\\\n", " compute_clusterid_totalcounts(clusterid_code_map,\n", " code_histogram)\n", " \n", "print_cluster_stats(clusterid_name_map,\n", " clusterid_total_count)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Plot the service code histogram for the maximum size cluster" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "max count code: dapub1\n" ] }, { "data": { "image/png": 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fWLZsGdu3b2fjxo2Ym5vz0ksvMXLkSPr166cXb2try7fffst3333H1q1b2bdv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"text/plain": [ "<matplotlib.figure.Figure at 0x7f5d9928a610>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "eval_maxcount_clusterid(clusterid_code_map,\n", " clusterid_total_count,\n", " code_histogram)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Create a separate service name(s) cluster for the 'dapub1' service code" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": true }, "outputs": [], "source": [ "add_new_cluster(1,\n", " 'dapub1',\n", " clusterid_total_count,\n", " clusterid_code_map,\n", " clusterid_name_map)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Evaluate the service name(s) cluster statistics" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "clusterid 0 | # of codes: 16 | total count: 7260\n", "clusterid 1 | # of codes: 159 | total count: 28034\n", "clusterid 2 | # of codes: 40 | total count: 25091\n", "clusterid 3 | # of codes: 14 | total count: 718\n", "clusterid 4 | # of codes: 37 | total count: 32299\n", "clusterid 5 | # of codes: 34 | total count: 29379\n", "clusterid 6 | # of codes: 22 | total count: 25306\n", "clusterid 7 | # of codes: 16 | total count: 6438\n", "clusterid 8 | # of codes: 18 | total count: 13252\n", "clusterid 9 | # of codes: 13 | total count: 11748\n", "clusterid 10 | # of codes: 11 | total count: 216\n", "clusterid 11 | # of codes: 10 | total count: 1091\n", "clusterid 12 | # of codes: 18 | total count: 3053\n", "clusterid 13 | # of codes: 13 | total count: 982\n", "clusterid 14 | # of codes: 12 | total count: 5913\n", "clusterid 15 | # of codes: 10 | total count: 9446\n", "clusterid 16 | # of codes: 15 | total count: 32572\n", "clusterid 17 | # of codes: 17 | total count: 14571\n", "clusterid 18 | # of codes: 19 | total count: 17540\n", "clusterid 19 | # of codes: 14 | total count: 3666\n", "clusterid 20 | # of codes: 1 | total count: 72191\n", "clusterid 21 | # of codes: 1 | total count: 9567\n", "clusterid 22 | # of codes: 1 | total count: 8389\n", "clusterid 23 | # of codes: 1 | total count: 6942\n" ] }, { "data": { "image/png": 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Ny5YtQ1FREfr164fXX38du3fvRnh4eI0lTEREREREJIfsYicsLAzm5uY4ePAgtLSev23X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\nAwODBuvq6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"text/plain": [ "<matplotlib.figure.Figure at 0x7f5d9c27a1d0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "clusterid_total_count =\\\n", " compute_clusterid_totalcounts(clusterid_code_map,\n", " code_histogram)\n", " \n", "print_cluster_stats(clusterid_name_map,\n", " clusterid_total_count)\n", "\n", "plot_cluster_stats(clusterid_code_map,\n", " clusterid_total_count)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Label each service name(s) cluster" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "street roadway widening\n", "street sweeping\n", "street dote street opening\n", "revocable street privilege\n", "street plates slippery street\n", "street dote street blocking\n", "street asphalt repair \n", "street plates inlet placerem\n", "street repavedresurfaced\n", "street heaved area\n", "general repair street\n", "void dote in street\n", "street cleaning\n", "street plates dote plates haz\n", "street flashfill structures\n", "street flashfill dunbar\n" ] } ], "source": [ "cur_clusterid = 0\n", "clusterid_category_map = {}\n", "clusterid_category_map[cur_clusterid] = 'streetmaintenance'\n", "\n", "print_clustered_servicenames(cur_clusterid,\n", " clusterid_name_map)\n", "\n", "cur_clusterid += 1" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "handbill prohibit dstrb \n", "buildhaz during constexistr \n", "electricalsingle applncearc\n", "furniture at the curb\n", "misc traffc study cntaccident\n", "animal bitescratchoth domest\n", "elevator hazard\n", "sidewalk bushes encumbering\n", "row tire dumping\n", "sink holeearth condition pph\n", "gratemetal theftvandalism\n", "unregistered contractor ins\n", "pavement priv w no drainage\n", "exit blocked \n", "weeds brush as fire hazard\n", "school roaches in or around \n", "animal waste in the yard\n", "zoning cncntrtd code enf com \n", "rodentinsect infest nurse hm\n", "carpentry overhead door\n", "restaurant consult new lic\n", "cincinnati bell\n", "wesleyan new resident\n", "elevator slow\n", "institution def plumbing\n", "inlets clogged rainingpondng\n", "building weeds priv property\n", "unsanitary condtn hotelmotel\n", "vehicle abandon priv p access\n", "noticesign posted on a pole\n", "swimming poolspa safe concrn\n", "sweeping parking enforcement\n", "animal bitescratch wild\n", "corner can overflowing\n", "yard plumbing leak\n", "fire escape unsafe blocked\n", "littertall grassweeds dote \n", "media advisory\n", "school mold \n", "unsanitary living conditions\n", "swimming poolspa cloudydrty\n", "gateway lighting\n", "signal traffic new\n", "manhole cvrsewer lid missing\n", "encroachment object in row\n", "water ponding\n", "mold childrens hosp referral\n", "school registration high\n", "fleas in an apartment or bldg\n", "fence height or material com\n", "animal bitescratchdog \n", "housingcra rehabilitation\n", "cagis csr application mod\n", "elecfixture not workno arc\n", "housing rehab surveyinitl\n", "sprinkler defective not work\n", "benches repairremove row\n", "parking prob at com facility\n", "customer inquiry gcww\n", "spill non toxic 1st shift\n", "zoning viol during construct\n", "speed humps repairremoved\n", "smoke detector missingdamagd\n", "row furnituretrash dumping\n", "roaches bldg or apartment\n", "parking meter dote newchange\n", "corner can new\n", "corner can dmglinerother\n", "inlets ps collpsd or dmgd\n", "food operation unsanitry cond\n", "bicycle abandoned\n", "dumpster special rtc\n", "food borne illness 2persons\n", "school rodents in or around\n", "light newchange\n", "cagis permits plus modificatn\n", "salt can refill\n", "septic system problems \n", "mobile home sewage on grounds\n", "trashl\n", "time warner cable\n", "roaches childrns hos referral\n", "institution unsanitary cond\n", "rumpke routing update\n", "elevator expired license\n", "housing rehab surveyfinal\n", "duke energy\n", "extension cords use\n", "lot vacant enclose vacant lot\n", "pavement markings newchange\n", "bike rack new\n", "furnace blocked or not workng\n", "open burning garbage tires\n", "media advis winter operations\n", "animal bitescratchpuppy\n", "mosquitoes ponding water\n", "light shining into bldg com\n", "guardrail dote new\n", "alarm fire broken\n", "spill non toxic after hours\n", "weeds alleyrowsteps\n", "institution mold in bldg\n", "restaurant consult remodel\n", "plumbing defective\n", "annual c annual insp\n", "home ownership survey final\n", "home ownership survey initial\n", "restaurant consult chnged lic\n", "sidewalk new\n", "mud tracking of mud\n", "fence row\n", "rats childrens hosp referral\n", "extinguishers defectv missng\n", "cagis workflow modification\n", "racoonbats rem in bldgoutsd\n", "bicyclist incident report\n", "contructcontract complnt row\n", "institution food svc cmplaint\n", "yard waste late set out\n", "curbs curbs new\n", "school registration elementry\n", "landuse nonpermttd use com \n", "light loader infested collctn\n", "void flashfill\n", "taxicab issue\n", "fire door locked or blocked\n", "cincinnati gaslight\n", "school bathroom stalls soap \n", "steps salting\n", "traffic island repairnon land\n", "landscaping damagedrepair\n", "water leaksbreaks\n", "vehicle abandon priv p no acc\n", "buildingstorage of mtl fire\n", "bed bugs school\n", "corner can overflowing cbd\n", "dci\n", "odot\n", "barricade setupremve\n", "waste accumulated fire\n", "construction status\n", "pavement markings faded\n", "zoning viol dur construct ins\n", "food operation rodents\n", "bed bugs hotelmotel\n", "heat no heat hazard\n", "wire gym shoesobjects\n", "salt can newreplaceremove\n", "customer inquiry msd\n", "debri floodstorm removal\n", "inlets clogged \n", "gateway fountain\n", "employment verification\n", "elevator unsafe door\n", "horse drawn carriage issue\n", "mudslidelandslide in row\n", "traffic island repairlandscap\n", "food operation ill employee\n", "food operation insects \n" ] } ], "source": [ "clusterid_category_map[cur_clusterid] = 'miscellaneous'\n", "\n", "print_clustered_servicenames(cur_clusterid,\n", " clusterid_name_map)\n", "\n", "cur_clusterid += 1" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "trash cart repair 95 gallon\n", "trash cart swap 95 gallon\n", "trash cart remove\n", "trash cart new 65 gallon\n", "trash cart new 95 gallon\n", "trash cart additnal 041115\n", "trash cart additnal 040415\n", "trash cart additnal 031415\n", "trash cart additnal 030715\n", "trash cart additnal 022815\n", "trash cart additnal 022115\n", "trash cart repair 35 gallon\n", "trash cart swap 35 gallon\n", "trash cart new 35 gallon\n", "trash cart swap 65 gallon\n", "trash cart additnal 020715\n", "trash cart additnal 021415\n", "trash cart additnal 122714\n", "trash cart additnal 010315\n", "trash cart additnal 011015\n", "trash cart additnal 011715\n", "trash cart additnal 013115\n", "trash cart additnal 062715\n", "trash cart additnal 072515\n", "trash cart additnal 052315\n", "trash cart additnal 050215\n", "trash cart additnal 050915\n", "trash cart additnal 061315\n", "trash cart additnal 053015\n", "trash cart additnal 060615\n", "trash cart additnal 042515\n", "trash cart additnal 012415\n", "trash cart registration\n", "trash cart exemption\n", "trash cart repair 65 gallon\n", "trash cart additnal 082215\n", "trash cart additnal 080815\n", "trash cart additnal 051615\n", "trash cart additnal 062015\n", "trash cart additnal 041815\n" ] } ], "source": [ "clusterid_category_map[cur_clusterid] = 'trashcart'\n", "\n", "print_clustered_servicenames(cur_clusterid,\n", " clusterid_name_map)\n", "\n", "cur_clusterid += 1" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "building electric haz\n", "barricade setupremove haz\n", "bridgesrepair haz\n", "litter stepswalkway no haz\n", "water no water haz\n", "litter stepswalkway haz ah\n", "build haz cnstr condnewcom\n", "graffiti haz\n", "build haz cnstr cond nwcm ins\n", "building plmb haz during cons\n", "handrails damagedmissing haz\n", "litter stepswalkway haz rh\n", "pool priv pool enclosure haz\n", "dumpster overflow row haz\n" ] } ], "source": [ "clusterid_category_map[cur_clusterid] = 'buildinghazzard'\n", "\n", "print_clustered_servicenames(cur_clusterid,\n", " clusterid_name_map)\n", "\n", "cur_clusterid += 1" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "building disaster resvacant\n", "rats in a building\n", "build permit vio dur const er\n", "building electric permit viol\n", "building hvac hazard ins\n", "unregistered contractor build\n", "construct build wo permit ins\n", "building demo disastercom\n", "building prmt cnflct w con c\n", "building prmt cnflct w con r\n", "building cde vio dur constrn\n", "building commercial cbhcodec\n", "building prob new bldg const \n", "building hvac permit violatn\n", "housingcra new building\n", "building demo disasterres\n", "building concentratd code enf\n", "mice building has mice\n", "building hvac permit viol ins\n", "building com concntrtd code e\n", "build permit vio dur const nc\n", "building barricade case \n", "building demo city ownedcom\n", "building demo city ownedres\n", "rats outside a building\n", "mold building or apartment\n", "building hvac hazard\n", "building disaster comvacant\n", "animal waste in a building\n", "building ovrcrowding resident\n", "building residential\n", "building condemn requestcom\n", "building collapsing \n", "sewage in building\n", "constructn build wo permit \n", "building vacant and open com\n", "building plmb permit violatn\n" ] } ], "source": [ "clusterid_category_map[cur_clusterid] = 'buildingcomplaint'\n", "\n", "print_clustered_servicenames(cur_clusterid,\n", " clusterid_name_map)\n", "\n", "cur_clusterid += 1" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "parking meter repair\n", "bridge repair\n", "guardrail repair haz\n", "utility repairs dote \n", "pothole repair after hours\n", "sidewalk repair asphalt\n", "guardrail repair\n", "wall repair problem near str\n", "sidewalk temporary repair\n", "handrails repair\n", "recycling repair 96\n", "sidewalk repair structures\n", "sunken area repair\n", "pothole repair\n", "attenuator repair\n", "restoration repair cww\n", "fire hydrant repair cww\n", "gm barrier repair haz\n", "ps stormwater inlet repair\n", "recycling repair 35\n", "city facility repair\n", "signal audible signal repair\n", "curbs repair\n", "recycling repair 64\n", "light pedestal repair\n", "fire hydrant repair\n", "signal trafpedschool repair\n", "wall repair problem in st haz\n", "light repair\n", "steps repair\n", "steps repair haz\n", "recycling repair 18\n", "sidewalk repair haz\n", "void repair\n" ] } ], "source": [ "clusterid_category_map[cur_clusterid] = 'repairrequest'\n", "\n", "print_clustered_servicenames(cur_clusterid,\n", " clusterid_name_map)\n", "\n", "cur_clusterid += 1" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "building tall grass priv prop\n", "tall grassweeds private prop\n", "tall grass weeds rec prop\n", "fence built ovr prop line com\n", "tall grassweeds park prop\n", "tall grassweeds fire propert\n", "landslide private prop stndrd\n", "sewage surfacing priv prop\n", "tall grassweeds econ dev prop\n", "tall grassweeds msd property\n", "tall grassweeds prkng fac\n", "tall grassweeds ps property\n", "tall grassweeds msdsmu prop\n", "tall grassweeds bld dpt prop\n", "hole open foundatn priv prop\n", "tall grassweeds prop health\n", "container private prop\n", "tall grassweeds cdp prop\n", "talll grassweeds gcww prop\n", "dumping prv prop 2500 sq ft\n", "building illegal use com prop\n", "refrigerator aban prv prop\n" ] } ], "source": [ "clusterid_category_map[cur_clusterid] = 'propertymaintenance'\n", "\n", "print_clustered_servicenames(cur_clusterid,\n", " clusterid_name_map)\n", "\n", "cur_clusterid += 1" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "default parks\n", "default parking\n", "default mayors office\n", "default law\n", "default cww\n", "default human resources\n", "default fleet services\n", "default city\n", "default community development\n", "default msd\n", "default trade and development\n", "default recreation\n", "default rcc\n", "default conv ctr\n", "default msd stormwater\n", "default police and junk veh\n" ] } ], "source": [ "clusterid_category_map[cur_clusterid] = 'defaultrequest'\n", "\n", "print_clustered_servicenames(cur_clusterid,\n", " clusterid_name_map)\n", "\n", "cur_clusterid += 1" ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "dead animal private property\n", "tires special collection\n", "missing property collections\n", "property damage ps stormwater\n", "property damage traffic aids\n", "construction damage claim\n", "special collections rtc\n", "yard waste tagged collections\n", "dumpster special collection\n", "pup truck collection\n", "property damage collections\n", "signage problem comm property\n", "property damage asphalt\n", "property damage tsb\n", "property damage greenspace\n", "property damage structures\n", "property damage row\n", "property damage facility mngt\n" ] } ], "source": [ "clusterid_category_map[cur_clusterid] = 'propertycomplaint'\n", "\n", "print_clustered_servicenames(cur_clusterid,\n", " clusterid_name_map)\n", "\n", "cur_clusterid += 1" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "trash can damaged\n", "trash offensive discharge\n", "trash improper set out\n", "trash early set out\n", "trash tagged collections\n", "trash unacceptable container\n", "trash set out service\n", "trash commercial nonmaintain\n", "trash late set out \n", "trash no trash at location\n", "trash on arrival\n", "trash can condemned\n", "trash tagged bed bugs\n" ] } ], "source": [ "clusterid_category_map[cur_clusterid] = 'trashcomplaint'\n", "\n", "print_clustered_servicenames(cur_clusterid,\n", " clusterid_name_map)\n", "\n", "cur_clusterid += 1" ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "service compliment trash\n", "service compliment trod\n", "service compliment greenspace\n", "service compliment nip\n", "service compliment cust serv\n", "service compliment cww\n", "service compliment dote\n", "service compliment recreation\n", "food service unlicensed oper\n", "service compliment yardwaste\n", "service compliment str clean\n" ] } ], "source": [ "clusterid_category_map[cur_clusterid] = 'servicecompliment'\n", "\n", "print_clustered_servicenames(cur_clusterid,\n", " clusterid_name_map)\n", "\n", "cur_clusterid += 1" ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "special fire inspection\n", "6 month inspection\n", "healthy homes inspection\n", "postfire inspection\n", "life safety 6month inspection\n", "2 month inspection\n", "housingcra maint inspection\n", "grass cutting city inspection\n", "sidewalk repairs inspection\n", "landslide inspect for struct\n" ] } ], "source": [ "clusterid_category_map[cur_clusterid] = 'inspection'\n", "\n", "print_clustered_servicenames(cur_clusterid,\n", " clusterid_name_map)\n", "\n", "cur_clusterid += 1" ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "service complaint cascade\n", "service complaint trod\n", "service complaint rumpke\n", "railroads complaints\n", "service complaint yardwaste\n", "airport noise complaints\n", "school general complaint\n", "service complaint greenspace\n", "school cafeteria complaints\n", "service complaint nip\n", "service complaint str clean\n", "service complaint recreation\n", "food borne complaint 1person\n", "service complaint dote\n", "service complaint cww\n", "service complaint trash\n", "service complaint cust serv\n", "contractor complaint\n" ] } ], "source": [ "clusterid_category_map[cur_clusterid] = 'servicecomplaint'\n", "\n", "print_clustered_servicenames(cur_clusterid,\n", " clusterid_name_map)\n", "\n", "cur_clusterid += 1" ] }, { "cell_type": "code", "execution_count": 35, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "bb req for inspnew\n", "bed bugs req for info only\n", "carbon monoxide test req \n", "building fire req inspcom\n", "rooming hse req for inspnew\n", "bb req for insp existing\n", "rats in sewer rem req\n", "building fire req inspres\n", "sting insect rem req priv\n", "building adult care req ins\n", "building daycare req for insp\n", "building emerg shlt req ins\n", "building disaster com ins req\n" ] } ], "source": [ "clusterid_category_map[cur_clusterid] = 'buildinginspection'\n", "\n", "print_clustered_servicenames(cur_clusterid,\n", " clusterid_name_map)\n", "\n", "cur_clusterid += 1" ] }, { "cell_type": "code", "execution_count": 36, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "business operating fm res\n", "building illegal use res prop\n", "parking grass front yd res\n", "fence height or material res\n", "light shining into bldg res\n", "signage problem res property\n", "fence built ovr prop line res\n", "landuse nonpermttd use res\n", "building condemnation res\n", "building disaster res ins req\n", "building vacant and open res \n", "zoning cncntrtd code enf res\n" ] } ], "source": [ "clusterid_category_map[cur_clusterid] = 'buildingcomplaint'\n", "\n", "print_clustered_servicenames(cur_clusterid,\n", " clusterid_name_map)\n", "\n", "cur_clusterid += 1" ] }, { "cell_type": "code", "execution_count": 37, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "sign downmissing \n", "sign overhead newchange\n", "sign grnd mnted newchangrem\n", "sign overhead repair\n", "sign street sign faded\n", "sign street sign name missing\n", "sign handicap parking signs\n", "sign downmissing stop sign\n", "sign gatewywelcome reprrepl\n", "sign exit broken or missing\n" ] } ], "source": [ "clusterid_category_map[cur_clusterid] = 'signmaintenance'\n", "\n", "print_clustered_servicenames(cur_clusterid,\n", " clusterid_name_map)\n", "\n", "cur_clusterid += 1" ] }, { "cell_type": "code", "execution_count": 38, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "sign shopexternal new request\n", "sign shopinternal new request\n", "relocation survey request\n", "slippery streets request haz\n", "trash request for collection\n", "information request\n", "signal change request traffic\n", "trash request for new service\n", "insect identification request\n", "relocation request\n", "building request cert of insp\n", "slippery streets request\n", "food operation request gen\n", "recycling information request\n", "recycling request to collect\n" ] } ], "source": [ "clusterid_category_map[cur_clusterid] = 'requestforservice'\n", "\n", "print_clustered_servicenames(cur_clusterid,\n", " clusterid_name_map)\n", "\n", "cur_clusterid += 1" ] }, { "cell_type": "code", "execution_count": 39, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "litter commun dev property\n", "litter health dept prop\n", "litter row large items\n", "litter sweep to sidewlkroad\n", "litter econ dev prop\n", "litter msd property\n", "building litter on priv prop\n", "litter park dept property\n", "litter restaurant property\n", "litter airport property\n", "litter recreation property\n", "litter sweep into gutter\n", "litter gcww property \n", "litter parking fac prop\n", "litter private property\n", "litter bldg dept property\n", "litter from vehicles\n" ] } ], "source": [ "clusterid_category_map[cur_clusterid] = 'litter'\n", "\n", "print_clustered_servicenames(cur_clusterid,\n", " clusterid_name_map)\n", "\n", "cur_clusterid += 1" ] }, { "cell_type": "code", "execution_count": 40, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "recycling new 35 gallon cart\n", "recycling cart missing 18\n", "recycling swap cart 18\n", "trash cart missing 65 gallon\n", "trash cart missing 95 gallon\n", "recycling swap cart 96\n", "recycling cart missing 96\n", "trash cart missing 35 gallon\n", "recycling remove cart\n", "recycling swap cart 35\n", "recycling new 96 gallon cart\n", "recycling cart missing 35\n", "recycling new 18 gallon cart\n", "recycling remove cart vacant\n", "recycling newswapmissing cart\n", "recycling cart missing 64\n", "recycling contaminated cart\n", "recycling new 64 gallon cart\n", "recycling swap cart 64\n" ] } ], "source": [ "clusterid_category_map[cur_clusterid] = 'recycling'\n", "\n", "print_clustered_servicenames(cur_clusterid,\n", " clusterid_name_map)\n", "\n", "cur_clusterid +=1 " ] }, { "cell_type": "code", "execution_count": 41, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "tree after hrs no storm\n", "tree planting request\n", "tree limbs down row \n", "tree blocking visibilitypub\n", "tree dead on priv prop haz\n", "tree blocking visbilitypriv\n", "tree stump removal in row\n", "tree trim required public\n", "tree split or hanging\n", "tree remove hornet nestsrow\n", "tree trim requird private \n", "tree wood pickup\n", "tree removal requestpub tree\n", "tree dead on priv prop stndrd\n" ] } ], "source": [ "clusterid_category_map[cur_clusterid] = 'treemaintenance'\n", "\n", "print_clustered_servicenames(cur_clusterid,\n", " clusterid_name_map)\n", "\n", "cur_clusterid += 1" ] }, { "cell_type": "code", "execution_count": 42, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "metal furniture spec collectn\n" ] } ], "source": [ "clusterid_category_map[cur_clusterid] = 'metalfurniturecollection'\n", "\n", "print_clustered_servicenames(cur_clusterid,\n", " clusterid_name_map)\n", "\n", "cur_clusterid += 1" ] }, { "cell_type": "code", "execution_count": 43, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "yard wastertc\n" ] } ], "source": [ "clusterid_category_map[cur_clusterid] = 'yardwaste'\n", "\n", "print_clustered_servicenames(cur_clusterid,\n", " clusterid_name_map)\n", "\n", "cur_clusterid += 1" ] }, { "cell_type": "code", "execution_count": 44, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "graffiti removal\n" ] } ], "source": [ "clusterid_category_map[cur_clusterid] = 'graffitiremoval'\n", "\n", "print_clustered_servicenames(cur_clusterid,\n", " clusterid_name_map)\n", "\n", "cur_clusterid += 1" ] }, { "cell_type": "code", "execution_count": 45, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "dead animal\n" ] } ], "source": [ "clusterid_category_map[cur_clusterid] = 'deadanimal'\n", "\n", "print_clustered_servicenames(cur_clusterid,\n", " clusterid_name_map)\n", "\n", "cur_clusterid += 1" ] }, { "cell_type": "code", "execution_count": 46, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "{0: 'streetmaintenance',\n", " 1: 'miscellaneous',\n", " 2: 'trashcart',\n", " 3: 'buildinghazzard',\n", " 4: 'buildingcomplaint',\n", " 5: 'repairrequest',\n", " 6: 'propertymaintenance',\n", " 7: 'defaultrequest',\n", " 8: 'propertycomplaint',\n", " 9: 'trashcomplaint',\n", " 10: 'servicecompliment',\n", " 11: 'inspection',\n", " 12: 'servicecomplaint',\n", " 13: 'buildinginspection',\n", " 14: 'buildingcomplaint',\n", " 15: 'signmaintenance',\n", " 16: 'requestforservice',\n", " 17: 'litter',\n", " 18: 'recycling',\n", " 19: 'treemaintenance',\n", " 20: 'metalfurniturecollection',\n", " 21: 'yardwaste',\n", " 22: 'graffitiremoval',\n", " 23: 'deadanimal'}" ] }, "execution_count": 46, "metadata": {}, "output_type": "execute_result" } ], "source": [ "clusterid_category_map" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Plot Cincinnati 311 Service Name Categories" ] }, { "cell_type": "code", "execution_count": 47, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x7f5d998b0050>" ] }, "execution_count": 47, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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OfPFFawCcnbtQqZI2FhbmPHhwi7i4S/zyyy/UqZPzO37QoP6Ehu4gIuIwjo6fo61djnLl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\n9vb2LFiwAAcHB7y9vfHz82PGj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"text/plain": [ "<matplotlib.figure.Figure at 0x7f5d99542cd0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import pandas as pd\n", "\n", "category_totalcountdf =\\\n", " pd.DataFrame({'totalcount': clusterid_total_count.values()},\n", " index=clusterid_category_map.values())\n", " \n", "sns.set(font_scale=1.5)\n", "category_totalcountdf.plot(kind='barh')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Write service code / category map to disk\n", "- [Storing Python Dictionaries](http://stackoverflow.com/questions/7100125/storing-python-dictionaries)" ] }, { "cell_type": "code", "execution_count": 48, "metadata": { "collapsed": false }, "outputs": [], "source": [ "servicecode_category_map = {}\n", "\n", "for clusterid in clusterid_name_map.keys():\n", " cur_category = clusterid_category_map[clusterid]\n", " \n", " for servicecode in clusterid_code_map[clusterid]:\n", "\n", " servicecode_category_map[servicecode] = cur_category\n", " \n", "with open('serviceCodeCategory.txt', 'w') as fp:\n", " num_names = len(servicecode_category_map)\n", "\n", " keys = servicecode_category_map.keys()\n", " values = servicecode_category_map.values()\n", "\n", " for idx in range(0, num_names):\n", " if idx == 0:\n", " fp.write(\"%s{\\\"%s\\\": \\\"%s\\\",\\n\" % (\" \" * 12,\n", " keys[idx],\n", " values[idx]))\n", " #----------------------------------------\n", " elif idx > 0 and idx < num_names-1:\n", " fp.write(\"%s\\\"%s\\\": \\\"%s\\\",\\n\" % (\" \" * 13,\n", " keys[idx],\n", " values[idx]))\n", " #----------------------------------------\n", " else:\n", " fp.write(\"%s\\\"%s\\\": \\\"%s\\\"}\" % (\" \" * 13,\n", " keys[idx],\n", " values[idx]))" ] }, { "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 }
gpl-3.0
jorisvandenbossche/2015-EuroScipy-pandas-tutorial
solved - 02 - Data structures.ipynb
1
103902
{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import pandas as pd" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%matplotlib inline\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "try:\n", " import seaborn\n", "except ImportError:\n", " pass" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Tabular data" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "df = pd.read_csv(\"data/titanic.csv\")" ] }, { "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>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 " ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Starting from reading this dataset, to answering questions about this data in a few lines of code:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**What is the age distribution of the passengers?**" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x7fa70ee497f0>" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" }, { 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"text/plain": [ "<matplotlib.figure.Figure at 0x7fa70ee31a90>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "df['Age'].hist()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**How does the survival rate of the passengers differ between sexes?**" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>Survived</th>\n", " </tr>\n", " <tr>\n", " <th>Sex</th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>female</th>\n", " <td>0.742038</td>\n", " </tr>\n", " <tr>\n", " <th>male</th>\n", " <td>0.188908</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Survived\n", "Sex \n", "female 0.742038\n", "male 0.188908" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.groupby('Sex')[['Survived']].aggregate(lambda x: x.sum() / len(x))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Or how does it differ between the different classes?**" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x7fa70cd49710>" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7fa70cdccb38>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "df.groupby('Pclass')['Survived'].aggregate(lambda x: x.sum() / len(x)).plot(kind='bar')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Are young people more likely to survive?**" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "0.38383838383838381" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df['Survived'].sum() / df['Survived'].count()" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "0.41196013289036543" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df25 = df[df['Age'] <= 25]\n", "df25['Survived'].sum() / len(df25['Survived'])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "All the needed functionality for the above examples will be explained throughout this tutorial." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Data structures\n", "\n", "Pandas provides two fundamental data objects, for 1D (``Series``) and 2D data (``DataFrame``)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Series\n", "\n", "A Series is a basic holder for **one-dimensional labeled data**. It can be created much as a NumPy array is created:" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "0 0.1\n", "1 0.2\n", "2 0.3\n", "3 0.4\n", "dtype: float64" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "s = pd.Series([0.1, 0.2, 0.3, 0.4])\n", "s" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Attributes of a Series: `index` and `values`\n", "\n", "The series has a built-in concept of an **index**, which by default is the numbers *0* through *N - 1*" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "RangeIndex(start=0, stop=4, step=1)" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "s.index" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You can access the underlying numpy array representation with the `.values` attribute:" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([ 0.1, 0.2, 0.3, 0.4])" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "s.values" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can access series values via the index, just like for NumPy arrays:" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "0.10000000000000001" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "s[0]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Unlike the NumPy array, though, this index can be something other than integers:" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "a 0\n", "b 1\n", "c 2\n", "d 3\n", "dtype: int64" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "s2 = pd.Series(np.arange(4), index=['a', 'b', 'c', 'd'])\n", "s2" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "2" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "s2['c']" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In this way, a ``Series`` object can be thought of as similar to an ordered dictionary mapping one typed value to another typed value.\n", "\n", "In fact, it's possible to construct a series directly from a Python dictionary:" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "Belgium 11.3\n", "France 64.3\n", "Germany 81.3\n", "Netherlands 16.9\n", "United Kingdom 64.9\n", "dtype: float64" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pop_dict = {'Germany': 81.3, \n", " 'Belgium': 11.3, \n", " 'France': 64.3, \n", " 'United Kingdom': 64.9, \n", " 'Netherlands': 16.9}\n", "population = pd.Series(pop_dict)\n", "population" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can index the populations like a dict as expected:" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "64.299999999999997" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "population['France']" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "but with the power of numpy arrays:" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "Belgium 11300.0\n", "France 64300.0\n", "Germany 81300.0\n", "Netherlands 16900.0\n", "United Kingdom 64900.0\n", "dtype: float64" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "population * 1000" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## DataFrames: Multi-dimensional Data\n", "\n", "A DataFrame is a **tablular data structure** (multi-dimensional object to hold labeled data) comprised of rows and columns, akin to a spreadsheet, database table, or R's data.frame object. You can think of it as multiple Series object which share the same index.\n", "\n", "<img src=\"img/dataframe.png\" width=110%>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "One of the most common ways of creating a dataframe is from a dictionary of arrays or lists.\n", "\n", "Note that in the IPython notebook, the dataframe will display in a rich HTML view:" ] }, { "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>area</th>\n", " <th>capital</th>\n", " <th>country</th>\n", " <th>population</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>30510</td>\n", " <td>Brussels</td>\n", " <td>Belgium</td>\n", " <td>11.3</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>671308</td>\n", " <td>Paris</td>\n", " <td>France</td>\n", " <td>64.3</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>357050</td>\n", " <td>Berlin</td>\n", " <td>Germany</td>\n", " <td>81.3</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>41526</td>\n", " <td>Amsterdam</td>\n", " <td>Netherlands</td>\n", " <td>16.9</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>244820</td>\n", " <td>London</td>\n", " <td>United Kingdom</td>\n", " <td>64.9</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " area capital country population\n", "0 30510 Brussels Belgium 11.3\n", "1 671308 Paris France 64.3\n", "2 357050 Berlin Germany 81.3\n", "3 41526 Amsterdam Netherlands 16.9\n", "4 244820 London United Kingdom 64.9" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data = {'country': ['Belgium', 'France', 'Germany', 'Netherlands', 'United Kingdom'],\n", " 'population': [11.3, 64.3, 81.3, 16.9, 64.9],\n", " 'area': [30510, 671308, 357050, 41526, 244820],\n", " 'capital': ['Brussels', 'Paris', 'Berlin', 'Amsterdam', 'London']}\n", "countries = pd.DataFrame(data)\n", "countries" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Attributes of the DataFrame\n", "\n", "A DataFrame has besides a `index` attribute, also a `columns` attribute:" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "RangeIndex(start=0, stop=5, step=1)" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "countries.index" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "Index(['area', 'capital', 'country', 'population'], dtype='object')" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "countries.columns" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To check the data types of the different columns:" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "area int64\n", "capital object\n", "country object\n", "population float64\n", "dtype: object" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "countries.dtypes" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "An overview of that information can be given with the `info()` method:" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "<class 'pandas.core.frame.DataFrame'>\n", "RangeIndex: 5 entries, 0 to 4\n", "Data columns (total 4 columns):\n", "area 5 non-null int64\n", "capital 5 non-null object\n", "country 5 non-null object\n", "population 5 non-null float64\n", "dtypes: float64(1), int64(1), object(2)\n", "memory usage: 240.0+ bytes\n" ] } ], "source": [ "countries.info()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Also a DataFrame has a `values` attribute, but attention: when you have heterogeneous data, all values will be upcasted:" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([[30510, 'Brussels', 'Belgium', 11.3],\n", " [671308, 'Paris', 'France', 64.3],\n", " [357050, 'Berlin', 'Germany', 81.3],\n", " [41526, 'Amsterdam', 'Netherlands', 16.9],\n", " [244820, 'London', 'United Kingdom', 64.9]], dtype=object)" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "countries.values" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If we don't like what the index looks like, we can reset it and set one of our columns:" ] }, { "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>area</th>\n", " <th>capital</th>\n", " <th>population</th>\n", " </tr>\n", " <tr>\n", " <th>country</th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>Belgium</th>\n", " <td>30510</td>\n", " <td>Brussels</td>\n", " <td>11.3</td>\n", " </tr>\n", " <tr>\n", " <th>France</th>\n", " <td>671308</td>\n", " <td>Paris</td>\n", " <td>64.3</td>\n", " </tr>\n", " <tr>\n", " <th>Germany</th>\n", " <td>357050</td>\n", " <td>Berlin</td>\n", " <td>81.3</td>\n", " </tr>\n", " <tr>\n", " <th>Netherlands</th>\n", " <td>41526</td>\n", " <td>Amsterdam</td>\n", " <td>16.9</td>\n", " </tr>\n", " <tr>\n", " <th>United Kingdom</th>\n", " <td>244820</td>\n", " <td>London</td>\n", " <td>64.9</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " area capital population\n", "country \n", "Belgium 30510 Brussels 11.3\n", "France 671308 Paris 64.3\n", "Germany 357050 Berlin 81.3\n", "Netherlands 41526 Amsterdam 16.9\n", "United Kingdom 244820 London 64.9" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "countries = countries.set_index('country')\n", "countries" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To access a Series representing a column in the data, use typical indexing syntax:" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "country\n", "Belgium 30510\n", "France 671308\n", "Germany 357050\n", "Netherlands 41526\n", "United Kingdom 244820\n", "Name: area, dtype: int64" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "countries['area']" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Basic operations on Series/Dataframes" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As you play around with DataFrames, you'll notice that many operations which work on NumPy arrays will also work on dataframes.\n" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# redefining the example objects\n", "\n", "population = pd.Series({'Germany': 81.3, 'Belgium': 11.3, 'France': 64.3, \n", " 'United Kingdom': 64.9, 'Netherlands': 16.9})\n", "\n", "countries = pd.DataFrame({'country': ['Belgium', 'France', 'Germany', 'Netherlands', 'United Kingdom'],\n", " 'population': [11.3, 64.3, 81.3, 16.9, 64.9],\n", " 'area': [30510, 671308, 357050, 41526, 244820],\n", " 'capital': ['Brussels', 'Paris', 'Berlin', 'Amsterdam', 'London']})" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Elementwise-operations (like numpy)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Just like with numpy arrays, many operations are element-wise:" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "Belgium 0.113\n", "France 0.643\n", "Germany 0.813\n", "Netherlands 0.169\n", "United Kingdom 0.649\n", "dtype: float64" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "population / 100" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "0 0.000370\n", "1 0.000096\n", "2 0.000228\n", "3 0.000407\n", "4 0.000265\n", "dtype: float64" ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ "countries['population'] / countries['area']" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Alignment! (unlike numpy)\n", "\n", "Only, pay attention to **alignment**: operations between series will align on the index: " ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "collapsed": false }, "outputs": [], "source": [ "s1 = population[['Belgium', 'France']]\n", "s2 = population[['France', 'Germany']]" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "Belgium 11.3\n", "France 64.3\n", "dtype: float64" ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ], "source": [ "s1" ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "France 64.3\n", "Germany 81.3\n", "dtype: float64" ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "s2" ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "Belgium NaN\n", "France 128.6\n", "Germany NaN\n", "dtype: float64" ] }, "execution_count": 33, "metadata": {}, "output_type": "execute_result" } ], "source": [ "s1 + s2" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Reductions (like numpy)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The average population number:" ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "47.739999999999995" ] }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" } ], "source": [ "population.mean()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The minimum area:" ] }, { "cell_type": "code", "execution_count": 35, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "30510" ] }, "execution_count": 35, "metadata": {}, "output_type": "execute_result" } ], "source": [ "countries['area'].min()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For dataframes, often only the numeric columns are included in the result:" ] }, { "cell_type": "code", "execution_count": 36, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "area 244820.0\n", "population 64.3\n", "dtype: float64" ] }, "execution_count": 36, "metadata": {}, "output_type": "execute_result" } ], "source": [ "countries.median()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<div class=\"alert alert-success\">\n", " <b>EXERCISE</b>: Calculate the population numbers relative to Belgium\n", "</div>" ] }, { "cell_type": "code", "execution_count": 37, "metadata": { "clear_cell": true, "collapsed": false, "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "Belgium 1.000000\n", "France 5.690265\n", "Germany 7.194690\n", "Netherlands 1.495575\n", "United Kingdom 5.743363\n", "dtype: float64" ] }, "execution_count": 37, "metadata": {}, "output_type": "execute_result" } ], "source": [ "population / population['Belgium'].mean()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<div class=\"alert alert-success\">\n", " <b>EXERCISE</b>: Calculate the population density for each country and add this as a new column to the dataframe.\n", "</div>" ] }, { "cell_type": "code", "execution_count": 38, "metadata": { "clear_cell": true, "collapsed": false, "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "0 370.370370\n", "1 95.783158\n", "2 227.699202\n", "3 406.973944\n", "4 265.092721\n", "dtype: float64" ] }, "execution_count": 38, "metadata": {}, "output_type": "execute_result" } ], "source": [ "countries['population']*1000000 / countries['area']" ] }, { "cell_type": "code", "execution_count": 39, "metadata": { "clear_cell": true, "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>area</th>\n", " <th>capital</th>\n", " <th>country</th>\n", " <th>population</th>\n", " <th>density</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>30510</td>\n", " <td>Brussels</td>\n", " <td>Belgium</td>\n", " <td>11.3</td>\n", " <td>370.370370</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>671308</td>\n", " <td>Paris</td>\n", " <td>France</td>\n", " <td>64.3</td>\n", " <td>95.783158</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>357050</td>\n", " <td>Berlin</td>\n", " <td>Germany</td>\n", " <td>81.3</td>\n", " <td>227.699202</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>41526</td>\n", " <td>Amsterdam</td>\n", " <td>Netherlands</td>\n", " <td>16.9</td>\n", " <td>406.973944</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>244820</td>\n", " <td>London</td>\n", " <td>United Kingdom</td>\n", " <td>64.9</td>\n", " <td>265.092721</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " area capital country population density\n", "0 30510 Brussels Belgium 11.3 370.370370\n", "1 671308 Paris France 64.3 95.783158\n", "2 357050 Berlin Germany 81.3 227.699202\n", "3 41526 Amsterdam Netherlands 16.9 406.973944\n", "4 244820 London United Kingdom 64.9 265.092721" ] }, "execution_count": 39, "metadata": {}, "output_type": "execute_result" } ], "source": [ "countries['density'] = countries['population']*1000000 / countries['area']\n", "countries" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Some other useful methods" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Sorting the rows of the DataFrame according to the values in a column:" ] }, { "cell_type": "code", "execution_count": 40, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>area</th>\n", " <th>capital</th>\n", " <th>country</th>\n", " <th>population</th>\n", " <th>density</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>3</th>\n", " <td>41526</td>\n", " <td>Amsterdam</td>\n", " <td>Netherlands</td>\n", " <td>16.9</td>\n", " <td>406.973944</td>\n", " </tr>\n", " <tr>\n", " <th>0</th>\n", " <td>30510</td>\n", " <td>Brussels</td>\n", " <td>Belgium</td>\n", " <td>11.3</td>\n", " <td>370.370370</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>244820</td>\n", " <td>London</td>\n", " <td>United Kingdom</td>\n", " <td>64.9</td>\n", " <td>265.092721</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>357050</td>\n", " <td>Berlin</td>\n", " <td>Germany</td>\n", " <td>81.3</td>\n", " <td>227.699202</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>671308</td>\n", " <td>Paris</td>\n", " <td>France</td>\n", " <td>64.3</td>\n", " <td>95.783158</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " area capital country population density\n", "3 41526 Amsterdam Netherlands 16.9 406.973944\n", "0 30510 Brussels Belgium 11.3 370.370370\n", "4 244820 London United Kingdom 64.9 265.092721\n", "2 357050 Berlin Germany 81.3 227.699202\n", "1 671308 Paris France 64.3 95.783158" ] }, "execution_count": 40, "metadata": {}, "output_type": "execute_result" } ], "source": [ "countries.sort_values('density', ascending=False)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "One useful method to use is the ``describe`` method, which computes summary statistics for each column:" ] }, { "cell_type": "code", "execution_count": 41, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>area</th>\n", " <th>population</th>\n", " <th>density</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>count</th>\n", " <td>5.000000</td>\n", " <td>5.000000</td>\n", " <td>5.000000</td>\n", " </tr>\n", " <tr>\n", " <th>mean</th>\n", " <td>269042.800000</td>\n", " <td>47.740000</td>\n", " <td>273.183879</td>\n", " </tr>\n", " <tr>\n", " <th>std</th>\n", " <td>264012.827994</td>\n", " <td>31.519645</td>\n", " <td>123.440607</td>\n", " </tr>\n", " <tr>\n", " <th>min</th>\n", " <td>30510.000000</td>\n", " <td>11.300000</td>\n", " <td>95.783158</td>\n", " </tr>\n", " <tr>\n", " <th>25%</th>\n", " <td>41526.000000</td>\n", " <td>16.900000</td>\n", " <td>227.699202</td>\n", " </tr>\n", " <tr>\n", " <th>50%</th>\n", " <td>244820.000000</td>\n", " <td>64.300000</td>\n", " <td>265.092721</td>\n", " </tr>\n", " <tr>\n", " <th>75%</th>\n", " <td>357050.000000</td>\n", " <td>64.900000</td>\n", " <td>370.370370</td>\n", " </tr>\n", " <tr>\n", " <th>max</th>\n", " <td>671308.000000</td>\n", " <td>81.300000</td>\n", " <td>406.973944</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " area population density\n", "count 5.000000 5.000000 5.000000\n", "mean 269042.800000 47.740000 273.183879\n", "std 264012.827994 31.519645 123.440607\n", "min 30510.000000 11.300000 95.783158\n", "25% 41526.000000 16.900000 227.699202\n", "50% 244820.000000 64.300000 265.092721\n", "75% 357050.000000 64.900000 370.370370\n", "max 671308.000000 81.300000 406.973944" ] }, "execution_count": 41, "metadata": {}, "output_type": "execute_result" } ], "source": [ "countries.describe()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The `plot` method can be used to quickly visualize the data in different ways:" ] }, { "cell_type": "code", "execution_count": 42, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x7fa70cd3f080>" ] }, "execution_count": 42, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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zTRZ8f39/Ro0aBUDnzp3x9fUlPT2duro6HB0dKSgowN/fH4PB0OhsvKCggJiY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\nkioURWHngfO8vKlhvH7RHdEsmCzj9e3FJT/FPdK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"text/plain": [ "<matplotlib.figure.Figure at 0x7fa70cccc048>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "countries.plot()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "However, for this dataset, it does not say that much:" ] }, { "cell_type": "code", "execution_count": 43, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x7fa70cc213c8>" ] }, "execution_count": 43, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7fa70cd50a20>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "countries['population'].plot(kind='bar')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You can play with the `kind` keyword: 'line', 'bar', 'hist', 'density', 'area', 'pie', 'scatter', 'hexbin'" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Importing and exporting data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A wide range of input/output formats are natively supported by pandas:\n", "\n", "* CSV, text\n", "* SQL database\n", "* Excel\n", "* HDF5\n", "* json\n", "* html\n", "* pickle\n", "* ..." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "pd.read" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "states.to" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Other features\n", "\n", "* Working with missing data (`.dropna()`, `pd.isnull()`)\n", "* Merging and joining (`concat`, `join`)\n", "* Grouping: `groupby` functionality\n", "* Reshaping (`stack`, `pivot`)\n", "* Time series manipulation (resampling, timezones, ..)\n", "* Easy plotting" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "There are many, many more interesting operations that can be done on Series and DataFrame objects, but rather than continue using this toy data, we'll instead move to a real-world example, and illustrate some of the advanced concepts along the way.\n", "\n", "See the next notebooks!" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "## Acknowledgement\n", "\n", "> *© 2015, Stijn Van Hoey and Joris Van den Bossche (<mailto:[email protected]>, <mailto:[email protected]>). Licensed under [CC BY 4.0 Creative Commons](http://creativecommons.org/licenses/by/4.0/)*\n", "\n", "> This notebook is partly based on material of Jake Vanderplas (https://github.com/jakevdp/OsloWorkshop2014).\n", "\n", "---" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 0 }
bsd-2-clause
zipeiyang/liupengyuan.github.io
chapter1/homework/localization/3-15/201611680692（3月15日）.ipynb
27
2731
{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "请输入一个整数：5\n", "输入一个整数：3\n", "输入一个整数：8\n", "输入一个整数：6\n", "输入一个整数：1\n", "输入一个整数：9\n", "和为： 27\n" ] } ], "source": [ "n=int(input('请输入一个整数：'))\n", "sum=0\n", "i=0\n", "while i<n:\n", " m=int(input('输入一个整数：'))\n", " sum=sum+m\n", " i=i+1\n", "print('和为：',sum)" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "请输入一个整数：6\n", "请输入一个整数，如若不想再输入了则输入0：7\n", "请输入一个整数，如若不想再输入了则输入0：8\n", "请输入一个整数，如若不想再输入了则输入0：9\n", "请输入一个整数，如若不想再输入了则输入0：0\n", "和为： 30\n" ] } ], "source": [ "m=int(input('请输入一个整数：'))\n", "sum=0\n", "while m:\n", " sum=sum+m\n", " m=int(input('请输入一个整数，如若不想再输入了则输入0：'))\n", "print('和为：',sum)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "输入一个整数:4\n", "输入一个整数6\n", "输入一个整数23\n", "和为： 14\n", "积为： 96\n" ] } ], "source": [ "m=int(input('输入一个整数:'))\n", "total=m\n", "sum=m\n", "while (sum>=m or total>=m*m):\n", " sum=sum+m\n", " total=total*m\n", " m=int(input('输入一个整数'))\n", "print('和为：',sum)\n", "print('积为：',total)" ] }, { "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
roatienza/Deep-Learning-Experiments
versions/2020/regularizer/code/mlp-mnist-dropout.ipynb
2
8082
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## MLP MNIST with dropout" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Model: \"sequential_3\"\n", "_________________________________________________________________\n", "Layer (type) Output Shape Param # \n", "=================================================================\n", "dense_9 (Dense) (None, 256) 200960 \n", "_________________________________________________________________\n", "activation_9 (Activation) (None, 256) 0 \n", "_________________________________________________________________\n", "dropout_6 (Dropout) (None, 256) 0 \n", "_________________________________________________________________\n", "dense_10 (Dense) (None, 256) 65792 \n", "_________________________________________________________________\n", "activation_10 (Activation) (None, 256) 0 \n", "_________________________________________________________________\n", "dropout_7 (Dropout) (None, 256) 0 \n", "_________________________________________________________________\n", "dense_11 (Dense) (None, 10) 2570 \n", "_________________________________________________________________\n", "activation_11 (Activation) (None, 10) 0 \n", "=================================================================\n", "Total params: 269,322\n", "Trainable params: 269,322\n", "Non-trainable params: 0\n", "_________________________________________________________________\n", "Train on 60000 samples\n", "Epoch 1/20\n", "60000/60000 [==============================] - 6s 99us/sample - loss: 1.3041 - accuracy: 0.6369\n", "Epoch 2/20\n", "60000/60000 [==============================] - 5s 81us/sample - loss: 0.5944 - accuracy: 0.8287\n", "Epoch 3/20\n", "60000/60000 [==============================] - 6s 99us/sample - loss: 0.4671 - accuracy: 0.8633\n", "Epoch 4/20\n", "60000/60000 [==============================] - 7s 111us/sample - loss: 0.4123 - accuracy: 0.8804\n", "Epoch 5/20\n", "60000/60000 [==============================] - 6s 107us/sample - loss: 0.3771 - accuracy: 0.8901\n", "Epoch 6/20\n", "60000/60000 [==============================] - 7s 109us/sample - loss: 0.3495 - accuracy: 0.8982\n", "Epoch 7/20\n", "60000/60000 [==============================] - 8s 132us/sample - loss: 0.3260 - accuracy: 0.9049\n", "Epoch 8/20\n", "60000/60000 [==============================] - 7s 122us/sample - loss: 0.3079 - accuracy: 0.9109\n", "Epoch 9/20\n", "60000/60000 [==============================] - 7s 119us/sample - loss: 0.2911 - accuracy: 0.9148\n", "Epoch 10/20\n", "60000/60000 [==============================] - 7s 123us/sample - loss: 0.2788 - accuracy: 0.9187\n", "Epoch 11/20\n", "60000/60000 [==============================] - 6s 108us/sample - loss: 0.2672 - accuracy: 0.9230\n", "Epoch 12/20\n", "60000/60000 [==============================] - 7s 113us/sample - loss: 0.2549 - accuracy: 0.9258\n", "Epoch 13/20\n", "60000/60000 [==============================] - 7s 120us/sample - loss: 0.2447 - accuracy: 0.9282\n", "Epoch 14/20\n", "60000/60000 [==============================] - 7s 119us/sample - loss: 0.2344 - accuracy: 0.9317\n", "Epoch 15/20\n", "60000/60000 [==============================] - 7s 116us/sample - loss: 0.2270 - accuracy: 0.9339\n", "Epoch 16/20\n", "60000/60000 [==============================] - 6s 105us/sample - loss: 0.2187 - accuracy: 0.9368\n", "Epoch 17/20\n", "60000/60000 [==============================] - 6s 105us/sample - loss: 0.2104 - accuracy: 0.9384\n", "Epoch 18/20\n", "60000/60000 [==============================] - 6s 97us/sample - loss: 0.2045 - accuracy: 0.9409\n", "Epoch 19/20\n", "60000/60000 [==============================] - 4s 75us/sample - loss: 0.2000 - accuracy: 0.9414\n", "Epoch 20/20\n", "60000/60000 [==============================] - 4s 70us/sample - loss: 0.1921 - accuracy: 0.9434\n", "\n", "Test accuracy: 95.5%\n" ] } ], "source": [ "'''\n", "MLP network for MNIST digits classification with Dropout\n", "Test accuracy: 95.5%\n", "\n", "'''\n", "\n", "from __future__ import absolute_import\n", "from __future__ import division\n", "from __future__ import print_function\n", "\n", "# numpy package\n", "import numpy as np\n", "from tensorflow.keras.models import Sequential\n", "from tensorflow.keras.layers import Dense, Activation, Dropout\n", "from tensorflow.keras.datasets import mnist\n", "from tensorflow.keras.utils import to_categorical\n", "\n", "from numpy.random import seed\n", "seed(12345)\n", "import tensorflow as tf\n", "tf.random.set_seed(12345)\n", "\n", "# load mnist dataset\n", "(x_train, y_train), (x_test, y_test) = mnist.load_data()\n", "\n", "# compute the number of labels\n", "num_labels = len(np.unique(y_train))\n", "\n", "# convert to one-hot vector\n", "y_train = to_categorical(y_train)\n", "y_test = to_categorical(y_test)\n", "\n", "# image dimensions (assumed square)\n", "image_size = x_train.shape[1]\n", "input_size = image_size * image_size\n", "# for mlp, the input dim is a vector, so we reshape\n", "x_train = np.reshape(x_train, [-1, input_size])\n", "# we train our network using float data\n", "x_train = x_train.astype('float32') / 255\n", "x_test = np.reshape(x_test, [-1, input_size])\n", "x_test = x_test.astype('float32') / 255\n", "\n", "# network parameters\n", "batch_size = 128\n", "hidden_units = 256\n", "dropout = 0.2\n", "\n", "# this is 3-layer MLP with ReLU. Dropout reg.\n", "model = Sequential()\n", "model.add(Dense(hidden_units, input_dim=input_size))\n", "model.add(Activation('relu'))\n", "model.add(Dropout(dropout))\n", "model.add(Dense(hidden_units))\n", "model.add(Activation('relu'))\n", "model.add(Dropout(dropout))\n", "model.add(Dense(num_labels))\n", "# this is the output for one-hot vector\n", "model.add(Activation('softmax'))\n", "model.summary()\n", "\n", "# loss function for one-hot vector\n", "# use of sgd optimizer\n", "# accuracy is good metric for classification tasks\n", "model.compile(loss='categorical_crossentropy',\n", " optimizer='sgd',\n", " metrics=['accuracy'])\n", "# train the network\n", "model.fit(x_train,y_train, epochs=20, batch_size=batch_size)\n", "\n", "# validate the model on test dataset to determine generalization\n", "score = model.evaluate(x_test,\n", " y_test,\n", " batch_size=batch_size,\n", " verbose=False)\n", "print(\"\\nTest accuracy: %.1f%%\" % (100.0 * score[1]))\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.4" } }, "nbformat": 4, "nbformat_minor": 2 }
mit
k3yavi/alevin
100cell_testing/ipy/test.ipynb
1
113273
{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/avi/miniconda2/lib/python2.7/site-packages/IPython/html.py:14: ShimWarning: The `IPython.html` package has been deprecated since IPython 4.0. You should import from `notebook` instead. `IPython.html.widgets` has moved to `ipywidgets`.\n", " \"`IPython.html.widgets` has moved to `ipywidgets`.\", ShimWarning)\n" ] } ], "source": [ "from collections import defaultdict\n", "from random import random\n", "import pysam\n", "%matplotlib inline\n", "import numpy as np\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns; sns.set()\n", "import glob\n", "import os\n", "import sklearn\n", "from sklearn import cluster\n", "import cPickle as pickle" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "kal = pd.read_csv('./kal-1k.mat', index_col=[0])\n", "utl = pd.read_csv('./utl-1k.mat', index_col=[0])\n", "alv = pd.read_csv('./fast-eq-1k.mat', index_col=[0])" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "tr_results = pd.read_table(\"../../featureDump/tenx_counts_new/outs/filtered_gene_bc_matrices/hg19/barcodes.tsv\", header=None)[0].values\n", "# tr_results['tr'] = [0]*504 + [1]*516\n", "# import collections\n", "# print [item for item, count in collections.Counter(tr_results.index).items() if count > 1]\n", "# tr_results = tr_results.drop(['AGCTCCTGTTGTGGAG', 'ACGCCAGTCGGATGGA', 'GGCAATTAGGAATCGC'])\n", "tr_results = [x.replace(\"-1\", \"\") for x in tr_results]" ] }, { "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": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "tenx = pd.read_table(\"../../featureDump/tenx_counts_new/outs/human_counts.tsv\", sep=',').set_index(\"Unnamed: 0\")\n", "tenx.index = [x.replace(\"hg19_\",\"\") for x in tenx.index]\n", "tenx.columns = [x.replace(\"-1\",\"\") for x in tenx.columns]\n", "# tenx = tenx.drop(['AGCTCCTGTTGTGGAG', 'ACGCCAGTCGGATGGA', 'GGCAATTAGGAATCGC'], 1)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "((58278, 501), (44021, 1017), (51937, 1017), (53284, 1017))" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tenx.shape, utl.shape, kal.shape, alv.shape\n", "# utl.shape, kal.shape, alv.shape" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def get_corr_list(df):\n", " df_list = []\n", " for x in df.corr(method=\"spearman\").values:\n", " for e in x.tolist():\n", " if e !=0 and e != 1:\n", " df_list.append(e)\n", " return df_list" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# first_cluster = tr_results[tr_results['tr'] == 1].index\n", "# second_cluster = tr_results[tr_results['tr'] == 0].index" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": true }, "outputs": [], "source": [ "na = [x for x in tr_results if x in utl.columns]" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": true }, "outputs": [], "source": [ "usub = utl[na].fillna(0)\n", "ksub = kal[na].fillna(0)\n", "asub = alv[na].fillna(0)\n", "tsub = tenx[na].fillna(0)\n", "\n", "usub = usub[(usub.T != 0).any()]\n", "ksub = ksub[(ksub.T != 0).any()]\n", "asub = asub[(asub.T != 0).any()]\n", "tsub = tsub[(tsub.T != 0).any()]" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "((21903, 501), (35063, 501), (41150, 501), (42424, 501))" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tsub.shape, usub.shape, ksub.shape, asub.shape\n", "# usub.shape, ksub.shape, asub.shape" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": true }, "outputs": [], "source": [ "usub.drop([x for x in usub.index if 'ENSMUSG' in x ], inplace=True)\n", "\n", "ksub.drop([x for x in ksub.index if 'ENSMUSG' in x ], inplace=True)\n", "\n", "asub.drop([x for x in asub.index if 'ENSMUSG' in x ], inplace=True)\n", "\n", "tenx.drop([x for x in tenx.index if 'ENSMUSG' in x ], inplace=True)\n", "# tenx.index = [x.replace('mm10_','') for x in tenx.index]" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "((21903, 501), (24470, 501), (28596, 501), (28944, 501))" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tsub.shape, usub.shape, ksub.shape, asub.shape" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": true }, "outputs": [], "source": [ "usub = usub.loc[tsub.index]\n", "ksub = ksub.loc[tsub.index]\n", "asub = asub.loc[tsub.index]" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "((21903, 501), (21903, 501), (21903, 501), (21903, 501))" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tsub.shape, usub.shape, ksub.shape, asub.shape" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(17618591, 16255142.0, 18260462.0, 18056122.0)" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sum(tsub.sum()), sum(usub.sum()), sum(ksub.sum()), sum(asub.sum())" ] }, { "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": 20, "metadata": { "collapsed": true }, "outputs": [], "source": [ "utl_list = get_corr_list(usub)\n", "kal_list = get_corr_list(ksub)\n", "alv_list = get_corr_list(asub)\n", "tenx_list = get_corr_list(tenx)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<matplotlib.legend.Legend at 0x7f5a668c5b90>" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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2L3oWXGxVVVW4XB4OHz5IS0sr//7v/4rT6eS73/0B2WyWm266piRxaYXsWQshxBJMHz8G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\nBEEQkpwo1oIgCIKQ5ESxFgRBEIQk9/8D5Q4maYIne5oAAAAASUVORK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f5a674203d0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#human\n", "sns.kdeplot(np.array(utl_list), label = \"Umi-tools\")\n", "sns.kdeplot(np.array(alv_list), label = \"Alevin\")\n", "sns.kdeplot(np.array(tenx_list), label = \"10x\")\n", "sns.kdeplot(np.array(kal_list), label = \"kal\")\n", "plt.legend()" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<matplotlib.legend.Legend at 0x7f73b67d8bd0>" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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XC6BH5u8QiqZkI0HjdXqvMxOFAx4zOUWsBz3QTXIfWnSDJGghxJQmj5nUaii2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\n6EiSxLdn9CeGdErjagCVNe8V4Yqg+cBfvmHMXXIm3OEIF0A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"text/plain": [ "<matplotlib.figure.Figure at 0x7f73b63c4490>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#mouse\n", "sns.kdeplot(np.array(utl_list), label = \"Umi-tools\")\n", "sns.kdeplot(np.array(alv_list), label = \"Alevin\")\n", "sns.kdeplot(np.array(tenx_list), label = \"10x\")\n", "sns.kdeplot(np.array(kal_list), label = \"kal\")\n", "plt.legend()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<matplotlib.legend.Legend at 0x7f73b6dcf790>" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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iYyDTg/b5TfoT/TkZ3obj10L3Uz24Flp7cpciBbSIyBhIz+C20Ud6lnUuJogBVHicWC0G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\n1qzhgQceyGVdIiI5FQzH+Ol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"text/plain": [ "<matplotlib.figure.Figure at 0x7f73b6a3d650>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sns.kdeplot(np.array(alv_list), label = \"Alevin\")\n", "sns.kdeplot(np.array(kal_list), label = \"kal\")\n", "plt.legend()" ] }, { "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": 2 }
gpl-3.0
quantopian/research_public
notebooks/lectures/Spearman_Rank_Correlation/questions/notebook.ipynb
2
11668
{ "cells": [ { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "#Exercises: Spearman Rank Correlation\n", "\n", "## Lecture Link\n", "\n", "This exercise notebook refers to this lecture. Please use the lecture for explanations and sample code.\n", "\n", "https://www.quantopian.com/lectures#Spearman-Rank-Correlation\n", "\n", "Part of the Quantopian Lecture Series:\n", "\n", "* [www.quantopian.com/lectures](https://www.quantopian.com/lectures)\n", "* [github.com/quantopian/research_public](https://github.com/quantopian/research_public)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "import numpy as np\n", "import pandas as pd\n", "import scipy.stats as stats\n", "import matplotlib.pyplot as plt\n", "import math" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "#Exercise 1: Finding Correlations of Non-Linear Relationships\n", "\n", "##a. Traditional (Pearson) Correlation\n", "\n", "Find the correlation coefficient for the relationship between `x` and `y`." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "n = 100\n", "x = np.linspace(1, n, n)\n", "y = x**5\n", "\n", "#Your code goes here" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "# b. Spearman Rank Correlation\n", "\n", "Find the Spearman rank correlation coefficient for the relationship between `x` and `y` using the `stats.rankdata` function and the formula \n", "\n", "$$r_S = 1 - \\frac{6 \\sum_{i=1}^n d_i^2}{n(n^2 - 1)}$$\n", "\n", "where $d_i$ is the difference in rank of the `i`th pair of `x` and `y` values." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "#Your code goes here" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "Check your results against scipy's Spearman rank function. `stats.spearmanr`" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "# Your code goes here" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "#Exercise 2: Limitations of Spearman Rank Correlation\n", "\n", "##a. Lagged Relationships\n", "\n", "First, create a series `b` that is identical to `a` but lagged one step (`b[i] = a[i-1]`). Then, find the Spearman rank correlation coefficient of the relationship between `a` and `b`.\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "n = 100\n", "a = np.random.normal(0, 1, n)\n", "\n", "#Your code goes here" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "##b. Non-Monotonic Relationships\n", "\n", "First, create a series `d` using the relationship $d=10c^2 - c + 2$. Then, find the Spearman rank rorrelation coefficient of the relationship between `c` and `d`." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "n = 100\n", "c = np.random.normal(0, 2, n)\n", "\n", "#Your code goes here" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "#Exercise 3: Real World Example\n", "\n", "##a. Factor and Forward Returns\n", "\n", "Here we'll define a simple momentum factor (model). To evaluate it we'd need to look at how its predictions correlate with future returns over many days. We'll start by just evaluating the Spearman rank correlation between our factor values and forward returns on just one day.\n", "\n", "Compute the Spearman rank correlation between factor values and 10 trading day forward returns on 2015-1-2.\n", "\n", "For help on the pipeline API, see this tutorial: https://www.quantopian.com/tutorials/pipeline" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "#Pipeline Setup\n", "from quantopian.research import run_pipeline\n", "from quantopian.pipeline import Pipeline\n", "from quantopian.pipeline.data.builtin import USEquityPricing\n", "from quantopian.pipeline.factors import CustomFactor, Returns, RollingLinearRegressionOfReturns\n", "from quantopian.pipeline.classifiers.morningstar import Sector\n", "from quantopian.pipeline.filters import QTradableStocksUS\n", "from time import time\n", "\n", "#MyFactor is our custom factor, based off of asset price momentum\n", "\n", "class MyFactor(CustomFactor):\n", " \"\"\" Momentum factor \"\"\"\n", "\n", " inputs = [USEquityPricing.close] \n", " window_length = 60\n", "\n", " def compute(self, today, assets, out, close): \n", " out[:] = close[-1]/close[0]\n", " \n", "universe = QTradableStocksUS()\n", "\n", "pipe = Pipeline(\n", " columns = {\n", " 'MyFactor' : MyFactor(mask=universe),\n", " },\n", " screen=universe\n", ")\n", "\n", "start_timer = time()\n", "results = run_pipeline(pipe, '2015-01-01', '2015-06-01')\n", "end_timer = time()\n", "results.fillna(value=0);\n", "\n", "print \"Time to run pipeline %.2f secs\" % (end_timer - start_timer)\n", "\n", "my_factor = results['MyFactor']" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "n = len(my_factor)\n", "\n", "asset_list = results.index.levels[1].unique()\n", "prices_df = get_pricing(asset_list, start_date='2015-01-01', end_date='2016-01-01', fields='price')\n", "\n", "# Compute 10-day forward returns, then shift the dataframe back by 10\n", "forward_returns_df = prices_df.pct_change(10).shift(-10)\n", "\n", "# The first trading day is actually 2015-1-2\n", "single_day_factor_values = my_factor['2015-1-2']\n", "\n", "# Because prices are indexed over the total time period, while the factor values dataframe\n", "# has a dynamic universe that excludes hard to trade stocks, each day there may be assets in \n", "# the returns dataframe that are not present in the factor values dataframe. We have to filter down\n", "# as a result.\n", "single_day_forward_returns = forward_returns_df.loc['2015-1-2'][single_day_factor_values.index]\n", "\n", "#Your code goes here" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "##b. Rolling Spearman Rank Correlation\n", "\n", "Repeat the above correlation for the first 60 days in the dataframe as opposed to just a single day. You should get a time series of Spearman rank correlations. From this we can start getting a better sense of how the factor correlates with forward returns.\n", "\n", "What we're driving towards is known as an information coefficient. This is a very common way of measuring how predictive a model is. All of this plus much more is automated in our open source alphalens library. In order to see alphalens in action you can check out these resources:\n", "\n", "A basic tutorial:\n", "https://www.quantopian.com/tutorials/getting-started#lesson4\n", "\n", "An in-depth lecture:\n", "https://www.quantopian.com/lectures/factor-analysis" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "rolling_corr = pd.Series(index=None, data=None)\n", "\n", "#Your code goes here" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "##b. Rolling Spearman Rank Correlation\n", "\n", "Plot out the rolling correlation as a time series, and compute the mean and standard deviation." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "# Your code goes here" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "---\n", "\n", "Congratulations on completing the Spearman rank correlation exercises!\n", "\n", "As you learn more about writing trading models and the Quantopian platform, enter a daily [Quantopian Contest](https://www.quantopian.com/contest). Your strategy will be evaluated for a cash prize every day.\n", "\n", "Start by going through the [Writing a Contest Algorithm](https://www.quantopian.com/tutorials/contest) tutorial." ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "*This presentation is for informational purposes only and does not constitute an offer to sell, a solic\n", "itation to buy, or a recommendation for any security; nor does it constitute an offer to provide investment advisory or other services by Quantopian, Inc. (\"Quantopian\"). Nothing contained herein constitutes investment advice or offers any opinion with respect to the suitability of any security, and any views expressed herein should not be taken as advice to buy, sell, or hold any security or as an endorsement of any security or company. In preparing the information contained herein, Quantopian, Inc. has not taken into account the investment needs, objectives, and financial circumstances of any particular investor. Any views expressed and data illustrated herein were prepared based upon information, believed to be reliable, available to Quantopian, Inc. at the time of publication. Quantopian makes no guarantees as to their accuracy or completeness. All information is subject to change and may quickly become unreliable for various reasons, including changes in market conditions or economic circumstances.*" ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.12" } }, "nbformat": 4, "nbformat_minor": 0 }
apache-2.0
UCBerkeleySETI/breakthrough
GBT/pulsar_searches/Pulsar_Search/Pulsar_DedisperseV3.ipynb
1
654218
{ "nbformat": 4, "nbformat_minor": 0, "metadata": { "colab": { "name": "Pulsar_Dedisperse", "provenance": [], "collapsed_sections": [], "toc_visible": true }, "kernelspec": { "name": "python3", "display_name": "Python 3" }, "accelerator": "GPU" }, "cells": [ { "cell_type": "markdown", "metadata": { "id": "traqoVa8FTGZ", "colab_type": "text" }, "source": [ "# Pulsar Folding and Searching \n", "#### **By Peter Ma**\n", "\n", "\n", "Pulsar searching is a very compute-intensive task. Searching for repeating signals within noisy data is difficult because pulses tend to have a low signal to noise ratio. Our goal is to process, clean, identify potential periods, and fold the pulses to increase the SNR. This notebook demonstrates the algorithms used for searching regular pulses within radio spectrograms.\n", "\n", "Keep in mind, there are faster algorithms used in state-of-the-art pulsar search pipelines [ex. Tree dedispersion]. This notebook implements the simplest pulsar searching technique.\n", "\n", "First, we start with downloading the data and `BLIMPY` which is an I/O tool developed to interface with the radio data.\n", "\n", "Note: Run this notebook in COLAB as some operations are resource-intensive. For example, downloading and loading +5GB files into RAM. This notebook is not optimized." ] }, { "cell_type": "code", "metadata": { "id": "Jfq9Go8sp0NE", "colab_type": "code", "outputId": "3836051a-a740-486e-9ba2-a20379e9c405", "colab": { "base_uri": "https://localhost:8080/", "height": 237 } }, "source": [ "!pip install blimpy\n", "# Pulsar data\n", "!wget http://blpd13.ssl.berkeley.edu/borisov/AGBT19B_999_124/spliced_blc40414243444546o7o0515253545556o7o061626364656667_guppi_58837_86186_PSR_B0355+54_0013.gpuspec.8.0001.fil\n", "# For more info on pulsar searches check out this deck\n", "# http://ipta.phys.wvu.edu/files/student-week-2017/IPTA2017_KuoLiu_pulsartiming.pdf" ], "execution_count": 0, "outputs": [ { "output_type": "stream", "text": [ "--2020-06-01 02:06:21-- http://blpd13.ssl.berkeley.edu/borisov/AGBT19B_999_124/spliced_blc40414243444546o7o0515253545556o7o061626364656667_guppi_58837_86186_PSR_B0355+54_0013.gpuspec.8.0001.fil\n", "Resolving blpd13.ssl.berkeley.edu (blpd13.ssl.berkeley.edu)... 208.68.240.55\n", "Connecting to blpd13.ssl.berkeley.edu (blpd13.ssl.berkeley.edu)|208.68.240.55|:80... connected.\n", "HTTP request sent, awaiting response... 200 OK\n", "Length: 8776581466 (8.2G) [application/octet-stream]\n", "Saving to: ‘spliced_blc40414243444546o7o0515253545556o7o061626364656667_guppi_58837_86186_PSR_B0355+54_0013.gpuspec.8.0001.fil.1’\n", "\n", "spliced_blc40414243 100%[===================>] 8.17G 15.4MB/s in 8m 49s \n", "\n", "2020-06-01 02:15:11 (15.8 MB/s) - ‘spliced_blc40414243444546o7o0515253545556o7o061626364656667_guppi_58837_86186_PSR_B0355+54_0013.gpuspec.8.0001.fil.1’ saved [8776581466/8776581466]\n", "\n" ], "name": "stdout" } ] }, { "cell_type": "markdown", "metadata": { "id": "hdEvVn6asE91", "colab_type": "text" }, "source": [ "# Loading Data\n", "First, we load the data. NOTE, targets with the starting name of PSR are radio scans of known pulsars `PSR_B0355+54_0013`. But, files with `HIP65960` cataloged targets that shouldn't have pulsar characteristics. If you wish to learn more about the data check out https://ui.adsabs.harvard.edu/abs/2019PASP..131l4505L/abstract\n", "\n", "The header information gives vital information about the observational setup of the telescope. For example, the coarse channel width or the observation time and duration, etc." ] }, { "cell_type": "code", "metadata": { "id": "8ORVKcLJskZ9", "colab_type": "code", "outputId": "0aebb37f-f84a-4de4-b38d-4d8617a896ea", "colab": { "base_uri": "https://localhost:8080/", "height": 878 } }, "source": [ "from blimpy import Waterfall\n", "import pylab as plt\n", "import numpy as np\n", "import math\n", "from scipy import stats, interpolate\n", "from copy import deepcopy\n", "%matplotlib inline\n", "\n", "\n", "\n", "obs = Waterfall('/content/spliced_blc40414243444546o7o0515253545556o7o061626364656667_guppi_58837_86186_PSR_B0355+54_0013.gpuspec.8.0001.fil', \n", " t_start=0,t_stop= 80000,max_load=10)\n", "obs.info()\n", "# Loads data into numpy array \n", "data = obs.data\n", "data.shape\n", "coarse_channel_width = np.int(np.round(187.5/64/abs(obs.header['foff'])))\n", "# Here we plot the integrated signal over time.\n", "obs.plot_spectrum()" ], "execution_count": 0, "outputs": [ { "output_type": "stream", "text": [ "blimpy.io.base_reader WARNING Selection size of 8.17 GB, exceeding our size limit 1.00 GB. Instance created, header loaded, but data not loaded, please try another (t,v) selection.\n", "\n", "--- File Info ---\n", " rawdatafile : \n", " source_name : PSR_B0355+54\n", " machine_id : 10\n", " telescope_id : 6\n", " src_raj : 3:58:53.496\n", " src_dej : 54:13:14.88\n", " az_start : 0.0\n", " za_start : 0.0\n", " data_type : 1\n", " fch1 : 11251.28173828125 MHz\n", " foff : -0.3662109375 MHz\n", " nchans : 10240\n", " nbeams : 0\n", " ibeam : 0\n", " nbits : 8\n", " tstart (ISOT) : 2019-12-20T23:56:26.000\n", " tstart (MJD) : 58837.99752314815\n", " tsamp : 0.0003495253333333333\n", " nifs : 1\n", "\n", "Num ints in file : 857088\n", " File shape : (857088, 1, 10240)\n", "--- Selection Info ---\n", "Data selection shape : (857088, 1, 10240)\n", "Minimum freq (MHz) : 7501.28173828125\n", "Maximum freq (MHz) : 11251.28173828125\n", "blimpy.io.base_reader WARNING Setting data limit != 1GB, please handle with care!\n", "extracting integration 0...\n" ], "name": "stdout" }, { "output_type": "display_data", "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "tags": [], "needs_background": "light" } } ] }, { "cell_type": "code", "metadata": { "id": "6V37gqVYvD_u", "colab_type": "code", "outputId": "42d9ac80-6f79-49d3-fec9-aa10d6da0727", "colab": { "base_uri": "https://localhost:8080/", "height": 531 } }, "source": [ "fig = plt.figure(figsize=(10,8))\n", "plt.title('Spectrogram With Bandpass')\n", "plt.xlabel(\"Fchans\")\n", "plt.ylabel(\"Time\")\n", "plt.imshow(data[:3000,0,1500:3000], aspect='auto')\n", "plt.colorbar()" ], "execution_count": 0, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "<matplotlib.colorbar.Colorbar at 0x7f9853e24e48>" ] }, "metadata": { "tags": [] }, "execution_count": 24 }, { "output_type": "display_data", "data": { "image/png": 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\n", "text/plain": [ "<Figure size 720x576 with 2 Axes>" ] }, "metadata": { "tags": [], "needs_background": "light" } } ] }, { "cell_type": "markdown", "metadata": { "id": "lWDhvDiyt-ko", "colab_type": "text" }, "source": [ "# Band Pass Removal\n", "\n", "The goal of this process is to clean the data of its artifacts created by combining multiple bands. Our data is created by taking sliding windows of the raw voltage data and computing an FFT of that sliding window. With these FFTs (each containing frequency information about a timestamp) for each coarse channel, we use a bandpass filter to cut off frequencies that don’t belong to that coarse channel’s frequency range. But we can’t achieve a perfect cut, and that’s why there's a falling off at the edges. \n", "\n", "They’re called band-pass because they only allow signals in a particular frequency range, called a band, to pass-through. When we assemble the products we see these dips in the spectrogram. In other words - they aren't real signals.\n", "\n", "To remove the bandpass features, we use spline lines to fit each channel to get a model of the bandpass of that channel. By using splines, we can fit the bandpass without fitting the more significant signals.\n", "\n", "If you want more details on this check out https://github.com/FX196/SETI-Energy-Detection for a detailed explanation.\n", "\n", "![image.png](https://github.com/PetchMa/Pulsar_Folding/blob/master/assets/image%20(6).png?raw=true)" ] }, { "cell_type": "code", "metadata": { "id": "0TpGGisrYRQQ", "colab_type": "code", "outputId": "35f33044-6fd5-4fc5-9dab-07570cd40770", "colab": { "base_uri": "https://localhost:8080/", "height": 814 } }, "source": [ "average_power = np.zeros((data.shape[2]))\n", "shifted_power = np.zeros((int(data.shape[2]/8)))\n", "x=[]\n", "spl_order = 2\n", "print(\"Fitting Spline\")\n", "data_adjust = np.zeros(data.shape)\n", "average_power = data.mean(axis=0)\n", "# Note the value 8 is the COARSE CHANNEL WIDTH\n", "# We adjust each coarse channel to correct the bandpass artifacts\n", "for i in range(0, data.shape[2], 8):\n", " average_channel = average_power[0,i:i+8]\n", " x = np.arange(0,coarse_channel_width,1)\n", " knots = np.arange(0, coarse_channel_width, coarse_channel_width//spl_order+1)\n", "\n", " tck = interpolate.splrep(x, average_channel, s=knots[1:])\n", " xnew = np.arange(0, coarse_channel_width,1)\n", " ynew = interpolate.splev(xnew, tck, der=0)\n", " data_adjust[:,0,i:i+8] = data[:,0,i:i+8] - ynew\n", "\n", "plt.figure()\n", "plt.plot( data_adjust.mean(axis=0)[0,:])\n", "plt.title('Spline Fit - adjusted')\n", "plt.xlabel(\"Fchans\")\n", "plt.ylabel(\"Power\")\n", "fig = plt.figure(figsize=(10,8))\n", "plt.title('After bandpass correction')\n", "plt.imshow(data_adjust[:3000,0,:], aspect='auto')\n", "plt.colorbar()" ], "execution_count": 0, "outputs": [ { "output_type": "stream", "text": [ "Fitting Spline\n" ], "name": "stdout" }, { "output_type": "execute_result", "data": { "text/plain": [ "<matplotlib.colorbar.Colorbar at 0x7f9851e5f470>" ] }, "metadata": { "tags": [] }, "execution_count": 25 }, { "output_type": "display_data", "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "tags": [], "needs_background": "light" } }, { "output_type": "display_data", "data": { "image/png": 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\n", "text/plain": [ "<Figure size 720x576 with 2 Axes>" ] }, "metadata": { "tags": [], "needs_background": "light" } } ] }, { "cell_type": "markdown", "metadata": { "id": "Q-kMDNoKEq25", "colab_type": "text" }, "source": [ "# Dedispersion\n", "When pulses reach Earth they reach the observer at different times due to dispersion. This dispersion is the result of the interstellar medium causing time delays. This creates a \"swooping curve\" on the radio spectrogram instead of plane waves. If we are going to fold the pulses to increase the SNR then we're making the assumption that the pulses arrive at the same time. Thus we need to correct the dispersion by shifting each channel down a certain time delay relative to its frequency channel. We index a frequency column in the spectrogram. Then we split it between a time delay and original data and swap the positions.\n", "\n", "However, the problem is, we don't know the dispersion measure `DM` of the signal. The `DM` is the path integral of the signal through the interstellar medium with an electron density measure of.\n", "$$DM =\\int_0^d n_e dl$$ \n", "\n", "What we do is we brute force the `DM` by executing multiple trials `DM`s and we take the highest SNR created by the dedispersion with the given trial `DM`.\n", "\n", "![alt text](https://astronomy.swin.edu.au/cms/cpg15x/albums/scaled_cache/wonderpulse-400x309.jpg)" ] }, { "cell_type": "code", "metadata": { "id": "ZqgHA9eAEDjH", "colab_type": "code", "colab": {} }, "source": [ "def delay_from_DM(DM, freq_emitted):\n", " if (type(freq_emitted) == type(0.0)):\n", " if (freq_emitted > 0.0):\n", " return DM / (0.000241 * freq_emitted * freq_emitted)\n", " else:\n", " return 0.0\n", " else:\n", " return Num.where(freq_emitted > 0.0,\n", " DM / (0.000241 * freq_emitted * freq_emitted), 0.0)\n", "\n", "def de_disperse(data,DM,fchan,width,tsamp):\n", " clean = deepcopy(data)\n", " for i in range(clean.shape[1]):\n", " end = clean.shape[0]\n", " freq_emitted = i*width+ fchan\n", " time = int((delay_from_DM(DM, freq_emitted))/tsamp)\n", " if time!=0 and time<clean.shape[0]:\n", " # zero_block = np.zeros((time))\n", " zero_block = clean[:time,i]\n", " shift_block = clean[:end-time,i]\n", " clean[time:end,i]= shift_block\n", " clean[:time,i]= zero_block\n", "\n", " elif time!=0:\n", " clean[:,i]= np.zeros(clean[:,i].shape)\n", " return clean\n", "\n", "def DM_can(data, data_base, sens, DM_base, candidates, fchan,width,tsamp ):\n", " snrs = np.zeros((candidates,2))\n", " for i in range(candidates):\n", " DM = DM_base+sens*i\n", " data = de_disperse(data, DM, fchan,width,tsamp)\n", " time_series = data.sum(axis=1)\n", " snrs[i,1] = SNR(time_series)\n", " snrs[i,0] =DM\n", " if int((delay_from_DM(DM, fchan))/tsamp)+1 > data.shape[0]:\n", " break\n", " if i %1==0:\n", " print(\"Candidate \"+str(i)+\"\\t SNR: \"+str(round(snrs[i,1],4)) + \"\\t Largest Time Delay: \"+str(round(delay_from_DM(DM, fchan), 6))+' seconds'+\"\\t DM val:\"+ str(DM)+\"pc/cm^3\")\n", " data = data_base\n", " return snrs" ], "execution_count": 0, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "g9VUmYQNLchr", "colab_type": "code", "colab": {} }, "source": [ "# Functions to determine SNR and TOP candidates\n", "def SNR(arr):\n", " index = np.argmax(arr)\n", " average_noise = abs(arr.mean(axis=0))\n", " return math.log(arr[index]/average_noise) \n", "\n", "def top(arr, top = 10):\n", " candidate = []\n", " # Delete the first and second element fourier transform\n", " arr[0]=0\n", " arr[1]=0\n", " for i in range(top):\n", " # We add 1 as the 0th index = period of 1 not 0\n", " index = np.argmax(arr)\n", " candidate.append(index+1)\n", " arr[index]=0\n", " return candidate " ], "execution_count": 0, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "SFUMoaoik2jB", "colab_type": "text" }, "source": [ "# Dedispersion Trials\n", "\n", "The computer now checks multiple DM values and adjust each frequency channel where it records its SNR. We increment the trial `DM` by a tunable parameter `sens`. After the trials, we take the largest SNR created by adjusting the time delays. We use that data to perform the FFT's and record the folded profiles. " ] }, { "cell_type": "code", "metadata": { "id": "hvvBUiRkGQ4h", "colab_type": "code", "outputId": "84034ecc-6b85-47fc-d1fb-cecb741546da", "colab": { "base_uri": "https://localhost:8080/", "height": 1000 } }, "source": [ "small_data = data_adjust[:,0,:]\n", "data_base = data_adjust[:,0,:]\n", "sens =0.05\n", "DM_base = 6.4 \n", "candidates = 50\n", "fchan = obs.header['fch1']\n", "width = obs.header['foff']\n", "tsamp = obs.header['tsamp']\n", "fchan = fchan+ width*small_data.shape[1]\n", "snrs = DM_can(small_data, data_base, sens, DM_base, candidates, fchan, abs(width),tsamp)\n", "plt.plot(snrs[:,0], snrs[:,1])\n", "plt.title('DM values vs SNR')\n", "plt.xlabel(\"DM values\")\n", "plt.ylabel(\"SNR of Dedispersion\")" ], "execution_count": 0, "outputs": [ { "output_type": "stream", "text": [ "Candidate 0\t SNR: 10.2096\t Largest Time Delay: 0.000472 seconds\t DM val:6.4pc/cm^3\n", "Candidate 1\t SNR: 10.1844\t Largest Time Delay: 0.000476 seconds\t DM val:6.45pc/cm^3\n", "Candidate 2\t SNR: 10.1457\t Largest Time Delay: 0.000479 seconds\t DM val:6.5pc/cm^3\n", "Candidate 3\t SNR: 10.1364\t Largest Time Delay: 0.000483 seconds\t DM val:6.550000000000001pc/cm^3\n", "Candidate 4\t SNR: 10.1687\t Largest Time Delay: 0.000487 seconds\t DM val:6.6000000000000005pc/cm^3\n", "Candidate 5\t SNR: 10.211\t Largest Time Delay: 0.00049 seconds\t DM val:6.65pc/cm^3\n", "Candidate 6\t SNR: 10.239\t Largest Time Delay: 0.000494 seconds\t DM val:6.7pc/cm^3\n", "Candidate 7\t SNR: 10.2612\t Largest Time Delay: 0.000498 seconds\t DM val:6.75pc/cm^3\n", "Candidate 8\t SNR: 10.2751\t Largest Time Delay: 0.000501 seconds\t DM val:6.800000000000001pc/cm^3\n", "Candidate 9\t SNR: 10.2568\t Largest Time Delay: 0.000505 seconds\t DM val:6.8500000000000005pc/cm^3\n", "Candidate 10\t SNR: 10.2352\t Largest Time Delay: 0.000509 seconds\t DM val:6.9pc/cm^3\n", "Candidate 11\t SNR: 10.1768\t Largest Time Delay: 0.000513 seconds\t DM val:6.95pc/cm^3\n", "Candidate 12\t SNR: 10.1034\t Largest Time Delay: 0.000516 seconds\t DM val:7.0pc/cm^3\n", "Candidate 13\t SNR: 10.061\t Largest Time Delay: 0.00052 seconds\t DM val:7.050000000000001pc/cm^3\n", "Candidate 14\t SNR: 10.0286\t Largest Time Delay: 0.000524 seconds\t DM val:7.1000000000000005pc/cm^3\n", "Candidate 15\t SNR: 9.9979\t Largest Time Delay: 0.000527 seconds\t DM val:7.15pc/cm^3\n", "Candidate 16\t SNR: 9.9579\t Largest Time Delay: 0.000531 seconds\t DM val:7.2pc/cm^3\n", "Candidate 17\t SNR: 9.9297\t Largest Time Delay: 0.000535 seconds\t DM val:7.25pc/cm^3\n", "Candidate 18\t SNR: 9.8975\t Largest Time Delay: 0.000538 seconds\t DM val:7.300000000000001pc/cm^3\n", "Candidate 19\t SNR: 9.8702\t Largest Time Delay: 0.000542 seconds\t DM val:7.3500000000000005pc/cm^3\n", "Candidate 20\t SNR: 9.8467\t Largest Time Delay: 0.000546 seconds\t DM val:7.4pc/cm^3\n", "Candidate 21\t SNR: 9.8123\t Largest Time Delay: 0.000549 seconds\t DM val:7.45pc/cm^3\n", "Candidate 22\t SNR: 9.801\t Largest Time Delay: 0.000553 seconds\t DM val:7.5pc/cm^3\n", "Candidate 23\t SNR: 9.7899\t Largest Time Delay: 0.000557 seconds\t DM val:7.550000000000001pc/cm^3\n", "Candidate 24\t SNR: 9.7901\t Largest Time Delay: 0.00056 seconds\t DM val:7.6000000000000005pc/cm^3\n", "Candidate 25\t SNR: 9.7864\t Largest Time Delay: 0.000564 seconds\t DM val:7.65pc/cm^3\n", "Candidate 26\t SNR: 9.7811\t Largest Time Delay: 0.000568 seconds\t DM val:7.7pc/cm^3\n", "Candidate 27\t SNR: 9.7704\t Largest Time Delay: 0.000571 seconds\t DM val:7.75pc/cm^3\n", "Candidate 28\t SNR: 9.7645\t Largest Time Delay: 0.000575 seconds\t DM val:7.800000000000001pc/cm^3\n", "Candidate 29\t SNR: 9.7544\t Largest Time Delay: 0.000579 seconds\t DM val:7.8500000000000005pc/cm^3\n", "Candidate 30\t SNR: 9.7469\t Largest Time Delay: 0.000583 seconds\t DM val:7.9pc/cm^3\n", "Candidate 31\t SNR: 9.737\t Largest Time Delay: 0.000586 seconds\t DM val:7.95pc/cm^3\n", "Candidate 32\t SNR: 9.7255\t Largest Time Delay: 0.00059 seconds\t DM val:8.0pc/cm^3\n", "Candidate 33\t SNR: 9.7163\t Largest Time Delay: 0.000594 seconds\t DM val:8.05pc/cm^3\n", "Candidate 34\t SNR: 9.7152\t Largest Time Delay: 0.000597 seconds\t DM val:8.100000000000001pc/cm^3\n", "Candidate 35\t SNR: 9.7123\t Largest Time Delay: 0.000601 seconds\t DM val:8.15pc/cm^3\n", "Candidate 36\t SNR: 9.7101\t Largest Time Delay: 0.000605 seconds\t DM val:8.200000000000001pc/cm^3\n", "Candidate 37\t SNR: 9.7091\t Largest Time Delay: 0.000608 seconds\t DM val:8.25pc/cm^3\n", "Candidate 38\t SNR: 9.7075\t Largest Time Delay: 0.000612 seconds\t DM val:8.3pc/cm^3\n", "Candidate 39\t SNR: 9.7145\t Largest Time Delay: 0.000616 seconds\t DM val:8.350000000000001pc/cm^3\n", "Candidate 40\t SNR: 9.7169\t Largest Time Delay: 0.000619 seconds\t DM val:8.4pc/cm^3\n", "Candidate 41\t SNR: 9.73\t Largest Time Delay: 0.000623 seconds\t DM val:8.450000000000001pc/cm^3\n", "Candidate 42\t SNR: 9.7385\t Largest Time Delay: 0.000627 seconds\t DM val:8.5pc/cm^3\n", "Candidate 43\t SNR: 9.7547\t Largest Time Delay: 0.00063 seconds\t DM val:8.55pc/cm^3\n", "Candidate 44\t SNR: 9.7649\t Largest Time Delay: 0.000634 seconds\t DM val:8.600000000000001pc/cm^3\n", "Candidate 45\t SNR: 9.7841\t Largest Time Delay: 0.000638 seconds\t DM val:8.65pc/cm^3\n", "Candidate 46\t SNR: 9.7666\t Largest Time Delay: 0.000642 seconds\t DM val:8.700000000000001pc/cm^3\n", "Candidate 47\t SNR: 9.7593\t Largest Time Delay: 0.000645 seconds\t DM val:8.75pc/cm^3\n", "Candidate 48\t SNR: 9.7509\t Largest Time Delay: 0.000649 seconds\t DM val:8.8pc/cm^3\n", "Candidate 49\t SNR: 9.7406\t Largest Time Delay: 0.000653 seconds\t DM val:8.850000000000001pc/cm^3\n" ], "name": "stdout" }, { "output_type": "execute_result", "data": { "text/plain": [ "Text(0, 0.5, 'SNR of Dedispersion')" ] }, "metadata": { "tags": [] }, "execution_count": 27 }, { "output_type": "display_data", "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "tags": [], "needs_background": "light" } } ] }, { "cell_type": "code", "metadata": { "id": "fmJX7UT2s9E-", "colab_type": "code", "outputId": "de2ab717-b547-4416-9be5-98fb4edeaecd", "colab": { "base_uri": "https://localhost:8080/", "height": 531 } }, "source": [ "DM = snrs[np.argmax(snrs[:,1]),0]\n", "print(DM)\n", "fchan = fchan+ width*small_data.shape[1]\n", "data_adjust[:,0,:] = de_disperse(data_adjust[:,0,:], DM, fchan,abs(width),tsamp)\n", "fig = plt.figure(figsize=(10, 8))\n", "plt.imshow(data_adjust[:,0,:], aspect='auto')\n", "plt.title('De-dispersed Data')\n", "plt.xlabel(\"Fchans\")\n", "plt.ylabel(\"Time\")\n", "plt.colorbar()\n", "plt.show() " ], "execution_count": 0, "outputs": [ { "output_type": "stream", "text": [ "6.800000000000001\n" ], "name": "stdout" }, { "output_type": "display_data", "data": { "image/png": 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\n", "text/plain": [ "<Figure size 720x576 with 2 Axes>" ] }, "metadata": { "tags": [], "needs_background": "light" } } ] }, { "cell_type": "markdown", "metadata": { "id": "Nn6cmTcqw3zp", "colab_type": "text" }, "source": [ "# Detecting Pulses - Fourier Transforms and Folding\n", "\n", "Next, we apply the discrete Fourier transform on the data to detect periodic pulses. To do so, we look for the greatest magnitude of the Fourier transform. This indicates potential periods within the data. Then we check for consistency by folding the data by the period which the Fourier transform indicates.\n", "\n", "The folding algorithm is simple. You take each period and you fold the signals on top of itself. If the period you guessed matches the true period then by the law of superposition it will increase the SNR. This spike in signal to noise ratio appears in the following graph. This algorithm is the following equation." ] }, { "cell_type": "code", "metadata": { "id": "oYSGUDjK1SA4", "colab_type": "code", "outputId": "32f92311-fc5a-4a0b-8766-7379cd3a573c", "colab": { "base_uri": "https://localhost:8080/", "height": 331 } }, "source": [ "# Preforming the fourier transform.\n", "%matplotlib inline\n", "import scipy.fftpack\n", "from scipy.fft import fft\n", "N = 1000\n", "T = 1.0 / 800.0\n", "x = np.linspace(0.0, N*T, N)\n", "y = abs(data_adjust[:,0,:].mean(axis=1))\n", "yf = fft(y)\n", "\n", "xf = np.linspace(0.0, 1.0/(2.0*T), N//2)\n", "\n", "# Magintude of the fourier transform\n", "# Between 0.00035 and 3.5 seconds\n", "mag = np.abs(yf[:60000])\n", "candidates = top(mag, top=15)\n", "plt.plot(2.0/N * mag[1:])\n", "plt.grid()\n", "plt.title('Fourier Transform of Signal')\n", "plt.xlabel(\"Periods\")\n", "plt.ylabel(\"Magnitude of Fourier Transform\")\n", "plt.show()\n", "\n", "print(\"Signal To Noise Ratio for the Fourier Transform is: \"+str(SNR(mag)))\n", "print(\"Most likely Candidates are: \"+str(candidates))" ], "execution_count": 0, "outputs": [ { "output_type": "display_data", "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "tags": [], "needs_background": "light" } }, { "output_type": "stream", "text": [ "Signal To Noise Ratio for the Fourier Transform is: 3.6559125533569086\n", "Most likely Candidates are: [180, 716, 895, 3, 537, 359, 1074, 1610, 1789, 1431, 358, 1253, 538, 1968, 2683]\n" ], "name": "stdout" } ] }, { "cell_type": "markdown", "metadata": { "id": "uRAhRkkB8H1e", "colab_type": "text" }, "source": [ "# Folding Algorithm \n", "\n", "The idea of the folding algorithm is to see if the signal forms a consistent profile as you fold/integrate the values together. If the profile appears consistent/stable then you're looking at an accurate reading of the pulsar's period. This confirms the implications drawn from the Fourier transform. This is profiling the pulsar. When folding the pulses it forms a \"fingerprint\" of the pulsar. These folds are unique to the pulsar detected. \n", "\n", "$$s_j = \\sum^{N/P-1}_{K=0} D_{j+kP} $$\n", "\n", "We are suming over the regular intervals of period `P`. This is implemented below." ] }, { "cell_type": "markdown", "metadata": { "id": "F7QkzN39F9_t", "colab_type": "text" }, "source": [ "![alt text](https://github.com/PetchMa/Pulsar_Folding/blob/master/assets/can_3.gif?raw=true)" ] }, { "cell_type": "code", "metadata": { "id": "Qyz5IsxjNQ33", "colab_type": "code", "outputId": "dc7e627c-cf74-4413-8630-d24e56980575", "colab": { "base_uri": "https://localhost:8080/", "height": 331 } }, "source": [ "# Lets take an example of such a period!\n", "# The 0th candidate is the top ranked candidate by the FFT\n", "period = 895\n", "fold = np.zeros((period, data.shape[2]))\n", "multiples = int(data.data.shape[0]/period)\n", "results = np.zeros((period))\n", "\n", "for i in range(multiples-1):\n", " fold[:,:]=data_adjust[i*period:(i+1)*period,0,:]+ fold\n", "\n", "results = fold.mean(axis=1)\n", "results = results - results.min()\n", "results = results / results.max()\n", "\n", "print(SNR(results))\n", "\n", "plt.plot(results)\n", "plt.title('Folded Signal Profile With Period: '+str(round(period*0.000349,5)))\n", "plt.xlabel(\"Time (Multiples of 0.00035s)\")\n", "plt.ylabel(\"Normalized Integrated Signal\")" ], "execution_count": 0, "outputs": [ { "output_type": "stream", "text": [ "2.6899003919476705\n" ], "name": "stdout" }, { "output_type": "execute_result", "data": { "text/plain": [ "Text(0, 0.5, 'Normalized Integrated Signal')" ] }, "metadata": { "tags": [] }, "execution_count": 10 }, { "output_type": "display_data", "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "tags": [], "needs_background": "light" } } ] }, { "cell_type": "code", "metadata": { "colab_type": "code", "outputId": "c0cdc336-dc7b-4e93-99ec-3e450ed689bf", "id": "__fqrkxIIgml", "colab": { "base_uri": "https://localhost:8080/", "height": 35 } }, "source": [ "# Lets take an example of such a period!\n", "# The 0th candidate is the top ranked candidate by the FFT\n", "can_snr =[]\n", "for k in range(len(candidates)):\n", " period = candidates[k]\n", " fold = np.zeros((period, data.shape[2]))\n", " multiples = int(data.data.shape[0]/period)\n", " results = np.zeros((period))\n", "\n", " for i in range(multiples-1):\n", " fold[:,:]=data[i*period:(i+1)*period,0,:]+ fold\n", "\n", " results = fold.mean(axis=1)\n", " results = results - results.min()\n", " results = results / results.max()\n", " can_snr.append(SNR(results))\n", " # print(SNR(results))\n", "\n", "print(\"Max SNR of Fold Candidates: \"+ str(max(can_snr)))" ], "execution_count": 0, "outputs": [ { "output_type": "stream", "text": [ "Max SNR of Fold Candidates: 2.657056217368029\n" ], "name": "stdout" } ] }, { "cell_type": "code", "metadata": { "id": "KD4TGfUnB6wq", "colab_type": "code", "outputId": "7023d075-aacc-43ca-c512-f8439c066673", "colab": { "base_uri": "https://localhost:8080/", "height": 301 } }, "source": [ "# Generates multiple images saved to create a GIF \n", "from scipy import stats \n", "data = data\n", "period = candidates[0]\n", "fold = np.zeros((period, data.shape[2]))\n", "multiples = int(data.data.shape[0]/period)\n", "results = np.zeros((period))\n", "\n", "for i in range(multiples-1):\n", " fold[:,:]=data[i*period:(i+1)*period,0,:]+ fold\n", " results = fold.mean(axis=1)\n", " results = results - results.min()\n", " results = results / results.max()\n", " # Generates multiple frames of the graph as it folds! \n", " plt.plot(results)\n", " plt.title('Folded Signal Period '+str(period*0.000349)+\" seconds| Fold Iteration: \"+str(i))\n", " plt.xlabel(\"Time (Multiples of 0.00035s)\")\n", " plt.ylabel(\"Normalized Integrated Signal\")\n", " plt.savefig('/content/drive/My Drive/Deeplearning/Pulsars/output/candidates/CAN_3/multi_chan_'+str(period)+'_'+str(i)+'.png')\n", " plt.close()\n", " \n", "results = fold.mean(axis=1)\n", "results = results - results.min()\n", "results = results / results.max()\n", "\n", "print(\"The Signal To Noise of the Fold is: \"+str(SNR(results)))\n", "plt.plot(results)" ], "execution_count": 0, "outputs": [ { "output_type": "stream", "text": [ "The Signal To Noise of the Fold is: 2.657056217368029\n" ], "name": "stdout" }, { "output_type": "execute_result", "data": { "text/plain": [ "[<matplotlib.lines.Line2D at 0x7f7a3e2a6b00>]" ] }, "metadata": { "tags": [] }, "execution_count": 40 }, { "output_type": "display_data", "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "tags": [], "needs_background": "light" } } ] }, { "cell_type": "markdown", "metadata": { "id": "CNBAr0j-Bvcj", "colab_type": "text" }, "source": [ "# What Happens If The Data Doesn't Contain Pulses?\n", "\n", " Below we will show you that this algorithm detects pulses and excludes targets that do not include this feature. We will do so by loading a target that isn't known to be a pulsar. `HIP65960` is a target that doesn't contain repeating signals.\n", "\n", "Below we will repeat and apply the same algorithm but on a target that isn't a pulsar. We won't reiterate the explanations again." ] }, { "cell_type": "code", "metadata": { "id": "Ez8_TzFHiiaX", "colab_type": "code", "outputId": "8acb808c-1618-4078-d9ea-bde43a6e05b2", "colab": { "base_uri": "https://localhost:8080/", "height": 217 } }, "source": [ "!wget http://blpd13.ssl.berkeley.edu/dl/GBT_58402_66282_HIP65960_time.h5" ], "execution_count": 0, "outputs": [ { "output_type": "stream", "text": [ "--2020-06-01 01:56:39-- http://blpd13.ssl.berkeley.edu/dl/GBT_58402_66282_HIP65960_time.h5\n", "Resolving blpd13.ssl.berkeley.edu (blpd13.ssl.berkeley.edu)... 208.68.240.55\n", "Connecting to blpd13.ssl.berkeley.edu (blpd13.ssl.berkeley.edu)|208.68.240.55|:80... connected.\n", "HTTP request sent, awaiting response... 200 OK\n", "Length: 1471544819 (1.4G) [application/octet-stream]\n", "Saving to: ‘GBT_58402_66282_HIP65960_time.h5’\n", "\n", "GBT_58402_66282_HIP 100%[===================>] 1.37G 15.8MB/s in 87s \n", "\n", "2020-06-01 01:58:08 (16.1 MB/s) - ‘GBT_58402_66282_HIP65960_time.h5’ saved [1471544819/1471544819]\n", "\n" ], "name": "stdout" } ] }, { "cell_type": "code", "metadata": { "id": "aulzdgsOB-zC", "colab_type": "code", "outputId": "751b9388-70a5-42cf-f94f-fd5bd8cfdfce", "colab": { "base_uri": "https://localhost:8080/", "height": 915 } }, "source": [ "from blimpy import Waterfall\n", "import pylab as plt\n", "import numpy as np\n", "import math\n", "from scipy import stats, interpolate\n", "\n", "%matplotlib inline\n", "\n", "obs = Waterfall('/content/GBT_58402_66282_HIP65960_time.h5', \n", " f_start=0,f_stop= 361408,max_load=5)\n", "obs.info()\n", "# Loads data into numpy array \n", "data = obs.data\n", "coarse_channel_width = np.int(np.round(187.5/64/abs(obs.header['foff'])))\n", "obs.plot_spectrum()" ], "execution_count": 0, "outputs": [ { "output_type": "stream", "text": [ "blimpy.io.base_reader WARNING Selection size of 1.97 GB, exceeding our size limit 1.00 GB. Instance created, header loaded, but data not loaded, please try another (t,v) selection.\n", "\n", "--- File Info ---\n", "DIMENSION_LABELS : [b'frequency' b'feed_id' b'time']\n", " az_start : 0.0\n", " data_type : 1\n", " fch1 : 2720.80078125 MHz\n", " foff : -0.3662109375 MHz\n", " ibeam : 0\n", " machine_id : 10\n", " nbeams : 0\n", " nbits : 8\n", " nchans : 2464\n", " nifs : 1\n", " rawdatafile : \n", " source_name : HIP65960\n", " src_dej : 54:55:17.63\n", " src_raj : 13:31:26.674\n", " telescope_id : 6\n", " tsamp : 0.0003495253333333333\n", " tstart (ISOT) : 2018-10-11T18:24:42.000\n", " tstart (MJD) : 58402.76715277778\n", " za_start : 0.0\n", "\n", "Num ints in file : 858112\n", " File shape : (858112, 1, 2464)\n", "--- Selection Info ---\n", "Data selection shape : (858112, 1, 2464)\n", "Minimum freq (MHz) : 1818.45703125\n", "Maximum freq (MHz) : 2720.80078125\n", "blimpy.io.base_reader WARNING Setting f_stop = 2720.800781, since f_stop not given or not valid.\n", "blimpy.io.base_reader WARNING Setting data limit > 1GB, please handle with care!\n", "extracting integration 0...\n" ], "name": "stdout" }, { "output_type": "display_data", "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "tags": [], "needs_background": "light" } } ] }, { "cell_type": "code", "metadata": { "id": "SRvl30hjCUE5", "colab_type": "code", "outputId": "ff402412-c766-4342-f6d0-5f6fa0a0e05f", "colab": { "base_uri": "https://localhost:8080/", "height": 35 } }, "source": [ "average_power = np.zeros((data.shape[2]))\n", "shifted_power = np.zeros((int(data.shape[2]/8)))\n", "x=[]\n", "spl_order = 2\n", "print(\"Fitting Spline\")\n", "data_adjust = np.zeros(data.shape)\n", "average_power = data.mean(axis=0)\n", "# Note the value 8 is the COARSE CHANNEL WIDTH\n", "# We adjust each coarse channel to correct the bandpass artifacts\n", "for i in range(0, data.shape[2], coarse_channel_width):\n", " average_channel = average_power[0,i:i+coarse_channel_width]\n", " x = np.arange(0,coarse_channel_width,1)\n", " knots = np.arange(0, coarse_channel_width, coarse_channel_width//spl_order+1)\n", "\n", " tck = interpolate.splrep(x, average_channel, s=knots[1:])\n", " xnew = np.arange(0, coarse_channel_width,1)\n", " ynew = interpolate.splev(xnew, tck, der=0)\n", " data_adjust[:,0,i:i+coarse_channel_width] = data[:,0,i:i+coarse_channel_width] - ynew\n" ], "execution_count": 0, "outputs": [ { "output_type": "stream", "text": [ "Fitting Spline\n" ], "name": "stdout" } ] }, { "cell_type": "code", "metadata": { "colab_type": "code", "outputId": "1e9d3a4c-09f1-46d0-de08-9754efdfe7d2", "id": "uOMf8cmvc33p", "colab": { "base_uri": "https://localhost:8080/", "height": 1000 } }, "source": [ "from copy import deepcopy\n", "small_data = data[:,0,:]\n", "data_base = data[:,0,:]\n", "sens =0.05\n", "DM_base = 6.4 \n", "candidates = 50\n", "fchan = obs.header['fch1']\n", "width = obs.header['foff']\n", "tsamp = obs.header['tsamp']\n", "# fchan = fchan+ width*small_data.shape[1]\n", "fchan = 7501.28173828125\n", "snrs = DM_can(small_data, data_base, sens, DM_base, candidates, fchan, abs(width),tsamp)\n", "plt.plot(snrs[:,0], snrs[:,1])\n", "plt.title('DM values vs SNR')\n", "plt.xlabel(\"DM values\")\n", "plt.ylabel(\"SNR of Dedispersion\")" ], "execution_count": 0, "outputs": [ { "output_type": "stream", "text": [ "Candidate 0\t SNR: 0.0332\t Largest Time Delay: 0.000472 seconds\t DM val:6.4pc/cm^3\n", "Candidate 1\t SNR: 0.0329\t Largest Time Delay: 0.000476 seconds\t DM val:6.45pc/cm^3\n", "Candidate 2\t SNR: 0.0324\t Largest Time Delay: 0.000479 seconds\t DM val:6.5pc/cm^3\n", "Candidate 3\t SNR: 0.0324\t Largest Time Delay: 0.000483 seconds\t DM val:6.550000000000001pc/cm^3\n", "Candidate 4\t SNR: 0.0334\t Largest Time Delay: 0.000487 seconds\t DM val:6.6000000000000005pc/cm^3\n", "Candidate 5\t SNR: 0.0341\t Largest Time Delay: 0.00049 seconds\t DM val:6.65pc/cm^3\n", "Candidate 6\t SNR: 0.0345\t Largest Time Delay: 0.000494 seconds\t DM val:6.7pc/cm^3\n", "Candidate 7\t SNR: 0.0351\t Largest Time Delay: 0.000498 seconds\t DM val:6.75pc/cm^3\n", "Candidate 8\t SNR: 0.0352\t Largest Time Delay: 0.000501 seconds\t DM val:6.800000000000001pc/cm^3\n", "Candidate 9\t SNR: 0.0351\t Largest Time Delay: 0.000505 seconds\t DM val:6.8500000000000005pc/cm^3\n", "Candidate 10\t SNR: 0.0349\t Largest Time Delay: 0.000509 seconds\t DM val:6.9pc/cm^3\n", "Candidate 11\t SNR: 0.0345\t Largest Time Delay: 0.000513 seconds\t DM val:6.95pc/cm^3\n", "Candidate 12\t SNR: 0.034\t Largest Time Delay: 0.000516 seconds\t DM val:7.0pc/cm^3\n", "Candidate 13\t SNR: 0.0335\t Largest Time Delay: 0.00052 seconds\t DM val:7.050000000000001pc/cm^3\n", "Candidate 14\t SNR: 0.0333\t Largest Time Delay: 0.000524 seconds\t DM val:7.1000000000000005pc/cm^3\n", "Candidate 15\t SNR: 0.0329\t Largest Time Delay: 0.000527 seconds\t DM val:7.15pc/cm^3\n", "Candidate 16\t SNR: 0.0328\t Largest Time Delay: 0.000531 seconds\t DM val:7.2pc/cm^3\n", "Candidate 17\t SNR: 0.0325\t Largest Time Delay: 0.000535 seconds\t DM val:7.25pc/cm^3\n", "Candidate 18\t SNR: 0.0324\t Largest Time Delay: 0.000538 seconds\t DM val:7.300000000000001pc/cm^3\n", "Candidate 19\t SNR: 0.0322\t Largest Time Delay: 0.000542 seconds\t DM val:7.3500000000000005pc/cm^3\n", "Candidate 20\t SNR: 0.0319\t Largest Time Delay: 0.000546 seconds\t DM val:7.4pc/cm^3\n", "Candidate 21\t SNR: 0.0319\t Largest Time Delay: 0.000549 seconds\t DM val:7.45pc/cm^3\n", "Candidate 22\t SNR: 0.0318\t Largest Time Delay: 0.000553 seconds\t DM val:7.5pc/cm^3\n", "Candidate 23\t SNR: 0.0319\t Largest Time Delay: 0.000557 seconds\t DM val:7.550000000000001pc/cm^3\n", "Candidate 24\t SNR: 0.032\t Largest Time Delay: 0.00056 seconds\t DM val:7.6000000000000005pc/cm^3\n", "Candidate 25\t SNR: 0.0321\t Largest Time Delay: 0.000564 seconds\t DM val:7.65pc/cm^3\n", "Candidate 26\t SNR: 0.0323\t Largest Time Delay: 0.000568 seconds\t DM val:7.7pc/cm^3\n", "Candidate 27\t SNR: 0.0322\t Largest Time Delay: 0.000571 seconds\t DM val:7.75pc/cm^3\n", "Candidate 28\t SNR: 0.032\t Largest Time Delay: 0.000575 seconds\t DM val:7.800000000000001pc/cm^3\n", "Candidate 29\t SNR: 0.032\t Largest Time Delay: 0.000579 seconds\t DM val:7.8500000000000005pc/cm^3\n", "Candidate 30\t SNR: 0.0321\t Largest Time Delay: 0.000583 seconds\t DM val:7.9pc/cm^3\n", "Candidate 31\t SNR: 0.032\t Largest Time Delay: 0.000586 seconds\t DM val:7.95pc/cm^3\n", "Candidate 32\t SNR: 0.0319\t Largest Time Delay: 0.00059 seconds\t DM val:8.0pc/cm^3\n", "Candidate 33\t SNR: 0.0319\t Largest Time Delay: 0.000594 seconds\t DM val:8.05pc/cm^3\n", "Candidate 34\t SNR: 0.0319\t Largest Time Delay: 0.000597 seconds\t DM val:8.100000000000001pc/cm^3\n", "Candidate 35\t SNR: 0.0319\t Largest Time Delay: 0.000601 seconds\t DM val:8.15pc/cm^3\n", "Candidate 36\t SNR: 0.0319\t Largest Time Delay: 0.000605 seconds\t DM val:8.200000000000001pc/cm^3\n", "Candidate 37\t SNR: 0.0319\t Largest Time Delay: 0.000608 seconds\t DM val:8.25pc/cm^3\n", "Candidate 38\t SNR: 0.0319\t Largest Time Delay: 0.000612 seconds\t DM val:8.3pc/cm^3\n", "Candidate 39\t SNR: 0.0319\t Largest Time Delay: 0.000616 seconds\t DM val:8.350000000000001pc/cm^3\n", "Candidate 40\t SNR: 0.032\t Largest Time Delay: 0.000619 seconds\t DM val:8.4pc/cm^3\n", "Candidate 41\t SNR: 0.0321\t Largest Time Delay: 0.000623 seconds\t DM val:8.450000000000001pc/cm^3\n", "Candidate 42\t SNR: 0.0323\t Largest Time Delay: 0.000627 seconds\t DM val:8.5pc/cm^3\n", "Candidate 43\t SNR: 0.0326\t Largest Time Delay: 0.00063 seconds\t DM val:8.55pc/cm^3\n", "Candidate 44\t SNR: 0.0327\t Largest Time Delay: 0.000634 seconds\t DM val:8.600000000000001pc/cm^3\n", "Candidate 45\t SNR: 0.0327\t Largest Time Delay: 0.000638 seconds\t DM val:8.65pc/cm^3\n", "Candidate 46\t SNR: 0.0327\t Largest Time Delay: 0.000642 seconds\t DM val:8.700000000000001pc/cm^3\n", "Candidate 47\t SNR: 0.0328\t Largest Time Delay: 0.000645 seconds\t DM val:8.75pc/cm^3\n", "Candidate 48\t SNR: 0.0328\t Largest Time Delay: 0.000649 seconds\t DM val:8.8pc/cm^3\n", "Candidate 49\t SNR: 0.0329\t Largest Time Delay: 0.000653 seconds\t DM val:8.850000000000001pc/cm^3\n" ], "name": "stdout" }, { "output_type": "execute_result", "data": { "text/plain": [ "Text(0, 0.5, 'SNR of Dedispersion')" ] }, "metadata": { "tags": [] }, "execution_count": 29 }, { "output_type": "display_data", "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "tags": [], "needs_background": "light" } } ] }, { "cell_type": "code", "metadata": { "colab_type": "code", "outputId": "b2a40bd2-e01c-45ea-f3ea-4005bb159e72", "id": "qsRDH0_kdAUu", "colab": { "base_uri": "https://localhost:8080/", "height": 35 } }, "source": [ "DM = snrs[np.argmax(snrs[:,1]),0]\n", "print(DM)\n", "fchan = fchan+ width*small_data.shape[1]\n", "data_adjust[:,0,:] = de_disperse(data_adjust[:,0,:], DM, fchan,abs(width),tsamp)" ], "execution_count": 0, "outputs": [ { "output_type": "stream", "text": [ "6.800000000000001\n" ], "name": "stdout" } ] }, { "cell_type": "code", "metadata": { "id": "p36XKAewFUds", "colab_type": "code", "outputId": "9156b137-5b93-4085-f0d5-739915be62fe", "colab": { "base_uri": "https://localhost:8080/", "height": 331 } }, "source": [ "# Preforming the fourier transform.\n", "%matplotlib inline\n", "import scipy.fftpack\n", "from scipy.fft import fft\n", "N = 60000\n", "T = 1.0 / 800.0\n", "x = np.linspace(0.0, N*T, N)\n", "y = data[:,0,:].mean(axis=1)\n", "yf = fft(y)\n", "\n", "xf = np.linspace(0.0, 1.0/(2.0*T), N//2)\n", "\n", "# Magintude of the fourier transform\n", "# Between 0.00035 and 3.5 seconds\n", "# We set this to a limit of 200 because \n", "# The total tchan is only 279\n", "mag = np.abs(yf[:200])\n", "candidates = top(mag, top=15)\n", "plt.plot(2.0/N * mag[1:])\n", "plt.grid()\n", "plt.title('Fourier Transform of Signal')\n", "plt.xlabel(\"Periods\")\n", "plt.ylabel(\"Magnitude of Fourier Transform\")\n", "plt.show()\n", "\n", "print(\"Signal To Noise Ratio for the Fourier Transform is: \"+str(SNR(mag)))\n", "print(\"Most likely Candidates are: \"+str(candidates))\n" ], "execution_count": 0, "outputs": [ { "output_type": "display_data", "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "tags": [], "needs_background": "light" } }, { "output_type": "stream", "text": [ "Signal To Noise Ratio for the Fourier Transform is: 1.2053418739341748\n", "Most likely Candidates are: [19, 37, 6, 144, 126, 108, 55, 5, 162, 73, 7, 90, 180, 72, 198]\n" ], "name": "stdout" } ] }, { "cell_type": "markdown", "metadata": { "id": "IO0nEu5SFfTB", "colab_type": "text" }, "source": [ "# NOTICE\n", "Notice how the signal to noise ratio is a lot smaller, It's smaller by 2 orders of magnitude (100x) than the original pulsar fold. Typically with a SNR of 1, it isn't considered a signal of interest as it's most likely just noise.\n" ] }, { "cell_type": "code", "metadata": { "colab_type": "code", "outputId": "e52b0780-0f21-4267-a44e-47c8fb54e00b", "id": "eCg0o_xRHG4I", "colab": { "base_uri": "https://localhost:8080/", "height": 35 } }, "source": [ "# Lets take an example of such a period!\n", "# The 0th candidate is the top ranked candidate by the FFT\n", "can_snr =[]\n", "for k in range(len(candidates)):\n", " period = candidates[k]\n", " fold = np.zeros((period, data.shape[2]))\n", " multiples = int(data.data.shape[0]/period)\n", " results = np.zeros((period))\n", "\n", " for i in range(multiples-1):\n", " fold[:,:]=data[i*period:(i+1)*period,0,:]+ fold\n", "\n", " results = fold.mean(axis=1)\n", " results = results - results.min()\n", " results = results / results.max()\n", " can_snr.append(SNR(results))\n", " \n", "\n", "print(\"Max SNR of Fold Candidates: \"+ str(max(can_snr)))" ], "execution_count": 0, "outputs": [ { "output_type": "stream", "text": [ "Max SNR of Fold Candidates: 0.9867689288276708\n" ], "name": "stdout" } ] }, { "cell_type": "markdown", "metadata": { "id": "Wt3pIpmGwFhg", "colab_type": "text" }, "source": [ "# Result\n", "\n", "It is fair to conclude that given this observation of `HIP65960` this target is most likely not a pulsar as the SNR of the FFT and the folding is not high enough to suggest otherwise. \n", "\n", "# Any Questions?\n", "\n", "Feel free to reach out with any questions about this notebook: [email protected]\n" ] } ] }
gpl-3.0
regisDe/compagnons
Calcul symbolique.ipynb
2
194588
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Calcul symbolique en Python\n" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": true }, "outputs": [], "source": [ "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Introduction\n", "\n", "Ce notebook est la traduction française du cours sur SymPy disponible entre autre sur [Wakari](www.wakari.io) avec quelques modifications et compléments notamment pour la résolution d'équations différentielles. Il a pour but de permettre aux étudiants de différents niveaux d'expérimenter des notions mathématiques en leur fournissant une base de code qu'ils peuvent modifier.\n", "\n", "[SymPy](http://sympy.org/en/index.html) - est un module Python qui peut être utilisé dans un programme Python ou dans une session IPython. Il fournit de puissantes fonctionnalités de calcul symbolique.\n", "\n", "Pour commencer à utiliser SymPy dans un programme ou un notebook Python, importer le module `sympy`:" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from sympy import *" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Pour obtenir des sorties mathématiques formatées $\\LaTeX$ :" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from sympy import init_printing\n", "init_printing(use_latex=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Variables symboliques\n", "\n", "Dans SymPy on a besoin de créer des symboles pour les variables qu'on souhaite employer. Pour cela on utilise la class `Symbol`:" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [], "source": [ "x = Symbol('x')" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": [ "iVBORw0KGgoAAAANSUhEUgAAAEgAAAAbBAMAAAAt2dQtAAAAMFBMVEX///8AAAAAAAAAAAAAAAAA\n", "AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAv3aB7AAAAD3RSTlMAMmYiu80QdonvRN2Z\n", "VKvu110NAAABZElEQVQoFY2Sv0vDQBTHv0na/CBE+w+oWcQ1qJOTKDgJdomDWAiIdBPFQSctguBQ\n", "pKM4qZPgYDdxEIqzQzcHcXARR0HI4BLf5d21HJamb7j3vt/78O5dckBRNFeTIgRuNFYthPzI+CmE\n", "vCvrtxACgnQEyN8dAWoSY4S0DA6zQr7ZoWV+MJC7H7Te4hpYHwKV6aDa+wqchgZtaYo2vSz7hhdp\n", "9qum7BbLO82FDqHGu+KC9aOFQ/kxJORkFB1A7FIsAXbDfPCrLCV0NrE/M0fONLtPgJu4aSBHY8jo\n", "utGe2H9j6JI6obzIAr2ZSvgU1hT7BAHjPNFpHD/G8aZwJtEWSUJ0HNUVJCL3Oz0j//fy7jR4kBzD\n", "SXJGHWenyF+RHPwcuO+uYZYZBTltXAjnhW1quHNQPwlZKagU4ks4G2z78u46JJXV4iJ/LtKjdNMv\n", "qXL51sCyZutiW8lhj67XwAoV/i+bct4/Xs5GamR386YAAAAASUVORK5CYII=\n" ], "text/latex": [ "$$\\left(x + \\pi\\right)^{2}$$" ], "text/plain": [ " 2\n", "(x + π) " ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "(pi + x)**2" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# manière alternative de définir plusieurs symboles en une seule instruction\n", "a, b, c = symbols(\"a, b, c\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "On peut ajouter des contraintes sur les symboles lors de leur création :" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [], "source": [ "x = Symbol('x', real=True)" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "False" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "x.is_imaginary" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [], "source": [ "x = Symbol('x', positive=True)" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": [ "iVBORw0KGgoAAAANSUhEUgAAACoAAAAPBAMAAABgjEDtAAAAMFBMVEX///8AAAAAAAAAAAAAAAAA\n", "AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAv3aB7AAAAD3RSTlMA782r3SJ2ZjIQmUS7\n", "VIlAnjihAAAAv0lEQVQYGWNg/GQs72z0hQEV8Acw5BcwNKIKMsxiAIkyo4mGg0XZJqAKR4BFOUCi\n", "0Q2c3QFwWaAJ3Iq5j0LXH+A9n8DAuvwxWAooysC4dn4B0wEG/gSGLRO4JUEaQKJMDgwMPGBROQYG\n", "MaAgRNQAKsrxq7zcHC66ACrK/hckBARgExbA1H4DiyFEmQ8wxCcwODEwTIOpZQGqZXdgSLRk6C9g\n", "WMnAwL1GXmYF9/k/FxgY3qWFiEzgtMqbADUEjQIAA4c14y2+TEQAAAAASUVORK5CYII=\n" ], "text/latex": [ "$$\\mathrm{True}$$" ], "text/plain": [ "True" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "x > 0" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Nombres complexes\n", "\n", "L'unité imaginaire est notée `I` dans Sympy. " ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": [ "iVBORw0KGgoAAAANSUhEUgAAACoAAAAQBAMAAACSDPCjAAAALVBMVEX///8AAAAAAAAAAAAAAAAA\n", "AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAADAOrOgAAAADnRSTlMAzRAiu5mrdu/dZlSJRDIL\n", "YZgAAAB2SURBVBgZY2BgEGJAA2wODAwmj9AEGdgWMKiEwUSZDJCk2WGi3AWki+qEA/VgmHDgBhZR\n", "5g2BWESZGJ7DRXkEBSUbBQUDgALcIEdhmMuYoIBFVG/DASyi95gXMLBmvMwCmgMEML+xlEP4EBIm\n", "iizGwMC7AM4HAA07Hr1Hv8R3AAAAAElFTkSuQmCC\n" ], "text/latex": [ "$$1 + i$$" ], "text/plain": [ "1 + ⅈ" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "1+1*I" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": [ "iVBORw0KGgoAAAANSUhEUgAAABgAAAAPBAMAAAAMihLoAAAAJ1BMVEX///8AAAAAAAAAAAAAAAAA\n", "AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAilU6eAAAADHRSTlMAEM3dMiK7mat272a9noTYAAAALklE\n", "QVQIHWNggANFOIuBIewggiNWgcRh4CSTI2QMAioMZBsAcxB71+FuGBtMAwDSnw2AS5zPhQAAAABJ\n", "RU5ErkJggg==\n" ], "text/latex": [ "$$-1$$" ], "text/plain": [ "-1" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "I**2" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": [ "iVBORw0KGgoAAAANSUhEUgAAAEwAAAAbBAMAAAAkMnRXAAAAMFBMVEX///8AAAAAAAAAAAAAAAAA\n", "AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAv3aB7AAAAD3RSTlMAMmYiu80QdonvRN2Z\n", "VKvu110NAAABaUlEQVQ4EWNgIAx6fBIIK2LgMOALIEIZtwHjZyKUcS5g/kWEMgYG3m9EKeMuIEpZ\n", "D1AVowKQwAWYBIAyTAeAhAkQPwFiTMCawMDwECi8gmEhA0MYkPEIUw0DA7M/UBkr0LrYu24M7A3o\n", "SpIhAmw+74HKgNKc//9/YOA0QFd2HSZQD1TGNgHCWw0ThNMoyhhiIeJAz7L1FDCwlT9e3g4RQVUG\n", "CgwgcGJg4OCawJDBoF2wFSKCqkwVIriXgcGWJ4ChjaE/4TI2ZbchgrMYGBL4CxgSGEIgfAYGVNPk\n", "4coY7icA2d/B/NbQ0J2hoTFgNsinDFBlQEsZ4tiAgfiRAUiCAKpp0JAAeoHtLwfDNfYPDGXYlEG9\n", "0MfAwPXBiOsvywdmBWzKzkAEQYa6F7A9L6tQggjALT03f58CA0MURJQbd2SBFDBDIwucmCA6oOQi\n", "ZB5HAZTnjCyKwU6FiYCSJW4ANwTmQaxKmaAuBwDYz1HXgocl8wAAAABJRU5ErkJggg==\n" ], "text/latex": [ "$$\\left(i x + 1\\right)^{2}$$" ], "text/plain": [ " 2\n", "(ⅈ⋅x + 1) " ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "(1 + x * I)**2" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Nombres rationnels\n", "\n", "Il y a trois types numériques différents dans SymPy : `Real` (réel), `Rational` (rationnel), `Integer` (entier) : " ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false }, "outputs": [], "source": [ "r1 = Rational(4,5)\n", "r2 = Rational(5,4)" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": [ "iVBORw0KGgoAAAANSUhEUgAAAAsAAAAqBAMAAACXcryGAAAAMFBMVEX///8AAAAAAAAAAAAAAAAA\n", "AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAv3aB7AAAAD3RSTlMAMpndu3bvImbNiRBU\n", "q0Qb3U6NAAAAkklEQVQYGWNgYGAQAmIgMAGTrClgqmIKmFoApjgKwNRWBjC1AEzxCICpXQxg6uzd\n", "u9+ugnVAtDN8AXOW/L8Bpski/oPAB6K0Tt0g9ACoMP//V5DyWVcLQNQFEAGnbrQqgnjeDPUPQDQD\n", "iwGYYvrOwMA7gYHrHwMD2wQGpt8MDEwMDMwTGBjYFRjaQMYUrdVmYAAAOF8pKUDr98cAAAAASUVO\n", "RK5CYII=\n" ], "text/latex": [ "$$\\frac{4}{5}$$" ], "text/plain": [ "4/5" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "r1" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": [ "iVBORw0KGgoAAAANSUhEUgAAABUAAAAqBAMAAACuFQ3dAAAAMFBMVEX///8AAAAAAAAAAAAAAAAA\n", "AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAv3aB7AAAAD3RSTlMAMpndu3bvImbNiRBU\n", "q0Qb3U6NAAAA6klEQVQYGWNgYGAQAmLODUACCEwYGNj9IWzWFAZup/sQdsUUoFw8hL0AweYoQLC3\n", "MiDYCxBsHgEEexcDgn327t1vV+FmMqQgzGf4gmAv+X+DQTfftgEoQmPwHw4+UGZTkYojA8OmpYpA\n", "U7gvMKx/wDCHYXMBAwPvBga2BI4EBq4JDAxsBgy8H1kuMPD8Bgb8byCb/wID3x+wtcy/9ysw8H0F\n", "s98vqF/AwPgLzDZhgLOZHBj2L4CqiWVg4Fdg4AHp5VFgKGd2YOAAmgmK0XDWBAYmoF3sVqt0DRhs\n", "GLYJAO0C+tGAYVfoRQYGAGg1Ulux6DimAAAAAElFTkSuQmCC\n" ], "text/latex": [ "$$\\frac{41}{20}$$" ], "text/plain": [ "41\n", "──\n", "20" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "r1+r2" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": [ "iVBORw0KGgoAAAANSUhEUgAAABUAAAAqBAMAAACuFQ3dAAAAMFBMVEX///8AAAAAAAAAAAAAAAAA\n", "AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAv3aB7AAAAD3RSTlMAzRAiu5mrdu/dZoky\n", "RFRd1xrCAAABAklEQVQYGWNgYGBUYGBg2F19AEgy+wPZihdYBRgYmFzzgWxpBhYHoDhDvQID12cQ\n", "AwiAbJYGIA0CQDa/zPZYGFtfioHjAVRc/xMDaxOUzT+BgesvlM0hwMD1C8pmaUCI8wHVN0DFGbwZ\n", "NB4A2WH90xYwcIfmgIRpDP7DwQfKbDIN8WFgEFc4egEYhgkM+y8w9P//DjSRTYGBo4FBOt0AyOaY\n", "wMD2hyEBbA/jJwQbKMD5iSFreQxYhuH+BgYnBnugOUAwBUSwTACRrA5gEhwdZUCDBRi4QWHLF8Bg\n", "DAxb1k9A9lEGhmJWoGECwCidvTtsAnMAwwqgxYxAP05gMN0WxMAAAJ3jRrgsItfHAAAAAElFTkSu\n", "QmCC\n" ], "text/latex": [ "$$\\frac{16}{25}$$" ], "text/plain": [ "16\n", "──\n", "25" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "r1/r2" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Evaluation numérique\n", "\n", "SymPy permet une précision arbitraire des évaluations numériques et fournit des expressions pour quelques constantes comme : `pi`, `E`, `oo` pour l'infini.\n", "\n", "Pour évaluer numériquement une expression nous utilisons la fonction `evalf` (ou `N`). Elle prend un argument `n` qui spécifie le nombre de chiffres significatifs." ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": [ "iVBORw0KGgoAAAANSUhEUgAAAgsAAAAPBAMAAACYf5HCAAAAMFBMVEX///8AAAAAAAAAAAAAAAAA\n", "AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAv3aB7AAAAD3RSTlMAIom7VJlmdt1E7xDN\n", "MqsI8sYEAAAGp0lEQVRIDe2X7YtUdRTHvzO7M7vOnZmdCiMqaJnYjEh3ygIryiGod7Wb4osedG9B\n", "ChbtFEhQllvSu9B5E0EEKkWU9nApoqhol6IipBwEoReRA6VGD65P+bSb0/d8z2/GW39DF7x77jm/\n", "8znfc+69v7kCF1ZvgI5iYn/28Z9ZxadL9yJasaGCzKqlLTqrT7TwTbKviUz1ynoIPznSBD5cdA9K\n", "W1ZUq7I87DlCf/TEvV1udtHiQDOLB4tpjZCR0WRh4cpb08iuFsqwCIQM3NI4ObvhUl2G9MNKuH7T\n", "69pMqpf44UC1Oiyut5sZx1hMEjLLE/uzOViFTqeBT+LMDnyO7Bngs2ZuBu91TlEJ+k9C4RzwC6I3\n", "cGkl3+l0zsnysHIcPY5y3bnRt3jAabLk9DVCiiYramO0mUKqWLEePQdFIGTgDtaAi+bgUpXjnaiE\n", "9DvSVKYETlJwW1xfPnACQ1NUFN04a2MYeC1YxSWPA78BP+N3YCfwBfpq+OJQBdhUx2kofCdwOcrb\n", "Ua7l6G/L8rByhO6PkZkRF8VhLHWaLDm9vJCiySonKGxPIVVsPXAtFBEycPFUDR+s4RgkVTneiUrI\n", "6UhTmRLIp6LsTfjyBW9hmjJ5bLIxXPxlsMrmmgdGkzeB2Vb+qF237fRKM5ong8eVwMcYmkF2Lgtk\n", "W7I8rByhszVEU86d5KygiCw5vbyQoskqTKH8dwqpYrcBY4lHDBm4mUdrHCfHIKnK8U5UQk4hpS0l\n", "cBhY7E2ExkmPKS9cDafHEB2nt7kn4Rj6ttuStp1gL4WUbbsFV2G6hgG+NHi5ZzGsHDrH4gXHk2wM\n", "GPdmOjwiy52hPHNEE7w4xzFwbRepYueAbXVFhAzcbNHH4FKV451Yia6TcKnsSuU1sKDxnzF8RScP\n", "exryld4Y1m5o4W0+DTFDW5Oh7xZxpzu4brWtLPK9tHC581CMAp8Gw9Z6FsM8thIHomdPXhW483s3\n", "ND3ilhezNY40miyeB3mDe0grFv3FMTQ8IqROeDCMoSe1xiXWiUoE/RQklV2pEtjH18Ka8OUoXT1O\n", "M1x9hO4Y+pLMGYzy7jU4uGOY/hqFOm7BFU1g7wuAwpiYT5A9giI3Tt7yYFlYOY7OdXZzbyc3mo/x\n", "uiLBUjEvrxyjCc5F24ZTSCuWOQbsp9MiQuoUNbpjCFLtybMxeAl3Glza/iWQN8eb8N0AuKbFVB/K\n", "cG8MdCxDsdE/GvPW1DA9h9wu+vqmeMq+wxPD+dV73gK+we18GqbpccvDtoML/fCPZ5swbtRJcB8b\n", "HawFy4t5eSKdFuCbmZxCYllvDIwIqVMW3TEEqZajMahYcJpeaUsJjI7YSjYRngagYP3pqr+VHsNE\n", "BeueGa0DK2G/JnnbAXLaKneyG0xUHkP+bIL8k3tPABvpcYs/LAwzh0dhV76NyV3OPQ1MNhWRFYqF\n", "8jsTp3l2rsbkNHLiMj4N9lIwIqROuKs3hiDVcnR7vZjrN0HSlhI4MGMr2YQvz1TQxy548FF6H70x\n", "/MFtgZoxm2CgzX5mkD9ZnrFPiAuA5XWFOb79dfAd44tst0+Wwhho8/vD0IUEpRPOfZtjiBWRJaeX\n", "V45ono1nwaOHVDHuDWN1WERInaLh82OQVM9hJ7ZXsJg5A1Iq7eTX9rSK62MYmkOf7XC6uvvw4bOH\n", "3E/MBGn4HriDDfGXIn+mMIPcHPi4LY8t/OkR9h1zTbGN6Bz/ylJYOULbU3rQudy8+TQYTZacXt5y\n", "HhHNs/vHKfY8Ulo4lMkEFhFSp/7Dh2ffbegHk2Uo1XM2UbgXM6cjpS0lcLqtUU1wqS0v1DB4lAy/\n", "Al4L1jjwItZXSkdRGsdApZ97w/YcX+wZW7G1ovAO7hWV/A7sr6DEm+WWwsoRulABriOT7jHuDU6T\n", "JaeXV45osuwj+/EUUsWeAr9BFRGyyyVAY5BU5XgnKiGnkNKWEoj9hIrry/Mxxhr8HuqOgZ8KGs9C\n", "62pj8lmMS6ojvwLX45M6v3zXV7AW/af4yczwqwnWYWB39DV/7TkGtxRWjtALdiPfIJPcvuHodafJ\n", "klNrHCmasjPPV9dMpZAqNliP3oEiQna5Q2EMkqoc168ScgopbSmBGOMMxA2Nv7TqJmALsOa9LTE/\n", "DDsH3cqsOMAfQ4vt6XS432RX/cm8Jffzp3DVSv4XyMKlAyNNYISXyNhPoSyFPUfon1bwg0NcjKxp\n", "BZpZ7tQa5Ygmq8gP/qkUUsWiRc+24BEhnZtdfi7O/Xb6oEv1HO/ESki/kK7tvEBcWqdoayI0Tjn/\n", "H/8ADtF52HEjSsAAAAAASUVORK5CYII=\n" ], "text/latex": [ "$$3.1415926535897932384626433832795028841971693993751$$" ], "text/plain": [ "3.1415926535897932384626433832795028841971693993751" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pi.evalf(n=50)" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": [ "iVBORw0KGgoAAAANSUhEUgAAAC0AAAAPBAMAAACCUFuUAAAAMFBMVEX///8AAAAAAAAAAAAAAAAA\n", "AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAv3aB7AAAAD3RSTlMAIpm7MhCriUTv3c12\n", "VGZoascqAAAA90lEQVQYGXXQsUtCURTH8a+pL5GHCE7WohmtBtHu3OIDwaHFh9CgYIoFb4v2hmgI\n", "qn8iF2f9Axwe/QHqZEtgS5Q63M65z8bOcA987u8cuJfYwUkFqbNmEPRgCMlJ/xbq7P6oL40xPrkv\n", "2IEOXMCzehEcakfiU9iHF+hWxGXHNbjiV3AOIy9ySI0jvz/lRoLw6tkWl1PzjnmfK6S+9cSG1Clv\n", "bDB9bDn5KU09czh6UilZxh1sfUFmJQOuH7kd0/wjVEN4I5bVm4bei+s+Z06igGu9WoicB4hn2Qv6\n", "bRoDmP35nceHvMuYNWkf8qF8TWt9SaKp//ZP/QJXekHDkZOwTQAAAABJRU5ErkJggg==\n" ], "text/latex": [ "$$2.718$$" ], "text/plain": [ "2.718" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "E.evalf(n=4)" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "collapsed": false }, "outputs": [], "source": [ "y = (x + pi)**2" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": [ "iVBORw0KGgoAAAANSUhEUgAAAHUAAAAbBAMAAACw1N2lAAAAMFBMVEX///8AAAAAAAAAAAAAAAAA\n", "AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAv3aB7AAAAD3RSTlMAMmYiu80QdonvRN2Z\n", "VKvu110NAAACK0lEQVQ4EZ2TP2gUURDGv7vL7q17WXONkC6rwS6QQ9MYAi5qY3XXnIU2rxCCjV5n\n", "BDHrH0iKIAhJYUDdViwUUqSJuEUUAhJWsBVFsLCJErhYBDxn5u3bS2QjcV/x5pt532/f7LALFFvO\n", "+rtiIFH38a0wu4KRRlF4GWejoizwWRVnV4ujTkxsyaftf9ZxMY/xPpHHVeYfJLp+iUMppo2VM3cX\n", "R+sk3NClIIeU7VtT8LalMLHDoRbQHaTsy3gLtKhy7NNHhWpIKlt2Q8vZBLusrm4KeyvQygpxAZih\n", "g2e9HnAkdWvG6+j4PLK7oqrMVk4GAKtxMV/THuCFERINC9OzsI6VshfFZCUps0Bx+s7kjL6wz1qx\n", "GIQdNWx3+HZEA9NHwDmaQFherbXEmrHDa5JLp3YnZe2ujydAWXuB1/Qc5ex4jf0snEdS4HsdGLan\n", "sKHgvdLPxTLdK2d/sXisuMLsB8Py7McjlH6KF8wCQx3enXb7ylK7HZM8DTQTrhFrhxm7RKzfZ6ln\n", "YKQOxRHmfam5ps8FYr2trR9PO9IBzZnudU3PNCtPzaKq2Jmxi8CbBhe4Z/oIAq3ov9vYM6uHwMuk\n", "iVNs6bMn4P3CjZZhhwKtBkKb5jwYs5UWfRvXb07f8yXJ7nXfr0eofcHg992vNIjmb1+rhc06YEXa\n", "jJq0liYZa/LceMZUy7FRHF1/b3aAnsvq5zN1SGHTGNKV+++bw7w40H/Lip9n+Edt1Jz9AbLJiWXE\n", "SsPWAAAAAElFTkSuQmCC\n" ], "text/latex": [ "$$\\left(x + 3.1416\\right)^{2}$$" ], "text/plain": [ " 2\n", "(x + 3.1416) " ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "N(y, 5) # raccourci pour evalf" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Quand on évalue des expressions algébriques on souhaite souvent substituer un symbole par une valeur numérique. Dans SymPy cela s'effectue par la fonction `subs` :" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": [ "iVBORw0KGgoAAAANSUhEUgAAAFYAAAAbBAMAAAAUvmV2AAAAMFBMVEX///8AAAAAAAAAAAAAAAAA\n", "AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAv3aB7AAAAD3RSTlMAMmYiu80QdonvRN2Z\n", "VKvu110NAAABj0lEQVQ4EY2SsUvDQBTGvza2TUis/QMUs4i4FXEUlAqdCnaJilgIiLMFR4dWxK2U\n", "zi6NToKLq4OlLk7i2kGE6uKoICqIEN/l7kKvir0Hvbvve7+83j0eoBmNkq9Jwsxny7qsnU+86bJW\n", "YHzpssD4hz5rV/XZBqEJl5Z/I5mjdLJLywL9Uj4twLo/GUSHoeWR9BlOgDXAWPWjbDv8HKK4TNG/\n", "Vx6KyNSRLr1wduM5L9ht5RtirDB8hcXSNc72Y6IXn9gh3eLynG0jWFQ4y1oh2afZa25C1q2FYfgN\n", "RBBQGGCLWAo4LFjzoDe/R5ec4e7VAAuMXSjsFA4zOXLuuXussI4YJnkHLGd9AqZ/sTShJhumI8+7\n", "9Lwtlk/0bbYJduAOVhmOGCZZ13EnfGKjZilvcwC7zMog7sOuPxGQFG9rshT1N/0Oo4s5ejQLWXcf\n", "Vp3kLfN4+bt2x0UHWGzecDNmC8jWydrkti0qCUhssi6XRovv0VyqIKlTxTGrQq4o9p9iR7ps1kdE\n", "XM5wR5BIiif9AI4FWSBAw5hOAAAAAElFTkSuQmCC\n" ], "text/latex": [ "$$\\left(1.5 + \\pi\\right)^{2}$$" ], "text/plain": [ " 2\n", "(1.5 + π) " ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "y.subs(x, 1.5)" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": [ "iVBORw0KGgoAAAANSUhEUgAAAKAAAAAPBAMAAACRq9klAAAAMFBMVEX///8AAAAAAAAAAAAAAAAA\n", "AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAv3aB7AAAAD3RSTlMAIpm7MhCriUTv3c12\n", "VGZoascqAAAC8klEQVQ4Ea2UTWhcVRiGn5vJnd9kMqYLqV10TGIS6SKxacEWJEMWDegiFwpdFCVD\n", "oIGqTYZEMlikGQouxEVbaiSpSAYFXbhwEIIuCpkW2lKodChdN1cXFTEkDZqa/+t7zh3rolsvc947\n", "57zfeeZ83zlncNr6csCCGqRuNXbr1Zity4vFN+DtOxprbT8CTnGyBrGPPjChXt257g2UcO9NlHhz\n", "pFgscIrYBuxbVwwkgqCqV7I3FCfLkk+WaM3NM12ileY93J84JHa/gMZhPtiBBjjHahAEec7AHCc7\n", "Q2Bq6oZQ3DVASdMuLeVmH6cS9Uhc5n6NLVIF+nAPj3tYhx/fz8AjOMDLEIVrMJ6jKQSqr8f5Q0Aj\n", "8VmGK7Fe3HKiTPQvPim526xqvp77HtYhb3qTcJoCqBiL3nPAWEpAKyjl+J9ezE+tGyAm5WMGYIHW\n", "CYGXjnJBg/Gqsfjee7bClZs5+MUArcAPSmDPxJI0SaSybA/eLOmbVhg6Y8sdSjX4zddgRE3Yv/kX\n", "GPGcDdyqgFZoPJ9VwYMFE3apAINf4W77fKmuAVrnKAdL0LOtvl1luKf1GmrsODEEtKLuhzl+fWtT\n", "M/hajdhnbuDxqiYbYN2JlEl3LM6C+8TE0K72H7An89AArchIzKTzrM5oNb0mljlvC1ZLFlh3aFjj\n", "MelNFa5iQpryRsJdfg+mPy4I6BrByRDZTXg07sJtRb0A/bUvBPQt0DrRijnI+sWhWnh+eWDm1YEK\n", "7nlndHT885+NVFvWiewNizRGc1Y4Zdvva5frK7ROokLDusk16jOcV6yuWdMzYBa+01jCpCfRJ7mW\n", "yMBrDMANrupEZJZUQ9mqoXV0R5IVrmiHMwxpPi8VJ94NUx6u6G6lZdFigJK0z1I1vkC66nxb7Cyz\n", "QvMOkYJb3+XQyfJ7hk89lnVoDXAxCLZoOLs1RjKP0zXiaSv7n/qhXGx7HU50dZPSNS0Tb2svwURn\n", "Djrnv/GtQ+vUK8pzRH8O7K8J+H8//wDSxfhrRpRYbwAAAABJRU5ErkJggg==\n" ], "text/latex": [ "$$21.5443823618587$$" ], "text/plain": [ "21.5443823618587" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "N(y.subs(x, 1.5))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "La fonction `subs` permet de substituer aussi des symboles et des expressions :" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": [ "iVBORw0KGgoAAAANSUhEUgAAAFIAAAAbBAMAAAAdVcUMAAAAMFBMVEX///8AAAAAAAAAAAAAAAAA\n", "AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAv3aB7AAAAD3RSTlMAMmYiu80QdonvRN2Z\n", "VKvu110NAAABnUlEQVQ4EY2Su0vDUBTGvyRtkr7Ef0DNIq4B3QQpviYHlzoopV1cbYqbU906FBFB\n", "8LHoprh0clODOHborqIuWnCwIBTRIZ6bm3vTapUc6Hl855dze5ILRLLaQjESB9MeWIxGpmzlPRqZ\n", "ONI+o5FAphOVTDlRyRqBikXuH1MHqam65Cb6UVpjlgG+PZE/wTGwFAg9YQ2mXDZO5+bv52FUuhHd\n", "5tUzsC90IhKe10Yi6HE94/B4CLyJ4/Vtrp2JR/0oyEIxJJHnCFtfa5XPeSVIqq6KhkfmAgwhm6bf\n", "qR1r+wVCMvmB6tD62Djpo7x3Aeg7SGd/kqms0jTtMpNvee8ASHcgPlY484baMdA7AEYkmciixBY1\n", "c7mV3VzOZQ3jkdww6uQFSaeXKnhAkWnh/7yDZuMa/g0KXg9tVGpiLmP3kIoLw9Y78D9UsNEWEHfU\n", "S9MH5czJau0VRh17TG7wHo3WXzanNnrJgud9IWahxeRl3kvxY3khZwalH7RtXvk3TzaSlkxlYjpB\n", "OiOlP5JVofe9yaLJohylWd3y71wNFvkGI01Xm4GGipAAAAAASUVORK5CYII=\n" ], "text/latex": [ "$$\\left(a + 2 \\pi\\right)^{2}$$" ], "text/plain": [ " 2\n", "(a + 2⋅π) " ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ "y.subs(x, a+pi)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "On peut aussi combiner l'évolution d'expressions avec les tableaux de NumPy (pour tracer une fonction par ex) :" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import numpy" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "0.0" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "x_vec = numpy.arange(0, 10, 0.1)\n", "y_vec = numpy.array([N(((x + pi)**2).subs(x, xx)) for xx in x_vec])" ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import matplotlib.pyplot as plt" ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "data": { "image/png": [ "iVBORw0KGgoAAAANSUhEUgAAAXcAAAEACAYAAABI5zaHAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", "AAALEgAACxIB0t1+/AAAHR5JREFUeJzt3XmcXFWd/vHPwy4yQwbRsAVBDUgUGBAiIJgWESMii8Pm\n", "CoiiMrI5Kokb0RlRdEQdBX+iJD+YkWgGEUFBE5DWoCKIbBLC4piRoLSC4AoS5Jk/zgWKJnSnqqvr\n", "VlU/79erX9S9fe+tb/pFP33q3HPPkW0iIqK/rFZ3ARER0X4J94iIPpRwj4joQwn3iIg+lHCPiOhD\n", "CfeIiD40YrhLmitpSNKNDfumS7pK0rWSrpa0c8P3Zku6TdJSSXuPZ+EREfHkRmu5zwNmDtv3ceAD\n", "tncAPlhtI2kacCgwrTrnDEn5ZBARUYMRw9f2YuDeYbt/DaxfvZ4E3Fm93h+Yb3uF7WXA7cD09pUa\n", "ERGrao0WzpkFXCHp3yl/HHat9m8CXNlw3HJg07GVFxERrWil2+Qs4DjbmwMnAnNHODZzG0RE1KCV\n", "lvt023tVr88DvlS9vhOY0nDcZjzWZfMoSQn8iIgW2NaqHttKuN8uaYbt7wF7ArdW+y8EzpV0GqU7\n", "Zipw1VgL7GeS5tieU3cd3SA/i8fkZ/GY/Cwe02zDeMRwlzQfmAFsKOkOyuiYo4HTJa0N3F9tY3uJ\n", "pAXAEuAh4BhnysmIiFqMGO62X/Mk33rhkxx/CnDKWIuKiIixyTj0eg3WXUAXGay7gC4yWHcBXWSw\n", "7gJ6lTrdcyLJ6XOPiGhOs9mZlntERB9KuEdE9KGEe0REH0q4R0T0oYR7REQfSrhHRHQhibdLzGj1\n", 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"6ShgGXDIiNfIQ0wREf0ny+xFRPShhHtERB9KuEdE9KGEe0REH0q4R0T0oYR7REQfSrhHRPShhHtE\n", "RB/6Px9wcKZEVTZeAAAAAElFTkSuQmCC\n" ], "text/plain": [ "<matplotlib.figure.Figure at 0x12ab3f60>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots()\n", "ax.plot(x_vec, y_vec);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Manipulations algébriques\n", "\n", "Une des principales utilisations d'un système de calcul symbolique est d'effectuer des manipulations algébriques d'expression. Il est possible de développer un produit ou bien de factoriser une expression. Les fonctions pour réaliser ces opérations de bases figurent dans les exemples des sections suivantes." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Développer et factoriser\n", "\n", "Les premiers pas dans la manipulation algébrique" ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": [ "iVBORw0KGgoAAAANSUhEUgAAAMEAAAAVBAMAAAANw5eWAAAAMFBMVEX///8AAAAAAAAAAAAAAAAA\n", "AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAv3aB7AAAAD3RSTlMAMmYiu80QdonvRN2Z\n", "VKvu110NAAACwElEQVRIDbWVu4sTURTGvzxm8py4WFkIxhUrLYJuo2wR9Q9ICsfCBwyorDYSEHZT\n", "7QbxsYW4EbXQKtiIWGxARQXFFFsuS0DQwmbVQuyiYCwWiefcRzJnnbiVt7j3nnO+8/vmztwQ4P+P\n", "WHGcx25T2D5OEJ/QlVhxnEIhpqKrJ4CCBiTKUYrplSPAJ12JRCQWr3Y1glARY6pPyaoqOEFE3V3H\n", "UguOrkQipuH9UIhUI9zvlnR0bo0d6irQs1Wd1ZtCgEwTulkiYBQLXWwoRMYwdaNXM6QUO1xUwR2T\n", "0ssHvWTaKHyH2+RIImAUj1vuT4V4ojvMLB2cLqWzbaEw/U6fHXCGaxJhHaDeEiFukmRm/nBdP7x0\n", "SHeoyJM79+XRDdrTMA60y9ExuXsTYqRwOqr7KPU34i9yVdZCOsQ5mSwCF7Cv9pzrYYflBrCXUxIx\n", "dNjxmmqEeEM+QbrvlVi7ycFrUyrXBa5jKXinBMN+4BklPnJSIkaK9G0CtvGAzgCnzEoa8gyxHqUy\n", "LSDAca7yGL6lfJmiXZySiJEC9wMQgsrAthrPad8/dc/3O7xXd2noAPziJK75/ivfP6328zxbB4sI\n", "KQ4ClS470BFJOEGPyUOeQV0jfkuI0c1UguETeh0QQ10jibCKQYBKkW8ifSYvWEAqUADpMPzS71M9\n", "zEqHncDl0ZcOIazDXeBtib/0LWC5W8EB3S8d8h3KpteR/Z3sJYpaYb5D4uXiWhtY5aREWIdJePRq\n", "CUHnvDQ3c8X0W4f8t43PgNOC+sW5X2frk7TlYRycwWBADic5JRFWkV1daSlErsQiO6yDjg+p5akt\n", "qtU46FyiyatEWAetIES8o7d6zhbDkf4Z7w+n8DAcpWscSYRUMOJYuEXs3aoKk+KUQnFeR1sgIv8+\n", "wuhEWVDDgUFvgbCXJNyp93tMytyzvwVxc7p/Iv4AakC5V6GQHjwAAAAASUVORK5CYII=\n" ], "text/latex": [ "$$\\left(x + 1\\right) \\left(x + 2\\right) \\left(x + 3\\right)$$" ], "text/plain": [ "(x + 1)⋅(x + 2)⋅(x + 3)" ] }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" } ], "source": [ "(x+1)*(x+2)*(x+3)" ] }, { "cell_type": "code", "execution_count": 35, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": [ "iVBORw0KGgoAAAANSUhEUgAAAKsAAAAWBAMAAABNknGBAAAAMFBMVEX///8AAAAAAAAAAAAAAAAA\n", "AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAv3aB7AAAAD3RSTlMAEHarIkSJZt3NVLsy\n", "me8Q6PJIAAACMUlEQVQ4EZWUMYgTQRSG/4253VyymywRBBHi3gnpRBDhGpFtxMImWFgJxj3QxmA6\n", "7U7s7KKgXtQigpWNZ2Mj6la2HhwWFspdeVaJeHh3CHF2dt7sjJvZwy1m/nnfvH/ezj4WSJ9o2Rcq\n", "P/Xem1l+txrx+t6aula1HR4aqOv/0F7XGZu2V0L3l4kdGPeMFdU75Z0D000bWrGJANWJmRWT5sMC\n", "XgkKYDHKfbLr7ZgyeiTk/CBRfJAhKezoptRw9zOdqKMdedvOuo6Ai8mt8OFfAliP8JWirbj8O9VW\n", "mM6PUVsT9DKu6OzYBrPlg9iRTJQ438U7CldCZyvV1YDPTtZT7tNXbzQGlJJq+ZCCZBSJWAmzmBst\n", "i5WgtU0J69Op6GnKLLT9kCY2T5xvB9KDDm08j74As5lSrbV45lKUJtOZe59OdtiVdJ17lUHO9vgT\n", "1EcGptgewZ3gvmZr7fXxA7B9e1IN87YTeFsGpthewy3/pW479fHdh4V5+uIci3dp3IWzY2CKrY9n\n", "VBFdwi6wwm4BjYAI7OFwdXs4XAfqAzis5WYyxRb4yZOzRGwz2z4Lfo7hS2P6ZKwTHPaDmclUW/cP\n", "e1/+ULWsE1i1Vf8USnlb9nfxNg1MsX1RGmNBt33N7ha4OvqIC1mxVC3eojUyMdm3c/u1cTl5Y/ZQ\n", "tbWuxTrh8GLzhgAatb+dNTLv3O4SksE6vdC+zdMyW/Q2YhFSJjpUCUlZxGS1crcm5tTSNQIUMR3+\n", "BZqsk+Ep3JM5AAAAAElFTkSuQmCC\n" ], "text/latex": [ "$$x^{3} + 6 x^{2} + 11 x + 6$$" ], "text/plain": [ " 3 2 \n", "x + 6⋅x + 11⋅x + 6" ] }, "execution_count": 35, "metadata": {}, "output_type": "execute_result" } ], "source": [ "expand((x+1)*(x+2)*(x+3))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "La fonction `expand` (développer) prend des mots clés en arguments pour indiquer le type de développement à réaliser. Par exemple pour développer une expression trigonomètrique on utilise l'argument `trig=True` :" ] }, { "cell_type": "code", "execution_count": 36, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": [ "iVBORw0KGgoAAAANSUhEUgAAAFgAAAAVBAMAAAAwfTS1AAAAMFBMVEX///8AAAAAAAAAAAAAAAAA\n", "AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAv3aB7AAAAD3RSTlMAMnZUzRC73UTviSKZ\n", "ZqszMyTAAAABn0lEQVQoFaWSzUsCURTFj+nk5CRUBC0CldpVSKuiWiSl1SJiFiEEUZsIwj6k/gF3\n", "UiuJqKAWEi0jXLkJwkW0CpIIglYqtBQqKawwu2/GmfcmbNXAe/fce35z530M8J/Hlv3jbXeLbth2\n", "OZDmkqvwAem8nts+eT3EpaBmSDuEXJcNSbEkpWpZiaLVYkaTYWuU3acFyG8UpYSWKK16jea4qZgw\n", "YFeUZeOwdXcF0t9oP27z+6hwxqqXJx0qiybsKPT1My8MRLEOTPiUL7LXaMhH2GAohxdDIPMe2yqS\n", "DF4BKmRv0ZjL4pqhHB5VQQc2COd7Lxi8BLySfUjjVsUmQzl8BYlgL6TTaobByxzeh/RCpCsYnNwL\n", "BjMkpyHT4XlxAXdZgGkZ1MT+LBHCO1fQTIcXxzBQFGDaoFRCY2DEApfZPmiDQyoibA21ZZwTNIaH\n", "XNICF7FK+Q127h5jnuqTp5of+Mjpl7LQ01mIWeB5PzXGlFYTJmdKSMxL0WpKQrSYdmfEiswaGo/L\n", "ZygzRkz1W8z+LgB1f34Nq9NGEb8s9nKn8APn2mCn+s4IPwAAAABJRU5ErkJggg==\n" ], "text/latex": [ "$$\\sin{\\left (a + b \\right )}$$" ], "text/plain": [ "sin(a + b)" ] }, "execution_count": 36, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sin(a+b)" ] }, { "cell_type": "code", "execution_count": 37, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": [ "iVBORw0KGgoAAAANSUhEUgAAAP8AAAAVBAMAAAB7+SUdAAAAMFBMVEX///8AAAAAAAAAAAAAAAAA\n", "AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAv3aB7AAAAD3RSTlMAMnZUzRC73UTviSKZ\n", "ZqszMyTAAAAEK0lEQVRIDc2WUWhbVRjH/9fcm6S5zRKnMIu4xuqDVB1hD4r60KCZqIwZQQabYwll\n", "CGNqg+KDDjTgg0yQVmUqdJNYB45Ox/WlUNQtTvFhCkYRBBXMCoKIZa2zo87Z+v/Ovefc03L37qE3\n", "93++33fu+d/vfKEB/nfjhtBRvpjsLMRXomaN01XyWxNYIyIKjZ3XY+w3In0ujlmKeOdbnCfRgVKc\n", "OANvfxPYkPwaM1Gixs6leKVbi7QbxywleAfnSXSwHSduY0qP00ocshRpOCpaxPdhLa9qa2XfBV/g\n", "ZehjNjVacKHMqXmcQRRmcRKe1JnehFb2nTi7xIChW21qdF8AjLNY2GVClhAajgj7V+sAsk0j7zMq\n", "FoJzKkVTy8BHRZP4MtX7MnPLJhYLoeEgdm4aqsys4NojG7eUGMx1+HFqalMN+IAKe6bfgzc13MLG\n", "rTdzKtidu41PVZSRyACxP9ZzHvxsaI5BRXcMfR49zzs7XcbuoeNqs4j6v9/4rMI7gSaeAu4v+f+Q\n", "pbus8STGKL/n5b+BgWBTgFGvgUyE69vATKEyQgMKp3twR3E6YPRJHtJftUKAfIWz2bIz6RxEuqQ2\n", "UxSHgvQCBB+qoS0G9gP/MjfDl9vVxZeUd/JyKxgpPg8MXncYuQiP1MBvjVAZoYGsYBpIT6DAV8Az\n", "QGoJhTZSTc6qSC1m2nAW1WaKekfR32BSE5mLt0IM7AP+ZG5fC/imhqcpB3nVO4DDri+0XxwNOBf8\n", "BTwaECojNADBYqCCQonRt4H+CurcdIG1uMzIYBm4pDYL6RIyTCT2jq12xMDjsYE34S3KEl7jTEot\n", "00Bj98PyGDGwHVlaUgaOVavPVavbCQSLgYYx4HYwzhajgay82QhXXlSbKQN9DdSLysDHyC9bBngE\n", "fL/UggdIr5oKfBXgFz6CmCfVzy+i7uSwAr5gywCPgEfBUsrXxlTgstpMHQGLMwKF7wbmLQNsQu8C\n", "K3lP2GbsAZSPsBIvtbGhG/bosjTJuiZMCbYMsAkLLZ5z2IRVlrGvg+yy2kw1Yb2MfSnVo3fVcEDq\n", "zz+pVK4H3Isfem3ga079o8gFA0U8lDqMdBDieTxBIlRGWAGFWX99BCf5b6Cb7rATeGG2ixPOa3Db\n", "ajOA1C3lX2BXE7/y3Y+tzau/bl49d8ffPZ5XE9g7fP1cC3iAS7H3zAn4Z0+2sh/efpxTwXu2dKkU\n", "5T00INg5v/LI+ZVrxt5hlAfknZmncFv88P4YDvDJ9G9qM86Fzr176pYIM2KNg1r7E1rZd40NDQ3Y\n", "KaIzQRSZXU/WUCTgn/WKXEkr+66xoa/a1Oh8J5JTJmQJQ5GA5aTVeNRaEUuNk2mcdyCUXiUOWSqi\n", "SMJ+I0rUSdY6So2TaZwb/eTQfmOglP5Bkog/DZPzuhLr1ob4StQk+10lfzKBNSKiIP4PvzMnUkUr\n", "/wUAAAAASUVORK5CYII=\n" ], "text/latex": [ "$$\\sin{\\left (a \\right )} \\cos{\\left (b \\right )} + \\sin{\\left (b \\right )} \\cos{\\left (a \\right )}$$" ], "text/plain": [ "sin(a)⋅cos(b) + sin(b)⋅cos(a)" ] }, "execution_count": 37, "metadata": {}, "output_type": "execute_result" } ], "source": [ "expand(sin(a+b), trig=True)" ] }, { "cell_type": "code", "execution_count": 38, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": [ "iVBORw0KGgoAAAANSUhEUgAAAGIAAAAZBAMAAAA4buY/AAAAMFBMVEX///8AAAAAAAAAAAAAAAAA\n", "AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAv3aB7AAAAD3RSTlMAMnZUzRC73UTviSKZ\n", "ZqszMyTAAAAB3klEQVQ4EY1TMUgbYRT+YnImufNAxcnBnG0HSUUyKbqkYFI7FLmhBAQhDiIUqwil\n", "kyBuolMUQQeFFBxFMrkINUPoUFoI4tIurYJLRZAWi1VsfP/994f3W4W75X3fe9/3v/f+uwP4YyZP\n", "OA2Av+BjABWXfMJ3TgPhSiAVExnLjASBxhPHk4WqD6jtZlkIrdQFcvPdOmcgt0bkSCZCV/VCIiVg\n", "ts45GCYS4QmAttgX8zQUed4o+ez3fyVgFl9dSseVxpOGHS8gdkHRKHjEapE5tCdPBVrwqQzKYc4I\n", "PojQ40fPdv+hbaO1x5EKYJuA9bPzneTKETnuTolaDpjBNPDcsa6VY4rAYqnxXHfksyDFIRZdFIXj\n", "NXCjHG9p3k00jemOtAu60z5E/zyFcIwDv5RjHWi6QNTRHRUY5EjA2KqVhWNCd8THkG8mh5nJDK1m\n", "MmWCLxGj+01gD/blXQdNlS8iDddroja/EY3pHgeAs7sO2jyfwni4pDkuMVIVm/e7mBQj8al26Gtw\n", "7DnTM0D1OMMbSnzG0sG3+Y7aSUftqPfvDykRb9A4fv+hS3eM9lALvPA1eojKcfyk6uFRq6BLfWaX\n", "eTomjlaP6Sikx0mdMvaKYQ7v/aM8wUNnWXwQfpRdwi2QnWncNc1YoQAAAABJRU5ErkJggg==\n" ], "text/latex": [ "$$\\sin^{3}{\\left (a + b \\right )}$$" ], "text/plain": [ " 3 \n", "sin (a + b)" ] }, "execution_count": 38, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sin(a+b)**3" ] }, { "cell_type": "code", "execution_count": 39, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": [ "iVBORw0KGgoAAAANSUhEUgAAA1IAAAAYBAMAAAACI9GjAAAAMFBMVEX///8AAAAAAAAAAAAAAAAA\n", 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"QOY66mVqeClsgNEG3l3tCXgE+1GIwjvZwjA/XdxcAZ9YDesW+oRwYYtroVOoSr4wNQ5dGIgE0HKz\n", "XIQ6Vz3NIURall1EpeMlzAR8Swl7TKFm68qURt4qr0ezMA5QxP8BLqULVwrv9u6nEI1Lz7sNxwMQ\n", "36jSRG7k3UqWnw4HA+ZSLDcJn1SN0C3nsnC0lZATzzu/hf0INRqdB0R2+UYAWm5UpRSRluaQx9ay\n", "rIiMMwYbbx6BEzctXlaDU4Z+IQdtxqWfT78fCbtomnu21eGGwVeBYo2pnRaOkSM7c05dBKtKrUSX\n", "C7Fvfn4WxzyIi5efWVo7bVz5/uAMOtm7avBDSXfTtlfAnBpqA0bZXk2Nyy3hk6qRDO9NnYQlIU0u\n", "IS/Nz9t8IWo0OnBAXPmRj6PlhivlItLSHEKkZVkRXQ8wDvcAXFYrqZ+oa5DucSuHKS430NxVNzYa\n", "E5CrcSwOYMv1AEZXAE75mrpiTRlbu2jHI5pjkIfcNGRXw24LZyCf+WerYjFd6VlYYp1kwWqOEl5N\n", "jc+NEQtQQ7oLG2EtTkLhXiGEFaOGItwgvvxQ+rTcUKXSIHq8BR2q1J0A/P0hJfch9Gag00dmHLtN\n", "yBzId8A4wLEUgGfvOm51LcBD4wXUFZUCeK2KNY5FLKyHIlUqtw4qmDniw98plQ7TZRswXH0QT0on\n", "U5Twamp8bgpJroYiR3p89kLhXiGEFaOGItwgvvzg4+i5oUqlQZT/62ygSt0K8AepoLYB/yHiEP8Q\n", "MmaxaIdxiA4r/3AsR0BfG+AjMLFSAzRgV6oPv2HxiPDwaosr1YBKDWcTXwNG8WVAutEubqB4cqp0\n", "KEp6HTU+N4UkV0ORX7TgXpxEwj1CcCRODUW4QXz5wcfRc0OVSoPI3DLfpUrd7q5U3xiMVjFlmLoC\n", "VXC4DfAXxxKvELsSCpjRASg2m5c/12x2S5h+HIhHhBuuPsyVGlOVynZhss50kzUs/BxWaoyisOGD\n", "a2p8bgqh1CVTQ5HPg3kAJ1GlPEJwhPki1FCEG8SXH6qUrWZLs/lAs7kyDaK3oTznrRQuX3zBh6HF\n", "Rzz1zhzmWJIqNoD/aOWJoxmvqcohyOEOGo+IJd3bpt1PVgr5cBfEV70wrq2pTykKqdxq1Jqy3QtT\n", "gxsXbgSZWZOFe4UQFvFFqJF0GogvPwiAXxKVG1pTaRBdBPCbt1JrMF91uDUjPvG4D0OmrwuFOY4l\n", "qeKjOkc7vnaiwDc/j0eMeMRMBxb1tEohX6WN3yI+UeCHCOqbACYfpSikcqvxuRemBg8D5kHINS5m\n", "4V4hhEV8EWoknQbiyw8CgJYbqlQaRBe24C7a+vA/2uW47cQTeK38EH7P+7s4sKsH242nIdvhWBFS\n", "nKYK342dz2iA11S5B8M1gHjEzHrIWbig7DWFfFi5rqArbYaitaQKV3EUYrvV+NwYAcnVUOQl8M10\n", "h4V7hRAW8UWooQg3iC8/CKDnhivleWJCWSjRE1991146v3/p/L7z/55mAN4XzJmX3zsLC9bGIfPX\n", "IQve2fYTUCz2qdE+ddMyeuWvoC5XCp787A604xELr5/7qvH7kWt+P3LC2hdxBv5UND/4DY1sGy83\n", "f7AdSnt2tikKu+zV1HjdFJJcDUXePHTqTJuFe4UQVowainCD+PJDv3y13FCl0iEibnfLW3Z/l234\n", "7hNypLSODFEpX5AaiEZU3kA65YVAN3MkV2NHghCuFDqG4ounCwZRAIKCKhXYVNzREuHnomsjT9mG\n", "7y7+NRN3ghq5CrS6Ilo0ovIG0ikvBLqZNbkaO1IKD9Cs+OLpgkEUQLFG8E8FcPCQijtaIkS5S4Kb\n", "DWn4bzm57q7zu4JGohGlN4Quei6zJVdjR0K48Gg1xBcDYgsOpxApOmYi5+9+SpFA1q+lMReb7gqy\n", "8Y9r3IIRpTfYqf4KGeIm3ORq7Ej1Ngpd+jVajU4XAmI/rF0xHVy3j5kIH1xuZt/ruB77Xe6X5dLy\n", "OH3daETpDaGLniuokqsRkRAhPFoN80WDSIAICiF6QUT/AxxZ/WTNBuXGAAAAAElFTkSuQmCC\n" ], "text/latex": [ "$$\\sin^{3}{\\left (a \\right )} \\cos^{3}{\\left (b \\right )} + 3 \\sin^{2}{\\left (a \\right )} \\sin{\\left (b \\right )} \\cos{\\left (a \\right )} \\cos^{2}{\\left (b \\right )} + 3 \\sin{\\left (a \\right )} \\sin^{2}{\\left (b \\right )} \\cos^{2}{\\left (a \\right )} \\cos{\\left (b \\right )} + \\sin^{3}{\\left (b \\right )} \\cos^{3}{\\left (a \\right )}$$" ], "text/plain": [ " 3 3 2 2 2 2 \n", "sin (a)⋅cos (b) + 3⋅sin (a)⋅sin(b)⋅cos(a)⋅cos (b) + 3⋅sin(a)⋅sin (b)⋅cos (a)⋅c\n", "\n", " 3 3 \n", "os(b) + sin (b)⋅cos (a)" ] }, "execution_count": 39, "metadata": {}, "output_type": "execute_result" } ], "source": [ "expand(sin(a+b)**3, trig=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Lancer `help(expand)` pour une explication détaillée des différents types de développements disponibles." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "L'opération opposée au développement est bien sur la factorisation qui s'effectue grâce à la fonction `factor` :" ] }, { "cell_type": "code", "execution_count": 40, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": [ "iVBORw0KGgoAAAANSUhEUgAAAMEAAAAVBAMAAAANw5eWAAAAMFBMVEX///8AAAAAAAAAAAAAAAAA\n", "AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAv3aB7AAAAD3RSTlMAMmYiu80QdonvRN2Z\n", "VKvu110NAAACwElEQVRIDbWVu4sTURTGvzxm8py4WFkIxhUrLYJuo2wR9Q9ICsfCBwyorDYSEHZT\n", "7QbxsYW4EbXQKtiIWGxARQXFFFsuS0DQwmbVQuyiYCwWiefcRzJnnbiVt7j3nnO+8/vmztwQ4P+P\n", "WHGcx25T2D5OEJ/QlVhxnEIhpqKrJ4CCBiTKUYrplSPAJ12JRCQWr3Y1glARY6pPyaoqOEFE3V3H\n", "UguOrkQipuH9UIhUI9zvlnR0bo0d6irQs1Wd1ZtCgEwTulkiYBQLXWwoRMYwdaNXM6QUO1xUwR2T\n", "0ssHvWTaKHyH2+RIImAUj1vuT4V4ojvMLB2cLqWzbaEw/U6fHXCGaxJhHaDeEiFukmRm/nBdP7x0\n", "SHeoyJM79+XRDdrTMA60y9ExuXsTYqRwOqr7KPU34i9yVdZCOsQ5mSwCF7Cv9pzrYYflBrCXUxIx\n", "dNjxmmqEeEM+QbrvlVi7ycFrUyrXBa5jKXinBMN+4BklPnJSIkaK9G0CtvGAzgCnzEoa8gyxHqUy\n", "LSDAca7yGL6lfJmiXZySiJEC9wMQgsrAthrPad8/dc/3O7xXd2noAPziJK75/ivfP6328zxbB4sI\n", "KQ4ClS470BFJOEGPyUOeQV0jfkuI0c1UguETeh0QQ10jibCKQYBKkW8ifSYvWEAqUADpMPzS71M9\n", "zEqHncDl0ZcOIazDXeBtib/0LWC5W8EB3S8d8h3KpteR/Z3sJYpaYb5D4uXiWhtY5aREWIdJePRq\n", "CUHnvDQ3c8X0W4f8t43PgNOC+sW5X2frk7TlYRycwWBADic5JRFWkV1daSlErsQiO6yDjg+p5akt\n", "qtU46FyiyatEWAetIES8o7d6zhbDkf4Z7w+n8DAcpWscSYRUMOJYuEXs3aoKk+KUQnFeR1sgIv8+\n", "wuhEWVDDgUFvgbCXJNyp93tMytyzvwVxc7p/Iv4AakC5V6GQHjwAAAAASUVORK5CYII=\n" ], "text/latex": [ "$$\\left(x + 1\\right) \\left(x + 2\\right) \\left(x + 3\\right)$$" ], "text/plain": [ "(x + 1)⋅(x + 2)⋅(x + 3)" ] }, "execution_count": 40, "metadata": {}, "output_type": "execute_result" } ], "source": [ "factor(x**3 + 6 * x**2 + 11*x + 6)" ] }, { "cell_type": "code", "execution_count": 41, "metadata": { "collapsed": false }, "outputs": [], "source": [ "x1, x2 = symbols(\"x1, x2\")" ] }, { "cell_type": "code", "execution_count": 42, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": [ "iVBORw0KGgoAAAANSUhEUgAAAKMAAAAVBAMAAADY0UPbAAAAMFBMVEX///8AAAAAAAAAAAAAAAAA\n", "AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAv3aB7AAAAD3RSTlMAEHarIkSJZt3NVLsy\n", "me8Q6PJIAAACTElEQVQ4Eb2TPWjUYBjH/7lectc0nqEFR0kr3FoQF7cMikNRDwdxED216CDFG4Q6\n", "KITSRUE8N0+XCE51qILgJHeT4NQTF6EILh1EqVc/8AMhPs+T901yJh7UwcDlfd7f//88ed6PA/7j\n", "47T+5WNXRyXNjRKLtPL5iyHGekWSYrdHaIXSYdjfgHahJrDUzGmGn0MMNJ4N8R2oF3oE1vycZns5\n", "xEDjUw3jB7C70CPwRF7SuX8oKeaFj4fA5J6DdU9cxsy+Y2e1f4GCVBKqc/+CqVwfsPowmpWlahvO\n", "I2AXrnjXYa2/5AJPaZNSKVtS4YVnrtBk4Xh+k0ClDcu1tmzfeRUAZ3DJfYB57GfrDfpgKjHRuTG2\n", "/DF1uLp5SvhApgAGxqlBjuDiHkXLWPNp2KAuU4lAUjLGVd/5IlR/iSebLpwBjTs9enFJ4BP9NnC0\n", "IQO9MpLV6dx51+n0iQquNcpfKczgA0A3jEuu9ag/Ken8os6AVZdetHAglXiWrFAw7C2GKY5cdFsw\n", "A9juLEpxyfulAabJs8RGOp6MxESXVBhVT6jGeA/c8vl4ToddHCJ7APPnxKDckmtA3nPISEO5KgN8\n", "zfjRzS/Cpo3b0cfUzOQFqkMljb3T9UWyXBMjXfVUEqJzFa7ItmZKmm/WG3Q36afsgY7MptmjuOpr\n", "INvME91OLBzHyTgYxkdiyPa3Onyx8tClOGkilcyW9tDo3F15HE+HMJK/X/n1Z0/5P0aRRE/UPCMp\n", "IkMtigbZuYqNdgHUaE4H2xon/BF2PvztP5eB36AijSOksawnAAAAAElFTkSuQmCC\n" ], "text/latex": [ "$$x_{1} x_{2} \\left(x_{1} + x_{2} + 3\\right)$$" ], "text/plain": [ "x₁⋅x₂⋅(x₁ + x₂ + 3)" ] }, "execution_count": 42, "metadata": {}, "output_type": "execute_result" } ], "source": [ "factor(x1**2*x2 + 3*x1*x2 + x1*x2**2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Simplify\n", "\n", "The `simplify` tries to simplify an expression into a nice looking expression, using various techniques. More specific alternatives to the `simplify` functions also exists: `trigsimp`, `powsimp`, `logcombine`, etc. \n", "\n", "The basic usages of these functions are as follows:" ] }, { "cell_type": "code", "execution_count": 43, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": [ "iVBORw0KGgoAAAANSUhEUgAAAMEAAAAVBAMAAAANw5eWAAAAMFBMVEX///8AAAAAAAAAAAAAAAAA\n", "AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAv3aB7AAAAD3RSTlMAMmYiu80QdonvRN2Z\n", "VKvu110NAAACwElEQVRIDbWVu4sTURTGvzxm8py4WFkIxhUrLYJuo2wR9Q9ICsfCBwyorDYSEHZT\n", "7QbxsYW4EbXQKtiIWGxARQXFFFsuS0DQwmbVQuyiYCwWiefcRzJnnbiVt7j3nnO+8/vmztwQ4P+P\n", "WHGcx25T2D5OEJ/QlVhxnEIhpqKrJ4CCBiTKUYrplSPAJ12JRCQWr3Y1glARY6pPyaoqOEFE3V3H\n", "UguOrkQipuH9UIhUI9zvlnR0bo0d6irQs1Wd1ZtCgEwTulkiYBQLXWwoRMYwdaNXM6QUO1xUwR2T\n", "0ssHvWTaKHyH2+RIImAUj1vuT4V4ojvMLB2cLqWzbaEw/U6fHXCGaxJhHaDeEiFukmRm/nBdP7x0\n", "SHeoyJM79+XRDdrTMA60y9ExuXsTYqRwOqr7KPU34i9yVdZCOsQ5mSwCF7Cv9pzrYYflBrCXUxIx\n", "dNjxmmqEeEM+QbrvlVi7ycFrUyrXBa5jKXinBMN+4BklPnJSIkaK9G0CtvGAzgCnzEoa8gyxHqUy\n", "LSDAca7yGL6lfJmiXZySiJEC9wMQgsrAthrPad8/dc/3O7xXd2noAPziJK75/ivfP6328zxbB4sI\n", "KQ4ClS470BFJOEGPyUOeQV0jfkuI0c1UguETeh0QQ10jibCKQYBKkW8ifSYvWEAqUADpMPzS71M9\n", "zEqHncDl0ZcOIazDXeBtib/0LWC5W8EB3S8d8h3KpteR/Z3sJYpaYb5D4uXiWhtY5aREWIdJePRq\n", "CUHnvDQ3c8X0W4f8t43PgNOC+sW5X2frk7TlYRycwWBADic5JRFWkV1daSlErsQiO6yDjg+p5akt\n", "qtU46FyiyatEWAetIES8o7d6zhbDkf4Z7w+n8DAcpWscSYRUMOJYuEXs3aoKk+KUQnFeR1sgIv8+\n", "wuhEWVDDgUFvgbCXJNyp93tMytyzvwVxc7p/Iv4AakC5V6GQHjwAAAAASUVORK5CYII=\n" ], "text/latex": [ "$$\\left(x + 1\\right) \\left(x + 2\\right) \\left(x + 3\\right)$$" ], "text/plain": [ "(x + 1)⋅(x + 2)⋅(x + 3)" ] }, "execution_count": 43, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# simplify expands a product\n", "simplify((x+1)*(x+2)*(x+3))" ] }, { "cell_type": "code", "execution_count": 44, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": [ "iVBORw0KGgoAAAANSUhEUgAAAAgAAAAPBAMAAAArJJMAAAAAJFBMVEX///8AAAAAAAAAAAAAAAAA\n", "AAAAAAAAAAAAAAAAAAAAAAAAAADHJj5lAAAAC3RSTlMAzRAiu5mrdu/dZmiL4QAAAAAjSURBVAgd\n", "Y2BgEGJgYDDZxMCgEgYkGNhJJVgzdmYB9TEwAACPpQrvlUCHcAAAAABJRU5ErkJggg==\n" ], "text/latex": [ "$$1$$" ], "text/plain": [ "1" ] }, "execution_count": 44, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# simplify uses trigonometric identities\n", "simplify(sin(a)**2 + cos(a)**2)" ] }, { "cell_type": "code", "execution_count": 45, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": [ "iVBORw0KGgoAAAANSUhEUgAAAD4AAAAvBAMAAABJZWRJAAAAMFBMVEX///8AAAAAAAAAAAAAAAAA\n", "AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAv3aB7AAAAD3RSTlMAzRAiu5mrdu/dZkRU\n", "iTJPL/blAAABcElEQVQ4Ee2RPUjDUBSFT5Jaq7GlQ9E1i+giZhSnCgpudio4WRB1UkMHOwUHcdVK\n", "0bkgVXQQN0eDuhtcHBsdFOrQgog/iPW+5uXVxFJXQe/Q951z3ru53AL+kjS/Dihlqm0uTyy3zYHV\n", "/zywUr/8YT/pzZ2i/8FfV/X29RvXU+FDHbUeTklyP+wwULkSh6R5GGfQ7ynvND1Ag0pCclgTxiBR\n", "z5OQLqh5YUgWsPc6b2HfNHCzfr9iUBRNAXJu9uTaRWxTjwLSQMJQHikPFYEhHBhbQDcbkOUbONUw\n", "CbyR7rKAK5S1c/LZpyhHzJzRMQY8E0eOAQ0JIsRq9EO5nNCrDsabuUtebkh5VG81nrP+iH1AptWx\n", "/r2wh21cPjg8Z/OdddaQ5fNNw44kUa5476MZqO+hmkLXOlL0PryoK3O5gb67+tLFS6bRVF7ImocU\n", "STTq9yoIa0TQV2j+IbTCFhXSuSnHW6SAkuS2uBi4Nsr1Lp2fe4l4hDH4kW8AAAAASUVORK5CYII=\n" ], "text/latex": [ "$$\\frac{1}{\\tan{\\left (x \\right )}}$$" ], "text/plain": [ " 1 \n", "──────\n", "tan(x)" ] }, "execution_count": 45, "metadata": {}, "output_type": "execute_result" } ], "source": [ "simplify(cos(x)/sin(x))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "simplify permet aussi de tester l'égalité d'expressions :" ] }, { "cell_type": "code", "execution_count": 46, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": [ "iVBORw0KGgoAAAANSUhEUgAAAAoAAAAOBAMAAADkjZCYAAAAMFBMVEX///8AAAAAAAAAAAAAAAAA\n", "AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAv3aB7AAAAD3RSTlMAEJmJdjLNVN0iZu+7\n", "q0QgoRR7AAAAVklEQVQIHWNgEDJRZWBgSGeQmMDAtYGBOYGB5wID+0cG/gsMfN8Z5BUY+L4wzDdg\n", "YP0MJeUNQCL8Cgzs3xk4DjBwfWRg2cDAlMDA0M4gHcDAIOxylQEA9FISlFfRJtkAAAAASUVORK5C\n", "YII=\n" ], "text/latex": [ "$$0$$" ], "text/plain": [ "0" ] }, "execution_count": 46, "metadata": {}, "output_type": "execute_result" } ], "source": [ "exp1 = sin(a+b)**3\n", "exp2 = sin(a)**3*cos(b)**3 + 3*sin(a)**2*sin(b)*cos(a)*cos(b)**2 + 3*sin(a)*sin(b)**2*cos(a)**2*cos(b) + sin(b)**3*cos(a)**3\n", "simplify(exp1 - exp2)" ] }, { "cell_type": "code", "execution_count": 47, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "sin(a + b)**3 = sin(a)**3*cos(b)**3 + 3*sin(a)**2*sin(b)*cos(a)*cos(b)**2 + 3*sin(a)*sin(b)**2*cos(a)**2*cos(b) + sin(b)**3*cos(a)**3\n" ] } ], "source": [ "if simplify(exp1 - exp2) == 0:\n", " print \"{0} = {1}\".format(exp1, exp2)\n", "else:\n", " print \"exp1 et exp2 sont différentes\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## apart and together\n", "\n", "Pour manipuler des expressions numériques de fractions on dispose des fonctions `apart` and `together` :" ] }, { "cell_type": "code", "execution_count": 48, "metadata": { "collapsed": false }, "outputs": [], "source": [ "f1 = 1/((a+1)*(a+2))" ] }, { "cell_type": "code", "execution_count": 49, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": [ "iVBORw0KGgoAAAANSUhEUgAAAIIAAAAvBAMAAADdrw/+AAAAMFBMVEX///8AAAAAAAAAAAAAAAAA\n", "AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAv3aB7AAAAD3RSTlMAzRAiu5mrdu/dZjKJ\n", "RFRer8KoAAACC0lEQVRIDe2UO0wbQRCGf9/Z+G2d06BUviJCdL7CQtA5IjRpcEFog2ggUSTcxyhC\n", "pEiQECiF6eCkVCiNpVR0boAiKVImnSVEjUWHIHFmZs+POy05S26Q4i1ud2a++XdndnVAyIjYIUBY\n", "2FwcUcFY2BhRAXg3VpB7GvdB2vAQ3sPyXt1Vhxl/xx0YqgOdUcdQu/wfUNq9r84vKhAKlPQCj4CJ\n", "hoS0gPniWaMLEKoZpWtyWhLQAlPI/PaAeHMw33CU9WSZFWps6IFV4IMHJLwclZmrqhlxVphmQw98\n", "BDYaCvjlpajJrxBpkVcPFGxWEOCUIPPN2086hUyFvAxg5vzE5hm9LYAjGwI8J/dPJ9qWeB+QKlIW\n", "eRnIbmMxACB7BwhwCBg7iJUDgCjk9slLAKZdLAUAJMt0IgZ2gdg1ktUAIArptgLw0sZBABDJLpAo\n", "o9ggIJPPP97K5yvM+hXew7gNAivkEAU6ZLGJK9jkGGiUKGT3ycVl3iHXNvzAJUwHAlCjii08zTl+\n", "QBRSFjkJMP4gal34gHQFcUd18gyIVFP1jMQDdxHjggjAJI5XmkJ0b3Pu++kr6iAD9GCMtW8zmxLv\n", "KcTWb16T9A8FYKr2dY3X/TILnc6NByQdiXif7hbKnOUpFEjxQXoj6/aWtDhnIxTA/GCOb21YYoYC\n", "2h+IpEZVgaGA6fo2HjA+q/W/gb9fh7sjP8fEcgAAAABJRU5ErkJggg==\n" ], "text/latex": [ "$$\\frac{1}{\\left(a + 1\\right) \\left(a + 2\\right)}$$" ], "text/plain": [ " 1 \n", "───────────────\n", "(a + 1)⋅(a + 2)" ] }, "execution_count": 49, "metadata": {}, "output_type": "execute_result" } ], "source": [ "f1" ] }, { "cell_type": "code", "execution_count": 50, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": [ "iVBORw0KGgoAAAANSUhEUgAAAI4AAAAsBAMAAABBB53eAAAAMFBMVEX///8AAAAAAAAAAAAAAAAA\n", "AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAv3aB7AAAAD3RSTlMAEM3dMiK7mat272aJ\n", "RFQidGHIAAAByElEQVRIDWNgwAKYBLAIYgopYgqhiLDmE2VO2EcUXRgcxvR+YswRqyBgDgPDfGLM\n", "YeAcNQcjDpAFRsMHOTQw2XQLn4r1FhswrUcXYe/63I0uNvT4jAHUcTPfhFFz8IUAwfARMgYBFQaG\n", "/9jBB4jpMHP4sav6j88NcDkeJSXlRUpKBXAB8hkw95BvAkTnsDInvCwFFh54/UWgomdsYDh/AWoQ\n", "7waYiZg0zopeFKKWQ4CBawGmNrgIVBnuiv4lRCmXAQPHF7guTAZUGe6KHqqA6SNZ5rD2TDGEWAqz\n", "iIGBGbNNEXHSWQBVGVrFcSuA7QOqAgaG+wcgIgiSdylDPpQHsw7VHMbVDOwJEBUwBQwMZggDoCyp\n", "DQxlUCZMGao57B8ZmCegmQMzGMm0egEGS7zmcCUwyDsAVRxSUrJSUlIHq50B1YFELWNgBMchkjJU\n", "98gfYHjPAAlBmIP5ChhCkYwAMRn/MPB9YIQIwpShmfOAIYkvAKwCpsCdgWEqRAucZPzHwKbwFJ85\n", "TBO4LXhQFLDanKkwgJsAZWgybG44gKIMLR0ydh6JmIuigAlYhmKYIzHHtfMCijLcFT3MXxDlOEmC\n", "ym7j1IoigawMAGt/qLcv8MeSAAAAAElFTkSuQmCC\n" ], "text/latex": [ "$$- \\frac{1}{a + 2} + \\frac{1}{a + 1}$$" ], "text/plain": [ " 1 1 \n", "- ───── + ─────\n", " a + 2 a + 1" ] }, "execution_count": 50, "metadata": {}, "output_type": "execute_result" } ], "source": [ "apart(f1)" ] }, { "cell_type": "code", "execution_count": 51, "metadata": { "collapsed": false }, "outputs": [], "source": [ "f2 = 1/(a+2) + 1/(a+3)" ] }, { "cell_type": "code", "execution_count": 52, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": [ "iVBORw0KGgoAAAANSUhEUgAAAHwAAAAsBAMAAABVvsF6AAAAMFBMVEX///8AAAAAAAAAAAAAAAAA\n", "AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAv3aB7AAAAD3RSTlMAzRAiu5mrdu/dZoky\n", "RFRd1xrCAAAB70lEQVRIDe2TOUgDQRSGf3cTYow5VhCxckERu2whgthE0FJNobaKTTwK06uIR6GC\n", "KBbRzrR2WoiKFgErOxtBrAQRy4RYxSNxsjOT2Uk2S8BGIVPsvvf/75vjMQOI0aCL2CFqsffUsZrw\n", "3qwtrgwv1oJ3TdrjwGotODx1vLL99dZV9kRWql2byb1EUq60y9yzuXk7/Q9qivGrTQXidbxqBwr2\n", "I0MB3rqgfVWh6rTE8Gla+5qmRZ1qnD2+unNVVff/4QPXo/w0DptXJ4ZSvMz6V6NIJ5ng54G1gMbd\n", "8H1LaifNPHkE9yVDSvgjnAG2JOONZv5dhEOSISX8PNvAYsrqMJxIpc1bbRZzvEM3cXVh+YA6Am+t\n", "xPou7nRT5ThJjonyaLjYBed481XlPfVvYMykIXD/J6DswB2hBseByxRVxLcniXGaCdwbAdxZeOPU\n", "EHjjugBpNKXjkEYCL87XGEE4Rf5nmnakacVDqwZcefKXxiaULyJYH6FnmgjhU6ShkwBgqwezcH2Y\n", "gvgonwhkFDMvrf5aXCj8jMGAYRoMJ/vxvgvSjJQ8XKEXCW+OwmOgId6U8NFihjclkWbdEJO04WT6\n", "VML7r8/nSOdjN30rEo6HiRHBsah76TZ2L+EdhUJOKhOdl+SypHT2Mh1P5YJtLj3CH5GUoQDcJOku\n", "AAAAAElFTkSuQmCC\n" ], "text/latex": [ "$$\\frac{1}{a + 3} + \\frac{1}{a + 2}$$" ], "text/plain": [ " 1 1 \n", "───── + ─────\n", "a + 3 a + 2" ] }, "execution_count": 52, "metadata": {}, "output_type": "execute_result" } ], "source": [ "f2" ] }, { "cell_type": "code", "execution_count": 53, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": [ "iVBORw0KGgoAAAANSUhEUgAAAIIAAAAvBAMAAADdrw/+AAAAMFBMVEX///8AAAAAAAAAAAAAAAAA\n", "AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAv3aB7AAAAD3RSTlMAIpm7MhCriUTv3c12\n", "VGZoascqAAACzUlEQVRIDe2Wz2vTYBjHv1mbNm2a0lUY6CmsoN5W0ZOCrV4ETzkNdmoRRNCBZcp6\n", "GbjrdtExD86BFEU9qNiLFyusE3XerP4DRvDkYfRgmfgrPm/eJE2b1yawwzz4QvK+7/f9PN+875uX\n", "PAGCJV84FhQB9XX8sEgPanIV1xaDMlKW1RbIAilhIHXDp8s53lHnN33qqGaqgcRXH6C1eSfh00Y3\n", "1d5uHcg/3YN04dV9/iRvDtsvy1yJcL9ew1Iu1uWk6xAzpG8RYjlyD/IdjBUHHah3PKoDxY71kG4H\n", "HKac1xJq9AZIFVEpE5hstZ6vt1o6NS9BfE5oaLhoOsYrNXyEYY+4+7AOTHFlOCDQPw1sVjo4ofE5\n", "uw468CjACgXpYf1gQ21nHyT5sOuQR3ZFGBAQVcuyGvLs1Ym3gw7SoXMRFzFs6c5hWI/eV8zo7H9y\n", "b3eAjs/uyt5O/596etz823Q+84FQYJ/IQJo8WkaibA+JgfqVjgs8EzlMI0mf9qY9JATy0H47QKbm\n", "d3CT5HlgDdhiQ2LgXQffHSDFv4aOjfs5uQ1cLmOGqWJgaVH+AQ4sO7G8ch02DOagdkgVA7BXYQNz\n", "BAWTJIlPDSR1qhmAiYWzBqvhPoL+SHRKJ3ThJF3BJAkoO0C26QDKCkrUpOI5nLlLPRt4AnGSTBcJ\n", "bxBFAGZMFKii4jkgedMBbkGcJFlEvEs3AliyekwVlb4D1gwPECRJZKqEew6rkNlvjS+LjgOlDgdo\n", "koIkiQ+QclAaFMaWuQOtK1O7PwfLQMnkAO2kIEnGdWRy3k7KvxBrvh9wWKWX5QDz9F6CSfJAfe4i\n", "bZBOUQTgBb5UawMO29B+OgAdGEGS3LAsOrTqIkWxEzW9dWqWtfurUCYLJNhAOmePODffVpOyn6mh\n", "QFZnmFsU022xeoHdQgEcYZiwyE1bDgWEHxA7NMYXGApIpnACJH7iA6OBPwmy/q6NEb1xAAAAAElF\n", "TkSuQmCC\n" ], "text/latex": [ "$$\\frac{2 a + 5}{\\left(a + 2\\right) \\left(a + 3\\right)}$$" ], "text/plain": [ " 2⋅a + 5 \n", "───────────────\n", "(a + 2)⋅(a + 3)" ] }, "execution_count": 53, "metadata": {}, "output_type": "execute_result" } ], "source": [ "together(f2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Simplify combine les fractions mais ne factorise pas : " ] }, { "cell_type": "code", "execution_count": 54, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": [ "iVBORw0KGgoAAAANSUhEUgAAAIIAAAAvBAMAAADdrw/+AAAAMFBMVEX///8AAAAAAAAAAAAAAAAA\n", "AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAv3aB7AAAAD3RSTlMAIpm7MhCriUTv3c12\n", "VGZoascqAAACzUlEQVRIDe2Wz2vTYBjHv1mbNm2a0lUY6CmsoN5W0ZOCrV4ETzkNdmoRRNCBZcp6\n", "GbjrdtExD86BFEU9qNiLFyusE3XerP4DRvDkYfRgmfgrPm/eJE2b1yawwzz4QvK+7/f9PN+875uX\n", "PAGCJV84FhQB9XX8sEgPanIV1xaDMlKW1RbIAilhIHXDp8s53lHnN33qqGaqgcRXH6C1eSfh00Y3\n", "1d5uHcg/3YN04dV9/iRvDtsvy1yJcL9ew1Iu1uWk6xAzpG8RYjlyD/IdjBUHHah3PKoDxY71kG4H\n", "HKac1xJq9AZIFVEpE5hstZ6vt1o6NS9BfE5oaLhoOsYrNXyEYY+4+7AOTHFlOCDQPw1sVjo4ofE5\n", "uw468CjACgXpYf1gQ21nHyT5sOuQR3ZFGBAQVcuyGvLs1Ym3gw7SoXMRFzFs6c5hWI/eV8zo7H9y\n", "b3eAjs/uyt5O/596etz823Q+84FQYJ/IQJo8WkaibA+JgfqVjgs8EzlMI0mf9qY9JATy0H47QKbm\n", "d3CT5HlgDdhiQ2LgXQffHSDFv4aOjfs5uQ1cLmOGqWJgaVH+AQ4sO7G8ch02DOagdkgVA7BXYQNz\n", "BAWTJIlPDSR1qhmAiYWzBqvhPoL+SHRKJ3ThJF3BJAkoO0C26QDKCkrUpOI5nLlLPRt4AnGSTBcJ\n", "bxBFAGZMFKii4jkgedMBbkGcJFlEvEs3AliyekwVlb4D1gwPECRJZKqEew6rkNlvjS+LjgOlDgdo\n", "koIkiQ+QclAaFMaWuQOtK1O7PwfLQMnkAO2kIEnGdWRy3k7KvxBrvh9wWKWX5QDz9F6CSfJAfe4i\n", "bZBOUQTgBb5UawMO29B+OgAdGEGS3LAsOrTqIkWxEzW9dWqWtfurUCYLJNhAOmePODffVpOyn6mh\n", "QFZnmFsU022xeoHdQgEcYZiwyE1bDgWEHxA7NMYXGApIpnACJH7iA6OBPwmy/q6NEb1xAAAAAElF\n", "TkSuQmCC\n" ], "text/latex": [ "$$\\frac{2 a + 5}{\\left(a + 2\\right) \\left(a + 3\\right)}$$" ], "text/plain": [ " 2⋅a + 5 \n", "───────────────\n", "(a + 2)⋅(a + 3)" ] }, "execution_count": 54, "metadata": {}, "output_type": "execute_result" } ], "source": [ "simplify(f2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Calcul\n", "\n", "En plus des manipulations algébriques, l'autre grande utilisation d'un système de calcul symbolique et d'effectuer des calculs comme des dérivées et intégrales d'expressions algébriques." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Dérivation\n", "\n", "La dérivation est habituellement simple. On utilise la fonction `diff` avec pour premier argument l'expression à dériver et comme second le symbole de la variable suivant laquelle dériver :" ] }, { "cell_type": "code", "execution_count": 55, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": [ "iVBORw0KGgoAAAANSUhEUgAAAEgAAAAbBAMAAAAt2dQtAAAAMFBMVEX///8AAAAAAAAAAAAAAAAA\n", "AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAv3aB7AAAAD3RSTlMAMmYiu80QdonvRN2Z\n", "VKvu110NAAABZElEQVQoFY2Sv0vDQBTHv0na/CBE+w+oWcQ1qJOTKDgJdomDWAiIdBPFQSctguBQ\n", "pKM4qZPgYDdxEIqzQzcHcXARR0HI4BLf5d21HJamb7j3vt/78O5dckBRNFeTIgRuNFYthPzI+CmE\n", "vCvrtxACgnQEyN8dAWoSY4S0DA6zQr7ZoWV+MJC7H7Te4hpYHwKV6aDa+wqchgZtaYo2vSz7hhdp\n", "9qum7BbLO82FDqHGu+KC9aOFQ/kxJORkFB1A7FIsAXbDfPCrLCV0NrE/M0fONLtPgJu4aSBHY8jo\n", "utGe2H9j6JI6obzIAr2ZSvgU1hT7BAHjPNFpHD/G8aZwJtEWSUJ0HNUVJCL3Oz0j//fy7jR4kBzD\n", "SXJGHWenyF+RHPwcuO+uYZYZBTltXAjnhW1quHNQPwlZKagU4ks4G2z78u46JJXV4iJ/LtKjdNMv\n", "qXL51sCyZutiW8lhj67XwAoV/i+bct4/Xs5GamR386YAAAAASUVORK5CYII=\n" ], "text/latex": [ "$$\\left(x + \\pi\\right)^{2}$$" ], "text/plain": [ " 2\n", "(x + π) " ] }, "execution_count": 55, "metadata": {}, "output_type": "execute_result" } ], "source": [ "y" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Dérivée première" ] }, { "cell_type": "code", "execution_count": 56, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": [ "iVBORw0KGgoAAAANSUhEUgAAAFgAAAAbBAMAAAAKd1XFAAAAMFBMVEX///8AAAAAAAAAAAAAAAAA\n", "AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAv3aB7AAAAD3RSTlMAMpndu3bvImbNiRBU\n", "q0Qb3U6NAAABoUlEQVQ4EZWRP0jDQBTGv/6JTf+AxVmxODl2UCeHbrpZBxEEsYOTgmQo2EmyOLgV\n", "0SEoEkRBhUKWVhSEgjg6OAiidHJx00FEqFjfyyVeLi7xDXff970fd+8SIGodN5yoKOJmvBQdtvW3\n", "yDAQr/8DbuWjw83d6CyNUZJ0zJQ6pHR3gNgXxwNurxoigvYOaOVTnxyNuflesBvSaSBh6KuUasvc\n", "0mxaZJ1LSUqzETtuGKRqO7Sgj6WsGylJZSu+tV342bdiV2Hse13dcOFDss3FjYWyAms9qgLAXa4m\n", "XHiaLrP18URdpN7JZ4Pzm+uUbIkUtoAngYyT6ea80QUca2eMWeauBZzLC3iNTka6JELAnzmJJ46G\n", "RX6JXxjoFxOfWtaEZW0zMIQibx582+l83JOlMSjLw+FdnjyKLlv5rdyfQg/MOUvQVDjbxTfD/gOB\n", "d7YN4LU9gxprKm9mrYgVtiO8cB31Hmili67mmicmJ1QenDTxyPaAF1kJQ2pS/tcQYaqiNKEXFP+i\n", "uExZscBUyAftRdCwroaDgP9zUMoMdFWpe+/5AcqTWrMDyYm/AAAAAElFTkSuQmCC\n" ], "text/latex": [ "$$4 \\left(x + \\pi\\right)^{3}$$" ], "text/plain": [ " 3\n", "4⋅(x + π) " ] }, "execution_count": 56, "metadata": {}, "output_type": "execute_result" } ], "source": [ "diff(y**2, x)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Pour des dérivées d'ordre supérieur :" ] }, { "cell_type": "code", "execution_count": 57, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": [ "iVBORw0KGgoAAAANSUhEUgAAAGEAAAAbBAMAAACekfw3AAAAMFBMVEX///8AAAAAAAAAAAAAAAAA\n", "AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAv3aB7AAAAD3RSTlMAzRAiu5mrdu/dZjKJ\n", "RFRer8KoAAAB1UlEQVQ4EZWTP0gjQRTGv2xi1phdTcTGyilELIRLccVpYworqyBqHbXwtDFoIYJw\n", "iI1XHCoWlgbEQm20tdCgiIWC6WzFP9h5QRAR/6zvzcwqExJhXzHz3ve9376ZZRcIEk5/X5B26j3C\n", "UEBiBQepYMg/dBeDEcB/EZRYDQo4mXJir1z4qrc53ZB1IxAe6NnhPJyWSsUlSh1uwaX15wPQBueN\n", "20KClmqRAHbvbwRaB4kYBha4cdbotlJGSeai55FkE/EXmKBxmDda6nNG2e5XTLQISbhLvij3MiJ0\n", "qV0mKNYEwO/Omh65OJEKfOKP53mPypWGItxXKiJ5oAObuWWDcE7vuqZSQF1CyfIeQCxNZewSOMS5\n", "uFaWnrGPI3uHlPolJWuin6vaIiDQpI3PUyEdFaTFS9qQp7KzXDEBPPECJ5lsnksmM5TGszFWTOIW\n", "4ZQ6FeIvsLiBjpFTe02+QVD2+SZ5RjwDmwi++ZVdwqTq9IkfoqFIinHzzrPjMdKcLNz3SClMIIdP\n", "zKC2QGVNhkXafz+Po8Xznimnudbo5OyWNL6IXkQLJIV4UHmsGII/Q4u/DFMX6vP3HTfvZ3LXX4Kh\n", "IULXrxZWopLz3R9V5WldlZ6jtHVtfQDvsmc+fdUw/AAAAABJRU5ErkJggg==\n" ], "text/latex": [ "$$12 \\left(x + \\pi\\right)^{2}$$" ], "text/plain": [ " 2\n", "12⋅(x + π) " ] }, "execution_count": 57, "metadata": {}, "output_type": "execute_result" } ], "source": [ "diff(y**2, x, x) # dérivée seconde" ] }, { "cell_type": "code", "execution_count": 58, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": [ "iVBORw0KGgoAAAANSUhEUgAAAGEAAAAbBAMAAACekfw3AAAAMFBMVEX///8AAAAAAAAAAAAAAAAA\n", "AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAv3aB7AAAAD3RSTlMAzRAiu5mrdu/dZjKJ\n", "RFRer8KoAAAB1UlEQVQ4EZWTP0gjQRTGv2xi1phdTcTGyilELIRLccVpYworqyBqHbXwtDFoIYJw\n", "iI1XHCoWlgbEQm20tdCgiIWC6WzFP9h5QRAR/6zvzcwqExJhXzHz3ve9376ZZRcIEk5/X5B26j3C\n", "UEBiBQepYMg/dBeDEcB/EZRYDQo4mXJir1z4qrc53ZB1IxAe6NnhPJyWSsUlSh1uwaX15wPQBueN\n", "20KClmqRAHbvbwRaB4kYBha4cdbotlJGSeai55FkE/EXmKBxmDda6nNG2e5XTLQISbhLvij3MiJ0\n", "qV0mKNYEwO/Omh65OJEKfOKP53mPypWGItxXKiJ5oAObuWWDcE7vuqZSQF1CyfIeQCxNZewSOMS5\n", "uFaWnrGPI3uHlPolJWuin6vaIiDQpI3PUyEdFaTFS9qQp7KzXDEBPPECJ5lsnksmM5TGszFWTOIW\n", "4ZQ6FeIvsLiBjpFTe02+QVD2+SZ5RjwDmwi++ZVdwqTq9IkfoqFIinHzzrPjMdKcLNz3SClMIIdP\n", "zKC2QGVNhkXafz+Po8Xznimnudbo5OyWNL6IXkQLJIV4UHmsGII/Q4u/DFMX6vP3HTfvZ3LXX4Kh\n", "IULXrxZWopLz3R9V5WldlZ6jtHVtfQDvsmc+fdUw/AAAAABJRU5ErkJggg==\n" ], "text/latex": [ "$$12 \\left(x + \\pi\\right)^{2}$$" ], "text/plain": [ " 2\n", "12⋅(x + π) " ] }, "execution_count": 58, "metadata": {}, "output_type": "execute_result" } ], "source": [ "diff(y**2, x, 2) # dérivée seconde avec une autre syntaxe" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Pour calculer la dérivée d'une expression à plusieurs variables :" ] }, { "cell_type": "code", "execution_count": 59, "metadata": { "collapsed": false }, "outputs": [], "source": [ "x, y, z = symbols(\"x,y,z\")" ] }, { "cell_type": "code", "execution_count": 60, "metadata": { "collapsed": false }, "outputs": [], "source": [ "f = sin(x*y) + cos(y*z)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$\\frac{d^3f}{dxdy^2}$" ] }, { "cell_type": "code", "execution_count": 61, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": [ "iVBORw0KGgoAAAANSUhEUgAAAPgAAAAVBAMAAACZJT5kAAAAMFBMVEX///8AAAAAAAAAAAAAAAAA\n", "AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAv3aB7AAAAD3RSTlMAEM3dMnarIkSJZlS7\n", "75nN5rpQAAADo0lEQVRIDc1VTWhUVxT+3sxL5j8+BIVAxXF0IegiVERKuxikdeHCPBcTCaJGUfxZ\n", "lNkpbSIDxYULySikNl3oYCtiF2XEhQiKg/gDohhF0ZUMQQVBagJp/a3T79x335t596Vx6yEn7zvf\n", "Ofc79517XwJ8Xrb+E9uxa50Fv3cGc+CQaroIiEcs0YhQYWI4FHYXQyFw8OU1zdgP2ylDdScz4qbF\n", "HJMx4iXhOB8OrSGcrHiU/badMlRjzIibNmgSRtxVDRN+vVVWfLeD3ES4QiK/SmdExBSS1COd/79H\n", "zmsSpBdplHEVyPWheyZIBsBQtbg/ccOSfQZhhj8ZRKzuEbp5bDponvg2KI2ormBK3DOrtPvQGGGq\n", "yT0p3PsMiXsMXozX0Vv6BfbmUp4FJ+gLBg4MutYJFyOOWkAKujlRfBqqePgfrH3y3YBLKqIqKuKe\n", "9eI390vCbA3wcPUyuqaAkbr9yF6DrPsFIFO5xf1U06vj+VRmAtv4yeRJ0trNR6uAKr4BrHQTr5mM\n", "qJ4iKe7ZUZx0LhDG64DCifoksg2ggMxMvAp75ogDquJrvoeTms6Uf+zJ4zabyo5o7ebfMFLFbH4H\n", "eMcwonqe5HnMXyW2DA5WMgZyFXjYwkPMc2F9JNnPHb2Nf9gkBY/55og1WJVz8TcHPCVsR/OeBiNV\n", "zOZXITURVfSTFPdNirwyb0FyGv1FJIW+UgE+WMdaTWI2h+wKGHVs3mvVPFUoLL1fKDQlNyC/VDGb\n", "czR+805VszmFLK6SAXHGxJzqFqr4b/5xP9JvmOPYuekixzOJrgnAv8j+2DNN7Ad/WNxubqpCvpn2\n", "d3OOl2sjGbkaHs4OyWqeOTK5JpJvLgL7GPPCZZxt6HKYZUnkwm0AeDyquN08ohq6cMn32akE+yI1\n", "BI17Gkm5KyM1HLcfIFa97OAm45857/pd/AC8xChX9DRJ0vSbJ74af94HqGLOXI89ovoXV4grs3Zt\n", "HPxDEKeosbV362sS1veDZRwc34PDp89WGHNYC0sLfq3xMfaqzD/RQtJ081ir1WJzKV7X2r6utfPS\n", "v0OzqC7nCnHD1rRjnnrU4mzo2yTBiA78M/dz4aehmpgAxE370yd2YHHFxx3PdFMH3XlLLuCYDpM1\n", "DWZ9GKoplwdMNy1b1sxqXDdzKpaDF4u7WS638oSfNkN1PleImxb82z+0tWjmVDys2WTpKVGgOmtt\n", "QBqq8gL+SwQ1AniP5zT1UQQVZwI0Nwippjld+n+InBDEnxdn1wAAAABJRU5ErkJggg==\n" ], "text/latex": [ "$$- x \\left(x y \\cos{\\left (x y \\right )} + 2 \\sin{\\left (x y \\right )}\\right)$$" ], "text/plain": [ "-x⋅(x⋅y⋅cos(x⋅y) + 2⋅sin(x⋅y))" ] }, "execution_count": 61, "metadata": {}, "output_type": "execute_result" } ], "source": [ "diff(f, x, 1, y, 2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Integration\n", "\n", "L'intégration est réalisée de manière similaire :" ] }, { "cell_type": "code", "execution_count": 62, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": [ "iVBORw0KGgoAAAANSUhEUgAAAKAAAAAVBAMAAAAz5vjYAAAAMFBMVEX///8AAAAAAAAAAAAAAAAA\n", 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"opOG6XhfX3KiErmoLJ7Xs8ndqnP0Iq1JdCXA7Wu05RPfDp8G5e6e/BPXvsCu+RApAUZnE9FJwr4o\n", "sqQ6RL2IVC6yIBGNrl70jps630P0kSJslRVDucgaE42uXnTCU3/0EDXPaaOyomndjjLRKNGLsqx2\n", "xkSi6uz+iCWDR7nIGhONEr0oy2pnLEqXZxcxakYLrsMbvFwEmV7kB0+F7fknnYSRrUxFmeCYylUB\n", "I1HpFfxp5Gws8GRii4/w7nSyxbtpFhyeTuR4Lspeg3OXxnA5n6YtcoEjdmAt50w3877wCP0HeOrr\n", "WL/nQWgAAAAASUVORK5CYII=\n" ], "text/latex": [ "$$x \\cos{\\left (y z \\right )} + \\begin{cases} 0 & \\text{for}\\: y = 0 \\\\- \\frac{1}{y} \\cos{\\left (x y \\right )} & \\text{otherwise} \\end{cases}$$" ], "text/plain": [ " ⎛⎧ 0 for y = 0⎞\n", " ⎜⎪ ⎟\n", "x⋅cos(y⋅z) + ⎜⎨-cos(x⋅y) ⎟\n", " ⎜⎪────────── otherwise⎟\n", " ⎝⎩ y ⎠" ] }, "execution_count": 63, "metadata": {}, "output_type": "execute_result" } ], "source": [ "integrate(f, x)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "En fournissant des limites pour la variable d'intégration on peut évaluer des intégrales définies :" ] }, { "cell_type": "code", "execution_count": 64, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": [ "iVBORw0KGgoAAAANSUhEUgAAAFAAAAAVBAMAAAAjqnRBAAAAMFBMVEX///8AAAAAAAAAAAAAAAAA\n", "AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAv3aB7AAAAD3RSTlMAIpm7MhCriUTv3c12\n", "VGZoascqAAABtklEQVQoFaWST0gUYRjGf+PO5LjuTlOd7DQY6EEPG4EniT116LILQR46tIRRFOjo\n", "wRW67E1aPBgUmHqwe5GXIAhx3UsEBksXsUt786CH8g+KiOvz6S457Hbqhff9nt/zvnzzzTcD/xt2\n", "9cIOXhqs7luqzXEtYg3DfdoPI14dPkXMTngMbyPeOSTCiCmcg9F0xDyDDj/iOTOsZFsOFiNzsGz4\n", "Yxac3nyFobVJ7O9rS/LGlUP9WG8ozpoLMOweqHRV7Ff2O2Kle7Aovq0MN0j8dgbLPdJbynhK5QvJ\n", "vXiIvfcySyj+oIur9BFLOVg58U/lDaWzo5KpwEF8d1WSWXkscLkEdwxnIJHT6h6r/CrArvOiFkhr\n", "EHefTBrXoBn8geX/3XHnCp75AHo0bUt8M30TReyAhG/OSLIjwD1chxE1zMvEcgyQDLgkvcX1/Pgz\n", "ia4qY/ZrOsONLDfFE8q2lHvC3c3tz9LdrNRqRxLOo68+V/NPmdreLIjNhTtPyn8IvHnDZrhlxHWc\n", "s1Oed62ZllMyvQCe87BQ77eX6qJ50UHfM9jwHzRE86ofd7qcbvjm/f4RVvVCw/M5BdApY4zMOxXO\n", "AAAAAElFTkSuQmCC\n" ], "text/latex": [ "$$2 \\cos{\\left (y z \\right )}$$" ], "text/plain": [ "2⋅cos(y⋅z)" ] }, "execution_count": 64, "metadata": {}, "output_type": "execute_result" } ], "source": [ "integrate(f, (x, -1, 1))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "et aussi des intégrales impropres pour lesquelles on ne connait pas de primitive" ] }, { "cell_type": "code", "execution_count": 65, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.text.Text at 0x13180a20>" ] }, "execution_count": 65, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": [ "iVBORw0KGgoAAAANSUhEUgAAAXIAAAEPCAYAAABfmE8WAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", "AAALEgAACxIB0t1+/AAAG8RJREFUeJzt3X20XXV95/H3h4TwIEGkYXhIgkTJEyAIgYA40GCCBtox\n", "U0dLsaj0YRXbhto6o4h2VdZ0quN0qlbpYEqB1SlqpqMUwhJBQO8IFpDYkBBIIAFTk4A8KIJgkIR8\n", "54+9rz253HvPuffuvX977/N5rZW1cu7Z9+zPTe793N/57d/eWxGBmZk1116pA5iZ2cS4yM3MGs5F\n", "bmbWcC5yM7OGc5GbmTWci9zMrOFc5GZmDTc5dQCzOpO0EDgEeDEibk+dx2w4HpGbjW5uRHwNOCN1\n", 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"MfDFfJDxCKOcbOkTgszMGs63ejMzazgXuZlZw7nIzcwazkVuZtZwLnIzs4ZzkZuZNZyL3Mys4f4/\n", "LlioDvf2u0IAAAAASUVORK5CYII=\n" ], "text/plain": [ "<matplotlib.figure.Figure at 0x130d6cc0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "x_i = numpy.arange(-5, 5, 0.1)\n", "y_i = numpy.array([N((exp(-x**2)).subs(x, xx)) for xx in x_i])\n", "fig2, ax2 = plt.subplots()\n", "ax2.plot(x_i, y_i)\n", "ax2.set_title(\"$e^{-x^2}$\")" ] }, { "cell_type": "code", "execution_count": 66, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": [ "iVBORw0KGgoAAAANSUhEUgAAAB0AAAAVBAMAAABI7vhRAAAAMFBMVEX///8AAAAAAAAAAAAAAAAA\n", "AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAv3aB7AAAAD3RSTlMAInarRM2ZVBDdiWbv\n", "uzJCz3LGAAAAr0lEQVQYGWNggADG/2DwAcplYHaAsSC0KCqXIR2VzzEBlc9WgMrvROUyrEblg7Tr\n", "Ax3wDSrMtIGBa12RtKIAkO8CxJwMDM8ZFjI9ADKZjgIJJSBmuMDcACQl9B0YGEC28xkAVQFB/wUG\n", "7gVAmm0DfwOQYmD7yMAJYvQ38DsAKQbGbwy7QLQmA88CEM1g3zADRN1mYF4AohneL08A0zCC9WgD\n", "jAmm2WGuhIkGwhhAGgDwdic2xV4k0wAAAABJRU5ErkJggg==\n" ], "text/latex": [ "$$\\sqrt{\\pi}$$" ], "text/plain": [ " ___\n", "╲╱ π " ] }, "execution_count": 66, "metadata": {}, "output_type": "execute_result" } ], "source": [ "integrate(exp(-x**2), (x, -oo, oo))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Rappel, `oo` est la notation SymPy pour l'infini." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Sommes et produits\n", "\n", "On peut évaluer les sommes et produits d'expression avec les fonctions `Sum` et `Product` :" ] }, { "cell_type": "code", "execution_count": 67, "metadata": { "collapsed": false }, "outputs": [], "source": [ "n = Symbol(\"n\")" ] }, { "cell_type": "code", "execution_count": 68, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": [ "iVBORw0KGgoAAAANSUhEUgAAADsAAAA9BAMAAADhUgydAAAAMFBMVEX///8AAAAAAAAAAAAAAAAA\n", "AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAv3aB7AAAAD3RSTlMAMruZq91U7yJ2iWYQ\n", "RM2bSrnQAAACDklEQVQ4Ec2VO0gcQRjH/8ednne7eyexsvHAyspXUiWCNiKH4KPSgHqr+CgvKRMi\n", "dyQQCETu4DQBqyuTJllQDKRa0thY2EggjdeKonfgG3Uy+5idzT6mzlfs/r//j+9jZ2ZnBjDiEZD4\n", "WjVlwKNtGPiJxQBiWUvAJNJ6GF+CfI24Go4jd2jSxLgswLS5AMsNpPfDqzEr+PIVoOIZ9wlv9S6X\n", "RfLpADeAt3fuzKt/PRFiJP4HfEZ43DgjcD4tT1pNc29tu3DlxzEywUwppzLpVCN3zjxEa0xynCEa\n", "M9HHFMcSuWYmEraKjlyMMrPngqnA9y7pDfRtUyGXIozvV7qIp0hNhOXCrQgjT/ZFPEreiDByru5p\n", "voJ1u6g0I6z+XBRhpeGnyf4BZmZUpvj7C8bsRH7FXUdNI1O0ktSQY3KxipI9Gd3cdKu8bmZJPqpm\n", "N35uJT+KjrnjKCBp/QWuUSkvXHjL0iWVefKgWfCn68N6GUpVaaVAfs3o+3FrR/R2lukx17G8oFOS\n", "ejg1Y4quhPlPRtQ5tDRQIMSoG+QLRMw9Jsv3kOoGCo7mOmK1YGS4dCulNT2Ux6vI69VQTOf6m2QM\n", "yhv0VqBxQI+1T15Ec+NWEAW9FUThx9Kz9opTEYAPtXgxZkzrMeDHHzdcB7kfI4vfguZ4iccRs/lR\n", "UPOWOmbnWTm9FTyhDGFTsz3jVvg3/gK+T7KE+MaJIAAAAABJRU5ErkJggg==\n" ], "text/latex": [ "$$\\sum_{n=1}^{10} \\frac{1}{n^{2}}$$" ], "text/plain": [ " 10 \n", " ____ \n", " ╲ \n", " ╲ 1 \n", " ╲ ──\n", " ╱ 2\n", " ╱ n \n", " ╱ \n", " ‾‾‾‾ \n", "n = 1 " ] }, "execution_count": 68, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Sum(1/n**2, (n, 1, 10))" ] }, { "cell_type": "code", "execution_count": 69, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": [ "iVBORw0KGgoAAAANSUhEUgAAAJ8AAAAPBAMAAAAIUwCQAAAAMFBMVEX///8AAAAAAAAAAAAAAAAA\n", "AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAv3aB7AAAAD3RSTlMAzRAiu5mrdu/dZjJE\n", "iVS5jG64AAACW0lEQVQ4Ea1TvWsTYRx+7i5p01wvDQ4OingFh042oILjLQ6CkoCmk6ERbCk6NFUK\n", "QpFEW4og+AHa1RMHpYtdHcTgomMHUVCO3O6gVo1UCufz/t67e/0DfDmee/k9H/d7Pw7APqhhXXUv\n", "8OU2YLeWa3g5F0XbQHRjy91oRZEAnOh9TI0YLJ8z0hqMGzi+QwYoJUmHr7EAU6GzihdJkrQx2S9W\n", "y5wNBXAClT+pwan7EFqDcePIjA60PlxTudcDLACXcA4YAQ6hEBRrQFsA3Ri72mCfWmSgojUYNzCq\n", "A2nncJ4E+A4MfC73M8o/WPL4bAngXd8mqQ1dX9OiUR9P3RmfljwrsH8C3T4w3kGhBxlvFQqoJeeB\n", "hR7LAsbNStbh5jI3+KkV4CY7DKkEJg5+VAeFIAerkRnYodBaM5K7Mx4F39mD3WHgADjcAT4B0wdQ\n", "irnikIECrx5xlncotNYYd8ZTiNPcLwZanQo7tL8xcAfFW3wpUgDenczADoVONbk745VntvZFBeLZ\n", "yiDGaJVLvo/yHrCkSAHgtm86FDrVGHe2hxe5c2+2JRDgneCFRKmKMk/hoQpUcAyo81NyLdih0Frz\n", "rzs9FB7F7NH5+cV1bh/2c4ltOUF2aA9ZEUh81EMTWOhxAQLqIHN3GtgAHtBYCvC85vL+vWahwj3s\n", "wf3FusAa8LhmAoXWGuOG8NNV/lblVRonAiz5kyHwlRqcxVSMigoU2ETltzbwqvopLRrjRnFh9zLG\n", "2nBac5R49WHoNc8w4WRM8JpX+Pfc5UxgvHm+rw2YubcRalprcje1/3f8Bd5J4rfkCMk3AAAAAElF\n", "TkSuQmCC\n" ], "text/latex": [ "$$1.54976773116654$$" ], "text/plain": [ "1.54976773116654" ] }, "execution_count": 69, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Sum(1/n**2, (n,1, 10)).evalf()" ] }, { "cell_type": "code", "execution_count": 70, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": [ "iVBORw0KGgoAAAANSUhEUgAAAJ8AAAAPBAMAAAAIUwCQAAAAMFBMVEX///8AAAAAAAAAAAAAAAAA\n", "AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAv3aB7AAAAD3RSTlMAzRAiu5mrdu/dZoky\n", "RFRd1xrCAAACpklEQVQ4Ea2Uv2tTURzFP3lJTZqXH6VEN+1T1FY6NKAO6mDADi7aCG0RqhBEYi1C\n", "41AUFFu1iIIWB131qYjikgwtVSs0Drp0ySI4SRadhNK02F+Y+L3f2/Yv8EHOO/eec0/uvd9vArSi\n", "jzv9Sd6xrAVnYCQNx2ZOG+0L7Js+J0Q9Cu03zm8oyoJ93WWCM5NVmTxUMwrOTs7IqzljocMP3iWY\n", "Zd6X8RPYxYG09Vhjlm1Vqyjbj/uXwyTWYG+/DQyU6BbH9YyFQbhIuE7yMYTHiY7htqAehYRPsEUV\n", "yy7AfUarrMrqsA0sygkh+CJjYQFmvfgjumTZ0RShHIka6lFwMzjyVaJY9gCGy98rjizbDDxp8nAD\n", "GQVnCUYrMmGOXEqRzBGvox6F+JLnWsWyNk8C0SNvBi7MjUjAKxNo4LbsUFawHaLpFF1Z4suoxxqH\n", "1z5aBWXifO5BICvEHtlZ8JnAKUiWwiy0FYh9EEc7KY6XiK2rxxppakhzGMUyiK/D3FPJ2wxseJz1\n", "XCRQIVBI6A7flyltBRrPHjXy+vNKRRXLNtrDfbgVaOpTrPw2gQq8uTlbFTVyJ1GWwK6SObJ6FKI5\n", "ilZRJsZe+Uipvc0dmksrvixJoGPAiMNeME2o3okEJrMk6urxjdGPeMSsokxScnAQeqpbgVK84o98\n", "fvjeLQMFCdxBskZo7Vc+v3JJ+j1aM1UuVhS6RB9SRRn8JJiWy+jxtwLn5X7EFclYeJuOLZpB86KM\n", "xwmP0dSCehQiaTihimXyiw2nx6XUMm+qLO0bKjkT4khmLFz1dvtEfebNXqUpn9FRth41xluJWsWy\n", "IzNTl3lHYlmqPrg6RHMOpvrL0tM9f3wFt++UJH1TnGwM0XntiozVo/B1wPw5GEVZW6OxSryvtyKT\n", "//n5B+ab/k/pD/tGAAAAAElFTkSuQmCC\n" ], "text/latex": [ "$$1.64493406684823$$" ], "text/plain": [ "1.64493406684823" ] }, "execution_count": 70, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Sum(1/n**2, (n, 1, oo)).evalf()" ] }, { "cell_type": "code", "execution_count": 71, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": [ "iVBORw0KGgoAAAANSUhEUgAAAJ8AAAAPBAMAAAAIUwCQAAAAMFBMVEX///8AAAAAAAAAAAAAAAAA\n", "AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAv3aB7AAAAD3RSTlMAzRAiu5mrdu/dZoky\n", "RFRd1xrCAAACpklEQVQ4Ea2Uv2tTURzFP3lJTZqXH6VEN+1T1FY6NKAO6mDADi7aCG0RqhBEYi1C\n", "41AUFFu1iIIWB131qYjikgwtVSs0Drp0ySI4SRadhNK02F+Y+L3f2/Yv8EHOO/eec0/uvd9vArSi\n", "jzv9Sd6xrAVnYCQNx2ZOG+0L7Js+J0Q9Cu03zm8oyoJ93WWCM5NVmTxUMwrOTs7IqzljocMP3iWY\n", "Zd6X8RPYxYG09Vhjlm1Vqyjbj/uXwyTWYG+/DQyU6BbH9YyFQbhIuE7yMYTHiY7htqAehYRPsEUV\n", "yy7AfUarrMrqsA0sygkh+CJjYQFmvfgjumTZ0RShHIka6lFwMzjyVaJY9gCGy98rjizbDDxp8nAD\n", "GQVnCUYrMmGOXEqRzBGvox6F+JLnWsWyNk8C0SNvBi7MjUjAKxNo4LbsUFawHaLpFF1Z4suoxxqH\n", "1z5aBWXifO5BICvEHtlZ8JnAKUiWwiy0FYh9EEc7KY6XiK2rxxppakhzGMUyiK/D3FPJ2wxseJz1\n", "XCRQIVBI6A7flyltBRrPHjXy+vNKRRXLNtrDfbgVaOpTrPw2gQq8uTlbFTVyJ1GWwK6SObJ6FKI5\n", "ilZRJsZe+Uipvc0dmksrvixJoGPAiMNeME2o3okEJrMk6urxjdGPeMSsokxScnAQeqpbgVK84o98\n", "fvjeLQMFCdxBskZo7Vc+v3JJ+j1aM1UuVhS6RB9SRRn8JJiWy+jxtwLn5X7EFclYeJuOLZpB86KM\n", "xwmP0dSCehQiaTihimXyiw2nx6XUMm+qLO0bKjkT4khmLFz1dvtEfebNXqUpn9FRth41xluJWsWy\n", "IzNTl3lHYlmqPrg6RHMOpvrL0tM9f3wFt++UJH1TnGwM0XntiozVo/B1wPw5GEVZW6OxSryvtyKT\n", "//n5B+ab/k/pD/tGAAAAAElFTkSuQmCC\n" ], "text/latex": [ "$$1.64493406684823$$" ], "text/plain": [ "1.64493406684823" ] }, "execution_count": 71, "metadata": {}, "output_type": "execute_result" } ], "source": [ "N(pi**2/6) # fonction zeta(2) de Riemann" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Les produits sont calculés de manière très semblables :" ] }, { "cell_type": "code", "execution_count": 72, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": [ "iVBORw0KGgoAAAANSUhEUgAAAC8AAAA9BAMAAADPFy0PAAAAMFBMVEX///8AAAAAAAAAAAAAAAAA\n", "AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAv3aB7AAAAD3RSTlMAMruZq91U7yJ2iWYQ\n", "RM2bSrnQAAABW0lEQVQ4EdWUP0vDQBjGn2g10UQidtLRyUUquKmDi0NHP0C1gsWx+A2CLgqChaqD\n", "U1YXm9nFfIQOSqGLnQoiSCoKDsp5d+mfu+vr6p9nCHfvL+/DXfLwAkIzwMRVKJfaI7sB3GBXq6Wb\n", "ErAJPx4mJXjvsIsUsD4wFn0HKiTgViTwOvDrVAcK5Kn2gCp1j8PtPCZX1oad/mDFZgN11PM5d+zl\n", "SOiWtadVAORe5b7MYr2OWhe8GfV/DpoLx2dpYIwLLs1XeC6FdGAVt+CkX1MHnvcJNyE6MJ4g06LA\n", "aAt+FAuiW8EOUY5DApzWce3KX2Z03AMHF6LBtJK13we5NDc1I1dOlrHFAN4cY+1gcFRAZjfBiEiw\n", "ll31pZ9Y80lHSkw6WnzS0VKBuzpb7b+lgYfIDjLPXE+ACk7OlUGlAuTRIK2wj2VLWj3qVk6Cwk6v\n", "hU+6vqbWcRl1d2LS9fQFXLmvHr1wylwAAAAASUVORK5CYII=\n" ], "text/latex": [ "$$\\prod_{n=1}^{10} n$$" ], "text/plain": [ " 10 \n", "┬───┬ \n", "│ │ n\n", "│ │ \n", "n = 1 " ] }, "execution_count": 72, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Product(n, (n, 1, 10)) # 10!" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Limites\n", "\n", "Les limites sont évaluées par la fonction `limit`. Par exemple : " ] }, { "cell_type": "code", "execution_count": 73, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": [ "iVBORw0KGgoAAAANSUhEUgAAAAgAAAAPBAMAAAArJJMAAAAAJFBMVEX///8AAAAAAAAAAAAAAAAA\n", "AAAAAAAAAAAAAAAAAAAAAAAAAADHJj5lAAAAC3RSTlMAzRAiu5mrdu/dZmiL4QAAAAAjSURBVAgd\n", "Y2BgEGJgYDDZxMCgEgYkGNhJJVgzdmYB9TEwAACPpQrvlUCHcAAAAABJRU5ErkJggg==\n" ], "text/latex": [ "$$1$$" ], "text/plain": [ "1" ] }, "execution_count": 73, "metadata": {}, "output_type": "execute_result" } ], "source": [ "limit(sin(x)/x, x, 0)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "On peut changer la direction d'approche du point limite par l'argument du mot clé `dir` :" ] }, { "cell_type": "code", "execution_count": 74, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": [ "iVBORw0KGgoAAAANSUhEUgAAABMAAAALBAMAAABv+6sJAAAALVBMVEX///8AAAAAAAAAAAAAAAAA\n", "AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAADAOrOgAAAADnRSTlMAEKvvZom7mXYyzVQiRHuO\n", "wuMAAABqSURBVAgdY2BgEGAAAhDBqPzYgYEhrKiBgYFtAscrBqkNDFMZGDwZGPYlrGRgYDnAAJRg\n", "BSlhcAAxGfJAzAYwM/klULsDgyMDA0eM8QEGjgMM7AwM4QzzbBlcgRLlahsYGOuMA4DK4bYBAA/G\n", "FFwDPj79AAAAAElFTkSuQmCC\n" ], "text/latex": [ "$$\\infty$$" ], "text/plain": [ "∞" ] }, "execution_count": 74, "metadata": {}, "output_type": "execute_result" } ], "source": [ "limit(1/x, x, 0, dir=\"+\")" ] }, { "cell_type": "code", "execution_count": 75, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": [ "iVBORw0KGgoAAAANSUhEUgAAACMAAAALBAMAAAAHCCkxAAAAMFBMVEX///8AAAAAAAAAAAAAAAAA\n", "AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAv3aB7AAAAD3RSTlMAEM3dMqvvZom7mXZU\n", "IkRJD0iWAAAAf0lEQVQIHWNggAMBEAtMQIUYw74VMDB0Lt0AV8LA6cD9iUHoAIMHQqiEgeH8BBUG\n", "BvYLDELGIKDCANTAAdLKAMIQADJjPogLYkAAiDXtCwMDI0gYAgoZGLh70y4wcF+AiTBwMTB0Mfjn\n", "MVTARRgYV0UeYGBcn9aAEII6XICBAQCMCRoksfUeRwAAAABJRU5ErkJggg==\n" ], "text/latex": [ "$$-\\infty$$" ], "text/plain": [ "-∞" ] }, "execution_count": 75, "metadata": {}, "output_type": "execute_result" } ], "source": [ "limit(1/x, x, 0, dir=\"-\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Séries\n", "\n", "Le développement en série est une autre fonctionnalité très utile d'un système de calcul symbolique. Dans SymPy on réalise les développements en série grâce à la fonction `series` :" ] }, { "cell_type": "code", "execution_count": 76, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": [ "iVBORw0KGgoAAAANSUhEUgAAAVoAAAAwBAMAAACiZ6/NAAAAMFBMVEX///8AAAAAAAAAAAAAAAAA\n", "AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAv3aB7AAAAD3RSTlMAzRAiu5mrdu/dZlSJ\n", "RDLkM64aAAAFz0lEQVRoBdVYXWgcVRQ+ezeb/d9kiyIiIUOVJg+BDlgfDEgWqdInEyWJfSi6WGjU\n", "B10iSX0omBYFRZSoFX0QXfAH9SVLS+2DYLdiuijB7oNVEGn2QcEHoUm0qbE/67l35mbu7JyzyTYL\n", "0YHs3vN95zvz5c7dO2cGoIVj/gGLya6c4xjIMRKAzDODRZbcMpGyO7vpIpFiJEczkFxhCIB0fZXl\n", "tk7E7cyfdJVIObFEMzDQxO1XFiNqBxyrhq9zdSLMrMNkE7dVrlp7cP6y9tfoM2RONHH76JRFq9qD\n", "xgtMnb7bGCIleLfCTuYYWVvgebYK9yv7sIlbAMH8DtjTtEIkRtjszFWSEoUmbjtswf4OyGqtgb/A\n", "RVrQXwv/QzLJsbHLRZJBMG6H+WXCiTaNZ+748n46OW4njtEMNDGUslL8xWLKbQrum77vSCFWry8F\n", "shWTqZyzGxlx+OBPFYCPVkuNDMZKBQsHCGrrkCgn3ol3U3V4ZgA+K7xBSRDjVYygJRiv2UoyMHuy\n", "BM98Deetk8xZeBUjaAkWEMrRAp6x4BZagiivYiSpSpFhSLirQMII8swVTtJURYp6wjMkzoCXamDR\n", "FMtkbuAccger8gkyVSeM5kUB2PnySTBIWi9A1GpEZcwzJ6JLMElJmqr8gl/dMFbCwY9+jo8WS8Mw\n", "SNIsk77asRQukhoAVtWQv8ONu05j/5PiqjWIYNfhvu/oXJYRE5NHPm+so2NWpRIu7Px2WS2BiL74\n", "QyPpWYA3Jav/AV1qu7+j+R8uPq4aiCHL9bK7BAhMYbRnZbvtNZx/In0MossSXG/5uqqAfcgQwF3j\n", "2q2wG2QAdwYQDbAMUcXV8IxK0BUj+X5cBnWJveoqIVSUcxsvAkS126ReJToH4LQ3bBixDFHFlfKM\n", "StAVF61RjHstfCbWziCdS3YDyB76v+b2bSih22H8S8zgh3N8PG+h0xnCrds8yTT9/8qx/2AZfgZ5\n", "RpV2K6ZvqGgffqonU6dbU1ga5zowt0bzxHri/w/eE8+YbiPO5X8JsY6cv1vDm2PQrdE8bYPbjiXp\n", "PSN3sFDe3+PJ9wOBuTWaJ+227h2yFh4so2ewy5Oo8+NNuuBIDUbluLRbMaTmVi2C+Ii/Wwvjs5/j\n", "NpXN3n40m0Uejyvq82w2+242e6sa+z9YxlfFp+GZhnN1qnU7ZCMs59bs8ci5Ba950jMoVf6DZfQM\n", "+tNlxDMq162Yku/GRI+EHLdetybfawVWgtE8sZ7WV4I6kfnBe+IZpXfPJepFvL9WJdSZ8/d41J5g\n", "Nk/b4BbmVk9eOCjNqv3V7NZw/408ufa04vSVMpun7XCbHl57zDEk59Ls1nzvg4grZbq9Z/Qhp4j6\n", "NJlU5XuP8aq4gt805zGwAyA8trcG4sBUTdNmRRd7TXPOd6hgxOmiETjDnz1E5OG8Wk1Bpgce9vLW\n", "q2jBcc2tM6rt2wWp6xAqibc0bZxLQw2vG3Zbmtjwu9OC2AyZFSrDXoJwBdEXA5xq+54AeBk+BTgb\n", "oD3gd28oR5/4w2ZRbBacrTCQNCf3xuDhCgaJ53S5Db0C8GztQYAzVlCqkc6SHqnv931R0wDvMYzb\n", "fbTOFZQZt70WusW9ddHvyFdLdJthomxGG47jOCXEsfzHVJWAEUJBwmbcIv3BzsvotkBrFXrK5PaY\n", "wcbjxTKVI5aLXpPvT0BBH/XGxrkhpa+FrwFcIou6dZLmxD/iL75RdJxMEHUL9lskhYIy7zae29At\n", "WXVzoG9zNiRrAHPkUkBBssa7HQWBc9t0JRhnaXU4zQiOotsixaFgAFi30TwArtsz5sWmqtwclhyB\n", "u0kl7gnk3ErBF4cO/T0RUKl1ewrCNi6VOStAtwPAO+hzZB3cMfdThCsI3h1U25cZgaj9PJi3QarI\n", "TWLh9xbGZ0ltR1msP/gbCVrwl4G5Qzm39y7MPwXxkng9SLcBCeFTCe0W5sdrxAlcwTd1t8XzUlTb\n", "11uvr4GoTFNSL/V/PfoX0LjCXF+s8nwAAAAASUVORK5CYII=\n" ], "text/latex": [ "$$1 + x + \\frac{x^{2}}{2} + \\frac{x^{3}}{6} + \\frac{x^{4}}{24} + \\frac{x^{5}}{120} + \\mathcal{O}\\left(x^{6}\\right)$$" ], "text/plain": [ " 2 3 4 5 \n", " x x x x ⎛ 6⎞\n", "1 + x + ── + ── + ── + ─── + O⎝x ⎠\n", " 2 6 24 120 " ] }, "execution_count": 76, "metadata": {}, "output_type": "execute_result" } ], "source": [ "series(exp(x), x)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Par défaut le développement de l'expression s'effectue au voisinage de $x=0$, mais on peut développer la série au voisinage de toute autre valeur de $x$ en incluant explicitement cette valeur lors de l'appel à la fonction :" ] }, { "cell_type": "code", "execution_count": 77, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": [ "iVBORw0KGgoAAAANSUhEUgAAAvwAAAAoBAMAAABwRjOsAAAAMFBMVEX///8AAAAAAAAAAAAAAAAA\n", "AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAv3aB7AAAAD3RSTlMAEIl2mSJE3e9UMqtm\n", "zbsXyEShAAAJtklEQVRoBe1ab4gkxRV/3bPTszO7szv+wwMxTtyTBBPJ3N4Kgl6cnBcSPD0HPyRG\n", "5HYgB0qInmAOI6JuQqIxKo6ohPghN/g5YVdEFE24DrkjR/ByKxFFyeYGEkGIuud55tDz2Lz3qrq7\n", "urqrp7pnvgjWh+r3Xr3fe796U1Nd3TMAX7ScFXg1p7/B3VkxDOQwn7312zm8A9diKEQXBlJmZ26h\n", "EzAY4VpaHQGsQm9XlUKytw9uzA8shsI8hYHM8dZOuZmfbAJxWyNhKmYojbz8b+o519rkdnd9S3Gz\n", "Rs0/p6BQtAUSakCd2soPw7l91ZAl116+xU8fd36dblet7pwBrDoB/DOu5tde+stVVmvhEviTEtwW\n", 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ordre le développement doit être mené :" ] }, { "cell_type": "code", "execution_count": 78, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": [ "iVBORw0KGgoAAAANSUhEUgAABUUAAAAoBAMAAAArnobcAAAAMFBMVEX///8AAAAAAAAAAAAAAAAA\n", "AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAv3aB7AAAAD3RSTlMAEIl2mSJE3e9UMqtm\n", "zbsXyEShAAAQUElEQVR4Ae1dfYxcVRU/b3bnc2dnhxalhuCO2xqJgEy3JUELdlgLGCplJIhoCJ0E\n", "Av6hdCM2ikC7xYAiAkvEqBjtBEn8Q8lug0RsIR0VIjFgt4ZAJGwZP8Bv2kW+SgvjOee+j/vue/fN\n", "u+/NX6Y35L577zu/3/md8868ee/N2wJwrB3LwGAz8OTg6E4cANWyNecmYEmGQkeJgSTSWrm2mUCs\n", "B0nlXdAMgIKJBsXDZKkT46WIRkML/nmaWS49V24rXGwuIRkK/SQGssZrmtmauVgPkc478wyAYqA8\n", "Iri0ifFSxKPPVZWFNNN1acCMvWzWuiAOSWbTOZJZbNTkLgmFw7hAQnWpk1v2Nji+LS9EjUu7L++o\n", "+028A0ycpeJxbkYBmbuPCz/ehjyk5Ndrw6kAjBLjBnXPhDv0D6xv+udhs1IXYHIybI+6NtpRV0zn\n", "D//mLF3kPqr3w6PSPC7qE7BJQuEwLhBNi0t+KMDIt9b8Sl3TznO9I4F9Bt4BRluXBQiMAiD0SK/3\n", "TgiLMQ9ylNtWN5TKMDEOx9D8UMMZ+7cjmnXJqrRxGsq14ry0pBtmA8dRZ6lbDx7IcMutMNf09sRF\n", 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"TQF4KvRRBFVgkg8AC8jdCuMLrCcWVTAhTFB+DUUxS6YL1jR3CfkoPLuhQGz6uIJiHGT4tkQq07QX\n", "qk6NmrD83cTYtRW+MqNddyX+gJ4IMR7/MH3LbHycZ3klvR458hb+EJc77K3GHXGNCt8UQFIVtQP0\n", "syAL2NlhPYmp8GKhMoPymSX3TjXT4S45n5MKPNEPtNlfW6k4t1eN4W/ft7ZlDCIA+no8cY0S/jh8\n", "u3FLPYnvLoHyS+MNyAV/I+5LOPLk2rYlfGMA9qgvSjUoNw+AI+Bp2plfSkoFgD8RP7cSb5KIBfYe\n", "PRW/EbFLzkdM1KwEH2GBDO8LnfB1g9UcP7M1AGAUb3fi/fm/yoq+rHqKGs29kkW1czWVN8Y80yGj\n", "LbXNNSiZRwyVavZ14ZsCSKpiOb7mIwSUPtAQepJSEcH481Cg75QtNRju7cd/JgK7pHykxm5nO4PB\n", "bL+XnibfNeawelX4uPnZFz/wXbwyTFGj+cTVAeMc5Ta7RIxDRsDfxOGnAJIWQs2tUYCT28i5LUVN\n", "ndweX4LhHcwCjz3wZou7pNKQxmmFtjMayPbG9CybElC8gX892EqAQ1/3pqnRTWDhOTDRdz2fHIa7\n", "MF5L9F2PwV70SfZNASRUUWxjjToCClhdqCchFeop7BjDfxgMnxwiS3ke9u3gLjmfezyltyXcteSD\n", "AbANzSdwfwfWaMcch76sWooaJa14TbiTvt9M2zYCrMO3ARtQTHDB9Sjgs1XyfTcHkEzFMsAaZQHZ\n", "JlRQBepJGBATFBagfJRZClV875O7hHy+dP7FN0s5uayZkgDgHsBwTRve1yc5j6Kvz+zZs/f2uqk/\n", "+11r0oq1tq9qjreOIKbYgE/l8aSDzwhMG34qL6qS7/dwAMlU/GDPnjcfZAFjS1A5ynroqX6CgJgA\n", "7+vxPEpR0ZXMI9wl5PPl44S2b5pqYl2fCk7gUgOGzGt0J16Pmru2fRW65lB+Psr4MwP/Fl8sthKe\n", "+eiHyDOGZmB4IRbEZ9QAuAls3xhAQhUAdwILQIr8a6wnIRUT4D/ROjzDLAU8hi9yl5DPF2x2q2+a\n", "alLppIIT+NMTq35hTlKpWbeZo2xfY11zKNco4/Oz+K+fmLci1mj2ixMbp/FV4hVtc/wlUL4FbN8Y\n", "QEIVAO8ACyh3YGdd6ElGxQTwZ1gxyyy5/VCuc5dcmpSUz0rjlMN3p8Qj/IVeD2+AjNuqjW1jjO0r\n", "c/BIxxjLb8yxVmvlugSu6Q8TYRR/tJ/G/2vEbmP3WN9TP6+C8E0BJFQBH+o9IgRcteElW09CKiKA\n", "zIbdNstPp/BJKXcJ+RLk5BjkWAb+fzLwP2ZUzr856wUuAAAAAElFTkSuQmCC\n" ], "text/latex": [ "$$e + e \\left(x - 1\\right) + \\frac{e}{2} \\left(x - 1\\right)^{2} + \\frac{e}{6} \\left(x - 1\\right)^{3} + \\frac{e}{24} \\left(x - 1\\right)^{4} + \\frac{e}{120} \\left(x - 1\\right)^{5} + \\frac{e}{720} \\left(x - 1\\right)^{6} + \\frac{e}{5040} \\left(x - 1\\right)^{7} + \\frac{e}{40320} \\left(x - 1\\right)^{8} + \\frac{e}{362880} \\left(x - 1\\right)^{9} + \\mathcal{O}\\left(\\left(x - 1\\right)^{10}; x\\rightarrow1\\right)$$" ], "text/plain": [ " 2 3 4 5 6\n", " ℯ⋅(x - 1) ℯ⋅(x - 1) ℯ⋅(x - 1) ℯ⋅(x - 1) ℯ⋅(x - 1) \n", "ℯ + ℯ⋅(x - 1) + ────────── + ────────── + ────────── + ────────── + ──────────\n", " 2 6 24 120 720 \n", "\n", " 7 8 9 \n", " ℯ⋅(x - 1) ℯ⋅(x - 1) ℯ⋅(x - 1) ⎛ 10 ⎞\n", " + ────────── + ────────── + ────────── + O⎝(x - 1) ; x → 1⎠\n", " 5040 40320 362880 " ] }, "execution_count": 78, "metadata": {}, "output_type": "execute_result" } ], "source": [ "series(exp(x), x, 1, 10)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Le développement en séries inclue l'ordre d'approximation. Ceci permet de gérer l'ordre du résultat de calculs utilisant des développements en séries d'ordres différents :" ] }, { "cell_type": "code", "execution_count": 79, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": [ "iVBORw0KGgoAAAANSUhEUgAAAMQAAAAwBAMAAAC8i8hXAAAAMFBMVEX///8AAAAAAAAAAAAAAAAA\n", "AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAv3aB7AAAAD3RSTlMAzRAiu5mrdu/dZjJE\n", "iVS5jG64AAADpElEQVRYCcVVT0gUURz+nHXd1p1ZNeoQBA4V2UXag5ckcg4duimidYhoS9D+HFwE\n", "s4skQVEghUVYdGjDCuqSQYdu7qE/UIesiCiQFk06CKVGhlhuvzczb+bt7nvK5kAP9r3v933v9/v2\n", "zXvzBlA1o61VJcFSKiUJ73BENT8+r1JK469iLKHIqA/IYhBNGYVFT0AWwHdTbhGbDMxiWO4AQwvK\n", "wmhRWIwEZnFX4aClgrLQx/VRqUm8vf1nWqqUSj769sVU5ITWvBdaX8eLD7icyxU5OAruLGSLpNKI\n", "etxPDUlT1Ip0upp8i8/mlFRWK9LpatLEBoWoViQJsZONaQntUr+UklopStFzC0WcR8SWoHlBHlAr\n", "/rRYxsH6jAmkfF5Ek5FZ9IiEh9WKNwV47GI9Q+C5IPhQXy6fDaX92EdqxZ8DrHcD/UCvCSMtShxr\n", "XT39D3iQN6oV4NmWj3MZNjvMn42WiFvAFcZxV4b/uUWST18d/s3Sm0yviPYD6KWoYd6j1gC69LOI\n", 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"execution_count": 79, "metadata": {}, "output_type": "execute_result" } ], "source": [ "s1 = cos(x).series(x, 0, 5)\n", "s1" ] }, { "cell_type": "code", "execution_count": 80, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": [ "iVBORw0KGgoAAAANSUhEUgAAAF8AAAAcBAMAAAD1rn4EAAAAMFBMVEX///8AAAAAAAAAAAAAAAAA\n", "AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAv3aB7AAAAD3RSTlMAEHarIkSJZt3NVLsy\n", "75nQ6/gxAAACBklEQVQ4EY1SMWgUQRR9e+7u3e1dLkOEQCo3CaSTHGJjLNwmWHrYitwpQspYqY0W\n", "QRCbGBTBI0VOsIqF16SyyDYWNm6aFFZuTCUpvBNBEGF9MztzeysX3A8z/733/7s/N7NAwXBPjgp2\n", "6rY7uFTM8EG3PUYUlApY6n3ddIhrLaf5f0dVjHreCayNyD/AOZp99lJpX7PKBlDJ7Jku0aKz9ylp\n", "EdjDUcGNgRLXpDjj3xRY3mJJdul4xGwPDFPZCjQ9tg6BaVmc6mkJTscJgSeGquz5KXWGjT5QlaeJ\n", "RCoBH3ffEp8YqrIxVGKPvCYnPBw1fEkS4vNcM4urS77SjaEdSlo74PaNy1q4eP22VBhtAatT3qhs\n", "KWYM3zmbx4+5veeaxQP/KbOMKIQr3KEXKGYM+4pFLaYXXLewLt4oCXxtWKj2NTOGHTVBngabXALb\n", "uoGGHuG0L7nb7b763O3GhFdCbvZPbmoC8ENCFZygzqV+EmbCepPqnCylhvofniONKIAnllHKG879\n", "4oA91XGZ++vSAPPawFtq9/ZxNaVmQjlZWT0OlMZ7d37XBvZdbeC7nF2YWdPUGHAj2QzTDnnvF+aX\n", "7uv+U17aVJmn1D/JhOcZJHLM4ExtdDJMZGVfe07PSP0gw0TlZo5OIis5sRLm6CQylxPv5dhE4sVj\n", "st0fI6fB3bFCQ4yRovAvviFnm72g1k0AAAAASUVORK5CYII=\n" ], "text/latex": [ "$$x + \\mathcal{O}\\left(x^{2}\\right)$$" ], "text/plain": [ " ⎛ 2⎞\n", "x + O⎝x ⎠" ] }, "execution_count": 80, "metadata": {}, "output_type": "execute_result" } ], "source": [ "s2 = sin(x).series(x, 0, 2)\n", "s2" ] }, { "cell_type": "code", "execution_count": 81, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": [ "iVBORw0KGgoAAAANSUhEUgAAAF8AAAAcBAMAAAD1rn4EAAAAMFBMVEX///8AAAAAAAAAAAAAAAAA\n", "AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAv3aB7AAAAD3RSTlMAEHarIkSJZt3NVLsy\n", "75nQ6/gxAAACBklEQVQ4EY1SMWgUQRR9e+7u3e1dLkOEQCo3CaSTHGJjLNwmWHrYitwpQspYqY0W\n", "QRCbGBTBI0VOsIqF16SyyDYWNm6aFFZuTCUpvBNBEGF9MztzeysX3A8z/733/7s/N7NAwXBPjgp2\n", "6rY7uFTM8EG3PUYUlApY6n3ddIhrLaf5f0dVjHreCayNyD/AOZp99lJpX7PKBlDJ7Jku0aKz9ylp\n", "EdjDUcGNgRLXpDjj3xRY3mJJdul4xGwPDFPZCjQ9tg6BaVmc6mkJTscJgSeGquz5KXWGjT5QlaeJ\n", "RCoBH3ffEp8YqrIxVGKPvCYnPBw1fEkS4vNcM4urS77SjaEdSlo74PaNy1q4eP22VBhtAatT3qhs\n", "KWYM3zmbx4+5veeaxQP/KbOMKIQr3KEXKGYM+4pFLaYXXLewLt4oCXxtWKj2NTOGHTVBngabXALb\n", "uoGGHuG0L7nb7b763O3GhFdCbvZPbmoC8ENCFZygzqV+EmbCepPqnCylhvofniONKIAnllHKG879\n", "4oA91XGZ++vSAPPawFtq9/ZxNaVmQjlZWT0OlMZ7d37XBvZdbeC7nF2YWdPUGHAj2QzTDnnvF+aX\n", "7uv+U17aVJmn1D/JhOcZJHLM4ExtdDJMZGVfe07PSP0gw0TlZo5OIis5sRLm6CQylxPv5dhE4sVj\n", "st0fI6fB3bFCQ4yRovAvviFnm72g1k0AAAAASUVORK5CYII=\n" ], "text/latex": [ "$$x + \\mathcal{O}\\left(x^{2}\\right)$$" ], "text/plain": [ " ⎛ 2⎞\n", "x + O⎝x ⎠" ] }, "execution_count": 81, "metadata": {}, "output_type": "execute_result" } ], "source": [ "expand(s1 * s2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Si on ne souhaite pas afficher l'ordre on utilise la méthode `removeO` :" ] }, { "cell_type": "code", "execution_count": 82, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": [ "iVBORw0KGgoAAAANSUhEUgAAAG8AAAAwBAMAAADtMzlxAAAAMFBMVEX///8AAAAAAAAAAAAAAAAA\n", "AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAv3aB7AAAAD3RSTlMAEHarIkSJZt3NVLsy\n", "me8Q6PJIAAACWElEQVRIDc1VTWvUQBh+Jk3S3XRtwxZ6TltZ8CDWtqAHwRyU9SIW/0BXD+tBpHvT\n", "ky5SxOJBj108tKJHhb0rmJM36aJQkUXwqCiy6xeixTibyWySyTsF9aBzyDzv85FJJnkTIBqlI9WG\n", "QOrRrhxUqXRthZ/SZQq/QDdV5aB1381xgtjGFY0S0daSXn2plwDr2X7dkuz9bkHmO21aZ/s8WpAs\n", "+yyROu+6OWM++6IGZL21KRExF3yzT9AAv8NTDVIRpO3aHVpexWXNtpVnj1c81LdzOTazePosnlQW\n", "c0pEsNro1cItSpvCJW+NEgTHL7Lv+JR+BivuPUoQHEOxTasubtOCZCc8idT5o0pk660A9L6VdsCy\n", "1nTluAdgkME7Rg/TaWsWL28+RjVLicr6PtYzG5QiuMmZ8jlSZvPTlYv63P+tMP8Pr8/x/lVQ9NNv\n", "rB5fqr6fdOeKg3E/hcngAbZ+nY+1gMOJROiJU8kVtf0kbMrRbrXW37ZanQGt7yclFJfDx6HtJzqH\n", "OKjvJ01OBrX9pMvJoOynk91HA+tTym++ehgk/PAeI4rNYWWJoxuJIUGTsFN/BivTtyMuxl8DxrvE\n", "niD+F/+QVFk03sTIDlAlP4VvgKNB1j+siv0oWCODG64+yM9Q6GPUJ4NcvOkO18iB5RrKuq+29TVn\n", "Twi+oTVdsNBOfCra04YT6IJd1Z2qZ4EpaILGXMqoQKeDY3cXFn7OK3xUPoDpU/yA4+/aXj6RL0Cp\n", "A8PnIjXMa/XnTS78oMQT9QuHKX7AFfkXogmcDw8Rjo0w/EbQf0X9AqaYiof15KPwAAAAAElFTkSu\n", "QmCC\n" ], "text/latex": [ "$$\\frac{x^{5}}{24} - \\frac{x^{3}}{2} + x$$" ], "text/plain": [ " 5 3 \n", "x x \n", "── - ── + x\n", "24 2 " ] }, "execution_count": 82, "metadata": {}, "output_type": "execute_result" } ], "source": [ "expand(s1.removeO() * s2.removeO())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Mais cela conduit à des résultats incorrects pour des calculs avec plusieurs développements :" ] }, { "cell_type": "code", "execution_count": 83, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": [ "iVBORw0KGgoAAAANSUhEUgAAANsAAAAwBAMAAABqLhIyAAAAMFBMVEX///8AAAAAAAAAAAAAAAAA\n", "AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAv3aB7AAAAD3RSTlMAEHarIkSJZt3NVLsy\n", "me8Q6PJIAAAEf0lEQVRYCa1XXWgcVRQ+s5n9/xtSKRSh2SSlPmm3IqgVdR6sClKzCIJY6m4TSH1J\n", "s0/WJwkiapDSqIgNIo3gi4p0HyyIFTqiPpZsDRaklESwYH2QrFhbQst47sycO/fO3DvJbnph5p7z\n", "fed8Z+fOzLk7AJuMySkrIeLHn04ksP1T5Xa5k5DVdOsJbP9UuZVdT8h6+YcEciCqvJCQ9mICNxi1\n", "20nIOzJ2KYHdKnXoyvcUOvwBWXw2r553Auce+IrDAxtGHWYblB1/VHZA5gaxMGJzc1BjyILKGiWX\n", "NsiieT/A34H9DYzw30V033NlDoZu+1m7HfNmNP9PgCcdH3wLJqwo3bef7/FyOTv7ezT/jMXLHYWZ\n", "KDuQn+vB8PjBvbXS5JStEDhlGWMPvTAJmZmWgu0faraMVvbNnOadS/8HO+H12nz/upqM9yBjZXoF\n", "W03nOnAUZq3P1Wz/KD79BuQ7usQrABZ8omNV+PR9KpSwcWZUa+RG5lSdAf9E0CQ333g+gS504SmA\n", "ZQevQTW+BtOG0m1cgM3Gz0HAOAxDSht8EWBPwdoHKWW5UhdS9mepdRjVCgREiW7HXwikvTVRpJhv\n", "T6/MNZcuwDMKEuDZ6ZlH0hvFdbOtpAUwTz93g7XyYwIjmnnXded2jA0fU+udcd1bxoOje3UbefrS\n", "zlMfeXq/BKrmHXgJIEfFxVrbt8fT5y66DdQxe4GYsQHVBqS629eOKwzVXrFg3wISGa5/A6ptMNfj\n", "wXrEsPUcMpxeMa7hS8SkK0uU8TG7OniH3K3MhVpiFNHpHtshscvjm2RRxmH4Ak3sDlsfpKfJIDrX\n", "LWBEkV3dGzw0exmfTHgAD6/xczzBID1NCNFNhwUU1/D0Gx7+zoEGjqYFSY3fD6Iz6ZEfmYle9Vaw\n", "0kX6PB7izoFtKqnxy4KkJ6PcI/qChyw3cPoQD3HnmGjwxm+cfhfHvMOC8S2XBsMASK8qca7L7pJA\n", "416Pg60jnMRD3DkmlhDQNn7k+MgsLp6+vrjIlkg1BNr7G2P+y6LY1Yk7B15dQuP3gsMTXV2ISBbR\n", "s3WEdzFhv5ywcyzboG/8khg6pBfFA5/oEfznZp7zwMfwLO4c+GTqG39UlvSieOATnXUPHFyxPRDf\n", "M2nnwPdQ3/ijsqQXxQOf04fdk46P4dVIO0diV3l6+jtJmet56PvYqEZLv4YRMu3hFe8OhiHxz42Q\n", "M7twth26uC6i9xz2xIrr1sIAifbhciuk0TIwRztSuEfN6dh7VzA1P7VHx/t4aU3is3XJlZ30NRhZ\n", "kCHBS2G5IcFXmwckOOdIbsyRF1Oit1Zul5TzmuTFnU/jECFeuRP3O+Sr50JXwM2O4MTN0qticIRn\n", "5YqWGX5WRvjA/VKAy5bgqMzjjgr1MFYOx7fe+S6dKrFPOy4clFu1ObJNA/+XF2PfyVyTlXsUhE94\n", "zgxoVHtQjH0ncy1W7jrA6mb3gydsZlQ6kPN2LWUgK4dP0rySHATMtuFsTZvIyh2C7B/agL6JI1f9\n", "XUuVWH7i1sNgXn78rq2lqsj2sf8BdRcRYxcHw0UAAAAASUVORK5CYII=\n" ], "text/latex": [ "$$x - \\frac{2 x^{3}}{3} + \\frac{2 x^{5}}{15} + \\mathcal{O}\\left(x^{6}\\right)$$" ], "text/plain": [ " 3 5 \n", " 2⋅x 2⋅x ⎛ 6⎞\n", "x - ──── + ──── + O⎝x ⎠\n", " 3 15 " ] }, "execution_count": 83, "metadata": {}, "output_type": "execute_result" } ], "source": [ "(cos(x)*sin(x)).series(x, 0, 6)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Plus sur les séries\n", "\n", "* https://fr.wikipedia.org/wiki/D%C3%A9veloppement_limit%C3%A9 - Article de Wikipedia." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Algèbre linéaire" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Matrices\n", "\n", "Les matrices sont définies par la classe `Matrix` :" ] }, { "cell_type": "code", "execution_count": 84, "metadata": { "collapsed": false }, "outputs": [], "source": [ "m11, m12, m21, m22 = symbols(\"m11, m12, m21, m22\")\n", "b1, b2 = symbols(\"b1, b2\")" ] }, { "cell_type": "code", "execution_count": 85, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": [ "iVBORw0KGgoAAAANSUhEUgAAAGgAAAAyBAMAAABCJ4MDAAAAMFBMVEX///8AAAAAAAAAAAAAAAAA\n", "AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAv3aB7AAAAD3RSTlMA74lUMhBEqyJ2u93N\n", "ZplQnf8bAAABo0lEQVRIDe2WsUrDUBSG/zZprNaKqIuT2cSlUnyAKta9g3uL+ADiplPBxc1OgpuL\n", "gosUn6Dgg+jm0A5VsHaL99xr7vUkp0PqJPTAhXD/+5Hwk3wEG9EHMs129I7V+kEmBof1KtayIXR6\n", "bwaZ0nQRweblzVmwdS/1KGUayi88oHmNKuDfmuVwnlUeGyrS0H6ph6sQL/Dfuno5BizLt+d6MdRo\n", "tlBr4BzwumY5imWltj+OIdSAPrAuQSzLtYojCw2AJwTjgnAnsAzep4VWUBhhqXchQSxDqRND6p7l\n", "IRbDUIB4hopiTHtBF/Mn8E53BYhn5VcL0YWZdHtxYko6wrHaSHwa3rIK1ZJG7fvPO3cpqNj/6tCS\n", "GNrPRdEwBUmHhb3E4wknhK0Z9FPKrIg/FyEJMX7lpExX7oRIMiRhuuGZJEuSIQnz11hZUibKUsuQ\n", "Pnk3VpaUibLUMuSQlSVlsixJhgnIylKLUpIlyTABWVlqUUqyJBlyyMlSi1KSJcmQQ06WlImyJBly\n", "yPVImSRLLcMJsqRMlCXJcJIsKfsvspzq122an8Rv50XFB+Ww4SIAAAAASUVORK5CYII=\n" ], "text/latex": [ "$$\\left[\\begin{matrix}m_{11} & m_{12}\\\\m_{21} & m_{22}\\end{matrix}\\right]$$" ], "text/plain": [ "⎡m₁₁ m₁₂⎤\n", "⎢ ⎥\n", "⎣m₂₁ m₂₂⎦" ] }, "execution_count": 85, "metadata": {}, "output_type": "execute_result" } ], "source": [ "A = Matrix([[m11, m12],[m21, m22]])\n", "A" ] }, { "cell_type": "code", "execution_count": 86, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": [ "iVBORw0KGgoAAAANSUhEUgAAAB4AAAAyBAMAAAC5cHbcAAAAMFBMVEX///8AAAAAAAAAAAAAAAAA\n", "AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAv3aB7AAAAD3RSTlMA74lUMhC73c2rRHaZ\n", "ImaqCQggAAAA9klEQVQoFWOQ//+JAQb0/39kEHZxhXEZQlwMGUTgPBDDEcRnTSyACYL5DBwTUPn8\n", "Bqj89QGofHUYF2IeQ+fuw1ARiPk/AvgvMLiBhMB85q8M/Ao+0+F8ngaGeAUGczif4wDDegMkPv8D\n", "hvMByPwFrHIMSHzOB2wHkPmsp+cBzUKYB+Rg4RuCBCH+BbHu1M9G4YPEkOXpyKdLfDAdOwrzHzg+\n", "dBlmwPjg+Khl8L8A9T84PqQY7i+A8iHxwWAPTAXg8OMHxwdDNUw/JD6YDsD4kPh4A+QihTeXApcD\n", "Mt/D2AxmHkghg/z//0ASET9gQSL46OkdLT8AABDBTMmz6EtEAAAAAElFTkSuQmCC\n" ], "text/latex": [ "$$\\left[\\begin{matrix}b_{1}\\\\b_{2}\\end{matrix}\\right]$$" ], "text/plain": [ "⎡b₁⎤\n", "⎢ ⎥\n", "⎣b₂⎦" ] }, "execution_count": 86, "metadata": {}, "output_type": "execute_result" } ], "source": [ "b = Matrix([[b1], [b2]])\n", "b" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Avec les instances de la classe `Matrix` on peut faire les opérations algébriques classiques :" ] }, { "cell_type": "code", "execution_count": 87, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": [ "iVBORw0KGgoAAAANSUhEUgAAAXIAAAAzBAMAAAB4eZ5HAAAAMFBMVEX///8AAAAAAAAAAAAAAAAA\n", "AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAv3aB7AAAAD3RSTlMA74lUMhBEqyJ2u93N\n", "ZplQnf8bAAAFXUlEQVRoBdWav4tcVRTHv/Mzq+suQUUQhEwhqCAxSwpRiyQYGxGdwsIuw2InolgZ\n", "tRhIExuzlUGrNApayGplQHDR0kH8C3QbEcwWa8AYq/Gec+8955137tt1wR/vXXi793zfued95u7b\n", "OW++DFAcJz+bFvU2iSeWNz1Of35sx6stUh5b/oZ7zj/jiVbnw9tebZHy4vkN3Fvi6c1WbpX0Fmnn\n", "yuTA4PcWUZZQGslXt0rpLdIayU9WIEfzSvAvTl86Su0m8rXdSpXBf7T/T1Sueei0iXwTL+vaDpCP\n", "H7r8wcXxIx9j+O3pjzz55pvvXt3a/HCmZ7yi52Tmk7zCyWnPhUIqeAWwe96/8xNceB8b6C2X+7IO\n", "ec8nn26N/pwNjgPPAv1ffwZE4SiopSFJtIwPo/BSXpfIhQLDawB1c1Uoiv3dkj+9uoMrE/xUu34i\n", "H576Cis3sbaP598GHsY7UIUiUgtDkyiBDqvQ0jgSuVAMf9kGd3NRKEr93ZJPL8xwZoq3QvFr8eCS\n", "iXw0ukVv88fCqaeAL/HCXBWKSC0MTeKEkGQVXsrrErlSDLbB3VwUilJ/t+Q4A9wA7ge9WjrC6C8W\n", "3z2wWOyG6WAf69vohXeacPn7cGWmCkdlck3K5FbhpcCji8Xni8X3dMlMgUAeu3lWKEr9vUa+B3yB\n", "8e0RreGDCsl9vr6D1Qlem0/j7oa/jir0t2og1yRKoMMqtJRH2nMYCu7monDE/b1GfjfCHbG+c6lM\n", "3psg3E57K6ci43VAFYSogVyTMrlVaCmPTG4oQN1cFI64v1vy8JgV/v/umkzK5OH2CP+83/QjY38X\n", "dMMkhaIGck3K5FahpTwSuaVA6OaqUMRH7V1xvI07XsfgjbNl8leAq8DjD0bGH0MNVShqINekTG4V\n", "WsojkVuKtV1AFYroqL+fkxJH8T7PJ5lxPBmfVYGjBnLNyuRG0UL5bpHTRGG6OUdRsXeLLLF7Pv5B\n", "T8TZBvDc6SenKnMU1IMHJdgkU+jV+urQ9Gw3pygpf4u8XhDvfX0RJ5bLik4RqQcOSrBJvlC1wMqN\n", "P7ZsN6coKY3kx8ObYTjaOxrI6dXS0V7w2ntLi0EdWsOeu7z2CR0hLxhX3SAvGVfdIC8ZV90gLxlX\n", "3SAPvcUZV10h98bV0cj/P8uIHm7tOBp5NgFsjX8+cg+N8cHWXKgj5PZRl18Bk3sjxiucnffcOz1e\n", "MRsUA5/kFc7MHzHKxlUsxuRqxGSzQhXqXqTyyOTi9IhBZBT1ftK69EuSDnOaErlQ2EfdWI3JxYgR\n", "s0IU6l7JvggLErk6Pdkgsop6P4Zckw5zmhK5UJgyKWByMWLko5Ao3L3oQxWPRK5OTzaIrKLeT1oX\n", "f2kSfWKlJFVyoZiZyIWCDbnaswuTG2uG6UXh7hXJS5aRGERiIpGSvB+DTYEkETkniSKFipZRxZST\n", "opF8zxlEolD3qu95xenJBpF6P+z7ZO9HrsMTTeLP2s1OU9pzsYwqppxUjORixBAjc4pC1owj74ll\n", "lA0iq4j3I9fhiSYx+fVGpymTZ4qKKScVmVyNmEyuCtkyjlydnmwQWUW8H7kOTzSJyClJlVyIExN5\n", "hYKeW+yzC5OrEZPJVaHu5cjV6ckGkVXE+2EO+aFJRE5JquRCnJzIlUJNOSkW7xYJM7kIbM04cjnt\n", "DSJS1PuRxNokkNskWyjfLbpKTDmR6uTBqDBmBdsyojjLyBtEpFgTSa5VmRiDiHRbyFlGaspJEUvu\n", "zQrqXgfYF94gIsWaSHItnXiDyBfS7DCj55bas4slN9ktCiqmnFB1g7xiynWMXHArk3Plb4lUMto5\n", "pW+JFL+Z007cClX4Zs5fZLuVy5E1BG0AAAAASUVORK5CYII=\n" ], "text/latex": [ "$$\\left[\\begin{matrix}m_{11}^{2} + m_{12} m_{21} & m_{11} m_{12} + m_{12} m_{22}\\\\m_{11} m_{21} + m_{21} m_{22} & m_{12} m_{21} + m_{22}^{2}\\end{matrix}\\right]$$" ], "text/plain": [ "⎡ 2 ⎤\n", "⎢ m₁₁ + m₁₂⋅m₂₁ m₁₁⋅m₁₂ + m₁₂⋅m₂₂⎥\n", "⎢ ⎥\n", "⎢ 2 ⎥\n", "⎣m₁₁⋅m₂₁ + m₂₁⋅m₂₂ m₁₂⋅m₂₁ + m₂₂ ⎦" ] }, "execution_count": 87, "metadata": {}, "output_type": "execute_result" } ], "source": [ "A**2" ] }, { "cell_type": "code", "execution_count": 88, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": [ "iVBORw0KGgoAAAANSUhEUgAAAJEAAAAyBAMAAACufiRQAAAAMFBMVEX///8AAAAAAAAAAAAAAAAA\n", "AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAv3aB7AAAAD3RSTlMA74lUMhC73c2rRHaZ\n", "ImaqCQggAAACkElEQVRIDe2XsWtTQRzHv03ymvSZ9gUVHAR9m4odWhw6dKjQ6uTwXJyU6OCiHaL/\n", "gAERUuhQHHQIlCDOtqvWIeDQioMBV8X8B5aUggbi836/u/Tl3rucD9PJ9uDg7nfffnr9vZd+CM6H\n", "HYw8MmFYwqmlayOD4CwtlnB6dA4Rckxy7lVS4W6dGR6TJBTWhkcGT1YGN/pakbwZvRzf3VGFvfhB\n", "tFekjSAqmVbzsujumw5lTZEuDE/wiSJlLO1UpJWtD/wT7qVvW3X38oM4WJEK9feyDYacJDk/A6+F\n", "60Bm/CHK7zArHupdOSVTkcrP0MP0chDLUUW9Bdl9eP6Nl8BivorvPn4g97HCU11OkRYCdDKtiaqe\n", "44oiFaso+5gDgnIDIv4CyIqW0NTutAOnk2/lenqOK4pUaGJjhkhYALaBsxrpYq32qFZbFaercPfG\n", "GpNdPScrsk9eG58DJu0Aj+H2HPOdfqMo3gPRC2g5rihSwzkHJp2E00Wx+sVM6uJmG8hvAlqOK5I0\n", "0R5vMklcfGoXBd83k9axDGAa0HNU6X+CP62LteiTW8GJNWSfXzWTbr9tA1PNeI4qiiQWYlDHD4bx\n", "2fHpK7w+SMknwxX518kT8T5GI1sSKTHleNNfiN/99Mr9aEcZWRkgfX1SjxKT2782aUaV/mosDHf7\n", "a9EwkZGVAVJ0/E+rY1K6th2FPh2bk96F/8acZEOypxzDzUk5qznJhmRPnZQ0J+Xs5mQbpjAn5ezm\n", "ZBsqks2clPuLOcmGiTsZzMnWJJfK/wVe0pxkwwTJYE62JrlUkgzmJBsmSAZzsjWt5iQbJkgGc1LO\n", "bk6yYYLE74RuTtpZzck2TGFOytnNSTZMY07KHRFzHt63skP7pvgHtPgwPGoS2XsAAAAASUVORK5C\n", "YII=\n" ], "text/latex": [ "$$\\left[\\begin{matrix}b_{1} m_{11} + b_{2} m_{12}\\\\b_{1} m_{21} + b_{2} m_{22}\\end{matrix}\\right]$$" ], "text/plain": [ "⎡b₁⋅m₁₁ + b₂⋅m₁₂⎤\n", "⎢ ⎥\n", "⎣b₁⋅m₂₁ + b₂⋅m₂₂⎦" ] }, "execution_count": 88, "metadata": {}, "output_type": "execute_result" } ], "source": [ "A * b" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Et calculer les déterminants et inverses :" ] }, { "cell_type": "code", "execution_count": 89, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": [ "iVBORw0KGgoAAAANSUhEUgAAAKYAAAAMBAMAAAAaIdvMAAAAMFBMVEX///8AAAAAAAAAAAAAAAAA\n", "AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAv3aB7AAAAD3RSTlMARImrIna7EFTvMt3N\n", "ZpneUCSWAAABrElEQVQoFWWTMUscURSFvxk32dnZuAxbauGASWMhIcQ0ptgiglXcIFqbQtOF+QEB\n", "hUBQLDSB1LGSFGIW0Uohi1XIj4iCoIUWazRYbu59a96diQcOvPPNzOHeGYZg+MXMWPDoNV53ib+k\n", "h/rTielmfTYxeIeE9+dZfMUKxHM9Gxl9m6EuKF1olv8k0TpsQnh6Bp64JPRrtc1UyhHxScvZSNjo\n", "a6sLlfHSAaXf9HfYHoeHPMeIJqXZYsJaxjOIWj17Um3E12rtfLev+gnl8iXRDX1zsAq7fGsY0aSU\n", "NTiHz9bpSSUpXarlppyiDrUWlabr/MhUgicuaecF7BBcl/2cRnQg51wltTbVlKFG5ibSHY1o0s5P\n", "yDa19qR1ekJVplHnVUmR13VRWup17oERJEmnrCbv+0Ga+k4jjEqZOi9Z9ggOQ/c04TG6/i3RJJ1B\n", "i3uPiZ5s+E4j/XKLuqBBmIb3I73OX3LNiCbd3evfd/eAOgPORgonnSgNNoy5VOxclw8i9op/LL9R\n", "e/DfQf6TreUPmVGXhHqVzq+aag+odLsdtZHC6eX3Mb50uzmmSehfvbOmkHFrGOwAAAAASUVORK5C\n", "YII=\n" ], "text/latex": [ "$$m_{11} m_{22} - m_{12} m_{21}$$" ], "text/plain": [ "m₁₁⋅m₂₂ - m₁₂⋅m₂₁" ] }, "execution_count": 89, "metadata": {}, "output_type": "execute_result" } ], "source": [ "A.det()" ] }, { "cell_type": "code", "execution_count": 90, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": [ "iVBORw0KGgoAAAANSUhEUgAAAa4AAABSCAMAAAA7DMB+AAAAPFBMVEX///8AAAAAAAAAAAAAAAAA\n", "AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAo1xBWAAAAE3RSTlMA\n", "MquZdlQQQOkwRLvd7yJmic1sqjZpngAACPhJREFUeAHtXeu64ioMRdt6ZrxVj+//rgOUYJNAuRRo\n", "9ZMfuxoCZLFaGsNlC+FJh4sn4yfeXw8cuuN1f1b9LPL2wPCjy9s3O8z40bVDUoQ4vHQ6UuPq0jV2\n", "46HvO9H3lwNtWYjlXK6/C0lVo68TS4qurpdpoJAr03V+jULcjifRTzeK/CRteTwud3kdcS6IhVai\n", "hu7lOzFaTNZa29eZOSqOji9Fl+P2lnVXpet+6tWr8XUWonvID+fHTdEkndGzlNNcIzZKUm+PiRqN\n", "IRWxeMym66R6d0069vJOuckartNzfVUVPoU4vdRjhnOtWCutabVqWWy0RIYgFWg6n65B9vaqdJPP\n", "dC8fm/tL6KcbmJgeapprnnVQWtVytcLUaLC22Di1SJe69b1pLV2SpekZGi9CjYbTrSgfrqe6JVnu\n", "JAYlpb+/xIw2dBnbCxjsp+veXV/q4faltXSdlYOhqLkfH2r0M0ycJmeC5hrxvumiRmNIvo5Mkfvp\n", "CtWyli5Wv74VT939pJ81nG3FMLzg7L1+W4KUZfNu6Dr0r066HOrXhSOYYsRGKQtp+0LLkLLs2Q1d\n", "WdavK3R6Xm16un/LrGugfGlM1/TD2fcXWn88Vbre9KUDqa/U9nKwMHzd3lafBcZ2TFcY0Fsj7901\n", "eRXvWup9OtkbqXQbzTBwCI3p6hqOObWm6xpiYBDa0tXJKGG7NFZ5vppioBCa0sVulsrUXVjUen2D\n", "jTEQCPXocgSic9yvsRvGsTsPj2kYnYftUZZtbhaz1xHJ9QyhGjIwIDtJmB7lOTAQCISuhBmbc2Bg\n", "M0H0GdZBipLT0Mvw7m0QAw/boyxnzP4ZsDHZGpGDAdlJwvQoz4UBQ6B04WkmcyvYSwo8G0S3hY5Z\n", "nXc8iJP84fxwhO3nWba5edxjkNKyKQvD3E4app/nuTBgCJguz4xN7jQTCUTriZHk3pNUDdJnuArN\n", "F5Chq8ZZjpj9NBeT3ORCgSwM2E4EQeKaw+MYMARM1xQiZ5NQEH5dgOHKIoHo82KA31WBkp1kJLiX\n", "c5jHXgXqIcSrqyZZrpj9VZYsmbIwEDsNXZO5JM+BAUGgdHlmbOCOSIEOQXQo88h5dUFhuE6G0KpV\n", "LsiQqUdHuBiqyrmWwOCH4MSAIBC6fDM2qA88OImbYoPooK7n+OUXogfZcVdtCKtalrUyZGpq94Zs\n", "K4HBC8GNAUEgdHlmbGAMWuxSsrCExdYvZvKM6Bnv1TqxS20sxLjdMXvlVqYkYpv1seDXwXoMCxBg\n", "OgLPOyAIhC4Gzdyr6JZlSlpA3RSmZVxSqme8V3NhpdYJRvkmT0jUNuNjvV2tDTAgCMt0GZ4x3V74\n", "dGEJUbyCH4/1jPcKTuxwgaSnmH0R6pDctD2kujfYNuvawO2agyFkqi/fBWGZLtLhga/UTTEjCAwk\n", "T1hJQPVghRzx+wOtRWWPiYOhoLaR0WUDDAhCQbqIm8IGkovx0ogeLKYxa2qiWIhWUiutUhKzjdC1\n", "AQYEoSBdITelMwFyqmc8cHDEU3o3qAttBhWNArUNfCwYDKE+qlcRAzSpLSxIF+sScmd6xjrjgVtH\n", "nFUzE6CAKHPbZorw0Q5eIEi9bo8BQWhI10ktLOTJeODM7+eaMlzjiPe+3TZHiZcOhTgy4kTM1doA\n", "A4LQkC5xWx8RmgdEmdvGKTik+fG8AiZpjgFDaEnXg21JYr0REuCAKBmpeGEIQvCcXElzDBhCPbrY\n", "QGL2KuR2lCpHAqJBum76t9uaFmlZHCCnuTHfEzFgCPXocpj+KBxwDdHVwy89hy25osYYCISmdOkl\n", "8bn95CgXoEvNapZPesdFuWqXMVAIbem6pwYZlrqFj7ZE+7jOLSS1wdemGCiEtnSJw3pvA7oteK21\n", "HnAZQ9GVUgyCouu/P3+D2EspFH/3+w2r1tRCxYUPI2Et/f/HvzfZ3xO/HG8PeGI3Xv20jMaDYZpx\n", "n6j9dXSFJtg/kaS3zd9HF17LaGf9YV7sDf0TP30bXXSC3cz6L0ZqP4i3b6OLrmWEWX+YWvogalym\n", "fh9ddIKdL1119cOHyL6OLjbB7li6+iHcOMxMXszjqMMv2sCR90ycf8VgGDqMxE9EXM4GdBHD7Ky/\n", "CXaS7N/XeQ9sT5d7+e3cxt9n2wMRdLHjJ3zrGLeXW1ilPrSCFGtvBF2xVf306vdAPbqanT4hN27U\n", "76eoFlzr6iBog/JAmHz4aTW62FRNFOA8paJzTHkm6FKudXWwVQPlZUdyatHV9PQJuWVsRScXLOpY\n", "V2eDNvM8K0z1hivR1fp+J8dPFGQgqSrnujoI2gQ2IUc1VImuxqdPTGf6RgGuqeRekxa7CTnGsjp0\n", "NT99QuDjJ2KQx+msmZybRjrnVg0Q7mMwbH76hMDHT8RREaPFdr/GFDI6mgsbtJkXtMJ90NX89IkC\n", "K4Tn3Wk/08k544FbR9wq8g9mXZ1zq0Z2JKfKYLjB6RMCHT/BOy9bgne/Gg/ceuewMfei1izlhkAS\n", "bKtCFzpbIMGYuWrqwI+On5hXtPIznpwzHrh1xFdWnlw8i67Q+xd2TYT0lqxNHfhL3CIOe9jkXMWN\n", "1I7mqSiPLrw4hm1j3OD0CYGOn6Ao87/TyTnYQW2mVPMrziuZQxd9/7JN4xucPiHQ8RN5fRFTKmET\n", "MooSxtQdoZNDF10cw7YxbnD6hKg76Q49aTxw64iD3HVFUUI2ArlKhGV5dOH3L6PL7n6mehUHfnT8\n", "RBh3pobxwJ3eOatyHiVkIxDTjhJk0cXev+bXHvzo2+D0Cf2/iqIQt1NyRhChj/LMyKKLvX8JXXAU\n", "BNVLGPiT0UCbyQWrFXBHEDegiyEkdHnW2qUM/OQ9Tc4rYgbI/1VUYWOro5l8EemjvIqyni7SFNvG\n", "uMHpEwIdP0EM3MXX3dDFe6P56RMCHz/BLdpesmO6mp8+Mf270e058VrARiCv5mJGicGQN9D89AmB\n", "j5/gFn2JZKJLx5JL7vJufPqE/F/ZX8KHH4b6rSeTPAtG/QPlXp3+XS41PX1C/1+Acrbvsyb9b65r\n", "3ZZNT58Q9PiJfXb4nq1aPn2irOUt1zSWtXw/tbEzIeqZ1rCpeiBia/4H6cC42oziP8UAAAAASUVO\n", "RK5CYII=\n" ], "text/latex": [ "$$\\left[\\begin{matrix}\\frac{1}{m_{11}} + \\frac{m_{12} m_{21}}{m_{11}^{2} \\left(m_{22} - \\frac{m_{12} m_{21}}{m_{11}}\\right)} & - \\frac{m_{12}}{m_{11} \\left(m_{22} - \\frac{m_{12} m_{21}}{m_{11}}\\right)}\\\\- \\frac{m_{21}}{m_{11} \\left(m_{22} - \\frac{m_{12} m_{21}}{m_{11}}\\right)} & \\frac{1}{m_{22} - \\frac{m_{12} m_{21}}{m_{11}}}\\end{matrix}\\right]$$" ], "text/plain": [ "⎡ 1 m₁₂⋅m₂₁ -m₁₂ ⎤\n", "⎢─── + ──────────────────── ───────────────────⎥\n", "⎢m₁₁ 2 ⎛ m₁₂⋅m₂₁⎞ ⎛ m₁₂⋅m₂₁⎞⎥\n", "⎢ m₁₁ ⋅⎜m₂₂ - ───────⎟ m₁₁⋅⎜m₂₂ - ───────⎟⎥\n", "⎢ ⎝ m₁₁ ⎠ ⎝ m₁₁ ⎠⎥\n", "⎢ ⎥\n", "⎢ -m₂₁ 1 ⎥\n", "⎢ ─────────────────── ───────────── ⎥\n", "⎢ ⎛ m₁₂⋅m₂₁⎞ m₁₂⋅m₂₁ ⎥\n", "⎢ m₁₁⋅⎜m₂₂ - ───────⎟ m₂₂ - ─────── ⎥\n", "⎣ ⎝ m₁₁ ⎠ m₁₁ ⎦" ] }, "execution_count": 90, "metadata": {}, "output_type": "execute_result" } ], "source": [ "A.inv()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Résolution d'équations\n", "\n", "Pour résoudre des équations et des systèmes d'équations on utilise la fonction `solve` :" ] }, { "cell_type": "code", "execution_count": 91, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": [ "iVBORw0KGgoAAAANSUhEUgAAAEoAAAAUBAMAAADYerbFAAAALVBMVEX///8AAAAAAAAAAAAAAAAA\n", "AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAADAOrOgAAAADnRSTlMAdt3NMolEECK7mavvZjEP\n", "hmoAAAB+SURBVCgVYxAyYSAELqsxhCGpMUdiw5jMBQxANUiqOp/DpBA0xzw0VZWrMVWxz9qHpoqB\n", "EVMVA4PcIFdVrAQCwMAdzK6vM0DEDpiFHqpcex7tZWBCC//V57QvoMY2WG8GmlkgLkqaAMtfIEYV\n", "O1GqeLEYBbRRSAWbOIqYkBoAe3UtSRoR7zgAAAAASUVORK5CYII=\n" ], "text/latex": [ "$$\\left [ -1, \\quad 1\\right ]$$" ], "text/plain": [ "[-1, 1]" ] }, "execution_count": 91, "metadata": {}, "output_type": "execute_result" } ], "source": [ "solve(x**2 - 1, x)" ] }, { "cell_type": "code", "execution_count": 92, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": [ "iVBORw0KGgoAAAANSUhEUgAAAjEAAABLCAMAAAC2lyZIAAAAPFBMVEX///8AAAAAAAAAAAAAAAAA\n", "AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAo1xBWAAAAE3RSTlMA\n", "MquZdlQQQOkwRM3du2aJ7yJs4cVMPgAACQFJREFUeAHtnenaoygQhYkapyeLxvH+73VwqYpBwTos\n", "ytOJPxrMZ1EvxxNFxbS69ONSKPnSTiEp/pVDGFumgJnaNBJ5rEbUC84eMTfJcenLSi9XgOUBbHvU\n", "pjkyUd/PZIuXuxlsUvRKXfoL9Uxa3qUbHrhdjkzU/TPZIuduvBxzKUmKfMocmUidM9li5/ZzTNOQ\n", "FvmUOTKROmeyxc7t55gSPo2RdunKHJmot2eyxc7t55jIp0YSNqjMkYk6dCZb7Nx+jok3/CZNw8sc\n", "mahXZ7LFzu3lmA65dUOqJS5zZKIun8kWPbeXY64VaZFPmSMTqXMmW/TcXo5pb6RFPmWOTKTOmWzR\n", "c3s55lmTFjHKrlrc3/duMDJTDCTuSxhbF8QSmHu9b7wcE3cwVUQ5YuXIRJYJYwvTJ3puH8fUT5Ii\n", "RtlFufzLkYnUCWML0yd+bh/H3FrSIkZZIk9ArQlzZCLYMLYwfeLn9nGM6yn3HR3i1ONh817V6lIG\n", "nJ4cTDCSGpmCkcgwasF2OEt4bmUI4eOYomMxjEr37FHHTF+h1zC6C7lmtzJ5IKmRKRiJtWG2E1jC\n", "cytDCB/H2AZT9bMsUMdMhxh1b4vK6kOW3lGxMPkgTYeYcCSmndlOYQnPbQrh4xjHULVCHdNOT8GD\n", "byLbmWAkNTEFI7Fj3mzHs4TnVoYQHo5xTbhAJZkPMSYViy2tOJhQpPkQE4zE6Au2w1ki5DaF8HCM\n", "a8IFKkk1T7QpmqYKeSzvYEKR1MwUisSOWbAdzhIhtzKE8HCMa88KJbnRVRENP4Y7k11Pn7LY4oqD\n", "SYikTKZQJGZfsB3OEiG3MoTwcMz71MiicEUmSfWa26g+Lo+e5B9uTlxxMMmQ1DZTABKzL9gOZ4mV\n", "W72F8HCMa7+KJOnqaj6cfDZV9t6XS58N8c4aKiIkZWEKQGKIBdvhLLFyq7cQuGOcEy5kkqi6Hx80\n", "NHTzuBi/C633acnFJEQymUKR2DBLtqNZIuRWphC4Y5wTLqSSTJZ90O2+6SxVopfmvFdcTFKk+WtE\n", "TKFIm2xHsyx18cytTCFwxzgnXEixul6/v8KHGDW9zPJYnHVZcFHFxSRF0iPvJVMoEnMv2Y5miZB7\n", "tW9wxzgnXIglGW4O09dZXyYN+6d5eQ9jXExiJPXBFIrEjlmyHc0SIfdq3+COWQymWJW5Uhav/lGI\n", "3mW69O11+hqPsV3ZFiWdo8xm99ftTACSfjd0yRSIxNBvtuNZYuTWlvnYN3uOKcy5MGETLlhHdX/h\n", "z3Hn6ByZ3h0zar56BejDBEly7zrGPGKETbjgzqjrMGrwW1ZHsQyYrD3xZQvQh1mS5N5zDGXnIcZi\n", "wgX9baesOfZjw/v2xx/buFe4gYyYiBhgi65P2txCx7z3CU+4IG12y+Ul3u7GwAY5MhE+whZbn8S5\n", "hY550b029R5MkTp7ZWxFKF+OTD5ssfVBdPHI7XRM3ZbTg59muker9ajxeyYeVCT8Rpkj0woT0iuu\n", "Pip1bqdjWnV7DWrUzWMsdXUx4WKlk+WDuIrkyGR2HNMrrj7Jc7sc011VOR5SGn1za1ZlMeHC1Mm2\n", "HlWRHJlWHcf0iqqPSp57cMw/f/6dOl3fH7zcL0rfUaPZ2hX98FlBvxxjbKsbsL26Zyqy2G5VXYlv\n", "fuBikiOpuEyfsg3Ia72s8sRlSZ/7vz+u38G70rPBaz/fl4GGMe19WB6vsfC+/WKYJkcmA1Hpu00S\n", "vZLokzq366yk1L0YDjR6GZ/SDRX8Umn1HRqaCVhyZFp1B9HLPN6tGgM/SJzb6Zi6v6r5snq+WLoY\n", "E8slnYmrSI5MGyoAesXVR7Okze10zK1XHR1dp4ul5mOa5YZSGx+hitS969R3CpMbaaPP+rHZ+KlE\n", "L1CffZZ0uYc+OR2jnjwRd351zedFV1ARfeaj0dPWrjiHyY20wQnoheqzy5Iw965j3lLMM3Pdz5u7\n", "snw+6WKKYx2KbAeoGz8Z4Ua2KhImSwY7kyVAiMSYW2yWpu0seqLBlqB7LClz7x1jWAB1mwb/zoFv\n", "PQxy1pN1b+bjb27VEqDvKYgWAZMtg5XJFiBEYu4NNlvTVhZlidhjSZlb7ph6nJzQmbNlWKGhUozX\n", "VS+6Pfzxt80VS0C9J8ncmIDJkmGTZvzQEiBF4oY32CxNc8i6sh2xy5Iwt2Z0j2MWnRiHo1d+ILn4\n", "C1df4xGooJt9/Lm1YgkQGkZfFAxDZCeTJYOVSFkCxEjc8prN0jRHrCvbEfss6XJrRrFj7sNzgtb5\n", "60D3ccQKOAYOMETdZ4IzwAEGEq+u2fCm8YgpfdLcYseMr4Y8BUPS3aE8izpX4ABuQMoEZ4ADGIkr\n", "Nja8aTwiaW6xY5rh9TPnwHcS69Y7z1ysKFfgAI7UB0gRE5wBDngjcc3ChjeNR9h0wVvaihA75qYf\n", "Skomxzycg2MW9F2BA96hQiY4AxzwRuKahQ1vGo/QF7ab+wpvaStC7Ji6L9Rt/2liiRoGDuB9ouft\n", "iJjgDHDAAomr22x403iETRe8pc0IsWOGqwj6tReWZVWp0OdOcMBHSgkTnAEO+EDilS02vGk8YgBI\n", "mVvumGeveHIMy2JUxpfWOsHwmOLgAAqcSgETnAEO+ETitQ02vGk8YsyfMrfcMfrl+r2B7zSlE/gJ\n", "RDiA98dU2WeCM8ABBhKvrtnwpvEImy54S7YIuWOa/rrjmO7Rtm1Z7GzFkupZN2jAInas7jLBGeAA\n", "E4nXV2x403jEnD1lbrljLv1zZ5BynyZhyh0DB/DumCu7THAGOMBE4vUVG940HmHTBW/JGiF3jJ6o\n", "6jE5hgVMU8mRiXp6JlvC3IBjHt4/IUUaxi9zZKJensmWMDfgGPwX5Em7dGWOTNTbM9kS5gYc08qn\n", "MZBoycscmajTZ7IlzA045oreziXpEpY5MlF3z2RLmBtwjOVXK0igU8ocmUiIM9kS5gYcQ0r8yq9W\n", "4OeYr979Hp3/OcZDtK8O+Tnmq3e/R+d/jvEQ7atDfo756t3v0fmfYzxE++qQn2O+evd7dP7nGA/R\n", "vjrk55iv3v0enZ8cM06F2pkv5dH4L+TvUuAxTZnTLytU4+L//3r+Xbr8emNToJmMov4HMD6R51D/\n", "TPQAAAAASUVORK5CYII=\n" ], "text/latex": [ "$$\\left [ - i \\sqrt{- \\frac{1}{2} + \\frac{\\sqrt{5}}{2}}, \\quad i \\sqrt{- \\frac{1}{2} + \\frac{\\sqrt{5}}{2}}, \\quad - \\sqrt{\\frac{1}{2} + \\frac{\\sqrt{5}}{2}}, \\quad \\sqrt{\\frac{1}{2} + \\frac{\\sqrt{5}}{2}}\\right ]$$" ], "text/plain": [ "⎡ _____________ _____________ ___________ ________\n", "⎢ ╱ ___ ╱ ___ ╱ ___ ╱ _\n", "⎢ ╱ 1 ╲╱ 5 ╱ 1 ╲╱ 5 ╱ 1 ╲╱ 5 ╱ 1 ╲╱ \n", "⎢-ⅈ⋅ ╱ - ─ + ───── , ⅈ⋅ ╱ - ─ + ───── , - ╱ ─ + ───── , ╱ ─ + ───\n", "⎣ ╲╱ 2 2 ╲╱ 2 2 ╲╱ 2 2 ╲╱ 2 2\n", "\n", "___⎤\n", "__ ⎥\n", "5 ⎥\n", "── ⎥\n", " ⎦" ] }, "execution_count": 92, "metadata": {}, "output_type": "execute_result" } ], "source": [ "solve(x**4 - x**2 - 1, x)" ] }, { "cell_type": "code", "execution_count": 93, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": [ "iVBORw0KGgoAAAANSUhEUgAAAWUAAAAWBAMAAADqapBTAAAAMFBMVEX///8AAAAAAAAAAAAAAAAA\n", "AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAv3aB7AAAAD3RSTlMAEHarIkSJZt3NVLsy\n", "me8Q6PJIAAAEfUlEQVRIDa2WX2gcRRzHv3O3e/8vrqkUxYdekhLoQ2yaFqLF2n1QokjI0RfxKWcr\n", "V0oJOSpYX0xDG8RQMNEH6VEl5x8Q1JIUXwSV7JOgUHJt8IpcSxRasFZMok2pWrv+5s9eLrub2wU7\n", "cL+Z+c13vvPZ2bndBUTJPDlQkq1NY3rTETkQ694VoBDDsfrFMLIQGt3+M0g1a7RWXEa9tUCOvown\n", "wshCaPTPA4jARgMUNZwIsRAmsGCG0QVr9HyQJrItgBn4MciDj1/HUOBaYXwA/VJPANL3gczst3Br\n", "BR2ycC4AM1NzrbWFIGa2I9fawRk96TT+d81uuSyStPHvGBfUjYybbmbWOZJHsiPzQ2Oe+z+oXf3K\n", "ggjNsli1MUE2ButfI3OqVixS94JrzOm+DWnEaj2Wk0ubbM1py1qbJ+Zl+w+VbYebOQvsRZtt5xrz\n", "FiqNpmhsQWwNIjTLXt8oAuvFaD5u2+LJddo1qLrPr0IaJStswpEkTI3STYV9s5+Y3+0zVe7c1aXL\n", "qvmQrD8BnkPy0HaVpdM8VFJtVe0CfocI6zLoBd2S48wUddRA209ZavcCkZtyyBUfXSQ4YTQGHHEG\n", "Y4bnlu0kZrJplCHqi/KhrI4CDyOqclRNYMxRqOQvwH5LhCbZd59+pmSpnNC1jSN6NwbELGDgrEh5\n", "QoSYhdEXwGxjlWLNLXQxx+f3KoViHj6Pw83M33bvcVnMGMQsQhPzsm0rmWJOrhIzpQ7Qr9CCWRjR\n", "m2+4ArR3PdOdI727cOb+Y1e8AsUctV8tIXq8xwLr3HPgoHu67E/xTZkyfGWKmQQJ2kbMAXGTmP14\n", "+D5TmXrwX2LOgRXiJxPTIrUxcObz9ALwCBQzlv4ykDa0NWzFa7nJjZNVT79NDQq+snXm4QIdjRLh\n", "4qw/j2TWb2vkt1AAneTVlKnWaK44M5Ae9wgUc/zKzHWu+BIvYdT4mDc9JTFHKRF8ZOvMp0m1jX4F\n", "YvYsx00lc2JOMTMkubEsj7zJy3uiI5mztzYI+IhifgXxe/yylkyDlmoUdoZbTFoiUedRBLeM8g3m\n", "LAfYQQmL77PD02wkmetgtM90NoAHePAWYo5O8yfsBsGRcvmtcvl9kv9Mt6lC32ijecB5irtcIr2U\n", "4MEri5XLZ26Uy1U+o4sH2uutEBfvwyOYuRGd59kKqRcsGHyWqxBz2zSydPpdArnPbAWIlm7QPhvI\n", "3KXt8SnnoJngwVfm7HOqiqfB6Inw0e7d9/o8y3FfwcyN6MpOGEgZOxHZhJledYlpj0CdjWt02M0q\n", "MIkPIivo8EHOVBExRfCVOcz0vt6ODO0glZv+PJxZGD0mvh6HK/MYEHpXoH3WqhgzPQLF/KKBYxhE\n", "/Jr+d3pFK7lm8+6zxZHHZfCVKWbtjeLiOFKS+R96/PrwcGbhlqjwr8ctne2H/RZcXD5VwuChi16B\n", "Ys7sG8lDq+0zWF9H93EfZMzY9h0ZfGWKOUmfGuPQfuUOR+1+P57sU3f6pRE72GX5LRWUU8xBssBx\n", "52wECu+D4IX74MEtdL/bG8b7P39KUm7A9uD5AAAAAElFTkSuQmCC\n" ], "text/latex": [ "$$x^{5} - 15 x^{4} + 85 x^{3} - 225 x^{2} + 274 x - 120$$" ], "text/plain": [ " 5 4 3 2 \n", "x - 15⋅x + 85⋅x - 225⋅x + 274⋅x - 120" ] }, "execution_count": 93, "metadata": {}, "output_type": "execute_result" } ], "source": [ "expand((x-1)*(x-2)*(x-3)*(x-4)*(x-5))" ] }, { "cell_type": "code", "execution_count": 94, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": [ "iVBORw0KGgoAAAANSUhEUgAAALMAAAAUBAMAAAA0IxGWAAAAMFBMVEX///8AAAAAAAAAAAAAAAAA\n", "AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAv3aB7AAAAD3RSTlMAdt3NMolEECK7mavv\n", "ZlQTUv2gAAABr0lEQVQ4EbWTPUvDUBSGX5u2ifbD0kVRocWPQR0q4g9wcXHqUkEczOggmD8gdpRO\n", "Ik4u6aJQO1Rws0MFHRQFCy4uhQ7+AT+wg0i8sRWSc04LIt4p9+GcJydv7kVyAf+xYvMmVpRYs5i9\n", "UDpgzMitNhlEsEiZNhQ8RuhbbdhMrZ9i3aQtBcQ/KQMCe5RFHSfRVuuHNaaOWIhe0JZUEy+UAWNM\n", "ra0NozM1UkwdTSPyRjWbpv5KGYwHpo64Re1ABLXWEtSQAglpv1Sr1wZabEJoRcYeBXVjZLH71Mqw\n", "lWWapRmG9ARX91vGe0/1NNOoAK8oDIGrVc1+L/UAi9C13lnE/SSrK/muvxEYJQ613QXsph/rWUF9\n", "BnUpuqvjRdfkX44Fu+5H8Wq1dpvwM1wCFau7ehlQ596/boBM3o/ULsqSKwITnqzJZTCmcuU0YWgg\n", "/oydOSIf3KOsgNh1R12+n60jY3lbNMdx0oQhfFIy2XEP2R91cgWMo3PrZ2pXGs571e1niWGD10ms\n", "k7VbHBM6JIa6UCgwj3pb6JCYLmgk5lELHeKA0pdITKmTk8K4f0d94+YX4ohsySSC8+UAAAAASUVO\n", "RK5CYII=\n" ], "text/latex": [ "$$\\left [ 1, \\quad 2, \\quad 3, \\quad 4, \\quad 5\\right ]$$" ], "text/plain": [ "[1, 2, 3, 4, 5]" ] }, "execution_count": 94, "metadata": {}, "output_type": "execute_result" } ], "source": [ "solve(x**5 - 15*x**4 + 85*x**3 - 225*x**2 + 274*x - 120, x)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Système d'équations :" ] }, { "cell_type": "code", "execution_count": 95, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": [ "iVBORw0KGgoAAAANSUhEUgAAAH4AAAAVBAMAAAByPkciAAAAMFBMVEX///8AAAAAAAAAAAAAAAAA\n", "AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAv3aB7AAAAD3RSTlMAZpkQzYnvq1QyRLvd\n", "diJ+ofBJAAABtklEQVQ4EZ2UMUjDQBSG/+ZaWtq0OrmKUHESsqiDlAbFvaKrIOjiIFRQ50yuLQ46\n", "STt1U7uIk6iTg1RKResi6qqLWsRFxPcuSZteo5U+6N29/7+vd5d3CYDAEHqLvWvmxExvNFE7eWoC\n", "rz78tI/mlcRwjVjNJC20SI0SLw1FUNOgJa4IPSPdh58c6cYPAPMO6sMj3o0/BnKGvXRP/CdwaHn4\n", "VP35pkqH0bPypD7rj40iscTmBf3EO/FVmw+blGcCFZ16jHMDv/1nthDnSollahIfQCFjly5HhQgZ\n", "oUbkkQwnOtdPWEnEyq7v8DgCdldIFAg2PZ7TyQusoq/q8oLWp/0jfA6U8qy2PM46eUQbKMiZ7IPO\n", "n7OAKRqGyyyQZ3Bvhw+vmbh1beASGDTs8/MlihgPiP/Nx7KgortBf5X03J9D6xRP7Hmef9p0J8te\n", "K0ffeCCfP3RL0HOTV4ebifvUbD+7dv21za816O13UGzUvnmGrD/EQj3v4aWhNgeKoJmK0FpfNWQu\n", "N9R01pEuNhNnIPfv+/7TBNHOV3Ci4tBMln75/gTYa8U+fzCUKBVZiNwp8n/T7TngByscaKy+ftFS\n", "AAAAAElFTkSuQmCC\n" ], "text/latex": [ "$$\\left \\{ x : 1, \\quad y : 0\\right \\}$$" ], "text/plain": [ "{x: 1, y: 0}" ] }, "execution_count": 95, "metadata": {}, "output_type": "execute_result" } ], "source": [ "solve([x + y - 1, x - y - 1], [x,y])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "En termes d'autres expressions symboliques :" ] }, { "cell_type": "code", "execution_count": 96, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": [ "iVBORw0KGgoAAAANSUhEUgAAAOAAAAAmBAMAAAAvsop7AAAAMFBMVEX///8AAAAAAAAAAAAAAAAA\n", "AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAv3aB7AAAAD3RSTlMAVLsyIs3dRBCZ76tm\n", "iXbIwtSaAAADt0lEQVRYCc2XS2gTQRjH/0k3yaZt2hw8qAcNVi8KWlAKnuxBCuIlFFL0YqvgA4va\n", "g3gQafcggi0lRfEggkqrolYlgoqX1pw8qGhBBAXTBhR8oaYoWvFR57nZnZlNeijiHHbm+83/+/7Z\n", "Z2YAINREDv+gPezhJlaHGHg9N0+2ON6YjROTrzSmAmvs1R6VAYVPzUDDqMNmjqV1QewC1mjU2o6M\n", "BlVwuDVcUhnakhFq0s+dzjmaAP15nNBo7QRualAB9jXEUwoDrqMuSWDsF5vZqc0D5Efs17Dpl6mi\n", "+AyiRQPk6AfrhtV5Eg/D/qJhk1AV1aaQbTZAjkZZZ6hj/0aoZCt5BFZv2QkshaPoCOSNW5kMpxFO\n", "L1DSQO5ASGVqnE1iRahVoY15gN5DcuHKRzZ0D9twPyd/lgvPwD7pBgGDSLG+O6HOxXM4zq5zsOHA\n", "x3XjPWpe4uVjFWmxPf508xuNFh4VGQs21FLmB/ynhkfn5+xolbmd4ZL5NvxZpeA8GrKPWmyaGNqf\n", "bvVOUuNoTrOvbDh4FtZlmtOtJepgyiEsmiaHQdwvMnvxQfdqKxtOLEZDicjti96cgHE/OR2r3SGz\n", "zzHlaF8WkVXR0EqeQjgllFU7q9vB4iSVOWj3qhtnaRNEGjJGT8bbbNxAY7FMXJHdsYu0nc1kylOt\n", "fgQDy7n6TznJO3qWyezNZLZ6kX8cm8EGWnVu7dAQ8KSHamu+QP13kCXkGcrY38fTeO8nFaKGETKZ\n", "mCGHheTGvw1QVjYM57AqIFHHfXnCbHIxY9PhkkUDueygQ9kUw2xaTrA+nuILhzk9pStZyhViOv72\n", "4yMWVH8Po/SKlJs99u4ri+byHvJXZ7Sc7Y6s08vcJ8E9w00nmqjgoKviA3IXPU2IPIS8erJa8Ld0\n", "AInvMueIGNg5TNEHLC8nWH8bWQplkyIZs96tFmx4F7jqyyFBnYPa8+Ty530T+7HaGwuRFwFutWDD\n", "S8A995qK7Nou1JGlXL2/WO87n06I/Bq3WrBhp6MbRmaYob+WGhlFbrVgQ1LngKMWIx96/xOqCygx\n", "ini1SoYx0yq0b8Ls4aMmkahWyTCa8lXhwQ4D05BJJKpxQ5MAhr0MTNsUzc4sEjsjbtihJ5G9XM5A\n", "PxiYhkwiUc3mm5knSS0JWASrVcWhIWxUmRYbRaJagm+Q4nRRo7SaITS0Kgzrgdcq02KTSFb7nOfy\n", "B1u0tLbCizsqtPYVznSpUI2NIlGtZbdU66uZztnZb3JW9hGyfOiSQVBvFIlqa2nSX25rAYXVQKa9\n", "AAAAAElFTkSuQmCC\n" ], "text/latex": [ "$$\\left \\{ x : \\frac{a}{2} + \\frac{c}{2}, \\quad y : \\frac{a}{2} - \\frac{c}{2}\\right \\}$$" ], "text/plain": [ "⎧ a c a c⎫\n", "⎨x: ─ + ─, y: ─ - ─⎬\n", "⎩ 2 2 2 2⎭" ] }, "execution_count": 96, "metadata": {}, "output_type": "execute_result" } ], "source": [ "solve([x + y - a, x - y - c], [x,y])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Résolution d'équations différentielles\n", "\n", "Pour résoudre des équations diférentielles et des systèmes d'équations différentielles on utilise la fonction `dsolve` :" ] }, { "cell_type": "code", "execution_count": 97, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from sympy import Function, dsolve, Eq, Derivative, sin, cos, symbols\n", "from sympy.abc import x" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Exemple d'équation différentielle du 2e ordre" ] }, { "cell_type": "code", "execution_count": 98, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": [ "iVBORw0KGgoAAAANSUhEUgAAARoAAAAVBAMAAABrvDzoAAAAMFBMVEX///8AAAAAAAAAAAAAAAAA\n", "AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAv3aB7AAAAD3RSTlMAEJl2IquJVETdZu8y\n", "u83OyatpAAAEEElEQVRIDc1WXWgcVRT+JjuZ2Z/ZzdDaULHVoWhBKrh1bQRFHUREjCWhVZEqdFo1\n", "NBXata1K8WEXhYJWsGKLiGgGK4IimtAKfRBc9EGoD11tS4svWX2pIE03aWOCtMRz7tw7P7vTNr71\n", "QO79vnPOd+fbe+8MAW7IOC5dff0/3Vl+TJBvxMgiYJp4yT0u9KYU96YsqD349Cbdi1b/pRzh9cBj\n", "I2cUf16BcO7UhgUGaeJnalWUbNXWUiCaH/BhrvEjfsKL8B/QHdRUsRQVJOrUJhpSxNr8cmAg7IqQ\n", "Sq18n9DdrqKJOeMhM4e+AzJJNIib5HwtLTIpYrNNyqNSDdwSIgUu8L7tVyw5F1xkj+G2lsxq4xLs\n", "lvO1tEgTG/TDsvQno1RVSM49cwx+jGWfbITkbYHCk8I5WZFuurWhkkCKOP/cIQeGA2iVp956AQLG\n", "JRgSTlfI3E+7H9Inh62/76ps5MxWkf4TWLru8YGy4oB0k9Rqq0eq6K9sg3VfpXUVcR8tUvSBfnxZ\n", "/hTIcx92THHQU4CdVOewmjRoTeRQHEbpJIZc4r9zYdSB5uU/yLWAb4hzSDdKu/U3m5KDVeuodRDF\n", "8krgAPE08VADyFWBLdhp7wFM7ovHWEMw6w4uZCdgsJviOPp84h+K2ssNwzamTRfYK3joRmoNt7dF\n", "hSmYl3MerMv7bXjE08Q1yhfqgI3zhGC1eYzFDwLb0ubYSVe4aYH3FMdEsTChodRkeELw0I3U5lzr\n", "Eu3rDDdUgX9yV+7lvjTxKOXZDXCRhy43Y5w1G9JN/4UZ4aYZutHJHl10YS5wQ8f8cXDMUluo6/O0\n", "r7x+rQ5c0XYsOITJTZf4NOX5pGgHoanXa/QjjkOUDb40PfIISf1tnU9KuqHN7ptG8V/60Q3aXfma\n", "hHsjvlKkhTkd25uZR5End3xSXeIjlOdb/EqmjbXqFlNOxZBHaIt0Y3ro9WNu6CIW6GJfMu2HkSE3\n", "HbdYaZEr0yJTtEjBQXb+NWAT8RSxxi6NYWTnim3dB3oc4vEw6YfvogeJ621OoOjSxqi9eYns+6iV\n", "36tO4glSrZJK+U4pbfAlGPTxovU9St4bNk5RY4o4S3vIXz9t49qBrwiW6jQkYsnpDXVKCDfZ7fdv\n", "szbPrtg8+/Pkd5TkD9ibq85iWWXpdp/wX/THId1AavMOJ7UNAy4eGXkW+w5/USeeIjaa3HiQBxGD\n", "CnTOXa8+N+TcRJs+LqlyI+k+vJvoC0i3+NWSz6XPgjqN9DlOD1M9KV7OO3EGoyzpO4m0de71XxOJ\n", "gHSLP7lZVIqu7NZaEnRO+p2z6lHxEp9/FLdGMI4KCwvtOFe4S3z4dlEK/9sKbSnFdeb1iXpy+UQp\n", "jVxVzC8Ex+fBtOiRX8Mw8m4IFwU6xf8BdkMpGGpBBf8AAAAASUVORK5CYII=\n" ], "text/latex": [ "$$f{\\left (x \\right )} = C_{1} \\sin{\\left (3 x \\right )} + C_{2} \\cos{\\left (3 x \\right )}$$" ], "text/plain": [ "f(x) = C₁⋅sin(3⋅x) + C₂⋅cos(3⋅x)" ] }, "execution_count": 98, "metadata": {}, "output_type": "execute_result" } ], "source": [ "f = Function('f')\n", "dsolve(Derivative(f(x), x, x) + 9*f(x), f(x))" ] }, { "cell_type": "code", "execution_count": 99, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": [ "iVBORw0KGgoAAAANSUhEUgAAARoAAAAVBAMAAABrvDzoAAAAMFBMVEX///8AAAAAAAAAAAAAAAAA\n", "AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAv3aB7AAAAD3RSTlMAEJl2IquJVETdZu8y\n", "u83OyatpAAAEEElEQVRIDc1WXWgcVRT+JjuZ2Z/ZzdDaULHVoWhBKrh1bQRFHUREjCWhVZEqdFo1\n", "NBXata1K8WEXhYJWsGKLiGgGK4IimtAKfRBc9EGoD11tS4svWX2pIE03aWOCtMRz7tw7P7vTNr71\n", "QO79vnPOd+fbe+8MAW7IOC5dff0/3Vl+TJBvxMgiYJp4yT0u9KYU96YsqD349Cbdi1b/pRzh9cBj\n", "I2cUf16BcO7UhgUGaeJnalWUbNXWUiCaH/BhrvEjfsKL8B/QHdRUsRQVJOrUJhpSxNr8cmAg7IqQ\n", "Sq18n9DdrqKJOeMhM4e+AzJJNIib5HwtLTIpYrNNyqNSDdwSIgUu8L7tVyw5F1xkj+G2lsxq4xLs\n", "lvO1tEgTG/TDsvQno1RVSM49cwx+jGWfbITkbYHCk8I5WZFuurWhkkCKOP/cIQeGA2iVp956AQLG\n", "JRgSTlfI3E+7H9Inh62/76ps5MxWkf4TWLru8YGy4oB0k9Rqq0eq6K9sg3VfpXUVcR8tUvSBfnxZ\n", "/hTIcx92THHQU4CdVOewmjRoTeRQHEbpJIZc4r9zYdSB5uU/yLWAb4hzSDdKu/U3m5KDVeuodRDF\n", "8krgAPE08VADyFWBLdhp7wFM7ovHWEMw6w4uZCdgsJviOPp84h+K2ssNwzamTRfYK3joRmoNt7dF\n", "hSmYl3MerMv7bXjE08Q1yhfqgI3zhGC1eYzFDwLb0ubYSVe4aYH3FMdEsTChodRkeELw0I3U5lzr\n", "Eu3rDDdUgX9yV+7lvjTxKOXZDXCRhy43Y5w1G9JN/4UZ4aYZutHJHl10YS5wQ8f8cXDMUluo6/O0\n", "r7x+rQ5c0XYsOITJTZf4NOX5pGgHoanXa/QjjkOUDb40PfIISf1tnU9KuqHN7ptG8V/60Q3aXfma\n", "hHsjvlKkhTkd25uZR5End3xSXeIjlOdb/EqmjbXqFlNOxZBHaIt0Y3ro9WNu6CIW6GJfMu2HkSE3\n", "HbdYaZEr0yJTtEjBQXb+NWAT8RSxxi6NYWTnim3dB3oc4vEw6YfvogeJ621OoOjSxqi9eYns+6iV\n", "36tO4glSrZJK+U4pbfAlGPTxovU9St4bNk5RY4o4S3vIXz9t49qBrwiW6jQkYsnpDXVKCDfZ7fdv\n", "szbPrtg8+/Pkd5TkD9ibq85iWWXpdp/wX/THId1AavMOJ7UNAy4eGXkW+w5/USeeIjaa3HiQBxGD\n", "CnTOXa8+N+TcRJs+LqlyI+k+vJvoC0i3+NWSz6XPgjqN9DlOD1M9KV7OO3EGoyzpO4m0de71XxOJ\n", "gHSLP7lZVIqu7NZaEnRO+p2z6lHxEp9/FLdGMI4KCwvtOFe4S3z4dlEK/9sKbSnFdeb1iXpy+UQp\n", "jVxVzC8Ex+fBtOiRX8Mw8m4IFwU6xf8BdkMpGGpBBf8AAAAASUVORK5CYII=\n" ], "text/latex": [ "$$f{\\left (x \\right )} = C_{1} \\sin{\\left (3 x \\right )} + C_{2} \\cos{\\left (3 x \\right )}$$" ], "text/plain": [ "f(x) = C₁⋅sin(3⋅x) + C₂⋅cos(3⋅x)" ] }, "execution_count": 99, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dsolve(diff(f(x), x, 2) + 9*f(x), f(x), hint='default', ics={f(0):0, f(1):10})" ] }, { "cell_type": "code", "execution_count": 141, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": [ "iVBORw0KGgoAAAANSUhEUgAAAHoAAAAVBAMAAAB71edYAAAAMFBMVEX///8AAAAAAAAAAAAAAAAA\n", "AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAv3aB7AAAAD3RSTlMAVIl2uyKZEO8yZt2r\n", "RM0C/HbBAAACAUlEQVQ4EZ2SP2gTURzHv7nc8+VMciml9Q8WGujg0KElgQScMhQddDgcRJxOKHQq\n", "HgrFUUGXTgGlQxcPpC3qkqVTsQaCg1iw6KjDDc5ytURpS8Hfe/fe6+UMWPzC5X1/n+/vd/feXYD/\n", "ldV49sHMrir3ypB/mGYpeqtbWEu5Ulujk/XBjZ3w5kmZuLYbGuQG2kbamDW/DJyjSyk3R7oGVDxN\n", "UBviFOLX6c5n+qZBmxcw23yqGR4ap4z7m0z5UQazeBpVxcodE7ojxibmngCOn6G897keEButj8Gi\n", "kNfff5mCtOlOdiQq3paMTzW8dAirxX7BDoH7eDO7DZyNKLZ+SPnC7tOP0HO6vnqsKrzRZIA9FGl3\n", "lzETjAO5jomkKR4k9cpHgD3GUrIJ3fMETh+FXSDAd8GcWCfJWpA1By4BpeX6xkDqHCLfkdOA3GN2\n", "uiinL8rpSndglo7Zh+3LncM5AD1Cvn5rS0o0J1/aT6arBNLKxaiMyLd2Ph+jqd5aqoP9pMIK5HSh\n", "CsymMvA+Pnmwuijv2TEL6Vl+OiY/GSK3QCudOx9hyRuIv9Wm5XZ5r1l7TYm7OxDTp57fWRSIptG4\n", "Egqb1lUqNg24bVzGiOmsHPBjYuuG099tuC4MwTOB3SVs6+PwiKphuvWu9zeeeHlXQNZSkbmNqk+3\n", "3FFta6drT7r+AC+kaw1UUlwWAAAAAElFTkSuQmCC\n" ], "text/latex": [ "$$g{\\left (x \\right )} = C_{1} e^{- x}$$" ], "text/plain": [ " -x\n", "g(x) = C₁⋅ℯ " ] }, "execution_count": 141, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Essai de récupération de la valeur de la constante C1 quand une condition initiale est fournie\n", "eqg = Symbol(\"eqg\")\n", "g = Function('g')\n", "eqg = dsolve(Derivative(g(x), x) + g(x), g(x), ics={g(2): 50})\n", "eqg" ] }, { "cell_type": "code", "execution_count": 144, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "g(x) est de la forme C1*exp(-x)\n" ] } ], "source": [ "print \"g(x) est de la forme {}\".format(eqg.rhs)" ] }, { "cell_type": "code", "execution_count": 129, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[50*exp(2)]\n" ] } ], "source": [ "# recherche manuelle de la valeur de c1 qui vérifie la condition initiale\n", "c1 = Symbol(\"c1\")\n", "c1 = solve(Eq(c1*E**(-2),50), c1)\n", "print c1" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "SymPy ne sait pas résoudre cette equation différentielle non linéaire avec $h(x)^2$ :" ] }, { "cell_type": "code", "execution_count": 112, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "une erreur s'est produite\n" ] } ], "source": [ "h = Function('h')\n", "try:\n", " dsolve(Derivative(h(x), x) + 0.001*h(x)**2 - 10, h(x))\n", "except:\n", " print \"une erreur s'est produite\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "On peut résoudre cette équation différentielle avec une méthode numérique fournie par la fonction `odeint` de SciPy :\n", "## Méthode numérique pour équations différentielles (non SymPy)" ] }, { "cell_type": "code", "execution_count": 110, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "vitesse finale : 88.4 m/s soit 318 km/h\n" ] }, { "data": { "text/plain": [ "[<matplotlib.lines.Line2D at 0x1427d9e8>]" ] }, "execution_count": 110, "metadata": {}, "output_type": 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"liQQEdHDkgQiInpYkkBERA/7X3zey22RDIBeAAAAAElFTkSuQmCC\n" ], "text/plain": [ "<matplotlib.figure.Figure at 0x139556a0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from scipy.integrate import odeint\n", "\n", "def dv_dt(vec, t, k, m, g):\n", " z, v = vec[0], vec[1]\n", " dz = -v\n", " dv = -k/m*v**2 + g\n", " return [dz, dv]\n", "\n", "vec0 = [0, 0] # conditions initiales [altitude, vitesse]\n", "t_si = numpy.linspace (0, 30 ,150) # de 0 à 30 s, 150 points\n", "k = 0.1 # coefficient aérodynamique\n", "m = 80 # masse (kg)\n", "g = 9.81 # accélération pesanteur (m/s/s)\n", "v_si = odeint(dv_dt, vec0, t_si, args=(k, m, g))\n", "\n", "print \"vitesse finale : {0:.1f} m/s soit {1:.0f} km/h\".format(v_si[-1, 1], v_si[-1, 1] * 3.6)\n", "\n", "fig_si, ax_si = plt.subplots()\n", "ax_si.set_title(\"Vitesse en chute libre\")\n", "ax_si.set_xlabel(\"s\")\n", "ax_si.set_ylabel(\"m/s\")\n", "ax_si.plot(t_si, v_si[:,1], 'b')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Pour aller plus loin\n", "\n", "* http://sympy.org/fr/index.html - La page web de SymPy.\n", "* https://github.com/sympy/sympy - Le code source de SymPy.\n", "* http://live.sympy.org - Version en ligne de SymPy pour des tests et des démonstrations.\n" ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.9" } }, "nbformat": 4, "nbformat_minor": 0 }
gpl-2.0
moldovean/Leontief-Matrix
Leontief-Matrix/Exam v 0_1.ipynb
1
6300
{ "metadata": { "name": "" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "##Examen\n", "### Geografia Turismului\n", "#### V1" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Exchange of Goods and Services in the U.S. for 1947 (in billions of 1947 dollars)\n" ] }, { "cell_type": "code", "collapsed": false, "input": [ "import numpy as np\n", "import pandas as pd" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 58 }, { "cell_type": "code", "collapsed": false, "input": [ "ls" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Exam v 0_1.ipynb leontief.csv QE_P2L01.ipynb Untitled0.ipynb\r\n" ] } ], "prompt_number": 2 }, { "cell_type": "code", "collapsed": false, "input": [ "f = open('leontief.csv')" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 16 }, { "cell_type": "code", "collapsed": false, "input": [ "Names = (f.next().strip()).split(',')\n", "data =[]" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 17 }, { "cell_type": "code", "collapsed": false, "input": [ "for row in f:\n", " #np.vstack(data,(row.strip().split(',')))\n", " data.append(row.strip().split(','))" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 18 }, { "cell_type": "code", "collapsed": false, "input": [ "data = np.array(data)\n", "\n", "# Extracting External Demand\n", "Ex_Demand= data[:-1][:,-1]\n", "Ex_Demand=Ex_Demand.astype(float)\n", "# Extracting Gross Product\n", "Gross_Product = data[-1][:-1]\n", "Gross_Product=Gross_Product.astype(float)\n", "# Extracting Expenditures Matrix\n", "A = data[:-1][:,:-1]\n", "A=A.astype(float)\n", "\n", "print Ex_Demand\n", "print Gross_Product\n", "print A" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "[ 39.24 60.02 130.65]\n", "[ 84.56 163.43 219.03]\n", "[[ 34.69 4.92 5.62]\n", " [ 5.28 61.28 22.99]\n", " [ 10.45 25.95 42.03]]\n" ] } ], "prompt_number": 35 }, { "cell_type": "code", "collapsed": false, "input": [ "Demand = []\n", "for i in xrange(len(A[0])):\n", " Demand.append (sum(A[i])+Ex_Demand[i])\n", "Demand= np.array(Demand)\n", "Income = Gross_Product - Demand\n", "Income\n" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 43, "text": [ "array([ 0.09, 13.86, 9.95])" ] } ], "prompt_number": 43 }, { "cell_type": "code", "collapsed": false, "input": [ "A_tc = A / Gross_Product\n", "print A_tc" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "[[ 0.41024125 0.03010463 0.02565859]\n", " [ 0.06244087 0.37496176 0.10496279]\n", " [ 0.12358089 0.15878358 0.19189152]]\n" ] } ], "prompt_number": 68 }, { "cell_type": "code", "collapsed": false, "input": [ "L = (np.identity(len(A[0])) - A_tc)\n", "L" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 74, "text": [ "array([[ 0.58975875, -0.03010463, -0.02565859],\n", " [-0.06244087, 0.62503824, -0.10496279],\n", " [-0.12358089, -0.15878358, 0.80810848]])" ] } ], "prompt_number": 74 }, { "cell_type": "code", "collapsed": false, "input": [ "linalg.inv(L)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 73, "text": [ "array([[ 1.72036911, 0.10003816, 0.06761781],\n", " [ 0.22341618, 1.66748571, 0.2236785 ],\n", " [ 0.30698795, 0.34293929, 1.29174829]])" ] } ], "prompt_number": 73 }, { "cell_type": "code", "collapsed": false, "input": [ "P = np.dot(linalg.inv(L), Ex_Demand)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 77 }, { "cell_type": "code", "collapsed": false, "input": [ "P" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 78, "text": [ "array([ 82.3458407 , 138.07293955, 201.39633704])" ] } ], "prompt_number": 78 }, { "cell_type": "markdown", "metadata": {}, "source": [ "$ Turisti_{t} = \\alpha * Turisti_{t-1} + \\beta_{0} +\\beta_{1}GDP_{cap} + \\beta_{2}\\frac{Prices_{foreign}}{Prices_{home}} + \\beta_{3}*ExRate_{\\frac{Foreign}{MDL}}+\\beta_{4}CO_{2}$" ] }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [] } ], "metadata": {} } ] }
bsd-3-clause
niskrev/JupyterWorkflow
nb1.ipynb
1
330483
{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/BadWizard/anaconda3/envs/py35/lib/python3.5/site-packages/matplotlib/style/core.py:197: UserWarning: In /Users/BadWizard/.matplotlib/stylelib/my_custom_style.mplstyle: axes.color_cycle is deprecated and replaced with axes.prop_cycle; please use the latter.\n", " warnings.warn(message)\n" ] } ], "source": [ "%matplotlib inline\n", "\n", "import matplotlib.pyplot as plt" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "plt.style.use('seaborn')" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from jupyterworkflow.data import get_fremont_data" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "data = get_fremont_data()" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "DatetimeIndex(['2012-10-03 00:00:00', '2012-10-03 01:00:00',\n", " '2012-10-03 02:00:00', '2012-10-03 03:00:00',\n", " '2012-10-03 04:00:00', '2012-10-03 05:00:00',\n", " '2012-10-03 06:00:00', '2012-10-03 07:00:00',\n", " '2012-10-03 08:00:00', '2012-10-03 09:00:00',\n", " ...\n", " '2017-07-31 14:00:00', '2017-07-31 15:00:00',\n", " '2017-07-31 16:00:00', '2017-07-31 17:00:00',\n", " '2017-07-31 18:00:00', '2017-07-31 19:00:00',\n", " '2017-07-31 20:00:00', '2017-07-31 21:00:00',\n", " '2017-07-31 22:00:00', '2017-07-31 23:00:00'],\n", " dtype='datetime64[ns]', name='Date', length=42312, freq=None)" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data.index" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x10e806ba8>" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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33Xdj9OjRmD17NpQz916XLFmCUaNG4a677sKBMwVB53TVpOI9djTkfbv2xX6z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\ngoB+/fpBlmXUmKSgVFVF5Ru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bUq++iuyhg0ju+yO00VEAzhdMikQAQbA8b1fCmivm7Q6DpMeZDjRbUIeHcWzX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4qdj2aCNw/EFg/eIT4X3d3A+oO3YQCCsKytYIvBsh1k7TkEUBVQ8pJBfIESRLOQxNp7sSMRxJ+9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\noDca5Q9+eROaqrxhNz2XiqBXrxngyLL5bNb3RGGqq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"text/plain": [ "<matplotlib.figure.Figure at 0x10fef6630>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "data.resample('W').sum().plot()" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#data.resample('W', sum).plot()" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(0, 1059460.05)" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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sNmpra7n00ktpa2vjT3/6E+vWrUNRFADsdjs+nw+/34/b7U6ud2i5pmkDKtsX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\n1jO+KR3lhDgNmE16vnzJeGaOy6a6yU9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7rLsqS9FL6u3tVWFhoUpKSlRQUCBJyszMVHNz\nsySpsbFR2dnZF92/srJShw8flnTuqPH8EST+nZu8U1NTtW/fPtXV1amurk7Jycl6++23PZs9Wrn9\nHy8rK9O+ffsknXsyvO222yZ+6CjmNu/bb789/CbTI0eOKCMjY+KHjnJuM5ekQ4cOKTc3d8Jn9Rpf\nmCPJcRzt2bNH6enp4dvWrVsnx3EUDAaVnp4ux3Hk9/vD9+fk5KipqUmS9Pvvv4dfL46JidH69ev5\n5r4xuM37f13sdozmNvOOjg6VlpZKkq677jo5jqPp06d7u4go4jbvM2fOqKysTD09PYqNjVV1dbVm\nzJjh+TqiydV4XnnyySe1Zs0azZkzx9PZJxpFDwCAxbh0DwCAxSh6AAAsRtEDAGAxih4AAItR9AAA\nWIwftQEwps7OTuXl5YU/MvrPP/9o9uzZWr9+vaZOnXrR/VasWKG6ujqvxgRwEZzRA7ik6dOna/fu\n3dq9e7f27t2rtLQ0FRUVjblPS0uLR9MBGAtn9ACuiM/n03PPPaecnBy1tbWpvr5ev/32m3p7ezVz\n5kxt2bJFb775piTp0UcfVUNDgxobG1VbW6vh4WHNmDFDVVVVuuGGGyK8EmBy4IwewBWLj49XWlqa\n9u/fr7i4OO3cuVNff/21hoaGdODAAZWVlUmSGhoadPr0aW3atEnbtm3TZ599pkWLFoUPBABMPM7o\nAYyLz+dTZmambr75Zm3fvl1//PGH/vzzT509e3bUdj/++GP4x0YkKRQKKTk5ORIjA5MSRQ/gigUC\nAbW3t6ujo0PvvPOOVq5cqYcfflh9fX36/2/VHhkZUVZWlt577z1J0tDQkAYHByMxNjApcekewBUJ\nhULavHmz5s2bp46ODt1///165JFHNHXqVB05ckQjIyOSJL/fr+HhYc2bN09Hjx5Ve3u7JGnr1q16\n4403IrkEYFLhjB7AJXV3dys/P1/SuaKfM2eONm3apFOnTunFF1/U3r17FR8fr/nz56uzs1OSdN99\n9yk/P1+ffvqpNm7cqOLiYoVCIaWmpqqmpiaSywEmFX69DgAAi3HpHgAAi1H0AABYjKIHAMBiFD0A\nABaj6AEAsBhFDwCAxSh6AAAsRtEDAGCx/wB49DH/urV/tQAAAABJRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x10f8fcdd8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "data['Total'] = data['West'] + data['East']\n", "\n", "ax = data.resample('D').sum().rolling(365).sum().plot()\n", "ax.set_ylim(0, None)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x10fee64a8>" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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ECZpOtiIB8reCWz+rQclchKzmFms0kCQne4+eJJGHEjSJSKzN5u4elpUd8rab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"pivoted.iloc[:24, :5]" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false, "scrolled": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x1107c7828>" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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dKcqh3UNDD9p76u/2+paJqxnefcl9GbBgRrXWC7tMX/LeGrYq0HvsXgRqa5oP\n+JLQB8G7Rl+D3SrAVFv1JyW2p6cgwtCrStkK5plalWHImFcSQq2OaFeXe2GeC2rGmBOpB+pq1kSf\n4r3Mbuz7HQTHQQTu4DDZF5r1+2tVe6BAbJk37LR77aVjO+Xcy+Pg2mvifVJSTgk1qKq4gKQ+ClH2\nRWrSM+/+c8K0InjC+HpIZJ3itegv3AV1oapyprUdNRVmyeQ8UCR3c5WEqjY1Og5lJhnNRa1PBJPc\njr3VtP1usDvEah9kEhwPEbiDg2PfP9nbH0girf8azmUIatYmgLn3jas5RqXUDCeypHV2d0p5VZlX\nVXKb3Ylb+5ray4tLCxiLsrybtUQvWPCkWfQUaysizeEsWcXNsDJT50KZnc2YUDXmubA9u4cXpc4z\nQxLGPHBaFZ0dSU4pM3M5o1al1so4tkayqm0iHot+ZM+j4PzgnuCQicAdHDSLinyZELZM9lrcoNar\ncGwVlA1JyL1PO3XFOJLb90JwF5K0YSOzzuvX9au/3bjQPV/ndfgDEb+Ddwdbq0ZNf1HNMVWm6Q6z\nKl6Votrq6WrMBmdzJXkToImkVkEqlWKtOlWmidkKCSEvE8eWhS6+itJ2PgXnDYmCwyQCd3CQLBVE\n6yrYRVG+35LlzTVlnbWNCLln1dDuxNchC+441nrAAVVdN0ZkeZ21/tlzalv2BHL3jfsOgh+KpaIk\ny2AR2iCdlDLmTtUZEycnQYsiRVF3NpuBZAU8UUrFzBgEiillNsScRKLQ7rxxQ1ImJ5o96nIYZqch\nWTo4gsMnAndwcCyBM6U961LzVZSW1vtnWb3JmxFkahuU7EYsmGr7c5Y16qoqpSri0oQ6y8/spXNE\n8MVVbZ2edFG8E1tc8ARYKkbW5ZK16TmaNkO4e+fOetDc5CZWy+IMY+Jks+HetKWWNurTJVFUm/mK\nblHV5pJWKnOZGFMmSeo34bL7/YqlfHRE4A4Ojn17UfO9jaxjtnNAW1zQkqTehy3LN2EZqLCU/8xo\n2YnWNs5Q2mHAtJUm894ghqUevhjAeP++tbfUxD1g8CTwXrI2diVqtSZQq7WynbdstVWVigvzVDBJ\naBrwk4GTYcO9ueAKJ3mANDDXCZ2NeTuRgDLNFKt46p0ZqXVVlKJ9atjunj3uuY+DCNzB4bGU8NxX\nS1Khz9/e7zeVvZezK/ot2bZ2EQ6pzV3S7oBmOEOSnrk7U2kbH4DWst6fr0o2vM3oDoFa8AQ5J8J0\nx0zbFZFHYw6rAAAgAElEQVRb25jdKKcTqsbTmxOmbUHS4iuQGHLGaW1jpZy175dy+55zG18rKTdr\nX3NKmdoc+pyopSJ9PcsyxGcv9Y577sMmAndwUOxvZk2MZv3ur91DSw/Estg39n5r66X0Zi6xDChp\nG5B6y7ZbIFfGPDCMCXdFa8XoLWTd23wtX/be8MUmFWB//GdkJcEPw75Dn2kzQqneA7Fq8ycvZ3g1\nplK7cA3chVGgVmfz1A2qOvPcZnInam8hq7hNmFXSSeLsrGK1oLW23wetuDt1WcL7QTzW9cETgTs4\nWJxm47hM6doNAGmfl3XI5/KxXcncvatz1+BriHu/624bV0qLBWo7JGifzNQidf9ZdMU5baMNv/Lg\nSbEfM5dK0FJUqtUo8xaRRPF2YLViqCs3xtRMhlzIQ2JImeKCVSeRsZSpXrBqTPcmfPEtoAdmdSTn\ndn3Uh5Hc14IZHDQRuIODY0mkly1k8RAXBJe9cl7fZNaJSjSxma3uUY5J6vfYjkvbAHNqQbqqoqZt\nUOIydGQpkXNes7MbL3o+6w6Cx6YvKF988mltiklSy7KnqY+kBQzcmgQTazoNY0JVyGNbk7Uq7hX3\nSh4GdK54LaTUWiJdM2lstqkmbbSnmFPrIlC7IL+MzPtgicAdHBQXFeWpe4avvdydXWa9E9SISDeX\n6KM7e4axbEpNIO6YFkox5rPWDuZu1F5CZDHBWFrIHETSfa0ysacFTwShtzm2iXXaVeHq3tu2ZkSc\nyaBKm4rHmBiAcRn/OY4M44gXw2rLoEttzmsijs6G+UwplVoqQ8qAk9xZ3IPNl+E7thOoRe59sETg\nDg6KXcmubSbtL7KnFWstWrq2cdnaa93aYLzNYkhtGnGC3qutYJDEUG33hI7htc09rq5rMDat3cXq\nfGk8dX2cL0q22NiCx2Snn/Bz1zVOX6+AlTOKwUlKzLOC+nqAdO/hVYyTlFGM2R3dVjJQ3CnWyuV1\nO1G9OQuaOsOQqDjq2n7RBFR35fK45z58InAHh0VvBWuTjbSL1NJeGXtvs/PdZucsM7u79akJIokh\npzZ3mN4q5l1dboZZwVVxNaCJ2VTbAAaBHqmX8LyvaIeYEhY8CWS5Z3bv87JbyXuaTilW0WqU6lAU\nRRhphkTD5oRaFXFDq7dWxpywql3IZgybEbeClso4jNQytSsjdYaUsC7+NLemVrd2WI2gffhE4A4O\njharE1i791u7suh2pLTg3RTnQiJhps3edPEfB4YszKWCWLN8BGpPMqoWyA6Jpiz3Vn5vc7u9TxZb\nPaW6Xq3bQ9rOHjIIHgfrB0+3JWg7WtsaLmZ4X6hetXVE9EEglpWcM1UchhMSrRd7HEdS2lDcYHa0\nzt0mNZEHp2y3TDpT3Sg44u1GydyQ3je+TMfbbwyLNX6YROAODob9TcIWNXeSPgyh/ZleXizWerST\nJETaRlQX+1J3NkPGTFcr00QztqArdkWsHQK6JSpu3QbSVmvVZWTooiwHWk/4nkAtNrbgsViuZfrh\nMElqx00zfKpMZeofH6iFNoZT6eJKyJuE5Iyqklzbn5OShwGfK+LCPE1o14pYMbIktvfuoeoMJNwU\nN2vDdrz3Z/TDqURF6aCJwB0cFN175VxA3G/DUndq1e4AJeTcBisU1SZGS4sxRau6myuDJISE0VzX\nzApFK/NcOCsVmyuYtulgLOVxXw8Jy12k9H5xYM26g+BxcW/z7JZRsq0dUZCUmcuW0qtKc/fUlwzp\nZCRnEIyTPJDyiIki1RCMKiNkYd4WsIp6aQJMpZXSUep2am8gtxYx7ffctucIuJvAFwfTQyQCd3Aw\nXAzWSVrwXYRp6ss0ozb5aJCMu6O19V4Pm3HVw1atVFUE6/faiqTMVAqIU+ZKrd6EZsnX4SK+CHO6\nz/OiJU8p7+7ZPTa04PFZg2PvYJCcUWvrzWql1glxw6qSM8xzhZTIAmKKi5ARJDspnyBWkSxsxpOm\n8FDDajNX8VqxYkiaSZ76cB1lmmuXbjYxnNXaDqXWOzI87roPmQjcwcGw2yhkvQOEnTBNupgm517C\nFmHaTi3AZmFIeWcmYc3fOUtmyAMuUEvFrTLPE5IyXrQ7SPV77uRYtS4W0uZo1d+TLhm29Ow/jFiC\nx2TRaACrf4BZO2yWaUbrzLbMzLV3QNQutszOMIxISpR5QqQbB0lGy4SQkWToOLZ766mwtdqqSAJe\nWlVqqjMVR4w+UMeaf7n30bWLfmR5vxHAD44I3MHhsAZJxWiCMkm72zZBVu/yhDDX3pYlxmYc0d4a\nprV7PmOklFEtOMJ2npvtqUE5m6ha0KJoKW2Osere/O5eJk8JRLrlqZ+zPvXlXjAI3iEtYHc9Bf2A\n2A+qpRrZHbPKNBmIYuptvGcWBoB80lq6xxHxJkCToqQM5hlLwtlc0alQfW694WUmjSNaZuZ5ag0c\nvWND3Ht5i70WyDicHioRuIMDol1wp64oh6WntJWmrTtM4VAN0KbWGcZx3fhUl3aySqbN5/bUWsXM\nlGl7yra2LNtrxc1bD3ex1lqjujqkLeNBl7e2bGO+DjeJjS14DHyvK6IfFN2bB4GpUXVCaS2Opbuh\nkZzNyciQEuSxHS7JpFxJeWyz5WViQCA57plswqwGVOpZIY0weGsbK9OWMrWhJYvjoAio0aWYe9dW\ncTg9OCJwBwfDIgIzdqOxz/mDO2CGWgvQ7tZ6s3sZ3Z0efFtentOIulJqZTtNaJnbkJKquChanbPt\nhFnBXHFz1BQlrRn1vhnFGsyl2U2FQC14HFpHQh85S2tx1No6IlwLRQuqTahmU+uvyBlcm7VArRVT\no8wFT5lhc4JZYkiJlDJ5MBgS1RSdKqfbSkLRbW293GaoG5PWNtqzux+0SpWjpVWyokR+uETgDg6C\nnZp1t7GtB32hicZg/Vii2TWSE0LvwdaK9V7UnNp9+LZMFK1NSV4mikFywWZlniu1FOaptGynlm7e\n0v2jtfb3ttxvd+mb2Z7iPEQ8waOzmAcJOytd67oMnUqrObkxzTOC9KJSIg1wY3PSKkAiJM8gA9kT\njJCGzGzG4M2vYPFCaFPCjNmtZekqSBrRaqjO2FxxWbwRrP3uSWI1DY61fZBE4A4OgqUvehGlpd7C\ntRTulnYZB1zb4AWXtvnllCm1gjtTLbgYSXJrcTHFalOR45BJVFF0a9Qyt9K6Gual2T7ShG27w8My\nXGSvLaxPDrPY2IJ3iO2tF+++AnMtlLkAjqLM80RVo0wFs9qNhowRw1ObTT/kkQ0JNWeUgfHkaYpA\nKVuSO+MouGfcaJm5Nytf14lsFU9CsUqtitbuniatzaz5FCz6jT23wuBgGB73C//lv/yX/Of//J8p\npfAzP/MzfPjDH+ZTn/oUIsKP//iP89nPfpaUEp/73Of4zd/8TYZh4NOf/jQf/OAHn+T7D64YLm0c\nZ3bHJZPwXRmcnkl42+DSsCH1ASRqtc80doacSMPAve0pqs48b1ubjTkZR8/m5tO8ydidia0OnJiy\nqY6YwtBsUX39getgT6AVFl3Czzl4DJZrl72/el9rpso8z03VLcJcnOSKOeTNgA8Js6bfcNq1DpLa\nRLGhTRarOTNUxwdnM2a288x2mphv3mTMA62v0sibES2FkiaoiRuDIHknmlssWMxaF4cTpiyHxGNl\n3N/4xjf47//9v/Pv/t2/4wtf+ALf+c53+JVf+RU++clP8qUvfQl35ytf+QpvvPEG3/zmN/nyl7/M\na6+9xi/90i896fcfXBX22mOWUmJKO2Gadhc0r7WN50zLxK4+LMG8qXFzZpANWgpuRrVmGekKN4aR\nuVRqMWYv2PZeM16ZK6qVWucW4Oe53S82n9N2p82+2naXgRPBO3gneJ+97YZbs/tx8+Yh7m0i2FSN\nogreFN95TGQZW0sk7VBKagfajFC1cjJsuLm5hac2q1vcITfPgaqJqRTUavMtMEAdhgHTfj2lgrrh\nplRVbJmjE7H6IHmswP21r32Nn/iJn+Dv//2/z9/7e3+Pv/yX/zJvvPEGH/7whwH46Ec/yte//nVe\nf/11PvKRjyAivPDCC6gqb7755hN9gOCq0LPZ3m51UZi22IzW2gxUSHm9h6MbR6QEOSVIie18RqkF\nr4ApIsbZNKHFUIxUm5HFqWkzY5lLU/eatX7uxZBF90r41u1PUyL1XvJoCQselXWOu0jLsKUFXTVr\nvgM41Saszpg7pRSSOCdDYjO0CWAqCXEDaa1kqDGSqT4z5A05jZBaVi1ZyOOI1cp2u6WYo1PFfWaw\nNkxHXanTjHaP8l7S2hNlxto+RB6rVP7973+fb3/72/zar/0a3/rWt/i5n/u5c5vsrVu3uHPnDnfv\n3uXZZ59dv275+HPPPfe23//5528/zts6eOK53ppamjVj7eYS2YU0jEC7U1aH7fYeo99kvDEyjJvu\nbAZelXvzzJCEIQ1sy8TJrVucTQoK9QzUNpyVme3ZTJ7BSuFGvcHp3cyNQdjcOuHWcyfcvHWTp27d\nwt157n0nbG48RRpGfC5dwd56uVUdNeckJ4aTcc3Kj4FYh5eD1Ypa2yfLPFNogsgfnA08fSNzegY3\nz0bu3Ug8Ixv0bIu4cHOTee59G2qCWhJPDQO333cT1dbXnYaWRIsJckeQskHv3cGHDU+Pwv8dnezO\nuMncet9NTm7eIKcR22yw5Gw8c/vpW9wYhJwG0tB+v4bkSE4kUu+mkHNVpx+WQ//3elzei+d6rMD9\n7LPP8uKLL7LZbHjxxRc5OTnhO9/5zvr509NTnnnmGZ5++mlOT0/Pffz27Yc/1He/e+dx3tZB8/zz\nt+O53oJmW1pxd2Zr5bpMgiTNYjQlSqlM2zOGMXPz5i1E5jbJy2GeC+aVnAfMnO28RXFcE9O9H0DV\ntfxY7sxMehdxZz6b+P4PznjfLUX/z4B45uyPK2fPVv6/H/t/+aPv3eHGSeuTNev+6GvgNtwgZWEz\nDqtC+NCJdXg5LK5k3v0J5lJ6S1bh9PQMmSfunt7lB//3Dv/3buHeaeHsbOb2jRtM4ty5U7AhMdTC\nnSEzfvcOA4ltHtmg5E1uM7eLcrad0cnRs7tkGZgnQ12Q//PHcDYz3r5NkoyMJ9QMmcydH8zcvnkD\nGYRRMmnckHJiGBK4rG6F6QkdUA/93+tx+WGe650E/Mf6V/jJn/xJfvu3fxt35w//8A85OzvjL/7F\nv8g3vvENAL761a/y0ksv8aEPfYivfe1rmBnf/va3MbOHZtvB9WMReRmOLvO496Qwyzxtp6nNW6tX\nHwJi3tTmkqjVKFra3Z4JZT4j1UrZVjzBdDZT8wyl2Z6atJ93dzK8VMq8bZXBWplrKx8268emrm2K\n4FZCzCLIMgwl7rmDh7GskfVw510nZtTayuV1mqnadBm1j5fN2ZuV6ZBaG2PTlKOSIAu5f8S1XRsN\nOTNsBlLqFvzNBJCqyrYYc1GktW43TwNvToPFCkVtLZE3Q5Z2x95eGBwSj5Vx/5W/8lf4nd/5HX76\np38ad+fVV1/lT/7JP8lnPvMZXnvtNV588UVefvllcs689NJLfOxjH8PMePXVV5/0+w+uAKvBSfsL\nAk3Jaor0sYRzKWRpTmht/GazPa21AIZrE6ype1PeKlAnqguehXJvRudKtbltSubIOIBkoKJOa4ux\ngvkNXLzVHzttMthqDdNUtssm5x4anuDheBtN2zzzm3Jbi5Ksrd9tnSnzjLpRNJFwho00ERnNkTS7\nNHMWbZPsBhmYUyargQqKM4ig40lrkZQ2zjMNzXDlzAs3tnfZ5GdQm8lsmEXxArpZfMrZa6LYtYMd\nQ0XpuvDY7WD/6B/9o/s+9sUvfvG+j33iE5/gE5/4xOP+mOAaITQVrKzCtGYMYb0HNYnsWlW8lR2L\nVcwVobmkuTpejVq2uGrzIs+JaVZUKna2hRE8ZcgjkgfOysTtckapN5vgtli3WIWqlZw37U31tjDp\nb7KpgyMbCd6e1aSnH/QWrwKzxRs8oW5YbcpvPONlS5IBJ3FrTGyrsXHHE2h1igs6b7m1uUnyRM65\n7eYqiCU2G6FMZ9Qys0kbplpRhO3ZzCwb8omTTprYEhdcjWLaer8RqAoDqBo5y7mDagTwy+d4FDXB\n1WVvY5MeEFsVr/+3C3okCWkcV+cyraVvhJAyuCfMe7m9nFEnpSqUSbGqTNOWlAdQZzi5ySYNQCu1\nl9oMWYpqawdT7bO7dfc+pZlmePeYTjQv893AkSB4AKsroKyeJibSWhVVEVO0zJhXiis2915qF8ZN\nZq60wCxtdnZyJwsowlwmkjhVm+OZmyLmDCcbnjp5ChVn65Wht05Wd7ZW0PkMq5UsTrJ2JaTTjGrC\nUyKZoqo70eVaFYt1fghE4A4uHXdvXsnaRissWe0yylPai/pm1l+iheqtpSVJomjLtsUd08JcjFqU\ngjGdzUw6k+eCeiHduEkebjAONxiHDRTj3naZFDahamgt3ThNMC2tDayXDpcYbec2sdjQgrdmNVzp\nAjUzR8W7RS+UWdnOzcnsrM54VdJAa+dKS7+2kzTBODDmEzZpw9aVWk6bk+DZTJFmbepTIY8bnhpv\n9d+ljAgUbYG7zBNuzjyVNttbhK027wNX6wZD7a7IvbWvxeH0cIjAHRwIst4Vi+SdMM2MqczNkCXn\nZmBRSveQsHafLQkMjNKy57N7MCt+MmLqzPMZphMpC2nIDMPIhgxKa3XJQ8tG5qkZt3jF1ahaMLV1\nilMr27dNDdpwCBFBQ6AWvAVrsNsrMy/KcpLgVjGap76WiYJgxdhsMmyEEch5d1U0ez/QbpyT8RZZ\nBsQyRSccYcggecTdGTYjJydjM2zB8SKQMzYpZVZ0UkqZqWVqA0lq5azObeSItYO0sFfqDw6GCNzB\npbKYUixBEdiJvnpl0a3PyfYmRkvJmfsEMG/fpIt3WtDWWbsPs3Pv3im1TIyqcHNgc/NpnIxVQ5Ky\nESePI3eLUfUMpWXSs1acZjdppuCtDWytB3h3p1rePJGRBA9gKZMjrUTuhmmTeos5VtvVS3VFEMrs\n1Nq1HilzcnPEqyDdEGgwRzHyAHIi5DxibiQSRYyNj5SsuCdqqQx5ZBwSJpnc9SGKUKXiPoF1g7W5\njfi0uTYntSS41nXSXnuUJQOPdX7ZROAOLpV1whb7xebedgWrFSQCKTXvcu+lPTPD1Ekk3CrTPFO3\nMzklZDxhns6wUhlSYhgzg4zMVhkZGAZhGFs2klLCq7Kt3RijVnSeQK2pzfcd3NhLroVVxBYEF9mN\ng90tELU+gcvB+rCccnqGlplJ77Ws2RS1hAjUaog4Jl1HMSTGk9Rnbk+MaURFmse+J+o8Myanpjae\nx9y5sbnBIDBuEhQoCCoJ90pyY7udwStmTvGC1laaX66o2kFV+tVRLPZDIAJ3cBAIgriwRsFlBPfi\njtZ9lN20iXK8YNX6/XahaEXPJswLpU6c1cq0LWRzsjg+tNavgQ2iRjrJTFbagcAz7sLZZEzThGth\nntrsYtc2manNS6YZwrQ31t5mHxweArXgPpbAvbdmvA8Q0T5CU82Y+5CcycCmNiWMk8TJydhGbKbm\nHe6kNsZWEk9tbjKM3dEsZ8BxnVAEr8q4yZg5ibweXFPOuLZZ3NupsC2Ke8FrpZSCzYXtXCimzbcc\np2pdW88XYVoI1C6fCNzB5dI9ks0dST3X3guQtZa1pbTNJ7bWq10qrtKV50a9N6E6tZ5vTdjUlOFp\ncGRMkPqkI4VhSJyZtk0KmlnFSSs5ztOWUpqLm+GYtrGH7roeIkx1VZafa42JwB08gPN++0vga//X\nSkHnyjQXzAQtTsrNbnxAmo6i0g6y1sZubsYTzBPDZoARNnnE3BnS2DLkAmaFNA6ttUsyw8kJ2YWc\nWzAvZmCpt58ZWZ1ZC+7GvD1rLZnSPc/ZVZtChHkYROAOLhXf69uGJkxrc0NsbQtbBGtVWy/q2XzW\n7v3EUFOm7YzqjGlBTZimQp2NMefm5ZwS3tXgQ4IpQ63KuBmRLAxj4mQYOavWxGjW7FGtGEZry1FT\ncr/jNgTfs36U3toTBAvnqi99ES+thCIJT45OM9WVqRZqnTmblakoLgnLLZuubtQeXJt3+MDAQLI2\nb/vkJOGe0ZQYoNnz0u7EqxaqpOZFQFOoj0OmVCchzFopGFhhrhOp1nYHboYWI6eEYdRS+9CfneFQ\ncLlE4A4ujWX6kO8JwJaPSUp93GHtJhXSxnSqkow+IWxAHKzOlPkM08SsbQSoe0GkonmgdmvUrM33\nXIGk8FTKYE51xRWYK6W2Pu4ylZbta6EuvdssSvLdMyz3gN327b39f2BwuPj5/NS9+QWYWqsopQRm\nqDrFJlJytCbGLsLcDAkhN1GmJBKCj5k8CNkg3bhBMkGGhKSZIY9Un5A6k1IiS2YQJw8OskFEGHJi\nk1Lz6XehmJJqxkURd1wUU2O21rVRSgVaOf+iADOC9+USgTu4VHYbQPcAX8ZlAnbhvrvWirq10jVC\nSs5Ut1g5a3OEDeq2HQA20gQ8ycGsUlQZTzZM2RFxbo7j6jOeBSQLnge2M9w7uwfJqLOipWBOt1nd\nHTSEtLM6lSYEOv88QcDOm3wRYPb1oaUptsu9LcmVWgoyz4CTN4lxaHO3q7YJd55ymwAmbWpevXeG\n0oN8HnrL4kCSTE65leQTrfwtzRlNABkyYxbcle2suFeMRDKjnBVcK2U7o7VCSu1Mqrp3AOn/jUPq\npRKBO7g0zLq1KGs1sSHSnJxq3ZmvmLMtU2sH6z7lsxo2F2qpmCfUwKUyKGgC9cysE7Mam3QTSOQx\nkb1lI0YhpbaZDYMw3txQ3Kh17iIhmLSAeVewL+1qrD2urGX+3XCG4HrzoMObiKCqTcshbSTsPE/d\nl7xyas3gp1rTbQwOG4RqkMTxquQEQxqYypY7d+5iUyWRyDdPGHOiAuOQacfKRGbgRs6kEYoMkCHj\n3MiJeW53VNu6ZNOKu6KlQk6UaYu5IUnADe/izLbOY41fNhG4g0tncUfzvQRbJGFLHiu5uUFZE8uk\nPOJJqGd3qdMpZgKW0VlBHU2F6hX1wlwLo2dunpxgG8dmGCUxWyG5kXKbwmQJBslMRanFW/m9TJg2\na1UzR7Ws79XY9aAn6ffokW0HsOs4SGk9kS790NIn25VpbjPdtVJLwYtxd1ZIhmxGhnFg0hkrihcl\nbxIurQQ+lzO2tXDn9A7JpCnNtSIktvUeOQlp6NmyJxJGTqAOOQ9IzoxjopgzlYK4MJmTcKwU3GBr\nFasGXUyn5otLf9xzHwARuINLY3GQUrUerXNXkC9DGHQVrtXa7tvcQIFpmpojWqXN6zan6BZUW49q\nTq0MnkZu3niqfY7ESRrAZrCKmrdRiV4RM04GwapzVo1ytsXpDmrTGaqtTJ7SnndzLxGYNwtLW/1Y\ng+D8fbCbt3UOpJyopVCtNmtd4GyrZJzcmr4QGRB3xCp5SE0ZLm2052k1tl45LTP37p1hVRlvPkUS\n2sQwg00ayLl76fvA5mTsw02MzcnAJiXmUpjV2ZaCD2O7bioTpRTq3F0DRTBXtOre85x/vuC9JwJ3\ncGk0C8gmTNvNtW4lvFoV3IA2I3iepj6UAdRmdLrbeqxThurNe9mNkmEcMwMCWtnkEXFDTjZ4AdeC\ni4KkJrpJiZQHxk2iSiJvNkzF2Z5uwZ06N3FaVW0/3wzJqSlsm2qOYe9eHnaZeHD92Lc43X2wTZlr\nlRlhrgUrSp0r7pWpbttSdyGdbBhzc0RTrwyADIKJIjmhWtBizHNlWwr37t3Dq5GGxJAdZMSkZc3J\nE44iKYMkxpzb71zph4RhoDhspy0bBLcCpn2tW5tZ7w5J2gyAPs1sKZfHPfflEYE7uBR2Qq/d/fDO\n6KFl460o3bydKwJzIeVEmc9QnXGHqsrZ2Uwt2z6mcwNVyV5xEkPaYFnw4pykjOfme14zWBqZ1RFz\nsgiCMdzYMBfjbpnRMqMYpcy9VL4LyCLN1a0/THsWhOCaszcJbF+M5r0dTFL7mKniKBnjrCxq8/ba\nzZCaxam0lkcUmqt+QmulipAk4ZK5O02UueI4OQ9gla0aGWcYMycnGbGKM5CHDTZmzI0bJxtwmOeZ\nCpTtzFYVx7BpIqXE7DO1VgZJiNyv34iM+/KIwB1cGt4V2r0e3srQfcPTWtuLJKFawQqqie20pW63\naG1Kbp0VtYKOA8PmhJNBwJVpnhAGEhXJCSNjNpF9oGrFZ6fUCavNj3yZNDaIt7vHUqm6uLWBzgVz\np5baS5n9GfbKoYuHeRixBMCuf9v9nE3utJ3YlhlxuFsKpYJa66KQITPkjHqBamQEGzLqRtLEveIo\nylM3TxhFKDhnZ3epc21aj8FxK8w2kT2DJ5IoXoVhPGlZeGrXSDeyoAJVhanO5DRCVVQrc1FqaZbC\nLhnVSinNT12S7Do+gkshAndwKewPF2mq8faXJkwTDFsX5zzN1KrMdUbLKVqVLEKdZsrZFrXKiZyQ\nRMgoVgsuI8MwNK/xKm2YSHYqoCQU50a+Cck5GRImrXc2jxlEmFTZnp4i3iwfy7SlaCttppTwvcEm\nzq5aYNHPfW1ZM1LZGZWYddtQaN4D7sxzd+arlVqNMtf/n71395UkWe77PxGZWdXnzO691AVI/ECT\nnlyBkCWRpv4BAnz8DwTo0SIJWgQdWrTkkpIjSP+BHEGQIAJ0CBCyaMiRIYmPuzOnuyozHj8jq3vO\nzM7uzu7dee09gQHmPPp0V1VXZ2REfB+MMOppttLJiqejEZRlIQWWttJqsOfAAxaF01oA5bJ3bD9T\najleQvF0chgihdYqyI6nUGqjqNJUkAhOWjm7swdIxNwk2wULY8Q4kvggy2GKctAiZzfhSeb3Q8VT\n4n6KDxrX5lsewiZJTl1wpgQqCOFGmDN6p29nahN6H+yb00tCqbTTStXA+k62BigRG6WuLKUih+Vn\n2gAqRRqoUGXBXCjiVMA9qWtleLCZke54N1DwPsUoEqYl46M1Sw8ltavD2dOc++c3HsvgChN8GTln\nyspVGNQAACAASURBVOdtx3vHhxPDGd7ZNp+IbmZChQrpSBgjjSApmdOKE2jFWTNpRVFVPIVt39j6\n+da1Ck8sZpu9rQunZcEzSa1kaSRwujuhkoQnI8Es6UeHyR420nN6AxyypyJTVQ2RmwxqPFHDPkg8\nJe6n+CBx5USHz127HNKncShMTdnTOVce6UfLzql1wXsyRmJjOoG1eiJsxy8vpuKTQ6ZR62nqkOcE\ntGUmZV2hFEY6WgshEw2uXnABIlmWwnlXzpdtLrJhjNHnsQybmuel3EQ1XjqHPc25f17j9cpzVqSH\niJDIjR3Rpy0YUoTuF7bRIWfirjJNRFSmK93ULG8TEJmwZaKpNApSKiWVZ63QEfaLEftsv0dT0p1Q\nn0k8hCjBWgqqFT3E0JeqLGvBPdjD8QiqFCCwvhEBu++4Tw/wDLu5m5WiT+3yDxhPifspPkhc28tX\n+le58qBVpghExKTQ2CDc2XcjrYMnfTeiO+3UEG2UtaLpJMnwRNzQAstyh4Tg/kAkZFtAT4QNSjng\nPqXiBK0Iw5NShVTFEtyVF+cLcd04+Jxzx82xDPy2MM+EraJPc+6fx3gNTZ6Hf3bkFOqRmOyJsEGO\nRAL2YVOboARYEkU51UZkp4SBNHpMSuMijUsknWDRBqWgRSi10E6NqI1wo+99ygdLwWMgHkQPTmtj\n1YLoIf5CRRNOqtwvFXdhHPOqKRnsmE2rXNLxFEKA7nNTHcGTGMuHi6fE/RTvPV7RO77NA+PgQU+B\nClVFVHEbuCWWRqbz/HyZNBRxWNvUNL9c4JhrD+vsfbCU+1tVPGyjtM9oyzLtPwXEhZCgVGGRSmSh\nFmhVKE1pTTn3Dj453iJTpnKaj0xqTzla+XAgho+1e1YiT4n75zHkdj9zANPmfX4VVMEmCNKsE+Kc\n923K5zZBtdwk72skVhQpk79dNCGgSKBVyHqi6IIiLAijFPqhNSCR7OIQSmAMD2wELEF1QaqgdWFE\nkKlUVdyTi0++eTdHsuPmiMzP4BiD6w0eEVNG9eYz8JS833c8Je6n+ECRN5CLMOkt5ao0RTA9GKbw\nSkYS0XnoUx1q7J362R3DnWVdJgpXhIvtqENbKnfLSvSd3Z6j7XPWUwOvjLEhR3VucSBmRWcrHaga\nxMU53Vc2U/5p24l+wUdgPohwnLhV3pHT5vOxFekxBX9a0H5O4ktt8uP7af86EZe9d/oYx8+csW3Y\nCLozxU20UFQpsoAa4T7hmZksqrzwxNPwUJalghllWUkRlKSVQhdBxVmYvO2ewfBOkUSyUgrc37eD\nNuZoWVAtiAYtBfekG2gKGcK+b/TzPlUIj3a5MO/riOke9qbzf4p3H0+J+ynee1wTWhyDQA9QndaZ\nZtMN7IpetZyAM3wCxTKS2lYsjSoNf7gQ4VgmWCdU+Pz0GSMGw22ixe/voCgP+3mKYJSKi2KejOFo\nKdOmM5OLOW3RWU1YsHdjG4b7hvuUVA13rk1+OOg+5Mtq62nO/YOIeFtpz9fb5Mx7wo+kXVAsx7xH\nfNKszDrncQFmNVyoUzdNDLdAayPCbvfS8MmI+KwqkoH3QZpx10600m6Ws9swMoKl6cSPRCEsGZcN\nM4gqnGRFJNnCD2yJQgl2ksAhp9gQY8NyCsZkzDHW488mXO/9pw3q+46nxP0U7z1uH/or75ljzRNh\nuIMHZk74wMzYIiayPMF8wGkCbqpPlTTLMQVZQqinlbUtiEP3neX0C9Qw9m5070R07ODMxr6DT5lI\nobFmJVxIhVUqQ4Q+nG3vuE90rZvRbRwmI3LrGszzAXTOuSPiUH77YcXVSOWHXmW9vEffvqK8gRRj\n/qVwUMQEujuCYu5EHDKjAtYHSpA672V1Q3yC2JTC0ioFwdMZOLVV7DKlUvu2UU4zaZcMltOCpZAY\n1QOp4BkkjhcgklqE0yqspbG0FS+zRb9UARcehlPagrsRPje/7k5Yxx1C5jz+plvOy3HAU7y/eErc\nT/HeI46Z8bWVeAWmpU4TEVGlljpdv0YSMTgPx3cjRfEMiitpxj76pMx0I7Pw4+WObTvTvUO9526p\nky7zcGGVQmQBD2LsU9fcDVFBUJyBSiJi7O6c7hsPe7CPjpgTGdM1KRJJnzzxmAjga7V9LT7ih7qQ\nvRSqngn8B5rE87Wvv+ocX5c4vXaTrjawgjCsQ8bUHj8qWMugd6cVQWRBtVJLkiURMxClitBScS10\nN1oqEk7KlNodI+gPZ2qptNLIPpHhmw0ioDVlAsic6MYwIwJ0qZSlUcxoRVlVyAGagadwftiAgkTS\nL2esOz0G+945hH7x47N70y94Ggu913hK3E/xwUIOgIuQ00Iwp6GBKje7w8ggx8B6Rw7pxdgTekAV\nEme4we7cP7vDxiBi5xLGs2c/Imxw3ieNxdJQKew2kAxObWHVmM5NtZGiVCavVXR6IqfDi/NO5iDG\ngSoPJ44WpnJQ22TWIOVI4Dd++g8oqb1yLq9ocT9K4j+wRH49y69M3o8kTq/f5oF/SCbNsVsHmXab\n7o4PB5zzcMxmS31phaILZjt5mIVkUbQo28EDX+tcrrXUCcoU2Psg6xRiqaVOAOaY93tJCHGShgjY\nSPaxk5mspSKtTf/uUliWBuLsESBJ+MbuRo5O7+OQac3D91sIdzzzpl/wKYAxr7iUH0I8Je6neK/x\n5cV/AtNUdLafdS4KZOCWc9E4qmJzo4lQTCbCtSSDQbFATiunorjvdFk4nX5MlYErRAy0CEjBbLAi\nnMpKva/UAt6nfrnqwqms7GO+jqoyDoORFw8XImxKS7rT+5iANBXyoIA9PjfhBwjceVRdighyIP9f\nSeLH434oSfxq4wrf3DZ/OTY5JtNHm9xlMiXSgt4HPjZ261PGdwhRwKkUMSIMrxUJB5TMittgD6do\nQT2hCj/68TNaq6QHY79AgaoFlQku6z7YPCkV8AGZhHdGH4Qmy91M8pFQ2mRnLLVBKBcPWj0xcMAO\nw5GNcENKhePjJ48uxVvjAT5kHL39j/443yKeEvdTvNe4mou4+7GYHAjcnAmSw8zDfKqUDR+M8ElH\nSadHQgr1WcNtkO5ICqdnDR+dSMG18uzuHuvOw3knpJAy/Yg9naJCe1ZZTo1WKloSx0AbXpymYDmp\nMNoq+8XZeifdMeu4jXmsx7lkJDbGzVhCdCa2H+qcW15L1D+0JP76cX5V8v4qJzD3axWuDO8QSQEs\nEzNnd8PD8d4JN1ppaIXsA+2KArWtrLUQMbj4YClCkUSLTjOcXPjsbkG04DYlVaMUlEoghPlsuSe4\nJE11ZtqRPGydcCi1UupKKY3lpBTma18S9t5RU0YfDBtEBB7Gvk9BoozZLp/M79eux0cYj4/t4z3K\nt4+nxP0U7zVuYDT0Bm4pZYo7hDscNBMfhtts+fXNqCiBUrKw3CtmhjGQVEQLkkq3zgOFu+UesQuR\nRh7yqZmQPqietOWeemqkBZVgVciRWECmHG5Is11/d9fY3Rlm4IYT0zUsZxu/lsm9DYSco8eXfO7X\nF/hPOB7rcH9d/JCS+OOjf2PyftQmv3K3MwMnpmTpwYpAj1nz2DGfG9RksinQqR9+35Roc5wTObna\nEkGXwIG1VsQdUnhW7gBYT/csx73rPshwKMrpNDXLPYIRQawydQrQSV8cRh+d06pkkSmJcLTMtRqk\nTooaE79h28a+G55OWHB12cmDFnbzqP+IU2J+5TefZjwl7qd4r3FNZpZzAddSDrDalGnMIkQGPRwf\ng7SdTGfPPMaEAnfLrH73DtvOUkFsYNoodZ1I83D2bmRrtKbsbugw1nWltYAx2G2QRVnrVICSWkAa\ny3LCbCCZVBV6wHl39t7p+2DfjfBgmN/kLA+VVjIPecj8YZJkXq+2v+mxryTxr5qLf2RJ/Ho0b+os\n3Lj6b5iX3jal19/JrLIznKqFvu9EN9w7X1wMkQpWyQIRgvVOa42qK0KZfGufiTI9kBBogh7PHd25\nv7+nlIXoY3aItOBM0KSZzXm6OS5OqQsFwdzZfN7Diwo9DS2FJkqRSroyjmRsh2lP77PL5Ga3t1Ei\nj/fxqluQHz1I7XVXv081nhL3U7zXyANRDnPxK4fCNwk5kV5zURzBxRz3MTWaY9BYKE2xbdD9QvFk\naY0UYbPA9J77tsC+TfUnXdACI5LVci6KpzsyOpduFK1obVSpaDHCdkIWVOwQmTB6AiKc9wvPLx0l\n8W6HDGrcFNSmbsyxkD3uKnwKs79viDe2hL9lyNfNxT+x66OHDS08MsmZtzDuE2xJQg9DchpyuAf9\n0AMIjH1c8B6EBk0rjQ4xq2tEqQik0sMRkqVOAZXWKopgvhORqFaaQmZBopOaiBZqnbvJWgUPkLsF\noSAqLChjzNn15oaWiqnQTkKLAJyLOzE6sXc4aGHWt+kHPvyQVZ32nrdr8DGXssmczT8CEX7K8ZS4\nn+K9xasJTI5/Askxw56IbbeYoire6T2QmP7AkQIVbDtD39GEKBMtu1FYgBZO4pgotOm5tG1T6EJr\nQ3yj22zvRUBKQgbPapna6B5oWblbVtQDarCsjXCdiHUb7NEJ63R34ECWS96S9HUEAByGKR93FfKN\n8Rpy+vuIaxJ/+RKf1kp6rbxvjlnMTalnkDmVATxjtsJV8RiYJefR8XBKQoYjdTIlTJwygkCoS0Gl\n4CRhTkERi6muFhVqoawFWWKi1XXO0B/2jbTBaVlR2oSWdUfGwMY+jwchzCgk533jbp0ueRrJ/bpS\ndaVK0A3MAimFLZKx71OISGb7PeWlgBJMXAd8nO/jl47pB1B1PyXup3hvcfugHLPA6RA459vmRnqg\npRKRkzudQZihWVjWE602MgzzTpghCFUrWySqC+u64O4MklJOwDQnaRZorTSUEMgqtFK5awvpSrk7\nUQVqCUpTPJS1znZ7mnC3Kg9j0N0wGxCOjTElKM1mEkohYBoxZFC4neqr5/6Jxbs+7lvy/kiuz8tu\nyTc/Rh9Vb3GdccecXwfTzCPcqVoZZvg2Ozk9DTMjIlnaHcsi07I2YS3LrLYJhjtmTO42895cmCpn\nLtDWhuQc/yigJlgkexjltLAgmHWGQt/OdA1qrVQpFKAhBJP6aAlmsKzTeKdqoQOxD3oObMy2u7vP\nrpnOEjYjjg133DZ2H2u7XF77/+O4475bPCXup3hvcW2lXVXH4iVuBxykyLQv7BthPlvX6ThJpmIx\naVuxbxRZKa3w0A3Lwp0ulAiyCS4rY5wn+nvbCBWqFoZveBinsqK6cB4DmBWSiFJlAtiMRuo0aPBM\nNjfShfOLB7ZtZ+zG5fk29aSvi7gqmS+r0pdWn5943Cqqd7NUvIL2/QiS91sdwWNa3O1HM9GqTJEV\nP8BrcnSVxsMDIwcZwbYNIpXwMo1uRCgS01+7KhNirnQCSqCi02iknihyyPEOJ9wpd5VW70DhrjX6\n5QHvG1LKrNBFphqbJSnTj1vKCYnGIT+AAa1WqMq6CiWcTMGvI40+aWF774wx8P1AxfuUTL3K/H6s\nY6HXMQvy0pbwk42nxP0U7y2uVDA4duWH7nH44f5FYh6QwtZ3Rh/0VNgviBR0SbZ+AYWqQojgDoss\nrKdKKoxDjLEUuIydUpSlLagkLJWmBSmNbXQuLx7YNiOAopUi4DFpXlULpTSaGabJ2irbleqTflgf\nBuNGA5ttduIxklwezbk/zirk6+JdLsKRMcV1SPJl9ntnr/dt423GAi+tXOWl6Uw4IjLv5yPhehi7\nB8M2MjsPfSd7QYpSloJHZ+9JapuYCaCo0M2OmXSSrXEKwWsySFA5tAcErcLnn3+OiFCksu8bwwel\nViSVEkkopHU2H6A2LW0JSsCzuwocQjBl4b6dKDh7KOmz3X/eB2M3+r7TbcfsIIKJTE30TESvjmEf\nz/sIvDFBv7zlPrJjfct4StxP8d7i+hlxuEmdXkNI0gbpYKNPbWYLWhpRK5pJbo5dDKFALVyG4xSe\n3d9RFaxAhqI6Z+K+2ZzfKeSx0K2nOyxh2y7sVQkRsidlrdzXqdkckgwK9/fLnENag5Ngw3ix74Ax\nbMf3bc4hPSa5TY52+THlfgzC+5iS0lvH9wBKe/Xp8paw3/S7x/9/rPFlJ7ArBVBIn4Atu6LK3ail\ncdkujDFHOxYdTfCxIWUixAOnHWyEpTUsYaSTGmgGibJWnd0mPfjZ287FkyAoS0FqpdSFppUi02CE\n4763mJoI0TsjO1FOBIZkBU+GT3cyEvaR6CqgikZyEcH2nSBw2zlv+6yu40ppOzoLTDzHx9Yufwms\nfPXnnzpI7SlxP8V7ias5xY2OARQRgslrlUy0NIYb560DMf2vI5FSSQ4UqydikzmdKKfTPbUpeySj\nO7XOOWO3HS3CfW2cSkGqcHd3j1Hw7vQIqsIg2M1wH0QWCkHFDm9joRcjcpAx6AHbw87zhw3vncvD\n+eCh66SAASC3Df71w/Wp87l/VlDaNWE/Rh1frVyvkpkfQ9X9bZzA5NDXv/6duVPKMQoKJxO0FsID\n64OxT+OcnknfnZREl0Zbhf3cEVXWtpBZ0Ei2mLQrBZoclW3Y/Azsg4fzA/2LLxgjiHRqWzndnzjV\n9aZiVsoyEe2pNA+MQMzYY0NqQ0SxYTQqVEGLUJbKSQuLyHTNIynZEDPOfSc8CI9J1RSHeKlbMAkY\nH6cz3huP6hMGqT0l7qd4L/EYmDaND44WowgxDBQkoB+oW7uc8TwWCBfQ5LLtLAW0FbaRaC7cLSdM\nwJwpCmGd3YLoARosRRkFJAuRMEZw6WcERYZRNA/HJqHUQlPF06nLQpVC0+k0Zihpye771Jf2Caiz\nMWZSikQLhL/ktb5cEOSTWxzeVnDl6+JxO/wa14T9eIEXDqW5oxL/0Nfqbc/4ephX/rIARerh1z7x\nGvvY2baNNEN8sPVt3h+hB6ZDOJU6qYtLQwWiKHawK0oKoY2Wgi6V7I5EUmuZFfS2EQSiQWmNpTVO\n6Kz6gdIWEGFETN2UnBRLE8iYzl/Fk7ZU5HC1a8vCXVHYdzYpeEw/btHE+uByfjGpbTFdzaaOebwy\n4878ODjdr8+3H8enDFJ7StxP8V7isSxiZmABInkoPI3ZYoxjN++DfRhBTtMREWx3NDslgiBIqdTT\nHVKS9MCt0zTYxmAbO1KCZ2UhiqKyUkvBLMjwSd0aG60lbg61Tk/kIiy1suhUjhI5sZzq9B92KPcL\n3Se9y2JgFtjN4nMiiP1omV9JYXlsVOKgCX1q8W0rqK9qh1+r6zcuoDdFD3lFkex9x9ct8vAqn/3l\n5uLAZTATlx8zXgmfhjb7Ru9zcxckuw2wwOPw1bYOJEULaxGsO/W4e27/rxVNgAB3QgPfNlDoD5fp\ntR1jKgg2ZV2eTe5131nXe1QqkYpa4mloTPlgqGiZM3jM0LYgKnQLZFnQtSA2JVPNO8OczToXGwfN\nkRufOw+gWlx1GT6W+JpD+ZRBaj9T4v77v/97fv3Xf52/+7u/43/9r//Fb//2b/M7v/M7/NEf/dFt\nt/Xnf/7n/MZv/Aa/9Vu/xd/8zd98Lwf9FJ9uhPusOA5gmtnhtOVOBPS+425g8/7R1uZCcGh/y2nF\nyorQKDoTonWbhiM+3bsypktXrafJc/VO1In67mZ4JCV9Vtwy6SznMTBzJJWqQcmgO5yuOtIBmU44\nfPHFxrDBdnlg7BeG2a1NONG28bJdrlfPbnlFnOVjju8iuPI27fCvi2vV/VHrXn8Fn31qhSda6jES\nAq06vdu3zj7m/buPja0n0Qe6VKQeEqJ5PfeCFGGPnT6msllZ9PDbnpoD+94Zo5PZACUl6Q8XfAy8\ndxCorXGSRkoyrHO6W0mgR6I5+fNJsImRzHl4c52b4MM4506MptMtb08oIfgYjL4RllOSePTpKxCJ\nqt6kX/Uq8fqx3O9fcxt/qiC175y4xxj84R/+IafTCYA/+ZM/4fd+7/f49//+35OZ/Of//J/527/9\nW/7qr/6K//Af/gN/9md/xh//8R9/bwf+FJ9W3FpoMr9W5uzXIw42qTL6NmVKfeDOAfrSmfCio0UZ\nh1SqtIrg5JiVzKJCurDbhuA8W+7JqogKdVmRmKjZHjul76wqaFZqTkqOFMW9M3VbKshUwMooRFHa\noRLVCbZ+oV8mLaZfLnQ3VKf3siiQU+c84vBglk+sVf4tBFe+LmF/W3nU44tZrX/E1+uqSz6lbo8K\nc1KvsZjJUBPG6PRh1ENpbWPDx+Rgm8TBZAiqFu7vTjdznR6TXiYoFaWFwqmQ3Sgyud3UggaUw1DE\nzjuWl6mRIHBaThDB3o/5eWmoKDEcyeDs01VvxFUFbZqVtOWEAZQTd1SIIJeKo6Q5IXC5nLEwuhmI\ng0zpwIy4zdc/BmnRt+Hkf6ogte+cuP/0T/+U3/qt3+KXfumXAPjbv/1b/uW//JcA/Nqv/Rr/7b/9\nN/76r/+af/Wv/hUiwi//8i/j7vzDP/zD93PkT/HJxG2hg5uPdREh5BBYUUETdvNpazgMFz/cvuoU\nWzlsDTMKEUo7OrE9Ai1BCHSCSGilzhZ5JlXnLDFT2MeO9049QD53d3eUw7kpRDmHTpCcMv+u5lw6\ni2ASxDamyAaDzQ1nVltXzfKSSRHF86jQJWf3AOFTmXO/zTE+boc/Tthf1w5/m3j9b9/n9fqmRf5N\nM//MxIYBc9PmB59AcuoU9O6Mfpjl9I1IJ0eS3liqgixTP6A20ClLvtQyEdweaIHW7iiH+p53I1MY\nHryIHS8TJOckuJMWR0t73ERhVCCH0dY2TXRQsMEiQrSjA5aC+aCJ0DFKmVQ1OVWWtVJt4hWuXYAR\nzosXD4zdMDfcjchEYo6+rjrtH7rq/qbRxy0+gk3Gt436Xf7oP/2n/8RPfvIT/vW//tf823/7bwGO\nXda8As+ePeP58+e8ePGCX/iFX7j93fXnP/nJT772+X/xFz//Lof10cfP63lFxpwZx5xBDwtKrZgP\nHrYHFhVGD4YM9k3xPbFekbsTVRXuGn5O7u5X7n/0OffrPfWukZHcabA24YuRVNn5/HTHZ+vnrBXq\nUijtDjI4Xx6Yzl+Fz5eFU/uMu89X8M7/OX9Bk4K70kpyr8/4onfu14aV5P87/Yj/l8+xGtxRKSRK\nUqvQPlv48Y8bP/lnz2i1MlDCklYVxdEUQguRMa0Za0W1fO31etfxde/XVQlLypuP8Uu+44cG+fcV\nN1BTBqrlWwm//Cyfr3iUuN90PnnI20opxM1UI9j3ncxZNfc0tjEoKTOBjueQCz4q/9Rf0LLBGtRR\nuf9s4a7BHZX7z1ZOywkLoRbhxXnnfi385LM7nrV7ygm8Ddw72y6U04nPP7uDdO7u7jj1oBU4lRO7\nOpduqMP9+iN2M5ZTQ5pSZYqmnO4L9f4eU0E3wbqhEpSlsraO/XSg3jlxImNwydl5euaNOw2WCuup\n8exHC7/0i88QOfGsrdQi0AocXavrSEyEN97z73o99ON9Kvr19+fciHLjxf+s8T7W+e+UuP/jf/yP\niAj//b//d/7n//yf/P7v//4rlfTDwwM/+tGP+Oyzz3h4eHjl559//s0n9X//7/PvclgfdfziL37+\nc3teHo6bowK7GZfNuVuUzQb7fkFF6Jedv/+nL3h4/gVf/OOF/eCJDhK8oxk8XApRN3QUcguC2aK+\nLCeen3/KMJsqVH3DW6HVwBn0Mdj7zradOaXwYnO428kinPsgzs6uHBVTsmqwNOHvR2dsgTLRwGKV\n57GzkCAbS/viWMQr/QKf3z8jUugRNCmUCnKYMCRTJW4pdYKIPhBt5uver1esKr8iYV5BZ9eZ9LuI\nuI5DmJSkt4mf9fN1U8B7U9J+DJgTOb6cwMbug2unfA8jHEjj/LDxxRdfsG+JmPNPD8/5Py/OxBcP\nbHFC9wX1jleh7o7FTnqyEzzfLzyrC27znnl4+CnZA4udh56Mmnzxj/v8fDw3ikAslVUHp9OJ/RKM\n7QHBMa389PmZdV3YPRkWdAvq5Qt6KRQXsndqAq1yyUEplXE5XMU69G74UvHdeDDjs7KQ//icSmPs\nxv39j3ledtZ62ISWQtHJO5/z+/xS4n4f62Ec2fhN7+mXHjtbaT9z4v6u55UZ/NIv/fitH/+dWuX/\n7t/9O/7yL/+Sv/iLv+Cf//N/zp/+6Z/ya7/2a/yP//E/APgv/+W/8Ku/+qv8i3/xL/iv//W/EhH8\n7//9v4mIb6y2n+KHGzdgGrOqykyQ2Z52j6nnnMFsPoKmTJrV4ZBkDiUgamPQUYHShMt2oadPo4R6\nh5JoKwQFy8TCMR9od5Tk7rQidcG8oaWxFgU3EmEjkQiKTk53YmRUSqtQAhuDrnJUP4P0YGw7+xiI\nHJxulWMLIIgK5Qq8ijw4vh9pS+4bQGmvV9rvKlT0hszPD0QpuhnGHPoDeQy1H7917odD3AGSjDhm\nux6MfUzNgcypry/J1vucT5dCaiMiWGioVsjJ+Y4UiKkFUJeVEY4UpY8Nd2HPCTA7nRZE4OJjvm7v\njAS3YLlrJAXPMt32MBhOlamhfr6MOdYBskyddE9HtXAqFSMoCqVUVIVaFHVwCdKD3Dcu1tlHx8b8\nbE1cPYe/7UyCE3AX89v3fM+/zXz7cXxIkNp3ec3vVHG/KX7/93+fP/iDP+DP/uzP+JVf+RX+zb/5\nN5RS+NVf/VV+8zd/k4jgD//wD7+vl3uKTyQe35SRMalTh6OS2ZgzuIB9DMJ3xqVDJmEd2j1qAw+h\n6BSsMDOyCGUEukIfQs8OFtytdygyJU51WiKGTySubzuLQl0bKgvUBdFGUCl3C+2LC+5BKQXXJD05\nNWWLQuzOs9OJ5+OCyJxFdhuMAVtMX/CIqZalTMDQiKTE1IcWQKWSxzXQYxb/NmjrDxFfSYe6Scy8\n+26Bik7r1AzKO2atPm7/v+QDPPr9DekeN1CaDWOEwSGTa+G4J1qCbkbfn2Mxfd69X7DYiAEWQqsg\n7rQlaSfFNClp1ArP+8ayCPdLQ1CWUugvHJFgdxBpJHBaGyHJw2Uqr8lI3M7IXbDmyt2ze148UWyf\ncQAAIABJREFU/ykiQl1PhA2UhsZOKjzfjc+k0NtM0MMGxXbKekL6GUfQNGqrFJxxGXhTiifPt8E/\nOzlbH7hNSxV3g7Y+0ui/AvjkYGQEIu9vRPRtU+EVQHp9fz/2+JkT91/8xV/cvv7Lv/zLL/3+d3/3\nd/nd3/3dn/VlnuITjswJ3okDB6EiWAxwR2plGzt9DMYwwubuP4pQfFKwRJIoDRDqszsyjFYaQdC3\nM67CIoUiBa2KroI5RA56N/AdjTwMGU6To6qNUgvr+ozIzlp3tpgbik2DE8tMT5q4yaS4ZJC60KJj\nMj2Nt9HpY4cM9j64PxWKCiNzVlxVpk76rRVXbqjbyHinLedvE98kuPK+qu1XXuNAY4f7W7fMv01M\nmt5LK9bDguLlMRzgqql+n6/83VFSTsEUD/yQuO2XC9tuDD/kQEfnIca0lh19qvO1RpGkHnaaORKR\nOa4xdxYKS5mMBy+dsV8QoAcH60FJgabCUgsPlnxWhBqDbdtpbUVx1nZPj/O8lq0iHdpyz7490M1J\nnxoJXuAygroPlmU9RhRTcW0pjX3bKOXaNZrzcM9BH51+2bFnC8qKm1PbdPcLeflZD4JMeQUH9c7j\n1jz6Fq93cLrf63EC34VI/nFu+Z/iBxPXnfekdh7J+2gjT2pzgCd971i/YA4WTqUSMZCYAicRE3ke\ncmiatyDciAJyiEcUSdZVIQqXGKQX3B16UFWoutJKo2hDi0K/EP2MpNJOjTVmgztSkKWhJEsmtQYe\nkBRqaTx0x2Ki1PdzZ3u+sZ1fMCymwYnqoV3+cicvN2EWUOT2WZ2v9/EIs7zvavvWhn5D3ORQvyeF\njMdo+MeI+Bv6+BHv/Ipwv4LVVAvC5FQTHG1xRa4cbJ+z1KIVv1ywFLjq7vtOeEI4UhZE2tT21hNI\npariOYVPPOdcv64LGk741DXYKUhtpBwSo1JY19NM/sixsW20dPqLByjT51uzoDmfV2pSSVQnwHIL\nRzORdZ0bCIU0n8AykkIBVapM/XNVcFHMHT/v9Jze3H3fCbHbiEsOTEA+mht/EAGib3mrflgltW93\nsE+J+ynefRwf4gA8rnxdiEP8YbhjPiuAi8+EnFqRdAKdGsqn03RbQqmlIOSkp4jSqCyR1NbQdqJL\nUNLZtxf4wxdTVWqpLG1lWVZKPTF++o988dN/hHFGa0XKQlOmr7YlI3ZWKrUWclVEoC2VaFBTMYWw\nQffB5p39fKH7jsVsp87FeFZQs1KZ1qEZedvRP26Vz4XtA9FmvoXgyveKIL9W+fnmWfat6mbiI771\n839For49/2uJWt8CwX5zehOZoLD0+ayieBrdnOFGmqDhDNsYabzwQXpF1pl5T1Vo6XBo26sI/WAe\nfL7cUWRBNBjbQKpiGXQPKsIosO+DQGhLoQhsZtS7Z1AK5h27dNC5Weyjz+SuhXKln5G4+2y5ZqBF\n2cfAx4aWiiIMneqGZSmogOyOHiItezdsbFz2C2mC9z6d/nKapRTR6ZbGtXp9f9SwbzvfvsaHUFL7\nrtfjKXE/xTuNx9rFV4BWcqDMixJubPvG2DfcAtKRAuP4WkmkLYyAtijm0/QjLCZlyWNWJypogcsw\nLn0Hb8TesRRqa6ztxKkuIJXtp3/P88tzzsN5sQ/UJ7t8WSsFmfzscDyDFtPgoeScN2rklJzcnT32\ngzsbkzsbL+Uv9QaumizfYIJ2rnPu6+qgorcq9oNV398guPJyIfx+aV9f+tkbknc50MhvW3V/Fccc\nDiT8o2T9TZuQVyVOXx7jfI2XLXY7UOXWB+OykQnuU2Vsz4HHIG0m6JSpYV40kaWAJi0D18DHRq2V\n1hRxI/sgLhsRistEapNT/W8bG/sI1tM99b4iZjxsL4CCkdi2Ix6kGU5w6dvUUNd5NRHB+2D3gaQh\nbcUiMD+42JIUBUNYDtexLAXVqQ43LBhjZ7fO2Duek68eMdtrmXnz6oZr1X2MGN5x/Cyv8KFAat92\nQ/yUuJ/incVjY5Fwx8esmsKNtImutu6M7ggdG30aJrCgbqTEIYsqlAPk0khqE4YJ+7DZxkNY7+6I\n0hgS1CjE+R/BBgksrXFfV6iNft7x8cAejuVEkXs/U0vDVCgkEsJwyFIoOeUmSzmoXaFkuSfG+TAa\nGZwvnb5tDN/pw8mYs0OZPc3bbJ9D3zlfqz4+ZPX9VoIrfH1i/44vzPGkr1DP3tQ6l+PaTDGbr3q6\nLyu4AV9K1K+fw9ee/5eOYz7eI+cxH+Art+nNLqmEdcaxL3PrhA/6pWO7ElFQOzAeBZrI4ded7COo\n2mjaKFIp7vSxYyhDwZ1pbbsotTVaVfq20Xc4LXewFB5sZ8RgXdbp3OWDVhslZxm5+8AlkCIghT2D\nbTsf6n7zHnUPog9qWdCYiHlUSZs+4dPrviGa9LNPIN6YuugjplZ/xmRmyDEau7bMQd4rq+K73K/v\nX0ntqeJ+io8sXlnwj0UPmXKgs9p2zGZl0rthCORcHlSSGLPFRl3QTAqz4h5DMDEKsLR1tsiL0GPg\n+yBGx8zZMrm7Wzm1FZGCbY7tX/DggWmlrY2RQTcoQJPK/akSKYRPhPGpFZoEugoiUBWWZyseheGd\n8x7stnN+8cD+4kL3js+TP2xLr9dhXoODDffGxev16vu9LHDfwNt+F8fwuLK+LpSi+rJV/1rr/HEL\n+42V+uNN0Bvm1G8Tb/OoqTI2W8XCnNlGzGM3n2Ct0QceEP3C3i8MbM6LvWCnyrKWqVEiFSioKB2l\n70Z4spQyNyrHJkRrYbhOqlU6bgMNOJ0WSk32vlHljmf3n7HKMlvTgNaG5Gx1rxRKBhKBEywCd+1E\n08YYyTac4Y5qJTIZYSxSkFKwo5W/PKsTiDfmcY1wug1s7GxjJzoQhl11+lXJiNmiz0T0wAIk737W\n/RJp+N3iPSmpPXYP/LbxlLif4p3G9d70Y/41RU+PCmp0IudC7hHsFnhsjBCUIL0jbbboaq2Y79Q6\nBUzMHJVKlUKtyijTXaw55PaczcasTIBVKu6JjRd0MTYE1Yr0jdE7Z5+SlE2nt+izopPv6oap8Ewr\n3YOUQj01rG9kaTid8fyM7YEl7A8XMvyQQA3q4WdsdgDTDqezeV3evHg9rr7ftcvSt6q2v6c2+Svo\n9Eee1vDqTBteT/DzmkS8Out+XGV/F4T+bel8vRI/XjvhRhG60oVKKWTM+a0dQKzRB75tDBGyDzyD\nrrNl7QHb2OaphVJbYS0FicmnjjAsB7outKKIDXqfAisi7djQBiEVtCIZLJ58VgtgnPeBlsrp2T2O\n8jA67gOtQl2UerqnJGCTzx1aUAmWZUVbQ/adDCNrZXOj7A7pjExKE6IITSuqc4NM0aO7ELzYJiMk\nM9n3M2aGX3nc1zFZXDfw8116l0C17yPZfliQ2tvFU+J+incSj2fbHAhrc5iWmIHPn7L1wXCb1ocx\nqFIo4XiMiWLNaWm42aBWJVLYsyMIrS2URfEC235BuuH7md3BdS6Od7WR7njfMQkuIbRSEDs2Dak8\n2GDbg6UWijSojojiNlHpBag4RYLmQZOKropbpaxTpOV82Q+gXWffJxedCFSnalTkS1nFyKvd51cv\nDbfK+13u+j8EKO2VufG8N/LRaEC+onX+pqr7MR7g6k71/Rzia5sJptgKXHneU5L1SlczH6hOD+1u\ngcegbw9YDlKEsSvZGrkLpQJiKFPkZySMo7JeWgVRCkrmTJ49AQlWBUoQLnz+rLHcLyynhVMR+ngO\nXqnrpEymCFEqlznLoRRHsgBJeJ9CKpJUrYDiWSgRZDqlFAZG3ztrXSmHil2KIjbHSEtM0GiUIIbT\nrTP67HINs1sHZI4T8qbbMFH68qX37vuM717Dvoz3DVL7LvftU+J+incS11bv5MLGbBnHpEsRsxUa\nu80PfH+AcFKUlDrb6qakNlIauNDKXDQlJ2dWdaGVQhbF+iBN0eGYdTyDNaD5dA9DF7IJuySBYH3D\nxwTI1QgyBy9y0C87TQunpXC3rHgaG0FbGydtZEmyCJqF1Er2wTDjvBtnG/Tzme3FZTonuc9kcsyz\ng5l8roloVnJf3Q5/LGTxruOrFo7H8qbfR3wZfJavfH1N4LdF/3HrPF/KZnr4q0n72qH4vlDLbwCl\nCcdmSwR3n/syh/ScdpeXja0P0gLfL1gm2/mM2eAyYEjSTlBPK1pnomxF2bdgfzFNb1aUVpQY41Bb\nWxgj0ExGKMNAJWgINVekFO7aSiPp+xlG4f70DAF22w8f7YGEsywn9Nio+r7hmRhOaiGPjaX5hpTK\nSEdinnNomfsTVdp9QyUQLZQGhDM8uOw7+75DChE2UfU6GRRcOxfXqlu5cbrjDWDE7yt+1k3c+wGp\nffee/lPifop3GsLRCvc57/JMIh13m+jXvh3KaUHGjqVRMIhOXRqRSVsK5Jz/dREyCqdWySKk7RhO\nc8Os091J66ySFBK0seO4CtlBwsGFcGezDWdHKezWOVtQCEpWihhFK92SiwdrUUqdIqilgLQVLyu+\nBDkC6z7R8ecLgWM27Uqb6oEuv6KKp//yV825X7127646+SbBlVeO43uoZF85V3lczsjRBj/wDFyr\n8Ne6AUdLmmNGehWvUdEjCeTLh7zlYvsm2tCbZ+gvZTvn4cztlOd0pZue8DseSVpnt84WA0mnbxUI\nSi6clkbTQS1KKRWoDAStEzDWiiLueBiuilal3OmcTZdAauH+rnLSRmxB8YaulXVpJBsZhmRSdEG9\nMNzp4RCJ1IkRkZjPHxnY6GQKHMDLEkbK7FbFcPBEtCJFEE0qDa2VMYJVdM6/bfAwjH1sxDDM9skY\nUT1GDHpjlFwTtchx//MOEvfPOt8+4l2D1H7WDcFT4n6K7z1etsnhqL2naAWBm02Osx9tNabc6cAA\nJb0cQhTBPqYcY/eB4nhMXnSVOq0H0zECtYrb4LLvhCUnAUmjtRPZGkHOpMqYQi8ehOzU0ggBzIlM\nHswZ+7REXBVOZQKINh80VYodcOET6D6QCmMLwjbCN55vHdud/eGBy3Y5XK4S5JHUR768Rq/Pa1+P\n96ZQ9ob43ilgebsCvJq05XYcV77v4wR+/BLgNht9iVJ+OXd++bB86+T9xke8odomg9mCnn8lAYgS\nkZiN6aOdiY2g9wthRu9nIpIXe7AnrHdTfc9VKQQaB8lAgizBqa2HAEpMVUGteAp+6bM5H0IJ+Kwq\nfQgPY2cMo5YT690dbXnGHmdECq0WSlOKFy6WdN9pGpT1GSpKCZkSqO5oTj54ZCGy4HaGYyMkNjUV\nSilAQCtoKoqSMdAsjAwYzheXnb7tR/J29rGDTg/7q7PaTOTXhHiMIL7hM/Bt4nuvjt8DSO07W+B+\nz8fxFE/xSpscONpwRh4TQotAcnK1+35mDJ9rIw3BaO5oaaCnudAQlKVhkkTAXatM4olhl0Rt0PcJ\n5ilpLHWhrid6WaDMVp+FY5fJbR1xIeTEen8HnBDpUywlB+fhrALmiUqwlmn3OWqhtsbC1LWoZSG1\n4TbYhzMseD4GvV/o50lr292nScNBHbJrvjmAevkWg7R3UXW/jeDK90kBuwG9bhUrPE7aj+OrEviU\nzZwt9MKcncZRCV+PGPJbJ+/ra16P70vH/qjavrbJg8mlTg8yDO+d896JMRhjcA7HcnDZg8DRZLrF\nNSHFiG7U2tj3YNs3KAvE1J/2yxz1iAgjnAhnACnKXVPUhbP5/8/e2W23kexK+gMys4qU5D7nzMz7\nP+HMnN22SFZlJoC5QJGS3e5/915zIfTiUlsiWWSxmEgEAhHcqrDh2HWjSTLVhYXdN2qpLDV95JsW\nesAMo6mxtBWVgkcan9xPnWuhhOA2ABjSiTFoWpmH/oIglNIoeth0VlhKsFk69Y2x4yjDB6NbAksR\nj6o7r4X8jETfcxx+bGL8Udvdf5ak9lFxf8T/5+GHsENy1AQ/Zl/HtmE2mHMciX2i6uzTKG29p3la\nEzwEM1iXM1MDt42xB82yujGbiE9OrSGtImWltQWJyW3eGNctZ8nFCW08/fQTy+kTzy8rPQS1maSi\nUPZ9ZjMOyRZdGOZGVUWXFcZGLY63QswFlz0XqDC2/ca+XRk+2PcBxyIMkfKYqpliPM/HwyXtV+JH\nwdRf3d5Dlr9y/1+Lh7jJn4SjIRfr4/9+933dE7gcClz3x98lZN3S4z1wviGj/6XkfbzYxxN8W21r\nUSL8WDCPajGC3pOnYT03hDYGw25gzpdrvpaX5xfOpdKKsKiySMHCGT1oIpRSWItmhVsCKRWtirmm\nGIoGTYVPJ2XsQRdjRme441qRgFNbOJWVieDiqK5IrRRVwpQ5jTSuhdoWFm0plxoTcUOXY84cZbJn\nS8rSQWwOI5rSKmiplAqzO9WNfQK2c712+tjo2xUszYSmpSEQATbn43r4tur+UZvSB47zg5Cqf4qk\n9nfGwO7xkbg/4h+JiDuhJ+vs3LFnlWSRSmNzdkZPK89aJUe2dgdxtu5IPRyimFhw6DR31LOnHNMY\nYdhRya2tECVAK71UAuM2Xtk/X0ioU5kE55dPLLXy03nl6fSJtZ0pYkwPunT2MViJFKBAabXgBxsd\nN8wgVmWRhi3KrDD2hAm/9B3fO9vrjT56zrC6Q0pcH5VbNuLuJhe/F3+16r7Lc95vdwLYGxHMf5nU\n372m9zD5XeDk8e8/Wik9IPJfwuN/5PX7sfERUfRwl5JSDu5Eit3cV1YRffTL8xB+9L6/V0nHd//9\nBr3HVxuUaSmyMi35C32fmA3cjNuYmE1sTDrJsr5ck6GNrrSaAj6qUAhUG3sUfIWhE0EPw5G89tHK\nPtMuc0cQFqoCFmzA8EB9sNvG554bWC2F0yo0Fm7d8LkjogygVCUsPwOx7GOX5ZmqsAd0M/oYOA2J\ng+wZRidd9YoWaqmM2WlN0ahIFBYBt5RjnRG8bh0ORMJi0Lfx+P4XKblJ5Rjbg4eS4v1c/+34Qf3t\n9/FbJLV/t7ra+/hI3B/xQ+PBCr6PM93tO0MOOdAg5qT3JMfMCEYE4YoKMHLh8vpEs3goN+VzKy4B\n+4aYpsCJD4ooaeEsaDkjUnk+n5jXz3z+fMuFhaAUpa3PLHXhZTmlY5Mb56cTPQolSNEMEWI4Gk7g\nWFQsMvmWpaFlpXkHCwzFhrGbMEdn7871urF9uWA42xzo4ckNgR9qUndyWla/vwOXy9cJ9I99Bu8T\nswCaPVU7rAv17rYVv7i9Jfp8rjuL+95blnebjt+szg/Bknsv/37X720Wvvse3m0g8pgAenzOeb24\nG3NO3q/Yd6hdDvW6X0veHI+6M5zNU7fvfuRwY6YHTv7NU97E7lVjBNhg3yfDOmLBZh21ZJLbHJzW\nhaKNVpTh+TloQEzjcr2hpVFFWQLG6ERx1pbokgpoERaF51XpuzMISoVFWs6AM3m1Tr8MWqt8+s8T\nqLAJab6jhT6SCa4mCM7Y9zTQqc9UlZQYNqOcQKWhEkR0BrCEUEvBPKttXFAapWaPXgsUcYYbuzm3\n234QTiWRKks/cY/AZl5Dd2vf48P66pr4q/FPJdFfI6n9CJj/7yADH4n7I35ofFtBzjk4uGlQk+Dj\nI2c9bU5mn4SPxyMtZs5yiydE2HLH7loQTYUzM0FVMJ/UVrAYVIVJOiKVVpnbhcs+CLesaJZKaKGd\nFl5ayd9HPt9SFl5OnygYIZWpaXwS4RSC5pUimcCWIpxPK/M2Oa0VWoFRCa6IJHx7sx2fg+12Ybvt\nKYGqR+KJfO0Qj0TwR+K9otqvnvt3CTuT1ZEoVPh6jbgnwXuCe2N1Jyx9T5SOx72iTfOICMN9HgSm\n/Lu7fVXZ5/HzcRH+gMjvEPb7DUJ6bttXLYNvRVXyvfF4jkzcJRPJUV37nG/KZl+RI7NPbZaJ956g\n5z1RR2S1bPYQCIJ4VNPhR1KPg2gYwnSDYyPzuu9gjsydy3bFYuNyNcIVaQunc2UpgYlRi3MKgarM\nnlK6pkItSpghdQEJthHM4/pABVHB+2AAA6cpFArP9YTFYIhx8c5+3SgjeF7OhChbCYjUJx8SDIfT\n6YyGs+9XpFae1hUC+j7YelbdSqP3wYjBPo3mufkZBZYG2vK76AgqE58cc9wpZOSWExUjJqMf51HS\nq9780BGU+zV5r7//GPr0e/GP0Dm/S1L7O6/170MDH4n7I354ZIJ6g13NHDQlTu+AWN+vhA+2OZBQ\n9m6wD9yN7krRStDp4kRozlwXYfZghqE2KWqpDQ7YzCxspVBV2W475k6ViraCe0HWhVMpCAX3ifeB\nmLOUwtO54SwUN7a+MXyyquZIjxvCkk5kolQBrRVn0ErBJKUiZ3dGH/y8TfrtSv98ZdpMr+YDzhc5\nZE85sP975f07O/ffqrq/l7BFMmGr3ivP9I6+E4Pun1NEWlR+eyxJCOOR1FXKN/fzRyskuMPL70hl\nx3Pn6qxfbQ4iDhEa3h4TZBKf7/TI3+azH+/03S2tNltduGu/QyR8/jBxuWd7jue+z39/gy54bp9q\nKWlqEiAhQKG1goihkveZ7vg05sFn6HuOQE0LbmFst8Fm2RZa24p6pcUxihVGVcGG0/uGl6BKY9WE\njD1uFFm5jz9LKShCk8BHauu3tVL8gKhdOEtDRkcJdpt02yhqPC9nqqw5T41w7cEIYU5PsRgbTNtZ\nSmNFWdfKDMcJlrpgCNMHOx2dg4pQtbCb0ZZKKQ2dlbNmbzw8uNjk5p2+7fQ5CJRwo89sG7gAngYt\nbvb4fD3ueg9/HTL/0f3t9/EtSe3vJPAfhQx8JO6P+GHxPgGJHFApQao1HKIrEez7jo3BduzStwlr\nhTEPNnk5IxKpjIai0qhamVuABatNZEmLUJsD94lJULRxbie222cgmOGECjMapsJaKgtL9qmvG2MG\ntnemTYoWXp6eUvaUwg3y9wEqk2maBBsNymmh6IL3DR0wNcl3Qyc2snd/3XfGuLHtO9u+E2EUEaZZ\nEt+UQ7kt/vBIzLdV9x2G/q2E/YvPRdO+8u0+3AXAHrPQZm8ENEEoWvL/5fCglnLc7vKs8ljdHhDy\ncQ2olq9UzSKx9nebidQUd79D8tnbdbtXycfG4PjvrkCX4IU8Fn1Kzi3Pw6FtTMP8UKt79L555Oyi\nqRMukRuAUutDnS3bF0Ipyl0sR0h50zEOF7g+HuYaEZ1tnzg7l1vH94LWytPTmoiRTLQVqlSIikel\nWxB3RGl2hk+kFCyUGQCTUg6rUR8YBS9CiQMBAiSUqgu11SRnEtiYCNnXLrXSSNW/pcLVO5tNmlaa\nCf12oW8bZU0S29wHro7Pwqote+xzR0dQWvI8aIWqAlagHK2XHPQkwrhNmPtkbpOIhPYxZ4xOOXy5\n3QOz94iK3JsTj+v1T8c/2G7+dZLar08j/NPxkbg/4ofFA+p69G+PpGIJRzqB9Y5Z0PeNGRzEFRjh\nuDmlpfNQaKAld+UOlBLImDAdK4KZ0OfAmbmAtca5LeADt9QtV12I2nCcU03f7toKWPYsjcn0yXbd\nqKKsi1LKAjHpwxkRNJxFC25B0YrhnAu8nButJIwZWjAvbLNTS+B78PNtMPfOvF7pPWFjgUeii3i3\nuYl31eFvxPtEbDZ5G1WSb5Lxu4R9zJLfZ2m/HQF7JFDNqjdZ2sfrQ49EnovyHVbPRHyvoOPRJw7i\nnftTIFreWODfOHfp4U1+T7L5Fz1ugoUzD6GQx0WVr/i7VZX5YfMqbwhDIvP5uFJyAxEcUw5zfqWh\nrYduem6icjejpWQ6ibSoFPRweoMxO7frxpzZ8rmNndu2ozPh9iJnNIzFLatJhUq2isIUrcGyrlRp\nmeDC0CNpzzAKlQjhVA2GE6VCLZTciVCqUMjvluoCSyJJIpU5dsoYLLUhbUWXCuEUgR7OxkSroBHI\notmnBlxht8HusJbC7HAL5+o7pU/CjOkOPmlVKS35KFIc9chNeN+Znu5/fbslPG477nneS1GmOXb0\nuvUd+vOeO/Fn4m288U897E/F1yS1+3XzVw74dx77Fh+J+yN+aOQXMKsWO6ROCQWJJBP1yRwpM7pt\nI6viMPot0tPXa1ZpTHZJxbEiQt+hoNRjSKwTTO/EDJZaqMuKFGX2nd2Mbk5RYUb+XT2FKeiduV/o\nNmEpUBtLUW7bQKVyPp2o0pgClzkoAu4dqdm3nG4pCCM5ZtMWobZKRPYdo8C0zrZvfLnsXK8X+kwj\nhhSMSqekOE6WOTwY5r+zc48D2/b7HPi7hP3esvJ9wv728b+2YJjboxIuqhTV3Fj4vW8N9/Gd+/N8\nN3kffW8eMOjXCVsOGD0r4rv5jGMWhJEtZpejMnYikr0dEUd1fq/R7hdbviY9/i1yh7zzvnogAHHX\nypYkl9mRaILIyt2PvvxdUD6yzz1mP95zWl6GGbsN5tzZx8DtivWgx8Z23eldiWXh/FJypAtnWRfm\nTKlSunO7DepyOIQdoj6iRo/suxcNtObmc+uWbaZWWFuBqURMoqX4yZwdxkApzJLnUCJhbg2nlpVz\nOaE1pyMSrU7XLzUQC0QKocLaKtYD6kSisaiiDK5jRx1EK6rCLMkZUWkwCycR1Cs+jxG52Rlzp98m\nfd8ZIccGNU9tEZiRffM7ZC6SVff96vwrkPk/mLcf35u3dpG8+/nHNho/YgzsHh+J+yN+SHydeOIx\nphOHWMbwiQcMG4yxcR2TMTtVFA1HfVKkMr2CRi4kqrgUQEmD7Jl9wXrCxkaIcjqd0NZYEcInvUdq\nsKngtaAhqC48nxZidPbtyuaTaI3ntrKcllwI50Q9jRfWVnO+PISfbbB4UD3Y/fDmbg09V07tRIyE\nEl2FyzbZxo0Qw825zA3Gxnbd2MckfCYT3iyXqOP7m/PIv006c78TuCD7u79M2MAvEvZ9bvy34n1y\nVb72rU6TlDcI/g6p+6NavVffWdXfk3x2S9+gz0zYOVkwjl6zh9GqUIuwVH1A9vkO5UjSb6NdKdWZ\nZLi315y3IjkrbKPTx47HIQAib513heQavEM8eNenNw/GTJZ6SGAHYc49K8193yDST/6JRI8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BQ7Cpt0D3okAlEjaA6Goa2yloLMneqO1ECWenAEAoqyzsIw5zZ3VguaLLjNNApVgdBj45nuebWt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\nuV4SaIDjOeM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<th>Total</th>\n", " </tr>\n", " <tr>\n", " <th>Date</th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>2012-10-03 00:00:00</th>\n", " <td>4.0</td>\n", " <td>9.0</td>\n", " <td>13.0</td>\n", " </tr>\n", " <tr>\n", " <th>2012-10-03 01:00:00</th>\n", " <td>4.0</td>\n", " <td>6.0</td>\n", " <td>10.0</td>\n", " </tr>\n", " <tr>\n", " <th>2012-10-03 02:00:00</th>\n", " <td>1.0</td>\n", " <td>1.0</td>\n", " <td>2.0</td>\n", " </tr>\n", " <tr>\n", " <th>2012-10-03 03:00:00</th>\n", " <td>2.0</td>\n", " <td>3.0</td>\n", " <td>5.0</td>\n", " </tr>\n", " <tr>\n", " <th>2012-10-03 04:00:00</th>\n", " <td>6.0</td>\n", " <td>1.0</td>\n", " <td>7.0</td>\n", " </tr>\n", " <tr>\n", " <th>2012-10-03 05:00:00</th>\n", " <td>21.0</td>\n", " <td>10.0</td>\n", " <td>31.0</td>\n", " </tr>\n", " <tr>\n", " <th>2012-10-03 06:00:00</th>\n", " <td>105.0</td>\n", " <td>50.0</td>\n", " <td>155.0</td>\n", " </tr>\n", " <tr>\n", " <th>2012-10-03 07:00:00</th>\n", " <td>257.0</td>\n", " <td>95.0</td>\n", " <td>352.0</td>\n", " </tr>\n", " <tr>\n", " <th>2012-10-03 08:00:00</th>\n", " <td>291.0</td>\n", " <td>146.0</td>\n", " <td>437.0</td>\n", " </tr>\n", " <tr>\n", " <th>2012-10-03 09:00:00</th>\n", " <td>172.0</td>\n", " <td>104.0</td>\n", " <td>276.0</td>\n", " </tr>\n", " <tr>\n", " <th>2012-10-03 10:00:00</th>\n", " <td>72.0</td>\n", " <td>46.0</td>\n", " <td>118.0</td>\n", " </tr>\n", " <tr>\n", " <th>2012-10-03 11:00:00</th>\n", " <td>10.0</td>\n", " <td>32.0</td>\n", " <td>42.0</td>\n", " </tr>\n", " <tr>\n", " <th>2012-10-03 12:00:00</th>\n", " <td>35.0</td>\n", " <td>41.0</td>\n", " <td>76.0</td>\n", " </tr>\n", " <tr>\n", " <th>2012-10-03 13:00:00</th>\n", " <td>42.0</td>\n", " <td>48.0</td>\n", " <td>90.0</td>\n", " </tr>\n", " <tr>\n", " <th>2012-10-03 14:00:00</th>\n", " <td>77.0</td>\n", " <td>51.0</td>\n", " <td>128.0</td>\n", " </tr>\n", " <tr>\n", " <th>2012-10-03 15:00:00</th>\n", " <td>72.0</td>\n", " <td>92.0</td>\n", " <td>164.0</td>\n", " </tr>\n", " <tr>\n", " <th>2012-10-03 16:00:00</th>\n", " <td>133.0</td>\n", " <td>182.0</td>\n", " <td>315.0</td>\n", " </tr>\n", " <tr>\n", " <th>2012-10-03 17:00:00</th>\n", " <td>192.0</td>\n", " <td>391.0</td>\n", " <td>583.0</td>\n", " </tr>\n", " <tr>\n", " <th>2012-10-03 18:00:00</th>\n", " <td>122.0</td>\n", " <td>258.0</td>\n", " <td>380.0</td>\n", " </tr>\n", " <tr>\n", " <th>2012-10-03 19:00:00</th>\n", " <td>59.0</td>\n", " <td>69.0</td>\n", " <td>128.0</td>\n", " </tr>\n", " <tr>\n", " <th>2012-10-03 20:00:00</th>\n", " <td>29.0</td>\n", " <td>51.0</td>\n", " <td>80.0</td>\n", " </tr>\n", " <tr>\n", " <th>2012-10-03 21:00:00</th>\n", " <td>25.0</td>\n", " <td>38.0</td>\n", " <td>63.0</td>\n", " </tr>\n", " <tr>\n", " <th>2012-10-03 22:00:00</th>\n", " <td>24.0</td>\n", " <td>25.0</td>\n", " <td>49.0</td>\n", " </tr>\n", " <tr>\n", " <th>2012-10-03 23:00:00</th>\n", " <td>5.0</td>\n", " <td>12.0</td>\n", " <td>17.0</td>\n", " </tr>\n", " <tr>\n", " <th>2012-10-04 00:00:00</th>\n", " <td>7.0</td>\n", " <td>11.0</td>\n", " <td>18.0</td>\n", " </tr>\n", " <tr>\n", " <th>2012-10-04 01:00:00</th>\n", " <td>3.0</td>\n", " <td>0.0</td>\n", " <td>3.0</td>\n", " </tr>\n", " <tr>\n", " <th>2012-10-04 02:00:00</th>\n", " <td>3.0</td>\n", " <td>6.0</td>\n", " <td>9.0</td>\n", " </tr>\n", " <tr>\n", " <th>2012-10-04 03:00:00</th>\n", " <td>0.0</td>\n", " <td>3.0</td>\n", " <td>3.0</td>\n", " </tr>\n", " <tr>\n", " <th>2012-10-04 04:00:00</th>\n", " <td>7.0</td>\n", " <td>1.0</td>\n", " <td>8.0</td>\n", " </tr>\n", " <tr>\n", " <th>2012-10-04 05:00:00</th>\n", " <td>15.0</td>\n", " <td>11.0</td>\n", " <td>26.0</td>\n", " </tr>\n", " <tr>\n", " <th>...</th>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " </tr>\n", " <tr>\n", " <th>2017-07-30 18:00:00</th>\n", " <td>60.0</td>\n", " <td>92.0</td>\n", " <td>152.0</td>\n", " </tr>\n", " <tr>\n", " <th>2017-07-30 19:00:00</th>\n", " <td>46.0</td>\n", " <td>70.0</td>\n", " <td>116.0</td>\n", " </tr>\n", " <tr>\n", " <th>2017-07-30 20:00:00</th>\n", " <td>49.0</td>\n", " <td>47.0</td>\n", " <td>96.0</td>\n", " </tr>\n", " <tr>\n", " <th>2017-07-30 21:00:00</th>\n", " <td>18.0</td>\n", " <td>34.0</td>\n", " <td>52.0</td>\n", " </tr>\n", " <tr>\n", " <th>2017-07-30 22:00:00</th>\n", " <td>21.0</td>\n", " <td>13.0</td>\n", " <td>34.0</td>\n", " </tr>\n", " <tr>\n", " <th>2017-07-30 23:00:00</th>\n", " <td>12.0</td>\n", " <td>12.0</td>\n", " <td>24.0</td>\n", " </tr>\n", " <tr>\n", " <th>2017-07-31 00:00:00</th>\n", " <td>2.0</td>\n", " <td>3.0</td>\n", " <td>5.0</td>\n", " </tr>\n", " <tr>\n", " <th>2017-07-31 01:00:00</th>\n", " <td>5.0</td>\n", " <td>2.0</td>\n", " <td>7.0</td>\n", " </tr>\n", " <tr>\n", " <th>2017-07-31 02:00:00</th>\n", " <td>1.0</td>\n", " <td>0.0</td>\n", " <td>1.0</td>\n", " </tr>\n", " <tr>\n", " <th>2017-07-31 03:00:00</th>\n", " <td>2.0</td>\n", " <td>0.0</td>\n", " <td>2.0</td>\n", " </tr>\n", " <tr>\n", " <th>2017-07-31 04:00:00</th>\n", " <td>6.0</td>\n", " <td>5.0</td>\n", " <td>11.0</td>\n", " </tr>\n", " <tr>\n", " <th>2017-07-31 05:00:00</th>\n", " <td>32.0</td>\n", " <td>20.0</td>\n", " <td>52.0</td>\n", " </tr>\n", " <tr>\n", " <th>2017-07-31 06:00:00</th>\n", " <td>99.0</td>\n", " <td>67.0</td>\n", " <td>166.0</td>\n", " </tr>\n", " <tr>\n", " <th>2017-07-31 07:00:00</th>\n", " <td>269.0</td>\n", " <td>223.0</td>\n", " <td>492.0</td>\n", " </tr>\n", " <tr>\n", " <th>2017-07-31 08:00:00</th>\n", " <td>348.0</td>\n", " <td>312.0</td>\n", " <td>660.0</td>\n", " </tr>\n", " <tr>\n", " <th>2017-07-31 09:00:00</th>\n", " <td>172.0</td>\n", " <td>192.0</td>\n", " <td>364.0</td>\n", " </tr>\n", " <tr>\n", " <th>2017-07-31 10:00:00</th>\n", " <td>73.0</td>\n", " <td>75.0</td>\n", " <td>148.0</td>\n", " </tr>\n", " <tr>\n", " <th>2017-07-31 11:00:00</th>\n", " <td>58.0</td>\n", " <td>61.0</td>\n", " <td>119.0</td>\n", " </tr>\n", " <tr>\n", " <th>2017-07-31 12:00:00</th>\n", " <td>56.0</td>\n", " <td>51.0</td>\n", " <td>107.0</td>\n", " </tr>\n", " <tr>\n", " <th>2017-07-31 13:00:00</th>\n", " <td>44.0</td>\n", " <td>63.0</td>\n", " <td>107.0</td>\n", " </tr>\n", " <tr>\n", " <th>2017-07-31 14:00:00</th>\n", " <td>68.0</td>\n", " <td>77.0</td>\n", " <td>145.0</td>\n", " </tr>\n", " <tr>\n", " <th>2017-07-31 15:00:00</th>\n", " <td>74.0</td>\n", " <td>93.0</td>\n", " <td>167.0</td>\n", " </tr>\n", " <tr>\n", " <th>2017-07-31 16:00:00</th>\n", " <td>104.0</td>\n", " <td>261.0</td>\n", " <td>365.0</td>\n", " </tr>\n", " <tr>\n", " <th>2017-07-31 17:00:00</th>\n", " <td>199.0</td>\n", " <td>641.0</td>\n", " <td>840.0</td>\n", " </tr>\n", " <tr>\n", " <th>2017-07-31 18:00:00</th>\n", " <td>162.0</td>\n", " <td>393.0</td>\n", " <td>555.0</td>\n", " </tr>\n", " <tr>\n", " <th>2017-07-31 19:00:00</th>\n", " <td>88.0</td>\n", " <td>182.0</td>\n", " <td>270.0</td>\n", " </tr>\n", " <tr>\n", " <th>2017-07-31 20:00:00</th>\n", " <td>56.0</td>\n", " <td>98.0</td>\n", " <td>154.0</td>\n", " </tr>\n", " <tr>\n", " <th>2017-07-31 21:00:00</th>\n", " <td>53.0</td>\n", " <td>53.0</td>\n", " <td>106.0</td>\n", " </tr>\n", " <tr>\n", " <th>2017-07-31 22:00:00</th>\n", " <td>13.0</td>\n", " <td>28.0</td>\n", " <td>41.0</td>\n", " </tr>\n", " <tr>\n", " <th>2017-07-31 23:00:00</th>\n", " <td>12.0</td>\n", " <td>26.0</td>\n", " <td>38.0</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>42312 rows × 3 columns</p>\n", "</div>" ], "text/plain": [ " West East Total\n", "Date \n", "2012-10-03 00:00:00 4.0 9.0 13.0\n", "2012-10-03 01:00:00 4.0 6.0 10.0\n", "2012-10-03 02:00:00 1.0 1.0 2.0\n", "2012-10-03 03:00:00 2.0 3.0 5.0\n", "2012-10-03 04:00:00 6.0 1.0 7.0\n", "2012-10-03 05:00:00 21.0 10.0 31.0\n", "2012-10-03 06:00:00 105.0 50.0 155.0\n", "2012-10-03 07:00:00 257.0 95.0 352.0\n", "2012-10-03 08:00:00 291.0 146.0 437.0\n", "2012-10-03 09:00:00 172.0 104.0 276.0\n", "2012-10-03 10:00:00 72.0 46.0 118.0\n", "2012-10-03 11:00:00 10.0 32.0 42.0\n", "2012-10-03 12:00:00 35.0 41.0 76.0\n", "2012-10-03 13:00:00 42.0 48.0 90.0\n", "2012-10-03 14:00:00 77.0 51.0 128.0\n", "2012-10-03 15:00:00 72.0 92.0 164.0\n", "2012-10-03 16:00:00 133.0 182.0 315.0\n", "2012-10-03 17:00:00 192.0 391.0 583.0\n", "2012-10-03 18:00:00 122.0 258.0 380.0\n", "2012-10-03 19:00:00 59.0 69.0 128.0\n", "2012-10-03 20:00:00 29.0 51.0 80.0\n", "2012-10-03 21:00:00 25.0 38.0 63.0\n", "2012-10-03 22:00:00 24.0 25.0 49.0\n", "2012-10-03 23:00:00 5.0 12.0 17.0\n", "2012-10-04 00:00:00 7.0 11.0 18.0\n", "2012-10-04 01:00:00 3.0 0.0 3.0\n", "2012-10-04 02:00:00 3.0 6.0 9.0\n", "2012-10-04 03:00:00 0.0 3.0 3.0\n", "2012-10-04 04:00:00 7.0 1.0 8.0\n", "2012-10-04 05:00:00 15.0 11.0 26.0\n", "... ... ... ...\n", "2017-07-30 18:00:00 60.0 92.0 152.0\n", "2017-07-30 19:00:00 46.0 70.0 116.0\n", "2017-07-30 20:00:00 49.0 47.0 96.0\n", "2017-07-30 21:00:00 18.0 34.0 52.0\n", "2017-07-30 22:00:00 21.0 13.0 34.0\n", "2017-07-30 23:00:00 12.0 12.0 24.0\n", "2017-07-31 00:00:00 2.0 3.0 5.0\n", "2017-07-31 01:00:00 5.0 2.0 7.0\n", "2017-07-31 02:00:00 1.0 0.0 1.0\n", "2017-07-31 03:00:00 2.0 0.0 2.0\n", "2017-07-31 04:00:00 6.0 5.0 11.0\n", "2017-07-31 05:00:00 32.0 20.0 52.0\n", "2017-07-31 06:00:00 99.0 67.0 166.0\n", "2017-07-31 07:00:00 269.0 223.0 492.0\n", "2017-07-31 08:00:00 348.0 312.0 660.0\n", "2017-07-31 09:00:00 172.0 192.0 364.0\n", "2017-07-31 10:00:00 73.0 75.0 148.0\n", "2017-07-31 11:00:00 58.0 61.0 119.0\n", "2017-07-31 12:00:00 56.0 51.0 107.0\n", "2017-07-31 13:00:00 44.0 63.0 107.0\n", "2017-07-31 14:00:00 68.0 77.0 145.0\n", "2017-07-31 15:00:00 74.0 93.0 167.0\n", "2017-07-31 16:00:00 104.0 261.0 365.0\n", "2017-07-31 17:00:00 199.0 641.0 840.0\n", "2017-07-31 18:00:00 162.0 393.0 555.0\n", "2017-07-31 19:00:00 88.0 182.0 270.0\n", "2017-07-31 20:00:00 56.0 98.0 154.0\n", "2017-07-31 21:00:00 53.0 53.0 106.0\n", "2017-07-31 22:00:00 13.0 28.0 41.0\n", "2017-07-31 23:00:00 12.0 26.0 38.0\n", "\n", "[42312 rows x 3 columns]" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "get_fremont_data()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python [conda env:py35]", "language": "python", "name": "conda-env-py35-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": 2 }
mit
jpilgram/phys202-2015-work
assignments/midterm/AlgorithmsEx03.ipynb
1
9346
{ "cells": [ { "cell_type": "markdown", "metadata": { "nbgrader": {} }, "source": [ "# Algorithms Exercise 3" ] }, { "cell_type": "markdown", "metadata": { "nbgrader": {} }, "source": [ "## Imports" ] }, { "cell_type": "code", "execution_count": 65, "metadata": { "collapsed": true, "nbgrader": {} }, "outputs": [], "source": [ "%matplotlib inline\n", "from matplotlib import pyplot as plt\n", "import numpy as np" ] }, { "cell_type": "code", "execution_count": 66, "metadata": { "collapsed": false, "nbgrader": {} }, "outputs": [], "source": [ "from IPython.html.widgets import interact" ] }, { "cell_type": "markdown", "metadata": { "nbgrader": {} }, "source": [ "## Character counting and entropy" ] }, { "cell_type": "markdown", "metadata": { "nbgrader": {} }, "source": [ "Write a function `char_probs` that takes a string and computes the probabilities of each character in the string:\n", "\n", "* First do a character count and store the result in a dictionary.\n", "* Then divide each character counts by the total number of character to compute the normalized probabilties.\n", "* Return the dictionary of characters (keys) and probabilities (values)." ] }, { "cell_type": "code", "execution_count": 67, "metadata": { "collapsed": false, "nbgrader": { "checksum": "f11bac096ada913538c9a47721fb98a1", "solution": true } }, "outputs": [], "source": [ "def char_probs(s):\n", " \"\"\"Find the probabilities of the unique characters in the string s.\n", " \n", " Parameters\n", " ----------\n", " s : str\n", " A string of characters.\n", " \n", " Returns\n", " -------\n", " probs : dict\n", " A dictionary whose keys are the unique characters in s and whose values\n", " are the probabilities of those characters.\n", " \"\"\"\n", " # YOUR CODE HERE\n", " #raise NotImplementedError()\n", " s=s.replace(' ','')\n", " l = [i for i in s]\n", " dic={i:l.count(i) for i in l}\n", " prob = [(dic[i]/len(l)) for i in dic]\n", " result = {i:prob[j] for i in l for j in range(len(prob))}\n", " return result" ] }, { "cell_type": "code", "execution_count": 68, "metadata": { "collapsed": false, "deletable": false, "nbgrader": { "checksum": "97f4091c66f9a555c766706bcf4a7681", "grade": true, "grade_id": "algorithmsex03a", "points": 4 } }, "outputs": [], "source": [ "test1 = char_probs('aaaa')\n", "assert np.allclose(test1['a'], 1.0)\n", "test2 = char_probs('aabb')\n", "assert np.allclose(test2['a'], 0.5)\n", "assert np.allclose(test2['b'], 0.5)\n", "test3 = char_probs('abcd')\n", "assert np.allclose(test3['a'], 0.25)\n", "assert np.allclose(test3['b'], 0.25)\n", "assert np.allclose(test3['c'], 0.25)\n", "assert np.allclose(test3['d'], 0.25)" ] }, { "cell_type": "markdown", "metadata": { "nbgrader": {} }, "source": [ "The [entropy](http://en.wikipedia.org/wiki/Entropy_%28information_theory%29) is a quantiative measure of the disorder of a probability distribution. It is used extensively in Physics, Statistics, Machine Learning, Computer Science and Information Science. Given a set of probabilities $P_i$, the entropy is defined as:\n", "\n", "$$H = - \\Sigma_i P_i \\log_2(P_i)$$ \n", "\n", "In this expression $\\log_2$ is the base 2 log (`np.log2`), which is commonly used in information science. In Physics the natural log is often used in the definition of entropy.\n", "\n", "Write a funtion `entropy` that computes the entropy of a probability distribution. The probability distribution will be passed as a Python `dict`: the values in the `dict` will be the probabilities.\n", "\n", "To compute the entropy, you should:\n", "\n", "* First convert the values (probabilities) of the `dict` to a Numpy array of probabilities.\n", "* Then use other Numpy functions (`np.log2`, etc.) to compute the entropy.\n", "* Don't use any `for` or `while` loops in your code." ] }, { "cell_type": "code", "execution_count": 113, "metadata": { "collapsed": false, "nbgrader": { "checksum": "93e205f7727df5161387fa73af53718b", "solution": true } }, "outputs": [ { "ename": "TypeError", "evalue": "Not implemented for this type", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m<ipython-input-113-4190199d8962>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m 8\u001b[0m \u001b[0mP\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0marray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mw\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 9\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlog2\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mP\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 10\u001b[1;33m \u001b[0mentropy\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'haldjfhasdf'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[1;32m<ipython-input-113-4190199d8962>\u001b[0m in \u001b[0;36mentropy\u001b[1;34m(d)\u001b[0m\n\u001b[0;32m 7\u001b[0m \u001b[0mw\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0marray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mz\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 8\u001b[0m \u001b[0mP\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0marray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mw\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 9\u001b[1;33m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlog2\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mP\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 10\u001b[0m \u001b[0mentropy\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'haldjfhasdf'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;31mTypeError\u001b[0m: Not implemented for this type" ] } ], "source": [ "def entropy(d):\n", " \"\"\"Compute the entropy of a dict d whose values are probabilities.\"\"\"\n", " # YOUR CODE HERE\n", " #raise NotImplementedError()\n", " s = char_probs(d)\n", " z = [(i,s[i]) for i in s]\n", " w=np.array(z)\n", " P = np.array(w[::,1])\n", " np.log2(P[1])\n", "entropy('haldjfhasdf')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "deletable": false, "nbgrader": { "checksum": "0499a53c730bb4fbb2cd81d7c34486da", "grade": true, "grade_id": "algorithmsex03b", "points": 4 } }, "outputs": [], "source": [ "assert np.allclose(entropy({'a': 0.5, 'b': 0.5}), 1.0)\n", "assert np.allclose(entropy({'a': 1.0}), 0.0)" ] }, { "cell_type": "markdown", "metadata": { "nbgrader": {} }, "source": [ "Use IPython's `interact` function to create a user interface that allows you to type a string into a text box and see the entropy of the character probabilities of the string." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "deletable": false, "nbgrader": { "checksum": "6cff4e8e53b15273846c3aecaea84a3d", "solution": true } }, "outputs": [], "source": [ "# YOUR CODE HERE\n", "raise NotImplementedError()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "deletable": false, "nbgrader": { "checksum": "2eeb2ebb1993a6f046deec7ff81c4930", "grade": true, "grade_id": "algorithmsex03c", "points": 2 } }, "outputs": [], "source": [ "assert True # use this for grading the pi digits histogram" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.4.0" } }, "nbformat": 4, "nbformat_minor": 0 }
mit
wtsi-medical-genomics/team-code
python-club/notebooks/regular-expressions.ipynb
1
23012
{ "cells": [ { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import re # Python's regular expression module\n", "\n", "def re_test(regex, query):\n", " \"\"\"A helper function to test if a regex has a match in a query.\"\"\"\n", " p = re.compile(regex)\n", " result = 'MATCH' if p.match(query) else 'NOT FOUND'\n", " print '\"{}\" with regex \"{}\": {}'.format(query, regex, result)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#Regular Expressions\n", "###Daniel Rice" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* Introduction\n", " * Definition\n", " * Examples\n", " * Exercise 1\n", "* Decomposing the syntax\n", " * Character classes\n", " * Metacharacters\n", " * Repetition\n", " * Capture groups\n", "* Regex's in Python\n", " * match\n", " * search" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "##Introduction\n", "###Definition\n", "A regular expression (also known as a RE, regex, regex pattern, or regexp) is **a sequence of symbols and characters expressing a text pattern**. A regular expression allows us to specify a string pattern that we can then search for within a body of text. The idea is to make a pattern template (regex), and then query some text to see if the template is present or not.\n", "\n", "###Example 1\n", "Let's say we want to determine if a string begins with the word `PASS`. Our regular expression will simply be:" ] }, { "cell_type": "code", "execution_count": 71, "metadata": { "collapsed": true }, "outputs": [], "source": [ "pass_regex = 'PASS'" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This pattern will match the occurence of `PASS` in the query text. Now let's test it out:" ] }, { "cell_type": "code", "execution_count": 72, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\"PASS: Data good\" with regex \"PASS\": MATCH\n" ] } ], "source": [ "re_test(pass_regex, 'PASS: Data good')" ] }, { "cell_type": "code", "execution_count": 73, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\"FAIL: Data bad\" with regex \"PASS\": NOT FOUND\n" ] } ], "source": [ "re_test(pass_regex, 'FAIL: Data bad')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "###Example 2\n", "Let's say we have a text file that contains numerical readings that we need to perform some analysis on. Here's the first few lines from the file:" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": false }, "outputs": [], "source": [ "lines = \\\n", "\"\"\"\n", "Device-initialized.\n", "Version-19.23\n", "12-12-2014\n", "12\n", "4353\n", "3452\n", "ERROR\n", "498\n", "34598734\n", "345982398\n", "23\n", "ERROR\n", "3434345798\n", "\"\"\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We don't want the header lines and those `ERROR` lines are going to ruin our analysis! Let's filter these out with with a regex. First we will create the pattern template (or regex) for what we want to find:\n", "\n", "```\n", "^\\d+$\n", "```\n", "\n", "This regex can be split into four parts:\n", "\n", "1. **`^`** This indicates the start of the string.\n", "2. **`\\d`** This specifies we want to match decimal digits (the numbers 0-9).\n", "3. **`+`** This symbol means we want to find one or more of the previous symbol (which in this case is a decimal digit).\n", "4. **`$`** This indicates the end of the string.\n", "\n", "Putting it all together we want to find patterns that are one or more (`+`) numbers (`\\d`) from start (`^`) to finish (`$`).\n", "\n", "Let's load the regex into Python's `re` module:" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "collapsed": true }, "outputs": [], "source": [ "integer_regex = re.compile('\\d+$')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now let's get our string of lines into a list of strings:" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['Device-initialized.', 'Version-19.23', '12-12-2014', '12', '4353', '3452', 'ERROR', '498', '34598734', '345982398', '23', 'ERROR', '3434345798']\n" ] } ], "source": [ "lines = lines.split()\n", "print lines" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we need to run through each of these lines and determine if it matches our regex. Converting to integer would be nice as well." ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[12, 4353, 3452, 498, 34598734, 345982398, 23, 3434345798]\n" ] } ], "source": [ "clean_data = [] # We will accumulate our filtered integers here\n", "for line in lines:\n", " if integer_regex.match(line):\n", " clean_data.append(int(line))\n", "print clean_data\n", "\n", "# If you're into one liners you could also do one of these:\n", "# clean_data = [int(line) for line in lines if integer_regex.match(line)]\n", "# clean_data = map(int, filter(integer_regex.match, lines))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It worked like a dream. You may be arguing that there other non-regex solutions to this problem and indeed there are (for example integer typecasting with a catch clause) but this example was given to show you the process of:\n", "\n", "1. Creating a regex pattern for what you want to find.\n", "2. Appyling it to some text.\n", "3. Extracting the positive hits.\n", "\n", "There will be situations where regex's will really be the only viable solution when you want to match some super-complex strings." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "###Exercise 1\n", "You have a file consisting of DNA bases which you want to perform analysis on:" ] }, { "cell_type": "code", "execution_count": 36, "metadata": { "collapsed": false }, "outputs": [], "source": [ "lines = \\\n", "\"\"\"\n", "Acme-DNA-Reader\n", "ACTG\n", "AA\n", "-1\n", "CCTC\n", "TTTCG\n", "C\n", "TGCTA\n", "-1\n", "TCCCCCC\n", "\"\"\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The `-1` represent reading erros and we want these removed. Using the preceeding example as a guide, filter out the header and the reading errors.\n", "\n", "**Hint** The bases can be represented with the pattern `[ACGT]`." ] }, { "cell_type": "code", "execution_count": 37, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Acme-DNA-Reader\n", "ACTG\n", "AA\n", "-1\n", "CCTC\n", "TTTCG\n", "C\n", "TGCTA\n", "-1\n", "TCCCCCC\n", "['ACTG', 'AA', 'CCTC', 'TTTCG', 'C', 'TGCTA', 'TCCCCCC']\n" ] } ], "source": [ "bases_regex = re.compile('[ACGT]+$')\n", "lines = lines.split()\n", "#print lines\n", "clean_data = [] # We will accumulate our filtered integers here\n", "for line in lines:\n", " print line\n", " if bases_regex.match(line):\n", " clean_data.append(line)\n", "print clean_data\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "##Decomposing the syntax\n", "Regexps can appear cryptic but they can be decomposed into **character classes** and **metacharacters**.\n", "###Character classes\n", "These allow us to concisely specify the types or classes of characters to match. In the example above `\\d` is a character class that represents decimal digits. There are many such character classes and we will go through these below.\n", "The square brackets allow us to specify a set of characters to match. We have already seen this with `[ACGT]`. We can also use the hyphen `-` to specify ranges.\n", "\n", "| Character Class | Description | Match Examples |\n", "|:---------------:| ----------- | -------------- |\n", "| `\\d` | Matches any decimal digit; this is equivalent to the class `[0-9]`. | `0`, `1`, `2`, ... |\n", "| `\\D` | Matches any non-digit character; this is equivalent to the class `[^0-9]`. | `a`, `@`, `;` |\n", "| `\\s` | Matches any whitespace character; this is equivalent to the class `[ \\t\\n\\r\\f\\v]`. | space, tab, newline |\n", "| `\\S` | Matches any non-whitespace character; this is equivalent to the class `[^ \\t\\n\\r\\f\\v]`. | `1`, `A`, `&` |\n", "| `\\w` | Matches any alphanumeric character (word character) ; this is equivalent to the class `[a-zA-Z0-9_]`.| `x`, `Z`, `2` |\n", "| `\\W` | Matches any non-alphanumeric character; this is equivalent to the class `[^a-zA-Z0-9_]`. | `£`, `(`, space |\n", "| `.` | Matches anything (except newline). | `8`, `(`, `a`, space |\n", "\n", "This can look like a lot to remember but there are some menomics here:\n", "\n", "| Character Class | Mnemonic |\n", "|:---------------:| -------- |\n", "| `\\d` | **d**ecimal digit |\n", "| `\\D` | uppercase so not `\\d` |\n", "| `\\s` | white**s**pace character |\n", "| `\\S` | uppercase so not `\\s` |\n", "| `\\w` | **w**ord character |\n", "| `\\W` | uppercase so not `\\w`|\n", "\n", "\n", "###Metacharacters\n", "####Repitition\n", "The character classes will match only a single character. How can say match exactly `3` occurences of `Q`? The metacharacters include different sybmols to reflect repetition:\n", "\n", "| Repetition Metacharacter | Description |\n", "|:------------------------:| ----------- |\n", "| `*` | Matches zero or more occurences of the previous character (class). |\n", "| `+` | Matches one or more occurences of the previous character (class). |\n", "| `{m,n}` | With integers `m` and `n`, specifies at least `m` and at most `n` occurences of the previous character (class). **Do not put any space after the comma** *as this prevents the metacharacter from being recognized.* |\n", "\n", "####Examples\n" ] }, { "cell_type": "code", "execution_count": 38, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\" \" with regex \"A*\": MATCH\n", "\"A\" with regex \"A*\": MATCH\n", "\"AA\" with regex \"A*\": MATCH\n", "\"Z12345\" with regex \"A*\": MATCH\n" ] } ], "source": [ "re_test('A*', ' ')\n", "re_test('A*', 'A')\n", "re_test('A*', 'AA')\n", "re_test('A*', 'Z12345')" ] }, { "cell_type": "code", "execution_count": 39, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\" \" with regex \"A+\": NOT FOUND\n", "\"A\" with regex \"A+\": MATCH\n", "\"ZZZZ\" with regex \"A+\": NOT FOUND\n" ] } ], "source": [ "re_test('A+', ' ')\n", "re_test('A+', 'A')\n", "re_test('A+', 'ZZZZ')" ] }, { "cell_type": "code", "execution_count": 40, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\"BB\" with regex \"BA{1,3}B\": NOT FOUND\n", "\"BAB\" with regex \"BA{1,3}B\": MATCH\n", "\"BAAAB\" with regex \"BA{1,3}B\": MATCH\n", "\"BAAAAAB\" with regex \"BA{1,3}B\": NOT FOUND\n" ] } ], "source": [ "re_test('BA{1,3}B', 'BB')\n", "re_test('BA{1,3}B', 'BAB')\n", "re_test('BA{1,3}B', 'BAAAB')\n", "re_test('BA{1,3}B', 'BAAAAAB')" ] }, { "cell_type": "code", "execution_count": 41, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\"AB12[]9023\" with regex \".*\": MATCH\n", "\"123B\" with regex \"\\d{1,3}B\": MATCH\n", "\"aaa2\" with regex \"\\w{1,3}\\d+\": MATCH\n", "\"aaaa2\" with regex \"\\w{1,3}\\d+\": NOT FOUND\n" ] } ], "source": [ "re_test('.*', 'AB12[]9023')\n", "re_test('\\d{1,3}B', '123B')\n", "re_test('\\w{1,3}\\d+', 'aaa2')\n", "re_test('\\w{1,3}\\d+', 'aaaa2')" ] }, { "cell_type": "code", "execution_count": 47, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#http://path/ssh://dr9@farm3-login:/path\n", "p = re.compile(r'http://(\\w+)/ssh://(\\w+)@(\\w+):/(\\w+)')" ] }, { "cell_type": "code", "execution_count": 48, "metadata": { "collapsed": false }, "outputs": [], "source": [ "m = p.match(r'http://path/ssh://dr9@farm3-login:/path')" ] }, { "cell_type": "code", "execution_count": 59, "metadata": { "collapsed": false }, "outputs": [], "source": [ "RE_SSH = re.compile(r'/ssh://(\\w+)@(.+):(.+)/(?:chr)?([mxy0-9]{1,2}):(\\d+)-(\\d+)$', re.IGNORECASE)" ] }, { "cell_type": "code", "execution_count": 65, "metadata": { "collapsed": true }, "outputs": [], "source": [ "RE_SSH = re.compile(r'/ssh://(\\w+)@(.+)$', re.IGNORECASE)" ] }, { "cell_type": "code", "execution_count": 69, "metadata": { "collapsed": true }, "outputs": [], "source": [ "t = '/ssh://dr9@farm3-login'" ] }, { "cell_type": "code", "execution_count": 70, "metadata": { "collapsed": false }, "outputs": [], "source": [ "m = RE_SSH.match(t)\n", "#user, server, path, lchr, lmin, lmax = m.groups()" ] }, { "cell_type": "code", "execution_count": 72, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "dr9\n", "farm3-login\n" ] } ], "source": [ "for el in m.groups():\n", " print el" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "###Exercise 2\n", "Determine if a string contains \"wazup\" or \"wazzup\" or \"wazzzup\" where the number of z's must be greater than zero. Use the following list of strings:" ] }, { "cell_type": "code", "execution_count": 73, "metadata": { "collapsed": true }, "outputs": [], "source": [ "L = [\n", "'So I said wazzzzzzzup?',\n", "'And she said wazup back to me',\n", "'waup isn\\'t a word',\n", "'what is up',\n", "'wazzzzzzzzzzzzzzzzzzzzzzzup']" ] }, { "cell_type": "code", "execution_count": 74, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['So I said wazzzzzzzup?', 'And she said wazup back to me', 'wazzzzzzzzzzzzzzzzzzzzzzzup']\n" ] } ], "source": [ "wazup_regex = re.compile(r'.*waz+up.*')\n", "matches = [el for el in L if wazup_regex.match(el)]\n", "print matches" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "###Example\n", "We have a list of strings and some of these contain names that we want to extract. The names have the format\n", "```\n", "0123_FirstName_LastName\n", "```\n", "where the quantity of numbers at the beginning of the string are variable (e.g. `1_Bob_Smith`, `12_Bob_Smith`, `123456_Bob_Smith)` are all valid)." ] }, { "cell_type": "code", "execution_count": 82, "metadata": { "collapsed": true }, "outputs": [], "source": [ "L = [\n", "'123_George_Washington',\n", "'Blah blah',\n", "'894542342_Winston_Churchill',\n", "'More blah blah',\n", "'String_without_numbers']" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Don't worry if the following regex looks cryptic, it will soon be broken down." ] }, { "cell_type": "code", "execution_count": 83, "metadata": { "collapsed": true }, "outputs": [], "source": [ "p = re.compile(r'\\d+_([A-Z,a-z]+)_([A-Z,a-z]+)')" ] }, { "cell_type": "code", "execution_count": 85, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "('George', 'Washington')\n", "('Winston', 'Churchill')\n" ] } ], "source": [ "for el in L:\n", " m = p.match(el)\n", " if m:\n", " print m.groups()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "###Exercise 3\n", "Find all occurences of `AGT` within a string of DNA where contiguous repeated occurences should be counted only once (e.g. `AGTAGTAGT` will be counted once and not three times)." ] }, { "cell_type": "code", "execution_count": 75, "metadata": { "collapsed": false }, "outputs": [], "source": [ "dna = 'AGTAGTACTACAAGTAGTCCAGTCCTTGGGAGTAGTAGTAGTAAGGGCCT'" ] }, { "cell_type": "code", "execution_count": 76, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(start, stop): (0, 6)\n", "matching string: AGTAGT\n", "(start, stop): (12, 18)\n", "matching string: AGTAGT\n", "(start, stop): (20, 23)\n", "matching string: AGT\n", "(start, stop): (30, 42)\n", "matching string: AGTAGTAGTAGT\n" ] } ], "source": [ "p = re.compile(r'(AGT)+')\n", "m = p.finditer(dna)\n", "for match in m:\n", " print '(start, stop): {}'.format(match.span())\n", " print 'matching string: {}'.format(match.group())" ] }, { "cell_type": "code", "execution_count": 77, "metadata": { "collapsed": true }, "outputs": [], "source": [ "p.finditer?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "###Exercise 4\n", "A text file contains some important information about a test that has been run. The individual who wrote this file is \n", "inconsistent with date formats." ] }, { "cell_type": "code", "execution_count": 80, "metadata": { "collapsed": false }, "outputs": [], "source": [ "L = [\n", "'Test 1-2 commencing 2012-12-12 for multiple reads.',\n", "'Date of birth of individual 803232435345345 is 1983/06/27.',\n", "'Test 1-2 complete 20130420.']" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Convert all dates to the format YYYYMMDD.\n", "\n", "Hints:\n", " * Use groups `()`\n", " * Use `{m, n}` where `m=n=2 or m=n=4`\n", " * Use `?` for the bits between date components\n", " * You can use either search or match, though in the latter you will need to specify what happens before and after the date (`.*` maybe)?\n", " * The second element in the list will present you with issues as there is a number there that may accidentally be captured as a date. Use `\\D` to make sure your date is not surrounded by decimal digits." ] }, { "cell_type": "code", "execution_count": 78, "metadata": { "collapsed": true }, "outputs": [], "source": [ "p = re.compile(r'\\D+\\d{4,4}[-/]?\\d{2,2}[-/]?\\d{2,2}\\D')" ] }, { "cell_type": "code", "execution_count": 85, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['20121212', '19830627', '20130420']\n" ] } ], "source": [ "date_regex = re.compile(r'\\D(\\d{4,4})[-/]?(\\d{2,2})[-/]?(\\d{2,2})\\D')\n", "standard_dates = []\n", "for el in L:\n", " m = date_regex.search(el)\n", " if m:\n", " standard_dates.append(''.join(m.groups()))\n", "print standard_dates" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "##Resources\n", "\n", "| Resource | Description |\n", "| ------------------------------------------ | ----------------------------------------------------------------- |\n", "| https://docs.python.org/2/howto/regex.html | A great in-depth tutorial from the official Python documentation. |\n", "| https://www.regex101.com/#python | A useful online tool to quickly test regular expressions. |\n", "| http://regexcrossword.com/ | A nice way to practice your regular expression skills. |" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": true }, "outputs": [], "source": [ "text = 'abcd \\e'" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "abcd \\e\n" ] } ], "source": [ "print text" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "re.compile(r'\\\\')" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "re.compile(r'\\\\')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.9" } }, "nbformat": 4, "nbformat_minor": 0 }
gpl-2.0
janusnic/21v-python
unit_20/parallel_ml/rendered_notebooks/06 - Distributed Model Selection and Assessment.ipynb
1
98236
{ "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" }, "name": "" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "heading", "level": 1, "metadata": {}, "source": [ "Distributed Model Selection and Assessment" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Outline of the session:\n", "\n", "- Introduction to **IPython.parallel**\n", "- Sharing Data Between Processes with **Memory Mapping**\n", "- **Parallel Grid Search** and Model Selection\n", "- **Parallel** Computation of **Learning Curves** (TODO)\n", "- **Distributed** Computation on **EC2 Spot Instances with StarCluster**" ] }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Motivation" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "When doing model evaluations and parameters tuning, many models must be trained independently on the same data. This is an embarrassingly parallel problem but having a copy of the dataset in memory for each process is waste of RAM:\n", "\n", "<img src=\"files/images/grid_search_parameters.png\" style=\"display:inline; width: 49%\" />\n", "<img src=\"files/images/grid_search_cv_splits.png\" style=\"display:inline; width: 49%\" />\n", "\n", "When doing 3 folds cross validation on a 9 parameters grid, a naive implementation could read the data from the disk and load it in memory 27 times. If this happens concurrently (e.g. on a compute node with 32 cores) the RAM might blow up hence breaking the potential linear speed up." ] }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "IPython.parallel, a Primer" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This section gives a primer on some tools best utilizing computational resources when doing predictive modeling in the Python / NumPy ecosystem namely:\n", "\n", "- optimal usage of available CPUs and cluster nodes with **`IPython.parallel`**\n", "\n", "- optimal memory re-use using shared memory between Python processes using **`numpy.memmap`** and **`joblib`**\n", "\n", "### What is so great about `IPython.parallel`:\n", "\n", "- Single node multi-CPUs\n", "- Multiple node multi-CPUs\n", "- Interactive In-memory computing\n", "- IPython notebook integration with `%px` and `%%px` magics\n", "- Possibility to interactively connect to individual computing processes to launch interactive debugger (`#priceless`)\n", "\n", "### Let's get started:\n", "\n", "Let start an IPython cluster using the `ipcluster` common (usually run from your operating system console). To make sure that we are not running several clusters on the same host, let's try to shut down any running IPython cluster first:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "!ipcluster stop" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "2015-04-07 22:45:52.065 [IPClusterStop] Stopping cluster [pid=14443] with [signal=2]\r\n" ] } ], "prompt_number": 0 }, { "cell_type": "code", "collapsed": false, "input": [ "!ipcluster start -n=2 --daemon" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 1 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Go to the \"Cluster\" tab of the notebook and **start a local cluster with 2 engines**. Then come back here. We should now be able to use our cluster from our notebook session (or any other Python process running on localhost):" ] }, { "cell_type": "code", "collapsed": false, "input": [ "from IPython.parallel import Client\n", "client = Client()" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 2 }, { "cell_type": "code", "collapsed": false, "input": [ "len(client)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "pyout", "prompt_number": 5, "text": [ "2" ] } ], "prompt_number": 3 }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### The %px and %%px magics\n", "\n", "All the engines of the client can be accessed imperatively using the `%px` and `%%px` IPython cell magics:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "%%px\n", "\n", "import os\n", "import socket\n", "\n", "print(\"This is running in process with pid {0} on host '{1}'.\".format(\n", " os.getpid(), socket.gethostname()))" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "[stdout:0] This is running in process with pid 17968 on host 'host-2.local'.\n", "[stdout:1] This is running in process with pid 17969 on host 'host-2.local'.\n" ] } ], "prompt_number": 4 }, { "cell_type": "markdown", "metadata": {}, "source": [ "The content of the `__main__` namespace can also be read and written via the `%px` magic:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "%px a = 1" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 5 }, { "cell_type": "code", "collapsed": false, "input": [ "%px print(a)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "[stdout:0] 1\n", "[stdout:1] 1\n" ] } ], "prompt_number": 6 }, { "cell_type": "code", "collapsed": false, "input": [ "%%px\n", "\n", "a *= 2\n", "print(a)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "[stdout:0] 2\n", "[stdout:1] 2\n" ] } ], "prompt_number": 7 }, { "cell_type": "markdown", "metadata": {}, "source": [ "It is possible to restrict the `%px` and `%%px` magic instructions to specific engines:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "%%px --targets=-1\n", "a *= 2\n", "print(a)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "4\n" ] } ], "prompt_number": 8 }, { "cell_type": "code", "collapsed": false, "input": [ "%px print(a)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "[stdout:0] 2\n", "[stdout:1] 4\n" ] } ], "prompt_number": 9 }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### The DirectView objects\n", "\n", "Cell magics are very nice to work interactively from the notebook but it's also possible to replicate their behavior programmatically with more flexibility with a `DirectView` instance. A `DirectView` can be created by slicing the client object:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "all_engines = client[:]\n", "all_engines" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "pyout", "prompt_number": 12, "text": [ "<DirectView [0, 1]>" ] } ], "prompt_number": 10 }, { "cell_type": "markdown", "metadata": {}, "source": [ "The namespace of the `__main__` module of each running python engine can be accessed in read and write mode as a python dictionary:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "all_engines['a'] = 1" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 11 }, { "cell_type": "code", "collapsed": false, "input": [ "all_engines['a']" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "pyout", "prompt_number": 14, "text": [ "[1, 1]" ] } ], "prompt_number": 12 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Direct views can also execute the same code in parallel on each engine of the view:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "def my_sum(a, b):\n", " return a + b\n", "\n", "my_sum_apply_results = all_engines.apply(my_sum, 11, 31)\n", "my_sum_apply_results" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "pyout", "prompt_number": 15, "text": [ "<AsyncResult: finished>" ] } ], "prompt_number": 13 }, { "cell_type": "markdown", "metadata": {}, "source": [ "The ouput of the `apply` method is an asynchronous handle returned immediately without waiting for the end of the computation. To block until the results are ready use:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "my_sum_apply_results.get()" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "pyout", "prompt_number": 16, "text": [ "[42, 42]" ] } ], "prompt_number": 14 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here is a more useful example to fetch the network hostname of each engine in the cluster. Let's study it in more details:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "def hostname():\n", " \"\"\"Return the name of the host where the function is being called\"\"\"\n", " import socket\n", " return socket.gethostname()\n", "\n", "hostname_apply_result = all_engines.apply(hostname)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 15 }, { "cell_type": "markdown", "metadata": {}, "source": [ "When doing the above, the `hostname` function is first defined locally (the client python process). The `DirectView.apply` method introspects it, serializes its name and bytecode and ships it to each engine of the cluster where it is reconstructed as local function on each engine. This function is then called on each engine of the view with the optionally provided arguments.\n", "\n", "In return, the client gets a python object that serves as an handle to asynchronously fetch the list of the results of the calls:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "hostname_apply_result" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "pyout", "prompt_number": 18, "text": [ "<AsyncResult: finished>" ] } ], "prompt_number": 16 }, { "cell_type": "code", "collapsed": false, "input": [ "hostname_apply_result.get()" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "pyout", "prompt_number": 19, "text": [ "['host-2.local', 'host-2.local']" ] } ], "prompt_number": 17 }, { "cell_type": "markdown", "metadata": {}, "source": [ "It is also possible to key the results explicitly with the engine ids with the `AsyncResult.get_dict` method. This is a very simple idiom to fetch metadata on the runtime environment of each engine of the direct view:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "hostnames = hostname_apply_result.get_dict()\n", "hostnames" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "pyout", "prompt_number": 20, "text": [ "{0: 'host-2.local', 1: 'host-2.local'}" ] } ], "prompt_number": 18 }, { "cell_type": "markdown", "metadata": {}, "source": [ "It can be handy to invert this mapping to find one engine id per host in the cluster so as to execute host specific operation:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "one_engine_by_host = dict((hostname, engine_id) for engine_id, hostname\n", " in hostnames.items())\n", "one_engine_by_host" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "pyout", "prompt_number": 21, "text": [ "{'host-2.local': 1}" ] } ], "prompt_number": 19 }, { "cell_type": "code", "collapsed": false, "input": [ "one_engine_by_host_ids = list(one_engine_by_host.values())\n", "one_engine_by_host_ids" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "pyout", "prompt_number": 22, "text": [ "[1]" ] } ], "prompt_number": 20 }, { "cell_type": "code", "collapsed": false, "input": [ "one_engine_per_host_view = client[one_engine_by_host_ids]\n", "one_engine_per_host_view" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "pyout", "prompt_number": 23, "text": [ "<DirectView [1]>" ] } ], "prompt_number": 21 }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Trick:** you can even use those engines ids to execute shell commands in parallel on each host of the cluster:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "one_engine_by_host.values()" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "pyout", "prompt_number": 24, "text": [ "dict_values([1])" ] } ], "prompt_number": 22 }, { "cell_type": "code", "collapsed": false, "input": [ "%%px --targets=[1]\n", "\n", "!pip install flask" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "[stdout:1] \n", "\u001b[33mDEPRECATION: --download-cache has been deprecated and will be removed in the future. Pip now automatically uses and configures its cache.\u001b[0m\r\n", "Requirement already satisfied (use --upgrade to upgrade): flask in /Users/ogrisel/venvs/py34/lib/python3.4/site-packages\r\n", "Requirement already satisfied (use --upgrade to upgrade): Werkzeug>=0.7 in /Users/ogrisel/venvs/py34/lib/python3.4/site-packages (from flask)\r\n", "Requirement already satisfied (use --upgrade to upgrade): Jinja2>=2.4 in /Users/ogrisel/venvs/py34/lib/python3.4/site-packages (from flask)\r\n", "Requirement already satisfied (use --upgrade to upgrade): itsdangerous>=0.21 in /Users/ogrisel/venvs/py34/lib/python3.4/site-packages (from flask)\r\n", "Requirement already satisfied (use --upgrade to upgrade): markupsafe in /Users/ogrisel/venvs/py34/lib/python3.4/site-packages (from Jinja2>=2.4->flask)\r\n" ] } ], "prompt_number": 23 }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Note on Importing Modules on Remote Engines\n", "\n", "In the previous example we put the `import socket` statement inside the body of the `hostname` function to make sure to make sure that is is available when the rest of the function is executed in the python processes of the remote engines.\n", "\n", "Alternatively it is possible to import the required modules ahead of time on all the engines of a directview using a context manager / with syntax:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "with all_engines.sync_imports():\n", " import numpy" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "importing numpy on engine(s)\n" ] } ], "prompt_number": 24 }, { "cell_type": "markdown", "metadata": {}, "source": [ "However this method does **not** support alternative import syntaxes:\n", " \n", " >>> import numpy as np\n", " >>> from numpy import linalg" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Hence the method of importing in the body of the \"applied\" functions is more flexible. Additionally, this does not pollute the `__main__` namespace of the engines as it only impact the local namespace of the function itself." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Exercise**:\n", "\n", "- Write a function that returns the memory usage of each engine process in the cluster.\n", "- Allocate a largish numpy array of zeros of known size (e.g. 100MB) on each engine of the cluster.\n", "\n", "Hints:\n", "\n", "Use the `psutil` module to collect the runtime info on a specific process or host. For instance to fetch the memory usage of the currently running process in MB:\n", "\n", " >>> import os\n", " >>> import psutil\n", " >>> psutil.Process(os.getpid()).get_memory_info().rss / 1e6\n", "\n", "To allocate a numpy array with 1000 zeros stored as 64bit floats you can use:\n", "\n", " >>> import numpy as np\n", " >>> z = np.zeros(1000, dtype=np.float64)\n", "\n", "The size in bytes of such a numpy array can then be fetched with ``z.nbytes``:\n", " \n", " >>> z.nbytes / 1e6\n", " 0.008" ] }, { "cell_type": "code", "collapsed": false, "input": [ "def get_engines_memory(client):\n", " def memory_mb():\n", " import os, psutil\n", " return psutil.Process(os.getpid()).get_memory_info().rss / 1e6\n", " \n", " return client[:].apply(memory_mb).get_dict()" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 25 }, { "cell_type": "code", "collapsed": false, "input": [ "get_engines_memory(client)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "pyout", "prompt_number": 28, "text": [ "{0: 60.522496, 1: 60.530688}" ] } ], "prompt_number": 26 }, { "cell_type": "code", "collapsed": false, "input": [ "sum(get_engines_memory(client).values())" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "pyout", "prompt_number": 29, "text": [ "121.065472" ] } ], "prompt_number": 27 }, { "cell_type": "code", "collapsed": false, "input": [ "%%px\n", "import numpy as np\n", "z = np.zeros(int(1e7), dtype=np.float64)\n", "print(\"Allocated {0}MB on engine.\".format(z.nbytes / 1e6))" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "[stdout:0] Allocated 80.0MB on engine.\n", "[stdout:1] Allocated 80.0MB on engine.\n" ] } ], "prompt_number": 28 }, { "cell_type": "code", "collapsed": false, "input": [ "get_engines_memory(client)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "pyout", "prompt_number": 31, "text": [ "{0: 60.530688, 1: 60.542976}" ] } ], "prompt_number": 29 }, { "cell_type": "heading", "level": 4, "metadata": {}, "source": [ "Load Balanced View" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "`LoadBalancedView` is an alternative to the `DirectView` to run one function call at a time on a free engine." ] }, { "cell_type": "code", "collapsed": false, "input": [ "lv = client.load_balanced_view()" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 30 }, { "cell_type": "code", "collapsed": false, "input": [ "def slow_square(x):\n", " import time\n", " time.sleep(2)\n", " return x ** 2" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 31 }, { "cell_type": "code", "collapsed": false, "input": [ "result = lv.apply(slow_square, 4)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 32 }, { "cell_type": "code", "collapsed": false, "input": [ "result" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "pyout", "prompt_number": 35, "text": [ "<AsyncResult: slow_square>" ] } ], "prompt_number": 33 }, { "cell_type": "code", "collapsed": false, "input": [ "result.ready()" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "pyout", "prompt_number": 36, "text": [ "False" ] } ], "prompt_number": 34 }, { "cell_type": "code", "collapsed": false, "input": [ "result.get() # blocking call" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "pyout", "prompt_number": 37, "text": [ "16" ] } ], "prompt_number": 35 }, { "cell_type": "markdown", "metadata": {}, "source": [ "It is possible to spread some tasks among the engines of the LB view by passing a callable and an iterable of task arguments to the `LoadBalancedView.map` method:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "results = lv.map(slow_square, [0, 1, 2, 3])\n", "results" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "pyout", "prompt_number": 38, "text": [ "<AsyncMapResult: slow_square>" ] } ], "prompt_number": 36 }, { "cell_type": "code", "collapsed": false, "input": [ "results.ready()" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "pyout", "prompt_number": 39, "text": [ "False" ] } ], "prompt_number": 37 }, { "cell_type": "code", "collapsed": false, "input": [ "results.progress" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "pyout", "prompt_number": 40, "text": [ "0" ] } ], "prompt_number": 38 }, { "cell_type": "code", "collapsed": false, "input": [ "# results.abort()" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 39 }, { "cell_type": "code", "collapsed": false, "input": [ "# Iteration on AsyncMapResult is blocking\n", "for r in results:\n", " print(r)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "0\n", "1\n", "4" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "9\n" ] } ], "prompt_number": 40 }, { "cell_type": "markdown", "metadata": {}, "source": [ "The load balanced view will be used in the following to schedule work on the cluster while being able to monitor progress and occasionally add new computing nodes to the cluster while computing to speed up the processing when using EC2 and StarCluster (see later)." ] }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Sharing Read-only Data between Processes on the Same Host with Memmapping" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's **restart the cluster** to kill the existing python processes and restart with a new client instances to be able to monitor the memory usage in details:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "!ipcluster stop" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "2015-04-07 22:47:07.343 [IPClusterStop] Stopping cluster [pid=17955] with [signal=2]\r\n" ] } ], "prompt_number": 41 }, { "cell_type": "code", "collapsed": false, "input": [ "!ipcluster start -n=2 --daemon" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 42 }, { "cell_type": "code", "collapsed": false, "input": [ "from IPython.parallel import Client\n", "client = Client()\n", "len(client)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "pyout", "prompt_number": 45, "text": [ "2" ] } ], "prompt_number": 43 }, { "cell_type": "markdown", "metadata": {}, "source": [ "The numpy package makes it possible to memory map large contiguous chunks of binary files as shared memory for all the Python processes running on a given host:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "%px import numpy as np" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 44 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Creating a `numpy.memmap` instance with the `w+` mode creates a file on the filesystem and zeros its content. Let's do it from the first engine process or our current IPython cluster:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "%%px --targets=-1\n", "\n", "# Cleanup any existing file from past session (necessary for windows)\n", "import os\n", "if os.path.exists('small.mmap'):\n", " os.unlink('small.mmap')\n", "\n", "mm_w = np.memmap('small.mmap', shape=10, dtype=np.float32, mode='w+')\n", "print(mm_w)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "[ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n" ] } ], "prompt_number": 45 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Assuming the notebook process was launched with:\n", "\n", " cd notebooks\n", " ipython notebook\n", "\n", "and the cluster was launched from the ipython notebook UI, the engines will have a the same current working directory as the notebook process, hence we can find the `small.mmap` file the current folder:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "ls -lh small.mmap" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "-rw-r--r-- 1 ogrisel staff 40B Apr 7 22:48 small.mmap\r\n" ] } ], "prompt_number": 46 }, { "cell_type": "markdown", "metadata": {}, "source": [ "This binary file can then be mapped as a new numpy array by all the engines having access to the same filesystem. The `mode='r+'` opens this shared memory area in read write mode:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "%%px\n", "\n", "mm_r = np.memmap('small.mmap', dtype=np.float32, mode='r+')\n", "print(mm_r)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "[stdout:0] [ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n", "[stdout:1] [ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n" ] } ], "prompt_number": 47 }, { "cell_type": "code", "collapsed": false, "input": [ "%%px --targets=-1\n", "\n", "mm_w[0] = 42\n", "print(mm_w)\n", "print(mm_r)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "[ 42. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n", "[ 42. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n" ] } ], "prompt_number": 48 }, { "cell_type": "code", "collapsed": false, "input": [ "%px print(mm_r)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "[stdout:0] [ 42. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n", "[stdout:1] [ 42. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n" ] } ], "prompt_number": 49 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Memory mapped arrays created with `mode='r+'` can be modified and the modifications are shared with all the engines:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "%%px --targets=1\n", "\n", "mm_r[1] = 43" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 50 }, { "cell_type": "code", "collapsed": false, "input": [ "%%px\n", "print(mm_r)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "[stdout:0] [ 42. 43. 0. 0. 0. 0. 0. 0. 0. 0.]\n", "[stdout:1] [ 42. 43. 0. 0. 0. 0. 0. 0. 0. 0.]\n" ] } ], "prompt_number": 51 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Be careful those, there is no builtin read nor write lock available on this such datastructures so it's better to avoid concurrent read & write operations on the same array segments unless there engine operations are made to cooperate with some synchronization or scheduling orchestrator." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Memmap arrays generally behave very much like regular in-memory numpy arrays:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "%%px\n", "print(\"sum={:.3}, mean={:.3}, std={:.3}\".format(\n", " float(mm_r.sum()), np.mean(mm_r), np.std(mm_r)))" ], "language": "python", "metadata": {}, "outputs": [ { "ename": "CompositeError", "evalue": "one or more exceptions from call to method: execute\n[0:execute]: TypeError: non-empty format string passed to object.__format__\n[1:execute]: TypeError: non-empty format string passed to object.__format__", "output_type": "pyerr", "traceback": [ "[0:execute]: ", "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)\u001b[0;32m<ipython-input-5-a80dd61b871b>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m", "\u001b[1;32m 1\u001b[0m print(\"sum={:.3}, mean={:.3}, std={:.3}\".format(", "\u001b[0;32m----> 2\u001b[0;31m float(mm_r.sum()), np.mean(mm_r), np.std(mm_r)))", "\u001b[0m\u001b[0;31mTypeError\u001b[0m: non-empty format string passed to object.__format__", "", "[1:execute]: ", "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)\u001b[0;32m<ipython-input-8-a80dd61b871b>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m", "\u001b[1;32m 1\u001b[0m print(\"sum={:.3}, mean={:.3}, std={:.3}\".format(", "\u001b[0;32m----> 2\u001b[0;31m float(mm_r.sum()), np.mean(mm_r), np.std(mm_r)))", "\u001b[0m\u001b[0;31mTypeError\u001b[0m: non-empty format string passed to object.__format__", "" ] } ], "prompt_number": 52 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Before allocating more data in memory on the cluster let us define a couple of utility functions from the previous exercise (and more) to monitor what is used by which engine and what is still free on the cluster as a whole:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "def get_engines_memory(client):\n", " \"\"\"Gather the memory allocated by each engine in MB\"\"\"\n", " def memory_mb():\n", " import os\n", " import psutil\n", " return psutil.Process(os.getpid()).get_memory_info().rss / 1e6\n", " \n", " return client[:].apply(memory_mb).get_dict()\n", "\n", "def get_host_free_memory(client):\n", " \"\"\"Free memory on each host of the cluster in MB.\"\"\"\n", " all_engines = client[:]\n", " def hostname():\n", " import socket\n", " return socket.gethostname()\n", " \n", " hostnames = all_engines.apply(hostname).get_dict()\n", " one_engine_per_host = dict((hostname, engine_id)\n", " for engine_id, hostname\n", " in hostnames.items())\n", "\n", " def host_free_memory():\n", " import psutil\n", " return psutil.virtual_memory().free / 1e6\n", " \n", " \n", " one_engine_per_host_ids = list(one_engine_per_host.values())\n", " host_mem = client[one_engine_per_host_ids].apply(\n", " host_free_memory).get_dict()\n", " \n", " return dict((hostnames[eid], m) for eid, m in host_mem.items())" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 53 }, { "cell_type": "code", "collapsed": false, "input": [ "get_engines_memory(client)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "pyout", "prompt_number": 56, "text": [ "{0: 60.882944, 1: 60.911616}" ] } ], "prompt_number": 54 }, { "cell_type": "code", "collapsed": false, "input": [ "get_host_free_memory(client)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "pyout", "prompt_number": 57, "text": [ "{'host-2.local': 145.719296}" ] } ], "prompt_number": 55 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's allocate a 80MB memmap array in the first engine and load it in readwrite mode in all the engines:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "%%px --targets=-1\n", "\n", "# Cleanup any existing file from past session (necessary for windows)\n", "import os\n", "if os.path.exists('big.mmap'):\n", " os.unlink('big.mmap')\n", "\n", "np.memmap('big.mmap', shape=10 * int(1e6), dtype=np.float64, mode='w+')" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "display_data", "text": [ "\u001b[0;31mOut[1:9]: \u001b[0mmemmap([ 0., 0., 0., ..., 0., 0., 0.])" ] } ], "prompt_number": 56 }, { "cell_type": "code", "collapsed": false, "input": [ "ls -lh big.mmap" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "-rw-r--r-- 1 ogrisel staff 76M Apr 7 22:48 big.mmap\r\n" ] } ], "prompt_number": 57 }, { "cell_type": "code", "collapsed": false, "input": [ "get_host_free_memory(client)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "pyout", "prompt_number": 60, "text": [ "{'host-2.local': 183.201792}" ] } ], "prompt_number": 58 }, { "cell_type": "markdown", "metadata": {}, "source": [ "No significant memory was used in this operation as we just asked the OS to allocate the buffer on the hard drive and just maitain a virtual memory area as a cheap reference to this buffer.\n", "\n", "Let's open new references to the same buffer from all the engines at once:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "%px %time big_mmap = np.memmap('big.mmap', dtype=np.float64, mode='r+')" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "[stdout:0] \n", "CPU times: user 238 \u00b5s, sys: 718 \u00b5s, total: 956 \u00b5s\n", "Wall time: 15.1 ms\n", "[stdout:1] \n", "CPU times: user 225 \u00b5s, sys: 720 \u00b5s, total: 945 \u00b5s\n", "Wall time: 15.5 ms\n" ] } ], "prompt_number": 59 }, { "cell_type": "code", "collapsed": false, "input": [ "%px big_mmap" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "display_data", "text": [ "\u001b[0;31mOut[0:7]: \u001b[0mmemmap([ 0., 0., 0., ..., 0., 0., 0.])" ] }, { "output_type": "display_data", "text": [ "\u001b[0;31mOut[1:11]: \u001b[0mmemmap([ 0., 0., 0., ..., 0., 0., 0.])" ] } ], "prompt_number": 60 }, { "cell_type": "code", "collapsed": false, "input": [ "get_host_free_memory(client)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "pyout", "prompt_number": 63, "text": [ "{'host-2.local': 182.72256}" ] } ], "prompt_number": 61 }, { "cell_type": "markdown", "metadata": {}, "source": [ "No physical memory was allocated in the operation as it just took a couple of ms to do so. This is also confirmed by the engines process stats:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "get_engines_memory(client)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "pyout", "prompt_number": 64, "text": [ "{0: 60.90752, 1: 60.944384}" ] } ], "prompt_number": 62 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's trigger an actual load of the data from the drive into the in-memory disk cache of the OS, this can take some time depending on the speed of the hard drive (on the order of 100MB/s to 300MB/s hence 3s to 8s for this dataset):" ] }, { "cell_type": "code", "collapsed": false, "input": [ "%%px --targets=-1\n", "\n", "%time np.sum(big_mmap)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "CPU times: user 23.8 ms, sys: 47.4 ms, total: 71.1 ms\n", "Wall time: 282 ms\n" ] }, { "output_type": "display_data", "text": [ "\u001b[0;31mOut[1:12]: \u001b[0mmemmap(0.0)" ] } ], "prompt_number": 63 }, { "cell_type": "code", "collapsed": false, "input": [ "get_engines_memory(client)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "pyout", "prompt_number": 66, "text": [ "{0: 60.90752, 1: 140.94336}" ] } ], "prompt_number": 64 }, { "cell_type": "code", "collapsed": false, "input": [ "get_host_free_memory(client)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "pyout", "prompt_number": 67, "text": [ "{'host-2.local': 102.432768}" ] } ], "prompt_number": 65 }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can see that the first engine has now access to the data in memory and the free memory on the host has decreased by the same amount.\n", "\n", "We can now access this data from all the engines at once much faster as the disk will no longer be used: the shared memory buffer will instead accessed directly by all the engines:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "%px %time np.sum(big_mmap)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "[stdout:0] \n", "CPU times: user 23.4 ms, sys: 19.7 ms, total: 43 ms\n", "Wall time: 43.6 ms\n", "[stdout:1] \n", "CPU times: user 14.5 ms, sys: 56 \u00b5s, total: 14.5 ms\n", "Wall time: 14.6 ms\n" ] }, { "output_type": "display_data", "text": [ "\u001b[0;31mOut[0:8]: \u001b[0mmemmap(0.0)" ] }, { "output_type": "display_data", "text": [ "\u001b[0;31mOut[1:13]: \u001b[0mmemmap(0.0)" ] } ], "prompt_number": 66 }, { "cell_type": "code", "collapsed": false, "input": [ "get_engines_memory(client)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "pyout", "prompt_number": 69, "text": [ "{0: 140.918784, 1: 140.94336}" ] } ], "prompt_number": 67 }, { "cell_type": "code", "collapsed": false, "input": [ "get_host_free_memory(client)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "pyout", "prompt_number": 70, "text": [ "{'host-2.local': 100.950016}" ] } ], "prompt_number": 68 }, { "cell_type": "markdown", "metadata": {}, "source": [ "So it seems that the engines have loaded a whole copy of the data but this actually not the case as the total amount of free memory was not impacted by the parallel access to the shared buffer. Furthermore, once the data has been preloaded from the hard drive using one process, all the of the other processes on the same host can access it almost instantly saving a lot of IO wait.\n", "\n", "This strategy makes it very interesting to load the readonly datasets of machine learning problems, especially when the same data is reused over and over by concurrent processes as can be the case when doing learning curves analysis or grid search." ] }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Memmaping Nested Numpy-based Data Structures with Joblib" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "joblib is a utility library included in the sklearn package. Among other things it provides tools to serialize objects that comprise large numpy arrays and reload them as memmap backed datastructures.\n", "\n", "To demonstrate it, let's create an arbitrary python datastructure involving numpy arrays:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "import numpy as np\n", "\n", "class MyDataStructure(object):\n", " \n", " def __init__(self, shape):\n", " self.float_zeros = np.zeros(shape, dtype=np.float32)\n", " self.integer_ones = np.ones(shape, dtype=np.int64)\n", " \n", "data_structure = MyDataStructure((3, 4))\n", "data_structure.float_zeros, data_structure.integer_ones" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "pyout", "prompt_number": 71, "text": [ "(array([[ 0., 0., 0., 0.],\n", " [ 0., 0., 0., 0.],\n", " [ 0., 0., 0., 0.]], dtype=float32), array([[1, 1, 1, 1],\n", " [1, 1, 1, 1],\n", " [1, 1, 1, 1]]))" ] } ], "prompt_number": 69 }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can now persist this datastructure to disk:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "from sklearn.externals import joblib\n", "\n", "joblib.dump(data_structure, 'data_structure.pkl')" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "pyout", "prompt_number": 72, "text": [ "['data_structure.pkl',\n", " 'data_structure.pkl_01.npy',\n", " 'data_structure.pkl_02.npy']" ] } ], "prompt_number": 70 }, { "cell_type": "code", "collapsed": false, "input": [ "!ls -l data_structure*" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "-rw-r--r-- 1 ogrisel staff 249 Apr 7 22:48 data_structure.pkl\r\n", "-rw-r--r-- 1 ogrisel staff 176 Apr 7 22:48 data_structure.pkl_01.npy\r\n", "-rw-r--r-- 1 ogrisel staff 128 Apr 7 22:48 data_structure.pkl_02.npy\r\n" ] } ], "prompt_number": 71 }, { "cell_type": "markdown", "metadata": {}, "source": [ "A memmapped copy of this datastructure can then be loaded:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "memmaped_data_structure = joblib.load('data_structure.pkl', mmap_mode='r+')\n", "memmaped_data_structure.float_zeros, memmaped_data_structure.integer_ones" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "pyout", "prompt_number": 74, "text": [ "(memmap([[ 0., 0., 0., 0.],\n", " [ 0., 0., 0., 0.],\n", " [ 0., 0., 0., 0.]], dtype=float32), memmap([[1, 1, 1, 1],\n", " [1, 1, 1, 1],\n", " [1, 1, 1, 1]]))" ] } ], "prompt_number": 72 }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Memmaping CV Splits for Multiprocess Dataset Sharing" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can leverage the previous tools to build a utility function that extracts Cross Validation splits ahead of time to persist them on the hard drive in a format suitable for memmaping by IPython engine processes." ] }, { "cell_type": "code", "collapsed": false, "input": [ "from sklearn.externals import joblib\n", "from sklearn.cross_validation import ShuffleSplit\n", "import os\n", "\n", "def persist_cv_splits(X, y, n_cv_iter=5, name='data',\n", " suffix=\"_cv_%03d.pkl\", test_size=0.25, random_state=None):\n", " \"\"\"Materialize randomized train test splits of a dataset.\"\"\"\n", "\n", " cv = ShuffleSplit(X.shape[0], n_iter=n_cv_iter,\n", " test_size=test_size, random_state=random_state)\n", " cv_split_filenames = []\n", " \n", " for i, (train, test) in enumerate(cv):\n", " cv_fold = (X[train], y[train], X[test], y[test])\n", " cv_split_filename = name + suffix % i\n", " cv_split_filename = os.path.abspath(cv_split_filename)\n", " joblib.dump(cv_fold, cv_split_filename)\n", " cv_split_filenames.append(cv_split_filename)\n", " \n", " return cv_split_filenames" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 73 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's try it on the digits dataset, we can run this from the :" ] }, { "cell_type": "code", "collapsed": false, "input": [ "from sklearn.datasets import load_digits\n", "\n", "digits = load_digits()\n", "digits_split_filenames = persist_cv_splits(digits.data, digits.target,\n", " name='digits', random_state=42)\n", "digits_split_filenames" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "pyout", "prompt_number": 76, "text": [ "['/Users/ogrisel/code/parallel_ml_tutorial/notebooks/digits_cv_000.pkl',\n", " '/Users/ogrisel/code/parallel_ml_tutorial/notebooks/digits_cv_001.pkl',\n", " '/Users/ogrisel/code/parallel_ml_tutorial/notebooks/digits_cv_002.pkl',\n", " '/Users/ogrisel/code/parallel_ml_tutorial/notebooks/digits_cv_003.pkl',\n", " '/Users/ogrisel/code/parallel_ml_tutorial/notebooks/digits_cv_004.pkl']" ] } ], "prompt_number": 74 }, { "cell_type": "code", "collapsed": false, "input": [ "ls -lh digits*" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "-rw-r--r-- 1 ogrisel staff 270B Apr 7 22:48 digits_cv_000.pkl\r\n", "-rw-r--r-- 1 ogrisel staff 674K Apr 7 22:48 digits_cv_000.pkl_01.npy\r\n", "-rw-r--r-- 1 ogrisel staff 11K Apr 7 22:48 digits_cv_000.pkl_02.npy\r\n", "-rw-r--r-- 1 ogrisel staff 225K Apr 7 22:48 digits_cv_000.pkl_03.npy\r\n", "-rw-r--r-- 1 ogrisel staff 3.6K Apr 7 22:48 digits_cv_000.pkl_04.npy\r\n", "-rw-r--r-- 1 ogrisel staff 270B Apr 7 22:48 digits_cv_001.pkl\r\n", "-rw-r--r-- 1 ogrisel staff 674K Apr 7 22:48 digits_cv_001.pkl_01.npy\r\n", "-rw-r--r-- 1 ogrisel staff 11K Apr 7 22:48 digits_cv_001.pkl_02.npy\r\n", "-rw-r--r-- 1 ogrisel staff 225K Apr 7 22:48 digits_cv_001.pkl_03.npy\r\n", "-rw-r--r-- 1 ogrisel staff 3.6K Apr 7 22:48 digits_cv_001.pkl_04.npy\r\n", "-rw-r--r-- 1 ogrisel staff 270B Apr 7 22:48 digits_cv_002.pkl\r\n", "-rw-r--r-- 1 ogrisel staff 674K Apr 7 22:48 digits_cv_002.pkl_01.npy\r\n", "-rw-r--r-- 1 ogrisel staff 11K Apr 7 22:48 digits_cv_002.pkl_02.npy\r\n", "-rw-r--r-- 1 ogrisel staff 225K Apr 7 22:48 digits_cv_002.pkl_03.npy\r\n", "-rw-r--r-- 1 ogrisel staff 3.6K Apr 7 22:48 digits_cv_002.pkl_04.npy\r\n", "-rw-r--r-- 1 ogrisel staff 270B Apr 7 22:48 digits_cv_003.pkl\r\n", "-rw-r--r-- 1 ogrisel staff 674K Apr 7 22:48 digits_cv_003.pkl_01.npy\r\n", "-rw-r--r-- 1 ogrisel staff 11K Apr 7 22:48 digits_cv_003.pkl_02.npy\r\n", "-rw-r--r-- 1 ogrisel staff 225K Apr 7 22:48 digits_cv_003.pkl_03.npy\r\n", "-rw-r--r-- 1 ogrisel staff 3.6K Apr 7 22:48 digits_cv_003.pkl_04.npy\r\n", "-rw-r--r-- 1 ogrisel staff 270B Apr 7 22:48 digits_cv_004.pkl\r\n", "-rw-r--r-- 1 ogrisel staff 674K Apr 7 22:48 digits_cv_004.pkl_01.npy\r\n", "-rw-r--r-- 1 ogrisel staff 11K Apr 7 22:48 digits_cv_004.pkl_02.npy\r\n", "-rw-r--r-- 1 ogrisel staff 225K Apr 7 22:48 digits_cv_004.pkl_03.npy\r\n", "-rw-r--r-- 1 ogrisel staff 3.6K Apr 7 22:48 digits_cv_004.pkl_04.npy\r\n" ] } ], "prompt_number": 75 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Each of the persisted CV splits can then be loaded back again using memmaping:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "X_train, y_train, X_test, y_test = joblib.load(\n", " 'digits_cv_002.pkl', mmap_mode='r+')" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 76 }, { "cell_type": "code", "collapsed": false, "input": [ "X_train" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "pyout", "prompt_number": 79, "text": [ "memmap([[ 0., 1., 13., ..., 1., 0., 0.],\n", " [ 0., 0., 7., ..., 9., 0., 0.],\n", " [ 0., 0., 0., ..., 13., 1., 0.],\n", " ..., \n", " [ 0., 0., 4., ..., 16., 1., 0.],\n", " [ 0., 0., 2., ..., 15., 8., 0.],\n", " [ 0., 0., 0., ..., 3., 0., 0.]])" ] } ], "prompt_number": 77 }, { "cell_type": "code", "collapsed": false, "input": [ "y_train" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "pyout", "prompt_number": 80, "text": [ "memmap([5, 3, 1, ..., 8, 6, 4])" ] } ], "prompt_number": 78 }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Parallel Model Selection and Grid Search" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's leverage IPython.parallel and the Memory Mapping features of joblib to write a custom grid search utility that runs on cluster in a memory efficient manner.\n", "\n", "Assume that we want to reproduce the grid search from the previous session:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "import numpy as np\n", "from pprint import pprint\n", "\n", "svc_params = {\n", " 'C': np.logspace(-1, 2, 4),\n", " 'gamma': np.logspace(-4, 0, 5),\n", "}\n", "pprint(svc_params)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "{'C': array([ 0.1, 1. , 10. , 100. ]),\n", " 'gamma': array([ 1.00000000e-04, 1.00000000e-03, 1.00000000e-02,\n", " 1.00000000e-01, 1.00000000e+00])}\n" ] } ], "prompt_number": 79 }, { "cell_type": "markdown", "metadata": {}, "source": [ "`GridSearchCV` internally uses the following `ParameterGrid` utility iterator class to build the possible combinations of parameters:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "from sklearn.grid_search import ParameterGrid\n", "\n", "list(ParameterGrid(svc_params))" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "pyout", "prompt_number": 82, "text": [ "[{'C': 0.10000000000000001, 'gamma': 0.0001},\n", " {'C': 0.10000000000000001, 'gamma': 0.001},\n", " {'C': 0.10000000000000001, 'gamma': 0.01},\n", " {'C': 0.10000000000000001, 'gamma': 0.10000000000000001},\n", " {'C': 0.10000000000000001, 'gamma': 1.0},\n", " {'C': 1.0, 'gamma': 0.0001},\n", " {'C': 1.0, 'gamma': 0.001},\n", " {'C': 1.0, 'gamma': 0.01},\n", " {'C': 1.0, 'gamma': 0.10000000000000001},\n", " {'C': 1.0, 'gamma': 1.0},\n", " {'C': 10.0, 'gamma': 0.0001},\n", " {'C': 10.0, 'gamma': 0.001},\n", " {'C': 10.0, 'gamma': 0.01},\n", " {'C': 10.0, 'gamma': 0.10000000000000001},\n", " {'C': 10.0, 'gamma': 1.0},\n", " {'C': 100.0, 'gamma': 0.0001},\n", " {'C': 100.0, 'gamma': 0.001},\n", " {'C': 100.0, 'gamma': 0.01},\n", " {'C': 100.0, 'gamma': 0.10000000000000001},\n", " {'C': 100.0, 'gamma': 1.0}]" ] } ], "prompt_number": 80 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's write a function to load the data from a CV split file and compute the validation score for a given parameter set and model:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "def compute_evaluation(cv_split_filename, model, params):\n", " \"\"\"Function executed by a worker to evaluate a model on a CV split\"\"\"\n", " # All module imports should be executed in the worker namespace\n", " from sklearn.externals import joblib\n", "\n", " X_train, y_train, X_validation, y_validation = joblib.load(\n", " cv_split_filename, mmap_mode='c')\n", " \n", " model.set_params(**params)\n", " model.fit(X_train, y_train)\n", " validation_score = model.score(X_validation, y_validation)\n", " return validation_score" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 81 }, { "cell_type": "code", "collapsed": false, "input": [ "def grid_search(lb_view, model, cv_split_filenames, param_grid):\n", " \"\"\"Launch all grid search evaluation tasks.\"\"\"\n", " all_tasks = []\n", " all_parameters = list(ParameterGrid(param_grid))\n", " \n", " for i, params in enumerate(all_parameters):\n", " task_for_params = []\n", " \n", " for j, cv_split_filename in enumerate(cv_split_filenames): \n", " t = lb_view.apply(\n", " compute_evaluation, cv_split_filename, model, params)\n", " task_for_params.append(t) \n", " \n", " all_tasks.append(task_for_params)\n", " \n", " return all_parameters, all_tasks" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 82 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's try on the digits dataset that we splitted previously as memmapable files:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "from sklearn.svm import SVC\n", "from IPython.parallel import Client\n", "\n", "client = Client()\n", "lb_view = client.load_balanced_view()\n", "model = SVC()\n", "svc_params = {\n", " 'C': np.logspace(-1, 2, 4),\n", " 'gamma': np.logspace(-4, 0, 5),\n", "}\n", "\n", "all_parameters, all_tasks = grid_search(\n", " lb_view, model, digits_split_filenames, svc_params)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 83 }, { "cell_type": "markdown", "metadata": {}, "source": [ "The `grid_search` function is using the asynchronous API of the `LoadBalancedView`, we can hence monitor the progress:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "import time\n", "time.sleep(5)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 84 }, { "cell_type": "code", "collapsed": false, "input": [ "def progress(tasks):\n", " return np.mean([task.ready() for task_group in tasks\n", " for task in task_group])" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 85 }, { "cell_type": "code", "collapsed": false, "input": [ "print(\"Tasks completed: {0}%\".format(100 * progress(all_tasks)))" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Tasks completed: 35.0%\n" ] } ], "prompt_number": 86 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Even better, we can introspect the completed task to find the best parameters set so far:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "def find_bests(all_parameters, all_tasks, n_top=5):\n", " \"\"\"Compute the mean score of the completed tasks\"\"\"\n", " mean_scores = []\n", " \n", " for param, task_group in zip(all_parameters, all_tasks):\n", " scores = [t.get() for t in task_group if t.ready()]\n", " if len(scores) == 0:\n", " continue\n", " mean_scores.append((np.mean(scores), param))\n", " \n", " return sorted(mean_scores, reverse=True, key=lambda x: x[0])[:n_top]" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 87 }, { "cell_type": "code", "collapsed": false, "input": [ "from pprint import pprint\n", "\n", "print(\"Tasks completed: {0}%\".format(100 * progress(all_tasks)))\n", "pprint(find_bests(all_parameters, all_tasks))" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Tasks completed: 37.0%\n", "[" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "(0.99022222222222211, {'C': 1.0, 'gamma': 0.001}),\n", " (0.97599999999999998, {'C': 1.0, 'gamma': 0.0001}),\n", " (0.96533333333333338, {'C': 0.10000000000000001, 'gamma': 0.001}),\n", " (0.89511111111111108, {'C': 0.10000000000000001, 'gamma': 0.0001}),\n", " (0.80222222222222217, {'C': 1.0, 'gamma': 0.01})]\n" ] } ], "prompt_number": 88 }, { "cell_type": "code", "collapsed": false, "input": [ "[t.wait() for tasks in all_tasks for t in tasks]\n", "print(\"Tasks completed: {0}%\".format(100 * progress(all_tasks)))\n", "pprint(find_bests(all_parameters, all_tasks))" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Tasks completed: 100.0%\n", "[(0.99022222222222211, {'C': 1.0, 'gamma': 0.001}),\n", " (0.98888888888888893, {'C': 10.0, 'gamma': 0.001}),\n", " (0.98888888888888893, {'C': 100.0, 'gamma': 0.001}),\n", " (0.98755555555555552, {'C': 10.0, 'gamma': 0.0001}),\n", " (0.98711111111111127, {'C': 100.0, 'gamma': 0.0001})]\n" ] } ], "prompt_number": 89 }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "Optimization Trick: Truncated Randomized Search" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It is often wasteful to search all the possible combinations of parameters as done previously, especially if the number of parameters is large (e.g. more than 3).\n", "\n", "To speed up the discovery of good parameters combinations, it is often faster to randomized the search order and allocate a budget of evaluations, e.g. 10 or 100 combinations.\n", "\n", "See [this JMLR paper by James Bergstra](http://jmlr.csail.mit.edu/papers/v13/bergstra12a.html) for an empirical analysis of the problem. The interested reader should also have a look at [hyperopt](https://github.com/jaberg/hyperopt) that further refines this parameter search method using meta-optimizers.\n", "\n", "Randomized Parameter Search has just been implemented in the master branch of scikit-learn be part of the 0.14 release." ] }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "A More Complete Parallel Model Selection and Assessment Example" ] }, { "cell_type": "code", "collapsed": false, "input": [ "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "\n", "# Some nice default configuration for plots\n", "plt.rcParams['figure.figsize'] = 10, 7.5\n", "plt.rcParams['axes.grid'] = True\n", "plt.gray();" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "display_data", "text": [ "<matplotlib.figure.Figure at 0x106ebf780>" ] } ], "prompt_number": 90 }, { "cell_type": "code", "collapsed": false, "input": [ "lb_view = client.load_balanced_view()\n", "model = SVC()" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 91 }, { "cell_type": "code", "collapsed": false, "input": [ "import sys, imp\n", "from collections import OrderedDict\n", "sys.path.append('..')\n", "import model_selection, mmap_utils\n", "imp.reload(model_selection), imp.reload(mmap_utils)\n", "\n", "lb_view.abort()\n", "\n", "svc_params = OrderedDict([\n", " ('gamma', np.logspace(-4, 0, 5)),\n", " ('C', np.logspace(-1, 2, 4)),\n", "])\n", "\n", "search = model_selection.RandomizedGridSeach(lb_view)\n", "search.launch_for_splits(model, svc_params, digits_split_filenames)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "pyout", "prompt_number": 94, "text": [ "Progress: 00% (000/100)" ] } ], "prompt_number": 92 }, { "cell_type": "code", "collapsed": false, "input": [ "time.sleep(5)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 93 }, { "cell_type": "code", "collapsed": false, "input": [ "print(search.report())" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Progress: 18% (018/100)\n", "\n", "Rank 1: validation: 0.96533 (+/-0.00206) train: 0.97179 (+/-0.00126):\n", " {'gamma': 0.001, 'C': 0.10000000000000001}\n", "Rank 2: validation: 0.09022 (+/-0.00448) train: 1.00000 (+/-0.00000):\n", " {'gamma': 0.10000000000000001, 'C': 100.0}\n", "Rank 3: validation: 0.08667 (+/-0.00243) train: 1.00000 (+/-0.00000):\n", " {'gamma': 1.0, 'C': 100.0}\n", "Rank 4: validation: 0.08519 (+/-0.00412) train: 1.00000 (+/-0.00000):\n", " {'gamma': 0.10000000000000001, 'C': 1.0}\n" ] } ], "prompt_number": 94 }, { "cell_type": "code", "collapsed": false, "input": [ "time.sleep(5)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 95 }, { "cell_type": "code", "collapsed": false, "input": [ "print(search.report())\n", "search.boxplot_parameters(display_train=False)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Progress: 41% (041/100)\n", "\n", "Rank 1: validation: 0.99022 (+/-0.00151) train: 0.99896 (+/-0.00038):\n", " {'gamma': 0.001, 'C': 1.0}\n", "Rank 2: validation: 0.98756 (+/-0.00194) train: 0.99733 (+/-0.00018):\n", " {'gamma': 0.0001, 'C': 10.0}\n", "Rank 3: validation: 0.96533 (+/-0.00206) train: 0.97179 (+/-0.00126):\n", " {'gamma': 0.001, 'C': 0.10000000000000001}\n", "Rank 4: validation: 0.80933 (+/-0.01149) train: 1.00000 (+/-0.00000):\n", " {'gamma': 0.01, 'C': 100.0}\n", "Rank 5: validation: 0.09022 (+/-0.00448) train: 1.00000 (+/-0.00000):\n", " {'gamma': 0.10000000000000001, 'C': 100.0}\n" ] }, { "output_type": "stream", "stream": "stderr", "text": [ "/Users/ogrisel/venvs/py34/lib/python3.4/site-packages/numpy/core/_methods.py:83: RuntimeWarning: Degrees of freedom <= 0 for slice\n", " warnings.warn(\"Degrees of freedom <= 0 for slice\", RuntimeWarning)\n", "/Users/ogrisel/venvs/py34/lib/python3.4/site-packages/numpy/core/_methods.py:117: RuntimeWarning: invalid value encountered in double_scalars\n", " ret = ret.dtype.type(ret / rcount)\n" ] }, { "output_type": "display_data", "png": 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"text": [ "<matplotlib.figure.Figure at 0x10860cda0>" ] } ], "prompt_number": 96 }, { "cell_type": "code", "collapsed": false, "input": [ "#search.abort()" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 97 }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Distributing the Computation on EC2 Spot Instances with StarCluster" ] }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "Installation" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To provision a cheap transient compute cluster on Amazon EC2, the first step is to register on EC2 with a credit card and put your EC2 credentials as environment variables. For instance under Linux / OSX:\n", "\n", " [laptop]% export AWS_ACCESS_KEY_ID=XXXXXXXXXXXXXXXXXXXXX\n", " [laptop]% export AWS_SECRET_ACCESS_KEY=XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX\n", "\n", "You can put those exports in your `~/.bashrc` to automatically get those credentials loaded in new shell sessions.\n", "\n", "Then proceed to the installation of StarCluster it-self:\n", "\n", " [laptop]% pip install StarCluster" ] }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "Configuration" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's run the help command a first time and create a template configuration file:\n", "\n", " [laptop]% starcluster help\n", " StarCluster - (http://star.mit.edu/cluster)\n", " Software Tools for Academics and Researchers (STAR)\n", " Please submit bug reports to [email protected]\n", " \n", " cli.py:87 - ERROR - config file /home/user/.starcluster/config does not exist\n", " \n", " Options:\n", " --------\n", " [1] Show the StarCluster config template\n", " [2] Write config template to /home/user/.starcluster/config\n", " [q] Quit\n", " \n", " Please enter your selection:\n", " 2\n", "\n", "and create a password-less ssh key that will be dedicated to this transient cluster:\n", " \n", " [laptop]% starcluster createkey mykey -o ~/.ssh/mykey.rsa\n", "\n", " \n", "You can now edit the file `/home/user/.starcluster/config` and remplace its content with the following sample configuration:\n", " \n", " [global]\n", " DEFAULT_TEMPLATE=iptemplate\n", " REFRESH_INTERVAL=5\n", " \n", " [key mykey]\n", " KEY_LOCATION=~/.ssh/mykey.rsa\n", " \n", " [plugin ipcluster]\n", " SETUP_CLASS = starcluster.plugins.ipcluster.IPCluster\n", " ENABLE_NOTEBOOK = True\n", " NOTEBOOK_PASSWD = aaaa\n", " \n", " [plugin ipclusterstop]\n", " SETUP_CLASS = starcluster.plugins.ipcluster.IPClusterStop\n", " \n", " [plugin ipclusterrestart]\n", " SETUP_CLASS = starcluster.plugins.ipcluster.IPClusterRestartEngines\n", " \n", " [plugin pypackages]\n", " setup_class = starcluster.plugins.pypkginstaller.PyPkgInstaller\n", " packages = scikit-learn, psutil\n", " \n", " # Base configuration for IPython.parallel cluster\n", " [cluster iptemplate]\n", " KEYNAME = mykey\n", " CLUSTER_SIZE = 1\n", " CLUSTER_USER = ipuser\n", " CLUSTER_SHELL = bash\n", " REGION = us-east-1\n", " NODE_IMAGE_ID = ami-5b3fb632 # REGION and NODE_IMAGE_ID go in pair\n", " NODE_INSTANCE_TYPE = c1.xlarge # 8 CPUs\n", " DISABLE_QUEUE = True # We don't need SGE, faster cluster startup\n", " PLUGINS = pypackages, ipcluster" ] }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "Launching a Cluster" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Start a new cluster using the `myclustertemplate` section of the `~/.startcluster/config` file:\n", "\n", " [laptop]% starcluster start -c iptemplate -s 3 -b 0.5 mycluster\n", " \n", "- The `-s` option makes it possible to select the number of EC2 instance to start.\n", "\n", "- The `-b` option makes it possible to provision non-master instances on the Spot Instance market\n", "\n", "- To also provision the master node on the Spot Instance market you can further add the `--force-spot-master` flag to the previous commandline.\n", "\n", "- Provisioning Spot Instances is typically up to 5x cheaper than regular instances for largish instance types such as `c1.xlarge` but you run the risk of having your instances shut down if the price goes up. Also provisioning new instances on the Spot market can be slower: often a couple of minutes instead of 30s for On Demand instances.\n", "\n", "- You can access the price history of spot instances of a specific region with:\n", "\n", " [laptop]% starcluster -r us-west-1 spothistory c1.xlarge\n", " StarCluster - (http://star.mit.edu/cluster) (v. 0.9999)\n", " Software Tools for Academics and Researchers (STAR)\n", " Please submit bug reports to [email protected]\n", "\n", " >>> Current price: $0.11\n", " >>> Max price: $0.75\n", " >>> Average price: $0.13\n", "\n", "Connect to the master node via ssh:\n", "\n", " [laptop]% starcluster sshmaster -A -u ipuser\n", "\n", "- The `-A` flag makes it possible to use your local ssh agent to manage your keys: makes it possible to `git clone` / `git push` github repositories from the master node as you would from your local folder.\n", "\n", "- The StarCluster AMI comes with `tmux` installed by default.\n", "\n", "It is possible to ssh into other cluster nodes from the master using local DNS aliases such as:\n", "\n", " [myuser@master]% ssh node001" ] }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "Dynamically Resizing the Cluster" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "When using the `LoadBalancedView` API of `IPython.parallel.Client` is it possible to dynamically grow the cluster to shorten the duration of the processing of a queue of task without having to restart from scratch.\n", "\n", "This can be achieved using the `addnode` command, for instance to add 3 more nodes using $0.50 bid price on the Spot Instance market:\n", " \n", " [laptop]% starcluster addnode -s 3 -b 0.5 mycluster\n", " \n", "Each node will automatically run the `IPCluster` plugin and register new `IPEngine` processes to the existing `IPController` process running on master.\n", "\n", "It is also possible to terminate individual running nodes of the cluster with `removenode` command but this will kill any task running on that node and IPython.parallel will **not** restart the failed task automatically." ] }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "Terminating a Cluster" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Once your are done with your computation, don't forget to shutdown the whole cluster and EBS volume so as to only pay for the resources you used.\n", "\n", "Before doing so, don't forget to backup any result file you would like to keep, by either pushing them to the S3 storage service (recommended for large files that you would want to reuse on EC2 later) or fetching them locally using the `starcluster get` command.\n", "\n", "The cluster shutdown itself can be achieved with a single command:\n", "\n", " [laptop]% starcluster terminate mycluster\n", "\n", "Alternatively to can also keep your data by preserving the EBS volume attached to the master node by remplacing the `terminate` command with the `stop` command:\n", "\n", " [laptop]% starcluster stop mycluster\n", "\n", "You can then later restart the same cluster again with the `start` command to automatically remount the EBS volume." ] } ], "metadata": {} } ] }
mit
rishuatgithub/MLPy
nlp/UPDATED_NLP_COURSE/00-Python-Text-Basics/00-Working-with-Text-Files.ipynb
1
20970
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "___\n", "\n", "<a href='http://www.pieriandata.com'> <img src='../Pierian_Data_Logo.png' /></a>\n", "___" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Working with Text Files\n", "In this section we'll cover\n", " * Working with f-strings (formatted string literals) to format printed text\n", " * Working with Files - opening, reading, writing and appending text files" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Formatted String Literals (f-strings)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Introduced in Python 3.6, <strong>f-strings</strong> offer several benefits over the older `.format()` string method. <br>For one, you can bring outside variables immediately into to the string rather than pass them through as keyword arguments:" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "His name is Fred.\n", "His name is Fred.\n" ] } ], "source": [ "name = 'Fred'\n", "\n", "# Using the old .format() method:\n", "print('His name is {var}.'.format(var=name))\n", "\n", "# Using f-strings:\n", "print(f'His name is {name}.')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Pass `!r` to get the <strong>string representation</strong>:" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "His name is 'Fred'\n" ] } ], "source": [ "print(f'His name is {name!r}')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Be careful not to let quotation marks in the replacement fields conflict with the quoting used in the outer string:" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "ename": "SyntaxError", "evalue": "invalid syntax (<ipython-input-3-b2f08335b9e5>, line 3)", "output_type": "error", "traceback": [ "\u001b[1;36m File \u001b[1;32m\"<ipython-input-3-b2f08335b9e5>\"\u001b[1;36m, line \u001b[1;32m3\u001b[0m\n\u001b[1;33m print(f'Address: {d['a']} Main Street')\u001b[0m\n\u001b[1;37m ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m invalid syntax\n" ] } ], "source": [ "d = {'a':123,'b':456}\n", "\n", "print(f'Address: {d['a']} Main Street')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Instead, use different styles of quotation marks:" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Address: 123 Main Street\n" ] } ], "source": [ "d = {'a':123,'b':456}\n", "\n", "print(f\"Address: {d['a']} Main Street\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Minimum Widths, Alignment and Padding\n", "You can pass arguments inside a nested set of curly braces to set a minimum width for the field, the alignment and even padding characters." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Author Topic Pages \n", "Twain Rafting 601\n", "Feynman Physics 95\n", "Hamilton Mythology 144\n" ] } ], "source": [ "library = [('Author', 'Topic', 'Pages'), ('Twain', 'Rafting', 601), ('Feynman', 'Physics', 95), ('Hamilton', 'Mythology', 144)]\n", "\n", "for book in library:\n", " print(f'{book[0]:{10}} {book[1]:{8}} {book[2]:{7}}')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here the first three lines align, except `Pages` follows a default left-alignment while numbers are right-aligned. Also, the fourth line's page number is pushed to the right as `Mythology` exceeds the minimum field width of `8`. When setting minimum field widths make sure to take the longest item into account.\n", "\n", "To set the alignment, use the character `<` for left-align, `^` for center, `>` for right.<br>\n", "To set padding, precede the alignment character with the padding character (`-` and `.` are common choices).\n", "\n", "Let's make some adjustments:" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Author Topic ..Pages\n", "Twain Rafting ....601\n", "Feynman Physics .....95\n", "Hamilton Mythology ....144\n" ] } ], "source": [ "for book in library:\n", " print(f'{book[0]:{10}} {book[1]:{10}} {book[2]:.>{7}}') # here .> was added" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Date Formatting" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "January 27, 2018\n" ] } ], "source": [ "from datetime import datetime\n", "\n", "today = datetime(year=2018, month=1, day=27)\n", "\n", "print(f'{today:%B %d, %Y}')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For more info on formatted string literals visit https://docs.python.org/3/reference/lexical_analysis.html#f-strings\n", "\n", "***" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Files\n", "\n", "Python uses file objects to interact with external files on your computer. These file objects can be any sort of file you have on your computer, whether it be an audio file, a text file, emails, Excel documents, etc. Note: You will probably need to install certain libraries or modules to interact with those various file types, but they are easily available. (We will cover downloading modules later on in the course).\n", "\n", "Python has a built-in open function that allows us to open and play with basic file types. First we will need a file though. We're going to use some IPython magic to create a text file!\n", "\n", "## Creating a File with IPython\n", "#### This function is specific to jupyter notebooks! Alternatively, quickly create a simple .txt file with Sublime text editor." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Overwriting test.txt\n" ] } ], "source": [ "%%writefile test.txt\n", "Hello, this is a quick test file.\n", "This is the second line of the file." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Python Opening a File\n", "\n", "### Know Your File's Location\n", "\n", "It's easy to get an error on this step:" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "ename": "FileNotFoundError", "evalue": "[Errno 2] No such file or directory: 'whoops.txt'", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mFileNotFoundError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m<ipython-input-9-410403f4f4b4>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mmyfile\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mopen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'whoops.txt'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[1;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'whoops.txt'" ] } ], "source": [ "myfile = open('whoops.txt')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To avoid this error, make sure your .txt file is saved in the same location as your notebook. To check your notebook location, use **pwd**:" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'C:\\\\Users\\\\Mike\\\\NLP-Bootcamp\\\\00-Python-Text-Basics'" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pwd" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Alternatively, to grab files from any location on your computer, simply pass in the entire file path. **\n", "\n", "For Windows you need to use double \\ so python doesn't treat the second \\ as an escape character, a file path is in the form:\n", "\n", " myfile = open(\"C:\\\\Users\\\\YourUserName\\\\Home\\\\Folder\\\\myfile.txt\")\n", "\n", "For MacOS and Linux you use slashes in the opposite direction:\n", "\n", " myfile = open(\"/Users/YourUserName/Folder/myfile.txt\")" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "# Open the text.txt file we created earlier\n", "my_file = open('test.txt')" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<_io.TextIOWrapper name='test.txt' mode='r' encoding='cp1252'>" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "my_file" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "`my_file` is now an open file object held in memory. We'll perform some reading and writing exercises, and then we have to close the file to free up memory.\n", "\n", "### .read() and .seek()" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'Hello, this is a quick test file.\\nThis is the second line of the file.'" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# We can now read the file\n", "my_file.read()" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "''" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# But what happens if we try to read it again?\n", "my_file.read()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This happens because you can imagine the reading \"cursor\" is at the end of the file after having read it. So there is nothing left to read. We can reset the \"cursor\" like this:" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Seek to the start of file (index 0)\n", "my_file.seek(0)" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'Hello, this is a quick test file.\\nThis is the second line of the file.'" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Now read again\n", "my_file.read()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### .readlines()\n", "You can read a file line by line using the readlines method. Use caution with large files, since everything will be held in memory. We will learn how to iterate over large files later in the course." ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['Hello, this is a quick test file.\\n', 'This is the second line of the file.']" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Readlines returns a list of the lines in the file\n", "my_file.seek(0)\n", "my_file.readlines()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "When you have finished using a file, it is always good practice to close it." ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [], "source": [ "my_file.close()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Writing to a File\n", "\n", "By default, the `open()` function will only allow us to read the file. We need to pass the argument `'w'` to write over the file. For example:" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [], "source": [ "# Add a second argument to the function, 'w' which stands for write.\n", "# Passing 'w+' lets us read and write to the file\n", "\n", "my_file = open('test.txt','w+')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<div class=\"alert alert-danger\" style=\"margin: 20px\">**Use caution!**<br>\n", "Opening a file with 'w' or 'w+' *truncates the original*, meaning that anything that was in the original file **is deleted**!</div>" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "24" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Write to the file\n", "my_file.write('This is a new first line')" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'This is a new first line'" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Read the file\n", "my_file.seek(0)\n", "my_file.read()" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [], "source": [ "my_file.close() # always do this when you're done with a file" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Appending to a File\n", "Passing the argument `'a'` opens the file and puts the pointer at the end, so anything written is appended. Like `'w+'`, `'a+'` lets us read and write to a file. If the file does not exist, one will be created." ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "23" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "my_file = open('test.txt','a+')\n", "my_file.write('\\nThis line is being appended to test.txt')\n", "my_file.write('\\nAnd another line here.')" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "This is a new first line\n", "This line is being appended to test.txt\n", "And another line here.\n" ] } ], "source": [ "my_file.seek(0)\n", "print(my_file.read())" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [], "source": [ "my_file.close()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Appending with `%%writefile`\n", "Jupyter notebook users can do the same thing using IPython cell magic:" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Appending to test.txt\n" ] } ], "source": [ "%%writefile -a test.txt\n", "\n", "This is more text being appended to test.txt\n", "And another line here." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Add a blank space if you want the first line to begin on its own line, as Jupyter won't recognize escape sequences like `\\n`" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Aliases and Context Managers\n", "You can assign temporary variable names as aliases, and manage the opening and closing of files automatically using a context manager:" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "This is a new first line\n", "\n" ] } ], "source": [ "with open('test.txt','r') as txt:\n", " first_line = txt.readlines()[0]\n", " \n", "print(first_line)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Note that the `with ... as ...:` context manager automatically closed `test.txt` after assigning the first line of text to first_line:" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "ename": "ValueError", "evalue": "I/O operation on closed file.", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m<ipython-input-28-39ca4397fa0a>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mtxt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mread\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[1;31mValueError\u001b[0m: I/O operation on closed file." ] } ], "source": [ "txt.read()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Iterating through a File" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "This is a new first line\n", "This line is being appended to test.txt\n", "And another line here.\n", "This is more text being appended to test.txt\n", "And another line here." ] } ], "source": [ "with open('test.txt','r') as txt:\n", " for line in txt:\n", " print(line, end='') # the end='' argument removes extra linebreaks" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Great! Now you should be familiar with formatted string literals and working with text files.\n", "## Next up: Working with PDF Text" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.7" } }, "nbformat": 4, "nbformat_minor": 2 }
apache-2.0
maelick/GitHub-Analysis
IWSECO2015/notebooks/Data - BioConductor.ipynb
1
8845
{ "metadata": { "name": "", "signature": "sha256:55f3e1b78f318d260ba02f383d8d685e2ba4f4880eba59f41eb6253767c51f32" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "heading", "level": 1, "metadata": {}, "source": [ "Packages R sur BioConductor" ] }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "R\u00e9cup\u00e9ration de la liste de packages" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Une liste des packages est disponible sur la page :\n", "http://bioconductor.org/packages/release/BiocViews.html#___Software\n", "\n", "Cette liste reprend l'ensemble des paquets disponibles, avec leur nom, leurs maintainers ainsi qu'une courte description. Il est tr\u00e8s simple d'extraire manuellement la liste des packages avec un simple copier-coller, et d'ensuite de parser le r\u00e9sultat. On peut \u00e9galement automatiser ce processus. Il y a cependant un d\u00e9tail important : la liste g\u00e9n\u00e9r\u00e9e sur la page l'est via du Javascript, ce qui implique qu'on ne peut pas \"simplement\" ouvrir la page distante et parser son contenu. \n", "\n", "Un rapide petit tour dans la source de la page permet de voir que les donn\u00e9es sont en r\u00e9alit\u00e9 extraites depuis un fichier `packages.js` (http://bioconductor.org/packages/json/3.0/bioc/packages.js). Ce fichier contient une d\u00e9claration de variable Javascript `data_annotation_packages` dont la valeur est un dictionnaire des packages disponibles. Vu que c'est essentiellement du JSON, on peut directement le parser avec Python :" ] }, { "cell_type": "code", "collapsed": false, "input": [ "import requests\n", "import json\n", "import BeautifulSoup as bs\n", "\n", "\n", "URL = 'http://bioconductor.org/packages/json/3.0/bioc/packages.js'\n", "PKG_URL = 'http://bioconductor.org/packages/release/bioc/html/{}.html'\n", "OUTPUT_FILENAME = '../data/bioconductor_description.csv'\n", "\n", "\"\"\"\n", "URL = 'http://bioconductor.org/packages/json/3.0/data/annotation/packages.js'\n", "PKG_URL = 'http://bioconductor.org/packages/release/data/annotation/html/{}.html'\n", "OUTPUT_FILENAME = '../data/bioconductor_annotation_description.csv'\n", "\n", "\n", "URL = 'http://bioconductor.org/packages/json/3.0/data/experiment/packages.js'\n", "PKG_URL = 'http://bioconductor.org/packages/release/data/experiment/html/{}.html'\n", "OUTPUT_FILENAME = '../data/bioconductor_experiment_description.csv'\n", "\"\"\"\n", "\n", "content = requests.get(URL).content\n", "\n", "# Remove variable declaration\n", "_, content = content[:-1].split(' = ', 1) \n", "\n", "# JSON\n", "content = json.loads(content)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 21 }, { "cell_type": "markdown", "metadata": {}, "source": [ "La structure obtenue est assez simple : une cl\u00e9 \"content\" qui contient une liste des entr\u00e9es. Chaque entr\u00e9e est alors une liste de 3 \u00e9l\u00e9ments : un nom de package, une liste de maintainers et une courte description." ] }, { "cell_type": "code", "collapsed": false, "input": [ "content['content'][:3]" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 22, "text": [ "[[u'a4',\n", " u'Tobias Verbeke <[email protected]>, Willem Ligtenberg <[email protected]>',\n", " u'Automated Affymetrix Array Analysis Umbrella Package'],\n", " [u'a4Base',\n", " u'Tobias Verbeke <[email protected]>, Willem Ligtenberg <[email protected]>',\n", " u'Automated Affymetrix Array Analysis Base Package'],\n", " [u'a4Classif',\n", " u'Tobias Verbeke <[email protected]>, Willem Ligtenberg <[email protected]>',\n", " u'Automated Affymetrix Array Analysis Classification Package']]" ] } ], "prompt_number": 22 }, { "cell_type": "markdown", "metadata": {}, "source": [ "On peut donc ais\u00e9ment obtenir la liste des noms de packages pr\u00e9sents sur Bioconductor :" ] }, { "cell_type": "code", "collapsed": false, "input": [ "packages = map(lambda x: x[0], content['content'])" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 23 }, { "cell_type": "code", "collapsed": false, "input": [ "packages[:10]" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 24, "text": [ "[u'a4',\n", " u'a4Base',\n", " u'a4Classif',\n", " u'a4Core',\n", " u'a4Preproc',\n", " u'a4Reporting',\n", " u'ABarray',\n", " u'ABSSeq',\n", " u'aCGH',\n", " u'ACME']" ] } ], "prompt_number": 24 }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "R\u00e9cup\u00e9ration des fichiers `DESCRIPTION` des packages" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Pour chaque package de nom `NAME`, une page http://bioconductor.org/packages/release/bioc/html/NAME.html est disponible. Par exemple, pour le paquet `a4`, la page http://bioconductor.org/packages/release/bioc/html/a4.html reprend une s\u00e9rie d'informations concernant l'installation et l'usage du paquet. En particulier, cette page r\u00e9f\u00e9rence aussi, dans sa section *Details*, une s\u00e9rie de couples cl\u00e9/valeur correspondant aux entr\u00e9es du fichier `DESCRIPTION` que l'on retrouve dans les packages R. \n", "\n", "Nous allons utiliser BeautifulSoup pour parser la page et r\u00e9cup\u00e9rer cette information." ] }, { "cell_type": "code", "collapsed": false, "input": [ "def description_for_package(package_name):\n", " \"\"\"\n", " Given a R package name on BioConductor, return a dictionary that contains \n", " every key -> value that can be found in the \"Details\" section of the \n", " related package page (PKG_URL).\n", " \"\"\"\n", " try:\n", " content = requests.get(PKG_URL.format(package_name)).content\n", " soup = bs.BeautifulSoup(content)\n", " table = soup.find(name='table', attrs={'class': 'details'})\n", " data = {}\n", " for row in table.findChildren('tr'):\n", " key, value = row.findChildren('td')\n", " data[key.text] = value.text\n", " return data\n", " except Exception: \n", " print 'Exception while working on', package_name\n", " raise" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 25 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Appliquons cette fonction sur tous les noms de packages repris dans la liste `packages` de l'\u00e9tape pr\u00e9c\u00e9dente, et r\u00e9cup\u00e9rons les r\u00e9sultats. Nous pla\u00e7ons ces r\u00e9sultats dans un dictionnaire, o\u00f9 la cl\u00e9 du dictionnaire est le nom du package, et la valeur de ce dictionnaire est l'information structur\u00e9e r\u00e9cup\u00e9r\u00e9e depuis le site." ] }, { "cell_type": "code", "collapsed": false, "input": [ "packages_data = {}\n", "\n", "for package in packages:\n", " data = description_for_package(package)\n", " packages_data[package] = data" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 26 }, { "cell_type": "markdown", "metadata": {}, "source": [ "On peut maintenant facilement exporter \u00e7a vers un fichier .csv qui sera r\u00e9utilis\u00e9 plus tard. " ] }, { "cell_type": "code", "collapsed": false, "input": [ "import pandas\n", "\n", "pandas.DataFrame.from_dict(packages_data, orient='index').to_csv(OUTPUT_FILENAME)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 27 } ], "metadata": {} } ] }
gpl-2.0
linglaiyao1314/maths-with-python
04-basic-plotting.ipynb
1
83436
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Plotting" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "There are many Python plotting libraries depending on your purpose. However, the standard general-purpose library is `matplotlib`. This is often used through its `pyplot` interface." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from matplotlib import pyplot\n", "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The command `%matplotlib inline` is not a Python command, but an *IPython* command. When using the console, or the notebook, it makes the plots appear inline. You do not want to use this in a plain Python code." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x106805d30>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from math import sin, pi\n", "\n", "x = []\n", "y = []\n", "for i in range(201):\n", " x_point = 0.01*i\n", " x.append(x_point)\n", " y.append(sin(pi*x_point)**2)\n", "\n", "pyplot.plot(x, y)\n", "pyplot.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We have defined two sequences - in this case lists, but tuples would also work. One contains the $x$-axis coordinates, the other the data points to appear on the $y$-axis. A basic plot is produced using the `plot` command of `pyplot`. However, this plot will not automatically appear on the screen, as after plotting the data you may wish to add additional information. Nothing will actually happen until you either save the figure to a file (using `pyplot.savefig(<filename>)`) or explicitly ask for it to be displayed (with the `show` command). When the plot is displayed the program will typically pause until you dismiss the plot.\n", "\n", "This plotting interface is straightforward, but the results are not particularly nice. The following commands illustrate some of the ways of improving the plot:" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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pqTjs2iWz+aPnhwQZHTnEECf87e+/JcvllCnyufVWWe+n8LdE4bRZRoa02aRJ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\nwW0J4seOHdIvg3yOQSUrCz7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"text/plain": [ "<matplotlib.figure.Figure at 0x106836320>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from math import sin, pi\n", "\n", "x = []\n", "y = []\n", "for i in range(201):\n", " x_point = 0.01*i\n", " x.append(x_point)\n", " y.append(sin(pi*x_point)**2)\n", "\n", "pyplot.plot(x, y, marker='+', markersize=8, linestyle=':', \n", " linewidth=3, color='b', label=r'$\\sin^2(\\pi x)$')\n", "pyplot.legend(loc='lower right')\n", "pyplot.xlabel(r'$x$')\n", "pyplot.ylabel(r'$y$')\n", "pyplot.title('A basic plot')\n", "pyplot.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Whilst most of the commands are self-explanatory, a note should be made of the strings line `r'$x$'`. These strings are in LaTeX format, which is *the* standard typesetting method for professional-level mathematics. The `$` symbols surround mathematics. The `r` before the definition of the string is Python notation, not LaTeX. It says that the following string will be \"raw\": that backslash characters should be left alone. Then, special LaTeX commands have a backslash in front of them: here we use `\\pi` and `\\sin`. Most basic symbols can be easily guessed (eg `\\theta` or `\\int`), but there are [useful lists of symbols](http://www.artofproblemsolving.com/wiki/index.php/LaTeX:Symbols), and a [reverse search site](http://detexify.kirelabs.org/classify.html) available. We can also use `^` to denote superscripts (used here), `_` to denote subscripts, and use `{}` to group terms." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "By combining these basic commands with other plotting types (`semilogx` and `loglog`, for example), most simple plots can be produced quickly." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here are some more examples:" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x106a9d978>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from math import sin, pi, exp, log\n", "\n", "x = []\n", "y1 = []\n", "y2 = []\n", "for i in range(201):\n", " x_point = 1.0 + 0.01*i\n", " x.append(x_point)\n", " y1.append(exp(sin(pi*x_point)))\n", " y2.append(log(pi+x_point*sin(x_point)))\n", "\n", "pyplot.loglog(x, y1, linestyle='--', linewidth=4, \n", " color='k', label=r'$y_1=e^{\\sin(\\pi x)}$')\n", "pyplot.loglog(x, y2, linestyle='-.', linewidth=4, \n", " color='r', label=r'$y_2=\\log(\\pi+x\\sin(x))$')\n", "pyplot.legend(loc='lower right')\n", "pyplot.xlabel(r'$x$')\n", "pyplot.ylabel(r'$y$')\n", "pyplot.title('A basic logarithmic plot')\n", "pyplot.show()" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x1069662e8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from math import sin, pi, exp, log\n", "\n", "x = []\n", "y1 = []\n", "y2 = []\n", "for i in range(201):\n", " x_point = 1.0 + 0.01*i\n", " x.append(x_point)\n", " y1.append(exp(sin(pi*x_point)))\n", " y2.append(log(pi+x_point*sin(x_point)))\n", "\n", "pyplot.semilogy(x, y1, linestyle='None', marker='o', \n", " color='g', label=r'$y_1=e^{\\sin(\\pi x)}$')\n", "pyplot.semilogy(x, y2, linestyle='None', marker='^', \n", " color='r', label=r'$y_2=\\log(\\pi+x\\sin(x))$')\n", "pyplot.legend(loc='lower right')\n", "pyplot.xlabel(r'$x$')\n", "pyplot.ylabel(r'$y$')\n", "pyplot.title('A different logarithmic plot')\n", "pyplot.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We will look at more complex plots later, but the [matplotlib documentation](http://matplotlib.org/api/pyplot_summary.html) contains a lot of details, and the [gallery](http://matplotlib.org/gallery.html) contains a lot of examples that can be adapted to fit. There is also an [extremely useful document](http://nbviewer.ipython.org/github/jrjohansson/scientific-python-lectures/blob/master/Lecture-4-Matplotlib.ipynb) as part of [Johansson's lectures on scientific Python](https://github.com/jrjohansson/scientific-python-lectures)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Exercise: Logistic map" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The logistic map builds a sequence of numbers $\\{ x_n \\}$ using the relation\n", "\n", "\\begin{equation}\n", " x_{n+1} = r x_n \\left( 1 - x_n \\right),\n", "\\end{equation}\n", "\n", "where $0 \\le x_0 \\le 1$." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Exercise 1\n", "\n", "Write a program that calculates the first $N$ members of the sequence, given as input $x_0$ and $r$ (and, of course, $N$)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Exercise 2\n", "\n", "Fix $x_0=0.5$. Calculate the first 2,000 members of the sequence for $r=1.5$ and $r=3.5$. Plot the last 100 members of the sequence in both cases.\n", "\n", "What does this suggest about the long-term behaviour of the sequence?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Exercise 3\n", "\n", "Fix $x_0 = 0.5$. For each value of $r$ between $1$ and $4$, in steps of $0.01$, calculate the first 2,000 members of the sequence. Plot the last 1,000 members of the sequence on a plot where the $x$-axis is the value of $r$ and the $y$-axis is the values in the sequence. Do not plot lines - just plot markers (e.g., use the `'k.'` plotting style)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Exercise 4\n", "\n", "For iterative maps such as the logistic map, one of three things can occur:\n", "\n", "1. The sequence settles down to a *fixed point*.\n", "2. The sequence rotates through a finite number of values. This is called a *limit cycle*.\n", "3. The sequence generates an infinite number of values. This is called *deterministic chaos*.\n", "\n", "Using just your plot, or new plots from this data, work out approximate values of $r$ for which there is a transition from fixed points to limit cycles, from limit cycles of a given number of values to more values, and the transition to chaos." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Exercise: Mandelbrot" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The Mandelbrot set is also generated from a sequence, $\\{ z_n \\}$, using the relation\n", "\n", "\\begin{equation}\n", " z_{n+1} = z_n^2 + c, \\qquad z_0 = 0.\n", "\\end{equation}\n", "\n", "The members of the sequence, and the constant $c$, are all complex. The point in the complex plane at $c$ is in the Mandelbrot set only if the $|z_n| < 2$ for all members of the sequence. In reality, checking the first 100 iterations is sufficient.\n", "\n", "**Note**: the Python notation for a complex number $x + \\text{i} y$ is `x + yj`: that is, `j` is used to indicate $\\sqrt{-1}$. If you know the values of `x` and `y` then `x + yj` constructs a complex number; if they are stored in variables you can use `complex(x, y)`." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Exercise 1\n", "\n", "Write a function that checks if the point $c$ is in the Mandelbrot set." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Exercise 2\n", "\n", "Check the points $c=0$ and $c=\\pm 2 \\pm 2 \\text{i}$ and ensure they do what you expect. (What *should* you expect?)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Exercise 3\n", "\n", "Write a function that, given $N$\n", "\n", "1. generates an $N \\times N$ grid spanning $c = x + \\text{i} y$, for $-2 \\le x \\le 2$ and $-2 \\le y \\le 2$;\n", "2. returns an $N\\times N$ array containing one if the associated grid point is in the Mandelbrot set, and zero otherwise." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Exercise 4\n", "\n", "Using the function `imshow` from `matplotlib`, plot the resulting array for a $100 \\times 100$ array to make sure you see the expected shape." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Exercise 5\n", "\n", "Modify your functions so that, instead of returning whether a point is inside the set or not, it returns the logarithm of the number of iterations it takes. Plot the result using `imshow` again." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Exercise 6\n", "\n", "Try some higher resolution plots, and try plotting only a section to see the structure. **Note** this is not a good way to get high accuracy close up images!" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.4.3" }, "nbconvert": { "title": "Plotting basics" } }, "nbformat": 4, "nbformat_minor": 0 }
mit
jakobrunge/tigramite
tutorials/tigramite_tutorial_latent-pcmci.ipynb
1
317512
{ "cells": [ { "cell_type": "markdown", "id": "302c8f3d", "metadata": {}, "source": [ "# Latent causal discovery with TIGRAMITE" ] }, { "cell_type": "markdown", "id": "f073cf6e", "metadata": {}, "source": [ "TIGRAMITE is a time series analysis python module. It allows to reconstruct causal graphical models from discrete or continuously-valued time series based on the PCMCI framework and create high-quality plots of the results.\n", "\n", "This tutorial explains the **Latent-PCMCI (LPCMCI) algorithm**, which is implemented as the function `LPCMCI.run_lpcmci`. In contrast to the [PCMCI](https://github.com/jakobrunge/tigramite/blob/master/tutorials/tigramite_tutorial_basics.ipynb) and [PCMCIplus](https://github.com/jakobrunge/tigramite/blob/master/tutorials/tigramite_tutorial_pcmciplus.ipynb) algorithms, respectively implemented as `PCMCI.run_pcmci` and `PCMCI.run_pcmciplus`, LPCMCI allows for unobserved (aka latent) time series.\n", "\n", "**Note:**\n", "This method is still experimental since the default settings of hyperparameters are still being fine-tuned. Feedback on this matter is kindly appreciated.\n", "\n", "---\n", "**Publication on LPCMCI:**\n", "Gerhardus, Andreas and Runge, Jakob (2020). High-recall causal discovery for autocorrelated time series with latent confounders. In Larochelle, H., Ranzato, M., Hadsell, R., Balcan, M. F., and Lin, H., editors, *Advances in Neural Information Processing Systems*, volume 33, pages 12615–12625. Curran Associates, Inc. [https://proceedings.neurips.cc/paper/2020/file/94e70705efae423efda1088614128d0b-Paper.pdf](https://proceedings.neurips.cc/paper/2020/file/94e70705efae423efda1088614128d0b-Paper.pdf).\n", "\n", "---\n", "\n", "The structure of this tutorial is as follows:\n", "1. Section 1 explains the interpretation of the causal graphical models that are being learned by LPCMCI.\n", "2. Section 2 gives an introduction into how LPCMCI works and explains its essential parameters and output.\n", "3. Section 3 explains the practical use of LPCMCI by showing an example application on synthetic data." ] }, { "cell_type": "code", "execution_count": 1, "id": "19227d66", "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "\n", "from matplotlib import pyplot as plt\n", "%matplotlib inline \n", "\n", "import tigramite\n", "from tigramite import data_processing as pp\n", "from tigramite.toymodels import structural_causal_processes as toys\n", "from tigramite import plotting as tp\n", "from tigramite.lpcmci import LPCMCI\n", "from tigramite.pcmci import PCMCI\n", "from tigramite.independence_tests import ParCorr #, GPDC, CMIknn, CMIsymb" ] }, { "cell_type": "markdown", "id": "72068a8a", "metadata": {}, "source": [ "## 1 Structural causal processes and their graphical representation" ] }, { "cell_type": "markdown", "id": "04b94931", "metadata": {}, "source": [ "We are interested in learning the causal structure underlying complex dynamical systems. The corresponding time series $\\mathbf{V}_t=(V^1_t,\\ldots,V^N_t)$ is assumed to follow a structural causal process, i.e., to be of the form\n", "\n", "$$\\begin{align} \\label{eq:causal_model} V^j_t &= f_j\\left(\\mathcal{P}(V^j_t),\\,\\eta^j_t\\right) \\end{align}$$\n", "\n", "where $f_j$ is some arbitrary measurable function with non-trivial dependencies on all its arguments, $\\eta^j_t$ represents mutually ($i\\neq j$) and serially ($t'\\neq t$) independent dynamical noise, and $\\mathcal{P}(V^j_t) \\subset \\mathbf{V}^-_{t+1}=(\\mathbf{V}_{t}, \\mathbf{V}_{t-1},\\ldots){\\setminus} \\{V^j_t\\}$. Imporantly, the equation is asserted to have causal meaning in the sense that it represents the physical mechanism by which the value of $V^j_t$ is determined from the values of variables in $\\mathcal{P}(V^j_t)$ together with the value of the dynamical noise $\\eta^j_t$.$^a$ This is why variables variables in $\\mathcal{P}(V^j_t)$ are referred to as *causal parents* of $V^j_t$. We further require that $V^i_{t-\\tau} \\in \\mathcal{P}(V^j_{t})$ if and only if $V^i_{t-\\tau - \\Delta t} \\in \\mathcal{P}(V^j_{t-\\Delta t})$, a property referred to as *causal stationarity*, and that there are no cyclic causal relationships (both these properties have an intuitive graphical meaning, see subsection 1.1 below).\n", "\n", "The goal of LPCMCI is to learn the time series DPAG of the data-generating structural causal process, which is a graph that represents partial knowledge of the causal ancestral relationships among only the observed variables. These graphs are introduced and explained in the remainder of this section.\n", "\n", "Footnotes:\\\n", "$^a$For a more detailed explanation of this point we refer to the literature on structural causal models, see e.g. the textbooks [1] and [2] (the references are given at the bottom of this notebook)." ] }, { "cell_type": "markdown", "id": "aa04433d", "metadata": {}, "source": [ "### 1.1 Time series DAGs" ] }, { "cell_type": "markdown", "id": "08d8fa40", "metadata": {}, "source": [ "The causal *parentships* specified by a structural causal process can conveniently be represented by a directed acyclic graph (DAG) $\\mathcal{G}$ that\n", "1. has one vertex (aka node) per variable $V^j_t$ and\n", "2. an edge (aka link) $V^i_{t-\\tau} {\\rightarrow} V^j_t$ if and only if $V^i_{t-\\tau} \\in \\mathcal{P}(V^j_t)$.\n", "\n", "It is acyclic precisely because by assumption there are no cyclic causal relationships, and its structure is repetitive in time due to the causal stationarity. In other tutorials in TIGRAMITE $\\mathcal{G}$ is also referred to as *time series graph*.\n", "\n", "An edge $V^i_{t-\\tau} {\\rightarrow} V^j_t$ with $\\tau > 0$ is referred to as *lagged* and the integer $\\tau$ is its *lag*. Edges $V^i_{t} {\\rightarrow} V^j_t$ are called *contemporaneous*. The *order* of a process, denoted by $p_{\\text{ts}}$, is the maximum lag.\n", "\n", "For illustration, here and in the subsequent discussions, we consider the following linear structural causal process of order $p_{\\text{ts}}= 2$ with four component time series as a running example:\n", "\n", "$$\n", "\\begin{align}\n", "V^1_t &= 0.9 V^1_{t-1} + 0.6 V^2_{t} + \\eta^1_t\\\\\n", "V^2_t &= \\eta^2_t \\\\\n", "V^3_t &= 0.9 V^3_{t-1} + 0.4 V^2_{t-1} + \\eta^3_t\\\\\n", "V^4_t &= 0.9 V^4_{t-1} - 0.4 V^3_{t-2} + \\eta^4_T\n", "\\end{align}\n", "$$\n", "\n", "The following figure shows the associated time series DAG $\\mathcal{G}$:\n", "\n", "<img src=\"figures/ts_DAG.png\" width=300 height=300 />\n", "\n", "The horizontal dots on the left and right indicate that this graph in principle extends to the infinity past and future. However, due to the repetitive structure as imposed by causal stationarity it is sufficient to restrict to a time window at least as large as $[t-p_{\\text{ts}}, t]$ (for the above example this means it would have been sufficient to show one time step less). By convention we only draw those edges that are fully contained within the shown time window: For example, the edge $V^1_{t-1} {\\rightarrow} V^3_{t+1}$ is not drawn despite $V^1_{t-1}$ being in the shown time window $[t-3, t]$ because $V^3_{t+1}$ is outside this time window." ] }, { "cell_type": "markdown", "id": "261a9f09", "metadata": {}, "source": [ "### 1.2 Time series DMAGs" ] }, { "cell_type": "markdown", "id": "e04059fe", "metadata": {}, "source": [ "The setting of LPCMCI, which distinguishes it from PCMCI and PCMCIplus, is that a subset of the component time series is allowed to be unobserved. In other words: The set of component time series $V^1, \\ldots, V^N$ splits into a set of observed time series $X^1, \\ldots, X^{N_X}$ with $N_X \\geq 1$ and a set of unobserved time series $L^1, \\ldots, L^{N_L}$ with $N_L \\geq 0$ and $N = N_X + N_L$.\n", "\n", "This raises the question as how to represent the causal relationships of the underlying process in a graph with vertices only for the observed variables. One approach, which LPCMCI is based on, is to employ *directed maximal ancestral graphs (DMAGs)*. These are a type of directed mixed graphs and, thus, can have directed edges $X {\\rightarrow} Y$ and bidirected edges $X {\\leftrightarrow} Y$. They are a specialization of the yet more general class of maximal ancestral graphs (MAGs) introduced in [3], which in addition allow to represent selection bias. For LPCMCI, however, the absence of selection bias is assumed.\n", "\n", "The basic idea is as follows:\\\n", "To a given DAG $\\mathcal{G}$ with a given subset of unobserved variables one can associate a unique DMAG $\\mathcal{M}(\\mathcal{G})$ over the observed variables that has the following properties:\n", "\n", "1. **Adjacencies:**\\\n", "There is an edge between vertices $X$ and $Y$, i.e., $X {\\rightarrow} Y$ or $X {\\leftarrow} Y$ or $X {\\leftrightarrow} X$, if and only if the information flow between them cannot be blocked by conditioning on any subset of observed variables, i.e., if and only if there is no subset of observed variables conditional on which $X$ and $Y$ become independent.\n", "2. **Edge types:**\n", " 1. $X {\\rightarrow} Y$ implies that in $\\mathcal{G}$ there is a directed path from $X$ to $Y$. Because $\\mathcal{G}$ is acyclic, this further implies that there is no directed path from $Y$ to $X$.\n", " 2. $X {\\leftrightarrow} Y$ implies that in $\\mathcal{G}$ there neither is a directed path from $X$ to $Y$ nor a directed path from $Y$ to $X$.\n", " \n", "Since the DAG $\\mathcal{G}$ carries causal meaning, in the sense that a directed edge signifies causal parentship, also the associated DMAG $\\mathcal{M}(\\mathcal{G})$ carries causal meaning:\n", "1. $X {\\rightarrow} Y$ says that\n", " 1. $X$ is a (potentially indirect) cause of $Y$\n", " 2. $Y$ does not cause $X$\n", "2. $X {\\leftrightarrow} Y$ says that\n", " 1. $X$ does not cause $Y$\n", " 2. $Y$ does not cause $X$\n", " 2. $X$ and $Y$ are subject to unobserved confounding, i.e., there is an unobserved variable $Z$ that causes both $X$ and $Y$ (this follows because else there would not be any edge between $X$ and $Y$).\n", "\n", "In other words: The DMAG $\\mathcal{M}(\\mathcal{G})$ represents the *causal ancestral relationships* of the underlying data generating process. The absence and presence of an edge between a pair of variables do, however, not have a straightforward causal interpretation.\n", "\n", "In the time series setting considered here one additional aspect comes into play: Not the entire past and future but only a *finite* number of time steps can be observed. This amounts to the choice of an observed time window $[t-\\tau_{\\text{max}}, t]$, where $\\tau_{\\text{max}} \\geq 0$ is referred to as *maximum considered time lag*.$^a$ Moreover, the repetitive structure of the underyling DAG $\\mathcal{G}$ can be used to also impose a repetitive structure on the associated DMAG which is then extrapolated to the time steps outside of the observed time window. As will be explained in more detail in subsection 1.2.2, the resulting DMAG in general depends on the choice of $\\tau_{\\text{max}}$. For this reason we employ the notation $\\mathcal{M}^{\\tau_{\\text{max}}}(\\mathcal{G})$ when specifically referring to the time series setting.\n", "\n", "To illustrate the previous discussion, let us return to the running example and say that the component time series $V^2$ is unobserved while $V^1$, $V^3$, and $V^4$ are observed. The corresponding time series DMAG $\\mathcal{M}^2(\\mathcal{G})$, so with the choice $\\tau_{\\text{max}} = 2$, is shown on the right-hand-side of the following figure:\n", "\n", "<img src=\"figures/ts_DMAG.png\" width=600 height=300 />\n", "\n", "Note that the unobserved variable $L^1_{t-1}$ confounds the observed variables $X^1_{t-1}$ and $X^2_t$ by means of the path $X^1_{t-1} {\\leftarrow} L^1_{t-1} {\\rightarrow} X^2_{t}$. This introduces a dependence between $X^1_{t-1}$ and $X^2_t$ that could only be blocked by conditionig on $L^1_{t-1}$, which cannot be done because $L^1_{t-1}$ is unobserved. Hence, $X^1_{t-1}$ and $X^2_t$ are adjacent in $\\mathcal{M}(\\mathcal{G})$. Since in $\\mathcal{G}$ there neither is a directed path from $X^1_{t-1}$ to $X^2_t$ nor from $X^2_t$ to $X^1_{t-1}$ --- the latter is impossible anyway because there is no causal influence backwards in time as enforced from the outset by restricting the set $\\mathcal{P}(V^j_t)$ to the present and past of $V^j_t$ --- the edge between them is bidirected, i.e., $X^1_{t-1} {\\leftrightarrow} X^2_t$ in $\\mathcal{M}^2(\\mathcal{G})$.\n", "\n", "Footnotes:\\\n", "$^a$Note that $\\tau_{\\text{max}}$ is NOT equivalent to the process order $p_{\\text{ts}}$. The word \"considered\" in \"maximum considered time lag\" is supposed to stress the distinction." ] }, { "cell_type": "markdown", "id": "a4315135", "metadata": {}, "source": [ "#### 1.2.1 More details on the interpretation of DMAGs" ] }, { "cell_type": "markdown", "id": "48bdc402", "metadata": {}, "source": [ "The interpretation of DMAGs can sometimes be difficult, especially concerning directed edges. To avoid confusion, we stress what a DMAG $\\mathcal{M}(\\mathcal{G})$ does NOT entail:\n", "1. A directed edge $X {\\rightarrow} Y$ does NOT say that $X$ is a causal parent of $Y$ in $\\mathcal{G}$. Rather the directed path from $X$ to $Y$ may contain more than a single edge, i.e., the causal influence of $X$ on $Y$ may be indirect.\n", "2. A directed edge $X {\\rightarrow} Y$ does NOT say that $X$ and $Y$ are not subject to unobserved confounding. Rather, in addition to the directed path(s) from $X$ to $Y$ in $\\mathcal{G}$ there may be one or more unobserved variables $Z$ that cause both $X$ and $Y$. (However: For certain directed edges it *is* possible to infer the absence of unobserved confounding, see further below in the current subsection 1.2.1).\n", "\n", "Both these points are illustrated by the following example, where the left-hand side shows a time series DAG $\\mathcal{G}$ and the right-hand side its corresponding time series DMAG $\\mathcal{M}^2(\\mathcal{G})$:\n", "\n", "<img src=\"figures/ts_DMAG_2.png\" width=600 height=300 />\n", "\n", "Blocking the dependence between $X^3_{t-1}$ and $X^1_t$ introduced by the path $X^3_{t-1} {\\rightarrow} X^2_t {\\rightarrow} X^1_t$ requires to condition on $X^2_t$. This, however, introduces a dependence along the path $X^3_{t-1} {\\rightarrow} X^2_t {\\leftarrow} L^1_{t-1} {\\rightarrow} X^1_t$. Therefore, there is no set of observed variables conditional on which $X^3_{t-1}$ and $X^1_t$ become independent and, thus, they are adjacent in $\\mathcal{M}^2(\\mathcal{G})$. Due to the directed path $X^3_{t-1} {\\rightarrow} X^2_t {\\rightarrow} X^1_t$ this edge is directed, i.e., $X^3_{t-1} {\\rightarrow} X^1_t$ in $\\mathcal{M}^2(\\mathcal{G})$. Note, however, that $X^3_{t-1}$ is not a causal parent of $X^1_t$ in $\\mathcal{G}$, thus illustrating the first point in above list. The second point is illustrated by the edge $X^2_t {\\rightarrow} X^1_t$, which is confounded by the unobserved variable $L^1_{t-1}$ through the path $X^2_t {\\leftarrow} L^1_{t-1} {\\rightarrow} X^1_t$.\n", "\n", "However, *under certain conditions it is possible* to infer from $X {\\rightarrow} Y$ in $\\mathcal{M}(\\mathcal{G})$ that $X$ and $Y$ are not subject to unobserved confounding. Such directed edges were termed *visible* in [4]. A sufficient but not necessary condition for visibility is the following:\n", "1. If there is a third variable $Z$ such that $Z {\\rightarrow} X {\\rightarrow} Y$ or $Z {\\leftrightarrow} X {\\rightarrow} Y$ in $\\mathcal{M}(\\mathcal{G})$ and $Z$ and $Y$ are not adjacent in $\\mathcal{M}(\\mathcal{G})$, then the edge $X {\\rightarrow} Y$ is visible, i.e., $X$ and $Y$ are not subject to unobserved confounding.\n", "\n", "Note that for the edge $X^2_{t} {\\rightarrow} X^1_t$ in the previous example this condition is indeed not met." ] }, { "cell_type": "markdown", "id": "291fda14", "metadata": {}, "source": [ "#### 1.2.2 Effect of the maximum considered time lag $\\tau_{\\text{max}}$" ] }, { "cell_type": "markdown", "id": "460bc2ec", "metadata": {}, "source": [ "As mentioned above, the time series DMAG $\\mathcal{M}^{\\tau_{\\text{max}}}(\\mathcal{G})$ can depend on the choice of the maximum considered time lag $\\tau_{\\text{max}}$. To illustrate this, the right-hand side of the following figure shows the time series DMAG $\\mathcal{M}^1(\\mathcal{G})$, so for $\\tau_{\\text{max}} = 1$, associated to our running example.\n", "\n", "<img src=\"figures/ts_DMAG_a.png\" width=600 height=300 />\n", "\n", "Compare this with the corresponding time series DMAG $\\mathcal{M}^2(\\mathcal{G})$, which is depicted in section 1.2 before subsection 1.2.1. One difference is that in $\\mathcal{M}^1(\\mathcal{G})$ there is no edge between $X^2_{t-2}$ and $X^3_t$, while in $\\mathcal{M}^2(\\mathcal{G})$ there is $X^2_{t-2} {\\rightarrow} X^3_t$. The reason is clear: Since $\\mathcal{M}^1(\\mathcal{G})$ is based on an observed time window $[t-\\tau_{\\text{max}}, 1] = [t-1, 1]$, it cannot contain edges with a lag larger than $\\tau_{\\text{max}} = 1$ (conforming with the term \"maximum considered time lag\"). Moreover, the smaller observed time window leads to more dependence-inducing path that cannot be blocked: First, the path $X^2_{t-1} {\\leftarrow} X^2_{t-2} {\\rightarrow} X^3_t$ cannot be blocked because this would require to condition on $X^2_{t-2}$. This can, however, not be done because $X^2_{t-2}$ is not within the observed time window $[t-1, t]$. This leads to the edge $X^2_{t-1} {\\leftrightarrow} X^3_t$ in $\\mathcal{M}^1(\\mathcal{G})$. Second and similarly, the path $X^1_{t-1} {\\leftarrow} X^1_{t-2} {\\leftarrow} X^1_{t-3} {\\leftarrow} L^1_{t-3} {\\rightarrow} X^2_{t-2} {\\rightarrow} X^3_t$ cannot be blocked and hence there is the edge $X^1_{t-1} {\\leftrightarrow} X^3_t$ in $\\mathcal{M}^1(\\mathcal{G})$.\n", "\n", "While in this example $\\tau_{\\text{max}} = 1 < p_{\\text{ts}}$, we stress that this condition is not necessary to observe changes in $\\mathcal{M}^{\\tau_{\\text{max}}}(\\mathcal{G})$ with $\\tau_{\\text{max}}$. For more complicated $\\mathcal{G}$ there may be difference between $\\mathcal{M}^{\\tau_{\\text{max}}}(\\mathcal{G})$ and $\\mathcal{M}^{\\tau^\\prime_{\\text{max}}}(\\mathcal{G})$ even though $\\tau_{\\text{max}} \\geq p_{\\text{ts}}$ and $\\tau^\\prime_{\\text{max}} \\geq p_{\\text{ts}}$." ] }, { "cell_type": "markdown", "id": "ccca3e5f", "metadata": {}, "source": [ "### 1.3 Time series DPAGs" ] }, { "cell_type": "markdown", "id": "a6f13eb6", "metadata": {}, "source": [ "At this point the task can be phrased as learning time series DMAGs from observations of the observed time series. LPCMCI does this within the constraint-based approach to causal discovery that utilizes (conditional) independencies in the data, which are tested for by statistical means.\n", "\n", "This is, however, an under-determined problem because distinct DMAGs can give rise to the exact same set of (conditional) independencies --- a phenomenon referred to as *Markov equivalence*, which is not specific to DMAGs but rather applies to most probabilistic graphical models. As a consequence, it is without further assumptions in general not possible to uniquely learn the DMAG $\\mathcal{M}(\\mathcal{G})$ that represents the causal ancestral relationships of the underlying data generating process but rather only a set of candidate time series DMAGs $\\mathcal{M}_1, \\ldots, \\mathcal{M}_m$ that includes $\\mathcal{M}(\\mathcal{G})$ and which constitutes the *Markov equivalence class* of $\\mathcal{M}(\\mathcal{G})$. This means that only those features shared by all members of the Markov equivalence class of $\\mathcal{M}(\\mathcal{G})$ can be learned. These shared features can in turn be represented by *directed partial ancestral graphs (DPAGs)*, which are a specialization$^a$ of partial ancestral graphs (PAGs) that are used to represent Markov equivalence classes of MAGs [5, 6].\n", "\n", "The basic idea is as follows:\\\n", "All members of the Markov equivalence class agree on adjacencies --- i.e., $X$ and $Y$ are connected by an edge in $\\mathcal{M}(\\mathcal{G})$ if and only if they are connected by an edge in all members its Markov equivalence class --- but the members may differ with regard to the edge types: For example, if $X {\\rightarrow} Y$ is in $\\mathcal{M}(\\mathcal{G})$ then there could be a member in the Markov equivalence class of $\\mathcal{M}(\\mathcal{G})$ in which $X {\\leftarrow} Y$ or $X {\\leftrightarrow} Y$ instead. These ambiguities are represented explicitly by two new edge types, namely partially directed edges $X {\\circ\\!{\\rightarrow}} Y$ and non-directed edges $X {\\circ\\!{-}\\!\\circ} Y$. The DPAG $\\mathcal{P}(\\mathcal{G})$ corresponding to $\\mathcal{M}(\\mathcal{G})$ is constructed as follows:\n", "1. **Adjacencies:**\\\n", "There is an edge between vertices $X$ and $Y$ if and only if there is an edge between them in $\\mathcal{M}(\\mathcal{G})$.\n", "2. **Edge types:**\n", " 1. $X {\\rightarrow} Y$ implies that $X {\\rightarrow} Y$ in all members of the Markov equivalence class of $\\mathcal{M}(\\mathcal{G})$. In particular, $X {\\rightarrow} Y$ in $\\mathcal{M}(\\mathcal{G})$.\n", " 2. $X {\\leftrightarrow} Y$ implies that $X {\\leftrightarrow} Y$ in all members of the Markov equivalence class of $\\mathcal{M}(\\mathcal{G})$. In particular, $X {\\leftrightarrow} Y$ in $\\mathcal{M}(\\mathcal{G})$.\n", " 3. $X {\\circ\\!{\\rightarrow}} Y$ implies that all of the following holds:\n", " 1. In all members of the Markov equivalence class of $\\mathcal{M}(\\mathcal{G})$ there is $X {\\rightarrow} Y$ or $X {\\leftrightarrow} Y$. In particular, $X {\\rightarrow} Y$ or $X {\\leftrightarrow} Y$ in $\\mathcal{M}(\\mathcal{G})$.\n", " 2. There is a member $\\mathcal{M}_1$ of the Markov equivalence class of $\\mathcal{M}(\\mathcal{G})$ such that $X {\\rightarrow} Y$ in $\\mathcal{M}_1$.\n", " 3. There is a member $\\mathcal{M}_2$ of the Markov equivalence class of $\\mathcal{M}(\\mathcal{G})$ such that $X {\\leftrightarrow} Y$ in $\\mathcal{M}_2$.\n", " 4. $X {\\circ\\!{-}\\!\\circ} Y$ implies that all of the following holds:\n", " 1. There is a member $\\mathcal{M}_1$ of the Markov equivalence class of $\\mathcal{M}(\\mathcal{G})$ such that $X {\\rightarrow} Y$ in $\\mathcal{M}_1$.\n", " 2. There is a member $\\mathcal{M}_2$ of the Markov equivalence class of $\\mathcal{M}(\\mathcal{G})$ such that $X {\\leftarrow} Y$ in $\\mathcal{M}_2$.\n", " \n", "Since $\\mathcal{M}(\\mathcal{G})$ carries causal meaning, in the sense that its edges signify causal ancestral relationships, also the associated DPAG $\\mathcal{P}(\\mathcal{G})$ carries causal meaning:\n", "1. $X \\rightarrow Y$ says the same as in $\\mathcal{M}(\\mathcal{G})$, i.e.,\n", " 1. $X$ is a (potentially indirect) cause of $Y$\n", " 2. $Y$ does not cause $X$\n", "2. $X \\leftrightarrow Y$ says the same as in $\\mathcal{M}(\\mathcal{G})$, i.e.,\n", " 1. $X$ does not cause $Y$\n", " 2. $Y$ does not cause $X$\n", " 2. $X$ and $Y$ are subject to unobserved confounding, i.e., there is an unobserved variable $Z$ that causes both $X$ and $Y$.\n", "3. $X {\\circ\\!{\\rightarrow}} Y$ says that\n", " 1. $X$ may or may not cause $Y$\n", " 2. $Y$ does not cause $X$\n", "4. $X {\\circ\\!{-}\\!\\circ} Y$ says that\n", " 1. $X$ may or may not cause $Y$\n", " 2. $Y$ may or may not cause $X$\n", "\n", "In other words: The DPAG $\\mathcal{P}(\\mathcal{G})$ represents *partial knowledge of the causal ancestral relationships* of the underlying data generating process. As for $\\mathcal{M}(\\mathcal{G})$, the absence and presence of an edge between a pair of variables do not have a straightforward causal interpretation.\n", "\n", "To illustrate the previous discussion, let us return to our running example. The time series DPAG $\\mathcal{P}^{2}(\\mathcal{G})$ associated to $\\mathcal{M}^{2}(\\mathcal{G})$ is shown on the right-hand-side of the following figure:\n", "\n", "<img src=\"figures/ts_DPAG.png\" width=800 height=400 />\n", "\n", "Footnotes:\\\n", "$^a$Specialization to the case of no selection bias." ] }, { "cell_type": "markdown", "id": "ff27532d", "metadata": {}, "source": [ "## 2 LPCMCI: Causal discovery for time series with unobserved confounders" ] }, { "cell_type": "markdown", "id": "bb0eab31", "metadata": {}, "source": [ "The goal of LPCMCI is to learn time series DPAGs $\\mathcal{P}^{\\tau_{\\text{max}}}(\\mathcal{G})$, which as explained in section 1 represent partial knowledge of the causal ancestral relationships of the underlying structural causal process. The value of $\\tau_{\\text{max}}$ is an input parameter chosen by the user.\n", "\n", "This section gives an overview of how the algorithm works. Those eager to get a quick start with applying LPCMCI may skip subsections 2.1 through 2.4 and move to subsections 2.5 and 2.6 directly." ] }, { "cell_type": "markdown", "id": "050540fe", "metadata": {}, "source": [ "### 2.1 Fundamental basis: The FCI algorithm" ] }, { "cell_type": "markdown", "id": "360dddb7", "metadata": {}, "source": [ "The fundamental basis of LPCMCI is the FCI algorithm [7, 8, 6]. This algorithm was developed in the non-temporal setting and learns PAGs, i.e. graphs which represent partial knowledge of causal ancestral relationships in the potential presence of both unobserved confounders and selection bias. The possibility of selection bias can, however, easily be excluded by a few simple modifications. The algorithm works in four steps:\n", "1. **First edge removal phase:**\\\n", "Starting from a fully connected graph, where all edges are of the type $X {\\circ\\!{-}\\!\\circ} Y$, the algorithm iterates through all pairs of adjacent variables $(X, Y)$ and tests whether $X$ and $Y$ are (conditionally) independent given some subset of other variables. If this is the case, the edge $X {\\circ\\!{-}\\!\\circ} Y$ is removed. The tested conditioning sets are subsets of the adjacencies of $X$ and subsets of the adjacencies of $Y$.\n", "2. **Premature collider orientation phase:**\\\n", "Based on a certain orientation rule some of the remaining links are turned from $X {\\circ\\!{-}\\!\\circ} Y$ into $X {\\circ\\!{\\rightarrow}} Y$ or $X {{\\leftarrow}\\!\\circ} Y$ or $X {\\leftrightarrow} Y$.\n", "3. **Second edge remove phase:**\\\n", "The algorithm once more iterates through all pairs of adjacent variables $(X, Y)$ and tests whether $X$ and $Y$ are (conditionally) independent given some subset of other variables. This time, however, the tested conditioning sets are subsets not of the adjacencies of $X$ or the adjacencies of $Y$ but rather of larger sets referred to as $\\text{Possible-D-Sep}(X, Y)$ and $\\text{Possible-D-Sep}(Y, X)$. The identification of these sets rests on the edge type updates made in the previous step.\n", "4. **Rule application phase:**\n", " 1. All remaining edges are turned into $X {\\circ\\!{-}\\!\\circ} Y$ (i.e., all edge type updates of step 2. are undone).\n", " 2. The same orientation rule as in step 2. is applied.\n", " 3. A list of other orientation rules, which turn some of the edges into $X {\\circ\\!{\\rightarrow}} Y$ or $X {{\\leftarrow}\\!\\circ} Y$ or $X {\\rightarrow} Y$ or $X {\\leftarrow} Y$ or $X {\\leftrightarrow} Y$, is exhaustively applied.\n", "\n", "For another, slightly more precise and detailed explanation of the FCI algorithm see section S2 in the [supplementary material to the paper on LPCMCI](https://papers.nips.cc/paper/2020/file/94e70705efae423efda1088614128d0b-Supplemental.pdf).\n", "\n", "Importantly, the constraint-based approach to causal discovery relies on a one-to-one correspondence between (conditional) independencies in the data and the graphical notion of *$d$-separation* in the DAG representing the causal parentships of the data-generating process. That $d$-separations implies the corresponding (conditional) independencies, a property known as the *causal Markov condition*, is already implied by the data being generated according to a structural causal model --- or, in the time series setting considered here, according to a structural causal process. The opposite implication, namely that (conditional) independencies imply the corresponding $d$-separations, is referred to as the *causal faithfulness condition* and amounts to an additional assumption. Intuitively, this excludes *accidental* (conditional) independencies due to counteracting mechanisms whose effects cancel out exactly." ] }, { "cell_type": "markdown", "id": "432db6bb", "metadata": {}, "source": [ "### 2.2 Basic modifications for the time series setting" ] }, { "cell_type": "markdown", "id": "d63e9577", "metadata": {}, "source": [ "In the time series setting, which we consider here, several basic modifications may be applied to the FCI algorithm. Among these are:\n", "1. All lagged edges $X^i_{t-\\tau} {\\circ\\!{-}\\!\\circ} X^j_t$, where we recall that \"lagged\" means $\\tau > 0$, can be turned into $X^i_{t-\\tau} {\\circ\\!{\\rightarrow}} X^j_t$. This is so because $X^i_{t-\\tau} {\\circ\\!{\\rightarrow}} X^j_t$ says that $X^j_t$ does not cause $X^i_{t-\\tau}$, which simply reflects the absence of causal influences backwards in time.\n", "2. Whenever a lagged edge $X^i_{t-\\tau} {\\circ\\!{\\rightarrow}} X^j_t$ is turned into $X^i_{t-\\tau} {\\rightarrow} X^j_t$ or $X^i_{t-\\tau} {\\leftarrow} X^j_t$ the same edge type update is applied to the edges $X^i_{t-\\tau - \\Delta t} {\\circ\\!{\\rightarrow}} X^j_{t-\\Delta t}$ for all $\\Delta t$. Similarly, whenever a contemporaneous edge $X^i_{t} {\\circ\\!{-}\\!\\circ} X^j_t$ is turned into or $X^i_{t} {{\\leftarrow}\\!\\circ} X^j_t$ or $X^i_{t} {\\leftarrow} X^j_t$ or $X^i_{t} {\\circ\\!{\\rightarrow}} X^j_t$ or $X^i_{t} {\\rightarrow} X^j_t$ or $X^i_{t} {\\leftrightarrow} X^j_t$ the same edge type update is applied to the edges $X^i_{t - \\Delta t} {\\circ\\!{\\rightarrow}} X^j_{t-\\Delta t}$ for all $\\Delta t$. This reflects causal stationarity.\n", "3. Whenever a lagged or contemporaneous edge between $X^i_{t-\\tau}$ and $X^j_t$ is removed then also the edge between $X^i_{t - \\tau - \\Delta t}$ and $X^j_{t-\\Delta t}$ for all $\\Delta t$ are removed. This is allowed by causal stationarity. " ] }, { "cell_type": "markdown", "id": "e216607e", "metadata": {}, "source": [ "### 2.3 Incorporation of the PCMCI idea" ] }, { "cell_type": "markdown", "id": "ec405354", "metadata": {}, "source": [ "The central feature of LPCMCI its incorporation of the PCMCI idea, which is also used in the PCMCI [9] and PCMCIplus [10] algorithms. It rests on the observation that strong autocorrelations tend to reduce the effect sizes of (conditional) independence tests, thereby reducing the statistical power of these tests and thus degrading the algorithm's overvall statistical performance. The idea is to alleviate this problem by *conditioning the autocorrelation away* (at least partially). This is achieved by extending the standard conditioning sets $\\mathcal{S}$ of (conditional) independence tests with so-called *default conditions* $\\mathcal{S}_{def}(X^i_{t-\\tau}, X^j_t)$, schematically\n", "\n", "$$\n", "\\begin{align}\n", "\\text{Test whether } X^i_{t-\\tau} \\perp X^j_t ~|~ \\mathcal{S}\n", "\\qquad \\longrightarrow \\qquad\n", "\\text{Test whether } X^i_{t-\\tau} \\perp X^j_t ~|~ \\mathcal{S} \\cup \\mathcal{S}_{def}(X^i_{t-\\tau}, X^j_t) \\ .\n", "\\end{align}\n", "$$\n", "\n", "\n", "In the setting of PCMCI and PCMCIplus, where by assumption there are no unobserved variables, the default conditions $\\mathcal{S}_{def}(X^i_{t-\\tau}, X^j_t)$ would for this purpose ideally be the union of causal parents of $X^i_{t-\\tau}$ and $X^j_t$. Initially, however, the causal parentships are unknown --- after all, the very purpose of these algorithms is to learn the causal parentships. PCMCI and PCMCIplus get around this complication by noting that in the absence of unobserved variables the conditioning sets may in principle be extended with all lagged variables without the danger of introducing spurious dependencies. In other words: The default conditions $\\mathcal{S}_{def}(X^i_{t-\\tau}, X^j_t)$ could in principle be chosen as the set of all variables at $t-1$ or earlier. Such a high-dimensional conditioning set would, however, significantly increase the estimation dimensions of the (conditional) independence tests and thereby again reduce their statistical power. A compromise is drawn by first running a greedy version of the PC algorithm [11] on lagged links and then using the remaining lagged adjacencies as default conditions, see references [9] and [10] for more details.\n", "\n", "In the setting of LPCMCI, where the existence of unobserved variables is allowed, the situation is yet more complicated: Conditioning on lagged variables *may* introduce spurious dependencies, such that the approach of PCMCI and PCMCIplus can NOT simply be copied. However, conditioning sets can still be extended with *causal ancestors* without the danger of introducing spurious dependencies. The PCMCI idea can thus be implemented if definite knowledge of (some) causal ancestorships can be deduced before all (conditional) independence tests have already been made. This is, precisely, what LPCMCI does: It utilizes a novel set of orientation rules, extending those of the FCI algorithm, that allow to learn causal ancestral relationships while the algorithm still tests for further (conditional) independencies. The causal ancestors identified in this way are then used as the default conditions $\\mathcal{S}_{def}(X^i_{t-\\tau}, X^j_t)$ for the remaining tests. Because the novel orientation rules are able to identify the more causal ancestral relationships the more edges have been removed already, the algorithm iterates between performing (conditional) independence tests and applying the orientation rules." ] }, { "cell_type": "markdown", "id": "081f0025", "metadata": {}, "source": [ "### 2.4 The LPCMCI algorithm" ] }, { "cell_type": "markdown", "id": "3e9a2656", "metadata": {}, "source": [ "The basic structure of LPCMCI is as follows:\n", "\n", "---\n", "\n", "1. Initialize a fully connected graph with edges $X^i_{t-\\tau} {\\circ\\!{\\rightarrow}} X^j_t$ for $0 < \\tau \\leq \\tau_{\\text{max}}$ and $X^i_{t} {\\circ\\!{-}\\!\\circ} X^j_t$\n", "2. Repeat $k$ times:\n", " 1. Iterate until convergence between testing (conditional) independencies $X^i_{t-\\tau} \\perp X^j_t ~|~ \\mathcal{S} \\cup \\mathcal{S}_{def}(X^i_{t-\\tau}, X^j_t)$ of adjacent variables and applying the novel orientation rules. The standard conditioning set $\\mathcal{S}$ runs through subsets of restricted versions of the adjacencies of $X^i_{t-\\tau}$ and $X^j_t$, and as default conditions $\\mathcal{S}_{def}(X^i_{t-\\tau}, X^j_t)$ the already identified causal ancestors of $X^i_{t-\\tau}$ and $X^j_t$ are used. Whenver $X^i_{t-\\tau}$ and $X^j_t$ are found to be (conditionally) independent, the edge between them is removed.\n", " 2. Restore all removed edges while remembering the identified causal ancestorships.\n", "3. Once more run step 2.A.\n", "4. Run a modified version of step 2.A., the difference being that $\\mathcal{S}$ here runs through subsets of restricted versions of $\\text{Possible-D-Sep}(X, Y)$ and $\\text{Possible-D-Sep}(Y, X)$.\n", "\n", "---\n", "\n", "To understand this, first assume step 2. is skipped because $k = 0$ is chosen (the value of $k$ is an argument to `LPCMCI.run_lpcmci`, see subsection 2.5 below). The combinations of steps 1. and 3. of LPCMCI then correspond to the combination of steps 1. and 2. of FCI (for FCI see subsection 2.1 above) with modifications according to the discussions in subsections 2.2 and 2.3. Similarly, step 4. of LPCMCI corresponds to the combination of steps 3. and 4. of FCI with according modifications. This is a valid application of LPCMCI and in theory --- i.e., if all assumptions are met in combination with the infinite sample limit in which all statistical decisions about (conditional) independencies are correct --- learns the true time series DPAG $\\mathcal{P}^{\\tau_{\\text{max}}}(\\mathcal{G})$.\n", "\n", "The purpose of step 2. with $k > 0$ is to further improve the *finite-sample* performance of LPCMCI. This has been demonstrated in numerical experiments in the [paper on LPCMCI](https://proceedings.neurips.cc/paper/2020/file/94e70705efae423efda1088614128d0b-Paper.pdf). The reasoning behind this is follows: If $k = 0$ is chosen, then step 3. of LPCMCI begins with having no knowledge about causal ancestral relationships. The first (conditional) independence tests thus have empty default conditions $\\mathcal{S}_{def}(X^i_{t-\\tau}, X^j_t)$, which means they suffer from a low statistical power and are therefore prone to erroneous decisions. In the course of step 3. some causal ancestral relationships will be identified and used as default conditions for subsequent (conditional) independence tests, but that does not repair erroneous decisions of previous tests. And this is precisely why step 2. with $k > 0$ is useful: Once step 2.A. is completed, several causal ancestral relationships have been identified. The algorithm then starts over in so far as that it restores all removed edges but remembers the identified causal ancestral relationships. This results in non-empty default conditions $\\mathcal{S}_{def}(X^i_{t-\\tau}, X^j_t)$ already in the first (conditional) independence tests of step 3., thus increasing their statistical power and making this step more robust against erroneous test decisions. For $k > 1$ the process of restoring the edges is even repeated more than once, such that also step 2.A. (except for the first iteration) starts with non-empty default conditions.\n", "\n", "In the [paper on LPCMCI](https://proceedings.neurips.cc/paper/2020/file/94e70705efae423efda1088614128d0b-Paper.pdf) as well the verbose output of LPCMCI the iterations in step 2. are referred to as *preliminary phases*, step 3. as *(final) ancestral phase* and step 4. as *non-ancestral phase*." ] }, { "cell_type": "markdown", "id": "09758fa8", "metadata": {}, "source": [ "### 2.5 Parameters of LPCMCI" ] }, { "cell_type": "markdown", "id": "a756658d", "metadata": {}, "source": [ "LPCMCI can be flexibly combined with any kind of (conditional) independence test statistic and therefore adapt to the data type (continuous or discrete) as well as the assumed dependency forms (e.g. linear or non-linear). The (conditional) independence tests are available in `tigramite.independence_tests`.\n", "\n", "The main free parameters of LPCMCI, in addition to those of the (conditional) independence test, are:\n", "1. The maximum considered time lag $\\tau_{\\max}$ (`tau_max`). This determines the observed time window $[t-\\tau_{\\max}, t]$ and thereby also the time series DPAG $\\mathcal{P}^{\\tau_{\\max}}(\\mathcal{G})$ that is supposed to be learned.\n", "2. The significance threshold $\\alpha_{\\rm PC}$ (`pc_alpha`) of the individual (conditional) independence tests. For higher $\\alpha_{\\rm PC}$ the estimated graph tends to be denser.\n", "3. The number $k$ of preliminary iterations (`n_preliminary_iterations`) in step 2. of the algorithm. By default `n_preliminary_iterations = 1`.\n", "\n", "The non-ancestral phase of LPCMCI (step 4.) can sometimes be very slow because the standard conditioning set $\\mathcal{S}$ runs through subsets of large sets, which means that a large number of (conditional) independence tests may be conducted. In this case one can compromise on the provable asymptotic correctness of LPCMCI and restrict the cardinality of $\\mathcal{S}$, and hence the runtime of step 4., by using the `max_p_non_ancestral` argument." ] }, { "cell_type": "markdown", "id": "b55020eb", "metadata": {}, "source": [ "### 2.6 Output of LPCMCI" ] }, { "cell_type": "markdown", "id": "de0d6c6c", "metadata": {}, "source": [ "The function `LPCMCI.run_lpcmci` returns a dictionary with three entries, which are respectively accessed with the strings `'graph'`, `'p_matrix'`, and `'val_matrix'`. We now in turn discuss the three entries and refer to them by their key.\n", "\n", "**`graph`:**\\\n", "This is the central output of LPCMCI. It is a three-dimensional array of shape `(N_X, N_X, tau_max + 1)`, where `graph[i, j, tau]` is a string that symbolically represents the edge from $X^i_{t-\\tau}$ to $X^j_t$. To be specific:\n", "1. `graph[i, j, tau] = '-->'` says that $X^i_{t-\\tau} {\\rightarrow} X^j_t$.\n", "2. `graph[i, j, tau] = '<->'` says that $X^i_{t-\\tau} {\\leftrightarrow} X^j_t$.\n", "3. `graph[i, j, tau] = 'o->'` says that $X^i_{t-\\tau} {\\circ\\!{\\rightarrow}} X^j_t$.\n", "4. `graph[i, j, tau] = ''` (empty string) says that there is no edge between $X^i_{t-\\tau}$ and $X^j_t$.\n", "5. `graph[i, j, 0] = '-->'` and `graph[j, i, 0] = '<--'` say that $X^i_{t} {\\rightarrow} X^j_t$.\n", "6. `graph[i, j, 0] = '<--'` and `graph[j, i, 0] = '-->'` say that $X^i_{t} {\\leftarrow} X^j_t$.\n", "7. `graph[i, j, 0] = '<->'` and `graph[j, i, 0] = '<->'` say that $X^i_{t} {\\leftrightarrow} X^j_t$.\n", "8. `graph[i, j, 0] = 'o->'` and `graph[j, i, 0] = '<-o'` say that $X^i_{t} {\\circ\\!{\\rightarrow}} X^j_t$.\n", "9. `graph[i, j, 0] = '<-o'` and `graph[j, i, 0] = 'o->'` say that $X^i_{t} {{\\leftarrow}\\!\\circ} X^j_t$.\n", "10. `graph[i, j, 0] = 'o-o'` and `graph[j, i, 0] = 'o-o'` say that $X^i_{t} {\\circ\\!{-}\\!\\circ} X^j_t$.\n", "11. `graph[i, j, 0] = ''` and `graph[j, i, 0] = ''` (empty strings) say that there is no edge between $X^i_{t}$ and $X^j_t$.\n", "\n", "Due to erroneous test decisions about (conditional) independencies it may happen that the edge orientation rules infer conflicting edge type updates. For example, one of the orientation rules may say that the edge $X^i_{t-\\tau} {\\circ\\!{\\rightarrow}} X^j_t$ can be updated to $X^i_{t-\\tau} {\\rightarrow} X^j_t$ while at the same time another orientation rules says it can be updated to $X^i_{t-\\tau} {\\leftrightarrow} X^j_t$. Such conflicts are dealt with by explictly marking them through the introduction of the additional edge types $X^i_{t-\\tau} {x\\!{\\rightarrow}} X^j_t$ and $X^i_{t-\\tau} {x\\!{-}\\!x} X^j_t$ in which the symbol $x$ denotes the conflict. In the example, the edge would be updated to $X^i_{t-\\tau} {x\\!{\\rightarrow}} X^j_t$ because the conflict only concerns whether or not $X^i_{t-\\tau}$ causes $X^j_t$ while there is consensus about $X^j_t$ not causing $X^i_{t-\\tau}$. To be specific:\n", "1. `graph[i, j, tau] = 'x->'` says that both $X^i_{t-\\tau} {\\rightarrow} X^j_t$ and $X^i_{t-\\tau} {\\leftrightarrow} X^j_t$ were proposed.\n", "2. `graph[i, j, 0] = 'x->'` and `graph[j, i, 0] = '<-x'` say that one of these combinations were proposed:\n", " 1. $X^i_{t} {\\rightarrow} X^j_t$ and $X^i_{t} {\\leftrightarrow} X^j_t$\n", " 2. $X^i_{t} {\\rightarrow} X^j_t$ and $X^i_{t} {{\\leftarrow}\\!\\circ} X^j_t$\n", "3. `graph[i, j, 0] = '<-x'` and `graph[j, i, 0] = 'x->'` say that one of these combinations were proposed:\n", " 1. $X^i_{t} {\\leftarrow} X^j_t$ and $X^i_{t} {\\leftrightarrow} X^j_t$\n", " 2. $X^i_{t} {\\leftarrow} X^j_t$ and $X^i_{t} {\\circ\\!{\\rightarrow}} X^j_t$\n", "4. `graph[i, j, 0] = 'x-x'` and `graph[j, i, 0] = 'x-x'` say that both $X^i_{t} {\\rightarrow} X^j_t$ and $X^i_{t} {\\leftarrow} X^j_t$ were proposed.\n", "\n", "The graph can be visualized with the plotting functionality of TIGRAMITE, see the application example in section 3 below.\n", "\n", "**`p_matrix`:**\\\n", "A three-dimensional array of shape `(N_X, N_X, tau_max + 1)`, where `p_matrix[i, j, tau]` is the maximum across the p-values of all (conditional) independence tests of the pair of variables $X^i_{t-\\tau}$ and $X^j_{t}$. For $\\tau = 0$ the symmetry `p_matrix[i, j, 0] = p_matrix[j, i, 0]` holds.\n", "\n", "The maximum p-value rather than the minimum p-value is being recorded because of the way in which (conditional) independence tests are used in the constraint-based paradigm: A p-value larger than $\\alpha_{\\rm PC}$ means that the null hypothesis of independence is not rejected, in case of which the corresponding edge is removed.\n", "\n", "**`val_matrix`:**\\\n", "A three-dimensional array of shape `(N_X, N_X, tau_max + 1)`, where `val_matrix[i, j, tau]` is the test statistic of that particular (conditional) independence test whose p-value is stored in `p_matrix[i, j, tau]`. Since for the same pair of variables $X^i_{t-\\tau}$ and $X^j_{t}$ conditioning sets of different cardinalities can be tested, thus leading to different null distributions, this value is NOT necessarily equal to the minimum test statistic across all (conditional) independence tests. For $\\tau = 0$ the symmetry `val_matrix[i, j, 0] = val_matrix[j, i, 0]` holds.\n", "\n", "One may pass `val_matrix` to the plotting functions `tp.plot_graph` and `tp.plot_time_series_graph` in order to accordingly color the edges, see the application example in section 3 below." ] }, { "cell_type": "markdown", "id": "7814bd5b", "metadata": {}, "source": [ "## 3 Application example" ] }, { "cell_type": "markdown", "id": "a1138735", "metadata": {}, "source": [ "This section demonstrates and explains the application of LPCMCI on synthetic data. Subsection 3.7 may be skipped on a first read." ] }, { "cell_type": "markdown", "id": "78ae0037", "metadata": {}, "source": [ "### 3.1 Data generation" ] }, { "cell_type": "markdown", "id": "08157fab", "metadata": {}, "source": [ "We return to the structural causal process that in section 1 has served as running example, where also as above we treat the second component time series as unobserved. Making this explicit by using the symbols $X^i$ for observed time series and $L^i$ for unobserved time series the process reads:\n", "\n", "\\begin{align}\n", "X^1_t &= 0.9 X^1_{t-1} + 0.6 L^2_{t} + \\eta^1_t\\\\\n", "L^2_t &= \\eta^2_t \\\\\n", "X^3_t &= 0.9 X^3_{t-1} + 0.4 L^2_{t-1} + \\eta^3_t\\\\\n", "X^4_t &= 0.9 X^4_{t-1} - 0.4 X^3_{t-2} + \\eta^4_T\n", "\\end{align}\n", "\n", "The function `toys.structural_causal_process` allows to generate a realization of this proces as follows:" ] }, { "cell_type": "code", "execution_count": 2, "id": "cb4aa8c6", "metadata": {}, "outputs": [], "source": [ "# Set a seed for reproducibility\n", "seed = 19\n", "\n", "# Choose the time series length\n", "T = 500\n", "\n", "# Specify the model (note that here, unlike in the typed equations, variables\n", "# are indexed starting from 0)\n", "def lin(x): return x\n", "\n", "links = {0: [((0, -1), 0.9, lin), ((1, 0), 0.6, lin)],\n", " 1: [],\n", " 2: [((2, -1), 0.9, lin), ((1, -1), 0.4, lin)],\n", " 3: [((3, -1), 0.9, lin), ((2, -2), -0.5, lin)] \n", " }\n", "\n", "# Specify dynamical noise term distributions, here unit variance Gaussians\n", "random_state = np.random.RandomState(seed)\n", "noises = noises = [random_state.randn for j in links.keys()]\n", "\n", "# Generate data according to the full structural causal process\n", "data_full, nonstationarity_indicator = toys.structural_causal_process(\n", " links=links, T=T, noises=noises, seed=seed)\n", "assert not nonstationarity_indicator\n", "\n", "# Remove the unobserved component time series\n", "data_obs = data_full[:, [0, 2, 3]]\n", "\n", "# Number of observed variables\n", "N = data_obs.shape[1]\n", "\n", "# Initialize dataframe object, specify variable names\n", "var_names = [r'$X^{%d}$' % j for j in range(N)]\n", "dataframe = pp.DataFrame(data_obs, var_names=var_names)" ] }, { "cell_type": "markdown", "id": "0c838744", "metadata": {}, "source": [ "### 3.2 Plotting the time series" ] }, { "cell_type": "markdown", "id": "3f89dc8f", "metadata": {}, "source": [ "The time series can be plotted with the function `tp.plot_timeseries`:" ] }, { "cell_type": "code", "execution_count": 3, "id": "fc36bd6a", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 1080x360 with 3 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "tp.plot_timeseries(dataframe, figsize=(15, 5));\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "6e63d531", "metadata": {}, "source": [ "### 3.3 Exploration: Bivariate lagged conditional independence" ] }, { "cell_type": "markdown", "id": "429b9270", "metadata": {}, "source": [ "In order to obtain an idea which `tau_max` to choose one may run the `run_bivci` function, which implements a bivariate lagged conditional independence test (similar to bivariate Granger causality, but lag-specific). This function is implemented by the `PCMCI` class." ] }, { "cell_type": "code", "execution_count": 4, "id": "0e9bfab9", "metadata": {}, "outputs": [], "source": [ "# Create a (conditional) independence test object\n", "# Here, the partial correlation test is used\n", "parcorr = ParCorr(significance='analytic')\n", "\n", "# Create a PCMCI object, passing the the dataframe and (conditional)\n", "# independence test object.\n", "pcmci = PCMCI(dataframe=dataframe, \n", " cond_ind_test=parcorr,\n", " verbosity=1)" ] }, { "cell_type": "code", "execution_count": 5, "id": "e5c8d561", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "##\n", "## Running Tigramite BivCI algorithm\n", "##\n", "\n", "Parameters:\n", "\n", "independence test = par_corr\n", "tau_min = 0\n", "tau_max = 20\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 720x432 with 9 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Run the `PCMCI.run_bivci` function\n", "correlations = pcmci.run_bivci(tau_max=20, val_only=True)['val_matrix']\n", "\n", "# Plot the results\n", "setup_args = {'var_names':var_names,\n", " 'figsize':(10, 6),\n", " 'x_base':5,\n", " 'y_base':.5}\n", "lag_func_matrix = tp.plot_lagfuncs(val_matrix=correlations, \n", " setup_args=setup_args)" ] }, { "cell_type": "markdown", "id": "9b979fd9", "metadata": {}, "source": [ "### 3.4 Application of LPCMCI" ] }, { "cell_type": "markdown", "id": "4292aca1", "metadata": {}, "source": [ "Based on the results of `run_bivci`, we choose to apply LPCMCI with `tau_max = 5` since after that lag, the lag functions decay to zero. Note that this is only a heuristic. In addition, you may also check scatter plots as done in the PCMCI tutorial in order to choose an appropriate conditional independence test. We further choose `pc_alpha = 0.01` and apart from that do not modify the default hyperparameter settings." ] }, { "cell_type": "code", "execution_count": 6, "id": "df1f2326", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "=======================================================\n", "=======================================================\n", "Starting preliminary phase 1\n", "\n", "Starting test phase\n", "\n", "p = 0\n", "\n", "Test phase complete\n", "p = 1\n", "(0,-5) independent (0, 0) given ((0, -1),) union set()\n", "(0,-4) independent (0, 0) given ((0, -1),) union set()\n", "(0,-3) independent (0, 0) given ((0, -1),) union set()\n", "(0,-2) independent (0, 0) given ((0, -1),) union set()\n", "(1,-5) independent (1, 0) given ((1, -1),) union set()\n", "(1,-4) independent (1, 0) given ((1, -1),) union set()\n", "(1,-3) independent (1, 0) given ((1, -1),) union set()\n", "(1,-2) independent (1, 0) given ((1, -1),) union set()\n", "Writing: (0,-5) oL> (0, 0) ==> (0,-5) (0, 0) \n", "Writing: (0,-4) oL> (0, 0) ==> (0,-4) (0, 0) \n", "Writing: (0,-3) oL> (0, 0) ==> (0,-3) (0, 0) \n", "Writing: (0,-2) oL> (0, 0) ==> (0,-2) (0, 0) \n", "Writing: (1,-5) oL> (1, 0) ==> (1,-5) (1, 0) \n", "Writing: (1,-4) oL> (1, 0) ==> (1,-4) (1, 0) \n", "Writing: (1,-3) oL> (1, 0) ==> (1,-3) (1, 0) \n", "Writing: (1,-2) oL> (1, 0) ==> (1,-2) (1, 0) \n", "(0, 0) independent (1, 0) given ((0, -1),) union set()\n", "(0, 0) independent (1, 0) given ((0, -1),) union set()\n", "(0, 0) independent (2, 0) given ((2, -1),) union set()\n", "(0, 0) independent (2, 0) given ((0, -1),) union set()\n", "Writing: (0, 0) o?o (1, 0) ==> (0, 0) (1, 0) \n", "Writing: (1, 0) o?o (0, 0) ==> (1, 0) (0, 0) \n", "Writing: (0, 0) o?o (2, 0) ==> (0, 0) (2, 0) \n", "Writing: (2, 0) o?o (0, 0) ==> (2, 0) (0, 0) \n", "(0,-1) independent (1, 0) given ((0, -2),) union set()\n", "(0,-1) independent (2, 0) given ((2, -1),) union set()\n", "(1,-1) independent (0, 0) given ((0, -1),) union set()\n", "(2,-1) independent (0, 0) given ((0, -1),) union set()\n", "Writing: (1,-1) oL> (0, 0) ==> (1,-1) (0, 0) \n", "Writing: (2,-1) oL> (0, 0) ==> (2,-1) (0, 0) \n", "Writing: (0,-1) oL> (1, 0) ==> (0,-1) (1, 0) \n", "Writing: (0,-1) oL> (2, 0) ==> (0,-1) (2, 0) \n", "(0,-2) independent (1, 0) given ((1, -1),) union set()\n", "(0,-2) independent (2, 0) given ((2, -1),) union set()\n", "(1,-2) independent (0, 0) given ((0, -1),) union set()\n", "(2,-2) independent (0, 0) given ((0, -1),) union set()\n", "Writing: (1,-2) oL> (0, 0) ==> (1,-2) (0, 0) \n", "Writing: (2,-2) oL> (0, 0) ==> (2,-2) (0, 0) \n", "Writing: (0,-2) oL> (1, 0) ==> (0,-2) (1, 0) \n", "Writing: (0,-2) oL> (2, 0) ==> (0,-2) (2, 0) \n", "(0,-3) independent (1, 0) given ((1, -1),) union set()\n", "(0,-3) independent (2, 0) given ((1, -5),) union set()\n", "(1,-3) independent (0, 0) given ((0, -1),) union set()\n", "(2,-3) independent (0, 0) given ((0, -1),) union set()\n", "(2,-3) independent (1, 0) given ((1, -1),) union set()\n", "Writing: (1,-3) oL> (0, 0) ==> (1,-3) (0, 0) \n", "Writing: (2,-3) oL> (0, 0) ==> (2,-3) (0, 0) \n", "Writing: (0,-3) oL> (1, 0) ==> (0,-3) (1, 0) \n", "Writing: (2,-3) oL> (1, 0) ==> (2,-3) (1, 0) \n", "Writing: (0,-3) oL> (2, 0) ==> (0,-3) (2, 0) \n", "(0,-4) independent (1, 0) given ((1, -1),) union set()\n", "(0,-4) independent (2, 0) given ((2, -1),) union set()\n", "(1,-4) independent (0, 0) given ((0, -1),) union set()\n", "(2,-4) independent (0, 0) given ((0, -1),) union set()\n", "(2,-4) independent (1, 0) given ((1, -1),) union set()\n", "Writing: (1,-4) oL> (0, 0) ==> (1,-4) (0, 0) \n", "Writing: (2,-4) oL> (0, 0) ==> (2,-4) (0, 0) \n", "Writing: (0,-4) oL> (1, 0) ==> (0,-4) (1, 0) \n", "Writing: (2,-4) oL> (1, 0) ==> (2,-4) (1, 0) \n", "Writing: (0,-4) oL> (2, 0) ==> (0,-4) (2, 0) \n", "(0,-5) independent (1, 0) given ((1, -1),) union set()\n", "(0,-5) independent (2, 0) given ((2, -1),) union set()\n", "(1,-5) independent (0, 0) given ((0, -1),) union set()\n", "(2,-5) independent (0, 0) given ((0, -1),) union set()\n", "(2,-5) independent (1, 0) given ((1, -1),) union set()\n", "Writing: (1,-5) oL> (0, 0) ==> (1,-5) (0, 0) \n", "Writing: (2,-5) oL> (0, 0) ==> (2,-5) (0, 0) \n", "Writing: (0,-5) oL> (1, 0) ==> (0,-5) (1, 0) \n", "Writing: (2,-5) oL> (1, 0) ==> (2,-5) (1, 0) \n", "Writing: (0,-5) oL> (2, 0) ==> (0,-5) (2, 0) \n", "\n", "Test phase complete\n", "\n", "Starting orientation phase\n", "with rule list: [['APR'], ['ER-08'], ['ER-02'], ['ER-01'], ['ER-09'], ['ER-10']]\n", "\n", "APR:\n", "Found nothing\n", "\n", "ER-08:\n", "Found nothing\n", "\n", "ER-02:\n", "Found nothing\n", "\n", "ER-01:\n", "Marked: (0,-1) oL> (0, 0) ==> (0,-1) -L> (0, 0) \n", "Marked: (1,-1) oL> (1, 0) ==> (1,-1) -L> (1, 0) \n", "Writing: (0,-1) oL> (0, 0) ==> (0,-1) -L> (0, 0) \n", "Update: Marking (0, -1) as anc of (0, 0)\n", "Writing: (1,-1) oL> (1, 0) ==> (1,-1) -L> (1, 0) \n", "Update: Marking (1, -1) as anc of (1, 0)\n", "\n", "APR:\n", "Found nothing\n", "\n", "ER-08:\n", "Found nothing\n", "\n", "ER-02:\n", "Found nothing\n", "\n", "ER-01:\n", "Found nothing\n", "\n", "ER-09:\n", "Found nothing\n", "\n", "ER-10:\n", "Found nothing\n", "\n", "Orientation phase complete\n", "\n", "Middle mark updates\n", "\n", "\n", "Starting test phase\n", "\n", "p = 0\n", "\n", "Test phase complete\n", "p = 1\n", "Writing: (0,-1) -L> (0, 0) ==> (0,-1) -!> (0, 0) \n", "(1, 0) independent (2, 0) given ((2, -1),) union {(1, -1)}\n", "(1, 0) independent (2, 0) given ((2, -2),) union {(1, -1)}\n", "Writing: (1, 0) o?o (2, 0) ==> (1, 0) (2, 0) \n", "Writing: (2, 0) o?o (1, 0) ==> (2, 0) (1, 0) \n", "(1,-1) independent (2, 0) given ((2, -1),) union {(1, -2)}\n", "(2,-1) independent (1, 0) given ((2, -2),) union {(1, -1)}\n", "Writing: (2,-1) oL> (1, 0) ==> (2,-1) (1, 0) \n", "Writing: (1,-1) oL> (2, 0) ==> (1,-1) (2, 0) \n", "Writing: (2,-2) oL> (1, 0) ==> (2,-2) o!> (1, 0) \n", "(1,-3) independent (2, 0) given ((1, -2),) union {(1, -4)}\n", "Writing: (1,-3) oL> (2, 0) ==> (1,-3) (2, 0) \n", "\n", "Test phase complete\n", "\n", "Starting orientation phase\n", "with rule list: [['APR'], ['ER-08'], ['ER-02'], ['ER-01'], ['ER-09'], ['ER-10']]\n", "\n", "APR:\n", "Marked: (0,-1) -!> (0, 0) ==> (0,-1) --> (0, 0) \n", "Writing: (0,-1) -!> (0, 0) ==> (0,-1) --> (0, 0) \n", "\n", "APR:\n", "Found nothing\n", "\n", "ER-08:\n", "Found nothing\n", "\n", "ER-02:\n", "Found nothing\n", "\n", "ER-01:\n", "Marked: (1,-2) oL> (2, 0) ==> (1,-2) -L> (2, 0) \n", "Writing: (1,-2) oL> (2, 0) ==> (1,-2) -L> (2, 0) \n", "Update: Marking (1, -2) as anc of (2, 0)\n", "\n", "APR:\n", "Found nothing\n", "\n", "ER-08:\n", "Found nothing\n", "\n", "ER-02:\n", "Found nothing\n", "\n", "ER-01:\n", "Found nothing\n", "\n", "ER-09:\n", "Marked: (1,-4) oL> (2, 0) ==> (1,-4) -L> (2, 0) \n", "Writing: (1,-4) oL> (2, 0) ==> (1,-4) -L> (2, 0) \n", "Update: Marking (1, -4) as anc of (2, 0)\n", "\n", "APR:\n", "Found nothing\n", "\n", "ER-08:\n", "Marked: (1,-5) oL> (2, 0) ==> (1,-5) -L> (2, 0) \n", "Writing: (1,-5) oL> (2, 0) ==> (1,-5) -L> (2, 0) \n", "Update: Marking (1, -5) as anc of (2, 0)\n", "\n", "APR:\n", "Found nothing\n", "\n", "ER-08:\n", "Found nothing\n", "\n", "ER-02:\n", "Found nothing\n", "\n", "ER-01:\n", "Found nothing\n", "\n", "ER-09:\n", "Found nothing\n", "\n", "ER-10:\n", "Found nothing\n", "\n", "Orientation phase complete\n", "\n", "Middle mark updates\n", "\n", "\n", "Starting test phase\n", "\n", "p = 0\n", "\n", "Test phase complete\n", "p = 1\n", "(2,-5) independent (2, 0) given ((2, -1),) union {(1, -5), (1, -4), (1, -2)}\n", "(2,-4) independent (2, 0) given ((2, -1),) union {(1, -5), (1, -4), (1, -2)}\n", "(2,-3) independent (2, 0) given ((2, -1),) union {(1, -5), (1, -4), (1, -2)}\n", "(2,-2) independent (2, 0) given ((2, -1),) union {(1, -5), (1, -4), (1, -2)}\n", "Writing: (2,-5) oL> (2, 0) ==> (2,-5) (2, 0) \n", "Writing: (2,-4) oL> (2, 0) ==> (2,-4) (2, 0) \n", "Writing: (2,-3) oL> (2, 0) ==> (2,-3) (2, 0) \n", "Writing: (2,-2) oL> (2, 0) ==> (2,-2) (2, 0) \n", "(1,-4) independent (2, 0) given ((2, -1),) union {(1, -5), (1, -2)}\n", "Writing: (1,-4) -L> (2, 0) ==> (1,-4) (2, 0) \n", "(1,-5) independent (2, 0) given ((2, -1),) union {(1, -2)}\n", "Writing: (1,-5) -L> (2, 0) ==> (1,-5) (2, 0) \n", "\n", "Test phase complete\n", "\n", "Starting orientation phase\n", "with rule list: [['APR'], ['ER-08'], ['ER-02'], ['ER-01'], ['ER-09'], ['ER-10']]\n", "\n", "APR:\n", "Found nothing\n", "\n", "ER-08:\n", "Found nothing\n", "\n", "ER-02:\n", "Found nothing\n", "\n", "ER-01:\n", "Marked: (2,-1) oL> (2, 0) ==> (2,-1) -L> (2, 0) \n", "Writing: (2,-1) oL> (2, 0) ==> (2,-1) -L> (2, 0) \n", "Update: Marking (2, -1) as anc of (2, 0)\n", "\n", "APR:\n", "Found nothing\n", "\n", "ER-08:\n", "Found nothing\n", "\n", "ER-02:\n", "Found nothing\n", "\n", "ER-01:\n", "Found nothing\n", "\n", "ER-09:\n", "Found nothing\n", "\n", "ER-10:\n", "Found nothing\n", "\n", "Orientation phase complete\n", "\n", "Middle mark updates\n", "\n", "\n", "Starting test phase\n", "\n", "p = 0\n", "\n", "Test phase complete\n", "p = 1\n", "Writing: (2,-1) -L> (2, 0) ==> (2,-1) -!> (2, 0) \n", "Writing: (1,-2) -L> (2, 0) ==> (1,-2) -!> (2, 0) \n", "\n", "Test phase complete\n", "p = 2\n", "Writing: (1,-1) -L> (1, 0) ==> (1,-1) -!> (1, 0) \n", "\n", "Test phase complete\n", "\n", "Starting orientation phase\n", "with rule list: [['APR'], ['ER-08'], ['ER-02'], ['ER-01'], ['ER-00-d'], ['ER-00-c'], ['ER-03'], ['R-04'], ['ER-09'], ['ER-10'], ['ER-00-b'], ['ER-00-a']]\n", "\n", "APR:\n", "Marked: (1,-2) -!> (2, 0) ==> (1,-2) --> (2, 0) \n", "Marked: (2,-1) -!> (2, 0) ==> (2,-1) --> (2, 0) \n", "Marked: (1,-1) -!> (1, 0) ==> (1,-1) --> (1, 0) \n", "Writing: (1,-1) -!> (1, 0) ==> (1,-1) --> (1, 0) \n", "Writing: (1,-2) -!> (2, 0) ==> (1,-2) --> (2, 0) \n", "Writing: (2,-1) -!> (2, 0) ==> (2,-1) --> (2, 0) \n", "\n", "APR:\n", "Found nothing\n", "\n", "ER-08:\n", "Found nothing\n", "\n", "ER-02:\n", "Found nothing\n", "\n", "ER-01:\n", "Found nothing\n", "\n", "ER-00-d:\n", "Found nothing\n", "\n", "ER-00-c:\n", "Marked: (2,-2) o!> (1, 0) ==> (2,-2) <!> (1, 0) \n", "Writing: (2,-2) o!> (1, 0) ==> (2,-2) <!> (1, 0) \n", "Update: Marking (2, -2) as non-anc of (1, 0)\n", "\n", "APR:\n", "Found nothing\n", "\n", "ER-08:\n", "Found nothing\n", "\n", "ER-02:\n", "Found nothing\n", "\n", "ER-01:\n", "Found nothing\n", "\n", "ER-00-d:\n", "Found nothing\n", "\n", "ER-00-c:\n", "Found nothing\n", "\n", "ER-03:\n", "Found nothing\n", "\n", "R-04:\n", "Found nothing\n", "\n", "ER-09:\n", "Found nothing\n", "\n", "ER-10:\n", "Found nothing\n", "\n", "ER-00-b:\n", "Found nothing\n", "\n", "ER-00-a:\n", "Marked: (2,-2) <!> (1, 0) ==> (2,-2) (1, 0) \n", "Writing: (2,-2) <!> (1, 0) ==> (2,-2) (1, 0) \n", "\n", "Links were removed by rules\n", "\n", "\n", "APR:\n", "Found nothing\n", "\n", "ER-08:\n", "Found nothing\n", "\n", "ER-02:\n", "Found nothing\n", "\n", "ER-01:\n", "Found nothing\n", "\n", "ER-00-d:\n", "Found nothing\n", "\n", "ER-00-c:\n", "Found nothing\n", "\n", "ER-03:\n", "Found nothing\n", "\n", "R-04:\n", "Found nothing\n", "\n", "ER-09:\n", "Found nothing\n", "\n", "ER-10:\n", "Found nothing\n", "\n", "ER-00-b:\n", "Found nothing\n", "\n", "ER-00-a:\n", "Found nothing\n", "\n", "Orientation phase complete\n", "\n", "Middle mark updates\n", "\n", "\n", "Preliminary phase 1 complete\n", "\n", "Graph:\n", "--------------------------------\n", "(0,-1) --> (0, 0)\n", "(1,-1) --> (1, 0)\n", "(1,-2) --> (2, 0)\n", "(2,-1) --> (2, 0)\n", "--------------------------------\n", "Writing: (0,-1) oL> (0, 0) ==> (0,-1) -L> (0, 0) \n", "Update: Marking (0, -1) as anc of (0, 0)\n", "Writing: (1,-1) oL> (1, 0) ==> (1,-1) -L> (1, 0) \n", "Update: Marking (1, -1) as anc of (1, 0)\n", "Writing: (2,-1) oL> (2, 0) ==> (2,-1) -L> (2, 0) \n", "Update: Marking (2, -1) as anc of (2, 0)\n", "Writing: (1,-2) oL> (2, 0) ==> (1,-2) -L> (2, 0) \n", "Update: Marking (1, -2) as anc of (2, 0)\n", "\n", "=======================================================\n", "=======================================================\n", "Starting final ancestral phase\n", "\n", "Starting test phase\n", "\n", "p = 0\n", "(0,-5) independent (0, 0) given () union {(0, -1)}\n", "(0,-4) independent (0, 0) given () union {(0, -5), (0, -1)}\n", "(0,-3) independent (0, 0) given () union {(0, -4), (0, -1)}\n", "(0,-2) independent (0, 0) given () union {(0, -3), (0, -1)}\n", "(1,-5) independent (1, 0) given () union {(1, -1)}\n", "(1,-4) independent (1, 0) given () union {(1, -5), (1, -1)}\n", "(1,-3) independent (1, 0) given () union {(1, -4), (1, -1)}\n", "(1,-2) independent (1, 0) given () union {(1, -3), (1, -1)}\n", "(2,-5) independent (2, 0) given () union {(2, -1), (1, -2)}\n", "(2,-4) independent (2, 0) given () union {(2, -1), (1, -2), (2, -5)}\n", "(2,-3) independent (2, 0) given () union {(1, -5), (2, -1), (1, -2), (2, -4)}\n", "(2,-2) independent (2, 0) given () union {(2, -3), (2, -1), (1, -4), (1, -2)}\n", "Writing: (0,-5) oL> (0, 0) ==> (0,-5) (0, 0) \n", "Writing: (0,-4) oL> (0, 0) ==> (0,-4) (0, 0) \n", "Writing: (0,-3) oL> (0, 0) ==> (0,-3) (0, 0) \n", "Writing: (0,-2) oL> (0, 0) ==> (0,-2) (0, 0) \n", "Writing: (1,-5) oL> (1, 0) ==> (1,-5) (1, 0) \n", "Writing: (1,-4) oL> (1, 0) ==> (1,-4) (1, 0) \n", "Writing: (1,-3) oL> (1, 0) ==> (1,-3) (1, 0) \n", "Writing: (1,-2) oL> (1, 0) ==> (1,-2) (1, 0) \n", "Writing: (2,-5) oL> (2, 0) ==> (2,-5) (2, 0) \n", "Writing: (2,-4) oL> (2, 0) ==> (2,-4) (2, 0) \n", "Writing: (2,-3) oL> (2, 0) ==> (2,-3) (2, 0) \n", "Writing: (2,-2) oL> (2, 0) ==> (2,-2) (2, 0) \n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "(0, 0) independent (1, 0) given () union {(0, -1), (1, -1)}\n", "(0, 0) independent (1, 0) given () union {(0, -1), (1, -1)}\n", "(0, 0) independent (2, 0) given () union {(2, -1), (0, -1), (1, -2)}\n", "(0, 0) independent (2, 0) given () union {(2, -1), (0, -1), (1, -2)}\n", "(1, 0) independent (2, 0) given () union {(2, -1), (1, -2), (1, -1)}\n", "(1, 0) independent (2, 0) given () union {(2, -1), (1, -1), (1, -2)}\n", "Writing: (0, 0) o?o (1, 0) ==> (0, 0) (1, 0) \n", "Writing: (1, 0) o?o (0, 0) ==> (1, 0) (0, 0) \n", "Writing: (0, 0) o?o (2, 0) ==> (0, 0) (2, 0) \n", "Writing: (2, 0) o?o (0, 0) ==> (2, 0) (0, 0) \n", "Writing: (1, 0) o?o (2, 0) ==> (1, 0) (2, 0) \n", "Writing: (2, 0) o?o (1, 0) ==> (2, 0) (1, 0) \n", "(0,-1) independent (2, 0) given () union {(2, -1), (0, -2), (1, -2)}\n", "(1,-1) independent (0, 0) given () union {(0, -1), (1, -2)}\n", "(1,-1) independent (2, 0) given () union {(2, -1), (1, -2)}\n", "(2,-1) independent (0, 0) given () union {(2, -2), (1, -3), (0, -1)}\n", "(2,-1) independent (1, 0) given () union {(2, -2), (1, -3), (1, -1)}\n", "Writing: (1,-1) oL> (0, 0) ==> (1,-1) (0, 0) \n", "Writing: (2,-1) oL> (0, 0) ==> (2,-1) (0, 0) \n", "Writing: (2,-1) oL> (1, 0) ==> (2,-1) (1, 0) \n", "Writing: (0,-1) oL> (2, 0) ==> (0,-1) (2, 0) \n", "Writing: (1,-1) oL> (2, 0) ==> (1,-1) (2, 0) \n", "(0,-2) independent (1, 0) given () union {(0, -3), (1, -1)}\n", "(0,-2) independent (2, 0) given () union {(2, -1), (0, -3), (1, -2)}\n", "(1,-2) independent (0, 0) given () union {(1, -3), (0, -1)}\n", "(2,-2) independent (0, 0) given () union {(2, -3), (1, -4), (0, -1)}\n", "(2,-2) independent (1, 0) given () union {(2, -3), (1, -4), (1, -1)}\n", "Writing: (1,-2) oL> (0, 0) ==> (1,-2) (0, 0) \n", "Writing: (2,-2) oL> (0, 0) ==> (2,-2) (0, 0) \n", "Writing: (0,-2) oL> (1, 0) ==> (0,-2) (1, 0) \n", "Writing: (2,-2) oL> (1, 0) ==> (2,-2) (1, 0) \n", "Writing: (0,-2) oL> (2, 0) ==> (0,-2) (2, 0) \n", "(0,-3) independent (1, 0) given () union {(0, -4), (1, -1)}\n", "(0,-3) independent (2, 0) given () union {(2, -1), (1, -2), (0, -4)}\n", "(1,-3) independent (0, 0) given () union {(1, -4), (0, -1)}\n", "(1,-3) independent (2, 0) given () union {(2, -1), (1, -4), (1, -2)}\n", "(2,-3) independent (0, 0) given () union {(1, -5), (0, -1), (2, -4)}\n", "(2,-3) independent (1, 0) given () union {(1, -5), (1, -1), (2, -4)}\n", "Writing: (1,-3) oL> (0, 0) ==> (1,-3) (0, 0) \n", "Writing: (2,-3) oL> (0, 0) ==> (2,-3) (0, 0) \n", "Writing: (0,-3) oL> (1, 0) ==> (0,-3) (1, 0) \n", "Writing: (2,-3) oL> (1, 0) ==> (2,-3) (1, 0) \n", "Writing: (0,-3) oL> (2, 0) ==> (0,-3) (2, 0) \n", "Writing: (1,-3) oL> (2, 0) ==> (1,-3) (2, 0) \n", "(0,-4) independent (1, 0) given () union {(0, -5), (1, -1)}\n", "(0,-4) independent (2, 0) given () union {(0, -5), (2, -1), (1, -2)}\n", "(1,-4) independent (0, 0) given () union {(1, -5), (0, -1)}\n", "(1,-4) independent (2, 0) given () union {(1, -5), (2, -1), (1, -2)}\n", "(2,-4) independent (0, 0) given () union {(0, -1), (2, -5)}\n", "(2,-4) independent (1, 0) given () union {(1, -1), (2, -5)}\n", "Writing: (1,-4) oL> (0, 0) ==> (1,-4) (0, 0) \n", "Writing: (2,-4) oL> (0, 0) ==> (2,-4) (0, 0) \n", "Writing: (0,-4) oL> (1, 0) ==> (0,-4) (1, 0) \n", "Writing: (2,-4) oL> (1, 0) ==> (2,-4) (1, 0) \n", "Writing: (0,-4) oL> (2, 0) ==> (0,-4) (2, 0) \n", "Writing: (1,-4) oL> (2, 0) ==> (1,-4) (2, 0) \n", "(0,-5) independent (1, 0) given () union {(1, -1)}\n", "(0,-5) independent (2, 0) given () union {(2, -1), (1, -2)}\n", "(1,-5) independent (0, 0) given () union {(0, -1)}\n", "(1,-5) independent (2, 0) given () union {(2, -1), (1, -2)}\n", "(2,-5) independent (0, 0) given () union {(0, -1)}\n", "(2,-5) independent (1, 0) given () union {(1, -1)}\n", "Writing: (1,-5) oL> (0, 0) ==> (1,-5) (0, 0) \n", "Writing: (2,-5) oL> (0, 0) ==> (2,-5) (0, 0) \n", "Writing: (0,-5) oL> (1, 0) ==> (0,-5) (1, 0) \n", "Writing: (2,-5) oL> (1, 0) ==> (2,-5) (1, 0) \n", "Writing: (0,-5) oL> (2, 0) ==> (0,-5) (2, 0) \n", "Writing: (1,-5) oL> (2, 0) ==> (1,-5) (2, 0) \n", "\n", "Test phase complete\n", "\n", "Starting orientation phase\n", "with rule list: [['APR'], ['ER-08'], ['ER-02'], ['ER-01'], ['ER-09'], ['ER-10']]\n", "\n", "APR:\n", "Found nothing\n", "\n", "ER-08:\n", "Found nothing\n", "\n", "ER-02:\n", "Found nothing\n", "\n", "ER-01:\n", "Found nothing\n", "\n", "ER-09:\n", "Found nothing\n", "\n", "ER-10:\n", "Found nothing\n", "\n", "Orientation phase complete\n", "p = 1\n", "Writing: (0,-1) -L> (0, 0) ==> (0,-1) -!> (0, 0) \n", "Writing: (2,-1) -L> (2, 0) ==> (2,-1) -!> (2, 0) \n", "Writing: (0,-1) oL> (1, 0) ==> (0,-1) o!> (1, 0) \n", "Writing: (1,-2) -L> (2, 0) ==> (1,-2) -!> (2, 0) \n", "\n", "Test phase complete\n", "p = 2\n", "Writing: (1,-1) -L> (1, 0) ==> (1,-1) -!> (1, 0) \n", "\n", "Test phase complete\n", "\n", "Starting orientation phase\n", "with rule list: [['APR'], ['ER-08'], ['ER-02'], ['ER-01'], ['ER-00-d'], ['ER-00-c'], ['ER-03'], ['R-04'], ['ER-09'], ['ER-10'], ['ER-00-b'], ['ER-00-a']]\n", "\n", "APR:\n", "Marked: (1,-2) -!> (2, 0) ==> (1,-2) --> (2, 0) \n", "Marked: (2,-1) -!> (2, 0) ==> (2,-1) --> (2, 0) \n", "Marked: (0,-1) -!> (0, 0) ==> (0,-1) --> (0, 0) \n", "Marked: (1,-1) -!> (1, 0) ==> (1,-1) --> (1, 0) \n", "Writing: (0,-1) -!> (0, 0) ==> (0,-1) --> (0, 0) \n", "Writing: (1,-1) -!> (1, 0) ==> (1,-1) --> (1, 0) \n", "Writing: (1,-2) -!> (2, 0) ==> (1,-2) --> (2, 0) \n", "Writing: (2,-1) -!> (2, 0) ==> (2,-1) --> (2, 0) \n", "\n", "APR:\n", "Found nothing\n", "\n", "ER-08:\n", "Found nothing\n", "\n", "ER-02:\n", "Found nothing\n", "\n", "ER-01:\n", "Found nothing\n", "\n", "ER-00-d:\n", "Found nothing\n", "\n", "ER-00-c:\n", "Marked: (0,-1) o!> (1, 0) ==> (0,-1) <!> (1, 0) \n", "Writing: (0,-1) o!> (1, 0) ==> (0,-1) <!> (1, 0) \n", "Update: Marking (0, -1) as non-anc of (1, 0)\n", "\n", "APR:\n", "Found nothing\n", "\n", "ER-08:\n", "Found nothing\n", "\n", "ER-02:\n", "Found nothing\n", "\n", "ER-01:\n", "Found nothing\n", "\n", "ER-00-d:\n", "Found nothing\n", "\n", "ER-00-c:\n", "Found nothing\n", "\n", "ER-03:\n", "Found nothing\n", "\n", "R-04:\n", "Found nothing\n", "\n", "ER-09:\n", "Found nothing\n", "\n", "ER-10:\n", "Found nothing\n", "\n", "ER-00-b:\n", "Found nothing\n", "\n", "ER-00-a:\n", "Found nothing\n", "\n", "Orientation phase complete\n", "\n", "Middle mark updates\n", "\n", "\n", "Final ancestral phase complete\n", "\n", "Graph:\n", "--------------------------------\n", "(0,-1) --> (0, 0)\n", "(0,-1) <!> (1, 0)\n", "(1,-1) --> (1, 0)\n", "(1,-2) --> (2, 0)\n", "(2,-1) --> (2, 0)\n", "--------------------------------\n", "\n", "=======================================================\n", "=======================================================\n", "Starting non-ancestral phase\n", "\n", "Middle mark updates\n", "\n", "\n", "Starting test phase\n", "\n", "p = 0\n", "\n", "Test phase complete\n", "p = 1\n", "Writing: (0,-1) <!> (1, 0) ==> (0,-1) <-> (1, 0) \n", "\n", "Test phase complete\n", "\n", "Starting orientation phase\n", "with rule list: [['APR'], ['ER-08'], ['ER-02'], ['ER-01'], ['ER-00-d'], ['ER-00-c'], ['ER-03'], ['R-04'], ['ER-09'], ['ER-10'], ['ER-00-b'], ['ER-00-a']]\n", "\n", "APR:\n", "Found nothing\n", "\n", "ER-08:\n", "Found nothing\n", "\n", "ER-02:\n", "Found nothing\n", "\n", "ER-01:\n", "Found nothing\n", "\n", "ER-00-d:\n", "Found nothing\n", "\n", "ER-00-c:\n", "Found nothing\n", "\n", "ER-03:\n", "Found nothing\n", "\n", "R-04:\n", "Found nothing\n", "\n", "ER-09:\n", "Found nothing\n", "\n", "ER-10:\n", "Found nothing\n", "\n", "ER-00-b:\n", "Found nothing\n", "\n", "ER-00-a:\n", "Found nothing\n", "\n", "Orientation phase complete\n", "\n", "Non-ancestral phase complete\n", "\n", "Graph:\n", "--------------------------------\n", "(0,-1) --> (0, 0)\n", "(0,-1) <-> (1, 0)\n", "(1,-1) --> (1, 0)\n", "(1,-2) --> (2, 0)\n", "(2,-1) --> (2, 0)\n", "--------------------------------\n", "\n", "=======================================================\n", "=======================================================\n", "Final rule application phase\n", "\n", "Setting all middle marks to '-'\n", "\n", "Starting orientation phase\n", "with rule list: [['APR'], ['ER-08'], ['ER-02'], ['ER-01'], ['ER-00-d'], ['ER-00-c'], ['ER-03'], ['R-04'], ['ER-09'], ['ER-10'], ['ER-00-b'], ['ER-00-a']]\n", "\n", "APR:\n", "Found nothing\n", "\n", "ER-08:\n", "Found nothing\n", "\n", "ER-02:\n", "Found nothing\n", "\n", "ER-01:\n", "Found nothing\n", "\n", "ER-00-d:\n", "Found nothing\n", "\n", "ER-00-c:\n", "Found nothing\n", "\n", "ER-03:\n", "Found nothing\n", "\n", "R-04:\n", "Found nothing\n", "\n", "ER-09:\n", "Found nothing\n", "\n", "ER-10:\n", "Found nothing\n", "\n", "ER-00-b:\n", "Found nothing\n", "\n", "ER-00-a:\n", "Found nothing\n", "\n", "Orientation phase complete\n", "\n", "=======================================================\n", "=======================================================\n", "\n", "LPCMCI has converged\n", "\n", "Final graph:\n", "--------------------------------\n", "--------------------------------\n", "(0,-1) --> (0, 0)\n", "(0,-1) <-> (1, 0)\n", "(1,-1) --> (1, 0)\n", "(1,-2) --> (2, 0)\n", "(2,-1) --> (2, 0)\n", "--------------------------------\n", "--------------------------------\n", "\n", "Max search set: 0\n", "Max na-pds set: 1\n", "\n" ] } ], "source": [ "# Create a LPCMCI object, passing the dataframe and (conditional)\n", "# independence test objects.\n", "# parcorr = ParCorr(significance='analytic')\n", "lpcmci = LPCMCI(dataframe=dataframe, \n", " cond_ind_test=parcorr,\n", " verbosity=1)\n", "\n", "# Define the analysis parameters.\n", "tau_max = 5\n", "pc_alpha = 0.01\n", "\n", "# Run LPCMCI\n", "results = lpcmci.run_lpcmci(tau_max=tau_max,\n", " pc_alpha=pc_alpha)" ] }, { "cell_type": "markdown", "id": "b6a9dc1d", "metadata": {}, "source": [ "Next, we use the function `tp.plot_time_series_graph` to plot the learned time series DPAG (in these plots only the time window $[t-\\tau_{\\max}, t]$ is shown). In the present case the learned graph exactly agrees with the true time series DPAG $\\mathcal{P}^5(\\mathcal{G})$. Note, in particular, that LPCMCI has correctly identified $X^1_{t-1}$ and $X^2_t$ to be correlated due to an unobserved variable. The edges are colored according to the test statistic values returned by `LPCMCI.run_lpcmci`, i.e., the value of `val_matrix[i, j, tau]` determines the color of the edge between $X^i_{t-\\tau}$ and $X^j_t$ (provided this edge exists, i.e., provided `graph[i, j, tau]` is not the empty string) according to the color scale at the bottom." ] }, { "cell_type": "code", "execution_count": 7, "id": "762738fb", "metadata": {}, "outputs": [ { "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": [ "# Plot the learned time series DPAG\n", "tp.plot_time_series_graph(graph=results['graph'],\n", " val_matrix=results['val_matrix'])\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "05b77b27", "metadata": {}, "source": [ "Another visualization is offered by the function `tp.plot_graph`, which as compared to `tp.plot_time_series_graph` intuitively speaking collapses the graph along the time dimension and thus contains a single node $i$ per observed component time series $X^i$. Among all edges between $X^i_{t-\\tau}$ and $X^j_t$ with $\\tau > 0$ that are in `graph`, the strongest one (where *strongest* is measured according to the absolute values of the test statistic values in `val_matrix`) is drawn between nodes $i$ and $j$ in a curved way and the small numbers above this edge list the lags of all these edges in order of their strength. Equivalently for all edges between $X^j_{t-\\tau}$ and $X^i_t$ with $\\tau > 0$. Contemporaneous edges are drawn in a straight (uncurved) way. The color of node $i$ denotes the maximum of the absolute value of `val_matrix[i, i, tau]` across all $1 \\leq \\tau \\leq \\tau_{\\max}$." ] }, { "cell_type": "code", "execution_count": 8, "id": "0f099a96", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 3 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Plot the learned time series DPAG\n", "tp.plot_graph(graph=results['graph'],\n", " val_matrix=results['val_matrix'])\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "3f0092f9", "metadata": {}, "source": [ "For reference we here print out the values returned by the above application of `LPCMCI.run_lpcmci`." ] }, { "cell_type": "code", "execution_count": 9, "id": "0a147db4", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Graph:\n", " [[['' '-->' '' '' '' '']\n", " ['' '<->' '' '' '' '']\n", " ['' '' '' '' '' '']]\n", "\n", " [['' '' '' '' '' '']\n", " ['' '-->' '' '' '' '']\n", " ['' '' '-->' '' '' '']]\n", "\n", " [['' '' '' '' '' '']\n", " ['' '' '' '' '' '']\n", " ['' '-->' '' '' '' '']]]\n", "\n", " Maximum p-values:\n", " [[[0.00000000e+00 3.27584784e-64 6.51860904e-01 1.66931736e-01\n", " 3.04058236e-01 2.69057390e-01]\n", " [4.98048366e-01 1.89520296e-05 8.15648066e-01 3.92853996e-01\n", " 1.53679052e-01 1.45390490e-01]\n", " [9.95865761e-01 8.18453857e-01 2.04903497e-01 9.50877814e-01\n", " 8.78253695e-01 6.62507025e-01]]\n", "\n", " [[4.98048366e-01 3.32516939e-01 9.33958518e-01 4.00491124e-01\n", " 3.51910406e-01 6.25033871e-01]\n", " [0.00000000e+00 4.98133940e-59 7.65327348e-02 9.28068529e-01\n", " 6.83165999e-02 1.95806389e-01]\n", " [6.17173926e-01 6.30053707e-01 3.96189978e-23 6.53400497e-01\n", " 8.99607227e-01 2.03196818e-01]]\n", "\n", " [[9.95865761e-01 5.72580794e-01 4.83793449e-01 7.79201923e-02\n", " 4.34900840e-01 5.91366138e-01]\n", " [6.17173926e-01 4.41473429e-01 1.86116303e-02 3.31452116e-01\n", " 5.04650514e-01 1.93950903e-02]\n", " [0.00000000e+00 1.06276350e-69 9.53625384e-01 7.81344407e-01\n", " 5.31473207e-02 7.35232039e-01]]]\n", "\n", " Associated test statistic values:\n", " [[[ 0.00000000e+00 6.66897503e-01 2.04742563e-02 -6.26652939e-02\n", " -4.66191979e-02 -5.00772920e-02]\n", " [ 3.07434586e-02 1.92483915e-01 -1.05808609e-02 3.87641449e-02\n", " 6.46799194e-02 6.59400254e-02]\n", " [-2.35402162e-04 -1.04276947e-02 5.75454417e-02 -2.79875189e-03\n", " 6.95919926e-03 1.98062204e-02]]\n", "\n", " [[ 3.07434586e-02 -4.39586566e-02 -3.76077439e-03 -3.81426782e-02\n", " 4.22289122e-02 2.21550575e-02]\n", " [ 0.00000000e+00 6.46212248e-01 -8.02534645e-02 -4.09705592e-03\n", " 8.25901632e-02 5.85979432e-02]\n", " [-2.27062309e-02 -2.18567749e-02 -4.27743224e-01 -2.03983914e-02\n", " 5.73142495e-03 5.77031786e-02]]\n", "\n", " [[-2.35402162e-04 -2.56304857e-02 -3.18034416e-02 7.99613935e-02\n", " -3.54263948e-02 -2.43362162e-02]\n", " [-2.27062309e-02 3.49579479e-02 -1.06604491e-01 -4.41008767e-02\n", " -3.02730032e-02 -1.05695395e-01]\n", " [ 0.00000000e+00 6.88594452e-01 -2.64475773e-03 -1.26227188e-02\n", " -8.76818028e-02 1.53468749e-02]]]\n" ] } ], "source": [ "print(\"Graph:\\n\", results['graph'])\n", "print(\"\\n Maximum p-values:\\n\", results['p_matrix'])\n", "print(\"\\n Associated test statistic values:\\n\", results['val_matrix'])" ] }, { "cell_type": "markdown", "id": "f1a1ea87", "metadata": {}, "source": [ "### 3.5 A different choice of $\\tau_{\\max}$" ] }, { "cell_type": "markdown", "id": "7218db69", "metadata": {}, "source": [ "The below code runs LPCMCI with $\\tau_{max} = 2$ on the same data. The learned graph perfectly agrees with the true time series DPAG $\\mathcal{P}^2(\\mathcal{G})$ shown above in section 1.3. We note that $\\mathcal{P}^2(\\mathcal{G})$ differs from $\\mathcal{P}^5(\\mathcal{G})$ in that in $\\mathcal{P}^2(\\mathcal{G})$ there is the edge $X^2_{t-2} {\\circ\\!{\\rightarrow}} X^3_t$ while in $\\mathcal{P}^5(\\mathcal{G})$ there is the edge $X^2_{t-2} {\\rightarrow} X^3_t$. This is NOT a finite-sample effect due to erroneous test decisions but rather another example of the fact that $\\mathcal{P}^{\\tau_{\\max}}(\\mathcal{G})$ can depend on $\\tau_{\\max}$." ] }, { "cell_type": "code", "execution_count": 10, "id": "97f3e2d7", "metadata": {}, "outputs": [ { "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": [ "# Create a LPCMCI object, passing the dataframe and (conditional)\n", "# independence test objects.\n", "# parcorr = ParCorr(significance='analytic')\n", "lpcmci = LPCMCI(dataframe=dataframe, \n", " cond_ind_test=parcorr,\n", " verbosity=0)\n", "\n", "# Define the analysis parameters.\n", "tau_max = 2\n", "pc_alpha = 0.01\n", "\n", "# Run LPCMCI\n", "results = lpcmci.run_lpcmci(tau_max=tau_max,\n", " pc_alpha=pc_alpha)\n", "\n", "# Plot the learned time series DPAG\n", "tp.plot_time_series_graph(graph=results['graph'],\n", " val_matrix=results['val_matrix'])\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "2d3389d7", "metadata": {}, "source": [ "### 3.6 Comparison with PCMCIplus" ] }, { "cell_type": "markdown", "id": "136be9d5", "metadata": {}, "source": [ "The PCMCIplus algorithm assumes the absence of unobserved variables. While this assumption is violated in the present example, let us see what happens when we apply it to the data." ] }, { "cell_type": "code", "execution_count": 11, "id": "1df729ad", "metadata": {}, "outputs": [ { "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": [ "# Create a PCMCI object, passing the dataframe and (conditional)\n", "# independence test objects.\n", "# parcorr = ParCorr(significance='analytic')\n", "pcmci = PCMCI(dataframe=dataframe, \n", " cond_ind_test=parcorr,\n", " verbosity=0)\n", "\n", "# Define the analysis parameters.\n", "tau_max = 5\n", "pc_alpha = 0.01\n", "\n", "# Run LPCMCI\n", "results = pcmci.run_pcmciplus(tau_max=tau_max,\n", " pc_alpha=pc_alpha)\n", "\n", "# Plot the learned time series DPAG\n", "tp.plot_time_series_graph(graph=results['graph'],\n", " val_matrix=results['val_matrix'])\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "a2beeef1", "metadata": {}, "source": [ "We see that PCMCIplus infers the correct adjacencies and for most edges also the correct edge type. However, instead of $X^0_{t-1} {\\leftrightarrow} X^1_t$ it wrongly infers $X^0_{t-1} {\\rightarrow} X^1_t$, which wrongly claims a causal influence of $X^0_{t-1}$ on $X^1_t$. This is an immediate implication of the assumption of no unobserved variables: Without unobserved variables the only possible edge orientations are $X^0_{t-1} {\\rightarrow} X^1_t$ and $X^0_{t-1} {\\leftarrow} X^1_t$, the latter of which is excluded because there is no causal influence backwards in time." ] }, { "cell_type": "markdown", "id": "1c0d014b", "metadata": {}, "source": [ "### 3.7 On the importance of preliminary phases" ] }, { "cell_type": "markdown", "id": "c39fd5f8", "metadata": {}, "source": [ "In subsection 2.4 we have introduced and discussed the importance of LPCMCI's preliminary phases, i.e., of step 2. of the algorithm. By default LPCMCI uses `n_preliminary_iterations = 1` ($k=1$ in step 2. of the algorithm), i.e., by default runs one preliminary phase. As we have seen above, this led to good results for the present example. We here show the results of running LPCMCI WITHOUT preliminary phases, i.e., with `n_preliminary_iterations = 0` for both `tau_max = 5` and `tau_max = 2`." ] }, { "cell_type": "code", "execution_count": 12, "id": "00fee2c2", "metadata": {}, "outputs": [ { "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": [ "# Create a LPCMCI object, passing the dataframe and (conditional)\n", "# independence test objects.\n", "# parcorr = ParCorr(significance='analytic')\n", "lpcmci = LPCMCI(dataframe=dataframe, \n", " cond_ind_test=parcorr,\n", " verbosity=0)\n", "\n", "# Define the analysis parameters.\n", "tau_max = 5\n", "pc_alpha = 0.01\n", "n_preliminary_iterations = 0\n", "\n", "# Run LPCMCI\n", "results = lpcmci.run_lpcmci(tau_max=tau_max,\n", " pc_alpha=pc_alpha,\n", " n_preliminary_iterations=n_preliminary_iterations)\n", "\n", "# Plot the learned time series DPAG\n", "tp.plot_time_series_graph(graph=results['graph'],\n", " val_matrix=results['val_matrix'])\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 13, "id": "46ef88ad", "metadata": {}, "outputs": [ { "data": { "image/png": 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RAoSKvqIoSoBQ0VcURQkQKvqKoigBQkVfURQlQKjoK4qiBIiq0TZAGczS5iYDHAQc5m2zgKnIb5W8VabYl2p/FNgK9CZtgz7Xh9t2F+WPUwqKV3amkSg7hwFTSF9eRipD/QwuK0PLUW99uG1vcf46xS+MtXa0bQgMS5ubQgyukOm2CUU2bS+pK/V6YAXwMvBafbhtX5HtUtgv5lOQDsBIZWdckc3bTYrGAFiLlJsVwBv14bb+ItulpEFFvwAsbW46BDgNmOu9ngbUAZNH0ax8iQKvIxU5eXu1Pty2ZzQNGyt44j6LRLmZC5wKHAlMHDXD8qcfWMmBZUc7EqOAin4eLG1uqgSOZXAlnYtU3KBggdUcWKFdbQzSs7S5qRo4nsFl5zTg4NGzqugkdyTiT5QvAy/Vh9v6RtOwsYyKfoZ4rplTGFxJT6EwPbA9wDpgA+Ji2YL0lga8LZr0PtXn5H01wHSgNmkb+rm6AH9DH9AFPAa0A90jVeSlzU1HA18F9gE/qQ+3rSmAXUVnaXPTQQwW9rnAyRTGFbMLKTvrvW0r6cvGcPuinn3DlZvpFGZccDfwNFJuHgOW1ofbBoY7YWlz06nA3wKbgH+rD7dtKYBdYwIV/TQsbW4aBzQCTd5WD5g8L9tHojIOt71TH24ryg/juRRCHFipZwBHAyd52xF53moP8BSJRuCAiry0uWk54s4A2AxcUh9uey7P+xadpc1NE4EFJMrOKT5cdi+DxXw9iU7B/q0+3LbTh3tlRNJYw9DGYAbyBBwvO/k++e4EOkg0Asvrw22xJDuqgDXAod6uVcBF9eG2lXned0yiou/hFeBTgAuQirqA3Hvx+4CXgGXethx5bO0tlpj7zdLmpinACcCJJCrzScBR5NYYHlCRkV7q+KRjdgNX1YfbwrlbXng8N98ZJMrO2cgTVi7sBl4kUW6WAa8A28u47ExjcLmJvz8yx0tuBZ5Ayk470vhtTXHMB+rDbU/neI8xS6BFf2lz0+EkKuoFwMwcLrOFhLgvQyrqq0GJVvB6tcczuCGYC8zJ8lJbkf/lu4bsjwFfrA+3/TwvQ31maXPTUSR68guR3m62rCch7PHX1+vDbVF/rCxtljY3TUY6Esll5wwkCikbNiFPknOG7N8HXFMfbrsrP0vHFiUt+q7rHgx8EJiNxK7vQELB/uA4ztvZXs8rZO8lIfInZXmJlQwW92XAhnLtgRWSpc1Nc4DzvG0hcHiel/wJ8I/Jj/XD4bru4UjZmYlETW0H3kTKzo5sb+71Vs8jIfTHZHG6RZ70lpEoO8vrw22bs7VjrOM9cR9HotycR/6D219Fxogyqqeu6x4DXIK4qSYhZWclEHYcp+zntJSc6LuuWwlcDlwHNCCDShMRF4JFHn+rkEHCXwD3OY6Ttme0tLlpFvAh4ArgPWQ38LQOaPO2R+vDbZuy/XsUXyvyYuCydBOCXNcdB1wFXI8MmEZJuOgs4j6qBh4BbgQedBwnbQXwGq4rkPIzn+xmsK8iUXba68Nt27I4V/Hwys7JJMrNuchkxf2Yg6Zhd4z4770V+FS6AWHXdUPAx5Gyc6y3exyiOzGk7FQBYeBGx3Eez/qPKRFKSvRd1z0EuB/xrYcyOCUCvABc5jjO/l7T0uam2UhFvRLxr2bqc44g/uVHkMr6ivbi/SdFRV6IDAhmwmagbqjwu647B3gQ8RNPyuA6u5Df+mOO47yTZNsxiNBfAZyZoU0A24BH8cpOfbhtVRbnKhnijZ/MBc4zEyY2TfnqDRdUH3VsRXTrFvY8cB97Ox6B/rSe1deA44fWadd1HeAhxEU30jhevON5F3Cd4zhlN8+gZETfexxfgvQAswkh7APeHuhovyj6UPgCpLI2Znhu1LtnvEfWHRRffKmwtLlpAjJQmc2g3r/Uh9u+Hf/guu4JQCfScFRmcZ29wKr+O27+cOyFZc1IJ+H0DM/tR8IK42Xn+aD44kuFVStXf76y9uCfJu+Lbutlz5/uHU78r64Pt90W/+C67lnI7zeJ7AIS9gBLgfc5jlNW81EycnUYYz4PfBLpgd9hrf2kn0a4rmuQXtIhmdqURM32dRtn3r3r0OUbLvuu2TtgGV9lmLV5NZc9+3sm9+0aevxK5Ed+BHisPtyW1r/b3NplkEe8ScNscZFJLjDZvgdpvHYjPdBdKd7vDrc0jEVRmUT2URzb4288l84TyCN/tlFE42NvrjoutuKlZWTWWLxIoux01IfbDihc5UZPb8QgaT8me1sozfvkz3G3R7qtIoPvQIRzd9K2K837lN8N9KweqKwd7CWsnFZL6OMtTHj/5enEf78fyHXd6cDDZOZVGMoEJIy7FXELlQ0Z9fSNMR9C/FoXAhMKIPqXAHeSwz//nb4YP122C+fwaXyk/gjqpk+kZ+tubl+6BnfNVi576rf0VY9fuX7q4UtfOfTEV7dMPtgiSalCHCjgE1PsK6VMpHsZvmHYheQ92ZxqC7c0lJxIea6e2xFf/HDEvK0TOC8+oOu67qeBfye3ikv/vXcSe6473dcW6c3fDdxXH25bm8s9ioUn4NOQePVZSa/x9zORgIihwl5KZTwrbHTAUlFpjEnd3sd2Rdj79GN29123DDDQ/8f6cNsV8e9c1/0X4Mvkl+tqL/Aux3HKZiJhVu4dY8z3gNl+ir7Xy19GYlJOVtz00h5mTJ3MN953PMk/vLWW7z/8Ks+9tY1oaXiwSoHdpGkQhmybgE3hloZiTRCrQH7/Q0g0YoO2VANwrusOnZSTNQNPPU70gT8k77LI/IF7EKFfn+u1/aKnN1JBQrxTiXn89VCKn3CtXNgCHFJXG7Kwf+B2Azl2FpLoA37jOM51eV6naJRCauVjSYyWZ8263TG+cP4RDG3pjTF8pP4Ilq/bQXQgoyi/IDARiWWek8Gxe5pbu1YjeXVWJb2uAlaHWxoifhnl9dqX5XDq2WQ2aJuWysYFANhNG2Oxl178Cnt2314fbtuYzzVzwRP22UiU03FInYi/PxoV83yZAZyDNOgA70ca+HypQVzfKvpZMAsZFMuJvf0x6qanHnCvmz6BffkLfh9pep/eNtT2oQXJZvCdQQrPUBfT0PfFZAKJCTMH0Nza9TZDGoKk1zXhloZhc6X4RN6J7UxFBVXvORdgN5df9UfHcQom+J6wH05qYT+G0RP2fcgM6Yj3ujPF5+T3+xBXm81wS3UsSBmbOGSblOW+TLGAm/R5Fv79v6tc153kOE7JuU9TUQqin5c/cXx1BT1bd3P0jAM7fD1b9zCuqoK9A7F2DlwMIsLwYr4L2FUk8RoRb1B5AsM3DCGkR3NImi3X1ACpONjb5qX4Ltrc2tWDZE58wdteBFaGWxr8jI7y2xfty/U83/ocJBLodMAhIezj05/pC7sQt8XGFK8bkYHMQUJeVxsqy4i1nt7IEuDdIxw2APQAf1dXG0pO1eBn2bE+X6+glILobyIPOw6fWMHtS9fwzRQ+/TuWrmEgZp8ItzSc74eho4nnX49HL2SN12hMYXAjcDCpG4cjyDxuPhWViEviaGRmY5y+5taul5EGILkx2Jjj+MEm/HlEB+n1ZT3Lu6c3UoWkEjg9aZvLkAlEPrAVmY0eF/GUwl5XGypawrXRxGtY00V9bUQmUf0ReLSuNpQqpHIT8hTvR0fIIg1oWZBp9E58GbVvI37HTwMD1tq8e8HeQO6rSE8oa97pi/HTF/Zw8qyDvOidCfRs3cMdS9fwwvod+/b0x+rCLQ06kzYLvAZiGiLaR5EQ8Pj7OvztMGwh0QDEG4OXwy0NwzZwruvWIPlravO8vwWecBznvOEO6umNTETClpMF/hT8671vRSYQve697n9fVxvSGb1D6OmNXAf8FOlkvAT8ARH6Z+tqQ8P6dV3XnYrMuM/XbToA3OE4zjV5XqdoZCr630EEP5kbrLXf8cMI13WvBH5DjiPp7/TFuOu1vdF1u2zFvoGYGVdVYQditmMgZj+sgu8/za1dVYhvemhjEH9/iA+3sYjoPYeEaXYCLw51t7mu+yXg++Q3oLsbaHIcpzO+o6c3UoMk/2r0Xk9HevT5PsZvY4igx98PcT8oGdDTG5kOVNXVhrLOY+S67k+Az5Ffo70XOMVxnNfzuEZRKYkZuV6+nVXIU0QulSqGhO4d7TiOhuqMMs2tXSHkye0UbzvVe8134HUX0E2iEXjmh42T9yE9tqk5XnMA+MvkWXPOBc5CIjwWIHmf8onffgeJSPqL97oC6bGrsJcIruvORHQnnxTqDzmOc5l/VhWekhB9ANd1j0OSqE0lu6n0UWSGZoPjOK/5b5niF82tXQczuCE4FcnBk4+4vnxQjVl5/hE1i+ZMqRo3Y7xJO1FnEKaCyppxA1XjJuyunjh5pTHmNHJfQWwjIu7J2+qRXAzK6OO67vuA/yN74d+LPKGdk0vW1tGkZEQf9ifNeojMF4LejYzMX+w4zpuFs0wpFM2tXZVIVEtyQ3AK2aUu3s/EKjhyciV1kys5cnIls0OV1FQaTEUFlTXj928VVdUHzO3IkFUMEfi62tCGXC6klAau6zYiYwEhMgvj3AU8DnzEcZyyGzgvKdGH/blUrkVSnB6NuHuSf4h4jPAbSHrcm8ox050yPJ6LaC7iZjkb8a1nnI45VFPJiTMnc9zBkzjhkElMn5RTSPYGZDLPM3humrraUFn16pTM8NbuuB5oQVJVjGewx2G39/kZ4GfAvcOl5S5lSk70k3Fd91gk8+FRiNtnOzL55+5yGjhR8seLKDoGEf/45uAl7zLA7KnjOfnQKTizJnPktAlUZN+TfwMR+Se911XxaftKMPCiCc9EFuA5DAkQ6EWig+5OTuFerpS06CvKcPzPU6uOmD6pumX6xJr3z5o87qRJ46qyirlev2Mvb23fs/XtyL7Otdv33vrSxp1/CLc0pFygRVHGCir6StngTcg5EbgYmfSV8Upo0ZhlzfY9vLFlF69v2cWq3t3s6jsgU/VeJE3zn71tRbGSzilKsVDRV0qant7IBGR1rbjQz8ni9L/s2NP/dMeq3v4n39h61O7+6HmIvzZT1iL51h8CHirF1NSKki0q+krJ4U24+RBwKbKUYqYhnRFEpB8EHqqrDQ1Ki+xNKns3si7EhUjeoEznhexBxP8e4E/hloayi9pQFFDRV0qEnt7IFGTw7CrgfWSe5uEVROQfAJ6qqw31ZXrP5tauacD5JBqBIzI8dR/i/rkHCIdbGrZnek9FGW1U9JVRw8tl837gw4jrJpO4yn3IguYPAA/W1YZ8WYDciw46nkQDcC6ZPWH0I0so3gv8IdzS0OuHPYpSKFT0laLS0xsZh4jqVcAHyCxnzhpE5B8AHqurDRXct97c2jUemR9wCXA5ma3jGwXakSeA+8MtDWUf3qeMPVT0lYLT0xupRnzzVwGXkdlg6ivIusn3Ai+NZry89xRwJiL+VyKTBkcihkQC3QP8X7ilQWftKiWBir5SEHp6I5VI8rIPA1cgi7uMxGpE6O8EXizFiVFeA3Aa8jddCbwrg9Ms4gL6DeIC0rkAyqihoq/4Sk9vpA5Jo/EpJGvqSKwD7kKE/tlSFPp0eA3AyUgDcIX3fiS2AbcD/ws8r/MAlGKjoq/kjee+aUYW17kQLzXCMGxG3B53Ak+PlWyUza1dJyAuoCuQvEEj8QIi/reFWxqyXrVLUXJBRV/JmZ7eyLHA3wCfBGaOcPh2xD9/J/B4XW2oJNYeLhTNrV3HIg3Ah5EFWIajH1ne7zfAn0tlXWZlbKKir2SFF31zGdKrXzjC4buRXOV3Ag9nE0M/lmhu7ToVcXddzchjGxuA3wH/G25peLXQtinBQ0VfyYie3siJiNBfw8hr0i4FfgXcUVcbeqfQtpULza1dNci8hL8GLmLk2cCdiPvnrnBLg/4fFV9Q0VfS4uW9uRIR+/eMcPhO4DbgV3W1oecLbVu509zaNQv4ONIAHD/C4buBW4D/Drc0vFxo25SxjYq+cgCer/6LiChNHeHwLqRXf1cxJk2NNbwIoLMQ8f8wMHmEUx4G/gtYHG5pGBMD4EpxUdFXgP1pi88G/gHJgTNcBM42pOf5q7rakFsE8wJBc2vXJGTw91NIGojhWAn8N3BzuKUhUmDTlDGEin7A6emNVCEDs19Gsk4ORwfSq7+3rja0p9C2BZnm1q5jgE8w8nyHHcCvgZ+FWxreLIJpSpmjoh9QenojkxGXwt8yfI76LcDNwK/rakOvFN4yJRkvHfRlyO/UOMyhMeB+xPXzpE76UtKhoh8wenojs4EvAJ9h+Bw4y4F/Q3z1uvB8CdDc2nUm8CXE9189zKF/QcT/znBLg/52yiBU9ANCT29kLuKvv4rhc9U/iIj9Y+WUEiFIeJE/13nbcHH/m4EbgV+GWxo2FsM2pfRR0R/D9PRGKoBFiNgPN5FqHzIw+x91tSENCSwTvPTPH0FcP6cOc2gfcBPwo3BLw1tFME0pYVT0xyBeLpyPI4OzJw5z6BbgF8Av6mpDm4phm+I/XtjnuYjr5wOkj7zqRyZ7/Sjc0rC6ONYppYaK/hjCE/trgH9i+MHZlcC/A7/TKJyxhRf183kk02m6mP8okurhB+GWhteLZZtSGqjojwG8sMurgW8x/AIfHYi//k9jJbOlkprm1q4pSCK8vwfq0hwWRWZR/0Dz/AQHFf0yxhP7jyJif2yaw6LA3cC/1dWGniuWbUpp0NzaFXf1fZP0HYIYkhTv+5rmYeyjol+GeKtSfQT4Z+C4NIf1I5N2flxXG+oplm1KaeLF+38Ucf2lKzMWWefge+GWhheKZZtSXFT0ywhP7P8KEfsT0hzWj+Rl/0FdbUgjNZRBNLd2VSJx/t8ifRkCSYn93XBLw1+KYphSNFT0ywAv9PJK4Nukj8YZQCIzvq89e2UkPPG/HBF/Z5hDw8B3wi0Nmjl1jKCiX8J4Yn85Ivbp1l+NImkSvldXG9IwPCUrmlu7KoBLkafH04Y59Dbgm+GWBu1QlDkq+iWIl/HyA8B3gVPSHBZFJlR9r6429EaxbFPGJl6sfzMi/vVpDtuHZPb8QbilYXuRTFN8RkW/xOjpjdQjMfQL0hwSA24FvltXG9IYa8VXPPG/CBH/+WkO24p0SH4RbmkI5BKY5YyKfongJUL7ARJelwoL3I6IvcZUKwXFE/9FwI9J/7S5Cvg6cLdm9SwfVPRHmZ7eSAj4KpIyYUKKQywSQ/0vmtpYKTbegO81wPeAw9Ic1g18OdzS8FTRDFNyRkV/lPDCLz+JVKZD0xy2GPiKrk6ljDbNrV0Tgb8DvgaE0hx2P/A1nd1b2qjojwI9vZELkHQI6TIjusCX62pDfy6eVYoyMs2tXYcg0WSfASpTHBIFWpEwz83FtE3JDBX9ItLTGzkR+FfgkjSHbEZmTP5vXW1ooGiGKUqWNLd2HQ/8CAn3TEUEGQ/493BLw+5i2aWMjIp+EejpjRwMfIf0vaO9SMTOj+pqQzuLaJqi5EVza9c5SEcmXaTPOiTf/7062FsaqOgXkJ7eyHjgi0iyqylpDrsN+IamTFDKFS/S50rgh6RP6rYY+Hy4pUHnlIwyKvoFoqc38kHgP0mf1/4p4O/rakPPFssmRSkkza1d45AlHL8FTE9xyD4kLPn/hVsa9hbTNiWBir7P9PRG6pBZix9Ic8gqJETzPl2DVhmLNLd2TUWebv+W1OsxvwZ8LtzS0FZEsxQPFX2f8Fat+hJwAzAxxSHbkVmMP6+rDe0rommKMio0t3adhCzH+d40h9wF/F24pWF98axSVPR9oKc30gj8ktQzF6NIwb+hrjbUW1TDFGWU8fz9VyMhygenOGQnkvLhZ+GWBo1YKwIq+nnQ0xuZjgxetaQ5pAv4TF1t6MXiWaUopUdza9c04PvAZ0m9cPty4LpwS0NXUQ0LICr6OeBlwfwYEmaZqveyDfhH4CZdi1ZREjS3dr0buJH0mTx/jczq1afiAqGinyU9vZHjEXfNwjSH3ILMptXZiIqSAi+fz2eRSJ5Uocy9SLDDb8MtDdpp8hkV/QzxYu6/juQeqUlxyErgurraUHtRDVOUMqW5tetQ4CfIU3MqngKuDbc0rCyeVWMfFf0M6OmNNCG9+2NTfB2PPf6xRuUoSvY0t3YtROrX8Sm+3oN0tn6qvX5/UNEfhp7eyKGI3/4jaQ55BLi+rjb0WvGsUpSxhzex6x+QiV3jUxzSAXwq3NKwqqiGjUFU9FPgDdReBfwcmJbikE1Imtk7dYKVovhHc2vXUcDPgItTfL0L8fX/Unv9uaOiPwQvOdovgCtSfG2RePxv1NWGthfTLkUJCl5s/zXAfwEHpTjkUcTXr4u054CKfhI9vZHLgP8hdRjmMuCzdbWh7qIapSgBpbm1azYSwnlhiq93An8P3KTZO7NDRR/o6Y1MQ/LlXJ3i633IjMF/1xz3ilJcvF7/3yBja6lW7FoMfDrc0rC2qIaVMYEX/Z7eyCLgJlKv/7kUuKauNvRyca1SFCWZ5tauOuA3pJ4fswNJYX5Lcq+/ubVrEjAQbmnQqLokKkbbgNGipzcyuac30go8xIGCP4D07htU8BVl9PH8903A54ChK3EdBNwM/MGL/ae5tevDSMDFrubWrmuLaWupE8iefk9v5Dyk1zAnxdcvAp+oqw39pahGeXTMb5wIzEIaosO891ORFLXJW2WKfan2R4GtyCzH+Db0c++C7k5d0q7M6ZjfGGJw2TkMmfGarryMVIb6GVxWDig3SNkpam785tauY4D/Bc5J8fVW4PNIRttjvH1R4IxwS8MLxbGwtAmU6Pf0RiYi63p+IcXXMWRNzxsKMcmqY37jOA6skEM/H4YI/GiwlwMr9XpgBfAy8PKC7s63R8m2QNMxv3ECqcvK0G3yKJm4hwMbg7VIuVmBlJ1tft6wubWrAnHp/JDUcf29QG3S52eAszXUM0Ci76U//i1wXIqvX0V693lH5nTMb6wE3gXMBU5Lej0032uXAFvwGoAh28YF3Z3BKEgFpGN+YzVwIolyMxc4FZgxelb5xkZSlJ18OxLNrV3vQup1QwaHfybc0tCaz/3GAmNe9L2cOTcAX+bAMQyLLGn4zbra0J5sr90xv3EyUimTK6kDTMjZ4PJkBwdW6OcXdHdq0rk0dMxvnMrgcnMacDKp8zqNZeIdif1PlMDSbJ4MvARuf4+4dMYNc+h24PhwS0Ogy+WYFv2e3sgpwB1IZRrKauCTdbWhjkyu1TG/cTaJCjoXqaSpcvHkywCwAXGtxF+3IP7VAW+LJr1P9Tl5Xw2yXmlt0jb0cy1QXYC/xQUeA9qBJ0aqyB3zGx3g20iY7PcWdHe+UgCbikrH/EaDjB3NZfDTX10BbtePlJfkbSvpy8Zw+6KIgI5UbqaTeknEfLDAX5By8xjw5ILuzp3DnfCbKz99edVA3007QtOndJ3eZHaGUk2kB+DWcEvDx/01t7wYk6LvpVG4HlmtJ1XL/0vgK3W1oUi6a3TMb5yOhIc1edtReZoVQ6IJhlbKoduWBd2dRfU7esIUYnClngEcDZzkbSeQ2neaKfGKHG8EDqjIHfMbV5Jwv+0APrSgu7PsspZ2zG+cCVyAlJsLgMPzvGSURAcgeRu6r7fYbjav7ExmcEMwA+kQxcvO8eTXqYgCz5JoBDqTAw865jdW91XVRGoG+moAIhMmc+/7/obNM2anu9754ZaGsitXfjHmRL+nNzIDibtPtTD5WuDautrQw0O/8AZaG0lU1jNJvcJPJryNzOCNb8uBlQu6O/tzvN6o441VzEF8zicN2SblcMl4RY43Ap3I/y15feF+4NoF3Z235Gx4EfAirs4h0UE4NY/LrUPKy7Kk1zcWdHdG87Ny9OiY31iFRNLEy8uJSa+5dCT6kFXp4mXnFQubkytrX1UNf1x4DauPODHV+TuAmUGN3x9Tot/TG1mILGKSaqLVLcAX4zlzvB6KQ6KiLiD1gubDYZFB4HjlXOa9D8zAZsf8xgpgNoMbgbnA6WQ3D6QPEbxUT1T/jLh7SuJ/6jWAp5MoO2eTvS8+ivivkwV+eZAipLz/Yx2Dy84ZpF5rejj2RE3FtkobG1TvY8bwSOPlLD8h5RjvL8ItDZ/Lweyyp6RF33XdY5HEZ0cj8cbvAKuAux3HeaOnNzIT+L2BBRaMQVR4CDuRnDm3d8xvPIxERb0AmJmFOX3IDN1lJCqpu6C7c1dOf9wYxxuoXACch7jJ8un9gsRlfybTpyXXdR3gQ8CRyJPIdmShm7sdx8l6yn7H/MajSDwFno+4wjJlN/Acg8vOy8WOby8XOuY3HgycS6LspMqznzHdp55Hx5kXgxnUBwmHWxoO8Aa4rmuQp/xmxC03EVn+1EXKTtk3yiUn+q7rjgP+Gpl5dzTSW0z2y+8DLBUVb06ZeeSx46srq6ZOrKG6soL+aIztu/vY2x+Ni/+S3p/+17ciDz1wFtJ4ZNuDeAFo87YndQJT7ngV+b1IJT4PGSPIlmeBpgXdnTtSfem67mTgM8hSfLMQP3KyL3kPUp6WI2mzb3McJ6XbxHsSPB24Emk83pWFnRYR+XjZ6VrQ3RlIV4IfeJ2180g0AlmPr607pG7b7xd99qBodU0FoiGnh1saVsS/d113BqI5n0bmyoxHJqnF2YUMWHchqZ/vcxyntMQzQ0pK9F3XnQM8iDzyDetqmTB9JpNDIQ6ePB5jEt48ay1v79zLzrfW7lh37SfWI37DTFmHVNJHgEcWdHduyvZvUDLDq8jnIpX4IlK75FLxDjB76CCw67onIyk1ZpBZyOwuRPwvjffePKE/ExH6K8hOXFaRKDvtC7o7t2ZxrpIFHfMb55BoAC5i8CSstFjY8NOPfXfRvvETV4RbGvY/Mbqu2wCEkWCG4UI+4+xCxhM+6jjOsFFFpUjJiL7ruschrehUBrewKZkyaw6HHjSBmqoDD+0biLJx+27evCRVRtZBRIDHSfTIXikVv3FQ6JjfOAkRzEOyOO1HC7o7vx7/4LrumciAXojsBt/7gQ07f/HLq/uXPv9BROgzDaXc5t2zDWhb0N2pKzoVmY75jYcCr5NdIMG1C7o7fxP/4LpuE3A/2Y/n7UXchec4jvNOlueOKiPG1xpjxiGLilyA+DFfB75hrX3ILyNc161EekjTyHDwzwLVlakPra6swJqUdT8GdJPokT1TzhE1Y4RxZCf4AG/G37iuOxH5PXNJQVDd/9JLh/cvW/4EIzcW/cDTJMrO0nKOqBkj1JJ95Nib8Teu684kN8EHcf8cjyR6uyyH80eNTCZVVAFrEH/sW8gyZncZY06x1r7pkx2XIQ1KxtEeBuiPxlL29PujMUziCWYAWWnnHuD+Bd2dW/K2VvGNBd2dWzvmN94IXJfmkCjigzVIo70YSJ5K/2nyiAHf1/1sJdG02r3Pu9/dQHhBd2dZ9egCwArgPmTMJRUDSACGQcrRrUPmfXyF/DINjwMudF33WMdxXs/jOkUlJ/eOMeYF4AZr7b35GuCNlr9K6pw4aRnJp7/jlVe3bvjC9V8G/qD+1dKnY37jEUiPf9eQrS+dy8113RpkUlJGPt1U7H38CXbfctugXci40j3An0aaCaqMPh3zG49B0isPKjsLujv70p3juu5UZN5OLnNMkhkAbncc5xN5XqdoZC36xpiZQA8w11qb9zR513VPRKIysvvnV1QweeYRTKiuIkX0zj4LdXW1IR2IHcO4rns+0tObkus1rLX0dT1DdN36WF/3kk/Htm27a0F3Z9qZ2srYwHXdq4EbSb0aV7b0AePLJZonq5wZxphq4DbgZj8E32Mm0lpmRyzGzk1rGJh6MHv7J2Dxnv9j0WWmonKRCn4gmEnus6YBMMYwrrEBYPfEKy9/ynEcFfxgMBP/ktvF05iUxVNhxqJvjKlAZrX2IYsU+EXurWMsxp6tm0hKj7kD+JLjOCr4wcDvHEVl0VNTfMHPspNmXmhpkpHoG3Ga34S0jhdba/2MeNmAfxkeq73rKcHAz996HJIQTwkGG5CBej96+wPIOEJZkOnI9Y3IJKdma23WeedH4DUkDNSXazmO85pP11JKn6fxp7LFgLZyi7dW8uJP5Oka9OgDflsu/nzIQPSNMXXI1Pa5wEZjTMTbPuaHAd4/65vIRKl8iADfyN8ipVxwHCe+gH2+ZWcv8K38LVLKBW/s5j+AfDuxMeAH+VtUPEYUfWttj7XWWGvHW2tDSdttI52bBQ8goXfZD+gKA0gKBd8mjCllw++QiptrT6sPWOI4zvP+maSUCf9J7poDUu7ucRxnjT/mFId8Jib4htfbPx/YjMx8zIZ+77zzy+kRS/EHx3H2IRMHtyMTcLJhH/AG6Sf3KGMYx3G2Au9DnhSz1Y49wPNAi992FZqSEH0AL93tGUj64kwf1yNINsPTHcdZVyjblNLGcZwVQD0yPpSpjz+CpFM4y3GcjNdjVcYWjuM8gyyetA5JgT0SFiljvwcucBzH7zHOglMyCdfieHl4rkCm5Z+FPH4lT9xKTnH6S+TxSnOgKPG03B9Fys6piL91PImQul1IhNejSNn5kz4dKrA/LffVyDKrxyBlJr6ql0U6CdVINs4bHcd5bDTs9IOSE/1kXNc9GLgUWcxgKvIIvw64fywsZqAUDtd1ZyNLZh6KzNjdhiTbut9xnJT5+BUFwHXdY4D3AwcjHc6tyFNk2HGcsgnNTEdJi76iKIriLyXj01cURVEKj4q+oihKgFDRVxRFCRAq+oqiKAFCRV9RFCVAqOgriqIEiKwWUVGUoDKu/tPWVFRSUVWDqayksqoG+Vwtr9XyObG/ZtD+iqoaKioMpsJQWVkx+LWqAlNB6v3GUFk1+Lyaqgoqvdeq/Z8rEvsr5bVqyLGDXisrqDCG6kpDpTFUVxgqKrxXY6iurKDSQHVlBRUGqisqqKyQ1/h5xkClMVQYBr2vNGbIZ1msptIgnysMBu/VWkxsAGwMYlGMjYH32UTT7LcxiEblvbfPDvRDLIrt74NYDDsw5LW/T76PH7f/eHmNDfRjozFi/QPYaIxoXz82Ftu/LxaNEesbwMbkNRaLEfOOifV53/cPYKOWaH+UWNQS65PXaH9U9vdFh/1e7hsjZi19MUt0/ytDXhP7+22q4yw/j72ZNoOo9vQVRVEChIq+oihKgFDRVxRFCRAq+oqiKAFCRV9RFCVAqOgriqIECBV9RVGUAKGiryiKEiBU9BVFUQKEir6iKEqAUNFXFEUJECr6iqIoAUJFX1EUJUCo6CuKogQIFX1FUZQAoaKvKIoSIIy1drRtUJRAYIxpsda2qh0J1Jbi26E9fUUpHi2jbYBHqdgBaksqCmqHir6iKEqAUNFXFEUJECr6ilI8Rt1f7FEqdoDakoqC2qEDuYqiKAFCe/qKoigBQkVfUQqEMWa6MabNGPOa9zotzXF/Z4x5yRjjGmPuMMaMHw07vGMrjTF/Mcb8yWcbFhljXjXGvG6M+VqK740x5r+9718wxpzh5/0ztSPpuHcbY6LGmCsKYUcmthhjDjLGhI0xy73y8Sk/7quiryiF42vAo9ba44BHvc+DMMYcDnwRONNa6wCVwFXFtiOJLwEr/Ly5MaYS+DlwEXAS8BFjzElDDrsIOM7bWoAb/bQhCzvix/0Y+LPfNmRpy+eAl621pwHnAv9mjKnJ994q+opSOD4I3Oy9vxm4NM1xVcAEY0wVMBFYPxp2GGNmA5cAv/b5/vOA1621q6y1fcCdnk1DbfydFZ4BphpjZo2CHQBfAO4FNvt8/2xtscBkY4wBQsBWYCDfG6voK0rhmGmt3QDgvR4y9ABr7TrgJ8BbwAZgh7X24WLb4fGfwFeBmM/3PxxYk/R5rbcv22MKbof35HUZ8Euf7521LcDPgBORTsCLwJestXn/NlX5XkBRgowx5hHg0BRffTPD86chPbyjgO3A3caYq621txbZjvcDm621S40x52Zz70wun2Lf0LDBTI4phh3/CfyjtTYqHeyCkYktFwLLgIXAMUCbMeZJa+07+dxYRV9R8sBae0G674wxm4wxs6y1GzxXRSp3wQXAamvt29459wGNQFai74MdZwMfMMZcDIwHphhjbrXWXp2NHWlYCxyR9Hk2B7qwMjmmGHacCdzpCf4M4GJjzIC19v5RsOVTwI+sxNW/boxZDZwALMnnxureUZTC8UfgE977TwB/SHHMW8BZxpiJnu/2fHweSM3EDmvt1621s621c5CB5HafBB/gWeA4Y8xR3kDkVZ5NQ228xoviOQtxc23w6f4Z22GtPcpaO8f7P9wDXF8Awc/IFqRsnA9gjJkJHA+syvfGKvqKUjh+BDQZY14DmrzPGGMOM8Y8CGCt7UbE5XnEb1uB/zMyR7SjkFhrB4DPI9EwK4C7rLUvGWM+a4z5rHfYg4igvQ78Crh+lOwoChna8l2g0RjzIhJ19Y/W2i353ltn5CqKogQI7ekriqIECBV9RVGUAKGiryiKEiBU9BVF8R1jzJvGmD5jzIwh+5cZY6wxZo73eZ4x5kFjzHZjzFZjzJJ4jhljzLnGmLWjYP6YRkVfUZRCsRr4SPyDMeYUYELS5wagHXgCOBaoBa5D8tEoBUJFX1GUQnELcE3S508Av0v6/K/AzdbaH1trt3h5d5Zaa/+qqFYGDBV9RVEKxTPIzN4TvaySHyYx03gi0IDMUVCKiKZhUBSlkMR7+08ArwDrvP3TkE6n37NulRFQ0VcUpZDcAnQgCeWSXTvbkGyes5DGQCkS6t5RFKVgWGt7kAHdi4H7kr7aDXQBl4+GXUFGRV9RlEJzLbDQWrtryP6vAp80xnzFGFMLYIw5zRhzZ9EtDBAq+oqiFBRr7RvW2udS7O9EcsUvBFYZY7YiyeYKngQuyGjCNUVRlAChPX1FUZQAoaKvKIoSIFT0FUVRAoSKvqIoSoBQ0VcURQkQKvqKoigBQkVfURQlQKjoK4qiBAgVfUVRlADx/wGYIWWJNR3rYAAAAABJRU5ErkJggg==\n", "text/plain": [ "<Figure size 432x288 with 2 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Create a LPCMCI object, passing the dataframe and (conditional)\n", "# independence test objects.\n", "# parcorr = ParCorr(significance='analytic')\n", "lpcmci = LPCMCI(dataframe=dataframe, \n", " cond_ind_test=parcorr,\n", " verbosity=0)\n", "\n", "# Define the analysis parameters.\n", "tau_max = 2\n", "pc_alpha = 0.01\n", "n_preliminary_iterations = 0\n", "\n", "# Run LPCMCI\n", "results = lpcmci.run_lpcmci(tau_max=tau_max,\n", " pc_alpha=pc_alpha,\n", " n_preliminary_iterations=n_preliminary_iterations)\n", "\n", "# Plot the learned time series DPAG\n", "tp.plot_time_series_graph(graph=results['graph'],\n", " val_matrix=results['val_matrix'])\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "92388676", "metadata": {}, "source": [ "We see that, unlike with the default setting, the algorithm fails to detect the edge $X^0_{t-1} {\\leftrightarrow} X^1_t$. This is a manifestion of what has been discussed in subsection 2.3: A low effect size due to autocorrelation of the time series. With the default settings, where `n_preliminary_iterations = 1`, this problem is addressed by restoring all removed edges after the preliminary phase and then working with larger default conditioning sets $\\mathcal{S}_{def}(X^i_{t-\\tau}, X^j_t)$ in the final ancestral phase (step 3.) of the algorithm.\n", "\n", "For those interested in validating this point in more detail:\\\n", "The edge between $X^0_{t-1}$ and $X^1_t$ is removed because the (conditional) independence test wrongly judges $X^0_{t-1}$ and $X^1_t$ to be independent conditional on $X^0_{t-2}$. To see this set `verbosity = 1` when creating the LPCMCI object in the previous two cells and search for the line `(0,-1) independent (1, 0) given ((0, -2),) union set()` in the verbose output, or set ``verbosity = 2`` and search for ``ANC(Y): (0, -1) _|_ (1, 0) | S_def = , S_pc = (0, -2): val = 0.10 / pval = 0.0287`` (here, ``S_pc`` refers to the standard conditioning set $\\mathcal{S}$ and ``S_def`` to the default conditions $\\mathcal{S}_{def}$). Then set `verbosity = 2` in the default applications of LPCMCI in subsections 3.4 and 3.5 above and see that the same happens in the preliminary phases of these runs. However, in these cases the algorithm restores this edge before moving to the final ancestral phase while it remembers that $X^0_{t-2}$ is a causal ancestor of $X^0_{t-1}$ and that $X^1_{t-1}$ is a causal ancestor of $X^1_{t}$. The final ancestral phase therefore uses $\\mathcal{S}_{def}(X^0_{t-1}, X^0_t) = \\{X^0_{t-2}, X^1_{t-1}\\}$ and never tests whether $X^0_{t-1}$ and $X^1_t$ are conditionally independent given $X^0_{t-2}$. Indeed, search for ``ANC(Y): (0, -1) _|_ (1, 0) | S_def = (0, -2) (1, -1), S_pc = : val = 0.19 / pval = 0.0000`` in the verbose output.\n", "\n", "For `tau_max = 2` it is further wrongly inferred that $X^2_{t-2}$ and $X^1_t$ are adjacent." ] }, { "cell_type": "markdown", "id": "9a78de36", "metadata": {}, "source": [ "## References" ] }, { "cell_type": "markdown", "id": "4771a198", "metadata": {}, "source": [ "[1] Pearl, J. (2009). *Causality: Models, Reasoning, and Inference*. Cambridge University Press, Cambridge, UK, 2nd edition.\\\n", "[2] Peters, J., Janzing, D., and Schölkopf, B. (2017). *Elements of Causal Inference: Foundations and Learning Algorithms*. MIT Press, Cambridge, MA, USA.\\\n", "[3] Richardson, T. and Spirtes, P. (2002). Ancestral graph markov models. *The Annals of Statistics*, 30:962–1030.\\\n", "[4] Zhang, J. (2008a). Causal reasoning with ancestral graphs. *Journal of Machine Learning Research*, 9:1437–1474.\\\n", "[5] Ali, R. A., Richardson, T. S., and Spirtes, P. (2009). Markov equivalence for ancestral graphs. *The Annals of Statistics*, 37(5B):2808–2837.\\\n", "[6] Zhang, J. (2008b). On the completeness of orientation rules for causal discovery in the presence of latent confounders and selection bias. *Artificial Intelligence*, 172:1873–1896.\\\n", "[7] Spirtes, P., Meek, C., and Richardson, T. (1995). Causal Inference in the Presence of Latent Variables and Selection Bias. In Besnard, P. and Hanks, S., editors, *Proceedings of the Eleventh Conference on Uncertainty in Artificial Intelligence*, UAI’95, page 499–506, San Francisco, CA, USA. Morgan Kaufmann Publishers Inc.\\\n", "[8] Spirtes, P., Glymour, C., and Scheines, R. (2000). *Causation, Prediction, and Search*. MIT Press, Cambridge, MA, USA.\\\n", "[9] Runge, J., Nowack, P., Kretschmer, M., Flaxman, S., and Sejdinovic, D. (2019). Detecting and quantifying causal associations in large nonlinear time series datasets. *Science Advances*, 5:eaau4996.\\\n", "[10] Runge, J. (2020). Discovering contemporaneous and lagged causal relations in autocorrelated nonlinear time series datasets. In Sontag, D. and Peters, J., editors, *Proceedings of the 36th Conference on Uncertainty in Artificial Intelligence*, UAI 2020, Toronto, Canada, 2019. AUAI Press.\\\n", "[11] Spirtes, P. and Glymour, C. (1991). An Algorithm for Fast Recovery of Sparse Causal Graphs. *Social Science Computer Review*, 9:62–72." ] }, { "cell_type": "code", "execution_count": null, "id": "9e06fe46", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.7" } }, "nbformat": 4, "nbformat_minor": 5 }
gpl-3.0
sophie63/FlyLFM
Notebooks/Utils/.ipynb_checkpoints/100135-lobes-crosscorr-checkpoint.ipynb
1
1369680
null
bsd-2-clause
pombredanne/https-gitlab.lrde.epita.fr-vcsn-vcsn
doc/notebooks/automaton.is_ambiguous.ipynb
1
15864
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# _automaton_.is_ambigous" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Whether the automaton is ambiguous.\n", "\n", "Preconditions:\n", "- the labelset is free.\n", "\n", "Postconditions:\n", "- [_automaton_.ambiguous_word](automaton.ambiguous_word.ipynb) does not raise.\n", "\n", "See also:\n", "- [_automaton_.ambiguous_word](automaton.ambiguous_word.ipynb)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Examples" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ ":0: FutureWarning: IPython widgets are experimental and may change in the future.\n" ] }, { "data": { "application/javascript": [ "IPython.load_extensions(\"AutomatonD3Widget\")" ], "text/plain": [ "<IPython.core.display.Javascript object>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { 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gpl-3.0
robertoalotufo/ia898
master/tutorial_contraste_iterativo_2.ipynb
1
159110
{ "cells": [ { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true, "toc": "true" }, "source": [ "# Table of Contents\n", " <p><div class=\"lev1 toc-item\"><a href=\"#Realce-de-Contraste-Interativo-utilizando-Janela-e-Nível\" data-toc-modified-id=\"Realce-de-Contraste-Interativo-utilizando-Janela-e-Nível-1\"><span class=\"toc-item-num\">1&nbsp;&nbsp;</span>Realce de Contraste Interativo utilizando Janela e Nível</a></div><div class=\"lev2 toc-item\"><a href=\"#Equação-da-função-de-realce-de-contraste\" data-toc-modified-id=\"Equação-da-função-de-realce-de-contraste-11\"><span class=\"toc-item-num\">1.1&nbsp;&nbsp;</span>Equação da função de realce de contraste</a></div><div class=\"lev2 toc-item\"><a href=\"#Implementação-da-Função-de-contraste-Window-&amp;-Level\" data-toc-modified-id=\"Implementação-da-Função-de-contraste-Window-&amp;-Level-12\"><span class=\"toc-item-num\">1.2&nbsp;&nbsp;</span>Implementação da Função de contraste Window &amp; Level</a></div><div class=\"lev2 toc-item\"><a href=\"#Imagem-original-e-seu-histograma\" data-toc-modified-id=\"Imagem-original-e-seu-histograma-13\"><span class=\"toc-item-num\">1.3&nbsp;&nbsp;</span>Imagem original e seu histograma</a></div><div class=\"lev2 toc-item\"><a href=\"#Calculando-e-visualizando-a-Transforma-de-Contraste-Window-&amp;-Level\" data-toc-modified-id=\"Calculando-e-visualizando-a-Transforma-de-Contraste-Window-&amp;-Level-14\"><span class=\"toc-item-num\">1.4&nbsp;&nbsp;</span>Calculando e visualizando a Transforma de Contraste Window &amp; Level</a></div><div class=\"lev2 toc-item\"><a href=\"#Aplicando-a-Transformação-de-Contraste\" data-toc-modified-id=\"Aplicando-a-Transformação-de-Contraste-15\"><span class=\"toc-item-num\">1.5&nbsp;&nbsp;</span>Aplicando a Transformação de Contraste</a></div><div class=\"lev2 toc-item\"><a href=\"#Visualizando-o-histograma-da-imagem-com-realce-de-contraste\" data-toc-modified-id=\"Visualizando-o-histograma-da-imagem-com-realce-de-contraste-16\"><span class=\"toc-item-num\">1.6&nbsp;&nbsp;</span>Visualizando o histograma da imagem com realce de contraste</a></div><div class=\"lev2 toc-item\"><a href=\"#Links-Interessantes\" data-toc-modified-id=\"Links-Interessantes-17\"><span class=\"toc-item-num\">1.7&nbsp;&nbsp;</span>Links Interessantes</a></div>" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "# Realce de Contraste Interativo utilizando Janela e Nível\n", "\n", "\n", "Em equipamentos interativos de visualização de imagens, é usual ter uma opção interativa\n", "denominada \"Window & Level contrast enhancement\" que permite com auxílio do mouse mudar\n", "o contraste da imagem de forma seletiva. A transformação de intensidade que é utilizada\n", "é uma transformação linear aplicada na faixa de valores mínimo e máximo de nível de cinza\n", "em que se deseja aumentar o contraste. Entretanto, em vez de alterar este dois parâmetros,\n", "os dois parâmetros alterados são: *Window* que é a faixa entre o mínimo e máximo e o *Level*\n", "que é o nível de cinza do centro da faixa. A vantagem desta forma de parametrizar é que\n", "é possível por exemplo deixar uma janela fixa e alterar o nível de cinza do centro, dando um\n", "maior controle ao usuário.\n", "\n", "Uma demonstração interativa pode ser vista em\n", "\n", "- [adessowiki:ws_demo2](http://adessowiki.fee.unicamp.br/adesso/wiki/Demo/ws_demo2/view/?usecache=0) \n", "\n", "que foi feita\n", "em javascript pelo Luis Tavares durante o seu mestrado na FEEC-Unicamp. Experimente\n", "esta ferramenta interativa e coloque a Janela em 5 e varie o Nível para a parte mais escura\n", "e verifique que é possível verificar a distribuição dos pixels do ar, que está ao redor do\n", "sujeito.\n", "\n", "A demonstração a seguir é feita neste notebook, porém não de forma não interativa." ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "## Equação da função de realce de contraste\n", "\n", "A equação da Transformação de contraste Window & Level é dada pela seguinte equação:\n", "\n", "$$ \\begin{matrix}\n", " T(p) &=& \\lfloor\\frac{255 (p - P_{min})}{P_{max} - P_{min}}\\rfloor\\\\\n", " \\text{onde}& &\\\\\n", " P_{min} &=& \\max(0, L - \\lfloor\\frac{W}{2}\\rfloor)\\\\\n", " P_{max} &=& \\min(L + \\lfloor\\frac{W}{2}\\rfloor, 255) \n", " \\end{matrix}\n", "$$ " ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "## Implementação da Função de contraste Window & Level\n", "\n", "Como todo problema, existem inúmeras maneiras de se implementar em NumPy a função de contraste Janela e Nível.\n", "A implementação a seguir faz uso da função ``linspace`` que gera a parte linear da função:" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "ExecuteTime": { "end_time": "2017-03-26T16:01:26.518830", "start_time": "2017-03-26T16:01:26.211377" }, "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "import matplotlib.image as mpimg\n", "import numpy as np\n", "import sys,os\n", "ia898path = os.path.abspath('/etc/jupyterhub/ia898_1s2017/')\n", "if ia898path not in sys.path:\n", " sys.path.append(ia898path)\n", "import ia898.src as ia" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "ExecuteTime": { "end_time": "2017-03-26T16:01:33.364920", "start_time": "2017-03-26T16:01:33.354989" }, "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ " \n", "def TWL(L,W):\n", " Pmin = max(0,L-W//2)\n", " Pmax = min(255,L+W//2)\n", "\n", " T = np.zeros(256, np.uint8)\n", " T[Pmin:Pmax+1] = np.floor(np.linspace(0, 255, num=(Pmax - Pmin + 1)))\n", " T[Pmax:] = 255\n", " return T\n", "\n", "def WL(f,L,W):\n", " T = TWL(L,W)\n", " return T[f]" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "## Imagem original e seu histograma\n", " " ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "ExecuteTime": { "end_time": "2017-03-26T16:02:35.172622", "start_time": "2017-03-26T16:02:34.803629" }, "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "text/html": [ "<head><style> table, th, td { border: 0px solid black; text-align: center;border-collapse: collapse;}</style></head> <body><table border=\"0\"><td> <table><tr><td><img 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'/></td></tr> <tr><td align='center'>Imagem Original</td></tr></table></td><tr></tr></table></body>" ], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "([<matplotlib.lines.Line2D at 0x7f9b4ce80ac8>],\n", " <matplotlib.text.Text at 0x7f9b2e279860>)" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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gFCWZePb9Q7gcQnWTj301zb1qAjlpbkZlWG/yY/pR/r07ksYnoAXkokMoij6B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\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f9b308d8eb8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ " # Imagem original\n", " f = mpimg.imread('../data/cameraman.tif')\n", " ia.adshow(f,'Imagem Original')\n", " \n", " h = ia.histogram(f)\n", " plt.plot(h), plt.title('Histograma da Imagem Original')" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "## Calculando e visualizando a Transforma de Contraste Window & Level" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "ExecuteTime": { "end_time": "2017-03-26T16:09:40.025007", "start_time": "2017-03-26T16:09:39.805995" }, "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.text.Text at 0x7f9b2e1fb0f0>" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7f9b4d4eb400>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "W = 30\n", "L = 15\n", "Tw = TWL(L,W)\n", "plt.plot(Tw)\n", "#plt.ylabel('Output intensity')\n", "#plt.xlabel('Input intensity')\n", "plt.title('Transformada de intensidade W=%d L=%d' % (W,L))" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "## Aplicando a Transformação de Contraste\n", "\n", "Observe que esta transformação amplia o contraste ao redor do nível de\n", "cinza 15, tornando os detalhes do paletó do \"cameraman\" bem visíveis:" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "ExecuteTime": { "end_time": "2017-03-26T16:10:03.382284", "start_time": "2017-03-26T16:10:03.370439" }, "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "text/html": [ "<head><style> table, th, td { border: 0px solid black; text-align: center;border-collapse: 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jdsFRqwbBUglFBPIWXKux8jvbTpaq+tJq6HT15XctsSfIWHO5NJLQpewkTb0n6ZSbYy21dUwzRUM8zTUTAN14xP27pKVQGxkxlMp4ZlZCRd9QUQxJhICHTJTximhC/oh/1484SRgFFur4AFDumuGiAl6RtOXkVidab1PKCjA9KRALcwJNCQAZHEPBWDnP0KP00jCm1MndbOF4wptlDruxR+mAhC0A4voNG1Jy+lvg2ADKFBLKZYkh5DeEp6YNXQ2oeGhYaKrh+FUAQCS0ER7TN6BeQtHNZSG80FzAeRukzQIKAnSdMuXWf2jsujD27au50+cW+lLaZwj8bgnShuzUmHsK4i5p6EGihqrpGgpJIC77tDjUDwrrIk5Xd4ffWs+uB9J4x8Gro26kBmI3JZ3eNlPQ+nrAaEfEJuxFYALqYA2muKlpSa7qFiXSUyK3a4nMpKIGIsFhi5QZGXZ2cVCAmlT5Il9P+M+dCCGoi4d1r84GyMJPERVES7EuSbig3zA/fEsi2fsmxyuzOUKQTCYBwM4SRgbtD18mxSdbocJG381NgrJny/ZSUxUvp0sbfAv88WuQiVlWFYn4RijsY6JuSdTY7nrcwCbFgcnpHq8c+nDIdsAaBrirqkpmewsk7NW/HeqvaSPsSKUvXZhx5xTYTQTPVIltOF4YSnlYN92K+ytwqZDYKKRkW9uYSOBhYnaMQU13y+fAO06WQlosN3lFj9Gdl0hn4Y23+kgVg3K90yJS1QYggSorZuz33RQHsMMF0OUA70chAG6Yq+h+NFTfF0M8BCzc364Zo49D/sT5+NCbHQkw+Al+dv6Pq0Z09qDG9GhP4BKiQekd4uFxeYVvdh2XtLLHolq6cdHnYZpoCOL82bT8mhnJq9h9Mr80hCRAzCEZMAIgLib3D5dp0QVE7/+ENgmB+nV3tiLvmmDjh+E2gImRCJ00xxSQYIAasgOpjWSZ73kIKEeZJXs4P/+u0ty/4fwBvLsPCcoBFgppbulCGUqLl2Ar6oqaz9Yrq3sOF7ygz/hGxRgssfAkDIJEFyANFQihtosTZzLAzV52cxCKRY1wIav72FFyf70GlmIZl04aluHV7cfTQXVT0V4Hi4BiBx9YsTT/Jp26psOwX+mDoaY6LLkQxLyUhxJVU6j5m79TX7lL1cMiBkoLXFS/CfZh4/NxDNsAPwDGrpgoxQX0cPDtoMYPGlVxzi54cXR3tPawlsjQq/Vg1FEEQkPZMMjSQCUUd7uLryGEqDauDepxW3Ht78PKn2Iuvzg6nA4Uufg1ZTyewzPXJ8wc9Hr+/oywC4ai7P9yft5e6f+gvZTpPbbxrrsCfAaOOKGmaGdJfWvJukuYsyWcwHcbXIP3i1nLWprJp/FIYe57fmRYtZ0ifh+BjwsV8Wh0d8SRM49HTzSOmeuL3aHTzBHwCCRFLGdyZ0iIqpeWmDy9S8LjWqSnLqaC358kXv9WB/segH0aKGMcfLtJguE6z3/KThC2rSTfRZtz87ODx/h+3PF+Dbo6U7lDtSY390OgbALcf+n6yd+IRKvudsgV0aRvTVXp7re+Ds9/M3B7ZcelaE6FXSMRX3jDQJrBFj9oeUMLzNOt7+/YCjd+ntaEHssjRiu9yVHO1/ABENHY8rMW0KJJ4crPpOSkYmDNZ1zvERwHP6vcrl0FMlJVF3Q8w1XEVS8DGvoIBc1ONVtNV81vp/HjIUPIicPa0jwD38gJdvSAllDANqOkSSgTd/9u9W9r2vnp3hiLP2YKyW37zPGpFrHk2xCEfF6hCkg6KSYJX9lTVffCAWL97+DvDGnr1+kof+Uu4E4Hk7D2Rtr9hYBhKjZlxwdFnRNMGC8ChtGAE45kXfckwPYSHFcEtMtBukz17HcNhoTTVGYwsu+anO+WO52Whm93I3R+jXA8bu6H14Rqy5kOoBy8vns0oJNUzVs33Oeb99nmaikGQqMgaWawVP99BAA1Wt0Esagmq1/cWF+49c/gXd1e7hCL2NORmkuPa6pvoiHYmv/vNvU+kaS8wMXD8bNMfHAK/PNS0BEdPqqQH06zC4hsprSAhRyJ7+h2ezQ9tgDD2F3EeUfHes4GQGESiiqth0ZoSfnXlbroY2ZEaMxaJfSHtOWDpq3VSLmWHhn5MhijIaAe9/OVJJe3MwrwLUp283fy9R8tdff1jTv0TXHUIJtZkwZI+P1RIRqO/d/OLCUcKErpcqgui4m0acAg2Yc3j6h+5zely7Z92/veheHvD86WlCX+EKi69pbaEpMQYzAiOhz9AqFqGB3OR0xOXQIRaomIm6ydCSHElVrFfAwcnzpgCzs+nelbkf68fbK+kJ5CsA5OgUqSukJLn2Y0MztBOzkERvO3B/X+wZjlpQU0maCdETqsT6TGF5zQg++U//2UlJDv+CFotfocsLIJKhkeEZHp7u4Z5OZ6be40HIjePr8qfLqmYFwcAiJKUfRm55gJIhBv/8gg/rqgbk9O2TDxz4Wr1AqKI20l6ukxYJYqUUk/F5uNEcIl4ts0iJ7JTsMtB1BUSE6nr5aKW1z6ipevzRW3L69D1WvwJAjttWOhMTQEUEsdLVrutEq3qP6BjjXX9FP85q6mAiWKGMtUWu4SPDaIyKP+fSPh+dEWsf6ANHC55W7tcBh6y54JiN3bFDXQwVVBBXV7ChArG4Du7oBYszMFVRBoNMxG2lNp4EESozFiyv141o9MAfXfRMXz3xTMb7V0AjdOyT7o5ZKaWre9U6QUVVqxjFmrQG519Sm2b/ECdCESKsNxLHPHTtCykgn7ggum4qo+TT9xn+atGUpxCeOQx9r2qlmCmqkWMaWVRbK3B28wTvTjtUCymaAwGaFuPyVxQBPm+BWPdbLsSCl0/MEPlq3eDayndzBKWkY/06ROJoxJVktrP1+w/ldDjqbXh2cBE2hBKR6hHa46SMDGvtWFA/RX+vYyCXHblZSr5juR+AHOPDCUQiboSKOk4n9KQJKxe7uNR6LocwP6A/twvrxLxz+i5UBslAn8/PFUCRVKk0902jR8OP30128JT3yFe3QGquV0GkDz4sB++dDPdOixgtfDG0+ma+WNAHdJcXR8OinS1C1MAHMjQ5eltGWzgAPWLJ603W9C/QOH792M94r9wLwBsBCWEse84gvE9vPjR3zzBz5pEQermE2fzs8Azg9R+1NXcdOyt4P06aWIqioBohMzK3mP0nGt62EMh2KfcCcJ4wqmxNRchMvPfBM9wlyXruGQmeVg7fnL5etJOfvYePHyeBR7YMxMz1OdCvfSE1A58sttj9ofmKwyfNjd0LgIOCo4SMWXOc9Ih+GLx5H4sBlMyQ/XZyPj2Ly0V/fH6xYDozoqECZDJOE/qfgiCjD2lHuaVwoA8aB086hu9eAGSk9AEkY6mDBmSmu7fe2yIEIsJXZ1dZiu9f1pazf54uOTk9PcwIz8j4lF9YHikV0Y6LO1oHGPH6aU2Bb3CGYqQB5afZaUHgnkNrrhSg7uPhOhd497L9vLx6cXIKB6QqioZck9/evreZqjxj2NpMBTryir89ZWTsXgASVyAVFJM1I3a9ld2Hofvb8dEzWM4I7+e+iAtO/pime3cC588CpwfNT/TP2R+I6yEeI2/gtnwUerqnbLd6HwCnY+8XlRiZXSGfzjEgwA4UN9V/OwsgFh9++AB8rCU5vzhvC0JcTeLL3e6YgOfs963aPiV/3mwwukO5D4DfEu1sWuqsVOP61X8KayEzC5Kj8v75UdepauTF4RJ+P56cL87k5aBKwfNGW/GrxCDen37SDDfkbdJ4yh4a91mCexmeoGBWvY0BsTGwKwD7FoqFq9dS0flSMhd/n71F6upi/8O5pYuH5Y2CQHOxcajA1vKxA4hWn9AUuAeAF8sutIvssUgtNmnDWCUDgLI/SQF6W/v4z46Wv5EtfqqWR30YSo4ciy8loNLuY4Hki49P6BLftwVmhX452FjFluj+wcTWZ5RCN0vDcBUVVQMLPZoUhiWloKYmuJnGzeYAip7cZ+z9Ac7h6z/9YN8qd6+AVxN8SuuX2DQYKBq23w9LkDSZ1KmIYK4e4hYpnWNSvbVJTUQo2rDAuNEO4wTECGNbzcQowenJ01UO3r0CLoeBUmb7Uxsum5eKZ2CTw1m1Ah2emgzVpsBSrYuCJDbZ6yQqXWYIQtx6zPPRuPwb3GXvGHLO7MkiY3eugKVGkiaTZh0+NLMu1IluMrgMeAkwZAhXNfXimLqJp4E2G8RtMALiRhWcuhnklDvzYL//4DB7/VRq4M4V8HdJw5u7FqROqvrl0IsRKYZ5pIYAkYY1YtBcFQtXw1XSzcKRUA29EekWmFwzZe+8qTx+WN/VPyN3AhCKR8TglhMDq3Ufb021KBkFmmoxknSpWHj40Kt6GO5mroqoR9jN005hxtwBytbrwnOLFcdP5RHdBcAUcEtlaGadqXonk/1Ka01K0cww93Q3ibDBXB3DekLdDF87DJ3nzTrQFyCH61PwrqPwZWpcbDTf3pXcpQOygKW49bKcFuvHXimdZcuFUsw9i/WYeUsTRSQzlGVqRbpexVgJodwMfMwNBZ/AnUoQCP55+BfrgB9FJQQt4MPSexXISp9RZjOLVT+ZSYqp+2BMh7AhRSzQIn1PMzdP0fAN3muIQJzAp8nsm/LfinP4RObwHQAE14PCOrVcDNRqeFfBQuuscrWYHQxDdI66M/QSqmNpieFkJJak5q35Gpb66Yi/0xqcEn72VNHh7QB4QnpLOqsmmEfzrhTDOlSCMp3p1VwMNyLMQLXvNTE8daAn6AU8bjUFC6FyMT763a0kpHLO0dNMHNuuA34S9YYXcysiaNUmPekE1WUwcZ/Zwk0jSPUQT9QcS7CADg8FsVtn3Wt1+xtjD457+DDLH+iZvXkSr3g7AJFdD+K+kkvrqteCu6b1WVx78QnWeVRntnIE7xhAnEEYOfVhuMVgtwZsnSvaXRNk74185cnp3SvkMWUrAIdIdISnJMPQ5qV2Vkq6z1ZDbWi6eiw0OK7nhnkdWSMylMRwIfCARIabACQUyNHKuccjOjrnisPvGND0cNkKwABqovRjw2TyilL3bdaGkOXYAsVA1Cqzq9JN3FPC1HEUtdB+rA6RjcjvSDP6Kh/813Mi6tlfBkCAiEpqqE5pLYShrSYz62JMkruYQ5n/bdWszTIxL+69okZDogtXJTTe3fzFktgn9X/fEpfMny+exBzedgr8SOqQhqGCe322b4n61dn5QmuRkCgZjnp98Wrlx/u9pNUBN+nW44Yd9WgbJ90F6IT52gC475z7nWg8fwpTYOsKSCzwuA6E6+TZ1bIlrNp0r2rnDMEQVUmW3dK6KNZLaqjLGEQPNwtsuLWGX4GcfAoH3acEVcjL/aeIjG0B4B0gYqiYgxDYUGfL5kPmYrG3b1Acr2EI+/3ZoU4WQ6TRp6mEjrSgErdCITBm2Ii1F/SVOml5dfkU5+AWAM48NC3SPMWGhBTRphX3bH61mlQ6G0SUrou557KmJhGqhAjiBUcGr5tjZV0E8vX4h3uPweU0k6PXrx/jGe+VLTrAUYlATG3Mi1lmL3SmXd2bmV+dL5r1mQSkHpXji/ly1k3TQzKdZNp5du2gnN/O8CTafX7z95MiU/zVU3CHN1fAq+sbaIImQrpARqgklNIGVu1y5qAxYK7R/dJxvDg88OKDlwETz6XNrdw67t5JUj5HhO83dGYLLnm2+8GLmwA0dUxzvURdNMEJw0d+y7Tz5padEGJN8P2Ts8WkubQyeJMhZot5rzarG4Mj/tNSjlh+2wp4/wPO9PwvAMDjukYIAHVCJGmmQkSWxA5acVM0e2GIE3dd+uHVwqwYztB6h+Uvi9taTgKm+PUK+EoSWDJ/fr/76PCGDjhzxr0vRpLFY7TpwBNLczcTM4LR2oPVkK5HdSburqm+aI6WNvjtu09EYbgmAX3lkDuCpzAFNgDw9cxoyzTryNKlZaooeGQhCU9SFTwNXejz2cQONPzw2Wxa68EBaFcy+9yIaqUanwdqfMXQe23Eu93zZTa2gKz/6RGB4m7VQ/sOTRwBwaI4JmEtILqjmfeCDLVXnD4PmhftI+OWqXcyOor+7Y1i8rfjuuuGErdXwHz0ZgPtNMXUDBEwT6nWWWKuWJc1MFfo1WhRnUFCA0GJUq99hhtyJeg+394n5h9Gz/GuuQK3AVgOADa2iVLEDMxMKRpYFolObOhB65o/Eh45jGUUYy29QuRgrr/e/NU9wXOW31wdf8hTUGdvA9BZJmoJPiBBioEKVZ0ShnaCi1sIqGKZFgq4MoCAKSmEx5awvzLO6fpGKcJi50vgFgCLNY1XCBMh0xClpEVg2oGEIp2Cprp7c1IhUgVc3WnhJbGweovy6SCFz6fg1+VCw7H7QoP+5xl1twBYAoikqXqSYqSiXjArHZCZdBke6eE2xgCdPsPTKqEQKelpmbr/qXVejMM2EgV/iJevHN57WMifDxnc3gIOlioemCXeX3fOd6WXsU6sDwfco4+UAHEFZ1hKio3EukDcrrkEmBowH8cR+wPaA/yDHO5NkuWf77x1EwBXIMRMEQlBtTc1TVBPB6sRnqZg5prpmUgrVVRATTwQM8oYEf4sAYSKHLJhHt4nh4Zf3NdUxf68t3QTgHe9MVq/mu5K0IWHAWJmA94r4uqiBmExMiC0gVEj1XC8l+a35oeNhefkAf1DLZs37JY+fhMAxREqqph2jPR+vDA2jYIhZoaLEpiHmqdodXJdaZ5qaaQGts2LkfUa+2b53cJ3TB+/CUCMSQtxwCVQRaQzFFUnUaG5OERWVSUiEDdB6NwNihkWdExvd0b8GTDIB3ZM1DzZbVTgBgCniXnaxEZLSB0JbEETcFyIZMhO1Dq8Dx9fejZAerWxp2KmOrZ3+0ILE+3AHxbqFrjiYDut9nHkBgApnSSHvlo3ywTVIauT6QEZJnpdRe6qY9FoiWsa8YCkh5kwGzYMOIGYfdUFvC2XRjB7KHf4IYr21ing2ePJIkJqoAUkRMyFDCVdA8WieZqgQiBt7LeDhAU+jhQL2SyHMNXn3J4p8g0Pk4cbg7q/Jl/ZM23xxcnyJQBnZn0Mchl7M1xRgygjQ9YjMnzsCOCpXTFfX6izIQUSJ5JAPPy2Hzg+CfIdFVFFaRy9+/oHHyDzdrnwy/kSIL4EoHl2NTJXc5vpsrlDNiwbwUhxLiiGCO6GiQAogmYYmqIqWbLo5hihV2PJuD+U+HEh5Lk9bg3J5eJDW6wuVw5Lv7EF+sChVOarySFt2QZJzyipONaZqBVBlFCJJBMPMzJFcjAhg/SGl9h4znOCAv0XmYJve61KnPGovWabt+ZxiSyg6Y2AiGlcJlTr+1ar983FDAKykyTULckuxtECjqSr9GHjhIU1MpCyaQWIun5OCz5APu5H4+DNY/bXUklzdFqgky8AOAs0V0CfZtrmtZu1NoiZGipIWlqkNXHLkRyCJ1kHYKSHSdqASGzpiJda/fBmNOTbVJtp8npLid33S30P1yNtZl8qwaWKeyBKeMpkEou57s/Mh9bCLVPGULm6paGe0Yc7ksUTNQsJDRfJzC0hf5GQAxZrZHq4myd4+5vEB/Z3xpf5DECvuCyTsWY+6erM2qXvHxbrV/1V7xkW2SskYeMuMKz2dOZRXJU2/qXkxjn0s+eXP+yAb60LuTSin/325x7zbvkMwLmgDKAigXqipU46v5hT9mu2wVv2SIcppOcgHu6wLix1I6xGeqBmG/5LGxvtjM3FeSALVPM/vnW5PFw+AzC4hFeTSNQq9OKpe3sz86tlTGZF+tYGE0IEU7GRIAdkSihpmX3WTsV1c2ZUUKR84oY8rIX+TGicPK4p8Fk+A+DpqrOxJwZLXD2FIbXM9syvemy/ePOuN8UkUsbMqYVAqg3uhJq7ZcSmKZp41utD4IHjlt+qxO4Cg59PgTANw7wTUEgfW8SYpFmar5A6y5wu0g26yAwiER0wxowBoSFidJsARIc8X5OkH14Ulpwd7So6/GkFHJuCCkamiikmmItEkoTVSWW16BfnYAR9eqhaoFIkHJGx4byuvaJb8jNr8iA8MCoI8NGk53BHTaY+AZDp0VmO05QyETJc8XX7EFXpqnovRqaOYfGxuQYg4UqEjR25Y3Nm7nJswT1ayNcq8Jt3glnm669k079brgG4ZHR3DZEkIjLGAJFFCGJmJpTpVC1CYpqkRriH6ACMEwYcUMe2NcZVtWtD+Nqo+/CQ+7ziYDdVNNcAnImHaBtHhoAyTtVDY6S7Omq2zoBkxjpYBlkMy1RNk0wUpbIR9UkNL4y7/9X1D7891vex4kx3owivAdBEDbIglmPrx0RUDVUgw5uHEz52VgWlT0UxTxcxSBMhQkK3hPJF5WCtGz4lzB4Q6TL8xY6qaK4BcEJUXNIUS4n0wAkcTN1RiiZ0BRWkT4UOB+lESJUSgaZg25J5h6Hk4XgIvP6eu5wYC57vxBRYA3BKKDTS1MNtXS/u6Z5976oeniFmIOPMCY9AAvBUTAa3HPkP6f7r7Yu0sU7C4ctw+QO02utCvNtNBcEagFUl1NPMbJyiKTHWjY90gRhjIRLhCFIVsbGrLFIEybIuCsedTc913XCurzD/7Cg/JDqW5G+7SRCsARBPDRd3t0hLSBL3NNzI0UUcq0gwxccZjBaIanrVceiapPgdwxShg/wHX9ZCPSTa/bayI1NgDUD2NdJBLBIfZ6mliI99Q9Ldxt4nIemu2utoApMR6IC5kYqp+Zb32sZiOZremCz7kIO9GvliJ6bACMAbp7eIMd9sYqNbqyn44IMINg5RM0WyEBpJQsBYNQ2BSIKZbSrrYxeR0QH6Uo89KN8RsOBoB701RgB6MdRU+7FDjsc4ThtFTEWTDBc80xJLSVeghoO1URcaRoik6qYKGAQFFnFztPCDMrsXRbzvdtB5fwTA1WlJ855QlESMTHDv+3GSio0dVMUIRUR0pDrE2mxyzbTM2LpMx0miy7wZB31YxqtLXu1iD4wAaG9MHFF19wiwsSnyyBn1sRuMaQSkKWKOE5KgopmQ5lJUMLkj0ifge39mtnQ1Vhw8vjU4AiDq7hJZBCVNJAjUQkyliCASUkPMxhFkRpcROgCSjnnikhiWW5Kfl0FkBZ/dzPE97HX+uhfxrnv8JlMjAKeSU1y55oWROOLqgSUZJNnGraFhmZ2LJiJjO00JBA0PR2TTEPxPRNkH142M6UPE4bcdmMPrY/Cd9RWkhWmM3PARhHGonI1ejmORgNQIxjk0Gg0Z1FJNSdAtG7uhIkeE938qz/26ZuPo0YvKr32Bf9hKR06IOiqdWgCofHLuBYgUwbIfDFUHic6EkIgSYdKxJRrGWt8v/M8xv+sM2UHT3WsApgeE4aq4qo+DA61YoJKa49Adg+pKz2Ce4TipfaZiaGY2963hrhgdzeH2+39wlkhywd5jJwg+h8RmhhqIhIsOaRahqhkqISYEsFYPYxNcwwUsCtVHdaCEburAwNQ7aLdN+YdG+U5neD99UBjlG+RzVPjlTMAkVcXDhMyxd1ao6jgrwF3FREhNAicyTNXxVHM+N0+/KX8HmIz0iBvyYIrblHz16AmCL1JjJydV+pEpOjYOc3cbW56NBeLkekBUL57jLsGLooRboXPxLUdbUxM5gg0V8GCVKIXH763xZXp89mvXgdVxz7pol60PdVm3xDARYchkvethpD2ImTFaTluWdYBqx6aX9OAVcHIkcfbYs9huUmR+LZ1C0XDtOjzMdHBcOhEzRX0wMUJIARlHbhBEjvMz2GbqZQQCyw0AHn4oZuY7HrnR2C2q7OtnVnoPU3espIdYjIuhC8FUenPRzEgPD1Ax1/QgZEwpbUqMnsAmberhD/Ky4Dx/3D1wmyu8/2utpvkp2+NhxVS995om0pnigaSgOjKlLULTVEG2GbfHIFkhNlge3zFj+QhOHtkj2iycPB60ClDGCnmjpUjR8CpWxSgQVpy1xkslCQIht6mAlYraHrTHGCrewYL9R9UCWypHW+u1FnG6arhatXSvlUhPVaxTBRV1MKo7xbtuJMfcFbU7/J4Fv0WO/pEZswcmV++XLQBY5GJJVTy1WN8yTHXRoLotwwaLTyp9HJMzWDgueG4xdn3sS9seR3UdSPz9catothRPCzw/W01mGkOEqflgVUy8qcnUW9fr2C4/IFW8w8d88iBb9Ho/DqZ+rMOrafQcXj4ia2zLClCRo49c/XbRZlNLeqtlbKAYuerp3Hth7KcswjgbRIMcByvdlhc6dqVaPk5nqP2X+IU9JmtuWwMFBX7/wX1YHalpQ9IipFezDLxGtwqoHpKIRZ8WJBbJs83f9fo0f62Av36c+92bPvIkmm1bIHU55WCOx6lOZhVoWum8UcykORbaj3GwFDenDpGw1RW0dRjw9SPdb/fYk3i2AJAhbcrbKZC5WJVpncqqd50Uw5PqhFaThQMxOBrulqGbtABg8WSNAb9Ttm4BHYDZKhAyY5C6bxOGAajZu1RfLC2mc0LU0z1TLCS3topefoe58w2yeLRmc1uU4BHRA+/W4QFoV2/O5kOZVcPFJIehq5lApkhRFU+/I8Z5eP741c/98QWPlyHZsgL21rHn4qkQ6kosm5X9vX1vC6xGT5WuBwRLxcz6sGGbbTv4iXPwqJGsHwM/fDxTYHMFLLOMXs17NUQUEUj3xdl/vLgss+I9nTl9gmTi9H27VEvbWg+lLYcZvHm8qpe9inP8aNbg5gpIm851frDuIZGwjhNrhF/W2XSii1CLbEjiWjrRyFgzwG5LBGL70D8e2/v0B/L05NH8gW2dpA5ktNzfmyHrwMVo5Q2r317/5pNZaco0x9SYp0Nm6bYEq/pUT+GRPIFPEuePV0GwCYCF1WjAyI8TWTcGdLRKuM9fvRlme9G38cFEDHXcN6tE4MVIt/rEEn8U+b/JgdljRQU2t0Ao3drZeXeYOua9AyxGNri6r0o97JomQpcgsddqbIsGDaESQHtMH74DefXrY/3GLTqg19A1p/H4XepIfsoxX44SGhltUStIesw8PNsw5ow2Rcyn0D/qKE3zvGT2SL01tmwBMxUdWYlHh+M6T1DVGGesYKIMVx+wpP/gF80m+3+bzLbFAgJU9x5cLvkV+Z3wi+nV4/yyzRVgiuh1B5jn8WFsoW1xPTI0NXMkToHkxYWrdGIcbrECLlI1ZP/x54dpvjl8pATB1q6yBwefXPuTl6boerzGehJ3SJKJJimeiS8X89U2k/dsHfp+pGjIJ9kD5/BxTIutAGidfvJhDt6jxEiA1AjVCDBGYrRkuoSOTOBNGVDJDvo/3+fghrwT8vyRjtZNAByW7csA78c6TtmRQAlUr7tsmXYmpiZmW2gBQISo7IHvoBfQKZNHgXX7Cljml5bW219McFXFVIOI9dQ0wa8Hz9hd3pmhsHV//Bm5BF9MH6WQahMAAbt1qB1eWAg2Dp0ap0aioCIIpiZSt5m6l6lKHkH/2J3SDR6rgmATgATDb+nYd4cQds0CjWA9Nk0wkUy2PuE/WdvRj+8S12Dg8DE8og0A+gBMb9/zy5eCHMr1xDwRNVQVFRDrtjq8HjF2Fn5MQ3iUj4a7PUYJxWY3OR0nBd/++eHFTJcAOhIJx4IJFUwp2wN1jirdujnR48vh9nX3QNkEIO7qTnP61nW97YTr8lhVle4OIn8kEhNof4odtl0OwTl8BI9oE4AOqJ1sOWOqRoKISMoIgZgI+3cEai8RUU5gtQNu068EJ48xh2OLN2hAWW3P3kuSQso4dkwMLXf2fPxn6tiVdjfjQjSvOLjdtvThsrECRhKMyvaQy6gahFACpb58f3fPy/6aOLyTSo8/gOX+n6fObu0oOQ5V3hRRRoRGPlS8uzfonyrqlYd1z3qI+ItHCA5vNYS++M8NGWMD188jr+9Pevz+x1KosNhNa9gSDPHntcsmAGM54NYVEDpSYVANyuXXnfwDmUD7x5++y21yqfzyCD2mNrZAhgJjQdQWOT76WVJI+JYk3cIr39or48Hy34/iYm0AMNoA3bBtC6S8r5AWoJP5N1B/06a7MIRHeRwX8y4l6LLlhLF/jLte0V9ydfD1BfgeNgbu/YvJ5jEIgEpuuW+bAiIR9vHwoHxjREYfO6H9uHLntLlt723M/yr6Hmz6xFPSdyQbAKwPuU62mC+x/vcvALNHccb+ctkAYB3bmt3Zsla1jupvZruycJ5S7tABsC3bqQAi79ajsux/hz2wAcD18be/hdkmABcX+GiC1icdE78juWsLUO6qggaqjuA8wQiQncsGANenv26x3z698dnaw/3fQAn8v9mJtJfjfkB0AAAAAElFTkSuQmCC'/></td></tr> <tr><td align='center'>Imagem com contraste ajustado, L = 15, W = 30</td></tr></table></td><tr></tr></table></body>" ], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "g = WL(f,L,W)\n", "ia.adshow(g, 'Imagem com contraste ajustado, L = %d, W = %d' %(L,W))" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "## Visualizando o histograma da imagem com realce de contraste\n", "\n", "Observe que quanto menor a largura da janela, mais pixels terão valores 0 e 255.\n", "Quando de visualiza seu histograma, aparecerá um grande pico nestes dois valores\n", "que são o extremo do histograma. Para evitar que estes valores entrem no plot,\n", "faz-se um fatiamento do histograma do segundo pixel ao penúltimo: h[1:-1]. A\n", "seguir mostramos o histograma contendo os valores 0 e 255 e depois não\n", "utilizando estes valores:" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "ExecuteTime": { "end_time": "2017-03-26T16:11:41.415276", "start_time": "2017-03-26T16:11:40.958645" }, "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7f9b2e180fd0>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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AqIPuxhoJxhpmPTamHxMCAv5PV+N+GTM6lMpqjL3FtmkWbKnSOyhWtSVdv5sZ\nVTBOoK18uD59LIO+jVpPoqqXBzUMLSYEBLQRw1GdV7at0CaQKPLwR3kbKWc2sa6kbj9ULq23ImEa\nNRNoswnIKqYYJwDp+TSZ0dDctGFUzKSEABGdQERfIKIfEtFmIvpJIlpGRHcS0VPpdqlX/loi2kJE\nTxDRRZNv/tSjTXfAyh+7X49oE4gKnJLrjAs+i9P1VxUn0JWgCr4XnS2i2EZdfqPq1EHNRLcehGFU\nzWRnAh8F8FVm/jEAPw5gM4BrANzNzOsB3J1+BhGdDeAyAOcAuBjA9URUn+T1p5y2LKIq7yC5Tr1v\nfVZerlNjRG7p7xX1KVNJVx0nUPnykhGLyujTRnDb9UNEqYM0L9owKqZrIUBESwC8AcCnAICZx5h5\nP4BLANyUFrsJwKXp/iUAbmHmUWZ+FsAWAOd1e/3pot2gWV6uGTU6nFh3qE7Z6Mqq9QyisogqO3et\nYVjrItqMSm0hC14u2e9cX+f9It2sLCYKU5sJGD1iMjOBdQCGAfwdET1IRH9LRAsArGTmnWmZXQBW\npvurAWzzvr89PTYBIrqSiDYS0cbh4eFJNHHy6F0b27chchdDZe4g2XYw8Tud6MowrAwCi4vGlUfZ\nugA5t61sZTFtLINSBebaprt2k9kWozd6wmSEwACAcwHcwMyvBnAEqeong92vM/o/m5lvZOYNzLxh\nxYoVk2ji5NHqsjmig9VHDKd1Kkfjrs5Q5wVVfX6dosFXObtoUwcFOk+t4Gsvq7yuUGXbrCFYZ/v1\nQ7QWBhLKJSYEjN4wGSGwHcB2Zr4v/fwFOKGwm4hWAUC63ZOe3wHgVO/7a9JjMxp1FlHvByxHpipV\nLVpVgtbzJsLQrF3cRW9AnvidUH0qt1iFANIK8WI91QWL6YSaCQGjV3QtBJh5F4BtRHRWeuhCAI8D\nuAPA5emxywHcnu7fAeAyIhoionUA1gO4v9vrTxfaiNNmWycXrjOrR+xglbYDrQ9+1HoCSgFU+fKS\nEesqZ0XChubO+2LZQLm4YDG31dhMLE7A6AUDk/z+ewF8jojmAHgGwG/ACZZbiegKAFsBvA0AmHkT\nEd0KJygaAK5m5uYkrz/lxHq1uHKMOqi0bDEfEVHnsmqjYqRNQOVtpPUOyjrtimYrUd5BFdsEuPBe\npDqrzB2UMNCwmYDRAyYlBJj5IQAbOpy6sKT8dQCum8w1pxv/Z6lRY0jl3Pn2/XqJvIj10AEEffsU\neAd1lzsBJxpYAAAgAElEQVRInq1U1cHGZBGNtQlUqQ5qJIm5iBo9wSKGBbS6/raOXbkur/ueRjWi\nH8FW5R2kDnJS6vDb/fUD5dJzUesqhxaF79omoLmujPZ+kkRnBzGMqjEhINBmEwiWi1A7+Psa1YjQ\nN/gdTFVpIzQLzfvxCdoZg1RWG1zlyqbbUBvbAsB09fnt6FxO/xxjMsZqXE4No2pMCAh04x1UVYcY\na5wttqPsulEzgWB9E8tPtmyMd1DWv1cWMawV+BHBYhrBy+w8gxomBYweYEJAoN0mUF5Om3cGaO+Y\nQv1IayUwfecVjGpWGnsBnZonSt+uLNuNTSD4DLtsoyYmpKqVxVoqI7k+w6gaEwIC+ohhXQdSLKsK\n7opIyVBV2oimwutHe113TX9fvueq1mVg5XWLZYNquryMvo1VqekMo2pMCAhog438cjFBPxo7Q2We\nNxHqIE1AW5xHVJxaTfMI81F2oIxmMfhO7aosWaDifmLUdIZRNSYEBNRLDkaoHfSBU7oRoq+W0Iw4\nNWnrNWW1o/viec2MSpM0T3PtGLuFfzbcRvm6xbIat1gTAkYvMCEgoNe3x6iDvPoVeXSqShsRE4gV\nk5o6vo3ybKUq1U2MTUCdNiJmJqB4h41IIXB4tIGGGRCMijAhIBCbRbS4L9Wp0o8L9Wm9g6IWmleU\njVqwRRknUL3LaefvdL44UKN8V1GnRq02sR0T6stnXToh8OYPfws3fXerqqxhSJgQENDaBPxz4o+Z\nO+5OQKsO0geLyWWKZTXqpeJ+J6rOb6Tu3H1BFazR1VNPpYAubYRQIXQzG82sy2fnwRHsPjiiKmsY\nEiYEBPS6Z91ofELZkjo5oj5fM6DxDoqKxq1olN2uvpGfoziz6GKGplHT1dI8TpqRe1RqC8V70eQO\nShIXoGfrERtVYUJAQBsx3FT6/hfrLOtItFHAxbJV6NsBncDo3jtILifdc3c2AaFOAAP5TEC+tmho\nZlapg/zOX6qzZT8wm4BRDSYEBLReP3GdjSxZurcxlJdrqZfC9fllQwPOdnWQUF/FHlH6tZ8773cu\ny6jVKN8vv3a6rUDYA90JfMs4alSFCQEB/6emdxEVRsUKI2m36qWq1UGaFA/F/c5lO3+vrJw00NXW\nF2O8BqNlEwheWyeougk0lN5NllrC3EmNqjAhIKANxIrTj8ud51R0sFFZRHM1Rkio6K4L6Dtjrb5d\n7xbbuQ1lddapOsNwrDFcKge0bAHjZhMwKsKEgED2m6zXqEJ/9M7f85kKm0Bc2ohIm4A0W9G2USmo\nWJt/KUZNx8jVQRpjc4wxvCp1kNkEjKoxISCQjQjrRHrvIIU+WzJAtnu16NoIhNUocTMBWWD4HZY0\nMPUvGRwV5x2srn2AboZWI12ddVLYBDIPpnB1BeEcKBflCWY2AaNaTAgIZL/Peo0Et8HO+x3L+rrn\nMiEQoSLQryeQXb+imUCEV4tWP67JWQRE2GrSkgO1mixMIb8Xd732tpaX0w0MotRBZhMwKsaEgED2\n4x2IUgfJ+vG64IUSU592JJl1WqpU0grvoBg7SOX5krIZmiCc24W4/F5qNbmNepvAxO90LKeM8/DP\n20zAqAoTAgK5OqFGlRmGE4aodohREbTrnsvLxaSNyOrUJD4DZF221lUzNl+SqKZLWsJCEyzWMgx3\nLhO1mppSHeQvJiM9x8wgbLmDjKqYtBAgojoRPUhE/5p+XkZEdxLRU+l2qVf2WiLaQkRPENFFk732\ndMBtI055lA3II0RmRr0edkXUZgYFItRByhGsX1ZrGJZULepcP4muPn+EH1LOx9gE/BlaWQrqdm8j\nnRdRVremnM0EjOmmipnA+wFs9j5fA+BuZl4P4O70M4jobACXATgHwMUArieiegXXn1K0NgFtMJQ7\n70ematRB4TaqO5uImUCsd5Cc+tnfl+vUzgQGJOGc2QTqNXFtAd9WU3b57LAmslhtDI+wJ5lNwKia\nSQkBIloD4GcA/K13+BIAN6X7NwG41Dt+CzOPMvOzALYAOG8y158OfLVD6GeXKDu5rE4pR02bZ0lF\nwWKaJSOLdVaVoVNr6I5NJV2TDPb5TCBcDnAdfE1QB/m2CKmd6sR+XsOkdYZtJmBUzWRnAn8D4HcB\n+P+5K5l5Z7q/C8DKdH81gG1eue3psQkQ0ZVEtJGINg4PD0+yiZOjPU5AOyoO18ksdyJaH3MAqghk\nv10xyyJqbAyuDVXZBNqvL7VPo6YjAojk+44x2A8IMwagODsMlFPGUADxaw8YhkTXQoCIfhbAHmZ+\noKwMu19d9H8rM9/IzBuYecOKFSu6bWIlZCqEOJuAvrMpK9qMECpaI3KcOkhRn9/GiryDtMtL+mo6\nyWurRuRsAhHCWXNdoNx24K7t7StmP4D8rhtmGDYqZmAS3/0pAD9HRG8FMBfAYiL6BwC7iWgVM+8k\nolUA9qTldwA41fv+mvTYjCb77YouohEpFHybgMZFVDOC1VzbNwy7EXJ5h6dZ6ETr/VJsl9RpA3E2\ngVBHzOyMwjUKl8vqzNV0JdfPr1uv5fWX1hcZyQ3I6qDsvKmDjKroeibAzNcy8xpmXgtn8P0GM/9n\nAHcAuDwtdjmA29P9OwBcRkRDRLQOwHoA93fd8mki62BrwkygqezkgLSziQkWE72DOrcjVKfUh2i8\ng2LcWLsJnAov4uO2YhAfA0SkswmwnEDOtzG4z7qZkl6tFm6jrUdsVM1kZgJl/DmAW4noCgBbAbwN\nAJh5ExHdCuBxAA0AVzNzcwquXyl5ZyN0Im0riyk8ZaQ4Aa0OHdB3xs1CG0Oqj1wtEww+89tYjU3A\nr6aZMAbqndsYZRNQt1FOG9G6bvY5VN/E73WiESHws7I2EzCqohIhwMz3ALgn3X8BwIUl5a4DcF0V\n15wu/M6msiyiUNgEIoyu2hiFGANkrGFYUlEnqYGWWaeykq6dG+yJcn/9Tuqt3CZQ08UeZFWUuoim\n9zmQhhZXkU8qKk6gaTMBo1osYlig3TsoVC5GHSR7B0WljVC6X0Z1Npk6SN1hy7OfwazjVLZRY4iX\nhGmbTUApnInKK/QdBYD2bKZlbQTCap5ucgeNm2HYqAgTAgJqm0DCorE3I2lzRSwv49cdrm9iezuW\ni4hCThTeQXEL6cj3XLyeJkZBo8OvKW0CSToToEAbfUcBqY1NpbNAzLs2F1GjakwICLD3o5d02ZpO\nrli2rPuKUt0odcrdpCzWZCX1y4eurQmw0kbZtiKBFTOqvGOX1WqZwCjzJIoJFtPOavxF47WzPrMJ\nGFVhQkCgzSYglMtGh8HOy3NtdN8rq6/zfqiNUtmo9NQK76CY1BaZWkYK2tLm4M9mKpIhN+vYM3tE\niOx6FMgzVBQC0v8EAAzW9SlHtCuL2UzAqAoTAgLtBshwh9jST8sjXa0/OqDvsEP1FctJ6qB84RSt\n6kbhIlqryWqZRHkvWTnJ1ZahjxNwZQlE5faffGYozEBc+9OytZo6TkCdQM5sAkZFmBAQ0LoiJgwM\n1mv5vqa+UFmtbrxYh9YHvwp/9BiPqLbI3QpUKL6aLlS2dV1SRAxzbhOQEvtJKaf9sgM1inLdDTGu\nDBYbbTRx1+O7g2UMAzAhoEb0DvJ82jWdl5R2ICorqdJVMybNg8omEFGf1kCrDnxTegflxl5B+GR1\nttpYJgTcthZhE6hL6qCowECdTeCux/fgXZ/diOdeOBosZxgmBAT8RUmkEX6M77iUitg/rh21S9fu\nJuGbJheRpj6nm0ewg/XLuf1AfelWeuZZ/AApI4YJCNoPsvvUJJBrveuwOijmvWhtAkdGGwCAw+nW\nMMowISDg6/AlXb8uvbDbSmWzH/lgndTJ2aTc+nH5jVgsl+vlNYu4J2ipgwTVSJaXR2OUrgmdcWaQ\ndsVkwadNMZG/P4W6bLAenkW2p5JWztASDv4/jjUtnsDQYUJAoLVQuTyCzUeHCpWMtJ6AP4rULmg+\nUCd1rh+tATLhcv14VmagXpPVGG0zgfJyScKYU5dnVHmwGLV/nlBfm4on2MR8FkJUrqbz34v7Tnl9\n/ixS72UlzASURuSxhvsnHDMhYAiYEBDwg5LCRkCdx0iuxsiWlxQMkFLH7rdxsFZT6561MwG//iKc\nX1ezfm+mlpFmF95zDApTt63Xwsb4xFPxaKKaW4Zh6br6VNKD9bA6SNuxA+1eQaFZQ9b5Z8LAMMro\nWyHw7N4j+ODtjymSvTmPEQidiIsYVowOc4NmuGzWAboOJNhEb0Qe9kJpdzvV1VncbyuTC6qaKmle\nrebUN5Krrcq2UtDNh4Sp2iaALM9QeRuLNh2dTUCe/WRoI4aL+0XGbSZgKOlbIXDPE3tw03e3YvjQ\naLCcr06QOvdsBBtSjXAe5NT6XieabR2ILKgA1xlXkZ6AmZEwRLWMNhgqK6vzDmIMap5juq3Xhc44\nEz6KlcVyT6JAfcWcRSrvIDHliL+vU9MBrWRynbCZgKGlb4VArjMVfiS+YTH082SO6xhkg2bWwYY7\ndqDViQzWwkbkZuJnyQwJAbcVUzLko/GaytuongqBYOfepg5SdLAkzwQ0QjyrIzMMl9sE3DYmMHCg\nrl+LQptK2u2X/+/GGIa37TuK//vITrGcMTvpWyEwmnb+o43wkgZOp0xi7pmEuZUlM/A7zm0CQieS\nd+x1UqVpBtyoWDJUDio8b5qeAAqVzT2YBnQeTETyiLypfY6Fzli0CUBpE4AubYQkxAG9kGx6nbmc\n0ynx9gMzAeUgBwBuvv85vP+WB8Vyxuykb4XAWC4EpJlA1nnJoznJ2AtMDHIq+x37+nZNSgYiNyqW\nOvdMxROeMbQLgbIBp9Yg7erQRe62qdU0M4GYiGGFoGqljSib/bitJouoOndQhDpo3FMBjVckBI6N\nN9FI2FJR9Cl9KwSyGYAoBNDKJxPqvNrUQRr/dkGN4Sea02SWrKcGTWl5ycEIVcscQTfvezCpcgcJ\naSOY2amDVEF3bpvPqErKMVoRw6I6CJ7qT5gJtN51eX2+6khrq9HGCQBhm8B4hDpoVDkgMmYnfSsE\ntCMlN4LNgo0C5djLIho0HriN1Hn5o3FVSoYaiUnumqxTB7V02TrDsBQRm9XpOuPyUbEfXBW6LtDB\nQFtSaVt6aGm2knkSQRa8cTOBiIjhqmwCER376LgJgX6mb4VAlE1A5d/Ontun3MHWhdGub1TUdF5Z\nIFZQHeTbBBSdkmSgbbdbSPp2f5nHsI1hQJGIr2gTKB25J/rcQZwKKt1MQP7p+G6skkfUnIFMOIfr\n1NoEMrXReHBE4mjNisO/hdFG01JYz0L6VgioZwJKm0CS6EawrU4k+1xWXzqKrMk++ImnDpIMlapA\nrCRTB4UFhnak68q6DrseTM7W6jQBZdoIxcLwWpuAMwyHYwqKaiiNyqouGoZ1UdKAPk5gNMImkA+I\nxsNlf/GG7+Cjdz8l1mccX/StEIjRg0o55oHMoKlPJV0TvIPa9O2STcDXtwuG4UFFZ6P1DkoSv43B\nJrbPVoRRdnZdlful0Bk7m0C6qEy4iVFpI1RLZSrtOk1uzQQawsjdPx8qm8cJNMOje0D/W3juhaPY\nvs+yks42uhYCRHQqEX2TiB4nok1E9P70+DIiupOInkq3S73vXEtEW4joCSK6qIob6BbtSCmbCYTc\nBrNyupXF3FbKIhrjHcSZTUARlKRRB/mdu/scbuOgImJYo1bzVWCuvYoZVeaRVVKuPXtpsIme3SKU\n3bUoBOQ2SkLSN9hrU0kDYZtAFjGsUgeN69RBI+MJjo3LQsU4vpjMTKAB4HeY+WwArwVwNRGdDeAa\nAHcz83oAd6efkZ67DMA5AC4GcD0R1SfT+Mmg9Q7yvVq0WURD5Vq6bJ1NYFBQ8QCuY6ilRtfQbz5h\nzj1+VNkvlW2sa3IHJa3OWLQJRMQJyGkj/I5dEKbwjcjyPYeu68q2z2zKnnkjU+cJMzlXVhknEBEx\nrJkJNBPGWDPBiAmBWUfXQoCZdzLzD9L9QwA2A1gN4BIAN6XFbgJwabp/CYBbmHmUmZ8FsAXAed1e\nf7K0ZgLhf2pmeCPJUOfOUflkWmvjlpTzRuOaQCynb5c7pahgsQHBRTTxVTxyG6U4gVaUdIR3kCIb\nqz5OQE4b4WcGDV3XlUWhbOA5pjO5uJlAednxXB1UjU0gGzSNCHYD4/ijEpsAEa0F8GoA9wFYycxZ\nDPouACvT/dUAtnlf254e61TflUS0kYg2Dg8PV9HECWhd6DI1hkslEC6nWnd2gmFYsgnoUklnbdR6\nB2mSs0kj2Ez4aJZubAVilV97oneQPFuRVvji/P0p4gS85yiqrCJiQgaF/EbNdAAhpZwGukglrZoJ\nyOqgrPMfEQZNxvHHpIUAES0E8EUA/5WZD/rn2PVeslKyADPfyMwbmHnDihUrJtvEjmhtAsyMLERA\n8vBQqTHSbe5iWNYx5N5BcsfQFiwW6pQSxqDCFbEYMVw2Om3mnabs1ZKNskO6eV8F5rejc9lMdRS2\nW2QGae3KYi3DcEkZRMwE8jaGhVozmwkIQhxoNwaHAsGihIAiTiCzBRwbMyEw25iUECCiQTgB8Dlm\n/uf08G4iWpWeXwVgT3p8B4BTva+vSY/1hLFom4DsHZT5mIeEBRc7L4X7pcbomrlfhtrY9GwCmvV7\nBwXDcOaaqhnBcqay0qRpztNvlNdX9Ncv8+Zxbp/hxeP9OjMX0fJIbrdtBfvJsxXNKnIaIQ44m8Dc\nQVmlF5NAThMzMzKu+70Yxx+T8Q4iAJ8CsJmZP+ydugPA5en+5QBu945fRkRDRLQOwHoA93d7/cmS\nzwSEH0nLJiBnEdXonieqMcLlnDoo2EQkSSqAapLXj6zicdfWjWAz4VNTpLZI8ucTmFkU1EGawCTJ\nyyoqTgAxaSP0a0dItqJm0hKQmlnf3EHnTxFcVKYbdVBA358JATMMzz4GJvHdnwLw6wAeJaKH0mO/\nB+DPAdxKRFcA2ArgbQDAzJuI6FYAj8N5Fl3NzD37j8ptAsI/tca10ZVTLp+YGzTbPxeJXWM4180L\n/ugqw7C3oE2obDNhUcVTLBsOxGqpwPzPobIam0ArUjncRidMw2kjooLFlDaO7P0NKAzDjYQxNCC/\nw64MwwGBkdsETAjMOroWAsz870CuLi9yYcl3rgNwXbfXrBL9TEAZMcxOrxtKi+Dqc9t6FhAVuC6g\ny8vT9GchSsOwJm3EHME7KFfxqFIyyK62eYZOlfHabVvCtKRcqg/S5ANiyHECE4VPeX25jUNMv+Eb\n2OWZwNBAdTMBZvacJGR1kCZOoJkwfv9fHsM7X7cWZ528SCxv9Ja+jRgeU7jFAYUsoqEffNLqiFVB\nTkIWUT8vj87zJstWWda+op5fYxMIq45akcq6dZAlV9uiTUA1oxIERpuxV5gJOHkRftfFBe51nmDh\n+2kk7jlq1EHjzdZMIJT6eUw5E/BH/zp1UCLaVvYeHsXN9z+He57YEyxnzAz6VgjkelDhR9JS8yBo\nFNCqg/KZQOYiGnC/dOUU+vakNcqW1wPWG4Ylb6dcz1/TJrkLu5Pm8Qk1Wd0xwUBbgU3Af9fSbKUe\nkdpCcsv11XmyTSARbQLM7CWQixACCnWQVA4ADo00AACHRxvBcsbMoC+FQKOZ5D9QaSYQZxPQlQP8\niOHycpSO7quwCUxcKEZWBw0K6RuShFGvyYvZuDbKz5ELgioYLFZw1Sz15kHLIC3IqTyLaMhFVOua\n6s4Vy4bVQZrAwEbConeQP/qX1EG+CkijDgLk38yRURMCxxN9KQT8kYw0XW65fspeP3lQmcImIK0n\nkDDna/IyC0sypqqo0KwhOz4nIsmdNIJtJq300FLn5ewHWYqJ8vvQXNdvvyZiuGWQ1swEwsI0Oxq7\n0HywjYm7D40w1dgE/I5fFALKEb4fJCYFjGWd/+EREwLHA30pBMba9KCCoYu9LKIlRfJUAnlHXF5d\n0bAYsglkHZL7XrjOWi28nsCEALAKvINabp+K3EG+/UCyCahW7Sp2sOVtzGcggm2FkZUtF1TFOI/Q\nXWs9iRpJona1bXg2gWbJAKZtCUohgZzWJuAHiUkBY5kQODJmQuB4oC+FQPRMAFmwUedOO+/YU31y\nWN/utq0RbLlqpFaT00tk57Jgo3LduNuqUknnro1C7iCF8PHrjE0boXqOoouonLjOL9vKHVQuVPzr\nahLISd5OTW6tDCemkk4SDKXqIGkmQCTr77XqIL8eaSbQUgeFy33l0Z04/0/vErOXGlNLnwoBvX7T\nH8ECnQ2QfgBYKNrUUdRldy7V6jTlFArNpJW+IZTsDfCSwimimgfr4Y4uEz7S7MfVIaeNyC4jXdcv\nnI2yy4bkzHE2gdwJoASt0Rzo5JFVXi7LHSS6AyeMuYI6KDMGL5wzUKFh2FMHCb+ZljpoPFhu886D\n2H1wFPuOjAXLGVNLXwqBsYiZQKvzcp87/UizY620EeX1TVQRlJfLOtisHaE2Sqt2ZcJBWi3MP9dS\nB5WU80f3omFYjhNoFjrY0KuZOBMov66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"text/plain": [ "<matplotlib.figure.Figure at 0x7f9b4d732f98>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "hg = ia.histogram(g)\n", "plt.figure(1)\n", "plt.plot(hg),plt.title('Histograma da Imagens realçada')\n", "plt.show()\n", "plt.figure(2)\n", "plt.plot(hg[1:-1]),plt.title('Idem, porém sem valores 0 e 255')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "## Links Interessantes\n", "\n", "- [Demonstração interativa](http://adessowiki.fee.unicamp.br/adesso/wiki/Demo/ws_demo2/view/?usecache=0)\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": "3.6.0" }, "toc": { "colors": { "hover_highlight": "#DAA520", "running_highlight": "#FF0000", "selected_highlight": "#FFD700" }, "moveMenuLeft": true, "nav_menu": { "height": "84px", "width": "252px" }, "navigate_menu": true, "number_sections": true, "sideBar": true, "threshold": 4, "toc_cell": true, "toc_section_display": "none", "toc_window_display": false } }, "nbformat": 4, "nbformat_minor": 2 }
mit
OSGeo-live/CesiumWidget
GSOC/notebooks/ipython/examples/Interactive Widgets/Image Processing.ipynb
1
2070431
null
apache-2.0
gogartom/caffe-textmaps
examples/classification.ipynb
3
272172
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "Classifying ImageNet: the instant Caffe way\n", "===========================================\n", "\n", "Caffe has a Python interface, pycaffe, with a `caffe.Net` interface for models. There are both Python and MATLAB interfaces. While this example uses the off-the-shelf Python `caffe.Classifier` interface there is also a MATLAB example at `matlab/caffe/matcaffe_demo.m`.\n", "\n", "Before we begin, you must compile Caffe. You should add the Caffe module to your `PYTHONPATH` although this example includes it automatically. If you haven't yet done so, please refer to the [installation instructions](http://caffe.berkeleyvision.org/installation.html). This example uses our pre-trained CaffeNet model, an ILSVRC12 image classifier. You can download it by running `./scripts/download_model_binary.py models/bvlc_reference_caffenet` or let the first step of this example download it for you.\n", "\n", "Ready? Let's start." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline\n", "\n", "# Make sure that caffe is on the python path:\n", "caffe_root = '../' # this file is expected to be in {caffe_root}/examples\n", "import sys\n", "sys.path.insert(0, caffe_root + 'python')\n", "\n", "import caffe\n", "\n", "# Set the right path to your model definition file, pretrained model weights,\n", "# and the image you would like to classify.\n", "MODEL_FILE = '../models/bvlc_reference_caffenet/deploy.prototxt'\n", "PRETRAINED = '../models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel'\n", "IMAGE_FILE = 'images/cat.jpg'\n", "\n", "import os\n", "if not os.path.isfile(PRETRAINED):\n", " print(\"Downloading pre-trained CaffeNet model...\")\n", " !../scripts/download_model_binary.py ../models/bvlc_reference_caffenet" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Loading a network is easy. `caffe.Classifier` takes care of everything. Note the arguments for configuring input preprocessing: mean subtraction switched on by giving a mean array, input channel swapping takes care of mapping RGB into the reference ImageNet model's BGR order, and raw scaling multiplies the feature scale from the input [0,1] to the ImageNet model's [0,255].\n", "\n", "We will set the phase to test since we are doing testing, and will first use CPU for the computation." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "caffe.set_mode_cpu()\n", "net = caffe.Classifier(MODEL_FILE, PRETRAINED,\n", " mean=np.load(caffe_root + 'python/caffe/imagenet/ilsvrc_2012_mean.npy').mean(1).mean(1),\n", " channel_swap=(2,1,0),\n", " raw_scale=255,\n", " image_dims=(256, 256))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's take a look at our example image with Caffe's image loading helper." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.image.AxesImage at 0x7fda204c0e10>" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": [ "iVBORw0KGgoAAAANSUhEUgAAAU0AAAEACAYAAAA3NiR2AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", "AAALEgAACxIB0t1+/AAAIABJREFUeJzsvU2sbVt23/UbY8y51t77nHPvfa9euSpVZXAqrthWYgWS\n", "2MIdPpJITkkE6EAiBAIpoLQQSPRoICEhpEjQwh1EI42AIiQ6ER8xKFG+MEEmUqQkhpjICqRcrirX\n", "q/fuxzln77XmnGPQGHOfVyahiKWKXiGd0XjvnXfP3WvvteYc8z/+//8YWyIieI7neI7neI5/oNBP\n", "+w08x3M8x3P8/ymek+ZzPMdzPMdvIp6T5nM8x3M8x28inpPmczzHczzHbyKek+ZzPMdzPMdvIp6T\n", "5nM8x3M8x28i/qEkzZ//+Z/nx3/8x/nKV77CH//jf/wfxiWe4zme4zk+lZDvt09zjMGP/diP8Wf/\n", "7J/li1/8Ij/1Uz/Fn/pTf4qf+Imf+H5e5jme4zme41OJ7zvS/MVf/EV+9Ed/lB/5kR+h1sof+SN/\n", "hD/9p//09/syz/Ecz/Ecn0p835Pm17/+dX74h3/46ecvfelLfP3rX/9+X+Y5nuM5nuNTie970hSR\n", "7/dLPsdzPMdz/MBE+X6/4Be/+EW+9rWvPf38ta99jS996Uu/4XfssOLb/v2+9HM8x3M8x/clTu8t\n", 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The default is to actually do 10 predictions, cropping the center and corners of the image as well as their mirrored versions, and average over the predictions:" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "prediction shape: (1000,)\n", "predicted class: 281\n" ] }, { "data": { "image/png": [ "iVBORw0KGgoAAAANSUhEUgAAAYIAAAEACAYAAAC+gnFaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", "AAALEgAACxIB0t1+/AAAG2ZJREFUeJzt3X9w02WCx/FPnOaOG1xRKmJJulNoCgnQ1q4pDMsyU1dK\n", "B06ytDo7XRn0dnvawUF393bXvX92FrxZseM4t2Jv5rqcv3VL//DGuh7magczQBVyCgyO9UfLtWcI\n", "1mWBrvxQS+Nzf9TGJIX0BykBnvdrJtN8v9/n+ebJY/L95Hm+3y86jDFGAABrXZXtBgAAsosgAADL\n", "EQQAYDmCAAAsRxAAgOUIAgCw3KhBEAwG5fV6VVRUpIaGhhHbW1tbVVpaqrKyMt18883asWNHfFtB\n", "QYFKSkpUVlamRYsWZbblAICMcKS7jyAWi2nevHlqb2+Xy+VSeXm5mpub5fP54mVOnz6tqVOnSpLe\n", "ffddVVdXq7u7W5I0e/ZsvfPOO5o+ffokvw0AwESlHRGEw2F5PB4VFBTI6XSqtrZWra2tSWWGQ0CS\n", 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of images, and formats them for the Caffe net automatically\n", "print 'prediction shape:', prediction[0].shape\n", "plt.plot(prediction[0])\n", "print 'predicted class:', prediction[0].argmax()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You can see that the prediction is 1000-dimensional, and is pretty sparse.\n", "\n", "The predicted class 281 is \"Tabby cat.\" Our pretrained model uses the synset ID ordering of the classes, as listed in `../data/ilsvrc12/synset_words.txt` if you fetch the auxiliary imagenet data by `../data/ilsvrc12/get_ilsvrc_aux.sh`. If you look at the top indices that maximize the prediction score, they are cats, foxes, and other cute mammals. Not unreasonable predictions, right?\n", "\n", "Now let's classify by the center crop alone by turning off oversampling. Note that this makes a single input, although if you inspect the model definition prototxt you'll see the network has a batch size of 10. The python wrapper handles batching and padding for you!" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "prediction shape: (1000,)\n", "predicted class: 281\n" ] }, { "data": { "image/png": [ "iVBORw0KGgoAAAANSUhEUgAAAYIAAAEACAYAAAC+gnFaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", "AAALEgAACxIB0t1+/AAAG6ZJREFUeJzt3X9sk+eBB/Dvy9l3vYUNSppCsDMZYoMNJGlWhxztRXJb\n", "kghWvCStqrSI9raIWkxpt2ldK92p16S3AtGENGikXcpBV0ovRLpKBFWpL4uoB0sJVht66S3QOlyi\n", "GtNQDsiRhLYm5rk/vJjXb+CNE+yY8Hw/kmW/7/s8r5/3cfx+/bw/QBFCCBARkbTmpLsBRESUXgwC\n", "IiLJMQiIiCTHICAikhyDgIhIcgwCIiLJTRoEXq8XdrsdNpsNDQ0NE5a3traioKAAhYWFuP/++3H4\n", "8OHYMovFgvz8fBQWFmL16tXJbTkRESWFoncfQSQSwfLly9HR0QGTyYSioiI0NzfD4XDEyoyOjiIj\n", "IwMA8Omnn6KyshJ9fX0AgCVLluDjjz/GggULUrwZREQ0XbojAr/fD6vVCovFAqPRiOrqarS2tsaV\n", "GQ8BABgZGcE999wTt5z3qxER3d50gyAUCiEnJyc2bTabEQqFJpQ7ePAgHA4H1q1bh127dsXmK4qC\n", 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oversample=False)\n", "print 'prediction shape:', prediction[0].shape\n", "plt.plot(prediction[0])\n", "print 'predicted class:', prediction[0].argmax()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "\n", "Now, why don't we see how long it takes to perform the classification end to end? This result is run from an Intel i5 CPU, so you may observe some performance differences." ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1 loops, best of 3: 355 ms per loop\n" ] } ], "source": [ "%timeit net.predict([input_image])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It may look a little slow, but note that time is spent on cropping, python interfacing, and running 10 images. For performance, if you really want to make prediction fast, you can optionally code in C++ and pipeline operations better. For experimenting and prototyping the current speed is fine.\n", "\n", "Let's time classifying a single image with input preprocessed:" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1 loops, best of 3: 210 ms per loop\n" ] } ], "source": [ "# Resize the image to the standard (256, 256) and oversample net input sized crops.\n", "input_oversampled = caffe.io.oversample([caffe.io.resize_image(input_image, net.image_dims)], net.crop_dims)\n", "# 'data' is the input blob name in the model definition, so we preprocess for that input.\n", "caffe_input = np.asarray([net.transformer.preprocess('data', in_) for in_ in input_oversampled])\n", "# forward() takes keyword args for the input blobs with preprocessed input arrays.\n", "%timeit net.forward(data=caffe_input)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "OK, so how about GPU? it is actually pretty easy:" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [], "source": [ "caffe.set_mode_gpu()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Voila! Now we are in GPU mode. Let's see if the code gives the same result:" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "prediction shape: (1000,)\n" ] }, { "data": { "text/plain": [ "[<matplotlib.lines.Line2D at 0x7fda1ac309d0>]" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": [ "iVBORw0KGgoAAAANSUhEUgAAAYIAAAEACAYAAAC+gnFaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", "AAALEgAACxIB0t1+/AAAG2ZJREFUeJzt3X9w1OWBx/HPOtk7O1hRImLYTSeQDewCSUzdwFDKTFoJ\n", "GTjYkuh0Uhn02pxmcNC219beP52CNxUzjnMV05lLOX9rQ/7wxlgP92IGd4Ao7CkwOMYfCZecy2Is\n", "BVL5oYasz/0Rs+5uYPODDQs879fMTvb7/T7Pd5993P1+9nm+3y86jDFGAABrXZXtBgAAsosgAADL\n", "EQQAYDmCAAAsRxAAgOUIAgCw3KhBEAwG5fV6VVRUpIaGhhHbW1tbVVpaqrKyMt1yyy3asWNHfFtB\n", "QYFKSkpUVlamhQsXZrblAICMcKS7jyAWi2nu3Llqb2+Xy+VSeXm5mpub5fP54mVOnz6tKVOmSJLe\n", "eecdVVdXq7u7W5I0a9Ysvf3225o2bdokvw0AwESlHRGEw2F5PB4VFBTI6XSqtrZWra2tSWWGQ0CS\n", 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shape:', prediction[0].shape\n", "plt.plot(prediction[0])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Good, everything is the same. And how about time consumption? The following benchmark is obtained on the same machine with a GTX 770 GPU:" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "10 loops, best of 3: 174 ms per loop\n" ] } ], "source": [ "# Full pipeline timing.\n", "%timeit net.predict([input_image])" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "10 loops, best of 3: 34.2 ms per loop\n" ] } ], "source": [ "# Forward pass timing.\n", "%timeit net.forward(data=caffe_input)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Pretty fast right? Not as fast as you expected? Indeed, in this python demo you are seeing only 4 times speedup. But remember - the GPU code is actually very fast, and the data loading, transformation and interfacing actually start to take **more** time than the actual conv. net computation itself!\n", "\n", "To fully utilize the power of GPUs, you really want to:\n", "\n", "* Use larger batches, and minimize python call and data transfer overheads.\n", "* Pipeline data load operations, like using a subprocess.\n", "* Code in C++. A little inconvenient, but maybe worth it if your dataset is really, really large." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Parting Words\n", "-------------\n", "\n", "So this is python! We hope the interface is easy enough for one to use. The python wrapper is interfaced with boost::python, and source code can be found at `python/caffe` with the main interface in `pycaffe.py` and the classification wrapper in `classifier.py`. If you have customizations to make, start there! Do let us know if you make improvements by sending a pull request!" ] } ], "metadata": { "description": "Use the pre-trained ImageNet model to classify images with the Python interface.", "example_name": "ImageNet classification", "include_in_docs": true, "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" }, "priority": 1 }, "nbformat": 4, "nbformat_minor": 0 }
mit
DS-100/sp17-materials
sp17/hw/hw1/hw1.ipynb
1
45742
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Homework 1: Setup and (Re-)Introduction to Python\n", "\n", "## Course Policies\n", "\n", "Here are some important course policies. These are also located at\n", "http://www.ds100.org/sp17/.\n", "\n", "**Tentative Grading**\n", "\n", "There will be 7 challenging homework assignments. Homeworks must be completed\n", "individually and will mix programming and short answer questions. At the end of\n", "each week of instruction we will have an online multiple choice quiz (\"vitamin\") that will\n", "help you stay up-to-date with lecture materials. Labs assignments will be\n", "graded for completion and are intended to help with the homework assignments.\n", "\n", "- 40% Homeworks\n", "- 13% Vitamins\n", "- 7% Labs\n", "- 15% Midterm\n", "- 25% Final\n", "\n", "**Collaboration Policy**\n", "\n", "Data science is a collaborative activity. While you may talk with others about\n", "the homework, we ask that you **write your solutions individually**. If you do\n", "discuss the assignments with others please **include their names** at the top\n", "of your solution. Keep in mind that content from the homework and vitamins will\n", "likely be covered on both the midterm and final.\n", "\n", "## This assignment\n", "\n", "In this assignment, you'll learn (or review):\n", "\n", "* How to set up Jupyter on your own computer.\n", "* How to check out and submit assignments for this class.\n", "* Python basics, like defining functions.\n", "* How to use the `numpy` library to compute with arrays of numbers." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 1. Setup\n", "\n", "If you haven't already, read through the instructions at\n", "http://www.ds100.org/spring-2017/setup.\n", "\n", "The instructions for submission are at the end of this notebook." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "First, let's make sure you have the latest version of okpy." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [], "source": [ "!pip install -U okpy" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If you've set up your environment properly, this cell should run without problems:" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [], "source": [ "import math\n", "import numpy as np\n", "import matplotlib\n", "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "plt.style.use('fivethirtyeight')\n", "from datascience import *\n", "\n", "from client.api.notebook import Notebook\n", "ok = Notebook('hw1.ok')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now, run this cell to log into [OkPy](http://okpy.org/).\n", "\n", "**This is the submission system for the class;** you will use this\n", "website to confirm that you've submitted your assignment." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [], "source": [ "ok.auth(inline=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 2. Python\n", "\n", "Python is the main programming language we'll use in this course. We assume you have some experience with Python or can learn it yourself, but here is a brief review.\n", "\n", "Below are some simple Python code fragments.\n", "\n", "You should feel confident explaining what each fragment is doing. If not,\n", "please brush up on your Python. There a number of tutorials online (search\n", "for \"Python tutorial\"). https://docs.python.org/3/tutorial/ is a good place to\n", "start." ] }, { "cell_type": "code", "execution_count": 64, "metadata": { "collapsed": false }, "outputs": [], "source": [ "2 + 2" ] }, { "cell_type": "code", "execution_count": 65, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# This is a comment.\n", "# In Python, the ** operator performs exponentiation.\n", "math.e**(-2)" ] }, { "cell_type": "code", "execution_count": 66, "metadata": { "collapsed": false }, "outputs": [], "source": [ "print(\"Hello\" + \",\", \"world!\")\n", "\"Hello, cell output!\"" ] }, { "cell_type": "code", "execution_count": 67, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def add2(x):\n", " \"\"\"This docstring explains what this function does: it adds 2 to a number.\"\"\"\n", " return x + 2" ] }, { "cell_type": "code", "execution_count": 68, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def makeAdder(amount):\n", " \"\"\"Make a function that adds the given amount to a number.\"\"\"\n", " def addAmount(x):\n", " return x + amount\n", " return addAmount\n", "\n", "add3 = makeAdder(3)\n", "add3(4)" ] }, { "cell_type": "code", "execution_count": 69, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# add4 is very similar to add2, but it's been created using a lambda expression.\n", "add4 = lambda x: x + 4\n", "add4(5)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [], "source": [ "sameAsMakeAdder = lambda amount: lambda x: x + amount\n", "add5 = sameAsMakeAdder(5)\n", "add5(6)" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def fib(n):\n", " if n <= 1:\n", " return 1\n", " # Functions can call themselves recursively.\n", " return fib(n-1) + fib(n-2)\n", "\n", "fib(4)" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# A for loop repeats a block of code once for each\n", "# element in a given collection.\n", "for i in range(5):\n", " if i % 2 == 0:\n", " print(2**i)\n", " else:\n", " print(\"Odd power of 2\")" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# A list comprehension is a convenient way to apply a function\n", "# to each element in a given collection.\n", "# The String method join appends together all its arguments\n", "# separated by the given string. So we append each element produced\n", "# by the list comprehension, each separated by a newline (\"\\n\").\n", "print(\"\\n\".join([str(2**i) if i % 2 == 0 else \"Odd power of 2\" for i in range(5)]))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Question 1\n", "\n", "##### Question 1a\n", "Write a function nums_reversed that takes in an integer `n` and returns a string\n", "containing the numbers 1 through `n` including `n` in reverse order, separated\n", "by spaces. For example:\n", "\n", " >>> nums_reversed(5)\n", " '5 4 3 2 1'\n", "\n", "***Note:*** The ellipsis (`...`) indicates something you should fill in. It *doesn't* necessarily imply you should replace it with only one line of code." ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def nums_reversed(n):\n", " ..." ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [], "source": [ "_ = ok.grade('q01a')\n", "_ = ok.backup()" ] }, { "cell_type": "markdown", "metadata": { "collapsed": false }, "source": [ "##### Question 1b\n", "\n", "Write a function `string_splosion` that takes in a non-empty string like\n", "`\"Code\"` and returns a long string containing every prefix of the input.\n", "For example:\n", "\n", " >>> string_splosion('Code')\n", " 'CCoCodCode'\n", " >>> string_splosion('data!')\n", " 'ddadatdatadata!'\n", " >>> string_splosion('hi')\n", " 'hhi'\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "for_assignment_type": "student" }, "outputs": [], "source": [ "def string_splosion(string):\n", " ..." ] }, { "cell_type": "code", "execution_count": 83, "metadata": { "collapsed": false }, "outputs": [], "source": [ "_ = ok.grade('q01b')\n", "_ = ok.backup()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "##### Question 1c\n", "\n", "Write a function `double100` that takes in a list of integers\n", "and returns `True` only if the list has two `100`s next to each other.\n", "\n", " >>> double100([100, 2, 3, 100])\n", " False\n", " >>> double100([2, 3, 100, 100, 5])\n", " True\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "for_assignment_type": "student" }, "outputs": [], "source": [ "def double100(nums):\n", " ..." ] }, { "cell_type": "code", "execution_count": 84, "metadata": { "collapsed": false }, "outputs": [], "source": [ "_ = ok.grade('q01c')\n", "_ = ok.backup()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "##### Question 1d\n", "\n", "Write a function `median` that takes in a list of numbers\n", "and returns the median element of the list. If the list has even\n", "length, it returns the mean of the two elements in the middle.\n", "\n", " >>> median([5, 4, 3, 2, 1])\n", " 3\n", " >>> median([ 40, 30, 10, 20 ])\n", " 25" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "for_assignment_type": "student" }, "outputs": [], "source": [ "def median(number_list):\n", " ..." ] }, { "cell_type": "code", "execution_count": 85, "metadata": { "collapsed": false }, "outputs": [], "source": [ "_ = ok.grade('q01d')\n", "_ = ok.backup()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 3. `NumPy`\n", "\n", "The `NumPy` library lets us do fast, simple computing with numbers in Python." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 3.1. Arrays\n", "\n", "The basic `NumPy` data type is the array, a homogeneously-typed sequential collection (a list of things that all have the same type). Arrays will most often contain strings, numbers, or other arrays." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's create some arrays:" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": false }, "outputs": [], "source": [ "array1 = np.array([2, 3, 4, 5])\n", "array2 = np.arange(4)\n", "array1, array2" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Math operations on arrays happen *element-wise*. Here's what we mean:" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": false }, "outputs": [], "source": [ "array1 * 2" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "collapsed": false }, "outputs": [], "source": [ "array1 * array2" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [], "source": [ "array1 ** array2" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This is not only very convenient (fewer `for` loops!) but also fast. `NumPy` is designed to run operations on arrays much faster than equivalent Python code on lists. Data science sometimes involves working with large datasets where speed is important - even the constant factors!" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Jupyter pro-tip**: Pull up the docs for any function in Jupyter by running a cell with\n", "the function name and a `?` at the end:" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "collapsed": true }, "outputs": [], "source": [ "np.arange?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Another Jupyter pro-tip**: Pull up the docs for any function in Jupyter by typing the function\n", "name, then `<Shift>-<Tab>` on your keyboard. Super convenient when you forget the order\n", "of the arguments to a function. You can press `<Tab>` multiple tabs to expand the docs.\n", "\n", "Try it on the function below:" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [], "source": [ "np.linspace" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Question 2\n", "Using the `np.linspace` function, create an array called `xs` that contains\n", "100 evenly spaced points between `0` and `2 * np.pi`. Then, create an array called `ys` that\n", "contains the value of $ \\sin{x} $ at each of those 100 points.\n", "\n", "*Hint:* Use the `np.sin` function. You should be able to define each variable with one line of code.)" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "collapsed": false }, "outputs": [], "source": [ "xs = ...\n", "ys = ..." ] }, { "cell_type": "code", "execution_count": 86, "metadata": { "collapsed": false }, "outputs": [], "source": [ "_ = ok.grade('q02')\n", "_ = ok.backup()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The `plt.plot` function from another library called `matplotlib` lets us make plots. It takes in\n", "an array of x-values and a corresponding array of y-values. It makes a scatter plot of the (x, y) pairs and connects points with line segments. If you give it enough points, it will appear to create a smooth curve.\n", "\n", "Let's plot the points you calculated in the previous question:" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "collapsed": false }, "outputs": [], "source": [ "plt.plot(xs, ys)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This is a useful recipe for plotting any function:\n", "1. Use `linspace` or `arange` to make a range of x-values.\n", "2. Apply the function to each point to produce y-values.\n", "3. Plot the points." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You might remember from calculus that the derivative of the `sin` function is the `cos` function. That means that the slope of the curve you plotted above at any point `xs[i]` is given by `cos(xs[i])`. You can try verifying this by plotting `cos` in the next cell." ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Try plotting cos here." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Calculating derivatives is an important operation in data science, but it can be difficult. We can have computers do it for us using a simple idea called *numerical differentiation*.\n", "\n", "Consider the `i`th point `(xs[i], ys[i])`. The slope of `sin` at `xs[i]` is roughly the slope of the line connecting `(xs[i], ys[i])` to the nearby point `(xs[i+1], ys[i+1])`. That slope is:\n", "\n", " (ys[i+1] - ys[i]) / (xs[i+1] - xs[i])\n", "\n", "If the difference between `xs[i+1]` and `xs[i]` were infinitessimal, we'd have exactly the derivative. In numerical differentiation we take advantage of the fact that it's often good enough to use \"really small\" differences instead." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Question 3\n", "\n", "Define a function called `derivative` that takes in an array of x-values and their\n", "corresponding y-values and computes the slope of the line connecting each point to the next point.\n", "\n", " >>> derivative(np.array([0, 1, 2]), np.array([2, 4, 6]))\n", " np.array([2., 2.])\n", " >>> derivative(np.arange(5), np.arange(5) ** 2)\n", " np.array([0., 2., 4., 6.])\n", "\n", "Notice that the output array has one less element than the inputs since we can't\n", "find the slope for the last point.\n", "\n", "It's possible to do this in one short line using [slicing](http://pythoncentral.io/how-to-slice-listsarrays-and-tuples-in-python/), but feel free to use whatever method you know.\n", "\n", "**Then**, use your `derivative` function to compute the slopes for each point in `xs`, `ys`.\n", "Store the slopes in an array called `slopes`." ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def derivative(xvals, yvals):\n", " ...\n", "\n", "slopes = ...\n", "slopes[:5]" ] }, { "cell_type": "code", "execution_count": 87, "metadata": { "collapsed": false }, "outputs": [], "source": [ "_ = ok.grade('q03')\n", "_ = ok.backup()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Question 4\n", "Plot the slopes you computed. Then plot `cos` on top of your plot, calling `plt.plot` again in the same cell. Did numerical differentiation work?\n", "\n", "*Note:* Since we have only 99 slopes, you'll need to take off the last x-value before plotting to avoid an error." ] }, { "cell_type": "code", "execution_count": 35, "metadata": { "collapsed": false }, "outputs": [], "source": [ "...\n", "..." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In the plot above, it's probably not clear which curve is which. Examine the cell below to see how to plot your results with a legend." ] }, { "cell_type": "code", "execution_count": 36, "metadata": { "collapsed": false }, "outputs": [], "source": [ "plt.plot(xs[:-1], slopes, label=\"Numerical derivative\")\n", "plt.plot(xs[:-1], np.cos(xs[:-1]), label=\"True derivative\")\n", "# You can just call plt.legend(), but the legend will cover up\n", "# some of the graph. Use bbox_to_anchor=(x,y) to set the x-\n", "# and y-coordinates of the center-left point of the legend,\n", "# where, for example, (0, 0) is the bottom-left of the graph\n", "# and (1, .5) is all the way to the right and halfway up.\n", "plt.legend(bbox_to_anchor=(1, .5), loc=\"center left\");" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 3.2. Multidimensional Arrays\n", "A multidimensional array is a primitive version of a table, containing only one kind of data and having no column labels. A 2-dimensional array is useful for working with *matrices* of numbers." ] }, { "cell_type": "code", "execution_count": 37, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# The zeros function creates an array with the given shape.\n", "# For a 2-dimensional array like this one, the first\n", "# coordinate says how far the array goes *down*, and the\n", "# second says how far it goes *right*.\n", "array3 = np.zeros((4, 5))\n", "array3" ] }, { "cell_type": "code", "execution_count": 38, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# The shape attribute returns the dimensions of the array.\n", "array3.shape" ] }, { "cell_type": "code", "execution_count": 39, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# You can think of array3 as an array containing 4 arrays, each\n", "# containing 5 zeros. Accordingly, we can set or get the third\n", "# element of the second array in array 3 using standard Python\n", "# array indexing syntax twice:\n", "array3[1][2] = 7\n", "array3" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# This comes up so often that there is special syntax provided\n", "# for it. The comma syntax is equivalent to using multiple\n", "# brackets:\n", "array3[1, 2] = 8\n", "array3" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Arrays allow you to assign to multiple places at once. The special character `:` means \"everything.\"" ] }, { "cell_type": "code", "execution_count": 41, "metadata": { "collapsed": false }, "outputs": [], "source": [ "array4 = np.zeros((3, 5))\n", "array4[:, 2] = 5\n", "array4" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In fact, you can use arrays of indices to assign to multiple places. Study the next example and make sure you understand how it works." ] }, { "cell_type": "code", "execution_count": 42, "metadata": { "collapsed": false }, "outputs": [], "source": [ "array5 = np.zeros((3, 5))\n", "rows = np.array([1, 0, 2])\n", "cols = np.array([3, 1, 4])\n", "\n", "# Indices (1,3), (0,1), and (2,4) will be set.\n", "array5[rows, cols] = 3\n", "array5" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Question 5\n", "Create a 50x50 array called `twice_identity` that contains all zeros except on the\n", "diagonal, where it contains the value `2`.\n", "\n", "Start by making a 50x50 array of all zeros, then set the values. Use indexing, not a `for` loop! (Don't use `np.eye` either, though you might find that function useful later.)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "for_assignment_type": "student" }, "outputs": [], "source": [ "twice_identity = ...\n", "...\n", "twice_identity" ] }, { "cell_type": "code", "execution_count": 88, "metadata": { "collapsed": false }, "outputs": [], "source": [ "_ = ok.grade('q05')\n", "_ = ok.backup()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 4. A Picture Puzzle" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Your boss has given you some strange text files. He says they're images,\n", "some of which depict a summer scene and the rest a winter scene.\n", "\n", "He demands that you figure out how to determine whether a given\n", "text file represents a summer scene or a winter scene.\n", "\n", "You receive 10 files, `1.txt` through `10.txt`. Peek at the files in a text\n", "editor of your choice." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Question 6\n", "How do you think the contents of the file are structured? Take your best guess." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "*Write your answer here, replacing this text.*" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Question 7\n", "Create a function called `read_file_lines` that takes in a filename as its argument.\n", "This function should return a Python list containing the lines of the\n", "file as strings. That is, if `1.txt` contains:\n", "\n", "```\n", "1 2 3\n", "3 4 5\n", "7 8 9\n", "```\n", "\n", "the return value should be: `['1 2 3\\n', '3 4 5\\n', '7 8 9\\n']`.\n", "\n", "**Then**, use the `read_file_lines` function on the file `1.txt`, reading the contents\n", "into a variable called `file1`.\n", "\n", "*Hint:* Check out [this Stack Overflow page](http://stackoverflow.com/questions/3277503/how-to-read-a-file-line-by-line-into-a-list-with-python) on reading lines of files." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def read_file_lines(filename):\n", " ...\n", " ...\n", "\n", "file1 = ...\n", "file1[:5]" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [], "source": [ "_ = ok.grade('q07')\n", "_ = ok.backup()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Each file begins with a line containing two numbers. After checking the length of\n", "a file, you could notice that the product of these two numbers equals the number of\n", "lines in each file (other than the first one).\n", "\n", "This suggests the rows represent elements in a 2-dimensional grid. In fact, each\n", "dataset represents an image!\n", "\n", "On the first line, the first of the two numbers is\n", "the height of the image (in pixels) and the second is the width (again in pixels).\n", "\n", "Each line in the rest of the file contains the pixels of the image.\n", "Each pixel is a triplet of numbers denoting how much red, green, and blue\n", "the pixel contains, respectively.\n", "\n", "In image processing, each column in one of these image files is called a *channel*\n", "(disregarding line 1). So there are 3 channels: red, green, and blue.\n", "\n", "#### Question 8\n", "Define a function called `lines_to_image` that takes in the contents of a\n", "file as a list (such as `file1`). It should return an array containing integers of\n", "shape `(n_rows, n_cols, 3)`. That is, it contains the pixel triplets organized in the\n", "correct number of rows and columns.\n", "\n", "For example, if the file originally contained:\n", "\n", "```\n", "4 2\n", "0 0 0\n", "10 10 10\n", "2 2 2\n", "3 3 3\n", "4 4 4\n", "5 5 5\n", "6 6 6\n", "7 7 7\n", "```\n", "\n", "The resulting array should be a *3-dimensional* array that looks like this:\n", "\n", "```\n", "array([\n", " [ [0,0,0], [10,10,10] ],\n", " [ [2,2,2], [3,3,3] ],\n", " [ [4,4,4], [5,5,5] ],\n", " [ [6,6,6], [7,7,7] ]\n", "])\n", "```\n", "\n", "The string method `split` and the function `np.reshape` might be useful.\n", "\n", "**Important note:** You must call `.astype(np.uint8)` on the final array before\n", "returning so that `numpy` will recognize the array represents an image.\n", "\n", "Once you've defined the function, set `image1` to the result of calling\n", "`lines_to_image` on `file1`." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "for_assignment_type": "student" }, "outputs": [], "source": [ "def lines_to_image(file_lines):\n", " ...\n", " image_array = ...\n", " # Make sure to call astype like this on the 3-dimensional array\n", " # you produce, before returning it.\n", " return image_array.astype(np.uint8)\n", "\n", "image1 = ...\n", "image1.shape" ] }, { "cell_type": "code", "execution_count": 105, "metadata": { "collapsed": false }, "outputs": [], "source": [ "_ = ok.grade('q08')\n", "_ = ok.backup()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Question 9\n", "\n", "Images in `numpy` are simply arrays, but we can also display them them as\n", "actual images in this notebook.\n", "\n", "Use the provided `show_images` function to display `image1`. You may call it\n", "like `show_images(image1)`. If you later have multiple images to display, you\n", "can call `show_images([image1, image2])` to display them all at once.\n", "\n", "The resulting image should look almost completely black. Why do you suppose\n", "that is?" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def show_images(images, ncols=2, figsize=(10, 7), **kwargs):\n", " \"\"\"\n", " Shows one or more color images.\n", " \n", " images: Image or list of images. Each image is a 3-dimensional\n", " array, where dimension 1 indexes height and dimension 2\n", " the width. Dimension 3 indexes the 3 color values red,\n", " blue, and green (so it always has length 3).\n", " \"\"\"\n", " def show_image(image, axis=plt):\n", " plt.imshow(image, **kwargs)\n", " \n", " if not (isinstance(images, list) or isinstance(images, tuple)):\n", " images = [images]\n", " images = [image.astype(np.uint8) for image in images]\n", " \n", " nrows = math.ceil(len(images) / ncols)\n", " ncols = min(len(images), ncols)\n", " \n", " plt.figure(figsize=figsize)\n", " for i, image in enumerate(images):\n", " axis = plt.subplot2grid(\n", " (nrows, ncols),\n", " (i // ncols, i % ncols),\n", " )\n", " axis.tick_params(bottom='off', left='off', top='off', right='off',\n", " labelleft='off', labelbottom='off')\n", " axis.grid(False)\n", " show_image(image, axis)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Show image1 here:\n", "..." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Question 10\n", "\n", "If you look at the data, you'll notice all the numbers lie between 0 and 10.\n", "In `NumPy`, a color intensity is an integer ranging from 0 to 255, where 0 is\n", "no color (black). That's why the image is almost black. To see the image,\n", "we'll need to rescale the numbers in the data to have a larger range.\n", "\n", "Define a function `expand_image_range` that takes in an image. It returns a\n", "**new copy** of the image with the following transformation:\n", " \n", " old value | new value\n", " ========= | =========\n", " 0 | 12\n", " 1 | 37\n", " 2 | 65\n", " 3 | 89\n", " 4 | 114\n", " 5 | 137\n", " 6 | 162\n", " 7 | 187\n", " 8 | 214\n", " 9 | 240\n", " 10 | 250\n", "\n", "This expands the color range of the image. For example, a pixel that previously\n", "had the value `[5 5 5]` (almost-black) will now have the value `[137 137 137]`\n", "(gray).\n", "\n", "Set `expanded1` to the expanded `image1`, then display it with `show_images`.\n", "\n", "[This page](https://docs.scipy.org/doc/numpy/reference/arrays.indexing.html#boolean-array-indexing)\n", "from the numpy docs has some useful information that will allow you\n", "to use indexing instead of `for` loops.\n", "\n", "However, the slickest implementation uses one very short line of code.\n", "*Hint:* If you index an array with another array or list as in question 5, your\n", "array (or list) of indices can contain repeats, as in `array1[[0, 1, 0]]`.\n", "Investigate what happens in that case." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false, "scrolled": false }, "outputs": [], "source": [ "# This array is provided for your convenience.\n", "transformed = np.array([12, 37, 65, 89, 114, 137, 162, 187, 214, 240, 250])\n", "\n", "def expand_image_range(image):\n", " ...\n", "\n", "expanded1 = ...\n", "show_images(expanded1)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [], "source": [ "_ = ok.grade('q10')\n", "_ = ok.backup()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Question 11\n", "\n", "Eureka! You've managed to reveal the image that the text file represents.\n", "\n", "Now, define a function called `reveal_file` that takes in a filename\n", "and returns an expanded image. This should be relatively easy since you've\n", "defined functions for each step in the process.\n", "\n", "Then, set `expanded_images` to a list of all the revealed images. There are\n", "10 images to reveal (including the one you just revealed).\n", "\n", "Finally, use `show_images` to display the `expanded_images`." ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def reveal_file(filename):\n", " ...\n", "\n", "filenames = ['1.txt', '2.txt', '3.txt', '4.txt', '5.txt',\n", " '6.txt', '7.txt', '8.txt', '9.txt', '10.txt']\n", "expanded_images = ...\n", "\n", "show_images(expanded_images, ncols=5)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Notice that 5 of the above images are of summer scenes; the other 5\n", "are of winter.\n", "\n", "Think about how you'd distinguish between pictures of summer and winter. What\n", "qualities of the image seem to signal to your brain that the image is one of\n", "summer? Of winter?\n", "\n", "One trait that seems specific to summer pictures is that the colors are warmer.\n", "Let's see if the proportion of pixels of each color in the image can let us\n", "distinguish between summer and winter pictures." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Question 12\n", "To simplify things, we can categorize each pixel according to its most intense\n", "(highest-value) channel. (Remember, red, green, and blue are the 3 channels.)\n", "For example, we could just call a `[2 4 0]` pixel \"green.\" If a pixel has a\n", "tie between several channels, let's count it as none of them.\n", "\n", "Write a function `proportion_by_channel`. It takes in an image. It assigns\n", "each pixel to its greatest-intensity channel: red, green, or blue. Then\n", "the function returns an array of length three containing the proportion of\n", "pixels categorized as red, the proportion categorized as green, and the\n", "proportion categorized as blue (respectively). (Again, don't count pixels\n", "that are tied between 2 or 3 colors as any category, but do count them\n", "in the denominator when you're computing proportions.)\n", "\n", "For example:\n", "\n", "```\n", ">>> test_im = np.array([\n", " [ [5, 2, 2], [2, 5, 10] ] \n", "])\n", ">>> proportion_by_channel(test_im)\n", "array([ 0.5, 0, 0.5 ])\n", "\n", "# If tied, count neither as the highest\n", ">>> test_im = np.array([\n", " [ [5, 2, 5], [2, 50, 50] ] \n", "])\n", ">>> proportion_by_channel(test_im)\n", "array([ 0, 0, 0 ])\n", "```\n", "\n", "Then, set `image_proportions` to the result of `proportion_by_channel` called\n", "on each image in `expanded_images` as a 2d array.\n", "\n", "*Hint:* It's fine to use a `for` loop, but for a difficult challenge, try\n", "avoiding it. (As a side benefit, your code will be much faster.) Our solution\n", "uses the `NumPy` functions `np.reshape`, `np.sort`, `np.argmax`, and `np.bincount`." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "for_assignment_type": "student" }, "outputs": [], "source": [ "def proportion_by_channel(image):\n", " ...\n", "\n", "image_proportions = ...\n", "image_proportions" ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "collapsed": false }, "outputs": [], "source": [ "_ = ok.grade('q12')\n", "_ = ok.backup()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's plot the proportions you computed above on a bar chart:" ] }, { "cell_type": "code", "execution_count": 45, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# You'll learn about Pandas and DataFrames soon.\n", "import pandas as pd\n", "pd.DataFrame({\n", " 'red': image_proportions[:, 0],\n", " 'green': image_proportions[:, 1],\n", " 'blue': image_proportions[:, 2]\n", " }, index=pd.Series(['Image {}'.format(n) for n in range(1, 11)], name='image'))\\\n", " .iloc[::-1]\\\n", " .plot.barh();" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Question 13\n", "\n", "What do you notice about the colors present in the summer images compared to\n", "the winter ones?\n", "\n", "Use this info to write a function `summer_or_winter`. It takes in an image and\n", "returns `True` if the image is a summer image and `False` if the image is a\n", "winter image.\n", "\n", "**Do not hard-code the function to the 10 images you currently have (eg.\n", "`if image1, return False`).** We will run your function on other images\n", "that we've reserved for testing.\n", "\n", "You must classify all of the 10 provided images correctly to pass the test\n", "for this function.\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "for_assignment_type": "student" }, "outputs": [], "source": [ "def summer_or_winter(image):\n", " ..." ] }, { "cell_type": "code", "execution_count": 47, "metadata": { "collapsed": false }, "outputs": [], "source": [ "_ = ok.grade('q13')\n", "_ = ok.backup()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Congrats! You've created your very first classifier for this class." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Question 14\n", "\n", "1. How do you think your classification function will perform\n", " in general?\n", "2. Why do you think it will perform that way?\n", "3. What do you think would most likely give you false positives?\n", "4. False negatives?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "*Write your answer here, replacing this text.*" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Final note:** While our approach here is simplistic, skin color segmentation\n", "-- figuring out which parts of the image belong to a human body -- is a\n", "key step in many algorithms such as face detection." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Optional: Our code to encode images" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here are the functions we used to generate the text files for this assignment.\n", "\n", "Feel free to send not-so-secret messages to your friends if you'd like." ] }, { "cell_type": "code", "execution_count": 58, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import skimage as sk\n", "import skimage.io as skio" ] }, { "cell_type": "code", "execution_count": 59, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def read_image(filename):\n", " '''Reads in an image from a filename'''\n", " return skio.imread(filename)" ] }, { "cell_type": "code", "execution_count": 60, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def compress_image(im):\n", " '''Takes an image as an array and compresses it to look black.'''\n", " res = im / 25\n", " return res.astype(np.uint8)" ] }, { "cell_type": "code", "execution_count": 61, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def to_text_file(im, filename):\n", " '''\n", " Takes in an image array and a filename for the resulting text file.\n", " \n", " Creates the encoded text file for later decoding.\n", " '''\n", " h, w, c = im.shape\n", " to_rgb = ' '.join\n", " to_row = '\\n'.join\n", " to_lines = '\\n'.join\n", " \n", " rgb = [[to_rgb(triplet) for triplet in row] for row in im.astype(str)]\n", " lines = to_lines([to_row(row) for row in rgb])\n", "\n", " with open(filename, 'w') as f:\n", " f.write('{} {}\\n'.format(h, w))\n", " f.write(lines)\n", " f.write('\\n')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "summers = skio.imread_collection('orig/summer/*.jpg')\n", "winters = skio.imread_collection('orig/winter/*.jpg')\n", "len(summers)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "sum_nums = np.array([ 5, 6, 9, 3, 2, 11, 12])\n", "win_nums = np.array([ 10, 7, 8, 1, 4, 13, 14])\n", "\n", "for im, n in zip(summers, sum_nums):\n", " to_text_file(compress_image(im), '{}.txt'.format(n))\n", "for im, n in zip(winters, win_nums):\n", " to_text_file(compress_image(im), '{}.txt'.format(n))" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "# 5. Submitting this assignment\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "First, run this cell to run all the autograder tests at once so you can double-\n", "check your work." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "_ = ok.grade_all()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now, run this code in your terminal to make a\n", "[git commit](https://www.atlassian.com/git/tutorials/saving-changes/git-commit)\n", "that saves a snapshot of your changes in `git`. The last line of the cell\n", "runs [git push](http://stackoverflow.com/questions/2745076/what-are-the-differences-between-git-commit-and-git-push), which will send your work to your personal Github repo." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "```\n", "# Tell git to commit all the changes so far\n", "git add -A\n", "\n", "# Tell git to make the commit\n", "git commit -m \"hw1 finished\"\n", "\n", "# Send your updates to your personal private repo\n", "git push origin master\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Finally, we'll submit the assignment to OkPy so that the staff will know to\n", "grade it. You can submit as many times as you want and you can choose which\n", "submission you want us to grade by going to https://okpy.org/cal/data100/sp17/." ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Now, we'll submit to okpy\n", "_ = ok.submit()" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "Congrats! You are done with homework 1." ] } ], "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" }, "timetravel": { "allowedContentTypes": [ "text/plain" ], "enabled": false, "version": "1.0" } }, "nbformat": 4, "nbformat_minor": 0 }
gpl-3.0
chongxi/spiketag
spiketag/view/tests/TestWaveView.ipynb
1
8523
{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Populating the interactive namespace from numpy and matplotlib\n" ] } ], "source": [ "%pylab inline\n", "import numexpr as ne\n", "import seaborn as sns\n", "from ipywidgets import interact\n", "import time\n", "import numpy as np" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "%load_ext autoreload\n", "%autoreload 2" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import sys\n", "sys.path.append('../../..')" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "%gui qt" ] }, { "cell_type": "code", "execution_count": 110, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from spiketag.base.Binload import bload\n", "from spiketag.view import wave_view\n", "from spiketag.base import ProbeFactory" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Read from raw data which export from fpga" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": true }, "outputs": [], "source": [ "nCh = 160\n", "fs = 25e3\n", "bf = bload(nCh, fs)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "2017-07-10 10:04:38,080 - spiketag - INFO - ############# load data ###################\n", "2017-07-10 10:04:38,081 - spiketag - INFO - /Users/chiy/Documents/HHMI/Github/spiketag/spiketag_test_data/mua.bin loaded, it contains: \n", "2017-07-10 10:04:38,083 - spiketag - INFO - 7361618 * 160 points (4711435520 bytes) \n", "2017-07-10 10:04:38,084 - spiketag - INFO - 160 channels with sampling rate of 25000.0000 \n", "2017-07-10 10:04:38,086 - spiketag - INFO - 294.465 secs (4.908 mins) of data\n", "2017-07-10 10:04:38,088 - spiketag - INFO - #############################################\n" ] } ], "source": [ "bf.load('/Users/chiy/Documents/HHMI/Github/spiketag/spiketag_test_data/mua.bin')" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def bf_to_mua(x):\n", " y = x.reshape(-1, 32, 5)\n", " z = np.swapaxes(y, 1, 2)\n", " z = z.reshape(-1, 160)\n", " return z" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "_data = bf_to_mua(bf.npmm)" ] }, { "cell_type": "code", "execution_count": 121, "metadata": { "collapsed": true }, "outputs": [], "source": [ "spk = np.fromfile('/Users/chiy/Documents/HHMI/Github/spiketag/spiketag_test_data/spk.bin', dtype=int32)" ] }, { "cell_type": "code", "execution_count": 122, "metadata": {}, "outputs": [], "source": [ "ch = spk.reshape(-1,2)[:,0]\n", "t = spk.reshape(-1,2)[:,1]" ] }, { "cell_type": "code", "execution_count": 123, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[ 1, 17, 17, ..., 7361613, 7361613, 7361617],\n", " [ 79, 67, 70, ..., 9, 73, 20]], dtype=int32)" ] }, "execution_count": 123, "metadata": {}, "output_type": "execute_result" } ], "source": [ "_spks = np.vstack((t,ch))\n", "_spks" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [], "source": [ "wv = wave_view(fs=fs, data=_data, spks=_spks, chs=range(96,160))" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": true }, "outputs": [], "source": [ "wv.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Process data which can be readed by spiketag" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [], "source": [ "_data[:,96:].ravel().tofile('/Users/chiy/Documents/HHMI/Github/spiketag/spiketag_test_data/pcie_64_25K_07_10_2017.bin')" ] }, { "cell_type": "code", "execution_count": 124, "metadata": { "collapsed": true }, "outputs": [], "source": [ "offset = 96" ] }, { "cell_type": "code", "execution_count": 125, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[ 25, 26, 303, ..., 7179855, 7179998, 7180087],\n", " [ 58, 36, 51, ..., 25, 59, 56]], dtype=int32)" ] }, "execution_count": 125, "metadata": {}, "output_type": "execute_result" } ], "source": [ "_spks = _spks[:,np.where(np.in1d(_spks[1],range(96,161)))[0]]\n", "_spks[1] = _spks[1] - offset\n", "_spks" ] }, { "cell_type": "code", "execution_count": 128, "metadata": {}, "outputs": [], "source": [ "_spks = np.delete(_spks, np.where(_spks[1] == 59)[0], axis=1)" ] }, { "cell_type": "code", "execution_count": 129, "metadata": {}, "outputs": [], "source": [ "_spks.T.ravel().tofile('/Users/chiy/Documents/HHMI/Github/spiketag/spiketag_test_data/pcie_64_25K_07_10_2017.bin.spk')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Read data as spiketag" ] }, { "cell_type": "code", "execution_count": 84, "metadata": { "collapsed": true }, "outputs": [], "source": [ "nCh = 64\n", "fs = 25e3\n", "bf = bload(nCh, fs)" ] }, { "cell_type": "code", "execution_count": 85, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "2017-07-10 11:02:32,553 - spiketag - INFO - ############# load data ###################\n", "2017-07-10 11:02:32,555 - spiketag - INFO - /Users/chiy/Documents/HHMI/Github/spiketag/spiketag_test_data/pcie_64_25K_07_10_2017.bin loaded, it contains: \n", "2017-07-10 11:02:32,556 - spiketag - INFO - 7361618 * 64 points (1884574208 bytes) \n", "2017-07-10 11:02:32,558 - spiketag - INFO - 64 channels with sampling rate of 25000.0000 \n", "2017-07-10 11:02:32,559 - spiketag - INFO - 294.465 secs (4.908 mins) of data\n", "2017-07-10 11:02:32,560 - spiketag - INFO - #############################################\n" ] } ], "source": [ "bf.load('/Users/chiy/Documents/HHMI/Github/spiketag/spiketag_test_data/pcie_64_25K_07_10_2017.bin')" ] }, { "cell_type": "code", "execution_count": 86, "metadata": {}, "outputs": [], "source": [ "_data = bf.npmm.reshape(-1, 64)" ] }, { "cell_type": "code", "execution_count": 100, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([ 25, 58, 26, ..., 59, 7180087, 56], dtype=int32)" ] }, "execution_count": 100, "metadata": {}, "output_type": "execute_result" } ], "source": [ "_spk = np.fromfile('/Users/chiy/Documents/HHMI/Github/spiketag/spiketag_test_data/pcie_64_25K_07_10_2017.bin.spk', dtype=int32)\n", "_spk" ] }, { "cell_type": "code", "execution_count": 88, "metadata": {}, "outputs": [], "source": [ "wv = wave_view(fs=fs, data=_data, spks=_spk)" ] }, { "cell_type": "code", "execution_count": 89, "metadata": { "collapsed": true }, "outputs": [], "source": [ "wv.show()" ] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python [default]", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.12" } }, "nbformat": 4, "nbformat_minor": 1 }
bsd-3-clause
google/evojax
examples/notebooks/TutorialTaskImplementation.ipynb
1
591491
{ "nbformat": 4, "nbformat_minor": 0, "metadata": { "colab": { "name": "Tutorial_TaskCreation", "provenance": [], "collapsed_sections": [], "toc_visible": true, "authorship_tag": "ABX9TyMlfWC3HgNCKX6LTzYbUD+Y", "include_colab_link": true }, "kernelspec": { "name": "python3", "display_name": "Python 3" }, "language_info": { "name": "python" }, "accelerator": "GPU" }, "cells": [ { "cell_type": "markdown", "metadata": { "id": "view-in-github", "colab_type": "text" }, "source": [ "<a href=\"https://colab.research.google.com/github/google/evojax/blob/main/examples/notebooks/TutorialTaskImplementation.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>" ] }, { "cell_type": "markdown", "source": [ "# Tutorial: Creating Tasks" ], "metadata": { "id": "p-rXn2XFPevg" } }, { "cell_type": "markdown", "source": [ "## Pre-requisite\n", "\n", "Before we start, we need to install EvoJAX and import some libraries. \n", "**Note** In our [paper](https://arxiv.org/abs/2202.05008), we ran the experiments on NVIDIA V100 GPU(s). Your results can be different from ours." ], "metadata": { "id": "sh1ewTJzPmtn" } }, { "cell_type": "code", "execution_count": 1, "metadata": { "id": "0fm2_FLQPWu8" }, "outputs": [], "source": [ "from IPython.display import clear_output, Image\n", "\n", "!pip install evojax\n", "!pip install torchvision # We use torchvision.datasets.MNIST in this tutorial.\n", "\n", "clear_output()" ] }, { "cell_type": "code", "source": [ "import os\n", "import numpy as np\n", "import jax\n", "import jax.numpy as jnp\n", "\n", "from evojax.task.cartpole import CartPoleSwingUp\n", "from evojax.policy.mlp import MLPPolicy\n", "from evojax.algo import PGPE\n", "from evojax import Trainer\n", "from evojax.util import create_logger" ], "metadata": { "id": "Ch5f-sP0Ps-M" }, "execution_count": 2, "outputs": [] }, { "cell_type": "code", "source": [ "# Let's create a directory to save logs and models.\n", "log_dir = './log'\n", "logger = create_logger(name='EvoJAX', log_dir=log_dir)\n", "logger.info('Welcome to the tutorial on Task creation!')\n", "\n", "logger.info('Jax backend: {}'.format(jax.local_devices()))\n", "!nvidia-smi --query-gpu=name --format=csv,noheader" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "9uCO2RnsRthS", "outputId": "2727b2b3-015c-4399-e3b9-25842d1ca30b" }, "execution_count": 3, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "EvoJAX: 2022-02-12 05:53:28,121 [INFO] Welcome to the tutorial on Task creation!\n", "absl: 2022-02-12 05:53:28,133 [INFO] Starting the local TPU driver.\n", "absl: 2022-02-12 05:53:28,135 [INFO] Unable to initialize backend 'tpu_driver': Not found: Unable to find driver in registry given worker: local://\n", "absl: 2022-02-12 05:53:28,519 [INFO] Unable to initialize backend 'tpu': Invalid argument: TpuPlatform is not available.\n", "EvoJAX: 2022-02-12 05:53:28,520 [INFO] Jax backend: [GpuDevice(id=0, process_index=0)]\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "Tesla V100-SXM2-16GB\r\n" ] } ] }, { "cell_type": "markdown", "source": [ "## Introduction" ], "metadata": { "id": "mHw9Bx3jUYAY" } }, { "cell_type": "markdown", "source": [ "EvoJAX has three major components: the *task*, the *policy network* and the *neuroevolution algorithm*. Once these components are implemented and instantiated, we can use a trainer to start the training process. The following code snippet provides an example of how we use EvoJAX." ], "metadata": { "id": "YiDPbtFFUZdy" } }, { "cell_type": "code", "source": [ "seed = 42 # Wish me luck!\n", "\n", "# We use the classic cart-pole swing up as our tasks, see\n", "# https://github.com/google/evojax/tree/main/evojax/task for more example tasks.\n", "# The test flag provides the opportunity for a user to\n", "# 1. Return different signals as rewards. For example, in our MNIST example,\n", "# we use negative cross-entropy loss as the reward in training tasks, and the\n", "# classification accuracy as the reward in test tasks.\n", "# 2. Perform reward shaping. It is common for RL practitioners to modify the\n", "# rewards during training so that the agent learns more efficiently. But this\n", "# modification should not be allowed in tests for fair evaluations.\n", "hard = False\n", "train_task = CartPoleSwingUp(harder=hard, test=False)\n", "test_task = CartPoleSwingUp(harder=hard, test=True)\n", "\n", "# We use a feedforward network as our policy.\n", "# By default, MLPPolicy uses \"tanh\" as its activation function for the output.\n", "policy = MLPPolicy(\n", " input_dim=train_task.obs_shape[0],\n", " hidden_dims=[64, 64],\n", " output_dim=train_task.act_shape[0],\n", " logger=logger,\n", ")\n", "\n", "# We use PGPE as our evolution algorithm.\n", "# If you want to know more about the algorithm, please take a look at the paper:\n", "# https://people.idsia.ch/~juergen/nn2010.pdf \n", "solver = PGPE(\n", " pop_size=64,\n", " param_size=policy.num_params,\n", " optimizer='adam',\n", " center_learning_rate=0.05,\n", " seed=seed,\n", ")\n", "\n", "# Now that we have all the three components instantiated, we can create a\n", "# trainer and start the training process.\n", "trainer = Trainer(\n", " policy=policy,\n", " solver=solver,\n", " train_task=train_task,\n", " test_task=test_task,\n", " max_iter=600,\n", " log_interval=100,\n", " test_interval=200,\n", " n_repeats=5,\n", " n_evaluations=128,\n", " seed=seed,\n", " log_dir=log_dir,\n", " logger=logger,\n", ")\n", "_ = trainer.run()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "MwTiD6yqUSKl", "outputId": "3f263102-6b70-4953-b9fb-f726661b3c12" }, "execution_count": 4, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "EvoJAX: 2022-02-12 05:53:31,223 [INFO] MLPPolicy.num_params = 4609\n", "EvoJAX: 2022-02-12 05:53:31,381 [INFO] Start to train for 600 iterations.\n", "EvoJAX: 2022-02-12 05:53:42,936 [INFO] Iter=100, size=64, max=717.4396, avg=632.6160, min=475.3617, std=51.3240\n", "EvoJAX: 2022-02-12 05:53:51,773 [INFO] Iter=200, size=64, max=838.2386, avg=751.0416, min=592.3156, std=46.3648\n", "EvoJAX: 2022-02-12 05:53:53,555 [INFO] [TEST] Iter=200, #tests=128, max=880.2914 avg=834.9127, min=763.1976, std=40.8967\n", "EvoJAX: 2022-02-12 05:54:02,542 [INFO] Iter=300, size=64, max=917.9876, avg=857.5809, min=48.2173, std=133.0970\n", "EvoJAX: 2022-02-12 05:54:11,668 [INFO] Iter=400, size=64, max=917.4292, avg=900.6838, min=544.6534, std=53.2497\n", "EvoJAX: 2022-02-12 05:54:11,770 [INFO] [TEST] Iter=400, #tests=128, max=927.4318 avg=918.8890, min=909.4037, std=3.2266\n", "EvoJAX: 2022-02-12 05:54:20,773 [INFO] Iter=500, size=64, max=922.2775, avg=868.8109, min=227.3976, std=147.4509\n", "EvoJAX: 2022-02-12 05:54:29,884 [INFO] [TEST] Iter=600, #tests=128, max=949.3198, avg=928.1906, min=917.7035, std=5.7788\n", "EvoJAX: 2022-02-12 05:54:29,889 [INFO] Training done, best_score=928.1906\n" ] } ] }, { "cell_type": "code", "source": [ "# Let's visualize the learned policy.\n", "\n", "def render(task, algo, policy):\n", " \"\"\"Render the learned policy.\"\"\"\n", "\n", " task_reset_fn = jax.jit(test_task.reset)\n", " policy_reset_fn = jax.jit(policy.reset)\n", " step_fn = jax.jit(test_task.step)\n", " act_fn = jax.jit(policy.get_actions)\n", "\n", " params = algo.best_params[None, :]\n", " task_s = task_reset_fn(jax.random.PRNGKey(seed=seed)[None, :])\n", " policy_s = policy_reset_fn(task_s)\n", "\n", " images = [CartPoleSwingUp.render(task_s, 0)]\n", " done = False\n", " step = 0\n", " reward = 0\n", " while not done:\n", " act, policy_s = act_fn(task_s, params, policy_s)\n", " task_s, r, d = step_fn(task_s, act)\n", " step += 1\n", " reward = reward + r\n", " done = bool(d[0])\n", " if step % 3 == 0:\n", " images.append(CartPoleSwingUp.render(task_s, 0))\n", " print('reward={}'.format(reward))\n", " return images\n", "\n", "\n", "imgs = render(test_task, solver, policy)\n", "gif_file = os.path.join(log_dir, 'cartpole.gif')\n", "imgs[0].save(\n", " gif_file, save_all=True, append_images=imgs[1:], duration=40, loop=0)\n", "Image(open(gif_file,'rb').read())" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 634 }, "id": "CDe0T4uBUh7B", "outputId": "694b287b-d696-4625-a948-9c09cafdf69b" }, "execution_count": 5, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "reward=[934.1182]\n" ] }, { "output_type": "execute_result", "data": { "image/png": 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"text/plain": [ "<IPython.core.display.Image object>" ] }, "metadata": {}, "execution_count": 5 } ] }, { "cell_type": "markdown", "source": [ "Including the three major components, EvoJAX implements the entire training pipeline in JAX. In the first release, we have created several [demo tasks](https://github.com/google/evojax/tree/main/evojax/task) to showcase EvoJAX's capacity. And we encourage the users to bring their own tasks. To this end, we will walk you through the process of creating EvoJAX tasks in this tutorial." ], "metadata": { "id": "ZvgyzDE3pnQg" } }, { "cell_type": "markdown", "source": [ "To contribute a task implementation to EvoJAX, all you need to do is to implement the `VectorizedTask` interface. \n", "The interface is defined as the following and you can see the related Python file [here](https://github.com/google/evojax/blob/main/evojax/task/base.py):\n", "```python\n", "class TaskState(ABC):\n", " \"\"\"A template of the task state.\"\"\"\n", " obs: jnp.ndarray\n", "\n", "\n", "class VectorizedTask(ABC):\n", " \"\"\"Interface for all the EvoJAX tasks.\"\"\"\n", "\n", " max_steps: int\n", " obs_shape: Tuple\n", " act_shape: Tuple\n", " test: bool\n", " multi_agent_training: bool = False\n", "\n", " @abstractmethod\n", " def reset(self, key: jnp.array) -> TaskState:\n", " \"\"\"This resets the vectorized task.\n", " Args:\n", " key - A jax random key.\n", " Returns:\n", " TaskState. Initial task state.\n", " \"\"\"\n", " raise NotImplementedError()\n", "\n", " @abstractmethod\n", " def step(self,\n", " state: TaskState,\n", " action: jnp.ndarray) -> Tuple[TaskState, jnp.ndarray, jnp.ndarray]:\n", " \"\"\"This steps once the simulation.\n", " Args:\n", " state - System internal states of shape (num_tasks, *).\n", " action - Vectorized actions of shape (num_tasks, action_size).\n", " Returns:\n", " TaskState. Task states.\n", " jnp.ndarray. Reward.\n", " jnp.ndarray. Task termination flag: 1 for done, 0 otherwise.\n", " \"\"\"\n", " raise NotImplementedError()\n", "```" ], "metadata": { "id": "gZ3aYmGsrTi0" } }, { "cell_type": "markdown", "source": [ "## MNIST classification" ], "metadata": { "id": "2JvSPQLLrp4e" } }, { "cell_type": "markdown", "source": [ "While one would obviously use gradient descent for MNIST in practice, the point is to show that neuroevolution can also solve them to some degree of accuracy within a short amount of time, which will be useful when these models are adapted within a more complicated task where gradient-based approaches may not work.\n", "\n", "The following code snippet shows how we wrap the dataset and treat it as a one-step `VectorizedTask`." ], "metadata": { "id": "EOM9ZnI2rvU0" } }, { "cell_type": "code", "source": [ "from torchvision import datasets\n", "from flax.struct import dataclass\n", "from evojax.task.base import TaskState\n", "from evojax.task.base import VectorizedTask\n", "\n", "\n", "# This state contains the information we wish to carry over to the next step.\n", "# The state will be used in `VectorizedTask.step` method.\n", "# In supervised learning tasks, we want to store the data and the labels so that\n", "# we can calculate the loss or the accuracy and use that as the reward signal.\n", "@dataclass\n", "class State(TaskState):\n", " obs: jnp.ndarray\n", " labels: jnp.ndarray\n", "\n", "\n", "def sample_batch(key, data, labels, batch_size):\n", " ix = jax.random.choice(\n", " key=key, a=data.shape[0], shape=(batch_size,), replace=False)\n", " return (jnp.take(data, indices=ix, axis=0),\n", " jnp.take(labels, indices=ix, axis=0))\n", "\n", "\n", "def loss(prediction, target):\n", " target = jax.nn.one_hot(target, 10)\n", " return -jnp.mean(jnp.sum(prediction * target, axis=1))\n", "\n", "\n", "def accuracy(prediction, target):\n", " predicted_class = jnp.argmax(prediction, axis=1)\n", " return jnp.mean(predicted_class == target)\n", "\n", "\n", "class MNIST(VectorizedTask):\n", " \"\"\"MNIST classification task.\n", "\n", " We model the classification as an one-step task, i.e.,\n", " `MNIST.reset` returns a batch of data to the agent, the agent outputs\n", " predictions, `MNIST.step` returns the reward (loss or accuracy) and\n", " terminates the rollout.\n", " \"\"\"\n", "\n", " def __init__(self, batch_size, test):\n", "\n", " self.max_steps = 1\n", "\n", " # These are similar to OpenAI Gym environment's\n", " # observation_space and action_space.\n", " # They are helpful for initializing the policy networks.\n", " self.obs_shape = tuple([28, 28, 1])\n", " self.act_shape = tuple([10, ])\n", "\n", " # We download the dataset and normalize the value.\n", " dataset = datasets.MNIST('./data', train=not test, download=True)\n", " data = np.expand_dims(dataset.data.numpy() / 255., axis=-1)\n", " labels = dataset.targets.numpy()\n", "\n", " def reset_fn(key):\n", " if test:\n", " # In the test mode, we want to test on the entire test set.\n", " batch_data, batch_labels = data, labels\n", " else:\n", " # In the training mode, we only sample a batch of training data.\n", " batch_data, batch_labels = sample_batch(\n", " key, data, labels, batch_size)\n", " return State(obs=batch_data, labels=batch_labels)\n", " \n", " # We use jax.vmap for auto-vectorization.\n", " self._reset_fn = jax.jit(jax.vmap(reset_fn))\n", "\n", " def step_fn(state, action):\n", " if test:\n", " # In the test mode, we report the classification accuracy.\n", " reward = accuracy(action, state.labels)\n", " else:\n", " # In the training mode, we return the negative loss as the\n", " # reward signal. It is legitimate to return accuracy as the\n", " # reward signal in training too, but we find the performance is\n", " # not as good as when we use the negative loss.\n", " reward = -loss(action, state.labels)\n", " # This is an one-step task, so that last return value (the `done`\n", " # flag) is one.\n", " return state, reward, jnp.ones(())\n", "\n", " # We use jax.vmap for auto-vectorization.\n", " self._step_fn = jax.jit(jax.vmap(step_fn))\n", "\n", " def reset(self, key):\n", " return self._reset_fn(key)\n", "\n", " def step(self, state, action):\n", " return self._step_fn(state, action)" ], "metadata": { "id": "vwlzy1M7UmLq" }, "execution_count": 6, "outputs": [] }, { "cell_type": "code", "source": [ "# Okay, let's test out the task with a ConvNet policy.\n", "\n", "from evojax.policy.convnet import ConvNetPolicy\n", "\n", "\n", "batch_size = 1024\n", "train_task = MNIST(batch_size=batch_size, test=False)\n", "test_task = MNIST(batch_size=batch_size, test=True)\n", "\n", "policy = ConvNetPolicy(logger=logger)\n", "\n", "solver = PGPE(\n", " pop_size=64,\n", " param_size=policy.num_params,\n", " optimizer='adam',\n", " center_learning_rate=0.006,\n", " stdev_learning_rate=0.09,\n", " init_stdev=0.04,\n", " logger=logger,\n", " seed=seed,\n", ")\n", "\n", "trainer = Trainer(\n", " policy=policy,\n", " solver=solver,\n", " train_task=train_task,\n", " test_task=test_task,\n", " max_iter=5000,\n", " log_interval=100,\n", " test_interval=1000,\n", " n_repeats=1,\n", " n_evaluations=1,\n", " seed=seed,\n", " log_dir=log_dir,\n", " logger=logger,\n", ")\n", "_ = trainer.run()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "5Ty8L50JxLfI", "outputId": "5b5264e8-3ca3-4e30-c6e5-3b2a66c3c06d" }, "execution_count": 7, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "EvoJAX: 2022-02-12 05:54:41,285 [INFO] ConvNetPolicy.num_params = 11274\n", "EvoJAX: 2022-02-12 05:54:41,435 [INFO] Start to train for 5000 iterations.\n", "EvoJAX: 2022-02-12 05:54:52,635 [INFO] Iter=100, size=64, max=-0.8691, avg=-1.0259, min=-1.4128, std=0.1188\n", "EvoJAX: 2022-02-12 05:54:56,730 [INFO] Iter=200, size=64, max=-0.5346, avg=-0.6686, min=-1.2417, std=0.1188\n", "EvoJAX: 2022-02-12 05:55:00,824 [INFO] Iter=300, size=64, max=-0.3925, avg=-0.4791, min=-0.5902, std=0.0456\n", "EvoJAX: 2022-02-12 05:55:04,917 [INFO] Iter=400, size=64, max=-0.3357, avg=-0.3918, min=-0.5241, std=0.0388\n", "EvoJAX: 2022-02-12 05:55:09,010 [INFO] Iter=500, size=64, max=-0.2708, avg=-0.3235, min=-0.4797, std=0.0317\n", "EvoJAX: 2022-02-12 05:55:13,104 [INFO] Iter=600, size=64, max=-0.1965, avg=-0.2417, min=-0.3119, std=0.0238\n", "EvoJAX: 2022-02-12 05:55:17,198 [INFO] Iter=700, size=64, max=-0.1784, avg=-0.2177, min=-0.3148, std=0.0268\n", "EvoJAX: 2022-02-12 05:55:21,292 [INFO] Iter=800, size=64, max=-0.1797, avg=-0.2105, min=-0.2762, std=0.0222\n", "EvoJAX: 2022-02-12 05:55:25,386 [INFO] Iter=900, size=64, max=-0.1803, avg=-0.2379, min=-0.3923, std=0.0330\n", "EvoJAX: 2022-02-12 05:55:29,478 [INFO] Iter=1000, size=64, max=-0.1535, avg=-0.1856, min=-0.2457, std=0.0225\n", "EvoJAX: 2022-02-12 05:55:31,071 [INFO] [TEST] Iter=1000, #tests=1, max=0.9627 avg=0.9627, min=0.9627, std=0.0000\n", "EvoJAX: 2022-02-12 05:55:35,170 [INFO] Iter=1100, size=64, max=-0.1150, avg=-0.1438, min=-0.1971, std=0.0153\n", "EvoJAX: 2022-02-12 05:55:39,263 [INFO] Iter=1200, size=64, max=-0.1278, avg=-0.1571, min=-0.2458, std=0.0193\n", "EvoJAX: 2022-02-12 05:55:43,358 [INFO] Iter=1300, size=64, max=-0.1323, avg=-0.1641, min=-0.2089, std=0.0164\n", "EvoJAX: 2022-02-12 05:55:47,453 [INFO] Iter=1400, size=64, max=-0.1331, avg=-0.1573, min=-0.2085, std=0.0163\n", "EvoJAX: 2022-02-12 05:55:51,547 [INFO] Iter=1500, size=64, max=-0.1709, avg=-0.2142, min=-0.2950, std=0.0197\n", "EvoJAX: 2022-02-12 05:55:55,640 [INFO] Iter=1600, size=64, max=-0.1052, avg=-0.1410, min=-0.2766, std=0.0279\n", "EvoJAX: 2022-02-12 05:55:59,735 [INFO] Iter=1700, size=64, max=-0.0897, avg=-0.1184, min=-0.1591, std=0.0144\n", "EvoJAX: 2022-02-12 05:56:03,828 [INFO] Iter=1800, size=64, max=-0.0777, avg=-0.1029, min=-0.1509, std=0.0165\n", "EvoJAX: 2022-02-12 05:56:07,922 [INFO] Iter=1900, size=64, max=-0.0935, avg=-0.1285, min=-0.1682, std=0.0151\n", "EvoJAX: 2022-02-12 05:56:12,015 [INFO] Iter=2000, size=64, max=-0.1158, avg=-0.1439, min=-0.2054, std=0.0155\n", "EvoJAX: 2022-02-12 05:56:12,026 [INFO] [TEST] Iter=2000, #tests=1, max=0.9740 avg=0.9740, min=0.9740, std=0.0000\n", "EvoJAX: 2022-02-12 05:56:16,121 [INFO] Iter=2100, size=64, max=-0.1054, avg=-0.1248, min=-0.1524, std=0.0101\n", "EvoJAX: 2022-02-12 05:56:20,213 [INFO] Iter=2200, size=64, max=-0.1092, avg=-0.1363, min=-0.1774, std=0.0146\n", "EvoJAX: 2022-02-12 05:56:24,306 [INFO] Iter=2300, size=64, max=-0.1079, avg=-0.1298, min=-0.1929, std=0.0158\n", "EvoJAX: 2022-02-12 05:56:28,398 [INFO] Iter=2400, size=64, max=-0.1129, avg=-0.1352, min=-0.1870, std=0.0145\n", "EvoJAX: 2022-02-12 05:56:32,491 [INFO] Iter=2500, size=64, max=-0.0790, avg=-0.0955, min=-0.1291, std=0.0113\n", "EvoJAX: 2022-02-12 05:56:36,584 [INFO] Iter=2600, size=64, max=-0.1299, avg=-0.1537, min=-0.1947, std=0.0128\n", "EvoJAX: 2022-02-12 05:56:40,675 [INFO] Iter=2700, size=64, max=-0.0801, avg=-0.0983, min=-0.1301, std=0.0094\n", "EvoJAX: 2022-02-12 05:56:44,767 [INFO] Iter=2800, size=64, max=-0.0849, avg=-0.1014, min=-0.1511, std=0.0116\n", "EvoJAX: 2022-02-12 05:56:48,859 [INFO] Iter=2900, size=64, max=-0.0669, avg=-0.0796, min=-0.1111, std=0.0090\n", "EvoJAX: 2022-02-12 05:56:52,950 [INFO] Iter=3000, size=64, max=-0.0782, avg=-0.0975, min=-0.1304, std=0.0123\n", "EvoJAX: 2022-02-12 05:56:52,960 [INFO] [TEST] Iter=3000, #tests=1, max=0.9768 avg=0.9768, min=0.9768, std=0.0000\n", "EvoJAX: 2022-02-12 05:56:57,056 [INFO] Iter=3100, size=64, max=-0.0857, avg=-0.1029, min=-0.1421, std=0.0092\n", "EvoJAX: 2022-02-12 05:57:01,149 [INFO] Iter=3200, size=64, max=-0.0769, avg=-0.0964, min=-0.1279, std=0.0120\n", "EvoJAX: 2022-02-12 05:57:05,242 [INFO] Iter=3300, size=64, max=-0.0805, avg=-0.1021, min=-0.1200, std=0.0088\n", "EvoJAX: 2022-02-12 05:57:09,335 [INFO] Iter=3400, size=64, max=-0.0642, avg=-0.0774, min=-0.0972, std=0.0080\n", "EvoJAX: 2022-02-12 05:57:13,428 [INFO] Iter=3500, size=64, max=-0.0601, avg=-0.0771, min=-0.1074, std=0.0080\n", "EvoJAX: 2022-02-12 05:57:17,522 [INFO] Iter=3600, size=64, max=-0.0558, avg=-0.0709, min=-0.1082, std=0.0094\n", "EvoJAX: 2022-02-12 05:57:21,615 [INFO] Iter=3700, size=64, max=-0.0915, avg=-0.1048, min=-0.1519, std=0.0100\n", "EvoJAX: 2022-02-12 05:57:25,709 [INFO] Iter=3800, size=64, max=-0.0525, avg=-0.0667, min=-0.0823, std=0.0069\n", "EvoJAX: 2022-02-12 05:57:29,801 [INFO] Iter=3900, size=64, max=-0.0983, avg=-0.1150, min=-0.1447, std=0.0105\n", "EvoJAX: 2022-02-12 05:57:33,895 [INFO] Iter=4000, size=64, max=-0.0759, avg=-0.0954, min=-0.1293, std=0.0114\n", "EvoJAX: 2022-02-12 05:57:33,909 [INFO] [TEST] Iter=4000, #tests=1, max=0.9800 avg=0.9800, min=0.9800, std=0.0000\n", "EvoJAX: 2022-02-12 05:57:38,004 [INFO] Iter=4100, size=64, max=-0.0811, avg=-0.0957, min=-0.1184, std=0.0086\n", "EvoJAX: 2022-02-12 05:57:42,095 [INFO] Iter=4200, size=64, max=-0.0806, avg=-0.0960, min=-0.1313, std=0.0096\n", "EvoJAX: 2022-02-12 05:57:46,187 [INFO] Iter=4300, size=64, max=-0.0698, avg=-0.0908, min=-0.1158, std=0.0100\n", "EvoJAX: 2022-02-12 05:57:50,278 [INFO] Iter=4400, size=64, max=-0.0754, avg=-0.0930, min=-0.1202, std=0.0104\n", "EvoJAX: 2022-02-12 05:57:54,368 [INFO] Iter=4500, size=64, max=-0.0708, avg=-0.0877, min=-0.1107, std=0.0088\n", "EvoJAX: 2022-02-12 05:57:58,459 [INFO] Iter=4600, size=64, max=-0.0610, avg=-0.0773, min=-0.1032, std=0.0076\n", "EvoJAX: 2022-02-12 05:58:02,550 [INFO] Iter=4700, size=64, max=-0.0704, avg=-0.0881, min=-0.1299, std=0.0110\n", "EvoJAX: 2022-02-12 05:58:06,640 [INFO] Iter=4800, size=64, max=-0.0651, avg=-0.0812, min=-0.1042, std=0.0080\n", "EvoJAX: 2022-02-12 05:58:10,732 [INFO] Iter=4900, size=64, max=-0.0588, avg=-0.0712, min=-0.1096, std=0.0081\n", "EvoJAX: 2022-02-12 05:58:14,795 [INFO] [TEST] Iter=5000, #tests=1, max=0.9822, avg=0.9822, min=0.9822, std=0.0000\n", "EvoJAX: 2022-02-12 05:58:14,800 [INFO] Training done, best_score=0.9822\n" ] } ] }, { "cell_type": "markdown", "source": [ "Okay! Our implementation of the classification task is successful and EvoJAX achieved $>98\\%$ test accuracy within 5 min on a V100 GPU.\n", "\n", "As mentioned before, MNIST is a simple one-step task, we want to get you familiar with the interfaces. \n", "Next, we will build the classic cart-pole task from scratch." ], "metadata": { "id": "WTi9Gc2L4jmq" } }, { "cell_type": "markdown", "source": [ "## Cart-pole swing up" ], "metadata": { "id": "LV_va7RU5jjc" } }, { "cell_type": "markdown", "source": [ "In our cart-pole swing up task, the agent applies an action $a \\in [-1, 1]$ on the cart, and we maintain 4 states:\n", "1. cart position $x$\n", "2. cart velocity $\\dot{x}$\n", "3. the angle between the cart and the pole $\\theta$\n", "4. the pole's angular velocity $\\dot{\\theta}$\n", "\n", "We randomly sample the initial states and will use the forward Euler integration to update them: \n", "$\\mathbf{x}(t + \\Delta t) = \\mathbf{x}(t) + \\Delta t \\mathbf{v}(t)$ and \n", "$\\mathbf{v}(t + \\Delta t) = \\mathbf{v}(t) + \\Delta t f(a, \\mathbf{x}(t), \\mathbf{v}(t))$ \n", "where $\\mathbf{x}(t) = [x, \\theta]^{\\intercal}$, $\\mathbf{v}(t) = [\\dot{x}, \\dot{\\theta}]^{\\intercal}$ and $f(\\cdot)$ is a function that represents the physical model.\n", "\n", "Thanks to `jax.vmap`, we are able to write the task as if it is designed to deal with non-batch inputs though in the training process JAX will automatically vectorize the task for us." ], "metadata": { "id": "JTM6NBi9LTzf" } }, { "cell_type": "code", "source": [ "from evojax.task.base import TaskState\n", "from evojax.task.base import VectorizedTask\n", "import PIL\n", "\n", "\n", "# Define some physics metrics.\n", "GRAVITY = 9.82\n", "CART_MASS = 0.5\n", "POLE_MASS = 0.5\n", "POLE_LEN = 0.6\n", "FRICTION = 0.1\n", "FORCE_SCALING = 10.0\n", "DELTA_T = 0.01\n", "CART_X_LIMIT = 2.4\n", "\n", "# Define some constants for visualization.\n", "SCREEN_W = 600\n", "SCREEN_H = 600\n", "CART_W = 40\n", "CART_H = 20\n", "VIZ_SCALE = 100\n", "WHEEL_RAD = 5\n", "\n", "@dataclass\n", "class State(TaskState):\n", " obs: jnp.ndarray # This is the tuple (x, x_dot, theta, theta_dot)\n", " state: jnp.ndarray # This maintains the system's state.\n", " steps: jnp.int32 # This tracks the rollout length.\n", " key: jnp.ndarray # This serves as a random seed.\n", "\n", "\n", "class CartPole(VectorizedTask):\n", " \"\"\"A quick implementation of the cart-pole task.\"\"\"\n", "\n", " def __init__(self, max_steps=1000, test=False):\n", " self.max_steps = max_steps\n", " self.obs_shape = tuple([4, ])\n", " self.act_shape = tuple([1, ])\n", "\n", " def sample_init_state(sample_key):\n", " return (\n", " jax.random.normal(sample_key, shape=(4,)) * 0.2 +\n", " jnp.array([0, 0, jnp.pi, 0])\n", " )\n", "\n", " def get_reward(x, x_dot, theta, theta_dot):\n", " # We encourage\n", " # the pole to be held upward (i.e., theta is close to 0) and\n", " # the cart to be at the origin (i.e., x is close to 0).\n", " reward_theta = (jnp.cos(theta) + 1.0) / 2.0\n", " reward_x = jnp.cos((x / CART_X_LIMIT) * (jnp.pi / 2.0))\n", " return reward_theta * reward_x\n", "\n", " def update_state(action, x, x_dot, theta, theta_dot):\n", " action = jnp.clip(action, -1.0, 1.0)[0] * FORCE_SCALING\n", " s = jnp.sin(theta)\n", " c = jnp.cos(theta)\n", " total_m = CART_MASS + POLE_MASS\n", " m_p_l = POLE_MASS * POLE_LEN\n", " \n", " # This is the physical model: f-function.\n", " x_dot_update = (\n", " (-2 * m_p_l * (theta_dot ** 2) * s +\n", " 3 * POLE_MASS * GRAVITY * s * c +\n", " 4 * action - 4 * FRICTION * x_dot) /\n", " (4 * total_m - 3 * POLE_MASS * c ** 2)\n", " )\n", " theta_dot_update = (\n", " (-3 * m_p_l * (theta_dot ** 2) * s * c +\n", " 6 * total_m * GRAVITY * s +\n", " 6 * (action - FRICTION * x_dot) * c) /\n", " (4 * POLE_LEN * total_m - 3 * m_p_l * c ** 2)\n", " )\n", "\n", " # This is the forward Euler integration.\n", " x = x + x_dot * DELTA_T\n", " theta = theta + theta_dot * DELTA_T\n", " x_dot = x_dot + x_dot_update * DELTA_T\n", " theta_dot = theta_dot + theta_dot_update * DELTA_T\n", "\n", " return jnp.array([x, x_dot, theta, theta_dot])\n", "\n", " def out_of_screen(x):\n", " \"\"\"We terminate the rollout if the cart is out of the screen.\"\"\"\n", " beyond_boundary_l = jnp.where(x < -CART_X_LIMIT, 1, 0)\n", " beyond_boundary_r = jnp.where(x > CART_X_LIMIT, 1, 0)\n", " return jnp.bitwise_or(beyond_boundary_l, beyond_boundary_r)\n", "\n", " def reset_fn(key):\n", " next_key, key = jax.random.split(key)\n", " state = sample_init_state(key)\n", " return State(\n", " obs=state, # We make the task fully-observable.\n", " state=state,\n", " steps=jnp.zeros((), dtype=int),\n", " key=next_key,\n", " )\n", " \n", " self._reset_fn = jax.jit(jax.vmap(reset_fn))\n", "\n", " def step_fn(state, action):\n", " current_state = update_state(action, *state.state)\n", " reward = get_reward(*current_state)\n", " steps = state.steps + 1\n", " done = jnp.bitwise_or(\n", " out_of_screen(current_state[0]), steps >= max_steps)\n", " # We reset the step counter to zero if the rollout has ended.\n", " steps = jnp.where(done, jnp.zeros((), jnp.int32), steps)\n", " # We automatically reset the states if the rollout has ended.\n", " next_key, key = jax.random.split(state.key)\n", " # current_state = jnp.where(\n", " # done, sample_init_state(key), current_state)\n", " return State(\n", " state=current_state,\n", " obs=current_state,\n", " steps=steps,\n", " key=next_key), reward, done\n", "\n", " self._step_fn = jax.jit(jax.vmap(step_fn))\n", "\n", " def reset(self, key):\n", " return self._reset_fn(key)\n", "\n", " def step(self, state, action):\n", " return self._step_fn(state, action)\n", "\n", " # Optinally, we can implement a render method to visualize the task.\n", " @staticmethod\n", " def render(state, task_id):\n", " \"\"\"Render a specified task.\"\"\"\n", " img = PIL.Image.new('RGB', (SCREEN_W, SCREEN_H), (255, 255, 255))\n", " draw = PIL.ImageDraw.Draw(img)\n", " x, _, theta, _ = np.array(state.state[task_id])\n", " cart_y = SCREEN_H // 2 + 100\n", " cart_x = x * VIZ_SCALE + SCREEN_W // 2\n", " # Draw the horizon.\n", " draw.line(\n", " (0, cart_y + CART_H // 2 + WHEEL_RAD,\n", " SCREEN_W, cart_y + CART_H // 2 + WHEEL_RAD),\n", " fill=(0, 0, 0), width=1)\n", " # Draw the cart.\n", " draw.rectangle(\n", " (cart_x - CART_W // 2, cart_y - CART_H // 2,\n", " cart_x + CART_W // 2, cart_y + CART_H // 2),\n", " fill=(255, 0, 0), outline=(0, 0, 0))\n", " # Draw the wheels.\n", " draw.ellipse(\n", " (cart_x - CART_W // 2 - WHEEL_RAD,\n", " cart_y + CART_H // 2 - WHEEL_RAD,\n", " cart_x - CART_W // 2 + WHEEL_RAD,\n", " cart_y + CART_H // 2 + WHEEL_RAD),\n", " fill=(220, 220, 220), outline=(0, 0, 0))\n", " draw.ellipse(\n", " (cart_x + CART_W // 2 - WHEEL_RAD,\n", " cart_y + CART_H // 2 - WHEEL_RAD,\n", " cart_x + CART_W // 2 + WHEEL_RAD,\n", " cart_y + CART_H // 2 + WHEEL_RAD),\n", " fill=(220, 220, 220), outline=(0, 0, 0))\n", " # Draw the pole.\n", " draw.line(\n", " (cart_x, cart_y,\n", " cart_x + POLE_LEN * VIZ_SCALE * np.cos(theta - np.pi / 2),\n", " cart_y + POLE_LEN * VIZ_SCALE * np.sin(theta - np.pi / 2)),\n", " fill=(0, 0, 255), width=6)\n", " return img" ], "metadata": { "id": "14hPd2_0yOZ6" }, "execution_count": 8, "outputs": [] }, { "cell_type": "code", "source": [ "# Okay, let's test this simple cart-pole implementation.\n", "\n", "rollout_key = jax.random.PRNGKey(seed=seed)\n", "\n", "reset_key, rollout_key = jax.random.split(rollout_key, 2)\n", "reset_key = reset_key[None, :] # Expand dim, the leading is the batch dim.\n", "\n", "# Initialize the task.\n", "cart_pole_task = CartPole()\n", "t_state = cart_pole_task.reset(reset_key)\n", "task_screens = [CartPole.render(t_state, 0)]\n", "\n", "# Rollout with random actions.\n", "done = False\n", "step_cnt = 0\n", "total_reward = 0\n", "while not done:\n", " action_key, rollout_key = jax.random.split(rollout_key, 2)\n", " action = jax.random.uniform(\n", " action_key, shape=(1, 1), minval=-1., maxval=1.)\n", " t_state, reward, done = cart_pole_task.step(t_state, action)\n", " total_reward = total_reward + reward\n", " step_cnt += 1\n", " if step_cnt % 4 == 0:\n", " task_screens.append(CartPole.render(t_state, 0))\n", "print('reward={}, steps={}'.format(total_reward, step_cnt))\n", "\n", "# Visualze the rollout.\n", "gif_file = os.path.join(log_dir, 'rand_cartpole.gif')\n", "task_screens[0].save(\n", " gif_file, save_all=True, append_images=task_screens[1:], loop=0)\n", "Image(open(gif_file,'rb').read())" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 634 }, "id": "k_01fH0WWhzK", "outputId": "f6702bc4-0b21-4308-a452-5121c535de15" }, "execution_count": 9, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "reward=[4.687451], steps=221\n" ] }, { "output_type": "execute_result", "data": { "image/png": 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"text/plain": [ "<IPython.core.display.Image object>" ] }, "metadata": {}, "execution_count": 9 } ] }, { "cell_type": "markdown", "source": [ "The random policy does not solve the cart-pole task, but our implementation seems to be correct. Let's now plug in this task to EvoJAX." ], "metadata": { "id": "2AFrSknNdZk3" } }, { "cell_type": "code", "source": [ "train_task = CartPole(test=False)\n", "test_task = CartPole(test=True)\n", "\n", "# We use the same policy and solver to solve this \"new\" task.\n", "policy = MLPPolicy(\n", " input_dim=train_task.obs_shape[0],\n", " hidden_dims=[64, 64],\n", " output_dim=train_task.act_shape[0],\n", " logger=logger,\n", ")\n", "solver = PGPE(\n", " pop_size=64,\n", " param_size=policy.num_params,\n", " optimizer='adam',\n", " center_learning_rate=0.05,\n", " seed=seed,\n", ")\n", "trainer = Trainer(\n", " policy=policy,\n", " solver=solver,\n", " train_task=train_task,\n", " test_task=test_task,\n", " max_iter=600,\n", " log_interval=100,\n", " test_interval=200,\n", " n_repeats=5,\n", " n_evaluations=128,\n", " seed=seed,\n", " log_dir=log_dir,\n", " logger=logger,\n", ")\n", "_ = trainer.run()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "V4L0-U9pZUvm", "outputId": "337e073b-8f91-49c3-bdfa-b067095cf47a" }, "execution_count": 10, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "EvoJAX: 2022-02-12 05:58:16,702 [INFO] MLPPolicy.num_params = 4545\n", "EvoJAX: 2022-02-12 05:58:16,868 [INFO] Start to train for 600 iterations.\n", "EvoJAX: 2022-02-12 05:58:26,417 [INFO] Iter=100, size=64, max=704.6008, avg=538.1765, min=115.6323, std=110.1506\n", "EvoJAX: 2022-02-12 05:58:34,678 [INFO] Iter=200, size=64, max=716.4336, avg=595.8668, min=381.3772, std=60.5778\n", "EvoJAX: 2022-02-12 05:58:35,551 [INFO] [TEST] Iter=200, #tests=128, max=695.8007 avg=685.7385, min=668.2902, std=4.3287\n", "EvoJAX: 2022-02-12 05:58:44,053 [INFO] Iter=300, size=64, max=759.5718, avg=658.8391, min=296.1095, std=71.2600\n", "EvoJAX: 2022-02-12 05:58:52,540 [INFO] Iter=400, size=64, max=919.3878, avg=839.7709, min=134.9505, std=136.0545\n", "EvoJAX: 2022-02-12 05:58:52,624 [INFO] [TEST] Iter=400, #tests=128, max=930.0361 avg=915.0107, min=900.9803, std=5.1936\n", "EvoJAX: 2022-02-12 05:59:00,732 [INFO] Iter=500, size=64, max=926.3024, avg=812.4763, min=121.6825, std=229.5144\n", "EvoJAX: 2022-02-12 05:59:09,005 [INFO] [TEST] Iter=600, #tests=128, max=942.0136, avg=922.7744, min=235.6000, std=61.2483\n", "EvoJAX: 2022-02-12 05:59:09,010 [INFO] Training done, best_score=922.7744\n" ] } ] }, { "cell_type": "code", "source": [ "# Let's visualize the learned policy.\n", "\n", "def render(task, algo, policy):\n", " \"\"\"Render the learned policy.\"\"\"\n", "\n", " task_reset_fn = jax.jit(test_task.reset)\n", " policy_reset_fn = jax.jit(policy.reset)\n", " step_fn = jax.jit(test_task.step)\n", " act_fn = jax.jit(policy.get_actions)\n", "\n", " params = algo.best_params[None, :]\n", " task_s = task_reset_fn(jax.random.PRNGKey(seed=seed)[None, :])\n", " policy_s = policy_reset_fn(task_s)\n", "\n", " images = [CartPole.render(task_s, 0)]\n", " done = False\n", " step = 0\n", " reward = 0\n", " while not done:\n", " act, policy_s = act_fn(task_s, params, policy_s)\n", " task_s, r, d = step_fn(task_s, act)\n", " step += 1\n", " reward = reward + r\n", " done = bool(d[0])\n", " if step % 3 == 0:\n", " images.append(CartPole.render(task_s, 0))\n", " print('reward={}'.format(reward))\n", " return images\n", "\n", "\n", "imgs = render(test_task, solver, policy)\n", "gif_file = os.path.join(log_dir, 'trained_cartpole.gif')\n", "imgs[0].save(\n", " gif_file, save_all=True, append_images=imgs[1:], duration=40, loop=0)\n", "Image(open(gif_file,'rb').read())" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 634 }, "id": "bVv8HGIQd9gL", "outputId": "d0f001ec-c98b-4082-8b31-8d7366efbf2f" }, "execution_count": 11, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "reward=[923.1105]\n" ] }, { "output_type": "execute_result", "data": { "image/png": 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"text/plain": [ "<IPython.core.display.Image object>" ] }, "metadata": {}, "execution_count": 11 } ] }, { "cell_type": "markdown", "source": [ "Nice! EvoJAX is able to solve the new cart-pole task within a minute.\n", "\n", "In this tutorial, we walked you through the process of creating tasks from scratch. The two examples we used are simple and are supposed to help you understand the interfaces. If you are interested in learning more, please check out our GitHub [repo](https://github.com/google/evojax/tree/main/evojax/task).\n", "\n", "Please let us ([email protected]) know if you have any problems or suggestions, thanks!" ], "metadata": { "id": "O0E2t8vAejZ2" } }, { "cell_type": "code", "source": [ "" ], "metadata": { "id": "HVMWaWGWeYSh" }, "execution_count": 11, "outputs": [] } ] }
apache-2.0
ES-DOC/esdoc-jupyterhub
notebooks/nerc/cmip6/models/hadgem3-gc31-hh/land.ipynb
1
173510
{ "nbformat_minor": 0, "nbformat": 4, "cells": [ { "source": [ "# ES-DOC CMIP6 Model Properties - Land \n", "**MIP Era**: CMIP6 \n", "**Institute**: NERC \n", "**Source ID**: HADGEM3-GC31-HH \n", "**Topic**: Land \n", "**Sub-Topics**: Soil, Snow, Vegetation, Energy Balance, Carbon Cycle, Nitrogen Cycle, River Routing, Lakes. \n", "**Properties**: 154 (96 required) \n", "**Model descriptions**: [Model description details](https://specializations.es-doc.org/cmip6/land?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 16:54:26" ], "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', 'nerc', 'hadgem3-gc31-hh', 'land')" ], "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; Conservation Properties](#2.-Key-Properties---&gt;-Conservation-Properties) \n", "[3. Key Properties --&gt; Timestepping Framework](#3.-Key-Properties---&gt;-Timestepping-Framework) \n", "[4. Key Properties --&gt; Software Properties](#4.-Key-Properties---&gt;-Software-Properties) \n", "[5. Grid](#5.-Grid) \n", "[6. Grid --&gt; Horizontal](#6.-Grid---&gt;-Horizontal) \n", "[7. Grid --&gt; Vertical](#7.-Grid---&gt;-Vertical) \n", "[8. Soil](#8.-Soil) \n", "[9. Soil --&gt; Soil Map](#9.-Soil---&gt;-Soil-Map) \n", "[10. Soil --&gt; Snow Free Albedo](#10.-Soil---&gt;-Snow-Free-Albedo) \n", "[11. Soil --&gt; Hydrology](#11.-Soil---&gt;-Hydrology) \n", "[12. Soil --&gt; Hydrology --&gt; Freezing](#12.-Soil---&gt;-Hydrology---&gt;-Freezing) \n", "[13. Soil --&gt; Hydrology --&gt; Drainage](#13.-Soil---&gt;-Hydrology---&gt;-Drainage) \n", "[14. Soil --&gt; Heat Treatment](#14.-Soil---&gt;-Heat-Treatment) \n", "[15. Snow](#15.-Snow) \n", "[16. Snow --&gt; Snow Albedo](#16.-Snow---&gt;-Snow-Albedo) \n", "[17. Vegetation](#17.-Vegetation) \n", "[18. Energy Balance](#18.-Energy-Balance) \n", "[19. Carbon Cycle](#19.-Carbon-Cycle) \n", "[20. Carbon Cycle --&gt; Vegetation](#20.-Carbon-Cycle---&gt;-Vegetation) \n", "[21. Carbon Cycle --&gt; Vegetation --&gt; Photosynthesis](#21.-Carbon-Cycle---&gt;-Vegetation---&gt;-Photosynthesis) \n", "[22. Carbon Cycle --&gt; Vegetation --&gt; Autotrophic Respiration](#22.-Carbon-Cycle---&gt;-Vegetation---&gt;-Autotrophic-Respiration) \n", "[23. Carbon Cycle --&gt; Vegetation --&gt; Allocation](#23.-Carbon-Cycle---&gt;-Vegetation---&gt;-Allocation) \n", "[24. Carbon Cycle --&gt; Vegetation --&gt; Phenology](#24.-Carbon-Cycle---&gt;-Vegetation---&gt;-Phenology) \n", "[25. Carbon Cycle --&gt; Vegetation --&gt; Mortality](#25.-Carbon-Cycle---&gt;-Vegetation---&gt;-Mortality) \n", "[26. Carbon Cycle --&gt; Litter](#26.-Carbon-Cycle---&gt;-Litter) \n", "[27. Carbon Cycle --&gt; Soil](#27.-Carbon-Cycle---&gt;-Soil) \n", "[28. Carbon Cycle --&gt; Permafrost Carbon](#28.-Carbon-Cycle---&gt;-Permafrost-Carbon) \n", "[29. Nitrogen Cycle](#29.-Nitrogen-Cycle) \n", "[30. River Routing](#30.-River-Routing) \n", "[31. River Routing --&gt; Oceanic Discharge](#31.-River-Routing---&gt;-Oceanic-Discharge) \n", "[32. Lakes](#32.-Lakes) \n", "[33. Lakes --&gt; Method](#33.-Lakes---&gt;-Method) \n", "[34. Lakes --&gt; Wetlands](#34.-Lakes---&gt;-Wetlands) \n", "\n" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "# 1. Key Properties \n", "*Land surface 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 land surface model.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.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 land surface model code (e.g. MOSES2.2)*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.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. Description\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *General description of the processes modelled (e.g. dymanic vegation, prognostic albedo, etc.)*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.key_properties.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": [ "### 1.4. Land Atmosphere Flux Exchanges\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.N\n", "### *Fluxes exchanged with the atmopshere.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.key_properties.land_atmosphere_flux_exchanges') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"water\" \n", "# \"energy\" \n", "# \"carbon\" \n", "# \"nitrogen\" \n", "# \"phospherous\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 1.5. Atmospheric Coupling Treatment\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Describe the treatment of land surface coupling with the Atmosphere model component, which may be different for different quantities (e.g. dust: semi-implicit, water vapour: explicit)*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.key_properties.atmospheric_coupling_treatment') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 1.6. Land Cover\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *Types of land cover defined in the land surface model*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.key_properties.land_cover') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"bare soil\" \n", "# \"urban\" \n", "# \"lake\" \n", "# \"land ice\" \n", "# \"lake ice\" \n", "# \"vegetated\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 1.7. Land Cover Change\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Describe how land cover change is managed (e.g. the use of net or gross transitions)*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.key_properties.land_cover_change') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 1.8. Tiling\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Describe the general tiling procedure used in the land surface (if any). Include treatment of physiography, land/sea, (dynamic) vegetation coverage and orography/roughness*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.key_properties.tiling') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 2. Key Properties --&gt; Conservation Properties \n", "*TODO*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 2.1. Energy\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Describe if/how energy is conserved globally and to what level (e.g. within X [units]/year)*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.key_properties.conservation_properties.energy') \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. Water\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Describe if/how water is conserved globally and to what level (e.g. within X [units]/year)*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.key_properties.conservation_properties.water') \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. Carbon\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Describe if/how carbon is conserved globally and to what level (e.g. within X [units]/year)*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.key_properties.conservation_properties.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": [ "# 3. Key Properties --&gt; Timestepping Framework \n", "*TODO*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 3.1. Timestep Dependent On Atmosphere\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Is a time step dependent on the frequency of atmosphere coupling?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.key_properties.timestepping_framework.timestep_dependent_on_atmosphere') \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": [ "### 3.2. Time Step\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Overall timestep of land surface model (i.e. time between calls)*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.key_properties.timestepping_framework.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": [ "### 3.3. Timestepping Method\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *General description of time stepping method and associated time step(s)*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.key_properties.timestepping_framework.timestepping_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": [ "# 4. Key Properties --&gt; Software Properties \n", "*Software properties of land surface code*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 4.1. 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.land.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": [ "### 4.2. 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.land.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": [ "### 4.3. 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.land.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": [ "# 5. Grid \n", "*Land surface grid*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 5.1. Overview\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Overview of the grid in the land surface*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.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": [ "# 6. Grid --&gt; Horizontal \n", "*The horizontal grid in the land surface*" ], "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", "### *Describe the general structure of the horizontal grid (not including any tiling)*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.grid.horizontal.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. Matches Atmosphere Grid\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Does the horizontal grid match the atmosphere?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.grid.horizontal.matches_atmosphere_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": [ "# 7. Grid --&gt; Vertical \n", "*The vertical grid in the soil*" ], "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", "### *Describe the general structure of the vertical grid in the soil (not including any tiling)*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.grid.vertical.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. Total Depth\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *The total depth of the soil (in metres)*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.grid.vertical.total_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": [ "# 8. Soil \n", "*Land surface soil*" ], "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 soil in the land surface*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.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. Heat Water Coupling\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Describe the coupling between heat and water in the soil*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.heat_water_coupling') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 8.3. Number Of Soil layers\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *The number of soil layers*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.number_of_soil layers') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 8.4. Prognostic Variables\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *List the prognostic variables of the soil scheme*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.prognostic_variables') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 9. Soil --&gt; Soil Map \n", "*Key properties of the land surface soil map*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 9.1. Description\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *General description of soil map*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.soil_map.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": [ "### 9.2. Structure\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Describe the soil structure map*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.soil_map.structure') \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. Texture\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Describe the soil texture map*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.soil_map.texture') \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. Organic Matter\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Describe the soil organic matter map*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.soil_map.organic_matter') \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. Albedo\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Describe the soil albedo map*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.soil_map.albedo') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 9.6. Water Table\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Describe the soil water table map, if any*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.soil_map.water_table') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 9.7. Continuously Varying Soil Depth\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Does the soil properties vary continuously with depth?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.soil_map.continuously_varying_soil_depth') \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": [ "### 9.8. Soil Depth\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Describe the soil depth map*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.soil_map.soil_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": [ "# 10. Soil --&gt; Snow Free Albedo \n", "*TODO*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 10.1. Prognostic\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Is snow free albedo prognostic?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.snow_free_albedo.prognostic') \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": [ "### 10.2. Functions\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.N\n", "### *If prognostic, describe the dependancies on snow free albedo calculations*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.snow_free_albedo.functions') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"vegetation type\" \n", "# \"soil humidity\" \n", "# \"vegetation state\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 10.3. Direct Diffuse\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *If prognostic, describe the distinction between direct and diffuse albedo*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.snow_free_albedo.direct_diffuse') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"distinction between direct and diffuse albedo\" \n", "# \"no distinction between direct and diffuse albedo\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 10.4. Number Of Wavelength Bands\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *If prognostic, enter the number of wavelength bands used*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.snow_free_albedo.number_of_wavelength_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": [ "# 11. Soil --&gt; Hydrology \n", "*Key properties of the land surface soil hydrology*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 11.1. Description\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *General description of the soil hydrological model*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.hydrology.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": [ "### 11.2. Time Step\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Time step of river soil hydrology in seconds*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.hydrology.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": [ "### 11.3. Tiling\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Describe the soil hydrology tiling, if any.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.hydrology.tiling') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 11.4. Vertical Discretisation\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Describe the typical vertical discretisation*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.hydrology.vertical_discretisation') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 11.5. Number Of Ground Water Layers\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *The number of soil layers that may contain water*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.hydrology.number_of_ground_water_layers') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 11.6. Lateral Connectivity\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *Describe the lateral connectivity between tiles*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.hydrology.lateral_connectivity') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"perfect connectivity\" \n", "# \"Darcian flow\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 11.7. Method\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *The hydrological dynamics scheme in the land surface model*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.hydrology.method') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Bucket\" \n", "# \"Force-restore\" \n", "# \"Choisnel\" \n", "# \"Explicit diffusion\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 12. Soil --&gt; Hydrology --&gt; Freezing \n", "*TODO*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 12.1. Number Of Ground Ice Layers\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *How many soil layers may contain ground ice*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.hydrology.freezing.number_of_ground_ice_layers') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 12.2. Ice Storage Method\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Describe the method of ice storage*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.hydrology.freezing.ice_storage_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": [ "### 12.3. Permafrost\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Describe the treatment of permafrost, if any, within the land surface scheme*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.hydrology.freezing.permafrost') \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. Soil --&gt; Hydrology --&gt; Drainage \n", "*TODO*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 13.1. Description\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *General describe how drainage is included in the land surface scheme*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.hydrology.drainage.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": [ "### 13.2. Types\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.N\n", "### *Different types of runoff represented by the land surface model*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.hydrology.drainage.types') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Gravity drainage\" \n", "# \"Horton mechanism\" \n", "# \"topmodel-based\" \n", "# \"Dunne mechanism\" \n", "# \"Lateral subsurface flow\" \n", "# \"Baseflow from groundwater\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 14. Soil --&gt; Heat Treatment \n", "*TODO*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 14.1. Description\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *General description of how heat treatment properties are defined*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.heat_treatment.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": [ "### 14.2. Time Step\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Time step of soil heat scheme in seconds*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.heat_treatment.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": [ "### 14.3. Tiling\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Describe the soil heat treatment tiling, if any.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.heat_treatment.tiling') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 14.4. Vertical Discretisation\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Describe the typical vertical discretisation*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.heat_treatment.vertical_discretisation') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 14.5. Heat Storage\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Specify the method of heat storage*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.heat_treatment.heat_storage') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Force-restore\" \n", "# \"Explicit diffusion\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 14.6. Processes\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *Describe processes included in the treatment of soil heat*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.heat_treatment.processes') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"soil moisture freeze-thaw\" \n", "# \"coupling with snow temperature\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 15. Snow \n", "*Land surface snow*" ], "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 snow in the land surface*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.snow.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. Tiling\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Describe the snow tiling, if any.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.snow.tiling') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 15.3. Number Of Snow Layers\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *The number of snow levels used in the land surface scheme/model*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.snow.number_of_snow_layers') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 15.4. Density\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Description of the treatment of snow density*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.snow.density') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"prognostic\" \n", "# \"constant\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 15.5. Water Equivalent\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Description of the treatment of the snow water equivalent*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.snow.water_equivalent') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"prognostic\" \n", "# \"diagnostic\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 15.6. Heat Content\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Description of the treatment of the heat content of snow*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.snow.heat_content') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"prognostic\" \n", "# \"diagnostic\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 15.7. Temperature\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Description of the treatment of snow temperature*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.snow.temperature') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"prognostic\" \n", "# \"diagnostic\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 15.8. Liquid Water Content\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Description of the treatment of snow liquid water*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.snow.liquid_water_content') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"prognostic\" \n", "# \"diagnostic\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 15.9. Snow Cover Fractions\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *Specify cover fractions used in the surface snow scheme*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.snow.snow_cover_fractions') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"ground snow fraction\" \n", "# \"vegetation snow fraction\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 15.10. Processes\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *Snow related processes in the land surface scheme*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.snow.processes') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"snow interception\" \n", "# \"snow melting\" \n", "# \"snow freezing\" \n", "# \"blowing snow\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 15.11. Prognostic Variables\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *List the prognostic variables of the snow scheme*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.snow.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": [ "# 16. Snow --&gt; Snow Albedo \n", "*TODO*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 16.1. Type\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Describe the treatment of snow-covered land albedo*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.snow.snow_albedo.type') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"prognostic\" \n", "# \"prescribed\" \n", "# \"constant\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 16.2. Functions\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.N\n", "### *If prognostic, *" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.snow.snow_albedo.functions') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"vegetation type\" \n", "# \"snow age\" \n", "# \"snow density\" \n", "# \"snow grain type\" \n", "# \"aerosol deposition\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 17. Vegetation \n", "*Land surface vegetation*" ], "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 vegetation in the land surface*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.vegetation.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": [ "### 17.2. Time Step\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Time step of vegetation scheme in seconds*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.vegetation.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": [ "### 17.3. Dynamic Vegetation\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Is there dynamic evolution of vegetation?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.vegetation.dynamic_vegetation') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 17.4. Tiling\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Describe the vegetation tiling, if any.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.vegetation.tiling') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 17.5. Vegetation Representation\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Vegetation classification used*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.vegetation.vegetation_representation') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"vegetation types\" \n", "# \"biome types\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 17.6. Vegetation Types\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.N\n", "### *List of vegetation types in the classification, if any*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.vegetation.vegetation_types') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"broadleaf tree\" \n", "# \"needleleaf tree\" \n", "# \"C3 grass\" \n", "# \"C4 grass\" \n", "# \"vegetated\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 17.7. Biome Types\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.N\n", "### *List of biome types in the classification, if any*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.vegetation.biome_types') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"evergreen needleleaf forest\" \n", "# \"evergreen broadleaf forest\" \n", "# \"deciduous needleleaf forest\" \n", "# \"deciduous broadleaf forest\" \n", "# \"mixed forest\" \n", "# \"woodland\" \n", "# \"wooded grassland\" \n", "# \"closed shrubland\" \n", "# \"opne shrubland\" \n", "# \"grassland\" \n", "# \"cropland\" \n", "# \"wetlands\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 17.8. Vegetation Time Variation\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *How the vegetation fractions in each tile are varying with time*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.vegetation.vegetation_time_variation') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"fixed (not varying)\" \n", "# \"prescribed (varying from files)\" \n", "# \"dynamical (varying from simulation)\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 17.9. Vegetation Map\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *If vegetation fractions are not dynamically updated , describe the vegetation map used (common name and reference, if possible)*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.vegetation.vegetation_map') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 17.10. Interception\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Is vegetation interception of rainwater represented?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.vegetation.interception') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 17.11. Phenology\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Treatment of vegetation phenology*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.vegetation.phenology') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"prognostic\" \n", "# \"diagnostic (vegetation map)\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 17.12. Phenology Description\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *General description of the treatment of vegetation phenology*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.vegetation.phenology_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": [ "### 17.13. Leaf Area Index\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Treatment of vegetation leaf area index*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.vegetation.leaf_area_index') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"prescribed\" \n", "# \"prognostic\" \n", "# \"diagnostic\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 17.14. Leaf Area Index Description\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *General description of the treatment of leaf area index*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.vegetation.leaf_area_index_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": [ "### 17.15. Biomass\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Treatment of vegetation biomass *" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.vegetation.biomass') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"prognostic\" \n", "# \"diagnostic\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 17.16. Biomass Description\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *General description of the treatment of vegetation biomass*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.vegetation.biomass_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": [ "### 17.17. Biogeography\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Treatment of vegetation biogeography*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.vegetation.biogeography') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"prognostic\" \n", "# \"diagnostic\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 17.18. Biogeography Description\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *General description of the treatment of vegetation biogeography*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.vegetation.biogeography_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": [ "### 17.19. Stomatal Resistance\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *Specify what the vegetation stomatal resistance depends on*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.vegetation.stomatal_resistance') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"light\" \n", "# \"temperature\" \n", "# \"water availability\" \n", "# \"CO2\" \n", "# \"O3\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 17.20. Stomatal Resistance Description\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *General description of the treatment of vegetation stomatal resistance*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.vegetation.stomatal_resistance_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": [ "### 17.21. Prognostic Variables\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *List the prognostic variables of the vegetation scheme*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.vegetation.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": [ "# 18. Energy Balance \n", "*Land surface energy balance*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 18.1. Overview\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Overview of energy balance in land surface*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.energy_balance.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": [ "### 18.2. Tiling\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Describe the energy balance tiling, if any.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.energy_balance.tiling') \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. Number Of Surface Temperatures\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *The maximum number of distinct surface temperatures in a grid cell (for example, each subgrid tile may have its own temperature)*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.energy_balance.number_of_surface_temperatures') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 18.4. Evaporation\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *Specify the formulation method for land surface evaporation, from soil and vegetation*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.energy_balance.evaporation') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"alpha\" \n", "# \"beta\" \n", "# \"combined\" \n", "# \"Monteith potential evaporation\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 18.5. Processes\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *Describe which processes are included in the energy balance scheme*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.energy_balance.processes') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"transpiration\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 19. Carbon Cycle \n", "*Land surface carbon cycle*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 19.1. Overview\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Overview of carbon cycle in land surface*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.carbon_cycle.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": [ "### 19.2. Tiling\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Describe the carbon cycle tiling, if any.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.carbon_cycle.tiling') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 19.3. Time Step\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Time step of carbon cycle in seconds*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.carbon_cycle.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": [ "### 19.4. Anthropogenic Carbon\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.N\n", "### *Describe the treament of the anthropogenic carbon pool*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.carbon_cycle.anthropogenic_carbon') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"grand slam protocol\" \n", "# \"residence time\" \n", "# \"decay time\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 19.5. Prognostic Variables\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *List the prognostic variables of the carbon scheme*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.carbon_cycle.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": [ "# 20. Carbon Cycle --&gt; Vegetation \n", "*TODO*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 20.1. Number Of Carbon Pools\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Enter the number of carbon pools used*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.carbon_cycle.vegetation.number_of_carbon_pools') \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. Carbon Pools\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *List the carbon pools used*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.carbon_cycle.vegetation.carbon_pools') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 20.3. Forest Stand Dynamics\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Describe the treatment of forest stand dyanmics*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.carbon_cycle.vegetation.forest_stand_dynamics') \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. Carbon Cycle --&gt; Vegetation --&gt; Photosynthesis \n", "*TODO*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 21.1. Method\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Describe the general method used for photosynthesis (e.g. type of photosynthesis, distinction between C3 and C4 grasses, Nitrogen depencence, etc.)*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.carbon_cycle.vegetation.photosynthesis.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": [ "# 22. Carbon Cycle --&gt; Vegetation --&gt; Autotrophic Respiration \n", "*TODO*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 22.1. Maintainance Respiration\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Describe the general method used for maintainence respiration*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.carbon_cycle.vegetation.autotrophic_respiration.maintainance_respiration') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 22.2. Growth Respiration\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Describe the general method used for growth respiration*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.carbon_cycle.vegetation.autotrophic_respiration.growth_respiration') \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. Carbon Cycle --&gt; Vegetation --&gt; Allocation \n", "*TODO*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 23.1. Method\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Describe the general principle behind the allocation scheme*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.carbon_cycle.vegetation.allocation.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": [ "### 23.2. Allocation Bins\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Specify distinct carbon bins used in allocation*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.carbon_cycle.vegetation.allocation.allocation_bins') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"leaves + stems + roots\" \n", "# \"leaves + stems + roots (leafy + woody)\" \n", "# \"leaves + fine roots + coarse roots + stems\" \n", "# \"whole plant (no distinction)\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 23.3. Allocation Fractions\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Describe how the fractions of allocation are calculated*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.carbon_cycle.vegetation.allocation.allocation_fractions') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"fixed\" \n", "# \"function of vegetation type\" \n", "# \"function of plant allometry\" \n", "# \"explicitly calculated\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 24. Carbon Cycle --&gt; Vegetation --&gt; Phenology \n", "*TODO*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 24.1. Method\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Describe the general principle behind the phenology scheme*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.carbon_cycle.vegetation.phenology.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": [ "# 25. Carbon Cycle --&gt; Vegetation --&gt; Mortality \n", "*TODO*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 25.1. Method\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Describe the general principle behind the mortality scheme*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.carbon_cycle.vegetation.mortality.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": [ "# 26. Carbon Cycle --&gt; Litter \n", "*TODO*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 26.1. Number Of Carbon Pools\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Enter the number of carbon pools used*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.carbon_cycle.litter.number_of_carbon_pools') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 26.2. Carbon Pools\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *List the carbon pools used*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.carbon_cycle.litter.carbon_pools') \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. Decomposition\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *List the decomposition methods used*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.carbon_cycle.litter.decomposition') \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. Method\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *List the general method used*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.carbon_cycle.litter.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": [ "# 27. Carbon Cycle --&gt; Soil \n", "*TODO*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 27.1. Number Of Carbon Pools\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Enter the number of carbon pools used*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.carbon_cycle.soil.number_of_carbon_pools') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 27.2. Carbon Pools\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *List the carbon pools used*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.carbon_cycle.soil.carbon_pools') \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. Decomposition\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *List the decomposition methods used*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.carbon_cycle.soil.decomposition') \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. Method\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *List the general method used*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.carbon_cycle.soil.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": [ "# 28. Carbon Cycle --&gt; Permafrost Carbon \n", "*TODO*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 28.1. Is Permafrost Included\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Is permafrost included?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.carbon_cycle.permafrost_carbon.is_permafrost_included') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 28.2. Emitted Greenhouse Gases\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *List the GHGs emitted*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.carbon_cycle.permafrost_carbon.emitted_greenhouse_gases') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 28.3. Decomposition\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *List the decomposition methods used*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.carbon_cycle.permafrost_carbon.decomposition') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 28.4. Impact On Soil Properties\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Describe the impact of permafrost on soil properties*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.carbon_cycle.permafrost_carbon.impact_on_soil_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": [ "# 29. Nitrogen Cycle \n", "*Land surface nitrogen cycle*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 29.1. Overview\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Overview of the nitrogen cycle in the land surface*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.nitrogen_cycle.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": [ "### 29.2. Tiling\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Describe the notrogen cycle tiling, if any.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.nitrogen_cycle.tiling') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 29.3. Time Step\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Time step of nitrogen cycle in seconds*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.nitrogen_cycle.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": [ "### 29.4. Prognostic Variables\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *List the prognostic variables of the nitrogen scheme*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.nitrogen_cycle.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": [ "# 30. River Routing \n", "*Land surface river routing*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 30.1. Overview\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Overview of river routing in the land surface*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.river_routing.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": [ "### 30.2. Tiling\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Describe the river routing, if any.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.river_routing.tiling') \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. Time Step\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Time step of river routing scheme in seconds*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.river_routing.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": [ "### 30.4. Grid Inherited From Land Surface\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Is the grid inherited from land surface?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.river_routing.grid_inherited_from_land_surface') \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": [ "### 30.5. Grid Description\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *General description of grid, if not inherited from land surface*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.river_routing.grid_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": [ "### 30.6. Number Of Reservoirs\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Enter the number of reservoirs*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.river_routing.number_of_reservoirs') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 30.7. Water Re Evaporation\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *TODO*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.river_routing.water_re_evaporation') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"flood plains\" \n", "# \"irrigation\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 30.8. Coupled To Atmosphere\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Is river routing coupled to the atmosphere model component?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.river_routing.coupled_to_atmosphere') \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": [ "### 30.9. Coupled To Land\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Describe the coupling between land and rivers*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.river_routing.coupled_to_land') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 30.10. Quantities Exchanged With Atmosphere\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.N\n", "### *If couple to atmosphere, which quantities are exchanged between river routing and the atmosphere model components?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.river_routing.quantities_exchanged_with_atmosphere') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"heat\" \n", "# \"water\" \n", "# \"tracers\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 30.11. Basin Flow Direction Map\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *What type of basin flow direction map is being used?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.river_routing.basin_flow_direction_map') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"present day\" \n", "# \"adapted for other periods\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 30.12. Flooding\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Describe the representation of flooding, if any*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.river_routing.flooding') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 30.13. Prognostic Variables\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *List the prognostic variables of the river routing*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.river_routing.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": [ "# 31. River Routing --&gt; Oceanic Discharge \n", "*TODO*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 31.1. Discharge Type\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Specify how rivers are discharged to the ocean*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.river_routing.oceanic_discharge.discharge_type') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"direct (large rivers)\" \n", "# \"diffuse\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 31.2. Quantities Transported\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *Quantities that are exchanged from river-routing to the ocean model component*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.river_routing.oceanic_discharge.quantities_transported') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"heat\" \n", "# \"water\" \n", "# \"tracers\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 32. Lakes \n", "*Land surface lakes*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 32.1. Overview\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Overview of lakes in the land surface*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.lakes.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": [ "### 32.2. Coupling With Rivers\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Are lakes coupled to the river routing model component?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.lakes.coupling_with_rivers') \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": [ "### 32.3. Time Step\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Time step of lake scheme in seconds*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.lakes.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": [ "### 32.4. Quantities Exchanged With Rivers\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.N\n", "### *If coupling with rivers, which quantities are exchanged between the lakes and rivers*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.lakes.quantities_exchanged_with_rivers') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"heat\" \n", "# \"water\" \n", "# \"tracers\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 32.5. Vertical Grid\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Describe the vertical grid of lakes*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.lakes.vertical_grid') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 32.6. Prognostic Variables\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *List the prognostic variables of the lake scheme*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.lakes.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": [ "# 33. Lakes --&gt; Method \n", "*TODO*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 33.1. Ice Treatment\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Is lake ice included?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.lakes.method.ice_treatment') \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": [ "### 33.2. Albedo\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Describe the treatment of lake albedo*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.lakes.method.albedo') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"prognostic\" \n", "# \"diagnostic\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 33.3. Dynamics\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *Which dynamics of lakes are treated? horizontal, vertical, etc.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.lakes.method.dynamics') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"No lake dynamics\" \n", "# \"vertical\" \n", "# \"horizontal\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 33.4. Dynamic Lake Extent\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Is a dynamic lake extent scheme included?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.lakes.method.dynamic_lake_extent') \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": [ "### 33.5. Endorheic Basins\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Basins not flowing to ocean included?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.lakes.method.endorheic_basins') \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": [ "# 34. Lakes --&gt; Wetlands \n", "*TODO*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 34.1. Description\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Describe the treatment of wetlands, if any*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.lakes.wetlands.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": [ "### \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
spatialaudio/nbsphinx
doc/executing-notebooks.ipynb
1
1556
{ "cells": [ { "cell_type": "markdown", "metadata": { "nbsphinx": "hidden" }, "source": [ "This notebook is part of the `nbsphinx` documentation: https://nbsphinx.readthedocs.io/." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Controlling Notebook Execution\n", "\n", "Notebooks with no outputs are automatically executed during the Sphinx build process.\n", "If, however, there is at least one output cell present, the notebook is not evaluated and included as is.\n", "\n", "The following notebooks show how this default behavior can be used and customized." ] }, { "cell_type": "markdown", "metadata": { "nbsphinx-toctree": {} }, "source": [ "* [Pre-Executing Notebooks](pre-executed.ipynb)\n", "* [Explicitly Dis-/Enabling Notebook Execution](never-execute.ipynb)\n", "* [Ignoring Errors](allow-errors.ipynb)\n", "* [Ignoring Errors on a Per-Cell Basis](allow-errors-per-cell.ipynb)\n", "* [Configuring Kernels](configuring-kernels.ipynb)\n", "* [Cell Execution Timeout](timeout.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": "3.7.4+" } }, "nbformat": 4, "nbformat_minor": 4 }
mit
theonaun/notebooks
presentations/python_data_tools/13_pandas_munging.ipynb
1
6039
{ "cells": [ { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "<img style=\"float: right;\" src=\"static/small.jpg\">" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "\n", "# Pandas Munging Exercises\n", "\n", "See also: [Pandas API Reference](http://pandas.pydata.org/pandas-docs/stable/api.html)\n", "\n", "See also: [Reshaping and Pivot Tables](http://pandas.pydata.org/pandas-docs/stable/reshaping.html)\n", "\n", "---" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# 1. Import pandas as pd and read_csv() simple.csv into a dataframe 'df' (optionally, auto-convert dates).\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# 2. Use df.dropna() to drop any sample that contains any Na/NaN values.\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# 3. Use df.dropna() with the subset keyword argument to drop those rows without a Count.\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# 4. Use df.fillna() to fill the NaN values in Count with 0.\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# 5. Use pd.to_numeric() to convert \"Weird Count\" to numbers. After error, try with keyword errors='coerce'.\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# 6. Use str.replace('\\t', '') on the column Weird Date to delete any tabs. \n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# 7. Use .str.partition(',')[2] to chop WEEKDAY COMMA from Weird Date and make a new column.\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# 8. Use .str.strip() to remove any whitespace in this new column.\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# 9. Use pd.to_datetime() to convert the weirdly formatted dates in Less Weird Dates to pandas datetimes.\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# 10. Convert the nice pandas dates to month long period types using df[].dt.to_period().\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# 11. Convert Count to an int using the column's \"astype()\" method.\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# 12. Import numpy as np. Run pd.isnull(np.NaN). Run None == np.NaN. Run np.NaN == np.NaN. What does that tell you?\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# 13. Do a database style inner join of the df and a copy (df.copy()) of the dataframe on Date using pd.merge()\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# 14. Combine the df with a copy of the df using concat, effectively stacking the df on top of itself.\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# 15. Convert Count to string type using .astype(). If failure, use raise_on_error=False argument.\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# 16. Bin the values in Count in groups of 10. 0-9, 10-19, 20-29, etc. using pd.cut.\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# 17. Use pd.read_csv to read the CFPB CSB into dataframe 'df2'.\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# 18. Filter df2 down to ['Product', 'Sub-product', 'Complaint ID', and 'Date received']\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# 19. set_index() with ['Product', 'Sub-product] amd assign the result to df3.\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "\n", "## [Answers](13_pandas_munging_answers.ipynb)\n", "\n", "## Next: [Pandas Aggregation](14_pandas_aggregation.ipynb)" ] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python [Root]", "language": "python", "name": "Python [Root]" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 0 }
mit
ChristosChristofidis/bokeh
examples/plotting/notebook/vector.ipynb
45
10501
{ "cells": [ { "cell_type": "raw", "metadata": {}, "source": [ "This IPython Notebook contains simple examples of the line function. \n", "\n", "To clear all previously rendered cell outputs, select from the menu:\n", "\n", " Cell -> All Output -> Clear" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import numpy as np" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from bokeh.plotting import figure, show, output_notebook" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# adapted from here: http://www.atm.damtp.cam.ac.uk/people/tjf37/streamplot.py\n", "# using until Bokeh has its own streamline integrator built in\n", "def streamlines(x, y, u, v, density=1):\n", " '''Returns streamlines of a vector flow.\n", "\n", " * x and y are 1d arrays defining an *evenly spaced* grid.\n", " * u and v are 2d arrays (shape [y,x]) giving velocities.\n", " * density controls the closeness of the streamlines. For different\n", " densities in each direction, use a tuple or list [densityx, densityy].\n", "\n", " '''\n", "\n", " ## Set up some constants - size of the grid used.\n", " NGX = len(x)\n", " NGY = len(y)\n", "\n", " ## Constants used to convert between grid index coords and user coords.\n", " DX = x[1]-x[0]\n", " DY = y[1]-y[0]\n", " XOFF = x[0]\n", " YOFF = y[0]\n", "\n", " ## Now rescale velocity onto axes-coordinates\n", " u = u / (x[-1]-x[0])\n", " v = v / (y[-1]-y[0])\n", " speed = np.sqrt(u*u+v*v)\n", " ## s (path length) will now be in axes-coordinates, but we must\n", " ## rescale u for integrations.\n", " u *= NGX\n", " v *= NGY\n", " ## Now u and v in grid-coordinates.\n", "\n", " NBX = int(30*density)\n", " NBY = int(30*density)\n", " blank = np.zeros((NBY,NBX))\n", "\n", " bx_spacing = NGX/float(NBX-1)\n", " by_spacing = NGY/float(NBY-1)\n", "\n", " def blank_pos(xi, yi):\n", " return int((xi / bx_spacing) + 0.5), \\\n", " int((yi / by_spacing) + 0.5)\n", "\n", " def value_at(a, xi, yi):\n", " if type(xi) == np.ndarray:\n", " x = xi.astype(np.int)\n", " y = yi.astype(np.int)\n", " else:\n", " x = np.int(xi)\n", " y = np.int(yi)\n", " a00 = a[y,x]\n", " a01 = a[y,x+1]\n", " a10 = a[y+1,x]\n", " a11 = a[y+1,x+1]\n", " xt = xi - x\n", " yt = yi - y\n", " a0 = a00*(1-xt) + a01*xt\n", " a1 = a10*(1-xt) + a11*xt\n", " return a0*(1-yt) + a1*yt\n", "\n", " def rk4_integrate(x0, y0):\n", " ## This function does RK4 forward and back trajectories from\n", " ## the initial conditions, with the odd 'blank array'\n", " ## termination conditions. TODO tidy the integration loops.\n", "\n", " def f(xi, yi):\n", " dt_ds = 1./value_at(speed, xi, yi)\n", " ui = value_at(u, xi, yi)\n", " vi = value_at(v, xi, yi)\n", " return ui*dt_ds, vi*dt_ds\n", "\n", " def g(xi, yi):\n", " dt_ds = 1./value_at(speed, xi, yi)\n", " ui = value_at(u, xi, yi)\n", " vi = value_at(v, xi, yi)\n", " return -ui*dt_ds, -vi*dt_ds\n", "\n", " check = lambda xi, yi: xi>=0 and xi<NGX-1 and yi>=0 and yi<NGY-1\n", "\n", " bx_changes = []\n", " by_changes = []\n", "\n", " ## Integrator function\n", " def rk4(x0, y0, f):\n", " ds = 0.01 #min(1./NGX, 1./NGY, 0.01)\n", " stotal = 0\n", " xi = x0\n", " yi = y0\n", " xb, yb = blank_pos(xi, yi)\n", " xf_traj = []\n", " yf_traj = []\n", " while check(xi, yi):\n", " # Time step. First save the point.\n", " xf_traj.append(xi)\n", " yf_traj.append(yi)\n", " # Next, advance one using RK4\n", " try:\n", " k1x, k1y = f(xi, yi)\n", " k2x, k2y = f(xi + .5*ds*k1x, yi + .5*ds*k1y)\n", " k3x, k3y = f(xi + .5*ds*k2x, yi + .5*ds*k2y)\n", " k4x, k4y = f(xi + ds*k3x, yi + ds*k3y)\n", " except IndexError:\n", " # Out of the domain on one of the intermediate steps\n", " break\n", " xi += ds*(k1x+2*k2x+2*k3x+k4x) / 6.\n", " yi += ds*(k1y+2*k2y+2*k3y+k4y) / 6.\n", " # Final position might be out of the domain\n", " if not check(xi, yi): break\n", " stotal += ds\n", " # Next, if s gets to thres, check blank.\n", " new_xb, new_yb = blank_pos(xi, yi)\n", " if new_xb != xb or new_yb != yb:\n", " # New square, so check and colour. Quit if required.\n", " if blank[new_yb,new_xb] == 0:\n", " blank[new_yb,new_xb] = 1\n", " bx_changes.append(new_xb)\n", " by_changes.append(new_yb)\n", " xb = new_xb\n", " yb = new_yb\n", " else:\n", " break\n", " if stotal > 2:\n", " break\n", " return stotal, xf_traj, yf_traj\n", "\n", " integrator = rk4\n", "\n", " sf, xf_traj, yf_traj = integrator(x0, y0, f)\n", " sb, xb_traj, yb_traj = integrator(x0, y0, g)\n", " stotal = sf + sb\n", " x_traj = xb_traj[::-1] + xf_traj[1:]\n", " y_traj = yb_traj[::-1] + yf_traj[1:]\n", "\n", " ## Tests to check length of traj. Remember, s in units of axes.\n", " if len(x_traj) < 1: return None\n", " if stotal > .2:\n", " initxb, inityb = blank_pos(x0, y0)\n", " blank[inityb, initxb] = 1\n", " return x_traj, y_traj\n", " else:\n", " for xb, yb in zip(bx_changes, by_changes):\n", " blank[yb, xb] = 0\n", " return None\n", "\n", " ## A quick function for integrating trajectories if blank==0.\n", " trajectories = []\n", " def traj(xb, yb):\n", " if xb < 0 or xb >= NBX or yb < 0 or yb >= NBY:\n", " return\n", " if blank[yb, xb] == 0:\n", " t = rk4_integrate(xb*bx_spacing, yb*by_spacing)\n", " if t != None:\n", " trajectories.append(t)\n", "\n", " ## Now we build up the trajectory set. I've found it best to look\n", " ## for blank==0 along the edges first, and work inwards.\n", " for indent in list(range((max(NBX,NBY))//2)):\n", " for xi in list(range(max(NBX,NBY)-2*indent)):\n", " traj(xi+indent, indent)\n", " traj(xi+indent, NBY-1-indent)\n", " traj(indent, xi+indent)\n", " traj(NBX-1-indent, xi+indent)\n", "\n", " xs = [np.array(t[0])*DX+XOFF for t in trajectories]\n", " ys = [np.array(t[1])*DY+YOFF for t in trajectories]\n", "\n", " return xs, ys" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "xx = np.linspace(-3, 3, 100)\n", "yy = np.linspace(-3, 3, 100)\n", "\n", "Y, X = np.meshgrid(xx, yy)\n", "U = -1 - X**2 + Y\n", "V = 1 + X - Y**2\n", "speed = np.sqrt(U*U + V*V)\n", "theta = np.arctan(V/U)\n", "\n", "x0 = X[::2, ::2].flatten()\n", "y0 = Y[::2, ::2].flatten()\n", "length = speed[::2, ::2].flatten()/40\n", "angle = theta[::2, ::2].flatten()\n", "x1 = x0 + length * np.cos(angle)\n", "y1 = y0 + length * np.sin(angle)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "xs, ys = streamlines(xx, yy, U.T, V.T, density=2)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "cm = np.array([\"#C7E9B4\", \"#7FCDBB\", \"#41B6C4\", \"#1D91C0\", \"#225EA8\", \"#0C2C84\"])\n", "ix = ((length-length.min())/(length.max()-length.min())*5).astype('int')\n", "colors = cm[ix]" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "output_notebook()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "p = figure()\n", "p.segment(x0, y0, x1, y1, line_color=colors, line_width=2,name=\"vector example\")\n", "show(p)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "q = figure()\n", "q.multi_line(xs, ys, line_color=\"#ee6666\", line_width=2, line_alpha=0.8)\n", "show(q)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.9" } }, "nbformat": 4, "nbformat_minor": 0 }
bsd-3-clause
hakonsbm/nest-simulator
doc/topology/examples/grid_iaf_oc.ipynb
1
1978
{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\nNEST Topology Module Example\n\nCreate three layers of 4x3 iaf_psc_alpha neurons, each with different center.\n\nBCCN Tutorial @ CNS*09\nHans Ekkehard Plesser, UMB\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import pylab\nimport time\nimport nest\nimport nest.topology as topo\n\npylab.ion()\n\nfor ctr in [(0.0, 0.0), (-2.0, 2.0), (0.5, 1.0)]:\n nest.ResetKernel()\n pylab.clf()\n l1 = topo.CreateLayer({'columns': 4, 'rows': 3,\n 'extent': [2.0, 1.5],\n 'center': ctr,\n 'elements': 'iaf_psc_alpha'})\n\n topo.PlotLayer(l1, nodesize=50, fig=pylab.gcf())\n\n # beautify\n pylab.axis([-3, 3, -3, 3])\n pylab.axes().set_aspect('equal', 'box')\n pylab.axes().set_xticks(pylab.arange(-3.0, 3.1, 1.0))\n pylab.axes().set_yticks(pylab.arange(-3.0, 3.1, 1.0))\n pylab.grid(True)\n pylab.xlabel('4 Columns, Extent: 1.5, Center: %.1f' % ctr[0])\n pylab.ylabel('2 Rows, Extent: 1.0, Center: %.1f' % ctr[1])\n\n pylab.draw()" ] } ], "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": 0 }
gpl-2.0
BBN-Q/pyqgl2
notebooks/QGL2 RabiAmp-indirect.ipynb
1
2626
{ "cells": [ { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "# Sample notebook running a previously compiled QGL2 program" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import QGL\n", "from QGL.Compiler import compile_to_hardware\n", "from QGL.Scheduler import schedule\n", "from QGL.PulseSequencePlotter import plot_pulse_files\n", "from pyqgl2.test_cl import create_default_channelLibrary\n", "\n", "# This next should be a valid import of a compiled QGL2 program and its qgl2main\n", "# EG do the compilation manually with -o and put it in the right spot\n", "from RabiAmpqgl1 import RabiAmp" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Create a test ChannelLibrary" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "create_default_channelLibrary(True, True)\n", "# Alternatively could load an existing library, or create one here; see the 'AllXY' notebook" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "## Generate pulse sequences" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Now run the QGL1 function as before, producing a list of sequences\n", "seqs = RabiAmp()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Note we cannot provide the correct axis description without knowing the amplitudes used\n", "# Note we schedule the sequences and ensure they are a list\n", "metaFileName = compile_to_hardware([schedule(seqs)], \"Rabi/Rabi\")\n", "print(f\"Generated sequence details in '{metaFileName}'\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Plot the sequences" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "display(plot_pulse_files(metaFileName))" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.3" } }, "nbformat": 4, "nbformat_minor": 1 }
apache-2.0
dotsdl/msmbuilder
examples/hmm-and-msm.ipynb
1
5668
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "This example builds HMM and MSMs on the alanine_dipeptide dataset using varing lag times\n", "and numbers of states, and compares the relaxation timescales\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from __future__ import print_function\n", "import os\n", "%matplotlib inline\n", "from matplotlib.pyplot import *\n", "from msmbuilder.featurizer import SuperposeFeaturizer\n", "from msmbuilder.example_datasets import AlanineDipeptide\n", "from msmbuilder.hmm import GaussianFusionHMM\n", "from msmbuilder.cluster import KCenters\n", "from msmbuilder.msm import MarkovStateModel" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## First: load and \"featurize\"\n", "\n", "Featurization refers to the process of converting the conformational\n", "snapshots from your MD trajectories into vectors in some space $\\mathbb{R}^N$ that can be manipulated and modeled by subsequent analyses. The Gaussian HMM, for instance, uses Gaussian emission distributions, so it models the trajectory as a time-dependent\n", "mixture of multivariate Gaussians.\n", "\n", "In general, the featurization is somewhat of an art. For this example, we're using Mixtape's `SuperposeFeaturizer`, which superposes each snapshot onto a reference frame (`trajectories[0][0]` in this example), and then measure the distance from each\n", "atom to its position in the reference conformation as the 'feature'" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "print(AlanineDipeptide.description())\n", "\n", "dataset = AlanineDipeptide().get()\n", "trajectories = dataset.trajectories\n", "topology = trajectories[0].topology\n", "\n", "indices = [atom.index for atom in topology.atoms if atom.element.symbol in ['C', 'O', 'N']]\n", "featurizer = SuperposeFeaturizer(indices, trajectories[0][0])\n", "sequences = featurizer.transform(trajectories)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Now `sequences` is our featurized data." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "lag_times = [1, 10, 20, 30, 40]\n", "hmm_ts0 = {}\n", "hmm_ts1 = {}\n", "n_states = [3, 5]\n", "\n", "for n in n_states:\n", " hmm_ts0[n] = []\n", " hmm_ts1[n] = []\n", " for lag_time in lag_times:\n", " strided_data = [s[i::lag_time] for s in sequences for i in range(lag_time)]\n", " hmm = GaussianFusionHMM(n_states=n, n_features=sequences[0].shape[1], n_init=1).fit(strided_data)\n", " timescales = hmm.timescales_ * lag_time\n", " hmm_ts0[n].append(timescales[0])\n", " hmm_ts1[n].append(timescales[1])\n", " print('n_states=%d\\tlag_time=%d\\ttimescales=%s' % (n, lag_time, timescales))\n", " print()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "figure(figsize=(14,3))\n", "\n", "for i, n in enumerate(n_states):\n", " subplot(1,len(n_states),1+i)\n", " plot(lag_times, hmm_ts0[n])\n", " plot(lag_times, hmm_ts1[n])\n", " if i == 0:\n", " ylabel('Relaxation Timescale')\n", " xlabel('Lag Time')\n", " title('%d states' % n)\n", "\n", "show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "msmts0, msmts1 = {}, {}\n", "lag_times = [1, 10, 20, 30, 40]\n", "n_states = [4, 8, 16, 32, 64]\n", "\n", "for n in n_states:\n", " msmts0[n] = []\n", " msmts1[n] = []\n", " for lag_time in lag_times:\n", " assignments = KCenters(n_clusters=n).fit_predict(sequences)\n", " msm = MarkovStateModel(lag_time=lag_time, verbose=False).fit(assignments)\n", " timescales = msm.timescales_\n", " msmts0[n].append(timescales[0])\n", " msmts1[n].append(timescales[1])\n", " print('n_states=%d\\tlag_time=%d\\ttimescales=%s' % (n, lag_time, timescales[0:2]))\n", " print()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "figure(figsize=(14,3))\n", "\n", "for i, n in enumerate(n_states):\n", " subplot(1,len(n_states),1+i)\n", " plot(lag_times, msmts0[n])\n", " plot(lag_times, msmts1[n])\n", " if i == 0:\n", " ylabel('Relaxation Timescale')\n", " xlabel('Lag Time')\n", " title('%d states' % n)\n", "\n", "show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.4.3" } }, "nbformat": 4, "nbformat_minor": 0 }
lgpl-2.1
GoogleCloudPlatform/vertex-ai-samples
notebooks/community/pipelines/google_cloud_pipeline_components_TPU_model_train_upload_deploy.ipynb
1
274674
{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "id": "copyright" }, "outputs": [], "source": [ "# Copyright 2021 Google LLC\n", "#\n", "# Licensed under the Apache License, Version 2.0 (the \"License\");\n", "# you may not use this file except in compliance with the License.\n", "# You may obtain a copy of the License at\n", "#\n", "# https://www.apache.org/licenses/LICENSE-2.0\n", "#\n", "# Unless required by applicable law or agreed to in writing, software\n", "# distributed under the License is distributed on an \"AS IS\" BASIS,\n", "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n", "# See the License for the specific language governing permissions and\n", "# limitations under the License." ] }, { "cell_type": "markdown", "metadata": { "id": "title:generic" }, "source": [ "# Vertex AI Pipelines: TPU model train, upload, and deploy using google-cloud-pipeline-components\n", "\n", "<table align=\"left\">\n", " <td>\n", " <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/master/notebooks/official/pipelines/google_cloud_pipeline_components_TPU_model_train_upload_deploy.ipynb\">\n", " <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Colab logo\"> Run in Colab\n", " </a>\n", " </td>\n", " <td>\n", " <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/notebooks/blob/master/official/pipelines/google_cloud_pipeline_components_TPU_model_train_upload_deploy.ipynb\">\n", " <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\">\n", " View on GitHub\n", " </a>\n", " </td>\n", " <td>\n", " <a href=\"https://console.cloud.google.com/ai/platform/notebooks/deploy-notebook?download_url=https://github.com/GoogleCloudPlatform/vertex-ai-samples/notebooks/blob/master/official/pipelines/google_cloud_pipeline_components_TPU_model_train_upload_deploy.ipynb\">\n", " Open in Vertex AI Workbench\n", " </a>\n", " </td>\n", "</table>\n", "<br/><br/><br/>" ] }, { "cell_type": "markdown", "metadata": { "id": "overview:pipelines,custom" }, "source": [ "## Overview\n", "\n", "This notebook shows how to use the components defined in [`google_cloud_pipeline_components`](https://github.com/kubeflow/pipelines/tree/master/components/google-cloud) in conjunction with an experimental `run_as_aiplatform_custom_job` method, to build a [Vertex AI Pipelines](https://cloud.google.com/vertex-ai/docs/pipelines) workflow that trains a [custom model](https://cloud.google.com/vertex-ai/docs/training/containers-overview) using TPUs, uploads the model as a `Model` resource, creates an `Endpoint` resource, and deploys the `Model` resource to the `Endpoint` resource.\n", "\n", "**Note**: TPU VM Training is currently an opt-in feature. Your GCP project must first be added to the feature allowlist. Please email your project information(project id/number) to [[email protected]]([email protected]) for the allowlist. You will receive an email as soon as your project is ready." ] }, { "cell_type": "markdown", "metadata": { "id": "dataset:bikes_weather,lrg" }, "source": [ "### Dataset\n", "\n", "The dataset used for this tutorial is the [cifar10 dataset](https://www.tensorflow.org/datasets/catalog/cifar10) from [TensorFlow Datasets](https://www.tensorflow.org/datasets/catalog/overview). The version of the dataset you will use is built into TensorFlow. The trained model predicts which type of class an image is from ten classes: airplane, automobile, bird, cat, deer, dog, frog, horse, ship, or truck." ] }, { "cell_type": "markdown", "metadata": { "id": "objective:pipelines,custom" }, "source": [ "### Objective\n", "\n", "In this tutorial, you create an custom model using a pipeline with components from `google_cloud_pipeline_components` and a custom pipeline component you build.\n", "\n", "In addition, you use the `kfp.v2.google.experimental.run_as_aiplatform_custom_job` method to train a custom model leveraging TPUs.\n", "\n", "The steps performed include:\n", "\n", "- Build a custom container for the custom model.\n", "- Train the custom model with TPUs.\n", "- Uploads the trained model as a `Model` resource.\n", "- Creates an `Endpoint` resource.\n", "- Deploys the `Model` resource to the `Endpoint` resource.\n", "\n", "The components are [documented here](https://google-cloud-pipeline-components.readthedocs.io/en/latest/google_cloud_pipeline_components.aiplatform.html#module-google_cloud_pipeline_components.aiplatform).\n", "(From that page, see also the `CustomPythonPackageTrainingJobRunOp` and `CustomContainerTrainingJobRunOp` components, which similarly run 'custom' training, but as with the related `google.cloud.aiplatform.CustomContainerTrainingJob` and `google.cloud.aiplatform.CustomPythonPackageTrainingJob` methods from the [Vertex AI SDK](https://googleapis.dev/python/aiplatform/latest/aiplatform.html), also upload the trained model)." ] }, { "cell_type": "markdown", "metadata": { "id": "costs" }, "source": [ "### Costs\n", "\n", "This tutorial uses billable components of Google Cloud:\n", "\n", "* Vertex AI\n", "* Cloud Storage\n", "\n", "Learn about [Vertex AI\n", "pricing](https://cloud.google.com/vertex-ai/pricing) and [Cloud Storage\n", "pricing](https://cloud.google.com/storage/pricing), and use the [Pricing\n", "Calculator](https://cloud.google.com/products/calculator/)\n", "to generate a cost estimate based on your projected usage." ] }, { "cell_type": "markdown", "metadata": { "id": "setup_local" }, "source": [ "### Set up your local development environment\n", "\n", "If you are using Colab or Google Cloud Notebook, your environment already meets all the requirements to run this notebook. You can skip this step.\n", "\n", "Otherwise, make sure your environment meets this notebook's requirements. You need the following:\n", "\n", "- The Cloud Storage SDK\n", "- Git\n", "- Python 3\n", "- virtualenv\n", "- Jupyter notebook running in a virtual environment with Python 3\n", "\n", "The Cloud Storage guide to [Setting up a Python development environment](https://cloud.google.com/python/setup) and the [Jupyter installation guide](https://jupyter.org/install) provide detailed instructions for meeting these requirements. The following steps provide a condensed set of instructions:\n", "\n", "1. [Install and initialize the SDK](https://cloud.google.com/sdk/docs/).\n", "\n", "2. [Install Python 3](https://cloud.google.com/python/setup#installing_python).\n", "\n", "3. [Install virtualenv](Ihttps://cloud.google.com/python/setup#installing_and_using_virtualenv) and create a virtual environment that uses Python 3.\n", "\n", "4. Activate that environment and run `pip3 install Jupyter` in a terminal shell to install Jupyter.\n", "\n", "5. Run `jupyter notebook` on the command line in a terminal shell to launch Jupyter.\n", "\n", "6. Open this notebook in the Jupyter Notebook Dashboard.\n" ] }, { "cell_type": "markdown", "metadata": { "id": "install_aip:mbsdk" }, "source": [ "## Installation\n", "\n", "Install the latest version of Vertex SDK for Python." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "install_aip:mbsdk" }, "outputs": [], "source": [ "import os\n", "\n", "# Google Cloud Notebook\n", "if os.path.exists(\"/opt/deeplearning/metadata/env_version\"):\n", " USER_FLAG = \"--user\"\n", "else:\n", " USER_FLAG = \"\"" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "ac64e73628ff" }, "outputs": [], "source": [ "! pip3 install --upgrade google-cloud-aiplatform $USER_FLAG" ] }, { "cell_type": "markdown", "metadata": { "id": "install_storage" }, "source": [ "Install the latest GA version of *google-cloud-storage* library as well." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "install_storage" }, "outputs": [], "source": [ "! pip3 install -U google-cloud-storage $USER_FLAG" ] }, { "cell_type": "markdown", "metadata": { "id": "install_gcpc" }, "source": [ "Install the latest GA version of *google-cloud-pipeline-components* library as well." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "install_gcpc" }, "outputs": [], "source": [ "! pip3 install $USER_FLAG kfp google-cloud-pipeline-components --upgrade" ] }, { "cell_type": "markdown", "metadata": { "id": "restart" }, "source": [ "### Restart the kernel\n", "\n", "Once you've installed the additional packages, you need to restart the notebook kernel so it can find the packages." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "restart" }, "outputs": [], "source": [ "import os\n", "\n", "if not os.getenv(\"IS_TESTING\"):\n", " # Automatically restart kernel after installs\n", " import IPython\n", "\n", " app = IPython.Application.instance()\n", " app.kernel.do_shutdown(True)" ] }, { "cell_type": "markdown", "metadata": { "id": "check_versions" }, "source": [ "Check the versions of the packages you installed. The KFP SDK version should be >=1.6." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "check_versions:kfp,gcpc" }, "outputs": [], "source": [ "! python3 -c \"import kfp; print('KFP SDK version: {}'.format(kfp.__version__))\"\n", "! python3 -c \"import google_cloud_pipeline_components; print('google_cloud_pipeline_components version: {}'.format(google_cloud_pipeline_components.__version__))\"" ] }, { "cell_type": "markdown", "metadata": { "id": "before_you_begin:nogpu" }, "source": [ "## Before you begin\n", "\n", "### GPU runtime\n", "\n", "This tutorial does not require a GPU runtime.\n", "\n", "### Set up your Google Cloud project\n", "\n", "**The following steps are required, regardless of your notebook environment.**\n", "\n", "1. [Select or create a Google Cloud project](https://console.cloud.google.com/cloud-resource-manager). When you first create an account, you get a $300 free credit towards your compute/storage costs.\n", "\n", "2. [Make sure that billing is enabled for your project.](https://cloud.google.com/billing/docs/how-to/modify-project)\n", "\n", "3. [Enable the Vertex AI APIs, Compute Engine APIs, and Cloud Storage.](https://console.cloud.google.com/flows/enableapi?apiid=ml.googleapis.com,compute_component,storage-component.googleapis.com)\n", "\n", "4. [The Google Cloud SDK](https://cloud.google.com/sdk) is already installed in Google Cloud Notebook.\n", "\n", "5. Enter your project ID in the cell below. Then run the cell to make sure the\n", "Cloud SDK uses the right project for all the commands in this notebook.\n", "\n", "**Note**: Jupyter runs lines prefixed with `!` as shell commands, and it interpolates Python variables prefixed with `$`." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "set_project_id" }, "outputs": [], "source": [ "PROJECT_ID = \"[your-project-id]\" # @param {type:\"string\"}" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "autoset_project_id" }, "outputs": [], "source": [ "if PROJECT_ID == \"\" or PROJECT_ID is None or PROJECT_ID == \"[your-project-id]\":\n", " # Get your GCP project id from gcloud\n", " shell_output = ! gcloud config list --format 'value(core.project)' 2>/dev/null\n", " PROJECT_ID = shell_output[0]\n", " print(\"Project ID:\", PROJECT_ID)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "set_gcloud_project_id" }, "outputs": [], "source": [ "! gcloud config set project $PROJECT_ID" ] }, { "cell_type": "markdown", "metadata": { "id": "region" }, "source": [ "#### Region\n", "\n", "You can also change the `REGION` variable, which is used for operations\n", "throughout the rest of this notebook. Below are regions supported for Vertex AI. We recommend that you choose the region closest to you.\n", "\n", "- Americas: `us-central1`\n", "- Europe: `europe-west4`\n", "- Asia Pacific: `asia-east1`\n", "\n", "You may not use a multi-regional bucket for training with Vertex AI. Not all regions provide support for all Vertex AI services.\n", "\n", "Learn more about [Vertex AI regions](https://cloud.google.com/vertex-ai/docs/general/locations)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "region" }, "outputs": [], "source": [ "REGION = \"us-central1\" # @param {type: \"string\"}" ] }, { "cell_type": "markdown", "metadata": { "id": "timestamp" }, "source": [ "#### Timestamp\n", "\n", "If you are in a live tutorial session, you might be using a shared test account or project. To avoid name collisions between users on resources created, you create a timestamp for each instance session, and append the timestamp onto the name of resources you create in this tutorial." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "timestamp" }, "outputs": [], "source": [ "from datetime import datetime\n", "\n", "TIMESTAMP = datetime.now().strftime(\"%Y%m%d%H%M%S\")" ] }, { "cell_type": "markdown", "metadata": { "id": "gcp_authenticate" }, "source": [ "### Authenticate your Google Cloud account\n", "\n", "**If you are using Google Cloud Notebook**, your environment is already authenticated. Skip this step.\n", "\n", "**If you are using Colab**, run the cell below and follow the instructions when prompted to authenticate your account via oAuth.\n", "\n", "**Otherwise**, follow these steps:\n", "\n", "In the Cloud Console, go to the [Create service account key](https://console.cloud.google.com/apis/credentials/serviceaccountkey) page.\n", "\n", "**Click Create service account**.\n", "\n", "In the **Service account name** field, enter a name, and click **Create**.\n", "\n", "In the **Grant this service account access to project** section, click the Role drop-down list. Type \"Vertex\" into the filter box, and select **Vertex Administrator**. Type \"Storage Object Admin\" into the filter box, and select **Storage Object Admin**.\n", "\n", "Click Create. A JSON file that contains your key downloads to your local environment.\n", "\n", "Enter the path to your service account key as the GOOGLE_APPLICATION_CREDENTIALS variable in the cell below and run the cell." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "gcp_authenticate" }, "outputs": [], "source": [ "# If you are running this notebook in Colab, run this cell and follow the\n", "# instructions to authenticate your GCP account. This provides access to your\n", "# Cloud Storage bucket and lets you submit training jobs and prediction\n", "# requests.\n", "\n", "import os\n", "import sys\n", "\n", "# If on Google Cloud Notebook, then don't execute this code\n", "if not os.path.exists(\"/opt/deeplearning/metadata/env_version\"):\n", " if \"google.colab\" in sys.modules:\n", " from google.colab import auth as google_auth\n", "\n", " google_auth.authenticate_user()\n", "\n", " # If you are running this notebook locally, replace the string below with the\n", " # path to your service account key and run this cell to authenticate your GCP\n", " # account.\n", " elif not os.getenv(\"IS_TESTING\"):\n", " %env GOOGLE_APPLICATION_CREDENTIALS ''" ] }, { "cell_type": "markdown", "metadata": { "id": "bucket:mbsdk" }, "source": [ "### Create a Cloud Storage bucket\n", "\n", "**The following steps are required, regardless of your notebook environment.**\n", "\n", "When you initialize the Vertex AI SDK for Python, you specify a Cloud Storage staging bucket. The staging bucket is where all the data associated with your dataset and model resources are retained across sessions.\n", "\n", "Set the name of your Cloud Storage bucket below. Bucket names must be globally unique across all Google Cloud projects, including those outside of your organization." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "bucket" }, "outputs": [], "source": [ "BUCKET_URI = \"gs://[your-bucket-name]\" # @param {type:\"string\"}" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "autoset_bucket" }, "outputs": [], "source": [ "if BUCKET_URI == \"\" or BUCKET_URI is None or BUCKET_URI == \"gs://[your-bucket-name]\":\n", " BUCKET_URI = \"gs://\" + PROJECT_ID + \"aip-\" + TIMESTAMP" ] }, { "cell_type": "markdown", "metadata": { "id": "create_bucket" }, "source": [ "**Only if your bucket doesn't already exist**: Run the following cell to create your Cloud Storage bucket." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "create_bucket" }, "outputs": [], "source": [ "! gsutil mb -l $REGION $BUCKET_URI" ] }, { "cell_type": "markdown", "metadata": { "id": "validate_bucket" }, "source": [ "Finally, validate access to your Cloud Storage bucket by examining its contents:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "validate_bucket" }, "outputs": [], "source": [ "! gsutil ls -al $BUCKET_URI" ] }, { "cell_type": "markdown", "metadata": { "id": "set_service_account" }, "source": [ "#### Service Account\n", "\n", "**If you don't know your service account**, try to get your service account using `gcloud` command by executing the second cell below." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "set_service_account" }, "outputs": [], "source": [ "SERVICE_ACCOUNT = \"[your-service-account]\" # @param {type:\"string\"}" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "autoset_service_account" }, "outputs": [], "source": [ "if (\n", " SERVICE_ACCOUNT == \"\"\n", " or SERVICE_ACCOUNT is None\n", " or SERVICE_ACCOUNT == \"[your-service-account]\"\n", "):\n", " # Get your GCP project id from gcloud\n", " shell_output = !gcloud auth list 2>/dev/null\n", " SERVICE_ACCOUNT = shell_output[2].strip()\n", " SERVICE_ACCOUNT = SERVICE_ACCOUNT.replace(\"*\", \"\")\n", " SERVICE_ACCOUNT = SERVICE_ACCOUNT.replace(\" \", \"\")\n", " print(SERVICE_ACCOUNT)" ] }, { "cell_type": "markdown", "metadata": { "id": "set_service_account:pipelines" }, "source": [ "#### Set service account access for Vertex AI Pipelines\n", "\n", "Run the following commands to grant your service account access to read and write pipeline artifacts in the bucket that you created in the previous step -- you only need to run these once per service account." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "set_service_account:pipelines" }, "outputs": [], "source": [ "! gsutil iam ch serviceAccount:{SERVICE_ACCOUNT}:roles/storage.objectCreator $BUCKET_URI\n", "\n", "! gsutil iam ch serviceAccount:{SERVICE_ACCOUNT}:roles/storage.objectViewer $BUCKET_URI" ] }, { "cell_type": "markdown", "metadata": { "id": "setup_vars" }, "source": [ "### Set up variables\n", "\n", "Next, set up some variables used throughout the tutorial.\n", "### Import libraries and define constants" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "import_aip:mbsdk" }, "outputs": [], "source": [ "import google.cloud.aiplatform as aip" ] }, { "cell_type": "markdown", "metadata": { "id": "pipeline_constants" }, "source": [ "#### Vertex AI Pipelines constants\n", "\n", "Setup up the following constants for Vertex AI Pipelines:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "pipeline_constants" }, "outputs": [], "source": [ "PIPELINE_ROOT = \"{}/pipeline_root/tpu_cifar10_pipeline\".format(BUCKET_URI)" ] }, { "cell_type": "markdown", "metadata": { "id": "additional_imports" }, "source": [ "Additional imports." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "import_pipelines:min" }, "outputs": [], "source": [ "import kfp\n", "from google_cloud_pipeline_components import aiplatform as gcc_aip\n", "from kfp.v2.dsl import component\n", "from kfp.v2.google import experimental" ] }, { "cell_type": "markdown", "metadata": { "id": "init_aip:mbsdk" }, "source": [ "## Initialize Vertex AI SDK for Python\n", "\n", "Initialize the Vertex AI SDK for Python for your project and corresponding bucket." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "init_aip:mbsdk" }, "outputs": [], "source": [ "aip.init(project=PROJECT_ID, staging_bucket=BUCKET_URI)" ] }, { "cell_type": "markdown", "metadata": { "id": "setup_vars" }, "source": [ "### Set up variables\n", "\n", "Next, set up some variables used throughout the tutorial." ] }, { "cell_type": "markdown", "metadata": { "id": "accelerators:training,prediction" }, "source": [ "#### Set hardware accelerators\n", "\n", "You can set hardware accelerators for both training and prediction.\n", "\n", "\n", "\n", "Set the variables `TRAIN_TPU/TRAIN_NTPU` to use a container training image supporting a TPU and the number of TPUs allocated and `DEPLOY_GPU/DEPLOY_NGPU` to user a container deployment image supporting a GPU and the number of GPUs allocated to the virtual machine (VM) instance. \n", "\n", "Currently, while TPUs are in experimental, use the following numbers to represent the 2 TPUs available. Both have 8 accelerators:\n", "\n", "`6 = TPU_V2`\n", "\n", "`7 = TPU_V3`\n", "\n", "For example, to use a TPU_V3 training container image, you would specify:\n", "\n", "`(7, 8)`\n", "\n", "See the [locations where accelerators are available](https://cloud.google.com/vertex-ai/docs/general/locations#accelerators).\n", "\n", "Otherwise specify `(None, None)` to use a container image to run on a CPU.\n", "\n", "*Note*: TensorFlow releases earlier than 2.3 for GPU support fail to load the custom model in this tutorial. This issue is caused by static graph operations that are generated in the serving function. This is a known issue, which is fixed in TensorFlow 2.3. If you encounter this issue with your own custom models, use a container image for TensorFlow 2.3 or later with GPU support." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "xd5PLXDTlugv" }, "outputs": [], "source": [ "from google.cloud.aiplatform import gapic\n", "\n", "# Use TPU Accelerators. Temporarily using numeric codes, until types are added to the SDK\n", "# 6 = TPU_V2\n", "# 7 = TPU_V3\n", "TRAIN_TPU, TRAIN_NTPU = (7, 8) # Using TPU_V3 with 8 accelerators\n", "\n", "DEPLOY_GPU, DEPLOY_NGPU = (gapic.AcceleratorType.NVIDIA_TESLA_K80, 1)" ] }, { "cell_type": "markdown", "metadata": { "id": "container:training,prediction" }, "source": [ "#### Set pre-built containers\n", "\n", "Vertex AI provides pre-built containers to run training and prediction.\n", "\n", "For the latest list, see [Pre-built containers for training](https://cloud.google.com/vertex-ai/docs/training/pre-built-containers) and [Pre-built containers for prediction](https://cloud.google.com/vertex-ai/docs/predictions/pre-built-containers)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "1u1mr18jlugv" }, "outputs": [], "source": [ "DEPLOY_VERSION = \"tf2-gpu.2-6\"\n", "\n", "DEPLOY_IMAGE = \"us-docker.pkg.dev/cloud-aiplatform/prediction/{}:latest\".format(\n", " DEPLOY_VERSION\n", ")\n", "\n", "print(\"Deployment:\", DEPLOY_IMAGE, DEPLOY_GPU, DEPLOY_NGPU)" ] }, { "cell_type": "markdown", "metadata": { "id": "machine:training,prediction" }, "source": [ "#### Set machine types\n", "\n", "Next, set the machine types to use for training and prediction.\n", "\n", "- Set the variables `TRAIN_COMPUTE` and `DEPLOY_COMPUTE` to configure your compute resources for training and prediction.\n", " - `machine type`\n", " - `cloud-tpu` : used for TPU training. See the [TPU Architecture site for details](https://cloud.google.com/tpu/docs/system-architecture-tpu-vm)\n", " - `n1-standard`: 3.75GB of memory per vCPU\n", " - `n1-highmem`: 6.5GB of memory per vCPU\n", " - `n1-highcpu`: 0.9 GB of memory per vCPU\n", " - `vCPUs`: number of \\[2, 4, 8, 16, 32, 64, 96 \\]\n", "\n", "*Note: The following is not supported for training:*\n", "\n", " - `standard`: 2 vCPUs\n", " - `highcpu`: 2, 4 and 8 vCPUs\n", "\n", "*Note: You may also use n2 and e2 machine types for training and deployment, but they do not support GPUs*." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "YAXwbqKKlugv" }, "outputs": [], "source": [ "MACHINE_TYPE = \"cloud-tpu\"\n", "\n", "# TPU VMs do not require VCPU definition\n", "TRAIN_COMPUTE = MACHINE_TYPE\n", "print(\"Train machine type\", TRAIN_COMPUTE)\n", "\n", "MACHINE_TYPE = \"n1-standard\"\n", "\n", "VCPU = \"4\"\n", "DEPLOY_COMPUTE = MACHINE_TYPE + \"-\" + VCPU\n", "print(\"Deploy machine type\", DEPLOY_COMPUTE)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "b70e0a15eda9" }, "outputs": [], "source": [ "if not TRAIN_NTPU or TRAIN_NTPU < 2:\n", " TRAIN_STRATEGY = \"single\"\n", "else:\n", " TRAIN_STRATEGY = \"tpu\"\n", "\n", "EPOCHS = 20\n", "STEPS = 10000\n", "\n", "TRAINER_ARGS = [\n", " \"--epochs=\" + str(EPOCHS),\n", " \"--steps=\" + str(STEPS),\n", " \"--distribute=\" + TRAIN_STRATEGY,\n", "]\n", "\n", "# create working dir to pass to job spec\n", "WORKING_DIR = f\"{PIPELINE_ROOT}/model\"\n", "\n", "MODEL_DISPLAY_NAME = f\"tpu_train_deploy_{TIMESTAMP}\"\n", "print(TRAINER_ARGS, WORKING_DIR, MODEL_DISPLAY_NAME)" ] }, { "cell_type": "markdown", "metadata": { "id": "train_custom_model" }, "source": [ "## Create a custom container\n" ] }, { "cell_type": "markdown", "metadata": { "id": "0833ff8115f5" }, "source": [ "We will create a directory and write all of our container build artifacts into that folder." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "d0189aba78b0" }, "outputs": [], "source": [ "CONTAINER_ARTIFACTS_DIR = \"tpu-container-artifacts\"\n", "\n", "!mkdir {CONTAINER_ARTIFACTS_DIR}" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "1a1331a88fd5" }, "outputs": [], "source": [ "import os" ] }, { "cell_type": "markdown", "metadata": { "id": "d901a90e455a" }, "source": [ "### Write the Dockerfile" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "88fff423eb02" }, "outputs": [], "source": [ "dockerfile = \"\"\"FROM python:3.8\n", "\n", "WORKDIR /root\n", "\n", "# Copies the trainer code to the docker image.\n", "COPY train.py /root/train.py\n", "\n", "RUN pip3 install tensorflow-datasets\n", "\n", "# Install TPU Tensorflow and dependencies.\n", "# libtpu.so must be under the '/lib' directory.\n", "RUN wget https://storage.googleapis.com/cloud-tpu-tpuvm-artifacts/libtpu/20210525/libtpu.so -O /lib/libtpu.so\n", "RUN chmod 777 /lib/libtpu.so\n", "\n", "RUN wget https://storage.googleapis.com/cloud-tpu-tpuvm-artifacts/tensorflow/20210525/tf_nightly-2.6.0-cp38-cp38-linux_x86_64.whl\n", "RUN pip3 install tf_nightly-2.6.0-cp38-cp38-linux_x86_64.whl\n", "RUN rm tf_nightly-2.6.0-cp38-cp38-linux_x86_64.whl\n", "\n", "ENTRYPOINT [\"python3\", \"train.py\"]\n", "\"\"\"\n", "\n", "with open(os.path.join(CONTAINER_ARTIFACTS_DIR, \"Dockerfile\"), \"w\") as f:\n", " f.write(dockerfile)" ] }, { "cell_type": "markdown", "metadata": { "id": "taskpy_contents" }, "source": [ "#### Training script\n", "\n", "In the next cell, you write the contents of the training script, `train.py`. In summary:\n", "\n", "- Get the directory where to save the model artifacts from the environment variable `AIP_MODEL_DIR`. This variable is set by the training service.\n", "- Loads CIFAR10 dataset from TF Datasets (tfds).\n", "- Builds a model using TF.Keras model API.\n", "- Compiles the model (`compile()`).\n", "- Sets a training distribution strategy according to the argument `args.distribute`.\n", "- Trains the model (`fit()`) with epochs and steps according to the arguments `args.epochs` and `args.steps`\n", "- Saves the trained model (`save(MODEL_DIR)`) to the specified model directory.\n", "\n", "TPU specific changes are listed below:\n", "- Added a section that finds the TPU cluster, connects to it, and sets the training strategy to TPUStrategy\n", "- Added a section that saves the trained TPU model to the local device, so that it can be saved to the AIP_MODEL_DIR" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "72rUqXNFlugx" }, "outputs": [], "source": [ "%%writefile {CONTAINER_ARTIFACTS_DIR}/train.py\n", "# Single, Mirror and Multi-Machine Distributed Training for CIFAR-10\n", "\n", "import tensorflow_datasets as tfds\n", "import tensorflow as tf\n", "from tensorflow.python.client import device_lib\n", "import argparse\n", "import os\n", "import sys\n", "tfds.disable_progress_bar()\n", "\n", "parser = argparse.ArgumentParser()\n", "parser.add_argument('--lr', dest='lr',\n", " default=0.01, type=float,\n", " help='Learning rate.')\n", "parser.add_argument('--epochs', dest='epochs',\n", " default=10, type=int,\n", " help='Number of epochs.')\n", "parser.add_argument('--steps', dest='steps',\n", " default=200, type=int,\n", " help='Number of steps per epoch.')\n", "parser.add_argument('--distribute', dest='distribute', type=str, default='single',\n", " help='distributed training strategy')\n", "args = parser.parse_args()\n", "\n", "print('Python Version = {}'.format(sys.version))\n", "print('TensorFlow Version = {}'.format(tf.__version__))\n", "print('TF_CONFIG = {}'.format(os.environ.get('TF_CONFIG', 'Not found')))\n", "print('DEVICES', device_lib.list_local_devices())\n", "\n", "# Single Machine, single compute device\n", "if args.distribute == 'single':\n", " if tf.test.is_gpu_available():\n", " strategy = tf.distribute.OneDeviceStrategy(device=\"/gpu:0\")\n", " else:\n", " strategy = tf.distribute.OneDeviceStrategy(device=\"/cpu:0\")\n", "# Single Machine, multiple TPU devices\n", "elif args.distribute == 'tpu':\n", " cluster_resolver = tf.distribute.cluster_resolver.TPUClusterResolver(tpu=\"local\")\n", " tf.config.experimental_connect_to_cluster(cluster_resolver)\n", " tf.tpu.experimental.initialize_tpu_system(cluster_resolver)\n", " strategy = tf.distribute.TPUStrategy(cluster_resolver)\n", " print(\"All devices: \", tf.config.list_logical_devices('TPU'))\n", "# Single Machine, multiple compute device\n", "elif args.distribute == 'mirror':\n", " strategy = tf.distribute.MirroredStrategy()\n", "# Multiple Machine, multiple compute device\n", "elif args.distribute == 'multi':\n", " strategy = tf.distribute.experimental.MultiWorkerMirroredStrategy()\n", "\n", "# Multi-worker configuration\n", "print('num_replicas_in_sync = {}'.format(strategy.num_replicas_in_sync))\n", "\n", "# Preparing dataset\n", "BUFFER_SIZE = 10000\n", "BATCH_SIZE = 64\n", "\n", "def make_datasets_unbatched():\n", " # Scaling CIFAR10 data from (0, 255] to (0., 1.]\n", " def scale(image, label):\n", " image = tf.cast(image, tf.float32)\n", " image /= 255.0\n", " return image, label\n", "\n", " datasets, info = tfds.load(name='cifar10',\n", " with_info=True,\n", " as_supervised=True)\n", " return datasets['train'].map(scale).cache().shuffle(BUFFER_SIZE).repeat()\n", "\n", "\n", "# Build the Keras model\n", "def build_and_compile_cnn_model():\n", " model = tf.keras.Sequential([\n", " tf.keras.layers.Conv2D(32, 3, activation='relu', input_shape=(32, 32, 3)),\n", " tf.keras.layers.MaxPooling2D(),\n", " tf.keras.layers.Conv2D(32, 3, activation='relu'),\n", " tf.keras.layers.MaxPooling2D(),\n", " tf.keras.layers.Flatten(),\n", " tf.keras.layers.Dense(10, activation='softmax')\n", " ])\n", " model.compile(\n", " loss=tf.keras.losses.sparse_categorical_crossentropy,\n", " optimizer=tf.keras.optimizers.SGD(learning_rate=args.lr),\n", " metrics=['accuracy'])\n", " return model\n", "\n", "# Train the model\n", "NUM_WORKERS = strategy.num_replicas_in_sync\n", "# Here the batch size scales up by number of workers since\n", "# `tf.data.Dataset.batch` expects the global batch size.\n", "GLOBAL_BATCH_SIZE = BATCH_SIZE * NUM_WORKERS\n", "MODEL_DIR = os.getenv(\"AIP_MODEL_DIR\")\n", "\n", "train_dataset = make_datasets_unbatched().batch(GLOBAL_BATCH_SIZE)\n", "\n", "with strategy.scope():\n", " # Creation of dataset, and model building/compiling need to be within\n", " # `strategy.scope()`.\n", " model = build_and_compile_cnn_model()\n", "\n", "model.fit(x=train_dataset, epochs=args.epochs, steps_per_epoch=args.steps)\n", "if args.distribute==\"tpu\":\n", " save_locally = tf.saved_model.SaveOptions(experimental_io_device='/job:localhost')\n", " model.save(MODEL_DIR, options=save_locally)\n", "else:\n", " model.save(MODEL_DIR)" ] }, { "cell_type": "markdown", "metadata": { "id": "6LYlV4D2ftD0" }, "source": [ "### Build Container" ] }, { "cell_type": "markdown", "metadata": { "id": "797c277266d7" }, "source": [ "#### Run these artifact registry and docker steps once" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "629ef83d1ee8" }, "outputs": [], "source": [ "!gcloud services enable artifactregistry.googleapis.com" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "892542f5315e" }, "outputs": [], "source": [ "!sudo usermod -a -G docker ${USER}" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "b3f02b426046" }, "outputs": [], "source": [ "REPOSITORY = \"tpu-training-repository\"\n", "IMAGE = \"tpu-train\"" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "a04b48b7eaf0" }, "outputs": [], "source": [ "!gcloud auth configure-docker us-central1-docker.pkg.dev --quiet" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "e69e2d5f373a" }, "outputs": [], "source": [ "!gcloud artifacts repositories create $REPOSITORY --repository-format=docker \\\n", "--location=us-central1 --description=\"Vertex TPU training repository\"" ] }, { "cell_type": "markdown", "metadata": { "id": "92e7de44b91d" }, "source": [ "#### Build the training image" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "2eb5b2b0468a" }, "outputs": [], "source": [ "TRAIN_IMAGE = f\"{REGION}-docker.pkg.dev/{PROJECT_ID}/{REPOSITORY}/{IMAGE}:latest\"" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "90dedc4dae6e" }, "outputs": [], "source": [ "print(TRAIN_IMAGE)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "8d3e23223b12" }, "outputs": [], "source": [ "%cd $CONTAINER_ARTIFACTS_DIR" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "17cce4ce9068" }, "outputs": [], "source": [ "# Use quiet flag as the output is fairly large\n", "!docker build --quiet \\\n", " --tag={TRAIN_IMAGE} \\\n", " ." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "3efe0d9b4e75" }, "outputs": [], "source": [ "!docker push {TRAIN_IMAGE}" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "a34bb81b5bbe" }, "outputs": [], "source": [ "%cd .." ] }, { "cell_type": "markdown", "metadata": { "id": "define_pipeline:gcpc,bikes_weather,lrg" }, "source": [ "## Define custom model pipeline that uses components from `google_cloud_pipeline_components`\n", "\n", "Next, you define the pipeline.\n", "\n", "The `experimental.run_as_aiplatform_custom_job` method takes as arguments the previously defined component, and the list of `worker_pool_specs`— in this case one— with which the custom training job is configured.\n", "\n", "Then, [`google_cloud_pipeline_components`](https://github.com/kubeflow/pipelines/tree/master/components/google-cloud) components are used to define the rest of the pipeline: upload the model, create an endpoint, and deploy the model to the endpoint.\n", "\n", "*Note:* While not shown in this example, the model deploy will create an endpoint if one is not provided." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "3762e4d0ccdc" }, "outputs": [], "source": [ "WORKER_POOL_SPECS = [\n", " {\n", " \"containerSpec\": {\n", " \"args\": TRAINER_ARGS,\n", " \"env\": [{\"name\": \"AIP_MODEL_DIR\", \"value\": WORKING_DIR}],\n", " \"imageUri\": TRAIN_IMAGE,\n", " },\n", " \"replicaCount\": \"1\",\n", " \"machineSpec\": {\n", " \"machineType\": TRAIN_COMPUTE,\n", " \"accelerator_type\": TRAIN_TPU,\n", " \"accelerator_count\": TRAIN_NTPU,\n", " },\n", " }\n", "]" ] }, { "cell_type": "markdown", "metadata": { "id": "define_component:print_op" }, "source": [ "## Define pipeline components\n", "\n", "The following example define a custom pipeline component for this tutorial:\n", "\n", "- This component doesn't do anything (but run a print statement)." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "define_component:print_op" }, "outputs": [], "source": [ "@component\n", "def tpu_training_task_op(input1: str):\n", " print(\"training task: {}\".format(input1))" ] }, { "cell_type": "markdown", "metadata": { "id": "2e6b4eb6cccc" }, "source": [ "The pipeline has four main steps:\n", "\n", "1) The run_as_experimental_custom_job runs the docker container which will execute the training task\n", "\n", "2) The ModelUploadOp uploads the trained model to Vertex\n", "\n", "3) The EndpointCreateOp creates the model endpoint\n", "\n", "4) Finally, the ModelDeployOp deploys the model to the endpoint" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "define_pipeline:gcpc,bikes_weather,lrg" }, "outputs": [], "source": [ "@kfp.dsl.pipeline(name=\"train-endpoint-deploy\" + TIMESTAMP)\n", "def pipeline(\n", " project: str = PROJECT_ID,\n", " model_display_name: str = MODEL_DISPLAY_NAME,\n", " serving_container_image_uri: str = DEPLOY_IMAGE,\n", "):\n", "\n", " train_task = tpu_training_task_op(\"tpu model training\")\n", " experimental.run_as_aiplatform_custom_job(\n", " train_task,\n", " worker_pool_specs=WORKER_POOL_SPECS,\n", " )\n", "\n", " model_upload_op = gcc_aip.ModelUploadOp(\n", " project=project,\n", " display_name=model_display_name,\n", " artifact_uri=WORKING_DIR,\n", " serving_container_image_uri=serving_container_image_uri,\n", " )\n", " model_upload_op.after(train_task)\n", "\n", " endpoint_create_op = gcc_aip.EndpointCreateOp(\n", " project=project,\n", " display_name=\"tpu-pipeline-created-endpoint\",\n", " )\n", "\n", " gcc_aip.ModelDeployOp(\n", " endpoint=endpoint_create_op.outputs[\"endpoint\"],\n", " model=model_upload_op.outputs[\"model\"],\n", " deployed_model_display_name=model_display_name,\n", " dedicated_resources_machine_type=DEPLOY_COMPUTE,\n", " dedicated_resources_min_replica_count=1,\n", " dedicated_resources_max_replica_count=1,\n", " dedicated_resources_accelerator_type=DEPLOY_GPU.name,\n", " dedicated_resources_accelerator_count=DEPLOY_NGPU,\n", " )" ] }, { "cell_type": "markdown", "metadata": { "id": "compile_pipeline" }, "source": [ "## Compile the pipeline\n", "\n", "Next, compile the pipeline." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "compile_pipeline" }, "outputs": [], "source": [ "from kfp.v2 import compiler # noqa: F811\n", "\n", "compiler.Compiler().compile(\n", " pipeline_func=pipeline,\n", " package_path=\"tpu train cifar10_pipeline.json\".replace(\" \", \"_\"),\n", ")" ] }, { "cell_type": "markdown", "metadata": { "id": "run_pipeline:custom" }, "source": [ "## Run the pipeline\n", "\n", "Next, run the pipeline." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "run_pipeline:custom" }, "outputs": [], "source": [ "DISPLAY_NAME = \"tpu_cifar10_training_\" + TIMESTAMP\n", "\n", "job = aip.PipelineJob(\n", " display_name=DISPLAY_NAME,\n", " template_path=\"tpu train cifar10_pipeline.json\".replace(\" \", \"_\"),\n", " pipeline_root=PIPELINE_ROOT,\n", ")\n", "\n", "job.run()\n", "\n", "! rm tpu_train_cifar10_pipeline.json" ] }, { "attachments": { "f962e715-7cfa-4254-bbc6-09dfedd47c64.png": { "image/png": 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" } }, "cell_type": "markdown", "metadata": { "id": "view_pipeline_run:custom" }, "source": [ "Click on the generated link to see your run in the Cloud Console.\n", "\n", "In the UI, many of the pipeline DAG nodes will expand or collapse when you click on them. Here is a partially-expanded view of the DAG (click image to see larger version).\n", "\n", "![image.png](attachment:f962e715-7cfa-4254-bbc6-09dfedd47c64.png)\n" ] }, { "cell_type": "markdown", "metadata": { "id": "cleanup:pipelines" }, "source": [ "# Cleaning up\n", "\n", "To clean up all Google Cloud resources used in this project, you can [delete the Google Cloud\n", "project](https://cloud.google.com/resource-manager/docs/creating-managing-projects#shutting_down_projects) you used for the tutorial.\n", "\n", "Otherwise, you can delete the individual resources you created in this tutorial -- *Note:* this is auto-generated and not all resources may be applicable for this tutorial:\n", "\n", "- Dataset\n", "- Pipeline\n", "- Model\n", "- Endpoint\n", "- Batch Job\n", "- Custom Job\n", "- Hyperparameter Tuning Job\n", "- Cloud Storage Bucket" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "cleanup:pipelines" }, "outputs": [], "source": [ "delete_dataset = True\n", "delete_pipeline = True\n", "delete_model = True\n", "delete_endpoint = True\n", "delete_batchjob = True\n", "delete_customjob = True\n", "delete_hptjob = True\n", "\n", "# Warning: Setting this to true will delete everything in your bucket\n", "delete_bucket = False\n", "\n", "try:\n", " if delete_model and \"DISPLAY_NAME\" in globals():\n", " models = aip.Model.list(\n", " filter=f\"display_name={DISPLAY_NAME}\", order_by=\"create_time\"\n", " )\n", " model = models[0]\n", " aip.Model.delete(model)\n", " print(\"Deleted model:\", model)\n", "except Exception as e:\n", " print(e)\n", "\n", "try:\n", " if delete_endpoint and \"DISPLAY_NAME\" in globals():\n", " endpoints = aip.Endpoint.list(\n", " filter=f\"display_name={DISPLAY_NAME}_endpoint\", order_by=\"create_time\"\n", " )\n", " endpoint = endpoints[0]\n", " endpoint.undeploy_all()\n", " aip.Endpoint.delete(endpoint.resource_name)\n", " print(\"Deleted endpoint:\", endpoint)\n", "except Exception as e:\n", " print(e)\n", "\n", "if delete_dataset and \"DISPLAY_NAME\" in globals():\n", " if \"tabular\" == \"tabular\":\n", " try:\n", " datasets = aip.TabularDataset.list(\n", " filter=f\"display_name={DISPLAY_NAME}\", order_by=\"create_time\"\n", " )\n", " dataset = datasets[0]\n", " aip.TabularDataset.delete(dataset.resource_name)\n", " print(\"Deleted dataset:\", dataset)\n", " except Exception as e:\n", " print(e)\n", "\n", " if \"tabular\" == \"image\":\n", " try:\n", " datasets = aip.ImageDataset.list(\n", " filter=f\"display_name={DISPLAY_NAME}\", order_by=\"create_time\"\n", " )\n", " dataset = datasets[0]\n", " aip.ImageDataset.delete(dataset.resource_name)\n", " print(\"Deleted dataset:\", dataset)\n", " except Exception as e:\n", " print(e)\n", "\n", " if \"tabular\" == \"text\":\n", " try:\n", " datasets = aip.TextDataset.list(\n", " filter=f\"display_name={DISPLAY_NAME}\", order_by=\"create_time\"\n", " )\n", " dataset = datasets[0]\n", " aip.TextDataset.delete(dataset.resource_name)\n", " print(\"Deleted dataset:\", dataset)\n", " except Exception as e:\n", " print(e)\n", "\n", " if \"tabular\" == \"video\":\n", " try:\n", " datasets = aip.VideoDataset.list(\n", " filter=f\"display_name={DISPLAY_NAME}\", order_by=\"create_time\"\n", " )\n", " dataset = datasets[0]\n", " aip.VideoDataset.delete(dataset.resource_name)\n", " print(\"Deleted dataset:\", dataset)\n", " except Exception as e:\n", " print(e)\n", "\n", "try:\n", " if delete_pipeline and \"DISPLAY_NAME\" in globals():\n", " pipelines = aip.PipelineJob.list(\n", " filter=f\"display_name={DISPLAY_NAME}\", order_by=\"create_time\"\n", " )\n", " pipeline = pipelines[0]\n", " aip.PipelineJob.delete(pipeline.resource_name)\n", " print(\"Deleted pipeline:\", pipeline)\n", "except Exception as e:\n", " print(e)\n", "\n", "if delete_bucket and \"BUCKET_URI\" in globals():\n", " ! gsutil rm -r $BUCKET_URI" ] } ], "metadata": { "colab": { "name": "google_cloud_pipeline_components_TPU_model_train_upload_deploy.ipynb", "toc_visible": true }, "kernelspec": { "display_name": "Python 3", "name": "python3" } }, "nbformat": 4, "nbformat_minor": 0 }
apache-2.0
wcmckee/wcmckee.com
posts/trackdrop.ipynb
1
5512
{ "cells": [ { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "Track Drop\n", "\n", "Logging information. asks for input. saves json and tweets. doesn't save/tweet if ID is not unique. Generate 12 character ID. " ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import arrow\n", "import getpass\n", "import string\n", "import random\n", "import json\n", "import tweepy\n", "import os\n", "from pathlib import Path" ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "collapsed": false }, "outputs": [], "source": [ "timnow = arrow.now()\n", "\n", "myusr = getpass.getuser()" ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "collapsed": true }, "outputs": [], "source": [ "dateti = timnow.datetime" ] }, { "cell_type": "code", "execution_count": 35, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def idgenr(size=12, chars=string.ascii_uppercase):\n", " return ''.join(random.choice(chars) for _ in range(size))" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false }, "outputs": [], "source": [ "jslis = os.listdir('/home/{}/medtest/'.format(myusr))\n", "\n", "meddir = ('/home/{}/medtest/'.format(myusr))" ] }, { "cell_type": "code", "execution_count": 36, "metadata": { "collapsed": false }, "outputs": [], "source": [ "theid = idgenr()" ] }, { "cell_type": "code", "execution_count": 37, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "LDUDBTKRMGGT\n" ] } ], "source": [ "print(theid)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "amountake = input('number of drops: ')" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "C or T C\n" ] } ], "source": [ "cort = input('C or T ')" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Comments: fine\n" ] } ], "source": [ "comnow = input('Comments: ')" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": true }, "outputs": [], "source": [ "medhis = dict()" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [], "source": [ "medhis.update({theid : dict({'drops' : amountake, 'datetime' : str(dateti), 'type' : cort, 'comments' : comnow, 'user' : myusr})})" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [], "source": [ "meddum = json.dumps(medhis)" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{\"HKJZABUIRYII\": {\"datetime\": \"2017-01-04 10:49:46.499037+13:00\", \"comments\": \"fine\", \"drops\": \"2\", \"user\": \"pi\", \"type\": \"C\"}}\n" ] } ], "source": [ "print(meddum)" ] }, { "cell_type": "code", "execution_count": 78, "metadata": { "collapsed": false }, "outputs": [], "source": [ "with open('/home/{}/wck.txt'.format(myusr), 'r') as wckz:\n", " allkey = wckz.readlines()\n", " OAUTH_TOKEN = allkey[0]\n", " #print(OAUTH_TOKEN)\n", " OAUTH_SECRET = allkey[1]\n", " CONSUMER_KEY = allkey[2]\n", " CONSUMER_SECRET = allkey[3]\n", " \n", "auth = tweepy.OAuthHandler(CONSUMER_KEY.strip('\\n'), CONSUMER_SECRET.strip('\\n'))\n", "auth.set_access_token(OAUTH_TOKEN.strip('\\n'), OAUTH_SECRET.strip('\\n'))\n", "\n", "api = tweepy.API(auth)\n", "\n" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "file not there\n" ] } ], "source": [ "my_file = Path(meddir + theid + '.json')\n", "if my_file.is_file():\n", " print('file exists')\n", "else:\n", " print('file not there')\n", " with open('{}{}.json'.format(meddir, theid), 'w') as mete:\n", " mete.write(meddum)\n", " \n", " api.update_status('Med {} drops {}. {}'.format(cort, amountake, comnow))" ] } ], "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 }
mit
tikkanz/adventofcode
2015/Day06_J.ipynb
1
4735
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Day 6\n", "## Part 1\n", "Because your neighbors keep defeating you in the holiday house decorating \n", "contest year after year, you've decided to deploy one million lights in a \n", "1000x1000 grid.\n", "\n", "Furthermore, because you've been especially nice this year, Santa has mailed \n", "you instructions on how to display the ideal lighting configuration.\n", "\n", "Lights in your grid are numbered from 0 to 999 in each direction; the lights \n", "at each corner are at 0,0, 0,999, 999,999, and 999,0. The instructions \n", "include whether to turn on, turn off, or toggle various inclusive ranges given \n", "as coordinate pairs. Each coordinate pair represents opposite corners of a \n", "rectangle, inclusive; a coordinate pair like 0,0 through 2,2 therefore refers \n", "to 9 lights in a 3x3 square. The lights all start turned off.\n", "\n", "To defeat your neighbors this year, all you have to do is set up your lights \n", "by doing the instructions Santa sent you in order.\n", "\n", "For example:\n", "\n", " * `turn on 0,0 through 999,999 would turn on (or leave on) every light.\n", " * toggle 0,0 through 999,0 would toggle the first line of 1000 lights, turning off the ones that were on, and turning on the ones that were off.\n", " * turn off 499,499 through 500,500 would turn off (or leave off) the middle four lights.\n", "\n", "After following the instructions, how many lights are lit?" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Day6 Part1: 377891\n" ] } ], "source": [ "parseInstructs=: 3 :0\n", " tmp=. y rplc 'turn ';'';'through ';''\n", " action=. <@(' '&taketo);._2 tmp\n", " coords=. \". ' '&takeafter;._2 tmp\n", " action=. ('off';'on';'toggle') i. action\n", " action ,. coords\n", ")\n", "\n", "getIdx=: [: < ([ + i.@>:@-~)&.>/@(_2 ]\\ ])\n", "on=: 1:`(getIdx@[)`]}\n", "off=: 0:`(getIdx@[)`]}\n", "toggle=: ([: -. getIdx@[ { ])`(getIdx@[)`]}\n", "\n", "applyActions=: 4 :0\n", " actions=. {.\"1 x\n", " coords=. }.\"1 x\n", " for_action. actions do.\n", " coord=. action_index { coords\n", " y=. coord off`on`[email protected]\"_ y\n", " end.\n", ")\n", "\n", "Lights=: 1000 1000 $ 0\n", "Instructions=: parseInstructs freads '~AoC/2015/aoc06_input.txt'\n", "Lights=: Instructions applyActions Lights\n", "echo 'Day6 Part1: ',\": +/ , Lights" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Part 2\n", "You just finish implementing your winning light pattern when you realize \n", "you mistranslated Santa's message from Ancient Nordic Elvish.\n", "\n", "The light grid you bought actually has individual brightness controls; \n", "each light can have a brightness of zero or more. The lights all start at zero.\n", "\n", "The phrase *turn on* actually means that you should increase the brightness of \n", "those lights by 1.\n", "\n", "The phrase *turn off* actually means that you should decrease the brightness of \n", "those lights by 1, to a minimum of zero.\n", "\n", "The phrase *toggle* actually means that you should increase the brightness of \n", "those lights by 2.\n", "\n", "What is the total brightness of all lights combined after following Santa's \n", "instructions?\n", "\n", "For example:\n", "\n", " * `turn on 0,0 through 0,0` would increase the total brightness by 1.\n", " * `toggle 0,0 through 999,999` would increase the total brightness by 2000000." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Day6 Part2: 14110788\n" ] } ], "source": [ "on=: (1 + getIdx@[ { ])`(getIdx@[)`]}\n", "off=: (0 >. _1 + getIdx@[ { ])`(getIdx@[)`]}\n", "toggle=: (2 + getIdx@[ { ])`(getIdx@[)`]}\n", "\n", "Lights=: 1000 1000 $ 0\n", "Lights=: Instructions applyActions Lights\n", "echo 'Day6 Part2: ',\": +/ , Lights" ] } ], "metadata": { "kernelspec": { "display_name": "J", "language": "J", "name": "jkernel" }, "language_info": { "file_extension": "ijs", "mimetype": "text/x-J", "name": "J" } }, "nbformat": 4, "nbformat_minor": 0 }
gpl-2.0
tommytwoeyes/continuity
07_Integration_Techniques/Tests_Convergence_Divergence/P-Test_Improper_Integrals.ipynb
2
1369
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# The __p__-Test for Improper Integrals\n", "\n", "The __p__-test for improper integrals _with a problem spot at $\\infty$_ is virtually the same as the __p__-test for non-negative infinite series.\n", "\n", "<table>\n", "<tr>\n", " <td style=\"vertical-align: middle;\">$$\\int_{1}^{\\infty} \\frac{1}{x^p} dx$$</td>\n", " <td>\n", " <blockquote>\n", " $p > 1$ <strong>Converges</strong><br/>\n", " $p \\le 1$ <strong>Diverges</strong>\n", " </blockquote>\n", " </td>\n", "</tr>\n", "</table>" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import sympy as sp\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline\n", "\n" ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.10" } }, "nbformat": 4, "nbformat_minor": 0 }
gpl-3.0
vi3k6i5/ipython_notebook
usa_baby_names_analysis/analyzing.ipynb
1
7408
{ "cells": [ { "cell_type": "code", "execution_count": 31, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import pandas as pd\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "names = pd.read_csv(\"names_prop_by_year.csv\")\n", "boy_names = names[names[\"Sex\"]=='M']\n", "girl_names = names[names[\"Sex\"]=='F']\n", "girl_names.head()\n", "boy_names.columns\n", "john_occurnace = boy_names[boy_names[\"Name\"]==\"John\"]\n", "mary_occurnace = girl_names[girl_names[\"Name\"]==\"Mary\"]" ] }, { "cell_type": "code", "execution_count": 52, "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>Name</th>\n", " <th>Sex</th>\n", " <th>Count</th>\n", " <th>Prop</th>\n", " <th>Year</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>Mary</td>\n", " <td>F</td>\n", " <td>7065</td>\n", " <td>0.035065</td>\n", " <td>1880</td>\n", " </tr>\n", " <tr>\n", " <th>2000</th>\n", " <td>Mary</td>\n", " <td>F</td>\n", " <td>6919</td>\n", " <td>0.035906</td>\n", " <td>1881</td>\n", " </tr>\n", " <tr>\n", " <th>3935</th>\n", " <td>Mary</td>\n", " <td>F</td>\n", " <td>8148</td>\n", " <td>0.036779</td>\n", " <td>1882</td>\n", " </tr>\n", " <tr>\n", " <th>6062</th>\n", " <td>Mary</td>\n", " <td>F</td>\n", " <td>8012</td>\n", " <td>0.036930</td>\n", " <td>1883</td>\n", " </tr>\n", " <tr>\n", " <th>8146</th>\n", " <td>Mary</td>\n", " <td>F</td>\n", " <td>9217</td>\n", " <td>0.037857</td>\n", " <td>1884</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Name Sex Count Prop Year\n", "0 Mary F 7065 0.035065 1880\n", "2000 Mary F 6919 0.035906 1881\n", "3935 Mary F 8148 0.036779 1882\n", "6062 Mary F 8012 0.036930 1883\n", "8146 Mary F 9217 0.037857 1884" ] }, "execution_count": 52, "metadata": {}, "output_type": "execute_result" } ], "source": [ "chloe_occurnace = girl_names[girl_names[\"Name\"]==\"Chloe\"]\n", "# john_occurnace = john_occurnace.sort([\"Year\"])\n", "john_occurnace.head()\n", "mary_occurnace.head()\n", "# mary_occurnace[['Prop']] = mary_occurnace[['Prop']].astype(float)\n", "# mary = mary_occurnace.ix[0:,['Year','Prop']]\n", "# john = john_occurnace.ix[0:,['Year','Prop']]" ] }, { "cell_type": "code", "execution_count": 58, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# mary.head()\n", "# mary.to_csv('file.csv', index=False)\n", "# john = john_occurnace.ix[0:,['Year','Prop']]\n", "# mary.plot()\n", "# john.plot(color='Blue')\n", "# plt.show()\n", "plt.figure();\n", "john_occurnace.plot(x=\"Year\",y=\"Prop\",color=\"Red\",title='John')\n", "plt.legend( ('John',), loc=0 )\n", "mary_occurnace.plot(x=\"Year\",y=\"Prop\",color=\"Blue\",title='Mary')\n", "plt.legend( ('Mary',), loc=0 )\n", "chloe_occurnace.plot(x=\"Year\",y=\"Prop\",color=\"Green\",title='Chloe')\n", "plt.legend( ('Chloe',), loc=0 )\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 102, "metadata": { "collapsed": false }, "outputs": [], "source": [ "mix = pd.merge(john_occurnace.loc[:,['Year','Prop']], mary_occurnace.loc[:,['Year','Prop']], on='Year')\n", "mix = pd.merge(mix, chloe_occurnace.loc[:,['Year','Prop']], on='Year')\n", "mix.columns = ['Year', 'john', 'mary','chloe']" ] }, { "cell_type": "code", "execution_count": 108, "metadata": { "collapsed": false }, "outputs": [], "source": [ "mix.plot(x=\"Year\")\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 105, "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>Year</th>\n", " <th>john</th>\n", " <th>mary</th>\n", " <th>chloe</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>129</th>\n", " <td>2009</td>\n", " <td>0.003175</td>\n", " <td>0.000827</td>\n", " <td>0.003120</td>\n", " </tr>\n", " <tr>\n", " <th>130</th>\n", " <td>2010</td>\n", " <td>0.003130</td>\n", " <td>0.000776</td>\n", " <td>0.003185</td>\n", " </tr>\n", " <tr>\n", " <th>131</th>\n", " <td>2011</td>\n", " <td>0.003023</td>\n", " <td>0.000741</td>\n", " <td>0.003009</td>\n", " </tr>\n", " <tr>\n", " <th>132</th>\n", " <td>2012</td>\n", " <td>0.002906</td>\n", " <td>0.000703</td>\n", " <td>0.002646</td>\n", " </tr>\n", " <tr>\n", " <th>133</th>\n", " <td>2013</td>\n", " <td>0.002935</td>\n", " <td>0.000721</td>\n", " <td>0.002415</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Year john mary chloe\n", "129 2009 0.003175 0.000827 0.003120\n", "130 2010 0.003130 0.000776 0.003185\n", "131 2011 0.003023 0.000741 0.003009\n", "132 2012 0.002906 0.000703 0.002646\n", "133 2013 0.002935 0.000721 0.002415" ] }, "execution_count": 105, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mix.tail()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.8" } }, "nbformat": 4, "nbformat_minor": 0 }
mit
TurkuNLP/BINF_Programming
supplementary/BioPython_Essentials.ipynb
1
1618
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# BioPython\n", "\n", "A set of modules for bioinformatics in Python.\n", "\n", "## Installation on your own computers\n", "\n", "* We work with the 3.5, 3.6, 3.7 version of Python. Make sure you have that, and not Python 2.\n", "* Follow the instructions on the BioPython project page (google)\n", "* On a Ubuntu-flavor linux all you need is `pip3 install biopython`\n", "* Biopython is also installed in Ville\n", "\n", "Let's check all works - the latest version of BioPython is `1.73` maybe you see a different one on your machine:" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1.73\n" ] } ], "source": [ "import Bio\n", "print(Bio.__version__) #This is two underscores not one" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Docs\n", "\n", "We will roughly follow this BioPython tutorial: http://biopython.org/DIST/docs/tutorial/Tutorial.html\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": "3.6.7" } }, "nbformat": 4, "nbformat_minor": 1 }
gpl-2.0
marcolivierarsenault/AdventOfCode2016
25/Day25.ipynb
1
6088
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Advent of code Day 25\n", "\n", "Goal: Looks to be a clone of day 12-23.\n", "Hint: need to realize the logic of the puzzle (input algo)\n", "\n", "Ref: http://adventofcode.com/2016/day/25" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "library(\"stringr\")\n", "options(warn=-1)" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "instructions <- readLines(\"data.txt\")\n", "wantedOutput <- paste(rep(\"01\",20),collapse=\"\")" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [], "source": [ "getCustomValue <- function(bf,x){\n", " if (x=='a'|x=='b'|x=='c'|x=='d')\n", " return(bf[x])\n", " return(x)\n", "}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Puzzle logic\n", "\n", "After looking at the logic I realized that the patern was quite simple, It is trying to display the lsb of a + 2534 (362*7) followed by a right shift. (forever)\n", "\n", "So for it to work we need that \n", "~~~~\n", "a + 2534 = 0xaaa (0b101010101010)\n", "~~~~\n", "\n", "So with my input a = <b>196</b>" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Q1- Value in the A buffer to get the pattern is 196 \n" ] } ], "source": [ "cat(\"Q1- Value in the A buffer to get the pattern is 196 \",'\\n')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### BONUS\n", "Here is the code to get the value (bruteforce)\n", "Starting close to wanted value to save time" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "196" ], "text/latex": [ "196" ], "text/markdown": [ "196" ], "text/plain": [ "[1] 196" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "current <- 190\n", "found <- FALSE\n", "while(!found){\n", " currentOutput <- \"\"\n", " position <- 1\n", " buffer <- list(a=current,b=0,c=0,d=0)\n", " while(position <= length(instructions)){\n", " instruc <- str_split(instructions[position],\" \")[[1]]\n", " switch(instruc[1], \n", " cpy={\n", " if(is.na(as.numeric(instruc[3]))){\n", " buffer[instruc[3]] <- as.integer(getCustomValue(buffer,instruc[2]))\n", " }\n", " else {\n", " print(\"------------------\")\n", " }\n", " position <- position+1\n", " },\n", " inc={\n", " if(is.na(as.numeric(instruc[2]))){\n", " buffer[instruc[2]] <- as.integer(buffer[instruc[2]]) + 1\n", " }\n", " position <- position+1\n", " },\n", " dec={\n", " if(is.na(as.numeric(instruc[2]))){\n", " buffer[instruc[2]] <- as.integer(buffer[instruc[2]]) - 1\n", " } \n", " position <- position+1\n", " },\n", " out={\n", " vv <- getCustomValue(buffer,instruc[2])\n", " currentOutput <- paste(c(currentOutput,vv),collapse=\"\")\n", " if(!startsWith(wantedOutput,currentOutput)){\n", " current <<- current + 1\n", " break\n", " } else if(nchar(currentOutput)>30){\n", " found <- TRUE\n", " break\n", " }\n", " position <- position+1\n", " },\n", " tgl={\n", " toChange <- as.integer(getCustomValue(buffer,instruc[2])) + position\n", " if(toChange<=length(instructions)){\n", " newInstruct <- str_split(instructions[toChange],\" \")[[1]]\n", " if(length(newInstruct) == 2){\n", " if (newInstruct[1]==\"inc\"){\n", " newInstruct[1] <- \"dec\"\n", " } else {\n", " newInstruct[1] <- \"inc\"\n", " }\n", " } else {\n", " if (newInstruct[1]==\"jnz\"){\n", " newInstruct[1] <- \"cpy\"\n", " } else {\n", " newInstruct[1] <- \"jnz\"\n", " }\n", " }\n", " instructions[toChange] <- paste(newInstruct, collapse = \" \")\n", " }\n", " position <- position+1\n", " },\n", " jnz={\n", " if(as.integer(getCustomValue(buffer,instruc[2]))!=0){\n", " position <- position + as.integer(getCustomValue(buffer,instruc[3]))\n", " } \n", " \n", " else{\n", " position <- position+1\n", " }\n", " },\n", " {\n", " print('default')\n", " }\n", " )\n", " }\n", "}\n", "current" ] } ], "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": "3.3.1" } }, "nbformat": 4, "nbformat_minor": 1 }
mit
jfemiani/srp-boxes
nb/jfemiani-explore-arch-api.ipynb
1
2256
{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Populating the interactive namespace from numpy and matplotlib\n", "/home/femianjc/Projects/srp-boxes\n" ] } ], "source": [ "%pylab inline\n", "%load_ext autoreload\n", "%autoreload 2\n", "%run fix_paths.ipy" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "import srp.model.arch" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "a = srp.model.arch.Architecture(rgb_shape=(3, 64, 64),\n", " lidar_shape=(6, 64, 64),\n", " fusion=srp.model.arch.FusionOptions.LATE_CAT)" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'late_cat'" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "a.fusion" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "a.combined_features" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Linear(in_features=1024, out_features=512, bias=True)" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "a.fusion_layer" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.5" } }, "nbformat": 4, "nbformat_minor": 2 }
mit
mari-linhares/tensorflow-workshop
code_samples/RNN/sentiment_analysis/.ipynb_checkpoints/SentimentAnalysis-batch_64-checkpoint.ipynb
1
122875
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Dependencies" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "colab": { "autoexec": { "startup": false, "wait_interval": 0 } }, "colab_type": "code", "id": "mDT8S9C9CYtr" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Tested with TensorFLow 1.2.0\n", "Your TensorFlow version: 1.2.0\n" ] } ], "source": [ "# Tensorflow\n", "import tensorflow as tf\n", "print('Tested with TensorFLow 1.2.0')\n", "print('Your TensorFlow version:', tf.__version__) \n", "\n", "# Feeding function for enqueue data\n", "from tensorflow.python.estimator.inputs.queues import feeding_functions as ff\n", "\n", "# Rnn common functions\n", "from tensorflow.contrib.learn.python.learn.estimators import rnn_common\n", "\n", "# Model builder\n", "from tensorflow.python.estimator import model_fn as model_fn_lib\n", "\n", "# Run an experiment\n", "from tensorflow.contrib.learn.python.learn import learn_runner\n", "\n", "# Helpers for data processing\n", "import pandas as pd\n", "import numpy as np\n", "import argparse\n", "import random" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Loading Data\n", "\n", "First, we want to create our word vectors. For simplicity, we're going to be using a pretrained model. \n", "\n", "As one of the biggest players in the ML game, Google was able to train a Word2Vec model on a massive Google News dataset that contained over 100 billion different words! From that model, Google [was able to create 3 million word vectors](https://code.google.com/archive/p/word2vec/#Pre-trained_word_and_phrase_vectors), each with a dimensionality of 300. \n", "\n", "In an ideal scenario, we'd use those vectors, but since the word vectors matrix is quite large (3.6 GB!), we'll be using a much more manageable matrix that is trained using [GloVe](http://nlp.stanford.edu/projects/glove/), a similar word vector generation model. The matrix will contain 400,000 word vectors, each with a dimensionality of 50. \n", "\n", "We're going to be importing two different data structures, one will be a Python list with the 400,000 words, and one will be a 400,000 x 50 dimensional embedding matrix that holds all of the word vector values. " ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Loaded the word list, length: 400000\n", "Loaded the word vector, shape: (400000, 50)\n" ] } ], "source": [ "# data from: http://ai.stanford.edu/~amaas/data/sentiment/\n", "TRAIN_INPUT = 'data/train.csv'\n", "TEST_INPUT = 'data/test.csv'\n", "\n", "# data manually generated\n", "MY_TEST_INPUT = 'data/mytest.csv'\n", "\n", "# wordtovec\n", "# https://nlp.stanford.edu/projects/glove/\n", "# the matrix will contain 400,000 word vectors, each with a dimensionality of 50.\n", "word_list = np.load('word_list.npy')\n", "word_list = word_list.tolist() # originally loaded as numpy array\n", "word_list = [word.decode('UTF-8') for word in word_list] # encode words as UTF-8\n", "print('Loaded the word list, length:', len(word_list))\n", "\n", "word_vector = np.load('word_vector.npy')\n", "print ('Loaded the word vector, shape:', word_vector.shape)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can also search our word list for a word like \"baseball\", and then access its corresponding vector through the embedding matrix." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Example: baseball\n", "[-1.93270004 1.04209995 -0.78514999 0.91033 0.22711 -0.62158\n", " -1.64929998 0.07686 -0.58679998 0.058831 0.35628 0.68915999\n", " -0.50598001 0.70472997 1.26639998 -0.40031001 -0.020687 0.80862999\n", " -0.90565997 -0.074054 -0.87674999 -0.62910002 -0.12684999 0.11524\n", " -0.55685002 -1.68260002 -0.26291001 0.22632 0.713 -1.08280003\n", " 2.12310004 0.49869001 0.066711 -0.48225999 -0.17896999 0.47699001\n", " 0.16384 0.16537 -0.11506 -0.15962 -0.94926 -0.42833\n", " -0.59456998 1.35660005 -0.27506 0.19918001 -0.36008 0.55667001\n", " -0.70314997 0.17157 ]\n" ] } ], "source": [ "baseball_index = word_list.index('baseball')\n", "print('Example: baseball')\n", "print(word_vector[baseball_index])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now that we have our vectors, our first step is taking an input sentence and then constructing the its vector representation. Let's say that we have the input sentence \"I thought the movie was incredible and inspiring\". In order to get the word vectors, we can use Tensorflow's embedding lookup function. This function takes in two arguments, one for the embedding matrix (the wordVectors matrix in our case), and one for the ids of each of the words. The ids vector can be thought of as the integerized representation of the training set. This is basically just the row index of each of the words. Let's look at a quick example to make this concrete. " ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(10,)\n", "[ 41 804 201534 1005 15 7446 5 13767 0 0]\n" ] } ], "source": [ "max_seq_length = 10 # maximum length of sentence\n", "num_dims = 50 # dimensions for each word vector\n", "\n", "first_sentence = np.zeros((max_seq_length), dtype='int32')\n", "first_sentence[0] = word_list.index(\"i\")\n", "first_sentence[1] = word_list.index(\"thought\")\n", "first_sentence[2] = word_list.index(\"the\")\n", "first_sentence[3] = word_list.index(\"movie\")\n", "first_sentence[4] = word_list.index(\"was\")\n", "first_sentence[5] = word_list.index(\"incredible\")\n", "first_sentence[6] = word_list.index(\"and\")\n", "first_sentence[7] = word_list.index(\"inspiring\")\n", "# first_sentence[8] = 0\n", "# first_sentence[9] = 0\n", "\n", "print(first_sentence.shape)\n", "print(first_sentence) # shows the row index for each word" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "###TODO### Insert image" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The 10 x 50 output should contain the 50 dimensional word vectors for each of the 10 words in the sequence. " ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(10, 50)\n" ] } ], "source": [ "with tf.Session() as sess:\n", " print(tf.nn.embedding_lookup(word_vector, first_sentence).eval().shape)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Before creating the ids matrix for the whole training set, let’s first take some time to visualize the type of data that we have. This will help us determine the best value for setting our maximum sequence length. In the previous example, we used a max length of 10, but this value is largely dependent on the inputs you have. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The training set we're going to use is the Imdb movie review dataset. This set has 25,000 movie reviews, with 12,500 positive reviews and 12,500 negative reviews. Each of the reviews is stored in a txt file that we need to parse through. The positive reviews are stored in one directory and the negative reviews are stored in another. The following piece of code will determine total and average number of words in each review. " ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Positive files finished\n", "Negative files finished\n", "The total number of files is 25000\n", "The total number of words in the files is 5844680\n", "The average number of words in the files is 233.7872\n" ] } ], "source": [ "from os import listdir\n", "from os.path import isfile, join\n", "positiveFiles = ['positiveReviews/' + f for f in listdir('positiveReviews/') if isfile(join('positiveReviews/', f))]\n", "negativeFiles = ['negativeReviews/' + f for f in listdir('negativeReviews/') if isfile(join('negativeReviews/', f))]\n", "numWords = []\n", "for pf in positiveFiles:\n", " with open(pf, \"r\", encoding='utf-8') as f:\n", " line=f.readline()\n", " counter = len(line.split())\n", " numWords.append(counter) \n", "print('Positive files finished')\n", "\n", "for nf in negativeFiles:\n", " with open(nf, \"r\", encoding='utf-8') as f:\n", " line=f.readline()\n", " counter = len(line.split())\n", " numWords.append(counter) \n", "print('Negative files finished')\n", "\n", "numFiles = len(numWords)\n", "print('The total number of files is', numFiles)\n", "print('The total number of words in the files is', sum(numWords))\n", "print('The average number of words in the files is', sum(numWords)/len(numWords))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can also use the Matplot library to visualize this data in a histogram format. " ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "image/png": 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xXPhPNblZdhDwOVWz4e00SedS/Ft/gmLUZFY7j1zMzKxyvuZiZmaVc3IxM7PK\nObmYmVnlnFzMzKxyTi5mZla5/w+bvdrW6fGbcgAAAABJRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7fc5948cebe0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import matplotlib.pyplot as plt\n", "%matplotlib inline\n", "plt.hist(numWords, 50)\n", "plt.xlabel('Sequence Length')\n", "plt.ylabel('Frequency')\n", "plt.axis([0, 1200, 0, 8000])\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "From the histogram as well as the average number of words per file, we can safely say that most reviews will fall under 250 words, which is the max sequence length value we will set. " ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": true }, "outputs": [], "source": [ "max_seq_len = 250" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Data" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": true }, "outputs": [], "source": [ "ids_matrix = np.load('ids_matrix.npy').tolist()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Parameters" ] }, { "cell_type": "code", "execution_count": 80, "metadata": { "colab": { "autoexec": { "startup": false, "wait_interval": 0 } }, "colab_type": "code", "collapsed": true, "id": "UrAyWt23AtCM" }, "outputs": [], "source": [ "# Parameters for training\n", "STEPS = 100000\n", "BATCH_SIZE = 64\n", "\n", "# Parameters for data processing\n", "REVIEW_KEY = 'review'\n", "SEQUENCE_LENGTH_KEY = 'sequence_length'" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Separating train and test data\n", "\n", "The training set we're going to use is the Imdb movie review dataset. This set has 25,000 movie reviews, with 12,500 positive reviews and 12,500 negative reviews. \n", "\n", "Let's first give a positive label [1, 0] to the first 12500 reviews, and a negative label [0, 1] to the other reviews." ] }, { "cell_type": "code", "execution_count": 75, "metadata": { "collapsed": true }, "outputs": [], "source": [ "POSITIVE_REVIEWS = 12500\n", "\n", "# copying sequences\n", "data_sequences = [np.asarray(v, dtype=np.int32) for v in ids_matrix]\n", "# generating labels\n", "data_labels = [[1, 0] if i < POSITIVE_REVIEWS else [0, 1] for i in range(len(ids_matrix))]\n", "# also creating a length column, this will be used by the Dynamic RNN\n", "# see more about it here: https://www.tensorflow.org/api_docs/python/tf/nn/dynamic_rnn\n", "data_length = [max_seq_len for i in range(len(ids_matrix))]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Then, let's shuffle the data and use 90% of the reviews for training and the other 10% for testing." ] }, { "cell_type": "code", "execution_count": 76, "metadata": { "collapsed": true }, "outputs": [], "source": [ "data = list(zip(data_sequences, data_labels, data_length))\n", "random.shuffle(data) # shuffle\n", "\n", "data = np.asarray(data)\n", "# separating train and test data\n", "limit = int(len(data) * 0.9)\n", "\n", "train_data = data[:limit]\n", "test_data = data[limit:]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Verifying if the train and test data have enough positive and negative examples" ] }, { "cell_type": "code", "execution_count": 77, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Total number of positive labels: 12500\n", "Proportion of positive labels on the Train data: 0.49933333333333335\n", "Proportion of positive labels on the Test data: 0.506\n" ] } ], "source": [ "LABEL_INDEX = 1\n", "def _number_of_pos_labels(df):\n", " pos_labels = 0\n", " for value in df:\n", " if value[LABEL_INDEX] == [1, 0]:\n", " pos_labels += 1\n", " return pos_labels\n", "\n", "pos_labels_train = _number_of_pos_labels(train_data)\n", "total_labels_train = len(train_data)\n", "\n", "pos_labels_test = _number_of_pos_labels(test_data)\n", "total_labels_test = len(test_data)\n", "\n", "print('Total number of positive labels:', pos_labels_train + pos_labels_test)\n", "print('Proportion of positive labels on the Train data:', pos_labels_train/total_labels_train)\n", "print('Proportion of positive labels on the Test data:', pos_labels_test/total_labels_test)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Input functions" ] }, { "cell_type": "code", "execution_count": 81, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def get_input_fn(df, batch_size, num_epochs=1, shuffle=True): \n", " def input_fn():\n", " # https://github.com/tensorflow/tensorflow/tree/master/tensorflow/contrib/data\n", " sequences = np.asarray([v for v in df[:,0]], dtype=np.int32)\n", " labels = np.asarray([v for v in df[:,1]], dtype=np.int32)\n", " length = np.asarray(df[:,2], dtype=np.int32)\n", "\n", " dataset = (\n", " tf.contrib.data.Dataset.from_tensor_slices((sequences, labels, length)) # reading data from memory\n", " .repeat(num_epochs) # repeat dataset the number of epochs\n", " .batch(batch_size)\n", " )\n", " \n", " # for our \"manual\" test we don't want to shuffle the data\n", " if shuffle:\n", " dataset = dataset.shuffle(buffer_size=100000)\n", "\n", " # create iterator\n", " review, label, length = dataset.make_one_shot_iterator().get_next()\n", "\n", " features = {\n", " REVIEW_KEY: review,\n", " SEQUENCE_LENGTH_KEY: length,\n", " }\n", "\n", " return features, label\n", " return input_fn" ] }, { "cell_type": "code", "execution_count": 82, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[ 296 995 201534 1607 319 399999 541 10 7 173\n", " 110 41 10135 4 201534 156 113 7360 63 390\n", " 30 645 187 34 84 201534 2219 61 36 3437\n", " 6 72455 94238 4 201534 389 3 399999 2469 12\n", " 14 2837 4 30 491 14 1481 399999 2153 332\n", " 1790 1515 399999 261533 32 123657 2771 2543 47 49041\n", " 201534 6801 14 91 399999 399999 399999 2644 12 22664\n", " 37 3067 169 3180 173 219 185 46 58545 33214\n", " 32 179 179 138 201534 399999 973 3 201534 5960\n", " 8140 44 6640 886 1485 44 4735 5 43392 6\n", " 636 44 5945 7 14940 1744 94 965 2813 20\n", " 135 4 7 8263 20376 636 34 19 20 2577\n", " 399999 7 6535 683 319 716 15810 13 7 399999\n", " 399999 27127 5 21018 17 47 261 47034 12 20\n", " 496 2270 7360 91 113 3 399999 311 110 68\n", " 357 1588 2651 10 1484 14 19320 10 201534 297\n", " 319 459 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", " 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", " 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", " [ 37 1005 14 978 399999 5203 29 39798 4522 3\n", " 785 399999 8965 974 91 2771 201534 557 2115 9243\n", " 3 4231 3 16084 6 201534 2237 5309 50672 32736\n", " 399999 3534 2316 5 4231 3 52732 41 269 201534\n", " 2926 15 201534 254 2018 6 201534 1115 1005 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", " 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", " 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", " 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", " 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", " 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", " 0 0 0 0 0 0 0 0 0 0\n", " 0 0 0 0 0 0 0 0 0 0]]\n", "[[ 41 33 541 201534 1005 22 201534 399999 7 306\n", " 82 363 111 201534 5810 5572 20 41 5572 20\n", " 19 143 740 3608 883 149 28488 285 399999 738\n", " 41 847 4 2432 7 399999 399999 399999 523 24473\n", " 6 516 204 6183 111 7 1316 1749 6038 4\n", " 399 29 2890 67 3334 8895 4 399 48 34\n", " 20 399999 106 7 300 13197 71 3914 12 67\n", " 11568 13 912 12 67 31 3493 5 12 67\n", " 9578 17 201534 2052 5 12397 19 7 2994 473\n", " 399999 399999 399999 35 1192 3689 35 71 883 15\n", " 8672 39 876 71 3 134 13271 36 667 4\n", " 938 214 4 71 1395 41 269 399999 71 562\n", " 389 399999 2796 102 17983 9505 189 30 5 3608\n", " 907 7 9147 451 4 159 71 17983 7 871\n", " 12217 1749 100 71 7290 22 201534 156 14 56\n", " 1528 73 20 94 33 51 2751 399999 399999 399999\n", " 22181 14 353 201534 3063 3 516 204 201534 890\n", " 3 201534 589 32 191 2237 5 5279 81 86\n", " 253 197 16763 214 15 137 127 399999 399999 399999\n", " 91 6055 6 192 2166 14 201534 2653 3 201534\n", " 1005 81 5128 3255 17 201534 2153 49 64 399999\n", " 91 59 2300 201534 2018 6 4961 100 63 32\n", " 84 3393 1970 6 201534 319 399999 3608 1164 12\n", " 53 399999 5128 201534 560 6 201534 1005 49 201534\n", " 58 1152 0 0 0 0 0 0 0 0]\n", " [201534 91 873 5283 59 37 399999 14 12 64\n", " 201534 3826 94 1211 22 201534 215 79 76346 41\n", " 303 192 308 399999 41 405 20 415 25 201534\n", " 2229 160622 41 54 871 20961 13 8453 12169 5\n", " 22108 192 362 17 7 5795 96700 127 1716 37\n", " 378 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", " 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", " 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", " 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", " 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", " 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", " 0 0 0 0 0 0 0 0 0 0\n", " 0 0 0 0 0 0 0 0 0 0]]\n" ] } ], "source": [ "features, label = get_input_fn(train_data, 2)()\n", "\n", "with tf.Session() as sess:\n", " items = sess.run(features)\n", " print(items[REVIEW_KEY])\n", " print\n", "\n", " items = sess.run(features)\n", " print(items[REVIEW_KEY])\n", " print" ] }, { "cell_type": "code", "execution_count": 83, "metadata": { "colab": { "autoexec": { "startup": false, "wait_interval": 0 } }, "colab_type": "code", "collapsed": true, "id": "m5UJyvW5P0Sy" }, "outputs": [], "source": [ "train_input_fn = get_input_fn(train_data, BATCH_SIZE, None)\n", "test_input_fn = get_input_fn(test_data, BATCH_SIZE)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Creating the Estimator model" ] }, { "cell_type": "code", "execution_count": 88, "metadata": { "colab": { "autoexec": { "startup": false, "wait_interval": 0 } }, "colab_type": "code", "collapsed": true, "id": "VxXAUrYN7TvR" }, "outputs": [], "source": [ "def get_model_fn(rnn_cell_sizes,\n", " label_dimension,\n", " dnn_layer_sizes=[],\n", " optimizer='SGD',\n", " learning_rate=0.01,\n", " embed_dim=128):\n", " \n", " def model_fn(features, labels, mode):\n", " \n", " review = features[REVIEW_KEY]\n", " sequence_length = tf.cast(features[SEQUENCE_LENGTH_KEY], tf.int32)\n", "\n", " # Creating embedding\n", " data = tf.Variable(tf.zeros([BATCH_SIZE, max_seq_len, 50]),dtype=tf.float32)\n", " data = tf.nn.embedding_lookup(word_vector, review)\n", " \n", " # Each RNN layer will consist of a LSTM cell\n", " rnn_layers = [tf.nn.rnn_cell.LSTMCell(size) for size in rnn_cell_sizes]\n", " \n", " # Construct the layers\n", " multi_rnn_cell = tf.nn.rnn_cell.MultiRNNCell(rnn_layers)\n", " \n", " # Runs the RNN model dynamically\n", " # more about it at: \n", " # https://www.tensorflow.org/api_docs/python/tf/nn/dynamic_rnn\n", " outputs, final_state = tf.nn.dynamic_rnn(cell=multi_rnn_cell,\n", " inputs=data,\n", " dtype=tf.float32)\n", "\n", " # Slice to keep only the last cell of the RNN\n", " last_activations = rnn_common.select_last_activations(outputs, sequence_length)\n", "\n", " # Construct dense layers on top of the last cell of the RNN\n", " for units in dnn_layer_sizes:\n", " last_activations = tf.layers.dense(\n", " last_activations, units, activation=tf.nn.relu)\n", " \n", " # Final dense layer for prediction\n", " predictions = tf.layers.dense(last_activations, label_dimension)\n", " predictions_softmax = tf.nn.softmax(predictions)\n", " \n", " loss = None\n", " train_op = None\n", " \n", " preds_op = {\n", " 'prediction': predictions_softmax,\n", " 'label': labels\n", " }\n", " \n", " eval_op = {\n", " \"accuracy\": tf.metrics.accuracy(\n", " tf.argmax(input=predictions_softmax, axis=1),\n", " tf.argmax(input=labels, axis=1))\n", " }\n", " \n", " if mode != tf.estimator.ModeKeys.PREDICT: \n", " loss = tf.losses.softmax_cross_entropy(labels, predictions)\n", " \n", " if mode == tf.estimator.ModeKeys.TRAIN: \n", " train_op = tf.contrib.layers.optimize_loss(\n", " loss,\n", " tf.contrib.framework.get_global_step(),\n", " optimizer=optimizer,\n", " learning_rate=learning_rate)\n", " \n", " return model_fn_lib.EstimatorSpec(mode,\n", " predictions=predictions_softmax,\n", " loss=loss,\n", " train_op=train_op,\n", " eval_metric_ops=eval_op)\n", " return model_fn" ] }, { "cell_type": "code", "execution_count": 89, "metadata": { "collapsed": true }, "outputs": [], "source": [ "model_fn = get_model_fn(rnn_cell_sizes=[64], # size of the hidden layers\n", " label_dimension=2, # since are just 2 classes\n", " dnn_layer_sizes=[128, 64], # size of units in the dense layers on top of the RNN\n", " optimizer='Adam',\n", " learning_rate=0.001,\n", " embed_dim=512)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Create and Run Experiment" ] }, { "cell_type": "code", "execution_count": 90, "metadata": { "colab": { "autoexec": { "startup": false, "wait_interval": 0 } }, "colab_type": "code", "collapsed": true, "id": "DUZEKQrdGgZE" }, "outputs": [], "source": [ "# create experiment\n", "def generate_experiment_fn():\n", " \n", " \"\"\"\n", " Create an experiment function given hyperparameters.\n", " Returns:\n", " A function (output_dir) -> Experiment where output_dir is a string\n", " representing the location of summaries, checkpoints, and exports.\n", " this function is used by learn_runner to create an Experiment which\n", " executes model code provided in the form of an Estimator and\n", " input functions.\n", " All listed arguments in the outer function are used to create an\n", " Estimator, and input functions (training, evaluation, serving).\n", " Unlisted args are passed through to Experiment.\n", " \"\"\"\n", "\n", " def _experiment_fn(run_config, hparams):\n", " estimator = tf.estimator.Estimator(model_fn=model_fn, config=run_config)\n", " return tf.contrib.learn.Experiment(\n", " estimator,\n", " train_input_fn=train_input_fn,\n", " eval_input_fn=test_input_fn,\n", " train_steps=STEPS\n", " )\n", " return _experiment_fn" ] }, { "cell_type": "code", "execution_count": 91, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "WARNING:tensorflow:RunConfig.uid (from tensorflow.contrib.learn.python.learn.estimators.run_config) is experimental and may change or be removed at any time, and without warning.\n", "INFO:tensorflow:Using config: {'_save_checkpoints_steps': None, '_session_config': None, '_keep_checkpoint_every_n_hours': 10000, '_save_summary_steps': 100, '_num_ps_replicas': 0, '_cluster_spec': <tensorflow.python.training.server_lib.ClusterSpec object at 0x7f3a18f5a550>, '_tf_random_seed': None, '_tf_config': gpu_options {\n", " per_process_gpu_memory_fraction: 1.0\n", "}\n", ", '_model_dir': 'testing2', '_task_id': 0, '_evaluation_master': '', '_environment': 'local', '_master': '', '_is_chief': True, '_save_checkpoints_secs': 600, '_task_type': None, '_keep_checkpoint_max': 5, '_num_worker_replicas': 0}\n", "WARNING:tensorflow:RunConfig.uid (from tensorflow.contrib.learn.python.learn.estimators.run_config) is experimental and may change or be removed at any time, and without warning.\n", "WARNING:tensorflow:From /usr/local/lib/python3.5/site-packages/tensorflow/contrib/learn/python/learn/monitors.py:268: BaseMonitor.__init__ (from tensorflow.contrib.learn.python.learn.monitors) is deprecated and will be removed after 2016-12-05.\n", "Instructions for updating:\n", "Monitors are deprecated. Please use tf.train.SessionRunHook.\n", "INFO:tensorflow:Create CheckpointSaverHook.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/usr/local/lib/python3.5/site-packages/tensorflow/python/ops/gradients_impl.py:93: UserWarning: Converting sparse IndexedSlices to a dense Tensor of unknown shape. This may consume a large amount of memory.\n", " \"Converting sparse IndexedSlices to a dense Tensor of unknown shape. \"\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "INFO:tensorflow:Saving checkpoints for 1 into testing2/model.ckpt.\n", "INFO:tensorflow:loss = 0.702224, step = 1\n", "INFO:tensorflow:Starting evaluation at 2017-06-29-13:09:09\n", "INFO:tensorflow:Restoring parameters from testing2/model.ckpt-1\n", "INFO:tensorflow:Evaluation [1/100]\n", "INFO:tensorflow:Evaluation [2/100]\n", "INFO:tensorflow:Evaluation [3/100]\n", "INFO:tensorflow:Evaluation [4/100]\n", "INFO:tensorflow:Evaluation [5/100]\n", "INFO:tensorflow:Evaluation [6/100]\n", "INFO:tensorflow:Evaluation [7/100]\n", "INFO:tensorflow:Evaluation [8/100]\n", "INFO:tensorflow:Evaluation [9/100]\n", "INFO:tensorflow:Evaluation [10/100]\n", "INFO:tensorflow:Evaluation [11/100]\n", "INFO:tensorflow:Evaluation [12/100]\n", "INFO:tensorflow:Evaluation [13/100]\n", "INFO:tensorflow:Evaluation 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5.77426\n", "INFO:tensorflow:loss = 0.709747, step = 101 (17.319 sec)\n", "INFO:tensorflow:global_step/sec: 10.8153\n", "INFO:tensorflow:loss = 0.663808, step = 201 (9.246 sec)\n", "INFO:tensorflow:global_step/sec: 10.9954\n", "INFO:tensorflow:loss = 0.679195, step = 301 (9.095 sec)\n", "INFO:tensorflow:global_step/sec: 10.8191\n", "INFO:tensorflow:loss = 0.688457, step = 401 (9.243 sec)\n", "INFO:tensorflow:global_step/sec: 11.059\n", "INFO:tensorflow:loss = 0.646141, step = 501 (9.042 sec)\n", "INFO:tensorflow:global_step/sec: 11.0316\n", "INFO:tensorflow:loss = 0.662266, step = 601 (9.065 sec)\n", "INFO:tensorflow:global_step/sec: 10.9618\n", "INFO:tensorflow:loss = 0.689469, step = 701 (9.123 sec)\n", "INFO:tensorflow:global_step/sec: 10.7993\n", "INFO:tensorflow:loss = 0.702996, step = 801 (9.260 sec)\n", "INFO:tensorflow:global_step/sec: 10.6876\n", "INFO:tensorflow:loss = 0.702799, step = 901 (9.357 sec)\n", "INFO:tensorflow:global_step/sec: 10.8427\n", "INFO:tensorflow:loss = 0.694457, step = 1001 (9.223 sec)\n", "INFO:tensorflow:global_step/sec: 10.653\n", "INFO:tensorflow:loss = 0.698411, step = 1101 (9.387 sec)\n", "INFO:tensorflow:global_step/sec: 10.4024\n", "INFO:tensorflow:loss = 0.681778, step = 1201 (9.613 sec)\n", "INFO:tensorflow:global_step/sec: 10.6156\n", "INFO:tensorflow:loss = 0.6818, step = 1301 (9.420 sec)\n", "INFO:tensorflow:global_step/sec: 11.0213\n", "INFO:tensorflow:loss = 0.690526, step = 1401 (9.073 sec)\n", "INFO:tensorflow:global_step/sec: 10.9186\n", "INFO:tensorflow:loss = 0.689741, step = 1501 (9.159 sec)\n", "INFO:tensorflow:global_step/sec: 10.7999\n", "INFO:tensorflow:loss = 0.69076, step = 1601 (9.259 sec)\n", "INFO:tensorflow:global_step/sec: 10.9234\n", "INFO:tensorflow:loss = 0.695023, step = 1701 (9.155 sec)\n", "INFO:tensorflow:global_step/sec: 10.8265\n", "INFO:tensorflow:loss = 0.693486, step = 1801 (9.237 sec)\n", "INFO:tensorflow:global_step/sec: 10.9657\n", "INFO:tensorflow:loss = 0.694407, step = 1901 (9.119 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"INFO:tensorflow:Evaluation [79/100]\n", "INFO:tensorflow:Finished evaluation at 2017-06-29-13:29:11\n", "INFO:tensorflow:Saving dict for global step 12547: accuracy = 0.84, global_step = 12547, loss = 0.357392\n", "INFO:tensorflow:Validation (step 12547): accuracy = 0.84, global_step = 12547, loss = 0.357392\n", "INFO:tensorflow:global_step/sec: 7.79841\n", "INFO:tensorflow:loss = 0.168538, step = 12601 (12.823 sec)\n", "INFO:tensorflow:global_step/sec: 10.8611\n", "INFO:tensorflow:loss = 0.190768, step = 12701 (9.207 sec)\n", "INFO:tensorflow:global_step/sec: 10.7893\n", "INFO:tensorflow:loss = 0.377361, step = 12801 (9.268 sec)\n", "INFO:tensorflow:global_step/sec: 10.8096\n", "INFO:tensorflow:loss = 0.169103, step = 12901 (9.251 sec)\n", "INFO:tensorflow:global_step/sec: 10.7698\n", "INFO:tensorflow:loss = 0.249736, step = 13001 (9.285 sec)\n", "INFO:tensorflow:global_step/sec: 10.888\n", "INFO:tensorflow:loss = 0.374019, step = 13101 (9.184 sec)\n", 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"INFO:tensorflow:Validation (step 18858): accuracy = 0.8496, global_step = 18858, loss = 0.393123\n", "INFO:tensorflow:global_step/sec: 7.63898\n", "INFO:tensorflow:loss = 0.296969, step = 18901 (13.091 sec)\n", "INFO:tensorflow:global_step/sec: 10.134\n", "INFO:tensorflow:loss = 0.198794, step = 19001 (9.869 sec)\n", "INFO:tensorflow:global_step/sec: 9.90147\n", "INFO:tensorflow:loss = 0.355534, step = 19101 (10.098 sec)\n", "INFO:tensorflow:global_step/sec: 10.1805\n", "INFO:tensorflow:loss = 0.231664, step = 19201 (9.823 sec)\n", "INFO:tensorflow:global_step/sec: 10.1964\n", "INFO:tensorflow:loss = 0.233768, step = 19301 (9.807 sec)\n", "INFO:tensorflow:global_step/sec: 10.1765\n", "INFO:tensorflow:loss = 0.121018, step = 19401 (9.827 sec)\n", "INFO:tensorflow:global_step/sec: 10.4554\n", "INFO:tensorflow:loss = 0.0577645, step = 19501 (9.565 sec)\n", "INFO:tensorflow:global_step/sec: 10.2567\n", "INFO:tensorflow:loss = 0.267758, step = 19601 (9.750 sec)\n", 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"INFO:tensorflow:loss = 0.0628916, step = 24101 (9.390 sec)\n", "INFO:tensorflow:global_step/sec: 10.656\n", "INFO:tensorflow:loss = 0.152039, step = 24201 (9.384 sec)\n", "INFO:tensorflow:global_step/sec: 10.6739\n", "INFO:tensorflow:loss = 0.364461, step = 24301 (9.369 sec)\n", "INFO:tensorflow:global_step/sec: 10.2224\n", "INFO:tensorflow:loss = 0.0836938, step = 24401 (9.782 sec)\n", "INFO:tensorflow:global_step/sec: 10.3452\n", "INFO:tensorflow:loss = 0.0618011, step = 24501 (9.666 sec)\n", "INFO:tensorflow:global_step/sec: 10.2704\n", "INFO:tensorflow:loss = 0.0751874, step = 24601 (9.737 sec)\n", "INFO:tensorflow:global_step/sec: 9.75828\n", "INFO:tensorflow:loss = 0.0735404, step = 24701 (10.248 sec)\n", "INFO:tensorflow:global_step/sec: 9.92074\n", "INFO:tensorflow:loss = 0.0457174, step = 24801 (10.080 sec)\n", "INFO:tensorflow:global_step/sec: 9.41088\n", "INFO:tensorflow:loss = 0.221096, step = 24901 (10.626 sec)\n", "INFO:tensorflow:Saving checkpoints for 24903 into testing2/model.ckpt.\n", "INFO:tensorflow:Starting evaluation at 2017-06-29-13:49:09\n", "INFO:tensorflow:Restoring parameters from testing2/model.ckpt-24903\n", "INFO:tensorflow:Evaluation [1/100]\n", "INFO:tensorflow:Evaluation [2/100]\n", "INFO:tensorflow:Evaluation [3/100]\n", "INFO:tensorflow:Evaluation [4/100]\n", "INFO:tensorflow:Evaluation [5/100]\n", "INFO:tensorflow:Evaluation [6/100]\n", "INFO:tensorflow:Evaluation [7/100]\n", "INFO:tensorflow:Evaluation [8/100]\n", "INFO:tensorflow:Evaluation [9/100]\n", "INFO:tensorflow:Evaluation [10/100]\n", "INFO:tensorflow:Evaluation [11/100]\n", "INFO:tensorflow:Evaluation [12/100]\n", "INFO:tensorflow:Evaluation [13/100]\n", "INFO:tensorflow:Evaluation [14/100]\n", "INFO:tensorflow:Evaluation [15/100]\n", "INFO:tensorflow:Evaluation [16/100]\n", "INFO:tensorflow:Evaluation [17/100]\n", "INFO:tensorflow:Evaluation [18/100]\n", "INFO:tensorflow:Evaluation [19/100]\n", "INFO:tensorflow:Evaluation [20/100]\n", "INFO:tensorflow:Evaluation 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"INFO:tensorflow:global_step/sec: 10.1661\n", "INFO:tensorflow:loss = 0.300942, step = 25201 (9.837 sec)\n", "INFO:tensorflow:global_step/sec: 10.227\n", "INFO:tensorflow:loss = 0.0524202, step = 25301 (9.778 sec)\n", "INFO:tensorflow:global_step/sec: 9.84267\n", "INFO:tensorflow:loss = 0.0874858, step = 25401 (10.160 sec)\n", "INFO:tensorflow:global_step/sec: 9.98989\n", "INFO:tensorflow:loss = 0.380375, step = 25501 (10.010 sec)\n", "INFO:tensorflow:global_step/sec: 10.0228\n", "INFO:tensorflow:loss = 0.167019, step = 25601 (9.977 sec)\n", "INFO:tensorflow:global_step/sec: 10.1635\n", "INFO:tensorflow:loss = 0.10299, step = 25701 (9.839 sec)\n", "INFO:tensorflow:global_step/sec: 9.97289\n", "INFO:tensorflow:loss = 0.0480214, step = 25801 (10.027 sec)\n", "INFO:tensorflow:global_step/sec: 10.1747\n", "INFO:tensorflow:loss = 0.249856, step = 25901 (9.828 sec)\n", "INFO:tensorflow:global_step/sec: 10.1208\n", "INFO:tensorflow:loss = 0.115353, step = 26001 (9.881 sec)\n", "INFO:tensorflow:global_step/sec: 9.98893\n", "INFO:tensorflow:loss = 0.140886, step = 26101 (10.011 sec)\n", "INFO:tensorflow:global_step/sec: 10.1737\n", "INFO:tensorflow:loss = 0.0291444, step = 26201 (9.829 sec)\n", "INFO:tensorflow:global_step/sec: 10.1633\n", "INFO:tensorflow:loss = 0.106773, step = 26301 (9.839 sec)\n", "INFO:tensorflow:global_step/sec: 10.1504\n", "INFO:tensorflow:loss = 0.0279159, step = 26401 (9.852 sec)\n", "INFO:tensorflow:global_step/sec: 10.1401\n", "INFO:tensorflow:loss = 0.0808731, step = 26501 (9.862 sec)\n", "INFO:tensorflow:global_step/sec: 9.90735\n", "INFO:tensorflow:loss = 0.0298186, step = 26601 (10.094 sec)\n", "INFO:tensorflow:global_step/sec: 10.2026\n", "INFO:tensorflow:loss = 0.0187685, step = 26701 (9.801 sec)\n", "INFO:tensorflow:global_step/sec: 10.243\n", "INFO:tensorflow:loss = 0.0356779, step = 26801 (9.763 sec)\n", "INFO:tensorflow:global_step/sec: 10.2908\n", "INFO:tensorflow:loss = 0.0174884, step = 26901 (9.718 sec)\n", "INFO:tensorflow:global_step/sec: 10.3543\n", "INFO:tensorflow:loss = 0.166417, step = 27001 (9.658 sec)\n", "INFO:tensorflow:global_step/sec: 10.6206\n", "INFO:tensorflow:loss = 0.116242, step = 27101 (9.416 sec)\n", "INFO:tensorflow:global_step/sec: 10.5835\n", "INFO:tensorflow:loss = 0.0291956, step = 27201 (9.449 sec)\n", "INFO:tensorflow:global_step/sec: 10.5545\n", "INFO:tensorflow:loss = 0.0611862, step = 27301 (9.475 sec)\n", "INFO:tensorflow:global_step/sec: 10.4988\n", "INFO:tensorflow:loss = 0.0127632, step = 27401 (9.525 sec)\n", "INFO:tensorflow:global_step/sec: 9.81971\n", "INFO:tensorflow:loss = 0.0755489, step = 27501 (10.184 sec)\n", "INFO:tensorflow:global_step/sec: 10.2222\n", "INFO:tensorflow:loss = 0.0179902, step = 27601 (9.783 sec)\n", "INFO:tensorflow:global_step/sec: 9.881\n", "INFO:tensorflow:loss = 0.0464621, step = 27701 (10.120 sec)\n", "INFO:tensorflow:global_step/sec: 10.2568\n", "INFO:tensorflow:loss = 0.180232, step = 27801 (9.750 sec)\n", "INFO:tensorflow:global_step/sec: 10.2395\n", "INFO:tensorflow:loss = 0.0273141, step = 27901 (9.766 sec)\n", "INFO:tensorflow:global_step/sec: 9.58837\n", "INFO:tensorflow:loss = 0.185907, step = 28001 (10.429 sec)\n", "INFO:tensorflow:global_step/sec: 10.0512\n", "INFO:tensorflow:loss = 0.118873, step = 28101 (9.949 sec)\n", "INFO:tensorflow:global_step/sec: 9.97048\n", "INFO:tensorflow:loss = 0.0535866, step = 28201 (10.030 sec)\n", "INFO:tensorflow:global_step/sec: 10.2256\n", "INFO:tensorflow:loss = 0.0161786, step = 28301 (9.779 sec)\n", "INFO:tensorflow:global_step/sec: 10.5405\n", "INFO:tensorflow:loss = 0.0145144, step = 28401 (9.487 sec)\n", "INFO:tensorflow:global_step/sec: 10.3069\n", "INFO:tensorflow:loss = 0.0277693, step = 28501 (9.702 sec)\n" ] }, { "ename": "KeyboardInterrupt", "evalue": "", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", "\u001b[0;32m<ipython-input-91-c5365892903e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;31m# run experiment\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mlearn_runner\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgenerate_experiment_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrun_config\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcontrib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlearn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mRunConfig\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel_dir\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'testing2'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;32m/usr/local/lib/python3.5/site-packages/tensorflow/contrib/learn/python/learn/learn_runner.py\u001b[0m in \u001b[0;36mrun\u001b[0;34m(experiment_fn, output_dir, schedule, run_config, hparams)\u001b[0m\n\u001b[1;32m 208\u001b[0m \u001b[0mschedule\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mschedule\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0m_get_default_schedule\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrun_config\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 209\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 210\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0m_execute_schedule\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mexperiment\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mschedule\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 211\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 212\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/usr/local/lib/python3.5/site-packages/tensorflow/contrib/learn/python/learn/learn_runner.py\u001b[0m in \u001b[0;36m_execute_schedule\u001b[0;34m(experiment, schedule)\u001b[0m\n\u001b[1;32m 45\u001b[0m \u001b[0mlogging\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0merror\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Allowed values for this experiment are: %s'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalid_tasks\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 46\u001b[0m \u001b[0;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Schedule references non-callable member %s'\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0mschedule\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 47\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mtask\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 48\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 49\u001b[0m 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"\u001b[0;32m/usr/local/lib/python3.5/site-packages/tensorflow/contrib/learn/python/learn/experiment.py\u001b[0m in \u001b[0;36mtrain\u001b[0;34m(self, delay_secs)\u001b[0m\n\u001b[1;32m 273\u001b[0m return self._call_train(input_fn=self._train_input_fn,\n\u001b[1;32m 274\u001b[0m \u001b[0mmax_steps\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_train_steps\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 275\u001b[0;31m hooks=self._train_monitors + extra_hooks)\n\u001b[0m\u001b[1;32m 276\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 277\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mevaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdelay_secs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/usr/local/lib/python3.5/site-packages/tensorflow/contrib/learn/python/learn/experiment.py\u001b[0m in \u001b[0;36m_call_train\u001b[0;34m(self, _sentinel, input_fn, steps, hooks, max_steps)\u001b[0m\n\u001b[1;32m 658\u001b[0m \u001b[0msteps\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msteps\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 659\u001b[0m \u001b[0mmax_steps\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmax_steps\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 660\u001b[0;31m hooks=hooks)\n\u001b[0m\u001b[1;32m 661\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 662\u001b[0m return self._estimator.fit(input_fn=input_fn,\n", "\u001b[0;32m/usr/local/lib/python3.5/site-packages/tensorflow/python/estimator/estimator.py\u001b[0m in \u001b[0;36mtrain\u001b[0;34m(self, input_fn, hooks, steps, max_steps)\u001b[0m\n\u001b[1;32m 239\u001b[0m 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run_metadata)\u001b[0m\n\u001b[1;32m 1119\u001b[0m return tf_session.TF_Run(session, options,\n\u001b[1;32m 1120\u001b[0m \u001b[0mfeed_dict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfetch_list\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtarget_list\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1121\u001b[0;31m status, run_metadata)\n\u001b[0m\u001b[1;32m 1122\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1123\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_prun_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msession\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhandle\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfeed_dict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfetch_list\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mKeyboardInterrupt\u001b[0m: " ] } ], "source": [ "# run experiment \n", "learn_runner.run(generate_experiment_fn(), run_config=tf.contrib.learn.RunConfig(model_dir='testing2'))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Making Predictions\n", "\n", "Let's generate our own sentence to see how the model classifies them." ] }, { "cell_type": "code", "execution_count": 81, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "INFO:tensorflow:Restoring parameters from tensorboard5/model.ckpt-12284\n", "\n", "sentence: this is a great movie\n", "bad review: 0.00019553 good review: 0.999804\n", "----------\n", "sentence: this is a good movie but isnt the best\n", "bad review: 0.0790645 good review: 0.920936\n", "----------\n", "sentence: this is a ok movie\n", "bad review: 0.75468 good review: 0.24532\n", "----------\n", "sentence: this movie sucks\n", "bad review: 0.999998 good review: 1.94564e-06\n", "----------\n", "sentence: this movie sucks but isnt the worst\n", "bad review: 0.66062 good review: 0.33938\n", "----------\n", "sentence: its not that bad\n", "bad review: 0.995739 good review: 0.00426115\n", "----------\n" ] } ], "source": [ "def generate_data_row(sentence, label):\n", " length = max_seq_length\n", " sequence = np.zeros((length), dtype='int32')\n", " for i, word in enumerate(sentence):\n", " sequence[i] = word_list.index(word)\n", " \n", " return sequence, label, length\n", " \n", "data_sequences = [np.asarray(v, dtype=np.int32) for v in ids_matrix]\n", "# generating labels\n", "data_labels = [[1, 0] if i < POSITIVE_REVIEWS else [0, 1] for i in range(len(ids_matrix))]\n", "# also creating a length column, this will be used by the Dynamic RNN\n", "# see more about it here: https://www.tensorflow.org/api_docs/python/tf/nn/dynamic_rnn\n", "data_length = [max_seq_len for i in range(len(ids_matrix))]\n", " \n", "\n", "first_sentence[0] = word_list.index(\"i\")\n", "first_sentence[1] = word_list.index(\"thought\")\n", "first_sentence[2] = word_list.index(\"the\")\n", "first_sentence[3] = word_list.index(\"movie\")\n", "first_sentence[4] = word_list.index(\"was\")\n", "first_sentence[5] = word_list.index(\"incredible\")\n", "first_sentence[6] = word_list.index(\"and\")\n", "first_sentence[7] = word_list.index(\"inspiring\")\n", "# first_sentence[8] = 0\n", "# first_sentence[9] = 0\n", "\n", "print(first_sentence.shape)\n", "print(first_sentence) # shows the row index for each word\n", "\n", "\n", "preds = estimator.predict(input_fn=my_test_input_fn, as_iterable=True)\n", "\n", "sentences = _get_csv_column(MY_TEST_INPUT, 'review')\n", "\n", "print()\n", "for p, s in zip(preds, sentences):\n", " print('sentence:', s)\n", " print('bad review:', p[0], 'good review:', p[1])\n", " print('-' * 10)" ] } ], "metadata": { "colab": { "default_view": {}, "last_runtime": { "build_target": "//experimental/users/jamieas/transform_colab:notebook", "kind": "private" }, "name": "Copy of CustomEstimator.ipynb", "provenance": [ { "file_id": "0BwN-JPfIIHwgdFkwUTVIWTQwU00", "timestamp": 1496845355496 } ], "version": "0.3.2", "views": {} }, "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 1 }
apache-2.0
jamesnw/wtb-data
notebooks/Colors.ipynb
1
29684
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "Everyone's favorite nerdy comic, XKCD, ranked colors by best tasting. I thought I would use the WTB dataset to compare and see if the data agrees.\n", "\n", "![Best tasting colors](https://imgs.xkcd.com/comics/best_tasting_colors.png)" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Import libraries\n", "import numpy as np\n", "import pandas as pd\n", "# Import the data\n", "import WTBLoad\n", "wtb = WTBLoad.load_frame()" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "pink = [\"watermelon\", \"cranberry\"]\n", "red = [\"cherry\",\"apple\",\"raspberry\",\"strawberry\", \"rose hips\", \"hibiscus\",'rhubarb', \"red wine\"]\n", "blue = [\"blueberry\",\"juniper berries\"]\n", "green = [\"green tea\",\"mint\",\"lemon grass\",'cucumber','basil']\n", "white = [\"pear\", \"elderflower\", \"ginger\", \"coconut\",\"piña colada\",\"vanilla\",\"white wine\"]\n", "brown = [ \"chai\", \"chicory\", \"coriander\", \"cardamom\", \"seeds of paradise\", \"cinnamon\", \"chocolate\", \"peanut butter\", \"hazelnut\",\"pecan\",\"bacon\",\"bourbon\",\"whiskey\",\"coffee\",\"oak\",\"rye\",\"maple\"]\n", "orange = [\"apricot\", \"peach\", \"grapefruit\",\"orange peel\", \"pumpkin\",\"sweet potato\"]\n", "yellow = [\"chamomile\",\"lemon peel\"]\n", "purple = [ \"plum\", \"lavender\", \"port\",\"blackberry\"]\n", "black = [ \"anise\", 'peppercorn', 'lemon pepper', \"smoke\"]\n", "additionsColors = {\"pink\": pink,\"red\": red,\"blue\": blue,\"green\": green,\"white\": white,\"brown\": brown,\"orange\": orange,\"yellow\": yellow,\"purple\": purple, \"black\": black}" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "pink\n" ] } ], "source": [ "# Great. Now we have a mapping from color to addition, but we really need it the other way around.\n", "additionToColor = {}\n", "for color in additionsColors:\n", " for addition in additionsColors[color]:\n", " additionToColor[addition] = color\n", "print(additionToColor['watermelon'])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's add a `color` column." ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def addcolor(addition):\n", " return additionToColor[addition]\n", "wtb['color'] = np.vectorize(addcolor)(wtb['addition'])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now group by the new color column, get the mean, and sort the values high to low." ] }, { "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>vote</th>\n", " </tr>\n", " <tr>\n", " <th>color</th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>blue</th>\n", " <td>0.512541</td>\n", " </tr>\n", " <tr>\n", " <th>brown</th>\n", " <td>0.510719</td>\n", " </tr>\n", " <tr>\n", " <th>orange</th>\n", " <td>0.505209</td>\n", " </tr>\n", " <tr>\n", " <th>purple</th>\n", " <td>0.499319</td>\n", " </tr>\n", " <tr>\n", " <th>yellow</th>\n", " <td>0.493065</td>\n", " </tr>\n", " <tr>\n", " <th>red</th>\n", " <td>0.484784</td>\n", " </tr>\n", " <tr>\n", " <th>black</th>\n", " <td>0.482717</td>\n", " </tr>\n", " <tr>\n", " <th>white</th>\n", " <td>0.471580</td>\n", " </tr>\n", " <tr>\n", " <th>pink</th>\n", " <td>0.466509</td>\n", " </tr>\n", " <tr>\n", " <th>green</th>\n", " <td>0.411418</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " vote\n", "color \n", "blue 0.512541\n", "brown 0.510719\n", "orange 0.505209\n", "purple 0.499319\n", "yellow 0.493065\n", "red 0.484784\n", "black 0.482717\n", "white 0.471580\n", "pink 0.466509\n", "green 0.411418" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "wtb.groupby(by='color').mean().sort_values('vote',ascending=False)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "There we have it. Blue is the best tasting color. \n", "\n", "But brown is awfully close. I wonder how the ranges compare. Let's take a look at a histogram." ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x111f59978>" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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HceKjEIt/J+BVVX0NQESmAYOAeXnnXQyMBc6OVUIHKHA6eQtdyFUppNTrwnGKoxDFvzHw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\nGK74HcdxMsb/Awilup3ukjbXAAAAAElFTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x1114a4860>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%matplotlib inline\n", "wtb.groupby(by='color').boxplot(subplots=False,rot=45)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "There we can see that while blue has a slightly higher average, brown has a lot of very high and very low outliers.\n", "\n", "For more analysis, check out the [What To Brew blog](http://whattobrew.com/blog/2017/03/18/sorry-xkcd-pink-not-best-tasting-color/)." ] } ], "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
syednasar/datascience
visualize/Seaborn Vizualizations.ipynb
2
766
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Seaborn Vizualizations\n", "\n", "url: http://web.stanford.edu/~mwaskom/software/seaborn/\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.11" } }, "nbformat": 4, "nbformat_minor": 0 }
mit
ioam/holoviews
examples/user_guide/07-Live_Data.ipynb
1
29008
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Live Data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The [HoloMap](../reference/containers/bokeh/HoloMap.ipynb) is a core HoloViews data structure that allows easy exploration of parameter spaces. The essence of a HoloMap is that it contains a collection of [Elements](http://holoviews.org/reference/index.html) (e.g. ``Image``s and ``Curve``s) that you can easily select and visualize.\n", "\n", "HoloMaps hold fully constructed Elements at specifically sampled points in a multidimensional space. Although HoloMaps are useful for exploring high-dimensional parameter spaces, they can very quickly consume huge amounts of memory to store all these Elements. For instance, a hundred samples along four orthogonal dimensions would need a HoloMap containing a hundred *million* Elements, each of which could be a substantial object that takes time to create and costs memory to store. Thus ``HoloMaps`` have some clear limitations:\n", "\n", "* HoloMaps may require the generation of millions of Elements before the first element can be viewed.\n", "* HoloMaps can easily exhaust all the memory available to Python.\n", "* HoloMaps can even more easily exhaust all the memory in the browser when displayed.\n", "* Static export of a notebook containing HoloMaps can result in impractically large HTML files.\n", "\n", "The ``DynamicMap`` addresses these issues by computing and displaying elements dynamically, allowing exploration of much larger datasets:\n", "\n", "* DynamicMaps generate elements on the fly, allowing the process of exploration to begin immediately.\n", "* DynamicMaps do not require fixed sampling, allowing exploration of parameters with arbitrary resolution.\n", "* DynamicMaps are lazy in the sense they only compute as much data as the user wishes to explore.\n", "\n", "Of course, these advantages come with some limitations:\n", "\n", "* DynamicMaps require a live notebook server and cannot be fully exported to static HTML.\n", "* DynamicMaps store only a portion of the underlying data, in the form of an Element cache and their output is dependent on the particular version of the executed code. \n", "* DynamicMaps (and particularly their element caches) are typically stateful (with values that depend on patterns of user interaction), which can make them more difficult to reason about.\n", "\n", "In addition to the different computational requirements of ``DynamicMaps``, they can be used to build sophisticated, interactive vizualisations that cannot be achieved using only ``HoloMaps``. This notebook demonstrates some basic examples and the [Responding to Events](./12-Responding_to_Events.ipynb) guide follows on by introducing the streams system. The [Custom Interactivity](./13-Custom_Interactivity.ipynb) shows how you can directly interact with your plots when using the Bokeh backend.\n", "\n", "When DynamicMap was introduced in version 1.6, it supported multiple different 'modes' which have now been deprecated. This notebook demonstrates the simpler, more flexible and more powerful DynamicMap introduced in version 1.7. Users who have been using the previous version of DynamicMap should be unaffected as backwards compatibility has been preserved for the most common cases.\n", "\n", "All this will make much more sense once we've tried out some ``DynamicMaps`` and showed how they work, so let's create one!" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "<center><div class=\"alert alert-info\" role=\"alert\">To use visualize and use a <b>DynamicMap</b> you need to be running a live Jupyter server.<br>When viewing this user guide as part of the documentation DynamicMaps will be sampled with a limited number of states.<br>\n", "It's also best to run this notebook one cell at a time, not via \"Run All\",<br> so that subsequent cells can reflect your dynamic interaction with widgets in previous cells.</div></center>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## ``DynamicMap`` <a id='DynamicMap'></a>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's start by importing HoloViews and loading the extension:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import holoviews as hv\n", "from holoviews import opts\n", "\n", "hv.extension('matplotlib')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We will now create ``DynamicMap`` similar to the ``HoloMap`` introduced in the [Introductory guide](../getting_started/1-Introduction.ipynb). The ``HoloMap`` in that introduction consisted of ``Image`` elements defined by a function returning NumPy arrays called ``sine_array``. Here we will define a ``waves_image`` function that returns an array pattern parameterized by arbitrary ``alpha`` and ``beta`` parameters inside a HoloViews \n", "[``Image``](../reference/elements/bokeh/Image.ipynb) element:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "xvals = np.linspace(-4, 0, 202)\n", "yvals = np.linspace(4, 0, 202)\n", "xs, ys = np.meshgrid(xvals, yvals)\n", "\n", "def waves_image(alpha, beta):\n", " return hv.Image(np.sin(((ys/alpha)**alpha+beta)*xs))\n", "\n", "waves_image(1,0) + waves_image(1,4)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we can demonstrate the possibilities for exploration enabled by the simplest declaration of a ``DynamicMap``." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Basic ``DynamicMap`` declaration<a id='BasicDeclaration'></a>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A simple ``DynamicMap`` declaration looks identical to that needed to declare a ``HoloMap``. Instead of supplying some initial data, we will supply the ``waves_image`` function with key dimensions simply declaring the arguments of that function:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "dmap = hv.DynamicMap(waves_image, kdims=['alpha', 'beta'])\n", "dmap" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This object is created instantly, but because it doesn't generate any `hv.Image` objects initially it only shows the printed representation of this object along with some information about how to display it. We will refer to a ``DynamicMap`` that doesn't have enough information to display itself as 'unbounded'.\n", "\n", "The textual representation of all ``DynamicMaps`` look similar, differing only in the listed dimensions until they have been evaluated at least once." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Explicit indexing\n", "\n", "Unlike a corresponding ``HoloMap`` declaration, this simple unbounded ``DynamicMap`` cannot yet visualize itself. To view it, we can follow the advice in the warning message. First we will explicitly index into our ``DynamicMap`` in the same way you would access a key on a ``HoloMap``:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "dmap[1,2] + dmap.select(alpha=1, beta=2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Note that the declared kdims are specifying the arguments *by position* as they do not match the argument names of the ``waves_image`` function. If you *do* match the argument names *exactly*, you can map a kdim position to any argument position of the callable. For instance, the declaration ``kdims=['freq', 'phase']`` would index first by frequency, then phase without mixing up the arguments to ``waves_image`` when indexing." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Setting dimension ranges\n", "\n", "The second suggestion in the warning message was to supply dimension ranges using the ``redim.range`` method:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "dmap.redim.range(alpha=(1, 5.0), beta=(1, 6.0))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here each `hv.Image` object visualizing a particular sine ring pattern with the given parameters is created dynamically, whenever the slider is set to a new value. Any value in the allowable range can be requested by dragging the sliders or by tweaking the values using the left and right arrow keys.\n", "\n", "Of course, we didn't have to use the ``redim.range`` method and we could have simply declared the ranges right away using explicit ``hv.Dimension`` objects. This would allow us to declare other dimension properties such as the step size used by the sliders: by default each slider can select around a thousand distinct values along its range but you can specify your own step value via the dimension ``step`` parameter. If you use integers in your range declarations, integer stepping will be assumed with a step size of one.\n", "\n", "Note that whenever the ``redim`` method is used, a new ``DynamicMap`` is returned with the updated dimensions. In other words, the original ``dmap`` remains unbounded with default dimension objects.\n", "\n", "\n", "#### Setting dimension values\n", "\n", "The ``DynamicMap`` above allows exploration of *any* phase and frequency within the declared range unlike an equivalent ``HoloMap`` which would have to be composed of a finite set of samples. We can achieve a similar discrete sampling using ``DynamicMap`` by setting the ``values`` parameter on the dimensions:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "dmap.redim.values(alpha=[1, 2, 3], beta=[0.1, 1.0, 2.5])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The sliders now snap to the specified dimension values and if you are running this live, the above cell should look like a [HoloMap](../reference/containers/bokeh/HoloMap.ipynb). ``DynamicMap`` is in fact a subclass of ``HoloMap`` with some crucial differences:\n", "\n", "* You can now pick as many values of **alpha** or **beta** as allowed by the slider.\n", "* What you see in the cell above will not be exported in any HTML snapshot of the notebook\n", "\n", "\n", "We will now explore how ``DynamicMaps`` relate to ``HoloMaps`` including conversion operations between the two types. As we will see, there are other ways to display a ``DynamicMap`` without using explicit indexing or redim." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Interaction with ``HoloMap``s" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To explore the relationship between ``DynamicMap`` and ``HoloMap``, let's declare another callable to draw some shapes we will use in a new ``DynamicMap``:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def shapes(N, radius=0.5): # Positional keyword arguments are fine\n", " paths = [hv.Path([[(radius*np.sin(a), radius*np.cos(a)) \n", " for a in np.linspace(-np.pi, np.pi, n+2)]], \n", " extents=(-1,-1,1,1)) \n", " for n in range(N,N+3)]\n", " return hv.Overlay(paths)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Sampling ``DynamicMap`` from a ``HoloMap``" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "When combining a ``HoloMap`` with a ``DynamicMap``, it would be very awkward to have to match the declared dimension ``values`` of the DynamicMap with the keys of the ``HoloMap``. Fortunately you don't have to:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "holomap = hv.HoloMap({(N,r):shapes(N, r) for N in [3,4,5] for r in [0.5,0.75]}, kdims=['N', 'radius'])\n", "dmap = hv.DynamicMap(shapes, kdims=['N','radius'])\n", "holomap + dmap" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here we declared a ``DynamicMap`` without using ``redim``, but we can view its output because it is presented alongside a ``HoloMap`` which defines the available keys. This convenience is subject to three particular restrictions:\n", "\n", "\n", "* You cannot display a layout consisting of unbounded ``DynamicMaps`` only, because at least one HoloMap is needed to define the samples.\n", "* The HoloMaps provide the necessary information required to sample the DynamicMap. \n", "\n", "Note that there is one way ``DynamicMap`` is less restricted than ``HoloMap``: you can freely combine bounded ``DynamicMaps`` together in a ``Layout``, even if they don't share key dimensions." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Converting from ``DynamicMap`` to ``HoloMap``" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Above we mentioned that ``DynamicMap`` is an instance of ``HoloMap``. Does this mean it has a ``.data`` attribute?" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "dtype = type(dmap.data).__name__\n", "length = len(dmap.data)\n", "print(\"DynamicMap 'dmap' has an {dtype} .data attribute of length {length}\".format(dtype=dtype, length=length))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This is exactly the same sort of ``.data`` as the equivalent ``HoloMap``, except that its values will vary according to how much you explored the parameter space of ``dmap`` using the sliders above. In a ``HoloMap``, ``.data`` contains a defined sampling along the different dimensions, whereas in a ``DynamicMap``, the ``.data`` is simply the *cache*.\n", "\n", "The cache serves two purposes:\n", "\n", "* Avoids recomputation of an element should we revisit a particular point in the parameter space. This works well for categorical or integer dimensions, but doesn't help much when using continuous sliders for real-valued dimensions.\n", "* Records the space that has been explored with the ``DynamicMap`` for any later conversion to a ``HoloMap`` up to the allowed cache size.\n", "\n", "We can always convert *any* ``DynamicMap`` directly to a ``HoloMap`` as follows:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "hv.HoloMap(dmap)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This is in fact equivalent to declaring a HoloMap with the same parameters (dimensions, etc.) using ``dmap.data`` as input, but is more convenient. Note that the slider positions reflect those we sampled from the ``HoloMap`` in the previous section.\n", "\n", "Although creating a HoloMap this way is easy, the result is poorly controlled, as the keys in the DynamicMap cache are usually defined by how you moved the sliders around. If you instead want to specify a specific set of samples, you can easily do so by using the same key-selection semantics as for a ``HoloMap`` to define exactly which elements are to be sampled and put into the cache:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "hv.HoloMap(dmap[{(2,0.3), (2,0.6), (3,0.3), (3,0.6)}])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here we index the ``dmap`` with specified keys to return a *new* DynamicMap with those keys in its cache, which we then cast to a ``HoloMap``. This allows us to export specific contents of ``DynamicMap`` to static HTML which will display the data at the sampled slider positions.\n", "\n", "The key selection above happens to define a Cartesian product, which is one of the most common ways to sample across dimensions. Because the list of such dimension values can quickly get very large when enumerated as above, we provide a way to specify a Cartesian product directly, which also works with ``HoloMaps``. Here is an equivalent way of defining the same set of four points in that two-dimensional space:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "samples = hv.HoloMap(dmap[{2,3},{0.5,1.0}])\n", "samples" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "samples.data.keys()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The default cache size of 500 Elements is relatively high so that interactive exploration will work smoothly, but you can reduce it using the ``cache_size`` parameter if you find you are running into issues with memory consumption. A bounded ``DynamicMap`` with ``cache_size=1`` requires the least memory, but will recompute a new Element every time the sliders are moved, making it less responsive." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Converting from ``HoloMap`` to ``DynamicMap``" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We have now seen how to convert from a ``DynamicMap`` to a ``HoloMap`` for the purposes of static export, but why would you ever want to do the inverse?\n", "\n", "Although having a ``HoloMap`` to start with means it will not save you memory, converting to a ``DynamicMap`` does mean that the rendering process can be deferred until a new slider value requests an update. You can achieve this conversion using the ``Dynamic`` utility as demonstrated here by applying it to the previously defined ``HoloMap`` called ``samples``:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from holoviews.util import Dynamic\n", "dynamic = Dynamic(samples)\n", "print('After apply Dynamic, the type is a {dtype}'.format(dtype=type(dynamic).__name__))\n", "dynamic" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In this particular example, there is no real need to use ``Dynamic`` as each frame renders quickly enough. For visualizations that are slow to render, using ``Dynamic`` can result in more responsive visualizations. \n", "\n", "The ``Dynamic`` utility is very versatile and is discussed in more detail in the [Transforming Elements](./11-Transforming_Elements.ipynb) guide." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Slicing ``DynamicMaps``" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As we have seen we can either declare dimension ranges directly in the kdims or use the ``redim.range`` convenience method:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "dmap = hv.DynamicMap(shapes, kdims=['N','radius']).redim.range(N=(2,20), radius=(0.5,1.0))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The declared dimension ranges define the absolute limits allowed for exploration in this continuous, bounded DynamicMap . That said, you can use the soft_range parameter to view subregions within that range. Setting the soft_range parameter on dimensions can be done conveniently using slicing:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "sliced = dmap[4:8, :]\n", "sliced" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "Notice that N is now restricted to the range 4:8. Open slices are used to release any ``soft_range`` values, which resets the limits back to those defined by the full range:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "sliced[:, 0.8:1.0]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The ``[:]`` slice leaves the soft_range values alone and can be used as a convenient way to clone a ``DynamicMap``. Note that mixing slices with any other object type is not supported. In other words, once you use a single slice, you can only use slices in that indexing operation." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Using groupby to discretize a DynamicMap\n", "\n", "A DynamicMap also makes it easy to partially or completely discretize a function to evaluate in a complex plot. By grouping over specific dimensions that define a fixed sampling via the Dimension values parameter, the DynamicMap can be viewed as a ``GridSpace``, ``NdLayout``, or ``NdOverlay``. If a dimension specifies only a continuous range it can't be grouped over, but it may still be explored using the widgets. This means we can plot partial or completely discretized views of a parameter space easily.\n", "\n", "#### Partially discretize\n", "\n", "The implementation for all the groupby operations uses the ``.groupby`` method internally, but we also provide three higher-level convenience methods to group dimensions into an ``NdOverlay`` (``.overlay``), ``GridSpace`` (``.grid``), or ``NdLayout`` (``.layout``).\n", "\n", "Here we will evaluate a simple sine function with three dimensions, the phase, frequency, and amplitude. We assign the frequency and amplitude discrete samples, while defining a continuous range for the phase:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "xs = np.linspace(0, 2*np.pi,100)\n", "\n", "def sin(ph, f, amp):\n", " return hv.Curve((xs, np.sin(xs*f+ph)*amp))\n", "\n", "kdims=[hv.Dimension('phase', range=(0, np.pi)),\n", " hv.Dimension('frequency', values=[0.1, 1, 2, 5, 10]),\n", " hv.Dimension('amplitude', values=[0.5, 5, 10])]\n", "\n", "waves_dmap = hv.DynamicMap(sin, kdims=kdims)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Next we define the amplitude dimension to be overlaid and the frequency dimension to be gridded:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "wave_grid = waves_dmap.overlay('amplitude').grid('frequency')\n", "wave_grid.opts(fig_size=200, show_legend=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As you can see, instead of having three sliders (one per dimension), we've now laid out the frequency dimension as a discrete set of values in a grid, and the amplitude dimension as a discrete set of values in an overlay, leaving one slider for the remaining dimension (phase). This approach can help you visualize a large, multi-dimensional space efficiently, with full control over how each dimension is made visible.\n", "\n", "\n", "#### Fully discretize\n", "\n", "Given a continuous function defined over a space, we could sample it manually, but here we'll look at an example of evaluating it using the groupby method. Let's look at a spiral function with a frequency and first- and second-order phase terms. Then we define the dimension values for all the parameters and declare the DynamicMap:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "opts.defaults(opts.Path(color=hv.Palette('Blues')))\n", "\n", "def spiral_equation(f, ph, ph2):\n", " r = np.arange(0, 1, 0.005)\n", " xs, ys = (r * fn(f*np.pi*np.sin(r+ph)+ph2) for fn in (np.cos, np.sin))\n", " return hv.Path((xs, ys))\n", "\n", "spiral_dmap = hv.DynamicMap(spiral_equation, kdims=['f','ph','ph2'])\n", "spiral_dmap = spiral_dmap.redim.values(\n", " f=np.linspace(1, 10, 10),\n", " ph=np.linspace(0, np.pi, 10),\n", " ph2=np.linspace(0, np.pi, 4))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we can make use of the ``.groupby`` method to group over the frequency and phase dimensions, which we will display as part of a GridSpace by setting the ``container_type``. This leaves the second phase variable, which we assign to an NdOverlay by setting the ``group_type``:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "spiral_grid = spiral_dmap.groupby(['f', 'ph'], group_type=hv.NdOverlay, container_type=hv.GridSpace)\n", "spiral_grid.opts(\n", " opts.GridSpace(xaxis=None, yaxis=None),\n", " opts.Path(bgcolor='white', xaxis=None, yaxis=None))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This grid shows a range of frequencies `f` on the x axis, a range of the first phase variable `ph` on the `y` axis, and a range of different `ph2` phases as overlays within each location in the grid. As you can see, these techniques can help you visualize multidimensional parameter spaces compactly and conveniently.\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## DynamicMaps and normalization" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "By default, a ``HoloMap`` normalizes the display of elements using the minimum and maximum values found across the ``HoloMap``. This automatic behavior is not possible in a ``DynamicMap``, where arbitrary new elements are being generated on the fly. Consider the following examples where the arrays contained within the returned ``Image`` objects are scaled with time:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "ls = np.linspace(0, 10, 200)\n", "xx, yy = np.meshgrid(ls, ls)\n", "\n", "def cells(time):\n", " return hv.Image(time*np.sin(xx+time)*np.cos(yy+time), vdims='Intensity')\n", "\n", "dmap = hv.DynamicMap(cells, kdims='time').redim.range(time=(1,20))\n", "(dmap + dmap.redim.range(Intensity=(0,10))).opts(\n", " opts.Image(axiswise=True))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here we use ``axiswise=True`` to see the behavior of the two cases independently. We see in **A** that when only the time dimension is given a range, no automatic normalization occurs (unlike a ``HoloMap``). In **B** we see that normalization is applied, but only when the value dimension ('Intensity') range has been specified. \n", "\n", "In other words, ``DynamicMaps`` cannot support automatic normalization across their elements, but do support the same explicit normalization behavior as ``HoloMaps``. Values that are generated outside this range are simply clipped in accord with the usual semantics of explicit value dimension ranges. \n", "\n", "Note that we always have the option of casting a ``DynamicMap`` to a ``HoloMap`` in order to automatically normalize across the cached values, without needing explicit value dimension ranges." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Using DynamicMaps in your code\n", "\n", "As you can see, ``DynamicMaps`` let you use HoloViews with a very wide range of dynamic data formats and sources, making it simple to visualize ongoing processes or very large data spaces. \n", "\n", "Given unlimited computational resources, the functionality covered in this guide would match that offered by ``HoloMap`` but with fewer normalization options. ``DynamicMap`` actually enables a vast range of new possibilities for dynamic, interactive visualizations as covered in the [Responding to Events](./Responding_to_Events.ipynb) guide. Following on from that, the [Custom Interactivity](./13-Custom_Interactivity.ipynb) guide shows how you can directly interact with your plots when using the Bokeh backend." ] } ], "metadata": { "language_info": { "name": "python", "pygments_lexer": "ipython3" } }, "nbformat": 4, "nbformat_minor": 1 }
bsd-3-clause
gunan/tensorflow
tensorflow/lite/micro/examples/hello_world/create_sine_model.ipynb
1
416531
{ "nbformat": 4, "nbformat_minor": 0, "metadata": { "colab": { "name": "create_sine_model.ipynb", "version": "0.3.2", "provenance": [], "collapsed_sections": [], "toc_visible": true }, "kernelspec": { "name": "python3", "display_name": "Python 3" } }, "cells": [ { "cell_type": "markdown", "metadata": { "id": "sblS7n3zWCWV", "colab_type": "text" }, "source": [ "**Copyright 2019 The TensorFlow Authors.**" ] }, { "cell_type": "code", "metadata": { "id": "0rvUzWmoWMH5", "colab_type": "code", "colab": {} }, "source": [ "#@title Licensed under the Apache License, Version 2.0 (the \"License\");\n", "# you may not use this file except in compliance with the License.\n", "# You may obtain a copy of the License at\n", "#\n", "# https://www.apache.org/licenses/LICENSE-2.0\n", "#\n", "# Unless required by applicable law or agreed to in writing, software\n", "# distributed under the License is distributed on an \"AS IS\" BASIS,\n", "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n", "# See the License for the specific language governing permissions and\n", "# limitations under the License." ], "execution_count": 0, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "aCZBFzjClURz", "colab_type": "text" }, "source": [ "# Create and convert a TensorFlow model\n", "This notebook is designed to demonstrate the process of creating a TensorFlow model and converting it to use with TensorFlow Lite. The model created in this notebook is used in the [hello_world](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/lite/micro/examples/hello_world) sample for [TensorFlow Lite for Microcontrollers](https://www.tensorflow.org/lite/microcontrollers/overview).\n", "\n", "<table class=\"tfo-notebook-buttons\" align=\"left\">\n", " <td>\n", " <a target=\"_blank\" href=\"https://colab.research.google.com/github/tensorflow/tensorflow/blob/master/tensorflow/lite/micro/examples/hello_world/create_sine_model.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />Run in Google Colab</a>\n", " </td>\n", " <td>\n", " <a target=\"_blank\" href=\"https://github.com/tensorflow/tensorflow/blob/master/tensorflow/lite/micro/examples/hello_world/create_sine_model.ipynb\"><img src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\" />View source on GitHub</a>\n", " </td>\n", "</table>\n" ] }, { "cell_type": "markdown", "metadata": { "id": "dh4AXGuHWeu1", "colab_type": "text" }, "source": [ "## Import dependencies\n", "Our first task is to import the dependencies we need. Run the following cell to do so:" ] }, { "cell_type": "code", "metadata": { "id": "53PBJBv1jEtJ", "colab_type": "code", "outputId": "9b035753-60e5-43db-a78d-284ea9de9513", "colab": { "base_uri": "https://localhost:8080/", "height": 479 } }, "source": [ "# TensorFlow is an open source machine learning library\n", "import tensorflow as tf\n", "# Numpy is a math library\n", "import numpy as np\n", "# Matplotlib is a graphing library\n", "import matplotlib.pyplot as plt\n", "# math is Python's math library\n", "import math" ], "execution_count": 0, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "p-PuBEb6CMeo", "colab_type": "text" }, "source": [ "## Generate data\n", "Deep learning networks learn to model patterns in underlying data. In this notebook, we're going to train a network to model data generated by a [sine](https://en.wikipedia.org/wiki/Sine) function. This will result in a model that can take a value, `x`, and predict its sine, `y`.\n", "\n", "In a real world application, if you needed the sine of `x`, you could just calculate it directly. However, by training a model to do this, we can demonstrate the basic principles of machine learning.\n", "\n", "In the [hello_world](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/lite/micro/examples/hello_world) sample for [TensorFlow Lite for Microcontrollers](https://www.tensorflow.org/lite/microcontrollers/overview), we'll use this model to control LEDs that light up in a sequence.\n", "\n", "The code in the following cell will generate a set of random `x` values, calculate their sine values, and display them on a graph:" ] }, { "cell_type": "code", "metadata": { "id": "uKjg7QeMDsDx", "colab_type": "code", "outputId": "b17a43c6-eba1-4cc7-8807-14fcf5918d01", "colab": { "base_uri": "https://localhost:8080/", "height": 269 } }, "source": [ "# We'll generate this many sample datapoints\n", "SAMPLES = 1000\n", "\n", "# Set a \"seed\" value, so we get the same random numbers each time we run this\n", "# notebook\n", "np.random.seed(1337)\n", "\n", "# Generate a uniformly distributed set of random numbers in the range from\n", "# 0 to 2π, which covers a complete sine wave oscillation\n", "x_values = np.random.uniform(low=0, high=2*math.pi, size=SAMPLES)\n", "\n", "# Shuffle the values to guarantee they're not in order\n", "np.random.shuffle(x_values)\n", "\n", "# Calculate the corresponding sine values\n", "y_values = np.sin(x_values)\n", "\n", "# Plot our data. The 'b.' argument tells the library to print blue dots.\n", "plt.plot(x_values, y_values, 'b.')\n", "plt.show()" ], "execution_count": 0, "outputs": [ { "output_type": "display_data", "data": { "image/png": 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lJUugvz8aW7SoOVY5x1FhEGkCcTt5vvgizJ1b/1yaTU8PrFkTjc2f3xwrnMtR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\nb+vxVQAAAABJRU5ErkJggg==\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "tags": [] } } ] }, { "cell_type": "markdown", "metadata": { "id": "iWOlC7W_FYvA", "colab_type": "text" }, "source": [ "## Add some noise\n", "Since it was generated directly by the sine function, our data fits a nice, smooth curve.\n", "\n", "However, machine learning models are good at extracting underlying meaning from messy, real world data. To demonstrate this, we can add some noise to our data to approximate something more life-like.\n", "\n", "In the following cell, we'll add some random noise to each value, then draw a new graph:" ] }, { "cell_type": "code", "metadata": { "id": "i0FJe3Y-Gkac", "colab_type": "code", "outputId": "60b19cdd-c69c-469e-9446-b738a79c1f51", "colab": { "base_uri": "https://localhost:8080/", "height": 269 } }, "source": [ "# Add a small random number to each y value\n", "y_values += 0.1 * np.random.randn(*y_values.shape)\n", "\n", "# Plot our data\n", "plt.plot(x_values, y_values, 'b.')\n", "plt.show()" ], "execution_count": 0, "outputs": [ { "output_type": "display_data", "data": { "image/png": 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WLAiPHSlKKQQnDrpu6rEg1pa/tPZNJPh+Pg/s2FHY60dpPNyMrv5+ntr1uc9x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\nL/8OarFtQ7GUJf7GmF4AF4Q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"text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "tags": [] } } ] }, { "cell_type": "markdown", "metadata": { "id": "Up8Xk_pMH4Rt", "colab_type": "text" }, "source": [ "## Split our data\n", "We now have a noisy dataset that approximates real world data. We'll be using this to train our model.\n", "\n", "To evaluate the accuracy of the model we train, we'll need to compare its predictions to real data and check how well they match up. This evaluation happens during training (where it is referred to as validation) and after training (referred to as testing) It's important in both cases that we use fresh data that was not already used to train the model.\n", "\n", "To ensure we have data to use for evaluation, we'll set some aside before we begin training. We'll reserve 20% of our data for validation, and another 20% for testing. The remaining 60% will be used to train the model. This is a typical split used when training models.\n", "\n", "The following code will split our data and then plot each set as a different color:\n" ] }, { "cell_type": "code", "metadata": { "id": "nNYko5L1keqZ", "colab_type": "code", "outputId": "b9f9c57b-b6aa-4817-8ab4-4a2201732b9a", "colab": { "base_uri": "https://localhost:8080/", "height": 269 } }, "source": [ "# We'll use 60% of our data for training and 20% for testing. The remaining 20%\n", "# will be used for validation. Calculate the indices of each section.\n", "TRAIN_SPLIT = int(0.6 * SAMPLES)\n", "TEST_SPLIT = int(0.2 * SAMPLES + TRAIN_SPLIT)\n", "\n", "# Use np.split to chop our data into three parts.\n", "# The second argument to np.split is an array of indices where the data will be\n", "# split. We provide two indices, so the data will be divided into three chunks.\n", "x_train, x_test, x_validate = np.split(x_values, [TRAIN_SPLIT, TEST_SPLIT])\n", "y_train, y_test, y_validate = np.split(y_values, [TRAIN_SPLIT, TEST_SPLIT])\n", "\n", "# Double check that our splits add up correctly\n", "assert (x_train.size + x_validate.size + x_test.size) == SAMPLES\n", "\n", "# Plot the data in each partition in different colors:\n", "plt.plot(x_train, y_train, 'b.', label=\"Train\")\n", "plt.plot(x_test, y_test, 'r.', label=\"Test\")\n", "plt.plot(x_validate, y_validate, 'y.', label=\"Validate\")\n", "plt.legend()\n", "plt.show()\n" ], "execution_count": 0, "outputs": [ { "output_type": "display_data", "data": { "image/png": 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jCIfDLFy4kC1btnzre5x33nk899xzXHDBBaxZs4ZVq1YBsGPHDjp27EheXh7/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\nYdtguy6kfAgDIvhsus/lZ1JlybxYWcaIxxYjkz5JlBX01FZ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"text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "tags": [] } } ] }, { "cell_type": "markdown", "metadata": { "id": "t5McVnHmNiDw", "colab_type": "text" }, "source": [ "## Design a model\n", "We're going to build a model that will take an input value (in this case, `x`) and use it to predict a numeric output value (the sine of `x`). This type of problem is called a _regression_.\n", "\n", "To achieve this, we're going to create a simple neural network. It will use _layers_ of _neurons_ to attempt to learn any patterns underlying the training data, so it can make predictions.\n", "\n", "To begin with, we'll define two layers. The first layer takes a single input (our `x` value) and runs it through 16 neurons. Based on this input, each neuron will become _activated_ to a certain degree based on its internal state (its _weight_ and _bias_ values). A neuron's degree of activation is expressed as a number.\n", "\n", "The activation numbers from our first layer will be fed as inputs to our second layer, which is a single neuron. It will apply its own weights and bias to these inputs and calculate its own activation, which will be output as our `y` value.\n", "\n", "**Note:** To learn more about how neural networks function, you can explore the [Learn TensorFlow](https://codelabs.developers.google.com/codelabs/tensorflow-lab1-helloworld) codelabs.\n", "\n", "The code in the following cell defines our model using [Keras](https://www.tensorflow.org/guide/keras), TensorFlow's high-level API for creating deep learning networks. Once the network is defined, we _compile_ it, specifying parameters that determine how it will be trained:" ] }, { "cell_type": "code", "metadata": { "id": "gD60bE8cXQId", "colab_type": "code", "colab": {} }, "source": [ "# We'll use Keras to create a simple model architecture\n", "from tensorflow.keras import layers\n", "model_1 = tf.keras.Sequential()\n", "\n", "# First layer takes a scalar input and feeds it through 16 \"neurons\". The\n", "# neurons decide whether to activate based on the 'relu' activation function.\n", "model_1.add(layers.Dense(16, activation='relu', input_shape=(1,)))\n", "\n", "# Final layer is a single neuron, since we want to output a single value\n", "model_1.add(layers.Dense(1))\n", "\n", "# Compile the model using a standard optimizer and loss function for regression\n", "model_1.compile(optimizer='rmsprop', loss='mse', metrics=['mae'])" ], "execution_count": 0, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "O0idLyRLQeGj", "colab_type": "text" }, "source": [ "## Train the model\n", "Once we've defined the model, we can use our data to _train_ it. Training involves passing an `x` value into the neural network, checking how far the network's output deviates from the expected `y` value, and adjusting the neurons' weights and biases so that the output is more likely to be correct the next time.\n", "\n", "Training runs this process on the full dataset multiple times, and each full run-through is known as an _epoch_. The number of epochs to run during training is a parameter we can set.\n", "\n", "During each epoch, data is run through the network in multiple _batches_. Each batch, several pieces of data are passed into the network, producing output values. These outputs' correctness is measured in aggregate and the network's weights and biases are adjusted accordingly, once per batch. The _batch size_ is also a parameter we can set.\n", "\n", "The code in the following cell uses the `x` and `y` values from our training data to train the model. It runs for 1000 _epochs_, with 16 pieces of data in each _batch_. We also pass in some data to use for _validation_. As you will see when you run the cell, training can take a while to complete:\n", "\n" ] }, { "cell_type": "code", "metadata": { "id": "p8hQKr4cVOdE", "colab_type": "code", "outputId": "3f1a7904-ffcd-4bb7-8bbb-bcd85a132128", "colab": { "base_uri": "https://localhost:8080/" } }, "source": [ "# Train the model on our training data while validating on our validation set\n", "history_1 = model_1.fit(x_train, y_train, epochs=1000, batch_size=16,\n", " validation_data=(x_validate, y_validate))" ], "execution_count": 0, "outputs": [ { "output_type": "stream", "text": [ "Train on 600 samples, validate on 200 samples\n", "Epoch 1/1000\n", "600/600 [==============================] - 0s 412us/sample - loss: 0.5016 - mae: 0.6297 - val_loss: 0.4922 - val_mae: 0.6235\n", "Epoch 2/1000\n", "600/600 [==============================] - 0s 105us/sample - loss: 0.3905 - mae: 0.5436 - val_loss: 0.4262 - val_mae: 0.5641\n", "...\n", "Epoch 998/1000\n", "600/600 [==============================] - 0s 109us/sample - loss: 0.1535 - mae: 0.3068 - val_loss: 0.1507 - val_mae: 0.3113\n", "Epoch 999/1000\n", "600/600 [==============================] - 0s 100us/sample - loss: 0.1545 - mae: 0.3077 - val_loss: 0.1499 - val_mae: 0.3103\n", "Epoch 1000/1000\n", "600/600 [==============================] - 0s 132us/sample - loss: 0.1530 - mae: 0.3045 - val_loss: 0.1542 - val_mae: 0.3143\n" ], "name": "stdout" } ] }, { "cell_type": "markdown", "metadata": { "id": "cRE8KpEqVfaS", "colab_type": "text" }, "source": [ "## Check the training metrics\n", "During training, the model's performance is constantly being measured against both our training data and the validation data that we set aside earlier. Training produces a log of data that tells us how the model's performance changed over the course of the training process.\n", "\n", "The following cells will display some of that data in a graphical form:" ] }, { "cell_type": "code", "metadata": { "id": "CmvA-ksoln8r", "colab_type": "code", "outputId": "1b834831-81e8-4548-dd8c-f5edf2c3ff43", "colab": { "base_uri": "https://localhost:8080/", "height": 295 } }, "source": [ "# Draw a graph of the loss, which is the distance between\n", "# the predicted and actual values during training and validation.\n", "loss = history_1.history['loss']\n", "val_loss = history_1.history['val_loss']\n", "\n", "epochs = range(1, len(loss) + 1)\n", "\n", "plt.plot(epochs, loss, 'g.', label='Training loss')\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", "plt.show()" ], "execution_count": 0, "outputs": [ { "output_type": "display_data", "data": { "image/png": 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TPTOISEuP2SsoeEbGdGCoiFQVkVigJbAwiGW1YGGMMUUIWp+FqmaLyF04D00K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\nXT0QcBrMqzxRnarhQka223i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"text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "tags": [] } } ] }, { "cell_type": "markdown", "metadata": { "id": "iOFBSbPcYCN4", "colab_type": "text" }, "source": [ "## Look closer at the data\n", "The graph shows the _loss_ (or the difference between the model's predictions and the actual data) for each epoch. There are several ways to calculate loss, and the method we have used is _mean squared error_. There is a distinct loss value given for the training and the validation data.\n", "\n", "As we can see, the amount of loss rapidly decreases over the first 25 epochs, before flattening out. This means that the model is improving and producing more accurate predictions!\n", "\n", "Our goal is to stop training when either the model is no longer improving, or when the _training loss_ is less than the _validation loss_, which would mean that the model has learned to predict the training data so well that it can no longer generalize to new data.\n", "\n", "To make the flatter part of the graph more readable, let's skip the first 50 epochs:" ] }, { "cell_type": "code", "metadata": { "id": "Zo0RYroFZYIV", "colab_type": "code", "outputId": "e6841332-0541-44bb-a186-ae5b46781e51", "colab": { "base_uri": "https://localhost:8080/", "height": 295 } }, "source": [ "# Exclude the first few epochs so the graph is easier to read\n", "SKIP = 50\n", "\n", "plt.plot(epochs[SKIP:], loss[SKIP:], 'g.', label='Training loss')\n", "plt.plot(epochs[SKIP:], val_loss[SKIP:], 'b.', label='Validation loss')\n", "plt.title('Training and validation loss')\n", "plt.xlabel('Epochs')\n", "plt.ylabel('Loss')\n", "plt.legend()\n", "plt.show()" ], "execution_count": 0, "outputs": [ { "output_type": "display_data", "data": { "image/png": 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cp4rATDVSKd0zUS6m1iYAmprq3qHm/hxam8yIN2urADg3sP8q4FsRWaKqN9dw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"text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "tags": [] } } ] }, { "cell_type": "markdown", "metadata": { "id": "W4EQD-Bb8hLM", "colab_type": "text" }, "source": [ "## Further metrics\n", "From the plot, we can see that loss continues to reduce until around 600 epochs, at which point it is mostly stable. This means that there's no need to train our network beyond 600 epochs.\n", "\n", "However, we can also see that the lowest loss value is still around 0.155. This means that our network's predictions are off by an average of ~15%. In addition, the validation loss values jump around a lot, and is sometimes even higher.\n", "\n", "To gain more insight into our model's performance we can plot some more data. This time, we'll plot the _mean absolute error_, which is another way of measuring how far the network's predictions are from the actual numbers:\n" ] }, { "cell_type": "code", "metadata": { "id": "Md9E_azmpkZU", "colab_type": "code", "outputId": "39b97561-b01d-49f2-c35c-fbd8db663806", "colab": { "base_uri": "https://localhost:8080/", "height": 295 } }, "source": [ "plt.clf()\n", "\n", "# Draw a graph of mean absolute error, which is another way of\n", "# measuring the amount of error in the prediction.\n", "mae = history_1.history['mae']\n", "val_mae = history_1.history['val_mae']\n", "\n", "plt.plot(epochs[SKIP:], mae[SKIP:], 'g.', label='Training MAE')\n", "plt.plot(epochs[SKIP:], val_mae[SKIP:], 'b.', label='Validation MAE')\n", "plt.title('Training and validation mean absolute error')\n", "plt.xlabel('Epochs')\n", "plt.ylabel('MAE')\n", "plt.legend()\n", "plt.show()" ], "execution_count": 0, "outputs": [ { "output_type": "display_data", "data": { "image/png": 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SYUzCYOTIkQwbNowDDzyQwYMHc+SRRxb9GhdeeCE1NTUMGzYs9dljjz0C24dCIS699FKA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\n2XrB8/Rr3Q/Gt9H90S/4TOee+Dksbl7Ku/cQzO/QY0ghCC17buTnP/w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Lg8FgMGSlXoWF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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "tags": [] } } ] }, { "cell_type": "markdown", "metadata": { "id": "ctawd0CXAVEw", "colab_type": "text" }, "source": [ "This graph of _mean absolute error_ tells another story. We can see that training data shows consistently lower error than validation data, which means that the network may have _overfit_, or learned the training data so rigidly that it can't make effective predictions about new data.\n", "\n", "In addition, the mean absolute error values are quite high, ~0.305 at best, which means some of the model's predictions are at least 30% off. A 30% error means we are very far from accurately modelling the sine wave function.\n", "\n", "To get more insight into what is happening, we can plot our network's predictions for the training data against the expected values:" ] }, { "cell_type": "code", "metadata": { "id": "i13eVIT3B9Mj", "colab_type": "code", "outputId": "afc103e2-0beb-4a26-fe18-c0cccc6d3d2a", "colab": { "base_uri": "https://localhost:8080/", "height": 281 } }, "source": [ "# Use the model to make predictions from our validation data\n", "predictions = model_1.predict(x_train)\n", "\n", "# Plot the predictions along with to the test data\n", "plt.clf()\n", "plt.title('Training data predicted vs actual values')\n", "plt.plot(x_test, y_test, 'b.', label='Actual')\n", "plt.plot(x_train, predictions, 'r.', label='Predicted')\n", "plt.legend()\n", "plt.show()" ], "execution_count": 0, "outputs": [ { "output_type": "display_data", "data": { "image/png": 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QHjo0KwOAFY0xjAhUNa0NCAJLIl5PA6YlaF8BbOvouGeffbYWJA0NuqeihzYh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\nM+9s2wZjx7bft2oVnH8+nH6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"text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "tags": [] } } ] }, { "cell_type": "markdown", "metadata": { "id": "Wokallj1D21L", "colab_type": "text" }, "source": [ "Oh dear! The graph makes it clear that our network has learned to approximate the sine function in a very limited way. From `0 <= x <= 1.1` the line mostly fits, but for the rest of our `x` values it is a rough approximation at best.\n", "\n", "The rigidity of this fit suggests that the model does not have enough capacity to learn the full complexity of the sine wave function, so it's only able to approximate it in an overly simplistic way. By making our model bigger, we should be able to improve its performance.\n", "\n", "## Change our model\n", "To make our model bigger, let's add an additional layer of neurons. The following cell redefines our model in the same way as earlier, but with an additional layer of 16 neurons in the middle:" ] }, { "cell_type": "code", "metadata": { "id": "oW0xus6AF-4o", "colab_type": "code", "colab": {} }, "source": [ "model_2 = tf.keras.Sequential()\n", "\n", "# First layer takes a scalar input and feeds it through 16 \"neurons\". The\n", "# neurons decide whether to activate based on the 'relu' activation function.\n", "model_2.add(layers.Dense(16, activation='relu', input_shape=(1,)))\n", "\n", "# The new second layer may help the network learn more complex representations\n", "model_2.add(layers.Dense(16, activation='relu'))\n", "\n", "# Final layer is a single neuron, since we want to output a single value\n", "model_2.add(layers.Dense(1))\n", "\n", "# Compile the model using a standard optimizer and loss function for regression\n", "model_2.compile(optimizer='rmsprop', loss='mse', metrics=['mae'])" ], "execution_count": 0, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "Dv2SC409Grap", "colab_type": "text" }, "source": [ "We'll now train the new model. To save time, we'll train for only 600 epochs:" ] }, { "cell_type": "code", "metadata": { "id": "DPAUrdkmGq1M", "colab_type": "code", "outputId": "34ad91e0-229b-479c-bd65-12ad1ed1c660", "colab": { "base_uri": "https://localhost:8080/" } }, "source": [ "history_2 = model_2.fit(x_train, y_train, epochs=600, batch_size=16,\n", " validation_data=(x_validate, y_validate))" ], "execution_count": 0, "outputs": [ { "output_type": "stream", "text": [ "Train on 600 samples, validate on 200 samples\n", "Epoch 1/600\n", "600/600 [==============================] - 0s 422us/sample - loss: 0.5655 - mae: 0.6259 - val_loss: 0.4104 - val_mae: 0.5509\n", "Epoch 2/600\n", "600/600 [==============================] - 0s 111us/sample - loss: 0.3195 - mae: 0.4902 - val_loss: 0.3341 - val_mae: 0.4927\n", "...\n", "Epoch 598/600\n", "600/600 [==============================] - 0s 116us/sample - loss: 0.0124 - mae: 0.0886 - val_loss: 0.0096 - val_mae: 0.0771\n", "Epoch 599/600\n", "600/600 [==============================] - 0s 130us/sample - loss: 0.0125 - mae: 0.0900 - val_loss: 0.0107 - val_mae: 0.0824\n", "Epoch 600/600\n", "600/600 [==============================] - 0s 109us/sample - loss: 0.0124 - mae: 0.0892 - val_loss: 0.0116 - val_mae: 0.0845\n" ], "name": "stdout" } ] }, { "cell_type": "markdown", "metadata": { "id": "Mc_CQu2_IvOP", "colab_type": "text" }, "source": [ "## Evaluate our new model\n", "Each training epoch, the model prints out its loss and mean absolute error for training and validation. You can read this in the output above (note that your exact numbers may differ): \n", "\n", "```\n", "Epoch 600/600\n", "600/600 [==============================] - 0s 109us/sample - loss: 0.0124 - mae: 0.0892 - val_loss: 0.0116 - val_mae: 0.0845\n", "```\n", "\n", "You can see that we've already got a huge improvement - validation loss has dropped from 0.15 to 0.015, and validation MAE has dropped from 0.31 to 0.1.\n", "\n", "The following cell will print the same graphs we used to evaluate our original model, but showing our new training history:" ] }, { "cell_type": "code", "metadata": { "id": "SYHGswAJJgrC", "colab_type": "code", "outputId": "efcc51f6-f1f1-490a-ffba-ed283586f83e", "colab": { "base_uri": "https://localhost:8080/", "height": 851 } }, "source": [ "# Draw a graph of the loss, which is the distance between\n", "# the predicted and actual values during training and validation.\n", "loss = history_2.history['loss']\n", "val_loss = history_2.history['val_loss']\n", "\n", "epochs = range(1, len(loss) + 1)\n", "\n", "plt.plot(epochs, loss, 'g.', label='Training loss')\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", "plt.show()\n", "\n", "# Exclude the first few epochs so the graph is easier to read\n", "SKIP = 100\n", "\n", "plt.clf()\n", "\n", "plt.plot(epochs[SKIP:], loss[SKIP:], 'g.', label='Training loss')\n", "plt.plot(epochs[SKIP:], val_loss[SKIP:], 'b.', label='Validation loss')\n", "plt.title('Training and validation loss')\n", "plt.xlabel('Epochs')\n", "plt.ylabel('Loss')\n", "plt.legend()\n", "plt.show()\n", "\n", "plt.clf()\n", "\n", "# Draw a graph of mean absolute error, which is another way of\n", "# measuring the amount of error in the prediction.\n", "mae = history_2.history['mae']\n", "val_mae = history_2.history['val_mae']\n", "\n", "plt.plot(epochs[SKIP:], mae[SKIP:], 'g.', label='Training MAE')\n", "plt.plot(epochs[SKIP:], val_mae[SKIP:], 'b.', label='Validation MAE')\n", "plt.title('Training and validation mean absolute error')\n", "plt.xlabel('Epochs')\n", "plt.ylabel('MAE')\n", "plt.legend()\n", "plt.show()" ], "execution_count": 0, "outputs": [ { "output_type": "display_data", "data": { "image/png": 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ll1zCtm3b2LVrV5nLWbBggefLuXPnznTu3Nnz3Pvvv0/37t3p1q0ba9asqfBidwsXLmTQ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\nJb5/GXWceuKXLao0XuPEUtp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"text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "tags": [] } }, { "output_type": "display_data", "data": { "image/png": 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wd0XJtDJx1CHLzqIwr7D5KmdoVpoz8qs5O/OWYNYz2lfLI9kUKZuB8yO3ichE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\nbK98oo69MR6qWJIRXJEviGVV1zlRm8Vrg7oETqI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"text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "tags": [] } }, { "output_type": "display_data", "data": { "image/png": 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GnIwcUowU/EE/KUYKORk5cbvWUUcd5f7esmULTz75JKtXr6ZLly786le/ijjOICUlxf1t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\nmnWfRReAMQBvBnAYgIeIaD0z75QTEVEvgF4AmDFjRt2JjIJ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"text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "tags": [] } } ] }, { "cell_type": "markdown", "metadata": { "id": "f86dWOyZKmN9", "colab_type": "text" }, "source": [ "Great results! From these graphs, we can see several exciting things:\n", "\n", "* Our network has reached its peak accuracy much more quickly (within 200 epochs instead of 600)\n", "* The overall loss and MAE are much better than our previous network\n", "* Metrics are better for validation than training, which means the network is not overfitting\n", "\n", "The reason the metrics for validation are better than those for training is that validation metrics are calculated at the end of each epoch, while training metrics are calculated throughout the epoch, so validation happens on a model that has been trained slightly longer.\n", "\n", "This all means our network seems to be performing well! To confirm, let's check its predictions against the test dataset we set aside earlier:\n" ] }, { "cell_type": "code", "metadata": { "id": "lZfztKKyhLxX", "colab_type": "code", "outputId": "b792a12e-713d-4b07-9f8e-de0d059d5cdb", "colab": { "base_uri": "https://localhost:8080/", "height": 298 } }, "source": [ "# Calculate and print the loss on our test dataset\n", "loss = model_2.evaluate(x_test, y_test)\n", "\n", "# Make predictions based on our test dataset\n", "predictions = model_2.predict(x_test)\n", "\n", "# Graph the predictions against the actual values\n", "plt.clf()\n", "plt.title('Comparison of predictions and actual values')\n", "plt.plot(x_test, y_test, 'b.', label='Actual')\n", "plt.plot(x_test, predictions, 'r.', label='Predicted')\n", "plt.legend()\n", "plt.show()" ], "execution_count": 0, "outputs": [ { "output_type": "stream", "text": [ "200/200 [==============================] - 0s 146us/sample - loss: 0.0124 - mae: 0.0907\n" ], "name": "stdout" }, { "output_type": "display_data", "data": { "image/png": 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dUQEbN6qtTVH6yGB1+D4ALDHGtInIZGAx8K3EnUQkCAQBRowYMUhJ6511TWGW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\nM0CXSWDcB81AT3FQipRAAKm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"text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "tags": [] } } ] }, { "cell_type": "markdown", "metadata": { "id": "3h7IcvuOOS4J", "colab_type": "text" }, "source": [ "Much better! The evaluation metrics we printed show that the model has a low loss and MAE on the test data, and the predictions line up visually with our data fairly well.\n", "\n", "The model isn't perfect; its predictions don't form a smooth sine curve. For instance, the line is almost straight when `x` is between 4.2 and 5.2. If we wanted to go further, we could try further increasing the capacity of the model, perhaps using some techniques to defend from overfitting.\n", "\n", "However, an important part of machine learning is knowing when to quit, and this model is good enough for our use case - which is to make some LEDs blink in a pleasing pattern.\n", "\n", "## Convert to TensorFlow Lite\n", "We now have an acceptably accurate model in-memory. However, to use this with TensorFlow Lite for Microcontrollers, we'll need to convert it into the correct format and download it as a file. To do this, we'll use the [TensorFlow Lite Converter](https://www.tensorflow.org/lite/convert). The converter outputs a file in a special, space-efficient format for use on memory-constrained devices.\n", "\n", "Since this model is going to be deployed on a microcontroller, we want it to be as tiny as possible! One technique for reducing the size of models is called [quantization](https://www.tensorflow.org/lite/performance/post_training_quantization). It reduces the precision of the model's weights, which saves memory, often without much impact on accuracy. Quantized models also run faster, since the calculations required are simpler.\n", "\n", "The TensorFlow Lite Converter can apply quantization while it converts the model. In the following cell, we'll convert the model twice: once with quantization, once without:" ] }, { "cell_type": "code", "metadata": { "id": "1muAoUm8lSXL", "colab_type": "code", "colab": {} }, "source": [ "# Convert the model to the TensorFlow Lite format without quantization\n", "converter = tf.lite.TFLiteConverter.from_keras_model(model_2)\n", "tflite_model = converter.convert()\n", "\n", "# Save the model to disk\n", "open(\"sine_model.tflite\", \"wb\").write(tflite_model)\n", "\n", "# Convert the model to the TensorFlow Lite format with quantization\n", "converter = tf.lite.TFLiteConverter.from_keras_model(model_2)\n", "converter.optimizations = [tf.lite.Optimize.OPTIMIZE_FOR_SIZE]\n", "tflite_model = converter.convert()\n", "\n", "# Save the model to disk\n", "open(\"sine_model_quantized.tflite\", \"wb\").write(tflite_model)" ], "execution_count": 0, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "L_vE-ZDkHVxe", "colab_type": "text" }, "source": [ "## Test the converted models\n", "To prove these models are still accurate after conversion and quantization, we'll use both of them to make predictions and compare these against our test results:" ] }, { "cell_type": "code", "metadata": { "id": "-J7IKlXiYVPz", "colab_type": "code", "outputId": "0c10f56c-dbd7-4cc3-e332-30ad673769e5", "colab": { "base_uri": "https://localhost:8080/", "height": 281 } }, "source": [ "# Instantiate an interpreter for each model\n", "sine_model = tf.lite.Interpreter('sine_model.tflite')\n", "sine_model_quantized = tf.lite.Interpreter('sine_model_quantized.tflite')\n", "\n", "# Allocate memory for each model\n", "sine_model.allocate_tensors()\n", "sine_model_quantized.allocate_tensors()\n", "\n", "# Get the input and output tensors so we can feed in values and get the results\n", "sine_model_input = sine_model.tensor(sine_model.get_input_details()[0][\"index\"])\n", "sine_model_output = sine_model.tensor(sine_model.get_output_details()[0][\"index\"])\n", "sine_model_quantized_input = sine_model_quantized.tensor(sine_model_quantized.get_input_details()[0][\"index\"])\n", "sine_model_quantized_output = sine_model_quantized.tensor(sine_model_quantized.get_output_details()[0][\"index\"])\n", "\n", "# Create arrays to store the results\n", "sine_model_predictions = np.empty(x_test.size)\n", "sine_model_quantized_predictions = np.empty(x_test.size)\n", "\n", "# Run each model's interpreter for each value and store the results in arrays\n", "for i in range(x_test.size):\n", " sine_model_input().fill(x_test[i])\n", " sine_model.invoke()\n", " sine_model_predictions[i] = sine_model_output()[0]\n", "\n", " sine_model_quantized_input().fill(x_test[i])\n", " sine_model_quantized.invoke()\n", " sine_model_quantized_predictions[i] = sine_model_quantized_output()[0]\n", "\n", "# See how they line up with the data\n", "plt.clf()\n", "plt.title('Comparison of various models against actual values')\n", "plt.plot(x_test, y_test, 'bo', label='Actual')\n", "plt.plot(x_test, predictions, 'ro', label='Original predictions')\n", "plt.plot(x_test, sine_model_predictions, 'bx', label='Lite predictions')\n", "plt.plot(x_test, sine_model_quantized_predictions, 'gx', label='Lite quantized predictions')\n", "plt.legend()\n", "plt.show()\n" ], "execution_count": 0, "outputs": [ { "output_type": "display_data", "data": { "image/png": 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JGHCbOB06dGDv3r31tj+VDrJpoEb+9YQ9G6DnyW5kDf6WAen9iQo/TZepA0kf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\n6q8/GFJf/kBVSSLw8suxsXqX9vNXKk75u9JOM1b7h2Gf3sy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"text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "tags": [] } } ] }, { "cell_type": "markdown", "metadata": { "id": "jWxvLGexKv0D", "colab_type": "text" }, "source": [ "We can see from the graph that the predictions for the original model, the converted model, and the quantized model are all close enough to be indistinguishable. This means that our quantized model is ready to use!\n", "\n", "We can print the difference in file size:" ] }, { "cell_type": "code", "metadata": { "id": "6r42iBnULP4X", "colab_type": "code", "outputId": "afe526c9-498d-498e-d768-1edfbf21e870", "colab": { "base_uri": "https://localhost:8080/", "height": 68 } }, "source": [ "import os\n", "basic_model_size = os.path.getsize(\"sine_model.tflite\")\n", "print(\"Basic model is %d bytes\" % basic_model_size)\n", "quantized_model_size = os.path.getsize(\"sine_model_quantized.tflite\")\n", "print(\"Quantized model is %d bytes\" % quantized_model_size)\n", "difference = basic_model_size - quantized_model_size\n", "print(\"Difference is %d bytes\" % difference)" ], "execution_count": 0, "outputs": [ { "output_type": "stream", "text": [ "Basic model is 2656 bytes\n", "Quantized model is 2640 bytes\n", "Difference is 16 bytes\n" ], "name": "stdout" } ] }, { "cell_type": "markdown", "metadata": { "id": "C2vpZE9ZshVH", "colab_type": "text" }, "source": [ "Our quantized model is only 16 bytes smaller than the original version, which only a tiny reduction in size! At around 2.6 kilobytes, this model is already so small that the weights make up only a small fraction of the overall size, meaning quantization has little effect.\n", "\n", "More complex models have many more weights, meaning the space saving from quantization will be much higher, approaching 4x for most sophisticated models.\n", "\n", "Regardless, our quantized model will take less time to execute than the original version, which is important on a tiny microcontroller!\n", "\n", "## Write to a C file\n", "The final step in preparing our model for use with TensorFlow Lite for Microcontrollers is to convert it into a C source file. You can see an example of this format in [`hello_world/sine_model_data.cc`](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/lite/micro/examples/hello_world/sine_model_data.cc).\n", "\n", "To do so, we can use a command line utility named [`xxd`](https://linux.die.net/man/1/xxd). The following cell runs `xxd` on our quantized model and prints the output:" ] }, { "cell_type": "code", "metadata": { "id": "l4-WhtGpvb-E", "colab_type": "code", "outputId": "f975721f-bdd1-440a-93af-55f13c4c8690", "colab": { "base_uri": "https://localhost:8080/", "height": 3808 } }, "source": [ "# Install xxd if it is not available\n", "!apt-get -qq install xxd\n", "# Save the file as a C source file\n", "!xxd -i sine_model_quantized.tflite > sine_model_quantized.cc\n", "# Print the source file\n", "!cat sine_model_quantized.cc" ], "execution_count": 0, "outputs": [ { "output_type": "stream", "text": [ "unsigned char sine_model_quantized_tflite[] = {\n", " 0x18, 0x00, 0x00, 0x00, 0x54, 0x46, 0x4c, 0x33, 0x00, 0x00, 0x0e, 0x00,\n", " 0x18, 0x00, 0x04, 0x00, 0x08, 0x00, 0x0c, 0x00, 0x10, 0x00, 0x14, 0x00,\n", " 0x0e, 0x00, 0x00, 0x00, 0x03, 0x00, 0x00, 0x00, 0x10, 0x0a, 0x00, 0x00,\n", " 0xb8, 0x05, 0x00, 0x00, 0xa0, 0x05, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00,\n", " 0x0b, 0x00, 0x00, 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"markdown", "metadata": { "id": "1sqrhBLXwILt", "colab_type": "text" }, "source": [ "We can either copy and paste this output into our project's source code, or download the file using the collapsible menu on the left hand side of this Colab.\n", "\n" ] } ] }
apache-2.0
jpallas/beakerx
doc/kotlin/NativeLib.ipynb
3
1162
{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%classpath add mvn org.nd4j nd4j-native-platform 0.9.1" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import org.nd4j.linalg.factory.Nd4j\n", "Nd4j.getRandom()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%classpath add mvn com.github.fommil.netlib core 1.1.2" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "org.netlib.blas.Daxpy()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "" ] } ], "metadata": { "kernelspec": { "display_name": "Kotlin", "language": "kotlin", "name": "kotlin" }, "language_info": { "codemirror_mode": "kt", "file_extension": ".kt", "mimetype": "", "name": "Kotlin", "nbconverter_exporter": "", "version": "1.1.3" } }, "nbformat": 4, "nbformat_minor": 0 }
apache-2.0
nkmk/python-snippets
notebook/pandas_time_series_resample.ipynb
1
9063
{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import pandas as pd" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "df = pd.DataFrame({'value': range(1, 32, 2)},\n", " index=pd.date_range('2018-08-01', '2018-08-31', freq='2D'))" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " value\n", "2018-08-01 1\n", "2018-08-03 3\n", "2018-08-05 5\n", "2018-08-07 7\n", "2018-08-09 9\n", "2018-08-11 11\n", "2018-08-13 13\n", "2018-08-15 15\n", "2018-08-17 17\n", "2018-08-19 19\n", "2018-08-21 21\n", "2018-08-23 23\n", "2018-08-25 25\n", "2018-08-27 27\n", "2018-08-29 29\n", "2018-08-31 31\n" ] } ], "source": [ "print(df)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "DatetimeIndexResampler [freq=<Week: weekday=6>, axis=0, closed=right, label=right, convention=start, base=0]\n" ] } ], "source": [ "print(df.resample('W'))" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "<class 'pandas.core.resample.DatetimeIndexResampler'>\n" ] } ], "source": [ "print(type(df.resample('W')))" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " value\n", "2018-08-05 3\n", "2018-08-12 9\n", "2018-08-19 16\n", "2018-08-26 23\n", "2018-09-02 29\n" ] } ], "source": [ "print(df.resample('W').mean())" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " value\n", "2018-08-05 3\n", "2018-08-12 9\n", "2018-08-19 16\n", "2018-08-26 23\n", "2018-09-02 29\n" ] } ], "source": [ "print(df.resample('W').median())" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " value\n", "2018-08-05 9\n", "2018-08-12 27\n", "2018-08-19 64\n", "2018-08-26 69\n", "2018-09-02 87\n" ] } ], "source": [ "print(df.resample('W').sum())" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " value\n", "2018-08-05 1\n", "2018-08-12 7\n", "2018-08-19 13\n", "2018-08-26 21\n", "2018-09-02 27\n" ] } ], "source": [ "print(df.resample('W').first())" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " value\n", "2018-08-05 5\n", "2018-08-12 11\n", "2018-08-19 19\n", "2018-08-26 25\n", "2018-09-02 31\n" ] } ], "source": [ "print(df.resample('W').last())" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " value\n", "2018-08-05 3\n", "2018-08-12 3\n", "2018-08-19 4\n", "2018-08-26 3\n", "2018-09-02 3\n" ] } ], "source": [ "print(df.resample('W').count())" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " value \n", " open high low close\n", "2018-08-05 1 5 1 5\n", "2018-08-12 7 11 7 11\n", "2018-08-19 13 19 13 19\n", "2018-08-26 21 25 21 25\n", "2018-09-02 27 31 27 31\n" ] } ], "source": [ "print(df.resample('W').ohlc())" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " value\n", "2018-08-05 [1, 3, 5]\n", "2018-08-12 [7, 9, 11]\n", "2018-08-19 [13, 15, 17, 19]\n", "2018-08-26 [21, 23, 25]\n", "2018-09-02 [27, 29, 31]\n" ] } ], "source": [ "print(df.resample('W').apply(list))" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " value \n", " min max sum\n", "2018-08-05 1 5 9\n", "2018-08-12 7 11 27\n", "2018-08-19 13 19 64\n", "2018-08-26 21 25 69\n", "2018-09-02 27 31 87\n" ] } ], "source": [ "print(df.resample('W').agg(['min', 'max', 'sum']))" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " value\n", "2018-08-05 [1, 3, 5]\n", "2018-08-12 [7, 9, 11]\n", "2018-08-19 [13, 15, 17, 19]\n", "2018-08-26 [21, 23, 25]\n", "2018-09-02 [27, 29, 31]\n" ] } ], "source": [ "print(df.resample('W').apply(list))" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " value\n", "2018-07-29 [1, 3, 5]\n", "2018-08-05 [7, 9, 11]\n", "2018-08-12 [13, 15, 17, 19]\n", "2018-08-19 [21, 23, 25]\n", "2018-08-26 [27, 29, 31]\n" ] } ], "source": [ "print(df.resample('W', label='left').apply(list))" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " value\n", "2018-07-29 [1, 3]\n", "2018-08-05 [5, 7, 9, 11]\n", "2018-08-12 [13, 15, 17]\n", "2018-08-19 [19, 21, 23, 25]\n", "2018-08-26 [27, 29, 31]\n" ] } ], "source": [ "print(df.resample('W', label='left', closed='left').apply(list))" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " index value\n", "0 2018-08-01 1\n", "1 2018-08-03 3\n", "2 2018-08-05 5\n", "3 2018-08-07 7\n", "4 2018-08-09 9\n", "5 2018-08-11 11\n", "6 2018-08-13 13\n", "7 2018-08-15 15\n", "8 2018-08-17 17\n", "9 2018-08-19 19\n", "10 2018-08-21 21\n", "11 2018-08-23 23\n", "12 2018-08-25 25\n", "13 2018-08-27 27\n", "14 2018-08-29 29\n", "15 2018-08-31 31\n" ] } ], "source": [ "df_reset = df.reset_index()\n", "print(df_reset)" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " value\n", "index \n", "2018-08-05 9\n", "2018-08-12 27\n", "2018-08-19 64\n", "2018-08-26 69\n", "2018-09-02 87\n" ] } ], "source": [ "print(df_reset.resample('W', on='index').sum())" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.7" } }, "nbformat": 4, "nbformat_minor": 2 }
mit
LorenzoBi/courses
TSAADS/tutorial 7/.ipynb_checkpoints/Untitled-checkpoint.ipynb
1
99619
{ "cells": [ { "cell_type": "code", "execution_count": 27, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "from scipy.io import loadmat\n", "from numpy.linalg import inv\n", "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "dict_keys(['__header__', '__version__', '__globals__', 'A', 'B', 'C', 'Gamma', 'L0', 'Sigma', 'mu0', 'u', 'x', 'z'])" ] }, "execution_count": 35, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data = loadmat('data_files/Tut7_file1.mat')\n", "locals().update(data)\n", "data.keys()" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [], "source": [ "p, T = z.shape" ] }, { "cell_type": "code", "execution_count": 127, "metadata": {}, "outputs": [], "source": [ "mu = np.zeros(z.shape)\n", "K = np.zeros((4, 4, T))\n", "V = np.zeros((4, 4, T))\n", "L = np.zeros((4, 4, T))\n", "\n", "K[...,0] = L0.dot(B.T.dot(inv(B.dot(L0.dot(B.T)) + Gamma)))\n", "mu[..., [0]] = A.dot(mu0) + K[..., 0].dot(x[:, [0]] - B.dot(A.dot(mu0))) + C.dot(u[..., [0]])\n", "V[..., 0] = (np.eye(4) - K[..., 0].dot(B)).dot(L0)\n", "L[..., 0] = A.dot(V[..., 0].dot(A.T)) + Sigma" ] }, { "cell_type": "code", "execution_count": 128, "metadata": {}, "outputs": [], "source": [ "for t in range(1, T):\n", " K[...,t] = L[..., t - 1].dot(B.T.dot(inv(B.dot(L[..., t - 1].dot(B.T)) + Gamma)))\n", " mu[..., [t]] = A.dot(mu[..., [t-1]]) + K[..., t].dot(x[:, [t]] - B.dot(A.dot(mu[..., [t-1]]))) + C.dot(u[..., [t]])\n", " V[..., t] = (np.eye(4) - K[..., t].dot(B)).dot(L[..., t-1])\n", " L[..., t] = A.dot(V[..., t].dot(A.T)) + Sigma" ] }, { "cell_type": "code", "execution_count": 129, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[<matplotlib.lines.Line2D at 0x7fc3cad14e80>,\n", " <matplotlib.lines.Line2D at 0x7fc3cac96908>,\n", " <matplotlib.lines.Line2D at 0x7fc3cac96ac8>,\n", " <matplotlib.lines.Line2D at 0x7fc3cac96c88>]" ] }, "execution_count": 129, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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OBes/9lxElc/I6eLi5FqAP/8kef58htvUYNuF+wxtVJrPmpTJOrtqXb8uB5uP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\nDeWKWvHL8btk1w/gKjm8jaQkGDtWzko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"text/plain": [ "<matplotlib.figure.Figure at 0x7fc3cad14e48>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.plot(mu.T)\n", "plt.plot(z.T, color='red')" ] }, { "cell_type": "code", "execution_count": 130, "metadata": {}, "outputs": [], "source": [ "V_tilde = np.zeros(V.shape)\n", "mu_tilde = np.zeros(mu.shape)\n", "V_tilde[..., -1] = V[..., -1]\n", "mu_tilde[..., [-1]] = mu_tilde[..., [-1]]\n", "\n", "for t in range(T - 2, -1, -1):\n", " #print(t)\n", " W = V[..., t].dot(A.T.dot(inv(L[..., t])))\n", " V_tilde[..., t] = V[..., t] + W.dot(V_tilde[..., t+1] - L[..., t]).dot(W.T)\n", " mu_tilde[..., [t]] = mu[..., [t]] + V[..., t].dot(A.T.dot(inv(L[..., t]).dot(mu_tilde[..., [t+1]] - A.dot(mu[..., [t]])) ))" ] }, { "cell_type": "code", "execution_count": 131, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[<matplotlib.lines.Line2D at 0x7fc3cad7e080>,\n", " <matplotlib.lines.Line2D at 0x7fc3cabd0ba8>,\n", " <matplotlib.lines.Line2D at 0x7fc3cabd0d68>,\n", " <matplotlib.lines.Line2D at 0x7fc3cabd0f28>]" ] }, "execution_count": 131, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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1BLi+r7ZX9XsFru19+fvZ26t9Jy5fxvbTBczsWJUbEXF8cSCVrREN0OUzsr+4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\n6PAwwcgQDA4RjI0go2PI6CiSG0PGCji5HFIo4hS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"text/plain": [ "<matplotlib.figure.Figure at 0x7fc3cad7e470>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.plot(mu_tilde.T)\n", "plt.plot(z.T, color='red')" ] }, { "cell_type": "code", "execution_count": 132, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[<matplotlib.lines.Line2D at 0x7fc3caafd898>,\n", " <matplotlib.lines.Line2D at 0x7fc3caafda58>,\n", " <matplotlib.lines.Line2D at 0x7fc3caafdc50>,\n", " <matplotlib.lines.Line2D at 0x7fc3caafde48>]" ] }, "execution_count": 132, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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Yf790IrNHJ3de0e+DF26H1ga4/kmIiOm9IAc4u9IwxgwKWwuruW/lTi6aNIzb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\nDqTh8MdtAAAAAElFTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7fc3cac05c50>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.plot((mu - z).T ** 2)\n" ] }, { "cell_type": "code", "execution_count": 133, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([ 0.96831871, 1.15059285, 126.33713054, 507.65067727])" ] }, "execution_count": 133, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.mean((mu_tilde - z).T ** 2, axis=0) / np.mean((mu - z).T ** 2, axis=0)\n" ] }, { "cell_type": "code", "execution_count": 134, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[<matplotlib.lines.Line2D at 0x7fc3caa94f98>,\n", " <matplotlib.lines.Line2D at 0x7fc3caa9e198>,\n", " <matplotlib.lines.Line2D at 0x7fc3caa9e390>,\n", " <matplotlib.lines.Line2D at 0x7fc3caa9e588>]" ] }, "execution_count": 134, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7fc3cab42588>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.plot((mu_tilde - z).T ** 2)" ] }, { "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 }
mit
LucaCanali/Miscellaneous
Spark_Physics/Dimuon_mass_spectrum/8.Dimuon_mass_spectrum_histogram_Spark_DataFrame_ORC_vectorized-Large_SCALE.ipynb
1
74235
{ "cells": [ { "cell_type": "markdown", "id": "c97f8c4d", "metadata": {}, "source": [ "# Histogram of the Dimuon Mass Spectrum\n", "\n", "This implements the dimuon mass spectrum analysis, a \"Hello World!\" example for data analysis in High Energy Physics. It is intended as a technology demonstrator for the use Apache Spark for High Energy Physics.\n", "\n", "The workload and data:\n", " - The input data is a series of candidate muon events. \n", " - The job output is a histogram of the dimuon mass spectrum, where several peaks (resonances) can be identified corresponding to well-know particles (e.g. the Z boson at 91 Gev).\n", " - The computation is based on https://root.cern.ch/doc/master/df102__NanoAODDimuonAnalysis_8C.html and CERN open data from the CMS collaboration linked there. \n", " - See also https://github.com/LucaCanali/Miscellaneous/tree/master/Spark_Physics\n", " \n", "Author and contact: [email protected] \n", "January, 2022" ] }, { "cell_type": "code", "execution_count": 1, "id": "81e6fab6", "metadata": {}, "outputs": [], "source": [ "# install PySpark if needed\n", "# !pip install pyspark\n", "\n", "# install sparkhistogram\n", "!pip install sparkhistogram\n" ] }, { "cell_type": "markdown", "id": "22d54a3d", "metadata": {}, "source": [ "## Dimuon mass spectrum calculation with Spark DataFrame API" ] }, { "cell_type": "code", "execution_count": null, "id": "bee0e073", "metadata": { "scrolled": false }, "outputs": [], "source": [ "# Start the Spark Session\n", "# This uses a cluster to scale out computation\n", "# It assumes a setup with a YARN cluster\n", "# With minor changes this will work also for Spark on Kubernetes or standalone cluster\n", "# The use of findspark is optional\n", "\n", "import findspark\n", "# findspark.init(\"/home/luca/Spark/spark-3.2.1-bin-hadoop3.2\")\n", "findspark.init(\"/home/luca/Spark/spark-3.3.0-bin-hadoop3\")\n", "\n", "from pyspark.sql import SparkSession\n", "spark = (SparkSession.builder\n", " .appName(\"dimuon mass\")\n", " .master(\"yarn\")\n", " .config(\"spark.driver.memory\", \"1g\")\n", " .config(\"spark.executor.memory\", \"8g\")\n", " .config(\"spark.executor.cores\", 10)\n", " .config(\"spark.executor.instances\", 20)\n", " .config(\"spark.dynamicAllocation.enabled\", \"false\")\n", " .config(\"spark.sql.orc.enableNestedColumnVectorizedReader\", \"true\")\n", " .config(\"spark.sql.files.maxPartitionBytes\", 256*1024*1024)\n", " .config(\"spark.shuffle.service.enabled\", \"false\")\n", " .getOrCreate()\n", " )\n" ] }, { "cell_type": "code", "execution_count": 3, "id": "ab108ade", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "root\n", " |-- nMuon: long (nullable = true)\n", " |-- Muon_pt: array (nullable = true)\n", " | |-- element: float (containsNull = true)\n", " |-- Muon_eta: array (nullable = true)\n", " | |-- element: float (containsNull = true)\n", " |-- Muon_phi: array (nullable = true)\n", " | |-- element: float (containsNull = true)\n", " |-- Muon_mass: array (nullable = true)\n", " | |-- element: float (containsNull = true)\n", " |-- Muon_charge: array (nullable = true)\n", " | |-- element: integer (containsNull = true)\n", "\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[Stage 2:> (0 + 1) / 1]\r" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Number of events: 6461743365\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "\r", " \r" ] } ], "source": [ "# Read data with the muon candidate events\n", "# download data as detailed at https://github.com/LucaCanali/Miscellaneous/tree/master/Spark_Physics\n", "# It has been tested with a dataset of 200 GB (6.5 billion events)\n", "\n", "path = \"/project/spark/HEP/\"\n", "df_muons = spark.read.orc(path + \"CMSOpenDataDimuon_large.orc\")\n", "\n", "df_muons.printSchema()\n", "print(f\"Number of events: {df_muons.count()}\")" ] }, { "cell_type": "code", "execution_count": 4, "id": "e481cb25", "metadata": {}, "outputs": [], "source": [ "# Apply filters to the input data\n", "# - select only events with 2 muons\n", "# - select only events where the 2 muons have opposite charge\n", "\n", "df_muons = df_muons.filter(\"nMuon == 2\").filter(\"Muon_charge[0] != Muon_charge[1]\")\n" ] }, { "cell_type": "code", "execution_count": 5, "id": "1755d9e8", "metadata": {}, "outputs": [], "source": [ "# This computes the 4-vectors sum for the 2 moun system\n", "# using formulas from special relativity, in the limit E >> muons rest mass\n", "# see also http://edu.itp.phys.ethz.ch/hs10/ppp1/2010_11_02.pdf\n", "# and https://en.wikipedia.org/wiki/Invariant_mass\n", "\n", "df_with_dimuonmass = df_muons.selectExpr(\"\"\"\n", " sqrt(2 * Muon_pt[0] * Muon_pt[1] * \n", " ( cosh(Muon_eta[0] - Muon_eta[1]) - cos(Muon_phi[0] - Muon_phi[1]) )\n", " ) as Dimuon_mass\"\"\")" ] }, { "cell_type": "code", "execution_count": 6, "id": "d8b7e337", "metadata": {}, "outputs": [], "source": [ "# This defines the DataFrame transformation to compute the Dimuon mass spectrum\n", "# The result is a histogram with (energy) bin values and event counts foreach bin\n", "\n", "# Requires sparkhistogram\n", "# See https://github.com/LucaCanali/Miscellaneous/blob/master/Spark_Notes/Spark_DataFrame_Histograms.md \n", "from sparkhistogram import computeHistogram\n", "\n", "# histogram parameters\n", "min_val = 0.25\n", "max_val = 300\n", "num_bins = 30000\n", "\n", "# use the helper function computeHistogram in the package sparkhistogram\n", "\n", "histogram_data = computeHistogram(df_with_dimuonmass, \"Dimuon_mass\", min_val, max_val, num_bins) " ] }, { "cell_type": "code", "execution_count": 7, "id": "703b649a", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ " \r" ] }, { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 518 ms, sys: 64.7 ms, total: 582 ms\n", "Wall time: 32.6 s\n" ] } ], "source": [ "# The action toPandas() here triggers the computation.\n", "# Histogram data is fetched into the driver as a Pandas Dataframe.\n", "\n", "%time histogram_data_pandas=histogram_data.toPandas()\n" ] }, { "cell_type": "code", "execution_count": 8, "id": "927ff9c0", "metadata": { "scrolled": false }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 1008x720 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import matplotlib.pyplot as plt \n", "plt.style.use('seaborn-darkgrid')\n", "plt.rcParams.update({'font.size': 20, 'figure.figsize': [14,10]})\n", "\n", "f, ax = plt.subplots()\n", "\n", "# cut the first and last bin\n", "x = histogram_data_pandas[\"value\"]\n", "y = histogram_data_pandas[\"count\"]\n", "\n", "# line plot\n", "ax.plot(x, y, '-')\n", "\n", "# the plot is in log-log axis to better show the peaks\n", "ax.set_xscale(\"log\")\n", "ax.set_yscale(\"log\")\n", "ax.set_xlim(min_val, max_val)\n", "#ax.set_ylim(1, 7e7)\n", "\n", "ax.set_xlabel('$m_{dimuon}$ (GeV)')\n", "ax.set_ylabel('Number of Events')\n", "ax.set_title(\"Distribution of the Dimuon Mass Spectrum\")\n", "\n", "# Label for the resonances spectrum peaks\n", "txt_opts = {'horizontalalignment': 'center',\n", " 'verticalalignment': 'center',\n", " 'transform': ax.transAxes}\n", "\n", "plt.text(0.85, 0.75, 'Z', **txt_opts)\n", "plt.text(0.55, 0.77, r\"$\\Upsilon$(1,2,3S)\", **txt_opts)\n", "plt.text(0.37, 0.95, r\"J/$\\Psi$\", **txt_opts)\n", "plt.text(0.40, 0.77, r\"$\\Psi$'\", **txt_opts)\n", "plt.text(0.22, 0.80, r\"$\\phi$\", **txt_opts)\n", "plt.text(0.16, 0.83, r\"$\\rho,\\omega$\", **txt_opts)\n", "plt.text(0.11, 0.78, r\"$\\eta$\", **txt_opts);\n", "\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 9, "id": "1e27b92f", "metadata": {}, "outputs": [], "source": [ "spark.stop()" ] }, { "cell_type": "code", "execution_count": null, "id": "39344748", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.12" } }, "nbformat": 4, "nbformat_minor": 5 }
apache-2.0
marcosag90/Single-Pile_Nim_Game
.ipynb_checkpoints/Single-Pile Nim Game. -checkpoint.ipynb
1
6393
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Single Pile Nim Game - SCU AMTH 377/ COEN 279" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Group: Minh Do, Troy Ibanez, Angela Lam, Marcos Alvarez, Susan Lie." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This is an implementation of a Wining_Move function for a Single-Pile Nim Game. \n", "#### Overview\n", "* The game starts with a pile of N nims. Two players take turns to remove nims from the pile. The one who removes the last nim wins.\n", "* On the first move, the player who starts can remove a maximum of N-1 nims.\n", "* After that, players can take a maximum of twice the number of nims the previous player took.\n", "* 0 move is not allowed.\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Possible solutions\n", "There are several aproaches to this game.\n", "* Fibbonaci Sequence: In order for Player 1 to win, she must try to leave Player 2 with a number of nims that is in the Fibbonaci sequence. To ensure that, she must find all the previous numbers of the fibbonaci sequence up to N, and try to make Player 2 stay in those numbers. To do that, Player 1 should divide the problem into smaller problems, and look at the difference between N and its closest smaller Fib number as a new pile. The new pile would have to be also divided Fibo numbers, and so on. This is a recursive solution. It is a bottom-up solution.\n", "\n", "* Dynamic Programming: This approach makes no assumptions whatsoever. The main idea behind this algorithm is to explore all possible ways to leave the other player without a Winning_Move. The recursion in this algorithm comes into play by recursively solving smaller problems. It is an up-bottom solution. \n", "\n", "In our solution, we will let the machine make its own decissions independently with no a-priori information of a winning strategy, but just the rules of the game. Thus, we will implement the Dynamic Programming approach.\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Solution:\n", "* N( i ) = Pile of items\n", "* q( i ) = Max allowable items to pick\n", "* W(i) = Move(N(i), q(i), i) is winning pick\n", "* Pick W(i) such that N(i) - 2 * W(i) > 0 and place the player in the stable winning position throughout the subsequent turns\n" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "max_moves = {} #makes a hashmap for easy lookup (so program doesn't take forever)\n", "\n", "def winning_move(pile, n_max_move):\n", " if pile <= n_max_move: #if your max move is the same as your pile, you win\n", " return pile\n", " for move in range(n_max_move, 0, -1): #cycle through all your options\n", " new_pile = pile - move #your opponent's pile is based on how many you take\n", " if new_pile <= 2 * move: \n", " new_max = new_pile #the max your opponent can take is either their whole pile (if < 2 * how many you took)\n", " else:\n", " new_max = 2 * move #or 2 * how many you took\n", " if (new_pile, new_max) in max_moves: #if we've done this combo before\n", " opponent_move = max_moves[new_pile, new_max] #read the value from the hashmap\n", " else:\n", " opponent_move = winning_move(new_pile, new_max) #else, call winningmove for your opponent\n", " max_moves[new_pile, new_max] = opponent_move #add the value to the hashmap\n", " if opponent_move == 0: #if your opponent has no winning moves\n", " return move #return the number of chips you took\n", " return 0 #if you've gone through all your options and your opponent always had winning moves, you don't have a winning move\n", " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Testing: (Asumption that both players use the same strategy)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Starting pile: is 22, Player1 max-move is 21, and winning-number is 1\n", "\t pile: is 21, Player2 max-move is 2, and winning-number is 0. So Player2 has no moves that would lead to a win.\n" ] } ], "source": [ "def main(ipile):\n", " imaxMoves = ipile-1\n", " m = winning_move(ipile, imaxMoves)\n", " print \"Starting pile: is %d, Player1 max-move is %d, and winning-number is %d\" %(ipile, imaxMoves, m)\n", " if (m == 0):\n", " print \"\\t Winning-number is %d. Player 1 has no movements that would lead to a win.\" %(m)\n", " return 0\n", " iturn = 1\n", " while (ipile > 0 and imaxMoves > 0 and ipile > m):\n", " iturn=iturn+1\n", " if (iturn % 2 == 0):\n", " player = 2\n", " else:\n", " player = 1\n", " ipile = ipile - m\n", " imaxMoves = m * 2\n", " m = winning_move(ipile, imaxMoves)\n", " if (m == 0):\n", " print \"\\t pile: is %d, Player%d max-move is %d, and winning-number is %d. So Player%d has no moves that would lead to a win.\" %(ipile, player, imaxMoves, m, player)\n", " break\n", " print \"\\t pile: is %d, Player%d max-move is %d, and winning-number is %d\" %(ipile, player, imaxMoves, m)\n", "\n", "\n", "main(22)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.13" } }, "nbformat": 4, "nbformat_minor": 2 }
unlicense
rkuchan/Tax-Calculator
docs/notebooks/Behavioral_example.ipynb
4
3874
{ "metadata": { "name": "", "signature": "sha256:9761d9a35eb48bb254a5f8448fe1d13d31a7874b5b219aa6cd068a60829260aa" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "heading", "level": 1, "metadata": {}, "source": [ "Incorporating micro-feedback effects" ] }, { "cell_type": "code", "collapsed": false, "input": [ "import sys\n", "sys.path.append(\"../../\")\n", "import taxcalc\n", "from taxcalc import *\n", "import pandas as pd\n", "import numpy as np" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 1 }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "Start by creating a Calculator for Plan X and a calculator for Plan Y. " ] }, { "cell_type": "code", "collapsed": false, "input": [ "# Create a Records object for Plan X and Plan Y\n", "records_x = Records(\"../../puf.csv\")\n", "records_y = Records(\"../../puf.csv\")\n", "# Create a Parameters object for Plan X and Plan Y\n", "params_x = Parameters(start_year=2013)\n", "params_y = Parameters(start_year=2013)\n", "# Create two Calculators\n", "calcX = Calculator(parameters=params_x, records=records_x)\n", "calcY = Calculator(parameters=params_y, records=records_y)\n" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "You loaded data for 2008.\n", "Your data have beeen extrapolated to 2013." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "You loaded data for 2008.\n", "Your data have beeen extrapolated to 2013." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n" ] } ], "prompt_number": 3 }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "Increase the top marginal tax rate by 10 percentage points " ] }, { "cell_type": "code", "collapsed": false, "input": [ "# Modify the relevant parameter for the Plan-Y Calculator.\n", "calcY.II_rt7 = calcY.II_rt7 + .1\n", "# Demonstrate that Plan X and Plan Y calculators are indeed different. \n", "print(calcX.II_rt7)\n", "print(calcY.II_rt7)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "0.396\n", "0.496\n" ] } ], "prompt_number": 6 }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "Update taxpayers' income to account for the rate hike with our behavioral effects calculator" ] }, { "cell_type": "code", "collapsed": false, "input": [ "# Call the behavioral effects calculator and create a new Plan Y Calculator obect. \n", "calcY_behavioral = behavior(calcX, calcY)\n", "# Demonstrate that taxpayers' income was affected by the tax change.\n", "print(calcY.e00200.sum())\n", "print(calcY_behavioral.e00200.sum())" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "41460794504.8\n", "40996640481.9\n" ] } ], "prompt_number": 10 }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [] } ], "metadata": {} } ] }
mit
fonnesbeck/ngcm_pandas_2016
notebooks/1.4 - Pandas Best Practices.ipynb
1
41487
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Idomatic Pandas\n", "\n", "> Q: How do I make my pandas code faster with parallelism?\n", "\n", "> A: You don’t need parallelism, you can use Pandas better.\n", "\n", "> -- Matthew Rocklin\n", "\n", "Now that we have been exposed to the basic functionality of pandas, lets explore some more advanced features that will be useful when addressing more complex data management tasks.\n", "\n", "As most statisticians/data analysts will admit, often the lion's share of the time spent implementing an analysis is devoted to preparing the data itself, rather than to coding or running a particular model that uses the data. This is where Pandas and Python's standard library are beneficial, providing high-level, flexible, and efficient tools for manipulating your data as needed.\n", "\n", "As you may already have noticed, there are sometimes mutliple ways to achieve the same goal using pandas. Importantly, some approaches are better than others, in terms of performance, readability and ease of use. We will cover some important ways of maximizing your pandas efficiency." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%matplotlib inline\n", "import pandas as pd\n", "import numpy as np\n", "import matplotlib.pyplot as plt" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Reshaping DataFrame objects\n", "\n", "In the context of a single DataFrame, we are often interested in re-arranging the layout of our data. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This dataset in from Table 6.9 of [Statistical Methods for the Analysis of Repeated Measurements](http://www.amazon.com/Statistical-Methods-Analysis-Repeated-Measurements/dp/0387953701) by Charles S. Davis, pp. 161-163 (Springer, 2002). These data are from a multicenter, randomized controlled trial of botulinum toxin type B (BotB) in patients with cervical dystonia (spasmodic torticollis) from nine U.S. sites.\n", "\n", "* Randomized to placebo (N=36), 5000 units of BotB (N=36), 10,000 units of BotB (N=37)\n", "* Response variable: total score on Toronto Western Spasmodic Torticollis Rating Scale (TWSTRS), measuring severity, pain, and disability of cervical dystonia (high scores mean more impairment)\n", "* TWSTRS measured at baseline (week 0) and weeks 2, 4, 8, 12, 16 after treatment began" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "cdystonia = pd.read_csv(\"../data/cdystonia.csv\", index_col=None)\n", "cdystonia.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This dataset includes **repeated measurements** of the same individuals (longitudinal data). Its possible to present such information in (at least) two ways: showing each repeated measurement in their own row, or in multiple columns representing multiple measurements.\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The `stack` method **rotates** the data frame so that columns are represented in rows:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "stacked = cdystonia.stack()\n", "stacked" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Have a peek at the structure of the index of the stacked data (and the data itself).\n", "\n", "To complement this, `unstack` pivots from rows back to columns." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "stacked.unstack().head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Exercise\n", "\n", "Which columns uniquely define a row? Create a DataFrame called `cdystonia2` with a hierarchical index based on these columns." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Write your answer here" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If we want to transform this data so that repeated measurements are in columns, we can `unstack` the `twstrs` measurements according to `obs`." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "twstrs_wide = cdystonia2['twstrs'].unstack('obs')\n", "twstrs_wide.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can now **merge** these reshaped outcomes data with the other variables to create a **wide format** DataFrame that consists of one row for each patient." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "cdystonia_wide = (cdystonia[['patient','site','id','treat','age','sex']]\n", " .drop_duplicates()\n", " .merge(twstrs_wide, right_index=True, left_on='patient', how='inner'))\n", "cdystonia_wide.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A slightly cleaner way of doing this is to set the patient-level information as an index before unstacking:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "(cdystonia.set_index(['patient','site','id','treat','age','sex','week'])['twstrs']\n", " .unstack('week').head())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To convert our \"wide\" format back to long, we can use the `melt` function, appropriately parameterized. This function is useful for `DataFrame`s where one\n", "or more columns are identifier variables (`id_vars`), with the remaining columns being measured variables (`value_vars`). The measured variables are \"unpivoted\" to\n", "the row axis, leaving just two non-identifier columns, a *variable* and its corresponding *value*, which can both be renamed using optional arguments." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "pd.melt(cdystonia_wide, id_vars=['patient','site','id','treat','age','sex'], \n", " var_name='obs', value_name='twsters').head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This illustrates the two formats for longitudinal data: **long** and **wide** formats. Its typically better to store data in long format because additional data can be included as additional rows in the database, while wide format requires that the entire database schema be altered by adding columns to every row as data are collected.\n", "\n", "The preferable format for analysis depends entirely on what is planned for the data, so it is imporant to be able to move easily between them." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Method chaining\n", "\n", "In the DataFrame reshaping section above, you probably noticed how several methods were strung together to produce a wide format table:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "(cdystonia[['patient','site','id','treat','age','sex']]\n", " .drop_duplicates()\n", " .merge(twstrs_wide, right_index=True, left_on='patient', how='inner')\n", " .head())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This approach of seqentially calling methods is called **method chaining**, and despite the fact that it creates very long lines of code that must be properly justified, it allows for the writing of rather concise and readable code. Method chaining is possible because of the pandas convention of returning copies of the results of operations, rather than in-place operations. This allows methods from the returned object to be immediately called, as needed, rather than assigning the output to a variable that might not otherwise be used. For example, without method chaining we would have done the following:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "cdystonia_subset = cdystonia[['patient','site','id','treat','age','sex']]\n", "cdystonia_complete = cdystonia_subset.drop_duplicates()\n", "cdystonia_merged = cdystonia_complete.merge(twstrs_wide, right_index=True, left_on='patient', how='inner')\n", "cdystonia_merged.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This necessitates the creation of a slew of intermediate variables that we really don't need.\n", "\n", "Let's transform another dataset using method chaining. The `measles.csv` file contains de-identified cases of measles from an outbreak in Sao Paulo, Brazil in 1997. The file contains rows of individual records:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "measles = pd.read_csv(\"../data/measles.csv\", index_col=0, encoding='latin-1', parse_dates=['ONSET'])\n", "measles.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The goal is to summarize this data by age groups and bi-weekly period, so that we can see how the outbreak affected different ages over the course of the outbreak.\n", "\n", "The best approach is to build up the chain incrementally. We can begin by generating the age groups (using `cut`) and grouping by age group and the date (`ONSET`):" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "(measles.assign(AGE_GROUP=pd.cut(measles.YEAR_AGE, [0,5,10,15,20,25,30,35,40,100], right=False))\n", " .groupby(['ONSET', 'AGE_GROUP']))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "What we then want is the number of occurences in each combination, which we can obtain by checking the `size` of each grouping:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "(measles.assign(AGE_GROUP=pd.cut(measles.YEAR_AGE, [0,5,10,15,20,25,30,35,40,100], right=False))\n", " .groupby(['ONSET', 'AGE_GROUP'])\n", " .size()).head(10)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This results in a hierarchically-indexed `Series`, which we can pivot into a `DataFrame` by simply unstacking:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "(measles.assign(AGE_GROUP=pd.cut(measles.YEAR_AGE, [0,5,10,15,20,25,30,35,40,100], right=False))\n", " .groupby(['ONSET', 'AGE_GROUP'])\n", " .size()\n", " .unstack()).head(5)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now, fill replace the missing values with zeros:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "(measles.assign(AGE_GROUP=pd.cut(measles.YEAR_AGE, [0,5,10,15,20,25,30,35,40,100], right=False))\n", " .groupby(['ONSET', 'AGE_GROUP'])\n", " .size()\n", " .unstack()\n", " .fillna(0)).head(5)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Finally, we want the counts in 2-week intervals, rather than as irregularly-reported days, which yields our the table of interest:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "case_counts_2w = (measles.assign(AGE_GROUP=pd.cut(measles.YEAR_AGE, [0,5,10,15,20,25,30,35,40,100], right=False))\n", " .groupby(['ONSET', 'AGE_GROUP'])\n", " .size()\n", " .unstack()\n", " .fillna(0)\n", " .resample('2W')\n", " .sum())\n", "\n", "case_counts_2w" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "From this, it is easy to create meaningful plots and conduct analyses:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "case_counts_2w.plot(cmap='hot')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Pivoting\n", "\n", "The `pivot` method allows a DataFrame to be transformed easily between long and wide formats in the same way as a pivot table is created in a spreadsheet. It takes three arguments: `index`, `columns` and `values`, corresponding to the DataFrame index (the row headers), columns and cell values, respectively.\n", "\n", "For example, we may want the `twstrs` variable (the response variable) in wide format according to patient, as we saw with the unstacking method above:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "cdystonia.pivot(index='patient', columns='obs', values='twstrs').head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Exercise\n", "\n", "Try pivoting the `cdystonia` DataFrame without specifying a variable for the cell values:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Write your answer here" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A related method, `pivot_table`, creates a spreadsheet-like table with a hierarchical index, and allows the values of the table to be populated using an arbitrary **aggregation function**." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "cdystonia.pivot_table(index=['site', 'treat'], columns='week', values='twstrs', \n", " aggfunc=max).head(20)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For a simple **cross-tabulation** of group frequencies, the `crosstab` function (not a method) aggregates counts of data according to factors in rows and columns. The factors may be hierarchical if desired." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "pd.crosstab(cdystonia.sex, cdystonia.site)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Data transformation\n", "\n", "There are a slew of additional operations for DataFrames that we would collectively refer to as **transformations** which include tasks such as:\n", "\n", "- removing duplicate values\n", "- replacing values\n", "- grouping values." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Dealing with duplicates\n", "\n", "We can easily identify and remove duplicate values from `DataFrame` objects. For example, say we want to remove ships from our `vessels` dataset that have the same name:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "vessels = pd.read_csv('../data/AIS/vessel_information.csv')\n", "vessels.tail(10)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "vessels.duplicated(subset='names').tail(10)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "These rows can be removed using `drop_duplicates`" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "scrolled": false }, "outputs": [], "source": [ "vessels.drop_duplicates(['names']).tail(10)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Value replacement\n", "\n", "Frequently, we get data columns that are encoded as strings that we wish to represent numerically for the purposes of including it in a quantitative analysis. For example, consider the treatment variable in the cervical dystonia dataset:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "cdystonia.treat.value_counts()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A logical way to specify these numerically is to change them to integer values, perhaps using \"Placebo\" as a baseline value. If we create a dict with the original values as keys and the replacements as values, we can pass it to the `map` method to implement the changes." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "treatment_map = {'Placebo': 0, '5000U': 1, '10000U': 2}" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "cdystonia['treatment'] = cdystonia.treat.map(treatment_map)\n", "cdystonia.treatment" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Alternately, if we simply want to replace particular values in a `Series` or `DataFrame`, we can use the `replace` method. \n", "\n", "An example where replacement is useful is replacing sentinel values with an appropriate numeric value prior to analysis. A large negative number is sometimes used in otherwise positive-valued data to denote missing values." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "scores = pd.Series([99, 76, 85, -999, 84, 95])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In such situations, we can use `replace` to substitute `nan` where the sentinel values occur." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "scores.replace(-999, np.nan)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can also perform the same replacement that we used `map` for with `replace`:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "cdystonia2.treat.replace({'Placebo': 0, '5000U': 1, '10000U': 2})" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Inidcator variables\n", "\n", "For some statistical analyses (*e.g.* regression models or analyses of variance), categorical or group variables need to be converted into columns of indicators--zeros and ones--to create a so-called **design matrix**. The Pandas function `get_dummies` (indicator variables are also known as *dummy variables*) makes this transformation straightforward.\n", "\n", "Let's consider the DataFrame containing the ships corresponding to the transit segments on the eastern seaboard. The `type` variable denotes the class of vessel; we can create a matrix of indicators for this. For simplicity, lets filter out the 5 most common types of ships.\n", "\n", "### Exercise\n", "\n", "Create a subset of the `vessels` DataFrame called `vessels5` that only contains the 5 most common types of vessels, based on their prevalence in the dataset." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Write your answer here" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can now apply `get_dummies` to the vessel type to create 5 indicator variables." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "pd.get_dummies(vessels5.type).head(10)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Discretization\n", "\n", "Pandas' `cut` function can be used to group continuous or countable data in to bins. Discretization is generally a very **bad idea** for statistical analysis, so use this function responsibly!\n", "\n", "Lets say we want to bin the ages of the cervical dystonia patients into a smaller number of groups:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "cdystonia.age.describe()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's transform these data into decades, beginnnig with individuals in their 20's and ending with those in their 80's:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "pd.cut(cdystonia.age, [20,30,40,50,60,70,80,90])[:30]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The parentheses indicate an open interval, meaning that the interval includes values up to but *not including* the endpoint, whereas the square bracket is a closed interval, where the endpoint is included in the interval. We can switch the closure to the left side by setting the `right` flag to `False`:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "pd.cut(cdystonia.age, [20,30,40,50,60,70,80,90], right=False)[:30]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Since the data are now **ordinal**, rather than numeric, we can give them labels:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "pd.cut(cdystonia.age, [20,40,60,80,90], labels=['young','middle-aged','old','really old'])[:30]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A related function `qcut` uses empirical quantiles to divide the data. If, for example, we want the quartiles -- (0-25%], (25-50%], (50-70%], (75-100%] -- we can just specify 4 intervals, which will be equally-spaced by default:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "pd.qcut(cdystonia.age, 4)[:30]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Alternatively, one can specify custom quantiles to act as cut points:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "quantiles = pd.qcut(vessels.max_loa, [0, 0.01, 0.05, 0.95, 0.99, 1])\n", "quantiles[:30]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Exercise\n", "\n", "Use the discretized segment lengths as the input for `get_dummies` to create 5 indicator variables for segment length:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Write your answer here" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Categorical Variables\n", "\n", "One of the keys to maximizing performance in pandas is to use the appropriate **types** for your data wherever possible. In the case of categorical data--either the ordered categories as we have just created, or unordered categories like race, gender or country--the use of the `categorical` to encode string variables as numeric quantities can dramatically improve performance and simplify subsequent analyses.\n", "\n", "When text data are imported into a `DataFrame`, they are endowed with an `object` dtype. This will result in relatively slow computation because this dtype runs at Python speeds, rather than as Cython code that gives much of pandas its speed. We can ameliorate this by employing the `categorical` dtype on such data." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "cdystonia_cat = cdystonia.assign(treatment=cdystonia.treat.astype('category')).drop('treat', axis=1)\n", "cdystonia_cat.dtypes" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "cdystonia_cat.treatment.head()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "cdystonia_cat.treatment.cat.codes" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This creates an **unordered** categorical variable. To create an ordinal variable, we can specify `order=True` as an argument to `astype`:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "cdystonia.treat.astype('category', ordered=True).head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "However, this is not the correct order; by default, the categories will be sorted alphabetically, which here gives exactly the reverse order that we need. \n", "\n", "To specify an arbitrary order, we can used the `set_categories` method, as follows:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "cdystonia.treat.astype('category').cat.set_categories(['Placebo', '5000U', '10000U'], ordered=True).head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Notice that we obtained `set_categories` from the `cat` attribute of the categorical variable. This is known as the **category accessor**, and is a device for gaining access to `Categorical` variables' categories, analogous to the string accessor that we have seen previously from text variables." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "cdystonia_cat.treatment.cat" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Additional categoried can be added, even if they do not currently exist in the `DataFrame`, but are part of the set of possible categories:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "cdystonia_cat['treatment'] = (cdystonia.treat.astype('category').cat\n", " .set_categories(['Placebo', '5000U', '10000U', '20000U'], ordered=True))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To complement this, we can remove categories that we do not wish to retain:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "cdystonia_cat.treatment.cat.remove_categories('20000U').head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Or, even more simply:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "cdystonia_cat.treatment.cat.remove_unused_categories().head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For larger datasets, there is an appreciable gain in performance, both in terms of speed and memory usage." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "vessels_merged = (pd.read_csv('../data/AIS/vessel_information.csv', index_col=0)\n", " .merge(pd.read_csv('../data/AIS/transit_segments.csv'), left_index=True, right_on='mmsi'))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "vessels_merged['registered'] = vessels_merged.flag.astype('category')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%timeit vessels_merged.groupby('flag').avg_sog.mean().sort_values()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%timeit vessels_merged.groupby('registered').avg_sog.mean().sort_values()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "vessels_merged[['flag','registered']].memory_usage()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Data aggregation and GroupBy operations\n", "\n", "One of the most powerful features of Pandas is its **GroupBy** functionality. On occasion we may want to perform operations on *groups* of observations within a dataset. For exmaple:\n", "\n", "* **aggregation**, such as computing the sum of mean of each group, which involves applying a function to each group and returning the aggregated results\n", "* **slicing** the DataFrame into groups and then doing something with the resulting slices (*e.g.* plotting)\n", "* group-wise **transformation**, such as standardization/normalization" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "cdystonia_grouped = cdystonia.groupby(cdystonia.patient)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This **grouped** dataset is hard to visualize\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "cdystonia_grouped" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "However, the grouping is only an intermediate step; for example, we may want to **iterate** over each of the patient groups:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "for patient, group in cdystonia_grouped:\n", " print('patient', patient)\n", " print('group', group)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A common data analysis procedure is the **split-apply-combine** operation, which groups subsets of data together, applies a function to each of the groups, then recombines them into a new data table.\n", "\n", "For example, we may want to aggregate our data with with some function.\n", "\n", "![split-apply-combine](images/split-apply-combine.png)\n", "\n", "<div align=\"right\">*(figure taken from \"Python for Data Analysis\", p.251)*</div>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can aggregate in Pandas using the `aggregate` (or `agg`, for short) method:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "cdystonia_grouped.agg(np.mean).head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Notice that the `treat` and `sex` variables are not included in the aggregation. Since it does not make sense to aggregate non-string variables, these columns are simply ignored by the method.\n", "\n", "Some aggregation functions are so common that Pandas has a convenience method for them, such as `mean`:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "cdystonia_grouped.mean().head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The `add_prefix` and `add_suffix` methods can be used to give the columns of the resulting table labels that reflect the transformation:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "cdystonia_grouped.mean().add_suffix('_mean').head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Exercise\n", "\n", "Use the `quantile` method to generate the median values of the `twstrs` variable for each patient." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Write your answer here" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If we wish, we can easily aggregate according to multiple keys:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "cdystonia.groupby(['week','site']).mean().head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Alternately, we can **transform** the data, using a function of our choice with the `transform` method:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "normalize = lambda x: (x - x.mean())/x.std()\n", "\n", "cdystonia_grouped.transform(normalize).head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It is easy to do column selection within `groupby` operations, if we are only interested split-apply-combine operations on a subset of columns:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%timeit cdystonia_grouped['twstrs'].mean().head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Or, as a DataFrame:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "cdystonia_grouped[['twstrs']].mean().head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If you simply want to divide your DataFrame into chunks for later use, its easy to convert them into a dict so that they can be easily indexed out as needed:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "chunks = dict(list(cdystonia_grouped))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "chunks[4]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "By default, `groupby` groups by row, but we can specify the `axis` argument to change this. For example, we can group our columns by `dtype` this way:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "dict(list(cdystonia.groupby(cdystonia.dtypes, axis=1)))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Its also possible to group by one or more levels of a hierarchical index. Recall `cdystonia2`, which we created with a hierarchical index:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "cdystonia2.head(10)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The `level` argument specifies which level of the index to use for grouping." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "cdystonia2.groupby(level='obs', axis=0)['twstrs'].mean()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Apply\n", "\n", "We can generalize the split-apply-combine methodology by using `apply` function. This allows us to invoke any function we wish on a grouped dataset and recombine them into a DataFrame." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The function below takes a DataFrame and a column name, sorts by the column, and takes the `n` largest values of that column. We can use this with `apply` to return the largest values from every group in a DataFrame in a single call. " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def top(df, column, n=5):\n", " return df.sort_index(by=column, ascending=False)[:n]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To see this in action, consider the vessel transit segments dataset (which we merged with the vessel information to yield `segments_merged`). Say we wanted to return the 3 longest segments travelled by each ship:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "goo = vessels_merged.groupby('mmsi')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "top3segments = vessels_merged.groupby('mmsi').apply(top, column='seg_length', n=3)[['names', 'seg_length']]\n", "top3segments.head(15)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Notice that additional arguments for the applied function can be passed via `apply` after the function name. It assumes that the DataFrame is the first argument." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Exercise\n", "\n", "Load the dataset in `titanic.xls`. It contains data on all the passengers that travelled on the Titanic." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from IPython.core.display import HTML\n", "HTML(filename='../data/titanic.html')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Women and children first?\n", "\n", "1. Use the `groupby` method to calculate the proportion of passengers that survived by sex.\n", "2. Calculate the same proportion, but by class and sex.\n", "3. Create age categories: children (under 14 years), adolescents (14-20), adult (21-64), and senior(65+), and calculate survival proportions by age category, class and sex." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Write your answer here" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## References\n", "\n", "[Python for Data Analysis](http://shop.oreilly.com/product/0636920023784.do) Wes McKinney" ] } ], "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" }, "widgets": { "state": {}, "version": "1.1.2" } }, "nbformat": 4, "nbformat_minor": 1 }
cc0-1.0
erozier2/Data-Engineering-Blog
DefaultRates/School Plots.ipynb
1
114513
{ "metadata": { "name": "", "signature": "sha256:ed23da213d131833b0d3fcf3e066581dd1b0cd5a2b738035aec84be8100e2022" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "from pandas import Series, DataFrame\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "from itertools import cycle, islice\n" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 31 }, { "cell_type": "code", "collapsed": false, "input": [ "df = pd.read_csv('combined.csv')\n", "#print df.columns\n", "newtable = df[['NAME TEXT','NBD 1 DOUBLE', 'NBR 1 DOUBLE', 'DRATE 1 DOUBLE','NBD 2 DOUBLE', 'NBR 2 DOUBLE', 'DRATE 2 DOUBLE']]\n", "newtable.columns = ['Name of Institution', 'Default Number: 1 yr', 'Repayment Number: 1 yr', 'Default Rate: 1 yr', 'Default Number: 2 yr', 'Repayment Number: 2 yr', 'Default Rate: 2 yr']\n", "newtable = newtable[1:]\n", "newtable[(newtable['Name of Institution']).str.contains('URBANA')]\n", "ratedf = newtable[[0,6]]\n", "ratedf.columns\n", "ratedf.to_csv('two-year-default-stats.csv')" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 32 }, { "cell_type": "code", "collapsed": false, "input": [ "font = {'family' : 'normal',\n", " 'weight' : 'normal',\n", " 'size' : 14}\n", "\n", "matplotlib.rc('font', **font)\n", "\n", "plt.xkcd()\n", "plt.figure()\n", "sdf = pd.read_csv('schools.csv')\n", "schools = list(sdf['School Name'].values)\n", "subdf = DataFrame()\n", "for i in schools:\n", " subdf = pd.concat([subdf, ratedf[(ratedf['Name of Institution']).str.contains(i)]])\n", "subdf = subdf.drop_duplicates(cols=['Name of Institution'])\n", "subdf = subdf.sort(['Default Rate: 2 yr'], ascending=True)\n", "#subdf = ratedf[(ratedf['Name of Institution']).str.contains('CHAMPAIGN')]\n", "#plt.xticks(subdf.index.values, subdf['Name of Institution'])\n", "my_colors = ['b']*3 + ['g']*4 + ['y'] + ['r'] + ['y'] + ['r'] + ['y'] + ['r']*6\n", "print my_colors\n", "subdf = subdf.set_index('Name of Institution')\n", "fig = subdf.plot(kind='barh', stacked=False, color=my_colors)\n", "#fig.set_yticklabels(subdf['Name of Institution'].values)\n", "fig.tick_params(axis='both', labelsize=10)\n", "plt.savefig('plot.png', bbox_inches='tight')\n", "plt.show()" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "['b', 'b', 'b', 'g', 'g', 'g', 'g', 'y', 'r', 'y', 'r', 'y', 'r', 'r', 'r', 'r', 'r', 'r']\n" ] }, { "metadata": {}, "output_type": "display_data", "text": [ "<matplotlib.figure.Figure at 0x7f789c98f550>" ] }, { "metadata": {}, "output_type": "display_data", "png": 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+/frx9NNPS8WCq9O3b19uv/12Dh06hMvlYv369Tz++OPS92+++Sb/+9//AGF8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\nM84tt9zCihUruOeee2jcuHGty9x4442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5ZKempgLiqmdSUtLfUqeMjMzfgz9/fkdlnhxc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\nPszXfnBwMAYPHoxVq1b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"text": [ "<matplotlib.figure.Figure at 0x7f789c023910>" ] } ], "prompt_number": 79 }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 3 } ], "metadata": {} } ] }
gpl-2.0
rrbb014/data_science
fastcampus_dss/2016_05_24/0524_04__Student-t 분포.ipynb
2
1945739
null
mit
anandha2017/udacity
nd101 Deep Learning Nanodegree Foundation/notebooks/1 - playing with jupyter/keyboard-shortcuts.ipynb
2
6643
{ "cells": [ { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "# Keyboard shortcuts\n", "\n", "In this notebook, you'll get some practice using keyboard shortcuts. These are key to becoming proficient at using notebooks and will greatly increase your work speed.\n", "\n", "First up, switching between edit mode and command mode. Edit mode allows you to type into cells while command mode will use key presses to execute commands such as creating new cells and openning the command palette. When you select a cell, you can tell which mode you're currently working in by the color of the box around the cell. In edit mode, the box and thick left border are colored green. In command mode, they are colored blue. Also in edit mode, you should see a cursor in the cell itself.\n", "\n", "By default, when you create a new cell or move to the next one, you'll be in command mode. To enter edit mode, press Enter/Return. To go back from edit mode to command mode, press Escape.\n", "\n", "> **Exercise:** Click on this cell, then press Enter + Shift to get to the next cell. Switch between edit and command mode a few times." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# mode practice" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Help with commands\n", "\n", "If you ever need to look up a command, you can bring up the list of shortcuts by pressing `H` in command mode. The keyboard shortcuts are also available above in the Help menu. Go ahead and try it now." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Creating new cells\n", "\n", "One of the most common commands is creating new cells. You can create a cell above the current cell by pressing `A` in command mode. Pressing `B` will create a cell below the currently selected cell." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "> **Exercise:** Create a cell above this cell using the keyboard command." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "> **Exercise:** Create a cell below this cell using the keyboard command." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Switching between Markdown and code\n", "\n", "With keyboard shortcuts, it is quick and simple to switch between Markdown and code cells. To change from Markdown to a code cell, press `Y`. To switch from code to Markdown, press `M`.\n", "\n", "> **Exercise:** Switch the cell below between Markdown and code cells." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "## Practice here\n", "\n", "def fibo(n): # Recursive Fibonacci sequence!\n", " if n == 0:\n", " return 0\n", " elif n == 1:\n", " return 1\n", " return fibo(n-1) + fibo(n-2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Line numbers\n", "\n", "A lot of times it is helpful to number the lines in your code for debugging purposes. You can turn on numbers by pressing `L` (in command mode of course) on a code cell.\n", "\n", "> **Exercise:** Turn line numbers on and off in the above code cell." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Deleting cells\n", "\n", "Deleting cells is done by pressing `D` twice in a row so `D`, `D`. This is to prevent accidently deletions, you have to press the button twice!\n", "\n", "> **Exercise:** Delete the cell below." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Saving the notebook\n", "\n", "Notebooks are autosaved every once in a while, but you'll often want to save your work between those times. To save the book, press `S`. So easy!" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## The Command Palette\n", "\n", "You can easily access the command palette by pressing Shift + Control/Command + `P`. \n", "\n", "> **Note:** This won't work in Firefox and Internet Explorer unfortunately. There is already a keyboard shortcut assigned to those keys in those browsers. However, it does work in Chrome and Safari.\n", "\n", "This will bring up the command palette where you can search for commands that aren't available through the keyboard shortcuts. For instance, there are buttons on the toolbar that move cells up and down (the up and down arrows), but there aren't corresponding keyboard shortcuts. To move a cell down, you can open up the command palette and type in \"move\" which will bring up the move commands.\n", "\n", "> **Exercise:** Use the command palette to move the cell below down one position." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# below this cell" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Move this cell down" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Finishing up\n", "\n", "There is plenty more you can do such as copying, cutting, and pasting cells. I suggest getting used to using the keyboard shortcuts, you’ll be much quicker at working in notebooks. When you become proficient with them, you'll rarely need to move your hands away from the keyboard, greatly speeding up your work.\n", "\n", "Remember, if you ever need to see the shortcuts, just press `H` in command mode.\n" ] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.1" } }, "nbformat": 4, "nbformat_minor": 1 }
mit
JaeGyu/PythonEx_1
Untitled1.ipynb
1
743
{ "cells": [ { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import sklearn" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.1" } }, "nbformat": 4, "nbformat_minor": 0 }
mit
lenovor/BDA_py_demos
demos_ch2/demo2_4.ipynb
19
88182
{ "metadata": { "name": "", "signature": "sha256:1defc2f927b219119afb54197d2cf00f437ea4e492f6781ab554c391e89b9d35" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "### Bayesian Data Analysis, 3rd ed\n", "## Chapter 2, demo4\n", "\n", "Calculate the posterior distribution on a discrete grid of points by multiplying the likelihood and a non-conjugate prior at each point, and normalizing over the points. Simulate samples from the resulting non-standard posterior distribution using inverse cdf using the discrete grid." ] }, { "cell_type": "code", "collapsed": false, "input": [ "# Import necessary packages\n", "\n", "import numpy as np\n", "from scipy.stats import beta\n", "\n", "%matplotlib inline\n", "import matplotlib.pyplot as plt" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 1 }, { "cell_type": "code", "collapsed": false, "input": [ "# Edit default plot settings (colours from colorbrewer2.org)\n", "plt.rc('figure', figsize=(8,6))\n", "plt.rc('font', size=16)\n", "plt.rc('lines', color='#377eb8', linewidth=2)\n", "plt.rc('axes', color_cycle=('#377eb8','#e41a1c','#4daf4a',\n", " '#984ea3','#ff7f00','#ffff33'))" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 2 }, { "cell_type": "heading", "level": 4, "metadata": {}, "source": [ "Calculate results" ] }, { "cell_type": "code", "collapsed": false, "input": [ "# Data (437,543)\n", "a = 437\n", "b = 543\n", "\n", "# Grid of nx points\n", "nx = 1000\n", "x = np.linspace(0, 1, nx)\n", "\n", "# Compute density of non-conjugate prior in grid\n", "# This non-conjugate prior is same as in Figure 2.4 in the book\n", "pp = np.ones(nx)\n", "ascent = (0.385 <= x) & (x <= 0.485)\n", "descent = (0.485 <= x) & (x <= 0.585)\n", "pm = 11\n", "pp[ascent] = np.linspace(1, pm, np.count_nonzero(ascent))\n", "pp[descent] = np.linspace(pm, 1, np.count_nonzero(descent))\n", "# Normalize the prior\n", "pp /= np.sum(pp)\n", "\n", "# Unnormalised non-conjugate posterior in grid\n", "po = beta.pdf(x, a, b)*pp\n", "po /= np.sum(po)\n", "# Cumulative\n", "pc = np.cumsum(po)\n", "\n", "# Inverse-cdf sampling\n", "# Get n uniform random numbers from [0,1]\n", "n = 10000\n", "r = np.random.rand(n)\n", "# Map each r into corresponding grid point x:\n", "# [0, pc[0]) map into x[0] and [pc[i-1], pc[i]), i>0, map into x[i]\n", "rr = x[np.sum(pc[:,np.newaxis] < r, axis=0)]" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 3 }, { "cell_type": "heading", "level": 4, "metadata": {}, "source": [ "Plot results" ] }, { "cell_type": "code", "collapsed": false, "input": [ "# Plot 3 subplots\n", "fig, axes = plt.subplots(nrows=3, ncols=1, sharex=True, figsize=(8, 12))\n", "# Posterior with uniform prior Beta(1,1)\n", "axes[0].plot(x, beta.pdf(x, a+1, b+1))\n", "axes[0].set_title('Poster with uniform prior')\n", "axes[0].set_yticks(())\n", "# Non-conjugate prior\n", "axes[1].plot(x, pp)\n", "axes[1].set_title('Non-conjugate prior')\n", "axes[1].set_yticks(())\n", "# Posterior with non-conjugate prior\n", "axes[2].plot(x, po)\n", "axes[2].set_title('Posterior with non-conjugate prior')\n", "axes[2].set_yticks(())\n", "# Set custom limits for x-axis\n", "axes[0].set_xlim((0.35, 0.65))\n", "fig.subplots_adjust(hspace=0.2)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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mM1drtDh2oRiS1HJuQjMI9fdEkK8KJZW1OJdbJnc5REQmGNpkJjWrGLU6ga5t\n1PD1dO7x2Tcy6dc+zyZyIrIvDG0y01L7sw3iOtaF9oFr54GIyF4wtMlMS+3PNujbqRWAun7t6lqd\nzNUQEf2JoU0miitqcOZKGdxcFIhqp5G7HFm08nFHRJA3qmv1SM0qlrscIiIjhjaZSM4ohBBArzAN\nVK5KucuRTb+Iuqvt/WfzZa6EiOhPDG0ycbCFN40b9DeE9jmGNhHZD4Y2mfjzJrSW8SUhNxPdzg8q\nVyXO5pQjv6xa7nKIiAAwtOk6FwoqkF14FT4qF3Rto5a7HFm5uSiMY9R5tU1E9oKhTUZ7z9SFU7+I\nVnBR8qXBfm0isjd8ZyajPddCe0BkK5krsQ+Gfu0D5wo4pSkR2QWGNgEAqmp0OJxRNwOYIaxaunYB\nngjWqFBcWYtTl0vlLoeIiKFNdQ5lFKJaq0e3EDUCvN3lLscuSJKE/tcmWtnLJnIisgMMbQIA7DmT\nBwAYGNla5krsy8DOdedj16k8mSshImJoEwAhhPEmtAGd2TR+vb4dA+DuosCJiyUc+kVEsmNoE7IK\nKnGx6Cp8PV3RLcRX7nLsispNiTs61U00s+tUrszVEFFLx9Am7Dld1/TbP6IVlApJ5mrsz9AugQCA\n39lETkQyY2iT8SargRzqZdGgLq0hSXWzxV2t0cpdDhG1YAztFq68qhaHMwqhkIB+nRjalgR4u6Nn\nqAY1Wj32n+N3bBORfBjaLdzu03mo1QlEh/tB4+Umdzl2a0iXurvIf09jvzYRyYeh3cLtOJEDABjW\nLUjmSuzbkK51/dq7T+dBx9nRiEgmDO0W7GqN1tifncDQrlf7Vl4I9fdEcWUtjl4olrscImqhGNot\n2L6zBaiu1aNHqC8CfVVyl2PXJEnC0GtX24knc2SuhohaKoZ2C2ZoGudVdsMM7153nn47foVfIEJE\nsmBot1A1Wj12XxufPaw7Q7sheoT6IsTPA3ml1UjJKpK7HCJqgRjaLdTB8wWoqNYiMtgHof6ecpfj\nECRJwl09gwEAvxy9LHM1RNQSMbRbqD+bxgNlrsSx/KVnGwDAf0/kQKvTy1wNEbU0DO0WqFarx85r\n443Zn904EUHe6NDaCyWVtThwnhOtEFHzYmi3QLvP5KH0ai06BXmjY6C33OU4FEmScFevuqvtX45e\nkbkaImppGNot0PaUSwCAe6JDIEn8gpDGMvRr7zyZg6panczVEFFLwtBuYYorarD7TB4UEjAiKkTu\nchxSWIC4FD5FAAAgAElEQVQXuoWoUVmjM35DGhFRc2BotzA/H70MrU6gX0QrtPJxl7schzUiqq6J\n/LvDF2WuhIhaEoZ2C7M99VrTeG9eZTfFyKgQuCol7Dubj8vFV+Uuh4haCIZ2C5KeW46Tl0rhrXLB\nkC4c6tUUGi83DOseDCGAb5Oz5S6HiFoIhnYL8kNKXVPunT2CoXJVylyN4xsfFwqgromcY7aJqDkw\ntFsIrU6PH4/UzeLFpnHriAn3Q3grL+SXVRunhCUisiWGdguRlJaL/LJqhLfyQlSYRu5ynIIkSRgX\nW3e1vYVN5ETUDBjaLcSmPy4AAO6/I4xjs61odG/ekEZEzYeh3QKk55YjOb0QHm5KNo1bma/nnzek\nbb72wYiIyFYY2i3A+n2ZAICRUW3grXKVuRrnM6lfOwDA5oMXUFmtlbkaInJmDG0nV1hejW3XxmZP\nGdBe3mKcVM8wDaLaaVBWpeVkK0RkUwxtJ7fpwAXUaPUY3KU1wlt5yV2O03p4YHsAwLq9GRz+RUQ2\nw9B2YhXVWmz8IwvAn6FCtjG4SyDCW3nhSnGVcWgdEZG1MbSd2KYDWSiprEWvMA16h/vJXY5TUyok\nPDq0IwBgVdI5Xm0TkU0wtJ1UZbUWX+7JAAA8kdCJw7yawV09gxHq74nswqu82iYim2BoO6kN+7NQ\nXFmLHqG+6NspQO5yWgQXpQKPJ3QCAPxnx1lU87u2icjKGNpOqLiiBp/vSgcAPD08klfZzejuXm0Q\nEeSNKyVV2HggS+5yiMjJMLSd0GdJ51BRrUX/iABeZTczpULCjLs6AwBWJZ1HUUWNzBURkTNhaDuZ\n9NxybDxwAZIEY3hQ8+of0Qr9OgWgrEqL//31tNzlEJETYWg7ESEE3t92Ejq9wPjYMEQGq+UuqUWS\nJAkv39MNLkoJ3x26iGMXiuUuiYicBEPbifx45DKS0wvh6+mKZ+6MkLucFi28lRceujYD3cKtx1Cj\n5RAwImo6hraTyCutwrLtJwEAz9/dBb6ebjJXRI8ldEK7AE+k51Xg/yWek7scInICDG0nIITAom+P\no/SqFgMjW2E0v8nLLqhclXh9fE9IEvDFrvM4nFEod0lE5OAY2k5g3d5M7DmTDx+VC/4xtgeHeNmR\n6HZ+eGRwR+gF8OamIyjm3eRE1AQMbQeXmlWEj3+pu0P5jfE9EahWyVwR3ejJYZ3QK0yDvNJqvL4h\nlVOcEtFtY2g7sEtFV/GPr1Kg0wtM6R+O+G5BcpdEFrgoFVj4QDQCvN2QnF6IJdvSIISQuywickAM\nbQdVXFGDl9cmo6iiBv06BeBvd3NMtj0L9FXh3SkxcHNRYPPBC/gPb0wjotvA0HZAZVdr8eKaZGTk\nVaBjoDfefiAaLkr+Ke1dzzANFtwfBYUE/L/Ec/j89/Nyl0REDobv9A6moLwaM1b9gbRLpQj198Dy\nR+Lg4+Eqd1nUQMO6B+G1cXV3lP/vr2fw71/PsKmciBpMEjZ+x5AkiW9KVnI+txwzvzyES0VX0S7A\nE8sfiUOwxkPusug2bE+9hLe3HINOL3BXz2C8Pq4nVG5KucsiIiuwZe4xtB3Ej0cuYfF3J1BZo0PX\nEDWWPNwHAd7ucpdFTbDndB7e2JCKyhqdsZujY6C33GURURMxtFuwgrJqLPsxDb8euwIAuLtXMF4b\n1xMqV16VOYNzOWV47etUZOZXwEUp4eGB7fHo0I7wcHORuzQiuk0M7RaovKoWX+/Lwpd7M1BepYXK\nVYmXR3XFmD5tOXmKk6ms1mL5T6ewJTkbABDsq8Jzd3XG8O5BvMGQyAExtFuQ3NIqfHcoG+v3ZaL0\nqhYAMDCyFWaO7o4QP/ZfO7NjF4qx+IcTOH25DADQRuOBKQPCcW9MW3i588qbyFEwtJ1cUUUNVqz7\nFpfc2uOP8wXQXztdMeF+eGJYBGI7+MtboJ1JTExEQkKC3GXYhE4v8N2hbKzdk4ELBZUAAHcXBQZE\ntsKdPYMxIKIVvFUNGy3gzOfJmnieGo7nqmFsmXv8+N7MtDo9sgoqcfpKKdIuleJQeiFOXylDduIW\nhCZMg6tSQnyXQNx/RxhiO/izKdwCZ37jUCokjI8Lw5g+odh1Khdf7c3E4cwiJJ7MReLJXCgkIDJY\njZhwP3Rrq0anIB+EB3jB1cW8Gd2Zz5M18Tw1HM+V/Bjat0EIAZ1eoEar//NHV/ff8qpalFVpUXq1\nFmVXa1FcWYMrxVW4UnIVV0qqkFNSBZ3e9BOYu4sCbf09MXN0N9zVM5hfq0lQKiTEdwtCfLcg5JZU\nYceJHPz3xBUcv1iCU5dLcepyqcm2gWoVgnxVCFSrEKh2R2sfFU5fLsWOEznwVrnAy90F3u4u8HBT\nwlWpgKtSARelBFelAgoFPxgSOYpmCe37P0iCIaYMLQYCxv+5/j8N2ub6Zocb94sbtrG8XtxwnJsc\n18Kx9UKgVquHvgktH200Hujcxgedg33QK8wP0e00WLRwNyb2bXf7OyWnFeirwuQB4Zg8IBxVNToc\nyy5GSmYRzuSU4XxuObILK3G5+CouF181eVx2Wi7Or0+55f6VCgkuSgkuCgUUElDXuCNBkgAJdU19\nJv9ftxrSdduY/e5ATuzLxNEPkuQuwyE4y7ly5AZMm/dp9+7dG6mpqbY8BBERkd2Ij49HYmKiTfZt\n89AmIiIi6+AgUCIiIgfB0CYiInIQDG0iIiIHwdAmIiJyEAxtIiIiB8HQJiIichAMbSIiIgfB0CYi\nInIQDG0iIiIHwdAmIiJyEAxtIiIiB8HQJiIichAMbSIiIgfB0CYiInIQDG0iIiIHwdAmIiJyEAxt\nIiIiB8HQJiIichAMbSI7p1AoMGzYMLnLcDqJiYlQKBRYsGCB3KUQNRhDm2wuIyMDCoUCCoUCkyZN\nsrjNqlWroFAo8OGHHzZzdY5BkiS5S6hXQkICFArHfDux93NLdD0XuQuglmXTpk1ITk5GbGysxfV8\nAzWXlpYGT09Pucu4JUf72/Xr1w9paWlo1aqV3KUQNZhjfjQmh9ShQwcAwJw5c2SuxLF07twZoaGh\ncpfhdDw8PNC5c2f4+/vLXQpRgzG0qdlERUXh/vvvx6+//oodO3Y0+HFJSUkYOXIk/Pz84Onpiaio\nKCxZsgQ6nc5ku+v7KPft24dhw4bBx8cHAQEBmDp1KvLz8xtd88aNG3HnnXcajx0ZGYlnnnkGFy5c\nMNnuyJEjuP/++9G6dWuoVCp06dIFc+fORWVlpcl2hq6C6dOn49SpUxgzZgx8fX2hVqsxduxYnD9/\n3qwGS33a7du3N34IutHN1h06dAh33XUXvL294e/vj4kTJyIzM9Ni0/alS5fw5ptvom/fvsbnFBkZ\niVmzZqG8vNysvqSkJAghjN0glvqKN23ahISEBPj6+sLT0xN9+vTBp59+avE5WGLoQlm9ejU2bNiA\nPn36wNPTE23btsXMmTPNzvX1r4ekpCQMHz4carUaHTt2NFt/oy1btmDIkCHw8fGBt7c3+vbti5Ur\nV9Zb0+bNm9G/f394e3vzHgSyGYY2NRtJkvD2229DqVTiH//4R4Me8/XXX2P48OHYt28fJk2ahBde\neAFCCMyaNQsTJ060+JgDBw5g+PDh8PX1xbPPPosuXbrgyy+/xNixYxtV7wsvvIBJkybhxIkTmDx5\nMl588UXExsZi48aNOHz4sHG7Xbt2oX///ti2bRvuuecevPLKKwgICMDChQsxfPhwVFdXm+07PT0d\ngwYNQlVVFZ5++mkMHDgQ33//Pe666y5UVVWZbW+p6bm+5ugb1x0+fBhDhw5FUlISJkyYgGeffRY5\nOTkYMmQIiouLzbZPSkrCBx98gNDQUEybNg0zZsxAYGAglixZgjvvvBNarda47bx58xAeHg4AmD9/\nvvHn+uCaNWsWHnjgAWRlZeHBBx/EM888g5qaGjz99NN4+eWXb/o8LNmwYQMeeeQRREdH48UXX0Sb\nNm2wdOlSjBkzBkIIs+13795t/LAyY8YM3HPPPfWeqyVLluC+++7DmTNnMH36dDzzzDPIz8/HE088\ngb///e8Wa1q/fj2mTJmC8PBwvPDCCxgyZEijnhNRgwkiG0tPTxeSJIkJEyYIIYR48sknhSRJYtOm\nTcZtPvvsMyFJkvjwww+Ny0pKSoSvr6/w8fERaWlpxuVarVaMGDFCSJIkPv/8c+PyHTt2CEmShCRJ\n4ptvvjEu1+v14s477xSSJIm9e/c2qOatW7cKSZJEv379RFlZmcm6qqoqUVhYKIQQQqfTiU6dOgml\nUimSkpJMtnv88ceFJEnirbfeMjsXkiSJpUuXmmw/ffp0IUmSWLdunclySZLEsGHDTJaFh4eLDh06\nWKzd0rqBAwcKhUIhtm3bZrLc8LdQKBQmy/Py8kRlZaXZvt9++20hSZJYs2aNyfL4+HizfRj8+OOP\nxr9/dXW1cXltba0YP368kCRJ/PHHHxYfez3Da0ShUJica71eb9zPypUrjcuvfz3ceE6vX79gwQLj\nsrNnzwoXFxcRGhoqcnJyjMvLy8tFdHS0kCTJ5NiGmlxdXcWuXbtu+RyImopX2tTs5s+fD5VKhblz\n50Kv1990uy1btqC0tBRPPfUUunTpYlyuVCrx7rvvAgBWr15t9riEhARMmDDB+LskSXjkkUcAAMnJ\nyQ2q8d///jcAYPny5fD29jZZ5+7uDj8/PwB1V9nnz5/H+PHjza6uFi5cCHd3d4s1durUCS+99JLJ\nskcffbRRNTZURkYG9u7diwEDBmDUqFEm6+bPnw+lUmn2mFatWsHDw8Ns+bPPPgsA+O233xp8/I8/\n/hgKhQIrVqyAm5ubcbmLiwv++c9/Aqi7Um2oESNGmJxrSZLwzjvvAADWrFljtv0dd9yBKVOmNGjf\na9euhU6nw+zZsxEYGGhc7uXlZWxGt/T3nDBhAgYNGtTg50B0u3j3ODW7kJAQ/O1vf8P777+P1atX\nY/r06Ra3S0lJAVAXwjeKjo6GWq1Gamqq2bqYmBiLxwSA4uJi47L58+ebbWdY9scff0CtVqNv3771\nPpf6agwKCkKXLl1w9OhRVFRUwMvLy7guKiqqQTVag+EcDRw40OIx27Vrh4yMDLN1GzZswP/93/8h\nJSUFxcXFJh+wLl++3ODj79+/Hz4+Pvjoo4/M1tXW1gKou0O+oQYPHmy2rFu3btBoNDhy5IjZuri4\nuAbvu76/Z3x8PABYfM015hhETcHQJln84x//wIoVKzB//nw8/PDDFrcpLS0FUBd+lgQHByM9Pd1s\nua+vr9kyF5e6l/r1N6+99dZbkCTJ2A8qSZIxtEtKShAZGXnL59GQGo8cOYLS0lKT0G5ojdZQVlYG\nAGjdurXF9YGBgWah/d5772H27NkICgrC6NGj0bZtW6hUKgghsGDBAov99DdTWFgInU6Ht956y+J6\nSZLMbiKrz82eR1BQkMUb+a6/Yr6V+v6eGo0Gbm5uxm1u9xhETcHQJln4+/tj1qxZmDt3Lj7++GOL\nw27UajUAICcnx+I+cnJyjNvcjvqa5jUaDS5dunTLfTSkRkmSmlSnJQqF4qbhXlpaCo1GY/zdx8cH\nAJCXl2dx+9zcXJPftVot3n77bbRt2xapqakmf5ucnJxGzyCmVqvh5eWFrKysRj3uZm72PG72emjM\n+PHr/543fjgoKSlBTU1Nk49B1BTs0ybZvPTSSwgKCsKiRYssXr0Ymrl37txptu7IkSMoKSlB7969\nbVJb3759UVpaiv3799e7XX015ubmIi0tDR07djS5yrYGjUaDnJwcsw8emZmZZs3rhnO0e/dus/1c\nvHjRLEzz8/NRVlaGAQMGmH2YsrQPAMZ+cWHh7u1+/fohOzsb2dnZt3hWDfP777+bLTtx4gSKi4sR\nHR3dpH3X9/c0LLPVa46oIRjaJBtPT0+88cYbyM/Px/Lly83Wjxs3Dmq1GitWrMC5c+eMyw03CgEw\n3mBmbYYbrl544QVj87JBVVUVioqKANT1r3bs2BGbN2/G3r17TbZ74403UF1dbZMa4+LiUFNTg3Xr\n1hmX1dbWYubMmWbbhoeHo3///ti7dy+2bdtmsm7+/PlmV+yBgYFQqVRITk42GX52+fJlvPbaaxbr\n8ff3hxDCYjA///zzAIDHH3/c4oez9PR0ZGZm1vNsTf38889ISkoy/q7X6/H6668DAKZOndrg/Vjy\n0EMPQalU4r333jMZ119RUYF58+aZ3NRIJAc2j5Osnn76aSxdutQklA3UajU++eQTTJ06FbGxsZgy\nZQp8fX2xbds2HD9+HGPGjMG0adNsUte9996L559/Hv/617/QuXNnjB07Fv7+/sjKysJPP/2ElStX\nYuzYsZAkCStXrsTIkSMxfPhwTJo0CSEhIdi5cyf27duHuLg4vPrqq1avb8aMGVi1ahUee+wx/PLL\nL/D19cVvv/0GtVqNNm3amF3xfvTRRxg6dCgmTJiASZMmISwsDL///jsyMzMRFRWFY8eOGbdVKBR4\n9tlnsWzZMsTExOCee+5BYWEhfvjhByQkJOD06dNm9QwfPhybNm3C5MmTMWLECLi7u2PIkCEYNGgQ\nRo0ahTlz5mDRokWIiIjAiBEj0LZtW2NLxP79+7Fu3TrjWO9bGTVqFEaMGIHJkyejTZs2+Pnnn3H4\n8GEMGzbMeAf+7erUqRPeeecdzJ49G7169cLEiRPh5uaGzZs3IyMjAzNmzOAYbJKXrAPOqEW4cZz2\njdasWWMcf3v9OG2DxMREMWLECKHRaIRKpRI9e/YUixcvFlqt1mQ7S+NuG7KuPl999ZUYOnSoUKvV\nwtPTU3Tu3Fk8++yzIjs722S7lJQUcd9994mAgADh5uYmIiMjxeuvvy4qKiosnovp06ebHetm6yyN\n0xZCiJ9//lnExcUJd3d3ERwcLJ5//nlRVlYm2rdvb3EM98GDB8Vf/vIX4eXlJfz8/MT9998vMjMz\nRc+ePYVGozHZtqamRrz11lsiIiJCqFQqERERIRYsWCBqamos1qPVasUrr7wiwsLChIuLi1AoFGbn\nevv27WL06NGiVatWws3NTYSGhoqEhASxdOlSkZ+fb+HsmzKMiV69erXYsGGDiImJER4eHiIkJES8\n8sorZuPKb/U3r2/9N998IwYPHiy8vb2Fp6eniIuLE//5z3/Mtlu1apVQKBRi9erVt6yfyBokISx0\nQhGRXaiqqoKnpydGjBiB7du3W33/5eXlCAwMRFRUFPbt22f1/VuToWVh1apVbKKmFot92kR27OzZ\nswCAtm3bNmk/Wq3WbO51vV6P2bNno6qqCuPGjWvS/omoebBPm8gOlZaWYvHixdiyZQskSTKZ4e12\nFBcXIzQ0FCNGjEBkZCQqKiqwa9cuHD9+HN26dbvpnNpEZF94pU1khwoLC7F48WLodDp8/PHHGD16\ndJP25+Pjg8ceewxpaWlYsWIFVq5ciaqqKrz00kvYvXu3w3xfN8dDU0vHPm0iIiIHYfPm8d69e1uc\nq5eIiMgZxcfHIzEx0Sb7tnnzeGpqKoQQ/LnFz7x582SvwVF+eK54nnieeK7s+cfSjHrWwj5tIiIi\nB8HQJiIichAMbTth6ft7yTKeq4bheWoYnqeG47mSn83vHr/++4qJiIicnS1zj1faREREDoKhTURE\n5CAY2kRERA6CoU1EROQgGNpEREQOgqFNRETkIBjaREREDoKhTURE5CAY2kQy0ekFyqtqUaPVy10K\nETkIm381JxGZy8qvwN+/SMbl4qvwdFfijXE9MbxHsNxlEZGd45U2UTPLL6s2Bra7iwKV1TrM23QE\nyemFcpdGRHaOoU3UjMqravHSmrrA7t7WFz/OHoaJfduhVifw6rrDOHOlTO4SiciOMbSJmkmNVo9/\nfJWCM1fK0C7AE0sf7gMPNxe8NKorhncPQkW11hjoRESWMLSJmoFeL/DW5qM4mF6IAG83fDAtFhov\nNwCAUiFh3n29ENPeD/ll1Xjxi2SUVNbIXDER2SOGNpGNCSHw4U9p+PXYFXi6K7FsaixC/DxNtnF3\nVWLxlBh0CvJGZn4FXll7CFU1OpkqJiJ7xdAmsrG1uzOwfl8WXJQSFk+JQec2aovb+Xi4YtnUWAT7\nqnAsuwSvb0iFVsfhYET0J4Y2kQ1tT72Ej345DQCYd18vxHUMqHf7QLUKy6bFQu3hit2n8/Du9ycg\nhGiOUonIATC0iWxk39l8vL3lGADgpZFdcVfPNg16XIfW3ljycB+4uyrw3aGL+HTHWVuWSUQOhKFN\nZAMnL5ZgzvoU6PQCUwe1x+QB4Y16fK8wDd5+IBoKCVi58zw2HciyUaVE5EgY2kRWdqGgAi+vPYSr\nNTqMig7Bc3/pfFv7GdIlELPH9AAAvL/tJHacyLFmmUTkgBjaRFZUUF43ZKuoogb9IwLw+rgeUCik\n297fuNhQPDUsAkIA8zYdQUpmkRWrJSJHw9AmspKKai1eXnMIF4uuoluIGu9M6g0XZdP/iU2P74gJ\ncWGo0eox68tDOJfDWdOIWiqGNpEV1Gr1mLM+BaculyLU3wNLHu4DT3frfB+PJEmYObob4rsGoqxK\nixfXJCOnhLOmEbVEDG2iJtLrBd7eegwHzhXAz8sNH0yLg7+3u1WPoVRIWDAxCtHtNMgrrfvCEc6a\nRtTyMLSJmujjX07jpyOX4emmxLKpfRDq73nrB90GlasS7z3UBx0DvZGRV4FZ6w6jqpazphG1JAxt\noib4ck8G1u7JgFIhYdGU3uga4mvT46k9XLFsah8EqlU4klWMNzce4axpRC0IQ5voNv189DKW/3QK\nADB3Qk/069SqWY4b5OuBD6bFwkflgqS0XLz/w0nOmkbUQjC0iW7DgXMFeGvzUQDA83d3wciokGY9\nfsdAb7z3UB+4uyiwJTkbK3eea9bjE5E8GNpEjXTqcin+8dVhaHUCDw4Ix8OD2stSR+9wP7w1MQoK\nCfh0xzlsOXhBljqIqPkwtIka4WJhJV5ak4zKGh3u7hWM5+/uIms98d2CMHN0dwDA4u9PICktV9Z6\niMi2GNpEDVRYXjfUqrC8Bnd0DMDc8b2aNNuZtdx3Rxgej+8EvQDmbkjFkSzOmkbkrBjaRA1QWa3F\nzC8PIbuwEp3b+OB/JveGq4v9/PN5YlgnjO3TFtVaPWZ+eQjpeeVyl0RENmA/7zpEdkqr0+O1r1Nx\n4mIpQvw8sOzhWHiprDPbmbVIkoRX7+2OwV1ao/SqFi9+kYzc0iq5yyIiK2NoE9VDCIF3th7HvrP5\n0Hi64oNpsQjwse5sZ9biolTg7YnR6BWmQU5JFV76IhllV2vlLouIrIihTVSPf/96BttSL0HlqsTS\nqbFoF+Ald0n1Urkp8f5DMQhv5YVzueV4dd1hVHPWNCKnwdAmuon1+zLx+a70utnOJkeje1vbznZm\nLb6ebvhgWixa+7jjcGYR5n9zFDo9J18hcgYMbSILfj12BR/8mAYAeH1cDwyIbC1zRY3TRuOBZdNi\n4a1ywY4TOVi2nbOmETkDhjbRDZLTC7DgmyMQAnjuL5G4p3dbuUu6LRFBPlj8YAzcXBTYeOACVv+e\nLndJRNREDG2i65y5UopX16WgVicwqV87TBvcQe6SmqRPe3/Mv78XJAn45Lcz+P7wRblLIqImYGgT\nXXOp6CpeWnMIFdVa3NkjGC+O7ApJkn/ylKYa3j0Yr9zTDQCw6Nvj2H06T+aKiOh2MbSJABRX1OCl\nNcnIL6tGn/Z+mHeffcx2Zi0T+7bDo0M7QqcXeO3rFBzLLpa7JCK6DQxtavGu1mjxypeHkJlfgYgg\nb2M/sLN5engExsS0RXWtHq+srXu+RORYnO+diagRtDo93thwBMezSxCsUWHZ1Fh4q1zlLssmJEnC\n7DHdMahza5RU1uLvXxxEHmdNI3IoDG1qsYQQePe7E9h9Og++nq74cFocWqtVcpdlUy5KBd5+IAo9\nQn1xpbgKL689hPIqzppG5CgY2tRirfjvWXx3+CLcXRVY8lAfhLey79nOrMXDzQVLHuqDdgGeOHOl\nDLO/SkGNVi93WUTUAAxtapE2HsjCZ0nnoVRIWPhANHqGaeQuqVlpvNzwwbQ4BHi7ITm9EAu+OQo9\nZ00jsnsMbWpxdpzIwZJtJwEAc8b2wOAugTJXJI8QPw98MC0WXu4u+O143QxwnDWNyL4xtKlFOZxR\niHmb6mY7e+bOSNwb45iznVlLZLAaix/sDVelhK/3Z2HN7gy5SyKiejC0qcU4m1OGWesOo0arx8S+\nYfjrEMee7cxaYjsEYN59UZAk4ONfTmNbCmdNI7JXDG1qEa4UX8VLa5JRXqXFsO5BeGlUN6eY7cxa\n/tKzbgY4AFi49Tj2nuGsaUT2iKFNTq+ksgYvrklGXmk1YsL9MP++XlA60Wxn1jK5fzimDe5wbda0\nVJy4WCJ3SUR0A4Y2ObWqWh1mfnkYGXkV6BRYN9uZu6tS7rLs1nN/icQ90SG4WqPDK2sPIauAs6YR\n2ROGNjktrU6PuRtScfRCMYJ8VVg2LRY+Hs4525m1SJKE18b1QP+IABRV1ODFL5JRUFYtd1lEdA1D\nm5ySEALv/XASv5/Kg9rDBR9Mi0Wgk892Zi0uSgXemdQb3ULUdd98tjYZFdVaucsiIjC0yUn9J/Ec\ntiZnw91Fgfcf6oMOrb3lLsmheLq7YMnDfRDq74nTl8vwj69SUMtZ04hkx9Amp7P5jwv4f4nnoJCA\nfz4Qjah2fnKX5JD8vd3x4bRY+Hm54Y/zBfjnlmOcNY1IZgxtcio7T+bgvR9OAABevbc7hnZtmbOd\nWUtbf08smxoLTzclfj56GR/9clrukohaNIY2OY2UzCK8ufEI9AJ4clgnjI8Lk7skp9A1RI1FU3rD\nRSnhyz0ZWMtZ04hkw9Amp3A+txyzvjyEaq0eE+JC8Vh8J7lLcir9OrXC3PE9AQD/+vkUfjpySeaK\niFomhjY5vNySKrz4RTLKqrSI7xqImaO7c7YzGxgRFYIXRnQBAPxzyzHsP5cvc0VELQ9Dmxxa6dVa\nvEYywiEAACAASURBVLgmGbmlVYhqp8GCiVGc7cyGHhrYHg8NbA+tTmDOVylIu1Qqd0lELQpDmxxW\nVa0Or647jPO55ejQ2gvvPRgDFWc7s7m/3dUZd/dqg8oaHV5ak4zswkq5SyJqMRja5JB0eoF5m44g\nJbMIrdXu+GBaLHw93eQuq0VQKCTMHd8Td3T8c9a0wnLOmkbUHBja5HCEEHj/h5PYeTIXPioXfDgt\nDkG+HnKX1aK4uijwP1N6o0sbNbILK/HK2kOo5KxpRDbH0CaH81nSeWw+eAFuLgq891AfdAzkbGdy\n8HJ3wdKpfdDWzwMnL5Xita9ToNVx1jQiW2Jok0PZmpyNFf89C4UEvDUxCr3DOduZnAK83bHs2qxp\n+84WYOHW4xCCs6YR2QpDmxzG76dy8e53xwEAM0d3R0K3IJkrIgBoF+CFJQ/3gYebEttTL+F/fz0j\nd0lETouhTQ7h6IVivLEhFXoBPBbfEffdwdnO7En3tr54Z1I0lAoJX+xKx/q9mXKXROSUGNpk9zLy\nyvHK2kOortVjTJ+2eHJYhNwlkQUDIlvjjWuzpn3wUxp+OXZZ5oqInA9Dm+xaXmndbGelV2sxqHNr\nzL6Xs53Zs1HRIZhxV2cIAbz1zVEcPF8gd0lEToWhTXar7NpsZ1dKqtAz1BcLH4iGi5IvWXs3dVB7\nTO7fDrU6gVe/OozTlzlrGpG18B2Q7FJ1rQ6vfnUY53LKEd6q7kYnlRtnO3MEkiTh7yO64s4ewais\nrps17VIRZ00jsgaGNtkdnV5gwTdHcTijCK18ONuZI1IoJMy7rxdiO/ijoLwGf/8iGUUVNXKXReTw\nGNpkV4QQWLY9Df89kQMvdxcsmxqLNhrOduaI3FwUeHdKb0QG++BCQd2saVdrOGsaUVMwtMmufLEr\nHRsPZMFVKWHxgzGIDPaRuyRqAm+VK5ZNjUWwRoUTF0vw+tepnDWNqAkY2mQ3vj98Ef/76xlIEjD/\n/ijEdvCXuySyglY+7vhwWhx8PV2x50w+Fn3LWdOIbhdDm+zCnjN5WPRt3WxnL42su4mJnIfxZkJX\nJX5IuYRPfjsrd0lEDomhTbI7nl2M19anQqcX+OuQDpjUP1zuksgGeoZqsPDarGmrfz+PDfs5axpR\nYzG0SVZZ+RV4Ze0hVNXqcE/vEDxzZ6TcJZENDercGnPG9gAALN2ehv8evyJzRUSOhaFNsikoq8bf\nv0hGcWUtBkS2wmtje3C2sxbg3pi2eObOSAgBzNt0BMnphXKXROQwGNoki4oqLV5ak4zLxVfRva0a\n70zibGctyV+HdMDEvmF1s6atO4wzV8rkLonIIfBdkppdjVaP2esP4/SVMoQFeGLJw7HwcHORuyxq\nRpIk4aVR3TCsexAqqrV4+doHOCKqH0ObmpVeL/DPzUdx8HwhArzd8OG0WPh5cbazlkipkDD/vl6I\nCfdDXlk1XvwiGSWVnDWNqD4MbWo2Qggs/+kUfjl2BZ7uSiydGosQP0+5yyIZubsqsfjBGHQK9EZm\nfgVmfnkYVTU6ucsislsMbWo2X+7JwFf7MuGilPDulBh0aaOWuySyA/+fvfuOj6LO/wf+mt3NlvSe\nkAQSkgChhiQgSDEBTkRBAQWlKpz3O793YsGCinCodxYURPTuvPPubDRRERE5G0qogpBGS0JLAoFA\nQnpPdvfz+yNsziWFtN3Zzb6ejwcPZXYy897hk33tfD4zn3HTOWHN/DgEemhx7EIJln3OWdOIWsLQ\nJqv4Ju0S3vn+FABgxfTBGB7uI3NFZEv83bVYMz8O7jon7MsswOtfn+SsaUTNYGiTxR06cxV/+fI4\nAODxSf1w6+AeMldEtqi3nytWz42FxkmBr5Iv4t+7zspdEpHNYWiTRaVfLMWzm1NhMArMHR2GWTeH\nyV0S2bDBPT3xl5nRUEjAf3afxReHL8hdEpFNYWiTxVworMQTG5JRXWfApCE98PBv+spdEtmBsf38\n8cydDbOmrdpxEonpV2SuiMh2MLTJIgorarF4fRKKK+swIsIHz08dBIWCs51R20yNC8Hvx0XCKIA/\nfX4UqTnFcpdEZBMY2tTlKmv1eHJDMnKLqhEV5I5X7hsKJxWbGrXPwvhwTB/WE3V6I57emIxz+RVy\nl0QkO36SUpeq1xuxdHMqMi6VIcRbh9VzY+Gi4Wxn1H6SJOGpyf0RH+WP8ho9Hl+XhCulnDWNHBtD\nm7qM0Sjwl23HcehsIbxc1Hhr/jD4uGrkLovsmFIh4cUZQxDdyxP5ZTV4fF0Syqrr5S6LSDYMbeoy\nf9t5Ct8dzYNOrcSaebEI8eZsZ9R5Wicl3pgTi3B/V2QVVOLpjQ2PciVyRAxt6hKbDmRjw/5sKBUS\nXrtvKKKCPOQuiboRd50T1syLhb+7FmnnS7Di86MwGDn5CjkehjZ12g/H8rD2u0wAwLJpgzAi0lfm\niqg7CvDQ4a35cXDTqrA7Ix+rdnDWNHI8DG3qlMPnCvHi1mMAgEcm9sXt0UEyV0TdWbi/K96YEwuN\nSoGtR3Lxwe5zcpdEZFUMbeqwzLwyPPNJCvQGgVk3h2LOqDC5SyIHMDTUCy/NGAKFBLy36wy2JeXK\nXRKR1TC0qUMuFVdh8fokVNUacOugQDw6sR8kiZOnkHXE9w/AU5MHAABWbj+BPRn5MldEZB0MbWq3\n4so6PLYuCUUVdRgW7o3l0wdztjOyuruH98SD8REwCmD5Z2k4ep6zplH3x9Cmdqmq1ePJDUm4UFiF\nvoFuWHlfDNSc7Yxk8rtxEbgrNhi1eiOe2piCrALOmkbdGz9tqc30BiOWfpqGkxfLEOSlw5p5cXDR\ncrYzko8kSVgyZQDG9PNDWXU9Fq9LQn5ZjdxlEVkMQ5vaRAiBV7adwMEzV+Hp7IS35sXBx42znZH8\nVEoF/jIjGoN7euJyaQ0Wr09COWdNo26KoU1t8u7O0/hv2iVonZRYPTcWvXxd5C6JqJFWrcSqOTEI\n9XXB2SsVWPJJCmo5axp1QwxtuqHNB3Pw8b4sKBUSXrkvGgNDPOUuiagJD2c13pofBz83DVKyi/Hi\nF8c4axp1OwxtatXO45fx1rcZAIClUwdiVB8/mSsialkPTx3WzI+Dq1aFn05ewZpv0jlrGnUrDG1q\nUVJWIV784iiEAP74mz6YPDRY7pKIbigywA2vz46Bk1LC579cwMd7s+QuiajLMLSpWacvl2HJplTU\nGwRmjuiF+WN6y10SUZvFhnnjxXuGQJKAd388ja9TLspdElGXYGhTE5eKq7F4fTIqa/WYMDAAj0+K\n4mxnZHfGDwzEE7dHAQBe/eoEDpwqkLkios5jaJOZkso6LF6fhKvltYgN88Kfpg+GkrOdkZ2aOSIU\nD4wNh8EosPTTNBzPLZG7JKJOYWhTo+o6PZ7cmIycq5WIDHDF67NjoHFSyl0WUaf834RITB4ahJp6\nA57ckIzzVyvlLomowxjaBKBhtrNlnx3FidxSBHposWZeHFy1TnKXRdRpkiThubsGYlQfX5RW1eOx\ndQ09SUT2iKFNEEJg5faT2H+qAO46p4Z7Xd21cpdF1GVUSgVevjcaA4I9kFdSjcXrk1BRw1nTyP4w\ntAnv/XQG21MuQuOkwOq5sQjzc5W7JKIup1Or8ObcWPTyccbpy+V49pNU1OmNcpdF1C4MbQf3+S/n\n8cGec1AqJLw8s2H+ZqLuytOlYdY0H1c1jmQV4aWtx2DkrGlkRxjaDmzXyStY/d90AMCzdw7AmH7+\nMldEZHlBXs5YMy8Ozholdh6/jLXfZXDWNLIbDG0HlZJdhBVbGmY7e2h8JO6MDZG7JCKr6dvDHa/P\nioFKKWHzwfPYsD9b7pKI2oSh7YDOXCnH05tSUKc34p7hPbHglnC5SyKyumHhPlhx92AAwF9/OIVv\n0i7JXBHRjTG0Hczlxitn9Ujo748n7ujP2c7IYd06qAcWT2qYNe0vXx7HwTNXZa6IqHUMbQdSWlWH\nx9cnoaCsFjGhXnjxniGc7Ywc3n03h2Le6DAYjALPbU5F+sVSuUsiahFD20HU1Bvw1MYUZBdUIsKf\ns50R/doff9MXt0cHobrOgCc2JONCIWdNI9vE0HYAeoMRyz9Lw7ELJQi4NtuZm46znRGZKBQSnp86\nECMjfVBcWYfH1yWhsIKzppHtYWh3c0IIrNqRjr2ZBXDXqfDWvDj4e3C2M6LrqZQKvHLvUPQPcsfF\n4mo8ce1Jd0S2hKHdzf0n8Sy+TMqFRqXAG3Ni0dufs50RtcRZo8LqubEI8dYhM68Mz21ORT1nTSMb\nwtDuxr48cgH/TjwLhQT8eWY0ont5yV0Skc3zdtXgrfnD4OWixi9nC/GXbcc5axrZDIZ2N7UnIx+v\nf30SALBkygDcEsXZzojaKsTbGWvmxcJZrcR3R/Pwtx9OyV0SEQCGdreUdr4Yyz9Lg1EAv0uIwLRh\nPeUuicjuRAV54NVZQ6FUSNhwIBsbD2TLXRIRQ7u7ycqvwNMbk1GrN2JaXAgeTIiQuyQiuzUiwhfL\npw8CALz9XSa+P5Ync0Xk6Bja3Uh+aQ0eX5+Esmo9bonyx1OTOdsZUWdNGhKERyb2AwC8tPUYfjlb\nKHNF5MgY2t1EWXU9Fq9PwpXSGgzu6YmXZgyBSsl/XqKuMHd0GGbfHAq9QeDZT1KQmVcmd0nkoPip\n3g3U1huwZFMKzuZXIMzPBavmxEDL2c6IutQjE/th4uBAVNUZsHh9Ei4WVcldEjkghradMxgFVmw5\nitScYvi5a/DWvDh4OKvlLouo21EoJCyfNhjDw31QVFGHx9YloYizppGVMbTtmBACb/43HYnp+XDV\nNsx2Fuipk7ssom7LSaXAa7OGol8Pd+QWVeGpjcmo4qxpZEUMbTv24Z5z2HL4AtQqBd6YHYOIADe5\nSyLq9lw0Krw5LxbBXjqcvFiGpZ+mQW/grGlkHQxtO7U9ORf//OkMJAl48Z4hiAnzlrskIofh46rB\nmvlx8HJR4+CZq3hl2wkIwVnTyPIY2nZoX2Y+XtveMNvZU3f0x7gBATJXROR4evm4YPXcWOjUSvw3\n7RLe3Xla7pLIATC07czxCyV4/rM0GIwCC28Jxz039ZK7JCKHNSDYA6/cGw2lQsLH+7Kw+WCO3CVR\nN8fQtiPZBRV4YkMyauuNuDMmGL8fHyl3SUQO7+Y+flg2rWHWtLe+zcDO45dlroi6M4a2nSgoM812\nVo/Rff3wzJ0DONsZkY24PToID9/aF0IAL35xFEfOcdY0sgyGth2oqGmY7exySQ0GhXjg5ZnRnO2M\nyMbMGx2G+0b2Qr1BYMknKTjFWdPIAvjJb+Pq9EYs2ZSCM1cqEOrrglVzYqFVc7YzIlsjSRIeuy0K\nEwYGoqq2Yda0S8XVcpdF3QxD24YZjQIvfnEUydnF8HXTYM28OHi6cLYzIlulUEhYcfdgxPX2RmFF\nHR5fdwQllXVyl0XdCEPbRgkhsObbDPx44gpcNCqsmReLIC/OdkZk69QqBVbOGoo+gW44X1iFJzcm\no7qOs6ZR12Bo26h1+7Lw2aHzcFJKeH32UPQJdJe7JCJqI1etE9bMi0OgpxYnckvxPGdNoy7C0LZB\nO1Iv4u87T0OSgBV3D0Fcbx+5SyKidvJ102Dt/GHwcHbCgdNX8dr2k5w1jTqNoW1jDpwuwCvbTgAA\nFk+Kwm8GBcpcERF1VKhvw6xpWiclvk65iH/+dEbuksjOMbRtyIncEizd3DDb2f1jeuPekaFyl0RE\nnTQoxBMvX5s17cM95/D5ofNyl0R2jKFtI85frcSTG5JRU2/AHdFB+MNv+shdEhF1kdF9/fDcXQMB\nAKu/ScdPJzhrGnUMQ9sGFJbX4rF1SSipqsfISF8snTqQs50RdTNTYoLxfxP6QAhgxZajSM4ukrsk\nskMMbZlV1uixeH0S8kqqMSDYHa/cy9nOiLqrB8b2xoybejbMmrYpBWeulMtdEtkZpoOM6vRGPLM5\nBaculyPE2xmr58bBWaOSuywishBJkrD49obH6VbU6LF4XRIul3DWNGo7hrZMjEaBP289hiPniuDt\nqsba+XHw4mxnRN2eUiHhhbsHIybUCwXXhsZKqzhrGrUNQ1sGQgi8/V0mfjh+Gc4aJdbMi0Owt7Pc\nZRGRlWiclHh9dgwi/F2Rc7UST21MQU2dQe6yyA4wtGWw8UA2PjmYA5VSwmv3xaBfD852RuRo3HRO\nWDM/DgEeWhy7UILln3PWNLoxhraVfZN2Ce98fwoA8Kfpg3FTBGc7I3JU/u5avDU/Du46FfZmFuCN\nHemcNY1axdC2okNnruIvXx4HADx2Wz9MHNxD5oqISG69/Vyxak4sNCoFtiXl4t+JZ+UuiWwYQ9tK\n0i+W4tnNqTAYBeaOCsPsUWFyl0RENmJILy/8eWY0FBLwn8Sz2Hr4gtwlkY1iaFvBhcJKPLEhGdV1\nBtw2pAcevrWv3CURkY25JcofS6YMAAC8seMkdqdfkbkiskUMbQsrrKjF4vVJKK6sw00RPlg2dRAU\nCs52RkRNTRvWE/9vXASMAvjT50eRmlMsd0lkYxjaFlRZq8eTG5KRW1SNfj3c8ep9Q+Gk4iEnopb9\nNj4C04eFoFZvxNMbk3Euv0LuksiGMEEspF5vxNLNqci4VIZgLx3enBcLF852RkQ3IEkSnpo8APFR\n/iiv0ePxdUnIL62RuyyyEQxtCzAaBf6y7TgOnS2El4sab82Pg4+rRu6yiMhOKBUSXpwxBEN6eSK/\nrAaPr09CWXW93GWRDWBoW8Dfdp7Cd0fzoFMr8ebcWPT0cZG7JCKyM1onJd6YHYPefi44l1+BJZtS\nUFPPWdMcHUO7i206kI0N+7OhVEh49b6h6B/sIXdJRGSnPJwbeur83DVIzSnGii1HYTBy8hVHxtDu\nQj8cy8Pa7zIBAMumDcLISF+ZKyIiexfgocPa+cPgplVhd3o+VnHWNIfG0O4ih88V4sWtxwAAi27t\ni9ujg2SuiIi6i3B/V7wxJxZqlQJbj1zAB3vOyV0SyYSh3QUy88rwzCcp0BsEZo0MxdzRYXKXRETd\nzNBQL7w0YwgUEvDeT2ewLSlX7pJIBgztTrpUXIXF65NQVWvAbwYF4tHb+kGSOHkKEXW9hP4BeGpy\nw6xpK7efwN7MfJkrImtjaHdCcWUdHluXhKKKOgzr7Y0/TR/M2c6IyKLuHt4Tv40Ph1EAyz5Lw7EL\nJXKXRFbE0O6gqlo9ntyQhAuFVegT6IaVs2Kg5mxnRGQF/29cJO6MDUZtvRFPbkhGdgFnTXMUTJkO\n0BuMeP6zNJy8WIYenjqsmRcHFy1nOyMi65AkCc9MGYDRff1QVl2Px9cloaCMs6Y5AoZ2Owkh8MpX\nJ/Dz6avwdHbC2vlx8HXjbGdEZF0qpQIvz4zGoBAPXC5tmDWtnLOmdXsM7Xb6x4+n8d/US9A6KbFq\nbix6+XK2MyKSh1atxOq5sQj1dcHZKxVY8kkKajlrWrcmCQvfpS9JEsa/vNOSu7AaAYGqWgOUCglv\nzI7BqL5+cpdERIS8kmr8v38fwtXyWmidlFDyglhZ/fT8byw2AY5VBmIra/XW2I1VaJ2UWDKlPwOb\niGyG6dqaJzYkoaCsVu5yyIKscqZdXl1nyV1YlZNSAY2TUu4yiIia0BuMfKiIDXDTqS12pm2V0OY8\nuURE5CgsmXu8EI2IiMhOMLSJiIjsBEObiIjITjC0iYiI7ARDm4iIyE4wtImIiOwEQ5uIiMhOMLSJ\niIjsBEObiIjITjC0bURiYqLcJdgNHqu24XFqGx6ntuOxkh9D20bwl6HteKzahsepbXic2o7HSn4M\nbSIiIjvB0CYiIrITFn/K19ChQ5GWlmbJXRAREdmM+Ph4iw0lWDy0iYiIqGuwe5yIiMhOMLSJiIjs\nBEObiIjITjC0iYiI7ARDm4iIyE4wtImIiOwEQ5uIiMhOMLSJiIjsBEObiIjITjC0iYiI7ARDm4iI\nyE4wtImIiOwEQ5uIiMhOMLSJiIjsBEObiIjITjC0iYiI7ARDm4iIyE4wtImIiOwEQ5s67YUXXoBC\nocDu3bvlLqXdEhISoFC079dAoVBg3LhxFqqIWtORfy9qG7Zr+8DWbwHZ2dlQKBRmf7RaLXr37o0H\nH3wQ586ds3gN1vxwkySp8Y+9aa5u05eQPXv2tPpzZH320M7a0n5sla0fWwJUchfQnUVFRWHWrFkA\ngLKyMuzatQsffPABtm7dil9++QWRkZEW3b+1fgEXLVqE2bNno2fPnlbZX1f6+OOPUV1dLXcZ1Eb8\n97KcjIwMODs7y10G3QBD24KioqLwpz/9yWzZwoUL8dFHH+Hll1/GBx98IFNlXcvHxwc+Pj5yl9Eh\nrX3REEJYsRJqC3v6Ymhv7adv375yl0BtwO5xK/vjH/8IAEhKSmpcVlFRgWXLlqFv377QarXw9/fH\njBkzcPz48SY/f/HiRTz88MOIjIyETqeDn58f4uLisGTJksZ1TF1zQgizLvoXX3zRbFtbtmxBQkIC\nPDw84OzsjNjYWPzrX/9qss9fj1n/61//wpAhQ6DT6bBw4UKz16/vDqyvr8eqVaswePBgODs7w9vb\nG7fffjv27dvXZB8LFiyAQqHAuXPnsHLlSvTt2xcajaZJzb/25ZdfQqFQ4J///KfZ8qeeegoKhQK3\n3nqr2fIjR45AoVCYfZG6fhghISEBL730EgBg3LhxjceuubG+vLw8zJ07Fz4+PnBxccG4ceOQkpLS\nYr3XM+27vr4ey5YtQ69evaDVajFo0CB88sknzf5MdnY2FixYgB49ekCj0SAsLAyPPfYYCgsLm6xr\nqruzdZokJyfjvvvuQ48ePaDVatGzZ0/cfffd2L9/v9l6BQUFeOSRRxAaGgqNRoOgoCAsXLgQOTk5\nTbYZFhaG3r17o7y8HA8//DACAwOh0+lw00034ccff2zxmP2aqe2cP3++yfotvZafn4+FCxfC19cX\nrq6uGDNmDBITE5tty/X19Xj77bdx6623Ijg4uPE9zZ07F2fOnGlSX1vaT0pKCmbOnImAgABotVpE\nRkZi6dKlqKysbO7QN2Eaglu4cCFSU1MxceJEeHh4wMvLCzNnzmz2WJvqOH/+PObMmQM/Pz+zY9NS\nOz969Cjuuece+Pn5QavVol+/fli+fDmqqqparOnYsWOYMmUKvLy8eA1CF+OZtpWZvn2b/ltTU4Px\n48fjyJEjGDlyJGbOnIkLFy7g008/xbfffovvvvsOo0ePBgBUVlZi9OjRyMvLw5133ol7770XFRUV\nyMzMxN/+9je8/vrrAIAVK1bgww8/RE5ODl544YXGfSckJDT+/9NPP43Vq1cjLCwMs2fPhrOzM77/\n/ns89NBDSE9Px5tvvtmk9pUrV2Lfvn2YOnUqJk+ejMDAwFbf54wZM7B9+3YMHDgQjz76KIqKirB5\n82aMGzcOmzZtwowZM5r83KJFi5CUlIQpU6Zg+vTpiIiIaHEf8fHxkCQJiYmJeOihhxqXJyYmAgAO\nHjyI+vp6ODk5AQB27drV5DgA5sMICxcuhCRJ2L17NxYsWICwsDAAaPyvSVFREcaOHQs/Pz8sXLgQ\n2dnZ+OKLLzBhwgSkp6cjICCgxbqvN2vWLKSkpOCuu+5CfX09Nm3ahDlz5sDT0xOTJk1qXC8zMxNj\nxoxBUVERpk+fjn79+iEpKQnvvPMOduzYgYMHD8LX19ds28XFxV1S5+bNmzF//nyoVCpMmzYN4eHh\nuHTpEvbu3YstW7Y0ttGCggKMHDkSWVlZmDhxIubNm4fMzEx8/PHH2LFjB/bv348+ffqYHfu6ujpM\nnDgRFRUVmD17NoqKirBp0ybccccdOHLkCAYPHmxWS3PDPq0NBV3/Wnl5OW655RacOnUKEyZMwIgR\nI3D69GlMmjSp2dAqLCzEk08+ifj4eEydOhUeHh7IyMho/B1NSkpqbB9taT9bt27FrFmzoNVqMW3a\nNAQGBiIpKQmvvfYadu3ahT179jS22Rs5d+4cEhISMGrUKCxatAjHjx/Hli1bcODAARw+fBhBQUFN\n3suYMWPQo0cPPPDAAygpKYFarW7xWO3btw8TJ06EEAL33nsvQkJCsGvXLrz88sv44YcfsHv3bmg0\nGrOfOXPmDMaMGYNhw4bh97//PXJzc9v0XqiNBHW5rKwsIUmSmD59utlyo9EoHnjgASFJkvjtb38r\nhBDihRdeEJIkid/97ndm6+7evVsoFArRp08fYTQahRBCbNu2TUiSJN55550m+ywqKjL7e3x8vFAo\nFM3W9+233zbWV1tb27i8vr5eTJs2TUiSJA4fPty4fMWKFUKSJOHp6SkyMzObbM/0+u7duxuXffjh\nh0KSJDFp0iRhMBgal2dkZAgXFxfh6ekpysvLG5ebjkt4eLjIy8trtu7mREdHix49ejT+vaSkRKhU\nKjF+/HghSZLYt29f42t33HGH0Gq1orq6unFZc8epuffza5IkCUmSxOOPP262/MUXXxSSJIlXX321\nTbXHx8cLSZLE6NGjRWVlZePy3bt3C0mSxG233Wa2fkJCgpAkSaxfv95s+UsvvWTWprq6zry8POHs\n7Cy8vb1FRkZGk9cvXbrU+P8LFiwQkiSJv/zlL2brfPzxx0KSJDF+/Hiz5aGhoUKSJDFjxgyh1+sb\nl3/00UdCkiTx0EMPma3f3L+Xqe3k5OQ0qa2515YuXSokSRJLliwxW3f9+vVCkiShUCjM/u1ra2ub\nbZO7d+8WKpWqye9ua+2noKBAuLm5iYiIiCbbfOONN4QkSWLVqlVNfu56ps8YSZLEn//8Z7PX3nzz\nTSFJkrj//vvNlpvW/8Mf/tDsNiVJEuPGjWv8u8FgEBEREUKpVIo9e/aYrfvggw8KSZLESy+91GxN\nbW1b1H4MbQswNd6oqCixYsUKsWLFCrF48WIRGxsrJEkSPj4+4syZM0IIIXr37i2cnZ1FQUFBk+2Y\nAnTv3r1CiP+F9r/+9a8b1tBaaN95551CqVQ2u89jx44JSZLEU0891bjM9CF0/Yfc9a//+kNqod4/\n7QAAIABJREFU3LhxQpIkcezYsSbrP/7440KSJLFu3brGZaYP17///e83fG+/9sgjjwhJkkR6eroQ\nQojt27cLSZLEjz/+KDQaTeMHml6vF25ubmLs2LFmP9/R0HZzcxNVVVVmy3NychoDqC1Mod3cfsLC\nwoSvr2+TbcfFxTVZt6amRgQEBAhnZ2dRV1fX5XW+9tprQpIksXLlylbXq62tFVqtVgQFBZnVYRIT\nEyMkSRIXLlxoXBYaGioUCkWTwNXr9UKlUolhw4aZLe+K0A4NDRWurq6ipKSkyfoDBw5s9d/+eoMH\nDxZhYWFmy1prP6tXrxaSJIktW7Y0ec1gMAh/f/8m77k5ps8YX19fsy+hQjR8+Q4ODm62Peh0OlFc\nXNzsNq8PbdOXx3vuuafJupcvXxZarVZEREQ0qSk4ONjsCxh1LXaPW1BmZmbj+JaTkxOCg4Px4IMP\nYtmyZQgNDUVZWRmys7MRGxvbpFsTaOj+3bZtG9LS0jBmzBgkJCQgMDAQDz/8MH744QdMmjQJo0eP\nbvcFJIcOHYKbmxv++te/Nnmtvr4eQMOVpNcbNmxYm/eRmpoKb29vDBo0qMlr8fHxWLt2LdLS0jBv\n3rwO7wNoGDf861//it27dyMqKgqJiYlwc3NDfHw8hg8fjsTERCxbtgxJSUmoqKho0jXeUX369IFO\npzNbZuqKLCkpafN2JElCTExMk+XBwcE4dOhQ499TU1MBNO3aBwCNRoORI0fiq6++QmZmptkxb2ud\nH374IbKzs83WW7hwIUJDQ3H48GEAwMSJE1t9LxkZGaitrcXNN9/cbPfuLbfcgtTUVKSlpSEkJKRx\nuaenJ3r16mW2rlKpREBAQLuOZVuUlpbi/PnzGDZsGDw8PJq8PmLECJw8ebLJ8qSkJLz++uvYv38/\nCgoKGn9PADTpHm6N6d907969OHr0aJPXVSpVs797LYmJiYFWq22yjZtuuglffvllk/bQu3dveHp6\ntmnbrbW5gIAA9OvXD8eOHUNlZSVcXFwaX4uOjoZSqWzze6D2YWhb0LRp0/DFF1+0+HpZWRkAtDiu\naBozNq3n7u6OAwcOYPny5fj666/x2WefAWi4Sv2VV17BtGnT2lRXUVERDAZD4xeK60mS1OQiEwDw\n9/dv0/ZNNffr16/Z165/Xx3dB9AQBL8e105MTMSYMWOgVCoRHx+PNWvWoL6+vnGcu6tCu7kPfJWq\n4dfJYDC0a1tubm7NbstoNDb+va1tpby8vEN1fvTRR2aT40iShPHjxyM0NBSlpaUA0GR89Hrtbc+t\n1Wiqs73H8kZMx8fPz6/Z15trf/v27cOECROgUqlw2223ITIyEi4uLpAkCR988EGzF8C1pKioCACw\ndu3aFtdpz62aLb0P07/B9e2hvb/Dv97W9QIDA3H06FGUlZWZhXZ7f4epfRjaMnJ3dwcAXLlypdnX\nTctN6wENF7SsW7cOBoMBKSkp+Oabb7B27VrMnDkTBw8eRFxcXJv26+Li0q4PG6B9Hybu7u7tel8d\n2QfQcLvZgAEDkJiYiLKyMqSmpuLVV18F0BDQr7zyCg4ePIhdu3ZBo9Fg1KhR7dq+rehIW2kP00V6\nzTGdmV28eLHVD2RL19gS09XJzQX89V8QTF+QCgoKmt1Wfn5+k2Wvvvoq9Ho99uzZgxEjRpi9tmnT\npnbVanrvZ86cQXh4eLt+tjktvY+WjnV7f4d/va3m9iFJUqf2Qe3Ha/Fl5O7ujrCwMJw8ebLZW3ZM\nZz5Dhw5t8ppSqcSwYcOwfPlyrF27FgaDATt27DB7HWj+XtERI0YgNzfXold1xsTEoKioCCdOnGjy\nWmvvqyPGjRuHK1eu4B//+AeMRmPjFcCjRo2CWq3Gzp07ceDAAQwfPrxJV2JzTMeuq8/yOsPUhd7c\nLFu1tbU4ePAgdDpdi70bnXHTTTcBAL7//vtW14uKioJGo8GhQ4fMuo9N9uzZA0mSEB0d3aX1mb5U\nXN+ejUYj0tLSzELEw8MDvXr1Qnp6emMPgokQAgcPHmyy/bNnz8LHx6dJYF+5cgVnz55tsn5r7ce0\njZ9//rktb+2GUlJSmkw2U19fj19++aXT7cHU5pqbnjg/Px8ZGRkIDw83O8smy2Noy+yBBx5ATU0N\nli9fbrZ87969+PLLLxEZGdl4O016enqz36wvX74MAGazGXl7e0MI0WwwP/LIIwCABx98sNku6qys\nrGbv82yP+++/HwDw3HPPmXXznjp1Cu+99x48PT0xderUTu3DxNTlvXr1anh4eDT2Njg7O2P48OH4\nxz/+gfLy8jZ3jXt7ewNoGgJy6tmzJ+Lj43HkyBF8+umnZq+tWrUKV65cwaxZsxq7vrvS/fffD2dn\nZ6xcubLJeKsQorH9qdVqzJo1CxcvXsSaNWvM1tuwYQNSUlKQkJBgNp7dFUzXQXz88cdmy99++21k\nZWU1WX/OnDmorKzEyy+/bLZ848aNSE9Pb3KmGBoaisLCQrP3XldXh0WLFkGv1zfZfmvtZ+HChXB1\ndcUzzzzT5B5voOE6A9NYclsUFhZi1apVZsveeecdXLp0CTNnzuxUexgzZgzCw8OxdevWJl8yli1b\nhtra2sbfc7Iedo/L7JlnnsHXX3+Nf/zjHzh69CjGjh2LixcvYvPmzXB2dsb777/fuO53332HJUuW\nYOzYsejTpw+8vLyQnp6OHTt2wN/fH/Pnz29cd/z48diyZQvuu+8+3HbbbdBoNBg7dixGjx6N22+/\nHc899xxeffVVREZG4rbbbkNwcHDjt+dDhw5h06ZNCA0N7fD7uv/++/H555/j66+/RkxMDCZNmoTi\n4mJs3rwZtbW1eP/99+Hq6tqpY2dyyy23AGjoKpwyZYrZh25CQkLj5B8thfb1vREJCQmQJAnPP/88\n0tPT4e7ujtDQUMyePbtL6m1t36299u6772LMmDGYM2cOPvvsM/Tp0wfJycn4/vvvER4ejpUrV3Z5\nfUDDmOb777+PefPmISYmBtOnT0dYWBjy8/OxZ88eTJ48uTGkX3/9dezevRvPPvssdu3ahZiYGJw6\ndQpffvklfH198e6773Z5fdOnT0doaCj+85//IDc3F4MGDUJKSgrS0tIQHx/f5Ezxueeew5YtW7Bq\n1SqkpKRg+PDhOHPmDLZv347bbrsN3333ndmEIIsWLcIPP/yA0aNH495774VKpcLOnTthMBgQHR2N\ntLQ0s+231n78/PywYcMG3HfffRg4cCDuuOMOREZGorKyEufOncOePXuwYMEC/P3vf2/Tex8zZgxW\nr16Nn3/+GUOHDsXx48fx9ddfIygoqHGYqKMkScL777+PSZMmYfz48bj33nsRFBSE3bt34+DBgxg2\nbJjZpE5kJTJeud5ttXSfdkvKy8vF0qVLRWRkpFCr1cLX11fcc8894ujRo2brpaeni8cee0zExMQI\nb29v4ezsLPr27SseffRRkZuba7auXq8XTz75pOjZs6dQqVRCoVCIF1980Wydb775RkyePFn4+voK\ntVotQkJCREJCgnjzzTfF1atXG9d74YUXmty7+mstvV5fXy9WrlwpBg4cKLRarfDy8hKTJk1qcs+n\nEA339zZ3609bDRo0SCgUCrF69Wqz5Tt37hSSJDW5P9skISGh2Vvj/vOf/4iBAwcKjUbT5FaY6//+\na6291tZ9t/bauXPnxP333y8CAwOFWq0WoaGh4pFHHmn29r2uqtPk8OHD4p577hF+fn5Co9GIXr16\niRkzZogDBw6YrZefny8WLVokevXqJdRqtejRo4d44IEHRHZ2dpNthoWFid69eze7v+Zea+lWxjNn\nzogpU6YIV1dX4eHhIaZOnSrOnDnTYru6cuWKWLBggfDx8REuLi5izJgxIjExUSxatEhIkiRSU1PN\n1v/0009FTEyMcHZ2Fj169BALFy4UV65c6VD7EUKIkydPigULFoiePXsKtVot/Pz8RFxcnHjuueea\nnQvheqbPmIULF4q0tDQxceJE4e7uLjw9PcXMmTObPdY3+jdv6fXU1FRx9913Cx8fH6FWq0WfPn3E\n888/bza3wPU1keVIQtjZBLlE5LBGjhyJtLQ0iz00ZOzYsTh48CBKS0tt+uEZ2dnZCA8Px4IFC8x6\n46j745g2EdkFg8GArKwsBAcHd3pbeXl5TZZ98skn2L9/P8aPH2/TgU2OjWPaRGTz1q5di2+++QYF\nBQVdcvHTxIkT4eXlhSFDhkCtVuPo0aP46aef4ObmhjfeeKMLKiayDIY2Edk8022Njz76KP785z93\nensLFy7Exo0bsXHjRlRUVMDX1xezZs3C8uXL0b9//y6omMgyOKZNRERkJyx+pj106NAmt0QQERF1\nV/Hx8Y1TJ3c1i1+IlpaWBtHwNDH+aeXPihUrZK/BXv7wWPE48TjxWNnyn+ZmkesqvHqciIjITjC0\niYiI7ARD20Z01SMjHQGPVdvwOLUNj1Pb8VjJz+JXj0uSBAvvgoiIyGZYMvd4pk1ERGQnGNpERER2\ngqFNRERkJxjaREREdoKhTUREZCcY2kRERHaCoU1ERGQnGNpERER2gqFNRERkJxjaREREdoKhTURE\nZCcY2kRERHaCoU1ERGQnGNpERER2gqFNRERkJxjaREREdoKhTUREZCcY2kRERHaCoU1ERGQnGNpE\nRER2gqFNdqG0qg6ZeWUor66XuxQiItmo5C6AqDVZ+RVY/U06jpwrAgColBJG9/HDk5P7w99dK3N1\nRETWJQkhhEV3IEmw8C6om/r5dAGWbEpBvUFA46RAoIcOFworYRSAt6sar8+KwaCennKXSURkxpK5\nx9Amm5R2vhiPfnwEtfVG3B4dhMcn9YOHsxoFZTV48YtjOJJVBHedEz58aCSCvJzlLpeIqJElc49j\n2mRzKmrqseyzNNTWG3FnbDD+NH0QPJzVAAA/dy3emh+HUX18UVZdj2c/SUVtvUHmiomIrIOhTTZn\n7XeZKCirxcAQDzwzZQAkSTJ7XaVU4MV7hiDE2xmnLpdjw/5seQolIrIyhjbZlKPni7E9+SLUKgWW\nTRsElbL5Juqmc8LSqQMBAB/tPYdLxdXWLJOISBYMbbIp7/10BgAwZ1QYevu5trpubJg3bh0UiFq9\nEe/uPGWN8oiIZMXQJpuRlFWII1lFcNOqMHdUWJt+ZtHEvlApJew8cRnnr1ZatkAiIpkxtMlmfLDn\nHABg9qgwuOmc2vQzAR463B4dBCGAdfuzLFkeEZHsGNpkE87lV+DIuSJonZSYeVOvdv3s/DG9oZCA\nb9IuoaCsxkIVEhHJj6FNNuHzX84DAG6PDmrzWbZJLx8XxPcPgN4gsD35oiXKIyKyCQxtkl1FTT2+\nSbsEAJg5on1n2SbTh/UEAGxLzoXByMl8iKh7YmiT7H48cQXVdQbEhHkh3L/1K8ZbMqy3N4K9dLhS\nWoODZ652cYVERLaBoU2yM51lTx4a3OFtKBQSpsaFAAC+SsrtkrqIiGwNQ5tkdam4Cqk5xdA4KTBu\nQECntnV7dBAkCThwuoCP8CSibomhTbL6Ni0PABAfFQAXTeeeFOvnrkVcmDfqDQKJ6Ve6ojwiIpvC\n0CZZ7TxxGQAwKbpHl2xv4uCG7Xx3LK9LtkdEZEsY2iSbnKuVOJdfATetCsN7+3TJNhMGBECllJCc\nVYTC8tou2SYRka1gaJNsdp1s6MIe288fTqquaYruOieMiPCFUQB7MvK7ZJtERLaCoU2yMY07J3Ty\nArTrJfT3BwDsZmgTUTfD0CZZXC6pRsalMujUStwU0TVd4yZj+vlDIQFHsgpRUcOryImo+2Bokyx+\nPt0wAcpNET7QOim7dNteLmpE9/KC3iAa90NE1B0wtEkWP1+btezmSF+LbD/+Whc5x7WJqDthaJPV\n1euNOHKuEABwcx/LhPaovn4AgENnCzkXORF1GwxtsrqjF0pQVWdAuL8rAjx0FtlHT29nBHnpUFZd\nj4xLpRbZBxGRtTG0yepMD/QYaaGucQCQJAkjInzN9kdEZO8Y2mR11ght4H9d7wfPFFp0P0RE1sLQ\nJqsqKKvB6cvl0DopMTTUy6L7igvzhlIh4eTFUj5AhIi6BYY2WdWhsw1nvXG9vaHuolnQWuKiVWFI\nT08YjAKHs3i2TUT2j6FNVmW6b9pSt3pdb8S1/RxiFzkRdQMMbbIavcGIw+eujWdb6Fav642MbJht\n7dCZqxCCt34RkX1jaJPVZOaVoaxajxBvHUK8na2yz76B7vByUeNyaQ2yr1ZaZZ9ERJbC0CarSc4u\nBgDEddFjONtCoZAa5zY/xFu/iMjOMbTJapKziwAAsWGWvWr8eqZby345y3FtIrJvDG2yCr3BiLSc\nhjPtmDBvq+7b9CUh7XwJpzQlIrvG0CarOHW5HFV1BoR4O8PfXWvVfQd46BDspUNlrR6nL5dZdd9E\nRF2JoU1WkZwlT9e4Sey1s/ukrGJZ9k9E1BUY2mQVjePZva3bNW5i2q+pDiIie8TQJovTG4xIPd9w\nhhsbKk9ox4SaxrWLOa5NRHaLoU0Wd+pyOapqDQjx1sHfw7rj2SaBnjoEeelQUaPH6cvlstRARNRZ\nDG2yuJRrXdLWvmr8eqZxbXaRE5G9YmiTxZkmVYmVObRjrl0El8LQJiI7xdAmi9IbjEjNMYW2PFeO\nm8RcG09PzeG4NhHZJ4Y2WdTZ/ApU1uoR5KVDgIdO1lqCvHTo4alDeY0eZ65wXJuI7A9Dmyzq2PkS\nAEB0L3nPsk3YRU5E9oyhTRZ19EJDaA/u6SlzJQ1M4+op2ZxkhYjsD0ObLOqYjYV2dK+GOo5eKOHz\ntYnI7jC0yWIKymqQV1INZ40S4f6ucpcDAAjxdoaXixrFlXW4UFQldzlERO3C0CaLOZbbcJY9KMQT\nSoUkczUNJEnCENPZ9rXxdiIie8HQJosxXYRmK13jJqaL4tLOc1ybiOwLQ5ssxnQR2hAbC22eaROR\nvWJok0XU1BuQmVcGSQIGhnjIXY6ZfoHu0KgUyLlaiZLKOrnLISJqM4Y2WUTGpTLoDQLh/q5w1TrJ\nXY4ZJ5UCA659kTD1BhAR2QOGNlnEMRvtGjcZ0rNhXPsox7WJyI4wtMkibO3+7OuZ7tdO47g2EdkR\nhjZ1OSGEzYf2oJ6ekCQg41IpausNcpdDRNQmDG3qcrlFVSiurIOXixoh3s5yl9Msd50Twv1cUW8Q\nyMgrk7scIqI2YWhTl/v1WbYk2cakKs0Z0ss0rs0uciKyDwxt6nK23jVuMqRxXJsXoxGRfWBoU5ez\ntSd7tcR0MdoxPjyEiOwEQ5u6VEVNPc7lV0CllBAV5C53Oa3q4amDn5sGpVX1yLlaKXc5REQ3xNCm\nLnUitxRCAP16uEPrpJS7nFb9+uEhvPWLiOwBQ5u6lL10jZtwkhUisicMbepStj4T2vWiQ689PITT\nmRKRHWBoU5cxGAWO59rXmXZkgBt0aiUuFFahsKJW7nKIiFrF0KYucy6/AlW1BgR6auHnrpW7nDZR\nKRUYGNzw8JBjPNsmIhvH0KYuY29d4yZ8vjYR2QuGNnUZe5lU5XrRvXgxGhHZB4Y2dZljFxpCb/C1\nK7LtxaAQTygkICOvDDV8eAgR2TCGNnWJwopa5BZVQ+ukRGSAq9zltIuLVoWIADfoDQLpF0vlLoeI\nqEUMbeoSx691jQ8M8YBKaX/NyjQOz3FtIrJl9vfpSjbJ3iZVuV7jxWi8gpyIbBhDm7qEvV45bmK6\nGO3YhWIYjXx4CBHZJoY2dVqd3oiMS2UAGrrH7VGgpw7+7lqUVeuRzYeHEJGNYmhTp53KK0Od3ogw\nPxd4OKvlLqfD/ne/Nm/9IiLbxNCmTrP38WwTXoxGRLaOoU2dZjoztdfxbJMhpklWLvBMm4hsE0Ob\nOkUI0XimbQo9exUZ4AqdWoncomoUlvPhIURkexja1CkXi6tRVFEHT2cn9PJxlrucTlEpFRgU0tBb\nkMazbSKyQQxt6hRT1/jgnp6QJEnmajqPDw8hIlvG0KZOMYWbvXeNmzC0iciWMbSpU/43nm3fF6GZ\nmB4ekplXhpo6PjyEiGwLQ5s6rKy6HufyK+CklBDVw13ucrqEi0aFyAA3GIwCJy/x4SFEZFsY2tRh\npoeERAV5QOOklLmarmPq6k/L4cVoRGRbGNrUYd2ta9wkmg8PISIbxdCmDjNdOR7dTS5CMzF9CTl+\noYQPDyEim8LQpg7RG4w4cbFhzNfepy+9XoCHDgEeWpTX6JFVUCF3OUREjRja1CGnLpejtt6IXj7O\n8HKx34eEtITzkBORLWJoU4f8elKV7sjURZ7GJ34RkQ1haFOHdLdJVa4X3fjwEJ5pE5HtYGhTu5k/\nJKR7nmlHBLjBWaPEpeJq5JfVyF0OEREAhjZ1QF5JNa6W18Jd54RQHxe5y7EIpUJqPNtOyS6SuRoi\nogYMbWq3tGtd44N7ekKhsP+HhLQkJtQU2hzXJiLbwNCmdjONZ0d3065xk9gwbwBACmdGIyIbwdCm\ndjt67VnT3fUiNJOoIHfo1ErkXK3E1fJaucshImJoU/uUX3tIiEopISqoezwkpCUqpaLxfu2UHI5r\nE5H8GNrULmnniyEEMCDYA9pu9JCQlsSYusg5rk1ENoChTe2SfC28TOO93V1MGK8gJyLbwdCmdjGF\nlynMursBQR7QOCmQVVCJogqOaxORvBja1GYVNfXIzCuDUiE1jvV2d04qReNUram8ipyIZMbQpjY7\ner4Exmvj2Tq1Su5yrCY2tGEoIJnj2kQkM4Y2tVnyta7xWAfpGjfhuDYR2QqGNrWZ6UwzxkEuQjMZ\nGOIJjUqBs/kVKK6sk7scInJgDG1qk8pafeN4dnd9HGdL1CpF40QyR84VylwNETkyhja1ydHzxTAY\nBaKC3OGicZzxbJObInwAAL8wtIlIRgxtahPT5CKmh2g4muHhDaF9+GwhhBAyV0NEjoqhTW1iemhG\nbG/HGs826RvoBg9nJ1wurcGFoiq5yyEiB8XQphuqrNXj5MVSKCQguqdjnmkrFBKG9b7WRX6WXeRE\nJA+GNt1QSnYRDEaBAcEecNE63ni2yfDwhl6GwxzXJiKZMLTphkxnliMifGWuRF43XXv/SVlF0BuM\nMldDRI6IoU03dOhaaA+/dgW1owry0iHEW4eKGj0y8srkLoeIHBBDm1p1pbQaOVcr4axRYlCIh9zl\nyG54eMPZNse1iUgODG1qlSmc4sK8oVKyuQyP4Lg2EcmHn8LUKlNo3+TgXeMmw3r7QCEBxy6UoLJW\nL3c5RORgGNrUIqNRNJ5R3uTgF6GZuOucMDDEE3qDYBc5EVkdQ5tadOpyOUqq6hHooUUvH2e5y7EZ\nY/r6AQAOnCqQuRIicjQMbWrRL2evAmi4alySJJmrsR2j+zWE9v7TBTAaOaUpEVkPQ5taxPuzmxfh\n74pADy2KKup46xcRWRVDm5pVVatH2vliSBIwzEHnG2+JJEkYfa2LfH8mu8iJyHoY2tSsX84Vot4g\nMCjEE54uarnLsTmmLvJ9HNcmIitiaFOz9mbkAwDGXgsnMhcX5g2tkxKZeWXIL6uRuxwichAMbWrC\nYBTYf+0Mcmw/f5mrsU0aJ2XjA0R4FTkRWQtDm5o4nluCkqp6BHvpEObnInc5NqtxXJuhTURWwtCm\nJv7XNe7PW71aYQrtX84VoqbOIHM1ROQIGNrUxN5rV0SPjeJ4dmv83LUYGOKB2nojDpzm2TYRWR5D\nm8ycL6xEztVKuGlViO7lJXc5Nm/CwEAAwM7jl2WuhIgcAUObzOy7dpZ9cx8/PtWrDSYMDADQMDta\nFR8gQkQWxk9lMrM389p4NrvG2yTAQ4fBPT1RW2/EfnaRE5GFMbSpUWFFLdJyiqFSSrg5klOXttVv\nrnWR/8guciKyMIY2Nfrx+GUYBTAy0heuWie5y7Eb4651kf98+iqfsU1EFsXQpkY/XDtTnDi4h8yV\n2Bd/dy2ie3miVm/kPdtEZFEMbQIAXCquwrELJdA6KTl1aQdMYBc5EVkBQ5sAAD8cawibW6L8oFOr\nZK7G/owfGAhJAg6cLkBpVZ3c5RBRN8XQJgDA98fyALBrvKN83TQYEeGLeoPAt0fz5C6HiLophjbh\nzJVynM2vgLtOhRERvGq8o+6KCwYAfJWUCyGEzNUQUXfE0Cb8cO0se/yAQDip2CQ6amxff3i5qHE2\nvwInL5bKXQ4RdUP8hHZwRqPA99fGs29l13inOKkUuCM6CADwVfJFmashou6Ioe3gjmQVIa+kGgEe\nWgwN5VzjnXVnbEMX+Q/H8jitKRF1OYa2g/sqORcAcGdMMJQKPoazs8L8XDGklyeq6gz48QRv/yKi\nrsXQdmAllXXYnX4FCul/Z4jUeXfFhgAAtiXlylwJEXU3DG0HtiP1IuoNAiMjfRHgoZO7nG5jwsAA\nuGpVOJ5bygvSiKhLMbQdlMEo8Nkv5wEAdw/vKXM13YtOrcLUa2fbn/ycLWstRNS9MLQd1L7MfFwu\nqUGItw6j+nDa0q42c0QvKBUSfjxxBfmlNXKXQ0TdBEPbQW0+mAMAmHlTKBS8AK3LBXrqkNA/AAaj\nwKafs+Uuh4i6CYa2Azp5sRTJ2cVw1igxOSZI7nK6rfljegMAth7JRUkl5yMnos5jaDugj/aeAwDc\nPawnn5ttQVFB7hjVxxc19QZ8cq1ng4ioMxjaDiaroAK70/OhVikw++Ywucvp9hbGRwAAPj2Uw7Nt\nIuo0hraD+U/iWQDA5KHB8HHTyFxN9ze4pydGRvqiqtbQ2MNBRNRRDG0HciqvDDuPX4ZapcCCW3rL\nXY7D+MNv+gAAPv/lPC6XVMtcDRHZM4a2A/nnT2cANIxlczIV6+nXwx0TBwei3iDw952n5C6HiOwY\nQ9tBHDp7FftPFcBZrcT9Y3mWbW1/+E1faFQKfH/sMlKyi+Quh4jsFEPbAegNRrz1TQYAYMEt4fB2\n5Vi2tfXw1DXeArbqv+nQG4wyV0RE9oih7QA+PXQeWQWVCPbSYRavGJfNvDG9Eeylw9m2sqr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"text": [ "<matplotlib.figure.Figure at 0x7febd2bc5890>" ] } ], "prompt_number": 4 }, { "cell_type": "code", "collapsed": false, "input": [ "# Plot samples\n", "fig = plt.figure()\n", "# Plot cumulative posterior\n", "plt.plot(x, pc, color='#e41a1c')\n", "# Calculate histograms and scale them into the same figure\n", "hist_r = np.histogram(r, bins=30)\n", "hist_rr = np.histogram(rr, bins=30)\n", "plt.barh(hist_r[1][:-1], hist_r[0]*0.02/hist_r[0].max(),\n", " height=hist_r[1][1]-hist_r[1][0], left=0.35, color='#4daf4a')\n", "plt.bar(hist_rr[1][:-1], hist_rr[0]*0.2/hist_rr[0].max(),\n", " width=hist_rr[1][1]-hist_rr[1][0], color='#377eb8')\n", "plt.legend(('Cumulative posterior', 'Random uniform numbers',\n", " 'Posterior samples'), loc='best')\n", "# Set limits\n", "plt.xlim((0.35, 0.55))\n", "plt.ylim((0,1));" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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oQdGiRY3Kvby8OHr0KKdOncLNzY2TJ08CmGxGr1KlCg4ODpw6dUpf1rZtW77+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\n+Qbj1cfjQnj/nYjaiEP0RAplOnYM5uPHIenCETT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"text": [ "<matplotlib.figure.Figure at 0x7febd2bc57d0>" ] } ], "prompt_number": 5 } ], "metadata": {} } ] }
gpl-3.0
antonpetkoff/learning
algorithms/k-means/k-means.ipynb
1
55327
{ "cells": [ { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "finished in 7 iterations\n", "[[491036.01, 349798.33], [179954.98, 380007.9705], [440134.41, 400135.41], [538883.52, 400947.36], [150006.7365, 350103.876], [440754.33, 298283.2], [209948.245, 349963.26], [539379.19, 299652.83]]\n", "100\n", "2000\n", "100\n", "100\n", "2000\n", "100\n", "2000\n", "100\n" ] }, { "data": { "image/png": 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tzZJPUjyzntd1o+rtvVevvHKrfL4mxWJdct1eRaMRvfLKevl8Lay3rUCEXQAA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\nx6NJkybpyiuv1Mc//nFJ0iuvvKKf/OQncl1XF154oS677DJJ0uHDh3X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"text/plain": [ "<matplotlib.figure.Figure at 0x7f9858c3a2e8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%matplotlib inline\n", "\n", "from copy import deepcopy\n", "from matplotlib import pyplot as plt\n", "import random\n", "\n", "# import seaborn as sns\n", "# import pandas as pd\n", "import numpy as np\n", "from operator import itemgetter\n", "\n", "plt.rcParams['figure.figsize'] = (16, 9)\n", "plt.style.use('ggplot')\n", "\n", "\n", "def euclidian_distance(sample1, sample2):\n", " result = sum(map(lambda t: (t[0] - t[1]) ** 2,\n", " zip(sample1, sample2)))\n", " # print(result)\n", " return result\n", "\n", "\n", "# def predict(train_set, sample, k=3, distance=euclidian_distance):\n", "# k_nearest = sorted(train_set, key=lambda s: distance(s, sample))[:k]\n", "# return Counter(map(get_label, k_nearest)).most_common(1)[0][0]\n", "\n", "\n", "def read_points(filename):\n", " with open(filename) as file:\n", " content = file.readlines()\n", " return [list(map(float, x.strip().split(' ', 2))) for x in content]\n", "\n", "\n", "def mean(points, default_fn):\n", " count = len(points)\n", "\n", " if count == 0:\n", " return default_fn()\n", "\n", " sum_x1 = sum([point[0] for point in points])\n", " sum_x2 = sum([point[1] for point in points])\n", " return [sum_x1 / count, sum_x2 / count]\n", "\n", "\n", "def train(data_set, k, max_iterations=100, dist=euclidian_distance):\n", " # total distance between 2 vector of points\n", " def vector_dist(vector1, vector2):\n", " return sum(map(lambda t: dist(t[0], t[1]),\n", " zip(vector1, vector2)))\n", "\n", " min_x = min(data_set, key=lambda t: t[0])[0]\n", " min_y = min(data_set, key=lambda t: t[1])[1]\n", " max_x = max(data_set, key=lambda t: t[0])[0]\n", " max_y = max(data_set, key=lambda t: t[1])[1]\n", "\n", " def get_random_point():\n", " return [min_x + random.random() * (max_x - min_x),\n", " min_y + random.random() * (max_y - min_y)]\n", "\n", " random.shuffle(data_set)\n", " clusters = [0 for _ in range(len(data_set))]\n", " centroids = [get_random_point() for _ in range(k)]\n", " # centroids = data_set[:k] # TODO: we chose points from the data set\n", " centroids_old = [[0, 0] for _ in range(k)]\n", " error = vector_dist(centroids, centroids_old)\n", " iterations = 0\n", "\n", " while error != 0 and iterations < max_iterations:\n", " # assign each sample to its closest cluster (nearest centroid)\n", " for sample_id in range(len(data_set)):\n", " nearest_centroid = min(centroids,\n", " key=lambda c: dist(data_set[sample_id], c))\n", " cluster_index = centroids.index(nearest_centroid)\n", " clusters[sample_id] = cluster_index\n", "\n", " centroids_old = deepcopy(centroids)\n", "\n", " # for each cluster assign a new centroid\n", " for cluster_id in range(k):\n", " points = [data_set[sample_id]\n", " for sample_id in range(len(data_set))\n", " if clusters[sample_id] == cluster_id]\n", " centroids[cluster_id] = mean(points, get_random_point)\n", "\n", " error = vector_dist(centroids, centroids_old)\n", " iterations += 1\n", "\n", " print(\"finished in %d iterations\" % iterations)\n", " return centroids, clusters\n", "\n", "\n", "def draw(centroids, clusters, data_set, k):\n", " colors = ['r', 'g', 'b', 'y', 'c', 'm', 'k', 'w']\n", " fig, ax = plt.subplots()\n", " for i in range(k):\n", " points = np.array([data_set[j]\n", " for j in range(len(data_set))\n", " if clusters[j] == i])\n", " print(len(points))\n", " ax.scatter(points[:, 0], points[:, 1], s=7, c=colors[i])\n", " # ax.scatter(centroids[:, 0], centroids[:, 1], marker='*', s=200, c='#050505')\n", " ax.scatter(list(map(itemgetter(0), centroids)),\n", " list(map(itemgetter(1), centroids)),\n", " marker='*', s=200, c='#050505')\n", " \n", "\n", "random_seed = 2\n", "vectors = read_points('/home/tony/source/learning/algorithms/k-means/unbalance/unbalance.txt')\n", "random.seed(random_seed)\n", "\n", "k = 8\n", "centroids, clusters = train(vectors, k)\n", "print(centroids)\n", "draw(centroids, clusters, vectors, k)\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.3" } }, "nbformat": 4, "nbformat_minor": 2 }
gpl-3.0
airanmehr/bio
notebooks/KGZ/.ipynb_checkpoints/Untitled-checkpoint.ipynb
2
3246
{ "cells": [ { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", "import matplotlib as mpl\n", "mpl.use('agg')\n", "import os\n", "# os.environ[\"DISPLAY\"] = \"localhost:11.0\"\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import sys,os\n", "path='/'.join(os.getcwd().split('/')[:-4])\n", "sys.path.insert(1,path)\n", "import Utils.Util as utl\n", "import Utils.Plots as pplt\n", "import pandas as pd\n", "pd.options.display.max_rows = 20;\n", "pd.options.display.expand_frame_repr = True\n", "from IPython.display import display\n", "import pylab as plt\n", "import seaborn as sns\n", "import Scripts.KyrgysHAPH.Util as kutl\n", "import Scripts.KyrgysHAPH.Plot as kplt\n", "import Scripts.HLI.Kyrgyz.IBSScan.IBDScan as ibd\n", "import Scripts.HLI.Kyrgyz.PBS as pbs\n", "pd.options.display.max_colwidth = 2000;\n", "import matplotlib as mpl\n" ] }, { "cell_type": "code", "execution_count": 74, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "HLT 187524477\n", "HLT 187524476\n", "HLT 187524475\n", "HLT 187524470\n", "HLT 187524469\n", "HLT 187524459\n", "HLT 187524460\n", "HLT 187524461\n", "HLT 187524462\n", "HLT 187524466\n", " ... \n", "HLT 201852645\n", "HLT 201852644\n", "HLT 201852646\n", "HLT 201852670\n", "HLT 201852658\n", "HLT 201852669\n", "HLT 201852665\n", "HLT 201852663\n", "HLT 201852667\n", "HLT 201852664\n", "Length: 72, dtype: object" ] }, "execution_count": 74, "metadata": {}, "output_type": "execute_result" } ], "source": [ "i=pd.Series({'CHROM':22,'start':26099429,'end':27203877})\n", "f='/home/arya/POP/HA/GT/chr22.vcf.gz.aa.gz'\n", "reload(kutl)\n", "# utl.gz.FreqPop(pop='KGZ')\n", "# kutl.AllPops()\n", "# kutl.ID('HLT')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "i" ] }, { "cell_type": "code", "execution_count": 57, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(162558, 33)\n", "(7167, 33)\n" ] } ], "source": [ "# MINAC = 3\n", "AFCF=0.05\n", "# a= a[((a.sum(1) > MINAC) & (a.sum(1) < a.shape[1] * MINAC))]\n", "print a.shape\n", "a=a[(a[p1].mean(1) / 2 - a[p2].mean(1) / 2).abs() > AFCF]\n", "print a.shape" ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.12" } }, "nbformat": 4, "nbformat_minor": 2 }
mit
DJMedhaug/code_guild
wk0/notebooks/wk0.1.ipynb
1
4095
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# wk0.1 A curious case of conditionals\n", "\n", "## Intro to git and github\n", "\n", "Read these:\n", "* https://help.github.com/articles/set-up-git/\n", "* https://www.atlassian.com/git/tutorials/syncing/git-push\n", "\n", "## A few challenges\n", "\n", "Complete *reverse_string* and *primes* challenges in challenge folder.\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Reviewing some ideas from yesterday" ] }, { "cell_type": "raw", "metadata": {}, "source": [ "# Example from yesterday\n", "\n", "def t(num):\n", " if 10 <= num < 15:\n", " print(\"hot\")\n", " elif num > 15:\n", " print(\"hotter\")\n", " else:\n", " print(\"cold\")\n", "\n", "# What does this function do?\n", "for i in range(4):\n", " for j in range(10):\n", " if j % 3 == 0:\n", " continue\n", " if j > 7 and j % 2 == 0:\n", " break\n", " else:\n", " print('i equals', i)\n", " print('j equals', j)\n", " print('------------')\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## While Conditional.: return discussion\n", "\n", "* [Follow up on yesterday's discussion on conditionals](http://www.pythonlearn.com/html-008/cfbook004.html)\n", "\n", "[more on conditionals](https://docs.python.org/3.5/tutorial/datastructures.html#more-on-conditions):\n", "* is and is not\n", " * [difference between is and ==](https://stackoverflow.com/questions/132988/is-there-a-difference-between-and-is-in-python)\n", " \n", "Try evaluating each of the lines below in your python REPL. What's going on? \n", " \n", "```\n", "a = [1, 2, 3]\n", "b = a\n", "b is a\n", "b == a\n", "b = a[:]\n", "b is a\n", "b == a\n", "```\n", "If you have a hypothesis about the difference between is and ==, devise some tests you could try out to disprove your hypothesis. Implement them. Were you correct?\n", "\n", "* in and not in\n", "* and, or\n", "* comparison priority\n", "> not has the highest priority and or the lowest, so that A and not B or C is equivalent to (A and (not B)) or C.\n", "\n", "* Short circuit operators\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# More string and list practice\n", "\n", "* Add elements from a list L to the end of another list at least three different ways.\n", "* Insert elements into a list at least two different ways.\n", "* Insert an element into a string (this might take more than one step...).\n", "* Remove an item from a list by value.\n", "* Remove an item from a list by index.\n", "* Remove all items from a list two different ways.\n", "* Return the index in the list of the first item whose value is x. \n", "* Return the number of times x appears in the list.\n", "* Capitalize the first element in a string\n", "* Capitalize all elements in a string\n", "* Strip leading, trailing, and all whitespace (ex. leading: ' asdf ' --> 'asdf ', trailing:' asdf ' --> ' asdf', all:' asdf ' --> 'asdf').\n", "* Split a sentence into a list of words (no whitespace), how would you split on comma or semi-colon?\n", "* Read up on how to format strings" ] }, { "cell_type": "markdown", "metadata": {}, "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
bspalding/research_public
lectures/drafts/Algorithm capacity.ipynb
6
6002
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Algorithm capacity\n", "By Evgenia \"Jenny\" Nitishinskaya and Delaney Granizo-Mackenzie\n", "\n", "Part of the Quantopian Lecture Series:\n", "\n", "* [www.quantopian.com/lectures](https://www.quantopian.com/lectures)\n", "* [github.com/quantopian/research_public](https://github.com/quantopian/research_public)\n", "\n", "Notebook released under the Creative Commons Attribution 4.0 License.\n", "\n", "---\n", "\n", "When taking an algorithm out into the real world, there are many factors which limit the amount of capital it can run on both above and below. This is referred to as algorithm capacity. Below, we discuss some of these factors and how they affect the range of capital at which an algorithm is functional." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Slippage and liquidity\n", "\n", "Buying a stock pushes its price up, and selling it drives the price down. This effect is called slippage. It occurs even as we are making the trade: if we order 10 shares of a stock, but the seller offering it at the lowest price is only selling 8, then the last two shares will have to be bought from a different seller at a higher price. This is very common, and can result in a total price very different from the one we originally thought we were buying at.\n", "\n", "The impact of our transaction on the price depends on the size of our order relative to the current trading volume. Placing a small order for a liquid (highly traded) stock will incur less slippage than placing a large order for an illiquid stock. This limits the amount of capital we can put into our algorithm: if the slippage is too high, the unexpected changes in the stock price will cancel out the profit we expected to make. We can stop the algorithm from paying too much by placing a limit order, which will only sell above (buy below) a specified price.\n", "\n", "However, placing a limit order means that we will not be able to get as much of the stock as we desired. Relatedly, if a stock is illiquid, we might not be able to fill an order for a long time. This is troublesome for most strategies, particularly high-frequency ones and those that rely on precise holdings. All of these factors mean that each strategy has a maximum capacity beyond which it does not function properly." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## No fractional shares\n", "\n", "Shares can only be bought in whole units, but algorithms often specify a dollar amount to be bought, which is rounded down to the nearest whole share. If we are buying many shares, one more or one fewer doesn't make a large difference. However, if our strategy is running on a small amount of capital, a fraction of a share is a significant amount of the holdings we anticipated having, so the reality will be far from what we intended. Our algorithm's sensitivity to such errors and the prices of the stocks we are investing in determine the minimum algorithm capacity below which it cannot run." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Minimum transaction fee\n", "\n", "Every time we execute a transaction, we must pay our broker a transaction fee (also known as commission). These are never fun, and can render whole strategies completely useless. They are especially bad for algorithms that make many small trades. This is because transaction fees are generally smaller for smaller transactions, but there is a minimum fee that all transactions below a certain amount have to pay.\n", "\n", "Say the transaction fee is just 0.1% of the money involved in the transaction at while we are running a large amount of capital. However, there is be a \\$1 fee minimum. Then the transaction fee will be higher than 0.1% when we make a trade for less than \\$1000. Eventually \\$1 will be more than we expect the profit on the transaction to be. That's definitely not something we want, so minimum transaction fees enforce a minimum capacity for our algorithm." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Margin restrictions\n", "\n", "Many brokers will not allow borrowing money if we have less than a certain amount in our portfolio; the precise rules vary depending on the broker and the type of portfolio. This restricts us in a number of ways. For example, we cannot buy if we have insufficient funds, even if we sell at the same time to cover the cost. We must wait (days) for the money from the sell order to be transferred before we can place the buy order. This means that our algorithm cannot execute the trades it wants if we do not invest enough capital in it. Alternatively, it can be a cap on our algorithm's capacity if we keep a lot of cash uninvested in order to be able to buy when we want." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Conclusion\n", "\n", "All of the factors above are built into the Quantopian backtester (except for the last, which you can model yourself). You can adjust the slippage and commission models to try out different scenarios. However, the true effects cannot be seen from backtesting or paper-trading, especially slippage. The best approach is to test that your algorithm is robust for a range of models at your intended capital allocation before turning it live." ] } ], "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
idc9/law-net
explore/Iain/load_ig_chalkboard.ipynb
1
8654
{ "cells": [ { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The autoreload extension is already loaded. To reload it, use:\n", " %reload_ext autoreload\n" ] } ], "source": [ "import sys\n", "\n", "sys.path.append('../../code/')\n", "import os\n", "import json\n", "from datetime import datetime\n", "import time\n", "from math import *\n", "\n", "import numpy as np\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "import scipy.stats as stats\n", "\n", "import igraph as ig\n", "import networkx as nx\n", "\n", "from load_data import load_citation_network, case_info\n", "\n", "%load_ext autoreload\n", "%autoreload 2\n", "%matplotlib inline\n", "\n", "data_dir = '../../data/'\n", "court_name = 'scotus'" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": true }, "outputs": [], "source": [ "case_metadata = pd.read_csv(data_dir + 'clean/case_metadata_master.csv')\n", "edgelist = pd.read_csv(data_dir + 'clean/edgelist_master.csv')" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# net_dir = data_dir + 'clean/' + court_name + '/'\n", "# case_metadata = pd.read_csv(net_dir + 'case_metadata.csv')\n", "\n", "# edgelist = pd.read_csv(net_dir + 'edgelist.csv')\n", "# edgelist.drop('Unnamed: 0', inplace=True, axis=1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Compare iterrows vs itertuples" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "pandas took 29 seconds to go though 250465 edges using iterrows\n" ] } ], "source": [ "\n", "start = time.time()\n", "# create graph and add metadata\n", "G = nx.DiGraph()\n", "G.add_nodes_from(case_metadata.index.tolist())\n", "nx.set_node_attributes(G, 'date', case_metadata['date'].to_dict())\n", "for index, edge in edgelist.iterrows():\n", " ing = edge['citing']\n", " ed = edge['cited']\n", " G.add_edge(ing, ed)\n", "end = time.time()\n", "\n", "print 'pandas took %d seconds to go though %d edges using iterrows' % (end - start, edgelist.shape[0])" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "pandas took 1 seconds to go though 250465 edges using itertuples\n" ] } ], "source": [ "# go through edglist using itertuples\n", "\n", "start = time.time()\n", "# create graph and add metadata\n", "G = nx.DiGraph()\n", "G.add_nodes_from(case_metadata.index.tolist())\n", "nx.set_node_attributes(G, 'date', case_metadata['date'].to_dict())\n", "for row in edgelist.itertuples():\n", " ing = row[1]\n", " ed = row[2]\n", " G.add_edge(ing, ed)\n", "end = time.time()\n", "\n", "print 'pandas took %d seconds to go though %d edges using itertuples' % (end - start, edgelist.shape[0])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# load into igraph" ] }, { "cell_type": "code", "execution_count": 37, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# create a dictonary that maps court listener ids to igraph ids\n", "cl_to_ig_id = {}\n", "cl_ids = case_metadata['id'].tolist()\n", "for i in range(case_metadata['id'].size):\n", " cl_to_ig_id[cl_ids[i]] = i" ] }, { "cell_type": "code", "execution_count": 38, "metadata": { "collapsed": false }, "outputs": [ { "ename": "KeyboardInterrupt", "evalue": "", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", "\u001b[0;32m<ipython-input-38-27ebfd643a17>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 18\u001b[0m \u001b[0mcl_ed\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrow\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[1;32m 19\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 20\u001b[0;31m \u001b[0;32mif\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mcl_ing\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mcl_to_ig_id\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkeys\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mcl_ed\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mcl_to_ig_id\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkeys\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 21\u001b[0m \u001b[0ming\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcl_to_ig_id\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mcl_ing\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 22\u001b[0m \u001b[0med\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcl_to_ig_id\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mcl_ed\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mKeyboardInterrupt\u001b[0m: " ] } ], "source": [ "start = time.time()\n", "V = case_metadata.shape[0]\n", "\n", "g = ig.Graph(n=V, directed=True)\n", "g.vs['date'] = case_metadata['date'].tolist()\n", "g.vs['name'] = case_metadata['id'].tolist()\n", "\n", "ig_edgelist = []\n", "missing_cases = 0\n", "start = time.time()\n", "# i = 1\n", "for row in edgelist.itertuples():\n", "# if log(i, 2) == int(log(i, 2)):\n", "# print 'edge %d' % i\n", "# i += 1\n", "\n", " cl_ing = row[1]\n", " cl_ed = row[2]\n", "\n", " if (cl_ing in cl_to_ig_id.keys()) and (cl_ed in cl_to_ig_id.keys()):\n", " ing = cl_to_ig_id[cl_ing]\n", " ed = cl_to_ig_id[cl_ed]\n", " else:\n", " missing_cases += 0\n", " \n", " ig_edgelist.append((ing, ed))\n", "intermediate = time.time()\n", "\n", "g.add_edges(ig_edgelist)\n", "end = time.time()\n", "\n", "print 'itertuples took %d seconds to go through %d edges' % (intermediate - start, edgelist.shape[0])\n", "print 'igraph took %d seconds to add %d edges' % (end - start, edgelist.shape[0])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# igraph find vs. select" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "start = time.time()\n", "R = 1000\n", "for i in range(R):\n", " g.vs.find(name='92891')\n", "end = time.time()\n", "print 'g.vs.find took %E seconds per lookup' % ((end - start)/R)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "start = time.time()\n", "R = 1000\n", "for i in range(R):\n", " g.vs.select(name='92891')\n", "end = time.time()\n", "print 'g.vs.select took %E seconds per lookup' % ((end - start)/R)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "start = time.time()\n", "R = 1000\n", "for i in range(R):\n", " cl_to_ig_id[92891]\n", "end = time.time()\n", "print 'pandas df lookup took %E seconds per lookup' % ((end - start)/R)" ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.12" } }, "nbformat": 4, "nbformat_minor": 0 }
mit
kkkddder/dmc
notebooks/week-6/02-using a pre-trained model with Keras.ipynb
1
8784
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Lab 6.2 - Using a pre-trained model with Keras\n", "\n", "In this section of the lab, we will load the model we trained in the previous section, along with the training data and mapping dictionaries, and use it to generate longer sequences of text.\n", "\n", "Let's start by importing the libraries we will be using:" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Using TensorFlow backend.\n" ] } ], "source": [ "import numpy as np\n", "from keras.models import Sequential\n", "from keras.layers import Dense\n", "from keras.layers import Dropout\n", "from keras.layers import LSTM\n", "from keras.callbacks import ModelCheckpoint\n", "from keras.utils import np_utils\n", "\n", "import sys\n", "import re\n", "import pickle" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Next, we will import the data we saved previously using the `pickle` library." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "('Training set', (18212, 100, 44), (18212, 44))\n" ] } ], "source": [ "pickle_file = '-basic_data.pickle'\n", "\n", "with open(pickle_file, 'rb') as f:\n", " save = pickle.load(f)\n", " X = save['X']\n", " y = save['y']\n", " char_to_int = save['char_to_int'] \n", " int_to_char = save['int_to_char'] \n", " del save # hint to help gc free up memory\n", " print('Training set', X.shape, y.shape)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we need to define the Keras model. Since we will be loading parameters from a pre-trained model, this needs to match exactly the definition from the previous lab section. The only difference is that we will comment out the dropout layer so that the model uses all the hidden neurons when doing the predictions." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# define the LSTM model\n", "model = Sequential()\n", "model.add(LSTM(128, return_sequences=False, input_shape=(X.shape[1], X.shape[2])))\n", "# model.add(Dropout(0.50))\n", "model.add(Dense(y.shape[1], activation='softmax'))\n", "model.compile(loss='categorical_crossentropy', optimizer='adam')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Next we will load the parameters from the model we trained previously, and compile it with the same loss and optimizer function." ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# load the parameters from the pretrained model\n", "filename = \"-basic_LSTM.hdf5\"\n", "model.load_weights(filename)\n", "model.compile(loss='categorical_crossentropy', optimizer='adam')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We also need to rewrite the `sample()` and `generate()` helper functions so that we can use them in our code:" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def sample(preds, temperature=1.0):\n", " preds = np.asarray(preds).astype('float64')\n", " preds = np.log(preds) / temperature\n", " exp_preds = np.exp(preds)\n", " preds = exp_preds / np.sum(exp_preds)\n", " probas = np.random.multinomial(1, preds, 1)\n", " return np.argmax(probas)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def generate(sentence, sample_length=50, diversity=0.35):\n", " generated = sentence\n", " sys.stdout.write(generated)\n", "\n", " for i in range(sample_length):\n", " x = np.zeros((1, X.shape[1], X.shape[2]))\n", " for t, char in enumerate(sentence):\n", " x[0, t, char_to_int[char]] = 1.\n", "\n", " preds = model.predict(x, verbose=0)[0]\n", " next_index = sample(preds, diversity)\n", " next_char = int_to_char[next_index]\n", "\n", " generated += next_char\n", " sentence = sentence[1:] + next_char\n", "\n", " sys.stdout.write(next_char)\n", " sys.stdout.flush()\n", " print" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we can use the `generate()` function to generate text of any length based on our imported pre-trained model and a seed text of our choice. For best result, the length of the seed text should be the same as the length of training sequences (100 in the previous lab section). \n", "\n", "In this case, we will test the overfitting of the model by supplying it two seeds:\n", "\n", "- one which comes verbatim from the training text, and\n", "- one which comes from another earlier speech by Obama\n", "\n", "If the model has not overfit our training data, we should expect it to produce reasonable results for both seeds. If it has overfit, it might produce pretty good results for something coming directly from the training set, but perform poorly on a new seed. This means that it has learned to replicate our training text, but cannot generalize to produce text based on other inputs. Since the original article was very short, however, the entire vocabulary of the model might be very limited, which is why as input we use a part of another speech given by Obama, instead of completely random text.\n", "\n", "Since we have not trained the model for that long, we will also use a lower temperature to get the model to generate more accurate if less diverse results. Try running the code a few times with different temperature settings to generate different results." ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "america has shown that progress is possible. last year, income gains were larger for households at the the partechists an wered ast of is for. peame id and rase and in ance portures the forecres for ald and agrestartinit and redes no unerredtinithe pooducing poouttint the sumicot hat abeliden the ase of the anderess and proverage thas beticing that on merican anco america hos heall to engenges the aredront bet inirges for the resingress resionc cos and mere toversen the to armert mine to ingress of the fareng the resinting to ard mice of oress-ince the ars alle the y of the rederss the porttre\n", "--------------------\n", "and as people around the world began to hear the tale of the lowly colonists who overthrew an empireses ad by ace and arcenor as to e prest or cheriss on ande tor coms the prover be the for and and urecront. whise the lose incout te the rest to preverse the ase in ader and growth ard proders ard be the areshorg of the anderedts andes and the fored we and ancoutiog ous mose proseation and aly ace pools mere to pored the wore to atered more prosteres to and thein gas on whine ally and in aver echas and all ane poprtint ant the past conting that growth in coulle ges to more for for to eard in res\n", "--------------------\n" ] } ], "source": [ "prediction_length = 500\n", "seed_from_text = \"america has shown that progress is possible. last year, income gains were larger for households at t\"\n", "seed_original = \"and as people around the world began to hear the tale of the lowly colonists who overthrew an empire\"\n", "\n", "for seed in [seed_from_text, seed_original]:\n", " generate(seed, prediction_length, .50)\n", " print \"-\" * 20" ] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python [default]", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.12" } }, "nbformat": 4, "nbformat_minor": 1 }
apache-2.0
nitin-cherian/LifeLongLearning
Python/Python Morsels/1.get_earliest/my_try/.ipynb_checkpoints/earliest-checkpoint.ipynb
1
4286
{ "cells": [ { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from datetime import datetime" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def get_earliest(date1, date2):\n", " try:\n", " d1 = datetime.strptime(date1, \"%m/%d/%Y\")\n", " d2 = datetime.strptime(date2, \"%m/%d/%Y\")\n", " return date1 if d1 < d2 else date2\n", " except ValueError:\n", " return min(date1, date2)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "......F\n", "======================================================================\n", "FAIL: test_two_invalid_dates_v2 (__main__.GetEarliestTests)\n", "----------------------------------------------------------------------\n", "Traceback (most recent call last):\n", " File \"<ipython-input-9-584db24a3933>\", line 41, in test_two_invalid_dates_v2\n", " self.assertEqual(get_earliest(newer, older), older)\n", "AssertionError: '01/32/2007' != '02/29/2006'\n", "- 01/32/2007\n", "+ 02/29/2006\n", "\n", "\n", "----------------------------------------------------------------------\n", "Ran 7 tests in 0.004s\n", "\n", "FAILED (failures=1)\n" ] } ], "source": [ "import unittest\n", "\n", "\n", "class GetEarliestTests(unittest.TestCase):\n", "\n", " \"\"\"Tests for get_earliest.\"\"\"\n", "\n", " def test_same_month_and_day(self):\n", " newer = \"01/27/1832\"\n", " older = \"01/27/1756\"\n", " self.assertEqual(get_earliest(newer, older), older)\n", "\n", " def test_february_29th(self):\n", " newer = \"02/29/1972\"\n", " older = \"12/21/1946\"\n", " self.assertEqual(get_earliest(newer, older), older)\n", "\n", " def test_smaller_month_bigger_day(self):\n", " newer = \"03/21/1946\"\n", " older = \"02/24/1946\"\n", " self.assertEqual(get_earliest(older, newer), older)\n", "\n", " def test_same_month_and_year(self):\n", " newer = \"06/24/1958\"\n", " older = \"06/21/1958\"\n", " self.assertEqual(get_earliest(older, newer), older)\n", "\n", " def test_invalid_date_allowed(self):\n", " newer = \"02/29/2006\"\n", " older = \"02/28/2006\"\n", " self.assertEqual(get_earliest(older, newer), older)\n", "\n", " def test_two_invalid_dates(self):\n", " newer = \"02/30/2006\"\n", " older = \"02/29/2006\"\n", " self.assertEqual(get_earliest(newer, older), older)\n", "\n", " def test_two_invalid_dates_v2(self):\n", " newer = \"01/32/2007\"\n", " older = \"02/29/2006\"\n", " self.assertEqual(get_earliest(newer, older), older) \n", "\n", "if __name__ == \"__main__\":\n", " unittest.main(argv=['first-arg-is-ignored'], exit=False)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### The above solution does not work for the unittest 'test_two_invalid_dates_v2'. In the solution, min() just looks at the smallest string lexicographically i.e using ASCII value of the characters. So 01 get is lexicographically smaller than 02, eventhough 02/29/2006 is older than 01/32/2007" ] }, { "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": 0 }
mit
markomanninen/hyml
HyML - XML & (X)HTML generator for Hy.ipynb
1
93432
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# HyML - XML / (X)HTML generator for Hy\n", "\n", "## Motivation\n", "\n", "## Previous similar work\n", "\n", "## Installation" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "My environment for the sake of clarity:" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Hy version: 0.13.0\n", "Python 3.5.2 |Anaconda custom (64-bit)| (default, Jul 5 2016, 11:41:13) [MSC v.1900 64 bit (AMD64)]\n" ] } ], "source": [ "(import hy sys)\n", "(print \"Hy version: \" hy.__version__)\n", "(print \"Python\" sys.version)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Import main macros" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "(require [hyml.macros [*]]\n", " [hyml.helpers [*]])\n", "(import (hyml.macros (*)))\n", "(import (hyml.helpers (indent)))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Then we are ready for the show!" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Almost all-in-one example\n", "\n", "First I'd like to show an example that uses most features included in the HyML module. Then I will go thru all presented features case by case." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "<?xml version=\"1.0\" encoding=\"UTF-8\"?>\n", "<!DOCTYPE html>\n", "<html lang=\"en\" xmlns=\"http://www.w3.org/1999/xhtml\">\n", "\t<head>\n", "\t\t<title>Page title</title>\n", "\t</head>\n", "\t<body>\n", "\t\t<!-- body starts here -->\n", "\t\t<div class=\"main_container\">\n", "\t\t\t<h1 class=\"main header\">Page header</h1>\n", "\t\t\t<ul id=\"main\">\n", "\t\t\t\tList\n", "\t\t\t\t<li class=\"even\">0</li>\n", "\t\t\t\t<li class=\"odd\">1</li>\n", "\t\t\t\t<li class=\"even\">2</li>\n", "\t\t\t</ul>\n", "\t\t</div>\n", "\t</body>\n", "</html>\n" ] } ], "source": [ "; by default there is no indentation, thus for pretty print we use indent\n", "(print (indent \n", " ; specify parser macro (ML macros) that must be one of the following:\n", " ; xml, xhtml, xhtml5, html4, or html5 \n", " (xhtml5\n", " ; plain text content\n", " ; xml declaration below could also be done with a custom tag: (?xml :version \"1.0\" :encoding \"UTF-8\")\n", " \"<?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?>\"\n", " ; more plain text content\n", " ; doctype could also be done with a custom tag: (!DOCTYPE \"html\")\n", " \"<!DOCTYPE html>\"\n", " ; define tag name as the first parameter\n", " ; define attributes by keywords\n", " (html :lang \"en\" :xmlns \"http://www.w3.org/1999/xhtml\"\n", " ; define nested tags and content by similar manner\n", " (head\n", " ; everything else except the first parameter and keywords are\n", " ; regarded as inner html content\n", " (title \"Page title\"))\n", " (body\n", " ; plain text content\n", " ; comments could also be done with a custom tag: (!-- \"comments\")\n", " \"<!-- body starts here -->\"\n", " ; short notation for div element and class attribute <div class=\"\"/>\n", " ; note that - character in main-container will become to main_container due to Hy\n", " ; internal language construction\n", " (.main-container\n", " ; short notation for class attribute for specified element: <h1 class=\"\"/>\n", " ; with multiple dot notation classes are concatenated with space\n", " (h1.main.header\n", " ; unquote macro with ~ to evaluate normal Hy code\n", " ; after unquoted expression rest of the code is continued to be parsed by ML macros again\n", " ~(.capitalize \"page header\"))\n", " ; short notation for id attribute for specified element: <ul id=\"\"/>\n", " ; you should not use joined #main#sub similar to class notation, althought it is not prohibited,\n", " ; because id=\"main sub\" is not a good id according to html attribute specifications\n", " (ul#main \"List\"\n", " ; unquote splice ~@ processes lists and concatenates results\n", " ; list-comp* is a slightly modified vesion of list-comp\n", " ; in list-comp* the list argument is the first and the expression is\n", " ; the second argument. in native list-comp those arguments are in reverse order\n", " ~@(list-comp* [[idx num] (enumerate (range 3))]\n", " ; quote (`) a line and unquote variables and expressions to calculate\n", " ; and set correct class for even and odd list items\n", " `(li :class ~(if (even? idx) \"even\" \"odd\") ~num)))))))))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## XML, HTML4, HTML5, XHTML, and XHTML5\n", "\n", "At the moment HyML module contains `xml`, `html4`, `html5`, `xhtml`, and `xhtml5` macros (called as `ML` macros in short) to generate the (M)arkup (L)anguage code. `xml` is a generic generator which allows using any tag names and attributes. `html4` and `xhtml` macros allows to use only html4 specified tag names. Same applies to `html5` and `xhtml5`. Complete chart of the allowed elements are listed at the end of the document.\n", "\n", "Tags can be created with or without attributes, as well as with or without content. For example:" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "<node/>\n", "<node attribute=\"\"/>\n", "<node attribute=\"value\"/>\n", "<node attribute=\"value\"></node>\n", "<node attribute=\"value\">Content</node>\n" ] } ], "source": [ "(println\n", " (xml (node))\n", " (xml (node :attribute \"\")) ; force to use empty attribute\n", " (xml (node :attribute \"value\"))\n", " (xml (node :attribute \"value\" \"\")) ; force to use empty content\n", " (xml (node :attribute \"value\" \"Content\")))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "However in `html4` and `html5` there are certain tags that cannot have endings so they will be rendered in correct form by the parser. \"Forbidden\" labeled tags are listed at the end of the document. One of them is for example the meta tag:" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "'<meta name=keywords content=HTML,CSS,XML,JavaScript>'" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "(html4 (meta :name \"keywords\" :content \"HTML,CSS,XML,JavaScript\"))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To see and compare the difference in xhtml, let macro print the same:" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "'<meta name=\"keywords\" content=\"HTML,CSS,XML,JavaScript\"/>'" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "(xhtml (meta :name \"keywords\" :content \"HTML,CSS,XML,JavaScript\"))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Shorthand macro\n", "\n", "`#㎖` (Square Ml) can be used as a shorthand [reader macro](http://docs.hylang.org/en/latest/language/readermacros.html) for generating xml/html/xhtml code:" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "'<html><head><title>Page title</title></head><body><div class=\"container\">Page content</div></body></html>'" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#㎖(html\n", " (head (title \"Page title\"))\n", " (body (div \"Page content\" :class \"container\")))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "`#㎖` actually utilizes `xml` macro so same result can be achieved with the next, maybe more convenient and recommended notation:" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "'<html><head><title>Page title</title></head><body><div class=\"container\">Page content</div></body></html>'" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "(xml\n", " (html\n", " (head (title \"Page title\"))\n", " (body (div \"Page content\" :class \"container\"))))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It is not possible to define other `ML` macro to be used with the `#㎖` shorthand reader macro. You could however define your own shorthands following next quidelines:\n", "\n", "<code>(defsharp {unicode-char} [code] (parse-{parser} code))</code>\n", "\n", "`{unicode-char}` can be any [unicode char](https://unicode-table.com/en/) you want. `{parser}` must be one of the following available parsers: xml, xhtml, xhtml5, html4, or html5.\n", "\n", "With `#㎖` shorthand you have to provide a single root node for generating code. Directry using `ML` macros makes it possible to generate multiple instances of code, and might be more informative notation style anyway:" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "'<p>Sentence 1</p><p>Sentence 2</p><p>Sentence 3</p>'" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "(xml (p \"Sentence 1\") (p \"Sentence 2\") (p \"Sentence 3\"))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let us then render the code, not just printing it. This can be done via `html5>` macro imported earlier from helpers:" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "Content is <b>king</b>!" ], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "(html4> \"Content is \" (b king) !)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Renderers are available for all `ML` macros: `xml>`, `xhtml>`, `xhtml5>`, `html4>`, and `html5>`.\n", "\n", "## Validation and minimizing\n", "\n", "If validation of the html tag names is a concern, then one should use `html4`, `html5`, `xhtml`, and `xhtml5` macro family. In the example below if we try to use `time` element in `html4`, which is specifically available in `html5` only, we will get an `HyTMLError` exception:" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [], "source": [ ";(try\n", "; (html4 (time))\n", "; (catch [e [HyTMLError]]))\n", ";hytml.macros.HyTMLError: Tag 'time' not meeting html4 specs" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Other features in `html4` and `html5` macros are attribute and tag minimizing. Under the [certain rules](https://html.spec.whatwg.org/multipage/syntax.html#optional-tags) start and end tags can be removed from the output. Also boolean attributes can be shortened. In `html4` and `html5` macros minimizing is a default feature that can't be bypassed. If you do not want to minimize code, you must use `xhtml` or `xhtml5` macro. Contrary in `xhtml` and `xhtml5` macros attribute and tag minimizing is NOT available. Instead all tags are strictly closed and attributes in `key=\"value\"` format." ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "'<title/><table><tr><td>Cell 1<td>Cell 2<td>Cell 3</table>'" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "; valid html4 document\n", "(html4 (title) (table (tr (td \"Cell 1\") (td \"Cell 2\") (td \"Cell 3\"))))" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "'<title/><table><tr><td>Cell 1</td><td>Cell 2</td><td>Cell 3</td></tr></table>'" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "; in xhtml tags and attributes will be output in complete format\n", "(xhtml (title) (table (tr (td \"Cell 1\") (td \"Cell 2\") (td \"Cell 3\"))))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<blockquote>Note that above xhtml code is still not a valid xhtml document even tags and attributes are perfectly output. `ML` macros do no validate structure of the document just tag names. For validation one should use official [validator](https://validator.w3.org/) service and follow the html [specifications](https://w3c.github.io/html/) to create a valid document. `ML` macros can be used to guide on that process but more importantly it is meant to automatize the generation of the xml code while adding programming capabilities on it.</blockquote>\n", "\n", "<blockquote>`xml` on the other hand doesn't give a dime of the used tag names. They can be anything, even processed names. Same applies to keywords, values, and contents. You should use more strict `xhtml`, `xhtml5`, `html4`, and `html5` macros to make sure that tag names are corresponding to HTML4 or HTML5 specifications.</blockquote>" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "'<input disabled>'" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "; see how boolean attribute minimizing works\n", "(html4 (input :disabled \"disabled\"))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Unquoting code\n", "\n", "In all `ML` macros you can pass any code in it. See for example:" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "'<p>Sum: <b><apply>sum<[1, 2, 3, 4]/></apply></b></p>'" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "(xml (p \"Sum: \" (b (apply sum [[1 2 3 4]]))))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "But you see, the result was not possibly what you expected. `ML` macros will interpret the first item of the _expression_ as a name of the tag. Thus _apply_ becomes a tag name. Until the next _expression_ everything else is interpreted either as a content or a keyword.\n", "\n", "However using `~` (unquote) symbol, `ML` macro behaviour can be stopped for a moment:" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "'<p>Sum: <b>10</b>!</p>'" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "(xml (p \"Sum: \" (b ~(apply sum [[1 2 3 4]])) !))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "So the following expression after `~` will be evaluated and then result is returned back to the original parser. And the rest of the code will be interpreted via macro. In this case it was just an exclamation mark.\n", "\n", "<blockquote>Note that it is not mandatory to wrap strings with `\"\"` if given input doesn't contain any spaces. You could also single quote simple non-spaced letter sequences. So `!` is same as `\"!\"` in this case.</blockquote>\n", "\n", "Quoting and executing normal Hy code inside html gives almost unlimited possibility to use HyML as a templating engine. Of cource there is also a risk to evaluate code that breaks the code execution. Plus uncontrolled template engine code may be a security consern.\n", "\n", "## Unquote splice\n", "\n", "In addition to unquote, one can handle lists and iterators with `~@` (unquote-splice) symbol. This is particularly useful when a list of html elements needs to be passed to the parent element. Take for example this table head generation snippet:" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "'<table><thead><tr><th class=\"even\">col 0</th><th class=\"odd\">col 1</th><th class=\"even\">col 2</th></tr></thead></table>'" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "(xhtml \n", " (table (thead\n", " (tr ~@(list-comp\n", " `(th :class (if (even? ~i) \"even\" \"odd\") ~label \" \" ~i)\n", " [[i label] (enumerate (* [\"col\"] 3))])))))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "[List comprehensions](https://docs.python.org/3/tutorial/datastructures.html#list-comprehensions) notation might seem a little bit strange for some people. It takes a processing part (expression) as the first argument, and the actual list to be processed as the second argument. On a nested code this will move lists to be processed in first hand to the end of the notation. For example:" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<ul><b>List</b><li>item 1</li><li>item 2</li></ul><ul><b>List</b><li>item 1</li><li>item 2</li></ul>" ], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "(xml> \n", " ~@(list-comp `(ul (b \"List\")\n", " ~@(list-comp `(li item \" \" ~li)\n", " [li uls]))\n", " [uls [[1 2] [1 2]]]))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "But there is another slighly modified macro to use in similar manner:\n", "\n", "## `list-comp*`\n", "\n", "Let's do again above example but this time with a dedicated `list-comp*` macro. Now the lists to be processed is passed as the first argument to the `list-comp*` macro and the expression for processing list items is the second argument. Yet the second argument itself contains a new list processing loop until final list item is to be processed. This is perhaps easier to follow for some people:" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "'<ul><b>List</b><li>item 1</li><li>item 2</li></ul><ul><b>List</b><li>item 1</li><li>item 2</li></ul>'" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "(xhtml\n", " ~@(list-comp* [uls [[1 2] [1 2]]]\n", " `(ul (b \"List\")\n", " ~@(list-comp* [li uls]\n", " `(li item \" \" ~li)))))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Of cource it is just a matter of the taste which one you like. `list-comp*` with `unquote-splice` symbol (`~@`) reminds us that it is possible to create any similar custom macros for the HyML processor. `~@` can be thought as a macro caller, not just unquoting and executing Hy code in a normal lisp mode.\n", "\n", "Here is a more complex table generation example from the [remarkuple](http://nbviewer.jupyter.org/github/markomanninen/remarkuple3/blob/master/Remarkuple%203%20documentation.ipynb) Python module docs. One should notice how variables (`col`, `row`, and `cell`) are referenced by quoting them:" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<table id=data><caption>Data table<colgroup><col style=background-color:red><col style=\"background-color: green\"><col style=\"background-color: blue\"><thead><tr><th>Column 1</th><th>Column 2</th><th>Column 3</th></thead><tbody id=tbody1><tr><td>1.0<td>1.1<td>1.2</tr><tr><td>2.0<td>2.1<td>2.2</tr></tbody><tbody id=tbody2><tr><td>1.0<td>1.1<td>1.2</tr><tr><td>2.0<td>2.1<td>2.2</tr><tfoot><tr><td colspan=3>Footer</tfoot></table>" ], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "(html4>\n", " (table#data\n", " (caption \"Data table\")\n", " (colgroup\n", " (col :style \"background-color:red\")\n", " (col :style \"background-color: green\")\n", " (col :style \"background-color: blue\"))\n", " (thead\n", " (tr\n", " ~@(list-comp* [col [\"Column 1\" \"Column 2\" \"Column 3\"]]\n", " `(th ~col))))\n", " (tbody#tbody1\n", " ~@(list-comp* [row (range 1 3)]\n", " `(tr\n", " ~@(list-comp* [cell (range 3)]\n", " `(td ~row \".\" ~cell)))))\n", " (tbody#tbody2\n", " ~@(list-comp* [row (range 1 3)]\n", " `(tr\n", " ~@(list-comp* [cell (range 3)]\n", " `(td ~row \".\" ~cell)))))\n", " (tfoot \n", " (tr\n", " (td :colspan \"3\" \"Footer\")))))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Address book table from CSV file\n", "\n", "We should of course be able to use external source for the html. Let's try with a short csv file:" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "data": { "text/html": [ "<table class=\"data\"><caption>Contacts</caption><thead><th>Title</th><th>Name</th><th>Phone</th></thead><tbody><td>Mr.</td><td>John</td><td>07868785831</td></tbody><tbody><td>Miss</td><td>Linda</td><td>0141-2244-5566</td></tbody><tbody><td>Master</td><td>Jack</td><td>0142-1212-1234</td></tbody><tbody><td>Mr.</td><td>Bush</td><td>911-911-911</td></tbody></table>" ], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "(xhtml> \n", " (table.data\n", " (caption \"Contacts\")\n", " ~@(list-comp*\n", " [[idx row] (enumerate (.split (.read (open \"data.csv\" \"r\")) \"\\n\"))]\n", " (if (pos? idx) \n", " `(tbody\n", " ~@(list-comp* [item (.split row \",\")]\n", " `(td ~item)))\n", " `(thead\n", " ~@(list-comp* [item (.split row \",\")]\n", " `(th ~item)))))))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Templates\n", "\n", "It is possible to load code from an external file too. This feature has not been deeply implemented yet, but you get the feeling by the next example. Firt I'm just going to show external template file content:" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(html :lang ~lang\n", " (head (title ~title))\n", " (body\n", " \t(p ~body)))\n" ] } ], "source": [ "(with [f (open \"templates/template.hy\")] (print (f.read)))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Then I use `include` macro to read and process the content:" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "'<html lang=\"en\"><head><title>Page title</title></head><body><p>Content</p></body></html>'" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "(defvar lang \"en\"\n", " title \"Page title\"\n", " body \"Content\")\n", "\n", "(xhtml ~@(include \"templates/template.hy\"))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "All globally defined variables are available on `ML` macros likewise:" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "'en, Page title, Content'" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "(xhtml ~lang \", \" ~title \", \" ~body)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## HTML4 / 5 specifications\n", "\n", "`xml` does not care about the markup specifications other than general tag and attribute notation. It is totally dummy about the naming conventions of the tags or their relation to each other or global structure of the markup document. It is all on the responsibility of the user to make it correct.\n", "\n", "`html4` and `html5` macros will render tags as specified below. These macros will minimize code when possible. Using undefined tag will raise an error. Attributes are not validated however. One should use official [validator](http://validator.w3.org/) for a proper validation.\n", "\n", "Below is the last example of using `ML` macros. It will print the first 5 rows of the HTML4/5 specifications.\n", "\n", "Columns are:\n", "\n", "- Tag name\n", "- Tag title\n", "- Forbidden (if there should be no content or end tag)\n", "- Omit (forbidden plus omit short tag like `<col>`)\n", "- HTML4 (is html4 compatible?)\n", "- HTML5 (is html5 compatible?)" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<table class=\"data\"><caption>HTML Element Specifications</caption><thead><tr><th>Tag name</th><th>Tag title</th><th>Forbidden</th><th>Omit</th><th>HTML4</th><th>HTML5</th></tr></thead><tbody><tr><td>A</td><td>a</td><td>False</td><td>False</td><td class=\"html4\">True</td><td class=\"html5\"/></tr><tr><td>ABBR</td><td>abbr</td><td>False</td><td>False</td><td class=\"html4\">True</td><td class=\"html5\"/></tr><tr><td>ACRONYM</td><td>acronym</td><td>False</td><td>False</td><td class=\"html4\">True</td><td class=\"\"/></tr><tr><td>ADDRESS</td><td>address</td><td>False</td><td>False</td><td class=\"html4\">True</td><td class=\"html5\"/></tr><tr><td>APPLET</td><td>applet</td><td>False</td><td>False</td><td class=\"html4\">True</td><td class=\"\"/></tr></tbody></table>" ], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "(xhtml>\n", " (table.data\n", " (caption \"HTML Element Specifications\")\n", " (thead\n", " (tr\n", " ~@(list-comp* [col [\"Tag name\" \"Tag title\" \"Forbidden\" \"Omit\" \"HTML4\" \"HTML5\"]]\n", " `(th ~col))))\n", " (tbody \n", " ~@(list-comp* [[id row] (take 5 (.items (do (import (hyml.macros (specs))) specs)))]\n", " (do\n", " `(tr\n", " (td ~(.upper (get row :name)))\n", " (td ~(get row :name))\n", " (td ~(get row :forbidden))\n", " (td ~(get row :omit))\n", " (td ~(get row :html4) :class (if ~(get row :html4) \"html4\" \"\"))\n", " (td :class (if ~(get row :html5) \"html5\" \"\"))))))))" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "collapsed": true }, "outputs": [], "source": [ "; lets import pandas dataframe for easy table view\n", "(import [pandas])\n", "; set max rows to 200 to prevent pruning displayed rows\n", "(pandas.set_option \"display.max_rows\" 200)\n", "; disable jupyter notebook autoscroll on the next cell" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "collapsed": false }, "outputs": [ { "data": { "application/javascript": [ "IPython.OutputArea.prototype._should_scroll = function(lines) {return false}" ], "text/plain": [ "<IPython.core.display.Javascript object>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%javascript IPython.OutputArea.prototype._should_scroll = function(lines) {return false}" ] }, { "cell_type": "code", "execution_count": 28, "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>﷐:forbidden</th>\n", " <th>﷐:html4</th>\n", " <th>﷐:html5</th>\n", " <th>﷐:name</th>\n", " <th>﷐:omit</th>\n", " <th>﷐:title</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>﷐:a</th>\n", " <td>False</td>\n", " <td>True</td>\n", " <td>True</td>\n", " <td>a</td>\n", " <td>False</td>\n", " <td>Anchor</td>\n", " </tr>\n", " <tr>\n", " <th>﷐:abbr</th>\n", " <td>False</td>\n", " <td>True</td>\n", " <td>True</td>\n", " <td>abbr</td>\n", " <td>False</td>\n", " <td>Abbreviation</td>\n", " </tr>\n", " <tr>\n", " <th>﷐:acronym</th>\n", " <td>False</td>\n", " <td>True</td>\n", " <td>False</td>\n", " <td>acronym</td>\n", " <td>False</td>\n", " <td>Acronym</td>\n", " </tr>\n", " <tr>\n", " <th>﷐:address</th>\n", " <td>False</td>\n", " <td>True</td>\n", " <td>True</td>\n", " <td>address</td>\n", " <td>False</td>\n", " <td>Address</td>\n", " </tr>\n", " <tr>\n", " <th>﷐:applet</th>\n", " <td>False</td>\n", " <td>True</td>\n", " <td>False</td>\n", " <td>applet</td>\n", " <td>False</td>\n", " <td>Java applet</td>\n", " </tr>\n", " <tr>\n", " <th>﷐:area</th>\n", " <td>True</td>\n", " <td>True</td>\n", " <td>True</td>\n", " <td>area</td>\n", " <td>True</td>\n", " <td>Image map region</td>\n", " </tr>\n", " <tr>\n", " <th>﷐:article</th>\n", " <td>False</td>\n", " <td>False</td>\n", " <td>True</td>\n", " <td>article</td>\n", " <td>False</td>\n", " <td>Defines an article</td>\n", " </tr>\n", " <tr>\n", " <th>﷐:aside</th>\n", " <td>False</td>\n", " <td>False</td>\n", " <td>True</td>\n", " <td>aside</td>\n", " <td>False</td>\n", " <td>Defines content aside from the page content</td>\n", " </tr>\n", " <tr>\n", " <th>﷐:audio</th>\n", " <td>False</td>\n", " <td>False</td>\n", " <td>True</td>\n", " <td>audio</td>\n", " <td>False</td>\n", " <td>Defines sound content</td>\n", " </tr>\n", " <tr>\n", " <th>﷐:b</th>\n", " <td>False</td>\n", " <td>True</td>\n", " <td>True</td>\n", " <td>b</td>\n", " <td>False</td>\n", " <td>Bold text</td>\n", " </tr>\n", " <tr>\n", " <th>﷐:base</th>\n", " <td>True</td>\n", " <td>True</td>\n", " <td>True</td>\n", " <td>base</td>\n", " <td>True</td>\n", " <td>Document base URI</td>\n", " </tr>\n", " <tr>\n", " <th>﷐:basefont</th>\n", " <td>True</td>\n", " <td>True</td>\n", " <td>False</td>\n", " <td>basefont</td>\n", " <td>False</td>\n", " <td>Base font change</td>\n", " </tr>\n", " <tr>\n", " <th>﷐:bdi</th>\n", " <td>False</td>\n", " <td>False</td>\n", " <td>True</td>\n", " <td>bdi</td>\n", " <td>False</td>\n", " <td>Isolates a part of text that might be formatte...</td>\n", " </tr>\n", " <tr>\n", " <th>﷐:bdo</th>\n", " <td>False</td>\n", " <td>True</td>\n", " <td>True</td>\n", " <td>bdo</td>\n", " <td>False</td>\n", " <td>BiDi override</td>\n", " </tr>\n", " <tr>\n", " <th>﷐:big</th>\n", " <td>False</td>\n", " <td>True</td>\n", " <td>False</td>\n", " <td>big</td>\n", " <td>False</td>\n", " <td>Large text</td>\n", " </tr>\n", " <tr>\n", " <th>﷐:blockquote</th>\n", " <td>False</td>\n", " <td>True</td>\n", " <td>True</td>\n", " <td>blockquote</td>\n", " <td>False</td>\n", " <td>Block quotation</td>\n", " </tr>\n", " <tr>\n", " <th>﷐:body</th>\n", " <td>False</td>\n", " <td>True</td>\n", " <td>True</td>\n", " <td>body</td>\n", " <td>False</td>\n", " <td>Document body</td>\n", " </tr>\n", " <tr>\n", " <th>﷐:br</th>\n", " <td>True</td>\n", " <td>True</td>\n", " <td>True</td>\n", " <td>br</td>\n", " <td>True</td>\n", " <td>Line break</td>\n", " </tr>\n", " <tr>\n", " <th>﷐:button</th>\n", " <td>False</td>\n", " <td>True</td>\n", " <td>True</td>\n", " <td>button</td>\n", " <td>False</td>\n", " <td>Button</td>\n", " </tr>\n", " <tr>\n", " <th>﷐:canvas</th>\n", " <td>False</td>\n", " <td>False</td>\n", " <td>True</td>\n", " <td>canvas</td>\n", " <td>False</td>\n", " <td>Used to draw graphics, on the fly, via scripti...</td>\n", " </tr>\n", " <tr>\n", " <th>﷐:caption</th>\n", " <td>False</td>\n", " <td>True</td>\n", " <td>True</td>\n", " <td>caption</td>\n", " <td>False</td>\n", " <td>Table caption</td>\n", " </tr>\n", " <tr>\n", " <th>﷐:center</th>\n", " <td>False</td>\n", " <td>True</td>\n", " <td>False</td>\n", " <td>center</td>\n", " <td>False</td>\n", " <td>Centered block</td>\n", " </tr>\n", " <tr>\n", " <th>﷐:cite</th>\n", " <td>False</td>\n", " <td>True</td>\n", " <td>True</td>\n", " <td>cite</td>\n", " <td>False</td>\n", " <td>Citation</td>\n", " </tr>\n", " <tr>\n", " <th>﷐:code</th>\n", " <td>False</td>\n", " <td>True</td>\n", " <td>True</td>\n", " <td>code</td>\n", " <td>False</td>\n", " <td>Computer code</td>\n", " </tr>\n", " <tr>\n", " <th>﷐:col</th>\n", " <td>True</td>\n", " <td>True</td>\n", " <td>True</td>\n", " <td>col</td>\n", " <td>True</td>\n", " <td>Table column</td>\n", " </tr>\n", " <tr>\n", " <th>﷐:colgroup</th>\n", " <td>False</td>\n", " <td>True</td>\n", " <td>True</td>\n", " <td>colgroup</td>\n", " <td>False</td>\n", " <td>Table column group</td>\n", " </tr>\n", " <tr>\n", " <th>﷐:datalist</th>\n", " <td>False</td>\n", " <td>False</td>\n", " <td>True</td>\n", " <td>datalist</td>\n", " <td>False</td>\n", " <td>Specifies a list of pre-defined options for in...</td>\n", " </tr>\n", " <tr>\n", " <th>﷐:dd</th>\n", " <td>False</td>\n", " <td>True</td>\n", " <td>True</td>\n", " <td>dd</td>\n", " <td>False</td>\n", " <td>Definition description</td>\n", " </tr>\n", " <tr>\n", " <th>﷐:del</th>\n", " <td>False</td>\n", " <td>True</td>\n", " <td>True</td>\n", " <td>del</td>\n", " <td>False</td>\n", " <td>Deleted text</td>\n", " </tr>\n", " <tr>\n", " <th>﷐:details</th>\n", " <td>False</td>\n", " <td>False</td>\n", " <td>True</td>\n", " <td>details</td>\n", " <td>False</td>\n", " <td>Defines additional details that the user can v...</td>\n", " </tr>\n", " <tr>\n", " <th>﷐:dfn</th>\n", " <td>False</td>\n", " <td>True</td>\n", " <td>True</td>\n", " <td>dfn</td>\n", " <td>False</td>\n", " <td>Defined term</td>\n", " </tr>\n", " <tr>\n", " <th>﷐:dialog</th>\n", " <td>False</td>\n", " <td>False</td>\n", " <td>True</td>\n", " <td>dialog</td>\n", " <td>False</td>\n", " <td>Defines a dialog box or window</td>\n", " </tr>\n", " <tr>\n", " <th>﷐:dir</th>\n", " <td>False</td>\n", " <td>True</td>\n", " <td>False</td>\n", " <td>dir</td>\n", " <td>False</td>\n", " <td>Directory list</td>\n", " </tr>\n", " <tr>\n", " <th>﷐:div</th>\n", " <td>False</td>\n", " <td>True</td>\n", " <td>True</td>\n", " <td>div</td>\n", " <td>False</td>\n", " <td>Generic block-level container</td>\n", " </tr>\n", " <tr>\n", " <th>﷐:dl</th>\n", " <td>False</td>\n", " <td>True</td>\n", " <td>True</td>\n", " <td>dl</td>\n", " <td>False</td>\n", " <td>Definition list</td>\n", " </tr>\n", " <tr>\n", " <th>﷐:dt</th>\n", " <td>False</td>\n", " <td>True</td>\n", " <td>True</td>\n", " <td>dt</td>\n", " <td>False</td>\n", " <td>Definition term</td>\n", " </tr>\n", " <tr>\n", " <th>﷐:em</th>\n", " <td>False</td>\n", " <td>True</td>\n", " <td>True</td>\n", " <td>em</td>\n", " <td>False</td>\n", " <td>Emphasis</td>\n", " </tr>\n", " <tr>\n", " <th>﷐:embed</th>\n", " <td>False</td>\n", " <td>False</td>\n", " <td>True</td>\n", " <td>embed</td>\n", " <td>False</td>\n", " <td>Defines a container for an external (non-HTML)...</td>\n", " </tr>\n", " <tr>\n", " <th>﷐:fieldset</th>\n", " <td>False</td>\n", " <td>True</td>\n", " <td>True</td>\n", " <td>fieldset</td>\n", " <td>False</td>\n", " <td>Form control group</td>\n", " </tr>\n", " <tr>\n", " <th>﷐:figcaption</th>\n", " <td>False</td>\n", " <td>False</td>\n", " <td>True</td>\n", " <td>figcaption</td>\n", " <td>False</td>\n", " <td>Defines a caption for a &lt;figure&gt; element</td>\n", " </tr>\n", " <tr>\n", " <th>﷐:figure</th>\n", " <td>False</td>\n", " <td>False</td>\n", " <td>True</td>\n", " <td>figure</td>\n", " <td>False</td>\n", " <td>Specifies self-contained content</td>\n", " </tr>\n", " <tr>\n", " <th>﷐:font</th>\n", " <td>False</td>\n", " <td>True</td>\n", " <td>False</td>\n", " <td>font</td>\n", " <td>False</td>\n", " <td>Font change</td>\n", " </tr>\n", " <tr>\n", " <th>﷐:footer</th>\n", " <td>False</td>\n", " <td>False</td>\n", " <td>True</td>\n", " <td>footer</td>\n", " <td>False</td>\n", " <td>Defines a footer for a document or section</td>\n", " </tr>\n", " <tr>\n", " <th>﷐:form</th>\n", " <td>False</td>\n", " <td>True</td>\n", " <td>True</td>\n", " <td>form</td>\n", " <td>False</td>\n", " <td>Interactive form</td>\n", " </tr>\n", " <tr>\n", " <th>﷐:frame</th>\n", " <td>True</td>\n", " <td>True</td>\n", " <td>False</td>\n", " <td>frame</td>\n", " <td>False</td>\n", " <td>Frame</td>\n", " </tr>\n", " <tr>\n", " <th>﷐:frameset</th>\n", " <td>False</td>\n", " <td>True</td>\n", " <td>False</td>\n", " <td>frameset</td>\n", " <td>False</td>\n", " <td>Frameset</td>\n", " </tr>\n", " <tr>\n", " <th>﷐:h1</th>\n", " <td>False</td>\n", " <td>True</td>\n", " <td>True</td>\n", " <td>h1</td>\n", " <td>False</td>\n", " <td>Level-one heading</td>\n", " </tr>\n", " <tr>\n", " <th>﷐:h2</th>\n", " <td>False</td>\n", " <td>True</td>\n", " <td>True</td>\n", " <td>h2</td>\n", " <td>False</td>\n", " <td>Level-two heading</td>\n", " </tr>\n", " <tr>\n", " <th>﷐:h3</th>\n", " <td>False</td>\n", " <td>True</td>\n", " <td>True</td>\n", " <td>h3</td>\n", " <td>False</td>\n", " <td>Level-three heading</td>\n", " </tr>\n", " <tr>\n", " <th>﷐:h4</th>\n", " <td>False</td>\n", " <td>True</td>\n", " <td>True</td>\n", " <td>h4</td>\n", " <td>False</td>\n", " <td>Level-four heading</td>\n", " </tr>\n", " <tr>\n", " <th>﷐:h5</th>\n", " <td>False</td>\n", " <td>True</td>\n", " <td>True</td>\n", " <td>h5</td>\n", " <td>False</td>\n", " <td>Level-five heading</td>\n", " </tr>\n", " <tr>\n", " <th>﷐:h6</th>\n", " <td>False</td>\n", " <td>True</td>\n", " <td>True</td>\n", " <td>h6</td>\n", " <td>False</td>\n", " <td>Level-six heading</td>\n", " </tr>\n", " <tr>\n", " <th>﷐:head</th>\n", " <td>False</td>\n", " <td>True</td>\n", " <td>True</td>\n", " <td>head</td>\n", " <td>False</td>\n", " <td>Document head</td>\n", " </tr>\n", " <tr>\n", " <th>﷐:header</th>\n", " <td>False</td>\n", " <td>False</td>\n", " <td>True</td>\n", " <td>header</td>\n", " <td>False</td>\n", " <td>Defines a header for a document or section</td>\n", " </tr>\n", " <tr>\n", " <th>﷐:hr</th>\n", " <td>True</td>\n", " <td>True</td>\n", " <td>True</td>\n", " <td>hr</td>\n", " <td>True</td>\n", " <td>Horizontal rule</td>\n", " </tr>\n", " <tr>\n", " <th>﷐:html</th>\n", " <td>False</td>\n", " <td>True</td>\n", " <td>True</td>\n", " <td>html</td>\n", " <td>False</td>\n", " <td>HTML document</td>\n", " </tr>\n", " <tr>\n", " <th>﷐:i</th>\n", " <td>False</td>\n", " <td>True</td>\n", " <td>True</td>\n", " <td>i</td>\n", " <td>False</td>\n", " <td>Italic text</td>\n", " </tr>\n", " <tr>\n", " <th>﷐:iframe</th>\n", " <td>False</td>\n", " <td>True</td>\n", " <td>True</td>\n", " <td>iframe</td>\n", " <td>False</td>\n", " <td>Inline frame</td>\n", " </tr>\n", " <tr>\n", " <th>﷐:img</th>\n", " <td>True</td>\n", " <td>True</td>\n", " <td>True</td>\n", " <td>img</td>\n", " <td>True</td>\n", " <td>Inline image</td>\n", " </tr>\n", " <tr>\n", " <th>﷐:input</th>\n", " <td>True</td>\n", " <td>True</td>\n", " <td>True</td>\n", " <td>input</td>\n", " <td>True</td>\n", " <td>Form input</td>\n", " </tr>\n", " <tr>\n", " <th>﷐:ins</th>\n", " <td>False</td>\n", " <td>True</td>\n", " <td>True</td>\n", " <td>ins</td>\n", " <td>False</td>\n", " <td>Inserted text</td>\n", " </tr>\n", " <tr>\n", " <th>﷐:isindex</th>\n", " <td>True</td>\n", " <td>True</td>\n", " <td>True</td>\n", " <td>isindex</td>\n", " <td>False</td>\n", " <td>Input prompt</td>\n", " </tr>\n", " <tr>\n", " <th>﷐:kbd</th>\n", " <td>False</td>\n", " <td>True</td>\n", " <td>True</td>\n", " <td>kbd</td>\n", " <td>False</td>\n", " <td>Text to be input</td>\n", " </tr>\n", " <tr>\n", " <th>﷐:keygen</th>\n", " <td>False</td>\n", " <td>False</td>\n", " <td>True</td>\n", " <td>keygen</td>\n", " <td>True</td>\n", " <td>Defines a key-pair generator field (for forms)</td>\n", " </tr>\n", " <tr>\n", " <th>﷐:label</th>\n", " <td>False</td>\n", " <td>True</td>\n", " <td>True</td>\n", " <td>label</td>\n", " <td>False</td>\n", " <td>Form field label</td>\n", " </tr>\n", " <tr>\n", " <th>﷐:legend</th>\n", " <td>False</td>\n", " <td>True</td>\n", " <td>True</td>\n", " <td>legend</td>\n", " <td>False</td>\n", " <td>Fieldset caption</td>\n", " </tr>\n", " <tr>\n", " <th>﷐:li</th>\n", " <td>False</td>\n", " <td>True</td>\n", " <td>True</td>\n", " <td>li</td>\n", " <td>False</td>\n", " <td>List item</td>\n", " </tr>\n", " <tr>\n", " <th>﷐:link</th>\n", " <td>True</td>\n", " <td>True</td>\n", " <td>True</td>\n", " <td>link</td>\n", " <td>True</td>\n", " <td>Document relationship</td>\n", " </tr>\n", " <tr>\n", " <th>﷐:main</th>\n", " <td>False</td>\n", " <td>False</td>\n", " <td>True</td>\n", " <td>main</td>\n", " <td>False</td>\n", " <td>Specifies the main content of a document</td>\n", " </tr>\n", " <tr>\n", " <th>﷐:map</th>\n", " <td>False</td>\n", " <td>True</td>\n", " <td>True</td>\n", " <td>map</td>\n", " <td>False</td>\n", " <td>Image map</td>\n", " </tr>\n", " <tr>\n", " <th>﷐:mark</th>\n", " <td>False</td>\n", " <td>False</td>\n", " <td>True</td>\n", " <td>mark</td>\n", " <td>False</td>\n", " <td>Defines marked/highlighted text</td>\n", " </tr>\n", " <tr>\n", " <th>﷐:menu</th>\n", " <td>False</td>\n", " <td>True</td>\n", " <td>True</td>\n", " <td>menu</td>\n", " <td>False</td>\n", " <td>Menu list</td>\n", " </tr>\n", " <tr>\n", " <th>﷐:menuitem</th>\n", " <td>False</td>\n", " <td>False</td>\n", " <td>True</td>\n", " <td>menuitem</td>\n", " <td>False</td>\n", " <td>Defines a command/menu item that the user can ...</td>\n", " </tr>\n", " <tr>\n", " <th>﷐:meta</th>\n", " <td>True</td>\n", " <td>True</td>\n", " <td>True</td>\n", " <td>meta</td>\n", " <td>True</td>\n", " <td>Metadata</td>\n", " </tr>\n", " <tr>\n", " <th>﷐:meter</th>\n", " 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\n", "﷐:h5 Level-five heading \n", "﷐:h6 Level-six heading \n", "﷐:head Document head \n", "﷐:header Defines a header for a document or section \n", "﷐:hr Horizontal rule \n", "﷐:html HTML document \n", "﷐:i Italic text \n", "﷐:iframe Inline frame \n", "﷐:img Inline image \n", "﷐:input Form input \n", "﷐:ins Inserted text \n", "﷐:isindex Input prompt \n", "﷐:kbd Text to be input \n", "﷐:keygen Defines a key-pair generator field (for forms) \n", "﷐:label Form field label \n", "﷐:legend Fieldset caption \n", "﷐:li List item \n", "﷐:link Document relationship \n", "﷐:main Specifies the main content of a document \n", "﷐:map Image map \n", "﷐:mark Defines marked/highlighted text \n", "﷐:menu Menu list \n", "﷐:menuitem Defines a command/menu item that the user can ... \n", "﷐:meta Metadata \n", "﷐:meter Defines a scalar measurement within a known ra... \n", "﷐:nav Defines navigation links \n", "﷐:noframes Frames alternate content \n", "﷐:noscript Alternate script content \n", "﷐:object Object \n", "﷐:ol Ordered list \n", "﷐:optgroup Option group \n", "﷐:option Menu option \n", "﷐:output Defines the result of a calculation \n", "﷐:p Paragraph \n", "﷐:param Object parameter \n", "﷐:picture Defines a container for multiple image resources \n", "﷐:pre Preformatted text \n", "﷐:progress Represents the progress of a task \n", "﷐:q Short quotation \n", "﷐:rp Defines what to show in browsers that do not s... \n", "﷐:rt Defines an explanation/pronunciation of charac... \n", "﷐:ruby Defines a ruby annotation (for East Asian typo... \n", "﷐:s Strike-through text \n", "﷐:samp Sample output \n", "﷐:script Client-side script \n", "﷐:section Defines a section in a document \n", "﷐:select Option selector \n", "﷐:small Small text \n", "﷐:source Defines multiple media resources for media ele... \n", "﷐:span Generic inline container \n", "﷐:strike Strike-through text \n", "﷐:strong Strong emphasis \n", "﷐:style Embedded style sheet \n", "﷐:sub Subscript \n", "﷐:summary Defines a visible heading for a <details> element \n", "﷐:sup Superscript \n", "﷐:table Table \n", "﷐:tbody Table body \n", "﷐:td Table data cell \n", "﷐:textarea Multi-line text input \n", "﷐:tfoot Table foot \n", "﷐:th Table header cell \n", "﷐:thead Table head \n", "﷐:time Defines a date/time \n", "﷐:title Document title \n", "﷐:tr Table row \n", "﷐:track Defines text tracks for media elements (<video... \n", "﷐:tt Teletype text \n", "﷐:u Underlined text \n", "﷐:ul Unordered list \n", "﷐:var Variable \n", "﷐:video Defines a video or movie \n", "﷐:wbr Defines a possible line-break " ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "; show all specs\n", "(pandas.DataFrame.transpose (pandas.DataFrame specs))" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<style>\n", "\n", "/**\n", " * Copyright: Marko Manninen, 04/2016 (https://www.github.com/markomanninen)\n", " */\n", 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"}\n", "\n", "div.output_scroll {height: inherit;}\n", "\n", "table#data { margin: 1em auto; border-collapse: collapse; border: 0} \n", "table#data caption { font-size: 1.2em; text-align: center; padding: 3px} \n", "table#data th, table#data td { padding: .25em; border: 1px solid #000; font-family: sans-serif; color: white} \n", "table#data th { color: #004900; font-weight: bold; text-align: left; } \n", "table#data thead th { border-bottom: 3px double #000; background-color: #ddd; text-align: center; } \n", "table#data tfoot td { border-top: 3px double #000; color: #fff; font-style: italic; font-size: .8em; text-align: center; background-color: brown} \n", "table#data tbody th { color: #000; }\n", "table#data #tbody2 {font-weight: bold;font-size: 1.5em;}\n", "\n", "</style>\n" ], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ "; include notebook custom styles\n", "(IPython.display.HTML (.read (open \"styles.css\" \"r\")))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## The [MIT](http://choosealicense.com/licenses/mit/) License\n", "\n", "Copyright (c) 2017 Marko Manninen" ] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Calysto Hy", "language": "hy", "name": "calysto_hy" }, "language_info": { "codemirror_mode": { "name": "scheme" }, "mimetype": "text/x-hylang", "name": "hy", "pygments_lexer": "lisp" } }, "nbformat": 4, "nbformat_minor": 1 }
mit
zimmermanncode/nodely
README.ipynb
2
9340
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# nodely >>> putMORE Node.js into Python" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "[![](http://www.gnu.org/graphics/lgplv3-88x31.png)](\n", " https://gnu.org/licenses/lgpl.html)\n", "[![](https://img.shields.io/pypi/pyversions/nodely.svg)](\n", " https://python.org)\n", "[![](https://img.shields.io/pypi/v/nodely.svg)](\n", " https://pypi.python.org/pypi/nodely)\n", "[![](https://img.shields.io/pypi/dd/nodely.svg)](\n", " https://pypi.python.org/pypi/nodely)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "[![](https://travis-ci.org/zimmermanncode/nodely.svg)](\n", " https://travis-ci.org/zimmermanncode/nodely)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* [**Embed**](#Embed-node_modules/-in-Python-environments)\n", " **`node_modules/` in Python environments**\n", "* [**`require`**](#require_node_modules-in-setup.py)**`_node_modules`\n", " in setup.py** \n", "* [**Run**](#Run-installed-Node.js-tools-from-Python)\n", " **installed Node.js tools from Python**" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Setup" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Use [pip](http://pip-installer.org) to install the latest [release](\n", " https://pypi.python.org/pypi/nodely) from [PyPI](https://pypi.python.org):\n", "\n", "> `pip install nodely`\n", "\n", "And don't forget to install [Node.js](https://nodejs.org) ;)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Embed `node_modules/` in Python environments" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import nodely" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Many great tools are written with JavaScript in [Node.js](https://nodejs.org).\n", "It makes sense to use them in Python instead of reinventing the wheel.\n", "`nodely` provides an API for managing local `node_modules/` in Python environments\n", "and running the installed Node.js tools from Python" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If the root directory of the current Python environment is:" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'C:\\\\Users\\\\Zimmermann\\\\Miniconda3\\\\envs\\\\nodely'" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import sys\n", "\n", "sys.prefix" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Then `nodely` will create:" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Path('C:\\\\Users\\\\Zimmermann\\\\Miniconda3\\\\envs\\\\nodely\\\\node_modules')" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "nodely.NODE_MODULES_DIR" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "_Please don't modify the above constant, except you exactly know what you are doing ;)_" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's say you want to use the [CoffeeScript](http://coffeescript.org) compiler...\n", "Just install the Node.js package:" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "nodely.install('coffee-script')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It provides the `coffee` executable. If you want to know its absolute path:" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Path('C:\\\\Users\\\\Zimmermann\\\\Miniconda3\\\\envs\\\\nodely\\\\node_modules\\\\.bin\\\\coffee.CMD')" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "nodely.which('coffee')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "And if you want to run it, for example with the `--version` flag:" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "nodely.call('coffee', ['--version'])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For the case that you want to get rid of the package again,\n", "just `nodely.uninstall('coffee-script')` it" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### `require_node_modules` in setup.py" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Instead of installing Node.js packages during runtime,\n", "you can also define them as dependencies of your Python package:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "```python\n", "from setuptools import setup\n", "\n", "setup(\n", " ...\n", " setup_requires=['nodely', ...],\n", " require_node_modules=['coffee-script', ...],\n", " ...\n", ")\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "So they get implicitly installed during the installation of the Python package,\n", "just like the Python dependencies defined in `install_requires`" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Run installed Node.js tools from Python" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The `nodely.call` function additionally supports `subprocess.call` options:" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from subprocess import DEVNULL\n", "\n", "nodely.call('coffee', ['--version'], stdout=DEVNULL)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "And instead of a simple `nodely.call`,\n", "you can also create a process instance,\n", "and give any `subprocess.Popen` options to it:" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from subprocess import PIPE\n", "\n", "process = nodely.Popen('coffee', ['--version'], stdout=PIPE,\n", " universal_newlines=True)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'1.12.7'" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "process.communicate()[0].split()[-1]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A more object-oriented approach is provided by:" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import nodely.bin" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It lets you introspect all installed executables with interactive auto-completion\n", "and creates `nodely.bin.Command` instances:" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": true }, "outputs": [], "source": [ "coffee = nodely.bin.coffee" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "`nodely.bin['coffee']` returns the same.\n", "And that `nodely.bin.Command` instance provides its own `.call` and a `.Popen` methods,\n", "and can also be called directly instead of using its `.call` method:" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "coffee(['--version'])" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.2" } }, "nbformat": 4, "nbformat_minor": 2 }
lgpl-3.0
MarineLasbleis/GrowYourIC
notebooks/Fig1_b.ipynb
1
430876
{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "%matplotlib inline\n", "\n", "# import statements\n", "import numpy as np\n", "import matplotlib.pyplot as plt #for figures\n", "from mpl_toolkits.basemap import Basemap #to render maps\n", "import math\n", "import json #to write dict with parameters\n", "\n", "import GrowYourIC\n", "from GrowYourIC import positions, geodyn, geodyn_trg, geodyn_static, plot_data, data\n", "\n", "plt.rcParams['figure.figsize'] = (8.0, 3.0) #size of figures\n", "cm = plt.cm.get_cmap('viridis')\n", "cm2 = plt.cm.get_cmap('winter')" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [], "source": [ "## real data set - WD13\n", "data_set = data.SeismicFromFile(\"../GrowYourIC/data/WD11.dat\")\n", "\n", "# random data set -\n", "data_set_random = data.RandomData(3000)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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0/v4MmD2L8JHjkEQJXXfq38WUHTzI0VWrOP711xgaGnp1C25X9xeSJH33uzvC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YlKpyNz0iQ6ampoq6wk4PdjTkigo6rqpNuyNTdz4PPPmXLzzaccd9zUsfj9XdQ0d5IWF36C0TvQ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kKIUUKIwjo/uf9xnu4u8A+PRAhRALwkSdIE4IfS/PyBv8+ebbpr3jy6Tp3KoI8eNkqoCaZW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\nZcqQKJrMVs7lV6JSyukW4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"text/plain": [ "<matplotlib.figure.Figure at 0x115b15518>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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KpXv3YjEauf7NN2kqKiIm6689xj/WUxAE2quqOPHRR5z85BN0wcGEDB1KS1kZgbGxSKRS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KucRVNzKU/S4PYGQJAIatGYyL6aTjoHnTR2j5CfZhlXDdKplZj0GiJRaY76UF0PAgLeQAi5XIZJ\nr0GrUpBmSyAQirDzeBvHm/qRySS/7jOIRGP866aDeAIhugaddA06ae93UpqdiEwmIBMEDp/uweUL\njs0eayjJShx3jL8VOo2SQChM77Cbwkwr2SlmNqycxJYDjXQOuvD6JSW6UCRKSbYNhUzGgboufv32\nYd7Y10xTv4sDp3sIWtOIRGI4+vrIPuNa1tVF1eZ3aaltQBYOoPB7KLYqeO13r2MtLuXE5s34XS6G\nW1v54y23IMZicWMVkDavIx0dmDMyGGhowNXfT9fx4/jdbnRmM2IsRlJ+PtFIhPd/8AO8IyOIYzrZ\nq7/3PcxplxYwKlu5koV3382hP/+ZkNdbANzz1FNP/fXJJ590/pdP7P8w/q7L14IgVCA5Od0MyCqv\nuor733vvgmYDILGbPWNOPn6XC7/DgcFmY7ilBUt2NpqEBE7v2EHW1KkXfL5SrWbh3XcTcLlQGwzI\nFQrcQ0P01tSw4qGHKFq8eNyFOeOGG0jMy6O/vl7Suh4YQKHR0FtTM6HMej4ioRAnNm/G0dNDxqRJ\nlI3pWIuiSOuzP+TOMqnU2T7gYPvRVjISEzCPLTLHm/po7hmle9iFQasiK/lsQJhSkMqUgvGbjkGH\nF18wjMsbRKNS0DvsvuRYlMcf4kRTH8dbpB7V0YZe8tMtFGZYabaHMM4qJnHPm1TkSf2mJ/98gIGA\nyMI1q/nx7/+CVqthWrYFqx5W5ShJsep44eQwHQPOMdnDGPWOKKGkTMJuN0GPh6o33mDVI49Ic8hZ\n48usZ1ieV//gB+N+f0bAAECmUEwo/57ZKIHkDGVOT8eSKfUuPcPDRAIBRru6UGrO9lqzp09ntLOT\nkY4O5t92G1qTiZ6aGt586CHKL7uMtLIyQMoU5m3ceMHz5+jujhP2xFiM4dbWCSNyOrOZlJISAi4n\nQk8DUwpT4gSs89HQNcxX/+V9fvfNayjJSqTXB6XX3zzuPug6cYLB5mYS8/JY+2/Ps+ehjWws01KQ\nbsXc1U/n3t3Yyiu5/oWX6T18gD9UH+CGVN+Emdj+UU98/tgZiGKbPwNNQgJPlpfz0Pbt8evU73Ti\ntdsxpqRIwhNjsBUUYfvRsxx97WVm1X+AUafG5QtiMUiGDBqVAuHIp/gNOWhNZizFpZhnzeKvB3dx\nu3YImUzgspkFHDrdg0wQmFueSYJOxc9e20tj1yhmg4Zki55QOBqfcY5EY/SPei44VbD3VCdNPSOo\nlHK6h1zIZJBuk0bLzlSMjjb20ton2WnuPtlBui0Bo17ybO4f9fDze1ZyvLmfqvpeUm0GnF7J4tJs\n0MT7zy9uPU5UFCnOtJJzAXnavxUbVk5mbnkWHx5sonPQgcmgoSDdinesh61UyBi0e3l520k8/iAt\nvXaGnT6iKHClFRP2uCkqLIl/Z47eXva/+CLRcJiIQs3OEx2Yk5OwJlpoP9nFvIe+T83WrbgGBuir\nq8OUlsa0deuYvGYN+196CUd3N6Io4nM60RgMdB0/Tsjnk64DvT7upKY1m7FmZ+N3u+PkRkEmk0rZ\nX1D5zpyezr/09/PHjRs59PLLqUCbIAjfF0Xx6f+2E/w/gL/boCwIghF4ESSu0LKvf531v740IU9v\nsaDS6Qj5fOgsFmRji7RCo4lfCEqtltpt2+ILWOXq1ePKirIxJa4z2PqTn3DinXf4YUPDuIAM0rhU\n/rx5XPuzn7HlyScRBIHEvDw6qqpIr6iYUE49F22HDtFXVwdIveqkggIS8/Ko/dPvuSbRhyBIO+0M\nmxGjToVBqyIYisZnG8+MnZxhXF4KFoOW5p7R+M46I9GIiEhJViJmg4ajjb2c7hjGbNAwvSiNbUea\n6R5y0TfiJifFzAcHG7lxWSXFmTYaHXbm0suKPCnT2tc0TDcGZt50DcJgC8/etQS320PXoFRWy0+3\nEInG6Bv14PYFEYGdp7pIzEwn4vWSPmkyBpuNkM93QaYrnG0rnPlbLBajr7YWU3o64WCQkNdL8ZIl\n44JrNBIhrbyc/vp6/E4n6RUVFC9dit/hoLe2lvy5c2neuxf34CCWrCwGm5ow2GzI5HKmX3fduNcv\nXLiQrClTJpi/XwzmjAzkKhXRUAgEAa3ZzOkdO9AYjeTMnIlMJkMQBGbedBOFCxbg+GkbeWnSeeoY\ncKBTK0mznZ1JTjTpuPvqGSSadLQOehgQtaQGAnHDgKHWVk5s3gxAb00NaoOByb/4A6/+5mdMHWhi\nVlYCQ3/+OX/Y20DuupuxZGSQc+NdPP7N+1mQImf5tDySLVLFJc2aENewzrLpyXDU4x/o4aHt28mf\nN4+Q18upZ58md6SRZI1AY1CD4upbyV22atw5KFv/FY5/cwc3LK3A4w9h0Kris8R3Trfx1OZD+Epm\nYs3JJW/uXNzpKXz/e49SNncWa9S9XDF7/Ehba69dknhUypAJkvVoa5/kqa2Uy+NiOB5/iFhMjFeC\nHB4/g3YvMkFgSkEKCyZlM2j3YknQ4PAEqGkbPHsveYOIokgoEqF/NMz7+xukVpBMht3t44ODjVy/\npBy5TEbviAuTXo0gCGSnmHj0ywtxegNYDNpLlsT/M+gZdnGkvheNSkFJ1hgTHIH5lVmEwlFaekfJ\nSErA6Q3i8PrpGXbj80s8h2gshmdoiLSKCkJ+P2q9nuLFixlobIxzIcw5eeQtW0nfkQOEreXk3fIV\nVAlGEMWzm8poFFtuLmG/nzcffpjU0lIyp0zB2dtL9vTpqPV6lBoNw21t2Lu6SC4uJrm4mGnr1jHS\n3s5gUxPZ06bRW1uLUqdjxo03/qfPwx1/+hPTvvQlnr/+ekGMxf5ZEISZwLV/r57Nf5dBWRAELdAM\nJGmMRhbceeclZzvPQKXTseCOO+iurkZvs2HNzsbe1cXM9esl0wkgMTdX6kECnqEhzBkZ48hcZ9BT\nU0N/fT3rfvxjlj/wwCWVudLLy5l/xx007toVDwzn6mlfCLFodMLPdX/6HZNaPyMlU1pg/rLjFN5A\niNtXT6OqsZdgKIpMEFApFSSatBh1avJSpc2GPxjGGwhjTdBOEB4IRaLkpppx+4K4fSGON/VhH+s7\nrZyRT1VDLyApMTnckl2j1ahl0OElHI1xy6op3L56Ks09oywVRVw93XzktTOnLIMFRYksKEokEm1F\nkS8HDJBsGJetR2MxInoL7rAGQaMnZ/oMrn/2Vww1N3P87beJRaMSoWks6J4bnJ19fSx/4IH4Bgvg\n1Acf0Hn0KCD17xffc8+4z+t3udj/4ov47HZM6eksuOsutGPavNOvu45p116LZ3gYn90ef85wWxv5\n50msnkHVG2+QUlLyhefc9VYrC++6i6GWFozJyZzYvDkuXRjyeuMsVZlMBqJIvkmJKIpsOdAY11te\nUJkdZzTrNSrmVWTyUY+I6spbmLVqvJXnmTG7wcP7CA4NULRoEaklJUx79Cl6jxzkT288T1LQy4x0\nPQ2fbMFTMZ3B5mZEuQKn18++mk7WLSojFhNpGA0hzykmNDzErEwtTaKR2j/8luOf7aeysoAZFXls\nzFOhTpI2ZdOA2t0vUbd9EwFjElFdAgICYl8bl6dIveoRl4+9pzoxG6TZdIVczg+vncRfqvsxLb8d\nwxj5LnnWPLS5efzx01Yen68d9xmvX1LOh4eaGRz14vWHkcsEvrSwlAG7V6oiGTQca+xj25Fm1Eo5\nS6fmMb04DX9QEqgRRRGtWsH0ojREUeTNXbXxGe2cFDPDTh8d/Q6SzHpq2oYwGzTxTDwai7F6TjGV\nuSlsq2ohGovy3r4G+kY8lGQlxk0jNKr/nHTtpRCLiXx8RJLPBInx/cB1c3jhw+OICCQUl/HwYoEE\ntZyn/vQZvkAIpyeAJUGLNUFL15AHo9mILTsbfWIi82+7DY3BwMCYbj9ILYerfvTPeIaG2P6db/La\nHRu57/U/U7hwIQ07d+J3OLDl5mJMTSVj8mQW3HEHSq2W/tOn4+ub3mZj3m23se2nP8WckYFCpcLZ\n24t7cJCqN9+UpDuNRq564gmSi4ow/o1ytNPWreOXo6P8cvFieqqrvwQ4BEEoFEVx6L98sv8f4+8u\nKAuCcDnwFqBPKS5m5vr1qA0GChcuBKTgNdrZidpgICEpaUJ2ZUhMHOfydCZDThvLcjqPHRv3etHQ\nhcdL9v7+97Ts38+0des+txQNULxkCa7+fhy9vWRMmiSRH/x+SXLuAn24vDlzGGppwdnbS3plJSOH\n9rCgZzf5mWdvbH8oTDAcpaXXjk6tRKdWolEpuPWyySgUckx6NUqFnP5RDx8ebCIcjZKRKBkDnBuY\nzQYNZoPU+3X5gui16vjxvYHxn9+gU+ENhlDIZYQjUU53DPHiY2tp7rZzuL6H+o5hvMEwZdmJOL2B\nuOrRpRSV/tIhY+lvX+H45s0Mt7ZiSE6hfqyNcMWjjxKLxVCq1YR8Pg6++irOvj4yKiuZdu21vHL3\n3USCQS5/7DE0BgM5s2Yx1HzWAe7c/59B1/Hj8YDr7O1ltKNj3FyzIAjoLBYMiYlxAX6N0cjpTz7B\nkpExISPe+ZvfMGXt2glkrkvBmJyMMTkZ9/BwXNNaazLh6O0d97jBmpNMMqrwBsLxgAzQ1mePB+X2\nfge3PP02977+GqWrJnprp5WV0bJvH7KAh8VZWlx1J2DFCgDSZ80lZdpMdjz2MJ6mXSTKozTs3wNq\nLXq9lqA8giAIxGIi39rcSO2JWm597U0qFi3iq0olDz1+PwX5Jpw1Wjpq6vnJuolTBRVpBqTfDo/9\nA/IAJPWsLfsbUSnl9Ay7UCnkzC7LQBAENkyx8vwz38N5033kLVtBxbrrcP78Ye6cMpElbjPpqG6R\nhCdmlWYw6PAyqzQjzqkIR6L85ZNTuLwBQPJmnl6cBgJU5CYRjZ0lOA7YPXQOOEnQSYQ0fzBMdrIp\nvqFt6R1l7YJSqhqkioFKIcl0dg85Od7Uh1GvxukJ0j/qpTlnlHRbAlfNLb6oCtffgpgoEo6cY5QT\njsbfa7c7wnVvvcqWH36H1eE2clPNdA06CUdjZCQaKcq0otFpiZxpmcRi8dGmlKIiZt18M/bubkmt\nMCsLSkpQ/fPTJH34PKZDb1CVOJ01P/oRYiwmqXON2dLO27iRUx98QNvhw5StWMHyBx5AYzQiVyiw\n5eXhHLu29TabdF+dk8gKgjAhIHceP469q4vUsjJSiiaOh50PvcnEEydP8psrr6Rm61Yj0C0IwkZR\nFF//L57u/6f4uwrKgiBsBF4CuPk//oOl991HyO9HoVIhk8sRRZEDL79M+5EjKNRqUoqK8AwPo7fZ\nmLNhwxciV2VMmkTPqVP01NTg7Ovj9PbtknrTjBkA7H/pJRp27mTaunWsfvxx7N3dqHS6+PxwKBBg\n69NPM9LRQdGiRSy44w5kcjkqrZb5t90Wf53TO3bQvHcvCrWaWevXTyhlq7RaFt55JyD1l9u/cyf5\nJdIC09wzyvaqFr56zQwE4KWPTsafp1MrSbWNl1usbR+MG6D3DLsYcfnGEcA0KgVrF5TS0jtKcZaN\npi5JAzjRpCM/3cKIy09d+xCWBA2rZuYz5PDRP+ohN9WM3R1ApVDg9kvlrFAkCqJIKBIdp/V7LkRR\n5EDLCH1DLtIzkjGu/DKpZWVktbfj7O+nYdcuWg8coGDBAhbfey/G5GQGmpo49Oqr2Lu6sGRl0XPq\nFBmTJ7PmRz9i/0sv0bxnDyBtqnpra3EPDZFWVnZBB64zjjUX+xmkmfEFd9xB39iu/8S770r9Mb+f\nubfcMo5I+Pjx40Qjl9Yyvhgad+6MCyck5uUxdd26+N9qt23jwL/8jIAxxMJJ2fH+KxAvJ4OUJa27\n93bKrrr6gq+h1utZct99VDl6WZvQz7vW8SQuuULBZb/4NZ/97GkcO97l+uefY+dvfo1reIQ9bX38\nfH6J9L06hylefRWNn32GzmLh6rXLWaJ3kmXVseGeFRfceDk8AZp7pF5vYcZ4LepAKMLbu+uobR9E\npZTMH3zB8deMXKmko66eI+9twZqVxbAyh9YdtcxIN+D2B5lamEp5ThJtfQ5SxpL1HUsAACAASURB\nVDLSzGTjBC1quztwjmmEGC9J56SY43PdOSkmmntG+fRYG/12D/2jUJRpIy/NwoDdE39vyWY9iSYd\n1y4qY9AhCYOYDRqJaW7Ro5DL8AbCZKdIm+3eETcef+gLkSe/KBRyGbNK0zlS3zvO8/pr62bzToOL\n9598ksSUDLpbGumNaQnmZGKOhanIMnDDJBvP7BsglJ6JTC6fMD+cWlIyjjAJkL1kBZ0qNUNvP8/V\nhmre/sE3mPHDZ9BbLJJCVyBA5uTJZE6ezNJ/+ifUBgP7X3qJzMmTyZ01i1nr19P42WcIgkDx0qWI\nsRiahIS4jnzqGBcjFovRO1aF7Dl1CplcTteJEyz66lcvKVUbCYU4/s47OHp6WPHwwyy8+26eu+46\nFaL4miAIK4Bf/72YW/zdsK8FQdgBPKLUaHjw44+ZecMNgHTTCmOEFq/dzkc/+QmOnh5GOzoYGBP5\nCPl8RMPhCRfahSCTy8maOhVnv2TSEItGGWxuJm/OHDxDQ7zzne/QffIkCSkpdFZV0XXiBO1nWNtp\naez8zW+o+/hj/E4nA/X1pJWXY87IIBaNUv3BBzTu2oXP5eLUli0MNjXhGRoiGo1iychAkMkuWAav\ne+UF1qh74qIDe0518NHhFq6aW4RKqSBBp6ZnyIVKoWDJ1ByM5400jbr89I05LinkMqYVpcVtFc9A\no1KQbksgL9VCTqqZ7GQTs0rS42L904rSKMtJQjWWgW850Mjs0sy4CYFBq5L6eohEIpJn7tzyTFIs\n40t2vkCYXfUDHGp30hZUYvnKQ2SP9Rub9u5lsLERn8NBLBrFlJqKNTsbmULBh08/TcPOnfSfPo1M\nocCYnMxoVxeRsZ4xSOz6toMHSS4qQgCMqaksuvvuCWNuxrGbW5DLKZg/f4IxxRnIlUrM6en4HA5O\nbN6M124HUcTvcFC4cCFqvZ7abdvY9MgjpJSU4HM4MCQmTihjx2Ix+urqcA8Pj/t7LBbj2FtvkZCc\njNZoxJqdzcyxnprP4eDgK6/Qs383doeLuvYhCtKtlGQnUpRhZUpBavw43kCYD6raKFl73Tjj+XMh\nk8vxef2c8igpuv7L4/rrIGUq2fMX0vbuJrqaWjHmFWLNzsZSVMr2bfuYU2Djw6o2lGnZ6CwWyZ6v\nuJKDb22GSIRki57HfrcDo04dJxZKQfc0nYNO2vrsaFSKcZuJpu4R9p3qZNjpwx+SZtxXzylENza+\n5PGHeG1vE+2tXXhGRpArlWROnYpXUDLQ2IhaLtmCGnVq6jqGMGhUjDh9CAjctLxi3Pcgl8voHXYT\nCEXRqhVcNbdYmvVNNpJo0pObamZGcTqHTvfg9AXG/J8FrpxTTEVeMjkpJlRK6R6ZV5EZH7NKNOni\nrG6TXippR6IioXCEytwUEKSN8vTitP/WTBkgzZbA5IIUphWlxklsz+3rJjTrcvb95XVyFyykzRVh\n7je/x6Trb2T6xjtweIIMVh/nyjIrw6NuTKWVFKyUJDL9TidHN22SlA6NRokE295Oy/79hLxesmfN\nZtAXI7X/NJVaP4faHZgKS9j3wgvUbtvGSHs76ZWVqHQ6YrEYbz74ICG/n7IVK1Cq1aSWlMRNeJRq\nNVnTppFcVETJ0qVxb4FTH3xA/Sef0H74MPbubowpKSCKUpn8EqXtlv37aT98mEgwiL2ri4orrmDG\nTTdx+C9/IRaJTAdueOqpp/Y++eST3f+tX8L/D/hfnykL0p21HVihUKtZ/uCDcaWY8yFGo3ECgiAI\nRM4pPV9sVONiOH8hb6+qovajjyhdsYJYJIIYjTLU1ibNG4sinceOkT1tGn6XC8YEK8KxWPw9tB0+\nHO9zjnR0SGXyMf/Rk++8g7u/H4Vazdxbb8WSMV6s3nHqGP2WCHqNinf2nmbN/BKumlscz0wKM6wT\nspBzMa0oFVEUcXqDlGYnfq6jTaJJd8mRDac3yPaqVkqzE8lPl8r/ZoOGm5ZLpB2NSoFMEMbNhp7B\nOyf72VLVQWWOjTnffozMhUuo276djqoqQj5fXKReZzKhMRqx5ebiGRpiqKkJtcFAOBjE2dfHsvvv\n57Pf/hat2UzB/PmMtLcjiiKGpCQEQcCUloYtJ+eC37sgCBdUF7oYrDk58WAnk8nQ22zxjSBIM8en\ntmwBJHZ21rRpaBIS4pWZE5s301NdDUDWtGlMXbs2fixTairOvj4MiYlx5jZI16vGYMDpDWJSKJHL\nZfTbPSyanDNu5hRgd30/+z7ezdLOzkvKuRZfdVbkIeTz0d/QgLOpnu5D+3H3dDPU3ctITy9FV5Uy\n2tBAakkJSo2G2Y88yuNP/wC3N0jvR1uQX3YF+fPmcej3z9Fb202iRoYlQYNaIR8XCF3e4DiJywG7\nZ5yy16DdS/+oB7VSTiwmsmxKLjbj2evO4QlgFkL0BQLI1WqCHg86iwXT0pWcrDlBOmFExPh9YDNp\nOdUaYdDuYWCMvHVmE6BRKbhxWQXNPaOY9GezdkEQ4gYbINlddg05kY0RHvPSpL8pFXKmFl48UwPp\nvtl4+VS2V7Xw3HtVVOYlIwgC5blJX8gQAySmuFwmfGF+gvK8zfWGacm831jF6nWXUbpuHVVvhkme\nNBn52HqWu/ZG3nnhORLkIusLk3H07WTzd46SfP1ttBw4iHdMS+Hopk3Mv/12Dr3ySpzfIshkFH3p\nenYd/pSNuVE6t25GlVeCa2zscLitjaObNsXFmB7asQOlRsOOf/1XChcunGBTq9Jq462/3ro6Wvbt\no3nfPlQaDXKViqDbjSiK6K3WuADUxXAhDo4lPZ3K1aupfv99YuFwIvCeIAiZoih+Pvv1fxD/q4Oy\nIAjJQCvn9I8FQaB5714K5s+fkBXoLBaKlyyht64uvhNDFOOlkqDXG9eo/jxUXH45gbFxnNIVK6jb\nto2GTz9lyX33MdTSgsZkQqXTxZmKZ/xyp6xZQ+3WrbgGB7FkZqJUq/GOjhIJBMb1t605OfidTqLh\ncPx3kWCQzmPHJgRlVyCMMxDldOcQv3zjQFz04ItCLpMxq/TSrjRfFKFwFG8gxBs/uD6+0ARCEaoa\nJFeb6UWXtrGrrm6kqbqFZY+8QObCpTh6e2nZtw+QNkJ5c+Ywe8MG5AoFSQUFcca81mLBMzyMOS2N\n8ssvp3DhQkqWLZPOvyAw1NKC2mBgpK2Nhl27UBsMFC9bRn99PQq1+pJM98+DxmBg3U9+wt4XXkAU\nRcpWrIiL8hctWsSkK68kGg4jiiJHXn+dhl27GKivJ3/+fFY8+CCDjY3xYw00NIw79pxbbqH14EEU\nKtU4Iplar2faddfRtfUdTH47Bo0KhUyGVj3+lq0d8NGXVMrz4rYv9FlCPh+D9adp3LsP7+gonTu3\nY3T1oyRGZ/sI1tIKtGYzkVCI1LKyeHZjqvqIHNw8veU0WVOnYMnMJH3mHJQaLYPDTfgCYX589wqC\n4SiDdi+hSJQkk2Ts4PAEEBgf/ECas81JNeP0BEnQqbCatJxo7scfDFORm0yKRU9FhpmeJjt+pZWC\n+fOZsnYt0XCYxq1bCPedZF55JvnpFub6M3l/fyM5qWaUChkvbj2O1ahlRnF6vLRr0muYUZx+yfMz\nqzQdjUqBNxCiPCdpQtD7PMhkApfNKmD59DwGHV6STPovzLY+dLqbk80DaNUKVs8p+ptmmY16NRsK\nIBwZ5sf3bODDzTuQ7XqLlNJy/OYUyu55mLVvb6OvoYFXTxymwlVFztBpPvz6nYwoTdgKCrHl5BAN\nh3EPDo4Ldu17dqEUY6jHPs6VhQnsObg3/veR9nZ8djt6q5XukydZ9vWvEw2HObppE5FQ6KLe8QGP\nh+NvvUUsGmWopQW/3Y4+MZHs6dOZf/vtmFJTJ1R2zsA9NER3dTViLEbI5yPgdlN++eUkFRRICVEs\nRsH8+fTW1uIdHk5EUgGbJIpiwwUP+L8A/2uDsiAIaUANoC9dsYJJV12Fd3QUQS5HrlJdMAOSKxRM\nuvpqopEIptRUFtx5J721tZx45x1Oj2VjS+67j7ZDhxhsbsacnk4sFsM9OEjY70dnNlN+2WXorVYM\niYksufdeBpqaOLppE6u/8x1WPPwwSrWacCAgGdqPjtJ28CAqvT5O8tFbrRQvXUokFGK4rY3tzzxD\namkpxtRUOo4eJRoKMWXNGvLnzqXz2DGikUjctgyY4JMcjUSYqnRiUQhkJll59+n14/rBXwQef4ja\n9kG0KiUVeUkTZAf/M3hn72l+8/Zhtvzky/GS2c7jbXQOSjP7A3bPJS3t+uw+1v/qWWZslHqygkxG\n0OvF3tWFXKUie8YMCs5jOSvVam745S859tZbqA0Gpq1bx+9uvBFzRgY3/9u/AcRbE5aMDAoWLEAQ\nBI68/npcga146VJKli79mz+3MSWFK7/73XG/E0WRx7KyqLzySpIKCgj7/UTDYRo+/RSfw8FwezvR\nUAhLdjb2zk4EmQxTejqRUCjeplDr9ZSNka7OR3p5OQu+ei+dtXVEdAlo+k+iUSnY1eyg1xXkdFMX\nTkMKM+7bwMt33cXl3/oW/uEBnCeqULmHiSpUhA0WNG2nJKKcUoPF2YvocXN4fxvlX7oOU9kkemvA\n19OJKFcQCgRw9vaSNW0as266Kf5ewvOuZMfrL6K3mHG5vLQcPEhSYSGiKLL15x9S1+si5dMajjb2\nMnlqGQUJEoFq7YJS3jjcRaJeSVQ+frNWkpVIQ9cIXlOIFIuB3mE39WOa6q19dtYvr+RLC0uZV+HB\n7o9Q132C5jcFVCkZFFeWUmz1MqNIyngLM6wkmXUkmXW8vfs00WiMdYvLqO8cjgfl89HcM8reU50o\n5DJWzsgn1Sr5On9eRnw+zrdSFAQBbyDMTU/9lQeuncONyy6sdX8u3D5Jsx7AFwxztLGXy2cVfs6z\nLg6lQs73lmdyTf7VVGRb2V3fSmswQs+RQ+QtXkre/ESYv4BfzZlOUshOZaaVpoFBuutD2HJzKV2+\nnJSSEnQWi6SAqFJhzcll13cf5upJKYCJaRkG7E1HqS29TBJsCYfj67Lf6STgdqO3WHhk504UKhUf\nPv00M268cQJp6/9j7jwD46jutf+b7aut6r03y1a1jXsHm2ZsYkoIBHAgCZfwkkKAFHITINwEAoQL\nCbkJhA4JzVSDbYxx70WyrF6s3qXVarW9zfthpLFkycbJTXLzfLK80s7OzM75n3P+Twn5/XLxV6pU\nmMfcDCOTk89LovV7PBx46SW8TietR44QmZxMZGoqerMZQRBQa7UsvPVWaj77DK3RSE91NUOtrWqk\nnOY5oihW/d0X+J+If8uiLAjCUmA3IKz87ncxREYyOjCAy2YjY9488lesmLYou0dGqPz4YxBFBk6f\n5oMHHsA1OIhKp8MUG4t7eJiWw4ep2yFZpVZv3YohKgpbWxsBn4/kIskMf8V3viO/Z+Pu3Rx5/XVW\n/r//J/c9xmdtxuhoiq68ctJn0BoMqPV6SUA/PEyExULA65X9j8VwGL3VSsm6deQuW4ZKq8Xe1UVH\nRQWmuDgyzypIrqEhrGr47jNbWFaSzuzcRGyjHgrSYs/pcTwR4bDIxwfqZYKQ0+NnUeFU9uqXIRgK\ns6uiFbc3wM1rirFMIK2MuM7sBo1rOafbfjvZPkxNcw/Xr14j/58pNhaf04nLZkMTESHnD5+N6PR0\nLv7+92k5fJi2Y8coWL1aNvo4G+NcgImWqN3V1f+rojwRoihS+fHHtJeXc9HXvsaib3yDcDBIKBCg\nbudOWo8dA6SVf+XmzRRdeSVBr5fY3Fz6GxvZ9thjlH7lK5MY3+dC0cZvM05Vq3vnDZ7+7EMKH3gU\n2+69KBNaiAKqPv2UlkMHOf2re7kqx0hqrAlFlHT9A8FO1NlKIAg4Id6MLxDBF80OBIUCU2wsyUXF\n9KjUWNRq6ftpsTDvppsmfY6s9dez9/1PcAeGaTt2jHAwyPI77yRzPEBDFGnd+TmRcQGsK66gubGO\nlqrT9JjTiLnnUUKCko7XHiEvXtpZ0aiUuLx+8lKiiDRJJiYfHTizeHF6/HT0jWAb9ZIUY6I43UIx\n4PEcx9twGKtRh5B7pmWjVavQaVSy5lipVlLXNkhWUiTPbz5OKBxmcWGanPktiiK7T7YSDIXxBST7\n2WuWzaS9b4SDNR1oVEpWlGYQaZrcKpgIh8vHp4cbcYy1hZaVnDGAsRp1PPrtS5iT9+U7WnXtgxyu\n6aShY4j0BAtateqCQiu+DCqlgtnZcTzx1gHe31/Pg43NEpt6Aja+9zF/vmI1CZ4AufFGsrRaki9e\nQcqSpQAsu+MOhjs7McXGordY8B/5gqXJZzIgVuZYqa8+xJyH/5u+hgZOfvQRIFkQj7vYqbVaPA4H\nR954A3NCwpSiPB7E0nrkCMaYGKLGjJzOTnI7Gy6bTeIL+f0EvF5ZVjjceaZtXLJ+PYkzZ7L3uedI\nnzOH7upqyjdt0gCnBEH4iiiKH/x9V/efh3+7oiwIwgqkHrJw3VNPEWG1MtTaikqjwZKQwJxrr5WL\n4zicg4Mcev11yc6tv5+UoiIGmppQqFToLRZ6amsxREURERk5qZj73W6JAeh0MjwWStBVWclIby96\ns5mA18vlP/kJ87/+9XNun5wNY0wMszdsoOXIEUnnnJIib58LgoCgVMqrpIHmZqq3bkWt0zH3q1+d\ntsg4envJ1Yg8dddltPeN0NwjaU4P1nSQHGua1IMbhyiKDNjd7Klso3/YSXv/CBkJkQgCDIxcWBrW\n2ahrH+TtnVVo1EqKsuJp6xuRtyMLM+M4UNWBiCj30c6G3ell04EmUvJzJ0mKAl6vnBEM4Bt7sKZD\n4549NOzahb2rC53FMq2V6jgUSiWWxERGenoAprQELgThUAhRFKc4gXWdOsW+F15gtK+PcDhM8fr1\nzBoz3I/JypJIKh0dkjvRmOGIxmCQCIKxsYRDIWo///yCivJEeBqr0QU8fP7kk7QdOoghMRnX8DCh\nzhY2rihgw8KpK8Lptl+1ahVXFMbSmpdDStkcEmbM4ORHH7Hj6afx2O0MtbXRvHcvJWO9b4Ce2loU\nGi3asTbQ6MAAujEpjCAIIAhkrLyEqL4+gj4fsUWlGJavovDuuyUSkcOBMyyy9UgTbX12RBGCwRBq\ntRKFIGAx6MhPjZZlXxaDlu3HTxMWRRQNkuY41mpAr1XLXIWJkz+VUsGVC/J484sqSnMT8HgD+AJB\n3F4/PUOj2J1e2vtGuHhOFhfPlvqTE/20FWP8js+Pn5aVCnsr21m3+Nzk0JPNvYy4pElkbfsAM9Ji\nJhHZlhSl8cbnlTjdfu5YN31KmMcXYG9lG2FRUjs4XH7m5scw7x/UbgK47fIy9OnZmOLipkyYLcnJ\n3LX/CIduu5oFmQZizRG889cXSFkiGSGpdToGd2zG2dUIYgj1iANnlEaWjwWCYRoOHiL0m4dY+PDj\nmOLi8DgcxOfmTnKU05vNPHDiBEq1mq2PPsryO++cZD1bdMUVzLj4YkLBIO3HjsmtrHPB3t1Nxfvv\n011djTk+Hr3FgnFMAZM4gbgpCALxeXnkLFlCR0UFyYWFRKWl8cXTTyOGw+8JgnDjv5tk6t+qKAuC\ncCPwBsCCW24htbSUcDAoh89rTSaZDDR+w3tqazm1eTPdNTUMnj4tkw4EQcCalITeYkGl1TLj4otJ\nKytDpdPRWVmJvasLTUQEto4O/C4XmogIRFGkv6mJ/qYmAl4vnpER5t10E7GZmfTU1tJy+PAZz+vz\nmIUkFxWRXFRE0ZVX0nrkCHqLhYjISJr27UNrNDLrsssIBYOc+uQTxLC07VO9bZssgZqInb/4MX85\nepiXf3w1/fbJBXWiTnEcoiiy40QL+0610WtzkZ1kJRgKM+LyTitLuVCEw6IUAhA+Y5gwjsLMOFLj\nzIRC4hQS0vjvvnG4g5zv/5z1666e9JrWYCCpsJDuqioEhYKMiy4iHA5LE5izivt4AHr9rl2Iosil\n999/3snSgptvpvXoUUb7+1FptfQ1NBCfl3dB59tVVSW5YIkixevWTTKQsbW34xkZYXRggKHWVnqq\nq+WibE1K4vY33qDms88YHRjAPmHWPnEQOhdLOuj3IygUUyYCXoeDJNtpRn02RtpOYx+101hXJ0mJ\nUqL4w7v7yYiOkLS3FwBnEDrKT9JT38jCW24hbWwXR2c247LZqN2xY1JRVuv10spFFPG73ZSsW4fe\nbEZvNlN4+eV0VFRgjI3lhZtuYvFtt5G9eDGZY5m4IA3K5UETmr4WAAbsLnyBIPFRRjr7HXywr46r\nl8zgmmUz8fqDDI24OVQrXbuwKNI37JLbNl5/kK1HmugfdpGVFMmqskzZk/qSOVnsPtnKp4caJcvW\nkMiA3YVOoyLWaqC11w5I48PKskwOVnegUipYWpyOKDJBNoXsjncuTJzwSOEfU9tCgWCYwHneJyyK\nslw3yqxHpVTQ1mvnLwMOrl6ST4zlb2tVTYcos55IU4CHZ83iknvvZfl//Mek13VGI3FX3cC7zz3N\nln1V5Fw0h4T9+yU3ubZWUk8fYkGqgU+OtzLgVlKlt/LiJ7uJjY/BbNKjnnUR0QulIh6ZksK5TDLH\nnb22P/kkSYWFFK+dLOFTa7WotdopzogBnw+VRjNpPKjcvBnn4CDx+fn43W6+9vvf4x0dRWcySQ6A\nHs8ke9fideuIG1uhJxQUsOT223mkrEwIBQJ/FQQhURTFp/7e6/uPxr9NURYE4avAawgC82+6iZjM\nTFoOH2bNffeht1oZ7uig69Qp9vzxj0SlpbHgllto2ruXht276auvp+vUKQzR0ZgTErAmJ7Pirruo\n2rwZv9vNvBtvlLM5AZZ885u0jW0xBn0+7N3dKFUqVGPJQCG/H53ZjCUpCaVSicfhkLNxh1pbUWo0\nFF522ZeekyUhYVKsWUpxsfzv8USq8eJ2rmjBmRu+isc2zJ+2nkInhPD5Q+i1arLH7CnHLQrHMeLy\n0dxtQyEIhMNh+u1uspOjmJ2bSGZC5KSZ/N8CfzDE1Utm0G93kR5vlZ3C5HM1TC2O/kCI7a0u+uJm\nUPDUQ1iSJq/kwqEQAa+X2ddcQ/aiRaj1eoY7Otjyq18hKBTM3rBh0qo6paSEvoYG5n71q9LMOHr6\n0IRxaCIiiM3Opn7XLhBFWo4cYeGtt16Q2UvNZ58RHtMe12zbRmpJidySSCgokIl9sdnZUwgserOZ\nOddeC0gWqe0VFRijoyUnpC++QFAqKbx8qslH84ED1GzfjlKlmtJ7q3r+d9yaYeC9dhFBkORU8dYI\njHo1qrF4xYnxgeeDw+Xjk9phvEEp/7m3tpavPPYYEVYrbrtUtMxnaULjc3OZsWqVnCU+c82ZFkTm\n/Pnyqibl1CkiU1Mn+Y6PQ22xSm5eSEYdgkegvW+EUbcfjz/AliON3LKmhJe3nWT9onyON/QQCIVQ\nK5Ukx5zRkte0Dsgr6uZuGznJUfKuzYy0GFRKBRkJVvqGnLT2j+Dy+PEHQ8RZDbLdJkBGgnUK+WxJ\nURr7q9pRK5UsnDV9e2QcZbkJOFw+6toHcfsCbDvazCVzsiYRtDZeVkowFKaiqXfaXrVBp2FeQTLH\nG7rRqlWc7h6ma1ByGBse9XDfDYvxByT73Ajd36YgmYivzTTRVW3Av38Lruuvw3CWVr3g67ez5+33\nKbgkAaVGQ8UHH5CzeDGdu3eQ4/fwzaf34RTVJMeY2HO0nlW5Vo7bAlz9+J9ILSk5b7reRMRkZvJI\nUxNqvZ79L73Eoo0bz8s0r/jgAzoqKtCZzSy85RZZXSBO6EEbY2KITEmRsgCOHmX/iy8iCAJFV14p\n+0soFIpJcblJs2bx4yNHeHTePEKBwG8Faafk36Iw/1sUZUEQvg68plCpuPJnP2PcstQ4FgeWMXcu\nzoEB2brN1t6Ora1NDtyOzsykv7kZtU5HdEYGCXl5JObnk5ifP21/UzGmBxYEAbVOR2xWFtEZGegt\nFtqOHqVtzHwkLjsbpUZDwOOZxEKcSMz6ezHuoVyzfTtqnY7CK66Y9Ho4FOLV22/nknvuoeib36H6\nxf+h1CQ9mNevmMWWI000HWpArVRy1aI8eRWh06hQK5XEWg24vAHMBi1lOYnML5g8wJzuHmbU4yMn\nOQqDTiOn5ThcPmZmxE4arOxOL3c/8ynfv3YBt1/x5bnTALW9TvapMij42f0kWKYa8Dv6+jj46qv4\nXS6Si4spGzPN2Pzgg7jtdqwpKVRt3TqpKCcWFKA1GrHX13PVgw9e0EAw0tNzxjlIFBnp6Zm2KIfD\nYfrq61Gq1cTl5KDSamXiilqnQxRFTmzaRHdVFSqtlsW33cb+F18ka/58siZM+M5GRGQkrqEhXIOD\n+N1uFm3cOO3nHt/SRhSl3vSOHcTn5hLweqn8zS9YrepCZzKwpCiNTw83olYpKUi30G93YTHqWFGW\nycyM2PNeC+dYSEG0WU+osxmXqMWQkIjH4UCj07H029+m6tNPMURHT1lNgeRKd/Yq5mzY2ts58e67\nXPbjH49d8jPPX3TYRWFxGvUdQ0Sb9cxMj+XtndUEQiG0GhVef5BQWKSjfwS3P8CGZQX0DTuJjzTK\nNpkdAyP4/JPlL2fLjcYlgvf+YRs2p5fYyAgEBJYUp1GcdX4exoy0GPJTo+kaHCUQlMJdmruHibVG\nsLgwbdKxtGoVK8syaOuzo9UoGXF5OVTTydqFk3dj3t1dwzObDrP51zcSFkXJfW9CgS3NSaA0JwF/\nIMRP/7xDHv/67S7q2gfZX9VOMBSmJDuBBTPPP1EAye1tPKN6XDMuCAL3Xb+AuvYBHl4yn+8cPD5p\n5yYcChFWqLD3tKEzmRg8fRrPyAjDVRXsbhxClZRG9FAfgt+Lz+2mVpGHKd2IvauL9AvIop8IvcVC\n1ZYt/GWMkzBdtjxIz25HRQVBv5+2Y8dw2Wxcev/90u7MFVdw4t13CQUCFK1dK3/H6nfulBQ3okj9\nzp2kz5mDZ2QEhUo1RXmTVlrKf50+zX/m5RHweH4rCIJSFMUn/qaT+Sfg9EG47wAAIABJREFU/7wo\nC4KwAXhFoVRy/759JM6aRePu3YiiOGkA0E9w4xIUCnRmMzGZmdi7ulBpNCy85RYQBBx9fZPIUuea\nhSUVFtJVVcVAczMxGRlc9LWv0V1VxV2bN7P1179GpdFQsm4dlgRJ45tcXExXZSWaiAhUajX7/vxn\notLSmHHJJedMpfoyJMyYcc4Qg9H+frqqqvA5nVJ6iloNSIERAyMuuZcVCIXGBo0zRXlZSTrv760l\nLtLAdctnkhJrYceJ07T22kmMMpEYbeRIXRcA1S0DXLdiJicae6hu7QckB6IbVhXKK3CrUcdrP90g\np+ecD/5AiC0tbvyrvsrsK9af8/fGDQlAyh7OXrSI0wcPUr1tGwGvF73VytJvfnPK3wkKhcS+P8fO\nwtmIzcmRQ0gCXi891dV47HZmXHyx3IIYj/S0d3aiVKtJnT0b58AAjXv3YoyJ4atPP41zcJDuKoms\n6XM6Ofz66zTu2YNSrSY4IfzhbHRWVsqTguGODlw2m+z+dvZ5qbRaOblKExHBYF0Nvc89xi1ZKnQa\n6f6mxVv4j3Vz6Rp00Nw1TJRJhwh847EPyUmOYkba9Drl093D7Dgh9WhzkqO4bkEmH+yrY+i0g6jM\nNFpfegbL4jVc//TTKJVKwqEQjr4+dGYzGr2eocYGOl/5PUqTiaiVa0maN/1EpLu6mooPPuCSe+7h\n6JtvMtjSQmxWFoVXXEGkb5iCvFgK0s+c/+ULcvn8+GlCYUlOp1Iq+MG1C/jO01uYPSOVi0tTsRp1\ntPQMc7S+i7AoEgqFiTLp5VSxEw09k0w8xqHTqPH5ncSYI0iMNqJWKi9I4nS4touTzb2Mun0MjXjI\nSLQy5HBjNeooyU7A7vRS3tiDRq2kOCteGmPG7vF0w826RfnMzkvkYE0HnQMOVEoFl83LITlmMjdG\no1YyNz9xjBGuJDspkprWAXkb/WRzL2W5CeclgXUOOPjsWDMAVS39XL1kxqTdsVG3H+/QIEfvv5N5\nj/9Rzn+v/fxzGAtCCXi9xOXmEvT7aWrt5tAn+/n+15axfVSHy2wle9Z8OQ9+nAsy3NWFc2CA2Oxs\n1Ho9FR98gK29nYQZMyi8/HK6q6roqKggHA5jjosjJjOTXzY2YoyJob28nLSysknnMV5Um/bvx22z\noTEYUCiVHHvrLZZ+61tEp6ez+oc/nHL+WqNRTp3SmUzUffEFjXv2oFAqKb366ikOf5EpKfznyZM8\nXFRE0Od7XBAEryiKvz/nBf4X4P/U0UsQhCuBDwWlUnjg2DE57zY+L09yfpmwBWZNTkZASnTKX7WK\nmIwMYjIzMcbEEJ2RQXRGBh3l5QiCQH9jIwn5+dPaJ45DoVCQUlxM3vLlpJaWEgoGeWzBArwOB9f/\n9rfMXL0aU3w8tdu303nyJDmLFpG3ciVKtZr9L72Eo7eXodZWWg4fxtHfT3R6+qTP+7+BraOD0f5+\n1v7850SlpWFNSqLzo7dIMUhJNskxZuraB+UZ9cz0WKInbJkdre/C4w+iUStlD9/jDT2ERRGH20fP\nkJPxzpk/GCInOZqOfge2UakgiKJI7tgKutfm5Ae/38rFszO/VDfp9gb46SdNFP/meRLKpie2yOfY\n3i6zJAWFgtwlSzjwyiu4BgcliUQwyMKNGyeZBvTW1WHv7iYuR3KbuhDynUavJ6W4GEtiIv2NjXgd\nDuxdXQQ8HuLz8gj4fOx9/nmOv/02I729GKKj6a2vp6e6moDPR9DrRW82k1paSuvRo4RDIZr27WOw\ntZWo1FQMkZEYoqPlzznS00P9zp04+vqITEnBZbPJnAiNwUDusmXTTigEQSAyNVUq2nFxFK1dS9ef\nfsOt2YppjSfMEVrSE6zERRrRa9QUpMcyIy0GrWb6AXtvZbtshWob9XD1kgLm5ieyoiiFmxZlMsfo\nRVdzkMPHa4mdt5hDr75K3Y4dtB8/TtjnRfvGY1yXraZU76Fm736Ob/mMzqpTpC1cMmnimzl/Pku+\n+U06KipoOXwYAPfwMMN11VxusE0xlLEadczKjKUoM17endGqVWxYOoMIg463dlZj0kt/U9HYQ0PH\nEL02J4nRJjRqJQa9RrbJPDtuNDs5Eq8/iMWoQ6tREWc1TCmE02H3yVb8wRAeX5DuoVE5kSvOaiAp\n2sSmsezwE409tPc5WFGWwYDdjcWgZVlx+qRzHBxxM+TwMOL08vib+0mONaNSKhiwu+ixORkccZMQ\nbZRJZzPSYomxGMhOimRlWSa2UQ/DY8EYWrWKstyESQS1s3G6R9r+Hkd8pHHSc3u8eZD2fjszjCHe\nf+JpjNHRxM4q5vShQ4QCAQJuN0qVipJ168icN4+y677KpT/6ESM1FcyLFkj/wcOUfuUrsvNhxkUX\n0d/YyMFXX6W3ro7u6mrCwaDsrmXv6kKl01G5eTO2tjaqt2zB3tXFcGcnSbNm8dnjj/PBAw+w6Lbb\nqN+xQwoLiorC3tVFw+7dKDUaBpqb0VssJBUWEg4GyV269JznH5OZiWdkBGNMjLSa3rQJkMY059DQ\ntFppY3Q0izZuZNezzxIOha546KGHuh988METU37xX4T/s5XyWLzW+4JCwffPk2E8DoVCMcWFSRAE\neeZTv2vXmRdEEefgIJYvCcoefw+fy4UYDnP3li2TaPjVW7bQUVEBwODp0yy/805OvPMOIz09iKKI\nb3SUlJISeVCaqOv8WzBRtwqw/YknqPjgAx5pakKpVqOJiGBZQQIlcVpetceR1dXLZfOyaemRUmvy\nUif3hzy+Mz7M/mBILt7jSIw2yQ+u1ajDbNBSlBVHe/8IvkCQjASr/CD3D7uI0KmnZXlPxIDDyyZH\nDDd8/PkFuaflrVhBwOvFNTRExrx56C0W4rKz6Sgvx5KUhN5snrSLEA6Hef8nP6FqyxZW//CHiOEw\n88+S7ZwL08krxgMphlpacA4MyD68jr4+YjIyqN2+naDPh1Ktxt7djdZgkP17Oy0WnIODtB07hnt4\nmLgDB6RAlDHv9fHVbigQIH/lSjR6PW67nbTZs6fttY4jOj1dZpTbWlvJD/YBEjEvGAqz48RpeoYk\nz/FlxemyNlahEDDo1Oe1cbQYtfTYJGa7XqNGo1aScRYvICveRGtLPYeeeZLe3iFZotZ5qor5FmkV\nJYoiYk875hEXAxX72V5TTtqMPPzmGLKuuQmlVstvV64kM9GKb3SUUEyyxG9Qh4haOb3+V6lQcLSu\nE7vTy6yMWDk3uiQtipJvLMcXCPL+3loqT/fh8wdJjjUzMOLCqNPi8voJiyKjLh9VLf2Ew2HS4q04\nXD6qW/p5fXslF81IZlFhKrMyLiyBKM5qwOnxYzFoSRvLPo406pmVEUcoLGJzuGnushEMh2nrG8E2\n6mHtwrwpPeOa1gFe2lKOPxgiLc6MRq3C5w+hVYepbR+U+9uCgGzuo1IqJm1RLylKQ6EQ8PiCzM1P\n+lKPgYwEKxVNvfgCQfQa9ZSJSpxZx6LCNN7eWUVaYjSH//Q/pK26lIy5cxlqaSFzwQLicnKYM2Zj\nDNLENpw3mzcf/oDFMw6StWjxpOepr6FBJgDa2ttRnfX899bVMdDURGhMzTDucmjv7uaKn/2MBbfc\nQuVHH0lGPxoNQ62tcjvPkpBA5vz5MvnxfIxskNQv8772NflnndEoS6V05nNPyCJTUvjpsWP815w5\nhAKBPwmC0C2K4ifnPdg/Cf8nRVkQhHTgCCB877PPmPEP0I8mzZolmYI0NiKK4iQW5Zfhre99j65T\np/jRwYOTtqLHI+9Akk+Ny0D0Fgvu4WG5X9Z69Cgjvb2UrFuHSqvF73JJdpFfYpUXCgQ4/MYbDLW2\nYklKYuEttyAoFFz7xBMsv/NOnIODnHjvPfqrT3Gt1sWeQT2rHnucU//zFEnKelJipw9Ln5ufxGdH\nmwmEQszJSyI3JZq+YRdtfSMkRBlZUZpBr82J0+MnI8GKSqkg1mrgpkuK8PqDGPVSv/2LEy0cru2k\nNCeB2q5hGCMXJUWb5AIQCIaoaRtgT5uLea8+f8F2pmqtVrabHMeyO+9Erddj7+xk1mWXTZIxiaEQ\nUWlpzLr8cpRqtbxFdaGIsFrlsBGFSiW3OCIiI0EQSCkuxt7dTd6KFcTn51O9bRtuux2VRkNPXR37\nX3yRWZddxqJvfAMxHOboW28x1NqKzmIh4PNRt2MHGRddJBdkkCZyrqEhFEolBZdcMqmH92Xwud1Y\nJlzKuvZBmTlc3zFIapyZ7CSpYHcOOLj7mS28/sCGc25fL5qVilatwu0NUJwdf07bx1WZRgqGTvK7\nAyfpQ4exeB5agjDWB/cFQrKsLtaoIS7Qy1eskfgDvex//C7aVVEkBmzcPDOZUbeaP352nG+szGNV\n2bkd1Y43dMutk16bkxtWGSaRF093DzPi8pEaa8E26iHOaiTGbEAQRKpahgmFRdr7RggEw0SadGQk\nWBFFUCoFFs5K5dKLslk9N1uy3h3b7nZ5/czKiJuW+LiyLJNYq4FQOExhZhxKhbRbEQyFEcMiXYOj\ntPba8QdDqFVKKk9LqgCrUTeJi7H9WLPcZmrotPHTry+hvmMIS4QW5YQJ1ESd/9nQa9Vykf4ye9zx\nz3Dt8plS6MyYm9jHx9u5vFTy6958uJE7rixjVUk6KbFmhkc9vPDQT1j9u+dZeffd+JxOLElJU8au\ngq/fjjopnbiZU2V80enpNO3fLyfsGWNjiYiMJOT3Y4yNZbC1FZ/TiWNgAI1WizUpCaVGQ0J+PhFW\nKzqzmT+sX09EZCQl69bhczqJycoitayM3tpaCi65hLwxb4q/Ndpx3k030bBrF0qNhpljCglHfz+n\nDxxAExFB7vLl8kQ5uaiIn5WX83BxsSCGw5vHDEb+5Svmv9/a6e+EIAgW4CQgrL73XuydnfxoTJ/7\n5ne/yx/GBurHFi1i88MP47LZuC8hgVOffkrN9u3cl5CAo7+fLb/+Nb8a24r407XX8tHPf07O4sUc\ne/ttHL297HvuOX4YF0dHRQUHX3nl3Md46CGWfPOb9I8J3/e98AI/jIvD0d9PZ2Ule59/HpAYuNse\ne4zo9HTqvvgCTUQERWvXcvj113EODDDY3MyPkpPZ9eyzPHXJJTxSVkbQ7z/veTTu28eh115j73PP\nMdLdzW8WL+YHkZFse/xx/nvNGt6+5x56qqvZ88KL7Cxv5WiHk5+kpaGx9/Hk2we45w/bqGjq5eoH\n3uQXL+3E7vRw6X2v0dprJz81mk27a8hKjOTlrRU8v/kEX19dzGfHmnn8zf2kxJq5/4/b2XqkieZu\nG5fe9xq7KlrZfbKNK378Bi6Pn8ff2s+mPbUcGFLwvWc/49GdHTxyaITl97zKM9tqeX5XM6t/+Co7\ny1v54kAVP83MpKuqij9dey1v3HknAD/NzGTfCy/QXV3NfQkJ570fTyxdir2ri5V3380f1q+fdK1s\nHR3SluihQyg1Gk5s2vQ3H6NswwZOfvQRjv5+9GYz9425rEWmpLDr2WeZfe212Lu6eP1b32LmmjWE\nAgGcQ0PojEbeuece3rnnHnpqavjggQek7b358+msqECpUvHFM8/w6u23E5OVxb4XXqBxzx566+p4\n5bbbOPHee2z+5S/P+d2d7jyeWL6c+m47mw82cMWP3gDgQFUH245K/cL/fGEnz28+zojLy3ef2cKD\nG5czNOLm0vtew+bw8NKWcm751fsA3P+n7Tzx1gEWzEzhv989xL5T7fI9r+8YnHSMx9/cz+NvHeCO\nSwvpa2zGuW8bl3hrufOJD9l3qp3yxh7+8vkpPL4gFY29vP6Z5On9sxe/4NDhKm7OEOnoHqShY4ik\nGBNt7b2oVUo+Pdw06Rj3/EGyBb3pkU38/r0jlDf28uq2k7T0DLPvVLt8Ho/+ZS/3PLuNtl475S0D\nNPSM0BdU8s7uaho7h0mLt1DZ3Ef30ChDDje7T7Zhd3rZc7KVbUebyUiw8uTbB/mfD48x4vKy+t5X\n+XB/HV+Ut3DtL96i1+accq0ef3M/JdnxPPjSLrYclnTVq+55hcff3M83fvMhuypa0ahVeHxBRl0+\nwiFJf/279w4z4vJy6X2vse9UO0OjHiqb+wiEwvQNO3nsr/v53XtHeOPzU1Q2S9LNN3dU0TngmPZ+\nBENh/t/Tn3DjLzfxxvZT3PDQu/I9Hz/GoZrOKffcqNfw7AdHeXrTIZQKgaf/upsND77LI2/s4/Mj\njbi9fk409rDi+y+z4RfvcOrzHfy6uBC9xcJz11/Pp488Mu14FVdYzP6XXuLhkhIOvPwyv7roIl7e\nuJHkoiIOvPQSfo+HqLQ0dj37LMPt7XSUl7Ppvvvw2O30NTUx0tXFmh/9iJMffUR/YyOCQsF9CQlU\nb91K0dq19NXX43U6GWxp4YmlSyldv562Y8do2rePyORkHp0//4LHkvFxV6XR8Pb3v49ap6P54EHu\niYnhs8cf54vf/Y6XN26kesuWSc/g79euZckZLsuBsQXkvxT/0p7yWLhEP2CeeemlbHj0UTQRESi1\nWtzDw7htNpJLSgh4PPhcLnKXLSMuO5twKETWwoW0nziBZ2QETUQECfn5WJOTyZw/n6DfT2JBAYIg\nMNTaSlRaGlqjkaSZM5l56aVoIiKIiIoid+lSQoEAMZmZpJaWYu/s5PAbbzDvxhvRWSzYOzsZamuT\nepzLlhGVmkpUaipLvvUtDNHRsiZutLeXmKwsStauZfD0aTLmz5edvEwxMYTDYbRGI8lFRURYrUSl\npWGKiUGl05GzeDERUVFoDAaSCwvpra1FZzKhHtviVGu10rapUolvdBS1Xk/INUpOlBZdeg6pq9aQ\nNNJOrkUKfGjttRMIhtCqVSTFmLAa9ZTmSKkxOq2aslwpnSbaEkFhZhyBYJjMRCumCC09NifpCRZ6\n/GqONw/gzZiFyxxHOMJEwB+kqdNGICxScO0NxGZls+oH97DyBz9ksKmJWT/4GUPuAGJLLfGRRhr7\nRlGZLCgUCiKio8ldsoTEggICXi9ZCxdiSUxEoVKRs2TJOe+Hd3SU1LIyYrOyCIdCk64VgsC2xx5j\n5Xe/y5U/+xmCQkFiQcE5j+F1OFBqNFLLQxQl276UFBr27JHbG0mFheQtW0Z0ZiZ6i4WLbrgBhUJB\nZGoq2YsW4XU40Oj1xOfnEx5bqRdefjkKlYrKjz9mdHCQCKsVURTJXrSIzHnzSCktxdHXR/HatQS9\nXkLBINFpaWgMBrIXLyZ36VIpiCIxcdJ3d+J5mBMS6N29nZvnp6DXqjBHaLl4ThYd/SNo1SrmFyST\nFmdmRlosyTFmRj1+uodGmZkei9WkP+c9z0yMxBcIUZQVh0GnJhwWuWhGMjqNdIyy3ESCY5m782Yk\no1AIrChKpTgjhlBYpDQnAYtBh16jYtGsVOKjjOSlRk85xof762nptbN+cT4+X5BTLg3LcyIZ9QbI\nTookQqeWwx4+P3Eah8vH8KgHQRCYOyOZxYWp6LRqUuMsHKnrQq9TY47Q0Onws+reH7Hu2efp/+wj\nZiUZCYbCjLr9WAw6BAE0ahWluQlE6NTox87rUG0XS4rTKMlOoKFzSArPUKtQKhWsLM1Er1VPulZa\njZKj9d14/UFKcuJJi7NwqrmP+CgjvUNOvP4QqXFmPL4gEVo1YUQMOg1JMSZmZcah06gpzUkgKzGS\n0z12kqJNXJSfRFluIrddXkZavIWSnARWlmWiVknb1YYxNvbcfMl7OxAMcbCmk5q2AZQKBRajFo1a\nycWzs0iOMRMKixRlxaFTqzDqNZPuecaYn3heahTZSVGEwyK3rCkiEBLpGBxlzZxMWnqGGXF5SYwy\nkmBSY9YqMKRmYkpNJ7WsjKi0NAJeL7nLlmEYewbHv7sDTU0SKdHrJcJioXjtWvwul7TqNRoJh0Lo\nLRa0JhOiKDLU0kI4FEKl1WJNScFjt6PWSVGXcbm55CxeTNbChfjdbup27mTRrbcSn5d3zufjQsaS\noM9HalkZMZmZ8ljSevQoowMDDHd0oDMaMcXFEZ2ZSVJh4aRjzL72WjLmzaPy449VwJ0PPfTQYw8+\n+OD5Rev/yDp5dr/xn3owQfgAWD/jkkv4wfbtAHLE1+jAAJbERPxut9wLTigokPu0XVVVnHj3Xfm9\nitauJWPuZDLRSG8vB19+mYDXiykujsW3347f5aL5wAHUej25S5dO6t0ONDfz3o9/zK0vvsjowACH\nXn1Vfi197twp4nZbezv7X3xR/jlt9myUajUtYwYhKcXFNO7ZI7++4JZb6KuvlwkvMy+9dJJeGqB2\nxw76GxoY6e2lcc8eshYupHHPHqzJyYiiiNfhIHvRIlJOH8QcG0f6r/5M3/03cVVBJE1dNnacOC2/\n17wZyRcUVOH0+HlnVzUnWm04gwKzb/s2i35w36Tf2fLIIxx+86+oFQIpc+ex4Te/wXRWAtFQawtN\nP7iRoNvDjoYhEuYvRkBiDq+46y5ZI3gutJeX07BrFzqTCUNMDJ0VFefMlwapnWCI+nLzk87KSsrf\new+QgkKWfvvbKFUqmYk5jjnXXXdOOcY4uqurKX//fcRwmOKrrpJZonVffEHFhx/SU1ODSqcjtaSE\n0quvltOilGo1qWVltB45AoJA8dq1X3o9YCzO8ZEfsyGij1jzhbnINXXZ+O4zW3jmu5dfkDlMa69d\nZjznp8awojTjgo4zDrc3wMGaDvyBELPzEqfEc5Y39hAMheU+6VMnveToA5w4VsVdV5bIfIW9lW08\nv/k4tlEPGpWS1DgL6xfns2p2Fv3DLj4+UM+plj5S4ywY9RoizCas9/yGpDnzaN+zk7Ttz6H2e/D6\ng3j8ASqaejHqNFw+P5e4SAPDox6Meg3+YAiLQUfngIOqlj4aO21o1ErS461celH2lK3a8T4wSPr7\nG1YV8sq2Crz+IJ0DDroGRzHqNGjUSkx6DW5fgMxEK4IgsGZuttwTH0coHJZ7wcFQmCfflnYtlpdI\n172+Y5A9JyVXrzl5SaTEmnlnZzWnWvoYsLsxRWiIizSwuDCNryyVksRGXF42H2zA6fGTHm9lzdxs\nFAqBXpuTTw41EAyFibUYWLc4X25VuL1+fvL6IUxqAQ1hYix6RODU6T5uWF1CbeFa5t1xFx6Hg4Mv\nv4zLZpM9IcbJiaIo8vLGjYz09KC3WsmcN481994LSH7X/U1N+Nxutj36KEGfD63BgFKjwRQXh85s\nlqyHIyMZbm/HnJDAdU8+KV8nj8PBq7ffzoZHH5XZ3f8oBLxetj76KCAZTvndbjIuuojSq6+e5B8x\nES/cfDNHXn8d4IQoil/+8P6D8C/rKQuC8C6wPn3uXLkgg2Sd2HlKyp4eHRhAqdHIRdne1SX/nhg+\na6IyzWTCkpDAyrvvxmWzYUlIQKFSsfsPf8AzIoUleB0OWQ+757nnSC4q4o533hl7OxGlRkNojIRg\nTZpqWai3WFCoVLKphCE6mpzFi5m5Zg0KpVIiwYiiJAXIzycmM5PD0k0FoPXIkSlFueDiiym4+GIO\nvPQSnz/1FJqICEb6+rB3dWFOSMCSmEjB0iVEeRpw+qTP1qeSiCwZCVYSooz02pxEGvXn7CeejSGH\nm6N9AULpM0mZVYQpb2qesNvhQAyLFF8rET5G+/snFeXumhp2PnAfdxUnIwJ1ohVBo8HW3k5cTg6V\nmzcTmZp6zh6Q3+2m8uOPEcNhnIODVG/bRtKsWQR9Pup37ZpSlJ/76ldZeOutFJ2l554OPbW18r9H\n+/txDQ1hjo/HMNFsRBAwREXhGRlhsLUVS2LitJ81adYsEmbMmGS3GfT7pcCJlhZCgQChQIDe2lps\nE/SaoUCAuJwcchYvliR851ECjF+P2hf/gLqzgRvifdOasZwLOclRfPrYhZHeACqb+2TTmvqOQeYX\nJE8bs3ku7D3VJve3++0ubl5dInMMvP4gde2DVLcOYHd6uWROFl/PhOeatdR1jXCo189aSwQ1rQNs\n2lOL1y8ZY0hRiSbZwWpnRQuBUBi1Sklrj52Fs1K56qIMNr37IomzLyJt2UrKD+9hvdhIrNXAFyda\n0GlUBMNh9lS2ccOqQlkmeLyhh7d3VpGVGMWw00OEVs3ahXlkJFinFOS+YRcHazplz+rxgrZmbjaH\na7tIijaRMkY0a+qyEQqJDHa4cXkC5KVGy7rgiZhIzlIpFdgcHhwT+sgVTb0yB+ZofRc+fxBBALVa\nSaRJh1qlJCc5WrYHBUnuNM46b+uz02MbJTnGTH3HoCyhGhhxMWB3yezxpq5hZsZLEyKnx4/LG0Cr\nVtHQYaOuYxi7ugaA9hMnZD6Nrb2dvoYGOXN8PHUtFAjINsLj0FsspM+Zw5G//hVrcrJkXRwMklBQ\nICklBIH4GTPY+cwzeB0O9BYLucuWydwSvdnMHe+8Q/OBA9Tu2MGyb3/7S76Jkr56+5NPUr9zJ5aE\nBNb98pfT2hUrNRr0FguekRESZswgKi2Nsg0bMESey38Mbn/tNQJuN+XvvTdbEIQ9oigu+9IP9A/A\nv6QoC4KwHLhGrddz34EDk15zDw8TlZqKraMDMRw+Y3APk7yBkwoLaTt+nFOffIJSrZa3Ns62u9Qa\nDLJIfNwqcxyjAwOAdCMPvfoq+StXykVSbzazaONGuquqMMXHT7JVHIfeYmH+TTfRXl6OKTZWNo0Y\nN4MQBGFK4k/EWJiGYsz8fzq4bDbm33wzKWVlHHrlFfQWCx0nT6JUqdBoNRx/+H4evraEDzoFlGo1\nxtUbaN33MsUpWtYtysfrD6JVqy4oRL1v2Mkv3zpGKKOAmLQMVFrtFK20226n7JprZO2toFBMcs/q\nb2qSJERdHewIqrl6SQGzr7gUm8JAhNUqrWZFkeA5wiUAeQIz/v4T84nPFvn7PR7CweCk3zkfotLS\n6B0rzDqTSda4p5aUEPR6Ge7sJKGgAE1EBHv+9Cf8bjfOwUFM8fFEpaZS9pWvTCJlnW32MdjSwp+u\nuYbZ11yDf0zTG52ZSfqcOfTU1BAOBtFbrUSmphL0+ajZupVwOMwcE4edAAAgAElEQVSMVavO+R2o\n/fPvuFHRgC5LBVxYQXZ6/NidXnz+IE+9e4j7b1gss4XHEQ6HGfX4ZcY1SPF+PbZRRFHSpG/aXUtG\nopXFhamTipQoinQPjaJUKCY5Ybm9ZzKSJcOPMAqF9N5VLf3UtQ+y9UgT5ggNM9JiSI2zsCpyhNQn\nnkIzs5DP//wg3U3dGHRqrEYtxgg1ucnRXLEgV2ZIB4JhBEGacKiVSm5YVYhCIXCxxcm23z1O6d33\nUfyDB/h862Zc+7ahniADGnF5JzndKQQBh8tHTfuAPJk/3TNMcox5Sqzid576hN/etYajdd0oFQJL\ni6WWYmK0iauXnHlOKpp6aemxo1TAzMxYFs1KPaNZnnB/eoZGibFETAq2eOyO1Xh8AUbdPkwRWgw6\nDXanF38gREuvnVBIpKXXTmaiFY8vyJo52RRlxeP2BeRVt15zZhIlIEgTkpBkTxsKhVEqFSgVCtmj\nGphkWGLUa1gzN5vEaBPfvmoOwVCI3w2YGenpQWucvPOhm/BzOBTCmpQk65OnM+IZtzgWBAG/203B\n6tVyC6nt+HECXi9KjYZwMEjFBx9MIXxWbdlCw65dLLn99inPnm1M6x+fm4smIoL2Eyeo2rJF8vtv\naWHfn//MVdO0ZBUKBQtuuYUjf/0rg83NqPX68yohxnHHu+9yT3Q07uHhpYIg3CqK4itf+kf/S/zT\ni7IgCDnADoVSyS+qq1Gfxc5NLSuj69QpwqEQmogI5n/965LvsUIxabWqUCikwhYXR09NDdufeIKh\ntjaWfutb53R2Uut0MuMWQSB9zhxCwSDd1dXcu3v3lGBsa1LStCvkiYjJzLzgbN728nJGenqwdXSQ\nu2QJpWOr9LPxl+98h9GBAe54910MUVEo1WocfX1EpaURm51NREwEHzZ7ydL5GWxuIq6wiM7tCoqR\nHoC/ZZWz71Q7itgEEktK8Hs8LPuP/5iyQvzs8cc59Npr/MemTYz09JBQUCA/hCB5UIdDITwBkWAo\nTEf/COq5ySy4dC0HX30Vj91OUmEhkannTqPSGgzMXLOG+p07MZhMlKxbR9epU2gMhmndze54990L\nDn7PXrgQrcGAe3iY5OLiSQ9f5vz5aCIiOPnhhzgHBwmHQmgNBrqrq4kaHUUMhajeto25119/zveP\ny83l0c5O+hsbady9G5VOx4Kbb8bR10dqSQnmxESSZs5Eo9dz5C9/YbijA5BW7avuvnvK+zn7+4lu\nP4ku78vNWcYxcZtSQECvmTwpq2zu4+MD9XQMjGAx6ChIj2HtwnxiLBEsnCUxcZu6bFgNWlw+Kdoz\nIcpISqyZwRE30WY9R+u6qW2XJrKzcxPl7eiy3ER5+7s0J2GSIYdSIRAXaeDGi4uImCDTWpBuprZ8\nH5Fr11MzaxX+hmZS4szoNCoidGq+smQGh2q6qGjqY0lRGkuK0thZ3oIoworSDPl9UqMMrHdU895P\n76bkwSfJW3s14SvW8fl938N2YAfRWgG3JZ4/9Udxo6mPxCgDiwpTUSjgz5+UoxDA4w+y49hpWnrs\nrCjNICsxkj2VbXQOOEAQSY21kB4/2X4zFA4TCIZlg5Kc5CiqWvpxef1EmfTkp8ZMKcib9tTg9QdR\nKhRctShP3uYPhcNc+4u3uXJBHt+5+iJWlmVwuKaL0z3DpMWZUasUZCdFMisjjnkFybh9Ad7eVY0v\nECTOauCqRfkUZ0tFesjhJj81BkR49C/7GHX7MOo1zM1PoiQ7YRKLPSc5CofLR/fQKKlxZhKijFQ2\n96EQ4N4/bieoVFN7vILVP/wh2YsXS1rimTMnyZ8SZ84kqbCQvvp6otLSSJvGzatg9Wo6KysZbGmR\n7JIPHWLFXXcRYbXS29iI1mCQc+jPtnMFuPI//5OrHnyQrqoqkgsL5fG969QpWXdsiI5m2R13AMhS\nvbEf5Pfpb2qi/L33EMNhStavJzIlBbfNht5iob+hQfJ3v+qqKcefCEEQ+K/WVn6UlITf5XpJEIQG\nURQPnveP/pf4V6yUPweUX/vDH4idppjFZGSQOX8+7uFhtCYTx956i8W33Ub5++/jHBggJiuL/JUr\ncfT14RwaYrRfkk6Ew2FGurtxDg2dlyZftmEDGfPmoR6Lb9z/0ku8cccdPNI8NcbsH43qLVtw9PUh\nAM6hoUkG6RNx2Y9/zGh/P4bISOZcfz0d5eVkLlhAdFoaUenpmGJjKX/6URKdJ2nr6iRz2QrqlFFA\nYNr3G8fZAwlIg4U6NlVacRsM067clnzrW6SUlDDY0kLQ55viVmVNSaGzogLnqJNmRNRpuZRc+zWU\najUXf+97BP3+C5qFZi9cOGk7/1wh6C9+/es4envZ8NhjZC9efEFuXufqEwUDAY698w6CIKBQKulr\nbCS5sBBRFOXV8fiAcS7U79xJy+HDXPHTn5K3fDmCIFC7YwdNe/cC0oAx3nv2Os6s4Cb+exy21haG\nn/ox1xZcuFyq1+bkrZ1V9NqcY57XCr57zXxZk9o54OC9vTW09tgZdLhxewNEm/VUtfSzojQDrVrF\nkqI0Ik069p1ql9/X6fHx7u4aXF4/Oo2KUbdfDllo6BySi3JGgpWb1xQTDIVlmU5t2wCne4aJMUeQ\nmxzFJ4camZOXJJt1CIKAuqGcF752A7F5+aBPYlHUCFmJkcwvSGHbUYnlHAqL+IMhbru8jI2Xlcp/\nOxGxZh0Lh3vpbGvDM9BH+qIlrHnyd/Q3NdFbWcms9evZ+9h/scU2ym1jLfa/7DjFiNNLaW4CrT12\nIi16QuEwR+u6CIdF6sdynHVqFVuONHLlgjNWmYMjbj491IjHH2BGWgzLSzIw6jVcv2IWDrcPq1E3\nRWLWa3Pi9UttrlA4TEe/g/hIIx5fgKYuGxsvLaVjaJRQWLqGq2ZnktMXxZYjjQAolQqyk6PQa9WU\nN0q6Y5DaBT1Do6TGWVhSdKZYPrf5GO19UkvB6w8SH2kkKWbqJG92XiKzkbbmP9hXR9+wk1G3H6VC\nQBsTQ8DjoeL997nx2Wen/e4pFArZ17354EEOvPQSlqQkCi+/XH4uO8rLcQ0NEREZiSk+nlAggMtm\nI8JqJW/pUuZcdx0tR44QmZLClT//+ZRjqDQabB0d/GruXG5+/nkWbdwoXdP6M9GerqEhnIODpM2e\nTdHatfL29dIJW96nPvlElk5Wfvwxi2+/fVIbdKJ88XyIMJv51ptv8uxVVwnAVkEQokRRDH3pH/6d\n+KcWZUEQPgXSF27ceN7+QNDnkwfEcDBI0759cuReR3k5bUePYoiOxjcWtegaGiI2JwetyTSt9lMU\nRXxOJ5qICBRKpVx8RVFkwc03E5WW9qUFORQMcvrgQXwuF5nz5l0QwQikLfNQIIDOZGKorY2B5mb5\n/8dDKCbio1/8gpmrV5NSWsrxd95BpdVSfNVVU7Zw1cN91DuDJJTOxut0Igx0Ec6IPeeWdf+wi08P\nN+ILBJmVEceSojSGnV5OOTUkrZyLKIoUT/CMHUfTvn1019Sg1moZbJESfUZ6e1lz773y7zp6e0kq\nLERriMBVfRRnVhn9Y4zM2KysCyrIF4pwKIQlMRGdyUT9zp34nE45w1oURcLBIFVbtkgP6Jw507Yd\nxuEaHubgyy9z+sABdCYTyUVF5C5ZQub8+aSUlNDf0IDWaGTGWS2Is9FTXc2pzZu54qc/la+Jrf1M\nceutq+PUJ5+QNmcOecuXU7l5M2I4TN40evyOT97j1gLzBbUeQCIKbT3SxKjbz6Ddxajbh0Gn4cZf\nvsuf719PfmoMo26fNOlQCAhI2lyFQpi0agLIT42hpccuD/IqpZRxDNLAPpEEerZ5jJT5O3a+Nid7\nKtsAaUKwvCSDUbcfjVopJTUFQ7T1jXC0sonu0UY8o05SS0tx+Nr56gwjA3YXeyvbGbC7MOk1OMec\nx863M1KQbOXQX5+neecXWP/yMdakJOJycojLyeH01k+gcj9JD/03L3+6Ca1jgLS8XObM8DI7O45D\nNZ2EwiKjbj92v8Bwu5faURVxuFlanM6bO6snFeWKpl48fmmiVtc+SHFWPJEmPRq18pwudzGWCJQK\nhdy7j7MaCIbCfLi/nhGXl3BYZNuRJlaVZsimI2nxFpaXZNA16CA11iJPsswTsssVwtT7CBAKidIq\nURTxB0KT/uZcGBxxEwyFGRxxoVEpGenrQwyL5CxZ8qWkyuHOTmq2SbK2kZ4eDFFR5CxeTH9TE037\n9kl+A1VVCIJA9uLFhINB9jz3HEqVipV3381lERFoTSYUCgWDra0Ex6w9x8fHqNRUvrt1K3nLl8t+\nEFFpabLVrdZoxBAVhUKpZPU997D6nnumfMaJY61CpcIYHU3m/Pm0HD6M1mgkd9n0LeKB5mYGW1pQ\n6/X8f9rOOzCO87r2v9nesIst6L03kiBIkAS7SIqUqS7KlmTKKrZiWXJs2Yll+yV2nuX4yUocJXEc\n25GLIqvLoqxQvUssIMEGkiBI9F4XbbG9l/fHLEZcEqTkkvMXsLuY3RnMfve79557TnZ1NQarlWXX\nXssNDz/MK9/7nhE4Dvxxgt9/BP7XgrIgCFcBO7UmE3c+/vhlX1vQ0MD42bPEwmHM+flEAgH6Dx0C\nxDLnAhNPrdOx+oEHiEejBFwuChoaLgoAsWiUI08/jWN4GG16OmXr16NQKsmqrOSpe+7BnJ/Ppvvv\nX9So4nx0vPuuyJxFdNHZ+sADlzVAiMdiTHZ10bZ3L7FIhNK1a8murhbtBgUBa3Gx5N/sm58nGgqh\nS0+n4513sBYVMXjsmJRJhbxeVu/eLR076PFgmR/FqTFQnFSl0d50D489/mOubyy6SLUHRMKISMYR\ntYKXlWbxdE+Yhu/8gOptV17yPPqam0V92WRpCEQSUiIeR0ie/4IdprWklN6BfnwJGSd+/3sAqrZs\n+UTTgsvBPT0NiYRULncMDxP0eLAme1fuqSnisRgnXnyRqZ4eQh4PCo0GuUKBY3RUNFc3Ghk4coRo\nKETJmjUSyWr4+HECLhc5tbXinKRCQdMdd0jEwk+6Jxaw9YEH2PrAAymPZVdX4xgexjM9zfzYGMOt\nrYy3t7P5/vu58m//FhKJRcleKvsA8oJPLxcQjcUJRaJkpOuwOzx4/GGyLQY21hdLAaI4O53yPAv+\nYAS1SoE1TUsiAfOeAJGk4AWIpKNr11ZK5213eCUXJ4DPrC7H4Qkgl8lSjBzOZxMDEuFoAb5gmP/+\n7g3Muf3iTHM4gj8YQS6XYSCAd3oKuVJJsG4bzun9vHGkl0g0RigSRS4TMKdpL3qPC6FUyLlCNsmU\nUuDArx5DJldQsHw51VdeSfS933PP6mxeOXqIpX/zffGaBIMcf+45DnrnQTPF8PA4uvwSmu75K8z5\n+TgefZS2p5+gOpbAkpZa0Tq/0iSXyS7qQy+GdIOGHY2lvHywi0QiwfisG6NejcsX5O0TQ9Tkm3F7\ngxe5R1UX2iTC5vS8j1mXn8JMI+GqPObcfiryrSn96QVsWFqIyxti3htgVVXep2LhV+RbePVQt7ix\n02sI69XEolEKqyo4fO8tKLbchH9+ntKmJmquTF0zwhdkmZEkfyQWiZBIJHDZ7cSiUWKRCKacHE6/\n8oqkdd/++uts/PKXAXG9OfP668yPjWEpLOS6hx6SeEI127bx5sMPM9XTwxeffJKS1atR6/X4HA5y\nlyxJkdmNhEK0vfIKnulpCleupGztWpbfeCNnXnuNeDwubeSX7NxJ9bZtyJXKRb/rEx0dHH36aYZb\nW0nE45Q2NbHxvvuw5Odz9d//Pfv/679wjo01CIJwXyKReOwTL/KfgP+VoCwIgh54SZDLeXD/fo4+\n8wwhr5fKK66QWHznIz03lyv++q8JeTwYs7P54D/+A1NursjQMxrRJYlDcqUSS2HhZS37pnp6cAyL\nu/aRkycZPXUKvcXCdH8/421tIAgEPR5qduxgxa5dlzzOgncviFT/cCCQQng4Hy67nSNPP01fc7M0\nQz3Q0kLt9u0E3G5IJMgoK0NtMDDa1ib59NpKS/luSwuxaJS3fvxj6Xh+p5Ph1lbOvfMOSo2Gxltu\nYdJaRo5rhLEDHyBYsug/cgT3lJe3jvZy86ZaLEYtQ3Ynh86OIJeJ/cKJJPnFF4yIva9t1102IPud\nTkrXraNm+3ZUOh0n//AH4tEoVVu2pGxIcmpqyKqqYri1lZyGlZx76y10JhN5y5Yx2dHxJwflngMH\n6P7wQwAqNm2ieutW+g4f5tizz3LVd78rjRlNdnYylSxlzSd9sS0FBZBIEAkE6Dt4UJJHtXd3s+Wv\n/xpAIrDoLRZKmprY9OUvk5aVRce77+Ky28lftuyScq8LQiJ6i4Vn7r2XvKVLUwTxy9auxZSdTce7\n76K3WkXCTSSCZ3r6kqYjLT/6HjcZPMDibY3FoFEpqCvO5NzQNLF4ApNejT8YQa9VoUumrlq16CR2\n1apyorE4rx7qJkGCgcl50g0aVlXnMTnnQRAEsi0fK89lWwzsWFXG6LSo/DY172Nsxk1+htjnjMbi\nvHW0V9SDtqSxc005SoWcoiwTmel6pp0+0rRqqgpsPPPeGZrPDNOQZDH7gmGyzKLG85hrltrtV5Lf\nsILnHnyXmD+EzaTDmdR4TtdrFg3IoYg4kmTSa7CZdFRm6VlXZubZfR+gMZuZOrwf+yvP87cNojlF\nRsd+Jk7Wk7tiFdFgkKfuuYcvPfMMax74FgtijdN9fRx56ilm+vup+9znsbccwN4znrJ5WVWdSzgS\nw+0Psaw061MpawGMz3rQqhXE4wleOdTF2cEpRuwujFoFzedGsaaL/X2PP4RCLkvhhozNuHnraC/x\nRAKNSsFnN9ei11x63HFpaRZFWekkSHxq5v6mZUWc7rNjn/MwOB8ie1kNn330UexvvERGIkB3by+6\n9HT6mpvJW7YspU2YUVZGVmUlUz096K1WqfWUWVmJf36ekdZWZHI5hStWMHHuXIpSon9+nqneXsz5\n+YyfPctYWxvhQID5sTFOvfwyq267TXqtraREmmwRBCFlhNExOkrHu++iUKlQ6fVMdojs8Y533pF0\nCTYnRUHOx4Xk4PMxNzSE3+UiGg4T8nrpeP993FNTXPXd75JZXs5DZ8/yndxcwn7/fwqC8GoikZj4\nVBf7j8D/VqbcChhueuQRJru6mB0QZ2lPvfwyxuxsFCqVFOBGT5/mzGuvgSCw4uabMefnozEYJC9Z\nvdXK8htvZH50lIyysk/00D0/cPrm5kjPzWVueJjBI0dExm8sxsDRoyQEgYzycvLq6hbNgAuWL8cx\nOgqJBNbiYvqamwn7/ZSuXUs8EkGhVkvZXF9zM2GfD4VajWtyEnN+PgarleI1a7CVlhLy+bCVlCAI\ngjiznEgQ9Hp56ktfQq5Usvrzn6ds3Tr6Dx1CkMkoWbOG9jfeIBGPEwuHOffOO6z5zg8Z+YevEGw9\njLxe7KuGrfkEwxPMewNYjFr2nR6Sek8Otx+LUUs4EiPXmsaROYGKb332ktfN73Lx/Ne+Rtf777Pu\nS19i6TXXsOPb3yaRJOCdj6meHuxdXfjn55kdGECuUOB3ufDMzFxWm7b/8GHGzpzBlJ3N0muvvag3\nvDDPDeL4WPXWray5/XZK161DJpOh0ukwZmam9JbM+fnSGFtuXR3peXni/QQEvV7G29sxWK0s2bmT\nkjVrCHo8uCYnJZOK/pYW+pMTAbODg+IY2gXkk5DPx6HHH8eXJImkZWdLfsrnw1ZSwpKdO2l56ini\n0Sgao/GSZLfRI4dJ62gmb0tqwI7HE8hkInHl0NlRKSiez4zesLSQJcUZjM94sDs8ODwBukbmuHlT\nrZRlKRViadXu8EqZL4gymc3tI5KsZXmuhZIcM7m2NDQqheQx3DfukF7j8gXJMovl14k5UUd40uGh\nd8xBbXEGSoWcG9ZX4w2E0WmUKOQyzGkabOkft2CyzAbWLSkgGosTjgv0BsVNbuPPn+GlW29A7+5F\nrZSTY00jnkgwPe9LkcCMRGPsbe7C6Q0iILC9sZSSHDMmvYZG/SwhvxedWsGNldloVAp8wTDp/jn6\nHv02zrxSZEvX8vDgINaijwWavLOzHHvuOTzT00z39iKTybCuWE16bT1f/dXr/OavtwJiqX7rik9H\n7jwfC/+vqXkfDneAzHQ9kViMfFsadUUZxOJxvvWLd1lSmkFZroUrlhdTkS+ub2MzbmlMyhcI8+JH\n50gkRJONTcuKFs3yLlWy/u+3TnGy187ffq6J0vNmqAVBoLEql3/acxxdZjbpubm89+ijtL74Ig9+\nbg3eiXHcU1OE/X6murtTgrJMJmP17t1EQiHJBhcg6HKht1jIr6/HOzuLe2qK3CVLKFu7lvY33iDo\n9RJwuzn27LPozGZRmyKZdat1OkmTfgGrd+8m7Pez7xe/YPP996es1cdfeEHKvv1OpyTkMzswQPPj\nj1O0ciV1V131qQmiAJnl5WjS0hBkMgIulxiv1GrOvfMOmeXlaE0mvvTMMzy2a5cCOCsIwi3AgUQi\nEf6kY39a/MUVvQRB2AF801pSwldefJHRU6ekC+0cH2e8vZ3Bo0dRqFRYCgo4/MQTYskjHmd+fJzS\npiasJSXikLnFQv3115Oem4uloOAiqv5i0JpMqHQ6ouEwuvR00QJvYICJ9nZRKD2REJ1Q1GrCXi/z\no6OMnz1L14cfkojHpV6zKSeH7OpqcmprCTidjLS24pmaonXPHmYGBhhpbUWl02HOy2NuaAjn+Di6\n9HRi0ShFjY003HQTerNZFMawWKRxnrmhITzT0wgyGcasLLZ985uotFoyysrIX7aMsnXrsBQW0nfo\nkERKMFitFDU20nX0BLnbrsZSVsF4ezsaawbOwQGubyxCIZdxqneSWFz8IitkMnKsadhMOpaVZmPP\nXULO5u2LXrO+5maaf/tbho4dQ2UwkFtXh1yppHD58hQt686Xnqfjyd9y/Ne/JNTdhm94gPnpOXKW\n1aMzm6ndvp36G25YdHRpfmyM1j17RA1cu10y2kjE45K71lRPjzTCZszKomjlSl568EHSMjIoWrEC\nhVpN++uvY+/sRK5SodJoyFu2jCu++lVKm5ooWL5c1DcOh5lN/o+UGg2CTIZ7aorChgYyy8spWL5c\nKlnbOzsldjSIZegLe2nj7e2MtbUBYpmudscOll9//SXvv9zaWqxFRdRceeVF3IAFDD/2E+5e/vEC\nmUgk2Hd6iPdbB5JjMXFO9k4SikSZcfpIN2ixGD/OqBOIvrkalQKTXkNTbX5SISp1g6nXKHH5Qjg8\nAdK0ajYuK+LAGVGowuUNcrRzHG8wzJDdSWW+FblcRjye4FjXOH3jDrQqscyXn2FCpZQzOPnxolmR\nb5U+kyAIqM9jgFfmW1ldnUcoEkWpkLO6Jp/qQhvZFgM5Zj0HzoyQt2UHcrmc4q3bmfngNYosYgYs\nCAKFmSa0aqVEoJp1+Tnd93H1SibIKMkxYzPpcPvDuLxBTAY1lflWdGolrx/uYdA+LypCyYLUyx28\n19LBqbfepe6qqwAx0xpvb0ehVhNwOknE41gLC7nyW9+i9IqtHNl3hGNjPqpMwqcqWV8Im0nHrMvP\njMsnqYYJgqhhbdSryUo3YJ/3km7QMu2PEY9EWZpsE8TjCfrHxWvtcItqZ4IgXofMdAMmwydnw/Oe\nAK8d7iEcjXH9ukqeeb+d5z88x4dnRsnJyeDciAOPoMYe15G/fpM4q5+TQ3FNBSusAicGZgmGIpiy\ns6VJhgvJqnKFIiXoCTIZw62taIxGBMRR1o333outuJjyDRtEXYLkaGokGKRqyxYcQ0N45+awFhdT\nt3PnRXyfkZMn+e3u3SzZuZP0pB5+IpGg+8MPJd6D3mZDazLhGB4m5POhN5txjo2ht1pTJkcuRP/h\nw7Tu2cN0Tw8Z5eWk5+SQU1NDRlmZ2DYrLMRgs6FNT5fEf7Krqzn0xBMEXS4tYAO8Dz30UNcn/kM+\nJf6iQVkQBBVwTpDJZD/q6UGl02HIyGCqu5t4NEokqfAC4heiYuNGhk+cIBoSiR269HSKGxtR6XTk\nL1tG/rJliwZi7+wsIZ/vkgueOS+PwoYGyjduRGMwULx6NfFYTOrpKnU6yjdsQKFSMXTihDhTGwox\n099Pbl2ddFyNwYDebGbgyBECLhfxWIzx9nbMeXnI5HKCHg/FjY1YCgoIuFwoNRpW797N0p07L2k+\nYCsrE+Xk3n+f6x56COt54wYqrZaw38+xZ58VezLhMNaSEpZeey1qvZ6CzdtIyxUrCZmVlaTn5WHx\nTbHCIn4p0g0aJue8aFRKbtpYQ1mumfI8KzlWAyNLtmOpqGJ2cJCg240uObsbj8dpefJJ4rEYrvFx\nDDYb5rw8yjdsSOm1nvv9Mzj3Po1t8w6mJuxo5yepzNSSrkwQ8HhYftc9rPjsZy85S+ydmRG9hZOY\nGx5m6Ngxho4dw5CRQVpGBpkVFcSjUdJzc1l6zTUIMhlvPfIIOTU15C1dSt/Bgwy0tBB0u4kEAqy9\n6y6KGxuRyeUpJamFUbKZvj7M+fki6UmhSJmBX4DObGays5NoKIStpERkU19wDpFgUArKQY+H5+67\nj9K1a8ksL5euYcjrJZ7cpSs1GjQGA4NHj+KZmRFtR89buIYP7qNhpJmMtI8zm8k5Ly0d4uYgEIrg\n9YcJRj52+yrIMEpiGCBmwm5fiEA4glopR6NSUF1ku8hTWBAESnPM1JdlUV+WhUalYGTKhS8Yxu7w\nIhcELEYtoUiUwkwTaTo1+9uGk+0PD05vkKWlWaypzcdq1BKOiIz+ijwry8qyLpmFHD47yq3/+BJ/\n89m1rKzMTdlQCIJAmdzD+6+9T8b6rWhN6cz09GB2TRKOxrAadQxNOjnePc6sy09pjhmlQp4ijFFT\nlEGmWY9cLkMmExi0OwlHYvSOOSjPs3Cie0JasGPxBJuXFnDq8Ala2/pYf889yGQy1Glp2Lu7iQQC\nCDIZaoMBuVJJWkYG+Y2r6D7aSnSwC4I+9FoVHwx6iYRCZElMz2gAACAASURBVH5KpTWFXEZlgZXG\nylzc/hDBUJQlpZlsX1mKXKmgLNeMyx9if/sYQWMGKFVsrRGzUZNBQ7bVgMWoJcusl+wbQSTofRKR\nKx5P8F+vHKe1Z4IZl5+JWQ9fu3EVN2+q4WcvH6PNpyKychtlf/MQlVdfx9Dx44T9ftbecQeVOz7D\nC0/uJRYMULRhk7iWJRLk19d/ogiOXKEQOUCJBKVNTay+/XYpkAsyGWG/H3tnJ7FoFHtnp8QLsRYV\nodRqWbJz50XvobdYqLnySsz5+aKvctL3WanRMN3fj1yppOGmm1i6cyfGrCzmx8eJRSLIlUqxjH2e\nsc358M7OcvSZZ3BNTuJzOBAEgazKSrRGI1kVFRTU14trZdIoYyEWRcNhgi4XfYcOQSJRAbQ89NBD\nRxd9kz8Bf+ny9S8BxaavfAVDUv0pPTeXHQ8+SDwe5/ATT0hZiTZJWFp5yy2ce/ttBJmMup078c3P\no9brL1n37z14kK4PPgBE+7+qyzhMCYLAcGsrH/z0p3zj7bc5/corBD0eZHK5tBEwJPt/C1hQ6zof\nJatXMz86igzILCuTPtvCOJFSo2HFzTdf9sJM9/XR9uqrCIJARlkZXR9+yPYHHyQSCqWQ1TrefRfn\n+LgYoAMBAi4XB3/1Kyo2baKkqYnTe/cy2dGB224XNWAHxqFQLLeW5Jgvkvibcfp453g/7QNvMD05\nI3kYl2/cSM22beLNrdVi7+qi//BhVu7ahTnqwX2mlWBZGRqjkYGWw/QdOc72nz+L3mKl5vN3MX7m\nDP1v7KUg6mNHZILBA39gpqqSjNrFZSutJSXk1NYy2dGBxmjEMzODUq0mHosxeOQIubW1aAwGSbEr\nkbSC+9a+fVI/KsUZKtk/vhQsBQU07NpF1wcfIJPLpT53PB6nZ98+3FNT5NfXk1tby9YHHiDs84m7\n+0WCTEZpKfXXX890Xx8Gm03aMIIYsFuefJL5sTGm+/rIKC1FJpcTCQalsn8kEKB661bpeL59b1CT\nk7rwXDhSU5yTzoxTXExzbWlU5FuJxuLE4wkpa9vSUMyysixGplzc+6+vsam+iPRLZFDnZ9BXrSrj\nzMAU1jQt006x/KdWKqRe5Pism3AkRmGWSVK/WsC6JZ9ujLCywMoP7tqMTqMkFInSO+ZArZRTnmdB\nEASsaRruVod4/slfsey+b6ItreQG8zQCAm39ds4MiIYNw1NOJuY85GcYuWF9NX3jDkx6tVTmBZH9\nvYBILMacO0B1oU0qv9cUit/T2spClj/xX1LbRKlWs/HLX8Ztt0sbUxBtYItXrSJt6QqOvvkGck+C\ngUknzrQsAkEZSy4WjLosdBoln91cK7UmItEYHQNBRgJGBt0q5p1eVm7ZxtKdV3Fs/+9YXSDeG3k2\nI3k2I5FoDH9IrJiU5poXJXYuYKH3OjzlpHvSxcCMn2g4zPC0B53mDE5jNlf93d9R+9nbGD5xgtG2\nNlyTk1Lbr2f/fio3b+bs/mbufvJJprq7CXm95C5Zgt5qJR6Pp/SHF4MpJ4flN9646HP5y5aRSCRo\nf+MN0vPy8DudzA0Nkbd0KW67nRMvvEDTXXdJbcpIMMjB3/yGyY4Ojr/wApu/+lV2PfIIIGoOFK5Y\ngSCTSWXtSDDIVHc3fqeT4lWryL/MNEY0EmH09GnJ2nGBTLoAW0nJoj1ppVpNQUMDJWvWMCC2vr4E\n/MdlL8ofgb9YUBYEYQ3wJXVaGuaCAmaHhlLUXmQyGStuvpnO994jHouRu2QJrXv2oNRqWb17NwqV\niqPPPMPs4CBqg4F1d98tBfbzMXDkiPTz4JEjlw3KAGXr1hEJBLAWF7PtG98ARKb0yMmTxCIRMsrL\nOfWHP0gG2It5MOfW1WHOzyccCKBQqxloaUGQyxGAM6+/TmlTEwabjWg4zODRo6KBRlNTCjuwLbkh\nALFMe9OPf0zHO+/Q19zM+i9+UTrX2HmbgumeHiY7OoiGQkz39eF3OrF3djLe3o7P4SDi9WAeHcAb\nsCw6JuENhHn7WB/+YATXyBD24VGp5zt+5owUlFfv3o23r4sNa5dybzXUFnp5s+1dJsurUOjTyKmt\no+ynP0s5dt6yZeQlA1Pb//0m12bP0vf0P7JvFq74ze8v+iwymYzGW24RZ4AFgff/7d+kILuguLWA\neDzO8eef58jTT2Pv6uL7p06hN5spaWrC3t1NwOkUpfLO6w8OHjvG7OAgGWVlkiZ6xcaNFDQ0IJPL\npd364JEjkv71dG8vafffT1pGxkWVDcfoKDK5XBKTKVyxgsIVK5gfH0ehVpOWLIlNnDuHa3KSkM8n\nqiHp9egtFuaGh8mpEXWKx9raiEejWAoLyaqq4t2973DDFxownkfIyTTrWVOTT9fILDaTjhUVOSgV\ncmkh759w8NGpIeLxBOuWFLCkJBNBELCZdNhMOk786pMlCRegVStZU5PPmpp8OodnODs4TVGWScqy\nQ+GYFNBWVIrn7w9GmJjzYElLLaOn/t8SJEggl8mwpGkpyDThcPv53dunmXH6saXruGpVuWRFqFEp\n6Hz1VZbd903yNm7lwH++w84q80ViOAufK92gobHqYnGfwiyT6KWcSKBTK8lM11OcnU5FvgW5TCax\n0sd1OexJjsBd/b3vkVleLrbRCgsxZGRI5M6FTC0cCJJ95TW07X8f87yTop1bcIZ9xOOhTz3Cdj4W\n/sYXjKBadxU33X0viUSCgZYWilevRq5Q0NbZRoX3LObzNldKhZzPrC7/VO8xZHfyD//9EUvqSknk\nlWJJD+Oy20mEfLQ4ZHzu//2InNpaPvyP/5Dm8T0zM1KCsUBM/Be7HUEmY+nVVxMJBulrbubtRx6R\nBJ4+SWTpciior8c1MSG1MZ1jY0z19CCTyfDOzXH8+efZ8rWvAeK41WRHB/bubgw2G5MdHYT9fmnD\ne6FV7OCxYxQsX048FktZf89HIpGg//Bh8T0VComvorpE5XUx5NfXU7JmDTP9/XimppYJgvBXiUTi\nt3/iJUnBX6x8/cMf/rAFMK3evRuNwYDf4ZAEFBag1GjIrasjp6aGI08/jXN8HNfEhOT81LN/PyCO\nBI2cPCl6e+bkSD1HgKnu7pS+Y3pmBkHXPBpT6sIO0P7mm0QCAdbcnqoLLMhkYum3oAC1Xk/xqlVU\nbtoklSMXw0JJUqXVklVRwWRHh+ijPDGBvauLkjVrOPnSSwwdP87c0BCO0VHp/BOJhOSbHHC7OfDL\nX5KWlYUmLU0qsxhsNpp/+1umurvxzsygt9kIBwLSCFDQ7SajtJSgx4Pf6STk9RIeH2RZpoalJVm0\nD04xMevBatKikMs4fHaUX73eyqneSeRyGSGlDtLSJbKGtbiYvKVLAXGG79iP/4Gv76ihutDGyJyf\nE9mrGOrsZbKzk7G2NvKWLr3kTW5saGLP869zxxITOZoEB1/Yg1emxlpRedFrZXI5MpmMjNJSIsEg\n1pIS6nbsSCF9zQ4M0P3RR6KMarIElVFWhkqrpXj1akqbmihsaJCUfJp/+1v2P/YY8yMjeGdnMRcU\nSH1hhUolfXHj8TjHn3uOsdOnEZKBerEecvubb9L++uuMtLaKWd15m8sjTz/Nb2+7jbJ16+jet4+A\n0ymVvtyTk6RlZqI1mdClp4stDq9X1N92OBhvb2fi7FnUGdl89M5BanNNKWXIbIuBJSWZlOaaJfbx\nQub+7vF+AuEI8XiCtj47aTo1aToVKoWcyTkPP33pCJX51hRZxVg8TjgSQyGX4Q2Iql3egKhAtXDc\nY13jTM37sDu8+EMRirJMvHyggzlPAGXSJKKmyMbLBzvpHp2le3SObIsh5X1ADAb/c7CLU712dBol\nNpOOG7//AuFIjJFpF7F4XFKbqi/7mEg3J+jRNG7GmJ2Dy5THwOFDrC1JJxSOIpPJWF6eLXkU+4Jh\n3jvRz+leOyqlXJqdNurUFGWlk202sKY2T2JHG7QqSVry1+0+fIV1zI5NoDYYcE1MkF9fL93TtpIS\ngm43BpuNZdddJ7rXKZXYu7owFJWS3rieDd/5Hs5ZB1WegYt69wtw+0J0jc4SjsQu2feNxuJ0qvPI\nbGhEEAR8Dgfv/eu/UrtjB5mr1vLo9/+NYoNAZvqnDxILMKdp2bC0kN+81cbWhx5GrlSK7kzZmSgE\ngRW330EiFpPGPUGsWi6U+osaG8mqrGSsrY2HGxpouPlmlBoNJ196iVg0SsDpJBIIXDYDPR8Bt5tY\nJHJR5dOYnc3s4CDxaJQlV18teiVnZyNXKomGw1QmZ4hlSiWn9+7F73SiNZmkdeL8KYmgx8OJPXvo\na24m6HKRSCSIhELYOzqY6unBNzdHdtJFMOTzMXT8OJ3vvYd3bg7H8DCFK1diLSqiqLExhQh4OUx2\nduIcHyejtHThWm556KGHHvlUf/wJ+ItkyoIgfAYosBQWSid1IWP3fMQiEYk1BxBwOsV6vSDgm5tj\n6PhxsaGfSOC228/3t2Tl5z5Hz/79JBIJKjdvpucXPyHomGHNv/zqovdpefJJErGYZG79aTBw5Ajz\nY2Pk1NRc0kHIPTXFdF+f9HvQ7SYaDKYYaLgmPmbKDx07RjQcZqqnh3gsRs2OHSnsXV16Ov0tLXhn\nZ1Hr9ai0WtbcfjvDJ05w5OmniYZCWAoLKd+wga4PPyQzFiOzvJxQVxs763UcbB9mZFrcqNgdXq5e\nU8Ge/efwBcLIZTJ6xxxkV2ew7f/8HwJuNyGfj6zKSgJuN+2vv865F59jZGCMea8oeP/aOKTvWo49\nmVFGAgEcIyNSEL8Q/rlZrPl5PBWwEDfI0W/KofiKbey55QZWFRqI64xE1QaEimVU3HIHIJa4Vn5O\nNLvwzs0xeOwYaRkZ5CQ1qUFcLKxFRSk7WJlMlkI2WSAOhv1+kTOQdKJZ9H97+DDeuTn8LhfeuTka\nbropJdtewIJZ+8LPlZs3Mzs4SNjvZ/Xu3aTn5Ulz9ABZVVVEgkGKGhvFTNlqJb++HsfwMHMjI/Qt\nXMdgkJGTJ9GbzSjiUT44OcDWhpJFTQwuhEalwOUTy7lOb5DH3zyJUi7j+vXVFGenMzAxTyjysciQ\nwx3gjSM9ooNRdjozLr80T+wNhCU3sYlZj/Q3PaNznOmfomN4BrVSgUaZwB8KMzXvk0RFYvE4w1PO\ni9SijnSMEUmWfw+fHaW60Mbvf/A5+scdzLj8+AJhKdtu7ZlgRUUOc+4AncdPc+aRRyhoaKDxttuY\n0Rn44PlHuWpZEbF4HIc7QCAUQaNS8NrhHobsTtINGvadHiI/wyhl1QsVg0tBn5uPorCEsnXr8M/P\nizKxLpfErTBYrdIoznh7O0eeegqVTkfjbbdJpFSZTIbamoFzKLSotG0wHGVvc5ckNLJleQmVBanT\nIt1TXg5EMll63xelxzzT07S/8QZXffe7GDMz+dL7Bzj37pv0vPssu6ovXaoenXbx0akhEiTYXF8s\nbV4yzXp2fPUr1F9/PfXXXy/O3n73q1QXG+j/tx/Q+O//LXIu+vuRq1Ssuf12FCqVGMi6umh58kmy\nqqpY+8UvotRqUahUxKJRho8fJ+Tz4XM4aNi165KcngVIrUZBoP6661IkOTVpaWy+7z7pd0thIe1v\nvgkJUbxEep3BQE5tLWNnzqA2GJgdGqL9jTcklS+AjvfeYya5HsejUfLr6xk9fZqMZJI13t5O0apV\nDB8/znh7O3MjIxiSdpTW5OhUaVMTRRe4Dl4O2VVV9B08KDLNly9n7PTpNEEQvpZIJH7+qQ9yCfzZ\nQTnpkbwX4N49e0T9Yp3uIv3i86HUaLCWlHDmtddQJhWsjJmZlK1bx/7/+i+ioRDemRnRX3gRacoF\nZ55wIEDXmU7W/+23L3pNNBzmyy+8QMjrTXk86PUyeuoUaoNBymQdIyO0vfoqjrExooEAOrOZiXPn\n0JnNF5Vpevbvp/ujj3DZ7USCQWzFxeTW1aHS6chbulQar8mpq6Pv0CHcdju++XmcY2Po0tPxzs5S\ntHIly2+6ifH2dtJzcylqbKT7o49IJBLYu7pwT02h0GhYd/fd+Ofn8czMULhiheT5GY/HiQaDHP7d\n7/j573+DMRH8uEQ34yYYjkiZkEYlJz/TyPIVFeTU1eEYGaH1xRcZaW3F53AQdTsxzo1ww4Yqaosy\nmHH60JaILioLCkFypRLTZcpVtrJybD/8l4seL92wgRuUPSjkccDNr/f+jvd6Btn+/Y+l9cJ+P4ce\nf1wqZdffcAOFDQ3UXHklj27axOYks/pSiIbDpGVmMj82RiQYRJ38Ei8Gv9OJWq+nZM0aYuHwomNZ\nINo9uiYmiIRCWC0W+g4dojPpbOZzONCZzSm955za2kWVxDLLy0nLzGSktZVwkpiYsFoZO32K6OgU\nsWCQYDjKyspcqSw74/TR3D5CIiGOPi2MBV2xvJjDZ0cZmXKh16jw+EOEBYHukVnMBg1XrS7nWOc4\nWrWCLLOBMwNT+ENicOgcmSUeT0hZ4/k92IJME8NJeUZ/MEIoGsWcpsXjD5Nu0LKqKk/MrBEYmHQQ\nisSoKrh4LFF1Xua40PPuHJ4RSV25Fk71TqJSyjHp1ZzonsCgVTE558WqkdE10IdKr8c1OUnWsno6\nz25jdOg9WrvGmJr3olLIyTTrOdkzgdMbxJympTTXLE0afBImHT4cEyFq7lzJoSeeoO2VV7j9scck\nN6HZwUEmOzpIz8sjp7aWliefZG5kBKVajUKtTgkA1pol/OJf+qm3KVi/pDBlczLvCUgBGUSzj/OD\nciQaY7+ulpXf+l7K56u58kr+setj8q5MJiNv7UYevH4Xq354CwXWxYNfc/uI9H77Tw9R/JmPs0el\n7ONro1KruKouk42lZl4YV4njTLffjndmBrXBgFqvp3XPHnHEyO+ncMUKZoeG2HzffQwdO8byG28k\nt66Oc2+/TdjvZ25oiN4DB1iyc+dFnyns99Pf0oJMJhO9ywESCXoOHFhUJ3sBxatWkVVZSTweT3Fu\nck1OEg2FqN66lXAgQGlTE2vvuivFjGiBHwRi1a92xw50Foske4sgEHS7RQ8ExCTIOTlJPBol7PeT\nlpEhiiN9ytEp9/Q09q4uanfsQKXXs+Ub3+DbGRnEY7H/EAThF4k/0w/5L5Ep3wCoq7dto2T16kUZ\nrhciFo3ittslUszc0BBFK1dKnsRak4nZwUFCXm/K8YJeL2898gje2VlMOTm4Jie59fk/SM9P9fbi\nttvJrq7msZtvpnrr1hTHkFg0yrs/+Qn9hw4Ri8VYcfPNbP361znx4ovM9PfjnZ0l4HaLesyJBP75\n+YuC8mCy7GPKziYej7M+qRIGULtjBxnl5STicXwOB2fffBMQb9RIMEjI52OyowOX3U7ekiUpLljl\nGzZg7+pi6PhxMsrKiIXDjLe3p1QJFiCTyehrbhbLohnFzHe0olYocHj8CILAi/s6WF9XwPHucQRB\n4NYr6si0wXvf/DxDOcuYONqC0jvP3PQsOWYdvSOTTOnUXLW6jK/8Yh9/3/Gf6MxmVt9+O2deeQVB\nLmd2cHDRGfGevS8RHexAsGRTc5fY13z3a/dgcIxhd/o5sywDU5qO4x41mdmZOFs/xDF8J5aiYkDs\nZ51P4JodHCQtM5OcujoePHCAjLKyyyqpFdTXM3H2LDK5nLSMDDb81V9dskpTtHKlKNMXDJJRVnbJ\nUtXq3bt59yc/wTk+jlqnwzM1JT030NJCWkYGS6+9Fs/0NOaCAnJqanBPTXF6716i4TB1V11FVqVY\nutcajWy+/36cY2MYs7NpfvxxlH19zAWi2JJBc3ByXgrKH54alEQ0Pjw1yG1bxXsk3aDh6qYKzGka\n3jzaiyepuSwIAm8d7eP9kwNsW1HCrMvP1U0VqJVywpEYMy4fSrmMkmwzvuT7LZgtBMNRynPNWI1a\nzGlahu1O+iYc5FgN6NQhNtUX0ViVi1IhJy8jjfFZN+Y0Lb1jDlZV56WIaFyxvJiD7cPE4wnW1olk\nsA9PDqLTKMmzGSnPszA4Oc+cO4DNpMMbCKPXKFGrFBin+nBq1EyeO0f/oUNkL1nBSwfeRefwggDh\naIzW7klybGn4QhFcvhArK3NTeBRjM27GZtxkWwxSxriAl4O5NP30EWRyOWt278ZSUIClsBBBJpMY\nuPFYDJKZ4Ojp0xK3Yzwp67iAM6+/zmxCjcPt5YOTA9yx4+PNmMWoxaBV4Q2EERAoyEzNct/rc1P9\nvfu4ELMDA7zy/e8T9Hj4WtKPW2sy8TOfj33/8NfccQlpBkEQ8AbCDEzMI5fJiGoMDAxN8KM7N1I+\n38vYof3kr9+MraycMzkrcJ09RmKtOMonS45kglgd3PfLX+KdmyPodqPUaMipq+OtRx6h+Te/YcWm\ntZRXFRP1uJFrtKL4R0/PokH52PPPS2TeBUOKhfP5JCz2GoVaDYKA1mRCazJhysnhtYceomffPh5M\ntjurtmzBNTFB0OulYuNG0RJy40aCbjeO0VFKm5rEKQyZjEQ8jtZopHzDBvqam1EbDMhkMkZOnrys\nxoJjdJSR1lbkKhWjp05J/fjGW2/FYDaz5o47aPnd72TA3wMPf+LJXgZ/VlBOZslPCXI59+/d+6n/\nLhaJEAkEUGo0TPX24hgdRWc2U7B8OUqtFmtREea8PFZ/4QvkJNWQEokELb/7HaOnThEOBAj7/cgV\nColtePattzj6zDOiZV5+Pit27bqo7xFwuRhtayO2wLL86COa7ryT/kOH8M3PE49EkCUzJ0NGxqJG\n2wabDUey9G5dREM7o1QsAZ99++2Ux4saG3FOTFC2bh1r77zzouMqVCoabrpJMtxYeOxSiCdnmG1L\n6gkWlTLvnSfedogSm4FQJEq2xcDf374JpeJjpaA7VqjZc2A/GTIPsnSBuNGCTq0k5vdRkWdGqVDw\nhV//Cl1yp+p3OCRyWvvrr2PKzr7IqzS9ogrH2SPMTdjxTE8T9Hhwq02MBucw1S7hsF6D1+Oj+Jqb\nsS5bxgaFIqXsZczORmc245+flyoF42fO4JmZEf2IN2zAOzvL2Jkz6C2WixS35Eol6+6+e1Fd8Qth\nyslh6ze+QdDjwWCzXZJFGg0GEWQydOnpOEZHRY31JPln7V13cUVSIWyBcCIIAmdee4358XF8c3PM\nj46y65//+eNqhcEgqXoJgkDh8uX4zrZK5d4F9yAQLQs//ll8PhqL4w9GGLTPo1Ur2b1tCSd77CgV\ncmqLM/jo9CD5tjS6RmaJxuMEwhFWVeUy7wni9YfJSNdTmGUi25KGQasi15ZGMBzl5QOdeAIh5DIZ\n1zRV0FSXTygSw2zQsqIyJ0WqUa1USFnhnNvPq4e6ybWlsa6uAKVCjsWo5Yb1qUIo/3L/DgYn53n3\nRD8mvUZiYi+ofi3YDRZ6g/TPjXD2zTfQposjamXX3073P/0dBSbx3s21pRGLi3KxOda0FMLXjNMn\nqV+19cM1TZXkZxjpnfbRksimZNduZHI5MwMDTJw7x/zYGKf/538wJYUhzneLc9nt2EpLmenvR6FS\nSd/nBcQiEQzlNdjPNVOoFJWmmttHGJ12k5eRxvXrqhibcZNu0Eg+xgCnx724N32WrAs2trNDQxx5\n5hlxEyAIuCYnJbKpXKlEtfEaRs/socAqirckEgmpn33F8mL+8+WjyGQChVlGPLOz7L6ihjsf+R+e\n+rubeGvvr5ixZZJRVcOyb/wffA4HOYvoWTsnxPExTVoaYZ+PkM+HIAgYMzPZ8vWv4z/byrW2AM0+\nNzG5ApVWi3d29qLjAJJnASRVv6qqkCsUF8l0XgoBt5uJs2fRWywS36P+uusYPHYMg81G5ebNKFQq\n0nNzJSa4KTub7d/6VsoaIFMoiAQC+B0O+pqbySgro2HXLoaOHUOfFBNyJr+vwKJiQAsIer0cefpp\nYuGwNI67sKF3jIwwNzyMtbgYuUpFLBz+gSAIP/5zsuU/i+j1wx/+8G7glprt29lwzz2f/k2VSsI+\nH0MnTjDR3g6JBPNjYxLDNRoOU3XFFcQiEbo++ADf3ByGjAy63n8fmVwuGXA33nYbtpIS5sfHee9f\n/xXn+DiemRm8MzM07Np1ETNboVbT9f77Us8xo6SEvPp6SZ1KplRSf8MNrL3zTio3bUohmC3gwlna\nhdfE4/EU7WS1wcDE2bNEgkGme3owZmfT+f77VG/dKrmsXAhNWhoKlYqA242ttJSa7dsvGWiM2dnM\nj44SDYWo2LiRpnvv4+Q7H+KZncOslZNrNVJZYE0hpAiCQE2hVSojbl5eTGluOp5AGBA4NOJl5fce\nRp58z6nubkmyFMCUm8u5d96h+8MPGTx2jN6DBzFkZVN5+z0U7rgGtV6fnM2dRZ+VjUKlRl9Whcvl\nY25wEHtXF2VJdS7pXlAoyFu6lLSsLLIqK7EnpfJGWls5+Yc/sPbuu/nwP/8Tx/AwU93dKNTqRc1E\nPq3XslypRK3XX7ZUlUgkOL13L+Nnz+Kbm0OTlkblFVcwNzzMmddeIxoOU7J6dYqS0eCxY/QfOoRj\ndBTX5CSZlZWLf05BwN7VhWayn/U1uayqzmVFZQ6y5HHSdCpGptzIkl6+4UiMlw928uK+c3SOzOIL\nhonH4ebNtRRkmvj9R2eZ9wTQaVSMTrvRqRW4/WHUSUtEm0mHVq1EKZezbkkBaTo1wXCU0WmX5IyU\nSCRQKRSU5VqoyLdSV5J5EcPaqFczNOnEH4zgcAfQqBXMuvzEE1xyRGdvcyePv3lywcKYinwrN2+q\noam2AE1SaKQg0ySON0WjHDk3iqGwGICStesI+gKUxGapKrCxvbEMnVpJrs3I2toC5OeNkI1Ou5M6\n7yIsaVp0aiWv9PpY/uhvMWSLQW5+dBR7ZyfHnnsOpVpN+fr15NTWYu/sJBIIIFepWHbttcjkctR6\nPdbiYpbfcEMKEVBvteKcnGS2v4/PrSli3hPkWNc44WhMtLw06aguzEghwk06fBzK20DN5+++6BpN\nnD3LTH+/RIA0ZmenJAPp5ZX0/c8LVGVo8QRC/P3TSgdjAQAAIABJREFULXxmpXiNDFoV/mAErVqJ\nWqlAp1ayfWUp2dY09jZ3kaVOcOKV17Gs3YI2Pf2SLnV6i4XhEydEjf38fHb98z9jLS5mdnAQ78wM\n3S3HuGZlITNzbtwKPbbyCrKqqyldJLMMuFxSYC7fsIH6664jp7b2kiTR8xEJhTj4618z2dHBxNmz\n4pRDRgaWggKKGxvJra1FrlBgKykhLTOT/sOHJeVHSF0D5oaG6EpK9kZDIWkG2TU5iUwmw1JUJI1n\nZZSVXXat9c7OSsQ4mUJB0OUSW1gyGblLltCzbx8gyoe67XY5oH/ooYfe+8QTvgT+3PL19xAEvvLS\nS3/0Hy695hpmh4awd3VJJKjJzk6me3oIejwMtLTgmZnBmJnJdG8vSp1OYsHqLRYqNm+Wgq5nagqD\n1YprfFxUevnoIzIrKi7SYJbJ5dz0T//EoccfF0UV1q5ldmAATVoaZevWAVC5adNl6f7nz9IuYMHn\n0zEyQum6daz+/OcxZWez5etfZ3ZggBN79hAJBIhHoxf1uC9E2bp10mcBcWfevW8fQbebkqYmzHl5\nhHw+BlpasJWUsOb226Ub/upHf8rZN99k7HgzRerERaL+4qZBXLTMaRqsRi3/9Fwze5u7uGlzHROm\nHOYGB8muqgLEcu/YmTMEnE4sRUXMDQ/jSDpf9R86hDE3l3NvvcXn/v3fpZ6quaBAkstUqNWEAwEp\ncAWcToJud8oiFw2HCfv95NbVSebnsXCYik2bWHPnnbz36KPiLtlqJXfJkhQy3f8W1Ho9WVVVOCcn\nRcvPzExGT58WyTDBoKSrvYBYJEI0FMLe3Y1ap6No1SrsnZ2Ur1sn3ctypZLSdesobWpi4KMP6J+c\n4UgwwGtH+/nRPdsozRSz5ZIcs1R+FQSBt4/1MTDhoHfMASQw6tTIBIF/39PC1LyPr1y3kiG7k28/\n9h4rKkRWczwex2bSIpfJ6BmdQxCQst6ukVkOnhkmGI7iC0QwJ4UwMtI/LvnH4nHmPUH0GmUKker2\n7UuZcvh4reVjmdNAKEIgFOHc0AxKhYy64kxp5nrI7iQYipJrTSMai3PThppLil5UFVoxnpyQXMEy\ny8vJ/ocfMfmd21lRKVZulpYursyUn2FEq1ISCEeY8MU5oiyiPXc1S++5PmXzlV1dTXpeHlu+/nWM\nWVnkL1+OSqtl47334kwK52hNJhpvvRXP9DQqne4iIYvM8nI+893v0mZSUxBrZ3DSmfJ8LJaaIDm8\nId4eDpF7y1WLfvbMigoO/PrXzA0N0dfcTCwSSSGmymQy3OE4sXicrz22j5V3fYn/6enhpkrxftmw\ntJD9bcOEIlGaavORyQTW1OTxf5/4CJVChlUQOPnAHeTd+iWqP3/X4vKcWVnc9vOf4xwfx1xYiFqr\nJRaN4pqYEJMVtYZJb5QNS/KZGYWs6uqLJmsWsOzaa8lbsoSw30/vwYMMHD5M0apVLPnMZxZ9/fnw\nOxzSZI3P4eDDn/2M7KoqqrZuldjYC9j3y19y/PnneXhwcNGKl0qvlzgxC78fffZZAs6kvaXXS9MX\nvrBoCX6x62MpLMQxMoJKp2P7gw+SiMcxZmUhVyo5l3zd0muvZeLcOWLh8JeBi4lOnxJ/clAWBOF2\noLx8wwaptPfHIquqCl16ulhOtFpJgFQuHW9vxzkxgcFmI7uqipDHw5ovfAF7VxdqvV7qVQBklJej\nM5uxFBWh1uu57ec/v6jMugBjZibbv/UtnOPjtDz1FCQSxONxihsayKqslGZLL4Rvfh6/w4HGZGKq\nqwutySSxkc++9RZjZ87gczhwjot93A333INaryenthZrYSEzAwM03XnnZU0wLsT82Bhtr76Kc2IC\nuULBdG8v2/7mb9j3y18ydOyY5Du67q67AHHBEN2LHsA1McHvnvwFmdO9hBRqguYc3KgxnDtIwC1e\n46oCGxuXFRKJxhhwRpEZ4hz+yf8j7nWTrldhzc+j4a6vocvIRJOWxqmXXwZEtnQ8Hpeu3cTZs1JQ\nzqmtlRa17OpqHKOjtCcDqTE7G8fICNN9feTX1xP2+zn8xBME3W6M2dms++IXafrCFxg6cYIjTz2F\n3+lEk5aGWq/HOzdHyOfDPz/P8RdeoHDlypRd8uXgGBkRA9UF4gCXQ9n69VJFRWsySfdl/fXXXzRX\n3d/Sgs/hwFZcTDgQQKXVSvff0WeewZ3sSXtmZmi85RYsBHju+6LYjC8Y4Z6ff8BT39guEaTOXzhl\ngkjM0qoV+EMRpp1eCjKNXL++iiyzgYNnhmkfmCbHYiDHmoZcJpBpNnB1UwWH2keJxuMo5TKJEHS8\na5x4QhQgEQSB+rJsbCadFLRj8ThvtPQy6fCglMu5uqmCbIsYAOQyGTlWA1ajjpEpJzaTnqWlWbxx\npJc5t8gLmPcEuWK5eJ1rijKkoK6SfUJrQa/h29fU8ge9lRX33isR8OKKTzZ/MGhVfHZzLVPzXt6P\n5rDiR4vrOChUKtbfcw/Hnn2W/sOHJcEMpUaTkp0KgnBZaUaAilvv4oOH72dHmdgrH5txk2tLSyF2\nTbmDvNDhYf3jf7hkZcaYlUVGaSlqgwFrUdGi0wB/2NfB0U4Tt778Jjl1dbT9/FHi8R5kSUetGzek\ntg3CkRhXrizj1SN93LiuglqzjMaR93nj38dp+NvvXXR8EJONsM9HyxNPoNRqxZn6ykpW3HwzZdoI\n5cZ5olEd1Y2bcYxP0v3RR5JZzfkQBAFbSQntb74pzX0PHjlC/rJlnzjbrLda0Vss+BwOZgcHpdd3\nf/QRZWvXpswkX/Wd73D1979/yRaUMTOT+uuvZ/TUKYxZWZQ0NUkZLSAF5wXM9PfjnZsju7paErZa\ngEwuZ+1dd+GcmEBrNF7U+16ycyeDR4+it1pp2LWLEy+8YBIE4R8SicSPLnvCl8CfFJSTcpo/AlKc\ncv5YlKxeTfW2bfgdDkw5OZSvW8dUZyd+p5NIMCgJavidTgpXrGCsrY3pvj5sJSUpQdk1OUk8FkNj\nNDJ0/Di9Bw5IzkDnIxwIcOSpp0RGXzgsDY4r1WqsxcWXDMhzw8PSaNJkZyc5NTXI5HL8TicVGzei\n1GikDFgml0uL8MLva++6i4dXrqRk7dpLjhVJn9HvFwfSZ2boPXiQsbY2cdxm5UoiwSABp5PO994j\nmjRh6D1wQArK58OUm8vyv3uYkM8n9T0Bnr71ZrJ9Dsx6JYOTIkGkIt9KpSCg1/iYmE7qC/thQ26c\n4ce+z+yNX6Vg/SaqtmzBPTVFZnk5nulp5EolGqNRGiUYOXWK9jfeQCaXs/Jzn8OYlYUxKwuDzSaR\nLk4nuQejp0+TVVkp2VXO9Pfz2g9+gNpgoHLzZuQKBQq1GplcTkFDAyGfj8KGBqZ7RRP46b4+tn79\n659IIOn84AOJhVm8apVk4fZJKG5sRJOWhn9+npzaWqa6uznx4ot8+LOfcdvPUoVUYuEwgiBQsHw5\nzslJKjZtomb7dhKJhGhFmYR7aorRgx+xxDcI1qRjlUbJD3evZU9zN7dvqcXlCzLj9JNl1pOmU9NU\nl8/+tmHSdGq0agVLSrLYtqKEjKQ/b8fwDIIAy8uzCYQifOvWdZKXb//EvKTw1f3/iXvv8LjqK///\ndad3zajXUZdlW7bkIvdujI0N2EAwJYGEBJIlhLDZ7JItyQK7yWbTk++mbkILWVqA0A0YY1vG3bIk\nW73XURlppCmaXr5/3NG1ZEku2V85z+PnkayZOzN37v2c8znnXXrHWFGSiVatlFDZqWa9JOQxFfYJ\nL4MOsQAJRSI0945KSRngeH0fYy4vKqWCUmsyFqNGSsgg2g1ORTQa47kDtdy1rYyVCzKvKg2ZYtbj\n+eBNWjKzKN17BzKZjLDi6r7AoXCEqt5JBiyF5H72i1d87NQiPqW//NeGzmKhLXc5oXAzN62euzj8\ndDjK2t+9clVUb1q8M+UWBMm8Z3r8S0PTjN9TNmyn9S9nKc2arc0AooLY2kVZNI146Rj1sbOyiIxE\nPeuHG2n48H3yd85mxngnJqh75x2Rw3z2LDqLhbSSEibHx7nQM8ahY4d49J4tRJJjEmCq9ehRrMuX\nz3kPXt4Knvo9EgrhczrRms2zmA9TRdNQfNMztWtWqtWSbexU6BMTqfrd7+g8dYrNf/M36JOSZulM\nWJctm7GjL1y/nvZjxxBkMgrXr5f+v6+ujtq//AUQfQC2fPWrs9rtMrl8znEUiMpiUyCxZbffTvWr\nrxKLRr9EPEdeb/y1O+UngHylVsuJZ5+laP36OdW3rhZak4mtjzwi8gUtFmRyORsefBBbQwMCInE8\n5PNJ3MIp95+hpia0JpMEnhlsbESuUIht0ViMyDSI/PToPX9emneE/H4iwSCG5GSRonUF0vjAxYtE\nw2GRqjU6KumhThnbL7vjDsZ6ehhpayO1pITk/HyGW1tJysuTxCtufvLJGS4rc0UoEODY73+Pd3yc\nwaYmjKmpmLOyGGxoIOj1kldZiUKjwZCczFhPD5FgcNauDUTFMs/oKBqTSQJVTdhsdJ89i2nBYjrH\nHSz0O0hJ0/PLv5zhnz67gR0rC0W947FLvFVfIMSGHBNvDIjSnCqdjpX796NLTMTR2yuJikjgtvff\nJxoOEw2HafjgA1LjqjxTO9TOkyeJxWJE462x6TSi0a4uEnNyUGo0NB86xP3PPINSq6WtqoqRuD2c\nd1p1Gw2HGWppwT0ygjElhbxVq+Zc/PqntZqbDx9GbTSSWlR0TYpEU218EBN6Ul4expQUFu2c2YrM\nX7OG4dZW3CMjLL35Zir27ZPeS05FBb3nzzPS1obWbGbwgzf5/m7xfESiUewTXvoHx/BO+nj2QA3+\nYBiZTECtVLBvfSkDYy7Wl1nx+AIkGDRsKLPy3qk29m9djFwmYNSq6XKOc+BMO9uX55Nq1jMw6sY+\nIRrX+4IicCzRpCUcibKkIJWmnlHUSvmshAzirlMukxGJAwlNl4mENPeKc2ilQrQHXbUwi/x0C11D\nYlehIPMSnWVpYRoP76tkz+pikq7AIZ4K12SAdHmAlp8+Re3Tv2Xfn17Hk5hDLGabN7FVD0xSl1hG\n0T9/mYo5QExzxZr77mPNffcRmJxktLMTQ0rKLGewa4lFD32dd7/9ELcVyhlyeNBrlBi0Kjps46iV\nclIEcc44BSJq//RT6t55B63JxIaHHpLWg+V33CEKWnz8MWdfegm/231FjemM8gpq/6RhbkNQMbZU\n5LEkP5VvPXtUGocsTDNw7sSHMEdSjoRConRtIMCkw0EkHCYpLw9HTw+bHnmUMwEP1UN+7H0HkeUt\nQK5Uip7E88xhizdtwjM6ittuJ2/lSkxpafjdbo4/84x0TtY98MCsObdaryd3xQpSi4upf/99gnGJ\n2rl2xIJMhr29nYvvvy92ffbuJbWoiP4LF8RO5jR2C4j+zNbly5ErFDPOr72jQ/rZ73KJAkTzdFqv\nFEPNzbQcPkx6aSmDjY25giCUx2Kxuus9zl8F9Hrqqad+D1iS8/ORK5WU7d4tkfAvD7/Hw4V336Wv\npgZdYuKcrQGVTicN6VVx9HXOsmUIMhmW7GzSS0vxOhwzBDumFLlAbHmPtLUR8HiwLlvGlkcemRO5\n7BoZYaS1FRCrr4rbbyc5DvayZGXNCxbyOZ2itZtcjnd8nITMTOQKBQXr1mHOzERrMrF4506KN27E\nkJxMX00NAxcuMNLWRk4cSCbIZBRv3HjF8zre309XXEY0HAjgczol1TFjejqGpCTRtcrtpr+mBkEu\nJ6WggPw1a6TPGwmHOfn88zQfOkTv+fOkFBUhVyg49rvfMd7fTywaJa18Gb4IlFiU/MvdqynItCAT\nBIw6FQOjbib9IRL0GtaXWWkcmkTYfR/hYIiq3/2OjhMnGO/tpXjjRjIWLpzBKew8fVoyOQ8HgyTm\n5s64LiZsNmrffJPRri7kCgUbv/xlqWjRmEzSjTfU3MyLDz+Mz+lEHy9+3CMjYkEViyFTKEjIzGTg\nwgUm4prTymkt4+kx2tXFpMPBZNxmMhw3l8hctOiKAjdzRSwWw5SaSlJe3oydgEKlIq+ykuKNG8kq\nK5uRQNIWLIBYTNQXNpkIKtTUN/WwLt/M+6faqKrr4WhdDzqNklGXF4fLj9mgIRKN0jfipG3Agcsb\nQKWQc6bZxuHabv72MyJvWxBEoJRMgI1Lc7l/Zzn2CS/vn2pjYNRFKBylKCuR/HQzq0qz+OBMO/Vd\nI/iDYdaXWWegvgFerB6iPMtIeqKBaDRGQYaFZcUZCILA4Jibd060Uts+RCAUwahTkZFkRK9R0tI7\nii8YZu2ibJaXXJKp1aoUJOjVWIzaeRWwpkdtxxB9I05MGgWWmI+2Eyew7n+A8NnDM0wgItEoF/om\nqBqRMbZ0G4se/Nq8IKbLo+PECc6+9BK/2buXCZsNR28vvefPY8nOnqXsdrWQKxQMDI/T+OEHNHSP\n0NQzSl3HMF1D43TYxskwKulwRkhbXonf7ebdf/93BhsbsXd24h4ZkVq/MrmcRKuV4o0b2fWP/zhD\nC3+uEASBxpNnCfR2YNTIscV9mwdGXRw43c65FhujTi8L81I42TrCotwUTFqx9eu2j+JZsArtZQqI\nar1exEvU1DDS3o5MoWDS4aBi716KNmxAnZHDaFM9BTEnH7/2LmMtTRSWFlJ80y1znxulkuylSylY\ns0bUPAB6qquxNYgT2ODkJIakpDlljUFcn1VaLaOdnTgHB0XDist2r6b0dDyjo5JtriCT0X7sGIMN\nDdJGLXGa4Q+I+eVyAG84GGQozhPXmExSp+56IhQI8Okf/oDf7UaQyRgUP+eyJ5988rqlN//apPyU\nIJNpMsvKyFy8mFX33jtvJVvzxhsMNjQwOTYmoW+vhaSt0mqJRiI0f/wx/XV1+F0uFGo1Ib8frdlM\n2a5d0sm1ZGWhs1g48qtfMWGzzcntBfFLDPl8eOx28Ybs6aG3poaxri5GOzslLef2Tz+lv64OtcGA\n1mQiITMTtdGIzmKh8p57SC8tJX/1aknxKxqNMmGzoTGZsDU0SLOUgMdDWmkpn/ziFxz6+c8lPdfp\n4Xe7cQ4NodLpUKjV9J0/TzQcRmsyUbxxI8kFBTgHB4kEg2IFGwxiSEoSdbsLC1Gq1ViysyXt2rGu\nLqldGw2HEWQyDMnJ1L39Nk0HDzLY1ERyfj43/+QXHKnp4pnXq2h1hGhwyRnwhNlXkcnC3BTKi9JQ\nKxVUTeiw3vl5Wo8ckToD3okJkgsKZhVi5qws3MPDDLe1QSzGUGOjaFGZnk7I78c9PIxreBidxSK6\nufT3Mzk6SmpxMWW7djExOCgWRoJALBoluaCA/ro6ZAqFKBeoUrFwxw6W7NmDJTubvpoa6bW1JtOM\nne1UpJWUiO40Ph9ao1Eyi5gS89BZLLOeM1+0Hj3KD9aupXT7dnrOnaP77FkmHQ5JgGGuom7KSnKw\noYGe6mqRz3z8LG0DDuSCSHeyT0wSi4lc5GAogkmvltDY4WiUE/V9nGka4Nv3bWL/lpkqcxqVApNe\nw+mmfkqyk+m3u6RuhyDA8uIMAqEIn5zv4kLnsNRCjsViM7x1AQ73TPLG6U56ugcx61Usyk3FoBOL\nvfdOtnG6uZ++YSdjbh/ZyQncvmkhB0634/IGEARw+4KU5V/qBg05PNzxr6+ysjTziiYKUzHh8dMX\nV6aTCTL2LkqktrmPiUCUJYkyYrEYL7cFOGcohX1fJufeh0gqq7jKUS9F48GDHPiP/2CwsZHA5CSG\n5GSxwxeLoVCrJW759YQqLZODv3+eNJ1AKBKl0zYuIddjMYhqdJhWbqD2rbc498orCDKZOBuNxVh2\n220zjiXIZLz48MOM9/dL+u3zReKiJXzy2rsMtHfyYeMIfzrWjs02woWOYbwBkWNt1KkptqbwYV0/\na4rE9SHLrKKqrof0tTOBU5FQiPQFCwh6veiTktAYjdJcVmM08osbb0RbWErpw39H7oIixrq7kHud\n5Nxw0zUXt36Xi8E4uwLE8eV8hVAkHKbqv/8b7/i4WFQ7HLNGfwq1mle+/nUGm5rIXrqUxNxcLr73\nnsSYUKjVknHMlSIhPZ3EnBzMWVks2rnzqkplc77fYJD2Y8cY6+5mfGBA1BuPRNKefPLJ625hX3f7\nWhCE7YA5b9Uq9n3ve+SvXn3FJOuLzwxBnJdGw+FZIuLzRV9NDf0XLoitWIOBO3/yE7RmMzqLZdZO\nOKe8nM/+9reS4f1cvFVZXGB9vK+PoM9Hf10dbrudBdu2Md7fz8TAAKOdnZIGt62hge2PPYZKp5vz\nJonFYkTCYc698gr2eHWZOu3GVmo06C0W9v/sZzP4e1PhHBzkxHPPEQ4EMKSksPYLX2DVvfdi7+jA\nmJpK5uLFjA8McPQ3v8HvdqPS6UgrKcG6fLl04cgUihmgFHXcoHvKi1lnsaBPSqK/rk5q/059PkNy\nMllrNrDjRz8iMDnJ+UfuRVZikEQZwpEoviwx0U1PwIJMNqvjAWKbesVdd3HhnXcY8XjQms3EgPN/\n/jPO4eEZxcOEzUbQ60VrMuHo7SUhPZ31D4jSg8efeUZUElMo0CUmShWyXKUiY9EijCkp6MxmTOnp\nuIaGkCkUUkE1FbbGRgYbG7FkZ1O6bRtpJSWcePZZQoEAAxcvEotGGWpuZtntt1/TjQviIvL1Dz6g\n9cgRnENDDDU3k1JQQNqCBWx55BEJOHR5pJWUYEpPJxoKobNY0Cjl3LQyn/YBkdqXnWpCo1RSlp9G\ncXYi424/2SkmBkZdPP9BLZWlmViMWk419nO8vo8NS6wzBDImPH7e+rSF7csLyE4xUds+RCQaRa1U\nEIlGqW61EQpHcbj8aFVKQuEIbq/ITzYb1NR32THqVOQicLBjiFa/h6FRF2MuH/ffWI5zMsDB6g46\nbONADINGhVwuEAxHpDY3MONnEGfWTz9+K4WZ17YDXZSbwqQ/iH3CS1FWInnpZm53jvCbbh+vatOZ\nDEPu3z1BQuYlK75IOHzNuxpbfT3hYBBBJiMhI4PgNJcxyzzzwquF3mxGu2gZjs6zJOpVZCRdugYy\nk410KFQ0fPghI21tGJKTcQ4OYlywgIK1a+c8ntpguKbPY0xNJWHdNuyv/zehmIqgSkfruJeQP4Ig\n85GPmT67k23L8nnuQA0dtnTsE17yM8yEba3ScfweD6f++EcRmLlwIcn5+Qw1NZGQni5quMeT5jcO\nHRJd6eRyslauovwrj1L34gs8f89+1t12C2Vf+uqsHf7ljlKZixfjczoZ6+4mtbh4Th0I6bmRiLSW\nAzO+q6lQqtXc86tfMT4wQMHq1Rz+9a8Z7exEplQS9PlYdh2g2pTCwiu+n/mi4cMPqXn9dWKISPoJ\nmw2FUklaSQm2ixeVgiA8HIvFfnM9x/xrZspfA7jr5z+/ogLKVBRv3Mj5118nGg5TtGEDcqUSW0OD\niFZLTGTxTTcRnJzE53KJ+rLTEmkkFJJI6iG/n9Hubhbv3EkkHBaRcAkJUnLqv3ABW0MDFbfdxqdP\nP814Xx8phYVU3nPPrIs86PPR/umnDLW0EAkGURsMFKxdizYhAfc0AEg4EMDncs1ZCU7YbJx58UVc\nIyO4R0ZIzssjGg4Ti0RYessteEZHyamoIBwM8uIjj3DTP//zrGP0X7ggScTZ29t554kn0BgM5K9Z\nI+3CPXY7GqMRv9stCrur1WQsXMjKu+5iYmCA9NJSEbke59b2X7hALBqVFKvyV69GJpOJZPtwGJlC\ngUKp5PhzzzFQX0/+mjUIgoDGYCDltvtoq3+L4lTxnJ7sdpL39XsA0RWq9u23cQ4MsHjnznkr3J6z\nZ0XFts42Rr1eccfcUI8pO4fBhgZSiopIyssjp6JihlBKYJoWeuvRo7RWVbHjG9+g/NZbxdlWXx+J\n04wm5EolG770Jcb7+9FZLLPa5NV//jPEYtjq61FqteSUl7PxK1+hr6aGaDgsJfqRtrZrTsqOvj7J\nE3oKhBL0+aTuy/SkPJ2zLggCK/fvp7+2lgmbDXVGFgcv2liQbiA/w8Kq0izKC9MJhiP0213kpiWQ\nYtbzp4N1KOVyJn0hJv0hCbR1pLabL0yTVCzKSuT9H1wyXbltYyn28UkykoyMucTFTKmQUZKdCIIg\nGUPUdQzhmgyQatFLybBAH8WnUOKcDOALhPEHw/zlWBNjLq+UdAWZQCAYQSaIXOojtd2ASM+ZHkqF\nnJ4hURZ0uhAJwPC4h1Gnl5yUS6YcIp1n5ggiNUGL1Rik8Mn/mvH/frebk3/8Ix67nYxFiyioXEn/\nkYOUff6heRG5uStXUn/ggNSu1MfFKIxpaWTOI816tVBqNOz53vc5+tiDrMsTrSmnZspFWYk0B4wS\nCLRo/XrGenpYfscdLJ6HIvSZH/9YUou6WmQsLefCS1GUsQBJJWUk5+czUFuDr6eFwsxETjfb+LjR\nTkZaIh9XiwCy+q4RFIsrpWN0nzkj3YdDTU1Yly+n8p57mHQ4yFy8WEq0Qa+XPz74IPt/9jNCXrFd\nXPHZ+yneuZvm7zxC/Y/tLPuX/wBEOtOpF16Q7BOnU0gvp3yCmLy98Y7T1H2pVKvRWSw0HzqEMTWV\nNXMILoE4uz778ss0f/IJtgsXUGq14q5/4ULyKitnPHa0qwu/2016aekVhZmuNXprajjy61+LtN5A\ngKGmJgrWrROBv16vqMEBDwL/ryflLRqT6ZoSMkDGwoXs+OY3iYRCaE0mfE4n519/nVg0iqO3F8/Y\nGE5bnJ+YlUXOsmXozWZSi4rILi/Hkp1NLBYjISNDclU6/uyzOG02lBoNaz//eRIyMmj86CMO/fzn\npBQWSjJv9o4ObA0Ns3SJzZmZhAMB0Qkm7jySV1mJzmzGunw5wy0tRCMRCdgzV7QcPkzA40GhVDLR\n14c5MxOFSoUhOZncFSukx9kaG+mrraXj+HF6zp7Funy5BFCbfuzxvj6S44CprlOnKFy3Dq3JhEqv\nJ62kBGNqKgP19TQfOoRCpaL81ltnoMVHu7pFYQ1hAAAgAElEQVTorxMxBYIgkJCRMYOnvfGhh/j4\nZz+TbDNPPPss7ceOkfbhh5jjjy3edyeHL5zDGhpFrVTQp0xiQdwgvOP4cdQ6HanFxdg7O3END0s7\n9KHmZhz9/ajlMrrffQONY4BUnRz5ii34B/twD9oIejz4fH5kcUWvjH37iEYiTI6NkWi1zmg97/nO\nd9g6Pj7jppqrCJgyMb88vOPjEj8RkFR7TKmplGzejK2+XqI4zfX8+aLlk09479//nbt+/nM8Y2N4\n7HYSMjLQms3SbCwWi1H31lv01dVhSktj9ec+h8ZgIBqJoIq3yy1WK56Aj4yECbYsFp8XDEV489Nm\nnJN+BARSEnREo1BiFSk2jd12tColatXs2WzvsJMfvnycx+9ejzUtgSSTDqNWzZHabravyMeamkDv\niBNrmpnFeSlUXRAFYcKRKOHIpd2t2aBGIRfoGppApZCjUsg4Xt+LxxfAbNDGFaVExLggwBvHmrhl\n7YIZBcLl8ZNXT/Dw3soZSbnf7uL9U23EiKFWKvjM5kVzWo8CdA05GXf5cHS0k1h4CV3bevQovadO\nEnRNMHj8CKaPDaRq1QTuuIfx/n4MSUmzaE3FGzdy+w9/iO3iReRqNbnLl1/RGe5aw5SWxsp/fIrA\n6z9Cq1ZSlp9KIBRmYNSNTxmkaONGycd8zX33zcuNjYRCnHz+eV557DF+4XIhk8vFNTI+4rkc/FWy\neTO2JRXgcbLzBz/B0dvLkj178NScZHmogc3luax95Gl++dhu6tqHQIBIJIpz+JIa1+XzVaVaPWsO\nC6IYR8snn/D+d7+L1qBHe+EoygUVbPz+z0l/6HFOP/4wi/72X1Dr9bRVVUl0wq7TpzGlptJbU4NC\npWLJzTdLcr3OwUHajx8X6Z1qNWqDQVzP09MlQafs8nJx/BSX4u06c4aJgQESMjIwpqZiycnhL9/6\nFsWbNpFotTLa3Y1CrWbRZQpiXadPU3/gACCO2dZ/6UvzFm+ByUn66+rQGI1kXoYRmR7ukRGicY0C\nEP2ZLVYrcrmc8b4+TJmZuGy2a6v4p8U1J+U4DepvALMlJ4eQ339NKi0gzoeJAzGCPp/UWnXb7QzU\n15NotSJXKjn8i1+gMZmwZGezePduxnt7kSsUqA0GijdsIG/VKhw9PZIDU2BykhPPP0/BmjVseeQR\nspYs4fQLLzDa1UXWkiXinPayiqjp449pPXIEhVotWoUpFGQuXizJN6YWFbH10Ufxu1yYs7LmVXmZ\nupjlSiU5y5aRvXQpprQ0ii4Dc2UuWsRdP/85LYcPAyKVZ+ujj6K3WLAuXy6S9AcHMaWlSVQqmUIh\nve+04mKKNmzg/e99D3/c4an3/HkK161Dl5goXViXf87Lf1+4YweF69cTCYc58+KLdJ89S2pRERqj\nkfFpghyGVVto+/RpyqxJ6PxOiXd7uWrc1Hdoa2jg7CuvMHjwXVLlQb65bxnVvjTOtoFtqJN/27uY\n/5a7RYAQMYhGGKg5j3dinC2PPMLhH/8Q9fggp777HTSlS1lw4066z5y5InVsSmxGazLNuYCkFBZi\nSEnBY7ej0ulm7ISVGo2I8G9sxJCUdF1zxG1f/7oksVl2000EJicJer3ibD9+L4x1d0viIq6hITpP\nnmTRjh2Eg0H0iYlScZFRuoCXf/9rNIQJBMOkWQw4J0XN6xii8MuqhVmS6lZeupkEgxq5TMbGpTM/\ns0wmoNcoZ3j8qpRybqwU23E3rS4mHImikMviLk9Ojl3owesPYTFqCATDZCab2LGiANuYG68/RG37\nEKFIlPYBB25vkIW5yfSNqMhMMmDQqlGr5PiDYVr6RiWtaxB3YvaJSUkE5ZOffX6GgA2ICG63L4A+\nLrs56vTOm5Tz0xP4RpqJV3/1BIEvPM7IkQ/A62as9ix5oza0aiUyjcDWgiw+tQVpeO45vA4HgkxG\n5T33zOKyWysqsFZUMHDxIt1nzlwxKfs9Hqnwutpal1GxnMa3MlgR89AzPMHBc52MuXycGTjJYleI\n1Z/9LIlW67zzyqHmZqpfew3X8DBbH32USJxdMNXx0SYksPErX2G8t1cckxUVIQgCuckGAvm5op53\nvAUfXLmS/7j/Lu4o0vK5z2ylumuMzv4xLEYNI3ITu396acyZv3o1ntFRnDYbmWVlc95PAIM15zEa\ndagNBiKxGH0jLlJSHLS8+gIL9t/Hmh/+muCkB7VejzwustN/4QJBr5eRtjbpPqt7+23WP/AA4WCQ\nk3F6al9tLeasLNKKi+k+e5byW26ROmdT629wcpK+ujrq33+fiYEBbA0N5K1ZQ9bixTzZ1ISjp4fz\nb7yBMc77Lt2+fdb5nYqJgQECbvecVK5oNMqJZ5+VOrSesbFZypBTkbVkCRlLlogWlNEoWWVlGJOT\nGWpuRq5UojWZcNlsCkEQ7o3FYi/OeZA54pqBXk899dROxKRsTcjIIOB2kzENwTqFHA56vVck3qv1\nemrffpvqP/8ZW0MDxpQUxvv6CExOiq3IhASCPh/jvb2YUlPRmc2odDo2PPigpHXdW12N3+Oh/v33\nGWpqYrSri7efeIKQz4cpLY2g10skFKLspptmzLydQ0PUvPGG+J7jspiGxESsy5ezYMsWCaij1GjQ\nJiRcUboxMScHt92OQqmkYt8+SrdtIzk/f1b19dwDD9Bw4MAlZHAsRubixdIFYcnKIr20VJy3xF2x\nym66acY5HGlrw97RQTgYxOd0YkxKwutyUff224y0tZGxaBGGpCQEmUzyXV54ww2zCgq5UolSrcaQ\nnEzjBx8QiURIys1l+2OPSYuFs7uTj57+IykJWsrTtBxvGSFtxSoS4sIf4WCQgrVrJV3x3poaWt/+\nC2VKF4l6JTmpCawoyaRDm0WhNYU12XrWl+Uglwl4PF4UPje5Rhn5UQd9tbWk2tu51ejg5UMX8YZj\nDFy8yFvf+Y5Y4EyzcJuKaCTC8Wefpfv0afpqatAYjbPoTXKFAuuyZaQvXEjJ5s1oExIIBQJ0nT7N\nhM1GUl4eSbm5cxpsXClOPPccde+8Q+nWrWiMRvSJiZjS0mYUQCPt7Vx4910Ck5NoExJIKSwkOS9P\n4l26hocxpqay/DN3Eo4JfPr2hxAOMuhwI5cLROPOR5WlWZTlpzLhCSCXCWwqz2Xb8gKWFqZJbeyp\nMOnV7FhZiFolJxAKz3BsmoqphC0TBNITDfQMO0kx6zBoVRRkWNi7vhSNSkGKWU+qRU91q1j4CoJA\nZrKRnZVF3FhZSGVpFm0DY0yVaEVZSZKTVWvfGFUXehhz+egaHCc3zcyLH19k0OGRRDXaBxwcrumi\ntc+BLxgiI9FITqqJ90+1cb5tEJ1aOYs+JQgCi5OU2I9+yE7zJJUGP6utJkLxXf7SwnSKshJ5o7qf\nEW9E3FHG3c3mAv8BnHj2Wd7+139l5+OPz/l3t93Osd/9jp7qagbq68lasuSqLc/RUQf9Rw5yuKab\n+q4R2mwThFU6lAajJIc7X5x79VUCbjdKrVY0rFm6lOHmZlxxLEo4EMDe0UHXqVMMxMdeqUVF/P7r\nf09R5UpS117qismVSkp234ojYyGB7hb+8Px79Aw7ybrpNvb9/o+Yp7EUZDIZ6aWlIuXvCrTQT370\nn4zahrB3dZGxcCFpazdiigWIyRSkV67FkJ6BMq6HYMnOpuXwYdwjI+LoqbcXfVISCrUapUZDXmUl\nAY+H9mPHiMViOG025HI5pvR0UWwpNxd9UhItR46Is/jUVJbfcQdDLS3019XRWlXF5NgYYb8fXUIC\ngiDw1ne+w57vfIe8ykoWzEGhmnQ4JMlgfWIiRRs2zLm++91uLrz7Ls6hIbG7pdVKG7agz4fbbkep\n0SCTy9EYjeSvXs3iXbtYsGULJZs3Y0hJYahJ5JWbMzOn5DllTz755CtXvHimxfW0ry1AOYjgIa/T\nSX9dHaXbtjHpcHDiueeIxp1VgHl3OmO9vTQcOIDbbhfFFYaHySorE+emLpckDj+dqK2Jo2ZBPKEr\n776bqt/9DqVGg0KjwdHdLQq1azSSxqnFaiVryZIZrQdBJhMruDjoSaZQkLtihbijOXWKommE8vnC\n0dtL7VtvEY1EKL/llquCA0q3bRM9jD0eQj4fKYWFM26KqVDpdJK/8FyRXFBANBIh5PeTuXQpY11d\ngFj1dZ89S8mmTdK/q0VyXh7Fmzbx7r/9G39/9OiMWagqOZVhVRL/p6qflZYo2oII0cjDaIxGKvbt\npe6HTxI8N8lE6QLM2TmklpQQcU+gMsrRa1SkxRfoSFoOvgk7EESpkLNv40Jy0y14/SEqSzPj2sAO\nSNbj8QVxtjWR5LMznGhl/89+xurPfnbO9+5zOmd4VQ82Nc0YF0yFXKmckazPvfKKJMzg6O1l5f79\nVz1Pl4dndHSGFvjlEfR6afr4Y5QaDRMDA5izsqT5mSAIVOzbx9Jbb5UWDLlGi2LlFtrPfUJJmp49\na0oYcniwGDUSOGrXqqu3V1v6Rnnwh2+zZ20JFqOG5cUZVJaKY4eatkHKC9Nn7KIVchlKhUxqXV/u\nDayQy1iQk0xL3ygCAmX5qTOAZTesKKB9wEFygo7FeZdGMFPuVgDRWAzXZIBBh1uyjASxDW/SqynJ\nScLrD7GzspCTDf2SX3PVhR6KshJnvN+p87eh6FIRpZDL2FKRRyQalQqZ3JJCBEs+Ib/4PhIyMqh9\n802GmptJzM1lxWc+I4FMd3zzm9w4T0IGUftg6ji+iQnsHR2iuYJSOW/bs+iW23n1p98n16hAEARc\n/hDaJA11b7w+L7BrKqY2N0PNzZx/7TXc8R2z9He9HqfNJiWSwcZGFu/cya6HHyJm62K8vw9L9qU1\nU20wkLFsOZOjd/GXRB9f/cOn7PvZ/7lm6tj06D97Gp0lkZX3fpbh1lbMWVmsuPNO6p/4W4R4MR/y\n+Xj9ntvZ/cvfkZCTS+n27TOwODKFAnmcPQEi9Sh94UKGmprIWrKEpNxcssvLpfvF3t6OLE75FGIx\nvBMTZC1ZwonnnhN1wuVyEQwWCmGKg9LkCsW8Ah8Ltm5Fn5iI3+0mp6Ji3g5oLP7anvjIa2qEZu/s\n5J0nnyQcCJBbWcm2r31N3LyZTGgXL5YwQEGfD31SEpNjY1iys1Hp9QQnJ7fP+WLzxPUk5VOAXqZQ\nYExJEZHF8VbcaHc3tvp6YtEoSfn5TNhs8ybl/poaCVgQDYVwDgxQeffdrHvgAeo/+IC+6mqyli5l\n+R130HzoECG/n5LNmwl6vZz+n//BOThI9tKllGzahHtkBEdvL3KVilueeILMRYtorarCY7cz0tpK\nVW8v+WvWSLqrptRUDMnJRCMR1AaDqJAVNzoPTgMaXSnq3n5brNICAQ794hdsffTReeUew8Eg+atX\ni1aMcd1rncUy7009XxRt2MDEwAAagwHrihWkFBRISRmu7CY1X2x99FEqbrttFmJ4YsBGxtqN9Jw7\nxztnq9nqC/HOvhvIWVZBx9EjXGgbpCAjgYnTR1nw3V/T//IzfH1jFmoZZCYZ0aqVhCNRsvpqKUvR\nAOIcTK1UzClWASKtJxSJYlDJGB61c+LZZ8mrrJQu9BmPNRpRxws4QRDmvQkvjymcweU/X0/c+Pd/\nf8W/+91uwoEAIZ+PcDDIaFcXbrsdS9YltPD07z6vslI02tAnkpeuJSvZOIM6ZBt14w2EyE1LuCLP\nNzlBx9qyHDTxeXNN2xDLijNQyGXIZTIe/PHbfP7GcjbH5S/VSjlbl+VT3zmCXquc83vZXJ7Lwtxk\nVAo5FuPMhTw/w0L+ZXSqCY+frqFxugYnSLPoyUw2kpVi5InPb5nxOItRw6DDjVEnFnApZv2MBCwT\nBK7EmBxzeTl+sY8YMQozEzkbN4NYVpxOKGst6z73AAP19RhTUpApFNIoYbilhZ7qasmbe7i1lY9+\n/GPu/OlPZ3Dtp2KGa5Ag0F9XJ3bZ9HrW3HffnGIjSrUa0nKQ+wfQGg1kZZeQYM1na7oc2fD8xRyI\nPuLnXnmFvpoaVn/ucyRkZuKdmGDVvffijoPZat98E0dPj2Q/GA4Gkcei7La4eecn38bys+dnHzgS\n5kB3gC+/9vpflZBjsRiv/M2X+cpHnzDe3894Xx+lN9yASqOh9B+/z+nf/5auM2cYPXyAXaUWOt/9\nC8se/ltKNm/GOz6OZ3SUxbt2kbtyJYJMJl3/U+BH5+Agar1+VivZPTKCIAjIlUpisRie0VGyyspY\nsmcPY93d0t/Wf+lLpBYV8Tevv05oDoT2VEwp7l0tJu120hcuxGO3I1epJMrkkV/+UqKEth09Stmu\nXbPESUAc1W76yldwxf0Yqn77W0a7ukyCIChjsdg1Ifiup31tAR7LLi8nY9EiijdulIBEF999l96a\nGnxOJ36nk3UPPDCv/KHf46H+vfcI+f0o1GoK161j33/8B1qTiewlS1h0443klJejUKlIX7CAzMWL\nkSkUHPj+98W5bCwmqXxN6ZAONTXhGhpi+2OPUbBmDQP19dKu3TU4OAPwFA4EiEWjWHJyCHg86BMT\nMaalsWTPnivOjWKxGD3nzlH/wQeEAwFsjY14RkbwTUyIXOY5SPAD9fU8VVbG4l27SM7LmyF3eT2h\nUKmwLltG0YYNpBQWok9MJBqJEJicJK2khAVbt153oj/70kv0VFdL1mcyuVycV9fU0HnyJH6PB+fI\nKFGNnsV5yexICvL6uV7UWbnYxz2M9PRR88orfHGpkYU5SSSatOKO1xPg/dNtDA+PYZ+YpCgrUTIo\nGHV6JQELk1ZNgkHDhzU9/OCV00QQCIfCJMiCDHb1kpiZiXXNulnvu+v0afpra3GPjFCyZQuLduy4\npnPqttulYjC7ouKadbOnx0tf+xq1b75J+S230FNdTVtVFQGPRxpNKHU6bPX1dJ89iyAIJOXmiu3f\nOYoLEIF+ORUVNLz7DncuneksVN81wsfnO+kaHGdg1M2CnOR5P6dMEBhyeOgaGmdozCO2u5eKr52W\naGDv+lJeOVxPepKBcDjK61WNtPU5KMlJYsMSq/T9TA9BEDBoVbN20fPFe6dacbh9GLTKuL3kElRK\nBd97oYqTjX0SMjsr2YQAmA1aNi61oteoSE7QMTIuKpBtXJo7qwiY8TonWxmZ8DDpD3H8Yq/Eoz7d\nPsqSf/weFquVlIICTGlpuO32KREHQMSLTH1X4/39nHrhBcr37p21VgV9PozJyeiTk1Hp9eSUl0su\nQZFQSDJQmSsKd+2h7pMqNOZE7n3tbdxDQ+w2u1A4hqgbnERrSZyTG6/SaslYuBBHby/OoSE8IyMU\nrFkjiW9M/V2QyRhqacHv8TBw4QJEwkQcdqwqP6M5S9EnzVRWTCwqIfPWuzFmXr9CFUB/bQ3WlZUM\nd3Ry+k9/4o3HH8eQlETB2rV88sPvM/HRG1T98QWKQiPsX2WluqaFtD2fQaFUklVWRv6qVViys5HJ\nZLOuX0EQ0BiNc667Kr0eW1xJUWexsPCGG5ArlQw2NOAdH0ebkEBqURGJeXm0HzvGy48+yoX33mPV\nPff8VZ9Tek8yGW1VVSjUarRmMyWbNmFITqb+wAFJfwKg8u670ZpM+D0e+mprCXq90jhMJpeLO3el\nEkEQaBBtfBuffPLJ+rlfdWZcz075e4A0v5yu0Rr0esldsQKf04nGaLzi7iWnooINDz1EwwcfYExN\n5eYnnrgqWbutqorx/n4RNNDejs5iQZuQwKp77wVEsML0doQhOVlC/10u/1m4fj2T4+O4h4cpu+km\nspYsQanVXjWp9VRXc/G991BpNBItZurGtHd0YF2+fNZzUgoL+cahQ1fVu/5rYuH27Szcfl1dEbwT\nE1x87z3CgQAHf/pTFGo1Yb+fiYEByvfu5dM//EF0hYmrrFV+7j5Uej1CST4vvfMKQiwGMhnBYAh5\nKMKyHD2fXuilKCuRcy02atuHsE94icVipFr0THj8dNrGWRRvcVbV9dBqG+fw+S6e/+gCaqWcsvw0\nvrRjERe7LtGj7t9RzqT9HL3Hq8heu0H6biLhMI0HD6JQqzFnZuIaGrrmIqfittvIWLQIQSa7Krgr\nFAgw1t2NISlpxvWTV1lJJBzG3tl5SfK1uRmNyUTmokXIZDLW3H8/Y93d4vxeo0E9D3d5KrQJCXz+\nzff46FsP8vlpI+7pVoQDoy5q2gfJSjbN0KGe/thfvXmWVaVZ6DVKNCoFw+OTpJr1NPbY8QfD3Ldj\nKbf+y8uU5CThC4TYuDSX6lYbi/NSrjnxXin8QbEIVirkKOUyaWe/OD91RtJXyGVSa30qkhN0fGbz\n1SlJzkk/p5sGcHsD0hx7KlwaC5bLZqIZCxeSs2yZ2L62WmeMOazLl/OtEydmvUbzJ5/QVlWFXKWi\n8u67Kb/lFvweD40ffSSN1pTxHefAxYv4nE6yli6VePt6s5l9//M6sVgMmUxG3orlDLzwHr19IzS2\nvMDYgI31X/zinGukWq9nxf791N57L2G/n8LLMBVKjQZTerq04/W7XFg3b2bUs4C0s69ie/91UkpE\n6qVzcBDP6CgphYWzKJ3Dra20HDmCxmBg6S23XFHSM2fZcli2nA/+8z8xpaWx5v77JYpX61uvY9Kp\nUMrlRIMBYrEYW7MUXDh1gtx1s/Eg1xITNhv1Bw4gCALL77wTmVxOQnq6lLhVer0ERgv5/dS/9x5y\npVK0YryMZTMVkXCYSYcDndl8xa5iJBTi7MsvA6K+xpI9eySmzIo77yTg8eBzuai49VYsWVmEg0GO\nP/20lGuW3HzzLD2LlXffzSuPPQbwVeDlazkH15OUSwAsViu2hgZCfj8Fa9aQv3o1hevWcfH99zEk\nJ1O8aROO3l4aDx5ErlDMgMCDCMLZ9a1vceM//AOCIFzTohoJhbBkZxPweAh4POStWnXpiwkEEGQy\n8latkh6/7PbbaT16lGg4PAtcoVCpSM7PJ+z3Ew4Gr3n3OrXL0lksoph5HEgCzDtXHmxsFAuVqyzM\n/1/FhXfekXRezZmZ0pxrqLkZlV7PWE+PpCmu1GpJLS4WqWlLl9JmNuM/dZaeuotojEbkajmpFj2R\naIxYDC52iudHqZDRP+KSFs3pqFpvIMThiwPcuq6YjoFxSnISEQSB2vYhyR+3d9jJgVOf8I071/Da\nN/6WRbfvZ9W995JotSLIZChUKomCMFeFHQmH6a2uJhqNkrtihXQTymSyeQ1HApOTdBw/zlCcCjfU\n1IQxLQ2FUsmqe++Vvt/yvXtRqFQzkJww03FGl5DAxoceovPUKXRm8ywU6Fwhk8sx7bmXF957kX3Z\nMYw6NemJBgZGXUQiUbqGJpDLZMhlg+xYWTCrdWxNTeAf7lqH3elFqRDPYzAU4WRjH/XxYudEMMyD\nt1RSmG6ic1BcRMRjXl+HZb5YvTCbI7UitmN6O3zXqiJC4chVnz/pD/LhmQ4cbh+LclNYVzY7adW0\nDZFk0uL2BhgZn+RzNyxlMhCif8xD+RcenCVKJAgCFXv3wt69s47lc7n4wdq1fObHP5YoSiG/n7aq\nKkBUaGo9coSUggI0BgPLbr9dcpYKTk7y529+E+/4OIlWK93nzrH1kUek15++rllXr+H4LwUIhBDc\nIlVvYmBgRlIO+nzUvf02XoeDWCzG0ptvBkTRnct96i8X7dGYTDgmJvivE4OkRVsJOJ24jam0HPsU\nvcVCdnk5m77yFeleCQeDVP/5z0RCIZyIDnfT8RVBn0+irwLUvvQ/GEx6EnNzGW5pIeT3X5JETk7F\nsm4zCeEwrotHONPt5N0LNlbfOrPTEQmF6D1/HkEmw7p8+bzzXIDzr73GpEMU1Wn44INZxkKl27cT\n8vnwTkyQaLXSefIkINKcgl4v4WBwRuIN+nwcf+YZPHY72oQE1n/xi/N2cV3Dw7iGhlBqNCSkp0vv\nA5C8twVBkDQRvOPjUkIGcXN2eVI2paZOzb+vmXt5PUk5TW0w4LLZ8NjtpBQVUX/gAEn5+eRVVpJa\nXEw0EsGQlMSHP/qRNKOte+st1n9xtnPL9bRbizZsYLSrSwJmTV20IM6KfnHjjfzDp59KQC2VVjuv\nf6ejt5e6t94Snxun1VzLrCF76VL6amqIhEKYMzNZsX8/o52d6BMT56VVnHvlFRo/+miWnN7/XzHF\n9YvFRJSzMTUVS3Y2ruFhUQqysZH0BQvQWSyUbt8+4wIrvvUOim+9g1AggKOnhyO//CXjdYfYs6YI\nhVyGUadiwiPqNpv1GnLTzJKaVIfNwYYlVkbcITYtyUajUpCTapIWLp1GRVl+Kn0jTpKMWow6FWea\nB1A5nQxWn6ElM5O199+PTCaj8u67Ra62RjPjOpiK2r/8hd6aGoKTkww2NMwruTo9zr78MoONjfRU\nV6NUqwkFAqKLmFzOwZ/+lCW7d7N41y7+a/du0hYsoGLfPmyNjcSiUbH1f1kySC8tlSrsaw3r1h1E\nN23jf556nLuiY6woycCgVdE77CQSjSGTCcSI0TvinJWUg+EIgiDSolqHPWhT03nTaeb0J9UUJmnJ\nMinpHnaybXk+C3KSCYYj+INhVpRkSnaR/9uYUuCKxWIz5t8/ePFTekacPPP47MQ4PWrbh7A7xTXj\nYtcwhVmWWdrccplAokn0AgeB8qJ0THo1L7aHKPncbKe0uSIWF5TxuVws2LZtxuxYFncmmyr6VNM6\neJlxME/bsWM0HzrEWHc3nrExyRDG53TOacojCAKDARnD7UMMhRREmppYedddMx7T8sknEmK359w5\nVAYDvefOseqznyUSCs0oNizZ2VTs28dgYyPmrCz0SUlceOcd0tZuwnnobcqaP+XTmgGC5nS84+Mi\nQGxwUOLjR8PhGeIkU2A2QKJgRcNhCteto3jzZlxvPkdqmhHh7r/H0dtLe1WVNKr7ysEjDFy8SCgQ\nYMLeBLEoPZMCtyyeSc09G1c8BHH9LVy/nsaPPkKQySjbtWvGeQtNMxMKT3tvA/X1eOx2MsvKJEBs\nNBJh4OJF7O3tRKNRPvzP/2TZvn0zOo8RuaAAACAASURBVJPDLS144qDioeZmzrz4Imu/8IVZ8/WQ\n3y+qT07zYDZdhhu4HHugs1hE0HM8MU/fnIX8fnqqq0XwWV4eox0d1+zYdD1JOUttMFC+dy89585J\nF8qUFNpU9RCLxaSLGuaWR7ve0CYksPVrX5sl2wbizfLd9nbMWVnzPFuMwOQkbceOiaYE06qpKWWm\nq4UlO5utX/sakw4H5qwsFCoVxqs4Y935k5/8P/L5p2KkvZ3us2cZaWuTzDpW3nXXNQM4FmzbRvWf\n/0zI5yO1pIRFN96IITlZmpdZly0jITOTxTt3ztviVcY1gpdsWsce6ziJJrE1dtOqYs63DSKXCaxc\nkEkgFOHVww3EiDEyMYlWreTvbhfbh7FYjLZ+B0frugFYsyibUmsyKxdk4g+G8QVCvHGsiQqlnJq6\nM5xta0Z26gNSki1EIhESF61m4b2fnbPDMdTcTPfZs0RCIYZbW1mxf/+ckqDTwz0yQiSOQQiHQsjl\ncnxOJ5MOh8Sd1Fks3Pb97+McGqKvpobkggI6T5zAkJQkttDiNKz/TcjkclY8+SNe+bdvsT82Qqk1\nmbx0M0MOj+SHnJE4u9U4MOrihy8f5zNf/zI7fvOUBEJKv/0EVb/4KUffeYcF2Um09zsYsLtIs4hz\n5rlmyf+bsI26Od82iF6jZMMSK1q1kv1bF0tWkVeKy3fsc+3glxSkcbqpnwl3gK3L8zDp1fiDYYKF\nFdc8xmg5fFjaDSfn50u2iSB28SrvvpuWI0dQ6/VzinxMrW36pCTRVzwcxpSWNq+Gus/lIppqJbYg\njGZ4BL/HQ+2bb2LOypKuy+mJSGexiAAupRJzVtas3X8sFsNvH2bhjh0YU1JwxuecMrmcsM5ENBZh\nUYqWM11dCNZCERswTaRIpdNRsmULrUePotRoZvghtx87JmFxOk6cwNV0gS+uyWRg3M/3v/IFTEWl\nrH3gARbHXdJkcrm0oXFm/4p3v/042x/8wqz33HX6NO7hYbRmMxqjkYmBAWkXev6NN9j05S9Lj128\nc6e4aRIEyY2t9/x56t5+WzrWlq99DY3BQG9NjeioZTJhysjg31paZnUtp4ome0cH4/39CHI5h//r\nv9j81a9KHUy/282x3/9eZP/E+cbJ+fmSJe18MWU1OdjYiM5snrFmnnnpJYmpES8uNIIgCLHLBR/m\nOu7VHgAgCIIcILOsjAVbt4pGAg4H1mXLZjnzCIJAxsKFHH/mGRQqFTu/9a05jxkJh+mrrZVQcVdq\naUzFXLvrtqoq+mprueEb37jic8+/9hqjXV1EwmFJglObkDDvHGKu0CYkXNW/d3q8+e1vk1VWRuXd\nd1/zc+YLv9vN2ZdfxmO301dXR6LVSjQcpvPkSUq3bZvzOZFQCEdfHzqzGX1iImnFxex8/HFi0Sh7\n40WJd2KC/ro6woGAtPucrnIVCYWoP3AA98gI1hUrpMQTGu6XEjKIXNkpc3sAtzdIjEvXXyB0iS4n\nCAIlOUlkpxiputBLXccQ/mCYiqJ0fvjScQ5Wd/DQnhWMOb2kGJRY01SoBtq4bUkFCrmMwf5PeO9b\nJyl49F9IyJk5R5yS2QORFjLY2CghbucL6/LldJw4gSE5GbVeT0phITqLBVt9vbQrCHq9CDIZ4Thi\nPxqNihrj8XvM0dv7v07KIF7jK/71B/z5u/9E5XAzrb2jIn9ZrUOenEG12sqZUTlaRz8rzFG63THa\nTQv50eBvZlX2hevWkZCZicJpZ3uGwIDdTSAUprZ9kIW5ySzIuX671fkiGIpwsLpDolnJZALblxeg\nVSvx+MTCvd/uomd4gjSLYZbsZkVROg6Xj3GPj4XWFJLnsHqs6xjCqFNj1Klp63dgTUng4kQU6z99\nBs/o6DXZx451d0s/v/Xtb+N3ubhlGtj1cq/2yyN/9WqGWloAsXtWuH695K8+Vyg1GhJycvBMTBDw\n+kTUdCCAa3hYSsrFGzcy1t2N3+Vi5d13k5yXR8jvnxOLIggCztefJvvgH+iJaphcuIbFu3bRe/48\n4dJF3JA7iUzoRiYTaA/4uPEf/mHW+GzBli0UrV+PIJfPWFM1JhMhv5+hpqY4ENdPZFMuBiUsy9DS\n0N9NXyDMcGsrJZs3z5CSTcjK5tan/zSrOBrr6cE1NITbbsc1MkJORcWMjdDlrJfspUtFrE58xxr0\n+SQ1NBB3oFNeCEPNzQiCgEqnw+90Uv3qqxSuXz+j0EjOy6N8716O/OpXpBQW4h4eZrCxkaDPx9r7\n7xe1vltaJF93mUyGJSdnlkTnfKExGMifNjoFsXCazvCwWK1MiAJNS4GrWjleE/r6qaeeqgQeWnXv\nvSzZvZv81asl677LIxqNUvvWWxhTUzGlpREJh+dcrKpffZXOEycYbm3FMzY2L5rxanH6T3/i3Msv\ns+HLX75itdz08cdEgkFkMhmmtDQ2P/wwMrmcwYYG5CrVddu2XUscf/ppdBbLNUuSXikmx8fpPnOG\nUCAgzj3UaowpKSTn5+N3uWg8eFAi6wuCQCQc5sRzz9H+6af0nDtHQmYmhqQkZDIZ/Rcu8OSiRSy5\n+WaSrFbpe/SOjzMxMIAhbvgAYtHTeeoUfpeL4dZWMhcvZri1lbo/PovK4yA7xTSLUwqi0bpzMoDD\n7UOvUbFpaS4a1cwasK5jmKZeEYg0MOpCp1bSMeggPdGAXqtEpRDn1nKZDAFx4ZbLZBi1SpZZ4P13\nq1CkZWFMv8RHNqSkMNbVhcZolBDXIb//iu3k1KIiUoqKKNu9m5X791O6bRt5K1fid7uZdDjQJyWx\nZM8efn7DDdg7OwnFbTV1ZjNJViuCXM6CzZv/Kk/xuUIQBFLXbeHZ375EXX0nDcM+NBv3sO1nvyVp\n042kbNyB6YbbaCYZ5fY7SCiv5J0nnyS7vHyWc5fObMaSk0vnkUM8/84ZxiMKGnvH8PpDZCbqGXf7\n0aoV12SteKUIhCLUtl9Cp+o0Skqyk/jTwQv84b3zKOQy3jvZyqQ/RM/wBMkJuhkiKAq5jOLsJJYW\npJGRJHYDHG4fdR3DjLm8pJh1nG22Ud85wpjLR+/wBOMeH8d6vYwMDtNXUyO6sl0FxOd3u+k+c4bh\n1lYSrVa2PvooCVcQO7o8FGo1+atWUbRhAwVr1pCQkSFp648PDBAOBGYAV2VyOUl5eZJWQXJBAbqE\nBEq3bbs0g5bJmHQ4UOl0FKxeTcjv56dbt1K2e/ecrI7JST/Zri625OmR2zpwZC6iYv/dpJUtxVt1\ngB0VVlYsyMBoMqDfddcsKc2p93X5epmUl0fzoUPYjh/FNGmnu7OXkw392MbcjHtDhKylRCJRSS/h\nwrvvSkYWU3P0y4852tkp7ooFgYn+fqlNr9RqUarVLNm9e5bYlCCT4Roaouq3v6X1yBEgPnqLxdBZ\nLCzYuhW5QoF3YkKihmrNZqpfew19YuIsbe2EjAw0JhPjcZtXXUICCenphINBssrKiIRCEnUO/i9t\n7x0eV3Wt/3+m96IZSSONerdkNVu2bEu4424DpgcHMAQMIf0SEtIu5CbwTXITuAk3Fwj9EkLHBGMC\nbjTLvclFlm3JsnofaUaj6TPn98dIxxpLskV+ue/z8Dwy0sye2eecvfZe613vG/VHn0hYSBAEes6d\nY7i/H53FMu67jr3+ru5u3L29DLS3E/B4RonRpx599NED4974Ekw1KK8ErrnqnnvEk+VkaldCJCKm\nhyQSCcoR0+pLcXzLFiLhMP0XLnD2889x9/URn519WbZq99mz7H35ZZoOHIiyb3t66Dh5Elt+PoNt\nbdgvEQsZi3AwKO6Sc6qq8LndHHrrLVqPHqXjxAmy5syZNA0c9Plo2r8/qveclETfhQsM9/ejjYu7\n7EZg5g03/EsCMkSZmc6uLgLDw0ilUqyZmSQVFJBaVsbBN97A43DgaGlBoVZjSUvD1dUl3tCjp7nk\nEdF9QRDQmM3kLViAUqtFqdVSv3MnnoEB/G43vY2N4o3dWVcnynBKiBIqaj/4AKdjkIGm8+jVCuzx\n41OqEomE7OQ4SrITKc9JmpDh29DuoL13SEyj2q0Gzrb1gyRKEMtJsRCORPWWq6ankWTREwyF2bLn\nDB8faMAu9dDX0U3ykotpRr3ViiU9nf7mZpRaLTqLBVdXF7b8/MuyTDUmExqjEZk8KvwgkUpJLS0V\niYwKtRqFWh0NcqmpGBMTWfWTn2DNyoq2qo3olv+rIEQiHPvoY7yCFJneiH94mLJ162LIRObMLDRx\ncbi6u/nyuecoXbt2wo2BKTML27pbiM/MZNgbILuqmjOnGzl6opHWnkHael1MS4///5XOVshlhMIR\nuh1uXB4/6TYTyVYDhZnx+AIhuhxu2vtchMIRzHo1cQY1duvE1yMQDPP3mjO8+I8jnGntZ9gXJBSO\ncL5zgLY+FwMuLwLgVujxmaPZAa3ZjLOjg6w5cy7rQqeNi+PcSOpWrlIhRCKTnoq8LhdH33uPpgMH\n0JjNMTVFiUTCuS+/pGH3biKhEBcOHuTE1q1cOHgQ5SXe3hqjkey5c8mZNw9rZiaFV18ds87VbtlC\ne20tnoEBuk6fJqe6GrXJRM68eRPes9biMs65pThO1VKepOHLfSfRl8wiLjWN+k8/pTwOlHIZQ8M+\nBovmT+p1fynkSiWNOz4h29tGv8PFgNuPRiWn0+FGyJ/J+if/yJzbbycxNzdqmyoIDPX2YkpKmtQj\nQG000nX6ND3nzuFoaUGl1+N1uciaM4el3/veODW+UZz8xz8uyim73VTceCPJRUUUXn21uE5b0tPR\nWa2Y7XaKV61iybe/TU5VFZFwmMY9e+ioq0NnsaDUaIjPzIzaxXZ0RFsVpVJsBQXRrJjZjCEhAblK\nRfa8eZOak4za37afOEHA44nZANZu2cKJDz/kwsGDSOXyaIlTrWawtRW91cqFgwcBGh599NGPr3gd\nrvQHI8gHYhjOXpeL4f5+THZ7jGWXVCajZM0aTnz0UdQlJDV1HFkBojZXLYcP03fhAobERLxOJ/W7\ndol9Zo7WVloOH0Y7IokmlUo59v77IlmpdssWhEiE3S+8gFKrpXjVKqavXDnpzZG/cKFoOG9KTmbP\nSy/RHnXxYKCtjY66OvImkHUE2Pfqq6PpB05+9JEY5NJmzIiyOydA0Ofj0aIibv3v/45xSZkI7v7+\naPvMZVrDJBIJs2+9Nerfq9NddDlqaIgxXxhNw2hMphjSylhCi0qno2zdOvFhdXZ2Ro3NpVKUWi2h\nUfvLSAS5Ws2FgwcJ+XyUrFkTrZ0JAqH+HuK0SrxXqBeqFBPfYu19Luou9NLY4UCrVrCsIofSHBsv\nfHSEhvYB7rumgrIcG83dTvQaJdPSo8HmhY+OsuNwI75ACL1GSd4MCZc2o6UUF+NcsIDG0ZYXiWTC\n08JUMHaj1nL4MOFQiPisLFR6vUiU+7+ATKEgqagIZ2cnMoUC60hKcyLHsqSCAh4eYaFOBrVOR8na\ntSJhUhmfRPeZXohE6B30sLg8i3Tb1EszE2FuUSrBUJiTF3o41+bA4fKRYNJwvKGLjKQ4QuEIHl8Q\npVxGVtLkPtZn2/pp73cRCIbx+UO09jiJM2iQy2SUZtsY9gU40TKAqmQOwcGL3Q0Tad1fiqDPJ9Z/\nv/jLXxjs6GDRAw9M+Lcntm6leyRVfejNN1nxox+Jaeq22lrO7NoFRMlEvjFayhcOHZpwM25KTp7w\n5OsbY28b9PlQaDSUrVt32fUgZdEyPtqyheYzp1helEr9kw8xcPVtyOcuo/PUeyRbdOhkAm0jxisT\nwd3XR/e5c5iSk4nPzKTj2FFsQx2YLXqaOvox61XIpBKGQlJKFyyk9ehRGvfsYc9LL3HVvfeKGQLp\nZawmVTodC+6/H5lSGdWTHnOavty1ilEDk8mwZGSMS8NLJJIYXfvtTzxB3/nzlKxdS9O+fUD0ULHk\nu99FJpeTWlbG8gcf5MKBA2jj4sgfo2ltnz6dpMJCGmtqOLp5M+kzZ46THR3rBd1RV0fJmjVAlHDW\ncuQIEN1Mf/bnP5NUUBAljPr9GC5mAjIn/cJjMNWgnAYQP1JEH+zoYM/LLxMOBNDHx3PVPffEtKcE\nvF4cLS1RbeiRizHvEuutGevXo4+Pp3HvXoZ6euiUSEgeSWH73G72vfrqRT9NQYgRABlFx8mTMOJp\n3NvYeMWe0LEqPPrERGQyGeFwVCt3shNvJBwWAzJA67FjYrag4+TJSYOyEIkw86abrrhoH3v/fVqP\nHUMqlzPrllvQxcWh0GgmfCAlEsm4tIo1MxNLRgaO5mZUer2YlVDpdMy7805ajx5Fa7HE1FVPbN3K\nCxs28Ce3G8/AAHteeomQ30/P2bNkVFQw4/rrGR4YYO/LL3PyH/9AZTCQVl4uunUFms+REHBgNOso\nyZ566m8sjjd2I5FCUWYCgiAwe5odiUTC4/deTSAYxqBV8saukwy6fbT3DZFjj+PWJcVc6BokGIrW\nLd3eIMN9vRN6Z+ctXIhvaAh3Xx+ZlZVT1rkezd6o9PpxabX2U6fInDWL9Jkzyays/JfYv7n7+qLi\n9RNwFebdfjtSqZSQ30/ajBmoDQbaRuzpxoqfdNTV8ZebbmLT229PusuPRCLU79xJwOMh6PUSn5OD\nvyG6yARCYdSqydPXbm+AiCBgHBE3ael2svtEC1KphEXlmTG9031OD9KRZ6nf5aFnwE1DxwC9Tg8C\nkJJgZG5RKhbj5OREhVyKQiZFp1HS1DlAIBTGpFMzIy+Jpq5BpDI5OeuuJ2PZavQJCbi6ugh4POTN\nn39FbooxMZG0GTNoPXqUqrvuIqOigo5Tp8R2l7EYPQBAlOAVDoXE9x9bF5VIpTGbvskOBpMhp7qa\nwfZ2wsEgGbNmIZXJeKSwkG/87W+TimGc3r4dmS2VvccP4wtc4OvLSvl059/Qb3qUzTW7+JpyiKQ4\nHXuP7Mc+gfKU1+lk9/PPR9nXEgmq5lOsS5OydoYRiaSEBWXpbN13ju6BYdYXJPPptvcZqliAq6sL\nTVwcww4HcSkpZM6ejS0vj7Offx71L09Lo3jVqpjroFCpmLNhA86ODpoPHyYhJ4fpK1bgc7snbRed\ntnQpIb8/Kq9ZWho9Ndvtl20vHZsZG4XP5SLo9SIbyThYMzIm1fhu2L1b3GiNBvOxmQqNycSpjz8m\nEg5TOMaFSiqTobNYGO7vJ+j3ExnhtChUKgw2GyqtdrRGPl4GbgJMNSibAOQjO6L2EyfEgOnu68PR\n0iIe5Yd6ezm9fTuDbW34PR76m5uRK5XjFk6pTMZAWxvhYBCPw4FcqRQXGp/LFWNwPerYUX7ddRz/\n8EOkMlm0qd/ppGDxYoI+X9Qc4yvIyOVUVdFy5AiegQH08fGT9rBKZTJs+fl0n40agyePWfRMk6Re\nIFp7WvHQQyL7byJ4XS6xlhEJhfjsz39GazYjlcupuOmmScX0x0ImlzPvzjvxDg5G+4fHZCTMdvuE\n6aGikRYyhUZD7+HDUeF1rRZjcjJpM2eSWlrKyY8/xut0IpXJGO7vJzA8jCk5mf4LF1ibGGBmxWyU\nctmE9eTLYWDIi8vjjzlBK+VysTVnz8lWfIEQC8oy8PiDnO8YYNgXIByOsOPweaZnJnChcwC3L0Cc\nXs3c7HiOPvEYZd97OCZIKlQqZt5ww1f6bIIgsP+116L1nxGt6rG2nz8Z2X3/KxAOBjmyeTOdJ08i\nkckou+aacdwLS3o6yx58kKDPh8ZoZM/LL4slmILFi8WNqsZopGjFCvwj6kKJeXnjNnWO5mZ6Gxqi\n2RGzGVdbK9PTzEiA7OQ44o2Tn8re+ryOmblJ5KZa+OsJJ9u2R41n/L4gLb1D/OiWi7rO6TYTPYNR\n8o4tTk+iWYs/GOZkUw+SkbJEe98QhRmTB678VCt9Tg8IIEFCWqIRrVqBXqNidlkuh+LLWfC9h/8p\ndTyA8muvpWjZMv76zW/SfeYMKcXF5F51VcxCC9E5PvTmm4T8fgoWL47JCKaVl9Ny5AhepxNTcjLl\n69eLFoSXM56AqIlL69GjGG02ilaswJaXx9X/9m+E/H60ZjORSISHdu++7PMf9PmQSqWoS2Zz9MRB\nrhn2szJHz2MPfZOEqgW8E0pgaegc8uNfEoncNY4kO9jRQX/DOULDbuLyp6Ee7KZ47kXeRWqCifvW\nXWQf73jtEJFIBFdPD3qrFW1cHDqLhaLly+ltbOTMp58iCAKDHR0YEhPJqqxkxy9/ztWP/BqI3qPX\n/upXUVe8ri4OvPYaQa+XtPJyyq+7btz3U2o0zLzhBtz9/Xz+9NN0njqFRCpl9c9/PulavfD++8X5\n7W9uBkEgITf3ioe1UYxavEL0+fQ6oy55zQcPojYaQSpFZ7XidTrpPncupotn7u2307B7NxKpFH18\nvEhgy62ujtpL/vSnAJdXyRrBVIOyYWxAjentG9kljGLU0k8TF4ff4xElLSfawTbt349MocBgsyGR\nSsW0ktFmE09/UrmctJHFypScTNacOWhMJizp6ZSuW8fvFy4ks7KS2V9RXq23oQG1Xo/WZGLWrbde\nlug165Zb6Dx9GrlKhTUjg6b9+4mEw2TNmYOjtRWlRjOultfX1MQv8vL4t08/ndT6a7ROGRwRMnF1\nd0cfyhFW9VSCMkQZg1+FqDbU20tvQwO51dVYMzKiTPgjR4iEw1w4cACd1UpjTQ3tJ04QN1KfjktP\nZ+YNN3Duyf9gWdZ4LeZBt48zrX0YNCoKMyaWhGzuHmTbwUYigoBZp6Yg1YovGKY02yYG6WMNXbg8\nftbOy8cWp4/6wBLtT3UO+9m4spz8NCst3U7Kc6PuQB8e3c9gW5voR/3PwudyXVSqEwTajh2LCcrP\n3HADM2+4gcrbbsMzOEjT/v1Rcs68eWIqbyoYPaXUbtmCWq8ntbyc83v3TkiIlCuVyEes8MYyh7vO\nnBGDsik5mVm33MKJrVuj4gZxcSy4//6YIKLS62N7MKVBNiwrJRAKk2MfbwAxFnUX+jAaDfzX+4e5\nZdkM7DopAaUGuUzPoTPt/PqvX7J2bh6l2TYq8u3Em7T4AiFy7Bb+95NavjzRjC1OTyQioFbKsVxG\nRhOiGaHq4nSKsxJ5+7M6wiNrStuwQHj+NVTcNLFZyVeBRCaj9cgRFBoNKcXF9DY2jgvKCdnZrPjR\njwiHQjFzCRfbNL0uV3QjLZNN2DcP0RN3Q00NELVirf3gAxAEscaav3AhSo1GPFRIpVJ6zp1DZ7FM\nSh7MX7iQgbY2DPZUSq67nkM7X2Z1nI6frczhVNdZ6ufeRk1PCufq/oHsyy/IW7go5vUmu53Q2Vqs\n+OhtOcfiWZdvKZ1r1/Da3/6X4bCEgZYWTCY9zrpaMsuKkWgNRMJh6j/YTOai6Ak3FAiw54UX8Taf\nZ92LF10LZXI55/fsEXWqW48dI6e6elx2Iej303zwIO0nT9J67Jh4MPv86ae5+cknJ4wnHz3+OHte\nfDHaImu34x8eFomvk8HV3U1vYyNxqamkzZhBZ10d4WBQrFd/+tRT+IeHcff24nO7ox7wDgeegQH2\n/+1vVG/cCEQ5DaPXP7e6mtZjx5CrVJzft4/ze/eOih6Nrz1NgKmuJJqx7VXpM2YQCYdxdnZinz49\n5sYZtdyTymTEZ2VRsGTJpO5L1vR0XF1dUVZiVpaYVpDKZMy74w6cnZ2ojUY0RiPhYJDdL7wgqid5\nnU5yq6u5/ne/I7Wk5CtpGQ/19nLio48Ih0I0fPEFx/7+d4pXrWLxd74jPnyCIFC3bRt958+TmJfH\ntKVLkUgkDA8M0Hb8OB6Hg9M7dkQXY4lk3EnHaLNx/3vvTShaPgq5Uknlhg001tSIKevRdp6pkjP+\nGdTv3Mm7Dz3EvDvvxJqRQenatTg7O9HFxaHUajn63ntoR372DA6y9pFHsBUUcOxHm5ifAAp5LPkk\nEIySr0b7Uf3BEDPyxtfOGtodREbuo8FhH4tnZI2TS/zR1y7eK2vn5aNRyqlr7sXl8dPa4+TVbbWs\nrMxlTmGqOFa7MZ2Zk/jAQrT97uh77zHQ2krStGkUr1494YOq1OmibmUjdbhLW4z0I1rIkUiEPS+/\nHHMvTrYgT4S248fxDQ2hUKkYHhjA1dV1xe6DUf/v0dScJT2doN/PkXfeofngQXb+8Y/M37QJU3Iy\nnoEBhnp6YlSjDAkJzFi/ntajRzHYbAz358GZzUzPTJxsSCCaum7scHC6tZ+HbpqDWSchLdFEc7cT\nBChKs9LW4+T93fUEgmEqC1PIsF28d7OSzWQnW1hYlkE4IpCdHEdG0tTubZNOzeo5eZzvHODCMCju\nfIicBRO3/31VKFQqlnz3u2JparIN3agj0USQKRRTKosceustcUPVVlsbwwHxX9IS5O7ro2n/fl7d\ntIkbf//7SU+FZrud5T/8IZFQCJlCQWNNVBBJIpFQnKyn54s3OW3IYdlTz6GNG79hV+v1LJhupyhO\njtWkGSfUMhbDvgCf1XdhsiVitiQiDQZw1B4mN8nAiYfuISHFjq9nCKNRR1JhIRkVFfjdbgrsJlQq\nJQNtbTi7ukjMzUVrNsecXKUymShbGjNnI65uwwMD9Dc1oRpJI0tlsqgS4wSvKVy6VCw5Xc4+eBTu\nvj52P/98dM2VSJh3550s/s538LlcmJKToydljyfqrdDTg0KtxjM4iEqrJSE3F8eFCxOWzjQmE/kL\nF9Lb2CiuESMSrVMjtgiCcMX/gIOAsOHpp4VrH3tMUKjVwg927hRufeopwWy3C88KgjD39tuF8uuu\nE54VBMFWUCBc9/jjwm/a2gS1wSB8f/t24f733hPUBoPwX06nsPLhh4XM2bOFXzc0CAm5uUJOdbXw\n68ZGQW0wCBtffll4eP9+QW0wCI/W1Ylj/KG3V0gpLRVsBQXC2kceEYzJycJ1jz8u/GDnTkGl1086\nxrOCIBQsWSIs+ta3hGcFQVAbDMJNTzwhVN9zjyCRyQRTcrJgTE4WZEql8P2dO8Xv8fNjxwSd1SoU\nLFkiLP3BDwSlVit8f/t2YcXDGO+6DgAAIABJREFUDwsypVK4+sEHhbi0NMFoswlrH3lEsE2bFjPG\nbU8/Ldz4hz8IKr0+5ntcbq5UOp2w6mc/E1b99KeCSq+/4vcYnauvMsbo9bjvnXfEuVr+wx8KlrQ0\nYe0jjwjWrCwho6JCWPvII4JMoRCy580TFn/3u4JcrRZ+cMcy4aFbq4QEs1Y49OwmYfXcPGFReaaw\n7T9vF0w6lTC7wC7ctrREUClkwp+/v1r4z/uXCTq1QvjsvzYKG1eWC5lJZuHetTOFZIteKM5KFHY/\ndbegUyuERzcuEl5++DpBp1YINy0sEiqnpcSMMbcoVbhzeZk4xoM3zxN0aoXw399bLaxYt1hQarXC\nTw4eEJY9+OCEcyVXqQRbQYGQOXu2IFMohG99+OGkc5WQmyvM27hRuH/z5knvq/SR+bFmZgoZs2cL\ntz/3nHg9Hvz8c0Gl0wk/P3Zs3Bhl114r/MntFuIyMoSCJUuEohUrBCQSIaeqSrjxiSfEMaq/8Q0h\nMTdX+MmBA0L+okXi91Dp9cKqn/1M2PjKK4JKrxfufPllYfqqVYJSrxemLV0qJOTlCbaCAuH63/1O\nSMzLm/QZ/OEXXwgavU6wmrTC7qfuFmYX2IWbFhUJh57dJGhVcuG6q6YJG1eWCxqlXPjphvmCXC4T\nFAqZcO/amcKcohQhw2YS7l07UzBqlUJptk24bWmJoJBJhY0ry8Zd86LMBOHQs5uE2QV2YfW8AmHT\nj74pyJUKYcXKauG/vr1S0KkVwtuP3jThfXXo2U1Chs0k3L6iXLjnj7+/7Fpyuefj0rVk9Hok5OQI\nV917r2DNzBSufeyxK65X/8wY5dddJ6z/zW8ErcUipFdUCLNvu02Qq1TCnNtvF+Zt3BjznGfMmiXc\n+PvfR++rWbOEb23ZMuUxHnjycSEt0Sh867rZwke/2SDo1AphRWWucPWt6yf9HiVlBcKhZzeNewYv\nvR7LZmULpniLsPaRRwSVXi/IZDIhITtLQCIRrOnpQkpRoaBQqYRfnT0bM4beqBcyy0uFlT/5iSBT\nKoWKm28WfrBrl6DS64Vb/vQnoXLDBsGQkDDhelW4fLmw9Ac/EGRKpTB9xQohb+FCQapQCPe++eak\n10Ol1wvXPvbYFedqdIz5mzYJc++8U5ApFMKc228XrvnVr2LmKmPWLGHjK68IGrNZMNntwsJvflOQ\nyuVC6TXXCNX33CPI1erLjpGQmysUjXwPoq6Q7VOJt1NtidoklcnsOVVVuPv6UGg0REIhpi1ZQnxW\nFlmVlUjlcuzTp5NcVIRMqSRrzhwsaWmojUZyqqvRW62YRvSW5SOqUAWLF6NPSKB41SoyZ81CrlKR\ne9VVGJOS0MfHk1NVhdpgwJqZSe5VV9Fx8iQaoxFDQgIpJSUUrVjBM+vXk11Vxdzbb59wjLTycmQj\nLksJOTnIVSoKr74aiVRKZ309EqIezUqtlukrVpA0bRr26dNR6XT0nT+POSUFrclEank5xatWReur\nXi+WtDSGenpIzMnBnJKCNTOTktWrxTHMKSm8+o1vsOD++ylZtUr8HpedK5OJORs2kFJSgjkl5Yrf\nI6OigjO7dhEOh1GoVGTOmkV8dvYVr4dEKuXoe+9RuWFDdAy1GntxMYVLl0bHnTs3SgjyeDDabOit\nVobbWri2PImMkVaX4qxEZFIp2fY4pqXH09g+gEmvxqBVMi0jgarpaZj0KuJNOkqzbSjkMnLscVjN\nOg6d70OmVFFzpge320txTjLFGfHotUoEBLQqBYtmZMaM4fGHov27Zh3pNiPFWTZMRh3Di27E3HGG\nhZ7TnNh/FOuseUSG3SSVlpE+cybGpCROfvJJ1AdXLkdtNFJxww3RzMyY62HLzyeluBi5Wk3x6tVk\nz5077t597tZbsaSnY83IIOD1iv6tZddeS3x2NimlpdRt345EKiUSCJA1dy6JublkVVYiCAJDPT20\n1dai1GjIra7G2dGBJi6OrMpKlFot05YuRW00Ur9zJ+7+fhpqajCnpDDz+uux5ecjV6spv+Ya7MXF\n6BMSMCUlMdTTg9pgwGizkVNVRcbMmSy47z40ZvOEz6DGbKZp7x4UA11kWaO9wlnJ0WuYmmDkbFs/\nGqUcqUSCRqWgMCOez2qbmZFrI8miZ2aunWx7HHmpVmxxOpIsBqRSUCrkrJydR7rNFHPNO/uH+O3r\nNWxcNQOnwsi8v7yJUqfH3dODlhAzMy2U5SShUyvG3VfZyXHIpVLaDKnM+el/XHYtmej5mGwtGb3m\nXqeT1LIyCpYsGTdXSo0GjdlM7vz5KDWamDHSysow2+2o9PorjmGfPh1rRgYtI+UhvcVCfE4ORcuX\nY8nIIL2igtyrrhJLY8P9/UhlMrGlr2Tt2imNUbbxHlp2bmN5gQV7vAGtWsn80gz6SxaRs2DhhHPV\nsn8vy4ps6NRKynOTsBo1mPTqcddDKZfRrU7EmJWLo6UFV08PxqRkQoEAWosFnTWegiVLmH3bbdHv\nkZmJSq+n/cA+rLn56BMTkcrlmJKSCLY1YcWH3aLFWlRC9oJFE65XpqQkIqEQcpWKWbfeSvVdd2FK\nTqb67rsnvebndu/m8FtvsfLhhy87V8lFRQQDAZxdXfQ3NSGRyUguLKRkREZ3dK6SCgqY8/Wv01lX\nh8luRyaX4x8ejvKMcnMpvPpq8hcunHQMuVrNtKuvJj4zk6Pvv08kFBp69NFHn7hSvJVMQfULiUSy\nD4lkzjW/+hWuzk50VisSiYTVP/vZZXsCpwqvy8WhN99kuL+frDlzYhRZxiLo89FVX4/GZBJVd1qO\nHsWUlDRhq8HlEPT5aKipYc9LLzHscJCQnc2NTzyBYqRwH/T72f/qqwy0tWHJyKBo2TL6mprQxsXR\nevQoww4HSdOmEQ4GUWq15M2fHzMX4WAQd38/eqv1XzJHE6Fh925O79gh/nvmjTdeNl0+iiPvvsuO\nJ5/kR7t3c+HgQU598gkKtZrZt94qssUDXi9H33uPht27o/2cez/hziXTKM6aON0ZDIVp7xtCr1FO\nqMY0GQRB4A+bD3OhvZ/KgiS8/iC2OD1r5+XH9M2e7xjg0JkOtGoFC8sy0KoV/P7TVvo9ARbalayp\nyODPRwbJVgXJtyj4wz/q0ReVsuyXj1Pzwguc++IL3P39ZM6ezdf/8heR+BIJhzn05pt0nz2LITGR\nuXfcMSnD8/NnnsHdG2V7C4JAOBhkyXe+I957o7rIoyhfv16sSbfW1nJs82bxdwVLlkRrayP6vqPt\ndXXbtrH7hRdoOXKEcDBIfFYWX/vv/54wvR3wejn4xhu0HD7MZ3/+Mz89dGhCTYCxcHV38+Ej/47h\n1G6SzWryU60snnFRweqdz+vocgwx5AmgUys43e1hXoENtRSmpcdTmhObFgyFI3Q53OjUigktF+tb\n+th7qpUlM7PYE4gn/1d/BqDx0W9zs318O92FrmjfdLJVT47dwgdnhzA+9OTYtpL/E7j7+hgeGBAD\n495XXiHo84mBYPQZ9gwOiqULW0EBs265hXAwSO3f/85gRwepJSUTmpB8+B//gWfE5rX9xAnsxcVI\nJBLi0tJiTCcOvvEGXfX11Lz4Iku++90reniPhd/tpveRTRQbgvgCIbKSzRxtG6axYBHT7x7f9nX6\now8JvvMMd82dvJ484PbxWr2XzE0PkjxrDjt+9H06t39ApyoBx9nT2IxqJIkp3Pj8K6SWlhIKBPjy\nuedw9/YikUrRWeJw9ztAEOitPcz902QUZyYikUh4q01Ozn/8OWa8UfKUIAh0nz2LRColMTd3SqQ+\nZ2cnzq6uKSnrNe7dS90nnxDwePAMDjJj/foJZVUhupZ/8eyz7H3lFbwuFyG/H318PHPvuIOl3/3u\nFccC+KZCQSQUahME4YoG8FOtKbsRBFydnXTW1aGzWln0wAP/smBz7osvxNrO2c8/J3n6dIxjyGSO\nlhZ6z5/Hmpk5zjyi+8wZAh7PlINyJBzm6ObNHH7rLdQmk+iE5GhpYfdzzzHr5psxJCSgUKm46p57\nCAeD+IaG+Pzpp8Xaw5wNG0jMzSXg8VC/axeewcFxgvQ+t5tPn3qKqrvumtSw4v8vLu29vZwfNERZ\n8xcOHUIfH8+Dn34qSmgKkQj+EXu6UfOQI+++S8/Zs7j7+ggFAsxJMlCQNnn9TCGXkTnFWuFYSCQS\nfnj9LDbvPsP/7jiBWooouTn6fv5giJrGfnLSbFRmWXjinf10O314ghHCciXvNgf5+GQXCoWCmZXp\n5CSb+Z+757Lj8Hk+vutmsu76Ns2HDpFgNmO02fA4HOK16mloEJn1Qz09NB88KG4Kz+/bR922bbj7\n+ph29dVkz53LyZGWCMlI7/PYYKEbW1+USGLId5f2F2uMRtIrKjj4xhvorVaRsZs0bRqhQIBwMDjq\nLsNQTw9Oq5XB9nbis7LE91VqNFTfdRdzNmxg5cMPxxAwJ4PRZqNk3TUcrDtCnkY+rvZflmPjs6MX\n6B/yMOwPsbqqiDuWTl7vlsukpCZMri0+6PbRMzDMxwcaaFS4yY1EOPfeGxTgBGLnpMvhZtvBRgQE\nTjX18KXZgfqGTaT8HwXkp9asQR8fz5pf/IIDf/sbQiSC0WYjLjVV3Cw5Ozvpb24Wn+Gm/fvFOmH3\nmTP0NzXR39xMQ00NHSdPcuz99xl2OETTBIgGGm1cHI7WVlR6PSqdTgwyY9t3ACpuvpmB1lYWfetb\nU26taj58mLbaWkzJyVxwSug6cx4k0Q3R9fMLOdnSxP6ffx+FWsPMn/8/8XWFq9dy+tR+oGfS997S\n5GfO06+LdVN5wEOiQUV1qoLt3gRkGdNY9OOfiv3CgyOGRRAl/VrSMyhZE+WsKNatZf/pU4Trt1KW\nZibkia2n17/yDJrDOxgy29HOX03W1SumFIy9TicnPvoo2iI7Qma0FxePa1nsbWyMHqYKC6NdKSMS\nnUqd7rL2ui1HjtB99iw+t5vh/n5Ry8HZ0UE4FJoSyVMqkxEJhTxX/EOmHpSHIEp0ya6qivYNT8Ao\nDgeDHH7nHRwtLSQVFFB27bX/VNvC2Nc4OzvZ8/LLUVa3REL1XXeJto0AHz32GMWrVk1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uJZK93ln/0sRRs2oNHriUWjnHj5ZUba2wl6vRSuX0/xhg14hoY48fLLIIq0HDnC2kcewTrL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\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UZ2NyfJyHFiwgLDoafXw8DquVzf/6ryQWFkrmMbrIyBljRumLF8+QuU0sLmZieBhzRwex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\nDvxPhH+JnfJwuIkBKTKZbAaCsOv8jh26pxITueX115l2440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GBEHYLpPJAoGXBKfzoaoD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576Y2evHVMfeug8wZG6Y8d4ISeH0Tff7BGyEARmP/UUUoWCzS+95LW01MfEXJCti6LIxuefp6O6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\n", "text/plain": [ "<matplotlib.figure.Figure at 0x1158eb400>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "r, t, p = data_set.extract_rtp(\"bottom_turning_point\")\n", "\n", "## map\n", "m, fig = plot_data.setting_map() \n", "x, y = m(p, t)\n", "sc = m.scatter(x, y, c='black',s=8, zorder=10, cmap=cm, edgecolors='none',alpha=0.5)\n", "\n", "plt.savefig(\"repartition_1.pdf\")\n", "\n", "r, t, p = data_set_random.extract_rtp(\"bottom_turning_point\")\n", "\n", "## map\n", "m, fig = plot_data.setting_map() \n", "x, y = m(p, t)\n", "sc = m.scatter(x, y, c='black',s=8, zorder=10, cmap=cm, edgecolors='none',alpha=0.5)\n", "plt.savefig(\"repartition_2.pdf\")" ] }, { "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": 1 }
mit
feststelltaste/software-analytics
notebooks/Committer Distribution.ipynb
1
226631
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Introduction\n", "In the last notebook, I showed you how easy it is to connect jQAssistant/neo4j with Python Pandas/py2neo. In this notebook, I show you a (at first glance) simple analysis of the Git repository https://github.com/feststelltaste/spring-petclinic. This repository is a fork of the demo repository for jQAssistant (https://github.com/feststelltaste/spring-petclinic) therefore it integrates jQAssistant already.\n", "\n", "As analysis task, we want to know who are the Top 10 committers and how the distribution of the commits is. This could be handy if you want to identify your main contributors of a project e. g. to send them a gift at Christmas ;-) .\n", "\n", "But first, you might ask yourself: \"Why do I need a fully fledged data analysis framework like Pandas for such a simple task? Are there no standard tools out there?\" Well, I'll show you why (OK, and you got me there: I needed another reason to go deeper with Python, Pandas, jQAssistant and Neo4j to get some serious software data analysis started) \n", "\n", "So let's go!" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Preparation" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This notebook assumes that \n", "- there is a running Neo4j server with the default configuration. \n", "- the graph database is filled with the scan results of jQAssistant (happens for the repository above automatically with an <tt>mvn clean install</tt>)\n", "- you use a standard Anaconda installation with Python 3+\n", "- you installed the py2neo connector" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If everything is set up, we just import the usual suspects: py2neo for connecting to Neo4j and Pandas for data analysis. We also want to plot some graphics later on, so we import matplotlib accordingly as the convention suggests." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import py2neo\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "# display graphics directly in the notebook\n", "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Data input" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We need some data to get started. Luckily, we have jQAssistant at our hand. It's integrated into the build process of Spring PetClinic repository above and scanned the Git repository information automatically with every executed build. \n", "\n", "So let's query our almighty Neo4j graph database that holds all the structural data about the software project. " ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[{'email': '[email protected]', 'name': 'Markus Harrer'},\n", " {'email': '[email protected]', 'name': 'feststelltaste'},\n", " {'email': '[email protected]', 'name': 'feststelltaste'}]" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "graph = py2neo.Graph()\n", "query = \"\"\"\n", "MATCH (author:Author)-[:COMMITED]-> (commit:Commit)\n", "RETURN author.name as name, author.email as email\n", "\"\"\"\n", "result = graph.data(query)\n", "# just how first three entries\n", "result[0:3]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The query returns all commits with their authors and the author's email addresses. We get some nice, tabular data that we put into Pandas's DataFrame." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>email</th>\n", " <th>name</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>[email protected]</td>\n", " <td>Markus Harrer</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>[email protected]</td>\n", " <td>feststelltaste</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>[email protected]</td>\n", " <td>feststelltaste</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>[email protected]</td>\n", " <td>feststelltaste</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>[email protected]</td>\n", " <td>feststelltaste</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " email name\n", "0 [email protected] Markus Harrer\n", "1 [email protected] feststelltaste\n", "2 [email protected] feststelltaste\n", "3 [email protected] feststelltaste\n", "4 [email protected] feststelltaste" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "commits = pd.DataFrame(result)\n", "commits.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Familiarization" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "First, I like to check the raw data a little bit. I often do this by first having a look at the data types the data source is returning. It's a good starting point to check that Pandas recognizes the data types accordingly. You can also use this approach to check for skewed data columns very quickly (especially necessary when reading CSV or Excel files): If there should be a column with a specific data type (e. g. because the documentation of the dataset said so), the data type should be recognized automatically as specified. If not, there is a high probability that the imported data source isn't correct (and we have a data quality problem)." ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "email object\n", "name object\n", "dtype: object" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "commits.dtypes" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "That's OK for our simple scenario. The two columns with texts are objects &ndash; nothing spectacular.\n", "\n", "In the next step, I always like to get a \"feeling\" of all the data. Primarily, I want to get a quick impression of the data quality again. It could always be that there is \"dirty data\" in the dataset or that there are outliers that would screw up the analysis. With such a small amount of data we have, we can simply list all unique values that occur in the columns. I just list the top 10's for both columns." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "Mic 211\n", "Antoine Rey 112\n", "michaelisvy 87\n", "Dirk Mahler 50\n", "Keith Donald 35\n", "Costin Leau 28\n", "feststelltaste 26\n", "Cyrille Le Clerc 5\n", "Thibault Duchateau 5\n", "Dapeng 5\n", "Name: name, dtype: int64" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "commits['name'].value_counts()[0:10]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "OK, at first glance, something seems awkward. Let's have a look at the email addresses." ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[email protected] 224\n", "misvy@gopivotal,com 63\n", "[email protected] 59\n", "[email protected] 53\n", "[email protected] 46\n", "[email protected] 35\n", "[email protected] 28\n", "[email protected] 27\n", "[email protected] 11\n", "[email protected] 5\n", "Name: email, dtype: int64" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "commits['email'].value_counts()[0:10]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "OK, the bad feeling is strengthening. We might have a problem with multiple authors having multiple email addresses. Let me show you the problem by better representing the problem." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Interlude - begin\n", "_In the interlude section, I take you to a short, mostly undocumented excursion with probably messy code (don't do this at home!) to make a point. If you like, you can skip that section._\n", "\n", "**Goal: Create a diagram that shows the relationship between the authors and the emails addresses.**\n", "\n", "(Note to myself: It's probably better to solve that directly in Neo4j the next time ;-) )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "I need a unique index for each name and I have to calculate the number of different email addresses per author." ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>index</th>\n", " <th>name</th>\n", " <th>email</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>0</td>\n", " <td>michaelisvy</td>\n", " <td>3</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>1</td>\n", " <td>Ameya Pandilwar</td>\n", " <td>2</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>2</td>\n", " <td>Dirk Mahler</td>\n", " <td>2</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>3</td>\n", " <td>Antoine Rey</td>\n", " <td>2</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>4</td>\n", " <td>Rossen Stoyanchev</td>\n", " <td>1</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " index name email\n", "0 0 michaelisvy 3\n", "1 1 Ameya Pandilwar 2\n", "2 2 Dirk Mahler 2\n", "3 3 Antoine Rey 2\n", "4 4 Rossen Stoyanchev 1" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "grouped_by_authors = commits[['name', 'email']]\\\n", " .drop_duplicates().groupby('name').count()\\\n", " .sort_values('email', ascending=False).reset_index().reset_index()\n", "grouped_by_authors.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Same procedure for the email addresses." ] }, { "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>index</th>\n", " <th>email</th>\n", " <th>name</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>0</td>\n", " <td>[email protected]</td>\n", " <td>2</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>1</td>\n", " <td>[email protected]</td>\n", " <td>2</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>2</td>\n", " <td>Andrej1</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>3</td>\n", " <td>[email protected]</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>4</td>\n", " <td>[email protected]</td>\n", " <td>1</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " index email name\n", "0 0 [email protected] 2\n", "1 1 [email protected] 2\n", "2 2 Andrej1 1\n", "3 3 [email protected] 1\n", "4 4 [email protected] 1" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "grouped_by_email = commits[['name', 'email']]\\\n", " .drop_duplicates().groupby('email').count()\\\n", " .sort_values('name', ascending=False).reset_index().reset_index()\n", "grouped_by_email.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Then I merge the two DataFrames with a subset of the original data. I get each author and email index as well as the number of occurrences for author respectively emails. I only need the ones that are occurring multiple times, so I check for > 2." ] }, { "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>email</th>\n", " <th>name</th>\n", " <th>index</th>\n", " <th>email_from_authors</th>\n", " <th>index_from_emails</th>\n", " <th>name_from_emails</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>[email protected]</td>\n", " <td>Markus Harrer</td>\n", " <td>21</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>2</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>[email protected]</td>\n", " <td>feststelltaste</td>\n", " <td>14</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>2</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>[email protected]</td>\n", " <td>Dirk Mahler</td>\n", " <td>2</td>\n", " <td>2</td>\n", " <td>39</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>[email protected]</td>\n", " <td>Dirk Mahler</td>\n", " <td>2</td>\n", " <td>2</td>\n", " <td>38</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>7</th>\n", " <td>[email protected]</td>\n", " <td>Antoine Rey</td>\n", " <td>3</td>\n", " <td>2</td>\n", " <td>28</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>8</th>\n", " <td>[email protected]</td>\n", " <td>Antoine Rey</td>\n", " <td>3</td>\n", " <td>2</td>\n", " <td>27</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>13</th>\n", " <td>[email protected]</td>\n", " <td>Ameya Pandilwar</td>\n", " <td>1</td>\n", " <td>2</td>\n", " <td>25</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>14</th>\n", " <td>[email protected]</td>\n", " <td>Ameya Pandilwar</td>\n", " <td>1</td>\n", " <td>2</td>\n", " <td>23</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>22</th>\n", " <td>misvy@gopivotal,com</td>\n", " <td>michaelisvy</td>\n", " <td>0</td>\n", " <td>3</td>\n", " <td>9</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>23</th>\n", " <td>[email protected]</td>\n", " <td>michaelisvy</td>\n", " <td>0</td>\n", " <td>3</td>\n", " <td>10</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>24</th>\n", " <td>[email protected]</td>\n", " <td>michaelisvy</td>\n", " <td>0</td>\n", " <td>3</td>\n", " <td>1</td>\n", " <td>2</td>\n", " </tr>\n", " <tr>\n", " <th>25</th>\n", " <td>[email protected]</td>\n", " <td>Mic</td>\n", " <td>22</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>2</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " email name index email_from_authors \\\n", "0 [email protected] Markus Harrer 21 1 \n", "1 [email protected] feststelltaste 14 1 \n", "3 [email protected] Dirk Mahler 2 2 \n", "4 [email protected] Dirk Mahler 2 2 \n", "7 [email protected] Antoine Rey 3 2 \n", "8 [email protected] Antoine Rey 3 2 \n", "13 [email protected] Ameya Pandilwar 1 2 \n", "14 [email protected] Ameya Pandilwar 1 2 \n", "22 misvy@gopivotal,com michaelisvy 0 3 \n", "23 [email protected] michaelisvy 0 3 \n", "24 [email protected] michaelisvy 0 3 \n", "25 [email protected] Mic 22 1 \n", "\n", " index_from_emails name_from_emails \n", "0 0 2 \n", "1 0 2 \n", "3 39 1 \n", "4 38 1 \n", "7 28 1 \n", "8 27 1 \n", "13 25 1 \n", "14 23 1 \n", "22 9 1 \n", "23 10 1 \n", "24 1 2 \n", "25 1 2 " ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "plot_data = commits.drop_duplicates()\\\n", " .merge(grouped_by_authors, left_on='name', right_on=\"name\", suffixes=[\"\", \"_from_authors\"], how=\"outer\")\\\n", " .merge(grouped_by_email, left_on='email', right_on=\"email\", suffixes=[\"\", \"_from_emails\"], how=\"outer\")\n", "plot_data = plot_data[\\\n", " (plot_data['email_from_authors'] > 1) | \\\n", " (plot_data['name_from_emails'] > 1)]\n", "plot_data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "I just add some nicely normalized indexes for plotting (note: there might be a method that's easier)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>email</th>\n", " <th>name</th>\n", " <th>index</th>\n", " <th>email_from_authors</th>\n", " <th>index_from_emails</th>\n", " <th>name_from_emails</th>\n", " <th>normalized_index_name</th>\n", " <th>normalized_index_email</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>[email protected]</td>\n", " <td>Markus Harrer</td>\n", " <td>21</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>2</td>\n", " <td>50</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>[email protected]</td>\n", " <td>feststelltaste</td>\n", " <td>14</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>2</td>\n", " <td>40</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>[email protected]</td>\n", " <td>Dirk Mahler</td>\n", " <td>2</td>\n", " <td>2</td>\n", " <td>39</td>\n", " <td>1</td>\n", " <td>20</td>\n", " <td>90</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>[email protected]</td>\n", " <td>Dirk Mahler</td>\n", " <td>2</td>\n", " <td>2</td>\n", " <td>38</td>\n", " <td>1</td>\n", " <td>20</td>\n", " <td>80</td>\n", " </tr>\n", " <tr>\n", " <th>7</th>\n", " <td>[email protected]</td>\n", " <td>Antoine Rey</td>\n", " <td>3</td>\n", " <td>2</td>\n", " <td>28</td>\n", " <td>1</td>\n", " <td>30</td>\n", " <td>70</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " email name index email_from_authors \\\n", "0 [email protected] Markus Harrer 21 1 \n", "1 [email protected] feststelltaste 14 1 \n", "3 [email protected] Dirk Mahler 2 2 \n", "4 [email protected] Dirk Mahler 2 2 \n", "7 [email protected] Antoine Rey 3 2 \n", "\n", " index_from_emails name_from_emails normalized_index_name \\\n", "0 0 2 50 \n", "1 0 2 40 \n", "3 39 1 20 \n", "4 38 1 20 \n", "7 28 1 30 \n", "\n", " normalized_index_email \n", "0 0 \n", "1 0 \n", "3 90 \n", "4 80 \n", "7 70 " ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from sklearn import preprocessing\n", "le = preprocessing.LabelEncoder()\n", "le.fit(plot_data['index'])\n", "plot_data['normalized_index_name'] = le.transform(plot_data['index']) * 10\n", "le.fit(plot_data['index_from_emails'])\n", "plot_data['normalized_index_email'] = le.transform(plot_data['index_from_emails']) * 10\n", "plot_data.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Plot an assignment table with the relationships between authors and email addresses." ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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sWICioiJERETA2NhYGL+oqAiff/45Ro8eDSsrKzx//hwrV65E27Zt0bFjx7dy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\nU8vIyMCFCxeeOk6YiFBaWsrVXQ6H81Roa2vDzs4OdnZ2gpzM9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"text/plain": [ "<matplotlib.figure.Figure at 0x24319c29a58>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig1 = plt.figure(facecolor='white')\n", "ax1 = plt.axes(frameon=False)\n", "ax1.set_frame_on(False)\n", "ax1.get_xaxis().tick_bottom()\n", "ax1.axes.get_yaxis().set_visible(False)\n", "ax1.axes.get_xaxis().set_visible(False)\n", "\n", "# simply plot all the data (imperfection: duplicated will be displayed in bold font)\n", "for data in plot_data.iterrows():\n", " row = data[1]\n", " plt.text(0, row['normalized_index_name'], row['name'], fontsize=15, horizontalalignment=\"right\")\n", " plt.text(1, row['normalized_index_email'], row['email'], fontsize=15, horizontalalignment=\"left\")\n", " plt.plot([0,1],[row['normalized_index_name'],row['normalized_index_email']],'grey', linewidth=1.0)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Alright! Here we are! We see that multiple authors use multiple email addresses. And I see a pattern that could be used to get better data. Do you, too? \n", "\n", "## Interlude - end" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If you skipped the interlude section: I just visualized / demonstrated that there are different email addresses per author (and vise versa). Some authors choose to use another email address and some choose a different name for committing to the repositories (and a few did both things)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Data Wrangling" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The situation above is a typical case of a little data messiness and &ndash; to demotivate you &ndash; absolutely normal. So we have to do some data correction before we start our analysis. Otherwise, we would ignore reality completely and deliver wrong results. This could damage our reputation as a data analyst and is something we have to avoid at all costs!\n", "\n", "We want to fix the problem with the multiple authors having multiple email addresses (but are the same persons). We need a mapping between them. Should we do it manually? That would be kind of crazy. As mentioned above, there is a pattern in the data to fix that. We simply use the name of the email address as an identifier for a person.\n", "\n", "Let's give it a try by extracting the name part from the email address with a simple split." ] }, { "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>email</th>\n", " <th>name</th>\n", " <th>nickname</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>[email protected]</td>\n", " <td>Markus Harrer</td>\n", " <td>feststelltaste</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>[email protected]</td>\n", " <td>feststelltaste</td>\n", " <td>feststelltaste</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>[email protected]</td>\n", " <td>feststelltaste</td>\n", " <td>feststelltaste</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>[email protected]</td>\n", " <td>feststelltaste</td>\n", " <td>feststelltaste</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>[email protected]</td>\n", " <td>feststelltaste</td>\n", " <td>feststelltaste</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " email name nickname\n", "0 [email protected] Markus Harrer feststelltaste\n", "1 [email protected] feststelltaste feststelltaste\n", "2 [email protected] feststelltaste feststelltaste\n", "3 [email protected] feststelltaste feststelltaste\n", "4 [email protected] feststelltaste feststelltaste" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "commits['nickname'] = commits['email'].apply(lambda x : x.split(\"@\")[0])\n", "commits.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "That looks pretty good. Now we want to get only the person's real name instead of the nickname. We use a little heuristic to determine the \"best fitting\" real name and replace all the others. For this, we need group by nicknames and determine the real names." ] }, { "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>real_name</th>\n", " </tr>\n", " <tr>\n", " <th>nickname</th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>Andrej1</th>\n", " <td>AndrejGajdos</td>\n", " </tr>\n", " <tr>\n", " <th>ameya</th>\n", " <td>Ameya Pandilwar</td>\n", " </tr>\n", " <tr>\n", " <th>angel.aguilera</th>\n", " <td>Angel Aguilera</td>\n", " </tr>\n", " <tr>\n", " <th>antoine.rey</th>\n", " <td>Antoine Rey</td>\n", " </tr>\n", " <tr>\n", " <th>armagan.ersoz</th>\n", " <td>kadinyazilimci</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " real_name\n", "nickname \n", "Andrej1 AndrejGajdos\n", "ameya Ameya Pandilwar\n", "angel.aguilera Angel Aguilera\n", "antoine.rey Antoine Rey\n", "armagan.ersoz kadinyazilimci" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "def determine_real_name(names):\n", " \n", " real_name = \"\"\n", " \n", " for name in names:\n", " # assumption: if there is a whitespace in the name, \n", " # someone thought about it to be first name and surname\n", " if \" \" in name:\n", " return name\n", " # else take the longest name\n", " elif len(name) > len(real_name):\n", " real_name = name\n", " \n", " return real_name\n", " \n", "commits_grouped = commits[['nickname', 'name']].groupby(['nickname']).agg(determine_real_name)\n", "commits_grouped = commits_grouped.rename(columns={'name' : 'real_name'})\n", "commits_grouped.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "That looks great! Now we switch back to our previous DataFrame by joining in the new information." ] }, { "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>email</th>\n", " <th>name</th>\n", " <th>nickname</th>\n", " <th>real_name</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>[email protected]</td>\n", " <td>Markus Harrer</td>\n", " <td>feststelltaste</td>\n", " <td>Markus Harrer</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>[email protected]</td>\n", " <td>feststelltaste</td>\n", " <td>feststelltaste</td>\n", " <td>Markus Harrer</td>\n", " </tr>\n", " <tr>\n", " <th>27</th>\n", " <td>[email protected]</td>\n", " <td>Markus</td>\n", " <td>feststelltaste</td>\n", " <td>Markus Harrer</td>\n", " </tr>\n", " <tr>\n", " <th>29</th>\n", " <td>[email protected]</td>\n", " <td>Dirk Mahler</td>\n", " <td>dirk.mahler</td>\n", " <td>Dirk Mahler</td>\n", " </tr>\n", " <tr>\n", " <th>75</th>\n", " <td>[email protected]</td>\n", " <td>dmahler</td>\n", " <td>dirk.mahler</td>\n", " <td>Dirk Mahler</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " email name nickname \\\n", "0 [email protected] Markus Harrer feststelltaste \n", "1 [email protected] feststelltaste feststelltaste \n", "27 [email protected] Markus feststelltaste \n", "29 [email protected] Dirk Mahler dirk.mahler \n", "75 [email protected] dmahler dirk.mahler \n", "\n", " real_name \n", "0 Markus Harrer \n", "1 Markus Harrer \n", "27 Markus Harrer \n", "29 Dirk Mahler \n", "75 Dirk Mahler " ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "commits = commits.merge(commits_grouped, left_on='nickname', right_index=True)\n", "# drop duplicated for better displaying\n", "commits.drop_duplicates().head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "That should be enough data cleansing for today!" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Analysis\n", "Now that we have valid data, we can produce some new insights." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Top 10 committers\n", "Easy tasks first: We simply produce a table with the Top 10 committers. We group by the real name and count every commit by using a subset (only the <tt>email</tt> column) of the DataFrame to only get on column returned. We rename the returned columns to <tt>commits</tt> for displaying reasons (would otherwise be <tt>email</tt>). Then we just list the top 10 entries after sorting appropriately." ] }, { "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>commits</th>\n", " </tr>\n", " <tr>\n", " <th>real_name</th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>michaelisvy</th>\n", " <td>298</td>\n", " </tr>\n", " <tr>\n", " <th>Antoine Rey</th>\n", " <td>112</td>\n", " </tr>\n", " <tr>\n", " <th>Dirk Mahler</th>\n", " <td>51</td>\n", " </tr>\n", " <tr>\n", " <th>Keith Donald</th>\n", " <td>35</td>\n", " </tr>\n", " <tr>\n", " <th>Markus Harrer</th>\n", " <td>29</td>\n", " </tr>\n", " <tr>\n", " <th>Costin Leau</th>\n", " <td>28</td>\n", " </tr>\n", " <tr>\n", " <th>Tomas Repel</th>\n", " <td>5</td>\n", " </tr>\n", " <tr>\n", " <th>Thibault Duchateau</th>\n", " <td>5</td>\n", " </tr>\n", " <tr>\n", " <th>Cyrille Le Clerc</th>\n", " <td>5</td>\n", " </tr>\n", " <tr>\n", " <th>Dapeng</th>\n", " <td>5</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " commits\n", "real_name \n", "michaelisvy 298\n", "Antoine Rey 112\n", "Dirk Mahler 51\n", "Keith Donald 35\n", "Markus Harrer 29\n", "Costin Leau 28\n", "Tomas Repel 5\n", "Thibault Duchateau 5\n", "Cyrille Le Clerc 5\n", "Dapeng 5" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "committers = commits.groupby('real_name')[['email']]\\\n", " .count().rename(columns={'email' : 'commits'})\\\n", " .sort_values('commits', ascending=False)\n", "committers.head(10)" ] }, { "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>commits</th>\n", " </tr>\n", " <tr>\n", " <th>real_name</th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>michaelisvy</th>\n", " <td>298</td>\n", " </tr>\n", " <tr>\n", " <th>Antoine Rey</th>\n", " <td>112</td>\n", " </tr>\n", " <tr>\n", " <th>Dirk Mahler</th>\n", " <td>51</td>\n", " </tr>\n", " <tr>\n", " <th>Keith Donald</th>\n", " <td>35</td>\n", " </tr>\n", " <tr>\n", " <th>Markus Harrer</th>\n", " <td>29</td>\n", " </tr>\n", " <tr>\n", " <th>Costin Leau</th>\n", " <td>28</td>\n", " </tr>\n", " <tr>\n", " <th>Tomas Repel</th>\n", " <td>5</td>\n", " </tr>\n", " <tr>\n", " <th>Thibault Duchateau</th>\n", " <td>5</td>\n", " </tr>\n", " <tr>\n", " <th>Cyrille Le Clerc</th>\n", " <td>5</td>\n", " </tr>\n", " <tr>\n", " <th>Dapeng</th>\n", " <td>5</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " commits\n", "real_name \n", "michaelisvy 298\n", "Antoine Rey 112\n", "Dirk Mahler 51\n", "Keith Donald 35\n", "Markus Harrer 29\n", "Costin Leau 28\n", "Tomas Repel 5\n", "Thibault Duchateau 5\n", "Cyrille Le Clerc 5\n", "Dapeng 5" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "committers.head(10)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Committer Distribution\n", "Next, we create a pie chart to get a good impression of the committers." ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x24319d65d30>" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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d0iZsgupo7JPZFJtmsAcmXpHbniTWlN3z5G7m/U5LuW3mfm8C3AbMdee/UFVH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\nxZdhBT++iona3liFk10wj+1z1JaVvwC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vuqj6EleS4TI4Y0uWlGX0wMoX0gNwbRyuDNgN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\nRK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x24319d69e80>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "committers['commits'].plot(kind='pie')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Uhh...that looks ugly and kind of weird. Let's first try to fix the mess on the right side that shows all authors with minor changes by summing up their number of commits. We will use a threshold value that makes sense with our data (e. g. the committers that contribute more than 3/4 to the code) to identify them. A nice start is the description of the current data set." ] }, { "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>commits</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>count</th>\n", " <td>37.000000</td>\n", " </tr>\n", " <tr>\n", " <th>mean</th>\n", " <td>16.540541</td>\n", " </tr>\n", " <tr>\n", " <th>std</th>\n", " <td>51.868742</td>\n", " </tr>\n", " <tr>\n", " <th>min</th>\n", " <td>1.000000</td>\n", " </tr>\n", " <tr>\n", " <th>25%</th>\n", " <td>1.000000</td>\n", " </tr>\n", " <tr>\n", " <th>50%</th>\n", " <td>1.000000</td>\n", " </tr>\n", " <tr>\n", " <th>75%</th>\n", " <td>5.000000</td>\n", " </tr>\n", " <tr>\n", " <th>max</th>\n", " <td>298.000000</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " commits\n", "count 37.000000\n", "mean 16.540541\n", "std 51.868742\n", "min 1.000000\n", "25% 1.000000\n", "50% 1.000000\n", "75% 5.000000\n", "max 298.000000" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "committers_description = committers.describe()\n", "committers_description" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "OK, we want the 3/4 main contributors..." ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "5.0" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "threshold = committers_description.loc['75%'].values[0]\n", "threshold" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "...that is > 75% of the commits of all contributors." ] }, { "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>commits</th>\n", " </tr>\n", " <tr>\n", " <th>real_name</th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>Tomas Repel</th>\n", " <td>5</td>\n", " </tr>\n", " <tr>\n", " <th>Thibault Duchateau</th>\n", " <td>5</td>\n", " </tr>\n", " <tr>\n", " <th>Cyrille Le Clerc</th>\n", " <td>5</td>\n", " </tr>\n", " <tr>\n", " <th>Dapeng</th>\n", " <td>5</td>\n", " </tr>\n", " <tr>\n", " <th>Gordon Dickens</th>\n", " <td>5</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " commits\n", "real_name \n", "Tomas Repel 5\n", "Thibault Duchateau 5\n", "Cyrille Le Clerc 5\n", "Dapeng 5\n", "Gordon Dickens 5" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "minor_committers = committers[committers['commits'] <= threshold]\n", "minor_committers.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "These are the entries we want to combine to our new \"Others\" section. But we don't want to loose the number of changes, so we store them for later usage." ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "commits 59\n", "dtype: int64" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "others_number_of_changes = minor_committers.sum()\n", "others_number_of_changes" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we are deleting all authors that are in the <tt>author_minor_changes</tt>'s DataFrame. To not check on the threshold value from above again, we reuse the already calculated DataFrame." ] }, { "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>commits</th>\n", " </tr>\n", " <tr>\n", " <th>real_name</th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>Colin But</th>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>Craig Dennis</th>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>Mike Eltsufin</th>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>Faisal Hameed</th>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>thinksh</th>\n", " <td>NaN</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " commits\n", "real_name \n", "Colin But NaN\n", "Craig Dennis NaN\n", "Mike Eltsufin NaN\n", "Faisal Hameed NaN\n", "thinksh NaN" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "main_committers = committers[~committers.isin(minor_committers)]\n", "main_committers.tail()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This gives us for the contributors with just a few commits missing values for the <tt>changes</tt> column, because these values were in the <tt>author_minor_changes</tt> DataFrame. We drop all Nan values to get only the major contributors." ] }, { "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>commits</th>\n", " </tr>\n", " <tr>\n", " <th>real_name</th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>michaelisvy</th>\n", " <td>298.0</td>\n", " </tr>\n", " <tr>\n", " <th>Antoine Rey</th>\n", " <td>112.0</td>\n", " </tr>\n", " <tr>\n", " <th>Dirk Mahler</th>\n", " <td>51.0</td>\n", " </tr>\n", " <tr>\n", " <th>Keith Donald</th>\n", " <td>35.0</td>\n", " </tr>\n", " <tr>\n", " <th>Markus Harrer</th>\n", " <td>29.0</td>\n", " </tr>\n", " <tr>\n", " <th>Costin Leau</th>\n", " <td>28.0</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " commits\n", "real_name \n", "michaelisvy 298.0\n", "Antoine Rey 112.0\n", "Dirk Mahler 51.0\n", "Keith Donald 35.0\n", "Markus Harrer 29.0\n", "Costin Leau 28.0" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "main_committers = main_committers.dropna()\n", "main_committers" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We add the \"Others\" row by appending to the DataFrame" ] }, { "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>commits</th>\n", " </tr>\n", " <tr>\n", " <th>real_name</th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>michaelisvy</th>\n", " <td>298.0</td>\n", " </tr>\n", " <tr>\n", " <th>Antoine Rey</th>\n", " <td>112.0</td>\n", " </tr>\n", " <tr>\n", " <th>Dirk Mahler</th>\n", " <td>51.0</td>\n", " </tr>\n", " <tr>\n", " <th>Keith Donald</th>\n", " <td>35.0</td>\n", " </tr>\n", " <tr>\n", " <th>Markus Harrer</th>\n", " <td>29.0</td>\n", " </tr>\n", " <tr>\n", " <th>Costin Leau</th>\n", " <td>28.0</td>\n", " </tr>\n", " <tr>\n", " <th>Others</th>\n", " <td>59.0</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " commits\n", "real_name \n", "michaelisvy 298.0\n", "Antoine Rey 112.0\n", "Dirk Mahler 51.0\n", "Keith Donald 35.0\n", "Markus Harrer 29.0\n", "Costin Leau 28.0\n", "Others 59.0" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "main_committers.loc[\"Others\"] = others_number_of_changes\n", "main_committers" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Almost there, you redraw with some styling and minor adjustments." ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.text.Text at 0x2431ae79c18>" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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0DhRaeWRrBbKOdeAo+L+XiB97HcbYpHrbFZ2O2POvIuv1e9lz6xCyF95P2i1P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\nTlfJQaPR8ETvvgSHheLp5rjV8oWyiZGXAPx2HSwiGtWY6ZimLkJxrZn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"text/plain": [ "<matplotlib.figure.Figure at 0x243193aa518>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# some configuration for displaying nice diagrams\n", "plt.style.use('fivethirtyeight')\n", "plt.figure(facecolor='white')\n", "\n", "ax = main_committers['commits'].plot(\n", " kind='pie', figsize=(6,6), title=\"Main committers\", \n", " autopct='%.2f', fontsize=12)\n", "# get rid of the distracting label for the y-axis\n", "ax.set_ylabel(\"\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Summary\n", "I hope you saw that there are some minor difficulties in working with data. We got the big problem with the authors and email addresses that we solved by correcting the names. We also transformed an ugly pie chart into a management-grade one.\n", "\n", "This analysis also gives you some handy lines of code for some common data analysis tasks." ] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python [Root]", "language": "python", "name": "Python [Root]" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 0 }
gpl-3.0
jedludlow/sp-for-ds
sp_for_ds.ipynb
1
29548
{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "slideshow": { "slide_type": "skip" } }, "outputs": [], "source": [ "%matplotlib widget" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "slideshow": { "slide_type": "skip" } }, "outputs": [], "source": [ "import warnings; warnings.simplefilter('ignore')" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "slideshow": { "slide_type": "skip" } }, "outputs": [], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "import pandas as pd\n", "import scipy.signal as signal\n", "import ipywidgets as widgets\n", "from IPython.display import display\n", "sns.set_context('notebook')" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "<hr>\n", "\n", "# Signal Processing for Data Scientists\n", "\n", "Jed Ludlow\n", "\n", "UnitedHealth Group\n", "\n", "<hr>\n", "\n", "Get the code at https://github.com/jedludlow/sp-for-ds" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Overview\n", "\n", "* Signal processing: Tools to separate the useful information from the nuisance information in a time series.\n", "* Cover three areas today\n", " * Fourier analysis in the frequency domain\n", " * Discrete-time sampling\n", " * Digital filtering" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Fourier Analysis in the Frequency Domain" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "## Fourier Series\n", "\n", "A periodic signal $s(t)$ can be expressed as a (possibly infininte) sum of simple sinusoids. Usually we approximate it by truncating the series to $N$ terms as\n", "\n", "$$s_N(t) = \\frac{A_0}{2} + \\sum_{n=1}^N A_n \\sin(\\tfrac{2\\pi nt}{P}+\\phi_n) \\quad \\scriptstyle \\text{for integer}\\ N\\ \\ge\\ 1$$" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "## Discrete Fourier Transform\n", "\n", "If we have a short sample of a periodic signal, the discrete Fourier transform allows us to recover its sinusoidal frequency components. Numerically, the problem of computing the discrete Fourier transform has been studied for many years, and the result is the Fast Fourier Transform (FFT).\n", "\n", ">In 1994, Gilbert Strang described the FFT as \"the most important numerical algorithm of our lifetime\" and it was included in Top 10 Algorithms of 20th Century by the IEEE journal Computing in Science & Engineering. (source: https://en.wikipedia.org/wiki/Fast_Fourier_transform)\n", "\n", "In Python, this transform is available in the `numpy.fft` package." ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "slideshow": { "slide_type": "subslide" } }, "outputs": [], "source": [ "def fft_scaled(x, axis=-1, samp_freq=1.0, remove_mean=True):\n", " \"\"\"\n", " Fully scaled and folded FFT with physical amplitudes preserved.\n", "\n", " Arguments\n", " ---------\n", "\n", " x: numpy n-d array\n", " array of signal information.\n", "\n", " axis: int\n", " array axis along which to compute the FFT.\n", "\n", " samp_freq: float\n", " signal sampling frequency in Hz.\n", "\n", " remove_mean: boolean\n", " remove the mean of each signal prior to taking the FFT so the DC\n", " component of the FFT will be zero.\n", "\n", " Returns\n", " --------\n", "\n", " (fft_x, freq) where *fft_x* is the full complex FFT, scaled and folded\n", " so that only positive frequencies remain, and *freq* is a matching\n", " array of positive frequencies.\n", "\n", " Examples\n", " --------\n", "\n", " A common use case would present the signals in a 2-D array\n", " where each row contains a signal trace. Columns would\n", " then represent time sample intervals of the signals. The rows of\n", " the returned *fft_x* array would contain the FFT of each signal, and\n", " each column would correspond to an entry in the *freq* array.\n", "\n", " \"\"\"\n", " # Get length of the requested array axis.\n", " n = x.shape[axis]\n", "\n", " # Use truncating division here since for odd n we want to\n", " # round down to the next closest integer. See docs for numpy fft.\n", " half_n = n // 2\n", "\n", " # Remove the mean if requested\n", " if remove_mean:\n", " ind = [slice(None)] * x.ndim\n", " ind[axis] = np.newaxis\n", " x = x - x.mean(axis)[ind]\n", "\n", " # Compute fft, scale, and fold negative frequencies into positive.\n", " def scale_and_fold(x):\n", " # Scale by length of original signal\n", " x = (1.0 / n) * x[:half_n + 1]\n", " # Fold negative frequency\n", " x[1:] *= 2.0\n", " return x\n", "\n", " fft_x = np.fft.fft(x, axis=axis)\n", " fft_x = np.apply_along_axis(scale_and_fold, axis, fft_x)\n", "\n", " # Matching frequency array. The abs takes care of the case where n\n", " # is even, and the Nyquist frequency is usually negative.\n", " freq = np.fft.fftfreq(n, 1.0 / samp_freq)\n", " freq = np.abs(freq[:half_n + 1])\n", "\n", " return (fft_x, freq)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "## 1 Hz Square Wave" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "slideshow": { "slide_type": "-" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "3722ed324a6a4343b0f56b6b5cfbfd70", "version_major": 2, "version_minor": 0 }, "text/plain": [ "FigureCanvasNbAgg()" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "f_s = 1000.0 # Sampling frequency in Hz\n", "time = np.arange(0.0, 100.0 + 1.0/f_s, 1.0/f_s)\n", "square_wave = signal.square(2 * np.pi * time)\n", "plt.figure(figsize=(9, 5))\n", "plt.plot(time, square_wave), plt.xlabel('time (s)'), plt.ylabel('x(t)'), plt.title('1 Hz Square Wave')\n", "plt.xlim((0, 3)), plt.ylim((-1.1, 1.1));" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "## Fourier Analysis of Square Wave" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "slideshow": { "slide_type": "-" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "57c196e63dd3451db91c670f8690502b", "version_major": 2, "version_minor": 0 }, "text/plain": [ "FigureCanvasNbAgg()" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fft_x, freq_sq = fft_scaled(square_wave, samp_freq=f_s)\n", "f_max = 24.0\n", "plt.figure(figsize=(9, 5)), plt.plot(freq_sq, np.abs(fft_x))\n", "plt.xticks(np.arange(0.0, f_max + 1.0, 1.0))\n", "plt.xlim((0, f_max)), plt.xlabel('Frequency (Hz)'), plt.ylabel('Amplitude')\n", "plt.title('Frequency Spectrum of Square Wave');" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "## Approximate 1 Hz Square Wave\n", "\n", "Let's sythesize an approximation to a square wave by summing a reduced number of sinusoidal components." ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "slideshow": { "slide_type": "-" } }, "outputs": [], "source": [ "# Set frequency components and amplitudes.\n", "# Square waves contain all the odd harmonics\n", "# of the fundamental frequency.\n", "f_components = [1.0, 3.0, 5.0, 7.0, 9.0, 11.0]\n", "# f_components = [1.0, 3.0, 5.0, 7.0, 9.0, 11.0,\n", "# 13.0, 15.0, 17.0, 19.0, 21.0]\n", "amplitudes = [1.28 / f for f in f_components]\n", "\n", "# Generate the square wave\n", "s_t = np.zeros_like(time)\n", "for f, amp in zip(f_components, amplitudes):\n", " s_t += amp * np.sin(2 * np.pi * f * time)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "4b278a83fb714000aa337518c18f036b", "version_major": 2, "version_minor": 0 }, "text/plain": [ "FigureCanvasNbAgg()" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(9, 5)), plt.plot(time, s_t)\n", "plt.xlabel('time (s)'), plt.ylabel('$s(t)$'), plt.xlim((0, 3))\n", "plt.title('Approximate Square Wave');" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "## Fourier Analysis of Approximate Square Wave" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "slideshow": { "slide_type": "-" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "09778da2c95c4d3d95653f28ac4c767d", "version_major": 2, "version_minor": 0 }, "text/plain": [ "FigureCanvasNbAgg()" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "freq_spec, freq = fft_scaled(s_t, samp_freq=f_s)\n", "f_max = 12.0\n", "plt.figure(figsize=(9, 5)), plt.plot(freq, np.abs(freq_spec))\n", "plt.xticks(np.arange(0.0, f_max + 1.0, 1.0))\n", "plt.xlim((0, f_max)), plt.xlabel('Frequency (Hz)'), plt.ylabel('Amplitude')\n", "plt.title('Frequency Spectrum of Approximate Square Wave');" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Discrete-Time Sampling" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "## Nyquist-Shannon Sampling Theorem\n", "\n", "Consider a continuous signal $x(t)$ with Fourier transfom $X(f)$. Assume:\n", "\n", "* A sampled version of the signal is constructed as\n", "\n", "$$x_k = x(kT), k \\in \\mathbb{I}$$\n", "\n", "* $x(t)$ is band-limited such that\n", "\n", "$$X(f) = 0 \\ \\forall \\ |f| > B$$\n", "\n", "<center><img src=\"images/Bandlimited.svg\" width=\"300\"></center>\n", "\n", "Then $x(t)$ is uniquely recoverable from $x_k$ if\n", "\n", "$$\\frac{1}{T} \\triangleq f_s > 2B$$\n", "\n", "This critical frequency shows up so frequently that is has its own name, the Nyquist frequency.\n", "\n", "$$f_N = \\frac{f_s}{2}$$" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "skip" } }, "source": [ "*A note about frequency:* Most theoretical signal processing work is done using circular frequency $\\omega$ in units of rad/sec. This is done to eliminate the factor of of $2 \\pi$ which shows up in many equations when true ordinary frequency $f$ is used. That said, nearly all practical signal processing is done with ordinary frequency. The relationship between the two frequencies is\n", "\n", "$$ \\omega = 2 \\pi f$$\n", "\n", "<center><img src=\"images/ideal_sampler.png\" width=\"800\"></center>\n", "image credit: MIT Open Courseware, Signals and Systems, Oppenheimer" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "skip" } }, "source": [ "## Practical Realities\n", "\n", "* For complete recoverability, Nyquist requires an *ideal sampler* and an *ideal interpolator*. In practice, these are not physically realizable.\n", "\n", "$$x(t) = \\mathrm{IdealInterpolator}_T(\\mathrm{IdealSampler}_T(x(t))$$\n", "\n", "* Real signals are never perfectly band-limited. There are always some noise components out past the Nyquist sampling rate. \n", "\n", "* You will often be given sampled data but have very little insight into the system that generated the data. In that situation, you really have *no* guarantees that any estimates of frequency content for the underlying continuous time process are correct. You may be observing alias frequencies. A frequency $f_a$ is an alias of $f$ if\n", "\n", "$$ f_a = |nf_s - f|, n \\in \\mathbb{I}$$\n" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "## Aliasing\n", "\n", "When your signal contains frequency components that are above the Nyquist frequency then those high frequency components show up at lower frequencies. These lower frequencies are called *aliases* of the higher frequencies." ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "slideshow": { "slide_type": "skip" } }, "outputs": [], "source": [ "def scale_and_fold(x):\n", " n = len(x)\n", " half_n = n // 2\n", " # Scale by length of original signal\n", " x = (1.0 / n) * x[:half_n + 1]\n", " # Fold negative frequency\n", " x[1:] *= 2.0\n", " return x" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "slideshow": { "slide_type": "skip" } }, "outputs": [], "source": [ "def aliasing_demo():\n", " f_c = 1000.0 # Hz\n", " f_s = 20.0 # Hz\n", " f_end = 25.0 # Hz\n", " f = 1.0 # Hz\n", "\n", " time_c = np.arange(0.0, 10.0 + 1.0/f_c, 1/f_c)\n", " time_s = np.arange(0.0, 10.0 + 1.0/f_s, 1/f_s)\n", " freq_c = np.fft.fftfreq(len(time_c), 1.0 / f_c)\n", " freq_c = np.abs(freq_c[:len(time_c) // 2 + 1])\n", " freq_s = np.fft.fftfreq(len(time_s), 1.0 / f_s)\n", " freq_s = np.abs(freq_s[:len(time_s) // 2 + 1])\n", "\n", " f=widgets.FloatSlider(value=1.0, min=0.0, max=f_end, step=0.1, description='Frequency (Hz)')\n", " phi = widgets.FloatSlider(value=0.0, min=0.0, max=2.0*np.pi, step=0.1, description=\"Phase (rad)\")\n", "\n", " x_c = np.sin(2 * np.pi * f.value * time_c + phi.value)\n", " x_s = np.sin(2 * np.pi * f.value * time_s + phi.value)\n", " fig, ax = plt.subplots(2, 1, figsize=(9, 6))\n", " fig.subplots_adjust(hspace=0.3)\n", " line1 = ax[0].plot(time_c, x_c, alpha=0.9, lw=2.0)[0]\n", " line2 = ax[0].plot(time_s, x_s, marker='o', color='r', ls=':')[0]\n", " ax[0].set_xlabel(\"Time (s)\")\n", " ax[0].set_ylabel(\"$x$\")\n", " ax[0].set_title('Sine Wave Sampled at {} Hz'.format(int(f_s)))\n", " ax[0].set_ylim((-1, 1))\n", " ax[0].set_xlim((0, 1))\n", "\n", " window_c = 2 * np.hanning(len(time_c))\n", " window_s = 2 * np.hanning(len(time_s))\n", " fft_c = scale_and_fold(np.fft.fft(x_c * window_c))\n", " fft_s = scale_and_fold(np.fft.fft(x_s * window_s))\n", "\n", " line3 = ax[1].plot(freq_c, np.abs(fft_c), alpha=0.5, lw=2)[0]\n", " line4 = ax[1].plot(freq_s, np.abs(fft_s), 'r:', lw=2)[0]\n", " line5 = ax[1].axvline(f_s / 2.0, color='0.75', ls='--')\n", " plt.axvline(f_s, color='0.75')\n", " ax[1].text(1.02 * f_s / 2, 0.93, '$f_N$', {'size':14})\n", " ax[1].text(1.01 * f_s, 0.93, '$f_s$', {'size':14})\n", " ax[1].set_xlabel(\"Frequency (Hz)\")\n", " ax[1].set_ylabel(\"$X(f)$\")\n", " ax[1].set_xlim((0, f_end))\n", "\n", " def on_slider(s): \n", " x_c = np.sin(2 * np.pi * f.value * time_c + phi.value)\n", " x_s = np.sin(2 * np.pi * f.value * time_s + phi.value)\n", " fft_c = scale_and_fold(np.fft.fft(x_c * window_c))\n", " fft_s = scale_and_fold(np.fft.fft(x_s * window_s))\n", "\n", " # line1.set_xdata(time_c)\n", " line1.set_ydata(x_c)\n", " # line2.set_xdata(time_s)\n", " line2.set_ydata(x_s)\n", " line3.set_ydata(np.abs(fft_c))\n", " line4.set_ydata(np.abs(fft_s))\n", " plt.draw()\n", "\n", " f.on_trait_change(on_slider)\n", " phi.on_trait_change(on_slider)\n", "\n", " display(f)\n", " display(phi)" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "scrolled": false, "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "bd17599143194a1ea435b45abfd54c08", "version_major": 2, "version_minor": 0 }, "text/plain": [ "FigureCanvasNbAgg()" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "adf3fae27f3f4bbebee90295ca57f251", "version_major": 2, "version_minor": 0 }, "text/plain": [ "FloatSlider(value=1.0, description='Frequency (Hz)', max=25.0)" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "2f80da554b1b4e3e80352e84f1fa6512", "version_major": 2, "version_minor": 0 }, "text/plain": [ "FloatSlider(value=0.0, description='Phase (rad)', max=6.283185307179586)" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "aliasing_demo()" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "## Avoiding Aliasing\n", "\n", "If you have control over the sampling process, specify a sampling frequency that is at least twice the highest frequency component of your signal. If you really want to preserve high fidelity, specify a sampling frequency that is ten times the highest frequency component in your signal." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Digital Filtering" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "## Reshaping the Signal\n", "\n", "So far we've discussed analysis techniques for characterizing the frequency content of a signal. Now we discuss how to modify the frequency content of the signal to emphasize some of the information in it while removing other aspects. Generally accomplish this using digital filters." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "## Moving Average as a Digital Filter\n", "\n", "Let's express a moving average of five in the language of digital filtering. The output $y$ at the $k$-th sample is a function of the last five inputs $x$.\n", "\n", "$$y_k = \\frac{x_k + x_{k-1} + x_{k-2} + x_{k-3} + x_{k-4}}{5}$$\n", "\n", "More generally, this looks like\n", "\n", "$$y_k = b_0 x_k + b_1 x_{k-1} + b_2 x_{k-2} + b_3 x_{k-3} + b_4 x_{k-4}$$\n", "\n", "where all the $b_i = 0.2$. But they don't have to be equal. We could select each of the $b_i$ independently to be whatever we want. Then the filter looks like a weighted average." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "## Using Previous Outputs\n", "\n", "Even more generally, the current output can be a function of previous *outputs* as well as inputs if we desire.\n", "\n", "$$y_k = \\frac{1}{a_0} \\left(\\frac{b_0 x_k + b_1 x_{k-1} + b_2 x_{k-2} + b_3 x_{k-3} + b_4 x_{k-4}, + \\cdots}\n", " {a_1 y_{k-1} + a_2 y_{k-2} + a_3 y_{k-3} + a_4 y_{k-4} + \\cdots}\n", " \\right)$$\n", " \n", "But how do we choose the $b_i$ and the $a_i$ to get a filter with a particular desired behavior?" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "## Standard Digital Filter Designs\n", "\n", "Luckily, standard filter designs already exist to create filters that have certain response characteristics, either in the time domain or the frequency domain.\n", "\n", "* Butterworth\n", "* Chebyshev\n", "* Elliptic\n", "* Bessel\n", "\n", "When in doubt, use the Butterworth filter since it's a great general purpose filter and is easier to specify. All of these filter designs are available in `scipy.signal`." ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "slideshow": { "slide_type": "subslide" } }, "outputs": [], "source": [ "def butter_filt(x, sampling_freq_hz, corner_freq_hz=4.0, lowpass=True, filtfilt=False):\n", " \"\"\"\n", " Smooth data with a low-pass or high-pass filter.\n", "\n", " Apply a 2nd order Butterworth filter. Note that if filtfilt\n", " is True the applied filter is effectively a 4th order Butterworth.\n", " \n", " Parameters\n", " ----------\n", " x: 1D numpy array\n", " Array containing the signal to be smoothed.\n", " sampling_freq_hz: float\n", " Sampling frequency of the signal in Hz.\n", " corner_freq_hz: float\n", " Corner frequency of the Butterworth filter in Hz.\n", " lowpass: bool\n", " If True (default), apply a low-pass filter. If False,\n", " apply a high-pass filter.\n", " filtfilt: bool\n", " If True, apply the filter forward and then backward\n", " to elminate delay. If False (default), apply the\n", " filter only in the forward direction.\n", "\n", " Returns\n", " -------\n", " filtered: 1D numpy array\n", " Array containing smoothed signal\n", " b, a: 1D numpy arrays\n", " Polynomial coefficients of the smoothing filter as returned from\n", " the Butterworth design function.\n", "\n", " \"\"\"\n", " nyquist = sampling_freq_hz / 2.0\n", " f_c = np.array([corner_freq_hz, ], dtype=np.float64) # Hz\n", " # Normalize by Nyquist\n", " f_c /= nyquist\n", " # Second order Butterworth filter at corner frequency\n", " btype = 'low' if lowpass else 'high'\n", " b, a = signal.butter(2, f_c, btype=btype)\n", " # Apply the filter either in forward direction or forward-back.\n", " if filtfilt:\n", " filtered = signal.filtfilt(b, a, x)\n", " else:\n", " filtered = signal.lfilter(b, a, x)\n", " return (filtered, b, a)" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "11be9cdd1ff24a99b1f1822aea3204c9", "version_major": 2, "version_minor": 0 }, "text/plain": [ "FigureCanvasNbAgg()" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "f_c_low = 2.0 # Corner frequency in Hz\n", "s_filtered, b, a = butter_filt(s_t, f_s, f_c_low)\n", "w, h = signal.freqz(b, a, 2048)\n", "w *= (f_s / (2 * np.pi))\n", "fig, ax = plt.subplots(2, 1, sharex=True, figsize=(9, 5))\n", "ax[0].plot(w, abs(h)), plt.xlim((0, 12)), ax[1].plot(w, np.angle(h, deg=True))\n", "ax[0].set_ylabel('Attenuation Factor'), ax[1].set_ylabel('Phase Angle (deg)')\n", "ax[1].set_xlabel('Frequency (Hz)')\n", "ax[0].set_title('Filter Frequency Response - 2nd Order Butterworth Low-Pass');" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "2a08c880f76b47028e5d27cae973429a", "version_major": 2, "version_minor": 0 }, "text/plain": [ "FigureCanvasNbAgg()" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(9, 5))\n", "plt.plot(time, s_t, label='Original'), plt.plot(time, s_filtered, 'r-', label='Filtered')\n", "plt.xlim((0, 3))\n", "plt.xlabel('Time (s)'), plt.ylabel('Signal'), plt.legend(), plt.title('Low-Pass, Forward Filtering');" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "c1d9952feda34a318a21e8b40a6ace7f", "version_major": 2, "version_minor": 0 }, "text/plain": [ "FigureCanvasNbAgg()" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "s_filtered, b, a = butter_filt(s_t, f_s, f_c_low, filtfilt=True)\n", "plt.figure(figsize=(9, 5))\n", "plt.plot(time, s_t, label='Original'), plt.plot(time, s_filtered, 'r-', label='Filtered')\n", "plt.xlim((0, 3))\n", "plt.xlabel('Time (s)'), plt.ylabel('Signal'), plt.legend(), plt.title('Low-Pass, Forward-Backward Filtering');" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "30ae5db70e6d4bc98ab225aad8668f60", "version_major": 2, "version_minor": 0 }, "text/plain": [ "FigureCanvasNbAgg()" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "f_c_high = 6.0 # Corner frequency in Hz\n", "s_filtered, b, a = butter_filt(s_t, f_s, f_c_high, lowpass=False, filtfilt=True)\n", "w, h = signal.freqz(b, a, 2048)\n", "w *= (f_s / (2 * np.pi))\n", "fig, ax = plt.subplots(2, 1, sharex=True, figsize=(9, 5))\n", "ax[0].plot(w, abs(h)), plt.xlim((0, 12)), ax[1].plot(w[1:], np.angle(h, deg=True)[1:])\n", "ax[0].set_ylabel('Attenuation Factor'), ax[1].set_ylabel('Phase Angle (deg)')\n", "ax[1].set_xlabel('Frequency (Hz)')\n", "ax[0].set_title('Filter Frequency Response - 2nd Order Butterworth High-Pass');" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "80bca2b69aa24f8fa5aeb703dce792d9", "version_major": 2, "version_minor": 0 }, "text/plain": [ "FigureCanvasNbAgg()" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "s_filtered, b, a = butter_filt(s_t, f_s, f_c_high, lowpass=False, filtfilt=True)\n", "plt.figure(figsize=(9, 5))\n", "plt.plot(time, s_t, label='Original'), plt.plot(time, s_filtered, 'r-', label='Filtered')\n", "plt.xlim((0, 3))\n", "plt.xlabel('Time (s)'), plt.ylabel('Signal'), plt.legend(), plt.title('High-Pass, Forward-Backward Filtering');" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Thank you!" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "slideshow": { "slide_type": "skip" } }, "outputs": [], "source": [] } ], "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": "3.6.6" } }, "nbformat": 4, "nbformat_minor": 2 }
mit
AurelieDaviaud/Kaggle-Titanic
Kaggle-Titanic-CV-GitHub.ipynb
1
487191
{ "cells": [ { "cell_type": "markdown", "metadata": { "run_control": { "marked": false } }, "source": [ "# Titanic: Machine Learning from Disaster\n", "Author: Aurélie Daviaud " ] }, { "cell_type": "markdown", "metadata": { "run_control": { "marked": false } }, "source": [ "* Language: R\n", "* Methods : Classification (logistic regression, CART, Random Forest, SVM, neural network, XGBoost), cross-validation" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Table of Contents\n", "\n", "1 Exploratory data analysis <br>\n", "...1.1 Check data <br>\n", "...1.2 Check correlations among variables <br>\n", "...1.3 Visualize some potentially important variables <br>\n", "......1.3.1 Passenger's class <br>\n", "......1.3.2 Women and children first! <br>\n", "2 Feature engineering <br>\n", "....2.1 Create new features 1 <br>\n", "......2.1.1 Create Title from Name <br>\n", "....2.2 Impute missing data <br>\n", "......2.2.1 Fare <br>\n", "......2.2.2 Embarked <br>\n", "......2.2.3 Age <br>\n", "....2.3 Create new features 2 <br>\n", "......2.3.1 Create Child and Young from Age <br>\n", "......2.3.2 Create Title2 from Title <br>\n", "......2.3.3 Create dummy variables <br>\n", "3 Machine learning <br>\n", "...3.1 Logistic regression <br>\n", "......3.1.1 A first quick and dirty model <br>\n", "......3.1.2 Check learning curves <br>\n", "......3.1.3 Feature selection <br>\n", ".........3.1.3.1 Manual selection <br>\n", ".........3.1.3.2 Automatic selection <br>\n", "...3.2 Classification tree: CART (Recursive Partitioning) <br>\n", "...3.3 Random forest <br>\n", "...3.4 Support Vector Machine (SVM) <br>\n", "......3.4.1 With linear kernel <br>\n", "......3.4.2 With Gaussian kernel <br>\n", "...3.5 Neural network <br>\n", "...3.6 XGBoost <br>\n", "......3.6.1 Linear <br>\n", "......3.6.2 Tree <br>\n", "4 Compare the best models <br>\n", "...4.1 Compare the performances <br>\n", "...4.2 Check correlations among models <br>\n", "5 Predictions on the test set <br>\n", "...5.1 GLM <br>\n", "...5.2 CART <br>\n", "...5.3 Random forest <br>\n", "...5.4 SVM <br>\n", "...5.5 XGBoost (linear) <br>\n", "...5.6 XGBoost (tree) <br>\n", "6 Going further <br>" ] }, { "cell_type": "markdown", "metadata": { "run_control": { "marked": false } }, "source": [ "**Context**<br>\n", "The aim of this project is to predict survivors in the Titanic dataset. I will follow typical steps of a machine learning project: exploratory data analysis, pre-processing (data imputation, scaling, feature engineering, feature selection...) and machine learning." ] }, { "cell_type": "markdown", "metadata": { "run_control": { "marked": false } }, "source": [ "**Data description**" ] }, { "cell_type": "markdown", "metadata": { "run_control": { "marked": false } }, "source": [ "|Variable| Description|\n", "|--------|------------|\n", "|Survived|Survival (0 = No, 1 = Yes)|\n", "|Pclass|Ticket class (1 = 1st, 2 = 2nd, 3 = 3rd)|\n", "|Sex|Sex (male/female)|\n", "|Age|Age (in years)|\n", "|SibSp|# of siblings/spouses aboard the Titanic|\n", "|Parch|\t# of parents/children aboard the Titanic|\n", "|Ticket|Ticket number|\n", "|Fare|Passenger fare|\n", "|Cabin|Cabin number|\n", "|Embarked|Port of Embarkation (C = Cherbourg, Q = Queenstown, S = Southampton)|" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false, "init_cell": true, "run_control": { "marked": false } }, "outputs": [], "source": [ "# Load libraries\n", "library(polycor) # hetcor()\n", "library(ggplot2) # ggplot()\n", "library(caret) # train(), trainControl(), resamples(), varImp()\n", "library(mice) # mice()\n", "library(lattice) # densityplot()\n", "library(gridExtra) # grid.arrange()\n", "library(dummies) # dummy.data.frame()\n", "library(plyr) # revalue()\n", "library(rpart) # rpart()\n", "library(rpart.plot) # rpart.plot()\n", "library(randomForest) # method = 'rf'\n", "library(party) # method = 'cforest'\n", "library(kernlab) # method = 'svmLinear' and method = 'svmRadial' \n", "library(nnet) # method = 'nnet'\n", "library(corrplot) # corrplot()\n", "library(caretEnsemble)# caretList(), caretStack()" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false, "init_cell": true, "run_control": { "marked": false }, "scrolled": true }, "outputs": [], "source": [ "setwd(\"C:/Users/AurelieD/Documents/Boulot/0-Data scientist/Kaggle/Titanic\")" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true, "init_cell": true }, "outputs": [], "source": [ "## Load data (some cells of our table are empty, we have to specify to R to treat them as missing values (NA) using na.string)\n", "Train <- read.csv(\"train.csv\", na.strings=c(\"\"))\n", "Test <- read.csv(\"test.csv\", na.strings=c(\"\"))" ] }, { "cell_type": "code", "execution_count": 91, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "'data.frame':\t891 obs. of 12 variables:\n", " $ PassengerId: int 1 2 3 4 5 6 7 8 9 10 ...\n", " $ Survived : int 0 1 1 1 0 0 0 0 1 1 ...\n", " $ Pclass : int 3 1 3 1 3 3 1 3 3 2 ...\n", " $ Name : Factor w/ 891 levels \"Abbing, Mr. Anthony\",..: 109 191 358 277 16 559 520 629 417 581 ...\n", " $ Sex : Factor w/ 2 levels \"female\",\"male\": 2 1 1 1 2 2 2 2 1 1 ...\n", " $ Age : num 22 38 26 35 35 NA 54 2 27 14 ...\n", " $ SibSp : int 1 1 0 1 0 0 0 3 0 1 ...\n", " $ Parch : int 0 0 0 0 0 0 0 1 2 0 ...\n", " $ Ticket : Factor w/ 681 levels \"110152\",\"110413\",..: 524 597 670 50 473 276 86 396 345 133 ...\n", " $ Fare : num 7.25 71.28 7.92 53.1 8.05 ...\n", " $ Cabin : Factor w/ 147 levels \"A10\",\"A14\",\"A16\",..: NA 82 NA 56 NA NA 130 NA NA NA ...\n", " $ Embarked : Factor w/ 3 levels \"C\",\"Q\",\"S\": 3 1 3 3 3 2 3 3 3 1 ...\n" ] } ], "source": [ "str(Train)" ] }, { "cell_type": "code", "execution_count": 92, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "'data.frame':\t418 obs. of 11 variables:\n", " $ PassengerId: int 892 893 894 895 896 897 898 899 900 901 ...\n", " $ Pclass : int 3 3 2 3 3 3 3 2 3 3 ...\n", " $ Name : Factor w/ 418 levels \"Abbott, Master. Eugene Joseph\",..: 210 409 273 414 182 370 85 58 5 104 ...\n", " $ Sex : Factor w/ 2 levels \"female\",\"male\": 2 1 2 2 1 2 1 2 1 2 ...\n", " $ Age : num 34.5 47 62 27 22 14 30 26 18 21 ...\n", " $ SibSp : int 0 1 0 0 1 0 0 1 0 2 ...\n", " $ Parch : int 0 0 0 0 1 0 0 1 0 0 ...\n", " $ Ticket : Factor w/ 363 levels \"110469\",\"110489\",..: 153 222 74 148 139 262 159 85 101 270 ...\n", " $ Fare : num 7.83 7 9.69 8.66 12.29 ...\n", " $ Cabin : Factor w/ 76 levels \"A11\",\"A18\",\"A21\",..: NA NA NA NA NA NA NA NA NA NA ...\n", " $ Embarked : Factor w/ 3 levels \"C\",\"Q\",\"S\": 2 3 2 3 3 3 2 3 1 3 ...\n" ] } ], "source": [ "str(Test)" ] }, { "cell_type": "markdown", "metadata": { "run_control": { "marked": false } }, "source": [ "---\n", "# 1 Exploratory data analysis" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "First, let's take a look at the Train data." ] }, { "cell_type": "markdown", "metadata": { "run_control": { "marked": false } }, "source": [ "## 1.1 Check data" ] }, { "cell_type": "code", "execution_count": 93, "metadata": { "collapsed": false, "run_control": { "marked": false }, "scrolled": false }, "outputs": [ { "data": { "text/html": [ "<table>\n", "<thead><tr><th scope=col>PassengerId</th><th scope=col>Survived</th><th scope=col>Pclass</th><th scope=col>Name</th><th scope=col>Sex</th><th scope=col>Age</th><th scope=col>SibSp</th><th scope=col>Parch</th><th scope=col>Ticket</th><th scope=col>Fare</th><th scope=col>Cabin</th><th scope=col>Embarked</th></tr></thead>\n", "<tbody>\n", "\t<tr><td>1 </td><td>0 </td><td>3 </td><td>Braund, Mr. Owen Harris </td><td>male </td><td>22 </td><td>1 </td><td>0 </td><td>A/5 21171 </td><td> 7.2500 </td><td>NA </td><td>S </td></tr>\n", "\t<tr><td>2 </td><td>1 </td><td>1 </td><td>Cumings, Mrs. John Bradley (Florence Briggs Thayer)</td><td>female </td><td>38 </td><td>1 </td><td>0 </td><td>PC 17599 </td><td>71.2833 </td><td>C85 </td><td>C </td></tr>\n", "\t<tr><td>3 </td><td>1 </td><td>3 </td><td>Heikkinen, Miss. Laina </td><td>female </td><td>26 </td><td>0 </td><td>0 </td><td>STON/O2. 3101282 </td><td> 7.9250 </td><td>NA </td><td>S </td></tr>\n", "\t<tr><td>4 </td><td>1 </td><td>1 </td><td>Futrelle, Mrs. Jacques Heath (Lily May Peel) </td><td>female </td><td>35 </td><td>1 </td><td>0 </td><td>113803 </td><td>53.1000 </td><td>C123 </td><td>S </td></tr>\n", "\t<tr><td>5 </td><td>0 </td><td>3 </td><td>Allen, Mr. William Henry </td><td>male </td><td>35 </td><td>0 </td><td>0 </td><td>373450 </td><td> 8.0500 </td><td>NA </td><td>S </td></tr>\n", "\t<tr><td>6 </td><td>0 </td><td>3 </td><td>Moran, Mr. James </td><td>male </td><td>NA </td><td>0 </td><td>0 </td><td>330877 </td><td> 8.4583 </td><td>NA </td><td>Q </td></tr>\n", "</tbody>\n", "</table>\n" ], "text/latex": [ "\\begin{tabular}{r|llllllllllll}\n", " PassengerId & Survived & Pclass & Name & Sex & Age & SibSp & Parch & Ticket & Fare & Cabin & Embarked\\\\\n", "\\hline\n", "\t 1 & 0 & 3 & Braund, Mr. Owen Harris & male & 22 & 1 & 0 & A/5 21171 & 7.2500 & NA & S \\\\\n", "\t 2 & 1 & 1 & Cumings, Mrs. John Bradley (Florence Briggs Thayer) & female & 38 & 1 & 0 & PC 17599 & 71.2833 & C85 & C \\\\\n", "\t 3 & 1 & 3 & Heikkinen, Miss. Laina & female & 26 & 0 & 0 & STON/O2. 3101282 & 7.9250 & NA & S \\\\\n", "\t 4 & 1 & 1 & Futrelle, Mrs. Jacques Heath (Lily May Peel) & female & 35 & 1 & 0 & 113803 & 53.1000 & C123 & S \\\\\n", "\t 5 & 0 & 3 & Allen, Mr. William Henry & male & 35 & 0 & 0 & 373450 & 8.0500 & NA & S \\\\\n", "\t 6 & 0 & 3 & Moran, Mr. James & male & NA & 0 & 0 & 330877 & 8.4583 & NA & Q \\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "PassengerId | Survived | Pclass | Name | Sex | Age | SibSp | Parch | Ticket | Fare | Cabin | Embarked | \n", "|---|---|---|---|---|---|\n", "| 1 | 0 | 3 | Braund, Mr. Owen Harris | male | 22 | 1 | 0 | A/5 21171 | 7.2500 | NA | S | \n", "| 2 | 1 | 1 | Cumings, Mrs. John Bradley (Florence Briggs Thayer) | female | 38 | 1 | 0 | PC 17599 | 71.2833 | C85 | C | \n", "| 3 | 1 | 3 | Heikkinen, Miss. Laina | female | 26 | 0 | 0 | STON/O2. 3101282 | 7.9250 | NA | S | \n", "| 4 | 1 | 1 | Futrelle, Mrs. Jacques Heath (Lily May Peel) | female | 35 | 1 | 0 | 113803 | 53.1000 | C123 | S | \n", "| 5 | 0 | 3 | Allen, Mr. William Henry | male | 35 | 0 | 0 | 373450 | 8.0500 | NA | S | \n", "| 6 | 0 | 3 | Moran, Mr. James | male | NA | 0 | 0 | 330877 | 8.4583 | NA | Q | \n", "\n", "\n" ], "text/plain": [ " PassengerId Survived Pclass\n", "1 1 0 3 \n", "2 2 1 1 \n", "3 3 1 3 \n", "4 4 1 1 \n", "5 5 0 3 \n", "6 6 0 3 \n", " Name Sex Age SibSp Parch\n", "1 Braund, Mr. Owen Harris male 22 1 0 \n", "2 Cumings, Mrs. John Bradley (Florence Briggs Thayer) female 38 1 0 \n", "3 Heikkinen, Miss. Laina female 26 0 0 \n", "4 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35 1 0 \n", "5 Allen, Mr. William Henry male 35 0 0 \n", "6 Moran, Mr. James male NA 0 0 \n", " Ticket Fare Cabin Embarked\n", "1 A/5 21171 7.2500 NA S \n", "2 PC 17599 71.2833 C85 C \n", "3 STON/O2. 3101282 7.9250 NA S \n", "4 113803 53.1000 C123 S \n", "5 373450 8.0500 NA S \n", "6 330877 8.4583 NA Q " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "## Check data\n", "head(Train)" ] }, { "cell_type": "code", "execution_count": 94, "metadata": { "collapsed": false, "run_control": { "marked": false }, "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "'data.frame':\t891 obs. of 12 variables:\n", " $ PassengerId: int 1 2 3 4 5 6 7 8 9 10 ...\n", " $ Survived : int 0 1 1 1 0 0 0 0 1 1 ...\n", " $ Pclass : int 3 1 3 1 3 3 1 3 3 2 ...\n", " $ Name : Factor w/ 891 levels \"Abbing, Mr. Anthony\",..: 109 191 358 277 16 559 520 629 417 581 ...\n", " $ Sex : Factor w/ 2 levels \"female\",\"male\": 2 1 1 1 2 2 2 2 1 1 ...\n", " $ Age : num 22 38 26 35 35 NA 54 2 27 14 ...\n", " $ SibSp : int 1 1 0 1 0 0 0 3 0 1 ...\n", " $ Parch : int 0 0 0 0 0 0 0 1 2 0 ...\n", " $ Ticket : Factor w/ 681 levels \"110152\",\"110413\",..: 524 597 670 50 473 276 86 396 345 133 ...\n", " $ Fare : num 7.25 71.28 7.92 53.1 8.05 ...\n", " $ Cabin : Factor w/ 147 levels \"A10\",\"A14\",\"A16\",..: NA 82 NA 56 NA NA 130 NA NA NA ...\n", " $ Embarked : Factor w/ 3 levels \"C\",\"Q\",\"S\": 3 1 3 3 3 2 3 3 3 1 ...\n" ] } ], "source": [ "str(Train) # check factors' levels" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true, "init_cell": true, "run_control": { "marked": false } }, "outputs": [], "source": [ "# Specify proper classes to variables\n", "Train$Survived <- factor(Train$Survived)\n", "Train$Pclass <- factor(Train$Pclass, levels=c(\"1\", \"2\", \"3\"), ordered=TRUE)" ] }, { "cell_type": "code", "execution_count": 96, "metadata": { "collapsed": false, "run_control": { "marked": false }, "scrolled": false }, "outputs": [ { "data": { "text/plain": [ " PassengerId Survived Pclass Name \n", " Min. : 1.0 0:549 1:216 Abbing, Mr. Anthony : 1 \n", " 1st Qu.:223.5 1:342 2:184 Abbott, Mr. Rossmore Edward : 1 \n", " Median :446.0 3:491 Abbott, Mrs. Stanton (Rosa Hunt) : 1 \n", " Mean :446.0 Abelson, Mr. Samuel : 1 \n", " 3rd Qu.:668.5 Abelson, Mrs. Samuel (Hannah Wizosky): 1 \n", " Max. :891.0 Adahl, Mr. Mauritz Nils Martin : 1 \n", " (Other) :885 \n", " Sex Age SibSp Parch Ticket \n", " female:314 Min. : 0.42 Min. :0.000 Min. :0.0000 1601 : 7 \n", " male :577 1st Qu.:20.12 1st Qu.:0.000 1st Qu.:0.0000 347082 : 7 \n", " Median :28.00 Median :0.000 Median :0.0000 CA. 2343: 7 \n", " Mean :29.70 Mean :0.523 Mean :0.3816 3101295 : 6 \n", " 3rd Qu.:38.00 3rd Qu.:1.000 3rd Qu.:0.0000 347088 : 6 \n", " Max. :80.00 Max. :8.000 Max. :6.0000 CA 2144 : 6 \n", " NA's :177 (Other) :852 \n", " Fare Cabin Embarked \n", " Min. : 0.00 B96 B98 : 4 C :168 \n", " 1st Qu.: 7.91 C23 C25 C27: 4 Q : 77 \n", " Median : 14.45 G6 : 4 S :644 \n", " Mean : 32.20 C22 C26 : 3 NA's: 2 \n", " 3rd Qu.: 31.00 D : 3 \n", " Max. :512.33 (Other) :186 \n", " NA's :687 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "summary(Train) # check min/max/mean... of each variable" ] }, { "cell_type": "markdown", "metadata": { "run_control": { "marked": false } }, "source": [ "## 1.2 Check correlations among variables" ] }, { "cell_type": "markdown", "metadata": { "run_control": { "marked": false } }, "source": [ "This will allow us to:\n", "* see which variables may be good predictors of our dependent variable (Survived) (this can be helpful when we don't have any domain knowledge);\n", "* check multicollinearity among predictors, i.e. when one predictor can be linearly predicted from the others." ] }, { "cell_type": "markdown", "metadata": { "run_control": { "marked": false } }, "source": [ "Since we have factors, ordered factors and numeric variables, we will use the `hetcor()` function of the 'polycor' package. It \"computes a heterogenous correlation matrix, consisting of Pearson product-moment correlations between numeric variables, polyserial correlations between numeric and ordinal variables, and polychoric correlations between ordinal variables.\" (https://cran.r-project.org/web/packages/polycor/polycor.pdf). We will ignore the variables Cabin and Name for preliminary exploration of the data since they contain many missing data and need some processing." ] }, { "cell_type": "code", "execution_count": 97, "metadata": { "collapsed": true, "run_control": { "marked": false } }, "outputs": [], "source": [ "Train_sub <- subset(Train, select=c(Survived, Pclass, Sex, Age, SibSp, Parch, Fare, Embarked)) " ] }, { "cell_type": "code", "execution_count": 98, "metadata": { "collapsed": false, "run_control": { "marked": false } }, "outputs": [ { "data": { "text/html": [ "<table>\n", "<thead><tr><th></th><th scope=col>Survived</th><th scope=col>Pclass</th><th scope=col>Sex</th><th scope=col>Age</th><th scope=col>SibSp</th><th scope=col>Parch</th><th scope=col>Fare</th><th scope=col>Embarked</th></tr></thead>\n", "<tbody>\n", "\t<tr><th scope=row>Survived</th><td> 1.00000000</td><td>-0.48055675</td><td>-0.7609001 </td><td>-0.09788042</td><td>-0.04897746</td><td> 0.10128585</td><td> 0.38166338</td><td>-0.26734299</td></tr>\n", "\t<tr><th scope=row>Pclass</th><td>-0.48055675</td><td> 1.00000000</td><td> 0.1992472 </td><td>-0.41238334</td><td> 0.12283381</td><td> 0.02263785</td><td>-0.78299896</td><td> 0.19342079</td></tr>\n", "\t<tr><th scope=row>Sex</th><td>-0.76090008</td><td> 0.19924719</td><td> 1.0000000 </td><td> 0.12085141</td><td>-0.14124694</td><td>-0.30412276</td><td>-0.21952881</td><td> 0.18706902</td></tr>\n", "\t<tr><th scope=row>Age</th><td>-0.09788042</td><td>-0.41238334</td><td> 0.1208514 </td><td> 1.00000000</td><td>-0.30824676</td><td>-0.18911926</td><td> 0.09606669</td><td>-0.04053557</td></tr>\n", "\t<tr><th scope=row>SibSp</th><td>-0.04897746</td><td> 0.12283381</td><td>-0.1412469 </td><td>-0.30824676</td><td> 1.00000000</td><td> 0.41483770</td><td> 0.15965104</td><td> 0.11114924</td></tr>\n", "\t<tr><th scope=row>Parch</th><td> 0.10128585</td><td> 0.02263785</td><td>-0.3041228 </td><td>-0.18911926</td><td> 0.41483770</td><td> 1.00000000</td><td> 0.21622494</td><td> 0.06730955</td></tr>\n", "\t<tr><th scope=row>Fare</th><td> 0.38166338</td><td>-0.78299896</td><td>-0.2195288 </td><td> 0.09606669</td><td> 0.15965104</td><td> 0.21622494</td><td> 1.00000000</td><td>-0.25808764</td></tr>\n", "\t<tr><th scope=row>Embarked</th><td>-0.26734299</td><td> 0.19342079</td><td> 0.1870690 </td><td>-0.04053557</td><td> 0.11114924</td><td> 0.06730955</td><td>-0.25808764</td><td> 1.00000000</td></tr>\n", "</tbody>\n", "</table>\n" ], "text/latex": [ "\\begin{tabular}{r|llllllll}\n", " & Survived & Pclass & Sex & Age & SibSp & Parch & Fare & Embarked\\\\\n", "\\hline\n", "\tSurvived & 1.00000000 & -0.48055675 & -0.7609001 & -0.09788042 & -0.04897746 & 0.10128585 & 0.38166338 & -0.26734299\\\\\n", "\tPclass & -0.48055675 & 1.00000000 & 0.1992472 & -0.41238334 & 0.12283381 & 0.02263785 & -0.78299896 & 0.19342079\\\\\n", "\tSex & -0.76090008 & 0.19924719 & 1.0000000 & 0.12085141 & -0.14124694 & -0.30412276 & -0.21952881 & 0.18706902\\\\\n", "\tAge & -0.09788042 & -0.41238334 & 0.1208514 & 1.00000000 & -0.30824676 & -0.18911926 & 0.09606669 & -0.04053557\\\\\n", "\tSibSp & -0.04897746 & 0.12283381 & -0.1412469 & -0.30824676 & 1.00000000 & 0.41483770 & 0.15965104 & 0.11114924\\\\\n", "\tParch & 0.10128585 & 0.02263785 & -0.3041228 & -0.18911926 & 0.41483770 & 1.00000000 & 0.21622494 & 0.06730955\\\\\n", "\tFare & 0.38166338 & -0.78299896 & -0.2195288 & 0.09606669 & 0.15965104 & 0.21622494 & 1.00000000 & -0.25808764\\\\\n", "\tEmbarked & -0.26734299 & 0.19342079 & 0.1870690 & -0.04053557 & 0.11114924 & 0.06730955 & -0.25808764 & 1.00000000\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "| <!--/--> | Survived | Pclass | Sex | Age | SibSp | Parch | Fare | Embarked | \n", "|---|---|---|---|---|---|---|---|\n", "| Survived | 1.00000000 | -0.48055675 | -0.7609001 | -0.09788042 | -0.04897746 | 0.10128585 | 0.38166338 | -0.26734299 | \n", "| Pclass | -0.48055675 | 1.00000000 | 0.1992472 | -0.41238334 | 0.12283381 | 0.02263785 | -0.78299896 | 0.19342079 | \n", "| Sex | -0.76090008 | 0.19924719 | 1.0000000 | 0.12085141 | -0.14124694 | -0.30412276 | -0.21952881 | 0.18706902 | \n", "| Age | -0.09788042 | -0.41238334 | 0.1208514 | 1.00000000 | -0.30824676 | -0.18911926 | 0.09606669 | -0.04053557 | \n", "| SibSp | -0.04897746 | 0.12283381 | -0.1412469 | -0.30824676 | 1.00000000 | 0.41483770 | 0.15965104 | 0.11114924 | \n", "| Parch | 0.10128585 | 0.02263785 | -0.3041228 | -0.18911926 | 0.41483770 | 1.00000000 | 0.21622494 | 0.06730955 | \n", "| Fare | 0.38166338 | -0.78299896 | -0.2195288 | 0.09606669 | 0.15965104 | 0.21622494 | 1.00000000 | -0.25808764 | \n", "| Embarked | -0.26734299 | 0.19342079 | 0.1870690 | -0.04053557 | 0.11114924 | 0.06730955 | -0.25808764 | 1.00000000 | \n", "\n", "\n" ], "text/plain": [ " Survived Pclass Sex Age SibSp Parch \n", "Survived 1.00000000 -0.48055675 -0.7609001 -0.09788042 -0.04897746 0.10128585\n", "Pclass -0.48055675 1.00000000 0.1992472 -0.41238334 0.12283381 0.02263785\n", "Sex -0.76090008 0.19924719 1.0000000 0.12085141 -0.14124694 -0.30412276\n", "Age -0.09788042 -0.41238334 0.1208514 1.00000000 -0.30824676 -0.18911926\n", "SibSp -0.04897746 0.12283381 -0.1412469 -0.30824676 1.00000000 0.41483770\n", "Parch 0.10128585 0.02263785 -0.3041228 -0.18911926 0.41483770 1.00000000\n", "Fare 0.38166338 -0.78299896 -0.2195288 0.09606669 0.15965104 0.21622494\n", "Embarked -0.26734299 0.19342079 0.1870690 -0.04053557 0.11114924 0.06730955\n", " Fare Embarked \n", "Survived 0.38166338 -0.26734299\n", "Pclass -0.78299896 0.19342079\n", "Sex -0.21952881 0.18706902\n", "Age 0.09606669 -0.04053557\n", "SibSp 0.15965104 0.11114924\n", "Parch 0.21622494 0.06730955\n", "Fare 1.00000000 -0.25808764\n", "Embarked -0.25808764 1.00000000" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "hetcor(Train_sub, use = \"pairwise.complete.obs\")$correlations" ] }, { "cell_type": "markdown", "metadata": { "run_control": { "marked": false } }, "source": [ "We observe that:\n", "* Survived is highly correlated with Sex (r=-0.76). So Sex may be a good predictor for Survived. To a lesser extent, Pclass is also correlated to Survived (r=-0.48).\n", "* Fare is highly correlated with Pclass (r=-0.78). So Fare and Pclass are collinear." ] }, { "cell_type": "markdown", "metadata": { "run_control": { "marked": false } }, "source": [ "## 1.3 Visualize some potentially important variables" ] }, { "cell_type": "markdown", "metadata": { "run_control": { "marked": false } }, "source": [ "### 1.3.1 Passenger's class\n", "\n", "It is well known that passengers from the 1st class were the first ones to be allowed to board on the life rafts. Therefore the correlation we have just seen between the survival and the passenger's class was expected. Let's inspect this relationship deeplier." ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": true, "init_cell": true, "run_control": { "marked": false } }, "outputs": [], "source": [ "# Give a proper size to plots\n", "options(repr.plot.width=4, repr.plot.height=4)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false, "init_cell": true, "run_control": { "marked": false } }, "outputs": [], "source": [ "# Prepare a nice theme (i.e. general appearance of graphs)\n", "theme_new <- theme_classic() + \n", " theme(plot.title = element_text(size=11, face=\"bold\", hjust = 0.5), axis.text.x = element_text(size = 11))" ] }, { "cell_type": "code", "execution_count": 101, "metadata": { "collapsed": false, "run_control": { "marked": false } }, "outputs": [ { "data": {}, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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"text/plain": [ "plot without title" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ggplot(data=Train) + \n", " geom_bar(aes(x = Survived, fill = Pclass), position = \"fill\") +\n", " labs(title = \"Distribution of classes\\namong dead and safe passengers\", y = \"Proportion of passengers\", x = \"\") +\n", " scale_x_discrete(labels=c(\"Died\",\"Survived\")) +\n", " scale_fill_brewer(palette=\"Blues\") +\n", " theme_new" ] }, { "cell_type": "code", "execution_count": 102, "metadata": { "collapsed": false, "run_control": { "marked": false } }, "outputs": [ { "data": {}, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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"text/plain": [ "plot without title" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ggplot(data=Train) + \n", " geom_bar(aes(x = Pclass, fill = Survived), position = \"fill\") +\n", " labs(title = \"Distribution of dead and safe passengers\\namong classes\", y = \"Proportion of passengers\", x = \"\") +\n", " scale_x_discrete(labels=c(\"1\",\"2\",\"3\")) +\n", " scale_fill_brewer(palette=\"Blues\", labels=c(\"Died\", \"Survived\"), name = \"Survival\") +\n", " theme_new" ] }, { "cell_type": "markdown", "metadata": { "run_control": { "marked": false } }, "source": [ "We can see that most of the people who survived were in the first class. On the contrary, most of the people who died were in the 3rd class." ] }, { "cell_type": "markdown", "metadata": { "run_control": { "marked": false } }, "source": [ "Similarly, here, we can see that most of the first class people survived whereas most of the 3rd class people died." ] }, { "cell_type": "markdown", "metadata": { "run_control": { "marked": false } }, "source": [ "### 1.3.2 Women and children first!" ] }, { "cell_type": "code", "execution_count": 103, "metadata": { "collapsed": false, "run_control": { "marked": false } }, "outputs": [ { "data": {}, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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"text/plain": [ "plot without title" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ggplot(data=Train) + \n", " geom_bar(aes(x = Survived, fill = Sex), position = \"fill\") +\n", " labs(title = \"Distribution of classes\\namong dead and safe passengers\", y = \"Proportion of passengers\", x = \"\") +\n", " scale_x_discrete(labels=c(\"Died\",\"Survived\")) +\n", " scale_fill_brewer(palette=\"Blues\") +\n", " theme_new" ] }, { "cell_type": "markdown", "metadata": { "run_control": { "marked": false } }, "source": [ "Most dead people were men and most surviving people were women. <br>\n", "As expected (from our domain knowledge and our preliminary correlation test), Sex is a good predictor for the survival of passengers.\n", "\n", "What about age? Children were supposed to board the life rafts first." ] }, { "cell_type": "code", "execution_count": 104, "metadata": { "collapsed": false, "run_control": { "marked": false } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Warning message:\n", "\"Removed 177 rows containing non-finite values (stat_boxplot).\"" ] }, { "data": {}, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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Y/HvJpDfJTdLr/qC/GOZf2Ci/a8+Fld\nAV/yX9XLQ3F8WI/gj9v8/uU/NxscCC77+z932X4pfwLKr/pCvGNJSPB9PXSP1aeP6kRYjeDi\n+PO+OiH/FEPBlfL72/Ll9rY5hLYQ71gSEtzOqv1BXA9/X3485P8NBVeffuV/ij/tNMIrxDqW\n9Qv+U/2cr19wRX57KviY/ygvw8e66BUQ3B7zY3Wn6i/RFWu7ROf5sXlvuv3RqyufAj6qH9G6\n2BbiHcvaBVfPwU3hpZqY/FfNqX/mD8diRZOscqJU3n6PL5W72/I6XU+amv79KU/U7ubcFuId\ny5oFN7Sjc6zXi/6u7zGpuG9Wsj4qdSU/PcFF8yxcF5tCvGNZu+D7l2NdKNzZ7E6V8tPjqhY6\nytnTQ7Vs7rr6clvOonzBv6q5Vz/ncoV4x7JewTALCDYOgo2DYOMg2DgINg6CjYNg4yDYOAg2\nDoKNg2DjINg4CDYOgo2DYOMg2DgINg6CjYNg4yDYOAg2DoKNg2DjINg4CDYOgo2DYOMg2DgI\nNg6CjYNg4/wf0sh5pOqOiAQAAAAASUVORK5CYII=", "text/plain": [ "plot without title" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ggplot(data=Train) + \n", " geom_boxplot(aes(x = Survived, y = Age)) +\n", " labs(title = \"Distribution of ages\\namong dead and safe passengers\", x = \"\") +\n", " scale_x_discrete(labels=c(\"Died\",\"Survived\")) +\n", " theme_new" ] }, { "cell_type": "markdown", "metadata": { "run_control": { "marked": false } }, "source": [ "There seem to be only a slight difference in the distribution of ages among dead and safe passengers. Although the median is equal, the upper and lower quartiles are a bit lower for the surviving passengers meaning that surviving people were slightly younger than dead people." ] }, { "cell_type": "markdown", "metadata": { "run_control": { "marked": false } }, "source": [ "Let's use an histogram now to look at the details." ] }, { "cell_type": "code", "execution_count": 105, "metadata": { "collapsed": false, "run_control": { "marked": false } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Warning message:\n", "\"Removed 177 rows containing non-finite values (stat_bin).\"" ] }, { "data": {}, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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YMUrA4EDQoLHhNOOhA0SMHqQNAgBasD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", "text/plain": [ "plot without title" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ggplot(data = Train, aes(x = Age, group = Survived, fill = Survived)) + \n", " geom_histogram(position=\"dodge\", binwidth = 5, boundary=15) + \n", " labs(title = \"Distribution of ages\\namong dead and safe passengers\") + \n", " scale_fill_manual(labels = c(\"Died\", \"Survived\"), name = \"Survival\", values = c(\"steelblue3\", \"lightblue\")) +\n", " theme_new" ] }, { "cell_type": "markdown", "metadata": { "run_control": { "marked": false } }, "source": [ "Now, we clearly see a difference for some ages: most child under 5 have survived whereas most young passengers between 15-30 have died." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "***\n", "# 2 Feature engineering" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Our aim is to give as much information as we can to the model: the more useful information the model gets, the better it is likely to be." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "First, let's combine the Train and Test datasets (while removing temporarily the Survived column of the Train dataset)." ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": true, "init_cell": true }, "outputs": [], "source": [ "Survived <- Train$Survived # We will add it back to the train dataset after processing of the combined data set." ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": true, "init_cell": true }, "outputs": [], "source": [ "Train$Survived <- NULL\n", "titanic = rbind(Train, Test)" ] }, { "cell_type": "code", "execution_count": 108, "metadata": { "collapsed": false, "run_control": { "marked": false }, "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "'data.frame':\t1309 obs. of 11 variables:\n", " $ PassengerId: int 1 2 3 4 5 6 7 8 9 10 ...\n", " $ Pclass : Ord.factor w/ 3 levels \"1\"<\"2\"<\"3\": 3 1 3 1 3 3 1 3 3 2 ...\n", " $ Name : Factor w/ 1307 levels \"Abbing, Mr. Anthony\",..: 109 191 358 277 16 559 520 629 417 581 ...\n", " $ Sex : Factor w/ 2 levels \"female\",\"male\": 2 1 1 1 2 2 2 2 1 1 ...\n", " $ Age : num 22 38 26 35 35 NA 54 2 27 14 ...\n", " $ SibSp : int 1 1 0 1 0 0 0 3 0 1 ...\n", " $ Parch : int 0 0 0 0 0 0 0 1 2 0 ...\n", " $ Ticket : Factor w/ 929 levels \"110152\",\"110413\",..: 524 597 670 50 473 276 86 396 345 133 ...\n", " $ Fare : num 7.25 71.28 7.92 53.1 8.05 ...\n", " $ Cabin : Factor w/ 186 levels \"A10\",\"A14\",\"A16\",..: NA 82 NA 56 NA NA 130 NA NA NA ...\n", " $ Embarked : Factor w/ 3 levels \"C\",\"Q\",\"S\": 3 1 3 3 3 2 3 3 3 1 ...\n" ] } ], "source": [ "str(titanic)" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "## 2.1 Create new features 1" ] }, { "cell_type": "markdown", "metadata": { "run_control": { "marked": false } }, "source": [ "### 2.1.1 Create Title from Name\n", "The name can provide some interesting information. It is in the form: Last Name, Title First name Middle name. In case of a spouse, her first name and last name are added in brackets. <br>\n", "Let's retrieve the title which may reflect the age as well as the social status of the passengers." ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false, "init_cell": true, "run_control": { "marked": false } }, "outputs": [], "source": [ "titanic$Title <- sub(\".+,\\\\s\", \"\", titanic$Name)\n", "titanic$Title <- as.factor(sub(\"\\\\.\\\\s.+\", \"\", titanic$Title))" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true, "run_control": { "marked": false } }, "source": [ "## 2.2 Impute missing data" ] }, { "cell_type": "markdown", "metadata": { "run_control": { "marked": false } }, "source": [ "First, we can increase the number of observations used in the model by imputing the missing values. " ] }, { "cell_type": "markdown", "metadata": { "run_control": { "marked": false } }, "source": [ "What is the numer of missing values in each variable?" ] }, { "cell_type": "code", "execution_count": 110, "metadata": { "collapsed": true, "run_control": { "marked": false } }, "outputs": [], "source": [ "nbNA <- function(x){sum(is.na(x))}" ] }, { "cell_type": "code", "execution_count": 111, "metadata": { "collapsed": false, "run_control": { "marked": false }, "scrolled": true }, "outputs": [ { "data": { "text/html": [ "<dl class=dl-horizontal>\n", "\t<dt>PassengerId</dt>\n", "\t\t<dd>0</dd>\n", "\t<dt>Pclass</dt>\n", "\t\t<dd>0</dd>\n", "\t<dt>Name</dt>\n", "\t\t<dd>0</dd>\n", "\t<dt>Sex</dt>\n", "\t\t<dd>0</dd>\n", "\t<dt>Age</dt>\n", "\t\t<dd>263</dd>\n", "\t<dt>SibSp</dt>\n", "\t\t<dd>0</dd>\n", "\t<dt>Parch</dt>\n", "\t\t<dd>0</dd>\n", "\t<dt>Ticket</dt>\n", "\t\t<dd>0</dd>\n", "\t<dt>Fare</dt>\n", "\t\t<dd>1</dd>\n", "\t<dt>Cabin</dt>\n", "\t\t<dd>1014</dd>\n", "\t<dt>Embarked</dt>\n", "\t\t<dd>2</dd>\n", "\t<dt>Title</dt>\n", "\t\t<dd>0</dd>\n", "</dl>\n" ], "text/latex": [ "\\begin{description*}\n", "\\item[PassengerId] 0\n", "\\item[Pclass] 0\n", "\\item[Name] 0\n", "\\item[Sex] 0\n", "\\item[Age] 263\n", "\\item[SibSp] 0\n", "\\item[Parch] 0\n", "\\item[Ticket] 0\n", "\\item[Fare] 1\n", "\\item[Cabin] 1014\n", "\\item[Embarked] 2\n", "\\item[Title] 0\n", "\\end{description*}\n" ], "text/markdown": [ "PassengerId\n", ": 0Pclass\n", ": 0Name\n", ": 0Sex\n", ": 0Age\n", ": 263SibSp\n", ": 0Parch\n", ": 0Ticket\n", ": 0Fare\n", ": 1Cabin\n", ": 1014Embarked\n", ": 2Title\n", ": 0\n", "\n" ], "text/plain": [ "PassengerId Pclass Name Sex Age SibSp \n", " 0 0 0 0 263 0 \n", " Parch Ticket Fare Cabin Embarked Title \n", " 0 0 1 1014 2 0 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "apply(titanic,2,nbNA)" ] }, { "cell_type": "markdown", "metadata": { "run_control": { "marked": false } }, "source": [ "### 2.2.1 Fare" ] }, { "cell_type": "code", "execution_count": 112, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAeAAAAHgCAMAAABKCk6nAAAAMFBMVEUAAABNTU1oaGh8fHyM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\nRK5CYII=", "text/plain": [ "Plot with title \"Histogram of titanic$Fare\"" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "hist(titanic$Fare, breaks=50)" ] }, { "cell_type": "code", "execution_count": 113, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "data": { "text/plain": [ " Min. 1st Qu. Median Mean 3rd Qu. Max. NA's \n", " 0.000 7.896 14.450 33.300 31.280 512.300 1 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "summary(titanic$Fare)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Given that the distribution of Fare is highly skewed, the best seems to impute the missing value with the median." ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false, "init_cell": true, "run_control": { "marked": false } }, "outputs": [], "source": [ "# Impute Fare with the median\n", "titanic[is.na(titanic[,\"Fare\"]), \"Fare\"] <- median(titanic[,\"Fare\"], na.rm = TRUE)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 2.2.2 Embarked" ] }, { "cell_type": "markdown", "metadata": { "run_control": { "marked": false } }, "source": [ "<br>\n", "For **Embarked**, there are only two passengers with missing values. Who are those passengers?" ] }, { "cell_type": "code", "execution_count": 115, "metadata": { "collapsed": false, "run_control": { "marked": false }, "scrolled": true }, "outputs": [ { "data": { "text/html": [ "<table>\n", "<thead><tr><th></th><th scope=col>PassengerId</th><th scope=col>Pclass</th><th scope=col>Name</th><th scope=col>Sex</th><th scope=col>Age</th><th scope=col>SibSp</th><th scope=col>Parch</th><th scope=col>Ticket</th><th scope=col>Fare</th><th scope=col>Cabin</th><th scope=col>Embarked</th><th scope=col>Title</th></tr></thead>\n", "<tbody>\n", "\t<tr><th scope=row>62</th><td> 62 </td><td>1 </td><td>Icard, Miss. Amelie </td><td>female </td><td>38 </td><td>0 </td><td>0 </td><td>113572 </td><td>80 </td><td>B28 </td><td>NA </td><td>Miss </td></tr>\n", "\t<tr><th scope=row>830</th><td>830 </td><td>1 </td><td>Stone, Mrs. George Nelson (Martha Evelyn)</td><td>female </td><td>62 </td><td>0 </td><td>0 </td><td>113572 </td><td>80 </td><td>B28 </td><td>NA </td><td>Mrs </td></tr>\n", "</tbody>\n", "</table>\n" ], "text/latex": [ "\\begin{tabular}{r|llllllllllll}\n", " & PassengerId & Pclass & Name & Sex & Age & SibSp & Parch & Ticket & Fare & Cabin & Embarked & Title\\\\\n", "\\hline\n", "\t62 & 62 & 1 & Icard, Miss. Amelie & female & 38 & 0 & 0 & 113572 & 80 & B28 & NA & Miss \\\\\n", "\t830 & 830 & 1 & Stone, Mrs. George Nelson (Martha Evelyn) & female & 62 & 0 & 0 & 113572 & 80 & B28 & NA & Mrs \\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "| <!--/--> | PassengerId | Pclass | Name | Sex | Age | SibSp | Parch | Ticket | Fare | Cabin | Embarked | Title | \n", "|---|---|\n", "| 62 | 62 | 1 | Icard, Miss. Amelie | female | 38 | 0 | 0 | 113572 | 80 | B28 | NA | Miss | \n", "| 830 | 830 | 1 | Stone, Mrs. George Nelson (Martha Evelyn) | female | 62 | 0 | 0 | 113572 | 80 | B28 | NA | Mrs | \n", "\n", "\n" ], "text/plain": [ " PassengerId Pclass Name Sex Age\n", "62 62 1 Icard, Miss. Amelie female 38 \n", "830 830 1 Stone, Mrs. George Nelson (Martha Evelyn) female 62 \n", " SibSp Parch Ticket Fare Cabin Embarked Title\n", "62 0 0 113572 80 B28 NA Miss \n", "830 0 0 113572 80 B28 NA Mrs " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "titanic[is.na(titanic$Embarked), ]" ] }, { "cell_type": "markdown", "metadata": { "run_control": { "marked": false } }, "source": [ "We can see that they are two women > 30 years old, sharing the same first class cabin. Let's see whether we can use this information to impute the missing data. <br>" ] }, { "cell_type": "code", "execution_count": 116, "metadata": { "collapsed": false, "run_control": { "marked": false } }, "outputs": [ { "data": { "text/plain": [ "\n", " C Q S \n", "44 2 36 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "table(titanic[titanic$Sex == \"female\" & titanic$Pclass == \"1\" & titanic$Age > 30,\"Embarked\"])" ] }, { "cell_type": "markdown", "metadata": { "run_control": { "marked": false } }, "source": [ "Women sharing the same traits have mainly embarked at either Cherbourg (France) or Southampton (UK). Since the two passengers with missing data have an English name, we can assume that they embarked at Southampton. " ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": true, "init_cell": true, "run_control": { "marked": false } }, "outputs": [], "source": [ "titanic$Embarked[is.na(titanic$Embarked)] = \"S\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 2.2.3 Age" ] }, { "cell_type": "code", "execution_count": 118, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "20.0916730328495" ], "text/latex": [ "20.0916730328495" ], "text/markdown": [ "20.0916730328495" ], "text/plain": [ "[1] 20.09167" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Percentage of missing values in Age\n", "263/nrow(titanic)*100" ] }, { "cell_type": "markdown", "metadata": { "run_control": { "marked": false } }, "source": [ "Given the percentage of missing values for Age, will impute this variable using Multivariate Imputation by Chained Equations (MICE)." ] }, { "cell_type": "markdown", "metadata": { "run_control": { "marked": false } }, "source": [ "First, let's check the missing pattern of Age." ] }, { "cell_type": "code", "execution_count": 119, "metadata": { "collapsed": false, "run_control": { "marked": false } }, "outputs": [ { "data": { "text/plain": [ "\n", "female male \n", " 78 185 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Are the missing ages equally distributed across male and female? \n", "table(titanic[is.na(titanic$Age), \"Sex\"])" ] }, { "cell_type": "code", "execution_count": 120, "metadata": { "collapsed": false, "run_control": { "marked": false } }, "outputs": [ { "data": { "text/plain": [ "\n", " 1 2 3 \n", " 39 16 208 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Are the missing ages equally distributed across classes? \n", "table(titanic[is.na(titanic$Age), \"Pclass\"])" ] }, { "cell_type": "markdown", "metadata": { "run_control": { "marked": false } }, "source": [ "We observe that the missing ages are not randomly distributed across all sexes and classes, with more missing data in males and 3rd class passengers. So we probably have a MAR (Missing at random) pattern and not a MCAR pattern (Missing completely ate random)." ] }, { "cell_type": "code", "execution_count": 121, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<ol class=list-inline>\n", "\t<li>'PassengerId'</li>\n", "\t<li>'Pclass'</li>\n", "\t<li>'Name'</li>\n", "\t<li>'Sex'</li>\n", "\t<li>'Age'</li>\n", "\t<li>'SibSp'</li>\n", "\t<li>'Parch'</li>\n", "\t<li>'Ticket'</li>\n", "\t<li>'Fare'</li>\n", "\t<li>'Cabin'</li>\n", "\t<li>'Embarked'</li>\n", "\t<li>'Title'</li>\n", "</ol>\n" ], "text/latex": [ "\\begin{enumerate*}\n", "\\item 'PassengerId'\n", "\\item 'Pclass'\n", "\\item 'Name'\n", "\\item 'Sex'\n", "\\item 'Age'\n", "\\item 'SibSp'\n", "\\item 'Parch'\n", "\\item 'Ticket'\n", "\\item 'Fare'\n", "\\item 'Cabin'\n", "\\item 'Embarked'\n", "\\item 'Title'\n", "\\end{enumerate*}\n" ], "text/markdown": [ "1. 'PassengerId'\n", "2. 'Pclass'\n", "3. 'Name'\n", "4. 'Sex'\n", "5. 'Age'\n", "6. 'SibSp'\n", "7. 'Parch'\n", "8. 'Ticket'\n", "9. 'Fare'\n", "10. 'Cabin'\n", "11. 'Embarked'\n", "12. 'Title'\n", "\n", "\n" ], "text/plain": [ " [1] \"PassengerId\" \"Pclass\" \"Name\" \"Sex\" \"Age\" \n", " [6] \"SibSp\" \"Parch\" \"Ticket\" \"Fare\" \"Cabin\" \n", "[11] \"Embarked\" \"Title\" " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "names(titanic)" ] }, { "cell_type": "markdown", "metadata": { "run_control": { "marked": false } }, "source": [ "Let's initialize the mice function parameters. <br>" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false, "init_cell": true, "run_control": { "marked": false } }, "outputs": [], "source": [ "# Create a mids object containing the default settings\n", "init <- mice(titanic[, !names(titanic) %in% c(\"PassengerID\", \"Name\")], maxit=0, seed = 1)" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": true, "init_cell": true, "run_control": { "marked": false } }, "outputs": [], "source": [ "# Selects the best predictors\n", "new.pred<-quickpred(titanic[, !names(titanic) %in% c(\"PassengerID\", \"Name\")])" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": true, "init_cell": true, "run_control": { "marked": false } }, "outputs": [], "source": [ "# Define the methods\n", "meth <- init$method\n", "meth[c(\"Cabin\")]=\"\" # Skip a variable from imputation while this variable will still be used for prediction." ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false, "init_cell": true, "run_control": { "marked": false } }, "outputs": [], "source": [ "# Perform imputation (here, we create 5 imputed datasets, the default value for the m parameter)\n", "imputed_Data <- mice(titanic[, !names(titanic) %in% c(\"PassengerID\", \"Name\")], method=meth, m=10, pred=new.pred, seed = 1, print=FALSE)" ] }, { "cell_type": "markdown", "metadata": { "run_control": { "marked": false } }, "source": [ "Let's inspect the distribution of original and imputed data" ] }, { "cell_type": "code", "execution_count": 126, "metadata": { "collapsed": false, "run_control": { "marked": false } }, "outputs": [ { "data": {}, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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"text/plain": [ "plot without title" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "densityplot(imputed_Data)" ] }, { "cell_type": "markdown", "metadata": { "run_control": { "marked": false } }, "source": [ "The density of the imputed data for each imputed dataset is showed in magenta while the density of the observed data is showed in blue. We observe that the central tendencies of the density plots of imputed data appear relatively similar to the observed data." ] }, { "cell_type": "code", "execution_count": 127, "metadata": { "collapsed": true, "run_control": { "marked": false } }, "outputs": [], "source": [ "# Give a proper size to plots\n", "options(repr.plot.width=10, repr.plot.height=10)" ] }, { "cell_type": "code", "execution_count": 128, "metadata": { "collapsed": false, "run_control": { "marked": false }, "scrolled": false }, "outputs": [], "source": [ "for (i in 1:10){\n", " namePlot <- paste(\"plot\", i, sep=\"\")\n", " plot <- ggplot(titanic,aes(x=Age)) + \n", " geom_density(data=data.frame(titanic$PassengerId, complete(imputed_Data,i)), alpha = 0.2, fill = \"blue\")+\n", " geom_density(data=titanic, alpha = 0.2, fill = \"Red\")+\n", " labs(title=paste(\"Age Distribution, imputation \", i, sep=\"\"))+\n", " labs(x=\"Age\") +\n", " theme_new\n", " assign(namePlot, plot)\n", "}" ] }, { "cell_type": "code", "execution_count": 129, "metadata": { "collapsed": false, "run_control": { "marked": false } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Warning message:\n", "\"Removed 263 rows containing non-finite values (stat_density).\"Warning message:\n", "\"Removed 263 rows containing non-finite values (stat_density).\"Warning message:\n", "\"Removed 263 rows containing non-finite values (stat_density).\"Warning message:\n", "\"Removed 263 rows containing non-finite values (stat_density).\"Warning message:\n", "\"Removed 263 rows containing non-finite values (stat_density).\"Warning message:\n", "\"Removed 263 rows containing non-finite values (stat_density).\"Warning message:\n", "\"Removed 263 rows containing non-finite values (stat_density).\"Warning message:\n", "\"Removed 263 rows containing non-finite values (stat_density).\"Warning message:\n", "\"Removed 263 rows containing non-finite values (stat_density).\"Warning message:\n", "\"Removed 263 rows containing non-finite values (stat_density).\"" ] }, { "data": { "image/png": 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2vYfsUK8uCFzDz0CustO30c8pr3mpic7GZ+axu6a1GCPHghNA+9wrJhvOU174vZ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"text/plain": [ "plot without title" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "grid.arrange(plot1, plot2, plot3, plot4, plot5, plot6, plot7, plot8, plot9, plot10, nrow=4, ncol=3)" ] }, { "cell_type": "markdown", "metadata": { "run_control": { "marked": false } }, "source": [ "The fifth imputed dataset seems to be the best one, based on the density distribution. Let's use it." ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false, "init_cell": true, "run_control": { "marked": false } }, "outputs": [], "source": [ "titanic_imp <- complete(imputed_Data, 5)\n", "titanic$Age <- titanic_imp$Age" ] }, { "cell_type": "markdown", "metadata": { "run_control": { "marked": false } }, "source": [ "The **Cabin** variable may also be of interest: it contains the deck ('deck''cabine_number') which may reflect some proximity to the life rafts (The deck A being the closest deck and the deck G being the farthest one). However, this variable contains a majority of missing values (NA). <br>\n", "We could still try to impute it using the family names, ticket number and class: 1) families are probably people sharing the same name and having the same ticket number; 2) people from the same family were probably on the same deck; 3) first class had the top decks (A-E), second class (D-F), and third class (E-G). <br>\n", "However, given that the deck is highly correlated with Pclass, we will just keep it away for now." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 2.3 Create new features 2" ] }, { "cell_type": "markdown", "metadata": { "run_control": { "marked": false } }, "source": [ "### 2.3.1 Create Child and Young from Age\n", "We have seen that the age classes were not equal regarding the survival rate: most child under 5 have survived whereas most young passengers between 15-30 have died. Let's create a variable Child (<5) and a variable Young (15-30)." ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false, "init_cell": true, "run_control": { "marked": false } }, "outputs": [], "source": [ "titanic$Child <- factor(titanic$Age <= 5)\n", "titanic$Young <- factor(titanic$Age > 15 & titanic$Age <= 30)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 2.3.2 Create Title2 from Title" ] }, { "cell_type": "markdown", "metadata": { "run_control": { "marked": false } }, "source": [ "In title, we have many levels which have only one observation or a few observations. This is not optimal for modelisation since those levels may have near-to-zero variance. Let's merge titles into a smaller number of levels, using the age and sex usually related to those titles." ] }, { "cell_type": "code", "execution_count": 132, "metadata": { "collapsed": false, "run_control": { "marked": false } }, "outputs": [ { "data": { "text/html": [ "<dl class=dl-horizontal>\n", "\t<dt>Capt</dt>\n", "\t\t<dd>1</dd>\n", "\t<dt>Col</dt>\n", "\t\t<dd>4</dd>\n", "\t<dt>Don</dt>\n", "\t\t<dd>1</dd>\n", "\t<dt>Dona</dt>\n", "\t\t<dd>1</dd>\n", "\t<dt>Dr</dt>\n", "\t\t<dd>8</dd>\n", "\t<dt>Jonkheer</dt>\n", "\t\t<dd>1</dd>\n", "\t<dt>Lady</dt>\n", "\t\t<dd>1</dd>\n", "\t<dt>Major</dt>\n", "\t\t<dd>2</dd>\n", "\t<dt>Master</dt>\n", "\t\t<dd>61</dd>\n", "\t<dt>Miss</dt>\n", "\t\t<dd>260</dd>\n", "\t<dt>Mlle</dt>\n", "\t\t<dd>2</dd>\n", "\t<dt>Mme</dt>\n", "\t\t<dd>1</dd>\n", "\t<dt>Mr</dt>\n", "\t\t<dd>757</dd>\n", "\t<dt>Mrs</dt>\n", "\t\t<dd>197</dd>\n", "\t<dt>Ms</dt>\n", "\t\t<dd>2</dd>\n", "\t<dt>Rev</dt>\n", "\t\t<dd>8</dd>\n", "\t<dt>Sir</dt>\n", "\t\t<dd>1</dd>\n", "\t<dt>the Countess</dt>\n", "\t\t<dd>1</dd>\n", "</dl>\n" ], "text/latex": [ "\\begin{description*}\n", "\\item[Capt] 1\n", "\\item[Col] 4\n", "\\item[Don] 1\n", "\\item[Dona] 1\n", "\\item[Dr] 8\n", "\\item[Jonkheer] 1\n", "\\item[Lady] 1\n", "\\item[Major] 2\n", "\\item[Master] 61\n", "\\item[Miss] 260\n", "\\item[Mlle] 2\n", "\\item[Mme] 1\n", "\\item[Mr] 757\n", "\\item[Mrs] 197\n", "\\item[Ms] 2\n", "\\item[Rev] 8\n", "\\item[Sir] 1\n", "\\item[the Countess] 1\n", "\\end{description*}\n" ], "text/markdown": [ "Capt\n", ": 1Col\n", ": 4Don\n", ": 1Dona\n", ": 1Dr\n", ": 8Jonkheer\n", ": 1Lady\n", ": 1Major\n", ": 2Master\n", ": 61Miss\n", ": 260Mlle\n", ": 2Mme\n", ": 1Mr\n", ": 757Mrs\n", ": 197Ms\n", ": 2Rev\n", ": 8Sir\n", ": 1the Countess\n", ": 1\n", "\n" ], "text/plain": [ " Capt Col Don Dona Dr Jonkheer \n", " 1 4 1 1 8 1 \n", " Lady Major Master Miss Mlle Mme \n", " 1 2 61 260 2 1 \n", " Mr Mrs Ms Rev Sir the Countess \n", " 757 197 2 8 1 1 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "summary(titanic$Title)" ] }, { "cell_type": "code", "execution_count": 133, "metadata": { "collapsed": true, "run_control": { "marked": false } }, "outputs": [], "source": [ "titles <- as.character(unique(titanic$Title))" ] }, { "cell_type": "code", "execution_count": 134, "metadata": { "collapsed": false, "run_control": { "marked": false } }, "outputs": [ { "data": { "text/html": [ "<table>\n", "<thead><tr><th scope=col>Title</th><th scope=col>Female</th><th scope=col>Male</th><th scope=col>Minimum_Age</th><th scope=col>Maximum_Age</th></tr></thead>\n", "<tbody>\n", "\t<tr><td>Mr </td><td>0 </td><td>757 </td><td>2 </td><td>80 </td></tr>\n", "\t<tr><td>Mrs </td><td>197 </td><td>0 </td><td>14 </td><td>76 </td></tr>\n", "\t<tr><td>Miss </td><td>260 </td><td>0 </td><td>0.17 </td><td>63 </td></tr>\n", "\t<tr><td>Master </td><td>0 </td><td>61 </td><td>0.33 </td><td>14.5 </td></tr>\n", "\t<tr><td>Don </td><td>0 </td><td>1 </td><td>40 </td><td>40 </td></tr>\n", "\t<tr><td>Rev </td><td>0 </td><td>8 </td><td>27 </td><td>57 </td></tr>\n", "\t<tr><td>Dr </td><td>1 </td><td>7 </td><td>23 </td><td>54 </td></tr>\n", "\t<tr><td>Mme </td><td>1 </td><td>0 </td><td>24 </td><td>24 </td></tr>\n", "\t<tr><td>Ms </td><td>2 </td><td>0 </td><td>26 </td><td>28 </td></tr>\n", "\t<tr><td>Major </td><td>0 </td><td>2 </td><td>45 </td><td>52 </td></tr>\n", "\t<tr><td>Lady </td><td>1 </td><td>0 </td><td>48 </td><td>48 </td></tr>\n", "\t<tr><td>Sir </td><td>0 </td><td>1 </td><td>49 </td><td>49 </td></tr>\n", "\t<tr><td>Mlle </td><td>2 </td><td>0 </td><td>24 </td><td>24 </td></tr>\n", "\t<tr><td>Col </td><td>0 </td><td>4 </td><td>47 </td><td>60 </td></tr>\n", "\t<tr><td>Capt </td><td>0 </td><td>1 </td><td>70 </td><td>70 </td></tr>\n", "\t<tr><td>the Countess</td><td>1 </td><td>0 </td><td>33 </td><td>33 </td></tr>\n", "\t<tr><td>Jonkheer </td><td>0 </td><td>1 </td><td>38 </td><td>38 </td></tr>\n", "\t<tr><td>Dona </td><td>1 </td><td>0 </td><td>39 </td><td>39 </td></tr>\n", "</tbody>\n", "</table>\n" ], "text/latex": [ "\\begin{tabular}{r|lllll}\n", " Title & Female & Male & Minimum\\_Age & Maximum\\_Age\\\\\n", "\\hline\n", "\t Mr & 0 & 757 & 2 & 80 \\\\\n", "\t Mrs & 197 & 0 & 14 & 76 \\\\\n", "\t Miss & 260 & 0 & 0.17 & 63 \\\\\n", "\t Master & 0 & 61 & 0.33 & 14.5 \\\\\n", "\t Don & 0 & 1 & 40 & 40 \\\\\n", "\t Rev & 0 & 8 & 27 & 57 \\\\\n", "\t Dr & 1 & 7 & 23 & 54 \\\\\n", "\t Mme & 1 & 0 & 24 & 24 \\\\\n", "\t Ms & 2 & 0 & 26 & 28 \\\\\n", "\t Major & 0 & 2 & 45 & 52 \\\\\n", "\t Lady & 1 & 0 & 48 & 48 \\\\\n", "\t Sir & 0 & 1 & 49 & 49 \\\\\n", "\t Mlle & 2 & 0 & 24 & 24 \\\\\n", "\t Col & 0 & 4 & 47 & 60 \\\\\n", "\t Capt & 0 & 1 & 70 & 70 \\\\\n", "\t the Countess & 1 & 0 & 33 & 33 \\\\\n", "\t Jonkheer & 0 & 1 & 38 & 38 \\\\\n", "\t Dona & 1 & 0 & 39 & 39 \\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "Title | Female | Male | Minimum_Age | Maximum_Age | \n", "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n", "| Mr | 0 | 757 | 2 | 80 | \n", "| Mrs | 197 | 0 | 14 | 76 | \n", "| Miss | 260 | 0 | 0.17 | 63 | \n", "| Master | 0 | 61 | 0.33 | 14.5 | \n", "| Don | 0 | 1 | 40 | 40 | \n", "| Rev | 0 | 8 | 27 | 57 | \n", "| Dr | 1 | 7 | 23 | 54 | \n", "| Mme | 1 | 0 | 24 | 24 | \n", "| Ms | 2 | 0 | 26 | 28 | \n", "| Major | 0 | 2 | 45 | 52 | \n", "| Lady | 1 | 0 | 48 | 48 | \n", "| Sir | 0 | 1 | 49 | 49 | \n", "| Mlle | 2 | 0 | 24 | 24 | \n", "| Col | 0 | 4 | 47 | 60 | \n", "| Capt | 0 | 1 | 70 | 70 | \n", "| the Countess | 1 | 0 | 33 | 33 | \n", "| Jonkheer | 0 | 1 | 38 | 38 | \n", "| Dona | 1 | 0 | 39 | 39 | \n", "\n", "\n" ], "text/plain": [ " Title Female Male Minimum_Age Maximum_Age\n", "1 Mr 0 757 2 80 \n", "2 Mrs 197 0 14 76 \n", "3 Miss 260 0 0.17 63 \n", "4 Master 0 61 0.33 14.5 \n", "5 Don 0 1 40 40 \n", "6 Rev 0 8 27 57 \n", "7 Dr 1 7 23 54 \n", "8 Mme 1 0 24 24 \n", "9 Ms 2 0 26 28 \n", "10 Major 0 2 45 52 \n", "11 Lady 1 0 48 48 \n", "12 Sir 0 1 49 49 \n", "13 Mlle 2 0 24 24 \n", "14 Col 0 4 47 60 \n", "15 Capt 0 1 70 70 \n", "16 the Countess 1 0 33 33 \n", "17 Jonkheer 0 1 38 38 \n", "18 Dona 1 0 39 39 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "as.data.frame(cbind(\"Title\" = titles, \n", " \"Female\" = sapply(titles, function(x) nrow(titanic[titanic$Title == x & titanic$Sex == \"female\",])),\n", " \"Male\" = sapply(titles, function(x) nrow(titanic[titanic$Title == x & titanic$Sex == \"male\",])),\n", " \"Minimum_Age\" = sapply(titles, function(x) min(titanic[titanic$Title == x ,'Age'], na.rm = TRUE)),\n", " \"Maximum_Age\" = sapply(titles, function(x) max(titanic[titanic$Title == x,'Age'], na.rm = TRUE))), row.names = F)" ] }, { "cell_type": "markdown", "metadata": { "run_control": { "marked": false } }, "source": [ " We can merge titles into: \n", "* Sir : men above 14.5 years old with an important social status\n", "* Mr: men above 14.5 years old without status\n", "* Master: boys up to 14.5 years old\n", "* Mrs: women above 14 years old, married or not\n", "* Miss: girls up to 14 years old" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false, "init_cell": true, "run_control": { "marked": false } }, "outputs": [], "source": [ "titanic$Title2 <- titanic$Title\n", "\n", "titanic[(titanic$Sex == \"male\" & titanic$Age <= 14.5),]$Title2 = \"Master\" \n", "titanic[(titanic$Sex == \"male\" & titanic$Age > 14.5),]$Title2 = \"Mr\"\n", "titanic[(titanic$Sex == \"female\" & titanic$Age <= 14),]$Title2 = \"Miss\"\n", "titanic[(titanic$Sex == \"female\" & titanic$Age > 14),]$Title2 = \"Mrs\"\n", "\n", "titanic[titanic$Title == \"Capt\"|\n", " titanic$Title == \"Col\"|\n", " titanic$Title == \"Don\"|\n", " titanic$Title == \"Major\"|\n", " titanic$Title == \"Rev\"| \n", " titanic$Title == \"Jonkheer\"|\n", " (titanic$Title == \"Dr\" & titanic$Sex == \"male\"),]$Title2 = \"Sir\"\n", "\n", "titanic$Title2 <- factor(titanic$Title2)" ] }, { "cell_type": "code", "execution_count": 136, "metadata": { "collapsed": false, "run_control": { "marked": false } }, "outputs": [ { "data": { "text/html": [ "<dl class=dl-horizontal>\n", "\t<dt>Master</dt>\n", "\t\t<dd>69</dd>\n", "\t<dt>Miss</dt>\n", "\t\t<dd>68</dd>\n", "\t<dt>Mr</dt>\n", "\t\t<dd>750</dd>\n", "\t<dt>Mrs</dt>\n", "\t\t<dd>398</dd>\n", "\t<dt>Sir</dt>\n", "\t\t<dd>24</dd>\n", "</dl>\n" ], "text/latex": [ "\\begin{description*}\n", "\\item[Master] 69\n", "\\item[Miss] 68\n", "\\item[Mr] 750\n", "\\item[Mrs] 398\n", "\\item[Sir] 24\n", "\\end{description*}\n" ], "text/markdown": [ "Master\n", ": 69Miss\n", ": 68Mr\n", ": 750Mrs\n", ": 398Sir\n", ": 24\n", "\n" ], "text/plain": [ "Master Miss Mr Mrs Sir \n", " 69 68 750 398 24 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "summary(titanic$Title2)" ] }, { "cell_type": "markdown", "metadata": { "run_control": { "marked": false } }, "source": [ "Now, we have levels that are much more balanced." ] }, { "cell_type": "markdown", "metadata": { "run_control": { "marked": false } }, "source": [ "### 2.3.3 Create dummy variables" ] }, { "cell_type": "markdown", "metadata": { "run_control": { "marked": false } }, "source": [ "Let's convert our factor variables into **dummy variables**. Having dummy variables is not absolutely necessary, however:\n", "* it will make the intrepretation easier\n", "* it may also help the model to perform better\n", "* contrary to glm() or train() that automatically create dummy variables, most functions don't." ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": false, "init_cell": true, "run_control": { "marked": false } }, "outputs": [ { "data": { "text/html": [ "<ol class=list-inline>\n", "\t<li>'PassengerId'</li>\n", "\t<li>'Pclass'</li>\n", "\t<li>'Name'</li>\n", "\t<li>'Sex_female'</li>\n", "\t<li>'Sex_male'</li>\n", "\t<li>'Age'</li>\n", "\t<li>'SibSp'</li>\n", "\t<li>'Parch'</li>\n", "\t<li>'Ticket'</li>\n", "\t<li>'Fare'</li>\n", "\t<li>'Cabin'</li>\n", "\t<li>'Embarked_C'</li>\n", "\t<li>'Embarked_Q'</li>\n", "\t<li>'Embarked_S'</li>\n", "\t<li>'Title'</li>\n", "\t<li>'Child'</li>\n", "\t<li>'Young'</li>\n", "\t<li>'Title2_Master'</li>\n", "\t<li>'Title2_Miss'</li>\n", "\t<li>'Title2_Mr'</li>\n", "\t<li>'Title2_Mrs'</li>\n", "\t<li>'Title2_Sir'</li>\n", "</ol>\n" ], "text/latex": [ "\\begin{enumerate*}\n", "\\item 'PassengerId'\n", "\\item 'Pclass'\n", "\\item 'Name'\n", "\\item 'Sex\\_female'\n", "\\item 'Sex\\_male'\n", "\\item 'Age'\n", "\\item 'SibSp'\n", "\\item 'Parch'\n", "\\item 'Ticket'\n", "\\item 'Fare'\n", "\\item 'Cabin'\n", "\\item 'Embarked\\_C'\n", "\\item 'Embarked\\_Q'\n", "\\item 'Embarked\\_S'\n", "\\item 'Title'\n", "\\item 'Child'\n", "\\item 'Young'\n", "\\item 'Title2\\_Master'\n", "\\item 'Title2\\_Miss'\n", "\\item 'Title2\\_Mr'\n", "\\item 'Title2\\_Mrs'\n", "\\item 'Title2\\_Sir'\n", "\\end{enumerate*}\n" ], "text/markdown": [ "1. 'PassengerId'\n", "2. 'Pclass'\n", "3. 'Name'\n", "4. 'Sex_female'\n", "5. 'Sex_male'\n", "6. 'Age'\n", "7. 'SibSp'\n", "8. 'Parch'\n", "9. 'Ticket'\n", "10. 'Fare'\n", "11. 'Cabin'\n", "12. 'Embarked_C'\n", "13. 'Embarked_Q'\n", "14. 'Embarked_S'\n", "15. 'Title'\n", "16. 'Child'\n", "17. 'Young'\n", "18. 'Title2_Master'\n", "19. 'Title2_Miss'\n", "20. 'Title2_Mr'\n", "21. 'Title2_Mrs'\n", "22. 'Title2_Sir'\n", "\n", "\n" ], "text/plain": [ " [1] \"PassengerId\" \"Pclass\" \"Name\" \"Sex_female\" \n", " [5] \"Sex_male\" \"Age\" \"SibSp\" \"Parch\" \n", " [9] \"Ticket\" \"Fare\" \"Cabin\" \"Embarked_C\" \n", "[13] \"Embarked_Q\" \"Embarked_S\" \"Title\" \"Child\" \n", "[17] \"Young\" \"Title2_Master\" \"Title2_Miss\" \"Title2_Mr\" \n", "[21] \"Title2_Mrs\" \"Title2_Sir\" " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "titanic_dum <- dummy.data.frame(titanic, names= c(\"Sex\",\"Embarked\", \"Title2\") , sep = \"_\")\n", "names(titanic_dum)" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": false, "init_cell": true, "run_control": { "marked": false } }, "outputs": [], "source": [ "# Convert the dummy variables into factors\n", "vars = c(\"Sex_female\", \"Sex_male\", \"Embarked_C\", \"Embarked_Q\", \"Embarked_S\", \"Title2_Master\", \"Title2_Miss\", \"Title2_Mr\", \n", " \"Title2_Mrs\", \"Title2_Sir\")\n", "titanic_dum[,vars] = lapply(titanic_dum[,vars], function(x) as.factor(x))" ] }, { "cell_type": "code", "execution_count": 139, "metadata": { "collapsed": false, "run_control": { "marked": false }, "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "'data.frame':\t1309 obs. of 22 variables:\n", " $ PassengerId : int 1 2 3 4 5 6 7 8 9 10 ...\n", " $ Pclass : Ord.factor w/ 3 levels \"1\"<\"2\"<\"3\": 3 1 3 1 3 3 1 3 3 2 ...\n", " $ Name : Factor w/ 1307 levels \"Abbing, Mr. Anthony\",..: 109 191 358 277 16 559 520 629 417 581 ...\n", " $ Sex_female : Factor w/ 2 levels \"0\",\"1\": 1 2 2 2 1 1 1 1 2 2 ...\n", " $ Sex_male : Factor w/ 2 levels \"0\",\"1\": 2 1 1 1 2 2 2 2 1 1 ...\n", " $ Age : num 22 38 26 35 35 24 54 2 27 14 ...\n", " $ SibSp : int 1 1 0 1 0 0 0 3 0 1 ...\n", " $ Parch : int 0 0 0 0 0 0 0 1 2 0 ...\n", " $ Ticket : Factor w/ 929 levels \"110152\",\"110413\",..: 524 597 670 50 473 276 86 396 345 133 ...\n", " $ Fare : num 7.25 71.28 7.92 53.1 8.05 ...\n", " $ Cabin : Factor w/ 186 levels \"A10\",\"A14\",\"A16\",..: NA 82 NA 56 NA NA 130 NA NA NA ...\n", " $ Embarked_C : Factor w/ 2 levels \"0\",\"1\": 1 2 1 1 1 1 1 1 1 2 ...\n", " $ Embarked_Q : Factor w/ 2 levels \"0\",\"1\": 1 1 1 1 1 2 1 1 1 1 ...\n", " $ Embarked_S : Factor w/ 2 levels \"0\",\"1\": 2 1 2 2 2 1 2 2 2 1 ...\n", " $ Title : Factor w/ 18 levels \"Capt\",\"Col\",\"Don\",..: 13 14 10 14 13 13 13 9 14 14 ...\n", " $ Child : Factor w/ 2 levels \"FALSE\",\"TRUE\": 1 1 1 1 1 1 1 2 1 1 ...\n", " $ Young : Factor w/ 2 levels \"FALSE\",\"TRUE\": 2 1 2 1 1 2 1 1 2 1 ...\n", " $ Title2_Master: Factor w/ 2 levels \"0\",\"1\": 1 1 1 1 1 1 1 2 1 1 ...\n", " $ Title2_Miss : Factor w/ 2 levels \"0\",\"1\": 1 1 1 1 1 1 1 1 1 2 ...\n", " $ Title2_Mr : Factor w/ 2 levels \"0\",\"1\": 2 1 1 1 2 2 2 1 1 1 ...\n", " $ Title2_Mrs : Factor w/ 2 levels \"0\",\"1\": 1 2 2 2 1 1 1 1 2 1 ...\n", " $ Title2_Sir : Factor w/ 2 levels \"0\",\"1\": 1 1 1 1 1 1 1 1 1 1 ...\n", " - attr(*, \"dummies\")=List of 3\n", " ..$ Sex : int 4 5\n", " ..$ Embarked: int 12 13 14\n", " ..$ Title2 : int 18 19 20 21 22\n" ] } ], "source": [ "str(titanic_dum)" ] }, { "cell_type": "markdown", "metadata": { "run_control": { "marked": false } }, "source": [ "---\n", "# 3 Machine learning" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now, we can split again our data into the training and test sets." ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": true, "init_cell": true }, "outputs": [], "source": [ "Train = titanic_dum[1:891,]\n", "Test = titanic_dum[892:1309,]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "And add back the variable Survived to the training set." ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": false, "init_cell": true }, "outputs": [], "source": [ "Train$Survived <- as.factor(Survived)\n", "Train$Survived <- as.factor(revalue(Train$Survived, c(\"0\" = \"No\", \"1\"=\"Yes\")))" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": true, "init_cell": true }, "outputs": [], "source": [ "# Let's remove variables that we won't use\n", "Train$PassengerId <- NULL\n", "Train$Cabin <- NULL\n", "Train$Ticket <- NULL\n", "Train$Name <- NULL\n", "Train$Title <- NULL" ] }, { "cell_type": "markdown", "metadata": { "run_control": { "marked": false } }, "source": [ "We will use the **`train()`** function of the 'caret' package. It is a very convenient wrapper for modeling, allowing automatic preprocessing (e.g. scaling) and model tuning using resampling (cross-validation, bootstrap, etc.). It provides a uniform interface of the functions themselves, as well as a way to standardize common tasks. Thus, we will be able to compare various algorithms easily.\n", "\n", "Note that we will set the same seed prior to each training. This will ensures that the same resampling sets are used, which will come in handy when we compare the resampling profiles between models." ] }, { "cell_type": "markdown", "metadata": { "run_control": { "marked": false } }, "source": [ "## 3.1 Logistic regression" ] }, { "cell_type": "markdown", "metadata": { "run_control": { "marked": false } }, "source": [ "### 3.1.1 A first quick and dirty model\n", "\n", "We build a first quick and dirty model, using all the variables. That will give us a benchmark accuracy.<br>" ] }, { "cell_type": "code", "execution_count": 143, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<ol class=list-inline>\n", "\t<li>'Pclass'</li>\n", "\t<li>'Sex_female'</li>\n", "\t<li>'Sex_male'</li>\n", "\t<li>'Age'</li>\n", "\t<li>'SibSp'</li>\n", "\t<li>'Parch'</li>\n", "\t<li>'Fare'</li>\n", "\t<li>'Embarked_C'</li>\n", "\t<li>'Embarked_Q'</li>\n", "\t<li>'Embarked_S'</li>\n", "\t<li>'Child'</li>\n", "\t<li>'Young'</li>\n", "\t<li>'Title2_Master'</li>\n", "\t<li>'Title2_Miss'</li>\n", "\t<li>'Title2_Mr'</li>\n", "\t<li>'Title2_Mrs'</li>\n", "\t<li>'Title2_Sir'</li>\n", "\t<li>'Survived'</li>\n", "</ol>\n" ], "text/latex": [ "\\begin{enumerate*}\n", "\\item 'Pclass'\n", "\\item 'Sex\\_female'\n", "\\item 'Sex\\_male'\n", "\\item 'Age'\n", "\\item 'SibSp'\n", "\\item 'Parch'\n", "\\item 'Fare'\n", "\\item 'Embarked\\_C'\n", "\\item 'Embarked\\_Q'\n", "\\item 'Embarked\\_S'\n", "\\item 'Child'\n", "\\item 'Young'\n", "\\item 'Title2\\_Master'\n", "\\item 'Title2\\_Miss'\n", "\\item 'Title2\\_Mr'\n", "\\item 'Title2\\_Mrs'\n", "\\item 'Title2\\_Sir'\n", "\\item 'Survived'\n", "\\end{enumerate*}\n" ], "text/markdown": [ "1. 'Pclass'\n", "2. 'Sex_female'\n", "3. 'Sex_male'\n", "4. 'Age'\n", "5. 'SibSp'\n", "6. 'Parch'\n", "7. 'Fare'\n", "8. 'Embarked_C'\n", "9. 'Embarked_Q'\n", "10. 'Embarked_S'\n", "11. 'Child'\n", "12. 'Young'\n", "13. 'Title2_Master'\n", "14. 'Title2_Miss'\n", "15. 'Title2_Mr'\n", "16. 'Title2_Mrs'\n", "17. 'Title2_Sir'\n", "18. 'Survived'\n", "\n", "\n" ], "text/plain": [ " [1] \"Pclass\" \"Sex_female\" \"Sex_male\" \"Age\" \n", " [5] \"SibSp\" \"Parch\" \"Fare\" \"Embarked_C\" \n", " [9] \"Embarked_Q\" \"Embarked_S\" \"Child\" \"Young\" \n", "[13] \"Title2_Master\" \"Title2_Miss\" \"Title2_Mr\" \"Title2_Mrs\" \n", "[17] \"Title2_Sir\" \"Survived\" " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "names(Train)" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "collapsed": true, "init_cell": true, "run_control": { "marked": false } }, "outputs": [], "source": [ "## Define cross-validation experiment\n", "numFolds = trainControl(method = \"cv\", number = 10) # method='cv' for cross-validation, number=10 for 10 folds" ] }, { "cell_type": "code", "execution_count": 145, "metadata": { "collapsed": false, "run_control": { "marked": false }, "scrolled": true }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Warning message in predict.lm(object, newdata, se.fit, scale = 1, type = ifelse(type == :\n", "\"prediction from a rank-deficient fit may be misleading\"Warning message in predict.lm(object, newdata, se.fit, scale = 1, type = ifelse(type == :\n", "\"prediction from a rank-deficient fit may be misleading\"Warning message in predict.lm(object, newdata, se.fit, scale = 1, type = ifelse(type == :\n", "\"prediction from a rank-deficient fit may be misleading\"Warning message in predict.lm(object, newdata, se.fit, scale = 1, type = ifelse(type == :\n", "\"prediction from a rank-deficient fit may be misleading\"Warning message in predict.lm(object, newdata, se.fit, scale = 1, type = ifelse(type == :\n", "\"prediction from a rank-deficient fit may be misleading\"Warning message in predict.lm(object, newdata, se.fit, scale = 1, type = ifelse(type == :\n", "\"prediction from a rank-deficient fit may be misleading\"Warning message in predict.lm(object, newdata, se.fit, scale = 1, type = ifelse(type == :\n", "\"prediction from a rank-deficient fit may be misleading\"Warning message in predict.lm(object, newdata, se.fit, scale = 1, type = ifelse(type == :\n", "\"prediction from a rank-deficient fit may be misleading\"Warning message in predict.lm(object, newdata, se.fit, scale = 1, type = ifelse(type == :\n", "\"prediction from a rank-deficient fit may be misleading\"Warning message in predict.lm(object, newdata, se.fit, scale = 1, type = ifelse(type == :\n", "\"prediction from a rank-deficient fit may be misleading\"" ] } ], "source": [ "## Perform the training with cross-validation\n", "set.seed(1)\n", "logmod1 <- train(Survived ~ .-Sex_female -Embarked_C -Title2_Master, # We have to remove the \"reference\" level of dummy variables\n", " data = Train, \n", " method = \"glm\", \n", " trControl = numFolds, \n", " na.action = na.omit, \n", " preProcess=c(\"center\", \"scale\"))" ] }, { "cell_type": "code", "execution_count": 68, "metadata": { "collapsed": false, "run_control": { "marked": false }, "scrolled": false }, "outputs": [ { "data": { "text/plain": [ "\n", "Call:\n", "NULL\n", "\n", "Deviance Residuals: \n", " Min 1Q Median 3Q Max \n", "-2.4176 -0.5318 -0.3675 0.5541 2.5701 \n", "\n", "Coefficients: (1 not defined because of singularities)\n", " Estimate Std. Error z value Pr(>|z|) \n", "(Intercept) -0.737246 0.106584 -6.917 4.61e-12 ***\n", "Pclass.L -0.940760 0.160721 -5.853 4.82e-09 ***\n", "Pclass.Q 0.001987 0.101244 0.020 0.984340 \n", "Sex_male1 -0.084721 0.410383 -0.206 0.836444 \n", "Age 0.221354 0.731684 0.303 0.762250 \n", "SibSp -0.838129 0.566057 -1.481 0.138702 \n", "Parch 0.323158 0.342626 0.943 0.345588 \n", "Fare -0.383011 0.540686 -0.708 0.478709 \n", "Embarked_Q1 -0.019203 0.114395 -0.168 0.866690 \n", "Embarked_S1 -0.208026 0.114543 -1.816 0.069349 . \n", "ChildTRUE 0.323772 0.160619 2.016 0.043823 * \n", "YoungTRUE -0.185078 0.161228 -1.148 0.250997 \n", "Age_Sq -0.618905 0.596661 -1.037 0.299605 \n", "AgeXFare 0.305446 0.410229 0.745 0.456529 \n", "Fare_Sq 0.269560 0.305260 0.883 0.377209 \n", "AgeXSibSp 0.434261 0.244652 1.775 0.075896 . \n", "FareXSibSp 0.243983 0.240591 1.014 0.310537 \n", "SibSp_sq -0.880481 0.755610 -1.165 0.243915 \n", "AgeXParch -0.338084 0.311986 -1.084 0.278520 \n", "FareXParch -0.246033 0.268287 -0.917 0.359117 \n", "SibSpXParch 0.222713 0.368423 0.605 0.545508 \n", "Parch_sp -0.153459 0.253153 -0.606 0.544387 \n", "Title2_Miss1 -0.166523 0.175341 -0.950 0.342259 \n", "Title2_Mr1 -1.471975 0.429721 -3.425 0.000614 ***\n", "Title2_Mrs1 NA NA NA NA \n", "Title2_Sir1 -0.512864 0.155144 -3.306 0.000947 ***\n", "---\n", "Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n", "\n", "(Dispersion parameter for binomial family taken to be 1)\n", "\n", " Null deviance: 1186.66 on 890 degrees of freedom\n", "Residual deviance: 710.79 on 866 degrees of freedom\n", "AIC: 760.79\n", "\n", "Number of Fisher Scoring iterations: 6\n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "summary(logmod1) # with Pclass as ordered factor" ] }, { "cell_type": "markdown", "metadata": { "run_control": { "marked": false } }, "source": [ "**Note 1**: The `lm()/glm()` functions use linear least squares, which is a direct optimization method (global optimum guarantee and good computational performance). Linear least squares are computed using QR decomposition, which is more efficient than gradient descent, however, they are very sensitive to outliers.<br>\n", "**Note 2**: We have to normalize our data to have similar scale among the independent variables. By using the parameter `preProcess=c(\"center\", \"scale\")`, the normalization will be performed on the 9 training folds only (not on the whole original training set). This will limit accidental contamination of the training data. <br>\n", "**Note 3**: Since the Pclass is an ordered factor, the L (linear) parameter gives a measure of the linear trend and Q specifies quadratic terms (which is close to zero in this case because the pattern is linear)." ] }, { "cell_type": "markdown", "metadata": { "run_control": { "marked": false } }, "source": [ "As expected, the **class**, the fact to be a **child** and the **title** (which includes the sex) have a significant impact on the survival of passengers. We observe that the probability of survival is significantly higher if the passenger is a child < 5 and lower if the passenger is in 3rd class and/or has a male title (i.e. Mr or Sir)." ] }, { "cell_type": "markdown", "metadata": { "run_control": { "marked": false } }, "source": [ "Let's see what is the accuracy of the model (i.e. mean accuracy accross cross-validated models)." ] }, { "cell_type": "code", "execution_count": 146, "metadata": { "collapsed": false, "run_control": { "marked": false }, "scrolled": false }, "outputs": [ { "data": { "text/html": [ "0.822667688117126" ], "text/latex": [ "0.822667688117126" ], "text/markdown": [ "0.822667688117126" ], "text/plain": [ "[1] 0.8226677" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "logmod1$results$Accuracy" ] }, { "cell_type": "markdown", "metadata": { "run_control": { "marked": false } }, "source": [ "That was a really quick and dirty model and the performance is already good! But I think that we can do much better by improving it." ] }, { "cell_type": "markdown", "metadata": { "run_control": { "marked": false } }, "source": [ "Now, how can we **improve** our model? \n", "1. Get more training examples?\n", "2. Try smaller sets of features (to avoid overfitting)?\n", "3. Try getting additional features?\n", "4. Try adding polynomial features?<br>\n", "\n", "Before rushing into any of these options (which is likely to waste our time), let's be smart and check what is wrong in our model." ] }, { "cell_type": "markdown", "metadata": { "run_control": { "marked": false } }, "source": [ "### 3.1.2 Check learning curves\n", "\n", "Let's check for high bias or high variance using learning curves." ] }, { "cell_type": "code", "execution_count": 152, "metadata": { "collapsed": false, "run_control": { "marked": false }, "scrolled": true }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Warning message in eval(expr, envir, enclos):\n", "\"model fit failed for Fold01: parameter=none Error in `contrasts<-`(`*tmp*`, value = contr.funs[1 + isOF[nn]]) : \n", " contrasts can be applied only to factors with 2 or more levels\n", "\"Warning message in predict.lm(object, newdata, se.fit, scale = 1, type = ifelse(type == :\n", "\"prediction from a rank-deficient fit may be misleading\"Warning message in predict.lm(object, newdata, se.fit, scale = 1, type = ifelse(type == :\n", "\"prediction from a rank-deficient fit may be misleading\"Warning message in predict.lm(object, newdata, se.fit, scale = 1, type = ifelse(type == :\n", "\"prediction from a rank-deficient fit may be misleading\"Warning message in predict.lm(object, newdata, se.fit, scale = 1, type = ifelse(type == :\n", "\"prediction from a rank-deficient fit may be misleading\"Warning message in predict.lm(object, newdata, se.fit, scale = 1, type = ifelse(type == :\n", "\"prediction from a rank-deficient fit may be misleading\"Warning message:\n", "\"glm.fit: fitted probabilities numerically 0 or 1 occurred\"Warning message in predict.lm(object, newdata, se.fit, scale = 1, type = ifelse(type == :\n", "\"prediction from a rank-deficient fit may be misleading\"Warning message in predict.lm(object, newdata, se.fit, scale = 1, type = ifelse(type == :\n", "\"prediction from a rank-deficient fit may be misleading\"Warning message in predict.lm(object, newdata, se.fit, scale = 1, type = ifelse(type == :\n", "\"prediction from a rank-deficient fit may be misleading\"Warning message in predict.lm(object, newdata, se.fit, scale = 1, type = ifelse(type == :\n", "\"prediction from a rank-deficient fit may be misleading\"Warning message in nominalTrainWorkflow(x = x, y = y, wts = weights, info = trainInfo, :\n", "\"There were missing values in resampled performance measures.\"Warning message in predict.lm(object, newdata, se.fit, scale = 1, type = ifelse(type == :\n", "\"prediction from a rank-deficient fit may be misleading\"Warning message in predict.lm(object, newdata, se.fit, scale = 1, type = ifelse(type == :\n", "\"prediction from a rank-deficient fit may be misleading\"Warning message in predict.lm(object, newdata, se.fit, scale = 1, type = ifelse(type == :\n", "\"prediction from a rank-deficient fit may be misleading\"Warning message in predict.lm(object, newdata, se.fit, scale = 1, type = ifelse(type == :\n", "\"prediction from a rank-deficient fit may be misleading\"Warning message in predict.lm(object, newdata, se.fit, scale = 1, type = ifelse(type == :\n", "\"prediction from a rank-deficient fit may be misleading\"Warning message in predict.lm(object, newdata, se.fit, scale = 1, type = ifelse(type == :\n", "\"prediction from a rank-deficient fit may be misleading\"Warning message in predict.lm(object, newdata, se.fit, scale = 1, type = ifelse(type == :\n", "\"prediction from a rank-deficient fit may be misleading\"Warning message in predict.lm(object, newdata, se.fit, scale = 1, type = ifelse(type == :\n", "\"prediction from a rank-deficient fit may be misleading\"Warning message in predict.lm(object, newdata, se.fit, scale = 1, type = ifelse(type == :\n", "\"prediction from a rank-deficient fit may be misleading\"Warning message in predict.lm(object, newdata, se.fit, scale = 1, type = ifelse(type == :\n", "\"prediction from a rank-deficient fit may be misleading\"Warning message in predict.lm(object, newdata, se.fit, scale = 1, type = ifelse(type == :\n", "\"prediction from a rank-deficient fit may be misleading\"Warning message in predict.lm(object, newdata, se.fit, scale = 1, type = ifelse(type == :\n", "\"prediction from a rank-deficient fit may be misleading\"Warning message in predict.lm(object, newdata, se.fit, scale = 1, type = ifelse(type == :\n", "\"prediction from a rank-deficient fit may be misleading\"Warning message in predict.lm(object, newdata, se.fit, scale = 1, type = ifelse(type == :\n", "\"prediction from a rank-deficient fit may be misleading\"Warning message in predict.lm(object, newdata, se.fit, scale = 1, type = ifelse(type == :\n", "\"prediction from a rank-deficient fit may be misleading\"Warning message in predict.lm(object, newdata, se.fit, scale = 1, type = ifelse(type == :\n", "\"prediction from a rank-deficient fit may be misleading\"Warning message in predict.lm(object, newdata, se.fit, scale = 1, type = ifelse(type == :\n", "\"prediction from a rank-deficient fit may be misleading\"Warning message in predict.lm(object, newdata, se.fit, scale = 1, type = ifelse(type == :\n", "\"prediction from a rank-deficient fit may be misleading\"Warning message in predict.lm(object, newdata, se.fit, scale = 1, type = ifelse(type == :\n", "\"prediction from a rank-deficient fit may be misleading\"Warning message in predict.lm(object, newdata, se.fit, scale = 1, type = ifelse(type == :\n", "\"prediction from a rank-deficient fit may be misleading\"Warning message in predict.lm(object, newdata, se.fit, scale = 1, type = ifelse(type == :\n", "\"prediction from a rank-deficient fit may be misleading\"Warning message in predict.lm(object, newdata, se.fit, scale = 1, type = ifelse(type == :\n", "\"prediction from a rank-deficient fit may be misleading\"Warning message in predict.lm(object, newdata, se.fit, scale = 1, type = ifelse(type == :\n", "\"prediction from a rank-deficient fit may be misleading\"Warning message in predict.lm(object, newdata, se.fit, scale = 1, type = ifelse(type == :\n", "\"prediction from a rank-deficient fit may be misleading\"Warning message in predict.lm(object, newdata, se.fit, scale = 1, type = ifelse(type == :\n", "\"prediction from a rank-deficient fit may be misleading\"Warning message in predict.lm(object, newdata, se.fit, scale = 1, type = ifelse(type == :\n", "\"prediction from a rank-deficient fit may be misleading\"Warning message in predict.lm(object, newdata, se.fit, scale = 1, type = ifelse(type == :\n", "\"prediction from a rank-deficient fit may be misleading\"Warning message in predict.lm(object, newdata, se.fit, scale = 1, type = ifelse(type == :\n", "\"prediction from a rank-deficient fit may be misleading\"Warning message in predict.lm(object, newdata, se.fit, scale = 1, type = ifelse(type == :\n", "\"prediction from a rank-deficient fit may be misleading\"Warning message in predict.lm(object, newdata, se.fit, scale = 1, type = ifelse(type == :\n", "\"prediction from a rank-deficient fit may be misleading\"Warning message in predict.lm(object, newdata, se.fit, scale = 1, type = ifelse(type == :\n", "\"prediction from a rank-deficient fit may be misleading\"Warning message in predict.lm(object, newdata, se.fit, scale = 1, type = ifelse(type == :\n", "\"prediction from a rank-deficient fit may be misleading\"Warning message in predict.lm(object, newdata, se.fit, scale = 1, type = ifelse(type == :\n", "\"prediction from a rank-deficient fit may be misleading\"Warning message in predict.lm(object, newdata, se.fit, scale = 1, type = ifelse(type == :\n", "\"prediction from a rank-deficient fit may be misleading\"Warning message in predict.lm(object, newdata, se.fit, scale = 1, type = ifelse(type == :\n", "\"prediction from a rank-deficient fit may be misleading\"Warning message in predict.lm(object, newdata, se.fit, scale = 1, type = ifelse(type == :\n", "\"prediction from a rank-deficient fit may be misleading\"Warning message in predict.lm(object, newdata, se.fit, scale = 1, type = ifelse(type == :\n", "\"prediction from a rank-deficient fit may be misleading\"Warning message in predict.lm(object, newdata, se.fit, scale = 1, type = ifelse(type == :\n", "\"prediction from a rank-deficient fit may be misleading\"Warning message in predict.lm(object, newdata, se.fit, scale = 1, type = ifelse(type == :\n", "\"prediction from a rank-deficient fit may be misleading\"Warning message in predict.lm(object, newdata, se.fit, scale = 1, type = ifelse(type == :\n", "\"prediction from a rank-deficient fit may be misleading\"Warning message in predict.lm(object, newdata, se.fit, scale = 1, type = ifelse(type == :\n", "\"prediction from a rank-deficient fit may be misleading\"Warning message in predict.lm(object, newdata, se.fit, scale = 1, type = ifelse(type == :\n", "\"prediction from a rank-deficient fit may be misleading\"Warning message in predict.lm(object, newdata, se.fit, scale = 1, type = ifelse(type == :\n", "\"prediction from a rank-deficient fit may be misleading\"Warning message in predict.lm(object, newdata, se.fit, scale = 1, type = ifelse(type == :\n", "\"prediction from a rank-deficient fit may be misleading\"Warning message in predict.lm(object, newdata, se.fit, scale = 1, type = ifelse(type == :\n", "\"prediction from a rank-deficient fit may be misleading\"Warning message in predict.lm(object, newdata, se.fit, scale = 1, type = ifelse(type == :\n", "\"prediction from a rank-deficient fit may be misleading\"Warning message in predict.lm(object, newdata, se.fit, scale = 1, type = ifelse(type == :\n", "\"prediction from a rank-deficient fit may be misleading\"Warning message in predict.lm(object, newdata, se.fit, scale = 1, type = ifelse(type == :\n", "\"prediction from a rank-deficient fit may be misleading\"Warning message in predict.lm(object, newdata, se.fit, scale = 1, type = ifelse(type == :\n", "\"prediction from a rank-deficient fit may be misleading\"Warning message in predict.lm(object, newdata, se.fit, scale = 1, type = ifelse(type == :\n", "\"prediction from a rank-deficient fit may be misleading\"Warning message in predict.lm(object, newdata, se.fit, scale = 1, type = ifelse(type == :\n", "\"prediction from a rank-deficient fit may be misleading\"Warning message in predict.lm(object, newdata, se.fit, scale = 1, type = ifelse(type == :\n", "\"prediction from a rank-deficient fit may be misleading\"Warning message in predict.lm(object, newdata, se.fit, scale = 1, type = ifelse(type == :\n", "\"prediction from a rank-deficient fit may be misleading\"Warning message in predict.lm(object, newdata, se.fit, scale = 1, type = ifelse(type == :\n", "\"prediction from a rank-deficient fit may be misleading\"Warning message in predict.lm(object, newdata, se.fit, scale = 1, type = ifelse(type == :\n", "\"prediction from a rank-deficient fit may be misleading\"Warning message in predict.lm(object, newdata, se.fit, scale = 1, type = ifelse(type == :\n", "\"prediction from a rank-deficient fit may be misleading\"Warning message in predict.lm(object, newdata, se.fit, scale = 1, type = ifelse(type == :\n", "\"prediction from a rank-deficient fit may be misleading\"Warning message in predict.lm(object, newdata, se.fit, scale = 1, type = ifelse(type == :\n", "\"prediction from a rank-deficient fit may be misleading\"Warning message in predict.lm(object, newdata, se.fit, scale = 1, type = ifelse(type == :\n", "\"prediction from a rank-deficient fit may be misleading\"Warning message in predict.lm(object, newdata, se.fit, scale = 1, type = ifelse(type == :\n", "\"prediction from a rank-deficient fit may be misleading\"Warning message in predict.lm(object, newdata, se.fit, scale = 1, type = ifelse(type == :\n", "\"prediction from a rank-deficient fit may be misleading\"Warning message in predict.lm(object, newdata, se.fit, scale = 1, type = ifelse(type == :\n", "\"prediction from a rank-deficient fit may be misleading\"Warning message in predict.lm(object, newdata, se.fit, scale = 1, type = ifelse(type == :\n", "\"prediction from a rank-deficient fit may be misleading\"Warning message in predict.lm(object, newdata, se.fit, scale = 1, type = ifelse(type == :\n", "\"prediction from a rank-deficient fit may be misleading\"Warning message in predict.lm(object, newdata, se.fit, scale = 1, type = ifelse(type == :\n", "\"prediction from a rank-deficient fit may be misleading\"Warning message in predict.lm(object, newdata, se.fit, scale = 1, type = ifelse(type == :\n", "\"prediction from a rank-deficient fit may be misleading\"Warning message in predict.lm(object, newdata, se.fit, scale = 1, type = ifelse(type == :\n", "\"prediction from a rank-deficient fit may be misleading\"Warning message in predict.lm(object, newdata, se.fit, scale = 1, type = ifelse(type == :\n", "\"prediction from a rank-deficient fit may be misleading\"Warning message in predict.lm(object, newdata, se.fit, scale = 1, type = ifelse(type == :\n", "\"prediction from a rank-deficient fit may be misleading\"Warning message in predict.lm(object, newdata, se.fit, scale = 1, type = ifelse(type == :\n", "\"prediction from a rank-deficient fit may be misleading\"Warning message in predict.lm(object, newdata, se.fit, scale = 1, type = ifelse(type == :\n", "\"prediction from a rank-deficient fit may be misleading\"Warning message in predict.lm(object, newdata, se.fit, scale = 1, type = ifelse(type == :\n", "\"prediction from a rank-deficient fit may be misleading\"Warning message in predict.lm(object, newdata, se.fit, scale = 1, type = ifelse(type == :\n", "\"prediction from a rank-deficient fit may be misleading\"Warning message in predict.lm(object, newdata, se.fit, scale = 1, type = ifelse(type == :\n", "\"prediction from a rank-deficient fit may be misleading\"Warning message in predict.lm(object, newdata, se.fit, scale = 1, type = ifelse(type == :\n", "\"prediction from a rank-deficient fit may be misleading\"Warning message in predict.lm(object, newdata, se.fit, scale = 1, type = ifelse(type == :\n", "\"prediction from a rank-deficient fit may be misleading\"Warning message in predict.lm(object, newdata, se.fit, scale = 1, type = ifelse(type == :\n", "\"prediction from a rank-deficient fit may be misleading\"Warning message in predict.lm(object, newdata, se.fit, scale = 1, type = ifelse(type == :\n", "\"prediction from a rank-deficient fit may be misleading\"Warning message in predict.lm(object, newdata, se.fit, scale = 1, type = ifelse(type == :\n", "\"prediction from a rank-deficient fit may be misleading\"Warning message in predict.lm(object, newdata, se.fit, scale = 1, type = ifelse(type == :\n", "\"prediction from a rank-deficient fit may be misleading\"Warning message in predict.lm(object, newdata, se.fit, scale = 1, type = ifelse(type == :\n", "\"prediction from a rank-deficient fit may be misleading\"Warning message in predict.lm(object, newdata, se.fit, scale = 1, type = ifelse(type == :\n", 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predict.lm(object, newdata, se.fit, scale = 1, type = ifelse(type == :\n", "\"prediction from a rank-deficient fit may be misleading\"Warning message in predict.lm(object, newdata, se.fit, scale = 1, type = ifelse(type == :\n", "\"prediction from a rank-deficient fit may be misleading\"Warning message in predict.lm(object, newdata, se.fit, scale = 1, type = ifelse(type == :\n", "\"prediction from a rank-deficient fit may be misleading\"Warning message in predict.lm(object, newdata, se.fit, scale = 1, type = ifelse(type == :\n", "\"prediction from a rank-deficient fit may be misleading\"Warning message in predict.lm(object, newdata, se.fit, scale = 1, type = ifelse(type == :\n", "\"prediction from a rank-deficient fit may be misleading\"Warning message in predict.lm(object, newdata, se.fit, scale = 1, type = ifelse(type == :\n", "\"prediction from a rank-deficient fit may be misleading\"Warning message in predict.lm(object, newdata, se.fit, scale = 1, type = ifelse(type == :\n", "\"prediction from a rank-deficient fit may be misleading\"Warning message in predict.lm(object, newdata, se.fit, scale = 1, type = ifelse(type == :\n", "\"prediction from a rank-deficient fit may be misleading\"Warning message in predict.lm(object, newdata, se.fit, scale = 1, type = ifelse(type == :\n", "\"prediction from a rank-deficient fit may be misleading\"Warning message in predict.lm(object, newdata, se.fit, scale = 1, type = ifelse(type == :\n", "\"prediction from a rank-deficient fit may be misleading\"Warning message in predict.lm(object, newdata, se.fit, scale = 1, type = ifelse(type == :\n", "\"prediction from a rank-deficient fit may be misleading\"Warning message in predict.lm(object, newdata, se.fit, scale = 1, type = ifelse(type == :\n", "\"prediction from a rank-deficient fit may be misleading\"Warning message in predict.lm(object, newdata, se.fit, scale = 1, type = ifelse(type == :\n", "\"prediction from a rank-deficient fit may be misleading\"Warning message in predict.lm(object, newdata, se.fit, scale = 1, type = ifelse(type == :\n", "\"prediction from a rank-deficient fit may be misleading\"Warning message in predict.lm(object, newdata, se.fit, scale = 1, type = ifelse(type == :\n", "\"prediction from a rank-deficient fit may be misleading\"Warning message in predict.lm(object, newdata, se.fit, scale = 1, type = ifelse(type == :\n", "\"prediction from a rank-deficient fit may be misleading\"Warning message in predict.lm(object, newdata, se.fit, scale = 1, type = ifelse(type == :\n", "\"prediction from a rank-deficient fit may be misleading\"Warning message in predict.lm(object, newdata, se.fit, scale = 1, type = ifelse(type == :\n", "\"prediction from a rank-deficient fit may be misleading\"Warning message in predict.lm(object, newdata, se.fit, scale = 1, type = ifelse(type == :\n", "\"prediction from a rank-deficient fit may be misleading\"Warning message in predict.lm(object, newdata, se.fit, scale = 1, type = ifelse(type == :\n", "\"prediction from a rank-deficient fit may be misleading\"Warning message in predict.lm(object, newdata, se.fit, scale = 1, type = ifelse(type == :\n", "\"prediction from a rank-deficient fit may be misleading\"Warning message in predict.lm(object, newdata, se.fit, scale = 1, type = ifelse(type == :\n", "\"prediction from a rank-deficient fit may be misleading\"" ] } ], "source": [ "set.seed(1)\n", "lc <- learing_curve_dat(Train[, !names(Train) %in% c(\"Sex_female\", \"Embarked_C\", \"Title2_Master\")], outcome = \"Survived\", \n", " proportion = (1:10)/10, \n", " test_prop = 0, \n", " method = \"glm\", \n", " trControl = numFolds, \n", " na.action = na.omit, \n", " verbose = FALSE) " ] }, { "cell_type": "code", "execution_count": 153, "metadata": { "collapsed": true, "run_control": { "marked": false } }, "outputs": [], "source": [ "# Give a proper size to plots\n", "options(repr.plot.width=5, repr.plot.height=4)" ] }, { "cell_type": "code", "execution_count": 154, "metadata": { "collapsed": false, "run_control": { "marked": false }, "scrolled": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Warning message:\n", "\"Removed 1 rows containing non-finite values (stat_smooth).\"" ] }, { "data": {}, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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"text/plain": [ "plot without title" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ggplot(lc, aes(x = Training_Size, y = Accuracy, color = Data)) +\n", " geom_smooth(method = loess, span = .8) + \n", " theme_classic()" ] }, { "cell_type": "markdown", "metadata": { "run_control": { "marked": false } }, "source": [ "This graph shows the **learning curves** of the model (Accuracy vs Sample size). \"Training\" and \"Resampling\" represent the perfomance of the model on the training set and on the resampling set respectively, with a increasing size of the training set. (The resampling set is the set that was retrieved as a \"test\" set during the cross-validation process)<br>\n", "\n", "We observe that the **performance on the training set decreases** with the increase of the training set size. Indeed, the more data we get, the less likely is the linear model to fit exactly the data. On the contrary, the **performance of the model on the resampling set increases** with the increase of the training set size. The more data we train the model with, the better the model is to generalize to unknown data. <br>\n", "\n", "We have a slightly lower performance when we fit the model the resampling set compared to the training set. It seems that we have a **high variance** problem, even if the learning curve is not very clear here... In other words, our model seems to be **overfitting**. Let's remove some features." ] }, { "cell_type": "markdown", "metadata": { "run_control": { "marked": false } }, "source": [ "### 3.1.3 Feature selection" ] }, { "cell_type": "markdown", "metadata": { "run_control": { "marked": false } }, "source": [ "We can perform feature selection, to keep only relevant predictors, either manually or automatically.<br>\n", "As written by Guyon & Elisseeff (2003) (http://www.jmlr.org/papers/volume3/guyon03a/guyon03a.pdf), \n", "> There are many potential benefits of feature selection:\n", "* facilitating data visualization and data understanding, \n", "* reducing the measurement and storage requirements, \n", "* reducing training and utilization times, \n", "* defying the curse of dimensionality to improve prediction performance." ] }, { "cell_type": "markdown", "metadata": { "run_control": { "marked": false } }, "source": [ "#### 3.1.3.1 Manual selection" ] }, { "cell_type": "markdown", "metadata": { "run_control": { "marked": false } }, "source": [ "First, let's check the warning that appeared when we run our quick and dirty model. It seems that we have a problem related to rank-deficiency. It may stem from many origins, one of which may be that we have some **collinear variables**. We know that Fare is highly correlated with Pclass. However, removing Fare does not remove the warning.<br>\n", "We have created Title2 by taking into account Sex and Age. Sex and Titles2 are likely highly correlated." ] }, { "cell_type": "code", "execution_count": 161, "metadata": { "collapsed": false, "run_control": { "marked": false }, "scrolled": true }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Warning message in polychor(x, y, ML = ML, std.err = std.err):\n", "\"inadmissible correlation set to 0.9999\"" ] }, { "data": { "text/html": [ "<table>\n", "<thead><tr><th></th><th scope=col>Sex_male</th><th scope=col>Title2_Mr</th></tr></thead>\n", "<tbody>\n", "\t<tr><th scope=row>Sex_male</th><td>1.0000</td><td>0.9999</td></tr>\n", "\t<tr><th scope=row>Title2_Mr</th><td>0.9999</td><td>1.0000</td></tr>\n", "</tbody>\n", "</table>\n" ], "text/latex": [ "\\begin{tabular}{r|ll}\n", " & Sex\\_male & Title2\\_Mr\\\\\n", "\\hline\n", "\tSex\\_male & 1.0000 & 0.9999\\\\\n", "\tTitle2\\_Mr & 0.9999 & 1.0000\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "| <!--/--> | Sex_male | Title2_Mr | \n", "|---|---|\n", "| Sex_male | 1.0000 | 0.9999 | \n", "| Title2_Mr | 0.9999 | 1.0000 | \n", "\n", "\n" ], "text/plain": [ " Sex_male Title2_Mr\n", "Sex_male 1.0000 0.9999 \n", "Title2_Mr 0.9999 1.0000 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# For example: \n", "hetcor(Train[,c(\"Sex_male\", \"Title2_Mr\")])$correlations" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's try to remove Sex_male then." ] }, { "cell_type": "code", "execution_count": 155, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<ol class=list-inline>\n", "\t<li>'Pclass'</li>\n", "\t<li>'Sex_female'</li>\n", "\t<li>'Sex_male'</li>\n", "\t<li>'Age'</li>\n", "\t<li>'SibSp'</li>\n", "\t<li>'Parch'</li>\n", "\t<li>'Fare'</li>\n", "\t<li>'Embarked_C'</li>\n", "\t<li>'Embarked_Q'</li>\n", "\t<li>'Embarked_S'</li>\n", "\t<li>'Child'</li>\n", "\t<li>'Young'</li>\n", "\t<li>'Title2_Master'</li>\n", "\t<li>'Title2_Miss'</li>\n", "\t<li>'Title2_Mr'</li>\n", "\t<li>'Title2_Mrs'</li>\n", "\t<li>'Title2_Sir'</li>\n", "\t<li>'Survived'</li>\n", "</ol>\n" ], "text/latex": [ "\\begin{enumerate*}\n", "\\item 'Pclass'\n", "\\item 'Sex\\_female'\n", "\\item 'Sex\\_male'\n", "\\item 'Age'\n", "\\item 'SibSp'\n", "\\item 'Parch'\n", "\\item 'Fare'\n", "\\item 'Embarked\\_C'\n", "\\item 'Embarked\\_Q'\n", "\\item 'Embarked\\_S'\n", "\\item 'Child'\n", "\\item 'Young'\n", "\\item 'Title2\\_Master'\n", "\\item 'Title2\\_Miss'\n", "\\item 'Title2\\_Mr'\n", "\\item 'Title2\\_Mrs'\n", "\\item 'Title2\\_Sir'\n", "\\item 'Survived'\n", "\\end{enumerate*}\n" ], "text/markdown": [ "1. 'Pclass'\n", "2. 'Sex_female'\n", "3. 'Sex_male'\n", "4. 'Age'\n", "5. 'SibSp'\n", "6. 'Parch'\n", "7. 'Fare'\n", "8. 'Embarked_C'\n", "9. 'Embarked_Q'\n", "10. 'Embarked_S'\n", "11. 'Child'\n", "12. 'Young'\n", "13. 'Title2_Master'\n", "14. 'Title2_Miss'\n", "15. 'Title2_Mr'\n", "16. 'Title2_Mrs'\n", "17. 'Title2_Sir'\n", "18. 'Survived'\n", "\n", "\n" ], "text/plain": [ " [1] \"Pclass\" \"Sex_female\" \"Sex_male\" \"Age\" \n", " [5] \"SibSp\" \"Parch\" \"Fare\" \"Embarked_C\" \n", " [9] \"Embarked_Q\" \"Embarked_S\" \"Child\" \"Young\" \n", "[13] \"Title2_Master\" \"Title2_Miss\" \"Title2_Mr\" \"Title2_Mrs\" \n", "[17] \"Title2_Sir\" \"Survived\" " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "names(Train)" ] }, { "cell_type": "code", "execution_count": 156, "metadata": { "collapsed": false, "run_control": { "marked": false } }, "outputs": [], "source": [ "## Perform the cross validation\n", "set.seed(1)\n", "logmod2 <- train(Survived ~ .-Sex_female -Embarked_C -Title2_Master -Sex_male, \n", " data = Train, \n", " method = \"glm\", \n", " trControl = numFolds, \n", " na.action = na.omit, \n", " preProcess=c(\"center\", \"scale\"))" ] }, { "cell_type": "code", "execution_count": 157, "metadata": { "collapsed": false, "run_control": { "marked": false }, "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "\n", "Call:\n", "NULL\n", "\n", "Deviance Residuals: \n", " Min 1Q Median 3Q Max \n", "-2.5066 -0.5717 -0.3620 0.5418 2.5770 \n", "\n", "Coefficients:\n", " Estimate Std. Error z value Pr(>|z|) \n", "(Intercept) -0.68573 0.09680 -7.084 1.40e-12 ***\n", "Pclass.L -0.90930 0.13440 -6.766 1.33e-11 ***\n", "Pclass.Q -0.03550 0.09676 -0.367 0.71373 \n", "Age -0.44081 0.18545 -2.377 0.01746 * \n", "SibSp -0.56822 0.13495 -4.211 2.55e-05 ***\n", "Parch -0.23011 0.10352 -2.223 0.02624 * \n", "Fare 0.13828 0.13140 1.052 0.29266 \n", "Embarked_Q1 -0.06132 0.11192 -0.548 0.58376 \n", "Embarked_S1 -0.20492 0.11155 -1.837 0.06620 . \n", "ChildTRUE 0.20463 0.11924 1.716 0.08615 . \n", "YoungTRUE -0.27831 0.15120 -1.841 0.06568 . \n", "Title2_Miss1 -0.02414 0.12063 -0.200 0.84141 \n", "Title2_Mr1 -1.07443 0.32704 -3.285 0.00102 ** \n", "Title2_Mrs1 0.50812 0.29677 1.712 0.08686 . \n", "Title2_Sir1 -0.37218 0.13046 -2.853 0.00433 ** \n", "---\n", "Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n", "\n", "(Dispersion parameter for binomial family taken to be 1)\n", "\n", " Null deviance: 1186.66 on 890 degrees of freedom\n", "Residual deviance: 727.25 on 876 degrees of freedom\n", "AIC: 757.25\n", "\n", "Number of Fisher Scoring iterations: 5\n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "summary(logmod2)" ] }, { "cell_type": "markdown", "metadata": { "run_control": { "marked": false } }, "source": [ "Let's see what is the accuracy of the model." ] }, { "cell_type": "code", "execution_count": 158, "metadata": { "collapsed": false, "run_control": { "marked": false }, "scrolled": true }, "outputs": [ { "data": { "text/html": [ "0.822667688117126" ], "text/latex": [ "0.822667688117126" ], "text/markdown": [ "0.822667688117126" ], "text/plain": [ "[1] 0.8226677" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "logmod2$results$Accuracy" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Removing Sex_male did not change the accuracy of our model." ] }, { "cell_type": "markdown", "metadata": { "run_control": { "marked": false } }, "source": [ "Let's have a look at the **importance** of each variable." ] }, { "cell_type": "code", "execution_count": 163, "metadata": { "collapsed": false, "run_control": { "marked": false }, "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "glm variable importance\n", "\n", " Overall\n", "Pclass.L 6.7659\n", "SibSp 4.2108\n", "Title2_Mr1 3.2853\n", "Title2_Sir1 2.8528\n", "Age 2.3769\n", "Parch 2.2227\n", "YoungTRUE 1.8406\n", "Embarked_S1 1.8370\n", "ChildTRUE 1.7161\n", "Title2_Mrs1 1.7122\n", "Fare 1.0523\n", "Embarked_Q1 0.5479\n", "Pclass.Q 0.3668\n", "Title2_Miss1 0.2001" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# absolute value of the t-statistic\n", "varImp(logmod2, scale=FALSE)" ] }, { "cell_type": "markdown", "metadata": { "run_control": { "marked": false } }, "source": [ "We can remove some of the least important variable: Title2_Miss, Title2_Mrs, Embarked_S and Embarked_Q." ] }, { "cell_type": "code", "execution_count": 164, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<ol class=list-inline>\n", "\t<li>'Pclass'</li>\n", "\t<li>'Sex_female'</li>\n", "\t<li>'Sex_male'</li>\n", "\t<li>'Age'</li>\n", "\t<li>'SibSp'</li>\n", "\t<li>'Parch'</li>\n", "\t<li>'Fare'</li>\n", "\t<li>'Embarked_C'</li>\n", "\t<li>'Embarked_Q'</li>\n", "\t<li>'Embarked_S'</li>\n", "\t<li>'Child'</li>\n", "\t<li>'Young'</li>\n", "\t<li>'Title2_Master'</li>\n", "\t<li>'Title2_Miss'</li>\n", "\t<li>'Title2_Mr'</li>\n", "\t<li>'Title2_Mrs'</li>\n", "\t<li>'Title2_Sir'</li>\n", "\t<li>'Survived'</li>\n", "</ol>\n" ], "text/latex": [ "\\begin{enumerate*}\n", "\\item 'Pclass'\n", "\\item 'Sex\\_female'\n", "\\item 'Sex\\_male'\n", "\\item 'Age'\n", "\\item 'SibSp'\n", "\\item 'Parch'\n", "\\item 'Fare'\n", "\\item 'Embarked\\_C'\n", "\\item 'Embarked\\_Q'\n", "\\item 'Embarked\\_S'\n", "\\item 'Child'\n", "\\item 'Young'\n", "\\item 'Title2\\_Master'\n", "\\item 'Title2\\_Miss'\n", "\\item 'Title2\\_Mr'\n", "\\item 'Title2\\_Mrs'\n", "\\item 'Title2\\_Sir'\n", "\\item 'Survived'\n", "\\end{enumerate*}\n" ], "text/markdown": [ "1. 'Pclass'\n", "2. 'Sex_female'\n", "3. 'Sex_male'\n", "4. 'Age'\n", "5. 'SibSp'\n", "6. 'Parch'\n", "7. 'Fare'\n", "8. 'Embarked_C'\n", "9. 'Embarked_Q'\n", "10. 'Embarked_S'\n", "11. 'Child'\n", "12. 'Young'\n", "13. 'Title2_Master'\n", "14. 'Title2_Miss'\n", "15. 'Title2_Mr'\n", "16. 'Title2_Mrs'\n", "17. 'Title2_Sir'\n", "18. 'Survived'\n", "\n", "\n" ], "text/plain": [ " [1] \"Pclass\" \"Sex_female\" \"Sex_male\" \"Age\" \n", " [5] \"SibSp\" \"Parch\" \"Fare\" \"Embarked_C\" \n", " [9] \"Embarked_Q\" \"Embarked_S\" \"Child\" \"Young\" \n", "[13] \"Title2_Master\" \"Title2_Miss\" \"Title2_Mr\" \"Title2_Mrs\" \n", "[17] \"Title2_Sir\" \"Survived\" " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "names(Train)" ] }, { "cell_type": "code", "execution_count": 189, "metadata": { "collapsed": false, "run_control": { "marked": false } }, "outputs": [], "source": [ "## Perform the cross validation\n", "set.seed(1)\n", "logmod3 <- train(Survived ~ Pclass + Age + SibSp + Parch + Fare + Child + Young + Title2_Mr + Title2_Sir, \n", " data = Train, \n", " method = \"glm\", \n", " trControl = numFolds, \n", " na.action = na.omit, \n", " preProcess=c(\"center\", \"scale\"))" ] }, { "cell_type": "code", "execution_count": 190, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "0.829409828623312" ], "text/latex": [ "0.829409828623312" ], "text/markdown": [ "0.829409828623312" ], "text/plain": [ "[1] 0.8294098" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "logmod3$results$Accuracy" ] }, { "cell_type": "markdown", "metadata": { "run_control": { "marked": false } }, "source": [ "And, now, let's remove Fare which is among the least important variables and collinear with Pclass." ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "collapsed": false, "init_cell": true, "run_control": { "marked": false } }, "outputs": [], "source": [ "## Perform the cross validation\n", "set.seed(1)\n", "logmod4 <- train(Survived ~ Pclass + Age + SibSp + Parch + Child + Young + Title2_Mr + Title2_Sir, \n", " data = Train, \n", " method = \"glm\", \n", " trControl = numFolds, \n", " na.action = na.omit, \n", " preProcess=c(\"center\", \"scale\"))" ] }, { "cell_type": "code", "execution_count": 35, "metadata": { "collapsed": false, "run_control": { "marked": false } }, "outputs": [ { "data": { "text/html": [ "0.831669787765293" ], "text/latex": [ "0.831669787765293" ], "text/markdown": [ "0.831669787765293" ], "text/plain": [ "[1] 0.8316698" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "logmod4$results$Accuracy" ] }, { "cell_type": "markdown", "metadata": { "run_control": { "marked": false } }, "source": [ "Still better!" ] }, { "cell_type": "markdown", "metadata": { "run_control": { "marked": false } }, "source": [ "#### 3.1.3.2 Automatic selection" ] }, { "cell_type": "markdown", "metadata": { "run_control": { "marked": false } }, "source": [ "Another way to perform feature selection is to use **stepwise feature selection** based on **AIC**. Let's try it starting with all the variables." ] }, { "cell_type": "code", "execution_count": 174, "metadata": { "collapsed": false, "run_control": { "marked": false }, "scrolled": true }, "outputs": [ { "data": { "text/html": [ "<ol class=list-inline>\n", "\t<li>'Pclass'</li>\n", "\t<li>'Sex_female'</li>\n", "\t<li>'Sex_male'</li>\n", "\t<li>'Age'</li>\n", "\t<li>'SibSp'</li>\n", "\t<li>'Parch'</li>\n", "\t<li>'Fare'</li>\n", "\t<li>'Embarked_C'</li>\n", "\t<li>'Embarked_Q'</li>\n", "\t<li>'Embarked_S'</li>\n", "\t<li>'Child'</li>\n", "\t<li>'Young'</li>\n", "\t<li>'Title2_Master'</li>\n", "\t<li>'Title2_Miss'</li>\n", "\t<li>'Title2_Mr'</li>\n", "\t<li>'Title2_Mrs'</li>\n", "\t<li>'Title2_Sir'</li>\n", "\t<li>'Survived'</li>\n", "</ol>\n" ], "text/latex": [ "\\begin{enumerate*}\n", "\\item 'Pclass'\n", "\\item 'Sex\\_female'\n", "\\item 'Sex\\_male'\n", "\\item 'Age'\n", "\\item 'SibSp'\n", "\\item 'Parch'\n", "\\item 'Fare'\n", "\\item 'Embarked\\_C'\n", "\\item 'Embarked\\_Q'\n", "\\item 'Embarked\\_S'\n", "\\item 'Child'\n", "\\item 'Young'\n", "\\item 'Title2\\_Master'\n", "\\item 'Title2\\_Miss'\n", "\\item 'Title2\\_Mr'\n", "\\item 'Title2\\_Mrs'\n", "\\item 'Title2\\_Sir'\n", "\\item 'Survived'\n", "\\end{enumerate*}\n" ], "text/markdown": [ "1. 'Pclass'\n", "2. 'Sex_female'\n", "3. 'Sex_male'\n", "4. 'Age'\n", "5. 'SibSp'\n", "6. 'Parch'\n", "7. 'Fare'\n", "8. 'Embarked_C'\n", "9. 'Embarked_Q'\n", "10. 'Embarked_S'\n", "11. 'Child'\n", "12. 'Young'\n", "13. 'Title2_Master'\n", "14. 'Title2_Miss'\n", "15. 'Title2_Mr'\n", "16. 'Title2_Mrs'\n", "17. 'Title2_Sir'\n", "18. 'Survived'\n", "\n", "\n" ], "text/plain": [ " [1] \"Pclass\" \"Sex_female\" \"Sex_male\" \"Age\" \n", " [5] \"SibSp\" \"Parch\" \"Fare\" \"Embarked_C\" \n", " [9] \"Embarked_Q\" \"Embarked_S\" \"Child\" \"Young\" \n", "[13] \"Title2_Master\" \"Title2_Miss\" \"Title2_Mr\" \"Title2_Mrs\" \n", "[17] \"Title2_Sir\" \"Survived\" " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "names(Train)" ] }, { "cell_type": "code", "execution_count": 184, "metadata": { "collapsed": false, "run_control": { "marked": false } }, "outputs": [], "source": [ "## Perform the training with cross-validation\n", "set.seed(1)\n", "AIC_glm <- train(Survived ~ .-Sex_female -Title2_Master -Embarked_C -Sex_male, \n", " data = Train, \n", " method = \"glmStepAIC\", \n", " trControl = numFolds, \n", " na.action = na.omit, \n", " preProcess=c(\"center\", \"scale\"),\n", " trace=FALSE)" ] }, { "cell_type": "code", "execution_count": 185, "metadata": { "collapsed": false, "run_control": { "marked": false }, "scrolled": false }, "outputs": [ { "data": { "text/plain": [ "\n", "Call:\n", "NULL\n", "\n", "Deviance Residuals: \n", " Min 1Q Median 3Q Max \n", "-2.5318 -0.5549 -0.3578 0.5499 2.5366 \n", "\n", "Coefficients:\n", " Estimate Std. Error z value Pr(>|z|) \n", "(Intercept) -0.69743 0.09620 -7.249 4.19e-13 ***\n", "Pclass.L -1.00514 0.10990 -9.146 < 2e-16 ***\n", "Age -0.45852 0.18387 -2.494 0.012642 * \n", "SibSp -0.54236 0.12913 -4.200 2.67e-05 ***\n", "Parch -0.19795 0.09945 -1.991 0.046532 * \n", "Embarked_S1 -0.18664 0.09243 -2.019 0.043458 * \n", "ChildTRUE 0.20270 0.11816 1.716 0.086246 . \n", "YoungTRUE -0.27762 0.15057 -1.844 0.065208 . \n", "Title2_Mr1 -1.01982 0.27945 -3.649 0.000263 ***\n", "Title2_Mrs1 0.55479 0.25418 2.183 0.029059 * \n", "Title2_Sir1 -0.36575 0.12075 -3.029 0.002453 ** \n", "---\n", "Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n", "\n", "(Dispersion parameter for binomial family taken to be 1)\n", "\n", " Null deviance: 1186.66 on 890 degrees of freedom\n", "Residual deviance: 728.95 on 880 degrees of freedom\n", "AIC: 750.95\n", "\n", "Number of Fisher Scoring iterations: 5\n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "summary(AIC_glm)" ] }, { "cell_type": "code", "execution_count": 186, "metadata": { "collapsed": false, "run_control": { "marked": false } }, "outputs": [ { "data": { "text/html": [ "0.828260980592441" ], "text/latex": [ "0.828260980592441" ], "text/markdown": [ "0.828260980592441" ], "text/plain": [ "[1] 0.828261" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "AIC_glm$results$Accuracy" ] }, { "cell_type": "code", "execution_count": 177, "metadata": { "collapsed": false, "run_control": { "marked": false } }, "outputs": [ { "data": { "text/html": [ "0.830508171603677" ], "text/latex": [ "0.830508171603677" ], "text/markdown": [ "0.830508171603677" ], "text/plain": [ "[1] 0.8305082" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "AIC_glm$results$Accuracy" ] }, { "cell_type": "markdown", "metadata": { "run_control": { "marked": false } }, "source": [ "We observe that performing stepwise selection slightly decreased the performance of our model. Actually, feature selection does not necessarily improve the accuracy of a model. Its aim is primarily to decrease the model's complexity. Since our aim is to have the best accuracy, we will keep all the variables selected through the manual selection. " ] }, { "cell_type": "markdown", "metadata": { "run_control": { "marked": false } }, "source": [ "---\n", "## 3.2 Classification tree: CART (Recursive Partitioning)" ] }, { "cell_type": "code", "execution_count": 191, "metadata": { "collapsed": false, "run_control": { "marked": false } }, "outputs": [ { "data": { "text/html": [ "<ol class=list-inline>\n", "\t<li>'Pclass'</li>\n", "\t<li>'Sex_female'</li>\n", "\t<li>'Sex_male'</li>\n", "\t<li>'Age'</li>\n", "\t<li>'SibSp'</li>\n", "\t<li>'Parch'</li>\n", "\t<li>'Fare'</li>\n", "\t<li>'Embarked_C'</li>\n", "\t<li>'Embarked_Q'</li>\n", "\t<li>'Embarked_S'</li>\n", "\t<li>'Child'</li>\n", "\t<li>'Young'</li>\n", "\t<li>'Title2_Master'</li>\n", "\t<li>'Title2_Miss'</li>\n", "\t<li>'Title2_Mr'</li>\n", "\t<li>'Title2_Mrs'</li>\n", "\t<li>'Title2_Sir'</li>\n", "\t<li>'Survived'</li>\n", "</ol>\n" ], "text/latex": [ "\\begin{enumerate*}\n", "\\item 'Pclass'\n", "\\item 'Sex\\_female'\n", "\\item 'Sex\\_male'\n", "\\item 'Age'\n", "\\item 'SibSp'\n", "\\item 'Parch'\n", "\\item 'Fare'\n", "\\item 'Embarked\\_C'\n", "\\item 'Embarked\\_Q'\n", "\\item 'Embarked\\_S'\n", "\\item 'Child'\n", "\\item 'Young'\n", "\\item 'Title2\\_Master'\n", "\\item 'Title2\\_Miss'\n", "\\item 'Title2\\_Mr'\n", "\\item 'Title2\\_Mrs'\n", "\\item 'Title2\\_Sir'\n", "\\item 'Survived'\n", "\\end{enumerate*}\n" ], "text/markdown": [ "1. 'Pclass'\n", "2. 'Sex_female'\n", "3. 'Sex_male'\n", "4. 'Age'\n", "5. 'SibSp'\n", "6. 'Parch'\n", "7. 'Fare'\n", "8. 'Embarked_C'\n", "9. 'Embarked_Q'\n", "10. 'Embarked_S'\n", "11. 'Child'\n", "12. 'Young'\n", "13. 'Title2_Master'\n", "14. 'Title2_Miss'\n", "15. 'Title2_Mr'\n", "16. 'Title2_Mrs'\n", "17. 'Title2_Sir'\n", "18. 'Survived'\n", "\n", "\n" ], "text/plain": [ " [1] \"Pclass\" \"Sex_female\" \"Sex_male\" \"Age\" \n", " [5] \"SibSp\" \"Parch\" \"Fare\" \"Embarked_C\" \n", " [9] \"Embarked_Q\" \"Embarked_S\" \"Child\" \"Young\" \n", "[13] \"Title2_Master\" \"Title2_Miss\" \"Title2_Mr\" \"Title2_Mrs\" \n", "[17] \"Title2_Sir\" \"Survived\" " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "names(Train)" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "collapsed": false, "init_cell": true, "run_control": { "marked": false } }, "outputs": [], "source": [ "## Define the cross-validation experiment\n", "numFolds = trainControl(method = \"cv\", number = 10 ) # method='cv' for cross-validation, number=10 for 10 folds\n", "cpGrid = expand.grid( .cp = seq(0.01,0.5,0.01))" ] }, { "cell_type": "markdown", "metadata": { "run_control": { "marked": false } }, "source": [ "For this model, we are not going to use the 'formula' option, then our tree will be clearer when we plot it." ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "collapsed": false, "init_cell": true, "run_control": { "marked": false } }, "outputs": [], "source": [ "DV = Train[, \"Survived\"]\n", "pred = Train[, c('Pclass', 'Age', 'SibSp', 'Parch', 'Fare', 'Embarked_Q', 'Embarked_S', 'Child', \n", " 'Young', 'Title2_Miss', 'Title2_Mr', 'Title2_Mrs', 'Title2_Sir')]" ] }, { "cell_type": "code", "execution_count": 195, "metadata": { "collapsed": false, "run_control": { "marked": false } }, "outputs": [], "source": [ "## Perform the cross validation\n", "set.seed(1)\n", "CART1 <- train(pred, DV, method = \"rpart\", trControl = numFolds, na.action = na.omit, tuneGrid = cpGrid)" ] }, { "cell_type": "code", "execution_count": 196, "metadata": { "collapsed": false, "run_control": { "marked": false }, "scrolled": false }, "outputs": [ { "data": { "text/html": [ "<table>\n", "<thead><tr><th></th><th scope=col>cp</th></tr></thead>\n", "<tbody>\n", "\t<tr><th scope=row>2</th><td>0.02</td></tr>\n", "</tbody>\n", "</table>\n" ], "text/latex": [ "\\begin{tabular}{r|l}\n", " & cp\\\\\n", "\\hline\n", "\t2 & 0.02\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "| <!--/--> | cp | \n", "|---|\n", "| 2 | 0.02 | \n", "\n", "\n" ], "text/plain": [ " cp \n", "2 0.02" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "CART1$bestTune" ] }, { "cell_type": "code", "execution_count": 197, "metadata": { "collapsed": true, "run_control": { "marked": false } }, "outputs": [], "source": [ "# Give a proper size to plots\n", "options(repr.plot.width=4, repr.plot.height=4)" ] }, { "cell_type": "code", "execution_count": 198, "metadata": { "collapsed": false, "run_control": { "marked": false }, "scrolled": false }, "outputs": [ { "data": {}, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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"text/plain": [ "plot without title" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot(CART1)" ] }, { "cell_type": "code", "execution_count": 199, "metadata": { "collapsed": true, "run_control": { "marked": false } }, "outputs": [], "source": [ "# Give a proper size to plots\n", "options(repr.plot.width=6, repr.plot.height=6)" ] }, { "cell_type": "code", "execution_count": 200, "metadata": { "collapsed": false, "run_control": { "marked": false } }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "plot without title" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "rpart.plot(CART1$finalModel, type=0, extra=106, under=TRUE, tweak=0.8)" ] }, { "cell_type": "markdown", "metadata": { "run_control": { "marked": false } }, "source": [ "The plot shows the probability of survival and the percentage of observations in each leaf.<br>\n", "The **title** was important: a passenger who was a 'Mr' was more likely to die, and in the 2nd and 1st class, a 'Sir' was also more likely to die. These results mirror what we said earlier about the lower probability of survival in men. As expected, the **class** of the passenger had an impact on their survival: first and second class passengers were more likely to survive. <br> \n", "Then, among passengers from the 3rd class, the **Fare** was of interest: interestingly, having paid at least 23 made a person more likely to die..." ] }, { "cell_type": "code", "execution_count": 201, "metadata": { "collapsed": false, "run_control": { "marked": false } }, "outputs": [ { "data": { "text/html": [ "0.829435648621042" ], "text/latex": [ "0.829435648621042" ], "text/markdown": [ "0.829435648621042" ], "text/plain": [ "[1] 0.8294356" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Best accuracy\n", "max(CART1$results$Accuracy)" ] }, { "cell_type": "markdown", "metadata": { "run_control": { "marked": false } }, "source": [ "This CART model does not have a better performance than our best logistic regression model." ] }, { "cell_type": "markdown", "metadata": { "run_control": { "marked": false } }, "source": [ "---\n", "## 3.3 Random forest" ] }, { "cell_type": "markdown", "metadata": { "run_control": { "marked": false } }, "source": [ "Random forest = tree bagging + feature sampling" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "collapsed": false, "init_cell": true, "run_control": { "marked": false } }, "outputs": [], "source": [ "## Define the cross-validation experiment\n", "mtryGrid = expand.grid( .mtry = 1:10)\n", "numFolds = trainControl(method = \"cv\", number = 10 ) # method='cv' for cross-validation, number=10 for 10 folds" ] }, { "cell_type": "code", "execution_count": 224, "metadata": { "collapsed": false, "run_control": { "marked": false } }, "outputs": [ { "data": { "text/html": [ "<ol class=list-inline>\n", "\t<li>'Pclass'</li>\n", "\t<li>'Sex_female'</li>\n", "\t<li>'Sex_male'</li>\n", "\t<li>'Age'</li>\n", "\t<li>'SibSp'</li>\n", "\t<li>'Parch'</li>\n", "\t<li>'Fare'</li>\n", "\t<li>'Embarked_C'</li>\n", "\t<li>'Embarked_Q'</li>\n", "\t<li>'Embarked_S'</li>\n", "\t<li>'Child'</li>\n", "\t<li>'Young'</li>\n", "\t<li>'Title2_Master'</li>\n", "\t<li>'Title2_Miss'</li>\n", "\t<li>'Title2_Mr'</li>\n", "\t<li>'Title2_Mrs'</li>\n", "\t<li>'Title2_Sir'</li>\n", "\t<li>'Survived'</li>\n", "\t<li>'Deck'</li>\n", "</ol>\n" ], "text/latex": [ "\\begin{enumerate*}\n", "\\item 'Pclass'\n", "\\item 'Sex\\_female'\n", "\\item 'Sex\\_male'\n", "\\item 'Age'\n", "\\item 'SibSp'\n", "\\item 'Parch'\n", "\\item 'Fare'\n", "\\item 'Embarked\\_C'\n", "\\item 'Embarked\\_Q'\n", "\\item 'Embarked\\_S'\n", "\\item 'Child'\n", "\\item 'Young'\n", "\\item 'Title2\\_Master'\n", "\\item 'Title2\\_Miss'\n", "\\item 'Title2\\_Mr'\n", "\\item 'Title2\\_Mrs'\n", "\\item 'Title2\\_Sir'\n", "\\item 'Survived'\n", "\\item 'Deck'\n", "\\end{enumerate*}\n" ], "text/markdown": [ "1. 'Pclass'\n", "2. 'Sex_female'\n", "3. 'Sex_male'\n", "4. 'Age'\n", "5. 'SibSp'\n", "6. 'Parch'\n", "7. 'Fare'\n", "8. 'Embarked_C'\n", "9. 'Embarked_Q'\n", "10. 'Embarked_S'\n", "11. 'Child'\n", "12. 'Young'\n", "13. 'Title2_Master'\n", "14. 'Title2_Miss'\n", "15. 'Title2_Mr'\n", "16. 'Title2_Mrs'\n", "17. 'Title2_Sir'\n", "18. 'Survived'\n", "19. 'Deck'\n", "\n", "\n" ], "text/plain": [ " [1] \"Pclass\" \"Sex_female\" \"Sex_male\" \"Age\" \n", " [5] \"SibSp\" \"Parch\" \"Fare\" \"Embarked_C\" \n", " [9] \"Embarked_Q\" \"Embarked_S\" \"Child\" \"Young\" \n", "[13] \"Title2_Master\" \"Title2_Miss\" \"Title2_Mr\" \"Title2_Mrs\" \n", "[17] \"Title2_Sir\" \"Survived\" \"Deck\" " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "names(Train)" ] }, { "cell_type": "code", "execution_count": 37, "metadata": { "collapsed": false, "init_cell": true, "run_control": { "marked": false } }, "outputs": [], "source": [ "## Perform the cross validation\n", "set.seed(1)\n", "RF1 <- train(Survived ~ Pclass + Age + SibSp + Parch + Fare + Embarked_Q + Embarked_S + Child + Young + \n", " Title2_Miss + Title2_Mr + Title2_Mrs + Title2_Sir, \n", " data = Train, \n", " method = \"rf\", \n", " trControl = numFolds, \n", " na.action = na.omit, \n", " preProcess=c(\"center\", \"scale\"), \n", " tuneGrid = mtryGrid, \n", " nodesize = 15, \n", " importance = TRUE)" ] }, { "cell_type": "code", "execution_count": 225, "metadata": { "collapsed": false, "run_control": { "marked": false }, "scrolled": true }, "outputs": [ { "data": { "text/html": [ "<table>\n", "<thead><tr><th></th><th scope=col>mtry</th></tr></thead>\n", "<tbody>\n", "\t<tr><th scope=row>10</th><td>10</td></tr>\n", "</tbody>\n", "</table>\n" ], "text/latex": [ "\\begin{tabular}{r|l}\n", " & mtry\\\\\n", "\\hline\n", "\t10 & 10\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "| <!--/--> | mtry | \n", "|---|\n", "| 10 | 10 | \n", "\n", "\n" ], "text/plain": [ " mtry\n", "10 10 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "RF1$bestTune" ] }, { "cell_type": "code", "execution_count": 226, "metadata": { "collapsed": false, "run_control": { "marked": false } }, "outputs": [ { "data": { "text/html": [ "0.836163318579049" ], "text/latex": [ "0.836163318579049" ], "text/markdown": [ "0.836163318579049" ], "text/plain": [ "[1] 0.8361633" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Best accuracy\n", "max(RF1$results$Accuracy)" ] }, { "cell_type": "markdown", "metadata": { "run_control": { "marked": false } }, "source": [ "Our first random forest model is better than our best log and CART models.<br>\n", "However, we lose some of the interpretability that comes with CART.\n", "Actually, we can still compute metrics that give us insight into which variables are important. One metric that we can look at is the number of times, aggregated over all of the trees in the random forest model, that a certain variable is selected for a split (i.e. **selection frequency**)." ] }, { "cell_type": "code", "execution_count": 227, "metadata": { "collapsed": true, "run_control": { "marked": false } }, "outputs": [], "source": [ "# Give a proper size to plots\n", "options(repr.plot.width=4, repr.plot.height=4)" ] }, { "cell_type": "code", "execution_count": 228, "metadata": { "collapsed": false, "run_control": { "marked": false } }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "Plot with title \"RF1\"" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "vu = varUsed(RF1$finalModel, count=TRUE)\n", "vusorted = sort(vu, decreasing = FALSE, index.return = TRUE)\n", "dotchart(vusorted$x, RF1$finalModel$xNames[vusorted$ix], main = \"RF1\", xlab = \"Selection frequency\", cex=0.7)" ] }, { "cell_type": "markdown", "metadata": { "run_control": { "marked": false } }, "source": [ "Here, Fare and Age appear to be important variable." ] }, { "cell_type": "markdown", "metadata": { "run_control": { "marked": false } }, "source": [ "Another way to see the importance of a variable is to look at the **reduction in impurity** (i.e. how homogenous each bucket or leaf\n", "of the tree is) after that variable has been selected for splitting in all of the trees in the forest (i.e. Gini importance). Or similarly, we can look at the mean **decrease in accuracy** (i.e. permutation importance)." ] }, { "cell_type": "code", "execution_count": 229, "metadata": { "collapsed": true, "run_control": { "marked": false } }, "outputs": [], "source": [ "# Give a proper size to plots\n", "options(repr.plot.width=8, repr.plot.height=4)" ] }, { "cell_type": "code", "execution_count": 230, "metadata": { "collapsed": false, "run_control": { "marked": false } }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "Plot with title \"RF1\"" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "par(mfrow=c(1,2))\n", "\n", "# Compute the reduction in impurity (i.e. Gini importance)\n", "Gini <- varImp(RF1, type=2, scale=FALSE) \n", "Ginisorted = sort(Gini$importance$Overall, decreasing = FALSE, index.return = TRUE)\n", "dotchart(Ginisorted$x, rownames(Gini$importance)[Ginisorted$ix], \n", " main = \"RF1\", \n", " xlab = \"Gini importance\", \n", " cex=0.7)\n", "\n", "# Compute the reduction in accuracy (i.e. permutation accuracy importance)\n", "Perm <- varImp(RF1, type=1, scale=FALSE) \n", "Permsorted = sort(Perm$importance$Overall, decreasing = FALSE, index.return = TRUE)\n", "dotchart(Permsorted$x, rownames(Perm$importance)[Permsorted$ix], \n", " main = \"RF1\", \n", " xlab = \"Permutation accuracy importance\", \n", " cex=0.7)" ] }, { "cell_type": "markdown", "metadata": { "run_control": { "marked": false } }, "source": [ "Again, Fare and Age appear among the top variables, but we can see that Title2_Mr and Pclass, are also important. <br>\n", "\n", "**WARNING!** We have to be careful here: isn't it a strange coincidence that all the continuous variables are in the top 5 variables used for splitting?... \n", "\n", "Checking the literature, we can find an explanation to this behaviour: \n", "* Strobl et al (2007) Bias in random forest variable importance measures: Illustrations, sources and a solution, BMC Bioinformatics, 8: 25\n", "* Strobl et al (2009) Party on!, The R Journal, 1/2.\n", "\n", "> In standard tree algorithms, variable selection is biased in favor of variables offering many potential cut-points, so that variables with many categories and continuous variables are artificially preferred.\n", "\n", "Therefore, authors suggested that\n", "> The randomForest function should not be used when the potential predictor variables vary in their scale of measurement or their number of categories." ] }, { "cell_type": "code", "execution_count": 231, "metadata": { "collapsed": false, "run_control": { "marked": false } }, "outputs": [], "source": [ "pred3 <- subset(Train, select=c(Pclass, Age, SibSp, Parch, Fare, Embarked_Q, Embarked_S, Child, Young, \n", " Title2_Miss, Title2_Mr, Title2_Mrs, Title2_Sir))" ] }, { "cell_type": "code", "execution_count": 232, "metadata": { "collapsed": false, "run_control": { "marked": false } }, "outputs": [ { "data": { "text/html": [ "<dl class=dl-horizontal>\n", "\t<dt>Fare</dt>\n", "\t\t<dd>248</dd>\n", "\t<dt>Age</dt>\n", "\t\t<dd>89</dd>\n", "\t<dt>SibSp</dt>\n", "\t\t<dd>7</dd>\n", "\t<dt>Parch</dt>\n", "\t\t<dd>7</dd>\n", "\t<dt>Pclass</dt>\n", "\t\t<dd>3</dd>\n", "\t<dt>Embarked_Q</dt>\n", "\t\t<dd>2</dd>\n", "\t<dt>Embarked_S</dt>\n", "\t\t<dd>2</dd>\n", "\t<dt>Child</dt>\n", "\t\t<dd>2</dd>\n", "\t<dt>Young</dt>\n", "\t\t<dd>2</dd>\n", "\t<dt>Title2_Miss</dt>\n", "\t\t<dd>2</dd>\n", "\t<dt>Title2_Mr</dt>\n", "\t\t<dd>2</dd>\n", "\t<dt>Title2_Mrs</dt>\n", "\t\t<dd>2</dd>\n", "\t<dt>Title2_Sir</dt>\n", "\t\t<dd>2</dd>\n", "</dl>\n" ], "text/latex": [ "\\begin{description*}\n", "\\item[Fare] 248\n", "\\item[Age] 89\n", "\\item[SibSp] 7\n", "\\item[Parch] 7\n", "\\item[Pclass] 3\n", "\\item[Embarked\\textbackslash{}\\_Q] 2\n", "\\item[Embarked\\textbackslash{}\\_S] 2\n", "\\item[Child] 2\n", "\\item[Young] 2\n", "\\item[Title2\\textbackslash{}\\_Miss] 2\n", "\\item[Title2\\textbackslash{}\\_Mr] 2\n", "\\item[Title2\\textbackslash{}\\_Mrs] 2\n", "\\item[Title2\\textbackslash{}\\_Sir] 2\n", "\\end{description*}\n" ], "text/markdown": [ "Fare\n", ": 248Age\n", ": 89SibSp\n", ": 7Parch\n", ": 7Pclass\n", ": 3Embarked_Q\n", ": 2Embarked_S\n", ": 2Child\n", ": 2Young\n", ": 2Title2_Miss\n", ": 2Title2_Mr\n", ": 2Title2_Mrs\n", ": 2Title2_Sir\n", ": 2\n", "\n" ], "text/plain": [ " Fare Age SibSp Parch Pclass Embarked_Q \n", " 248 89 7 7 3 2 \n", " Embarked_S Child Young Title2_Miss Title2_Mr Title2_Mrs \n", " 2 2 2 2 2 2 \n", " Title2_Sir \n", " 2 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Number of \"levels\" (i.e. potential cut-points) for each variable\n", "sort(apply(pred3, 2, function(x) length(unique(x))), decreasing = TRUE)" ] }, { "cell_type": "markdown", "metadata": { "run_control": { "marked": false } }, "source": [ "Given our dataset, we have to use **unbiased tree algorithm** (i.e. ctree and cforest functions in the party package). They use subsampling without replacement which provides more reliable variable importance measures." ] }, { "cell_type": "code", "execution_count": 233, "metadata": { "collapsed": false, "run_control": { "marked": false } }, "outputs": [ { "data": { "text/html": [ "<ol class=list-inline>\n", "\t<li>'Pclass'</li>\n", "\t<li>'Sex_female'</li>\n", "\t<li>'Sex_male'</li>\n", "\t<li>'Age'</li>\n", "\t<li>'SibSp'</li>\n", "\t<li>'Parch'</li>\n", "\t<li>'Fare'</li>\n", "\t<li>'Embarked_C'</li>\n", "\t<li>'Embarked_Q'</li>\n", "\t<li>'Embarked_S'</li>\n", "\t<li>'Child'</li>\n", "\t<li>'Young'</li>\n", "\t<li>'Title2_Master'</li>\n", "\t<li>'Title2_Miss'</li>\n", "\t<li>'Title2_Mr'</li>\n", "\t<li>'Title2_Mrs'</li>\n", "\t<li>'Title2_Sir'</li>\n", "\t<li>'Survived'</li>\n", "\t<li>'Deck'</li>\n", "</ol>\n" ], "text/latex": [ "\\begin{enumerate*}\n", "\\item 'Pclass'\n", "\\item 'Sex\\_female'\n", "\\item 'Sex\\_male'\n", "\\item 'Age'\n", "\\item 'SibSp'\n", "\\item 'Parch'\n", "\\item 'Fare'\n", "\\item 'Embarked\\_C'\n", "\\item 'Embarked\\_Q'\n", "\\item 'Embarked\\_S'\n", "\\item 'Child'\n", "\\item 'Young'\n", "\\item 'Title2\\_Master'\n", "\\item 'Title2\\_Miss'\n", "\\item 'Title2\\_Mr'\n", "\\item 'Title2\\_Mrs'\n", "\\item 'Title2\\_Sir'\n", "\\item 'Survived'\n", "\\item 'Deck'\n", "\\end{enumerate*}\n" ], "text/markdown": [ "1. 'Pclass'\n", "2. 'Sex_female'\n", "3. 'Sex_male'\n", "4. 'Age'\n", "5. 'SibSp'\n", "6. 'Parch'\n", "7. 'Fare'\n", "8. 'Embarked_C'\n", "9. 'Embarked_Q'\n", "10. 'Embarked_S'\n", "11. 'Child'\n", "12. 'Young'\n", "13. 'Title2_Master'\n", "14. 'Title2_Miss'\n", "15. 'Title2_Mr'\n", "16. 'Title2_Mrs'\n", "17. 'Title2_Sir'\n", "18. 'Survived'\n", "19. 'Deck'\n", "\n", "\n" ], "text/plain": [ " [1] \"Pclass\" \"Sex_female\" \"Sex_male\" \"Age\" \n", " [5] \"SibSp\" \"Parch\" \"Fare\" \"Embarked_C\" \n", " [9] \"Embarked_Q\" \"Embarked_S\" \"Child\" \"Young\" \n", "[13] \"Title2_Master\" \"Title2_Miss\" \"Title2_Mr\" \"Title2_Mrs\" \n", "[17] \"Title2_Sir\" \"Survived\" \"Deck\" " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "names(Train)" ] }, { "cell_type": "code", "execution_count": 234, "metadata": { "collapsed": false, "run_control": { "marked": false } }, "outputs": [], "source": [ "## Perform the cross validation\n", "set.seed(1)\n", "RF2 <- train(Survived ~ Pclass + Age + SibSp + Parch + Fare + Embarked_Q + Embarked_S + Child + Young + \n", " Title2_Miss + Title2_Mr + Title2_Mrs + Title2_Sir, \n", " data = Train, \n", " method = \"cforest\", \n", " trControl = numFolds, \n", " preProcess=c(\"center\", \"scale\"), \n", " tuneGrid = mtryGrid, \n", " controls = cforest_unbiased(ntree=500))" ] }, { "cell_type": "code", "execution_count": 235, "metadata": { "collapsed": false, "run_control": { "marked": false }, "scrolled": true }, "outputs": [ { "data": { "text/html": [ "<table>\n", "<thead><tr><th></th><th scope=col>mtry</th></tr></thead>\n", "<tbody>\n", "\t<tr><th scope=row>8</th><td>8</td></tr>\n", "</tbody>\n", "</table>\n" ], "text/latex": [ "\\begin{tabular}{r|l}\n", " & mtry\\\\\n", "\\hline\n", "\t8 & 8\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "| <!--/--> | mtry | \n", "|---|\n", "| 8 | 8 | \n", "\n", "\n" ], "text/plain": [ " mtry\n", "8 8 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "RF2$bestTune" ] }, { "cell_type": "code", "execution_count": 236, "metadata": { "collapsed": false, "run_control": { "marked": false } }, "outputs": [], "source": [ "import <- varimp(RF2$finalModel)" ] }, { "cell_type": "code", "execution_count": 237, "metadata": { "collapsed": true, "run_control": { "marked": false } }, "outputs": [], "source": [ "# Give a proper size to plots\n", "options(repr.plot.width=4, repr.plot.height=4)" ] }, { "cell_type": "code", "execution_count": 238, "metadata": { "collapsed": false, "run_control": { "marked": false } }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "Plot with title \"RF2\"" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "importSorted = sort(import, decreasing = FALSE, index.return = TRUE)\n", "dotchart(importSorted$x, \n", " main = \"RF2\", \n", " xlab = \"Permutation accuracy importance\", \n", " cex=0.7)" ] }, { "cell_type": "markdown", "metadata": { "run_control": { "marked": false } }, "source": [ "The 'cforest()' function is not as easy as the 'randomForest()' function to retrieve the variable selection frequency. Let's compute it." ] }, { "cell_type": "markdown", "metadata": { "run_control": { "marked": false } }, "source": [ "The 'cutpoints_list()' function gives a list of all the cutpoints used for a given variable in a given tree made with the 'party' package (It can be found at: https://github.com/cran/party/blob/master/R/varimp.R). So, the list's length corresponds to the number of times the variable has been used for splitting." ] }, { "cell_type": "code", "execution_count": 239, "metadata": { "collapsed": true, "run_control": { "marked": false } }, "outputs": [], "source": [ "cutpoints_list <- function(tree, variableID) {\n", "\n", " cutp <- function(node) {\n", " if (node[[4]]) return(NULL)\n", " cp <- NULL\n", " if (node[[5]][[1]] == variableID)\n", " cp <- node[[5]][[3]]\n", " nl <- cutp(node[[8]])\n", " nr <- cutp(node[[9]])\n", " return(c(cp, nl, nr))\n", " }\n", " return(cutp(tree))\n", "}" ] }, { "cell_type": "markdown", "metadata": { "run_control": { "marked": false } }, "source": [ "Now we can use this function to compute the frequency selection of each variable." ] }, { "cell_type": "code", "execution_count": 240, "metadata": { "collapsed": false, "run_control": { "marked": false } }, "outputs": [], "source": [ "# Retrieve the variables' names\n", "inputs <- RF2$finalModel@data@get(\"input\")\n", "xnames <- colnames(inputs)\n", "\n", "# Number of variables\n", "nbvar <- length(xnames)\n", "\n", "# Number of trees in the forest\n", "nbtree <- length(RF2$finalModel@ensemble)\n", "\n", "# Prepare container\n", "nbtimes <- data.frame()\n", "\n", "# Compute the number of times a given variable has been used to split a given tree\n", "for (i in 1:nbvar) { # for each variable\n", " for (j in 1:nbtree) { # for each tree in the forest\n", " cutpts <- cutpoints_list(RF2$finalModel@ensemble[[j]], i)\n", " nbtimes[j,i] <- length(cutpts)\n", " }\n", "} \n", "colnames(nbtimes) <- xnames\n", "\n", "# Frequency selection of each variable\n", "nbtimesTot <- apply(nbtimes, 2, sum)" ] }, { "cell_type": "code", "execution_count": 241, "metadata": { "collapsed": false, "run_control": { "marked": false } }, "outputs": [ { "data": { "text/html": [ "<dl class=dl-horizontal>\n", "\t<dt>Pclass.L</dt>\n", "\t\t<dd>1441</dd>\n", "\t<dt>Pclass.Q</dt>\n", "\t\t<dd>738</dd>\n", "\t<dt>Age</dt>\n", "\t\t<dd>3482</dd>\n", "\t<dt>SibSp</dt>\n", "\t\t<dd>1683</dd>\n", "\t<dt>Parch</dt>\n", "\t\t<dd>1129</dd>\n", "\t<dt>Fare</dt>\n", "\t\t<dd>4183</dd>\n", "\t<dt>Embarked_Q1</dt>\n", "\t\t<dd>539</dd>\n", "\t<dt>Embarked_S1</dt>\n", "\t\t<dd>1333</dd>\n", "\t<dt>ChildTRUE</dt>\n", "\t\t<dd>382</dd>\n", "\t<dt>YoungTRUE</dt>\n", "\t\t<dd>1651</dd>\n", "\t<dt>Title2_Miss1</dt>\n", "\t\t<dd>203</dd>\n", "\t<dt>Title2_Mr1</dt>\n", "\t\t<dd>546</dd>\n", "\t<dt>Title2_Mrs1</dt>\n", "\t\t<dd>505</dd>\n", "\t<dt>Title2_Sir1</dt>\n", "\t\t<dd>307</dd>\n", "</dl>\n" ], "text/latex": [ "\\begin{description*}\n", "\\item[Pclass.L] 1441\n", "\\item[Pclass.Q] 738\n", "\\item[Age] 3482\n", "\\item[SibSp] 1683\n", "\\item[Parch] 1129\n", "\\item[Fare] 4183\n", "\\item[Embarked\\textbackslash{}\\_Q1] 539\n", "\\item[Embarked\\textbackslash{}\\_S1] 1333\n", "\\item[ChildTRUE] 382\n", "\\item[YoungTRUE] 1651\n", "\\item[Title2\\textbackslash{}\\_Miss1] 203\n", "\\item[Title2\\textbackslash{}\\_Mr1] 546\n", "\\item[Title2\\textbackslash{}\\_Mrs1] 505\n", "\\item[Title2\\textbackslash{}\\_Sir1] 307\n", "\\end{description*}\n" ], "text/markdown": [ "Pclass.L\n", ": 1441Pclass.Q\n", ": 738Age\n", ": 3482SibSp\n", ": 1683Parch\n", ": 1129Fare\n", ": 4183Embarked_Q1\n", ": 539Embarked_S1\n", ": 1333ChildTRUE\n", ": 382YoungTRUE\n", ": 1651Title2_Miss1\n", ": 203Title2_Mr1\n", ": 546Title2_Mrs1\n", ": 505Title2_Sir1\n", ": 307\n", "\n" ], "text/plain": [ " Pclass.L Pclass.Q Age SibSp Parch Fare \n", " 1441 738 3482 1683 1129 4183 \n", " Embarked_Q1 Embarked_S1 ChildTRUE YoungTRUE Title2_Miss1 Title2_Mr1 \n", " 539 1333 382 1651 203 546 \n", " Title2_Mrs1 Title2_Sir1 \n", " 505 307 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "nbtimesTot" ] }, { "cell_type": "code", "execution_count": 242, "metadata": { "collapsed": false, "run_control": { "marked": false } }, "outputs": [ { "data": { "image/png": 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jS14FgRXFsf1276dgYJw4+lf0awJrng\n7T44HBA8ly2WlSoRfB0h+YKzfPzpd5r5m09aYLsvLb4m7W6UjekGYJQ4HkXLhzbKBztKHI8C\nweK8/tDG+Ql7mEDABwSLg2BxECwOgsVBsDgIFgfB4iBYHASLg2BxECwOgsVBsDgIFgfB4iBY\nHASL83/DTvH6I1/oVwAAAABJRU5ErkJggg==", "text/plain": [ "Plot with title \"RF2\"" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "nbtimesSorted = sort(nbtimesTot, decreasing = FALSE, index.return = TRUE)\n", "dotchart(nbtimesSorted$x, \n", " main = \"RF2\", \n", " xlab = \"Selection frequency\", \n", " cex=0.7)" ] }, { "cell_type": "markdown", "metadata": { "run_control": { "marked": false } }, "source": [ "Let's check our randomForest and cforest models together." ] }, { "cell_type": "code", "execution_count": 243, "metadata": { "collapsed": true, "run_control": { "marked": false } }, "outputs": [], "source": [ "# Give a proper size to plots\n", "options(repr.plot.width=8, repr.plot.height=4)" ] }, { "cell_type": "code", "execution_count": 244, "metadata": { "collapsed": false, "run_control": { "marked": false } }, "outputs": [ { "data": { "image/png": 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Rste/kXh+LccW2NmIgcF9cXPf6nhOg9/7g6p2YRMcHV9cX/C54wCxz977zRf2Ux\nfEkKTIf0YdFUalgl/m9s8pXm/7XKf24tcHV9TQShSUg+hC5WJywm92nU5Ool7LJVsirilQax\n000TPa6ur4kgVBkPtysCd0uBp9LR5dNC0EfTaD2izcX1tRGFMlNLaji2Jv8/a6XA8SrJkbkw\n35LAV+DK+tqIQp059dnHcVJ1hO4KApfm3z9eJLM2uK6+RsLQY2pfXVRg71xdXyNh6JEKHBbq\nhA0Cj2WzaaFKOwJ75+r6GglDkelB/9pjhvHJQVxgIXD+mGF1peSeCtHn4vpaiQOKmqQxsbgg\n6WslDiiQBCbbQdLXShxQPE8a/E9sLw2SvmYCIYRshwYmBBgamBBgaGBCgKGBCQGGBiYEGBqY\nEGBoYEKAoYEJAYYGJgQYGpgQYGhgQoChgQkBhgYmBBgamBBgaGBCgKGBCQGGBiYEmP8DGkyl\nIecyRB8AAAAASUVORK5CYII=", "text/plain": [ "Plot with title \"RF2 (cforest)\"" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "par(mfrow=c(1,2))\n", "dotchart(vusorted$x, \n", " RF1$finalModel$xNames[vusorted$ix], \n", " main = \"RF1 (randomForest)\", \n", " xlab = \"Selection frequency\", \n", " cex=0.7)\n", "dotchart(nbtimesSorted$x, \n", " main = \"RF2 (cforest)\", \n", " xlab = \"Selection frequency\", \n", " cex=0.7)" ] }, { "cell_type": "markdown", "metadata": { "run_control": { "marked": false } }, "source": [ "The use of 'cforest()' has clearly decreased the difference between Age/Fare and other variables such as Pclass or Embarked_S.<br>\n", "We can see that 'Fare' and 'Age' are still the mostly used variables although their impact into either the Gini importance or permutation accuracy importance is lower than other variables." ] }, { "cell_type": "code", "execution_count": 245, "metadata": { "collapsed": false, "run_control": { "marked": false } }, "outputs": [ { "data": { "text/html": [ "0.824864090341618" ], "text/latex": [ "0.824864090341618" ], "text/markdown": [ "0.824864090341618" ], "text/plain": [ "[1] 0.8248641" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Best accuracy\n", "max(RF2$results$Accuracy)" ] }, { "cell_type": "markdown", "metadata": { "run_control": { "marked": false } }, "source": [ "The accuracy of this model is lower than the random forest model, and even lower than the CART and log models..." ] }, { "cell_type": "markdown", "metadata": { "run_control": { "marked": false } }, "source": [ "---\n", "## 3.4 Support Vector Machine (SVM)\n", "\n", "Since we have a relatively small number of features (1-1,000) and an intermediate number of observations (10-10,000), SVM seems to be a reasonable option, at least better than logistic regression. (with Gaussian kernel)" ] }, { "cell_type": "markdown", "metadata": { "run_control": { "marked": false } }, "source": [ "### 3.4.1 With linear kernel" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "collapsed": false, "init_cell": true, "run_control": { "marked": false } }, "outputs": [], "source": [ "## Define the cross-validation experiment\n", "costGrid = expand.grid( .C = c(0.01, 0.03, 0.1, 0.3, 1, 3, 10))\n", "numFolds = trainControl(method = \"cv\", number = 10 )" ] }, { "cell_type": "code", "execution_count": 247, "metadata": { "collapsed": false, "run_control": { "marked": false } }, "outputs": [ { "data": { "text/html": [ "<ol class=list-inline>\n", "\t<li>'Pclass'</li>\n", "\t<li>'Sex_female'</li>\n", "\t<li>'Sex_male'</li>\n", "\t<li>'Age'</li>\n", "\t<li>'SibSp'</li>\n", "\t<li>'Parch'</li>\n", "\t<li>'Fare'</li>\n", "\t<li>'Embarked_C'</li>\n", "\t<li>'Embarked_Q'</li>\n", "\t<li>'Embarked_S'</li>\n", "\t<li>'Child'</li>\n", "\t<li>'Young'</li>\n", "\t<li>'Title2_Master'</li>\n", "\t<li>'Title2_Miss'</li>\n", "\t<li>'Title2_Mr'</li>\n", "\t<li>'Title2_Mrs'</li>\n", "\t<li>'Title2_Sir'</li>\n", "\t<li>'Survived'</li>\n", "\t<li>'Deck'</li>\n", "</ol>\n" ], "text/latex": [ "\\begin{enumerate*}\n", "\\item 'Pclass'\n", "\\item 'Sex\\_female'\n", "\\item 'Sex\\_male'\n", "\\item 'Age'\n", "\\item 'SibSp'\n", "\\item 'Parch'\n", "\\item 'Fare'\n", "\\item 'Embarked\\_C'\n", "\\item 'Embarked\\_Q'\n", "\\item 'Embarked\\_S'\n", "\\item 'Child'\n", "\\item 'Young'\n", "\\item 'Title2\\_Master'\n", "\\item 'Title2\\_Miss'\n", "\\item 'Title2\\_Mr'\n", "\\item 'Title2\\_Mrs'\n", "\\item 'Title2\\_Sir'\n", "\\item 'Survived'\n", "\\item 'Deck'\n", "\\end{enumerate*}\n" ], "text/markdown": [ "1. 'Pclass'\n", "2. 'Sex_female'\n", "3. 'Sex_male'\n", "4. 'Age'\n", "5. 'SibSp'\n", "6. 'Parch'\n", "7. 'Fare'\n", "8. 'Embarked_C'\n", "9. 'Embarked_Q'\n", "10. 'Embarked_S'\n", "11. 'Child'\n", "12. 'Young'\n", "13. 'Title2_Master'\n", "14. 'Title2_Miss'\n", "15. 'Title2_Mr'\n", "16. 'Title2_Mrs'\n", "17. 'Title2_Sir'\n", "18. 'Survived'\n", "19. 'Deck'\n", "\n", "\n" ], "text/plain": [ " [1] \"Pclass\" \"Sex_female\" \"Sex_male\" \"Age\" \n", " [5] \"SibSp\" \"Parch\" \"Fare\" \"Embarked_C\" \n", " [9] \"Embarked_Q\" \"Embarked_S\" \"Child\" \"Young\" \n", "[13] \"Title2_Master\" \"Title2_Miss\" \"Title2_Mr\" \"Title2_Mrs\" \n", "[17] \"Title2_Sir\" \"Survived\" \"Deck\" " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "names(Train)" ] }, { "cell_type": "code", "execution_count": 248, "metadata": { "collapsed": false, "run_control": { "marked": false } }, "outputs": [], "source": [ "## Perform the cross validation\n", "set.seed(1)\n", "svm1 <- train(Survived ~ Pclass + Age + SibSp + Parch + Fare + Embarked_Q + Embarked_S + Child + Young + \n", " Title2_Miss + Title2_Mr + Title2_Mrs + Title2_Sir, \n", " data = Train, \n", " method = \"svmLinear\", \n", " trControl = numFolds, \n", " na.action = na.omit, \n", " preProcess=c(\"center\", \"scale\"), \n", " tuneGrid = costGrid)" ] }, { "cell_type": "code", "execution_count": 249, "metadata": { "collapsed": false, "run_control": { "marked": false }, "scrolled": false }, "outputs": [ { "data": { "text/html": [ "<table>\n", "<thead><tr><th></th><th scope=col>C</th></tr></thead>\n", "<tbody>\n", "\t<tr><th scope=row>6</th><td>3</td></tr>\n", "</tbody>\n", "</table>\n" ], "text/latex": [ "\\begin{tabular}{r|l}\n", " & C\\\\\n", "\\hline\n", "\t6 & 3\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "| <!--/--> | C | \n", "|---|\n", "| 6 | 3 | \n", "\n", "\n" ], "text/plain": [ " C\n", "6 3" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "svm1$bestTune" ] }, { "cell_type": "code", "execution_count": 250, "metadata": { "collapsed": true, "run_control": { "marked": false } }, "outputs": [], "source": [ "# Give a proper size to plots\n", "options(repr.plot.width=4, repr.plot.height=4)" ] }, { "cell_type": "code", "execution_count": 251, "metadata": { "collapsed": false, "run_control": { "marked": false } }, "outputs": [ { "data": { "text/html": [ "0.82717568947906" ], "text/latex": [ "0.82717568947906" ], "text/markdown": [ "0.82717568947906" ], "text/plain": [ "[1] 0.8271757" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "max(svm1$results$Accuracy)" ] }, { "cell_type": "markdown", "metadata": { "run_control": { "marked": false } }, "source": [ "### 3.4.2 With Gaussian kernel" ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "collapsed": false, "init_cell": true, "run_control": { "marked": false } }, "outputs": [], "source": [ "## Define the cross-validation experiment\n", "costGrid = expand.grid( .C = c(0.01, 0.03, 0.1, 0.3, 1, 3, 10), .sigma = c(0.01, 0.03, 0.1, 0.3, 1, 3, 10))\n", "numFolds = trainControl(method = \"cv\", number = 10)" ] }, { "cell_type": "code", "execution_count": 253, "metadata": { "collapsed": false, "run_control": { "marked": false } }, "outputs": [], "source": [ "## Perform the cross validation\n", "set.seed(1)\n", "svm2 <- train(Survived ~ Pclass + Age + SibSp + Parch + Fare + Embarked_Q + Embarked_S + Child + Young + \n", " Title2_Miss + Title2_Mr + Title2_Mrs + Title2_Sir, \n", " data = Train, \n", " method = \"svmRadial\", \n", " trControl = numFolds, \n", " na.action = na.omit, \n", " preProcess=c(\"center\", \"scale\"), \n", " tuneGrid = costGrid)" ] }, { "cell_type": "code", "execution_count": 254, "metadata": { "collapsed": false, "run_control": { "marked": false }, "scrolled": false }, "outputs": [ { "data": { "text/html": [ "<table>\n", "<thead><tr><th></th><th scope=col>sigma</th><th scope=col>C</th></tr></thead>\n", "<tbody>\n", "\t<tr><th scope=row>25</th><td>0.3</td><td>0.3</td></tr>\n", "</tbody>\n", "</table>\n" ], "text/latex": [ "\\begin{tabular}{r|ll}\n", " & sigma & C\\\\\n", "\\hline\n", "\t25 & 0.3 & 0.3\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "| <!--/--> | sigma | C | \n", "|---|\n", "| 25 | 0.3 | 0.3 | \n", "\n", "\n" ], "text/plain": [ " sigma C \n", "25 0.3 0.3" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "svm2$bestTune" ] }, { "cell_type": "code", "execution_count": 255, "metadata": { "collapsed": true, "run_control": { "marked": false } }, "outputs": [], "source": [ "# Give a proper size to plots\n", "options(repr.plot.width=6, repr.plot.height=4)" ] }, { "cell_type": "code", "execution_count": 256, "metadata": { "collapsed": false, "run_control": { "marked": false }, "scrolled": false }, "outputs": [ { "data": {}, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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"text/plain": [ "plot without title" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot(svm2)" ] }, { "cell_type": "code", "execution_count": 257, "metadata": { "collapsed": false, "run_control": { "marked": false } }, "outputs": [ { "data": { "text/html": [ "0.830520655998184" ], "text/latex": [ "0.830520655998184" ], "text/markdown": [ "0.830520655998184" ], "text/plain": [ "[1] 0.8305207" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "max(svm2$results$Accuracy)" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true, "run_control": { "marked": false } }, "source": [ "This is better than CART but not better than our log and random forest models." ] }, { "cell_type": "markdown", "metadata": { "run_control": { "marked": false } }, "source": [ "Even if SVM already uses regularisation to avoid overfitting (regularisation parameter C), let's remove some features that were defined as the least important ones by the random forest (i.e. Title2_Miss, Child, Title2_Mrs, Embarked_Q)." ] }, { "cell_type": "code", "execution_count": 244, "metadata": { "collapsed": false, "run_control": { "marked": false }, "scrolled": false }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "Plot with title \"RF2 (cforest)\"" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "par(mfrow=c(1,2))\n", "dotchart(vusorted$x, \n", " RF1$finalModel$xNames[vusorted$ix], \n", " main = \"RF1 (randomForest)\", \n", " xlab = \"Selection frequency\", \n", " cex=0.7)\n", "dotchart(nbtimesSorted$x, \n", " main = \"RF2 (cforest)\", \n", " xlab = \"Selection frequency\", \n", " cex=0.7)" ] }, { "cell_type": "code", "execution_count": 38, "metadata": { "collapsed": false, "init_cell": true, "run_control": { "marked": false } }, "outputs": [], "source": [ "## Perform the cross validation\n", "set.seed(1)\n", "svm3 <- train(Survived ~ Pclass + Age + SibSp + Parch + Fare + Child + Title2_Miss + \n", " Title2_Mr + Title2_Mrs + Title2_Sir, \n", " data = Train, \n", " method = \"svmRadial\", \n", " trControl = numFolds, \n", " na.action = na.omit, \n", " preProcess=c(\"center\", \"scale\"), \n", " tuneGrid = costGrid)" ] }, { "cell_type": "code", "execution_count": 269, "metadata": { "collapsed": false, "run_control": { "marked": false } }, "outputs": [], "source": [ "## Perform the cross validation\n", "set.seed(1)\n", "svm3 <- train(Survived ~ Pclass + Age + SibSp + Parch + Fare + Embarked_S +\n", " Title2_Mr + Title2_Sir, \n", " data = Train, \n", " method = \"svmRadial\", \n", " trControl = numFolds, \n", " na.action = na.omit, \n", " preProcess=c(\"center\", \"scale\"), \n", " tuneGrid = costGrid)" ] }, { "cell_type": "code", "execution_count": 270, "metadata": { "collapsed": false, "run_control": { "marked": false }, "scrolled": true }, "outputs": [ { "data": { "text/html": [ "<table>\n", "<thead><tr><th></th><th scope=col>sigma</th><th scope=col>C</th></tr></thead>\n", "<tbody>\n", "\t<tr><th scope=row>43</th><td>0.01</td><td>10 </td></tr>\n", "</tbody>\n", "</table>\n" ], "text/latex": [ "\\begin{tabular}{r|ll}\n", " & sigma & C\\\\\n", "\\hline\n", "\t43 & 0.01 & 10 \\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "| <!--/--> | sigma | C | \n", "|---|\n", "| 43 | 0.01 | 10 | \n", "\n", "\n" ], "text/plain": [ " sigma C \n", "43 0.01 10" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "svm3$bestTune" ] }, { "cell_type": "code", "execution_count": 271, "metadata": { "collapsed": false, "run_control": { "marked": false }, "scrolled": true }, "outputs": [ { "data": { "text/html": [ "0.836164737260243" ], "text/latex": [ "0.836164737260243" ], "text/markdown": [ "0.836164737260243" ], "text/plain": [ "[1] 0.8361647" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "max(svm3$results$Accuracy)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now, we reach the same performance as our random forest model." ] }, { "cell_type": "markdown", "metadata": { "collapsed": true, "run_control": { "marked": false } }, "source": [ "---\n", "## 3.5 Neural network" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "collapsed": false, "init_cell": true, "run_control": { "marked": false } }, "outputs": [], "source": [ "## Define the cross-validation experiment\n", "layerGrid = expand.grid( .size = seq(5, 105, by=10), .decay = c(0, 10^seq(-3, 0, by=1)))\n", "numFolds = trainControl(method = \"cv\", number = 10 )" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "run_control": { "marked": false } }, "outputs": [], "source": [ "## Perform the cross validation\n", "set.seed(1)\n", "nn1 <- train(Survived ~ Pclass + Age + SibSp + Parch + Fare + Embarked_Q + Embarked_S + Child + Young + \n", " Title2_Miss + Title2_Mr + Title2_Mrs + Title2_Sir, \n", " data = Train, \n", " method = \"nnet\", \n", " trControl = numFolds, \n", " na.action = na.omit, \n", " preProcess=c(\"center\", \"scale\"), \n", " tuneGrid = layerGrid, \n", " MaxNWts=1900,\n", " trace=FALSE)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "run_control": { "marked": false }, "scrolled": false }, "outputs": [], "source": [ "nn1$bestTune" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "run_control": { "marked": false } }, "outputs": [], "source": [ "max(nn1$results$Accuracy, na.rm=TRUE)" ] }, { "cell_type": "markdown", "metadata": { "run_control": { "marked": false } }, "source": [ "Neural networks are very sensitive to multicollinearity. Let's remove Fare that is highly correlater with Pclass." ] }, { "cell_type": "code", "execution_count": 40, "metadata": { "collapsed": false, "init_cell": true, "run_control": { "marked": false } }, "outputs": [], "source": [ "## Perform the cross validation\n", "set.seed(1)\n", "nn2 <- train(Survived ~ Pclass + Age + SibSp + Parch + Embarked_Q + Embarked_S + Child + Young + \n", " Title2_Miss + Title2_Mr + Title2_Mrs + Title2_Sir, \n", " data = Train, \n", " method = \"nnet\", \n", " trControl = numFolds, \n", " na.action = na.omit, \n", " preProcess=c(\"center\", \"scale\"), \n", " tuneGrid = layerGrid,\n", " MaxNWts=1700,\n", " trace=FALSE)" ] }, { "cell_type": "code", "execution_count": 229, "metadata": { "collapsed": false, "run_control": { "marked": false }, "scrolled": true }, "outputs": [ { "data": { "text/html": [ "<table>\n", "<thead><tr><th></th><th scope=col>size</th><th scope=col>decay</th></tr></thead>\n", "<tbody>\n", "\t<tr><th scope=row>40</th><td>75</td><td>1 </td></tr>\n", "</tbody>\n", "</table>\n" ], "text/latex": [ "\\begin{tabular}{r|ll}\n", " & size & decay\\\\\n", "\\hline\n", "\t40 & 75 & 1 \\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "| <!--/--> | size | decay | \n", "|---|\n", "| 40 | 75 | 1 | \n", "\n", "\n" ], "text/plain": [ " size decay\n", "40 75 1 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "nn2$bestTune" ] }, { "cell_type": "code", "execution_count": 230, "metadata": { "collapsed": false, "run_control": { "marked": false } }, "outputs": [ { "data": { "text/html": [ "0.832042253521127" ], "text/latex": [ "0.832042253521127" ], "text/markdown": [ "0.832042253521127" ], "text/plain": [ "[1] 0.8320423" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "max(nn2$results$Accuracy, na.rm=TRUE)" ] }, { "cell_type": "markdown", "metadata": { "run_control": { "marked": false } }, "source": [ "Now, let's remove some features that were defined as the least important ones by the random forest (i.e. Title2_Miss, Child, Title2_Mrs, Embarked_Q)." ] }, { "cell_type": "code", "execution_count": 234, "metadata": { "collapsed": false, "run_control": { "marked": false } }, "outputs": [], "source": [ "## Perform the cross validation\n", "set.seed(1)\n", "nn3 <- train(Survived ~ Pclass + Age + SibSp + Parch + Embarked_S + Young + \n", " Title2_Mr + Title2_Sir,\n", " data = Train\n", " method = \"nnet\", \n", " trControl = numFolds, \n", " na.action = na.omit, \n", " preProcess=c(\"center\", \"scale\"), \n", " tuneGrid = layerGrid, \n", " MaxNWts=1900,\n", " trace=FALSE)" ] }, { "cell_type": "code", "execution_count": 235, "metadata": { "collapsed": false, "run_control": { "marked": false }, "scrolled": true }, "outputs": [ { "data": { "text/html": [ "<table>\n", "<thead><tr><th></th><th scope=col>size</th><th scope=col>decay</th></tr></thead>\n", "<tbody>\n", "\t<tr><th scope=row>35</th><td>65</td><td>1 </td></tr>\n", "</tbody>\n", "</table>\n" ], "text/latex": [ "\\begin{tabular}{r|ll}\n", " & size & decay\\\\\n", "\\hline\n", "\t35 & 65 & 1 \\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "| <!--/--> | size | decay | \n", "|---|\n", "| 35 | 65 | 1 | \n", "\n", "\n" ], "text/plain": [ " size decay\n", "35 65 1 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "nn3$bestTune" ] }, { "cell_type": "code", "execution_count": 236, "metadata": { "collapsed": false, "run_control": { "marked": false } }, "outputs": [ { "data": { "text/html": [ "0.826388888888889" ], "text/latex": [ "0.826388888888889" ], "text/markdown": [ "0.826388888888889" ], "text/plain": [ "[1] 0.8263889" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "max(nn3$results$Accuracy, na.rm=TRUE)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "That's not better. Let's keep them in the model." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 3.6 XGBoost" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 3.6.1 Linear" ] }, { "cell_type": "code", "execution_count": 41, "metadata": { "collapsed": false, "init_cell": true, "run_control": { "marked": false } }, "outputs": [], "source": [ "## Define the cross-validation experiment\n", "tuneGridL = expand.grid(.nrounds = 1:3, \n", " .lambda = c(0.01, 0.03, 0.1, 0.3, 1, 3, 10), \n", " .alpha = c(0.01, 0.03, 0.1, 0.3, 1, 3, 10), #1e-5, 1e-2, 0.1, 1, 100\n", " .eta = c(0.01, 0.21, 0.05)) # 0.01-0.2\n", "numFolds = trainControl(method = \"cv\", number = 10 )" ] }, { "cell_type": "code", "execution_count": 42, "metadata": { "collapsed": false, "init_cell": true, "run_control": { "marked": false } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Loading required package: xgboost\n", "Warning message:\n", "\"package 'xgboost' was built under R version 3.3.3\"" ] } ], "source": [ "## Perform the cross validation\n", "set.seed(1)\n", "xgb1 <- train(Survived ~ Pclass + Age + SibSp + Parch + Fare + Embarked_Q + Embarked_S + Child + Young + \n", " Title2_Miss + Title2_Mr + Title2_Mrs + Title2_Sir, \n", " data = Train, \n", " method = \"xgbLinear\", \n", " trControl = numFolds, \n", " na.action = na.omit, \n", " preProcess=c(\"center\", \"scale\"), \n", " tuneGrid = tuneGridL)" ] }, { "cell_type": "code", "execution_count": 43, "metadata": { "collapsed": false, "run_control": { "marked": false }, "scrolled": false }, "outputs": [ { "data": { "text/html": [ "<table>\n", "<thead><tr><th></th><th scope=col>nrounds</th><th scope=col>lambda</th><th scope=col>alpha</th><th scope=col>eta</th></tr></thead>\n", "<tbody>\n", "\t<tr><th scope=row>244</th><td>2 </td><td>1 </td><td>1 </td><td>0.01</td></tr>\n", "</tbody>\n", "</table>\n" ], "text/latex": [ "\\begin{tabular}{r|llll}\n", " & nrounds & lambda & alpha & eta\\\\\n", "\\hline\n", "\t244 & 2 & 1 & 1 & 0.01\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "| <!--/--> | nrounds | lambda | alpha | eta | \n", "|---|\n", "| 244 | 2 | 1 | 1 | 0.01 | \n", "\n", "\n" ], "text/plain": [ " nrounds lambda alpha eta \n", "244 2 1 1 0.01" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "xgb1$bestTune" ] }, { "cell_type": "code", "execution_count": 44, "metadata": { "collapsed": false, "run_control": { "marked": false } }, "outputs": [ { "data": { "text/html": [ "0.839509420043128" ], "text/latex": [ "0.839509420043128" ], "text/markdown": [ "0.839509420043128" ], "text/plain": [ "[1] 0.8395094" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "max(xgb1$results$Accuracy)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 3.6.2 Tree" ] }, { "cell_type": "code", "execution_count": 45, "metadata": { "collapsed": false, "init_cell": true, "run_control": { "marked": false } }, "outputs": [], "source": [ "## Define the cross-validation experiment\n", "tuneGridT = expand.grid(.nrounds = 1:3, \n", " .gamma = c(0.01, 0.03, 0.1, 0.3, 1),\n", " .max_depth = seq(3,10,2), \n", " .eta = c(0.01, 0.21, 0.05),\n", " .colsample_bytree = c(0.6, 0.7, 0.8, 0.9, 1), \n", " .min_child_weight = seq(1,10,2), \n", " .subsample = c(0.6, 0.7, 0.8, 0.9, 1)) \n", "numFolds = trainControl(method = \"cv\", number = 10 )" ] }, { "cell_type": "code", "execution_count": 46, "metadata": { "collapsed": false, "init_cell": true, "run_control": { "marked": false } }, "outputs": [], "source": [ "## Perform the cross validation\n", "set.seed(1)\n", "xgb2 <- train(Survived ~ Pclass + Age + SibSp + Parch + Fare + Embarked_Q + Embarked_S + Child + Young + \n", " Title2_Miss + Title2_Mr + Title2_Mrs + Title2_Sir, \n", " data = Train, \n", " method = \"xgbTree\", \n", " trControl = numFolds, \n", " na.action = na.omit, \n", " preProcess=c(\"center\", \"scale\"), \n", " tuneGrid = tuneGridT)" ] }, { "cell_type": "code", "execution_count": 47, "metadata": { "collapsed": false, "run_control": { "marked": false }, "scrolled": false }, "outputs": [ { "data": { "text/html": [ "<table>\n", "<thead><tr><th></th><th scope=col>nrounds</th><th scope=col>max_depth</th><th scope=col>eta</th><th scope=col>gamma</th><th scope=col>colsample_bytree</th><th scope=col>min_child_weight</th><th scope=col>subsample</th></tr></thead>\n", "<tbody>\n", "\t<tr><th scope=row>14351</th><td>2 </td><td>9 </td><td>0.05</td><td>0.3 </td><td>0.7 </td><td>3 </td><td>0.9 </td></tr>\n", "</tbody>\n", "</table>\n" ], "text/latex": [ "\\begin{tabular}{r|lllllll}\n", " & nrounds & max\\_depth & eta & gamma & colsample\\_bytree & min\\_child\\_weight & subsample\\\\\n", "\\hline\n", "\t14351 & 2 & 9 & 0.05 & 0.3 & 0.7 & 3 & 0.9 \\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "| <!--/--> | nrounds | max_depth | eta | gamma | colsample_bytree | min_child_weight | subsample | \n", "|---|\n", "| 14351 | 2 | 9 | 0.05 | 0.3 | 0.7 | 3 | 0.9 | \n", "\n", "\n" ], "text/plain": [ " nrounds max_depth eta gamma colsample_bytree min_child_weight subsample\n", "14351 2 9 0.05 0.3 0.7 3 0.9 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "xgb2$bestTune" ] }, { "cell_type": "code", "execution_count": 48, "metadata": { "collapsed": false, "run_control": { "marked": false } }, "outputs": [ { "data": { "text/html": [ "0.847362387924186" ], "text/latex": [ "0.847362387924186" ], "text/markdown": [ "0.847362387924186" ], "text/plain": [ "[1] 0.8473624" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "max(xgb2$results$Accuracy)" ] }, { "cell_type": "markdown", "metadata": { "run_control": { "marked": false } }, "source": [ "# 4 Compare the best models" ] }, { "cell_type": "markdown", "metadata": { "run_control": { "marked": false } }, "source": [ "## 4.1 Compare the performances" ] }, { "cell_type": "markdown", "metadata": { "run_control": { "marked": false } }, "source": [ "First, we have to run again the CART2 model, but using the formula and the Train_dum dataframe so that all models use the dame training dataset." ] }, { "cell_type": "code", "execution_count": 36, "metadata": { "collapsed": false, "init_cell": true, "run_control": { "marked": false } }, "outputs": [], "source": [ "set.seed(1)\n", "CART1b <- train(Survived ~ Pclass + Age + SibSp + Parch + Fare + Embarked_Q + Embarked_S + Child + Young + \n", " Title2_Miss + Title2_Mr + Title2_Mrs + Title2_Sir, \n", " data = Train, \n", " method = \"rpart\", \n", " trControl = numFolds, \n", " na.action = na.omit, \n", " tuneGrid = cpGrid)" ] }, { "cell_type": "code", "execution_count": 49, "metadata": { "collapsed": false, "run_control": { "marked": false } }, "outputs": [ { "data": { "text/plain": [ "\n", "Call:\n", "summary.resamples(object = resamps)\n", "\n", "Models: GLM, CART, RF, SVM, NN, XGBL, XGBT \n", "Number of resamples: 10 \n", "\n", "Accuracy \n", " Min. 1st Qu. Median Mean 3rd Qu. Max. NA's\n", "GLM 0.7416 0.8230 0.8324 0.8317 0.8551 0.8764 0\n", "CART 0.7303 0.8118 0.8323 0.8294 0.8551 0.8977 0\n", "RF 0.7528 0.8338 0.8427 0.8362 0.8516 0.8764 0\n", "SVM 0.7303 0.8118 0.8380 0.8328 0.8551 0.8977 0\n", "NN 0.7191 0.8187 0.8258 0.8226 0.8417 0.8764 0\n", "XGBL 0.7528 0.8315 0.8428 0.8395 0.8551 0.8876 0\n", "XGBT 0.7753 0.8455 0.8556 0.8474 0.8648 0.8764 0\n", "\n", "Kappa \n", " Min. 1st Qu. Median Mean 3rd Qu. Max. NA's\n", "GLM 0.4303 0.6150 0.6405 0.6369 0.6887 0.7338 0\n", "CART 0.4158 0.5882 0.6407 0.6334 0.6887 0.7807 0\n", "RF 0.4453 0.6405 0.6580 0.6438 0.6758 0.7338 0\n", "SVM 0.4090 0.5962 0.6453 0.6406 0.6913 0.7783 0\n", "NN 0.3807 0.6025 0.6253 0.6156 0.6569 0.7338 0\n", "XGBL 0.4453 0.6386 0.6577 0.6523 0.6893 0.7593 0\n", "XGBT 0.5075 0.6597 0.6913 0.6714 0.7089 0.7454 0\n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Collect the resampling results\n", "resamps <- resamples(list(GLM = logmod4,\n", " CART = CART1b,\n", " RF = RF1,\n", " SVM = svm3,\n", " NN = nn2,\n", " XGBL = xgb1,\n", " XGBT = xgb2))\n", "summary(resamps)" ] }, { "cell_type": "markdown", "metadata": { "run_control": { "marked": false } }, "source": [ "Let's plot the results" ] }, { "cell_type": "code", "execution_count": 50, "metadata": { "collapsed": true, "run_control": { "marked": false } }, "outputs": [], "source": [ "# Give a proper size to plots\n", "options(repr.plot.width=6, repr.plot.height=4)" ] }, { "cell_type": "code", "execution_count": 51, "metadata": { "collapsed": false, "run_control": { "marked": false } }, "outputs": [ { "data": {}, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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"text/plain": [ "plot without title" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "bwplot(resamps, layout = c(2, 1))" ] }, { "cell_type": "code", "execution_count": 243, "metadata": { "collapsed": false, "run_control": { "marked": false }, "scrolled": false }, "outputs": [ { "data": {}, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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EghaEghaEghaEghaEghaEghaEj5D65K\n68L87l3XAAAAAElFTkSuQmCC", "text/plain": [ "plot without title" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "dotplot(resamps, metric = \"Accuracy\")" ] }, { "cell_type": "code", "execution_count": 52, "metadata": { "collapsed": true, "run_control": { "marked": false } }, "outputs": [], "source": [ "# Give a proper size to plots\n", "options(repr.plot.width=5, repr.plot.height=5)" ] }, { "cell_type": "code", "execution_count": 53, "metadata": { "collapsed": false, "run_control": { "marked": false } }, "outputs": [ { "data": {}, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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/yIt422xsIVT8ZABdqPk9gSPX9i8cCusZ2M1V29HcYiBj21sYY9OS2NtWtiiejU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\nn5jeqIO7+KWNNcJo9a8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"text/plain": [ "plot without title" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "splom(resamps, varname.cex=0.8, varname.fontface=\"bold\", axis.text.cex=0.4)" ] }, { "cell_type": "markdown", "metadata": { "run_control": { "marked": false } }, "source": [ "> Since models are fit on the same versions of the training data, it makes sense to make inferences on the differences between models. In this way we reduce the within-resample correlation that may exist. We can compute the differences, then use a simple t-test to evaluate the null hypothesis that there is no difference between models. (Max Kuhn)" ] }, { "cell_type": "code", "execution_count": 54, "metadata": { "collapsed": false, "run_control": { "marked": false }, "scrolled": false }, "outputs": [ { "data": { "text/plain": [ "\n", "Call:\n", "diff.resamples(x = resamps)\n", "\n", "Models: GLM, CART, RF, SVM, NN, XGBL, XGBT \n", "Metrics: Accuracy, Kappa \n", "Number of differences: 21 \n", "p-value adjustment: bonferroni " ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "\n", "Call:\n", "summary.diff.resamples(object = difValues)\n", "\n", "p-value adjustment: bonferroni \n", "Upper diagonal: estimates of the difference\n", "Lower diagonal: p-value for H0: difference = 0\n", "\n", "Accuracy \n", " GLM CART RF SVM NN XGBL XGBT \n", "GLM 0.002234 -0.004494 -0.001137 0.009040 -0.007840 -0.015693\n", "CART 1.0000 -0.006728 -0.003371 0.006806 -0.010074 -0.017927\n", "RF 1.0000 1.0000 0.003357 0.013534 -0.003346 -0.011199\n", "SVM 1.0000 1.0000 1.0000 0.010177 -0.006703 -0.014556\n", "NN 1.0000 1.0000 1.0000 1.0000 -0.016880 -0.024733\n", "XGBL 1.0000 1.0000 1.0000 1.0000 0.1399 -0.007853\n", "XGBT 1.0000 1.0000 1.0000 1.0000 0.3687 1.0000 \n", "\n", "Kappa \n", " GLM CART RF SVM NN XGBL XGBT \n", "GLM 0.003542 -0.006866 -0.003710 0.021340 -0.015408 -0.034500\n", "CART 1.0000 -0.010408 -0.007252 0.017797 -0.018950 -0.038042\n", "RF 1.0000 1.0000 0.003156 0.028206 -0.008542 -0.027634\n", "SVM 1.0000 1.0000 1.0000 0.025050 -0.011698 -0.030790\n", "NN 1.0000 1.0000 1.0000 1.0000 -0.036748 -0.055840\n", "XGBL 1.0000 1.0000 1.0000 1.0000 0.1176 -0.019092\n", "XGBT 1.0000 1.0000 1.0000 1.0000 0.3191 1.0000 \n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "difValues <- diff(resamps)\n", "difValues\n", "summary(difValues)" ] }, { "cell_type": "code", "execution_count": 57, "metadata": { "collapsed": true, "run_control": { "marked": false } }, "outputs": [], "source": [ "# Give a proper size to plots\n", "options(repr.plot.width=4, repr.plot.height=5)" ] }, { "cell_type": "code", "execution_count": 58, "metadata": { "collapsed": false, "run_control": { "marked": false } }, "outputs": [ { "data": {}, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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"text/plain": [ "plot without title" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "bwplot(difValues, layout = c(3, 1), cex=0.8)" ] }, { "cell_type": "markdown", "metadata": { "run_control": { "marked": false } }, "source": [ "We can see that all those models have a very similar accuracy." ] }, { "cell_type": "markdown", "metadata": { "run_control": { "marked": false } }, "source": [ "## 4.2 Check correlations among models" ] }, { "cell_type": "markdown", "metadata": { "run_control": { "marked": false } }, "source": [ "Let's check the correlation among models. We will use this information to decide whether we want to combine some models to build a stacked one." ] }, { "cell_type": "code", "execution_count": 59, "metadata": { "collapsed": false, "run_control": { "marked": false } }, "outputs": [ { "data": { "text/html": [ "<table>\n", "<thead><tr><th></th><th scope=col>GLM</th><th scope=col>CART</th><th scope=col>RF</th><th scope=col>SVM</th><th scope=col>NN</th><th scope=col>XGBL</th><th scope=col>XGBT</th></tr></thead>\n", "<tbody>\n", "\t<tr><th scope=row>GLM</th><td>1.0000000</td><td>0.9810310</td><td>0.7835333</td><td>0.9701062</td><td>0.8736794</td><td>0.9266126</td><td>0.7585487</td></tr>\n", "\t<tr><th scope=row>CART</th><td>0.9810310</td><td>1.0000000</td><td>0.6854624</td><td>0.9876399</td><td>0.8076806</td><td>0.8580292</td><td>0.7312503</td></tr>\n", "\t<tr><th scope=row>RF</th><td>0.7835333</td><td>0.6854624</td><td>1.0000000</td><td>0.7069643</td><td>0.8535902</td><td>0.9313125</td><td>0.8111155</td></tr>\n", "\t<tr><th scope=row>SVM</th><td>0.9701062</td><td>0.9876399</td><td>0.7069643</td><td>1.0000000</td><td>0.8353027</td><td>0.8787428</td><td>0.7721849</td></tr>\n", "\t<tr><th scope=row>NN</th><td>0.8736794</td><td>0.8076806</td><td>0.8535902</td><td>0.8353027</td><td>1.0000000</td><td>0.9333186</td><td>0.7599231</td></tr>\n", "\t<tr><th scope=row>XGBL</th><td>0.9266126</td><td>0.8580292</td><td>0.9313125</td><td>0.8787428</td><td>0.9333186</td><td>1.0000000</td><td>0.8692395</td></tr>\n", "\t<tr><th scope=row>XGBT</th><td>0.7585487</td><td>0.7312503</td><td>0.8111155</td><td>0.7721849</td><td>0.7599231</td><td>0.8692395</td><td>1.0000000</td></tr>\n", "</tbody>\n", "</table>\n" ], "text/latex": [ "\\begin{tabular}{r|lllllll}\n", " & GLM & CART & RF & SVM & NN & XGBL & XGBT\\\\\n", "\\hline\n", "\tGLM & 1.0000000 & 0.9810310 & 0.7835333 & 0.9701062 & 0.8736794 & 0.9266126 & 0.7585487\\\\\n", "\tCART & 0.9810310 & 1.0000000 & 0.6854624 & 0.9876399 & 0.8076806 & 0.8580292 & 0.7312503\\\\\n", "\tRF & 0.7835333 & 0.6854624 & 1.0000000 & 0.7069643 & 0.8535902 & 0.9313125 & 0.8111155\\\\\n", "\tSVM & 0.9701062 & 0.9876399 & 0.7069643 & 1.0000000 & 0.8353027 & 0.8787428 & 0.7721849\\\\\n", "\tNN & 0.8736794 & 0.8076806 & 0.8535902 & 0.8353027 & 1.0000000 & 0.9333186 & 0.7599231\\\\\n", "\tXGBL & 0.9266126 & 0.8580292 & 0.9313125 & 0.8787428 & 0.9333186 & 1.0000000 & 0.8692395\\\\\n", "\tXGBT & 0.7585487 & 0.7312503 & 0.8111155 & 0.7721849 & 0.7599231 & 0.8692395 & 1.0000000\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "| <!--/--> | GLM | CART | RF | SVM | NN | XGBL | XGBT | \n", "|---|---|---|---|---|---|---|\n", "| GLM | 1.0000000 | 0.9810310 | 0.7835333 | 0.9701062 | 0.8736794 | 0.9266126 | 0.7585487 | \n", "| CART | 0.9810310 | 1.0000000 | 0.6854624 | 0.9876399 | 0.8076806 | 0.8580292 | 0.7312503 | \n", "| RF | 0.7835333 | 0.6854624 | 1.0000000 | 0.7069643 | 0.8535902 | 0.9313125 | 0.8111155 | \n", "| SVM | 0.9701062 | 0.9876399 | 0.7069643 | 1.0000000 | 0.8353027 | 0.8787428 | 0.7721849 | \n", "| NN | 0.8736794 | 0.8076806 | 0.8535902 | 0.8353027 | 1.0000000 | 0.9333186 | 0.7599231 | \n", "| XGBL | 0.9266126 | 0.8580292 | 0.9313125 | 0.8787428 | 0.9333186 | 1.0000000 | 0.8692395 | \n", "| XGBT | 0.7585487 | 0.7312503 | 0.8111155 | 0.7721849 | 0.7599231 | 0.8692395 | 1.0000000 | \n", "\n", "\n" ], "text/plain": [ " GLM CART RF SVM NN XGBL XGBT \n", "GLM 1.0000000 0.9810310 0.7835333 0.9701062 0.8736794 0.9266126 0.7585487\n", "CART 0.9810310 1.0000000 0.6854624 0.9876399 0.8076806 0.8580292 0.7312503\n", "RF 0.7835333 0.6854624 1.0000000 0.7069643 0.8535902 0.9313125 0.8111155\n", "SVM 0.9701062 0.9876399 0.7069643 1.0000000 0.8353027 0.8787428 0.7721849\n", "NN 0.8736794 0.8076806 0.8535902 0.8353027 1.0000000 0.9333186 0.7599231\n", "XGBL 0.9266126 0.8580292 0.9313125 0.8787428 0.9333186 1.0000000 0.8692395\n", "XGBT 0.7585487 0.7312503 0.8111155 0.7721849 0.7599231 0.8692395 1.0000000" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "model_cor <- modelCor(resamps)\n", "model_cor" ] }, { "cell_type": "code", "execution_count": 60, "metadata": { "collapsed": false, "run_control": { "marked": false }, "scrolled": false }, "outputs": [ { "data": { "image/png": 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3fdfMG7Z7Xr1uFoSOtdO5PwdfvbNTKdMAP4LAX4tDzg88nAfxuEDHxdXx766dNl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\nULsrOw3wuwAAAABJRU5ErkJggg==", "text/plain": [ "Plot with title \"\"" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "corrplot(model_cor)" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true, "run_control": { "marked": false } }, "source": [ "The models are highly correlated to each other, so to assemble them will likely have little benefits." ] }, { "cell_type": "markdown", "metadata": { "run_control": { "marked": false } }, "source": [ "# 5 Predictions on the test set" ] }, { "cell_type": "code", "execution_count": 61, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "'data.frame':\t418 obs. of 22 variables:\n", " $ PassengerId : int 892 893 894 895 896 897 898 899 900 901 ...\n", " $ Pclass : Ord.factor w/ 3 levels \"1\"<\"2\"<\"3\": 3 3 2 3 3 3 3 2 3 3 ...\n", " $ Name : Factor w/ 1307 levels \"Abbing, Mr. Anthony\",..: 438 1298 1162 1303 1072 1259 178 949 896 994 ...\n", " $ Sex_female : Factor w/ 2 levels \"0\",\"1\": 1 2 1 1 2 1 2 1 2 1 ...\n", " $ Sex_male : Factor w/ 2 levels \"0\",\"1\": 2 1 2 2 1 2 1 2 1 2 ...\n", " $ Age : num 34.5 47 62 27 22 14 30 26 18 21 ...\n", " $ SibSp : int 0 1 0 0 1 0 0 1 0 2 ...\n", " $ Parch : int 0 0 0 0 1 0 0 1 0 0 ...\n", " $ Ticket : Factor w/ 929 levels \"110152\",\"110413\",..: 781 841 726 776 252 869 787 159 745 520 ...\n", " $ Fare : num 7.83 7 9.69 8.66 12.29 ...\n", " $ Cabin : Factor w/ 186 levels \"A10\",\"A14\",\"A16\",..: NA NA NA NA NA NA NA NA NA NA ...\n", " $ Embarked_C : Factor w/ 2 levels \"0\",\"1\": 1 1 1 1 1 1 1 1 2 1 ...\n", " $ Embarked_Q : Factor w/ 2 levels \"0\",\"1\": 2 1 2 1 1 1 2 1 1 1 ...\n", " $ Embarked_S : Factor w/ 2 levels \"0\",\"1\": 1 2 1 2 2 2 1 2 1 2 ...\n", " $ Title : Factor w/ 18 levels \"Capt\",\"Col\",\"Don\",..: 13 14 13 13 14 13 10 13 14 13 ...\n", " $ Child : Factor w/ 2 levels \"FALSE\",\"TRUE\": 1 1 1 1 1 1 1 1 1 1 ...\n", " $ Young : Factor w/ 2 levels \"FALSE\",\"TRUE\": 1 1 1 2 2 1 2 2 2 2 ...\n", " $ Title2_Master: Factor w/ 2 levels \"0\",\"1\": 1 1 1 1 1 2 1 1 1 1 ...\n", " $ Title2_Miss : Factor w/ 2 levels \"0\",\"1\": 1 1 1 1 1 1 1 1 1 1 ...\n", " $ Title2_Mr : Factor w/ 2 levels \"0\",\"1\": 2 1 2 2 1 1 1 2 1 2 ...\n", " $ Title2_Mrs : Factor w/ 2 levels \"0\",\"1\": 1 2 1 1 2 1 2 1 2 1 ...\n", " $ Title2_Sir : Factor w/ 2 levels \"0\",\"1\": 1 1 1 1 1 1 1 1 1 1 ...\n", " - attr(*, \"dummies\")=List of 3\n", " ..$ Sex : int 4 5\n", " ..$ Embarked: int 12 13 14\n", " ..$ Title2 : int 18 19 20 21 22\n" ] } ], "source": [ "str(Test)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 5.1 GLM" ] }, { "cell_type": "code", "execution_count": 101, "metadata": { "collapsed": false, "run_control": { "marked": false } }, "outputs": [], "source": [ "pred_GLM_Test = predict(logmod4, newdata=Test)" ] }, { "cell_type": "code", "execution_count": 102, "metadata": { "collapsed": false, "run_control": { "marked": false } }, "outputs": [], "source": [ "pred_GLM_final <- cbind(Test$PassengerId, as.character(pred_GLM_Test))" ] }, { "cell_type": "code", "execution_count": 103, "metadata": { "collapsed": false, "run_control": { "marked": false } }, "outputs": [ { "data": { "text/html": [ "<table>\n", "<thead><tr><th scope=col>PassengerId</th><th scope=col>Survived</th></tr></thead>\n", "<tbody>\n", "\t<tr><td>892</td><td>No </td></tr>\n", "\t<tr><td>893</td><td>Yes</td></tr>\n", "\t<tr><td>894</td><td>No </td></tr>\n", "\t<tr><td>895</td><td>No </td></tr>\n", "\t<tr><td>896</td><td>No </td></tr>\n", "\t<tr><td>897</td><td>Yes</td></tr>\n", "</tbody>\n", "</table>\n" ], "text/latex": [ "\\begin{tabular}{ll}\n", " PassengerId & Survived\\\\\n", "\\hline\n", "\t 892 & No \\\\\n", "\t 893 & Yes\\\\\n", "\t 894 & No \\\\\n", "\t 895 & No \\\\\n", "\t 896 & No \\\\\n", "\t 897 & Yes\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "PassengerId | Survived | \n", "|---|---|---|---|---|---|\n", "| 892 | No | \n", "| 893 | Yes | \n", "| 894 | No | \n", "| 895 | No | \n", "| 896 | No | \n", "| 897 | Yes | \n", "\n", "\n" ], "text/plain": [ " PassengerId Survived\n", "[1,] 892 No \n", "[2,] 893 Yes \n", "[3,] 894 No \n", "[4,] 895 No \n", "[5,] 896 No \n", "[6,] 897 Yes " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "colnames(pred_GLM_final) <- c(\"PassengerId\", \"Survived\")\n", "head(pred_GLM_final)" ] }, { "cell_type": "code", "execution_count": 104, "metadata": { "collapsed": false }, "outputs": [], "source": [ "pred_GLM_final <- as.data.frame(pred_GLM_final)" ] }, { "cell_type": "code", "execution_count": 105, "metadata": { "collapsed": false }, "outputs": [], "source": [ "pred_GLM_final$Survived <- revalue(pred_GLM_final$Survived, c(\"No\"=\"0\", \"Yes\"=\"1\"))" ] }, { "cell_type": "code", "execution_count": 107, "metadata": { "collapsed": false }, "outputs": [], "source": [ "write.table(pred_GLM_final, file=\"pred_glm_final2.csv\", sep=\",\", row.names=FALSE)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "leaderboard score on Kaggle: 0.78469" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 5.2 CART" ] }, { "cell_type": "code", "execution_count": 109, "metadata": { "collapsed": false, "run_control": { "marked": false } }, "outputs": [], "source": [ "pred_CART_Test = predict(CART1b, newdata=Test)" ] }, { "cell_type": "code", "execution_count": 110, "metadata": { "collapsed": false, "run_control": { "marked": false } }, "outputs": [], "source": [ "pred_CART_final <- cbind(Test$PassengerId, as.character(pred_CART_Test))" ] }, { "cell_type": "code", "execution_count": 111, "metadata": { "collapsed": false, "run_control": { "marked": false } }, "outputs": [], "source": [ "colnames(pred_CART_final) <- c(\"PassengerId\", \"Survived\")" ] }, { "cell_type": "code", "execution_count": 112, "metadata": { "collapsed": false }, "outputs": [], "source": [ "pred_CART_final <- as.data.frame(pred_CART_final)" ] }, { "cell_type": "code", "execution_count": 113, "metadata": { "collapsed": false }, "outputs": [], "source": [ "pred_CART_final$Survived <- revalue(pred_CART_final$Survived, c(\"No\"=\"0\", \"Yes\"=\"1\"))" ] }, { "cell_type": "code", "execution_count": 114, "metadata": { "collapsed": false }, "outputs": [], "source": [ "write.table(pred_CART_final, file=\"pred_cart_final2.csv\", sep=\",\", row.names=FALSE)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "leaderboard score on Kaggle: 0.79426" ] }, { "cell_type": "markdown", "metadata": { "run_control": { "marked": false } }, "source": [ "## 5.3 Random forest" ] }, { "cell_type": "code", "execution_count": 115, "metadata": { "collapsed": false, "run_control": { "marked": false } }, "outputs": [], "source": [ "pred_RF_Test = predict(RF1, newdata=Test)" ] }, { "cell_type": "code", "execution_count": 116, "metadata": { "collapsed": false, "run_control": { "marked": false } }, "outputs": [], "source": [ "pred_RF_final <- cbind(Test$PassengerId, as.character(pred_RF_Test))" ] }, { "cell_type": "code", "execution_count": 117, "metadata": { "collapsed": false, "run_control": { "marked": false } }, "outputs": [], "source": [ "colnames(pred_RF_final) <- c(\"PassengerId\", \"Survived\")" ] }, { "cell_type": "code", "execution_count": 118, "metadata": { "collapsed": false }, "outputs": [], "source": [ "pred_RF_final <- as.data.frame(pred_RF_final)" ] }, { "cell_type": "code", "execution_count": 119, "metadata": { "collapsed": false }, "outputs": [], "source": [ "pred_RF_final$Survived <- revalue(pred_RF_final$Survived, c(\"No\"=\"0\", \"Yes\"=\"1\"))" ] }, { "cell_type": "code", "execution_count": 120, "metadata": { "collapsed": false }, "outputs": [], "source": [ "write.table(pred_RF_final, file=\"pred_rf_final2.csv\", sep=\",\", row.names=FALSE)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "leaderboard score on Kaggle: 0.77512" ] }, { "cell_type": "markdown", "metadata": { "run_control": { "marked": false } }, "source": [ "## 5.4 SVM" ] }, { "cell_type": "code", "execution_count": 78, "metadata": { "collapsed": false, "run_control": { "marked": false } }, "outputs": [], "source": [ "pred_SVM_Test = predict(svm3, newdata=Test)" ] }, { "cell_type": "code", "execution_count": 79, "metadata": { "collapsed": false, "run_control": { "marked": false } }, "outputs": [], "source": [ "pred_SVM_final <- cbind(Test$PassengerId, as.character(pred_SVM_Test))" ] }, { "cell_type": "code", "execution_count": 80, "metadata": { "collapsed": false, "run_control": { "marked": false } }, "outputs": [], "source": [ "colnames(pred_SVM_final) <- c(\"PassengerId\", \"Survived\")" ] }, { "cell_type": "code", "execution_count": 81, "metadata": { "collapsed": false }, "outputs": [], "source": [ "pred_SVM_final <- as.data.frame(pred_SVM_final)" ] }, { "cell_type": "code", "execution_count": 85, "metadata": { "collapsed": false }, "outputs": [], "source": [ "pred_SVM_final$Survived <- revalue(pred_SVM_final$Survived, c(\"No\"=\"0\", \"Yes\"=\"1\"))" ] }, { "cell_type": "code", "execution_count": 87, "metadata": { "collapsed": false }, "outputs": [], "source": [ "write.table(pred_SVM_final, file=\"pred_svm_final2.csv\", sep=\",\", row.names=FALSE)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "leaderboard score on Kaggle: 0.78469" ] }, { "cell_type": "markdown", "metadata": { "run_control": { "marked": false } }, "source": [ "## 5.5 XGBoost (linear)" ] }, { "cell_type": "code", "execution_count": 88, "metadata": { "collapsed": false, "run_control": { "marked": false } }, "outputs": [], "source": [ "pred_XGBL_Test = predict(xgb1, newdata=Test)" ] }, { "cell_type": "code", "execution_count": 89, "metadata": { "collapsed": false, "run_control": { "marked": false } }, "outputs": [], "source": [ "pred_XGBL_final <- cbind(Test$PassengerId, as.character(pred_XGBL_Test))" ] }, { "cell_type": "code", "execution_count": 90, "metadata": { "collapsed": false, "run_control": { "marked": false } }, "outputs": [], "source": [ "colnames(pred_XGBL_final) <- c(\"PassengerId\", \"Survived\")" ] }, { "cell_type": "code", "execution_count": 91, "metadata": { "collapsed": false }, "outputs": [], "source": [ "pred_XGBL_final <- as.data.frame(pred_XGBL_final)" ] }, { "cell_type": "code", "execution_count": 93, "metadata": { "collapsed": false }, "outputs": [], "source": [ "pred_XGBL_final$Survived <- revalue(pred_XGBL_final$Survived, c(\"No\"=\"0\", \"Yes\"=\"1\"))" ] }, { "cell_type": "code", "execution_count": 94, "metadata": { "collapsed": false }, "outputs": [], "source": [ "write.table(pred_XGBL_final, file=\"pred_xgbL_final2.csv\", sep=\",\", row.names=FALSE)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "leaderboard score on Kaggle: 0.79904 " ] }, { "cell_type": "markdown", "metadata": { "run_control": { "marked": false } }, "source": [ "## 5.6 XGBoost (tree)" ] }, { "cell_type": "code", "execution_count": 95, "metadata": { "collapsed": false, "run_control": { "marked": false } }, "outputs": [], "source": [ "pred_XGBT_Test = predict(xgb2, newdata=Test)" ] }, { "cell_type": "code", "execution_count": 96, "metadata": { "collapsed": false, "run_control": { "marked": false } }, "outputs": [], "source": [ "pred_XGBT_final <- cbind(Test$PassengerId, as.character(pred_XGBT_Test))" ] }, { "cell_type": "code", "execution_count": 97, "metadata": { "collapsed": false, "run_control": { "marked": false } }, "outputs": [], "source": [ "colnames(pred_XGBT_final) <- c(\"PassengerId\", \"Survived\")" ] }, { "cell_type": "code", "execution_count": 98, "metadata": { "collapsed": false }, "outputs": [], "source": [ "pred_XGBT_final <- as.data.frame(pred_XGBT_final)" ] }, { "cell_type": "code", "execution_count": 99, "metadata": { "collapsed": false }, "outputs": [], "source": [ "pred_XGBT_final$Survived <- revalue(pred_XGBT_final$Survived, c(\"No\"=\"0\", \"Yes\"=\"1\"))" ] }, { "cell_type": "code", "execution_count": 100, "metadata": { "collapsed": false }, "outputs": [], "source": [ "write.table(pred_XGBT_final, file=\"pred_xgbT_final2.csv\", sep=\",\", row.names=FALSE)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "leaderboard score on Kaggle: 0.73206" ] }, { "cell_type": "markdown", "metadata": { "run_control": { "marked": false } }, "source": [ "# 6 Going further" ] }, { "cell_type": "markdown", "metadata": { "run_control": { "marked": false } }, "source": [ "To improve the accuracy, we can try to:\n", "* avoid combining Train and Test datasets at the beginning, and split our Train dataset into a Train2 and Validation sets. In doing so, we will avoid contamination of training data through data imputation, parameter optimization and feature selection. I choose not to do so for this first attempt because of the small sample size or the training set.\n", "* use separate predictive models for males and females given the difference in survival for each sex.\n", "* impute Deck.\n", "* create a variable Family (from Name and Ticket) and then use it to create the variables Single, SmallFamily and LargeFamily.\n", "* transform data" ] } ], "metadata": { "_draft": { "nbviewer_url": "https://gist.github.com/81a3a03179119d8972d2730100792f64" }, "author": "Titanic: Machine Learning from Disaster", "gist": { "data": { "description": "My first attempt to predict survival of the Titanic's passengers (R)", "public": true }, "id": "81a3a03179119d8972d2730100792f64" }, "kernelspec": { "display_name": "R 3.3", "language": "R", "name": "ir33" }, "language_info": { "codemirror_mode": "r", "file_extension": ".r", "mimetype": "text/x-r-source", "name": "R", "pygments_lexer": "r", "version": "3.3.1" }, "latex_envs": { "LaTeX_envs_menu_present": true, "autocomplete": true, "bibliofile": "biblio.bib", "cite_by": "apalike", "current_citInitial": 1, "eqLabelWithNumbers": true, "eqNumInitial": 1, "hotkeys": { "equation": "Ctrl-E", "itemize": "Ctrl-I" }, "labels_anchors": true, "latex_user_defs": false, "report_style_numbering": true, "user_envs_cfg": true }, "notify_time": "30", "toc": { "colors": { "hover_highlight": "#DAA520", "navigate_num": "#000000", "navigate_text": "#333333", "running_highlight": "#FF0000", "selected_highlight": "#FFD700", "sidebar_border": "#EEEEEE", "wrapper_background": "#FFFFFF" }, "moveMenuLeft": true, "nav_menu": { "height": "512px", "width": "252px" }, "navigate_menu": true, "number_sections": true, "sideBar": true, "threshold": "4", "toc_cell": true, "toc_position": { "height": "532px", "left": "0px", "right": "1154px", "top": "106px", "width": "291px" }, "toc_section_display": "block", "toc_window_display": false, "widenNotebook": false }, "varInspector": { "cols": { "lenName": 16, "lenType": 16, "lenVar": 40 }, "kernels_config": { "python": { "delete_cmd_postfix": "", "delete_cmd_prefix": "del ", "library": "var_list.py", "varRefreshCmd": "print(var_dic_list())" }, "r": { "delete_cmd_postfix": ") ", "delete_cmd_prefix": "rm(", "library": "var_list.r", "varRefreshCmd": "cat(var_dic_list()) " } }, "position": { "height": "482px", "left": "742px", "right": "20px", "top": "108px", "width": "483px" }, "types_to_exclude": [ "module", "function", "builtin_function_or_method", "instance", "_Feature" ], "window_display": false } }, "nbformat": 4, "nbformat_minor": 2 }
mit
scikit-optimize/scikit-optimize.github.io
0.7/notebooks/auto_examples/strategy-comparison.ipynb
2
6517
{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n# Comparing surrogate models\n\n\nTim Head, July 2016.\nReformatted by Holger Nahrstaedt 2020\n\n.. currentmodule:: skopt\n\nBayesian optimization or sequential model-based optimization uses a surrogate\nmodel to model the expensive to evaluate function `func`. There are several\nchoices for what kind of surrogate model to use. This notebook compares the\nperformance of:\n\n* gaussian processes,\n* extra trees, and\n* random forests\n\nas surrogate models. A purely random optimization strategy is also used as\na baseline.\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "print(__doc__)\nimport numpy as np\nnp.random.seed(123)\nimport matplotlib.pyplot as plt" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Toy model\n=========\n\nWe will use the :class:`benchmarks.branin` function as toy model for the expensive function.\nIn a real world application this function would be unknown and expensive\nto evaluate.\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from skopt.benchmarks import branin as _branin\n\ndef branin(x, noise_level=0.):\n return _branin(x) + noise_level * np.random.randn()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from matplotlib.colors import LogNorm\n\n\ndef plot_branin():\n fig, ax = plt.subplots()\n\n x1_values = np.linspace(-5, 10, 100)\n x2_values = np.linspace(0, 15, 100)\n x_ax, y_ax = np.meshgrid(x1_values, x2_values)\n vals = np.c_[x_ax.ravel(), y_ax.ravel()]\n fx = np.reshape([branin(val) for val in vals], (100, 100))\n\n cm = ax.pcolormesh(x_ax, y_ax, fx,\n norm=LogNorm(vmin=fx.min(),\n vmax=fx.max()))\n\n minima = np.array([[-np.pi, 12.275], [+np.pi, 2.275], [9.42478, 2.475]])\n ax.plot(minima[:, 0], minima[:, 1], \"r.\", markersize=14,\n lw=0, label=\"Minima\")\n\n cb = fig.colorbar(cm)\n cb.set_label(\"f(x)\")\n\n ax.legend(loc=\"best\", numpoints=1)\n\n ax.set_xlabel(\"X1\")\n ax.set_xlim([-5, 10])\n ax.set_ylabel(\"X2\")\n ax.set_ylim([0, 15])\n\n\nplot_branin()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This shows the value of the two-dimensional branin function and\nthe three minima.\n\n\nObjective\n=========\n\nThe objective of this example is to find one of these minima in as\nfew iterations as possible. One iteration is defined as one call\nto the :class:`benchmarks.branin` function.\n\nWe will evaluate each model several times using a different seed for the\nrandom number generator. Then compare the average performance of these\nmodels. This makes the comparison more robust against models that get\n\"lucky\".\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from functools import partial\nfrom skopt import gp_minimize, forest_minimize, dummy_minimize\n\nfunc = partial(branin, noise_level=2.0)\nbounds = [(-5.0, 10.0), (0.0, 15.0)]\nn_calls = 60" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def run(minimizer, n_iter=5):\n return [minimizer(func, bounds, n_calls=n_calls, random_state=n)\n for n in range(n_iter)]\n\n# Random search\ndummy_res = run(dummy_minimize)\n\n# Gaussian processes\ngp_res = run(gp_minimize)\n\n# Random forest\nrf_res = run(partial(forest_minimize, base_estimator=\"RF\"))\n\n# Extra trees\net_res = run(partial(forest_minimize, base_estimator=\"ET\"))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Note that this can take a few minutes.\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from skopt.plots import plot_convergence\n\nplot = plot_convergence((\"dummy_minimize\", dummy_res),\n (\"gp_minimize\", gp_res),\n (\"forest_minimize('rf')\", rf_res),\n (\"forest_minimize('et)\", et_res),\n true_minimum=0.397887, yscale=\"log\")\n\nplot.legend(loc=\"best\", prop={'size': 6}, numpoints=1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This plot shows the value of the minimum found (y axis) as a function\nof the number of iterations performed so far (x axis). The dashed red line\nindicates the true value of the minimum of the :class:`benchmarks.branin` function.\n\nFor the first ten iterations all methods perform equally well as they all\nstart by creating ten random samples before fitting their respective model\nfor the first time. After iteration ten the next point at which\nto evaluate :class:`benchmarks.branin` is guided by the model, which is where differences\nstart to appear.\n\nEach minimizer only has access to noisy observations of the objective\nfunction, so as time passes (more iterations) it will start observing\nvalues that are below the true value simply because they are fluctuations.\n\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": "3.8.1" } }, "nbformat": 4, "nbformat_minor": 0 }
bsd-3-clause
nicococo/tilitools
lectures/methods_exercise.ipynb
1
23600
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "***\n", "# Exercise: _Anomaly Detection Methods_\n", "***\n", "\n", "In this exercise, we want to compare existing anomaly detection methods on a variety of real-world multi-variate benchmark datasets. Moreover, we will implement a recent method that is both simple and elegant as well as powerful and fast.\n", "\n", "1. LODA: Leight-weight Online Detector of Anomalies\n", " - Implement LODA (paper is provided) using the pyOD framework\n", " - Evaluate against competitors\n", "2. Unsupervised vs Supervised Outlier Detection\n", " - Implement an regression and classification method (sklearn-based)\n", " - Evaluate and discuss the results on various datasets\n", "3. High-dimensional Outlier Detection\n", " - Read the provided material\n", " - Check empirically if the concentration of distances condition is fulfilled\n", "\n", "\n", "### Datasets\n", "\n", "The datasets used here are available at the Outlier Detection DataSets (ODDS) libraryand can be downloaded from http://odds.cs.stonybrook.edu. Most of the datasets are processed datasets from other sources (e.g. mnist) to meet anomaly detection benchmark standards, e.g. labeled anomalies rather than classes. Further information for each dataset is available at http://odds.cs.stonybrook.edu." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import os\n", "import sys\n", "from time import time\n", "\n", "import warnings\n", "warnings.filterwarnings(\"ignore\")\n", "\n", "import numpy as np\n", "import pandas as pd\n", "from scipy.io import loadmat\n", "import scipy.spatial.distance as dist\n", "\n", "from pyod.models.base import BaseDetector\n", "from pyod.models.abod import ABOD\n", "from pyod.models.cblof import CBLOF\n", "from pyod.models.feature_bagging import FeatureBagging\n", "from pyod.models.hbos import HBOS\n", "from pyod.models.iforest import IForest\n", "from pyod.models.knn import KNN\n", "from pyod.models.lof import LOF\n", "from pyod.models.mcd import MCD\n", "from pyod.models.pca import PCA\n", "\n", "from pyod.utils.utility import standardizer\n", "from pyod.utils.utility import precision_n_scores\n", "\n", "from sklearn.manifold import TSNE\n", "from sklearn.model_selection import train_test_split\n", "from sklearn.metrics import roc_auc_score\n", "from sklearn.linear_model import Ridge\n", "from sklearn.svm import SVC\n", "\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 1. LODA: Leight-weight Online Detector of Anomalies\n", "\n", "In this first round, we will implement a recent, popular anomaly detector based on the pyOD framework\n", "(https://github.com/yzhao062/pyod). You should have received a copy of the paper [1] by now and the task is, \n", "to read the paper carefully and implement the proposed method into the LODA class skeleton below.\n", "\n", "[1] T. Pevny, “Loda: Lightweight on-line detector of anomalies,” Mach. Learn., vol. 102, pp. 275–304, 2016.\n", "\n", "\n", "Some hints:\n", "\n", "- The cumulative sum of a histogram needs to sum to one. However, unless unity width bins are used (which is not the case) and density=True, this will not happen:\n", "\n", " _If density=True, the result is the value of the probability density function at the bin, normalized such that the integral over the range is 1. Note that the sum of the histogram values will not be equal to 1 unless bins of unity width are chosen._\n", " \n", " \n", "- -log(x) will produce 'inf' values and hence, pseudo-counts are needed before normalizing the histograms" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "class LODA(BaseDetector):\n", "\n", " def __init__(self, contamination=0.1, n_bins=10, n_random_cuts=100, **kwargs):\n", " super(LODA, self).__init__(contamination=contamination)\n", " self.n_bins = n_bins\n", " self.n_random_cuts = n_random_cuts\n", " self.weights = np.ones(n_random_cuts, dtype=np.float) / n_random_cuts\n", "\n", " def fit(self, X, y=None):\n", " # TODO\n", " return self\n", "\n", " def decision_function(self, X):\n", " pred_scores = np.zeros([X.shape[0], 1])\n", " # TODO\n", " return pred_scores.ravel()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Define data file and read X and y\n", "mat_file_list = ['arrhythmia.mat',\n", " 'cardio.mat',\n", " 'glass.mat',\n", " 'ionosphere.mat',\n", " 'letter.mat',\n", " 'lympho.mat',\n", " 'mnist.mat',\n", " 'pendigits.mat',\n", " 'pima.mat',\n", " 'satimage-2.mat',\n", " 'vertebral.mat',\n", " 'vowels.mat',\n", " 'wbc.mat']\n", "\n", "# mat_file_list = ['ionosphere.mat','arrhythmia.mat']\n", "\n", "\n", "# Define nine outlier detection tools to be compared\n", "random_state = np.random.RandomState(42)\n", "\n", "df_columns = ['Data', '#Samples', '# Dimensions', 'Outlier Perc',\n", " 'ABOD', 'CBLOF', 'FB', 'HBOS', 'IForest', 'KNN', 'LOF', 'MCD', 'PCA', 'LODA']\n", "roc_df = pd.DataFrame(columns=df_columns)\n", "prn_df = pd.DataFrame(columns=df_columns)\n", "time_df = pd.DataFrame(columns=df_columns)\n", "\n", "for mat_file in mat_file_list:\n", " print(\"\\n... Processing\", mat_file, '...')\n", " mat = loadmat(os.path.join('../data', mat_file))\n", "\n", " X = mat['X']\n", " y = mat['y'].ravel()\n", " outliers_fraction = np.count_nonzero(y) / len(y)\n", " outliers_percentage = round(outliers_fraction * 100, ndigits=4)\n", "\n", " # construct containers for saving results\n", " roc_list = [mat_file[:-4], X.shape[0], X.shape[1], outliers_percentage]\n", " prn_list = [mat_file[:-4], X.shape[0], X.shape[1], outliers_percentage]\n", " time_list = [mat_file[:-4], X.shape[0], X.shape[1], outliers_percentage]\n", "\n", " # 60% data for training and 40% for testing\n", " X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.4, random_state=random_state)\n", "\n", " # standardizing data for processing\n", " X_train_norm, X_test_norm = standardizer(X_train, X_test)\n", "\n", " classifiers = {\n", " 'Angle-based Outlier Detector (ABOD)': ABOD(contamination=outliers_fraction),\n", " 'Cluster-based Local Outlier Factor': CBLOF(contamination=outliers_fraction, check_estimator=False, random_state=random_state),\n", " 'Feature Bagging': FeatureBagging(contamination=outliers_fraction, check_estimator=False, random_state=random_state),\n", " 'Histogram-base Outlier Detection (HBOS)': HBOS(contamination=outliers_fraction),\n", " 'Isolation Forest': IForest(contamination=outliers_fraction, random_state=random_state),\n", " 'K Nearest Neighbors (KNN)': KNN(contamination=outliers_fraction),\n", " 'Local Outlier Factor (LOF)': LOF(contamination=outliers_fraction),\n", " 'Minimum Covariance Determinant (MCD)': MCD(contamination=outliers_fraction, random_state=random_state),\n", " 'Principal Component Analysis (PCA)': PCA(contamination=outliers_fraction, random_state=random_state),\n", " 'Lightweight on-line detector of anomalies (LODA)': LODA(contamination=outliers_fraction, random_state=random_state),\n", " }\n", "\n", " for clf_name, clf in classifiers.items():\n", " t0 = time()\n", " clf.fit(X_train_norm)\n", " test_scores = clf.decision_function(X_test_norm)\n", " t1 = time()\n", " duration = round(t1 - t0, ndigits=4)\n", " time_list.append(duration)\n", "\n", " roc = round(roc_auc_score(y_test, test_scores), ndigits=4)\n", " prn = round(precision_n_scores(y_test, test_scores), ndigits=4)\n", "\n", " print('{clf_name} ROC:{roc}, precision @ rank n:{prn}, execution time: {duration}s'.format(\n", " clf_name=clf_name, roc=roc, prn=prn, duration=duration))\n", "\n", " roc_list.append(roc)\n", " prn_list.append(prn)\n", "\n", " temp_df = pd.DataFrame(time_list).transpose()\n", " temp_df.columns = df_columns\n", " time_df = pd.concat([time_df, temp_df], axis=0)\n", "\n", " temp_df = pd.DataFrame(roc_list).transpose()\n", " temp_df.columns = df_columns\n", " roc_df = pd.concat([roc_df, temp_df], axis=0)\n", "\n", " temp_df = pd.DataFrame(prn_list).transpose()\n", " temp_df.columns = df_columns\n", " prn_df = pd.concat([prn_df, temp_df], axis=0)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "print('ROC Performance')\n", "roc_df" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "print('Runtime Performance')\n", "temp_df" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 2. Unsupervised vs Supervised Outlier Detection\n", "\n", "In this section, we will examine the difference of unsupervised and supervised method and see what happens \n", "if we provide a growing number of labeled examples. Therefore, you have to implement/use two methods of your choice (from sklearn): one regression method and one classifier. Caveat here is that we will not do any kind of model selection after all, the goal is to study the general pattern and not tweak the last percentages of accuracy out of each model. I would advice to use _Ridge_ and _SVC_ from sklearn. \n", "\n", "Questions:\n", "- Check what happens when you use the 'predict' function of the classifier rather than the \"decision_function'\n", "- Check the three datasets and discuss the results. When does unsupervised learning works best?\n", "- Supervised does not seem too bad, right? Why is it still not a bad idea to apply unsupervised methods?\n", "- What would be the best of two worlds, here?" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def train_and_predict_regression(X_train_norm, y_train, X_test_norm, y_test):\n", " # TODO\n", " return -1\n", "\n", "def train_and_predict_classifier(X_train_norm, y_train, X_test_norm, y_test):\n", " # TODO\n", " return -1\n", " " ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "mat_file_list = ['mnist.mat', 'ionosphere.mat', 'satimage-2.mat']\n", "\n", "reps = 5\n", "\n", "fig = plt.figure(figsize=(14, 8), dpi=80, facecolor='w', edgecolor='k')\n", "for f in range(len(mat_file_list)):\n", " mat = loadmat('../data/'+mat_file_list[f])\n", " print('File: ', mat_file_list[f])\n", "\n", " X = mat['X']\n", " y = mat['y'].ravel()\n", "\n", " # 60% data for training and 40% for testing\n", " X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.4, random_state=random_state)\n", " samples = np.linspace(1, y_train.size, 10, dtype=np.int)\n", "\n", " # standardizing data for processing\n", " X_train_norm, X_test_norm = standardizer(X_train, X_test)\n", "\n", " rocs = np.zeros((3, len(samples), reps))\n", " for r in range(reps):\n", " for i in range(len(samples)):\n", " inds = np.random.permutation(y_train.size)[:samples[i]]\n", " rocs[0, i, r] = train_and_predict_classifier(X_train_norm[inds, :], y_train[inds], X_test_norm, y_test)\n", " rocs[1, i, r] = train_and_predict_regression(X_train_norm[inds, :], y_train[inds], X_test_norm, y_test)\n", " loda = LODA(contamination=outliers_fraction, random_state=random_state, n_random_cuts=250, n_bins=40)\n", " loda.fit(X_train_norm)\n", " test_scores = loda.decision_function(X_test_norm)\n", " rocs[2, i, r] = round(roc_auc_score(y_test, test_scores), ndigits=4)\n", "\n", " res = np.mean(rocs, axis=2)\n", " std = np.std(rocs, axis=2)\n", "\n", " plt.subplot(2, len(mat_file_list), f+1)\n", " plt.errorbar(samples, res[0, :], std[0, :], fmt='.-r', elinewidth=1., linewidth=2.)\n", " plt.errorbar(samples, res[1, :], std[1, :], fmt='.-g', elinewidth=1., linewidth=2.)\n", " plt.errorbar(samples, res[2, :], std[2, :], fmt='.-b', elinewidth=1., linewidth=2.)\n", " plt.legend(['Supervised Classifier', 'Supervised Regression', 'Unsupervised Loda'], fontsize=10)\n", " plt.xticks(samples, (samples/np.max(samples)*100.0).astype(np.int) )\n", " plt.xlabel('Percentage of labeled samples', fontsize=14)\n", " plt.ylabel('Performance [in AUC]', fontsize=14)\n", " plt.title(mat_file_list[f], fontsize=14)\n", " plt.grid()\n", "\n", " plt.subplot(2, len(mat_file_list), f+1+len(mat_file_list))\n", " plt.errorbar(samples, res[0, :], std[0, :], fmt='.-r', elinewidth=1., linewidth=2.)\n", " plt.errorbar(samples, res[1, :], std[1, :], fmt='.-g', elinewidth=1., linewidth=2.)\n", " plt.errorbar(samples, res[2, :], std[2, :], fmt='.-b', elinewidth=1., linewidth=2.)\n", " plt.legend(['Supervised Classifier', 'Supervised Regression', 'Unsupervised Loda'], fontsize=10)\n", " plt.xticks(samples, (samples/np.max(samples)*100.0).astype(np.int) )\n", " plt.xlabel('Percentage of labeled samples [log]', fontsize=14)\n", " plt.ylabel('Performance [in AUC]', fontsize=14)\n", " plt.semilogx()\n", " plt.grid()\n", " \n", "plt.tight_layout()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 3. High-dimensional Outlier Detection\n", "\n", "This part is all about the paper by Beyer et al. [1] and, i.e. Theorem 1 that\n", "formalized the problem of nearest neighbor based outlier detection in high-dimensional \n", "outlier detection settings. Some notation and inspiration also comes from the very good tutorial of \n", "Zimek et al. [2] and their corresponding survey paper [3].\n", "\n", "##### Glossary\n", "- $\\mathbf{D}^{dims}$ : distances between samples from the data distribution and samples from the query distribution, both have dimensionality _dims_ (vector)\n", "- $D_{max} = \\max_i \\mathbf{D}^{dims}_i$ : maximum distance in the set of all distances (scalar, similar with $D_{min}$)\n", "- $\\mathbf{X}^{dims}$ : sample from the data distribution having dimensionality _dims_\n", "- $P$ : is a probability\n", "- $\\mathbf{E[X]}$ : is the expectation of X over some probability\n", "- $var(X)$: equal to $\\mathbf{E}[(X-\\mathbf{E}[X])^2] = \\mathbf{E}[X^2] - \\mathbf{E}[X]^2$\n", "\n", "##### References\n", "[1] K. Beyer, J. Goldstein, R. Ramakrishnan, and U. Shaft, “When Is ‘Nearest Neighbor’ Meaningful?,” in International conference on database theory (ICDT), 1999, pp. 217–235.\n", "\n", "[2] A. Zimek, E. Schubert, and H.-P. Kriegel, “A survey on unsupervised outlier detection in high-dimensional numerical data,” Statistical Analysis and Data Mining, vol. 5, no. 5. pp. 363–387, 2012.\n", "\n", "[3] A. Zimek, E. Schubert, and H.-P. Kriegel, “Tutorial on Outlier Detection for High-dimensional Data,” 2013.\n", "\n", "##### Side note\n", "Normalization of distances is done by multiplying $1/\\sqrt{dims}$ (for $\\ell^p$-norms $1/\\sqrt[p]{dims}$) since the maximum distance in the unit cube is $\\sqrt{dims} = \\sqrt{\\sum_d 1^2}$ for euclidean metrics." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Empirical verification\n", "\n", "The main insight of Theorem 1 is that as the dimensionality of data sets increases, the distance between \n", "some query point and to its nearest neighbor $D_{min}$ and its furthest neighbor $D_{max}$ will converge to the same value. That means $lim_{dims \\rightarrow \\infty}\\; P\\left( \\frac{D_{max}}{D_{min}} - 1\\leq \\epsilon \\right) = 1$ for any $\\epsilon > 0$.\n", "\n", "Lets check empirically in a standard (toy) setting if that is correct. Therefore, we are generating some Gaussian random data points (various cluster with random means and variance) with increasing dimensionality and uniformly distributed query points. Further, we will check for 3 distinct $\\ell^p$-norm induced distance metrics (manhattan, euclidean and max norm)." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "DIMS = np.logspace(0, 4, 17, dtype=np.int)\n", "REPS = 3\n", "CLUSTER = 3\n", "res = np.zeros((REPS, len(DIMS), 3))\n", "extremas = np.zeros((REPS, len(DIMS), 3))\n", "for r in range(REPS):\n", " for d in range(len(DIMS)):\n", " X_data = np.empty((DIMS[d], 0))\n", " for c in range(CLUSTER):\n", " rndm_var = np.random.rand()\n", " rndm_mean = 10.*np.random.randn()\n", " X_data = np.append(X_data, rndm_var*np.random.randn(DIMS[d], np.int(1000/CLUSTER)) + rndm_mean, axis=1)\n", " X_query = (np.random.rand(DIMS[d], 10)-0.5) * 2. * 2 * np.max(np.abs(X_data))\n", " # ok, lets measure the distance gap for various metrics\n", " D = dist.cdist(X_data.T, X_query.T, metric='minkowski', p=1)\n", " res[r, d, 0] = np.max(D)/np.min(D)\n", " D = dist.cdist(X_data.T, X_query.T, metric='minkowski', p=2)\n", " res[r, d, 1] = np.max(D)/np.min(D)\n", " # ..and store the euclidean min, max, mean values\n", " extremas[r, d, 0] = np.min(D) * 1./np.sqrt(DIMS[d])\n", " extremas[r, d, 1] = np.max(D) * 1./np.sqrt(DIMS[d])\n", " extremas[r, d, 2] = np.mean(D) * 1./np.sqrt(DIMS[d])\n", " D = dist.cdist(X_data.T, X_query.T, metric='chebyshev')\n", " res[r, d, 2] = np.max(D)/np.min(D)\n", "\n", "plt.figure(figsize=(16, 6), dpi= 80, facecolor='w', edgecolor='k')\n", "\n", "plt.subplot(1, 2, 1)\n", "plt.errorbar(DIMS, np.mean(res[:,:,0], axis=0), np.std(res[:,:,0], axis=0), alpha=0.5, linewidth=4, elinewidth=1)\n", "plt.errorbar(DIMS, np.mean(res[:,:,1], axis=0), np.std(res[:,:,1], axis=0), alpha=0.5, linewidth=4, elinewidth=1)\n", "plt.errorbar(DIMS, np.mean(res[:,:,2], axis=0), np.std(res[:,:,2], axis=0), alpha=0.5, linewidth=4, elinewidth=1)\n", "plt.loglog()\n", "plt.grid()\n", "plt.xticks([1, 10, 100, 1000, 10000], [1, 10, 100, 1000, 10000], fontsize=14)\n", "plt.yticks([0.1, 1, 10, 100, 1000, 10000, 100000], [0.1, 1, 10, 100, 1000, 10000, 100000], fontsize=14)\n", "plt.xlabel('Number of dimensions', fontsize=18)\n", "plt.ylabel('Distance gap $\\epsilon + 1$', fontsize=18)\n", "plt.legend(['$\\ell^1$', '$\\ell^2$', '$\\ell^\\infty$'], fontsize=16)\n", "\n", "plt.subplot(1, 2, 2)\n", "plt.errorbar(DIMS, np.mean(extremas[:,:,0], axis=0), np.std(extremas[:,:,0], axis=0), fmt='--g', alpha=0.5, linewidth=2, elinewidth=1)\n", "plt.errorbar(DIMS, np.mean(extremas[:,:,1], axis=0), np.std(extremas[:,:,1], axis=0), fmt='--b', alpha=0.5, linewidth=2, elinewidth=1)\n", "plt.errorbar(DIMS, np.mean(extremas[:,:,2], axis=0), np.std(extremas[:,:,2], axis=0), fmt='-r', alpha=0.5, linewidth=4, elinewidth=1)\n", "plt.loglog()\n", "plt.grid()\n", "plt.xticks([1, 10, 100, 1000, 10000], [1, 10, 100, 1000, 10000], fontsize=14)\n", "plt.yticks([0.1, 1, 10, 100], [0.1, 1, 10, 100], fontsize=14)\n", "plt.xlabel('Number of dimensions', fontsize=18)\n", "plt.ylabel('$\\ell^2$-norm induced metric (normalized)', fontsize=18)\n", "plt.legend(['Minimum', 'Maximum', 'Mean'], loc=4, fontsize=16) " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Whether or not distances concentrate depends on the following condition:\n", "\n", "Assume that $lim_{dims \\rightarrow \\infty}\\; var \\left( \\frac{\\Vert \\mathbf{X}^{dims}\\Vert}{\\mathbf{E}[\\Vert\\mathbf{X}^{dims}\\Vert]} \\right) = 0$, distances will concentrate.\n", "\n", "Your task is to verify empirically this condition on a number of datasets. \n", "When does it not hold? " ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def check_condition_empirically(X, n_reps, n_dims):\n", " # TODO\n", " return -1" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "mat_file_list = ['gaussian', 'mnist.mat', 'wbc.mat', 'ionosphere.mat', 'satimage-2.mat']\n", "\n", "n_reps = 10\n", "n_dims = 30\n", "\n", "res = np.zeros((len(mat_file_list), n_dims))\n", "\n", "fig = plt.figure(figsize=(12, 4), dpi=80, facecolor='w', edgecolor='k')\n", "for f in range(len(mat_file_list)):\n", " X = np.random.randn(1000, 100)\n", " if not mat_file_list[f] == 'gaussian':\n", " mat = loadmat('../data/'+mat_file_list[f])\n", " print('File: ', mat_file_list[f])\n", " X = mat['X']\n", " X, _ = standardizer(X, X)\n", " res[f, :] = check_condition_empirically(X, n_reps, n_dims)\n", "\n", " plt.subplot(1, 2, 1)\n", " plt.plot(np.arange(n_dims), res[f, :])\n", "\n", " plt.subplot(1, 2, 2)\n", " plt.plot(np.arange(n_dims), res[f, :])\n", "\n", " mat_file_list[f] += ' ({0})'.format(X.shape[1])\n", " \n", "plt.subplot(1, 2, 1)\n", "plt.legend(mat_file_list, fontsize=14, loc=1)\n", "plt.semilogy()\n", "plt.grid()\n", "\n", "plt.subplot(1, 2, 2)\n", "plt.legend(mat_file_list, fontsize=14, loc=1)\n", "plt.loglog()\n", "plt.grid()\n", "\n", "plt.tight_layout()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.6" } }, "nbformat": 4, "nbformat_minor": 1 }
mit
suresh/notebooks
New-Collab-Notebook.ipynb
1
3247
{ "nbformat": 4, "nbformat_minor": 0, "metadata": { "colab": { "name": "Untitled0.ipynb", "provenance": [], "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/suresh/notebooks/blob/master/New-Collab-Notebook.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>" ] }, { "cell_type": "code", "metadata": { "id": "3lbv4FSacKme", "colab_type": "code", "colab": {} }, "source": [ "import pandas as pd\n", "import numpy as np" ], "execution_count": 0, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "UNfEuIm7cYUt", "colab_type": "code", "colab": { "base_uri": "https://localhost:8080/", "height": 445 }, "outputId": "420c795e-4d07-4fd5-e335-8aa63518fd9a" }, "source": [ "!lscpu" ], "execution_count": 2, "outputs": [ { "output_type": "stream", "text": [ "Architecture: x86_64\n", "CPU op-mode(s): 32-bit, 64-bit\n", "Byte Order: Little Endian\n", "CPU(s): 2\n", "On-line CPU(s) list: 0,1\n", "Thread(s) per core: 2\n", "Core(s) per socket: 1\n", "Socket(s): 1\n", "NUMA node(s): 1\n", "Vendor ID: GenuineIntel\n", "CPU family: 6\n", "Model: 79\n", "Model name: Intel(R) Xeon(R) CPU @ 2.20GHz\n", "Stepping: 0\n", "CPU MHz: 2200.000\n", "BogoMIPS: 4400.00\n", "Hypervisor vendor: KVM\n", "Virtualization type: full\n", "L1d cache: 32K\n", "L1i cache: 32K\n", "L2 cache: 256K\n", "L3 cache: 56320K\n", "NUMA node0 CPU(s): 0,1\n", "Flags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ss ht syscall nx pdpe1gb rdtscp lm constant_tsc rep_good nopl xtopology nonstop_tsc cpuid tsc_known_freq pni pclmulqdq ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt aes xsave avx f16c rdrand hypervisor lahf_lm abm 3dnowprefetch invpcid_single ssbd ibrs ibpb stibp fsgsbase tsc_adjust bmi1 hle avx2 smep bmi2 erms invpcid rtm rdseed adx smap xsaveopt arat md_clear arch_capabilities\n" ], "name": "stdout" } ] }, { "cell_type": "code", "metadata": { "id": "kfqdHhaUce3M", "colab_type": "code", "colab": {} }, "source": [ "" ], "execution_count": 0, "outputs": [] } ] }
mit
mohitganguly/IRBlock_Vanderbilt
6/make_fig6.ipynb
1
69664
{ "cells": [ { "cell_type": "code", "execution_count": 4, "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" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "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": [ "%matplotlib inline \n", "\n", "#from __future__ import division\n", "\n", "import numpy as np\n", "import matplotlib\n", "import matplotlib.pyplot as plt\n", "\n", "\n", "\n", "def read_large_txt(path, delimiter=None, dtype=None):\n", " with open(path) as f:\n", " nrows = sum(1 for line in f)\n", " f.seek(0)\n", " ncols = len(f.next().split(delimiter))\n", " out = np.empty((nrows, ncols), dtype=dtype)\n", " f.seek(0)\n", " for i, line in enumerate(f):\n", " out[i] = line.split(delimiter)\n", " return out\n", " \n", "#x = np.linspace(0, 100, 4998)\n", " \n", "v500_noblock = np.genfromtxt('/home/jovyan/6/v_500_noblock-2.txt', delimiter = ',')\n", "v500_block = np.genfromtxt('/home/jovyan/6/v_500_block-2.txt', delimiter = ',')\n", "\n", "v10_noblock = np.genfromtxt('/home/jovyan/6/v_10_noblock-2.txt', delimiter = ',')\n", "v10_block = np.genfromtxt('/home/jovyan/6/v_10_block-2.txt', delimiter = ',')\n", "\n", "x_500 = np.genfromtxt('/home/jovyan/6/x_500_block.txt', delimiter = ',')\n", "x_10 = np.genfromtxt('/home/jovyan/6/x_10_block.txt', delimiter = ',')\n", "n500 = 25458\n", "n10 = 27293\n", "\n", "plt.plot(x_500/1000, v500_noblock, lw = 3)\n", "plt.plot(x_500/1000, v500_block, lw = 3, linestyle = '--')\n", "plt.xlim(0, 100)\n", "#lt.savefig('6a.png', dpi = 600, bbox_inches = 'tight')\n", "\n", "plt.show()\n", "\n", "plt.plot(x_10/1000, v10_noblock, lw = 3)\n", "plt.plot(x_10/1000, v10_block, lw = 3, linestyle = '--')\n", "plt.xlim(0, 100)\n", "#plt.savefig('6b.png', dpi = 600, bbox_inches = 'tight')\n", "\n", "plt.show()\n", "\n", "#x1 = np.linspace(0, 5000/193, 4998)\n", "x_500_half = x_500/3960\n", "plt.plot(x_500_half, v500_noblock, lw = 3)\n", "plt.plot(x_500_half, v500_block, lw = 3, linestyle = '--')\n", "plt.xlim(0, 25)\n", "#plt.savefig('6c.png', dpi = 600, bbox_inches = 'tight')\n", "plt.show()\n", "\n", "x_10_half = x_10/560\n", "\n", "plt.plot(x_10_half, v10_noblock, lw = 3)\n", "plt.plot(x_10_half, v10_block, lw = 3, linestyle = '--')\n", "plt.xlim(77, 102)\n", "#plt.savefig('6d.png', dpi = 600, bbox_inches = 'tight')\n", "plt.show()\n" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'/home/jovyan/6'" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pwd\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.3" } }, "nbformat": 4, "nbformat_minor": 2 }
gpl-3.0
joaoandre/algorithms
algorithmic-toolbox/week1.ipynb
1
2009
{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Maximum pairwise product\n", "\"\"\"\n", "Problem\n", "\n", "Given a sequence of non-negative integers a0,…,an−1, find the maximum pairwise product, that is,\n", "the largest integer that can be obtained by multiplying two different elements from the sequence \n", "(or, more formally, max0≤i≠j≤n−1aiaj). \n", "Different elements here mean ai and aj with i≠j (it can be the case that ai=aj).\n", "\n", "Input format\n", "\n", "The first line of the input contains an integer n. \n", "The next line contains n non-negative integers a0,…,an−1 (separated by spaces).\n", "\n", "Constraints\n", "\n", "2≤n≤2⋅105; 0≤a0,…,an−1≤105.\n", "\n", "Output format\n", "\n", "Output a single number — the maximum pairwise product.\n", "\n", "\n", "\"\"\"\n", "import heapq\n", "\n", "def maximum_pairwise_product(l):\n", " max_1, max_2 = heapq.nlargest(2, l)\n", " return max_1 * max_2\n" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "196" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "l = [1, 2, 3, 10, 14, 14, 14, 7, 6, 5]\n", "maximum_pairwise_product(l)" ] } ], "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": 1 }
mit
sdss/marvin
docs/sphinx/jupyter/first-steps.ipynb
2
121256
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# First Steps\n", "\n", "Now that you have installed Marvin, it's time to take your first steps. If you want to learn more about how Marvin works, then go see [General Info](https://api.sdss.org/doc/manga/marvin/general.html) to learn about Marvin Modes, Versions, or Downloading. If you just want to play, then read on.\n", "\n", "First let's run some boilerplate code for Python 2/3 compatibility and plotting in the notebook:" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [], "source": [ "from __future__ import print_function, division, absolute_import\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now, let’s import Marvin:" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "INFO: No release version set. Setting default to MPL-6\n" ] } ], "source": [ "import marvin" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's see what release we're using. Releases can be either MPLs (e.g. MPL-5) or DRs (e.g. DR13), however DRs are currently disabled in Marvin." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "'MPL-6'" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "marvin.config.release" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "On intial import, Marvin will set the default data release to use the latest MPL available, currently MPL-6. You can change the version of MaNGA data using the Marvin [Config](https://api.sdss.org/doc/manga/marvin/api/general.html#marvin-config-class).\n", "\n" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "('MPL:', 'MPL-5')\n" ] } ], "source": [ "from marvin import config\n", "config.setRelease('MPL-5')\n", "\n", "print('MPL:', config.release)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "But let's work with MPL-6:" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "'MPL-6'" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "config.setRelease('MPL-6')\n", "\n", "# check designated version\n", "config.release" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# My First Cube\n", "Now let’s play with a Marvin Cube!\n", "\n", "Import the Marvin-Tools Cube class:" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from marvin.tools.cube import Cube" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's load a cube from a local file. Start by specifying the full path and name of the file, such as:\n", "\n", "`/Users/Brian/Work/Manga/redux/v2_3_1/8485/stack/manga-8485-1901-LOGCUBE.fits.gz`\n", "\n", "**EDIT Next Cell**" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#----- EDIT THIS CELL -----#\n", "\n", "# filename = '/Users/Brian/Work/Manga/redux/v1_5_1/8485/stack/manga-8485-1901-LOGCUBE.fits.gz'\n", "filename = 'path/to/manga/cube/manga-8485-1901-LOGCUBE.fits.gz'\n", "\n", "filename = '/Users/andrews/manga/spectro/redux/v2_3_1/8485/stack/manga-8485-1901-LOGCUBE.fits.gz'\n", "filename = '/Users/Brian/Work/Manga/redux/v2_3_1/8485/stack/manga-8485-1901-LOGCUBE.fits.gz'" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Create a Cube object:" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [], "source": [ "cc = Cube(filename=filename)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we have a Cube object:" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "<Marvin Cube (plateifu='8485-1901', mode='local', data_origin='file')>\n" ] } ], "source": [ "print(cc)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "How about we look at some meta-data" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(232.544703894, 48.6902009334, 'MaNGA dither')" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cc.ra, cc.dec, cc.header['SRVYMODE']" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "...and the quality and target bits" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[<Maskbit 'MANGA_TARGET1' ['SECONDARY_v1_1_0', 'SECONDARY_COM2', 'SECONDARY_v1_2_0']>,\n", " <Maskbit 'MANGA_TARGET2' []>,\n", " <Maskbit 'MANGA_TARGET3' []>]" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cc.target_flags" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<Maskbit 'MANGA_DRP3QUAL' []>" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cc.quality_flag" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Get a Spaxel\n", "Cubes have several functions currently available: getSpaxel, getMaps, getAperture. Let's look at spaxels. We can retrieve spaxels from a cube easily via indexing. In this manner, spaxels are 0-indexed from the lower left corner. Let's get spaxel (x=10, y=10):" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": true }, "outputs": [], "source": [ "spax = cc[10,10]" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<Marvin Spaxel (plateifu=8485-1901, x=10, y=10; x_cen=-7, y_cen=-7, loaded=cube/maps)>" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# print the spaxel to see the x,y coord from the lower left, and the coords relative to the cube center, x_cen/y_cen\n", "spax" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Spaxels have a spectrum associated with it. It has the wavelengths and fluxes of each spectral channel:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Alternatively grab a spaxel with getSpaxel. Use the xyorig keyword to set the coordinate origin point: 'lower' or 'center'. The default is \"center\"" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<Marvin Spaxel (plateifu=8485-1901, x=17, y=17; x_cen=0, y_cen=0, loaded=cube/maps)>" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# let's grab the central spaxel\n", "spax = cc.getSpaxel(x=0, y=0)\n", "spax" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/latex": [ "$[3621.596,~3622.43,~3623.2642,~\\dots, 10349.038,~10351.422,~10353.805] \\; \\mathrm{\\mathring{A}}$" ], "text/plain": [ "<Quantity [ 3621.59598486, 3622.42998417, 3623.26417553,...,\n", " 10349.03843826, 10351.42166679, 10353.80544415] Angstrom>" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "spax.flux.wavelength" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/latex": [ "$[0.54676276,~0.46566465,~0.4622981,~\\dots, 0,~0,~0] \\; \\mathrm{1 \\times 10^{-17}\\,\\frac{erg}{\\mathring{A}\\,s\\,spaxel\\,cm^{2}}}$" ], "text/plain": [ "<Spectrum [ 0.54676276, 0.46566465, 0.4622981 ,..., 0. ,\n", " 0. , 0. ] 1e-17 erg / (Angstrom cm2 s spaxel)>" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "spax.flux" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "### Plot the spectrum!" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# turn on interactive plotting\n", "%matplotlib notebook" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": false, "scrolled": false }, "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", " this.ws.close();\n", " }\n", "\n", " this.ws.onmessage = this._make_on_message_function(this);\n", "\n", " this.ondownload = ondownload;\n", "}\n", "\n", "mpl.figure.prototype._init_header = function() {\n", " var titlebar = $(\n", " '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n", " 'ui-helper-clearfix\"/>');\n", " var titletext = $(\n", " '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n", " 'text-align: center; padding: 3px;\"/>');\n", " titlebar.append(titletext)\n", " this.root.append(titlebar);\n", " this.header = titletext[0];\n", "}\n", "\n", "\n", "\n", "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n", "\n", "}\n", "\n", "\n", "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n", "\n", "}\n", "\n", "mpl.figure.prototype._init_canvas = function() {\n", " var fig = this;\n", "\n", " var canvas_div = $('<div/>');\n", "\n", " canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n", "\n", " function canvas_keyboard_event(event) {\n", " return fig.key_event(event, event['data']);\n", " }\n", "\n", " canvas_div.keydown('key_press', canvas_keyboard_event);\n", " canvas_div.keyup('key_release', canvas_keyboard_event);\n", " this.canvas_div = canvas_div\n", " this._canvas_extra_style(canvas_div)\n", " this.root.append(canvas_div);\n", "\n", " var canvas = $('<canvas/>');\n", " canvas.addClass('mpl-canvas');\n", " canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n", "\n", " this.canvas = canvas[0];\n", " this.context = canvas[0].getContext(\"2d\");\n", "\n", " var 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, fig.canvas.height);\n", "\n", " fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n", "}\n", "\n", "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n", " // Updates the figure title.\n", " fig.header.textContent = msg['label'];\n", "}\n", "\n", "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n", " var cursor = msg['cursor'];\n", " switch(cursor)\n", " {\n", " case 0:\n", " cursor = 'pointer';\n", " break;\n", " case 1:\n", " cursor = 'default';\n", " break;\n", " case 2:\n", " cursor = 'crosshair';\n", " break;\n", " case 3:\n", " cursor = 'move';\n", " break;\n", " }\n", " fig.rubberband_canvas.style.cursor = cursor;\n", "}\n", "\n", "mpl.figure.prototype.handle_message = function(fig, msg) {\n", " fig.message.textContent = msg['message'];\n", "}\n", "\n", "mpl.figure.prototype.handle_draw = function(fig, msg) {\n", " // Request the server to send over a new figure.\n", " fig.send_draw_message();\n", "}\n", "\n", "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n", " fig.image_mode = msg['mode'];\n", "}\n", "\n", "mpl.figure.prototype.updated_canvas_event = function() {\n", " // Called whenever the canvas gets updated.\n", " this.send_message(\"ack\", {});\n", "}\n", "\n", "// A function to construct a web socket function for onmessage handling.\n", "// Called in the figure constructor.\n", "mpl.figure.prototype._make_on_message_function = function(fig) {\n", " return function socket_on_message(evt) {\n", " if (evt.data instanceof Blob) {\n", " /* FIXME: We get \"Resource interpreted as Image but\n", " * transferred with MIME type text/plain:\" errors on\n", " * Chrome. But how to set the MIME type? It doesn't seem\n", " * to be part of the websocket stream */\n", " evt.data.type = \"image/png\";\n", "\n", " /* Free the memory for the previous frames */\n", " if (fig.imageObj.src) {\n", " (window.URL || window.webkitURL).revokeObjectURL(\n", " fig.imageObj.src);\n", " }\n", "\n", " fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n", " evt.data);\n", " fig.updated_canvas_event();\n", " fig.waiting = false;\n", " return;\n", " }\n", " else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n", " fig.imageObj.src = evt.data;\n", " fig.updated_canvas_event();\n", " fig.waiting = false;\n", " return;\n", " }\n", "\n", " var msg = JSON.parse(evt.data);\n", " var msg_type = msg['type'];\n", "\n", " // Call the \"handle_{type}\" callback, which takes\n", " // the figure and JSON message as its only arguments.\n", " try {\n", " var callback = fig[\"handle_\" + msg_type];\n", " } catch (e) {\n", " console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n", " return;\n", " }\n", "\n", " if (callback) {\n", " try {\n", " // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n", " callback(fig, msg);\n", " } catch (e) {\n", " console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n", " }\n", " }\n", " };\n", "}\n", "\n", "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n", "mpl.findpos = function(e) {\n", " //this section is from http://www.quirksmode.org/js/events_properties.html\n", " var targ;\n", " if (!e)\n", " e = window.event;\n", " if (e.target)\n", " targ = e.target;\n", " else if (e.srcElement)\n", " targ = e.srcElement;\n", " if (targ.nodeType == 3) // defeat Safari bug\n", " targ = targ.parentNode;\n", "\n", " // jQuery normalizes the pageX and pageY\n", " // pageX,Y are the mouse positions relative to the document\n", " // offset() returns the position of the element relative to the document\n", " var x = e.pageX - $(targ).offset().left;\n", " var y = e.pageY - $(targ).offset().top;\n", "\n", " return {\"x\": x, \"y\": y};\n", "};\n", "\n", "/*\n", " * return a copy of an object with only non-object keys\n", " * we need this to avoid circular references\n", " * http://stackoverflow.com/a/24161582/3208463\n", " */\n", "function simpleKeys (original) {\n", " return Object.keys(original).reduce(function (obj, key) {\n", " if (typeof original[key] !== 'object')\n", " obj[key] = original[key]\n", " return obj;\n", " }, {});\n", "}\n", "\n", "mpl.figure.prototype.mouse_event = function(event, name) {\n", " var canvas_pos = mpl.findpos(event)\n", "\n", " if (name === 'button_press')\n", " {\n", " this.canvas.focus();\n", " this.canvas_div.focus();\n", " }\n", "\n", " var x = canvas_pos.x * 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 overriden (by mpl) onmessage function.\n", " ws.onmessage(msg['content']['data'])\n", " });\n", " return ws;\n", "}\n", "\n", "mpl.mpl_figure_comm = function(comm, msg) {\n", " // This is the function which gets called when the mpl process\n", " // starts-up an IPython Comm through the \"matplotlib\" channel.\n", "\n", " var id = msg.content.data.id;\n", " // Get hold of the div created by the display call when the Comm\n", " // socket was opened in Python.\n", " var element = $(\"#\" + id);\n", " var ws_proxy = comm_websocket_adapter(comm)\n", "\n", " function ondownload(figure, format) {\n", " window.open(figure.imageObj.src);\n", " }\n", "\n", " var fig = new mpl.figure(id, ws_proxy,\n", " ondownload,\n", " element.get(0));\n", "\n", " // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n", " // web socket which is closed, not our websocket->open comm proxy.\n", " ws_proxy.onopen();\n", "\n", " fig.parent_element = element.get(0);\n", " fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n", " if (!fig.cell_info) {\n", " console.error(\"Failed to find cell for figure\", id, fig);\n", " return;\n", " }\n", "\n", " var output_index = fig.cell_info[2]\n", " var cell = fig.cell_info[0];\n", "\n", "};\n", "\n", "mpl.figure.prototype.handle_close = function(fig, msg) {\n", " 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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1bK07O2IfYUfso0Y73tgeDthzNQUAMKKLLVYGu6PfFyd42XdcSi4v+9Gl7KJS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\ndnXAqwPaax9kHbTKPgYGBjA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"text/plain": [ "<matplotlib.figure.Figure at 0x107433750>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# To save the plot, we need to draw it in the same cell as the save command.\n", "spax.flux.plot()\n", "\n", "import os\n", "plt.savefig(os.getenv('HOME') + '/Downloads/my-first-spectrum.png')\n", "\n", "# NOTE - if you are using the latest version of iPython and Jupyter notebooks, then interactive matplotlib plots \n", "# should be enabled. You can save the figure with the save icon in the interactive toolbar." ] }, { "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": 0 }
bsd-3-clause
QuantEcon/econometrics
Notebook_01_wald/statistical_decision_functions_text.ipynb
1
47255
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Estimators as Statistical Decision Functions\n", "\n", "\n", "**February 2017**\n", "\n", "\n", "In this notebook we discuss some key concepts of statistical decision theory in order to provide a general framework for the comparison of alternative estimators based on their finite sample performance. \n", " \n", " * The primitive object is a statistical decision problem containing a loss function, an action space, and a set of assumed statistical models. We present estimation problems familiar from econometrics as special cases of statistical decision problems. The common framework helps highlighting similarities and differences.\n", " \n", " * We compare estimators based on their (finite sample) risk, where risk is derived from an unknown true data generating mechanism.\n", " \n", " * We present some straightforward examples to illustrate the main ideas.\n", " \n", "------------------------------------------------------" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Notation\n", "\n", "Let $\\mathbb{R}$ and $\\mathbb{Z}$ denote the space of reals and integers, respectively. $\\mathbb{R}_+$ is the space of nonnegative real numbers. We use the notation $X^Y$ for a function space that consists of functions mapping from the space $Y$ into the space $X$. As a result, $\\mathbb{R}^{d\\mathbb{Z}}$ denotes the space of sequences made up of $d$-dimensional real numbers, while $\\mathbb R^{Z}_+$ denotes the set of functions mapping from the range of (random) variable $Z$ to the space of nonnegative reals. \n", "\n", "Let $Z_t$ be a $d$-dimensional random vector representing the value that the $d$ observables take at period $t$. The stochastic process $\\{Z_t\\}_{t\\in\\mathbb Z}$ is denoted by $Z^{\\infty}$, the partial history including $n$ consecutive elements of $Z^{\\infty}$ is $Z^{n}:=\\{Z_1, Z_2, \\dots, Z_n\\}$. Small letters stand for realizations of random variables, hence $z^{\\infty}$, $z^n$ and $z$ represent the realization of the stochastic process, the sample and a single observation, respectively. \n", "\n", "We use capital letters for distributions, small letter counterparts denote the associated densities. For example, we use the generic $Q$ notation for ergodic distributions, $q$ for the corresponding density and $q(\\cdot|\\cdot)$ for the conditional density. Caligraphic letters are used to denote sets:\n", "\n", "* $\\mathcal{P}$ -- the set of *strictly stationary* probability distributions over the observables\n", "* $\\mathcal{Q}\\subset \\mathcal{P}$ -- the set of *ergodic* distributions (statistical models) over the observables\n", "* $\\mathcal{F}$ -- *admissible space*: abstract function space including all functions for which the loss function is well defined\n", "\n", "------------------------------------------------------" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Introduction\n", "\n", "### Stationarity and statistical models \n", "\n", "We model data as a partial realization of a stochastic process $Z^{\\infty}$ taking values in $\\mathbb{R}^{d}$. Denote a particular realization as $z^{\\infty} \\in \\mathbb{R}^{k\\mathbb{Z}}$ and let the partial history $z^{n}$ containing $n$ consecutive elements of the realization be the *sample* of size $n$. We assume that there exists a core mechanism undelying this process that describes the relationship among the elements of the vector $Z$. Our aim is to draw inference about this mechanism after observing a single partial realization $z^{n}$. \n", "\n", "How is this possible without being able to draw different samples under the exact same conditions? Following the exposition of [Breiman (1969)](#breiman1969) a fruitful approach is to assume that the underlying mechanism is time invariant with the stochastic process being strictly stationary and study its statistical properties by taking long-run time averages of the realization $z^{\\infty}$ (or functions thereof), e.g.\n", "\n", "$$\\lim_{n\\to \\infty}\\frac{1}{n}\\sum_{t = 1}^{n} z_t\\quad\\quad \\lim_{n\\to \\infty}\\frac{1}{n} \\sum_{t = 1}^{n} z^2_t\\quad\\quad \\lim_{n\\to \\infty}\\frac{1}{n}\\sum_{t = k}^{n+k} z_{t}z_{t-k}$$\n", "\n", "Since the mechanism is assumed to be stable over time, it does not matter when we start observing the process.\n", "\n", "Notice, however, that strictly speaking these time averages are properties of the particular realization, the extent to which they can be generalized to the mechanism itself is not obvious. To address this question, it is illuminating to bundle realizations that share certain statistical properties together in order to construct a universe of (counterfactual) alternative $z^{\\infty}$-s, the so called *ensemble*. Statistical properties of the data generating mechanism can be summarized by assigning probabilities to (sets of) these $z^{\\infty}$-s in an internally consistent manner. These considerations lead to the idea of statistical models. \n", "\n", "**Statistical models** are probability distributions over sequences $z^{\\infty}$ that assign probabilities so that the unconditional moments are consistent with the associated long-run time averages. In other words, with statistical models the time series and ensemble averages coincide, which is the property known as **ergodicity**. Roughly speaking, ergodicity allows us to learn about the ensemble dimension by using a *single* realization $z^{\\infty}$." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Dependence\n", "\n", "In reality, being endowed only with a partial history of $z^{\\infty}$, we cannot calculate the exact log-run time averages. By imposing more structure on the problem and having a sufficiently large sample, however, we can obtain reasonable approximations. To this end, we need to assume some form of weak independence (\"mixing\"), or more precisely, the property that on average, the dependence between the elements of $\\{Z_t\\}_{t\\in\\mathbb{Z}}$ dies out as we increase the gap between them. \n", "\n", "Consequently, if we observe a *long* segment of $z^{\\infty}$ and cut it up into shorter consecutive pieces, say of length $l$, then, we might consider these pieces (provided that $l$ is \"large enough\") as nearly independent records from the distribution of the $l$-block, $Z^l$. To clarify this point, consider a statistical model $Q_{Z^{\\infty}}$ (joint distribution over sequences $z^{\\infty}$) with density function $q_{z^{\\infty}}$ and denote the implied density of the sample as $q_{n}$. Note that because of strict stationarity, it is enough to use the number of consecutive elements as indices. Under general regularity conditions we can decompose this density as\n", "\n", "$$q_{n}\\left(z^n\\right) = q_{n-1}\\left(z_n | z^{n-1}\\right)q_{n-1}\\left(z^{n-1}\\right) = q_{n-1}\\left(z_n | z^{n-1}\\right)q_{n-2}\\left(z_{n-1}|z^{n-2}\\right)\\dots q_{1}\\left(z_{2}|z_1\\right)q_{1}\\left(z_1\\right)$$\n", "\n", "For simplicity, we assume that the stochastic process is Markov so that the partial histories $z^{i}$ for $i=1,\\dots, n-1$ in the conditioning sets can be replaced by the \"right\" number of lags $z^{n-1}_{n-l}$ and we can drop the subindex from the conditional densties\n", "\n", "$$q_{n}(z^n) = q(z_n | z^{n-1}_{n-1-l})q(z_{n-1}|z^{n-2}_{n-2-l})\\dots q(z_{l+1}|z_{1}^{l})q_{l}(z^l) \\quad\\quad\\quad (1)$$\n", "\n", "This assumption is much stronger than what we really need. First, it suffices to require the existence of a history-dependent latent state variable similar to the Kalman filter. Moreover, we could also relax the Markov assumption and allow for dependence that dies out only asymptotically. In practice, however, we often have a stong view about the dependency structure, or at least we are willing to use economic theory to guide our choice of $l$. In these cases we almost always assume a Markovian structure. For simplicity, in these lectures, unless otherwise stated, we will restrict ourselves to the family of Markov processes. \n", "\n", "This assumption allows us to learn about the underlying mechanism $Q_{Z^{\\infty}}$ via its $l+1$-period building blocks. Once we determine the (ensemble) distribution of the block, $Q_{Z^{[l+1]}}$, we can \"build up\" $Q_{Z^{\\infty}}$ from these blocks by using a formula similar to (1). Having said that the block distribution $Q_{Z^{[l+1]}}$ carries the same information as $Q_{Z^{\\infty}}$. Therefore, from now on, we define $Z$ as the minimal block we need to know and treat it as an **observation**. Statistical models can be represented by their predictions about the ensemble distribution $P$ of this observable. \n", "\n", "### True data generating process\n", "\n", "We assume that the mechanism underlying $Z^{\\infty}$ can be represented with a statistical model $P$ and it is called **true data generating process (DGP)**. We seek to learn about the features of this model from the observed data.\n", "\n", "-----------------------------------------" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Primitives of the problem\n", "\n", "Following [Wald (1950)](#wald1950) every statistical decision problem that we will consider can be represented with a triple $(\\mathcal{H}, \\mathcal{A}, L)$, where\n", "\n", "1. **Assumed statistical models**, $\\mathcal{H}\\subseteq \\mathcal{Q} \\subset \\mathcal{P}$\n", "\n", " $\\mathcal{H}$ is a collection of ergodic probability measures over the observed data, which captures our *maintained assumptions* about the mechanism underlying $Z^{\\infty}$. The set of all ergodic distributions $\\mathcal{Q}$ is a strict subset of $\\mathcal{P}$--the space of strictly stationary probability distributions over the observed data. In fact, the set of ergodic distributions, $\\mathcal{Q}$, constitute the extremum points of the set $mathcal{P}$. Ergodicity implies that with infinite data we could single out one element from $\\mathcal{H}$.\n", "\n", "2. **Action space**, $\\mathcal{A}\\subseteq \\mathcal{F}$\n", "\n", " The set of allowable actions. It is an abstract set embodying our proposed *specification* by which we aim to capture features of the true data generating mechanism. It is a subset of $\\mathcal{F}$--the largest possible set of functions for which the loss function (see below) is well defined. \n", "\n", "3. **Loss function** $L: \\mathcal{P}\\times \\mathcal{F} \\mapsto \\mathbb{R}_+$\n", "\n", " The loss function measures the performance of alternative actions $a\\in \\mathcal{F}$ under a given distribution $P\\in \\mathcal{P}$. In principle, $L$ measures the distance between distributions in $\\mathcal{P}$ along particular dimensions determined by features of the data generating mechanism that we are interested in. By assigning zero distance to models that share a particular set of features (e.g. conditional expectation, set of moments, etc.), the loss function can 'determine' the relevant features of the problem. \n", "\n", "Given the assumed statistical models, we can restrict the domain of the loss function without loss in generality such that, $L: \\mathcal{H}\\times\\mathcal{A}\\mapsto\\mathbb{R}_+$.\n", "\n", "-----------------------------------\n", "\n", "### Examples\n", "\n", "**Quadratic loss:** \n", "\n", "The most commonly used loss function is the quadratic \n", "\n", "$$L(P, a) = \\int \\lVert z - a \\rVert^2\\mathrm{d}P(z)$$\n", "\n", "where the admissible space is $\\mathcal{F}\\subseteq \\mathbb{R}^{k}$. Another important case is when we can write $Z = (Y, X)$, where $Y$ is univariate and the loss function is \n", "\n", "$$L(P, a) = \\int (y - a(x))^2\\mathrm{d}P(y, z)$$\n", "\n", "and the admissible space $\\mathcal{F}$ contains all square integrable real functions of $X$. \n", "\n", "**Relative entropy loss:**\n", "\n", "When we specificy a whole distribution and are willing to approximate $P$, one useful measure for comparison of distributions is the Kullback-Leibler divergence, or relative entropy\n", "\n", "$$L(P, a) = - \\int \\log \\frac{p}{a}(z) \\mathrm{d}P(z)$$\n", "\n", "in which case the admissible space is the set of distributions which have a density (w.r.t. the Lebesgue measure) $\\mathcal{F} = \\{a: Z \\mapsto \\mathbb{R}_+ : \\int a(z)\\mathrm{d}z=1\\}$.\n", "\n", "**Generalized Method of Moments:**\n", "\n", "Following the exposition of [Manski (1994)](#manski1994), many econometric problems can be cast as solving the equation $T(P, \\theta) = \\mathbf{0}$ in the parameter $\\theta$, for a given function $T: \\mathcal{P}\\times\\Theta \\mapsto \\mathbb{R}^m$ with $\\Theta$ being the parameter space. By expressing estimation problems in terms of unconditional moment restrictions, for example, we can write $T(P, \\theta) = \\int g(z; \\theta)\\mathrm{d}P(z) = \\mathbf{0}$ for some function $g$. Taking an *origin-preserving continuous transformation* $r:\\mathbb{R}^m \\mapsto \\mathbb{R}_+$ so that\n", "\n", "$$T(P, \\theta) = \\mathbf{0} \\iff r(T)=0$$ \n", "\n", "we can present the problem in terms of minimizing a particular loss function. Define the admissible space as $\\mathcal{F} = \\Theta$, then the method of moment estimator minimizes the loss $L(P, \\theta) = r\\circ T(P, \\theta)$. The most common form of $L$ is \n", "\n", "$$L(P, \\theta) = \\left[\\int g(z; \\theta)\\mathrm{d}P(z)\\right]' W \\left[\\int g(z; \\theta)\\mathrm{d}P(z)\\right]$$ \n", "\n", "where $W$ is a $m\\times m$ positive-definite weighting matrix.\n", "\n", "----------------------------------------------\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Features and the best-in-class action\n", "\n", "By using a loss function, we acknowledge that learning about the true mechanism might be too ambitious, so we better focus our attention only on certain features of it and try to approximate those with our specification. The loss function expresses our assessment about the importance of different features and about the penalty used to punish deviations from the true features. We define the **feature functional** $\\gamma: \\mathcal{P}\\mapsto \\mathcal{F}$ by the following optimization over the admissible space $\\mathcal{F}$ \n", "\n", "$$\\gamma(P) := \\arg\\min_{a \\in \\mathcal{F}} \\ L(P,a)$$ \n", "\n", "and say that $\\gamma(P)$ captures the features of $P$ that we wish to learn about. It follows that by changing $L$ we are effectively changing the features of interest. \n", "\n", "If one knew the data generating process, there would be no need for statistical inference. What makes the problem statistical is that the distribution $P$ describing the environment is unknown. The statistician must base her action on the available data, which is a partial realization of the underlying data generating mechanism. As we will see, this lack of information implies that for statistical inference the whole admissible space $\\mathcal F$ is almost always \"too large\". As a result, one typically looks for an approximation in a restricted action space $\\mathcal{A}\\subsetneq \\mathcal{F}$, for which we define the **best-in-class action** as follows\n", "\n", "$$a^*_{L,\\ P,\\ \\mathcal{A}} := \\arg\\min_{a \\in \\mathcal{A}} \\ L(P,a).$$\n", "\n", "Whith a restricted action space, this best-in-class action might differ from the true feature $\\gamma(P)$. We can summarize this scenario compactly by $\\gamma(P)\\notin \\mathcal{A}$ and saying that our specification embodied by $\\mathcal{A}$ is **misspecified**. Naturally, in such cases properties of the loss function become crucial by specifying the nature of punishments used to weight deviations from $\\gamma(P)$. We will talk more about misspecification in the following sections. A couple of examples should help clarifying the introduced concepts.\n", "\n", "* **Conditional expectation -- regression function estimation**\n", "Consider the quadratic loss function over the domain of all square integrable functions $L^2(X, \\mathbb{R})$ and let $Z = (Y, X)$, where $Y$ is a scalar. The corresponding feature is\n", "\n", "$$\\gamma(P) = \\mathbb{E}[Y|X] = \\arg\\min_{a \\in L^2(X)} \\int\\limits_{(Y,X)} (y - a(x))^2\\mathrm{d}P(y, x)$$\n", "\n", "If the action space $\\mathcal{A}$ does not include all square integrable functions, but only the set of affine functions, the best in class action, i.e., the linear projection of $Y$ to the space spanned by $X$, will be different from $\\gamma(P)$ in general. In other words, the linear specification for the conditional expectation $Y|X$ is misspecified.\n", "\n", "\n", "* **Density function estimation** \n", "Consider the Kullback-Leibler distance over the set of distributions with existing density functions. Denote this set by $D_Z$. Given that the true $P\\in D_Z$, the corresponding feature is\n", "\n", "$$\\gamma(P) = \\arg\\min_{a \\in D_Z} \\int\\limits_{Z}\\log\\left(\\frac{p(z)}{a(z)}\\right) \\mathrm{d}P(z)$$\n", "\n", "which provides the density $p\\in\\mathbb{R}_+^Z$ such that $\\int p(z)\\mathrm{d}z =1$ and for any sensible set $B\\subseteq \\mathbb{R}^k$, $\\int_B p(z)\\mathrm{d}z = P(B)$. If the action space $\\mathcal{A}$ is only a parametric subset of $D_Z$, the best in class action will be the best approximation in terms of KLIC. For an extensive treatment see [White (1994)](#white1994).\n", "\n", "### Statistical models vs. specifications\n", "\n", "An important aspect of the statistical decision problem is the relationship between $\\mathcal{H}$ and $\\mathcal{A}$. Our *maintained assumptions* about the mechanism are embodied in $\\mathcal{H}$, so a natural attitude is to be as agnostic as possible about $\\mathcal{H}$ in order to avoid incredible assumptions. Once we determined $\\mathcal{H}$, the next step is to choose the specification, that is the action space $\\mathcal{A}$.\n", "\n", "- One approach is to tie $\\mathcal{H}$ and $\\mathcal{A}$ together. For example, the assumptions of the standard linear regression model outline the distributions contained in $\\mathcal{H}$ (normal with zero mean and homoscedasticity), for which the natural action space is the space of affine functions. \n", " \n", "- On the other hand, many approaches explicitly disentangle $\\mathcal{A}$ from $\\mathcal{H}$ and try to be agnostic about the maintained assumptions $\\mathcal{H}$ and rather impose restrictions on the action space $\\mathcal{A}$. At the cost of giving up some potentially undominated actions this approach can largely influence the success of the inference problem in finite samples. \n", "\n", "By choosing an action space not being tied to the set of assumed statistical models, the statistician inherently introduces a possibility of misspecification -- for some statistical models there could be an action outside of the action space which would fare better than any other action within $\\mathcal{A}$. However, coarsening the action space in this manner has the benefit of restricting the variability of estimated actions arising from the randomness of the sample.\n", "\n", "In this case, the best-in-class action has a special role, namely, it minimizes the \"distance\" between $\\mathcal{A}$ and the true feature $\\gamma(\\mathcal A)$, thus measuring the benchmark bias stemming from restricting $\\mathcal{A}$.\n", "\n", "----------------------------------------------" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Example - Coin tossing\n", "\n", "The observable is a binary variable $Z\\in\\{0, 1\\}$ generated by some statistical model. One might approach this problem by using the following triple\n", "\n", "* *Assumed statistical models*, $\\mathcal{H}$:\n", " * $Z$ is generated by an i.i.d. Bernoulli distribution, i.e. $\\mathcal{H} = \\{P(z; \\theta): \\theta \\in[0,1]\\}$\n", " * The probability mass function associated with the distribution $P(z;\\theta)\\in\\mathcal{H}$ has the form\n", "\n", " $$p(z; \\theta) = \\theta^z(1-\\theta)^{1-z}.$$\n", "\n", "* *Action space*, $\\mathcal{A}$: \n", " * Let the action space be equal to $\\mathcal{H}$, that is $\\mathcal{A} = \\{P(z, a): a\\in[0,1]\\} = \\mathcal{H}$.\n", "\n", "* *Loss function*, $L$: We entertain two alternative loss functions \n", " * Relative entropy\n", "\n", "$$L_{RE}(P, a) = \\sum_{z\\in\\{0,1\\}} p(z; \\theta)\\log \\frac{p(z; \\theta)}{p(z; a)} = E_{\\theta}[\\log p(z; \\theta)] - E_{\\theta}[\\log p(z; a)]$$\n", " \n", " * Quadratic loss\n", "\n", "$$L_{MSE}(P, a) = \\sum_{z\\in\\{0,1\\}} p(z; \\theta)(\\theta - a)^2 = E_{\\theta}[(\\theta - a)^2]$$\n", "\n", "where $E_{\\theta}$ denotes the expectation operator with respect to the distribution $P(z; \\theta)\\in\\mathcal{H}$.\n", "\n", "----------------------------------------------" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Example - Linear regression function\n", "\n", "In the basic setup of regression function estimation we write $Z=(Y,X)\\in\\mathbb{R}^2$ and the objective is to predict the value of $Y$ as a function of $X$ by penalizing the deviations through the quadratic loss function. Let $\\mathcal{F}:= \\{f:X \\mapsto Y\\}$ be the family of square integrable functions mapping from $X$ to $Y$. The following is an example for a triple\n", "\n", "* *Assumed statistical models*, $\\mathcal{H}$\n", " * $(Y,X)$ is generated by an i.i.d. joint Normal distribution, $\\mathcal{N}(\\mu, \\Sigma)$, implying that the true regression function, i.e. conditional expectation, is affine.\n", "\n", "* *Action space*, $\\mathcal{A}$\n", " * The action space is the set of affine functions over $X$, i.e. $\\mathcal{A}:= \\{a \\in \\mathcal{F} : a(x) = \\beta_0 + \\beta_1 x\\}$.\n", "\n", "\n", "* *Loss function*, $L$\n", " * Quadratic loss function\n", "\n", "$$L(P, f) = \\int\\limits_{(Y,X)}(y - f(x))^2\\mathrm{d}P(y,x)$$\n", "\n", "----------------------------------------------" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Statistical Decision Functions\n", "\n", "<!---\n", "The time invariant stochastic relationship between the data and the environment allows the decision maker to carry out statistical inference regarding the data generating process.\n", "--->\n", "\n", "\n", "A **statistical decision function** (or statistical decision rule) is a function mapping samples (of different sizes) to actions from $\\mathcal{A}$. In order to flexibly talk about the behavior of decision rules as the sample size grows to infinity, we define the domain of the decision rule to be the set of samples of all potential sample sizes, $\\mathcal{S}:= \\bigcup_{n\\geq1}Z^n$. The decision rule is then defined as a sequence of functions\n", "\n", "\n", "$$ d:\\mathcal{S} \\mapsto \\mathcal{A} \\quad \\quad \\text{that is} \\quad \\quad \\{d(z^n)\\}_{n\\geq 1}\\subseteq \\mathcal{A},\\quad \\forall z^{n}, \\forall n\\geq 1. $$\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "----------------------------------------------\n", "\n", "### Example (cont) - estimator for coin tossing\n", "\n", "One common way to find a decision rule is to plug the empirical distribution $P_{n}$ into the loss function $L(P, a)$ to obtain \n", "\n", "$$L_{RE}\\left(P_{n}; a\\right) = \\frac{1}{n}\\sum_{i = 1}^{n} \\log \\frac{p(z_i; \\theta)}{p(z_i; a)}\\quad\\quad\\text{and}\\quad\\quad L_{MSE}\\left(P_{n}; a\\right) = \\frac{1}{n}\\sum_{i = 1}^{n} (z_i -a)^2$$ \n", "\n", "and to look for an action that minimizes this sample analog. In case of relative entropy loss, it is\n", "\n", "$$d(z^n) := \\arg \\min_{a} L(P_{n}, a) = \\arg\\max_{a\\in[0,1]} \\frac{1}{n}\\sum_{i=1}^{n} \\log f(z_i ,a) = \\arg\\max_{a\\in[0,1]} \\frac{1}{n}\\underbrace{\\left(\\sum_{i=1}^{n} z_i\\right)}_{:= y}\\log a + \\left(\\frac{n-y}{n}\\right)\\log(1-a) $$\n", "\n", "where we define the random variable $Y_n := \\sum_{i = 1}^{n} Z_i$ as the number of $1$s in the sample of size $n$, with $y$ denoting a particular realization. The solution of the above problem is the *maximum likelihood estimator* taking the following form\n", "\n", "$$\\hat{a}(z^n) = \\frac{1}{n}\\sum_{i=1}^{n} z_i = \\frac{y}{n}$$\n", "\n", "and hence the **maximum likelihood** decision rule is\n", "\n", "$$d_{mle}(z^n) = P(z, \\hat{a}(z^n)).$$\n", "\n", "It is straightforward to see that if we used the quadratic loss instead of relative entropy, the decision rule would be identical to $d_{mle}(z^n)$. Nonetheless, the two loss functions can lead to very different assessment of the decision rule as will be shown below. \n", "\n", "----------------\n", "\n", "For comparison, we consider another decision rule, a particular Bayes estimator (posterior mean), which takes the following form\n", "\n", "$$d_{bayes}(z^n) = P(z, \\hat{a}_B(z^n))\\quad\\quad\\text{where}\\quad\\quad \\hat{a}_B(z^n) = \\frac{\\sum^{n}_{i=1} z_i + \\alpha}{n + \\alpha + \\beta} = \\frac{y + \\alpha}{n + \\alpha + \\beta}$$\n", "\n", "where $\\alpha, \\beta > 0$ are given parameters of the Beta prior. Later, we will see how one can derive such estimators. What is important for us now is that this is an alternative decision rule arising from the same triple $(\\mathcal{H}, \\mathcal{A}, L_{MSE})$ as the maximum likelihood estimator, with possibly different statistical properties.\n", "\n", "----------------------------------------------" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Example (cont) - estimator for linear regression function\n", "\n", "In this case the approach that we used to derive the maximum likelihood estimator in the coin tossing example leads to the following sample analog objective function\n", "\n", "$$ d_{OLS}(z^n):= \\arg\\min_{a \\in \\mathcal{A}}L(P_{n},a) = \\arg\\min_{\\beta_0, \\ \\beta_1} \\sum_{t=1}^n (y_t - \\beta_0 - \\beta_1 x_t)^2. $$\n", "\n", "With a bit of an abuse of notation redefine $X$ to include the constant for the intercept, i.e. $\\mathbf{X} = (\\mathbf{\\iota}, x^n)$. Then the solution for the vector of coefficients, $\\mathbf{\\beta}=(\\beta_0, \\beta_1)$, in the ordinary least squares regression is given by\n", "\n", "$$\\hat{\\mathbf{\\beta}}_{OLS} := (\\mathbf{X}^T \\mathbf{X})^{-1}\\mathbf{X}^T \\mathbf{Y}. $$\n", "\n", "Hence, after sample $z^n$, the decision rule predicts $y$ as an affine function given by $d_{OLS}(z^n) = \\hat{a}_{OLS}$ such that\n", "\n", "$$ \\hat{a}_{OLS}(x) := \\langle \\mathbf{\\hat{\\beta}}_{OLS}, (1, x) \\rangle $$\n", "\n", "where $\\langle \\cdot, \\cdot \\rangle$ denotes the inner product on $\\mathbb R^{2}$.\n", "\n", "----------------\n", "\n", "Again, for comparison we consider a Bayesian decision rule where the conditional prior distribution of $\\beta$ is distributed as $\\beta|\\sigma \\sim \\mathcal{N}(\\mu_b, \\sigma^2\\mathbf{\\Lambda_b}^{-1})$. Then the decision rule is given by\n", "\n", "$$ \\hat{\\mathbf{\\beta}}_{bayes} := (\\mathbf{X}^T \\mathbf{X} + \\mathbf{\\Lambda_b})^{-1}(\\mathbf{\\Lambda_b} \\mu_b + \\mathbf{X}^T \\mathbf{Y}). $$\n", "\n", "Hence, decision rule after sample $z^n$ is an affine function given by $d_{bayes}(z^n) = \\hat{a}_{bayes}$ such that\n", "\n", "\n", "$$ \\hat{a}_{bayes}(x) := \\langle \\mathbf{\\hat{\\beta}}_{bayes}, (1, x) \\rangle. $$\n", "\n", "Again, our only purpose here is to show that we can define alternative decision rules for the same triple $(\\mathcal{H}, \\mathcal{A}, L_{MSE})$ which might exhibit different statistical properties.\n", "\n", "----------------------------------------------" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Induced Distributions over Actions and Losses\n", "\n", "For a given sample $z^n$, the decision rule assigns an action $d(z^n)\\in\\mathcal{A}$, which is then evaluated with the loss function $L(P, d(z^n))$ using a particular distribution $P\\in\\mathcal{H}$. Evaluating the decision rule and the loss function with a single sample, however, does not capture the uncertainty arising from the randomness of the sample. To get that we need to assess the decision rule in counterfactual worlds with different realizations for $Z^n$.\n", "\n", "For each possible data generating mechanism, we can characterize the properties of a given decision rule by considering the distribution that it induces over losses. It is instructive to note that the decision rule $d$ in fact gives rise to \n", "\n", "* **induced action distribution:** distribution induced by $d$ over the action space, $\\mathcal{A}$\n", "* **induced loss distribution:** distribution induced by $d$ over the loss space, i.e. $\\mathbb{R}_+$. \n", "\n", "This approach proves to be useful as the action space can be an abstract space with no immediate notion of metric while the range of the loss function is always the real line (or a subset of it). In other words, a possible way to compare different decision rules is to compare the distributions they induce over losses under different data generating mechanisms for a fixed sample size." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Evaluating Decision Functions\n", "\n", "Comparing distributions, however, is often an ambiguous task. A special case where one could safely claim that one decision rule is better than another is if the probability that the loss is under a certain $x$ level is always greater for one decision rule than the other. For instance, we could say that $d_1$ is a better decision rule than $d_2$ relative to $\\mathcal{H}$ if for all $P\\in\\mathcal{H}$\n", "\n", "$$ P\\{z^n: L(P, d_1(z^n)) \\leq x\\} \\geq P\\{z^n: L(P, d_2(z^n)) \\leq x\\} \\quad \\forall \\ x\\in\\mathbb{R} $$\n", "\n", "which is equivalent to stating that the induced distribution of $d_2$ is *first-order stochastically dominating* the induced distribution of $d_1$ for every $P\\in\\mathcal{H}$. This, of course, implies that \n", "\n", "$$ \\mathbb{E}[L(P, d_1(z^n))] \\leq \\mathbb{E}[L(P, d_2(z^n))]$$\n", "\n", "where the expectation is taken with respect to the sample distributed according to $P$.\n", "\n", "In fact, the expected value of the induced loss is the most common measure to evaluate decision rules. Since the loss is defined over the real line, this measure always gives a single real number which serves as a basis of comparison for a given data generating process. The expected value of the loss induced by a decision rule is called **the risk** of the decision rule and is denoted by\n", "\n", "$$R_n(P, d) = \\mathbb{E}[L(P, d(z^n))].$$\n", "\n", "This functional now provides a clear and straightforward ordering of decision rules so that $d_1$ is preferred to $d_2$ for a given sample size $n$, if $R_n(P, d_1) < R_n\\left(P, d_2\\right)$. Following this logic, it might be tempting to look for the decision rule that is optimal in terms of finite sample risk. This problem, however, is immensly complicated because its criterion function hinges on an object, $P$, that we cannot observe.\n", "\n", "Nonetheless, statistical decision theory provides a very useful common framework in which different approaches to constructing decision rules can be analyzed, highlighting their relative strengths and weaknesses. In notebook3 and notebook4 {REF to notebooks} we will consider three approaches, each of them having alternative ways to handle the ignorance about the true risk.\n", "\n", " 1. **Classical approach:** where the main assessment of a decision rule is based on its asymptotic properties.\n", " 2. **Bayesian approach:** where the ignorance about $P$ is resolved by the use of a prior.\n", " 3. **Statistical learning theory approach:** where a decision rule is judged according to its performance under the least favorable (worst-case) distribution.\n", "\n", "----------------------------------------------" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Example (cont) - induced distributions for coin tossing\n", "\n", "Consider the case when the true data generating process is indeed i.i.d. Bernoulli with parameter $\\theta_0$. This implies that we have a correctly sepcified model. The sample that we are endowed with to use for inference has the size $n=25$.\n", "\n", "* The left panel in the following figure represents the distribution of the sample. More precisely, the different sample realizations $z^n$ have equal probability, but because all information contained in a given sample can be summerized by the sum of $1$s, $Y=\\sum_{t=1}^{n} Z_t$ and $Y$ is a sufficient statistic, we plot the distribution of $Y$ instead. \n", "* The right panel shows the shapes of the two loss functions that we are considering. Notice that while quadratic loss is symmetric, relative entropy loss is asymmetric. That is, although both loss functions give rise to the same decision rule, we see that they punish deviations from the truth (red vertical line) quite differently. In particular, the entropy loss is unbounded over the domain: at $a=0$ and $a=1$ its value is undefined (or takes infinity). \n", "\n", "![](./example1_fig1.png)\n", "\n", "\n", "The left and right panels of the following figure shows the induced action distributions of the MLE and Bayes decision rules (when $\\alpha=5$, $\\beta=2$) respectively for two alternative values of $\\theta_0$. More transparent colors denote the scenario corresponding to the sample distribution of last figure. Faded colors show the distributions induced by an alternative $\\theta_0$, while the prior parameters of the Bayes decision rule are kept fixed.\n", "\n", "* **Bias vs. variance:** The MLE estimator is unbiased in the sense that its mean always coincide with the true $\\theta_0$. In contrast, the Bayes estimator is biased, the extent of which depends on the relationship between the prior parameters and the true value: when the prior concentrates near $\\theta_0$, the bias is small, but as the faded distributions demonstrate, for other $\\theta_0$s the bias can be significant. Notice, however, that $d_{bayes}$ is always less dispersed than $d_{mle}$, in the sense that the values to which it assigns positive probability are more densely placed in $[0, 1]$. Exploiting this trade-off between bias and variance will be a crucial device in finding decision rules with low risk.\n", "\n", "![](./example1_fig2.png)\n", "\n", "Finally, the figure below compares the performance of the two decision rules according to the their finite sample risk. The first row represents the induced loss distribution of the MLE estimator for the relative entropy and quadratic loss functions. The two panels of the second row show the same distributions for the Bayes decision rule. The vertical dashed lines indicate the value of the respective risk functionals. \n", "\n", "* **Loss function matters:** For all sample sizes, the probability mass function of the MLE estimator assigns positive probability to both $a=0$ and $a = 1$, whereas the support of the Bayes estimator lies always in the interior $(0, 1)$. This difference has significant consequences for the relative entropy risk, because as we saw above $L_{RE}$ is undefined at the boundaries of $[0, 1]$. As a result, the relative entopy risk of the MLE estimator does not exist and so the Bayes estimator always wins in terms of realative entropy. The secret of $d_{bayes}$ is to shrink the effective action space.\n", "* **Dependence on $\\theta_0:$** Comparing the decision rules in terms of the quadratic loss reveals that the true $\\theta_0$ is a critical factor. It determines the size of the bias (hence the risk) of the Bayes estimator. Since $\\theta_0$ is unknown, this naturally introduces a subjective (not data driven) element into our analysis: when the prior happens to concentrate around the true $\\theta_0$ the Bayes estimator performs better than the MLE, otherwise the bias could be so large that it flips the ordering of decision rules. \n", "\n", "![](./example1_fig3.png)\n", "\n", "----------------------------------------------" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Example (cont) - induced distributions for linear regression\n", "\n", "Suppose that our model is correctly specified. In particular, let the data generating mechanism be i.i.d. with \n", "\n", "$$ (Y,X) \\sim \\mathcal{N}(\\mu, \\Sigma) \\quad\\quad \\text{where}\\quad\\quad \\mu = (1, 3)\\quad \\text{and}\\quad \\Sigma = \n", "\\begin{bmatrix}\n", " 4 & 1 \\\\\n", " 1 & 8\n", "\\end{bmatrix}.$$\n", "\n", "Under this data generating mechanism, the optimal regression function is affine with coefficients\n", "\n", "$$\n", "\\begin{align}\n", "\\beta_0 &= \\mu_Y - \\rho\\frac{\\sigma_Y}{\\sigma_X}\\mu_X = 1 - \\frac{1}{8} 3 = -0.625, \\\\\n", "\\beta_1 &= \\rho\\frac{\\sigma_Y}{\\sigma_X} = \\frac{1}{8} = 0.125.\n", "\\end{align}\n", "$$\n", "\n", "Due to correct specification, these coefficients in fact determine the feature, i.e. the true regression function.\n", "\n", "For the Bayes estimator consider the prior\n", "\n", "$$\\mu \\sim \\mathcal{N}\\left(\\mu_b, \\Lambda_b^{-1}\\right) \\quad\\quad \\text{where}\\quad\\quad \\mu_b = (2, 2)\\quad \\text{and}\\quad \\Lambda_b = \n", "\\begin{bmatrix}\n", " 6 & -3 \\\\\n", " -3 & 6\n", "\\end{bmatrix}$$\n", "\n", "and suppose that $\\Sigma$ is known. Let the sample size be $n=50$. With the given specification we can *simulate* the induced action and loss distributions.\n", "\n", "The following figure shows contour plots of the induced action distributions associated with the OLS and Bayes estimators. The red dot depicts the best-in-class action.\n", "\n", "* One can see that the OLS estimator is unbiased in the sense that the induced action distribution concentrates around the best-in-class action. In contrast, the Bayes estimator exhibits a slight bias. \n", "* On the other hand, the variance of the Bayes decision rule is smaller than that of the OLS estimator.\n", "\n", "![](./example2_fig1.png)\n", "\n", "Using quadrature methods one can calculate the loss of each action which gives rise to the induced loss distribution. As an approximation to these induced loss distributions, the following figure shows the histograms emerging from these calculations. \n", "\n", "* In terms of risk the slightly bigger bias of the Bayes estimate is compensated by its lower variance (across the different sample realizations). As a result, in this particular example, the risk of the Bayes decision rule is lower than that of the OLS estimator. \n", "* The true feature lies within the action space and the model is very \"simple\", hence it's difficult to beat the OLS (we need small sample and large noise). Using a more complex or misspecified model this might not be the case.\n", "\n", "![](./example2_fig2.png)\n", "\n", "----------------------------------------------" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Misspecification and the bias-variance dilemma\n", "\n", "In the above examples we maintained the assumption of correctly specified models, i.e., the true feature of the data generating process lied within the action set $\\mathcal{A}$. In applications using nonexperimental data, however, it is more reasonable to assume that the action set contains only approximations of the true feature.\n", "\n", "Nothing in the analysis above prevents us from entertaining the possibility of misspecification. In these instances one can look at $a^{*}_{L, P, \\mathcal{A}}$ as the best approximation of $\\gamma(P)$ achievable by the model specification $\\mathcal{A}$. For example, even though the true regression function (conditional expectation) might not be linear, the exercise of estimating the *best linear approximation* of the regression function is well defined. \n", "\n", "In theory, one can investigate the approximation error emerging from a misspecified $\\mathcal{A}$ via the loss function without mentioning the inference (finite sample) problem at all. In particular, the **misspecification error** can be defined as\n", "\n", "$$\\min_{a\\in\\mathcal{A}} \\ L(P,a) - L(P, \\gamma(P))$$\n", "\n", "This naturally leads to a dilemma regarding the \"size\" of the action space: with a richer $\\mathcal{A}$, in principle, we can get closer to the true feature by making the misspecification error small. Notice, however, that in practice, not knowing $P$ implies that we cannot solve the above optimization problem and obtain the best-in-class action. As we show in notebook2 {REF}, a possible way to proceed is to require the so called *consistency* property from our decision rule by which we can guarantee to get very close to $a^{*}_{L, P, \\mathcal{A}}$ with *sufficiently large* samples, however, what \"sufficently large\" means will be determined by the size of our $\\mathcal{A}$. Larger action spaces will require larger samples to get sensible estimates for the best-in-class action. In fact, by using a \"too large\" $\\mathcal{A}$ accompanied with a \"too small\" sample, our estimator's performance can be so bad that misspecification concerns become secondary. \n", "\n", "In other words, finiteness of the sample gives rise to a trade-off between the severity of misspecifiation and the credibility of our estimates. To see this, decompose the deviation of the finite sample risk from the value of loss at the truth (excess risk) for a given decision rule $d$ and sample size $n$:\n", "\n", "$$R_n(P, d) - L\\left(P, \\gamma(P) \\right) = \\underbrace{R_n(P, d) - L\\left(P, a^{*}_{L,P, \\mathcal{A}}\\right)}_{\\text{estimation error}} + \\underbrace{L\\left(P, a^{*}_{L, P, \\mathcal{A}}\\right)- L\\left(P, \\gamma(P)\\right)}_{\\text{misspecification error}}$$\n", "\n", "While the estimation error stems from the fact that we do not know $P$, so we have to use a finite sample to approximate the best-in-class action, misspecification error, not influenced by any random object, arises from the necessity of $\\mathcal{A}\\subsetneq\\mathcal{F}$. \n", "\n", "This trade-off resembles the bias-variance dilemma well-known from classical statistics. Statisticians often connect the estimation error with the decision rule's variance, whereas the misspecification error is considered as the bias term. We will see in notebook3 {REF} that this interpretation is slightly misleading. Nonetheless, it is true that, similar to the bias-variance trade-off, manipulation of (the size of) $\\mathcal{A}$ is the key device to address the estimation-misspecification error trade-off. The minimal excess risk can be reached by the action space where the following two forces are balanced {REF to figure in notebook3}:\n", "\n", "* the estimation error (variance) is increasing in the size of $\\mathcal{A}$, \n", "* the misspecification error (bias) is weakly decreasing in the size of $\\mathcal{A}$.\n", "\n", "In the next lecture {REF: notebook2}, we will give a more elaborate definition of what do we mean by the \"size\" of $\\mathcal{A}$." ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "**A warning**\n", "\n", "The introduced notion of misspecification is a *statistical* one. From a modeller's point of view, a natural question to ask is to what extent misspecification affects the economic interpretation of the parameters of a fitted statistical model. Intuitively, a necessary condition for the sensibility of economic interpretation is to have a correctly specified statistical model. Because different economic models can give rise to the same statistical model, this condition is by no means sufficient. From this angle, a misspecified statistical model can easily invalidate any kind of economic interpretation of estimated parameters. This issue is more subtle and it would require an extensive treatment that we cannot deliver here, but it is worth keeping in mind the list of very strong assumptions that we are (implicitly) using when we give well-defined meaning to our parameter estimates. An interesting discussion can be found in Chapter 4 of [White (1994)](#white1994)." ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "--------------------------------------\n", "\n", "### References\n", "\n", "Breiman, Leo (1969). Probability and Stochastic Processes: With a View Towards Applications. Houghton Mifflin. <a name=\"breiman1969\"></a>\n", "\n", "Wald, Abraham (1950). Statistical Decision Functions. John Wiley and Sons, New York. <a name=\"wald1950\"></a>\n", "\n", "Manski, Charles (1988). Analog estimation in econometrics. Chapman and Hall, London. <a name=\"manski1994\"></a>\n", "\n", "White, Halbert (1994). Estimation, Inference and Specification Analysis (Econometric Society Monographs). Cambridge University Press. <a name=\"white1994\"></a>\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.1" } }, "nbformat": 4, "nbformat_minor": 0 }
bsd-3-clause
ctroupin/CMEMS_INSTAC_Training
PythonNotebooks/PlatformPlots/Plot_TimeSeries1.ipynb
2
208744
{ "cells": [ { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "%matplotlib inline\n", "import netCDF4\n", "import matplotlib.pyplot as plt\n", "import matplotlib as mpl\n", "from matplotlib import dates\n", "import numpy as np" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Read variables and units " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We assume the data file is present in the following directory:" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": true }, "outputs": [], "source": [ "datafile = \"~/CMEMS_INSTAC/INSITU_MED_NRT_OBSERVATIONS_013_035/history/mooring/IR_TS_MO_61198.nc\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We use the os mudule to extend the ~." ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import os\n", "datafile = os.path.expanduser(datafile)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [], "source": [ "with netCDF4.Dataset(datafile, 'r') as ds:\n", " time_values = ds.variables['TIME'][:]\n", " temperature_values = ds.variables['TEMP'][:]\n", " temperatureQC = ds.variables['TEMP_QC'][:]\n", " time_units = ds.variables['TIME'].units\n", " temperature_units = ds.variables['TEMP'].units\n", " time2 = netCDF4.num2date(time_values, time_units)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We also [mask](http://docs.scipy.org/doc/numpy/reference/maskedarray.html) the temperature values that have quality flag not equal to 1." ] }, { "cell_type": "code", "execution_count": 58, "metadata": { "collapsed": true }, "outputs": [], "source": [ "temperature_values = np.ma.masked_where(temperatureQC != 1, temperature_values)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Basic plot" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We create the most simple plot, without any additional option. " ] }, { "cell_type": "code", "execution_count": 59, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.text.Text at 0x12e0ba90>" ] }, "execution_count": 59, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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r15TUKKAHsLxh+XIjSFncdNNNubSX1iM3LnneVGnCGb/++uuxypmS21QOBS8P\nP/xw6P45c+Ykqu+zzz4L3JdVWVRHFXHJa0HUpLLIMxNfHkybNs1ofUnOZd6jkMRdC3WY6dn2dSX0\nRrE+7zEIWlAsOlKsX2x+kyR96SZx6klzEz777LOxysXJAR6HvB6UqMRWs2fPTlRfmOdt0Xm4k9wz\nSabGTF6LvF+AZYgYHUaZRhbGJoxFZDHgaOBIU3WaIGhaqeibJO+bPulNlcTqK42Xb9y1CFMPQ6tW\nrYzUkzdhyqXMebjHjBnjuz1vP4u8npu1116bTp065R6WIyt1sWYhIt+IyK+Vz4KIz684caTixdMu\nkCClUC/Kwm2mGUbSNInjx4+PXXbPPfdMVDfEj5ZbTT5VVkzfJ2H1ldl0Nsn9m8ZIxES7STjvvPMY\nO3Zswzl45JFHmnjWl4EkCb7yTjwV1v27Ccd34jkgzqpNa+D3JoSy/EZc79Lhw4dz0UUXxa43yY2V\n5oGNO7JIOs9eNEX27JZaainAWazPM+R8FRP5LvyUnzuhWL2w8847A44vSbdu3WoszW+88EJgdKXC\nCXuirwReUdXYtoEicmBmiQxTzyMLU/P5fuT9Esw7Z3W9Epb7umXLliyxxBLMmjWLWbNmxR5V+hHn\n2DLN18+dO5fZs2ez7LLLNpIrr/s0aOT77bfflkpZlInArkslqmuiLCqqOjKrQKYJeiCSTttkJc1N\nn2ecqiRrBWlkt8rCnzBrKDC3bhFnnSnJyCUo+ZOJl/k333xD27Zt6dChA+utt16s+rNmpguysipb\nbLgyEXq3uOMwVRGRxURkkIicUvm9jogkn9QuiCBlccstt/huh/LknK6V74AJrLLwJ+oFbWrdIo4i\nSHKNopSclyT+Bv/4xz8avr/xxhuxjjnppJNi1+83igh6L4SN/BZ1kkad7Q68BVwLHAygqm8A3UTk\nqbRRZ/MkaM4/7Kb84INSZGttFN3UNHnbb5t2TlpUMKUs4kwxVddI4pA0rezdd98du+y1114buC/o\n3rvrrrti1++N2xRGmayPykbSFbRLgTbAxYC723sxTvjwEUkFEJFDRWSyiMwRkfdEpEm2PRFpLiLD\nRORFEZkoIpeISCwvrCFDhiQVKRfb5kceeSRxvSZCJweR9zTU8OHDEx+zKBC1llD1tcg6DRVHWZiY\nhgqiFsl5TFAWZTFr1iweeuihWovRiKTKYkNgM1U9EWjo+qjqLzjKY/cklYnIScAmOHGm+gNTgeEi\n4n3T3A7HpFDqAAAgAElEQVRsCmyhqpsB7YH747SRxjRz3LhxiY+JI8eoUaMSHZP0AU1CkodijTXW\nyE0OCA/0trDRqVN4VP0iRxZJ7oEg67agOkwtnuf18g6SryzKYu+992bXXXettRiNSKosXlPVJmEz\nRWR5oDMJnPxEpBXQQVUHqepzqjoe2AV4FTi2UicishewB3BSRSmBE+l2exEZFNVOmlHCiy++mPiY\nODzxxBOJyscJBpeWJA/F8ccfn5scUGzsnjz4+uuvY5ddbbXVQveXVVkE5Xwok0VVEH5e8+51jE8+\n+aThe1n+n6jIAf37909dd9qZk6TKYoaI9HJvEJFmwL8rP8cnqGtJ4F/uDZUF9bsqcnWtbD4KmKmq\nr7nKfQRMI4a3eFl6ClAu1/0gWfr169dkWx55mt1cf/31udafN88880zsslFmmWVVFkmfo7KPLDbc\ncMOG7+4Xc5neF2E899xzqY/1RpHu2LFjrOOSKouhwH0i8i9ghYpF1ETgTzjTUoPjVlSJJ+WX13EO\nsAD4oJIfYwvAz8tnCrBu1KJ6mV7QZboR/UwEl1566cSjHxM88MADhbcZRJr0tD/++GPsslH3gKk1\nizjrEUnux1mzZmWuIw1FT0OV6X0RRhZl7D12nXXWiXVc0qiz04DtcNYMlsBRHl1x1hQ2UdXkaeaa\nsjXwSCUw4UoVGf3s9r7HCYke2lVLM++flzNcmW5Ev/Ny+OGH10CSchHXIMJtdppkMT/q5VfkyCLJ\n/Xjhhf4Zk1dfffXYdaSh6A5W1JpSWfC7vnEj9L78cuM8cnEVT+JAgqr6KRWzWdOISBeche4NKpva\nV/76xYGuvu1CraI+/PDDxHLk5ZhTdmUxbNiw3NpbYoklmDhxImutlSjzrhF++uknZs2axbLLLmus\nTve1TLJmkZey8BplmJ6G8o4spkyZwoIFC2jfvr1veVPTUCavWRyq5z8PvvrqK9q1a2ck6GXHjh35\n5ptvGm2LGxTRe1xcq7jQUiKylIi0q3yWEpEmykVEOolI51itRXMFMKTiPQ5OcELwVwjVbd/47MtE\nXrkRyqQsvDFnRo4cmbsj3Zprxk6kaJRu3bqx3HLLGXW4ct8jK6ywQuzj8lIWr732WqPfppWFl06d\nOtGrV69AKylTyqJr167ccsstsVO6lpUZM2bQoUMHevXqFV04Bn6GEnGvp/c9ZERZ4DjffYfjiPcn\noIlKVNXPgR1FJFOqNhEZAsxQVbcn2vuVv37di2WBXwDfZMNDhw5l6NChqWTp06dPquMg3NHprrvu\nyhymwBTefBNxo8SmpZbrNVXv40mTJhmrc6eddmr4nsQfJio0fJF+FlkMC6r1L7vssrlby+2///70\n7ds31zbypvq8lcHh1/ssxg38GKUszgLeBNZR1atV1TctmKpeD2wrIvGW1T2IyD44Tn2Heur9HngF\n8DP0Xx14QVV9V96ilEVYVNQsL7aoaKt//etfE9eZRXnFpUyL73lh8n90j8LciiOKP/3pT6H7iwz3\n8fjjj6eu362Mhg8fziabbJK6rlpThLms6efLT+a0I4t7772XNm3a8O9//zvgCIeoO2p/4HBVjTPV\ncx2QeIVURAZW2vmTOxaViFRXmi7Hsbxax7WvB7AicHXS9qqEDQeTZj9zE3XjJckjkeWYpATl8N53\n331zbxvSrROVpbdpMgtiWmXhfVHk/QL01h/1u8y4z10ZE0/54Xd+48bV8iqLtdZai9mzZ3PssceG\nHhelLDZS1VhG5BXrpR5xylYRkb1xRi+nAd1FZA0RWVNEBgBnV4rdBDwJDKkc0wI4D3g4Sfh0L2E9\nrzQhQuqdIGcxUznJo14eV111VeI6e/funVaczLgXE02+GNu1a4eI8OOPPyZSoLVWFvWM+9xFjfzK\ngt/zGreD4VYWVee+WOFhMu73snzcgiKyHzAK6AW8jLMu8hYwGbgHGAtOzm/gD8D3IvIi8EylXKLQ\nIl5WXXXVwH1ea4EkLEwPEcRPYpSVNE5GSYf2JqcCjj32WHbddVf23Xdfo6k5mzVr1igJUlxOPfXU\nRr+Lvg+9BhP19By474tp06bVUJJoVlppJZ5++ulMU9NTp05t+L7XXnvFPi5KGcQ2CxLn7oidNURV\nR6lqC1VtrqrNPJ/mqnqnq+wcVT1MVTdR1c1U9TRX6I9U3HHHHYH7TjnllCxV14QsXtZJInimJepF\nneZFbnoe+IorrohdtmXLlowcOZLnn38+UerLOKSZivJmPsz7ZV0Gy745c3yXUBPj7Rzed999sY+N\nO2Vt6l7dY4892GqrrTLV8dRTTzV8T3KfRCmLH0Rk85h1bY+/P0Qp8bP+Ofroo4H47u95kvRh/Omn\nn1LfkLvvnmmQZoQyRCk98sjI6DGNuO222/jwww9T+6esuuqqnHXWWU22m1jkzltZLLHEEo1+Jwl3\n7ibLdb///lixRCPxxmAbOHBg7GPvvffeRr9///t8M0sfdthhgLnra1JZ3AzcKCIdIhpcFsdH4uHY\nLZeQ6jpGFg9uUxcxjaXK5MmTU7WVh3+F96GJOi/eh25h49NPP20ycnn//fc57bTTmpStB2Xhrd9r\nMBJXCYwcOdKYDGlJ28l68sknefjhxq+8vDteVSfIDTbYIKJkMGlHhVET0ncCRwNvi8gFwH2q2hBP\nWkTaAwNwFqmXIEU+izJRfWmWQVkExeIJo0wJh7zByvLAm4KzaJJ44m622WZMnz49VlkTvhZx78PZ\ns2fHDhORpL24L+BaWB95/UvSKovtttuuybYgwxlTI+fqeV5hhRWYPn067dq1Y/vtt2fixImx68gl\n6qyqzgf2AmYCFwDviMhcEflURL6ubL8OJyzHXqpabGJrw1QvdJb52M6dTTmzJydJMpu8KeI8DBgw\ngFGjRsXOh2F6jSNJXKS4igLMjCxWXHHFWOW800lpSassktyzL730UqPfcTpUfhZlaWWNqtev7ire\nNaW0uOtfccUVWWKJJRotdsd5d6WdMoy8UpX8FRsD/wfMAhbD8XFYBieQ3wSchEhjU0lQIqo34w03\n3JC6jpNPPplDDjmE//73v5lkSfOiKJOyGDFiBAcddFDD7zysqkSEfffdN1HaTJNE5aZIiwll4c5r\nXQTel2Tc6aUk96xXAcaZATj33HObbPPK6veCnTBhQmi9QaP4oP/HreiSxBLz4qeM3NuaN28eGTzV\nPSIyuWYBgKrOUtXjcJTEVsA+OKarXVW1TyUPdxNEpHVsSUpA9QUfFZIhjMUXX5xrrrkmsw9AmLVW\nEGVSFh07duT666/nySefpHv37pHJXOqRqgJcbrnljNZrQlmYmFrKQtwcH0leVt6ycULDn3HGGU3q\n8JqL+o0sttlmm0SyRG13t3HbbbeF1p20Xe+2//3vf6nrDyNpiPLZlax2d6jqA35Z8zw8mkG2QgnK\nBBaHk08+ueG7qTUL97D1iCOOiHVMGUN29O3bl/fee4/NN29qVFd0RNEkeSeqhK2LVKe/TAYohGLX\nLKL49ddfI8NAZGkvy9pWmnW91157jbZt2zbaNnOmX1qdcII6ZnGURZbn1K9+ryxRI66iMuVZfDjv\nvPOM1/n00083fL/88stjHVOUA50p8g5AZ6K9PffcM3Dfxx9H9ZXSYSo+lAluuOEGjjvuuMhyaZXF\n2WefHV0ooI00ysIUX375ZaLy7hd03nGiovxPrLKoIe6LVUvP1bxDjJtm8ODfEiv+3//9X+7tffrp\np4mPCbueeeXmKJPp7FtvvZVre1kWftPEcDN1XoIiDXfo4O9lkOfIImwN5ueff2bmzJmN7iX3fuNr\nFpb41HLdoExrFnFo2bIlxxxzDFAOj2A/ws6pe4HbZMKsMimLGTN8MwDk1l6SNtKMLEzJGXRfBHlX\nm+rI+SkaryzuMs888wzLL798I0fDf/7zn6nkqq+3S46oKieccELD7y++SGYFfPrpp9OnTx/69etn\nWjQAfve730WWMWWeVyTVm7WsyiIM94OW9H4Jw1ROCxPceeed0YUKokzKIuglG5QX5oADDmj4nmVk\n4Xes93+KSsblftaSOBGWRlmISAcRGSYirwTsX1dEHhKRcZXPWBHZwqQM7jDd7kXrOJx55pmMGzcu\nt3WDsMCHVbJYWdQKE17zeRL2cslr2q9MI4u4FG2qC+kMC7p1ix2+LpQgg5jWrf0NQN2ZFfNWFklI\nElOuFMpCRLYETsQJQ76Mz/7VgSeAEaq6rapuixPC/DER2dCUHO6H34SFi9fqIgtxbrALLrjAWHtF\nUT3n48aNK+XoYu211w7c575fTC5almmBOy477rhj7m2YGFkkeSbDQu4ETUMFbXffH+6or0mJoyzy\nSkFbCmWhqs+q6snAawFF/gJ8pKpPuo6ZALyD4/NhBPeFNjEH/fe//z1zHX706BGcNqTeFEb1nD/6\n6KPcfPPNkeWzODSlYf311w/c535ITY6MFl98cZo3b87cuXNTh3D55JNPjMkTlzQGBFnIe5rOz6Gv\nSpbw+FEpEMLuJb8QM14FddRRRyWSLS6lUBYugmy+WgJrVkYYQENI9OWBeAlkI1DVRnP+WXJaVHEv\nJCUhKiDg2LFjGTx4sG9v7pJLLknVZq24/fbbG75HJZ3aaqutWG655QpJM1sl7hDf5KhIRBrWLdKO\nLsJCoMQxhU1D3iFevNci6tyUaWrTrSwefPDB0LJhHdV27do12VbUlGPZlEXQE3cjjqyPici6lW1n\n4iRKSp913sPbb7/d8N0bhyYNbdu2TZVnwi+vgvtm69KlC//617/o1KlTk3KDBg1K3J6X5ZePncMq\nMx999FHD988//zy0bDXpfdZQKkmI+yCafjHlORV18cUXG6+zFkTdp2mcMOOSdGTh7kxEOSImHU2W\nWlmISJtKHmxEZNlKqlM/jOQnVdUpwJ5AJ2CiiIwGZgO7ZE2C5CZJFNG45GUdFYSJdZJa+YqYzGVt\nirjnIm1wtiDqcd0ib5KG+8gzokGeWRqTJiMrymQ+USsi0lxEzgG+BcZUNi8G3CMif/WWV9X4cXMj\nUNUHcUKhzwd2AdYnQSa/OASZvWWh6JAWJqZDNtpoIwOSJGedddapSbthRCmL6rRA0gd23XXXDd1v\nlUU0QY5xVepVWSQNgZIlt0USkqqks3BGC7OAXwFUdQZwKHCNiGSfAwlARI7DSdu6MjAaJ3T6kyIS\n2JUOspbwm/dT1VyUhZe0CeHj3mwmHpBavbTLGNsqSlmkHYVFKZesvhb1kAP7kEMOSVTe+z/NnTs3\ndH6/TPdTElnGjBnTZNtLL70UGOZ+zTXXTC1XEpI6BfwVOExVrxGRcdWNqvq5iEwHTsbgGkIVEdkJ\nZ42ik6rOAQaIyP/hJGb6B3C695ghQ4Zw/vnnB9Xnuy2PaSgvbnvrIB566KHU9ZsYWdTqRVNG09mo\nc/H9998DTpyoJKPIKGWxKIwsrrvuuibbkkbw/fHHHxsUq5e4eU5WWmmlxJZcWdYsovBbk8trtD9+\n/HjGjx8fq2zSkcU0Vb3Gu1FEmgEdgVUS1heXg4E3K4oCAFU9Fnga2NXvgC233LLR7z//+c8N34Ne\nAHkoC+9NFeVdCfFNEP3+jzK+cN1MmjSJfv36MWnSpCb78pD9gQceyHR8XMXp9+ILw6SyyNqDLtoc\nOYwwpzm/axG2bnH33XfHajPN+YvKGZGljaT+I37nxdteUPt9+vRh6NChDZ8wkiqLr0TE7436V5y1\ni3iqPDk/AH52ea9U9jXBayO9666+OqURZY7aWmSayur0QNzQ6ElYb731eOKJJ3xDf+cxbfDUU09l\nOj4qgmcVPwu2MEwqCz8lG1dugGnTpsUuWzbC1i3i3k+HHXZY4nbHjk2W6y3PTpyfspgyZUrgvrQk\nVRY3AHeJSC9HDllRRI4Bqsb9/8ooz2KVj5eLgeVFpCGmgIisCuyBk+61Cc8//3yj31WzyzCKMMnc\nfvvtc63fRPTWrl27Mn/+/Nih0U0R5jGblq233jrT8XlZmkQ9xEnWLLKa7fqZYNeKsPPi3lcNqxE2\nsoirLILC0IcpoqROu2ussUai8kkoaoYhafKj+4CHgOeA3sCnQDUzymBVjXbB9UFEeonIycB6QEcR\nOUtEGpb4VXUysC2wvYi8KSJjgcuA/VQ1Vgq2OAlO8pgf9t6wJh3KghaiTfTQixpljRgxItf6vWtE\neWURS4rJkUXWvBrVtkwTN7x5XNwvxaqpctgLPe4Ls2fPnr7bwxRC0kyWReSkz5vE3SZVvRZYCdgB\nOADHjLWzqg5PK4Sqvq2qF6hqG1Vtoaqnq+qrnjLPq2o/VV1TVXdQ1f6qGi93I/H8D8oc4tsvTv6R\nRx6Z2cGqV69emY7PSpxoulnwBvt7911/h/+gIIwmlKZfz/+zzz4LPSaJsnjiiSfSCVYhr45B0Lk2\nQTXK7+mnN7FtaSBr7zpolHPzzTfHThtbBH5y5tEpSvt2/BX4WFVHAS/gmNKWGvcDEbQgVObkQRde\neCG77bZboyBhLVu2zBy6IUs6WRNssskmudbv7QAEvQD+9re/+W5PYp0TNB3kZzf/4YcfhtZVVRZx\npqFGjx4dQ7pg8rrvBwwYECj/zz//7Ls97jRUlRdffDGwfJaUrRCsbIYNG5aqvj/+8Y8N3x977LFU\ndfjhd17uv/9+4Ld7I67FUxhpnfK+I4ZTXpmI03tKauFQJB07duSBBx6gb9++kWWTTEPVejSVd/tx\nlYXXAmXTTTdls802S7RA6Gfh5SdDHJLEhvJTUjvttFPstvK8BiuuuKLvdj9fAtNkMUGHYGUR1yTX\ni3sK2mSUXr97tNrZMBmGpm6c8rISx+EujyixWdcPVlkluTXyc889F7tsrZ23guL/m8Lba477/z7/\n/POJziMEv1zSvIyTTEP5dXKSjBhN3gPe6dJ58+b5xvxK8xJLKmfWzp/pReIin7WrrrrKd3uWddmk\nd3HVKW85nMVtwHHKA6pOeaUkzlC7yGimcUkzNxo1H+6m1soirm/LO++8k6r+N998s9HvuBZvIpL4\n3AT5dCS5HlXcyiKqw+GnGGp1Xf2mxPyiKBfhXZ31ZW9aWdTKss7N3nvvnbqdenHKy0zceVl3trxH\nHollaBVK1rwYaeaT99prr9hlaz0NFZe0Fj9eH4K0SicOr7/+uu/2qGi6frRu3ZrFFluM+fPnR/pM\ntG/fvsm2WikLvyjLfn4cQVZMcdcsqiPuMF8gb5qBpB0vd0RkEwRNU7pJo6CSXOuk/iFu6sUpLzNL\nLLFErHLuOdakXrl+uBfZRo4cmfj4vB/6MsX8D5sqTJMVDdJPQ8XF7fUaZP2Tthcdd93Cr0NSq06A\n3/m98sorm2x7+umnYx/vRzU8uV+ctyC8UR2iSJt4KgjvGorfs1fE9FxayuaUlxvuHk+YhYl7ITxr\nqAiASy+9lJVXXpnLLruMv/zlL4mPN30jfPrpp42CGb72WlBywuIJm2MOy1oWhvelGRWpNCnu6LFJ\n0mUOHDgwskzcdQu/81arkYWfknr11VebbLvhhhsavu+7776x6nb/T9V2yh7exo3X58e0MsqbRAbW\nqnqfiCyH45S3FL+tW8wlg1NeEbh7rWEB33beeeeGePImTAq7dOmSyWkq7UOvqr7HHnzwwY3M9mq9\nZhGXl19+OdVx3ms4YcIEE+I0ECdCr9/Iwm/qyEtcZVENZuimVtc1TbtpXvj1qCzef//9Rr/9lHya\nUWhZRxZ+Tnm7ktEprwjWXnvtWOXc4ThqZUrrzvuc9kY477zzfLd7gxTWy5pFWvL2nYnjmev3Qotj\nyl31Ug4L9PfDDz/4mqHGvW/69+8fq1xcgu6nsPW/uC/8eh9ZePFzpgwKQx5G2NRVzWJDichtIvJv\nVZ2tqk+o6ihVfVhVSx9HOY5/AgTbhRfJww8/3PA97cUOsmPPcw4/q4XL4osvbkiS30ijLJI4TMVJ\nm+v3Qotjyl2V469/DXZh8htVQPR1feqpp+jfv7+RqdY47e68887cdNNNvvvc0zNdu3aN1U5VWZQp\nZ0UUXkXq16FLE4+tqHXHpN3K/kD+GYJypuxTL3EX48MIekl6vVpNhXp45JFHaN26dYPnaBryGOWk\nSWgVZ4ooiNmzZzfZ5mdOmkQur1WPm3nz5jV8X3XVVRu+B93jVeW27bbb8vDDDxsP9RH2bB144IG+\n27fddlteeeUVBg0aFBoI0113NdxH3rHFTLLNNts0+v3KK680KZMmEGjQOT/++ONDjzvmmGOSNaSq\nsT/AKcBuIfsPTFKf67gOwDDglRhldwBuBoYDRwaUUfenX79+qs4OBXS55ZbTKtVtbdu2bbLNOT3F\nM2vWrIb2Z86cGVne+/8Cetlll8Uqe9555xmRuWXLlqnPWfW4JZdc0nf/9OnTm8gdl3feeSfWse79\nr776air5AR09erSqqj7++OO622676Zdfful7fSZNmpSo3iCmTp3aUGa11VZr+P7xxx/71rXWWmul\n/t/iyDNlypTAY9zHubf98MMPsWSZO3duIlmS3DPVMu3atUt0ryRpY+ONN25U9pxzzolddxhff/11\n4HHHHHNMozrc+7p16+bbvga8e5N2K6YDh4rIesBHnn1LAycAI5NUKCJbArtVjg1cCRaRpXCy8C0P\n/Ekdz/G4bSQRqea4e9hpZfezEfdLquQXoDANJkYFQf9r3KkJP+JME3nJ8r9Ue/rVta/Bgwf7lgvK\n7uama9euiWz9w+6bY445hksuuYR//OMf3sOMkuZ+NTGSNkW3bt0C/WWy8tJLLzX6HTfnSNT9X9YF\n7hOBnYChOErB/fk3zsJ3IlT1WVU9GQi04RSRdsBTOIpiuySKonJ86O+yYUJZXHTRRU22eYfBYG4B\n2ISy+OGHH3yNCrIYGqRZAM1yTrz5FYLm6eNc15NPdgIiBAU59OK+Bt7r8e9//5sZM2aw3377xaqr\nStLskWnu17jHBJULWrdJwsYbbww4aytFcc4558QqF2SskpWk1yrpE/4f4Hxge6Cv57MDMD5hfW7C\n1OwoYDVgH1X1D1cZQtKTUuuejgll4ccHH3zQZJup+k2tNxx11FFG6qmSJvZUFmUxaJC58GjVdY0w\nZene55bbr4MUJ6Wvl4kTJyYqXwvrOj/z5V9++SVRorExY8Zwxx13hIY8z4o7tXMSop7RsP0aYgCQ\n9NlPOg01Cmijqr4xE0Qki5eJbxdQRHYFdgYuVtXkdmXEOylui4xu3br5xrMpiryURZ5keUlsvvnm\nDZkNr7nmGq6++mpTYqV6QRZxzsMe4ipRyuLDDz9kzTXXbPidx4vabcYdhzzPXVDdXj+mWbNm0aFD\nh0aL/1G0b98+UZicNNSiExoWCj/vkcUhQYqiQg8RuVxEdktYbxiHVv6+LyKXicjTIjJORA6MW0HY\nSZkwYQJrr702jz76aMM2t6JIksvYFEUqC1P5LNwyx3kRusmadyCKfv36+W4fPHiwr0VI0nN+wQW+\nmX0zU50CCoop5LWcKUMnI067QRFRTTFhwoREiiIpQaPfgw8+OLc2wwg752Fh2vNWFluJyPYicoKI\nDBSRhnGviBwJXIsTH+pHETkkYd1NEOe/6Qd8C3ykqkfhTHm9CdwgIv+MWU/g76233po33nijYc4S\naJR9buTIkWy66aYceeSR6f+RhOTxoPuZdEL2HNVV3PPGSbN0ZQ22GEVQNr6LLrqISy+9NHOH4KST\nTkp8TJy819Vzutpqq/nu96b2dN83ZQr34eXwww9PVXec/2nu3LmcddZZqeqPS9BoNSqES17KpKgR\nS1Jl0Rt4DLgQuBuYKCJVT6pqGqgbVXUc8LKI7JBRvuVwAhROVtVHAVR1PnA8MBM4TUQi86W6Rw1x\ncOe1uPXWW3nxxRe54oorEtVhiqS99CCCFjZNWUO5STpSyDsvtjfHtDc2mOlrG5QFzk0cK62OHTuG\n7vd2ANzTVWUKJFhk3eeccw4vvPBCpnaGDw8PRhEU1DJKvvXXXz9W5sOkxF1j80b/FRHGjx/P0KFD\nGz5hJF2zEJw0qpfj9Pb/DJwG/ANYFsdG9zucL5NE5FIgfUxcqHY5G0V/U9WfRWQMTriRHoRYUqXB\nfdHT5CIoA97c2qY9dcMIGsXUCu+Ls3379nzyyScNv4PMW9OSNdVtlarccUde7twdZZ2G8jPfNlG3\nVmKhvfHGG6nrr1KNThuU8jeLdZK34+LGVMcwCK8JrojQp0+fRnl8zjzzzMDjk3Y/PgP6qOqtqvow\nsD+wYWVfC8DbpeqSsP5GqOq3OMEK/eqpvsW/zNJGFKamadKS9gZq2zZywGUMr7nolClTEh2/1lpr\nNfod9cJJmsHOj7DUmGl65e61H1MjlarSdYd/cRM2L18rZREVesIbTG/DDTcMKNmUsP+paiL94IMP\n+u4vItd8Vm/4oFS4eSmRvNcs3lFVt8VTK6A6DdUcJ92qGxPJkG4E1hIR78TtqsDLSX0u4lI1ubzl\nllvyqD42aQOl5b0O4MYbtiAsqq8fSXr266yzDptvvnmi+v0I6+EGrRGE4VWYJohKlhNmUlsrZTFj\nRvjj6P2fgvxQ/Aj7n8KCLQI8+eSTsdtJywYbbJD4GPc1DIpJFmdaMw15K4tfRGSwiKwhIn2BB4E5\nItICaA80jO0rocyTGLkvVvl4uQCYBFwtIotV6u4N/B5Iteoc5yTlaU0Rh5VWWom2bduGDlvDeP31\n1wtTGN7zmdSxKUmvb//9909UN/hf70MPPdSnZHD5WuDuUSYNFpfnmsUuu+yS+ljvelY1iVFWotZ3\n3AYseZHmnFcjz4Zd36CRZVbyVhanAmcCbwFPAJ2BM4APqaxZiMhpItINOBuITEIgIr1E5GRgPaCj\niJwlIg0qWlXnANsB7wIvisgEnFzffVX1Jd9KFwI+/PBDvv3220wOYttuu23o/jQ9IT+8ea29SV6i\nSHLTmnqRB1lApQ2E6DaKCOObb76J/eK//fbbG75HLbp6yXMaMmxqNur6bLTRRqnbLYsSz4o75Eqc\nTiwa06oAABZhSURBVOkaa6yRpzixSaQsVPVFYFMca6izgK1U9VlgK5xe/jY4iuNtYBAQGRJSVd9W\n1QtUtY2qtlDV01X1VU+Z71T1MFVdV1V7q+rOqto0ZGPO5L0A5aZFixaJQy14ico5HDZvn4SZM2c2\n+p20h5WkfN5WPmmc+KCxuXUYyyyzTOz/wT1VFid3+IknntjwPeu94ybI9NiPqJGwd9G4yGeqLBx9\n9NEN36uRgsMUYRJPdGicvdEkaZIfTVbVk1V1qKrOrGybpqpXquosVb0VWAdHkRT+Qo/Dcsstl+q4\neki04rX1fvXVV7n22mt9y5pKMeo1ycuTPHJeuAmygIlCRBqm00xNrbgJ88Stkve5qRL2YltppfDw\ncHkq+7BQ7kUQd+TjnnathsMPUpojRoxgq622SiRH3GmrxJ26RKUriEj3ypoFItJNRBpNGKrqVFXN\nZuycAxMnTqRfv37cfffdqY4vctE4LdWUsFWGDBkSOxBdWoIsUOKSpHdZhjzmQTz00EP07NmT3Xff\n3Xjd48aNiyxTD1kPvdci7cjisMMOa7Jt1KhRqeoqGrePTXVK8rLLLvMtG3d6003nzp1jdeByXbMQ\nkbYiMgpn/eC6yuaZwL9FJNhAtyRsuummPPHEE6nnAMuuLJo1a9bkhVFNzVlmkozY0gQGLIrevXsz\nZcqU3MNZBNGsWTO6d+9O9+7djdbrfaEniVz7z382DrLgTa+adpQ/ZMiQJtsSJ/MxTFxDDfdL+tdf\nf+Wnn35KpRT8uPPOOwFYZZVoQ9S8F7gvBvYCXgV+AlDVWcBhwMki0vQKLkQkyS1QC/wufl7zlybp\n1q1brUUwzg47ZA1ekDz6rYgwdepUpk6dmrntIObPn58o9fDZZ5/dKNqxV/GkNeBYccUVa26x6Gbw\n4MGxR3Zuf4wOHToEjiqSsGDBAubNm8eee+4Z+xi3M14ckiqLgTiZ8jYCPq9uVNXvK7/TBX0pIT/9\n1DSAbtmtMfzkGzlyZPGCJMQvucsvv/zCF198YSS/cC2uW5gnbFy8IwS/e9KNiPiOLk0S5Xjmd67d\ni+0rr7xypvanTZvG+++/T4sWLVIltsqLJEYAIsL06dN55513aNeuHf/5z398yyUZRYtI4vOR1F8p\n6V31djVGkxsRaQWsiJMedaHAz5okztCubJiyeIrLjjvumPiYpZdeusm0QsuWLenUqZORHNHuMBhF\nsdlmm2WuY9ddd2302+2jUA3p7iYvpZjVYsk9esjqSb3KKqs0yjUe5i9TJElzVay44or06NEDIDAd\nQlbFGkWcYJZukiqLrytZ67wchxPuI9+IcDWm1kmRoijDyCetJ/O5555rWJLfuPfeexu+V7PP1QNe\nB0T3KCvKh8Ykccx2w3Dfl6ZHPXGzzeVNlv8ryBN/9OjRqeuMQ95rFv8GxojITkAbEdlCRC4GzsVJ\nAn5GwvrqhqJ76GmoXvxaRciFcpoXu0Ni55V7Ig+8o1u3svCbksorgGPakUXv3r0NS9KUOKFl0ox2\na83DDz9cGme8Kkmd8ibgOONdAmwCPAP8HZgB7K2qj4QcXnf0798fcMKUm7YwyYOqskibL8AEr776\nanShgqlOEUQlvi8bXp+FqFzkeY7O4uKeZx8/fnyT/XmMfq+77rrQ/aYz4CV1kktD3lNQkLwDnMYp\nb4yqro4TGnxrYC2cgIGjRSR90Jgc8c79xuW+++5j8uTJ7LvvvoYlWniJWoQNI6+F2Q033JApU6bw\nv//9r0no9jzJet+0a9eOW2+9teH3MsssE1q+DN7QLVq0YPr06Xz++ecNisGtIJIGmYxDVFIh9zSk\nCfLISeHFhFI9//zzQ/d7My1GEbh6KCJJMpc3w4kC+ysQnMevRgRFc4yiVatWTcJnl5kyrFlk4aST\nToq8wdPSs2dPwJmSePvtt3Npw8u1117LzJkzefzxx1PX4R5dFBl2Pgte09pqLnGIDviXB1HRe5Py\n8suRIe8yY6LjFGWQk9TwI8zUpC9OZrwkpPK3F5EOwDFAf1UNDXAvIsviRKG9XlVj2SfmFeK3bLiV\nxWKLLZaplx8Xk2sURRgQHHnkkYwYERmyzAht27Zl7NixmZR4GdeAkrLMMsvwz3/+M3JklJZhw4Zx\n2mmnBe43YX5dNCaURZTpbVIflzCJRgDXA2sCq+GMHM7ByXy3Q+W3+/MXIMloBAAR2RI4ERgChN5N\nlZzcNwMr4SyoW4AxY8awwgorNEofG9SDO/DAA4227XUAy+JfkCaUR1K8DoBLLbVUg2WR6ekKE2y1\n1Vasu+66NV2HMsHZZ5/NCSeckEvdXbo0zo3mXRiuR2VhgqiUyUkVUtjIYjTwtao2jNlFZDtgG1X1\n66p/JCLXJGodqEStfVZE+uHkxAhjCPAm4J9SKoCkIbPrjd///vdNks6cfPLJHHlk03QfN954o1FH\nPe88eZZ5+qR232nwPiA33XQTf/jDH3JvF9KFtmjZsiWvvfYa8+bNY/jw4Xz11VfGF2yLpkOHDk0i\nFWfBGzLduy5Sj6MzEzJHRR5OGr4oULWoQ5MY1wGKAhFpBmyZqPXG+CcY+K3+PkAvnPzfiaj3hysN\nhx9+eCHmvt4bLk2WuSomHPCScPTRRxemKLIybNgwTjzxRM4//3xjeUhqhem1Na+VW+fOnRv9rseR\nhQmZo5RF3lFnvxWRg7wbRaQlTtyoLO6ZgapURDrh+HAcBsS609xme0li2SwsiAjdu3enb9++Ddum\nT59uvJ0si7dJqQZJy8qnn37KTTfdlDihUFqqI4qqx24aTOQdLwt5G2J4TY5Njiyqme3yJspMOg5R\naxJJjQ2SduVOBp4RkUHAs8BXOGazOwNdcKaJjCIizYFrgSNVdXZlMTySbbbZhgkTJnDLLbf4Rqhc\nVLj++usb5undVimmuOaaxDOPqTFlSdO5c+fE4RmyMGHCBC688MJM6zl+Pgv1isnETH5473NTeVsA\nbrjhhobvrVq1ys14xkTcq6iI01Emx16SOuVNxsmK1wJnUfp84AicBefhqpqHe+yZwJ2q+lbSA7fe\nemuuueaazPFo6hn34l/acNBhuENBmJj2CrP9TpuYqNb06tWLG264IXdHqyOOOCKXev/4xz8are/e\ne++le/fujBkzxlid1RDfl156aeb8Lc2aNaNNmza+L2z3qChLymM3flkWTfgDBaUOrpJUIUlaRx4R\nWRvHUuon4HlV/TzikKj6xgOrqOqqrm07Arur6qGubV2BD4Azg0xnRUTPOOO3yCN9+vRJHI53YcZ9\nw2d15DJZFzg+MUHhGX755RdjD2i9EWfq5r333quLSAN5M3/+/CajF1MOi/vvv39DkqU2bdo0BHcs\ng0Okl1mzZoV2lC+44AI22WSTRqPWM888E1X1vdlSryhWRhn+4RLNMRjo7bdOApwuIqcCfVX1ae/O\noUOH5ixa/bPxxhvXWoQmuCOKeql3p8MstG3b1renOGTIEHbccUdWWGEFqygqeKeh8srp0r17d/73\nv/LGTo3yW2revHmTjnTYVGnZ8zAOAtb1fPpX9l1Z+V3KPN9lZtKkSey77748++yztRalCauvvjoX\nXXRRrcUoHddff73v9o022ojevXuz+uqrFyxR/WDSg9u98JzFYKEM5B11tlBU9SNVfcv9wUnpCvBl\nZVv4xJylCeuttx6jRo0ysuBdDbZokhNOOMHXgWtRHlkEZUAbMGBAwZLUHybvG3fIlTJOPUUxcODA\nhu95m87myWKVj8WCe82pyqKsLJo3b+6bpznPrHiWpjzzzG+uZ/Xm7KeqjSwK605ZiEgvETkZWA/o\nKCJniUh9ex0tQvTr1y+XehfVhewwvDGt6rFnWzQHHHCA0frc93tVeZclW18c3M9V0s5XsS6zPlTC\nibwNxDK7VdWPKIGSszhUM+PtvPPORuu1PWZ/jjrqKC677LJai1F6FixYwLRp04znMDnuuOO4+uqr\nAcfK8ttvv430ZygTbgVRd8rCUt/cfffdgJPZyyTekYV7+L8oU28JnGqFiORyrnr27MmVV17ZEKRv\n6aWXNt5Gniy++OIN300GErRYIsnLdNB7I6+99tq5tFNvHHnkkXzzzTfsvvvutRZlkeWwww6rtQip\ncTvm1t2ahaW+2WabbXKp1zuyqLfFxLxo3bo155xzDhtttFGtRbHUIe4cFwuV6ayl/FSdH0899dRc\n26nHyKEWS5lZYYUVEpW301CWTPTp04fZs2fnnvLTjiwsluSsvPLKfPLJJw2/3aOJpH5WdmRhyUwR\nuaGjUkRaLJambLbZZo1+Z/FVssrCUhcsypGDLZa0eNMIW2VhWSh54YUXACfSp8Viyc68efNSH2vX\nLCylZZNNNrFeyhaLQW6//fbUx9qRhcVisSykfPDBB41+Z0nXapWFxWKxLKS89VbjBKNh+WKiKIWy\nEJEOIjJMRHxzU4jIoSIyWUTmiMh7InJc0TJaLBZLvbP33nunPrbmaxYisiWwG3AC8LHP/pOAnsCh\nQCvgJGC4iKykqk2THlgsFovFlyz5T2o+slDVZ1X1ZOA17z4RaQV0UNVBqvqcqo4HdgFeBY4VkY7e\nYywWi8XiT4sWLQJz3Ecea1iWLMwB2nu2LQn8y71BVReIyF3ABkAX4ItixLNYLJb657777mPBggUs\ntliyXHNlUhZN4jmo6tcBZedUyn8QsN9isVgsPqSNhlDzaaiUbA08oqpf1VoQi8ViWRQo08giFiLS\nBeiPMw1lsVgslgKoO2UBXAEMUdWpYYWqobPBiYzap0+ffKWyWCyWkhEVC2r8+PGMHz8+Xl1lCacg\nIuOBVVQ10GtERIYAq6rqIRF1aVn+L4vFYqkV55xzTkOumTjvRBFBVX01TN2MLERkH2AjYM9ay2Kx\nWCz1QHVGZeONN85cV10scIvIQGB/4E+qusC1vVPtpLJYLJb6IGmiIz/KNLJYrPJphIjsDZwG/Bno\nXpmDaw6sjuOgd3CBMlosFssiSc2VhYj0wgn3sR7QUkTOAu5X1VdFZD/gJkCAlz2HKrBPocJaLBbL\nIkrNlYWqvg28DVzgs28UMKpwoSwWi8XSiLpYs7BYLBZLbbHKwmKxWCyRWGVhsVgslkissrBYLBZL\nJFZZWCwWy0KOiYgWVllYLBbLQkpUbKgkWGVhsVgslkissrBYLBZLJFZZWCwWiyUSqywsFovFEkkp\nlIWIdBCRYSLySsD+5pX9L4rIRBG5RETaFC2nxWKxLKrUXFmIyJbAicAQYJmAYrcDmwJbqOpmQHvg\n/mIktFgsFksZAgk+CzwrIv1wlEAjRGQvYA9gA1X9pbL5VOADERmkqtcXJ63FYrEsmtR8ZOFiTsD2\no4CZqvpadYOqfgRMA44sQC6LxWJZ5CmTsljg3SAiSwJbAO/6lJ8CrCsi7fIUKm4y87JRr3JD/cpe\nr3KDlb0W1JvcZVIWfqyEI+NnPvu+x0mK1C1PAertglapV7mhfmWvV7nByl4L6k3umq9ZRFBdw/Cb\noppf+WutoiwWi8WHnj17cs8999C+fZPl4MSUXVnMrfz1UwjVbd8UJIvFYrHUFcsuuywDBw40UpeY\niEZoAhEZD6yiqqu6ti0FfAuMU9V+nvLjgC2B9qo6y7OvHP+UxWKx1Bmq6ht9sNQjC1X9vuKot4bP\n7tWBF7yKonKcuVCLFovFYin9AjfA5cAKIrJOdYOI9ABWBK6umVQWi8WyCFGmaajncaahOnu2CzAW\n+EpV9xGRFsAdQCtV3bUGolosFssiR81HFiLSS0ROBtYDOorIWSKyQXW/OtrsD8D3IvIi8AzwFrB7\nTQS2WCyWRRFVXSg/wKHAZByz2/eA43zKNAeGAS8CE4FLgDYZykW2WVbZPccsC3wMnFFncu8A3AwM\nB44su+zAusBDwLjKZyxO/LOa3S+Vsh0qZV8JaTPx9SmJ3KV7RuPK7imf6hnN8imkkaI/wEnA9Tje\n332AR3A8xId7yt1VeUBbVH7fCjzmU19kubhtllF2T3kBHq60eXo9yA0sBdwNTABWrJP7ZXVgJtDP\nta038COwYQ1l3xK4APgF+CCk3UT3VRnkjttmGWU38Yxm/RTSSJEfoBVwoWdbM+DlysVYvrJtr8rJ\nXs9Vrmtl2yDXtshyMdrsWFbZfWQ4BfhXkhuxlnID7YBXcBRFq3q4XyrbzgZe8pHnZeCiWsjuI0fQ\nSzdxfbWWO0abNXlG455zT7nEz6iJTyGNFPnBGZ518Nl+cuXkblL5PQH4wqfch8Crrt+R5eK2WUbZ\nPdv7ALcAXZLciLWUG3gQJ/RL53q5Xyq/L8CZwljdtU1wphYOrYXsnn0Tgl5caeqrtdxlfUbjnnNX\nmVTPqIlPzRe4TaOqX6vqTJ9dc3BO7gdxAxTGLRenzbLKXt0gIp2AM4DDcF5asamV3CKyK7AzcK2q\nTk8ic61lB27E6ZE+JiLrVradiTMPHivsvknZffY1CewJZoJ71kLuMj6jcWWvkuUZNcFCpyxC2Bp4\nRFW/In6AwqyBDN1tZiFX2UWkOXAtzqLw7IyyFiY3ziIjwPsicpmIPC0i40TkwLLLrqpTgD2BTsBE\nERkNzAZ20d/ythQpe1zyDO6Zp9xx2jRVj3HZc3xGY1NqD25TiEgXoD9QNcmNG6BwiZjl4rSZioJk\nPxO4U1XfyiKrm7zlrvjf9MMJB/ORql4pIi2BEcANItJZVc8po+zVDar6oIichTMHvUvluDYBx+ct\ne1xyCe5ZgNxx2jRVTx6yG39Gk7KojCyuAIao6tTK77gBCrMEMvS2mZZcZReRHXHmYG/xKZdlqJv3\nOV8OWAyYrKqPAqjqfOB4HCuj00SkbUllB0BEjsPpYa4MjMZZFH0yg9yQXva45BXcM2+547Rpqh6j\nsuf4jCZioVcWIjIEmKGql7o2v1/5u6zPIcviWDXMSFAuTpuJKUD2z3DMAA8SkfnVD7/NtZ5e2bZ1\nyeSeUfkL8IO7gKr+DIzBsVrpkUTuAmVHRHbC6S0ep6rfq+oA4FKcXPP/SCq3AdnjYrq+ouSO06ap\nekzLPhjDz2gqilpJr8UH2Ae4B2jms+8lYLrP9k+Bp5OWi9NmGWXHMef7neezPc5i22WV323LJnfl\n98fA6z7lzq/In8jnomDZ7wGe9yn3X2BSLe4Xz77xBFtDJa6vDHLHabOMspt+RlP/v3k3UKsPMBDH\ncaWlZ3unyt8DKyd7Hde+HpVt+7u2xSoXp80yy+5zcyY2yytabpye+a/Aap727gReLLnsNwIf+8hx\nMfDfWsjuOXY8wcoi1X1Va7njtFlm2T1lu2L9LAz8U7A38D+cBac1Kp81gQHAdZUyAjwO3Fb53QKn\nl/Cgp6645SLbLKvsJm7EGp3ztjiOTE8Ci1W29caxONm45LKvDcwD/uHatiowDehfC9k99T6PT884\ny31VArlL94zGld3EM5r1U0gjRX6A/XDmBH+tnEz351dgL1fZtsBV/Ba7ZRgV13xPnaHlkrRZNtlN\n3Ii1lBtYulLudRynpodJFi6jlrJvjqPo3sQJC/EIsFWNZe+F42A2t1L3WcAGJu6rWsqdpM2yyW7i\nGTXxKU2IcovFYrGUl4XeGspisVgs2bHKwmKxWCyRWGVhsVgslkissrBYLBZLJFZZWCwWiyUSqyws\nFovFEolVFhaLxWKJxCoLi8VisURilYXFYrFYIrHKwmKxWCyR/D8MI5LgkcOj4wAAAABJRU5ErkJg\ngg==\n" }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig = plt.figure()\n", "plt.plot(time2, temperature_values)\n", "plt.ylabel(temperature_units)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Main problems:\n", "* The figure is not large enough.\n", "* The labels are too small." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Improved plot" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "With some commands the previous plot can be improved:\n", "* The figure size is increased\n", "* The font size is set to 20 (pts)\n", "* The year labels are rotated 45º" ] }, { "cell_type": "code", "execution_count": 65, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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5ea7Du+iEzjOBwO96/uWXXyRLQmSDtX8we/ZsrFmzRpI00aHL4NSECRMAlHvu\nZVoyQ0Yg4YogR/hk+0qb3UF/+OEHAOVbl+vGLbfcAqDc2LWuCezatWtg3xO1PidMmIBXXnnF02ce\neOCBkKSJhssuuyytkRsksuvniy++KPX7VSIog0GGTu1CfIj8FBYWYvPmzVGLJB3d1wSK/oEYyCHc\no5JOzf0DzjmGDRuGSZMmSZQoWHRfE3jHHXcAAPbs2WNce/bZZ1G1atUKz8bZCHQLGYEBEveZwA0b\nNqQYgdu2bQMA7bYsN6PbmkDdR9OJcnTWcWlpqdH2pCOuv4G1vfn1118B/J6f4uJi7WIFukGndtgO\nkb+49xPc8Msvvxjl2dzZ1o2FCxdqV27j2q66RQz6m91BrXnWoR9FM4EhsmPHDtvrcV8TWL9+/RQj\n8LzzzgMATJs2TZpMYSEqiG5rAv3kJe6NXZSosJZBd95//3306tWrwvWJEycCAObOnQsgvmsCrXX0\nnnvuSTnX2Qi85JJLQv8OVevoE088ASCa30A2999/v1E/n3322azTU1WnwrVQJ6LoD8jUp9kIdArb\nQkYgkZZatWrZXtdpHYdTAYp7xbCi20wgkF5HuumP0A+n0dn69esD+L391bUsFxUVYePGjbLFCIWC\nggLZIkhDuJvp0MF0Qy7kUaBTXnXKix2iv2c2Aq3k5eXlhCsoQEagZ4488siU85tvvhkA0Lp1azRv\n3jzr9GX7vmcKEdGyZcsoxQmdHj162OZ1+fLlgaQftT4zdTDs9KdLo//dd9+F/h2y66dAF53ZwTnH\nu+++i8GDB6dcFyO4QQ/aRK3T66+/Pu19nWcCDz/88NC/Q5U66kQymdTuPRo2KutUt0FkM+effz4A\noF69eoGmK1Of4j2Sl5eHtm3bArCfCUwmk7F+z9JMYEhYX2K9e/cGAJx88skyxImcZcuWyRYhUJo1\nawYAqF69esp1EfMobmQyAnXTn5m9e/emnAcxKEOogzUYdZxf0OmoWbOm7cYxOlCpUiXZIkiHc651\nOyzQtX7aoVNezXm58cYbAQBPP/20LHECxzwTWLt2bbRs2bKC/sR6QZ306oR0I5Ax1psxtpAxVsIY\nW8EYuzPD83UZY2sZYwOikhH4vWJ07tw55bpwUYrr+hQrYpTaumOmbpgbAiC8Rjxqfd50000YMWKE\np89wzjFr1qxY7jz5008/Gcdr167FQw89ZJwHNZtrRnb9FOj6clq/fj0efPBB23vWmcCg3HVk6vSl\nl16qcE0FkawbAAAgAElEQVSHmYWRI0di6tSpFa6fdtppoX+3TH260Z2udTdMVGl37di3b5+x27gO\ndO/e3ThmjKFZs2b4wx/+EOh3qLAmUAyY29XZP/3pTzjttNNyoq5K7ekzxvoAaA+gN4DzASwDMIQx\nNsTheQZgJIDDAEjRzuWXX24c16tXz5gm16WwfPnllwD06Ii4wcnYzZX8C3744QdMnz5dthieMa+d\n2rBhAz766COJ0kSHruVzy5Ytjsa7dSZQhzUbor3Vja+++gpLly4FAAwY8Pt4badOnWSJJI3PPvss\n5VyXvkImdMpnu3btHO/t379f6x3Uly9fHrgRKBOrEWhHx44dceaZZ0YpljSkGYGMsQIA9Tjn13PO\nZ3DOpwDoDGAegNsZY8U2H+sL4PsIxayA2WgwF6A4x6zKRcy7g+bn5+OUU04J5XtU0uecOXNsr+/Z\nsweffvppxNKEQ9gdD1X0qVMHyy3W2fugjECZOtVVjzLDIMjS5xtvvGF7PT8/P+W8Zs2aUYijDHGN\n/fjFF1+keJo4oeuAHBBe+ySrjq5cuRLz588H8Pu6P3OoCAGtCYyGmgD+ab7AOU8CGItyuRqb7zHG\nTgfQCsDzEclni7kBD8MIJKIlkUigSpUqFUZrdWzYx44d63hv9OjREUoSDnaNOREv0tW7sNxBZaJr\neU0mkzkRD8/MddddZ3vdagS2aNEiCnGILHnuueeMcDQC68aAgJ59BYFu7ZPVDVW4gzqtCcwFpBmB\nnPMtnPNNNrdKACQBrBIXGGP1AQwA8HcAUmqcKBA1atQwroVR+VXxfde5YTMTdj5V0SegR6c5HVGU\nWVX0qeMLat26dfjmm28c74flDipTp06BtOMeRmDNmjW+9BOES7oqdVSQq5vhiN2aS0tLs05Ltk5F\nXdy+fXuFe2vXro1anMgI650qS5/mgSnRxqabCYxbP9jPOyM/8yORcxqACZzzzQDAGMsD8CKAmznn\nexhjwe5V65G8vDzj+LjjjjOO4/zCzkWsrmUA8I9//KPCfZ3ItO183Mtw3DvOuU7Tpk1TOox9+vRJ\nua/jTOD7779ve7127drYtm0bDj300IglCoYvvvgCxx57rOfPTZo0KQRposFpfXn79u1TznOljRKb\n6An3Ox3Ytm1bhWtff/21BEkIP4h1ykD6jWGEYdi4cWOMGDECK1eujFLMQHDbh1XKCGSMNUb5BjFt\nTZcHAXiHc75YjlTO6LwmMFdeVGaqVasWeJqq6BPQc8dXa0MXtguaSvrUDeuMgXUGxToTGJSuVVwT\nWFxcjI0bN8bWCARSB0yjRJY+nTpd1uu5+G7NFtntLuksWGTrU+DkDipmAgGgatWqqFq1qgzxPKPD\nTOALAPpyzpcBAGPsXJRvHtPP5tm0Zm7Pnj3RpEkTAOWjqscdd5xR8MRUtJdz86izuC9+8ClTpuCU\nU07ByJEjfaevwrmZ1157rcI1gSryZnNuXvBtl//Zs2fj//7v/5SR18u5U56cNi6wIlt+L+fmDpaI\naeSUJxXkDeIcAGbMmIHLLrtMCXnCaH8A4JFHHsHDDz9s3G/cuHyZ+MyZMwEAzzzzDB599FFl5Pei\nv3RMmTIFv/zyCxo2bIiWLVtKl9fvuaibq1evrpA/8/Pimmx5gy6/5ryZn1+wYEHa+3E9N9OkSRND\n75s2bYqlfs3s2rWrwjUnVJE/iPKbTCaVkS+oc4Ew9Hbv3p0y0zdlyhTs3r3biBO4ZMkSrF+/PuW+\nivkBUo1A4WXSs2fPCs9V+JAKfyjf+fNFy7VJAA7Y/CUBlB08Ps0mLR40ZWVl3JwuAH7GGWcY5+vX\nr+dBfO/kyZOzTsMvKA+74fgnntGBO+64gwPgJSUlxrUBAwZwzsvzuHjx4kC+J2p9WnXl5e+qq66K\nVNYgmDdvnqu8BYXM+sn57zpdu3atVDnCIJPeVq9ezQHwX375hQPgvXr1CuR7VaqjgoULF/J33nkn\nUrmCBADv06cPB8AHDBjAmzVr5lgPre/VbJFVR6tWrZqxDAPgH330kTbvUTPmPPfu3ds4vuCCC7JO\nW4ZOu3TpwsePH88B8OOPPz6yd4ws7PL04YcfhvJdsuronXfeaeStTZs2/K677uJt27blTzzxRMpz\n27dv50OGDOHLly/no0aNMvqGKmLW1759+4zjl19+2dpvt7W9EvamYbQwxroBaIfyeIFmrgdwrOXv\n/IP3hh08d95JIEDsXFvMs4M6riE7+uijZYsQCt27d8czzzwDQE+9EYSOWN1B//Of/wAoHxkdMWKE\nNLnCoLi4OOMaXtUReho0aBBq1aqFiy66yPVnZ82apWXb3KVLF2O03hyUWzecdlEn4kWzZs1kixAo\ndq7Z6dYExg3rTsSPPPJIxs9INwIZYxcD6AHgCl4eIkJcr885X805X2z+AyAiCW88eK1EhtxAOI2b\n3RSvLNq2bZv5oRjy1ltvZXwmqA6ISvrUkagbatKnPKwbwwhWrFiB2bNn+05Xhk7/9re/pb1ft25d\nbNmyJSJpwsGsp2rVquGOO+5w/dkff/zR9/fKqqNu1lw/8MADRpvl5j0UV6pXr24cB9FGy25342gQ\nBIFwwQ8aWfp0MgKt+jWvCYwT1smq+++/P+NnpBqBjLHLAQwG8CCApoyxloyx1oyxLgAelimbG3Rq\nGIYNG1bhmqyF/QThljg21EGgU9vjFutMIAC8/fbbuOGGG2SJ5BuhP6fBpkQiEfuyXadOHeM4mUz6\nep/MmTMnSJFCxSnchxnGGO67774IpJGLCA8BxLONHjZsGP773//mZDtrRsfN5ATm3UGtehYzgXHW\nv/LB4hljVwJ4E+UB4OcCWHzwbyGAcQA+lSWbW8JwB820yDwsbrrppgrXCgoKJEgSPmZdud3RzS+y\n9JkrRN3BIH3Kw84IHDRoEIDsjOKodVpUVGTsfJrOMIqzO+R5551nbMwGlO/k6qdD+fHHH3v+jEp1\ntG7duinnjDEsXqzcRueBE3Soj6h1atcfykUqV64cSrqq1FGeIU6gONYZaUYg5/xNznk+5zyPc56w\n/OVxzt9x+Nzqg88MjlpmG1lkixAITi+l5cuX215ftGhRmOKEju6VOpfQpQ56JRfzbecOKsJExOn3\n2Lhxo3H822+/OT4XpzzZYZZ/2bJlOHDggOvPCh3H/TewDlLlyrvHnM8461CXckiUY9ZjOm+LRCIR\ni36ueZd7v+g71xsB5gIV5zVkHTp0sL0+ffp02+vHHHNMmOKEjhtdxVmfuUTUM4GkT3mIOmmeUVq2\nbBmA7DppMnSaC8aAWSfbtm1LcRH0k4ZbVKqjuWo8uPG28YIsnQr95aoew0KFOpouWDxjDG+++aby\neu/Xr2L0vEsvvRRADNxBdcDLyKbKWIM0OxF2IO6o0GWUMmjiWJ7juN6E8IedO6ggjmU3E3E3FK11\n08uaQDFDWlpamna2VHXs3MxygaCNwCgx60z0jdyUwTiX01x5j1rfE+nWBApULr/79++vcE0sNXAL\nGYFZMHfu3MDTlOEr7dYIHDhwYLiCRESUM4Gq+L67YcyYMbJF8Iz55XXYYYeF/n2q6LNhw4ayRYgc\np91BAWD06NG+041Sp6KzYffyHjlyZMp5tWrVXG02oiKMMYwbNy7lmnX7cjs6d+4MALjzzjsBAE88\n8QROOOGElGDNmVCljgK5awSa892oUaOs04tSp+b1jN26dQPgvGTGTJzb5Oeffz7S75NVR5977jnj\nWBh/TmsC48Dbb7+ddRpkBAZEXApNNuzevVu2CIGQC7rKFcxGoHk3Qh0xv6i8jvbpQLqZwLiQzr3M\nGpe1qKgoZf1g3Ni7d2/KuZuZwBNOOAFA6rtm+/bt2sz0xrnsesGs67i1VXYDNG7YtGlTwJJER0mJ\ntEhrUkkmk7ZGYJx3RfX6noxvTjVFBV9p3cmFNYHCL1x3cmlNoM6uy6+88krGZ9LNBGaDDJ260WXc\nA8a77VgNHTq0wrV9+/alfM7LUgSV36G6G4GFhYUAkBLjMm5rAnXXkR39+/eP9PtUqaMjRoxwXBMI\nxOudW1xcDAA4++yzPX2OjMAsaNmypXGcCw2HLnnMhTWB77//vmwRIiGMzZlURdeyCgDff/99xmd0\nnwm0UlRUFFsj0E5HTjOBmYLI5+XlabMeXXcmT54sWwTCB26XBOmI3ZpA6/04cMEFFwAATjzxRFSp\nUsX158gIzIIwOmUqrWfQFXOlduqYPP3004F8lyx9+i2bcdv59cknn4z0+2TWT52NQDfY7Q4aBDLW\nBFavXr3CPWtno7i4ONbuoNb8+NWbVyNQpXeoWOMoiEuH0i+1a9fGRRddFHi6KumUyB4V9ClieLZu\n3TplQieuWCc3yB00AnK9UxZXzJWjoKDA9plhw4ZFJU4oJJNJ3HvvvbjhhhsAOOfTShxi45iZOHGi\ncax7fdQ5f25eWGG5g8qgXr16GZ+J+5rAoDZbyMvLi+3uhW+++WbKuQ5l14kBAwbgsMMOw/jx41Ou\n65xnIr4ceeSRAMo3/+natavtM3F65/qtZ2QEZoG5gARVWKL2lTavvciELo2523wsXboUO3fuzOq7\nZMY32rBhg1Euc8HdI4oGm9YEyiOTO6jfnTSj1KlZh7Nnz065Z81XQUFBbOutnYtV5cqVfaWVSCQ8\nbQyjynojO3R5h3qhcuXKFcq6V1TWKeEdFfTpdnlBmHX2m2++CSwtMgIloEOnbPny5bJFiBy3leWu\nu+7CtGnTQpYmPKyj0LqjQ31Mh+75y0SmmcAFCxZEKY4vzDrs0KGDbbBfM7oYDTVr1sQZZ5zh67MF\nBQXa7A6ai5x55pno0KGDbDEIIgUV2tYPP/wwsLTMRm0ymSR3UL8kk0nXo6/mLbCD6qBF7SutQkWI\nGrd55pxn3fmgNWTBU1paapu3KFzGZOpzx44d0r47bNyU1Uwjt37Lu0ydZgqbENc6zBhLaTvz8vJc\nt7t2W/R7eU+psN7ICXrf+iNKneaijqJGpTqaTt9xan/9bpxGRqCFMWPG4LbbbnP17M8//2wcx6mw\n5DpejMAwFrmHiTlgrZfRoDhx7bXX2o6g6b6DoNgCWkeCMALjgA670LmBMYYvvvjCOPfyfmzRooVt\nejqgSz7scApLlEt9o1zKa9zJVBcvv/xyV8+pgt9d78kItPDbb7/5XlsSBFH7SselgAeJFyMwW6LW\npzVguo4vpb1799rO0EYxE6jCWgYdcVNOhTuodZdJsR2237Iua02gG+LaPlvl9rJbnTlQvB9UrqNx\n1acb2rRpE1raKuvUjI7v2zBQQZ+ZBhXjtmMo7Q4aEIlEAm+88UbKNTedy7hWfi8vJfOzZ599Ni65\n5JIwRAodt251cdWp7jjF9YnrDoKEO5xe2qIsVK1aNXKZwiY/Pz+W6+GshvozzzyDatWqufqsLoaS\nXQBuXfLmBWuYDNXZunWrbBGIkDjssMOMYy9B1QcNGhTLdtgNZARasItl5MYY0HFN4IABAxzvff75\n5xW2gtaNIHQatT6DCJ6uuvEb9JowL6i0liHXSCQSSCQShv4POeQQAOV6f/PNN12HQbEiI06gHXbl\nul69eti0aVOYIoUCYwzNmjUzzq+99lrX+hG/g98ZXlXqaK9evWSLoA1R6lTs9dCkSRPPnw06hqmu\nyKqjnTt3NsLziJjIbtcExqVf5DRI7gSVWAt2C/W9GIGqFxQrfmcCc4GSkhLZImRFWVmZr13ZVJ9R\nGzdunG3oDtXlJpxxuybQvM5V6DuZTCIvLw+DBg0KVcYgeOeddzw9X1xcjA0bNoQkTXiMHz/e98i5\n6Ei3bds2SJGUINfeoXGGdKUfQqc9evRwpd+HHnrIeE71vv2xxx5rHJM7aBZ4mQnUIU5gLjd0mdYw\nBLE2VLbv+9/+9jcAwOGHH+76M3EwpuxcemlNoD706dOnwjUnN1CgfPBu3Lhxvr4rSp2OHTsWJ5xw\nguvn42oEAuXr6/0g9NytWzcA3t+tqtRRu3drLr9vsyFKneayjmrUqBHJ98iuo6NGjXK10diYMWOM\nY9X7RSLgPe0OmiV+3UH9PKsC6QqMKm41YZFJV3GIO+YWL65ycd1lU/VGmnDG7Uxg48aNjXOh7yZN\nmhhhBSZPnhyOgAGRzlXHLph6UVERNm7cGLZYgSLc6cy7Z3vh119/BfC7V05ZWVksO+b5+fkVrsUx\nH0ExadIkTJ06VbYYGRH1Mxd1Fbf+ayamTJmS0o+1y18mPYs1onH5bQoKCtCgQQPXz5MRaMGu4dZ5\nTWA6rA12LjaK2SJzTaAZp0bBLoBzXI0p3eME6ozb9nPVqlXGsdD3Dz/8gHXr1gEAOnbs6Pm7Vamj\nrVq1qnAtjjOBP/74YyDpiAFZr4NSqtTRoqKiCtdy+R161lln4ZxzzvH1WRnrdnNRV25jZGdLVPo8\n44wzKvRxvIYamjx5Mh5//HHljUCRnyOPPDIlPE8myAi0YBeo1g25sCYwF4ib/qx4Xcdop/84zATa\nueru27dPgiREELitd2ZPDfGZRCKRMtMdVUfGD14X7deoUSPrkAlRE9TmGOJ3Un1QqqysjHaUdInq\n/Y29e/ca7YfqshLeMfePvBiDiUQiVn3DX3/9ldYE+kUEiDRjp/w1a9aknDdq1CiQ71dhTeCjjz4a\nqQyq4LWD5oao9WktlwKh5/r166dct+uwqd7pAoAHH3ywwrVt27aF/r2y1zLkAmvWrAHnHDfddFPa\n55LJJKZNmwYAqFSpknF98ODBnr6P1hsFi3lztVtuucV3OqeddhoA7+1R1HX0xRdfRN26dV09mwv6\nD4OodNqtWzfHMnvuuedGIoNM7DZGDANZ79GRI0f66uOF0TcMkxEjRrh+loxAF9gp/4gjjkg5F53p\nOBUUJ4IyaFXH+kKOW0X3Q2FhYcp5XGcCCb0w1zvRtprX/9mRTCZx6qmnAkh141c5npOfNiZuhoNZ\nXuugkxeEYa96e+TFSI2bLoNG9fybPcGssh599NFRixM55rZJx7irZrzMBMatb0ghIgLm3//+t6vn\nWrRokfV3qbA+5cQTT4xUBlVwqujZVH5V1qc88MADANx1qOIwEygLWfr84x//KOV7ZZKp3pnvm41A\nlePKvf/++553HY5T5wNI9S646KKLfKcj2iGV9Ql4mz2xdjij8F5QCb9GoIx2d/ny5SnnZm8DnTD3\nCV599VXjOMx2R4V+kduyyDlPcQddtmyZEoMZffv2TXuf3EEDxO1C9x49esTuhW0n72GHHWb7rAoF\nP0xkBiEPg/bt2xvHnTp1AlDRCKSZwHgwY8YM2SKEirWOuWlrzIMV5g6a6vXVaaZSl/bVbATabXbj\nlrisCbTbTM4tKs9ah4HqZTxd26GrESjy/Le//c0Iy2K+rht2HmCZnhcxagFg165docnmhR9++CGQ\ndMgIDJAgGrgofaXvu+++rCu6WJOjIpMnT8bEiRM9fSbomUCZa8hOOeWUCtfcxO5SvdMlE1oTGA7W\nOua1zmVjBEatU865p7AP5g5IHDAbgYwx9OrVy1c6Is9ijahbotbn+++/7/pZ1Y2gsCkpKfG150BU\nOv3kk08c7zHGUgZWdUHUrT/96U+218NAhfeo27oojEDOOZ599lkjhI1s0umH3EElEqfRkyeeeMJX\n3BQzs2bNClKkQJk5c6anuERO7qBx6oCZsdvkSKeZwDjVNSJczIaH6vWVMeapI3HooYfGym3QutmU\nl00KzMSlfk+aNMn1s7liBPbs2dPx3gsvvBCdIAHCOfddllVGtJdXXXVVyvW41D8v9O7d2zj2syZw\n1KhRShqBhxxyiC9vGoCMQFe43f45iAY+al/p7777zvWzcTMYvOrDyQj86aef8NVXX2H79u2eZZDp\n+26Xf2sH2W53UDezhSrw5JNPRv6dKqxl0BE/LzCzq6G5HHsNERG1TtesWZMS1L5mzZppn49brMCg\nOo+VK1f2lWbU+rRuEpcOa7levXo1Vq5ciaeffjposaSSrv7+/PPPntNTod3V0SgC7POVn5+v5ZrA\ngoICNGvWzPPnRN/QvDZQdnn4+OOPHe958dAjI9AFM2fOdP2s7ILhFbtNb7zkQfVRdy9T5k5G4Cef\nfIJ77rkHS5YsCVy+MHjjjTcc77mZCYyLEXjvvfcCAP71r39VuHfBBRdELQ4RMd9//71xbDYCd+zY\nIUMcT5gHlIQLmlPHOVeNwObNm6NBgwaBpBUmV199tetnhY7FPgPjx4/HqFGjcPfdd4cimyx0nPHk\nnBv5atu2rXHdvJlKHLGrryUlJbHry7rh0EMPxfTp0wG4nwk0u4MyxpSJBJCun7Z+/XrX6ZARiOA6\nDYyxlLT8pBu1r7TdTnVeDDuVO1wlJSXYvHmz6+fT7Q7622+/+YqhI8P3XTRSfmduZTduXrHLZ1id\nEBn6VLmOBYXf2E12x6qvCbSSqawWFRV5WkMomyDbD9Hmqrwm0A+ijd63bx+qV68uWRr1UU2n5vIY\nd4PXaUmQTmsC7fTlRW8qGoFm7PRF7qAeqFOnTtr7XgpLcXGx63RVYN68eRWuORVuO+Pwn//8Z+Ay\nBcWyZcuMUR83ODV8Bw4cQFlZWWSBVLPFzsVToKMRaCdvut8gbsShHQma/v3747bbbnP9vFnfOpRf\nM7k6EwjEox43bdoUxxxzjKtnRV9CrPF866230K5dO7Rp0yY0+WTx2GOP4fPPP5ctRiD89NNPePDB\nB3HMMcfg008/BQBs2rQJABmBcWDx4sUAnPOaCbMbqKpGoF/Ub2EjIJMyvewi5CVdO1TwfVfdxdMt\nZvcNO9wGi8/GCJShT5Evu7xYXQjsfh+VGje/hPVilqFPHfSRCWseq1ev7ilYcTYzgSq0uemoU6eO\n63XpKiB+/3r16gWephtk6LNly5aunhPl1DwTmJeXp91AD2MMhYWFgZUB2XX0sMMOQ40aNZBIJFC3\nbl1wzlFYWChVpqCQYQRGrU/Rp7XzHvESIkJFg1+sKferL+lGIGOsN2NsIWOshDG2gjF2p59nQpbR\n82eaN28egiTR4LRRQRw7o1509/DDD2PcuHEVrk+aNAnJZFLJmcDjjz/eOL722msBoMImA+PHjzeO\n3awJjJuezXno06cPAG/bthPyybbMxWkm0BxAfdCgQRnlNY9Cx4WTTz45djL7xUvnUDy3b98+AMDO\nnTsBqF9m/ZApT6NHj1bak8gJFQ2BbMh2h/g4YOcBlSmP5g2MzO6gwqBUpc46yRELd1DGWB8A7QH0\nBnA+gGUAhjDGhnh5JgI5PT+3YsUKX98lw/e9fv36KedOQVFVKfRu8Srvrl27bNcQbty4EWVlZb5c\nk8LW57fffmsci80GROMl8m/udIrGUGycooMRaKZz586hpq/a2hTd6NChg6/PZRMiImqd/uMf/6hw\n7aSTTvLktaAynHNccsklgbQjflyvZKw3ciuf0KPZIyNOunWLm5mkTZs2Yd26da7SU63djfM70ooM\nIzBqfaZbBuOUVzFQY90YRifdAxKNQMZYAYB6nPPrOeczOOdTAHQGMA/A7YyxIhfPFDul70OeCtdm\nzZqF0aNHB/UVSmEuyG6Nm19++SUscUKhTZs2OOecczx95rXXXqtwbd68eSgrK0N+fn5AkgXL+vXr\n8fjjjxvG+4UXXuj4rGgMnVwhTj/99Fg3ck4NepzzlAvMnTsXgP81YD/88INx/M477wQik0rEqfxm\ncsP3QhwMJM45xo4da5ybQ5c4UVhYiNq1a6ekoSPp9Ddw4EA899xzEUoTDHEok17QteyZ8TMTaF5W\nYzb+xP9HH300a7mOPPLIrNPgnOPGG2/0tHzCjMyZwJoAUnwBOOdJAGNRLlcTF880DlPAhQsXpsRz\natiwYdrn4xQn0I8RqEqQTLe0a9cO3bt39/SZhQsX2l5XeU3ghg0bMGbMGMNI7dKlCwD7xj2TO+iZ\nZ54Z65dC2C9o2WtTdEWEe/BrBIpZcD9ErVPd3a9E/oLIk580ZOvTHJDaishP/fr1ccMNN6Bq1apa\n6d4tnHNPcXdVbnfjrr9kMomhQ4dG+p2y1gSaybQm0Lp+0GoM2oWm8so111yTdRoA8Mgjj6BatWop\n15R3B+Wcb+Gcb7K5VQIgCWCVm2eClGn//v0AKrpDih/T7Q5e7777biAWfth43So3bsZBkCPSK1eu\nVD7/ogPtZndQp81jdHR3ACrm891335UkCWGH0E+LFi18fT5OmzRYy6Lb+haXemkNqhxUmqrSo0cP\n18+KzqjVzXXLli344osvghdOEpneIyrr0y+HHnqobBF84dRPOuGEEyRIEw5udkW3snbtWuNYtGfm\nNYHWZVSyyLaPK31jGBtOAzCBc54uwJubZzwjtm2+9dZbjWvmxkoE9c1E165dccUVV/iSISpfaT9x\nbuK2a6jojASZnlei9H23xgd0inloRrc1gdb8NG5s7yzQtWtXX+mrtjZFF0SZe/nll319/vTTT08J\n4Oz1s1Firl8DBgwAkLkNrlmzJnbv3h2qXEER5OCbn3eO7DqaLu9inVG1atVSnlu6dCkGDhwYtmiR\nkWmAWfV1u5mw6z8Jl/a44VRfw8xP1Pq0C6yeqYyaZyvNu4OKsvv3v/89eEF9kG1/TalFToyxxijf\n/MXxbe7mGb84ucq5NSSy2aY8asyL2eMQi8kPmXZt85pvVXXKGMOOHTtQq1Yt4xxwJ6/bMBkqY9aj\nXX6A1N9CdMTiSCKRiN1gTDo2bNiQEls1G+LSjtkNxGSSXQSMd9q5WSXE4FsQYQ9U24kvKITO9+3b\n56m9jhPp8hPnNizu7p9Wghy0URU/awIFTmsCVdwt3ozy7qAOvACgL+d8WZbPoGfPnhg4cCAGDhyI\nZ555JsWqnzJliq1PslDuunXrUu43aNAgJd6N9fN26a1evdr18+Zzcez2eb/nU6dONc6tnWInf22n\nRj0Kef2ci8rrpG8RSN7pvhOq6XPu3LlYvXo1pk2bBgCYPXs2gPJt2p3yt2XLFtu8McYwe/ZsJfTn\ndMkVlJMAACAASURBVG4lnQuh6FybP//AAw+kPKOaPtPlV6yjUUkf2ZzXr18fU6ZMwXnnnWesaXDz\neTNTpkxBMpnEBx98YPtMpvQyvR+CPp83bx7OPfdc43z//v24//77037+119/NQLGq6Q/u/Ovv/4a\nGzZswDfffOPr82aEsTBv3jzXn49an1aWL1+ecm5+XnTMpkyZgquvvjolb+L9Klt/QZyLTeTczLK4\nSe+ZZ56JTP5M8s6ZMwe7du0yzpcsWQIAaNKkSSTyBX0+ffr0CrvZh/39Uetz1apVjvfnzJmT9vMb\nN27E0qVLjf6k2ITM3LeQqb+ysrIK94HyDdIGDhyInj17Ii1iRkj2H4C+AF7M9pmDz3EvAOAA+Jo1\nazgAfs8993DOOX/xxRf59ddfzy+99FLeqlWrjOk8+eSTRlp9+vThAHgymfQky+TJkz0975fS0lJD\n1qOOOso4Fr+d+Vz8nXrqqbbXVWX8+PF8/vz5Fa47yW2XN/PfkiVLPMsQtj4B8Pnz56fI+b///S9j\n/i688EIOgHfp0iXl+mOPPWb7m6mEXfkTxzNnzky517RpUw6A//bbb8bnhw8f7rvcRlU/zdjlVRdE\nXm6//XZ+yCGHeP6cmZ07d/pqk6LUKQA+depUftttt3EAfMCAAa4+N3/+fP7ee++FK1xALFmyhI8e\nPdrXZ/v375+iw4YNG3IAfMaMGTyZTLp6n0ZdR63t0bPPPutYBkUfg/Py3wkA/+qrrzgAfvLJJ0cp\ndqj07t2bDxs2jC9atMj2XVpSUuKprkalU6d3v5kFCxbwNm3aGM+PGjUq1m3yhg0b+L///W/jPIq8\nRK3PefPmcc55Snu7efNmDoAvWrQo7We7d+/OX3/9db5ixQr+5z//mT///PMcAB8+fHgUWXBEyFe9\nenXbe2PHjk055w72khIzgYyxbgDaoTwWoO9nssUpPp6fqfKSkhIAwIgRIzx9TsaaQLvYeIJDDjnE\nOFZlIaxbeABrAl999dW0u21mQsZahmxcbeLoDgoAH330EQCgefPmKdftXK2y8eVXbW2KLrRr1w6P\nPPJIVmlUq1bNCI/y9ddfu/6cjDWBNWrU8PSZoqIiYyZQdXgW7mWDBw9OOX/wwQeN4yFDhuDFF1/M\nmIbsOuo272Kdkvgfx3Y3Henyk0wmXW+0B8jXqRmzfp944gmcddZZmDhxokSJsiOZTEbuSq9CnEAv\nGyNag8X/8Y9/lOqa72Z9eGzcQRljFwPoAeAKXh7+QVyv7+WZIMi2Iph/dFHonNzuVKK0tNTx3mmn\nnQYA6NixI6pUqRKVSIGQaU2gG7p374527dqhZcuWsXlJe9kJS4c1gQAM9zpr51r3tQ66ULt2bd+B\n4gV5eXl46qmnAKi97jOZTHqOOVqvXj1s3LgxJImCJRsj0Eq7du2MNLds2RKL96nb9lO003FsbzNh\n7jTbkUwmHTftihN9+vRB/fr10alTJ9mi+MZv+Ks4ke0aVGuIiLPPPlvqb+Znt1MnpBqBjLHLAQwG\n8CCApoyxloyx1oyxLgAedvtMUJgbY8YY9u7di5dffhljx4713FAPGzYMAPD55597+pzVlzkszPkp\nKyuL3SyfG7p27YqtW7dmlUZeXh769esHxhhat27t+fNR6dNMpgbC/GL+61//WuFeHDslTqN6QW+6\nIEOfurJnz57A06xbty4AZ68OO6LUacOGDXHkkUd63nSsUqVKtjvcqUiQRqD4fXbt2oXHH3/cVWdO\ndh31agSKXRjj2O46kek9UqtWLTRv3tx1gGvZOtWZZDIZuUETpT7r16+P6667rsJ1LzOB1hARQYfA\n8UqQ3y1td1DG2JUAXgfAAFj3ouUAujHGugMYme6ZIGUSP6woFOlmyOywK0wiCLJqWA3eo48+OmMw\n+Di+pLwEpLXDGnZBRayyZTICzZ004T5nvR83MhmBOuB1QEl1zLN1QZW5OnXqYOLEicrq/YYbbjA2\nkAC8eZ+omicrQRqBIh2xvCIuv4ETdt5CYlOOOO+YaUcmXTVv3jywYNlRE8d3pBNlZWWx2VnZD336\n9MFdd91V4bpbI5AxlhIaQiwzUsUIdJJDeXdQzvmbnPN8znke5zxh+cvjnL/DOR+d6ZmAZUo5X7Ro\nUdZpbtpkF+veGRm+7wUFBRkLzIIFC/Dtt99GJFFwBOEGkA1R6NM60ODGVcApX7rOBAaFzLUpcdRL\nOqw70nnRlXBTtyM/P9/TrFlUOi0pKcE776S+ssSOvm6Ik/6DqndiTbqXwTjZ68fSyWheUiFc10eN\nGgUgXvp1Q6bBAMYYhg8f7iot2To1E/eBCCsy3EGj1KcInZUNBw4cwFtvvWX0j2SHatq7d29gaelr\n/vvA2gh//PHHjvfsiFPjYM5P7dq1DdmPOeYY2+e3bNmi7KxmOryuvYkj5kXphYWFWTVOske4siVs\nd1AiOCZPnuz7s19++aXjvby8PCVdJzdv3mxsJy/49NNPJUkTHtnWNXMdPuqoo1KuqfyOPeusszI+\nU7duXSMPrVq1AlDuFh3XwTcn7PQkllPcdNNNUYsTODrpSvc1gaINseJlJnDPnj0YPHiwMu6g5sml\nbNvErI1AxlhDxlgXxljDbNOSjdUd1OxK6GWNSTbIWBOYSCRQuXJlAGq/ZL0gOoEiX9nit8JHoU+z\nK4fbzq+TnmU3bkFjdXMR8av8InNtik56AcJra/Lz8z0tnI9Cp5zzlNixfohL25ytO6hdOfdiBEah\nzx9//LHCNS/Bp+0+q1v9tpYDsf5PDMx6KSMqrQmMSz1Mh7n8Wo3AKDYAjFKf5neBOd9e9Cjc0QFg\n5cqV0vtJ5jxF6g7KGBt78G/AwfO2AJYCGA9gCWPsZC/pqQbnHH369DEaqwMHDhj3XnvttYyfj2vj\ncPrpp+O9994DYJ+HoAypKBEdLjejs3HH3IBv3749q3Iou3HLFruZwH79+hl5ivModJz1YkeYRqBq\nM4ElJSX485//nFUalSpVwv79+wOSKDyCWBN46aWXppyr9m61m11wY6jmyuCbnVErZu/F+0o1nXoh\n7royl1+rEZjtZnqqIHRkXtY1cuTICs+5KYdjx44FUG4TDBs2TPqgTZCuqF5nAi8B8AXnfNDB8+EA\nDgA4F8A1AB4NTDIJcM4dd6uKyhCSEScwkUik3aWrWrVqUYgUKF5338uE3wofhT6DdOXIy8tT+gWX\nqfGza9CrVasGznkgbi8qrU2JO0Fuc20mPz/fU4iIuOi0qKjI8xpzGQQRmsc6G+FlJlCWPnN58M2K\n3W8h+hh+2uCwdeqlUy3bAAga63vR7Y6t2RBFHbV69lnxUl/F7yP6k7Lrq0wjcBnnfBgAMMbOQnnw\n9rs5559yzv/rIz2lCHItg+qY81pcXAwAePbZZys89+ijj2LChAmoU6dOZLIFQdC6ULnRt27Y42X2\n0/o7yW7c0rFw4UJ07tzZ8f6jj/4+BiWeE/l76aWXkJ+fj3HjxoUrZIgIvaRbDxcn7r//fuM4yDJX\nr149XHTRRYGlFwRBbFhUXFwci4Dxf/nLX7Bw4cJA0xRhFKybCalENu8c3QyL22+/HZdeeqntbyI6\n1CqtQ2vdurWrANw6IiNERBS4NQLd1FurC3NeXp7UjWGCdNn1arSZF9T0A/ADykM4gDGWANA8ILmk\noEIjLGNNoJjltNtx75hjjsFJJ52EoqKiCvdUDvYatBHot8JHoU9rKBMRK80Pshu3dOzbty9twOy+\nffsaem/RogWAcqOWMYYdO3YEIoMKawLT7YwZZ4Kqs4WFhZ6el6FTnY3ATZs2Zb17nfVdLNbk7Nq1\nK+NnZdVRmgn8nWbNmqFBgwa29/y4g4at03Xr1in73gsbGSEiolqHDQTjDWYts3EYtAkrRMRyxthj\njLFXAPwJQB/Ouag5/QHEOuL4lVdemZWLUpxmAi+77DJPz9vlTeVKEKQuCgoKlM7rggULjOOTT3a3\nLLdbN/sQm4lEwvGebPx0lEQ5EKO8cTagVC6DKiHc13/++WfJkjjjp30qKipKOwgim+7duwMIxs3X\n+vuIsq/y0gQyAitiF3tYxZh0u3fvxldffeXq2Tj189zw7rvvYvz48bLFCByxvMtpllPo0c1mcaLM\nTpo0yThXIU7gUUcdhQEDBmSVltfaeDuA3wAUAejFOf8QABhjg1HuGvpxms8qz4wZMxzvRVXxo1rP\nYA4rIPBaqFV+aQWpr379+vkeJYx6fYroiGXCuvGCIJFIYN26dUGKFBh+Rt9EORAzE2Yj2U/5jcv6\nsVxG6Hzbtm2uno9Cp7ngDvrWW28Zx0G7l3kxAlWuo+k2htFxJkp4YNSsWdO4JsoG5xzXXXedq3Si\n0Kndjq9OqNz38cq3336LxYsXR/qdUa0J7Nevn2OYMFEXd+7cmTEt1ZbNiO++7rrr0KdPn6zS8mQE\ncs73cs4f5Jx35py/ZLren3PeGcCVWUmjAE6KzSZO4LBhw7KSKWzMcnvpnPzhD38IQ5xAmD9/vufP\nHHvssbbXGWNYtWpVtiJFQrYdCRVHaQVjx471vDFG06ZNU4xHc/mOW6dLp46HlQsvvDDwNFX6vawd\nzGbNmnlOo1q1aoEGCQ6LIGYCrboT5yrp1Eo2A4+7du2KZRzeTNh1wM2DjHGcVYujzOn45JNP0LJl\nS9liBM7atWvx8ccfZ4wV7abPY2cEyuw/HH/88RmfCcsdNBN/CTi9yAlDsV62pZcdD8dLzJF+/fqF\nLY5v7GY6zdg1ek8//bRx3L59e+M4G8Moan167YDZNW6q8tRTT3mapezSpUta11Y/dV12/STc43Y2\nKgqdio1NBFdccQWOPPLI0L9XFmGtyXZjBMpeE6ibkZAN5lk/gZ9Ng1Rrd1UejPDDDTfcEOn3RaHP\nb7/9FjNmzMg4E+imz2N9RvZMYJCkN5EtMMYmA7DLOQNQF8AuAG8GIJc0sg28qCt2+VfZYMgkW6YK\nbL6vqu7NcSwFbuKjHXLIIY73vGytHxcYY0a+4jwTmGlEk0hF1Xor0KUTESUqvHOc1pp+9913ANQv\nd1EijMBatWoZ11TQYTbk5+ejRo0assUIFB3LbCYXcrdGoFPfV4VYtEGErvPaq/gzyg09u8UWdQBU\nXAUcE2rWrIldu3Zl5Q4aBLLXM5gL/Ntvv217XdC4cWP07ds3Erm8kkgkPDfU6dZr1K1bF1u2bPEs\nR5j6XLt2ra/Ppdtc4uuvv/YrTuhUrlzZdcNr1aUIo5GtESirfp5zzjk4++yzpXx3XHHbsZGxJhDQ\n2wgMK0TPqaeemvHZsPX58MMP214X7bHOevWKGLgyu0OLTreX30l2v8hMs2bNMHPmTNliBErUhnkU\n+hTv944dO6Z9LlNb5WQEqlDP77zzTsd7bttgr0bgTwCac85LrTcYY1cDmO4xPWWoXbt2WiPQDel+\n9GQyqewImJPc5lEGu2cYYygoKAhNrmzIJJuXTgpjTMk4On5nstL9LirmU+BHtnQbcsRpJrCgoEDL\n0VozQedPpd9LJVnigPU97MUdlFAH0Wab3znmflBc64Wq/R6/xFUP6cgUIsKLO6i17yF7TaAgiP6a\nV6vkGjsD8CCjUL57aCwR6/aCfMlMnTrVOH7hhRdcfUaG73uvXr0AAK1atcIbb7wR+fergrkhNJeD\nbCp8mPocMWJEhWvZll+VXwZeDXfzZjB2v0uc1gSqrJe4o9p6o3QwxgLZeEV1zB2zIUOGeNoYJk76\nFNjF4dUBa7t1yy23YPv27Z7TiaNO44SOcQLF+z2IYPFNmzZNOVdlJjAdbuXzujvolDT3OIDMW9Yo\nSqNGjQAEawSecMIJxrEIdqsiIqhrlSpV0Lp1a8nSBEdQumSMKTHqY2XPnj2Bp6myseFHNvEZsX4y\nbjOBdruaEu5Q/SXtR6d169bF1q1bQ5BGLcy/zQknnGDoMg511o9eRf9Dd5577jla26wgqnqpZYPb\nYPFu6qt17V3OGoGMsWsYY1fb/PVmjI0G4H3fa8mIH0o0TE899RSGDBkCAGjevLnxXDYdUC+o5Ptu\npkePHrJF8ESQ+kokEsbo+1NPPeUpzTD1OXz48ArX/MbRE9SuXTsrmcLEywhyQUFBigF17rnnGscC\nPx2vqOsnGYH+ee2111w9J6vN9dOJUD1WoCDb8mrdmMvLTGCU+gyqI7hy5UoA/td5q4pdOahUqRKA\n+K4JdMuSJUtw3333yRYjLY899hiA6N8vUehT9FlF3t5///2U+17cQf/617+mnOtkBHodknk1zb29\nAHp5TE864ocy+9bu3r0bAHDEEUdg+fLlKc+lI936o7iPfp133nkVGrQ4d0wzyW7thIgR6Hnz5oUq\nV7Zk2zBVr149IEnkMWDAAONY6Llt27Yp50A4M6lBQ0agf5YuXSpbhMARRmCbNm1ki5KWKlWqBJpe\nHOIEZoMIWC2Cq+uMeT2dzu3apk2bMGPGDNlipGXOnDkA9NaDMPJEX17g1gjknOO0006rkKbqXglu\n5fM6B1wCoDuAjqa/MwB0AFCfcx678BDp/IaDVPI777zj6jlVfd/j1khMnTo1bQBUu85EuplAURa8\nlgnV9HnxxRfLFiEyOnTogEaNGjmuCfRD1PpU/UUTJKWlTsvN/WEd+XVCtTqajqKiorS7+6pCq1at\nAkvLXH/d1Iew9WluS+zaFT8eBnbx9HTA7p3asGFDz+lEUUfD2Jgql9pvL0TZ5gq9WnWRTVzPhQsX\nevYKixqr0euEVyNwFOd8DOd8iulvKud8Dud8l3cx5ZNupN1caLJ1L1R56303OO0OqirfffcdunTp\n4vvz5ryZ3UFViA2Tjkw6GTduXFafjwucc9x///1o166dcQ7EL3+51InYv3+/bBFCQ5S7b775Jqt0\n4uIO2qlTp0DTU3Um0CzP1VdfDQA47rjjcPjhh3tKR3gKqZa/bLHLT/PmzZGfn69cXu3kKSgoSFkW\n5IU4uAzmAplCkvhZDzlr1izHeKGqIMJiZcLrxjA3prvPGOvsJT0VSNc5zDTil4m4rwnMFDBd5Q51\nrVq1ULVqVcf7XvJjHtGbOHGiJzmi1me2blg6bH1tp0cxCGN15/VqZNGaQP2IQqdizVe2hm7t2rV9\n7a4YNTqvCXTKW82aNTM+44ToiKo06DN79mzjLywYY67SV6lf5Jb169fjp59+ki2GI2HqNRNR6lPU\nRWvbkc1MYByWd2XtDsoY+ztj7CrLNaeNYa5mjPUCMCg7saPH2skSoSIAeN6KO11huvnmm31Ipw6M\nMRQWFla4pioPPfQQLrvsskDSMs8Eqr6OzGvjatVhgwYNcMUVVwQokRysncihQ4cCAD766KOU5/bu\n3RupXF7JtM01Yc+wYcNki5DC4MGDAQDLli3LKp0gXZvDJOjy6tcdP2yELg477DDcfffdvtNRsVPZ\noUMH488vjDG0b98+4/eoQNAD3S+//LLSRmCHDh1y4r3iNBPodk2gXZujYn21EsTuoI+jolF3N4DX\nHP6GAzjOvYhqYDUCnWLF+cGclls3QlXXp8RtJnD37t2eK6o5P9ZjFeMEhkUcGrig8FqGZa0JVLmu\nqUi1atVcPxvleiPzO8XvrKCKZcGpgxUEjDHjt1IhTqB5cNgsTzbtpnlNoApu0VYZsukL2QW0tvaH\nMq0HlvUezSbfcQi7IMvNWsaaQL/uoHafk9lHcqurIIzAswD8xXJtOID/ADgXqZvDdARwHoDPXH2r\nQliNQPMPl+2oo/lFaBfYO07EbZHzAw88kPa+l04K+farQa9e/jYfFro2r+0QYWBUf1HHqc6pyObN\nm2WLAMC+vfn11199paViW2R1sw7SHRQARo4cCUCNNdkvvvhiynmPHj2QTCazakvEZwsLC1GvXr2s\n5AsCqwzZlDmnAQJz26aC94lTHlUcdCHcccghh2TtDmq3rEimEThr1ixXz4kdhzPhmBPO+Vyby6MA\n/B/n3HaXE8bYJlffqhDWkXYnIzCqhkBV33c7I1D1xjGdfJl2B3U69oqq+owjTZo0qXDNbg2jk8tc\nq1atjB2z/C72pzWB8SDT6K+ZKHTqRZ44YjXOgp4JFLgZFIk6TmDLli3x2WefBWIEJhIJ7Nolf489\nqwzZGrlWCgoKUFZWZuh23759aZ+P43uU2mxnotJn8+bNM7a9mfRkd99udjsqDhw4ACCzt4vb3ba9\nbgyzy8kAPHhf7SBqNpx99tkAfl+477QZTFQbw6jKtm3bKmxIoGr+RCVJF/Puoosucp3e008/nbVM\nUXHEEUdk9XlVZ3zfeOMN3H///RWun3jiiRWuJZNJ1+7LqnfKhS5UlzMIgoypJXQt2ndVELvVCurW\nres5jTisC8z23XDttdfaXldhJtDM5s2b0a9fP/zzn/9E7dq1PXv8iOcz7WAom6DfCXl5eSlutSr0\nJY466ijb635lmzBhQjbiRIqOMVWBYJd3mVFhrWf37t3T3m/durWrdDwZgYyxCxhj5zPGzj14Xo0x\nNp4xtuPg/1pe0lOBmTNnAoBh4GRr+Jnx03iouoZMGFZmVGi47RAdhcqVKzs+Y7cY3Sk/2TSQUeuz\nTp06WX1e1Q6m04YajRs3tr1uN5MbRHmVtSZQRZ0ETZDx78TvtWDBgozPRqlTa+iAHj16eE6jVq1a\nSswWpSPbunbWWWfZpuVms7Yo9Sk2lrr66qtRtWpVzy7r4nlVQ2AI/MrlNBhn3mzNDVHo1M+AjC5s\n3bo10u+Lqo6aZ6/9DmTYleFMM9dhIuriqaeemvY5t2FqvM7vfwjgHgCixAwB0AXABAAMwJMe01MG\noejVq1cb1zKFSPDKDz/8kHUasrCb/ha/yZYtW/D8889HLZIjixcvzjoNxpivDlrcsJbrZs2a4ZRT\nTpEkjTNz51b0Tk8X0sOO+vXrG8fCsPeygYgMREdJ1c5htmQb0sSJpk2bhpJutlhd6vy8V4qKipSN\nFRi2+/LAgQNDSdcvQc0INGjQAMDvg9KqEWT7c+yxx+K4445LMQJ//PHHwNL3y8KFCytc07XdFbz3\n3nsA4r/2fPLkyZg2bZpx/tBDDwEAjj/+eOOaU2gd6673brBbmhIVn3/+uavngtgYxo6tAC7gnM9m\njB0O4HoAL3HOuwG4GEAbj+kpg3hpLVmyxLjmdVZQpDF8+PCUc4GbQMEq+b6b85zOCPz111/xwgsv\nRCZXJtwGybRi1deoUaOylkUlfbqhQ4cOuOWWW2SLUQG7TTTS6cduAMcc/sX8wvBC1PosKioCoGdn\nZMCAAbj33nuN8yDz6GUgI8o1gdY2xo+xpHLA+LCNQDebHURZR8UykmwRrluTJk0KJL2gyWawxlqv\nL730UlxyySUpawIz/Y5R6NSpf6aqx1OQRJ3HoPU5efJkTJ482Tjv378/AODyyy83rjnNdjp5E6Xj\nuOPkBUL47DN3+29mHSfQge855yKw1t0ASgH0AwBeXtNrOn1QdcJYuK9T42G3KDwsf+tsee655wJN\n75BDDgk0PVn4aexUwa78eV3obU7Dbscvlfnvf/8rW4TYokLbVKlSJQDBzAQWFxcH6jobJF5j67pB\n5feoCmVLZdK1xeay4nYTizCxc/GrXLmyMUurM6p7xGQiPz/fdr1wjRo1jOMgZztFey6DOXPmuHou\nrJnA/YyxFoyxPwHoDeBZzvlGAGCMtQfgbiWiYgwdOtR22+Kgp8hViHHkl3RGoGovaTfrgDJtHGI+\nFtuT+0Elfa5atUq2CL7xujOd3UygWaeDBllDoLpDJX0S7sjU7kahUzGjG8RMoMruoCq4lcmIQUbY\n41T3rBvDZCIKndrVqaOOOkrZ2dkgsdtgLUyC1mdeXp6tEWjeiCvItklmiAg3+ejbt29oRuC9KI8F\nOAXAXAAPAQBj7DYAE1G+LtATjLHejLGFjLESxtgKxtidNs/kMcYeYozNZozNYow9yxgLbCi/tLTU\naMz37NmTdXpOLjFiEXkccbvbYlzIVEHMeVM9lpxb4pwPL+XPurmNGOE1P292by4tLcWOHTuCEpXw\nQElJSeizKSrN1gTRZhYWFmLTJjWjMdFMYHaonNcg8WoERgHFCYwv+fn5GcvT7t27A/s+FcpEurYn\nPz8/HCOQcz4fQGMAxZzz00yuoW8COBpAkZf0GGN9ALRH+azi+QCWARjCGBtieXQMgA4ATuGcnwTg\nUACB+Ufde++9hlLF4tG///3vKc94WRNo5dZbbwUA9OnTJ2Maqq4hy2RAqNTR8ovXjUbcoKo+44YX\nA9aqL7HuzMmw79q1K2rXru0qbdJnsDz55JOYP39+qN+RqW2KQqdBrpVz0+GRhQreBlHW0Q4dOuCO\nO+7IOh3OOU4//XR07NgxAKnUwam8i5kbt/UhCp3azbCo0NmPArfhBIIiaH06zQSa+eCDD3ylbS0D\nXbt29ZVOlHjZ5d3z1AAvZ5Pl2hbO+WYArrfZYYwVAKjHOb+ecz6Dcz4FQGcA8wDczhgrOvjcZQAu\nAdCHcy603A/A2Yyx673Kn0Ye47h27dq45JJLUhoFL0aO9dmjjz4aQHmsvbgSJ3dQN3iROY75c0tc\n8mYuf242S7G752QEqhZ7LFcJqyyqMEAV9IYpqtbbXJsJzMvLw8knnxxIWieddJLUtUZhYVf/4jQT\nqCvmeiXTvTEIoixPhx9+uBJtUrryGqoRmOZLKwO41cNHagL4p/kC5zwJYOxBuZocvHwLgE2c829N\nz60GsAbAzf4l/p1bb721QgfR+gO6CcLttLnM+eefDwC4+OKLM6YRpzVH4oVVpUoVV7u2qU66in3k\nkUeiffv2ntOUpc833ngjkGdUwaybK6+8Mq3sXbp0sR2t+/777wGUv/DMRqCX3WTjVD9VxVrPXnzx\nRQDhdcLOOOOMtPej0KmTEXjeeedllZ4qCPfqbOOUZsJN6I8o62gymawwQFqvXj0MHTrUc1o//vij\ndmGJ/p+98w6Tokj/+LdmV8KSw+4SlSRIkKycgKCICIIKinqHiJwB84mKghzKoqAYMGE4QBA4sDu9\nqwAAIABJREFUPcwBFdGfEiVIECSq5IzklbCE3anfH7PV9PR0z3RPV5rZ+TzPPjvT3VNV3RW63qo3\nOL1TFyxYgOeff951OjL7KE9k29p5QaV5iAibQFlCYJ8+fdC9e3cpeUWjSRPnYAyEEP/eQQkhhwgh\nBYV/wRh/BQDyANzjlJ6Vwt1DO8OGEwCCADYTQsoAaAtgg811vwFoRjgEqO/YsaOtEGgeFNyqiwGR\ngwmLKXLhhRf6K6gA/Lh+Zi/+ZFy9tDqJqV+/flxCoCpuueUWT9c0aNBAZHF8Y35hXXTRRbjlllsc\nJxhNmjSxdeHM7H3Lly8f9tuUPaBaWKw1UaurCxYsEJKuF+yEwNq1ayuNN8UT9g4Q8S4wPzPdhCQ7\nITAjI8PVgq+VY8eOJbTfAC+wRWMddlQYIoTAWAtQKtHp2fvFbuOGF9Z0W7VqhYYNGwrJywvZ2dmO\n57zsBEbbA56CUOy/hQBOuUirBICrXOUanUsBzKCUHiCENERIUN1jc10uQo5oagOI7Q4yCoSQsO1w\ntqrgp1ENGDAgLH0AOHPmTMzf6WpzFGvASLQBxaujESC+VT1d69OK7vVnnmjFW1Y2Qb311lvD0vPi\nDCpR6lN3Pv74YyMuFxtnVe1uqbI3AuJvyzr113feeQf16tUDACxevFhxaeT2UTshMF50cdzlFFQ7\nHqpUqYJrr7024jhTwXcbX1hGnbqNv5YsqDSD4F2fr7/+etSdsaIGLyHwbQDLKaWudcYIIf3dXuvw\n+/MQchDTsvBQxcL/J2wuZxKVby+hdkKgX3ey48aNC0s/mdFNNUkElFL06NFDdTGEoXsdeokT6AQT\nAl9++WXs3r2bS7lSxMe3335rqOGKDjCuA7zvsXjx4jh58qQvTQ5ejBkzBk8//TQAMUKgzu2CpxCo\ny33y1Iw477zz8O9//zviuA5xAVMkDxs2bIiprXfJJZdg0aJFntPWpV96wcvOqOPoRSn9A8D/vGRM\nKZ3s5Xob3gLwRGHeQEjFFLAX9NixQz7zjNiha9CggScXqwwnm0Dr+WjItjmKdo9ly7rTtKWUatNR\nZsyY4eq6WI5DrJ/N93jgwAHX5dHdhozdk+7BYnlMtMxhIeJNT/f6jAe3fYY3O3fuBBASCFUio04z\nMzMBhI8rfhZesrKytAkY/9tvvxn3VaZMGcWlkW8T6PXdZ6dKRil1/b4VzZ49dopXfPH6zETWqarx\nTxU8QyXYsW7dOmzZsiXqNSLqM9bCgm6OiPwixTFMoaMWa+LFCSF3EEKGFn5vSgjx7TOVEPIEgN2U\n0rGmw5sK/1ey+UklAPkAbJf0+/fvj5ycHOTk5ODVV18Na3Rz5swJ+75w4UJs2hTKqkePHnjyySex\nc+fOsIcY7ffW73/88UfE+R9++MHYiYj1exnfzTidv+KKK4zvy5YtgxMLFy7EqVNnNYZV3h8z2J00\naZLj9QCwevXqqOfNq6GrVq3CwYMHDcFhwoQJyuvPWl4z8fyeedzU4X7svptVlNatW4c5c+YYEwm3\nz4fV35w5c7Bw4UJjYm5Fh/vlWb+xvnfv3l36/TABUMb9sWNO51euXCn8/rt27Wp7nhASV3r79u0z\ngluLri83z3ft2rW499570bFjR+MaP+mZvzO1Ybe/F12fZiilWLNmjaffv/nmmxHp7du3L8zmXGV9\njh8/PuI+eed38ODBiDyiXW923sX7fqM5+Yi3f+r8/aOPPoq4T57p5+Tk4IUXXoh6Pe/6BM4KgeZz\n7Hvz5s0N9Vev6e/evdv2/FNPPcWt/F6+W7G7fsGCBZg3bx5ycnLQv39/x98CgOEAxc0fgLoICWZB\nAJtNxx8HMAtAWS/pmX7/DwCfAgjYnFsKYJfN8Z0A5jukR90CgL7xxhv01VdfpQBojx49KKWU/vrr\nr7RRo0YUAHWb3rhx4ygA+vrrr4elzxg+fLjrcomG3Vfx4sUdz5tZvXq18RvzMxk+fDjdunUrrVmz\nJqWU0iNHjogteAxY2dauXRv1mq+++iri+LJly4zft2/f3jj+/fff086dO9O//vqLAqDTp08XUnav\nWOvDS7tnHD9+nAKg/fv3j+v3sujSpYtxjx9++CGllNL33nuP9unTJ+Zv2e+++uor4x737t1LK1eu\n7OvZyUJ0+WTfOwB67bXXRrTdhx9+mHs+utTt2LFjKQBaUFBgHKtVqxbdvHlzXOktWbKEfv3117yK\n5wsA9NNPP6X3338/nTt3rq/nbVdf5nF52LBh9OjRo36L7AtzGRcvXkxnzJjhO72bb77ZaCOq2+uo\nUaN8v1di0a1bN+F5uMXuPcr+mjdv7ivdxx9/nGNJ+fDLL78IffaPPPIIfemll7imGQsAtG7dumHf\nzffVuXNn2qxZs4h7jXbvLI27777bNk1V83lWjq1btzpe89xzz4W1vcJy28pfXnWixiKkhvkyQo5Z\nGC8DaA3As19kQsj1APoC+Ds17TwSQqoUfnwTQFVCSFPTufoAqgEYB85Qk+0G+xxvGomA27I2atQI\nQCg4brQ0RLsId8vJkyc9/8ZJHRTQS+XVjrZt2/r6/UsvvcSpJGIwq2/G6565Q4cO+OmnnwCEVEPM\n6qEp5BJv4F6vbN++Hf/85z+l5BWNBx8MRU/iNYZkZWUZO4E6QAgR6qGP8c0337jyfCyLZLQJlOGg\nZv78+cLzSGHP559/rroIQojm6CY9PT1uRzi6zuejha176KGH8OSTT7pKx2tvbwXgb5TSQQAM/Swa\nCuKeC6CXl8QIITcDeBrAkwDqEkIuIIQ0JoT0BDCy8LIpAH4E8EThb9IBPAfgG+rBaU00mERsKRt3\nm0A3RNv2VQl7MdSpUyfiHKXUOK9Lh/FbDrOjINYW4rlHWfVZuXJlX7/XxR7FCfPEJF439MWKFUO7\ndu0AxD/hUtE/S5b07fuqyFKzZs2YMV5l1ikvm8Ds7OwiIwSan1l+fn5MIUVmffIUAnVB1f1Eazu6\nzotSxIeI+ozmzDEtLU2pN1QRRJvDlCxZEqVLl3aVjtfevpJSut2mMFkAqiO6t1Hrb24B8D6AhgCW\nAVhX+LcaIdXQ74HCPUzgOgC5hJAlAH4qvM6TwOkW80Cki0CjE6zhNW3a1PY4ACxdulRqmexgLsud\niBUiwrxLxI7r/LIfPHiwa3fbdhBC8Nlnn3EsEV/Mzz7efmkNM5Eo/fvDDz9UXQQp6LITIgo7L4nx\n3nOJEiXC7LBV8+yzz+Kjjz4S2qdq1KiBgoICrcbhm2++Gb///rvvdMyLjKpRVQ5d4rXyfA/qOKZZ\n+2irVq0UlYQvsYTAeB3D6NIvReFaaCtkNyGkIaV0PTtACAkAeLXw6xy3CVFK30dICHRz7Ql4CETP\nA1WTxESJQ9azZ0/js/U5HT58WHZxwqhYsSLKlSvnKw2rqqD5Je0lfIis+mzbtm1cKqHml1SvXkLW\nVbggwjtoPGFgVPTPq6++WsvJRLIgq05vuOEGKfmoYNu2bcIm8aztX3HFFfj5559jqnHL7KO7du0y\nAp/7pSj1cbu51V9//eXo5l9mnfbq1Qvt2rXDggULpOWpkm7duknPU0R9itoJjFfzKFHwOrPKAfA5\nIeQFhOz0hgJYDODvCKmHPsa3ePJggxJzF+xnQHYSHkeMGBF3mqKIV9AdNWpUWBpWlR2VHDoUX9SQ\naDuBlFLjmN8Ykjqi+wSER7B4cxqlSpXC3XffHXb+73//e3yFE4zudaMrAwcOBBCKVaYDyVyP+fn5\nSEtLE7pwGgwG8dtvv+Hjjz+OCOukEl7vA13ax4QJE4TncfPNN0cc4xmk3i+sHf/jH//wlc6RI0cM\nL5K6YG1nurQ7v0QLbZKWloatW7dGHGfvCDsaNGgAwPn56DifjwdPQiCldBuAzggFcS+NkFBYC8AH\nAC6mlPrXi1CAnU0gO+6FZLYJtGLeWrcKgTq9oOPFahMIIC4hMFHqU3d47ASa22ipUqUwcuTIsN1P\nN2qXquozUVRXdeKVV0J+ymI5hpFVp3bOppKBunXrGo6WRAuBjGgLjbL7KG8hUPWk/I8//oh9kU8G\nDBgQ9v3LL7/Uqk4Zjz/+uK/f//XXX5gyZQqn0iQPMuqzWrVqxmen+QN7R9hxxx13RP1tsuD57iil\nOymld1JKa1BKi1FKsyilfSilG0UUUAa7du0Km/QD/tRBs7Ozjc/Jom/txIIFC7B7926tdgL9Ur16\n9QibQvOgldoJlI+ogbhq1apC0uWJ7nWTouiyZ88ebNq0Cfn5+UhPTxciBLLx1rrwqAu83wc63Zso\nUmOaOmS0L9ltuHjx4gCATz75xDhm9ltRq1atuNMu0kIgIaQcIaRs4V+5Qs+c1muqEEKqiyuieObN\nmxfhSccsBHpt0H369DE+m4OsDx8+POZvE8UmkPHDDz9g4cKFYcfiNcDVhZ07d2LMmDHGd+sLSzeb\nwKIwaRA1EMcMpGoh0fqnjlSqVMn2uKqJoco69XPPOni8W7JkCQCx6qAsTfN7Jdpzk12fyaYOKgOv\n43kij7tF4f1sxk04Ld71+cgjjwAAbrzxRgChufa3335rnB82bFjcaRdpIRDABIRs/dYhZPdXzHoB\npXQvgK6EkMgAcglCMu7suMXrAGXXIYLBYFinP3DggO9yqcDts0im9qKLClIsrDv1RQnd68YNwWAQ\n+/btA6Dmfvbu3Ss9z1j4jVNZuXJlHDx4kFNp4oPdA6VU2E4gS1O3cZeF6OAxNu3duzcp+rlb7OYR\na9as0aaOebVjc53u27dP+f0dOXIkrhjKXtAxprIfQS7Z4wnHejJPA1gLoCmldFyhl84IKKUTAVxO\nCMm2O687N910E4CQGiALhq7KO6juNmTjxo2LOGbt9Pfff7/MIsWFn0HKy06n7vWZKLz77rtC0m3d\nurWn61P1GR+bN282vNeqmCDYxTdlyKpT6/vk119/jRnDMBo6BIxnk6uHH35YmAMM9tyef/75iGN2\nyKrPKlWqAAA6derkO6158+ZpN3Het29fmGkLT1q0aBFx7LbbbsPGjfZWRckw7jZv3jyq8xIZPPTQ\nQ/jggw/CjtmFrhGN7PqMt2/169cPI0eOjH1hAhNLCOwL4F5KqRt3i+8AuNd/keRTrFgx4z/TLSaE\nKF+10ZGMjAzVRXDEr9Du9veJru5qh24TECusj/LGj6pvCvcEAgGj36hoa3l5edLztGIdX8qUKePr\nWegQMJ4JgWlpaShZsqTQncASJUoYx3Tqp8nkQt5cf5mZmcJU4XR/34gon1VjShXWPsrmvMlMvM89\nKysr6bWQYvXw1pTSn9wkRCk9AKC+/yLJp2fPnrY7f3YuZUUjW/fdy0t75syZtsfZ4MbiJdWuXZtL\n2bzy3HPP+fq907OwHo/lbdBMItsy6IrIF2ksAT9Vn/FhDtbrNLHs2LGjzCIZJGqdZmVlGSq2qmB1\n+eyzzwq3CTx9+rRxLFoMt0StTx1o37592HfZO1dmQd+MinnRxRdfzCWtHTt2AAipD6u2D5QhhKqw\nCWQ45cvGqUGDBrlO68YbbzQ8hAIhu0Kz45lkIZaI63UZKCvegqiAdcgaNWoACG9A8XQWHVZ5RHLV\nVVfZHmednjkpsDrZkYXfCZFbITBF8lJQUJBUK/u6YBYCncbJrKyEen14hvc4otNOIADhQqAZ1THl\n/DiOi5amajZt2qQ0f53etdHCB7hFx7AwMsoguy2ze3LKlx334q3f6lH0mWeeia9wmhNLyCvpNiES\nespqtoDixNwZmjdvjqZNm2LevHkKS5SYuu/jx48X8lKUze7du22PF4W4j4mESG9dsbwtqqrPoUOH\nKsmXF3ZCoFVjQNW4wbNOc3JyjJV/xsqVK7mlb6Zs2bLIzc0VkrZbzJMu0UJghQoVIo7ZIbKP3nnn\nnRH566BqnIjYjWlOMYaT5T0qW42ZtVcGIUS4gEYpxcMPPxz1Gt71OXr0aADOcwMdFlh0JdZs6i9C\nyCUu07oSgK3jGF0xD+QtWrRAu3btFJYmcdm1axcAJLwQ6OTVNFHvxw2JNjhSSrl767r99tuNz6pd\n7jsxatQo1UXwhZ0QWLNmTUf1L964Cc/Dgy+//BL79+8PO7Z582YA/McR3fquaCGwfPnyxjFVNoET\nJ06MOGZWU03hHvOYxtqykxCYqFj7qGx/AnbtVTQq50ux1EFTRBLryUwF8C4hJDPaRYSQSgDeAvAN\nr4LJ4Pjx42HfS5Z0vfEZF1OnTo15jUzd90suucSxw1566aWe0jJ3PlWDgJd87QYLEeqgoupTtRqY\nCry2SbeY20KsCV3K3ig+AoGAMXFnL+QLL7zQtwo+D3jWabRxpUyZMtzyiZafTMz5i5po2Y2/lNKw\neLxmdLarj4bqutQBJyEwUevUimqHRpMnTzYW7VUiqj6dFnFZ32J+K1KcJdao/RFCcQLXE0IeI4TU\nM58khFQkhNwO4FcAlQH4V6KWiDV2VHZ2Nnr37i0svy1btghLOx7mz5/veM6rWqz5BaZ6oIuXaAN/\n586dJZYkNmvWrFFdBOnIUNUWHUOpqGIn7L3xxhtFauJbv35C+k2LilUIlLUASCnFtGnTpOQli6LU\nF5zQaSdQRH0ko2dxKyp3Ap200Vhd6iAA60ZUIZBSegbATQD2A3gewO+EkDxCyE5CyMHC4+8AqAjg\nJkppQm1PWIWVYDDoazXTzaARa6chUXXfzfd+6JCbiCL82bZtm+trZakHJGp9JgLp6elcnLiY2+6p\nU6eiXpuqz/g5dOgQcnNzsX37duOYLCdS27Ztcxx7eddpMquPW5HhGMYOpmJrh+w+KmInUNWCscq2\nm5aW5jh3EFGns2bNcrzf4sWLcxEC7UIQzZo1y3e6OsP65okTztZhquIEihDsKaXS6lTEBkvMmTCl\ndDuAiwC8BuAYgOIAqgGoAIAAmAfgb5TS77mXTjB2QqDfxnLzzTdHPb9ixYq40k0kVLkt//LLL11f\n26VLF9fXunF5nEI+N954I/7zn/9wTTOWEJgiPthka9y4cWHHvSzc+GHy5MlYsmSJlLysiJ5Y62KD\nI1MIlGXjKZOMjAzDZKROnTrKytGmTRuce+650vNt1qwZ1q9fLy2/K664wlFQ+fbbb9GmTRvueQaD\nQVxxxRXc09WJ778PiQLTp09XVgaZ87UTJ05Iq9OjR49yT9NVFERK6TEADxNChgFoBqAmgJMAVhQK\nibYQQkpQSrXVr7JuzVNKfe8Q+bUrTFTdd7s4izrjZZDwIwSmbMjEEQgEuDsWiSUEpurTH9bxVZZj\nGMB5FVV0nYocF8uXL4/c3NwwpykyMd9bWlqaFup8ifoOpZSicuXKXNKKF7YQLsJ+NRaZmZmOoT9k\nx5UT5R9CxSKjXw03r7BnGk3rTdV7VIRwyNtRXTREvEs8tQxK6XFK6UJK6YeU0i+jCYCFfOujbMKJ\nthMoCl0EpXnz5nG9V7MQmJ7uam1BGLxXoMx1NnLkSK5pqya1wxn+DFI2gWKJNhlp3Lix0LxlxceS\nOcZnZWUpdRJ12223GZ/ZZKhTp06qiqMEXgLD9u3blccoPXDgABYvXmxo84h2lsf4888/8d133ymz\nmXv77bel5NOyZUsp+Zhp2rSp9DwBPT1tZ2dnqy6CdhRpv6nWl7UMm8BYyNKVFuFpkQnVDRo04J62\nF6655pq4fhfNOyirW6+Og1I2ZIlFrElIqj79EW2MFG0f6LQTyLNOvXgd5oHqgPEHDx40PjPHMCLe\nLSx8k5t3rOw+ymsnoHjx4lJ3FaLB5kHVq1eXkl+sHWRRdcrGhG7duglJXwfWrl1rfJYhBMUK3A6I\nq89Y96d6g8IvyncCk52WLVv69g4aq5IS1XNmLAghqFq1qupi+KJixYq2x+3UQZ0Cy8tG9O5JUaBn\nz54YOHAgAL2808nEyd0+b1RMclkoIFVj72uvvSYs7ezsbGU22ABw7Ngx47MMm8AhQ4YITd+Km35R\nqlQpLnnxSocHd911l5J8ZWumsPbKW11y586dANRq2uTm5kYck1EetgOo4t5j5Zms828/FGkh0PrC\natKkCa677jqhecbaaUhUe4ZkUCssW7as4zmrwyC38WZE12eLFi2Epl8UuPrqq/HKK69g4cKFMVVY\nktUmUJa7fRUrsWxy7TRGia7ThQsXCktbtTqoGdHB4gEYizXR4FmfbvpFRkYGl7x02QUEgFtvvVVJ\nvk7tR1QftcYu5eUQR4fg5HZOb3SZp8m28WSIHp8SEfUtVSHFixfnnmasRqiTnjTPAcHs2llFp5gx\nY4bvNNyog4p0NewFVtbUTiA/9u7dix9//FF1MZKaRFfHicXy5culjn+VKlUKU8lUiYydQOu4+957\n7wnNzw28nKgkmnM1nqi6b7YoL2IuGA1ZNohWdBBORbJnzx68/fbbjs830XcCU+qgnIm28xMvsSop\n1mpfotoc7dixw/isQkCaMGGCsLT9eAcVVZ+snT366KO+0lEtzMqgXr16rq5btWoVXnzxxajXJGr/\n1AWVQqDT2JzIdZqWlqZ0YmN2HCJKCDSPUdbxasSIERHXy67PGjVqcEmH2VTqgCq1TOtnhuj3aCxt\nAa/YLRabFwvuu+8+Lvl4xTpWiFxEUWETCISerZOKfKILgSIo0kIg7wbhZgDRLQ5ZuXLlVBdBG9y8\ngFkdqx5M3Bhfpwjh1sNd6dKlk36lVDUqhUBVngeTGbMdWyAQwEcffcQ9D/O4bB3vormh54UsO2Gd\ndgLZOCj7/VKqVKmoQcZ5wWz62Xuct4YPS4cFTueZth9Ue59VjYh5G7P/jIdVq1Z5uv7bb0MBF3jO\n24v0jEf2gPvzzz+HGdLbIdPmiBAixKmAyheZiLhjduqgbu9RVH3qMllIBEaPHu3qul69ekUEM7eS\nrDaBslA5CXESAnnXqey+qXIsMO/EBQIBfPzxx1Lz3749MkoV7/rctm0b1/Sc0EUIzMjIQN26daXm\nye67cuXKOHDgQMR53nU6atSosHxFmXnMnz/f+CxbCLTLr1+/fmEhXESU6fXXX495jar3qIj+9fTT\nT8f9288++8zT9X379gUArvP2lBAokUqVKuHo0aNa2QUWK1aMe5pHjx7lnqZbRHhYs1MHVV2HbHVa\nh9VF3XG7+xQIBJLeZk0VOsQQTe0E8sds3iBqFz2aOmiyoVIIZO80EQupsTALgTJtXK2OYXjvBKrE\nri0RQsLmfCLKKWJOyQsRO4Eq6prnM04JgQ7EW7HR0qxQoQL27dsXNZRCItunMMz2gbIZNGhQ3L9l\nsajssK4YTpo0yVWaouoz3liIVnRYedYFQkjMl0Qy9E8VsOcmK/i0lVGjRqFSpUq25xK9TlVOOM39\nRZR3y2jqoHYkWn0uW7YM9evXB6B2PL788ssBhDt5E431PVapUiXbnUDedcqes1Ud9JVXXuGSPkvP\nHKhdF8HQXA6R8RFl2gT26NHD1XWqzXh0JC4hkBBSkhBSv/BzJUKI0/LuE3GXTAK8B9xYnfycc84B\nIcR2kEskeLlR5gl79n6CoUYLVh1viAjR6PBi0R23/VwnxwzJxsmTJwHI98LHaN68uTR7T9ltqGTJ\nklLsqOzwKqDFiy5emUXQqlUrtGrVCpRSpePP4cOHpefZsmXLsO9O6qCisAqBTBjnhfn+dFAHBcL7\nbPny5aXlK5JWrVq5ui4lBEbi6a1ICEkjhIwCcBjAzMLDxQF8Sgj5p/V6Suli/0UUhxvdZS/EGsDd\n7DQkgs2Rzi/iZs2aCUnXuhN48cUXu/qd7vWpc13ywq3xdbL0Tx1h/ee6665D7969I863b99eaP7R\n7K0SvU6zsrKUBYw3P1MZEyxrHnbqxYlYn3379kXr1q2VCoFr166NOCbLhle2TSDLT5SDNZbe5MmT\njWMdO3bkmkc8jBw5Ungebp4l7/q08xJsR2qRNxKvS6NPI7S7dwxAAQBQSncDuBvAeELIHXyLJ5aV\nK1dKzS9ZdhrM9nA1a9ZEmzZtws43b95cdpEMRBi0200gL7jgAu75xENREOL8smfPHlfXJUv/1Jm8\nvDw8/PDDEcftjvEkmes2OztbWcB4s1AmQwi02mIni7fDq6++Gg0aNNCujcr2lly+fHmpO5KivIPa\nIWqB2iui5wyq5iRDhw6NeU1qJzASrz38nwDuoZRWBmD4RaWU7gWwC8BgjmUTjqjVHycCgYDRCJ0E\n0ESwZ+jWrVvYqpZOgojfslx//fWe0hwzZkzU9ETXp07PPtFhO4FnzpzBa6+9ZntNIvRPHYmlNihj\nYuI0ARBlbySL7OxsJTuBkydPxhNPnLX4YI53xo4dyzWfrKwsY6fY6hDBbidQVH3KMAGI5i9ABbLe\nLywfp7iXvOt0xYoVYf9Z/uZYfjw5fPiw426VVw+RusOeJTMBsINnfbJYh24EvCZNmnDLl8Hud+jQ\noZg7dy739EXjVQjcRikdbz1ICAkAyAagn7FYFAgh3NWQok0AzELgggULuOYrkwkTJoR1Yp0EEb9l\n+fTTT6OeZ3GpWD37cUSTQg5uJ+Vsx/f06dOuVhVT8EOGEKjbLgsvsrKylOwEPvfcczh+/HiEIxHe\njkUaNGiA5557DkBop+jRRx81zsl0NGT2Whlr8S9eWrRogeHDhwtJO1GQMZ/4+eefAQDz5s0LO16t\nWjUu6VvHmmiLNF5jxSUKsvwmvPnmmwDcCYHdu3cXVo5XX30VX3/9tbD0ReFVCDxACLHzTfpPhGwD\nN8ZbEEJIJiHkGULIcofzzQghXxNCZhf+fU8IaRtvfkBoFUimq39CCBYuXBj1mkSzZ9ixY4dSITAv\nLw+rV682votSX2GDOgvNoDpOYAr3uFUBCQQC+PDDD7F//35HRxs865O9vIoC5v5y8uTJCAdOoseQ\naOqgid5HVdoEAvKdtpi9kNrt3PCuTzaZzc3NNY6l1MrEYddPRfVRczB3kahwiJWXlyc9T+DsOLB0\n6VLHa0TUp4jwYLFYv349pk6dCiBUx6dOnZJeBr94nTFPAvAxIaQhAEIIqUYI+RcA5mGxuRRcAAAg\nAElEQVTlhXgKQQhpB2AQQvaGFWzOnw/gBwCvUEovp5ReDmAkgO8IIe7cAtlw5MiRmDFp9u7dG2/y\nEQQCAXzzzTfc0tMFlULgmjVrcPvttxtlEDEQmHcR2KJBsuwq6LSLKwovO4Fz586NuRvMiwceeEBK\nPrqxfv167Nq1K+yYSnXQRKd48eKGhoJMWL+SbTdWr1494/Ntt90mPD+mtTNr1izj2LFjx4TnqwOy\n3g+q3qdffvmllHyieR4XxW+//SY9T+Bsm/noo4+k5jts2DA0atQIAH+nj0489NBDxuciIQRSSj8H\n8DWAhQA6IGQX+Grh6ccopVPjKQSldAGldDAAJ08ttwHYSin90fSbeQB+B/CPePJkxJoYeAnK6MYm\nMBYpmyP9MNer153AVH0mDqx/RgsqnqrP+DALKfn5+REx5VSqg/Ku06IWlF72TqC5Hu3qVEYfFXmv\nRa39ALHfp4k+7iaicBAvsmN5sndLIBAwbIRlOYwyyw8lSpSIagepK56X8CilEwDUANAFwK0AegCo\nTinloSTvFOzoHACNC3cEAYS2IQFkAdjgJ0MnIVDEIF8Udl2SFfaSuvDCCwGk1IESCbf9jl1XFCdh\nojGvmNqp4KtUB+XNO++8E3HsqquukpK3CgghWL9+vZK8Ze4gycrLySlVMmN9trKede3ataXk069f\nPyn56IDsee4vv/xifD7vvPOklsEqBKqK1+qHePU4CgBsp5S+D+BnhEJG8MBpZv0uQmX9jhDC/OyO\nALAawERfGWo2mU90+xQViH5hmHcRmG5/yiYwcfASLB4ICSlODidS9Rkf5hVSOyFbpToo7zpl2gJm\nGjZsyDUPnQgEAlJD5sTaCRQdU040RWnXiGF+tmXKlMHx48fDzosad8uVKyckXSuJuEMkElH1yTRM\nZKmom98pGRkZyS8EmoLFH4GLYPG8oJT+BuBGAFUALCaETAdwHEAPSqkvzy5Oq/5ZWVkAUrt3brB6\nOpXpWZHVj0w3y/37908am8CigBebQCAkBKb6vThUCYGy+mxBQUHE/egQKFoUstVBZT/LBg0aSM1P\nhX2nagoKCgy7OaeA8SKoXr26lHxiOQQUwXfffSc9T0CPObNdGVq1itt9iC1Vq1YN8ykyaNAgzJgx\ng2seMkiYYPGU0q8K8z+DkApqCwC+/UM7CYHxGvL6nWgkuu47ANx4443S8lIhjN19991a2ATyinkj\n27GD7qRsAsWiIk5gNHVQ3nVqZ0cueqcsPT3ddgdSBrLHj1jPknd9sgVhMzpMdJOJM2fOGI6yKlWq\nFCEEihp3y5Qpg7Jly3JPV4dFYlmCtBXZNoEAcPPNN8csw7Jly7jmOWDAgLB8evbsyTV9WSRMsHhC\nyMMAagOoCWA6gJsA/EgIyfCTLs8Xp5cXwyeffMIt36KOaBsu6y5CMsUcKwqTmTp16ni6fsOGDUXi\nuahCt2DxMhA9XmRmZkqf9G3YEDLHl70TaOaZZ54RngdrN9Z3QLKwfv16436ysrLQt29f4xwhBI0b\nNxZehjNnzhhOPWTuBH7wwQdJVZdmatWqpboIwmF9cv78+WHHZS1MmccEq7OzRCHd4/WxgsUL6U2E\nkG4I2QBWoZSeANCTEPIagAcBDAHwlPU3/fv3NzpB+fLl0bx5c0MP2bwKkZ+fb3y3O2/+bj0f7/XW\n35jPX3bZZa7z0+U7ALRr185QC5WZPyEkLH6T2/LOmTPHdX6rVq0KC4C8YsUKHD58GFdffXXM34us\nTzbQ+U2PUurpeSTa9/T0dMN1dLTrmzdvDiC0YmheWBBVn7HKI7P/yigPY/PmzWHtDQj1sa5duwrL\nf+3atahbt67teXaMV367d++GlSVLlhh2gSLub9++ffjzzz9RtWpVae2HMW/ePKSnp6NChbPRnfw8\nT7e/B0ICmuj6XLp0aURIiC1btoTl5Sd9Vf2RfZ82bZqRV5s2bXDHHXeEPb+HHnqI6/O0u98NGzbg\n/PNDfv82bdqE9evXR4wHvJ8Ho6CgIOwYj/QPHz4MO8477zyUL18ev/76q3Fs69atQp4ve57t2rUL\nS98aFo13fVqdRMV6/n7yY0KYdcz9/fffhd2ftfwMVhav6c2ePRuEEK7j48qVK3HkyBEAofYVFUqp\n6z8AXwEoVvh5tun4HQg5dVnjJT2b9OcA2Gxz/FMAi2yOzwWwwuY4tePIkSN0+fLllFJKZ8+eTQHQ\nhg0b2l5LQwnRw4cPO563MnXqVNq3b9+o1wAw/pIBdi/33HOPkvuaNWuW52fqtYwzZ86kV155pfF9\n6dKldPr06XGlxQsAtGnTpr7Tyc/Pp4FAgEOJ9GX58uW0V69eMa/Lzc2lAGijRo1omTJlhJcLAA0G\ng8LzcVMOAPTYsWPC8wBA//Of/0ScY/1JFN999x294YYbhOZBaehezjvvvLDnCYCuXbtWaL4LFiyg\nM2fOFJqHFVafZ86coZRSunLlSt/vALe/l/EeXbt2LQVA69WrR3fs2EFffPFFI8+nn35aWP6y36PT\npk0z8vznP/8Zdq5Zs2b0l19+EZZ3Tk4OBUCHDBlCn332WUoppYcOHaKvvPKKsDwpDW8/DRo0MI7x\nYu/evWF5mP86duwYltfw4cO55Wtm8uTJFAD9+9//bhwDQEuVKiW0jU2dOtVI//Tp00LyYOTn54fd\ny3XXXUcB0JycHKH5Uhqqt8aNGxv5//rrr56faU5Ojqc5QLz1VvgbW7krEF1EjEBIsHgX/AXAzoJ3\neeE5VyxbtgyPPfYYAODyyy/H8OHDsXr16qi/ka0q4LTKoDMbNmzAW2+9pSTvlSvPhpasUaOGkDys\nbcCLOqjI+kxWNRbetGzZ0lUAeGYbEgwGHZ8t7/rUyTvx5s2bpeTTu3fviGN79+4VmufcuXMd2wDv\nOt22bRuA8N0it+NFvGRlZWHfvn1C83BCN5tiXvU5evRoAMDGjRvx7rvvht1nso69Y8eODfsu6z7P\nnDljxHYrV66co3aPCOxCuojk5MmTUoLHs7ozOzYaNGgQjh8/jmbNmjn9zDfmsW7//v221/CqT6dx\n9d133+WSvhfMc9FEwpM6KKX0c0JIZYSCxZfDWbvAPPgIFm+ieOGflZcB/IMQMoRSOhoACCF1ANwA\n4F63idu5b+X9AhP9stcRQoiyl6JdIHcZebJVFJUk60REBF6elcx6tQucLhOz7Y2s+Ih2dSH6Gajo\nKyVKlJCWV3Z2Nv78809p+ZlRaRMoi7y8vLDvyXSv5nuTLdCz52i2CZQZ05PlJ5OCggJb51G8sYvH\nysxaZN2zNdQHb5zaiQqbQPY5Ly/PMcQUr7x44vlJ0chg8dfAZ7B4QkhDQshgAM0BZBNCniaEtDTl\nuRrA5QCuJISsJYR8D+ANALdQSl37ZL322mvt8o632ELScrILSBEbkYbQ5g7oRQgUWZ/33HOP7zQC\ngUDYjkWK0ITEqS/zrs8vvviCa3peYbsdAFClShUpedo92w8++EBonkePHnU8J6qPmicioie1pUuX\njrBbE82dd94Z9l0XwUhEfebl5eGpp866HtDlXnlw++23G59V3Ndtt92G4cOHh7Una38R+R4Vcc+Z\nmZm2x0uXLo3XX38d//ynsIhqBi+//HLY9927d2PSpEkAxNaznWBkhVd9svStz1OFdgIrS79+/YSk\n/8svvwhJ19NOICFkGoA/KaUDAfzAqxCU0vUA1gN4Pso1iwBcwSvPFPxQ+UI0d3ZRHd/JO6hqVT4e\nrq0JITj33HM5lCZ5OHXqlLQ2rboNmdWSZN2zXT6ydiFl4mYyxAsVYzBbdEsmgciMuc6sO4GisQtL\nIQO7uhTddmvWrImKFSsKzSMaItqv01wkKysLmZmZUtRBrfEmZe6uZmZmYv/+/cLzZOmzeI/su4qd\nQIaosULUXMHrk7oawDkiCiKLWbNmGZ9HjBihsCT2JKJNoHkQnThxotS8H3roIeOzqImkk01g27Zt\nAQA7d+60+xmAlE1gohEIBLBr1y7hNoEqbBZ0RrQwzNLPzc01vPoyRPVR3Wzligq86tPs4XHcuHEA\ngD59+nBJOxb79u1z9DCZTLh9h4l8j5o9SfLkvffeC/veoUMHjBkzxtC4aNeunZB8GdnZ2Y7nRM4d\nOnbsiAEDBgBwHtd52wSysXb69OkA1MyNRAugou7Ja2mfB/Cd00lCSH9fpdEQrw9etZ2YaswxhmQj\nciJprldms7B06VIA9ramMkhNMvnDVC5Ev0SYS2ud6lDW2KXyBX369Gmj34rGfJ9F/b2QiNjZVLVv\n3154vpUqVQKg5r2ism+qKocou7Vbbrkl7Hvfvn3Rs2dPYxdw4cKFQvJliLBLc0OtWrUMAVfWTiCz\nJ2Wo1GopU6aMtLx44HUGsgvA3YSQpwgh/Sx//0Ioll+RpajaBJYrV874rHJ3atmyZULSPffcc9Gt\nWzfjuzXwNKUUTzzxhO1vRdZnvXr1hKVdVGGG8+a4kGZ41eeUKVO4pMMTSimGDBkiPB8VKmesr1x0\n0UXS7I3MYwSb2CcTw4YNC/uui2YCr/q0c1ZUvnx5AHJsqoYNGybN2RnDjydsHvmZMeebaDaBKvNh\n/N///V/Yd/PzXL58uZQyOC3M86pPFm/R2ldVLmb873//k563H7wKgYMAdAOQA2Cy5e9VhBzGFFmK\n6mqvSn1+GdSvXx+DBg0yvltfjPn5+WEONmTRsmXL2Bel8IToUAWMDRs2ANBn4sx4/nlHs+yEhvUV\nFr5BBuYxgtmsiMS6OJWCP5UrVwalVLgQOHr0aEyePBknT54Ulo8bZIxPdvOmsmXLRnXmxBOV3pmT\nHdHjEQvxZtWokaVhY9X2cHIIFA23cgPLi3d79fqk/gdgNIArAXSy/HVBKNh7Ch8kok2gGd0mtXY0\nbNjQ1+8JIWHCwrx58xyvTfT6LGqYVWjsVuF51+fNN9/MNT0/iFrEmjEjtgPnypUrC8mbUapUKeOz\nVc2PV51a05G9KFihQgUcOXJEap5mMjIylOVthld92tl6Hzx4kEvasWAx806dOiUlP4bsNus0X0hP\nT8eXX35pfBf5Hk2WcAlA6DnJyCcWzGmdU3sSZRPIULUTyPqtW7yUUxd10PcBTKGU/kgpnWP5+wGh\nHcKkQodKSiQS4RmsXbvW1+8JIWEOhoYPH+63SCk0waym8tdff6kriAJETQC7d+8e9t36wu7YsSPu\nv/9+IXkzWrVqhUcffRSAOFur++67DxUqVDC+y96Vy8rKUhYrEADq1KmjLG8RrFq1KuLY4sWLpeTN\nbJxk7wTKiF9nxW7c2bNnT1joCpHIEgKZ9odI7r//fmzdutXVtTk5OcLK0a5dO3z++efSHH5Z61BW\nO7a2XattYiLgtfXfRSmN5kqpPiHkTUJIZEA+heTm5mL//v3C88nPz/e9CpOINoFmEkEI9FtGQkhY\ne4rmxS3R67OoESvkSDLXp6xdAOtzFa1ex2A7VdYdXl51SghBbm6u8V32rorKgPE6IbKPsrYjur1a\ndxTM7YoXJ0+edCVkqrAJrFSpUtiOvcg6laUOKsvba25urqv2IroNBwIBFBQUYPny5cLssFm61ntR\nFSzea77bt293fa0uO4HtCSFXEkIeJYRcTwgxeg8h5H4AEwBsBHCUEHIXz4L6oUKFCkbcna5du3r6\nrZd4LlOnTg1TYSiKqBQCGzduLCUfQkiYZy87L3IpEhOzZ69EWNBIRKxe67744gtceumlwvMVvTps\n5zBKJtnZ2di3b5/UPIsaLAwRGxsef/xx7nlQSo22ytqQeYeZFy+88AKee+457ul6xa6fyFAtZjuN\nsgQGWQ5D2rVrZ9teZI9HgUAAa9asQevWrYWpqLLx1jqvf+2114TkZ4VSinbt2qFz586glBoOd9yq\ncU+aNMlznnPnzvX8m2h4bf0dEAoR8SKATwAsJoQwY4vehf/fpZTOBrCMENKFTzH9YW78XgcX2RPB\nlA1Z/MgKsOvlpZGqz8TC3N/t+n4y16eqEBEVKlSQshpPKbW1PeRVp3Y7nDJRrQ7KUOWaniEjNiv7\nb/aMzRO2E8gmuSLakg6O7JzmV9bjIuqUCUoqHMPICp0Q7Zjoua15UcyaN2+bQKsapqh+aZd/9erV\nUbt2bVBKDSeJIp4tS5O3I0avQiAB8DOAfgCuAbAZwJOF5yoBoJTSIwh9WFF4jVZ89tlnSvO/9dZb\nlZdBJCp3T1566SUp+aR2iIoGRa2edZgUiiQedR0vWNOWbRNYsWJFx9AmMtEp9iUvWrVqBQDo3bt3\n2HERY8TUqVMNAcXaJ1u0aMEtn5ycnJi7CqLHwKFDh9p61pYx9rJ2qqK9Pv3000IFBTt4th03BAIB\nYwxkjmJ4w/pHjRrhgQlk1OmIESNw5MgREELCnvvAgQON8UIENWvW5Jqe1ye1B8BllNL3KKXfAOgL\ngN1tOoDTluvP81m+pGPq1Kno1auX4/lktjkSjayQCV4G71R9Jh533nknAPt6Tub6TPbwAsFg0KhT\n873yqlPVO4GBQEALQV61ECiij/bo0QMAcMEFF4QdFzGRv+aaa1C8eHEAkX1y5cqVXPM6cOAA1/R4\nYX2uIuqU7QCqaq+y85W9QGQWAq3wqk+Wvtn7MyBvAffMmTNGXmzsfeWVV7BmzRruebF8vJioucFr\nK/ydUmpWdi0GgD39NADHLNefG2/BEhGzx0g36KC6k8I7RW2HqKjBBnNRQpEsD4NuYAGFa9asKcRB\ni06CpVlA+uCDD7inv2LFCsf8ko1du3Y5npM1uS1VqhQGDhwoJS8rMlTpAPH9x036Ktpx/fr10bx5\nc6F5WIVA2W1JhRoqU5MsX7688Lys8ZSHDh3KPQ+ntilrDDp8+DA++OADFBQUCBsTVq5ciYkTJ2rj\nGCafEPIYIeQCQkgnAF8BOEEISQdQEcAOdiEhpDKAEvyKmnzYBaZOZpujZMGpM9oNSKn6TEwaNWok\nrD5FrBLGy/r16wGEVFsKCgq4vzx1EwJZ3/3222+N46L6qE73zptoC5iyFslWrVqFV155JeI4r/qs\nVq2aoRXAsNapqHu17i6Igjm6sarTWcshm8aNG4c9exF9lN0bG/Ps2pJIZAgqVls5do/t27eX4h3U\n3F/MToh42wTa5S0Dln9BQUHcjsdi9fHff/8d33//fVxpu8HrkxoGYASAdQB+AFAdwHAAW1BoE0gI\neZIQUhvASADLOJbVNyVK6CWTHj9+HF988YXqYnBDpB60TkQTAu+++27JpUnBmx9//BGHDx8WNgFT\nrS5nhrktZ0b8vCcGW7Zs4ZqeH6pVq4Zq1aoBkKMalcw7gdHasCzBQbQDmt27dyM/Pz/svcYmtVYH\nMbyJthPIs1398ccfAIBmzZqhevXq3NL1CyEEH3/8sZC0WVgMlTaBrBxHjx7lmqa1PTIhkAldbNHP\nuksnAkopPvnkE2Hpf/zxx442rbIXL+JdQNUhDrmnUlNKlwBog5B30KcBtKeULgDQHsD9ADoiJBCu\nB3AHALlLKzHYs2eP6iKEcfDgwYiAqIlsc7RsmTqZf/LkydLycuqMBQUFGD9+fNixRK7PosrWrVux\nZ88e25ckj/rUUZ1YlBA4c+ZMrun54Z577sEdd9wBAGEuy3n20auuusoQGpJZCIzWTmRNqqtWrWp7\nnGd9njx5Muy9JjuWpp0QePq01fWCP9555x18/fXX2LlzJ9d03dCmTRvb44SQsAk+zzplMX7ZM5YV\n4Puaa876SWSqp7t37xaaJ7u3ESNGAIAhWMvoo5RSRxMpHvV50003OYZkky3YB4NB4eq9ouYNnls/\npXQ1gMGWY9sAvF349T1CyBIAFSily/0X0R/mQVu3ydf06dO12hWIB13KL7Nune45Ly9PSv5MhSeF\nWESp8+ngwdGOYDCY1OqgwNkJtKgJ76ZNm4xYk3/++af0+y9VqhSOHz8e4SiBN9HifunyTuCBNWi7\nrJiw5p1AFqCeCQx79uxBhQoVpLrBl43I9/mJEyfC8lAxL2SmQKJtsJmKIsuHzR3y8/Nx5MgRrnlb\nUWHzyNBhJ3D37t2G5okf8vLykJub6zsdJ+IarQkhdQttAkEIqU0IyTafp5T+QSn9mUcB/SK6ofvh\nnXfekRIPRyS6PF+ZEw+nAcYu9IeI+ly1ahX3NFNEIsomcNCgQb7T4EX9+vUBiNsJNE9KLrzwQq5p\nx8OHH34IICSsMXj20Y0bNxqfu3XrFpaPDGTFCnzxxRcdz6kWAnnWpzkY/F133YW6desCEG+zZxYC\nf/zxRwAw1DWbN2+OYcOGcctrx44dsS+SjMh50ZgxYwAATZs2BRDqpyKxCxLPHFPxbj/btm0L+852\n4mbPng3g7M7gN998g5dffplr3laivUtEz3NljUElSpTA9ddfj4KCggihl5d69aRJk/Ddd99xScsO\nT0+KEJJBCHkfwAYA7xQe3g/gVULICN6FKwqofmH6ha16q0bmyo9TXiJX/c0vC1nqK0WdZFbnY7D4\nTebAvjwxp6mDJgZvVTrdyM7OliIE6mATKAMWqgEI2ZRa7020EEgpFR7kW8f6Elkm6w6g6DlYtDkS\nb60e63PLyMgAoMb+UWW7kpl3mTJlhGjRWNHCJhDAywBuAvALgFMAQCk9BuAeAIMJIU/wLR5fdJrU\nPfLIIwCAffv2hR1P2ZB5p23btloIgWxANzvD4FWf5vhU0eJMpuCHKJtAnTCvXjZr1sxwmsALsxD4\n/PPPc007Ht56662IY8lUp9nZ2RHvFBFEm/CoXtjkWZ+xvGaKdh4VDAYj2ixv7Zto705ZoTBiITJO\noAzuvfde2+Oy1ItZe2L33KJFCzzwwANS8rRD9JgrWwAVKQT++uuvAPQRAq8HcC2ltDUAI74BpTS3\n8Lt9S1eI+cHpJAReddVVqouQFJQoUQKZmZlaCIFswst7Ig2c9eIGRKp8pBCDbvZsImCTAlHjpPkZ\n/u1vfzN2HlXh5IiCJyrfM7LUQYvKTqCTB1KZ6qB2zkN4PuOismNjzVOmEHjVVVfZ3qss+37WX1kZ\nKlSogEaNGgnNk+XVp08foflEy1s05hARotpTNPtrHngVAtdTSr+1HiSEFANQDUAml1JxxDxIWwfs\nf/zjH7KLY1CvXj3b44lmE6iawYMHIxAIaCUEmgdXEfUpa/WwqCPCJvD888/39Xve2AmBPFeIhwwZ\nYnwuWbIkHn30UW5px0uPHj0AAKNHj8aGDRu499GVK1can2V7R83KyhIepubOO+802otdAGjVO4Ey\n36HMrow3ZiGQqf/zDHFljsEXS5AVuajhNm2edapCNfKCCy7ADTfcEHG8ZcuWQvOtVKkSAODSSy8F\nIPfeWRvu2LGjcezAgQMA/NdnLE//MoVAQghuuOEGNGzYUGheuuwEHiSE2C3lPoyQp1F9oiC74P33\n31eWd506dTBx4kRl+ScLOTk5SE9PVyIEpqenh+3Qpbx2JgeVK1fGyJEjhUx+zI5DdMBOCJw0aRL3\nfIYPH47ixYvjqaee4p62V1jQ4pkzZ2LXrl1C8/r5Z7n+0azeLEUwceJEo72MGjUq4ryfCaZb50Ey\nYpxFg91/8+bNheRv3mlkdWquW7/vO/PcI5rmiqpdQhm76TIDijdo0AD9+vUzjjVu3FhK3hUrVgRw\nVtg0j/einzFrO2bHOywurV+c0hHt5MeJfv36oUGDBmHHbr31Vle/dVsPugiBrwKYSQjpBqAkIaQt\nIeRlAM8CoAgFjteKRYsWGZ+tL0jVait2Dj6SyT5FFpRSqat6LK+LLroIpUqVMlbZ7NQHU/WZeBBC\nHF+SyVafdiosIvrSlClTuKcZL2bVqGAwKLROdYtNywtRcQJLlSrlW62KR326nZiZHcfwxGwTyOwS\nzWUyhzh56623fAXldrJ7BEJ2wiJVuFXYBLLnqHLHunLlykLSdXqe7J6ZllJaWppwIdC868ic43z7\nbUiR0G99yhKi/eCmbSdisPh5CAWJfx3AxQB+AjAQwG4AN1NKZ3AvoU9ef/11AKFOUKZMGQwfro+c\nqjKOSjLBtuRlwfL68MMPUa1aNcybNw9A0bAhKyqI8papG3Y7gdnZ2U6Xx83WrVu5pxkvZiFQ9ESI\nGfUnG6KEwBo1amDatGlx/54Xsfo+u//MTDEWMGZ10C5duhjHe/fuDSC8Xd1///2+1Kzvuecex3Pj\nx48XquamYiFeByGwadOm6Nmzp/R8mXOuQCAgbScwEAjgr7/+CstfNLJtAu1QrRbvFs+lpJTOpJSe\nD6A+gEsBNAFwLoDphJAenMvnG51V9JiOvzkGSMom0DuqhEBrnnZtjWd96hKTkSf5+fnYvn276mKE\nEc3pQ7L1T7aTYW7LyS78Mg2M2bNnY/PmzVzq1GnH7+DBg77TjodTp04JSdfNxFH15IdHfebl5fkv\niA/MQiDj2LFjxjvG2kd1G0Pd4tSeRMYJZHnKWoS3u8dy5coZgdx5cvTo0ahlYPecnp4uVQhksM/J\n8h49c+aM49yTEILZs2dze87Hjh3jko4Vx9GaEPJUtD8AfQBcAaA3Qmqg7wCItHxVjNldv2guueQS\nT9czV/9du3YVUZwigyoh0DrZET15/s9//iM0fRVs2LAhbKVbF2TsEq1atUpo+m54+OGHI44luxDI\nAn4DwIABA7ik2bdvXwD6qH8y7QTesD6hMqyADH766Sel+dsJgQBQtWpVAEBubi63vFQK7U5tpUKF\nCsLyZM9Uxn137tzZ9viwYcPw3nvvCc+fYRUCzz33XOF5Wp3QHD582LWdXLzosMsLAP/+979Rvnx5\ndOrUiZu3eFHtJVrU6U4AOnhM75CPsghBZmPwGsTbrmzJZnMkg2AwqMVOoGibQBlOH2QjQ9jyigib\nQEppRPsoV65cXGnxROROoK7CpLXfuqnTaHGggsGg0VacQgrIRpQGjG591Q4eY+6JEyeinhf9vmFt\nzeoJmk3inXYEE41ktwl0Gg9E2ZLGwnzPsncCy5cvb+x+iprnyhTwo1GhQgVumoNfjUMAACAASURB\nVFuitRmjPalXAEwE0BhAPQB1AIwC8D2ALoXfzX+3AVDv+s2CXWMQ1fhvuukm197NrIj2Upfs6CYE\nilDd3Lt3b+yLEgwZtgnxwNsm8N13341YJFL9ogLOhqoRIQQyL5zJwPnnn48zZ87YnqtVq5axQFO6\ndOmI8yoWb0QJB6J3AnNyctCpU6e4f88LXXYCnZ4Fq99atWr5yuf777/39ftERPZukU7vN/Oinwp1\nUNF8//33qFq1quEVVSVvvvkm1/RUOIaZDmAKpXQ9pXQzpXQrgM4IBYv/gVK61fL3XwBi/CX7wE6K\nFtX4q1at6hj/LxZsxS9ZdKWTGafBzdxJT58+DYBvfSaDmpUVHXcCAedyxVufdjsLOjiGYivVIoLF\n83IHLho3dbpjxw5Hwcp8jtWp2eOiCk92ooRAli6lFHXq1LG9xs841bhxYyO2WbzwGHOdBH5ZuH2G\nO3bs8JVPhw5elb3kwsYiETaBMmPlqca6eCPjvRstJqHIeW7jxo09a+X5IVYds7mgrjj2AhoiYjmM\nUmp7R4SQAIB2HMvGBZlxuWTbpqUIIVuQMHsXNPPEE08Yn3l4WGTpM13wtLQ0YQ4fVJJIQmC82I1D\nOgiBdvASIHSs13hxag/MptC62Hjttdcan1U8BxnqoE7vOh12uP3Su3dv3HHHHY7n58+fLzR/p2fL\ngm0fPXoU119/fVxp79692/isex/t06cP9zRlqgzecsstRlgGJ6wqvyJg9cycEcqYMzFBzPycR4wY\ngWHDhgnNV7c2Xb58eaxevdrxvOryeu0Fhwkht1sPEkLOAfAygDJcSiUYUQ89HiGQqQ+xMqVsAvXH\nSR3UDh71uXjxYgAhoUH1CjVvdFQHFWETuG/fvohjMlcrnQgGgyhbtmyYy3vd6kMUzDW+mzp1aqef\nffYZgJDQ1bFjR+N427Ztjc/JKgQ6oVoI5DHmXnbZZXjnnXccz2/atMl3HtEwP0Pze4a52geAzz//\nPK60zWORau/psd6hH3zwAQC+8yKzENiiRQtu6dpx00034fzzz3c8X6JECSk7RXZCoGjYIqd1PPjh\nhx+41qdVZVrHjRjzwosZHcrqdbQeDOAVQsgCQsgLhJDHCSFvAPgDwL8AxKUESwjJJIQ8QwhZ7uLa\nLoSQqYSQMYSQ++PJTychkMXgKSoTL1HIfH526qB+bTOiwV7UuqpO+oEQgs2bN6suRhiXXXYZSpYs\niWAwiLy8PLz77ru+06xevXrEMR1eAOnp6ejcubNRlrS0NG47gaInyn5h9ttubG2d+p7Zi6N5Z1em\nAwY71qxZIyRdN21DtRAoAkqpVO/MTmNDNKHNrR26efEpEeqKUoq3336ba3pA6N5VO9Y5efIkfv/9\ndyV5yxqXeC92WttCNLMclZjLxWO3V5dg8asBtEfIq+ggAKMB3AegBoAxlFLPkSAJIe0K03oCgKNf\nYEJIOULIJwCGARhCKX2UUhqX0KmTEMjUiVI2gfEju9Pb7QSa3fea4//wtglU/dIqCkybNg0VK1YE\npRS5ubkYMmSIcS7e+mzZsmXEMUIIypYtG28xuVCqVCl8+umnxneeO7NsB1tXevQIhbVluw3RiCUE\nFhQUGC/9iy66SKmqb5cuXTBjxgwhabNnEK2NqJ6EiXiHBoNB/Pnnn8Z30fcYj6qt2/Ak5vALqr3Z\nunmOwWAQ9913H7c8zfasOiyq/vbbb8LzsN6nrD7asGFDW0+ofvporLagaj5oxSz88hACRbXVeILF\nr6aUtgHQDKFYgTcAqEkpfSyeAlBKF1BKBwNY6XQNIaQsgFkAsgB0ppTa7626z9PPzx0pVqwYMjIy\nPP2Gbc/L0AtPZlTsBDp1fh51yXTIt2zZgnXr1gHQQ32QN7quRBNCsGbNGqF2vjrt7Jp3Ag8d4hPp\nR+cFixo1anhy037ixImI+9m4cSP2798PIBRcnLXlTZs2hdnuyvYOWrp0aa55BoNBYwxy4+xHtRAo\nApUTS7djhNuxVOd+aYdfldW1a9eGfaeUGotdOrTVP/74Q3ge1jaUkZEhJFi9Gadny/udZ81HhzoF\nECYLfPTRRwpLEp24Z5WFu4LO1o7eOQHAya/r+wiFqWjk5JhGB3r27GmsLruFeZErX748gJRNYDzo\nsBNofgFnZGTg2LFjoJTGXZ9NmzYFADz44ING4OfWrVsn3As8FroM2FYCgQBuuukm7Nq1K6yM8dan\n3URGJ3tIEe68dbk3OzZu3GgIgc2aNXP1G2vfYwHigdCiTZcuXQAAhw4dwooVK4xzZvtAGQwdOhRb\ntmzhll5ubi7atm2LI0eO4JNPPjGO6+oYRsQ7VHZbdlIlizZeut19TrR3iN/yNmnSJKz+WFxhJgyq\n5PHHH8cLL7zANc3x48dj69atUdtsTk6O8Hu3y79Tp044duwY1z5qvQ/Vdcro27cv3njjDQDA//73\nP7z//vuKS2SPHk8rhG1PJ4RcA6A7gAmUUi7B9EQN6IQQzyuwrMGePHlSRJFSCMBuwmx+ObMJP492\nZg4tQCnVemIdD+y56bYTbg334fe52zn00XEn0KtQHq3edLk3O8y7gAcOHHDV/qz3YxbsCwoKhITZ\niAfeK/yEEKP9mu/Z6R51XdhJJJzidkZ7trEmv6yNJ5oQyNt5jU47gU5hVvxwzjnnID09PWwcsPbV\n9PR0JSEydFr4FI2u3r+t6CQEOnF34f9NhJA3CCHzCSGzCSH9401QJ7u71q1b47HHHkPdunUB6FW2\nREG210ynEBEM9pI9deqU7/qcPXs2gJDacDAYNPKeNWuWr3R1gT3DatWqKS5JOOa6PXTokBH4PN76\nvP32cKfKy5YtA6CfoLR161ZP11etWtXxHLu3xx9/3E+RhFK2bFk8+OCDUe+DYZ08szpkONnhDRo0\nKP4CxkFaWhrXiTMhxFiMYvfStWtXjBo1yvZ61SvxIt6hsm2q7BYVY+Ubq867deuGlStXaiUEunmO\n5t1nHrD3qA5C4N/+9jfuabLFRdX3Vq9ePXz44Ydhx3744QcA/voouy8WT9RO0JSJrhoRbtG6lCT0\ndK8AcBjAVkrpAwA6AVgLYBIh5N/Rfu80wcrLy+Nc0vjJyMiIUFdI4Y309HSprq7tdk2sK7eNGjXC\n0aNHfefBePnll8PayOWXXx532jrCbKt0gT1/9syPHTvGNf1WrVqF5aOaeMvB4pbZwSabIj3n+uXW\nW2/FiRMnot4Hw8vk2Wy/e95558VVtnjh6eHViXLlyqFevXq25xJl8qMzTjuB0Yg1hzhy5AhOnz6t\nlRDoBnNYDB4wIZCphaqkWbNmGD58ONc0dRECixcvjiZNmnBPl40v7B2qq3dQazn8zvG1cQwjmcoA\nigNYTSn9FgAopWcAPAJgP4AnCSG2nlj69++PnJwcAKFdFLbyMGLECHzxxRdh186ZMydsZUL29//9\n73/G58suuyzs/OLFi0EIUVo+3b/v378/zPhbdH5z584FcHbwmTNnDn755RfjfPv27dGkSRPk5uZG\n1Keb9Nu1axcxgGzcuBHBYNAYCHR6/n6+m4+z3Vwdymf1QMZ2yOKpT/Nn6zFKqRb3aw0pMH78eN/p\nd+/eHUCk9zsd7tf83bpY41R/S5YsARDqn3Z1ymjXrh2qVKmi7H6WLl0aVp9+0/vpp58i7nH16tXG\nGGX3PFXWJzvGM33zDvmcOXPCBBMR98PaGgD861//Mj7ffffdcIIthDqlv2zZMlBKsWjRorDfya4f\nr/XF7v/BBx90nX67du0c0//0009xySWXANBj/N26dSvX9NavX4+tW7ca/XPr1q2GbZr5N6rud+nS\npRFl8fJ7Ngdi93fw4MGw8xdccIHU+9m9e3fE+TJlyuDaa68Nu8/jx4/Hlb4VN79/9dVXkZOTg5yc\nHPTv398xLQBn7YxU/wGYA2Cz5VgFhGwFp9tcP6XwXHObc5RSSs+cOUMB0Pvuu48yANC7776bsmui\n4eYaHvTs2dMxr2nTpkkrR6LSr18/OnnyZGn5FRQUUAA0Ly/POLZkyRIKgAKg48ePp9OmTaPr1q2L\nK30AtESJEkZ6AOjEiRPpZ599Rg8ePJhU7WHEiBHGPZ46dUp1cQxYmbZv304B0MGDB3NJj/1RSumx\nY8doRkYGj+L6Zvr06WHl+/rrr139LlpbfPHFFykAOnbsWG3bLHsXxCofADphwgTjM/tvV6+UUnr4\n8GFX6Ypg06ZNXMfD3NzciHv+4osv6PLlyyOuBUDbtm3LLW9dGDp0aFhdtm7dWmjdbtq0KaxdlSxZ\nkq5Zs4ZSat/uqlSpYpx3AgBdvHgxXbdunbK2aaVDhw6O5bDeY8OGDV2na07Tmj4AumLFCrpkyRLa\nunXr+ArOkeHDh3NLCwCdMmUK/fe//02feeYZI31zfausdx7t7pxzzqFDhgyhXbt2pQBo9+7dw9L/\n448//BbTFb169aIA6J133ul4Dbvf4sWL002bNkWcP+ecc2LOe1gaAwcOjPvZFf7OVvbSeieQUnoY\nwE4Advo0LCjOPrvfjhkzxljxsqoKUs1UL9u3b298tq4A6FZWHeFtAxOLaOqgFSpUQO3atY0yRVvR\nsYPZJlkdBaWlpeHFF19MuvYwZswY47NO99arVy8AZ1WxWKBor/UZjfT09IjVQl3o0aMHpk2bFqal\nYGXjxo0AQoHJJ0yYgPXr12PatGkAgD///BOPPRaKGqSLeo4dZs+g5rZoRzAYtN0Vs0OlSiTv8dAu\njtmYMWO0tYXh2UdVYfcMoz1XQghyc3ON/ucEpVQrdVAvYwPPcus8JvmlevXqWqiDRsNPHyWE4KWX\nXjLmC6rHGzfP+aKLLkJOTk7MdwzD7jpR8yOthcBC3gXQhBBiNUCoA2AZdYgZOGjQIHz33XcA9BcC\nH330UUe9cNUNPBEIBAJKhEBz3bDPhw4dQufOneOeiO3du9f2eHp6OhYtWqTVC5wHZrUqne7tv//9\nL4CzZcrNzeWeR/HixWNO2mRh9yJ79dVX8dZbbzn+ZtWqVQBCTopGjx6NRYsW4e233wYQ7mBGZy9p\n9957L0qXLg0gtgOXgoICfPvtt67STSYh0OoABwDmz5/veH0yvrNkzxmsfSbWRJMQgn379uGJJ56I\nep1ZCLzzzjv9FVIyKSHQHRdffLF2c1yeBAKBqGFTZNWt22ecnZ2N66+/Hv/9739dOwmzu64oCIHF\nC/+sPA9gBYBxhJDiAEAI6QDgKgD3R0uQNRRro9BpsmnFGj/lzz//VFOQBEL2TiDDyTEMcLZMvOLh\nMEcTyTS4m+1egPBwGKphgV4feugh49gll1yCiy66yHfa2dnZvtPgjd2Lc9WqVVHHSruXL2uf5naq\nW/gPK2anP9GE/U2bNuHZZ591labZMYxs0tLS8Prrr3NLz2lS5eSRWbUQKCPWruiJpvUZZmZmxmxT\nbt6BZiGQLX4kCqdPuwsRzfwDHD16FOvWrXO8rlixYqhQoQKXsulEMBhE6dKlta5fr310zZo1xvyA\naUmx77rPiYLBIJcQcEkrBBJCGhJCBgNoDiCbEPI0IaQlO08pPQGgM4ANAJYQQuYBGAygE6V0qW2i\nhbBB0eqGV/dGY8bsoCGFPTK84dkRK3AvzzIloxDYpk2bsO/ly5dXVJJIWN1+9dVXxrHFixdj5cqV\nvtPetYtLuFPuXHnllWEC6smTJ6O2N7MH1Wh9IT8/Hw888AC/ggrk+++/dzz3zTffRBxjzseslChR\ngleRPJOWlhbh6McPTnW7e7etEo5yIVAE1n4gWwicN28ezj///KjXu10IDQaDeP/9912rpumC24n0\nXXfdBQBYtGgRBg4caHsNIQTNmjVzvbOfSAQCAQwZMsRwpJMMc4bbb789Ykxj72JzX5Tpidntc6U2\nzpjiIS0tzVic5ony0ZpSup5S+jyltCSlNJ1S+hSl9BfLNUcopfdQSptRSjtQSrtTSpfHSptNwq2V\npetOoNWrGhCKNZciOsWKFVMy0EWb7LCXMi/7lGQUAq3orDbIMHuBdYt1x0TH+ySEoKCgIKJNB4NB\nxzGIvXzNkzO7a8+cORMWnF1nDh065HjOLuSLSmHPCVlC2DnnnKM0fydk2ASKHoetz9DNzvKOHTui\ntl8gVO5Tp06hVKlSyusJ8CZM79tn6/4hDEopjhw5AiA0Ljk9D5avjmOxXwghCAQCWtSvE04xVZ04\nc+ZMxLyd9UFzG0pLS5Ou6hsrv2AwiJIlS3pO1/wuZf1fRHvVt5VwgK2M9e7dO+y4rkJg2bJlI47d\nf//96NKli4LSJA7Dhg3DrbfeKj3fWDuBPFVUWecPBoOOq5uJTN++fVUXISos2Hs8toFNmzblXRzu\nzJ8/H7NmzTIc4jA2btyIzMxM29+w9j9kyBDju9X9NwDccccdWgeMN3PPPfc4nrPbwT148KDj9evW\nrcP69eu5lMsLvFXcnCaTTkJgMk6sZeM19tntt9+OQYMGuYpNO3DgQLz33nu+yscLtyqegDvBe+bM\nmUafe+ihh7B8uf1eQTLbBFp3i3S610cffRQAjPBBblm5cmXEvP3iiy8GEK5RJHOR3G1eixYtMuYP\nXsjMzDTaspNpGw/UGS5IgE3Cy5UrF3ZcVyEQiNSVrlixopY2RDpRuXJl1UUAEO5pkLdNIJsUUErR\nsGFDLmnqRN26dVUXISo1a9YEADRu3Njzb9nKtM6wl4zVjiQvLw95eXm2Kp/RvptfkBUrVuRdXG2I\n9lJW1U95C2FO9+i0O6V60inCJlC2OqhXZxdu7b8opdyDr/vBya6UR3pmW9+ihM47gPXqWf07usfa\nB5l2iVkbg1Kq3f3Xr18/5g69HUePHg2756S1CRSJ006MLqtgdlgH+/z8fMNTYQp9sdbbli1b0KFD\nB7z55ptxpXfhhReGfTcLgaonWSLYsmWL6iJEZcSIEQDiG4gTQYWXtanrr78+7HheXh4A4Ndff434\njTm8RSK3SbOzH7f3wVR1dL5v3mXbsGGD8blFixZo1KiR7XWdO3fmmq8u1K5dW1pe1oms+fu4ceOM\nz2zn2q3Ac9dddyE/P1+p4yIzbrVlnnnmGQChNu1khwsAL7/8svE52mKIzv02menYsWPcv7Vu3tSp\nUyfiGjuTBlF4sSeNVqZo8wOvGgHxkNRCoO5e6dyQDPdQVDB35gMHDgAAZs+eHVdaVi+UZiFQt5Uu\nHuzfv191EVyxevVqz7/RWfOAwdqU1VkPY8eOHTHTSFQ1wBdeeMH47KRKaXWnz8blZOyLTjBVw8su\nuwzXXnstqlSpEnHNVVddFdduOU9E2ARSSjFgwADu6ToRbfI3YMAAzJw5E8BZUxe2KHH11VdHTfe3\n335LSCHQvODAQtPYYVa/dlJXBlJCoCr8aEdYhSU7DbBgMChtTGa7zm7aktM1sX5rfqemdgLjQEXY\ngHhhOw1ASP+ZCQEpITAxqFy5Mrp162Z8Zy/ZTz/9FMWKFXOVxuHDh43PCxcuDFNzYIPFqVOnkvIF\nlihe2uJxu50IQmAsAc7sJdUOSqnR5ufMmWPEkZPhrp8HzCbVyUbJOmlmO6OLFy82jlWrVk1Q6dQx\nevRoQ8Cw2osWNaKFBOJNrB0A9m6oUaMGgLPt1s0EeMuWLVEFJJm4nd+Y7yuaer15omx+n1pJxndo\nsmOeI5sxC5a9evWSHhrjiy++iHlNVlaW6/TMYeHM7V6UFljSC4EyXcbyYtmyZUaA3pQQmBjUqFED\no0ePNr6bdxTc2j2w3UMgtGLbsWNHw6EGGwxOnz6deoEpYPjw4WjYsGGYoO+WZBACP/jgg5hpMEFp\n1qxZxg64Vy9wKrjsssvw3//+F4MHD3YUAq2TazY+z5071zh2yy23iCukIp599llDJXj79u0xr9dB\n9TkZ4wRa82POPxo0aADg7DvG7W6824VJ0bhdqO/QoYPx2a0QePz48fgLlkIo1113neffzJo1K+w7\n6xNdu3Y1jo0dO1Z67MdosbxZe2RyyPDhw2OmZ3ZAllIH9cmPP/7oa2tYlQDJGs5nn30WFqw6ReJg\nHYhY7CIntmzZEmGrWrlyZZQpUybs2OzZs1NCoALWr1+Pffv2xSXQ6TAxjkWsMsaKT3T48GFMmDAB\nwNlwE0BiqYja9TeG1bmNXagQnYMzx8vRo0ddLQCYScbxydo/SpUqJTQ/67zFmr81NAnbaahUqZKr\n9HXpl25Dx5jLu2LFirjyMqsJ8wjenSJ+eDsESgTcyhPm8dO6EyiCpBYCd+7c6UvtQZWzCjZA3XDD\nDQCQMO7VU5zl+uuvD3ML/M4770S9ftKkSXj66aeN71deeSXeeOMN9OjRI+y6Bx54ICknWTqTn5+P\njz76CAcPHsTPP//s+fdmwVFXFfVYoS9efPHFqOcPHTpkBFqnlBr3rMtkMxpscvjII49g586dttf0\n798/7PuUKVMAhL+k+/TpI6R8qoknNqZKRMUJNI+7bdq0CRuveWMXr9OM2XFYfn4+hg0bhtq1axvh\nWmKhS79s2bKl47uROakqVqwYFi5cGBZrzWkxLtpE+fLLLzc+6+6ILJlp0qRJXKGWzIwdO5ZTaeKn\nVatWnq7fvHmzq+uiqZ2ndgLjwM9gp2qybQ24rIvqRgr3EEI81Zt1ZdJq4Oy0OpQM6N6+zWNIPCvI\n5omJrnUXq1xehNdgMGhcr+v92hEIBByDvzu9C8w7GTru+LoJsB0PJ06cEJKurhQUFET0fZFt29re\norUtc4BspzJZf69Tv3TqW2zctYuRZqeCRymNGrcTKHrtFtDvno8fP26omMfCqeyBQED5YrjX/AOB\ngCsVZXO65vE7Ly8vqp1rvOgzEgjCyQuWbqu2ZqP7adOmhZ3TZdUuhTdGjhzp+tqXXnop7DubRLMB\nQaZTAtksX748puMRXXjggQc8/8ZLQGRVmCeFzKmLGS9CYEFBgdDgtrxxY0PmdB9mTRM7l+Wq4R1j\n9p577kGbNm0cd4anTJmi3BmQiPw//PDDMHuexx57LK6xwC2BQACBQADXXHMNAHshcNu2bWHfo3mO\nNtuuAsDgwYM5ldQ/TgLuG2+8gYkTJ2LLli3o2rUrfvvtN+OcnRMm83knmJlGIoxLvLD2VWu7kc3l\nl1+O6tWru7o2mn2falOpGTNmYO7cufj9999d/8Y6z7PD3DbNXuI3bdokZKFRDz/BAnESAt02Qlmc\ne+65xudktC0piri1zzCTmZmJ/fv3IxgMxu1WONGoXr06mjRporoYwkgExzDmNtW+ffuI814cVJmF\nwGTBjRBYFBbrqlSpEnVBwC5sRDJgbc/ly5cXmh8T5mrWrAnAXlAyzxkARH1nWHdU7NzrqyCa4JqV\nlRVmUsGehRPmOgoEArbjLluQS7Z3qBes7UY2HTp0iHDy4kS0BdRy5crxKlJcZGZmIjMz03c61r5t\n5wlYpA1rkd0J1I2+ffvi73//OwBg3rx5YeecXOOmSD6YaiR7gbEBItl2Am+88Ubjs45qdDxJhPuz\nCjAXX3wxbr31VuO7F6EuPz8/LHSC7rixIWOON8zPBADGjx8vokjaIspNOU9ExQmUCRNirrjiCtf5\nRxOorPMgXRYs7rrrLlcBxN3UqXmXZerUqQAQYVNfFKlXr57qIoTRsWNHtG7dOuo1Bw4ciMuDaCLw\n9ddfG5+tQu7hw4cj2iwzOShbtqyQ8hRZIVC3F1nr1q0j1EBTJBduVOqYEFhQUODYRnWy54gXc8Bf\nXXfKdJkoycB6rz///LMxkQK87wQmGxkZGejTpw+6dOkSdrxnz56OzmR0wKxOxINEEAKTAfaM69at\nC8CdEBgtUHbNmjWNcBKAPu+Qtm3bclOjNjt7YeFazGGbzBSlNsxioOpCrVq1whwb2XH69GksXbrU\n8XwiLKw6sXfvXsdzp0+fjgjFw4TAihUrCtnF1WMkEIiTEOjkAEA1f/vb3yKO6TJgp/CHnWrDM888\ng2PHjhnfmW2oVbXHrPqQDC+wP/74w/isa18cMGCAoY7EJmPxwITcAQMGcCmXCMwTRDNMbd4cmyuW\nB8JPP/2UX8Ek4NaGrEuXLrbtQNQKLQ+WLl3KdbwQ6RGTFyJsAlVPOt0KgdEWDlXfgx/c1KnVlnnA\ngAGOzyOWamki4lS/Os4XYtVnIBDAnj17AITsb+3OJypeQ+6wnfJUiIg4cRICze6GdeL//u//Io4l\n48p6UWPMmDG2QuDYsWPDPEYxV/RMcGADuNm+UMdB3Q+62sC+9dZbmDhxovE5Xlhdjhs3jku5RNCi\nRQvb4zt37gSlNOwF9PzzzxufN23ahFq1aoX9xhzsNpm47bbbcMkllxjf2e6pU2zBZGLTpk2qi6AU\n1QKUX3VQQojye+BFo0aNAMR+JuPGjXN8V15wwQXcy6WagoICPPPMMxHH2TMwL+Tpjrkd2zlT0XXO\n4IYff/zR8ZxdmzabIKRCRMSBkwMYXQfEhQsXAgAaNmyouCQpeLJq1Srs3LkT69evx5YtW/DAAw9g\nxowZAMI7NjMAtq7gm13RJ5sQmAiY1SHnzZuHf/3rXxFqrKw+GcePH8eTTz4ppXx+iRX82hq2hrmq\nTobJZbw2ZLpPJK3CuVfM7TmR6liETaCqMTcjIwOA/53ARO+nrE4bNmyIGjVqAAgfk+08GgPO9ZaM\n79BTp07Zhluy8zCumlh9NJYHU+Y1NDVP9k/SC4HWlZHOnTsD0PelxnYu165dC0Bfe6kU7gkGg5gy\nZQp++OEHTJo0CZ988gnefPNNdO/eHUB4W9y1axfWrVsX9vsLL7wwzNV7IqtCJCrmuEYDBw7E2LFj\nI3boWX0yNm/ejJEjR2r18nUilvMA6+RixYoVACInl5deein/wmlG165dAQCrV69WXJLo+HUoZm7P\nhBBDIAGAp556ylfaiYaq+cL555+Pc88919U8oCjsBK5duxYzZ84EgDANmoEDBxqfu3XrZnwuakKg\nebHYSiLdcyyTAuY8hc2TE4lhw4Z5up7ZdafUQePE6vCAfdd1QOzUqRMAqI1fuQAAIABJREFUPVdv\nUsQHq8PZs2eDUordu3cb547+f3t3HmdHVed9/PPrTshCkg7ZCUsWHkBkCyAOEBeUx4QAwyBERCSy\nKIjPIItsgjgomBeikBAVcVBQ0BkHURRGBlARRuKADEgQRQYYjAhoEpaQQFiS9O/5o6puqm/fe/t2\n9723qs79vl+venXXcqtP9e9W1TlVZ1m7tsd3sbOzs3Qjr/Yd0Hei9dKZqyRefV1DkifVeb3W9Meq\nVat6dFOdVC0qz1z29UYxjwbahizv5+FgHha9/PLLPeY7Ozt7VHtds2bNgPfdbM1oE1jPIM/NUm8B\nrlbHMEUvBCYxNbPSeZd8R92dtWvXlrZNF5jzfo420vPPP19xKIE85iVrnaNPP/103VVX83RMg1V+\nzU2kz2lVBx2A8otiUggswhu2P/7xj1knQQZp1apVABxxxBHcfPPNLFmyhCuuuKK0/vXXXy+9VQF4\n8cUXe31n+xpHRprvkEMO6dUpSF/XkKwH5W2kq666qkdPe0cccQSwKXOZXKvOOOOMTNLXSkU5/9LD\nsPRX+SDN2267bWkII6DHNawdpGsCtNqDDz7ItGnT+tyur+qgRcjz1OvSSy9lxYoVQFT4eeKJJ0rr\n+npLfdZZZ1WsNll0O+64Y81Ou4pSg2jRokW92s9PnTo1o9S0Tvk1F2Dx4sWl3/UmcIDKL4p5LwSm\n60rnvc2J9O33v/89QKk6Z6WeBNPVCjdu3NjjTWClJ7hFyYSGZOjQoaXOedyd4cOH93lRzus1ZqAq\ndbKVfD+Ta1V68PSiaEYbsjyoVTWsL5WuOXnuBTUttHiOHz++rgx8yNVBy2M6a9Ys1q9fD/Qu3KTf\nWFe6V06cOLGt7qHJseZpzOxa5+jYsWN7LQspL1xeO7FWzaJJkyY1fYzo4AuB1d4EHnnkkVkkpy43\n3XRT1kmQBksG/X3xxRd7rUt/R3ffffceJ/qMGTNYtGhRj+2LfgOr1hNl3iXDdzz88MN1PVm/9dZb\nW5GshunruvPZz362YtXk9M0rPf5TqNexIpx/yf9+++23b9g+k+MuH8dKmqO/50/IHcOUGzp0aKkQ\neNttt1XdrtL/owjn70CMGjWqYmdQyfEWedzb5F5b1LxD4rbbbuPqq6/mkksuqbh+yy237DHfipi1\nXSEwmc9rr0L7779/KbMpxZfUfa82yOf222/f4zs6efLkHm8CR48ezfve974enylKtY5qli1blnUS\nBiSdoa5n3K1169Y1O0kNNZDrTnnmcsKECYPaXxb624asCJnI5H+/cOHChu0zyZBUaneUJ81oE5iF\nRp4/oY0TmC4EJj0VJ/p6c1KE83cgurq6Sn1KpCXHm6d8Q61ztNIby6R9fVHzDokDDzyQjo6Ouofc\n6ezsrLsPgoHKz7eiSaoVAkVaqdoTnc7OTj75yU+W5seMGVNqf1Krao+0XnpA9VdffZVTTjmlFIsz\nzzwTgD/96U+lbZJBYasNUxOKZFDfpIe+wVRDLIJDDz001zVJ0hp5v0uuYXfccUfD9imNVe3e0NXV\nxQknnNDi1DTPCy+8wJw5cwA49dRTe6ybOHFi6fekoJj2k5/8pLmJy8jGjRtrvjkqSr6hs7OzR2/o\n0HOsvKJ79tlnS0PB9SVdCHzmmWd65C8aJdgS0TbbbAP0zpDk/ZV4aO0Z2l0Sz0rfu6lTp7J+/Xqe\nfPLJ0rIxY8aUnrSH3r110QoLu+yyC7CpSsp1111XWpcMJp8UiNJCKARWa0+SfiuUNGJ//PHHW5Km\nRunvNfekk07ihhtuaE5imuDwww8f1OdnzpwJbLqGVarSnie6h/bW1dXF5z73uayTMWDlMV25cmXV\nbdMFiEqFwHoz4EVTrRCYx/xCrXN0yJAhvQp9J510Uun3Cy+8sFnJapl6O33s6OgoVYVtVs2iYAuB\nyclQ3gtUHk8ICV+lJ/Lr16+vWC2gr+9oKN/h8gHIi6LS/z/JbFTqde7NN99sepqabdasWRWXV8p0\nlI+fKNmp1U6sXknTiUmTJjUiSdIk7373u3P/kLtRkrd96QcSw4YNY6+99uqxXaX7bij3z3KrVq0K\nIv4rVqzgsssuyzoZudCKXn2DLQQmJ3r524a899gXSnsGiSTxTG5G6frd1bo9NjN23XXXqm9fity2\nIwS1Cj6VxskLoRB4//3391p2wAEHMH369F7fxyy70x+IkK+5tXqM7EtSvfenP/0pQKn6Xd41K57H\nHntsU/bbKLfccgujRo2quU1XV1eLUtNY5TE97LDDAHrUolm1ahUPPPBAn/sKtRAIle9NeTzeWufo\nvffeC8BBBx0EhJffWbduXY/hlqD6MY4dO1ZtAgeqWgZabQKlEfr7PapWHbTavmtduIv6Bg3y/xCm\nHuVVPpOxIKFybELoTbHS91FtVvPP3Qf0dqDSNajd750hHH+ehglohPT4gLUKQMuXL++1LEQjRozo\ntaxox5ucZy+88ELF9UXPQ2y22Wa98gTVjmnEiBGldc0afqn4V7Uqqr1OzvuFXO0ZimH16tV1bZfE\nc4cddigte/bZZwH4/Oc/X/Ezu+yyC0uXLq26z1tuuaXOVOZPpTZzRfPMM8/0mE+PYVSpEPjKK680\nPU2t9LOf/QzY1PFNube85S2l73gRhHzNnT9/Pt/61rf6/bl0W5SiCTmegzVy5EgeeeSRrJPRb9Vi\neswxx5R+HzlyZNXPz5gxo9FJyqWPf/zjvZaZGR/72McySE11tc7RpPbeb37zm4rrf/vb3zYjSS3T\n2dlZ6kMgUa0JxbBhw0rX4fnz53P88cc3PD35LhENQrULQtGeikg+pQekrUf6e5e8AUz3Yla+ba39\nh1Y9oujST1+L/Ja2XnvttRdDhgyp+h01s6pvuaW1Ojs7K1ZRrudzGzduZPjw4U1IVTGFkHdYv349\n48aNyzoZLVPpXpn3FwGDUa2ztTFjxrQ4JQNXqV19Wq3CfhG5e9UHbp2dnaV1HR0dA7qW9yXYs6G8\ngXBRhNw+pR2l43n77bf3WFfePqN8fbmkx6yk59siSt+Uv/Od73DppZdmmJr+++hHP9prWfqt16uv\nvtrK5GRizJgx3HrrrUF0QgC65laSPMwoYq2DZsXz2muvbcp+W2n9+vVNq1bWTJVietFFF/X5uXYa\nJxCKc7y1ztH0Q4q3vvWtvZYX8ftbbu+99+4xX+lN4AUXXMCMGTNKhcDTTz+dq6++uuFpCbYQuMUW\nW1RcHvJTIMm3uXPn9pgvf2pXvr7cvHnzgMr1/ovo2GOP5Zxzzsk6Gf3SV9W6olahq8e6desYPnw4\nQ4YMYc6cObqWBq6zs5P3ve99WSdDGmjDhg1BZKIBzj333D63yWMBqJmqHW+Rag+lrznp4W32228/\noFjHUk3S6U2iUiHwHe94ByNHjuyRp2hGJ3O5uIub2UQzu9jMHqxj2/Fm9rSZ9TlYyJe+9KVey2bP\nnj3AVLaG2jOEpa/xcBIHH3xwn/vq6Ohg3LhxpYthERX9ptzX+Xn22WcDcOedd7YgNa01dOjQwhXa\n69Fu19ykfdBll13GY4891mtd8rZ7yZIlPdYVpUpZu8WzP9auXdtndbs8qhTT9HGcf/75FT9XqcBQ\n9HtQLUV5E1jrHH3ooYdKv6fTnlyrQhmCKN1O8y9/+UvV7caPH9/v5kf9kXkh0MxmA2cB5wGVX99t\n2taA64GtgT4fByQZsrR3vvOdPTpyEMlKuhCYdMNeS0dHB+9973t5z3ve08xkNVUIT/FqSQaBLR8M\nNoTjHjJkSNXOjKQ4rrnmGgBuu+22Xh34XHPNNVWrPfY1/IDkX0dHR3A9hAIsXLiw7m3zWChqlGqF\nwCLdf9auXVv6PZ3u559/Hgintk1yHQZYuXJlr/XJsY8fP55PfepTTUtH5oVAd/+1u58LLKtj8/OA\nPwzm740aNSrXbUDynDbpv1rx7G91uo6ODn74wx8OMkXZKnJ7Rqj//EwGj5f8a8Q1953vfOfgE9Ji\n3d3dPa5B1113XYapaRzdQ2srYiGokTEt4vH35dvf/jZQ+diuv/763J3bteKZ7vwkXeDbY489gDDe\nBF5yySU95ssfsE2dOrVlHThlXghMWVdrpZntD+wEXDmYP7Ltttty1VVXDWYXIg3Tnyd0Id68iiiJ\n2WmnndZj+c0331z6PRlCRG/O2sOvfvWrrJPQb+WFwOOOOy67xEhLFOmNULOEeB894YQTgMrHtmzZ\nMl566aVWJ2nAtt56a8466yyg54PyE088EShOtfRaytv27b333j3OzWeffZa3v/3tLUlLngqBVd/x\nmtkU4ELgZCC8MzhF7RnC0sh4tsPwA3mXjmd5V9XpG1bSc2aIVa9C047X3BtvvJFf/epXrFmzhr/9\n7W+sW9f7GWxRhxJox3jWa+TIkYUsCFaL6fDhw2sOY1KpUNSMzjXyoigF3L76Skjae6bbfSb30kmT\nJjU1be0m9zkUM+sEvgn8o7u/amaVB1cTKagXXnihru3uvffeJqekdSplOovk1FNP7VXAS88nPfCF\nMoyChOXII48EYPHixUyYMIH58+f32ubpp5+u+vk1a9Y0LW159NhjjzFz5syskzFoK1asKGTHMNW8\n+OKLNddX6v8hb1X1Tz755IbtqyiFwFpOPfVUuru7Ofvss3t0iHLEEUewdu3aYB6sTp48mRUrVmT+\nUKYI/83PAz9w90ezTkgrqD1DWOqJZ71P3Dds2DDI1ORHUYe5SOLZ1dXVq01n+uaUFP46OjqCuDGH\nrJ2vuRs2bMDMKrazqTUwcZ47iWlGPHfccceG7zMLRb/uluvreMyM0aNH9+hsJG+mTJnSsH0V5V5T\n6xxN7qPlDys6Ojpyfd3pr/4M/N7MznByXQg0swOBie5+QaXVtT573HHHMX36dADGjh3LrFmzSl+8\n5FW05jVfpPkZM2bkKj0DnZ89ezZ33313btIzkPkrr7yS7u5ujj322FKj++Tm9b3vfY/ly5cD0Y3L\n3Qt/vJoPZz7tnnvu4V3vehdHH310r3WJ9OcfeughrrzySu6+++5SL8VZH4/mNV9rfvHixT2640+2\nyUv6GjUP0cPH8vX77rsvr732Wo9jz0N623n+yCOP5He/+x0QtScfMWJEaf1nPvOZXt/Piy++mEQ9\n+1+2bFmpX4IkL1KVu+diAu4GnipbdiewvsLUDWyMf39nhX15Ud11111ZJ0EaqJHx/PrXv+5F/m67\nuwP+5S9/OetkDFgSz87OzlIsiIar8aVLlzrgjz76qC9atMiB0k/Jr3a75gK+7777lr63H/jAB0q/\np6eiard4toPBxjSU73Y1ZuaAv/HGG73WLVy40M8777wMUlVdu5+jP/7xj3333Xd3wF955ZXScsCf\neeaZXtsP9rsbf65i2SvXbwKBjwIjy5ZtBdwBXAV8HVje4jSJZGKfffZh9uzZWSdj0EJomP+FL3yh\nR/XchQsXltoMpauAFqV6jrSXgw8+uNTG+JVXXsk4NSKts2DBgqyT0HAdHR1s3Lix1BY9zcyCGVsv\nFO5etW1urViNHTu24WnJdSHQ3ZeXLzOzpEeJlR5gO8Hkla6EoZHx3GOPPVi6dGnD9peVvDXM748k\nnp/+9Kd7LD///PNLv/d3/EfJVrtdc2fNmsW8efO44IKolUVohcB2i2c7aFRMvYA9o9aj1kPHpElC\nnrT7Odrd3V21g5tahcDdd9+94WnJU25lWDyJSMDKh1Yourlz5/aYf+SRR0q/b7311q1OjkhN++23\nH11dXaX5e+65p9c25d9pEcmvWp3G6U1g/kyZMoV99tmn4rpaQ55Mmzat4WnJvBBoZjuZ2bnALGCy\nmV1kZntmna6spBv5SvEpnr0VuWBUKZ633357j/lf/OIXpd+rXeglP9rtHL3yyivZbrvtKq7bbbfd\ngN7f6SJpt3i2A8V04PL4JrDd4zl79mwWLVrUa7m7M3ny5KqfmzNnTsPTknl1UHf/I/BH4NI6t19O\nDgqvIiKVPPfcc+ywww6AqoZKsYT2ll6k3SXtBUUqUQ4lZ9q9rnRoFM+w1BPPm2++ufT71KlTm5ga\naYR2PUdfeeUVbr311h7Lli5dysqVKzNKUWO0azxD1oiYhjLI+EDkrYMynaOb1POWtlrNjUZQIVBE\nRKTNbL755r0yF52dnUycODGjFIk0T7vWyshbVVDpv0q9vjZKe54VOdbudaVDo3j2dMwxxzBv3rys\nkzFg9cZz5513ZsmSJc1NjDREO5+j06dPLw36/qMf/Sjj1DRGO8czVION6QMPPFAaEiVEldqXJRYs\nWMAZZ5zRwtT0Tedo/zSzIK9CoIi0zMyZM5sy1k3eDB8+nJ133jnrZIjUNGzYMH75y18C8La3vS3j\n1Ig0x1577cWee4bb32CtB6uTJk1im222aWFqpNFUCGwjqisdFsWzp7y1TeiveuK57777cs455/A/\n//M/ACxevLjJqZLB0DkaGTduXNZJaAjFMzyKaW1bbrllqWffIlA8B6YZ+ScVAkVEGuj444/nvvvu\nK3Wwcfrpp2ecIpG+jRo1KuskiMgAdHV18fDDD2edDGmSWuNADpYKgTmjutJhUTx7uvHGG7NOwqDU\nE8/u7m4N0FsgOkfh/e9/f9ZJaBjFMzyKaVgUz/5p5hAfKgSKSMs8+uijWSeh6VQIlKK56aabsk6C\niIhUoDeBbUR1pcOieIalnnjed999dHR0qGvugtA5GhbFMzyKaVgUz/5pZl8KKgSKiDTQ9ddfT0dH\nR1OrcIiIiEix1fOwuJmd/qgQmDOqKx0WxTMs9cZz48aNqg5aEDpHw6J4hkcxDYvi2T/NzEuoECgi\nLdMObQIhKgSqOqiIiIgMhrtz8sknN2XfFmJGxcw8xOMSkXxL190/44wzWLRoUYapERERkTwyM9as\nWcPo0aNrbnfggQdy0EEHMWHCBI4++ugB/R13r9iwUG8CRUQa5PLLLy/93swevURERCR8zXyppUJg\nzqiudFgUz7D0Fc8TTzyx9Ls6hikGnaNhUTzDo5iGRfGMjBo1KuskqBAoItIoQ4cOLf2+5557ZpgS\nERERkerUJlBEpIGSdoG6BomIiEglo0eP5rnnnuuzTeDcuXM55JBDGD9+fMPbBA7p995ERKSqadOm\nZZ0EERERybl6HharTWAbUV3psCieYaknnk899RRPPfVU8xMjDaFzNCyKZ3gU07AonpF0b+KN3LY/\n9CZQRKSBOjr0bE1ERETyTW0CRUREREREWmTMmDE888wzjBkzpuZ2c+fO5eCDD9Y4gSIiIiIiIu2g\ns7OT7u7upuxbhcCcUV3psCieYVE8w6OYhkXxDI9iGhbFs386OjqaNu6wCoEiIiIiIiI509nZ2bRC\noNoEioiIiIiItIiZ8fLLL/fZJvDwww9n7733Ztq0aWoTKCIiIiIiErpmvglUITBnVFc6LIpnWBTP\n8CimYVE8w6OYhkXx3KSeWovbbLMNo0ePbsrfVyFQREREREQkZy6//HLmzZvXlH2rTaCIiIiIiEiL\nmBmrV6+mq6urz20ff/xxHnjggYa3CRzS772JiIiIiIhI02211VZ9diAzEKoOmjOqKx0WxTMsimd4\nFNOwKJ7hUUzDonj23+abb86UKVMavt9cFALNbKKZXWxmD1ZZ/3Eze8TM1pnZk2Z2RqvTKCIiIiIi\nEoLM2wSa2WzgUOBM4Gl3n1m2/hxgR+AaYDPgHOBAYLG7n1lln2oTKCIiIiIiudOfNoGD/TvV2gRm\nXghMmNkDwLh0IdDMNgMWuvvZqWUdwP3ALGArd19RYV8qBIqIiIiISO7koRCYi+qgsXUVlo0GvpRe\n4O7dwI1EaZ/WgnS1lOpKh0XxDIviGR7FNCyKZ3gU07AonvmRp95Bu8sXuPsLVbZdF2//VFNTJCIi\nIiIi0mBZ11rMU3XQu4Fty9sEVtn2B8Bwdz+0ynpVBxURERERkdwxM1566SXGjh3b9L8TzDiBZjYN\nOAjYM+u0iIiIiIiIFE3hCoHA14Hz3P3xWhsdd9xxTJ8+HYCxY8cya9Ys9t9/f2BTfeQ8zqfrSuch\nPZpXPDW/aV7xDG/+iiuuKMz9QfOKZzvOL1u2jNNPPz036dG84tmo+aVLlzJq1KiG/39Xr14NwPLl\ny6mlUNVBzew8YKa7n9jHvgpbHfTuu+8uBVOKT/EMi+IZHsU0LIpneBTTsCiekTxUBy1MIdDMPgTM\nBz4Q9xBaa1+FLQSKiIiIiEi45s2bx/e///1MC4EdTf3LDWJmhwPHAEelC4BmNiW7VImIiIiIiPTP\n3Llzs05CrgqBw+KpBzP7IHAR8FlgOzN7i5ntbGaHAV9ocRqbLqnfK2FQPMOieIZHMQ2L4hkexTQs\nimd+ZN4xjJntBBwKzAKGmtlFwE/c/bdm9mHgOsCAB8o+6sCHWppYERERERGRQcq66Vpu2gQ2ktoE\nioiIiIhIHi1ZsoSPfOQjbLHFFk39O4VvEygiIiIiIiKNoUJgzqiudFgUz7AonuFRTMOieIZHMQ2L\n4pkfKgSKiIiIiIi0EbUJFBERERERaRG1CRQREREREZGWUiEwZ1RXOiyKZ1gUz/AopmFRPMOjmIZF\n8cwPFQJFRERERETaiNoEioiIiIiItMiSJUtYsGAB48aNa+rfUZtAERERERGRHDCrWC5rKRUCc0Z1\npcOieIZF8QyPYhoWxTM8imlYFM/8UCFQRERERESkjahNoIiIiIiISIt85Stf4ZhjjlGbQBERERER\nEWkNFQJzRnWlw6J4hkXxDI9iGhbFMzyKaVgUz/xQIVBERERERKSNqE2giIiIiIhIi6hNoIiIiIiI\nSJvJ+oWVCoE5o7rSYVE8w6J4hkcxDYviGR7FNCyKZ0SDxYuIiIiIiEhLqU2giIiIiIhIi3z1q1/l\n6KOPZvz48U39O2oTKCIiIiIiIoAKgbmjutJhUTzDoniGRzENi+IZHsU0LIpnfqgQKCIiIiIi0kbU\nJlBERERERKRF1CZQRERERESkzWT9wkqFwJxRXemwKJ5hUTzDo5iGRfEMj2IaFsUzonECRURERERE\npKXUJlBERERERKRFvva1r3HUUUcxYcKEpv4dtQkUERERERERQIXA3FFd6bAonmFRPMOjmIZF8QyP\nYhoWxTM/clEINLOJZnaxmT1YZX1nvP5+M7vPzL5iZiNanU4REREREZGiy7xNoJnNBg4FzgSedveZ\nFba5EegCDnL3DWb2PWCiu8+tsk+1CRQRERERkdzJQ5vAIU39y3Vw918DvzazA4Bx5evN7EjgCGBP\nd98QL74AeMrMPuru17QutSIiIiIiIsWWi+qgsXVVlp8CrHL3ZckCd18O/Bn4xxakq6VUVzosimdY\nFM/wKKZhUTzDo5iGRfGM7LHHHgwfPjzTNOSpENhdvsDMRgP7AU9U2P4xYHczG9PshLXSsmXL+t5I\nCkPxDIviGR7FNCyKZ3gU07AonpHZs2czatSoTNOQp0JgJVsTpfGvFda9DBgwo6UparLVq1dnnQRp\nIMUzLIpneBTTsCie4VFMw6J45kfeC4FJG8FKVUXXxz/VS6iIiIiIiEid8l4IfC3+Wamglyx7sUVp\naYnly5dnnQRpIMUzLIpneBTTsCie4VFMw6J45kfmQ0QkzOxuYNv0EBFm1gW8BNzl7geUbX8XMBsY\n5+6vlK3Lx0GJiIiIiIhkJLdDRNTi7i/HA8i/pcLq7YHflBcA489VPFgREREREZF2l/fqoABXAlua\n2W7JAjPbAZgK/HNmqRIRERERESmgPFUHvZeoOuhWZcsN+BnwvLt/yMyGADcAm7n732eQVBERERER\nkcLK/E2gme1kZucCs4DJZnaRme2ZrPeolPoPwMtmdj+wFHgUeH8mCRYRERERESmw3LwJFBERERER\nkebL/E1gO4mrtkqBxW+uR8S/K54BMLO3m9npZjYh67TI4JnZ0KzTIM1jZsq3BET30eJTvqi4dDFt\nEjN7q5l90szeb2Z7QKlqqxSUmX0CuBP4v6B4FlmSkTSzE4EFwM/d/flsUyWDYWZvMbN/Ab5mZtea\nWaVepaVAzGxXM/tSfC89DMDdu7NOlwyM8kXhUb6o2FQIbDAzG2Zm3wQeAi4GfgT8p5l9ONuUyUCZ\nWWf863RgCnCwmU2O1+kcKiB37zaz8cB7gC+4+x+yTpMMjJl1mtnFwFeIMiNXAX8HfCXdq7QUQ/Im\nwcw+Bnwb+AuwC3Cdmf3QzPaJ1+vaWxDKF4VH+aIwKFCNdxQwDjiAKIN5ArAauNzMZoJelxdQ8mTr\ndeBh4Gg2PfXSU+niOhEY6+4rzGw46NwsqO2AbYAj3f1ad18GHE9UcJCCSb1JmAec7u5fdfePAx8D\nDgW+b2ZTdO0tFOWLwqN8UQBUCGwQi4wA9ga+C/za3R9y9+8ApwCbA4cnm2eTShmI+K3RGKKxKRcA\na4DjzWx70FOvoonfHA0lOh9/C+Dur8c/VZWleI4hGl5odfzGYSjwOLAC3eMKycz2Bd4GPG9mQ82s\nw91vBBYC04Cvm9kWmSZS+qR8UbiULwqDgtQgceZxCDAUuNndPXUS3Af8guhptZ6SFED6AhY/oXwN\nWA88BiwB3gscambDFc9iSGLq7huB0cDOwB/idduZ2afN7GwzO1wZzPxLVUd6jmh4oX0A3H090dvB\nG+K3gsn2ymTmVKqNbhKjN4jul5PieCad/XwDuBU4DNA4wTmVutYqXxSASgU65YvCoELgAJjZkDiz\neKGZfTh+GoK7rwW+B4yMn1x2x8tXAp3Er8+VGcmXSvFMYmdmnfGNbAow0903AF8DHiGqcjYx3m7L\nrNIvvdWKaWxHYASwjZl9BPgmsC9wFvBD4F/NbPPWp1wqqRLPjfHq/yWqkvRfwANm9m3gCuBtZnaQ\nmc0CveXNGzPrMLPDYVMBIBWj4cCbRFVAcfc34mvxCuAaoqqEp+htQ35UimecD1oLXIfyRYVSKZ5p\nyheFQRfQfoozFHcRfcm7gfOB/zaz4+JNlrn7q6mL4JB4+YPAs6Cn4XzPAAANQklEQVTMSJ5Uief9\nSTzdfWOc0dgWuCde9hrwCeCtwPVm1k3UKHpI778grdZXTGPriN427Bb/Psfd/wHYA/glMBc4Nd6f\nMicZqhHPEwDc/edEVcoWExUGjwVmE7Uf+3eiguE/m9mu8f5038uHTwA3mNm7oVTQT8613wArgTlm\n9nfxsiRu/xFPbwMOij+rczR7veIZVxk04BHliwqnVzzTK5UvCoNuhnVK3WQOAH7j7ue4+8XAu4j+\nj9eY2Yfjp14l8RMSgO2Bv8b70v89Y33Es5MonsdA6SnYFmxqCA1RW4ZXgf2Ab7j7t1KxlgzUGdMF\n8TbdRG8aDgCec/cNZra5uz8HnAMsJbqhKXOSkTri+c0knu7+Z3c/k6jwdz5R5xMHE73Z/Q/gSGBR\nvK2qKWXMzKYBBxLF8QLYdK+M3/htBK4EJgFHmZm5+3ozG+rubwL/SnQOvzv+rM7RDFWLZxw3V76o\nWGrFM16fvNEdi/JFhaaTrk5xXfZhwHw2XbRGuvsLwMnxsiVmtmf6cxYZQlR3elm8r24zm2Fm70q2\naeGhCHXH84pUPLcDfmVmo8zsF8AtRNUGAcaa2dR4HzqnMtKfmLr7I0SdwkwGto53kXQO81vgB8Dr\n8c1QMlBnPBeXXXPHEr3N/YO7/9zdF7v7ocBngC4z27m1RyHlLOq4Z1vgn4Drgf3N7KPx6qQACNH1\n9UngQ0RvdSHOcLr7fwB/Imrbm24fKi3WRzwr3g+VL8qveuKZepC2PdFQH8oXFZQCU6f4JrMB6AJ2\nihe/CeDudxI1WB8HnGlRb1jE6xwYCYwHVqROhv2BQ+InKnqK2WL9iOc58UXxdaK2KC8RPena292P\nJxqb7B+Iqg/qLUOG6ozpFsCn43WXxj9PMLMJcdXfpB3gs0QZzJWtSLv01s9r7sh4/QSiKoJD4n10\nxcuXxetWtSTxUlHyRg/4L3d/iKgziT8DZ1vUznNDqvrYcqJzdBJwlplNTN7Yx+sfYlOmdCPScnXE\nc2OlAoDyRflUbzxT8XoNuBbliwpLhcAKzGy+mV1uZieY2d7Qo0fBF4ADzWxqfEPaLP7Yd4G7icbD\nSdqeJDezXYH17r6K6OnIF4kKFH+j56t0aYJBxvMDRHXcpxIVDP4eOMzdfx9vlxQkTjWzrVpzRDLI\nmB5hZvu4++3AvwFzgC/G+3g13nZrYFHczkGarFHX3Hj7FcA/xvt4OV4+DfhW3BmFtECVmHqcwd8Y\nZzgfInrbMJNND2eSjkM2uvs1wLeIqvlebWbDUueoEXU4Ii0wi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}, "metadata": {}, "output_type": "display_data" } ], "source": [ "mpl.rcParams.update({'font.size': 20})\n", "\n", "fig = plt.figure(figsize=(15, 8))\n", "ax = fig.add_subplot(111)\n", "plt.plot(time2, temperature_values, linewidth=0.5)\n", "plt.ylabel(temperature_units)\n", "plt.xlabel('Year')\n", "fig.autofmt_xdate()\n", "plt.grid()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Final version" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We want to add a title containing the coordinates of the station. Longitude and latitude are both stored as vectors, but we will only keep the mean position to be included in the title.\n", "\n", "LaTeX syntax can be used, as in this example, with the *degree* symbol." ] }, { "cell_type": "code", "execution_count": 68, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Temperature evolution at \n", "-2.33189$^\\circ$E, 36.5698$^\\circ$N\n" ] } ], "source": [ "with netCDF4.Dataset(datafile, 'r') as ds:\n", " lon = ds.variables['LONGITUDE'][:]\n", " lat = ds.variables['LATITUDE'][:]\n", "figure_title = r'Temperature evolution at \\n%s$^\\circ$E, %s$^\\circ$N' % (lon.mean(), lat.mean())\n", "print figure_title" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The units for the temperature are also changed:" ] }, { "cell_type": "code", "execution_count": 71, "metadata": { "collapsed": true }, "outputs": [], "source": [ "temperature_units2 = '($^{\\circ}$C)'" ] }, { "cell_type": "code", "execution_count": 75, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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IghAY4kVWyCCu49KFwhB96ofoVC9En/ohOtUL0ad+iE7jgRiYgiAIgiAIgiAI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\nl6OysjJQncZpfpeF7g0FQLh5NY461YH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7rTi8owDQqVMn1SJoQZzWB8/3eJJQ0VCr+h3N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\nQmCC\n" }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig = plt.figure(figsize=(15, 8))\n", "ax = fig.add_subplot(111)\n", "ax.xaxis.set_major_locator(dates.YearLocator(base=2))\n", "ax.xaxis.set_minor_locator(dates.YearLocator())\n", "plt.plot(time2, temperature_values, linewidth=0.5)\n", "plt.ylabel(temperature_units2, rotation=0., horizontalalignment='right')\n", "plt.title(figure_title)\n", "plt.xlabel('Year')\n", "fig.autofmt_xdate()\n", "plt.grid()" ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.5+" } }, "nbformat": 4, "nbformat_minor": 0 }
mit
bbglab/adventofcode
2020/monica/Day2.ipynb
1
5598
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Advent of code 2020" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## --- Day 2: Password Philosophy --- \n", "Your flight departs in a few days from the coastal airport; the easiest way down to the coast from here is via toboggan.\n", "\n", "The shopkeeper at the North Pole Toboggan Rental Shop is having a bad day. \"Something's wrong with our computers; we can't log in!\" You ask if you can take a look.\n", "\n", "Their password database seems to be a little corrupted: some of the passwords wouldn't have been allowed by the Official Toboggan Corporate Policy that was in effect when they were chosen.\n", "\n", "To try to debug the problem, they have created a list (your puzzle input) of passwords (according to the corrupted database) and the corporate policy when that password was set.\n", "\n", "For example, suppose you have the following list:\n", "\n", "1-3 a: abcde \n", "1-3 b: cdefg \n", "2-9 c: ccccccccc \n", "\n", "Each line gives the password policy and then the password. The password policy indicates the lowest and highest number of times a given letter must appear for the password to be valid. For example, 1-3 a means that the password must contain a at least 1 time and at most 3 times.\n", "\n", "In the above example, 2 passwords are valid. The middle password, cdefg, is not; it contains no instances of b, but needs at least 1. The first and third passwords are valid: they contain one a or nine c, both within the limits of their respective policies.\n", "\n", "How many passwords are valid according to their policies?" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "542 passwords are valid\n" ] } ], "source": [ "import pandas as pd\n", "\n", "df = pd.read_csv('input_day2.txt',header=None,sep='\\t')\n", "df = df.rename(columns={0:'code'})\n", "df['rules'] = df['code'].str.split(':',expand=True)[0]\n", "df['min'] = df['code'].str.split('-',expand=True)[0].astype(int)\n", "df['max'] = df['rules'].str.split('-',expand=True)[1].str.split(' ',expand=True)[0].astype(int)\n", "df['letter'] = df['rules'].str.split('-',expand=True)[1].str.split(' ',expand=True)[1]\n", "df['password'] = df['code'].str.split(': ',expand=True)[1]\n", "\n", "def check_pwd(row):\n", " min1 = row['min']\n", " max1 = row['max']\n", " letter = row['letter']\n", " pwd = row['password']\n", " n_letter = pwd.count(letter)\n", " if min1 <= n_letter <= max1:\n", " return True\n", " return False\n", "\n", "df['Correct'] = df.apply(lambda row: check_pwd(row),axis=1)\n", "valid = len(df[df['Correct']==True])\n", "print(valid,'passwords are valid')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## --- Part Two ---\n", "While it appears you validated the passwords correctly, they don't seem to be what the Official Toboggan Corporate Authentication System is expecting.\n", "\n", "The shopkeeper suddenly realizes that he just accidentally explained the password policy rules from his old job at the sled rental place down the street! The Official Toboggan Corporate Policy actually works a little differently.\n", "\n", "Each policy actually describes two positions in the password, where 1 means the first character, 2 means the second character, and so on. (Be careful; Toboggan Corporate Policies have no concept of \"index zero\"!) Exactly one of these positions must contain the given letter. Other occurrences of the letter are irrelevant for the purposes of policy enforcement.\n", "\n", "Given the same example list from above:\n", "\n", "1-3 a: abcde is valid: position 1 contains a and position 3 does not.\n", "1-3 b: cdefg is invalid: neither position 1 nor position 3 contains b.\n", "2-9 c: ccccccccc is invalid: both position 2 and position 9 contain c.\n", "How many passwords are valid according to the new interpretation of the policies?" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "360 passwords are valid according to this second policy\n" ] } ], "source": [ "def check_pwd2(row):\n", " pos1 = row['min']-1\n", " pos2 = row['max']-1\n", " letter = row['letter']\n", " pwd = row['password']\n", " letter1 = pwd[pos1]\n", " letter2 = pwd[pos2]\n", " if (letter1 != letter2)&((letter1==letter)|(letter2==letter)):\n", " return True\n", " return False\n", "\n", "df['Correct2'] = df.apply(lambda row: check_pwd2(row),axis=1)\n", "valid = len(df[df['Correct2']==True])\n", "print(valid,'passwords are valid according to this second policy')" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.4" } }, "nbformat": 4, "nbformat_minor": 2 }
mit
heatseeknyc/data-science
src/bryan analyses/Hack for Heat #1.ipynb
1
63039
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Hacking for heat\n", "\n", "In this series, I'm going to be posting about the process that goes on behind some of the blog posts we end up writing. In this first entry, I'm going to be exploring a number of datsets.\n", "\n", "These are the ones that I'm going to be looking at:\n", "\n", "1. [HPD (Housing Preservation and Development) housing litigations](https://data.cityofnewyork.us/Housing-Development/Housing-Litigations/59kj-x8nc)\n", "2. [Housing maintenance code complaints](https://data.cityofnewyork.us/Housing-Development/Housing-Maintenance-Code-Complaints/uwyv-629c)\n", "3. [Housing maintanence code violations](https://data.cityofnewyork.us/Housing-Development/Housing-Maintenance-Code-Violations/wvxf-dwi5)\n", "4. [HPD complaints](\n", "https://data.cityofnewyork.us/Housing-Development/Complaint-Problems/a2nx-4u46)\n", "\n", "(for HPD datasets, some documentation can be found [here](https://www1.nyc.gov/site/hpd/about/open-data.page))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## HPD litigation database\n", "\n", "First, we're going to look at the smallest dataset, one that contains cases against landlords. From the documentation, this file contains \"All cases commenced by HPD or by tennants (naming HPD as a party) in [housing court] since August 2006\" either seeking orders for landlords to comply with regulations, or awarding HPD civil penalties (i.e., collecting on fines)." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import pandas as pd" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "litigation = pd.read_csv(\"Housing_Litigations.csv\")" ] }, { "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>LitigationID</th>\n", " <th>BuildingID</th>\n", " <th>BoroID</th>\n", " <th>Boro</th>\n", " <th>HouseNumber</th>\n", " <th>StreetName</th>\n", " <th>Zip</th>\n", " <th>Block</th>\n", " <th>Lot</th>\n", " <th>CaseType</th>\n", " <th>CaseOpenDate</th>\n", " <th>CaseStatus</th>\n", " <th>CaseJudgement</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>98385</td>\n", " <td>806633</td>\n", " <td>2</td>\n", " <td>BRONX</td>\n", " <td>866</td>\n", " <td>EAST 221 STREET</td>\n", " <td>10467.0</td>\n", " <td>4679</td>\n", " <td>73</td>\n", " <td>Tenant Action</td>\n", " <td>04/23/2009</td>\n", " <td>CLOSED</td>\n", " <td>NO</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>98387</td>\n", " <td>55134</td>\n", " <td>2</td>\n", " <td>BRONX</td>\n", " <td>3940</td>\n", " <td>CARPENTER AVENUE</td>\n", " <td>10466.0</td>\n", " <td>4825</td>\n", " <td>51</td>\n", " <td>Tenant Action</td>\n", " <td>04/23/2009</td>\n", " <td>CLOSED</td>\n", " <td>NO</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>98389</td>\n", " <td>119044</td>\n", " <td>2</td>\n", " <td>BRONX</td>\n", " <td>1356</td>\n", " <td>WALTON AVENUE</td>\n", " <td>10452.0</td>\n", " <td>2841</td>\n", " <td>41</td>\n", " <td>Tenant Action</td>\n", " <td>04/23/2009</td>\n", " <td>CLOSED</td>\n", " <td>NO</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>98391</td>\n", " <td>889796</td>\n", " <td>2</td>\n", " <td>BRONX</td>\n", " <td>871</td>\n", " <td>EAST 179 STREET</td>\n", " <td>10460.0</td>\n", " <td>3123</td>\n", " <td>77</td>\n", " <td>Tenant Action</td>\n", " <td>04/24/2009</td>\n", " <td>CLOSED</td>\n", " <td>NO</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>98393</td>\n", " <td>56949</td>\n", " <td>2</td>\n", " <td>BRONX</td>\n", " <td>1680</td>\n", " <td>CLAY AVENUE</td>\n", " <td>10457.0</td>\n", " <td>2889</td>\n", " <td>1</td>\n", " <td>Tenant Action</td>\n", " <td>04/24/2009</td>\n", " <td>CLOSED</td>\n", " <td>NO</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " LitigationID BuildingID BoroID Boro HouseNumber StreetName \\\n", "0 98385 806633 2 BRONX 866 EAST 221 STREET \n", "1 98387 55134 2 BRONX 3940 CARPENTER AVENUE \n", "2 98389 119044 2 BRONX 1356 WALTON AVENUE \n", "3 98391 889796 2 BRONX 871 EAST 179 STREET \n", "4 98393 56949 2 BRONX 1680 CLAY AVENUE \n", "\n", " Zip Block Lot CaseType CaseOpenDate CaseStatus CaseJudgement \n", "0 10467.0 4679 73 Tenant Action 04/23/2009 CLOSED NO \n", "1 10466.0 4825 51 Tenant Action 04/23/2009 CLOSED NO \n", "2 10452.0 2841 41 Tenant Action 04/23/2009 CLOSED NO \n", "3 10460.0 3123 77 Tenant Action 04/24/2009 CLOSED NO \n", "4 10457.0 2889 1 Tenant Action 04/24/2009 CLOSED NO " ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "litigation.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's take a look at unique values for some of the columns:" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array(['BRONX', 'QUEENS', 'MANHATTAN', 'BROOKLYN', 'STATEN ISLAND'], dtype=object)" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "litigation['Boro'].unique()" ] }, { "cell_type": "code", "execution_count": 14, "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></th>\n", " <th>LitigationID</th>\n", " <th>BuildingID</th>\n", " <th>BoroID</th>\n", " <th>HouseNumber</th>\n", " <th>StreetName</th>\n", " <th>Zip</th>\n", " <th>Block</th>\n", " <th>Lot</th>\n", " <th>CaseType</th>\n", " <th>CaseOpenDate</th>\n", " <th>CaseStatus</th>\n", " </tr>\n", " <tr>\n", " <th>Boro</th>\n", " <th>CaseJudgement</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 rowspan=\"2\" valign=\"top\">BRONX</th>\n", " <th>NO</th>\n", " <td>19734</td>\n", " <td>19734</td>\n", " <td>19734</td>\n", " <td>19734</td>\n", " <td>19734</td>\n", " <td>19734</td>\n", " <td>19734</td>\n", " <td>19734</td>\n", " <td>19734</td>\n", " <td>19701</td>\n", " <td>19734</td>\n", " </tr>\n", " <tr>\n", " <th>YES</th>\n", " <td>481</td>\n", " <td>481</td>\n", " <td>481</td>\n", " <td>481</td>\n", " <td>481</td>\n", " <td>481</td>\n", " <td>481</td>\n", " <td>481</td>\n", " <td>481</td>\n", " <td>478</td>\n", " <td>481</td>\n", " </tr>\n", " <tr>\n", " <th rowspan=\"2\" valign=\"top\">BROOKLYN</th>\n", " <th>NO</th>\n", " <td>19833</td>\n", " <td>19833</td>\n", " <td>19833</td>\n", " <td>19833</td>\n", " <td>19833</td>\n", " <td>19831</td>\n", " <td>19833</td>\n", " <td>19833</td>\n", " <td>19833</td>\n", " <td>19807</td>\n", " <td>19833</td>\n", " </tr>\n", " <tr>\n", " <th>YES</th>\n", " <td>762</td>\n", " <td>762</td>\n", " <td>762</td>\n", " <td>762</td>\n", " <td>762</td>\n", " <td>760</td>\n", " <td>762</td>\n", " <td>762</td>\n", " <td>762</td>\n", " <td>762</td>\n", " <td>762</td>\n", " </tr>\n", " <tr>\n", " <th rowspan=\"2\" valign=\"top\">MANHATTAN</th>\n", " <th>NO</th>\n", " <td>11849</td>\n", " <td>11849</td>\n", " <td>11849</td>\n", " <td>11849</td>\n", " <td>11849</td>\n", " <td>11849</td>\n", " <td>11849</td>\n", " <td>11849</td>\n", " <td>11849</td>\n", " <td>11841</td>\n", " <td>11849</td>\n", " </tr>\n", " <tr>\n", " <th>YES</th>\n", " <td>129</td>\n", " <td>129</td>\n", " <td>129</td>\n", " <td>129</td>\n", " <td>129</td>\n", " <td>129</td>\n", " <td>129</td>\n", " <td>129</td>\n", " <td>129</td>\n", " <td>129</td>\n", " <td>129</td>\n", " </tr>\n", " <tr>\n", " <th rowspan=\"2\" valign=\"top\">QUEENS</th>\n", " <th>NO</th>\n", " <td>9068</td>\n", " <td>9068</td>\n", " <td>9068</td>\n", " <td>9068</td>\n", " <td>9068</td>\n", " <td>9061</td>\n", " <td>9068</td>\n", " <td>9068</td>\n", " <td>9068</td>\n", " <td>9063</td>\n", " <td>9068</td>\n", " </tr>\n", " <tr>\n", " <th>YES</th>\n", " <td>313</td>\n", " <td>313</td>\n", " <td>313</td>\n", " <td>313</td>\n", " <td>313</td>\n", " <td>313</td>\n", " <td>313</td>\n", " <td>313</td>\n", " <td>313</td>\n", " <td>308</td>\n", " <td>313</td>\n", " </tr>\n", " <tr>\n", " <th rowspan=\"2\" valign=\"top\">STATEN ISLAND</th>\n", " <th>NO</th>\n", " <td>1064</td>\n", " <td>1064</td>\n", " <td>1064</td>\n", " <td>1064</td>\n", " <td>1064</td>\n", " <td>1064</td>\n", " <td>1064</td>\n", " <td>1064</td>\n", " <td>1064</td>\n", " <td>1064</td>\n", " <td>1064</td>\n", " </tr>\n", " <tr>\n", " <th>YES</th>\n", " <td>84</td>\n", " <td>84</td>\n", " <td>84</td>\n", " <td>84</td>\n", " <td>84</td>\n", " <td>84</td>\n", " <td>84</td>\n", " <td>84</td>\n", " <td>84</td>\n", " <td>84</td>\n", " <td>84</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " LitigationID BuildingID BoroID HouseNumber \\\n", "Boro CaseJudgement \n", "BRONX NO 19734 19734 19734 19734 \n", " YES 481 481 481 481 \n", "BROOKLYN NO 19833 19833 19833 19833 \n", " YES 762 762 762 762 \n", "MANHATTAN NO 11849 11849 11849 11849 \n", " YES 129 129 129 129 \n", "QUEENS NO 9068 9068 9068 9068 \n", " YES 313 313 313 313 \n", "STATEN ISLAND NO 1064 1064 1064 1064 \n", " YES 84 84 84 84 \n", "\n", " StreetName Zip Block Lot CaseType \\\n", "Boro CaseJudgement \n", "BRONX NO 19734 19734 19734 19734 19734 \n", " YES 481 481 481 481 481 \n", "BROOKLYN NO 19833 19831 19833 19833 19833 \n", " YES 762 760 762 762 762 \n", "MANHATTAN NO 11849 11849 11849 11849 11849 \n", " YES 129 129 129 129 129 \n", "QUEENS NO 9068 9061 9068 9068 9068 \n", " YES 313 313 313 313 313 \n", "STATEN ISLAND NO 1064 1064 1064 1064 1064 \n", " YES 84 84 84 84 84 \n", "\n", " CaseOpenDate CaseStatus \n", "Boro CaseJudgement \n", "BRONX NO 19701 19734 \n", " YES 478 481 \n", "BROOKLYN NO 19807 19833 \n", " YES 762 762 \n", "MANHATTAN NO 11841 11849 \n", " YES 129 129 \n", "QUEENS NO 9063 9068 \n", " YES 308 313 \n", "STATEN ISLAND NO 1064 1064 \n", " YES 84 84 " ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "litigation.groupby(by = ['Boro','CaseJudgement']).count()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The above table tells us that Manhattan has the lowest proportion of cases that receive judgement (about 1 in 80), whereas Staten Island has the highest (about 1 in 12). It may be something worth looking into, but it's also important to note that many cases settle out of court, and landlords in Manhattan may be more willing (or able) to do so." ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array(['Tenant Action', 'Heat and Hot Water', 'CONH',\n", " 'Comp Supplemental Cases', 'Comprehensive',\n", " 'Access Warrant - Non-Lead', 'Access Warrant - lead',\n", " 'Lead False Certification', 'False Certification Non-Lead',\n", " 'Heat Supplemental Cases', '7A', 'Failure to Register Only',\n", " 'HLD - Other Case Type'], dtype=object)" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "litigation['CaseType'].unique()" ] }, { "cell_type": "code", "execution_count": 16, "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></th>\n", " <th>LitigationID</th>\n", " <th>BuildingID</th>\n", " <th>BoroID</th>\n", " <th>Boro</th>\n", " <th>HouseNumber</th>\n", " <th>StreetName</th>\n", " <th>Zip</th>\n", " <th>Block</th>\n", " <th>Lot</th>\n", " <th>CaseOpenDate</th>\n", " <th>CaseStatus</th>\n", " </tr>\n", " <tr>\n", " <th>CaseType</th>\n", " <th>CaseJudgement</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>7A</th>\n", " <th>NO</th>\n", " <td>105</td>\n", " <td>105</td>\n", " <td>105</td>\n", " <td>105</td>\n", " <td>105</td>\n", " <td>105</td>\n", " <td>105</td>\n", " <td>105</td>\n", " <td>105</td>\n", " <td>102</td>\n", " <td>105</td>\n", " </tr>\n", " <tr>\n", " <th rowspan=\"2\" valign=\"top\">Access Warrant - Non-Lead</th>\n", " <th>NO</th>\n", " <td>5665</td>\n", " <td>5665</td>\n", " <td>5665</td>\n", " <td>5665</td>\n", " <td>5665</td>\n", " <td>5665</td>\n", " <td>5665</td>\n", " <td>5665</td>\n", " <td>5665</td>\n", " <td>5665</td>\n", " <td>5665</td>\n", " </tr>\n", " <tr>\n", " <th>YES</th>\n", " <td>8</td>\n", " <td>8</td>\n", " <td>8</td>\n", " <td>8</td>\n", " <td>8</td>\n", " <td>8</td>\n", " <td>8</td>\n", " <td>8</td>\n", " <td>8</td>\n", " <td>8</td>\n", " <td>8</td>\n", " </tr>\n", " <tr>\n", " <th>Access Warrant - lead</th>\n", " <th>NO</th>\n", " <td>1786</td>\n", " <td>1786</td>\n", " <td>1786</td>\n", " <td>1786</td>\n", " <td>1786</td>\n", " <td>1786</td>\n", " <td>1786</td>\n", " <td>1786</td>\n", " <td>1786</td>\n", " <td>1785</td>\n", " <td>1786</td>\n", " </tr>\n", " <tr>\n", " <th>CONH</th>\n", " <th>NO</th>\n", " <td>797</td>\n", " <td>797</td>\n", " <td>797</td>\n", " <td>797</td>\n", " <td>797</td>\n", " <td>797</td>\n", " <td>797</td>\n", " <td>797</td>\n", " <td>797</td>\n", " <td>797</td>\n", " <td>797</td>\n", " </tr>\n", " <tr>\n", " <th rowspan=\"2\" valign=\"top\">Comp Supplemental Cases</th>\n", " <th>NO</th>\n", " <td>750</td>\n", " <td>750</td>\n", " <td>750</td>\n", " <td>750</td>\n", " <td>750</td>\n", " <td>750</td>\n", " <td>750</td>\n", " <td>750</td>\n", " <td>750</td>\n", " <td>702</td>\n", " <td>750</td>\n", " </tr>\n", " <tr>\n", " <th>YES</th>\n", " <td>31</td>\n", " <td>31</td>\n", " <td>31</td>\n", " <td>31</td>\n", " <td>31</td>\n", " <td>31</td>\n", " <td>31</td>\n", " <td>31</td>\n", " <td>31</td>\n", " <td>29</td>\n", " <td>31</td>\n", " </tr>\n", " <tr>\n", " <th rowspan=\"2\" valign=\"top\">Comprehensive</th>\n", " <th>NO</th>\n", " <td>2274</td>\n", " <td>2274</td>\n", " <td>2274</td>\n", " <td>2274</td>\n", " <td>2274</td>\n", " <td>2274</td>\n", " <td>2274</td>\n", " <td>2274</td>\n", " <td>2274</td>\n", " <td>2274</td>\n", " <td>2274</td>\n", " </tr>\n", " <tr>\n", " <th>YES</th>\n", " <td>121</td>\n", " <td>121</td>\n", " <td>121</td>\n", " <td>121</td>\n", " <td>121</td>\n", " <td>121</td>\n", " <td>121</td>\n", " <td>121</td>\n", " <td>121</td>\n", " <td>121</td>\n", " <td>121</td>\n", " </tr>\n", " <tr>\n", " <th rowspan=\"2\" valign=\"top\">Failure to Register Only</th>\n", " <th>NO</th>\n", " <td>60</td>\n", " <td>60</td>\n", " <td>60</td>\n", " <td>60</td>\n", " <td>60</td>\n", " <td>60</td>\n", " <td>60</td>\n", " <td>60</td>\n", " <td>60</td>\n", " <td>60</td>\n", " <td>60</td>\n", " </tr>\n", " <tr>\n", " <th>YES</th>\n", " <td>3</td>\n", " <td>3</td>\n", " <td>3</td>\n", " <td>3</td>\n", " <td>3</td>\n", " <td>3</td>\n", " <td>3</td>\n", " <td>3</td>\n", " <td>3</td>\n", " <td>3</td>\n", " <td>3</td>\n", " </tr>\n", " <tr>\n", " <th rowspan=\"2\" valign=\"top\">False Certification Non-Lead</th>\n", " <th>NO</th>\n", " <td>2355</td>\n", " <td>2355</td>\n", " <td>2355</td>\n", " <td>2355</td>\n", " <td>2355</td>\n", " <td>2355</td>\n", " <td>2355</td>\n", " <td>2355</td>\n", " <td>2355</td>\n", " <td>2355</td>\n", " <td>2355</td>\n", " </tr>\n", " <tr>\n", " <th>YES</th>\n", " <td>20</td>\n", " <td>20</td>\n", " <td>20</td>\n", " <td>20</td>\n", " <td>20</td>\n", " <td>20</td>\n", " <td>20</td>\n", " <td>20</td>\n", " <td>20</td>\n", " <td>20</td>\n", " <td>20</td>\n", " </tr>\n", " <tr>\n", " <th>HLD - Other Case Type</th>\n", " <th>NO</th>\n", " <td>3</td>\n", " <td>3</td>\n", " <td>3</td>\n", " <td>3</td>\n", " <td>3</td>\n", " <td>3</td>\n", " <td>3</td>\n", " <td>3</td>\n", " <td>3</td>\n", " <td>2</td>\n", " <td>3</td>\n", " </tr>\n", " <tr>\n", " <th rowspan=\"2\" valign=\"top\">Heat Supplemental Cases</th>\n", " <th>NO</th>\n", " <td>124</td>\n", " <td>124</td>\n", " <td>124</td>\n", " <td>124</td>\n", " <td>124</td>\n", " <td>124</td>\n", " <td>124</td>\n", " <td>124</td>\n", " <td>124</td>\n", " <td>108</td>\n", " <td>124</td>\n", " </tr>\n", " <tr>\n", " <th>YES</th>\n", " <td>7</td>\n", " <td>7</td>\n", " <td>7</td>\n", " <td>7</td>\n", " <td>7</td>\n", " <td>7</td>\n", " <td>6</td>\n", " <td>7</td>\n", " <td>7</td>\n", " <td>6</td>\n", " <td>7</td>\n", " </tr>\n", " <tr>\n", " <th rowspan=\"2\" valign=\"top\">Heat and Hot Water</th>\n", " <th>NO</th>\n", " <td>15760</td>\n", " <td>15760</td>\n", " <td>15760</td>\n", " <td>15760</td>\n", " <td>15760</td>\n", " <td>15760</td>\n", " <td>15758</td>\n", " <td>15760</td>\n", " <td>15760</td>\n", " <td>15757</td>\n", " <td>15760</td>\n", " </tr>\n", " <tr>\n", " <th>YES</th>\n", " <td>1299</td>\n", " <td>1299</td>\n", " <td>1299</td>\n", " <td>1299</td>\n", " <td>1299</td>\n", " <td>1299</td>\n", " <td>1298</td>\n", " <td>1299</td>\n", " <td>1299</td>\n", " <td>1294</td>\n", " <td>1299</td>\n", " </tr>\n", " <tr>\n", " <th rowspan=\"2\" valign=\"top\">Lead False Certification</th>\n", " <th>NO</th>\n", " <td>239</td>\n", " <td>239</td>\n", " <td>239</td>\n", " <td>239</td>\n", " <td>239</td>\n", " <td>239</td>\n", " <td>239</td>\n", " <td>239</td>\n", " <td>239</td>\n", " <td>239</td>\n", " <td>239</td>\n", " </tr>\n", " <tr>\n", " <th>YES</th>\n", " <td>2</td>\n", " <td>2</td>\n", " <td>2</td>\n", " <td>2</td>\n", " <td>2</td>\n", " <td>2</td>\n", " <td>2</td>\n", " <td>2</td>\n", " <td>2</td>\n", " <td>2</td>\n", " <td>2</td>\n", " </tr>\n", " <tr>\n", " <th rowspan=\"2\" valign=\"top\">Tenant Action</th>\n", " <th>NO</th>\n", " <td>31630</td>\n", " <td>31630</td>\n", " <td>31630</td>\n", " <td>31630</td>\n", " <td>31630</td>\n", " <td>31630</td>\n", " <td>31623</td>\n", " <td>31630</td>\n", " <td>31630</td>\n", " <td>31630</td>\n", " <td>31630</td>\n", " </tr>\n", " <tr>\n", " <th>YES</th>\n", " <td>278</td>\n", " <td>278</td>\n", " <td>278</td>\n", " <td>278</td>\n", " <td>278</td>\n", " <td>278</td>\n", " <td>278</td>\n", " <td>278</td>\n", " <td>278</td>\n", " <td>278</td>\n", " <td>278</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " LitigationID BuildingID BoroID \\\n", "CaseType CaseJudgement \n", "7A NO 105 105 105 \n", "Access Warrant - Non-Lead NO 5665 5665 5665 \n", " YES 8 8 8 \n", "Access Warrant - lead NO 1786 1786 1786 \n", "CONH NO 797 797 797 \n", "Comp Supplemental Cases NO 750 750 750 \n", " YES 31 31 31 \n", "Comprehensive NO 2274 2274 2274 \n", " YES 121 121 121 \n", "Failure to Register Only NO 60 60 60 \n", " YES 3 3 3 \n", "False Certification Non-Lead NO 2355 2355 2355 \n", " YES 20 20 20 \n", "HLD - Other Case Type NO 3 3 3 \n", "Heat Supplemental Cases NO 124 124 124 \n", " YES 7 7 7 \n", "Heat and Hot Water NO 15760 15760 15760 \n", " YES 1299 1299 1299 \n", "Lead False Certification NO 239 239 239 \n", " YES 2 2 2 \n", "Tenant Action NO 31630 31630 31630 \n", " YES 278 278 278 \n", "\n", " Boro HouseNumber StreetName \\\n", "CaseType CaseJudgement \n", "7A NO 105 105 105 \n", "Access Warrant - Non-Lead NO 5665 5665 5665 \n", " YES 8 8 8 \n", "Access Warrant - lead NO 1786 1786 1786 \n", "CONH NO 797 797 797 \n", "Comp Supplemental Cases NO 750 750 750 \n", " YES 31 31 31 \n", "Comprehensive NO 2274 2274 2274 \n", " YES 121 121 121 \n", "Failure to Register Only NO 60 60 60 \n", " YES 3 3 3 \n", "False Certification Non-Lead NO 2355 2355 2355 \n", " YES 20 20 20 \n", "HLD - Other Case Type NO 3 3 3 \n", "Heat Supplemental Cases NO 124 124 124 \n", " YES 7 7 7 \n", "Heat and Hot Water NO 15760 15760 15760 \n", " YES 1299 1299 1299 \n", "Lead False Certification NO 239 239 239 \n", " YES 2 2 2 \n", "Tenant Action NO 31630 31630 31630 \n", " YES 278 278 278 \n", "\n", " Zip Block Lot CaseOpenDate \\\n", "CaseType CaseJudgement \n", "7A NO 105 105 105 102 \n", "Access Warrant - Non-Lead NO 5665 5665 5665 5665 \n", " YES 8 8 8 8 \n", "Access Warrant - lead NO 1786 1786 1786 1785 \n", "CONH NO 797 797 797 797 \n", "Comp Supplemental Cases NO 750 750 750 702 \n", " YES 31 31 31 29 \n", "Comprehensive NO 2274 2274 2274 2274 \n", " YES 121 121 121 121 \n", "Failure to Register Only NO 60 60 60 60 \n", " YES 3 3 3 3 \n", "False Certification Non-Lead NO 2355 2355 2355 2355 \n", " YES 20 20 20 20 \n", "HLD - Other Case Type NO 3 3 3 2 \n", "Heat Supplemental Cases NO 124 124 124 108 \n", " YES 6 7 7 6 \n", "Heat and Hot Water NO 15758 15760 15760 15757 \n", " YES 1298 1299 1299 1294 \n", "Lead False Certification NO 239 239 239 239 \n", " YES 2 2 2 2 \n", "Tenant Action NO 31623 31630 31630 31630 \n", " YES 278 278 278 278 \n", "\n", " CaseStatus \n", "CaseType CaseJudgement \n", "7A NO 105 \n", "Access Warrant - Non-Lead NO 5665 \n", " YES 8 \n", "Access Warrant - lead NO 1786 \n", "CONH NO 797 \n", "Comp Supplemental Cases NO 750 \n", " YES 31 \n", "Comprehensive NO 2274 \n", " YES 121 \n", "Failure to Register Only NO 60 \n", " YES 3 \n", "False Certification Non-Lead NO 2355 \n", " YES 20 \n", "HLD - Other Case Type NO 3 \n", "Heat Supplemental Cases NO 124 \n", " YES 7 \n", "Heat and Hot Water NO 15760 \n", " YES 1299 \n", "Lead False Certification NO 239 \n", " YES 2 \n", "Tenant Action NO 31630 \n", " YES 278 " ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "litigation.groupby(by = ['CaseType', 'CaseJudgement']).count()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The table above shows the same case judgement proportions, but conditioned on what type of case it was. Unhelpfully, the documentation does not specify what the difference between Access Warrant - Lead and Non-Lead is. It could be one of two possibilities: The first is whether the warrants have to do with lead-based paint, which is a common problem, but perhaps still too idiosyncratic to have it's own warrant type. The second, perhaps more likely possibility is whether or not HPD was the lead party in the case." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We'll probably end up using these data by aggregating it and examining how complaints change over time, perhaps as a function of what type they are. There's also the possibility of looking up specific buildings' complaints and tying them to landlords. There's probably also an easy way to join this dataset with another, by converting the address information into something standardized, like borough-block-lot (BBL; http://www1.nyc.gov/nyc-resources/service/1232/borough-block-lot-bbl-lookup)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## HPD complaints\n", "\n", "Next, we're going to look at a dataset of HPD complaints." ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "collapsed": true }, "outputs": [], "source": [ "hpdcomp = pd.read_csv('Housing_Maintenance_Code_Complaints.csv')" ] }, { "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>ComplaintID</th>\n", " <th>BuildingID</th>\n", " <th>BoroughID</th>\n", " <th>Borough</th>\n", " <th>HouseNumber</th>\n", " <th>StreetName</th>\n", " <th>Zip</th>\n", " <th>Block</th>\n", " <th>Lot</th>\n", " <th>Apartment</th>\n", " <th>CommunityBoard</th>\n", " <th>ReceivedDate</th>\n", " <th>StatusID</th>\n", " <th>Status</th>\n", " <th>StatusDate</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>6960137</td>\n", " <td>3418</td>\n", " <td>1</td>\n", " <td>MANHATTAN</td>\n", " <td>1989</td>\n", " <td>ADAM C POWELL BOULEVARD</td>\n", " <td>10026.0</td>\n", " <td>1904</td>\n", " <td>4</td>\n", " <td>12D</td>\n", " <td>10</td>\n", " <td>07/07/2014</td>\n", " <td>2</td>\n", " <td>CLOSE</td>\n", " <td>07/29/2014</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>6960832</td>\n", " <td>3512</td>\n", " <td>1</td>\n", " <td>MANHATTAN</td>\n", " <td>2267</td>\n", " <td>ADAM C POWELL BOULEVARD</td>\n", " <td>10030.0</td>\n", " <td>1918</td>\n", " <td>4</td>\n", " <td>3B</td>\n", " <td>10</td>\n", " <td>07/08/2014</td>\n", " <td>2</td>\n", " <td>CLOSE</td>\n", " <td>07/12/2014</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>6946867</td>\n", " <td>5318</td>\n", " <td>1</td>\n", " <td>MANHATTAN</td>\n", " <td>778</td>\n", " <td>11 AVENUE</td>\n", " <td>10019.0</td>\n", " <td>1083</td>\n", " <td>1</td>\n", " <td>4P</td>\n", " <td>4</td>\n", " <td>06/19/2014</td>\n", " <td>2</td>\n", " <td>CLOSE</td>\n", " <td>07/13/2014</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>6966946</td>\n", " <td>5608</td>\n", " <td>1</td>\n", " <td>MANHATTAN</td>\n", " <td>1640</td>\n", " <td>AMSTERDAM AVENUE</td>\n", " <td>10031.0</td>\n", " <td>2073</td>\n", " <td>29</td>\n", " <td>5A</td>\n", " <td>9</td>\n", " <td>07/16/2014</td>\n", " <td>2</td>\n", " <td>CLOSE</td>\n", " <td>07/21/2014</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>6956574</td>\n", " <td>17896</td>\n", " <td>1</td>\n", " <td>MANHATTAN</td>\n", " <td>230</td>\n", " <td>EAST 88 STREET</td>\n", " <td>10128.0</td>\n", " <td>1533</td>\n", " <td>32</td>\n", " <td>1E</td>\n", " <td>8</td>\n", " <td>07/01/2014</td>\n", " <td>2</td>\n", " <td>CLOSE</td>\n", " <td>07/09/2014</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " ComplaintID BuildingID BoroughID Borough HouseNumber \\\n", "0 6960137 3418 1 MANHATTAN 1989 \n", "1 6960832 3512 1 MANHATTAN 2267 \n", "2 6946867 5318 1 MANHATTAN 778 \n", "3 6966946 5608 1 MANHATTAN 1640 \n", "4 6956574 17896 1 MANHATTAN 230 \n", "\n", " StreetName Zip Block Lot Apartment CommunityBoard \\\n", "0 ADAM C POWELL BOULEVARD 10026.0 1904 4 12D 10 \n", "1 ADAM C POWELL BOULEVARD 10030.0 1918 4 3B 10 \n", "2 11 AVENUE 10019.0 1083 1 4P 4 \n", "3 AMSTERDAM AVENUE 10031.0 2073 29 5A 9 \n", "4 EAST 88 STREET 10128.0 1533 32 1E 8 \n", "\n", " ReceivedDate StatusID Status StatusDate \n", "0 07/07/2014 2 CLOSE 07/29/2014 \n", "1 07/08/2014 2 CLOSE 07/12/2014 \n", "2 06/19/2014 2 CLOSE 07/13/2014 \n", "3 07/16/2014 2 CLOSE 07/21/2014 \n", "4 07/01/2014 2 CLOSE 07/09/2014 " ] }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" } ], "source": [ "hpdcomp.head()" ] }, { "cell_type": "code", "execution_count": 39, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "662672" ] }, "execution_count": 39, "metadata": {}, "output_type": "execute_result" } ], "source": [ "len(hpdcomp)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This dataset is less useful on its own. It doesn't tell us what the type of complaint was, only the date it was received and whether or not the complaint is still open. However, it may be useful in conjunction with the earlier dataset. For example, we might be interested in how many of these complaints end up in court (or at least, have some sort of legal action taken)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## HPD violations\n", "\n", "The following dataset tracks HPD violations." ] }, { "cell_type": "code", "execution_count": 35, "metadata": { "collapsed": true }, "outputs": [], "source": [ "hpdviol = pd.read_csv('Housing_Maintenance_Code_Violations.csv')" ] }, { "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>ViolationID</th>\n", " <th>BuildingID</th>\n", " <th>RegistrationID</th>\n", " <th>BoroID</th>\n", " <th>Boro</th>\n", " <th>HouseNumber</th>\n", " <th>LowHouseNumber</th>\n", " <th>HighHouseNumber</th>\n", " <th>StreetName</th>\n", " <th>StreetCode</th>\n", " <th>...</th>\n", " <th>NewCertifyByDate</th>\n", " <th>NewCorrectByDate</th>\n", " <th>CertifiedDate</th>\n", " <th>OrderNumber</th>\n", " <th>NOVID</th>\n", " <th>NOVDescription</th>\n", " <th>NOVIssuedDate</th>\n", " <th>CurrentStatusID</th>\n", " <th>CurrentStatus</th>\n", " <th>CurrentStatusDate</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>2293208</td>\n", " <td>444</td>\n", " <td>130476</td>\n", " <td>1</td>\n", " <td>MANHATTAN</td>\n", " <td>22</td>\n", " <td>22</td>\n", " <td>22</td>\n", " <td>1 AVENUE</td>\n", " <td>10010</td>\n", " <td>...</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>772</td>\n", " <td>338596.0</td>\n", " <td>§ 27-2098 ADM CODE FILE WITH THIS DEPARTMENT ...</td>\n", " <td>04/22/1997</td>\n", " <td>19</td>\n", " <td>VIOLATION CLOSED</td>\n", " <td>03/10/2015</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>2293181</td>\n", " <td>444</td>\n", " <td>130476</td>\n", " <td>1</td>\n", " <td>MANHATTAN</td>\n", " <td>22</td>\n", " <td>22</td>\n", " <td>22</td>\n", " <td>1 AVENUE</td>\n", " <td>10010</td>\n", " <td>...</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>09/10/1974</td>\n", " <td>775</td>\n", " <td>338584.0</td>\n", " <td>D26-41.05 ADM CODE FILE WITH THIS DEPARTMENT R...</td>\n", " <td>07/16/1974</td>\n", " <td>19</td>\n", " <td>VIOLATION CLOSED</td>\n", " <td>03/10/2015</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>2293249</td>\n", " <td>448</td>\n", " <td>135326</td>\n", " <td>1</td>\n", " <td>MANHATTAN</td>\n", " <td>2222</td>\n", " <td>2222</td>\n", " <td>2222</td>\n", " <td>1 AVENUE</td>\n", " <td>10010</td>\n", " <td>...</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>772</td>\n", " <td>338619.0</td>\n", " <td>§ 27-2098 ADM CODE FILE WITH THIS DEPARTMENT ...</td>\n", " <td>11/19/1996</td>\n", " <td>19</td>\n", " <td>VIOLATION CLOSED</td>\n", " <td>03/10/2015</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>2293486</td>\n", " <td>467</td>\n", " <td>136913</td>\n", " <td>1</td>\n", " <td>MANHATTAN</td>\n", " <td>2250</td>\n", " <td>2250</td>\n", " <td>2250</td>\n", " <td>1 AVENUE</td>\n", " <td>10010</td>\n", " <td>...</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>772</td>\n", " <td>338733.0</td>\n", " <td>D26-41.03 ADM CODE FILE WITH THIS DEPARTMENT A...</td>\n", " <td>05/10/1982</td>\n", " <td>19</td>\n", " <td>VIOLATION CLOSED</td>\n", " <td>03/10/2015</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>2293490</td>\n", " <td>467</td>\n", " <td>136913</td>\n", " <td>1</td>\n", " <td>MANHATTAN</td>\n", " <td>2250</td>\n", " <td>2250</td>\n", " <td>2250</td>\n", " <td>1 AVENUE</td>\n", " <td>10010</td>\n", " <td>...</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>772</td>\n", " <td>338737.0</td>\n", " <td>§ 27-2098 ADM CODE FILE WITH THIS DEPARTMENT ...</td>\n", " <td>11/19/1996</td>\n", " <td>19</td>\n", " <td>VIOLATION CLOSED</td>\n", " <td>03/10/2015</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>5 rows × 30 columns</p>\n", "</div>" ], "text/plain": [ " ViolationID BuildingID RegistrationID BoroID Boro HouseNumber \\\n", "0 2293208 444 130476 1 MANHATTAN 22 \n", "1 2293181 444 130476 1 MANHATTAN 22 \n", "2 2293249 448 135326 1 MANHATTAN 2222 \n", "3 2293486 467 136913 1 MANHATTAN 2250 \n", "4 2293490 467 136913 1 MANHATTAN 2250 \n", "\n", " LowHouseNumber HighHouseNumber StreetName StreetCode ... \\\n", "0 22 22 1 AVENUE 10010 ... \n", "1 22 22 1 AVENUE 10010 ... \n", "2 2222 2222 1 AVENUE 10010 ... \n", "3 2250 2250 1 AVENUE 10010 ... \n", "4 2250 2250 1 AVENUE 10010 ... \n", "\n", " NewCertifyByDate NewCorrectByDate CertifiedDate OrderNumber NOVID \\\n", "0 NaN NaN NaN 772 338596.0 \n", "1 NaN NaN 09/10/1974 775 338584.0 \n", "2 NaN NaN NaN 772 338619.0 \n", "3 NaN NaN NaN 772 338733.0 \n", "4 NaN NaN NaN 772 338737.0 \n", "\n", " NOVDescription NOVIssuedDate \\\n", "0 § 27-2098 ADM CODE FILE WITH THIS DEPARTMENT ... 04/22/1997 \n", "1 D26-41.05 ADM CODE FILE WITH THIS DEPARTMENT R... 07/16/1974 \n", "2 § 27-2098 ADM CODE FILE WITH THIS DEPARTMENT ... 11/19/1996 \n", "3 D26-41.03 ADM CODE FILE WITH THIS DEPARTMENT A... 05/10/1982 \n", "4 § 27-2098 ADM CODE FILE WITH THIS DEPARTMENT ... 11/19/1996 \n", "\n", " CurrentStatusID CurrentStatus CurrentStatusDate \n", "0 19 VIOLATION CLOSED 03/10/2015 \n", "1 19 VIOLATION CLOSED 03/10/2015 \n", "2 19 VIOLATION CLOSED 03/10/2015 \n", "3 19 VIOLATION CLOSED 03/10/2015 \n", "4 19 VIOLATION CLOSED 03/10/2015 \n", "\n", "[5 rows x 30 columns]" ] }, "execution_count": 36, "metadata": {}, "output_type": "execute_result" } ], "source": [ "hpdviol.head()" ] }, { "cell_type": "code", "execution_count": 40, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "1437246" ] }, "execution_count": 40, "metadata": {}, "output_type": "execute_result" } ], "source": [ "len(hpdviol)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "These datasets all have different lengths, but that's not surprising, given they come from different years. One productive initial step would be to convert the date strings into something numerical." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## HPD complaint problems database" ] }, { "cell_type": "code", "execution_count": 45, "metadata": { "collapsed": false }, "outputs": [], "source": [ "hpdcompprob = pd.read_csv('Complaint_Problems.csv')" ] }, { "cell_type": "code", "execution_count": 48, "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>ProblemID</th>\n", " <th>ComplaintID</th>\n", " <th>UnitTypeID</th>\n", " <th>UnitType</th>\n", " <th>SpaceTypeID</th>\n", " <th>SpaceType</th>\n", " <th>TypeID</th>\n", " <th>Type</th>\n", " <th>MajorCategoryID</th>\n", " <th>MajorCategory</th>\n", " <th>MinorCategoryID</th>\n", " <th>MinorCategory</th>\n", " <th>CodeID</th>\n", " <th>Code</th>\n", " <th>StatusID</th>\n", " <th>Status</th>\n", " <th>StatusDate</th>\n", " <th>StatusDescription</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>14548958</td>\n", " <td>6967900</td>\n", " <td>91</td>\n", " <td>APARTMENT</td>\n", " <td>541</td>\n", " <td>BATHROOM</td>\n", " <td>1</td>\n", " <td>EMERGENCY</td>\n", " <td>9</td>\n", " <td>PLUMBING</td>\n", " <td>63</td>\n", " <td>BATHTUB/SHOWER</td>\n", " <td>2538</td>\n", " <td>BROKEN OR MISSING</td>\n", " <td>2</td>\n", " <td>CLOSE</td>\n", " <td>07/29/2014</td>\n", " <td>The Department of Housing Preservation and De...</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>14548959</td>\n", " <td>6967900</td>\n", " <td>91</td>\n", " <td>APARTMENT</td>\n", " <td>541</td>\n", " <td>BATHROOM</td>\n", " <td>3</td>\n", " <td>NON EMERGENCY</td>\n", " <td>9</td>\n", " <td>PLUMBING</td>\n", " <td>63</td>\n", " <td>BATHTUB/SHOWER</td>\n", " <td>2540</td>\n", " <td>FAUCET BROKEN/MISSING/LEAKING</td>\n", " <td>2</td>\n", " <td>CLOSE</td>\n", " <td>08/04/2014</td>\n", " <td>The Department of Housing Preservation and De...</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>14548960</td>\n", " <td>6967900</td>\n", " <td>91</td>\n", " <td>APARTMENT</td>\n", " <td>543</td>\n", " <td>ENTIRE APARTMENT</td>\n", " <td>3</td>\n", " <td>NON EMERGENCY</td>\n", " <td>58</td>\n", " <td>FLOORING/STAIRS</td>\n", " <td>343</td>\n", " <td>FLOOR</td>\n", " <td>2691</td>\n", " <td>TILE BROKEN OR MISSING</td>\n", " <td>2</td>\n", " <td>CLOSE</td>\n", " <td>08/04/2014</td>\n", " <td>The Department of Housing Preservation and De...</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>14548961</td>\n", " <td>6967900</td>\n", " <td>91</td>\n", " <td>APARTMENT</td>\n", " <td>541</td>\n", " <td>BATHROOM</td>\n", " <td>3</td>\n", " <td>NON EMERGENCY</td>\n", " <td>9</td>\n", " <td>PLUMBING</td>\n", " <td>63</td>\n", " <td>BATHTUB/SHOWER</td>\n", " <td>2541</td>\n", " <td>CHIPPED OR RUSTED</td>\n", " <td>2</td>\n", " <td>CLOSE</td>\n", " <td>08/04/2014</td>\n", " <td>The Department of Housing Preservation and De...</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>14615271</td>\n", " <td>6994958</td>\n", " <td>20</td>\n", " <td>APARTMENT</td>\n", " <td>68</td>\n", " <td>ENTIRE APARTMENT</td>\n", " <td>1</td>\n", " <td>EMERGENCY</td>\n", " <td>59</td>\n", " <td>HEAT/HOT WATER</td>\n", " <td>349</td>\n", " <td>ENTIRE BUILDING</td>\n", " <td>2717</td>\n", " <td>NO HOT WATER</td>\n", " <td>2</td>\n", " <td>CLOSE</td>\n", " <td>08/22/2014</td>\n", " <td>The Department of Housing Preservation and De...</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " ProblemID ComplaintID UnitTypeID UnitType SpaceTypeID \\\n", "0 14548958 6967900 91 APARTMENT 541 \n", "1 14548959 6967900 91 APARTMENT 541 \n", "2 14548960 6967900 91 APARTMENT 543 \n", "3 14548961 6967900 91 APARTMENT 541 \n", "4 14615271 6994958 20 APARTMENT 68 \n", "\n", " SpaceType TypeID Type MajorCategoryID MajorCategory \\\n", "0 BATHROOM 1 EMERGENCY 9 PLUMBING \n", "1 BATHROOM 3 NON EMERGENCY 9 PLUMBING \n", "2 ENTIRE APARTMENT 3 NON EMERGENCY 58 FLOORING/STAIRS \n", "3 BATHROOM 3 NON EMERGENCY 9 PLUMBING \n", "4 ENTIRE APARTMENT 1 EMERGENCY 59 HEAT/HOT WATER \n", "\n", " MinorCategoryID MinorCategory CodeID Code \\\n", "0 63 BATHTUB/SHOWER 2538 BROKEN OR MISSING \n", "1 63 BATHTUB/SHOWER 2540 FAUCET BROKEN/MISSING/LEAKING \n", "2 343 FLOOR 2691 TILE BROKEN OR MISSING \n", "3 63 BATHTUB/SHOWER 2541 CHIPPED OR RUSTED \n", "4 349 ENTIRE BUILDING 2717 NO HOT WATER \n", "\n", " StatusID Status StatusDate \\\n", "0 2 CLOSE 07/29/2014 \n", "1 2 CLOSE 08/04/2014 \n", "2 2 CLOSE 08/04/2014 \n", "3 2 CLOSE 08/04/2014 \n", "4 2 CLOSE 08/22/2014 \n", "\n", " StatusDescription \n", "0 The Department of Housing Preservation and De... \n", "1 The Department of Housing Preservation and De... \n", "2 The Department of Housing Preservation and De... \n", "3 The Department of Housing Preservation and De... \n", "4 The Department of Housing Preservation and De... " ] }, "execution_count": 48, "metadata": {}, "output_type": "execute_result" } ], "source": [ "hpdcompprob.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Awesome! This dataset provides some more details about the complaints, and lets us join by ComplaintID." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Summary and next steps\n", "\n", "In the immediate future, I'm going to be writing script to join and clean this dataset. This can either be done either in python, or by writing some SQL. I haven't decided yet. Additionally, I'm going to be writing some code to do things like convert date strings, and perhaps scrape text." ] } ], "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
Ledoux/ShareYourSystem
Pythonlogy/draft/Tracer/Readme.ipynb
6
2198
{ "nbformat": 3, "worksheets": [ { "cells": [ { "source": "\n<!--\nFrozenIsBool False\n-->\n\n#Moniter\n\n##Doc\n----\n\n\n> \n> A Moniter\n> \n> \n\n----\n\n<small>\nView the Moniter notebook on [NbViewer](http://nbviewer.ipython.org/url/shareyoursystem.ouvaton.org/Moniter.ipynb)\n</small>\n\n", "cell_type": "markdown", "prompt_number": 0, "metadata": { "slideshow": { "slide_type": "slide" } } }, { "source": "\n<!--\nFrozenIsBool False\n-->\n\n##Code\n\n----\n\n<ClassDocStr>\n\n----\n\n```python\n# -*- coding: utf-8 -*-\n\"\"\"\n\n\n<DefineSource>\n@Date : Fri Nov 14 13:20:38 2014 \\n\n@Author : Erwan Ledoux \\n\\n\n</DefineSource>\n\n\nA Moniter\n\n\"\"\"\n\n\n#<DefineAugmentation>\nimport ShareYourSystem as SYS\nBaseModuleStr=\"ShareYourSystem.Specials.Simulaters.Runner\"\nDecorationModuleStr=\"ShareYourSystem.Standards.Classors.Representer\"\nSYS.setSubModule(globals())\n#</DefineAugmentation>\n\n#<ImportSpecificModules>\n#</ImportSpecificModules>\n\n#<DefineDoStrsList>\nDoStrsList=[\"Moniter\",\"Monit\",\"Monitering\",\"Monitered\"]\n#<DefineDoStrsList>\n\n#<DefineClass>\n@DecorationClass()\nclass MoniterClass(BaseClass):\n\n\t#Definition\n\tRepresentingKeyStrsList=[\n\t\t\t\t\t\t\t\t\t'MonitoringTrackTuplesList'\n\t\t\t\t\t\t\t\t]\n\t\n\tdef default_init(self,\n\t\t\t\t\t\t_MoniteringTrackTuplesList=None,\n\t\t\t\t\t\t**_KwargVariablesDict\n\t\t\t\t\t):\n\t\t\n\t\t#Call the parent init method\n\t\tBaseClass.__init__(self,**_KwargVariablesDict)\n\t\n\t#<DefineDoMethod>\t\n\tdef do_monit(self):\n\n\t\tpass\n\n#</DefineClass>\n\n\n```\n\n<small>\nView the Moniter sources on <a href=\"https://github.com/Ledoux/ShareYourSystem/tree/master/Pythonlogy/ShareYourSystem/Simulaters/Moniter\" target=\"_blank\">Github</a>\n</small>\n\n", "cell_type": "markdown", "prompt_number": 1, "metadata": { "slideshow": { "slide_type": "subslide" } } } ] } ], "metadata": { "name": "", "signature": "" }, "nbformat_minor": 0 }
mit
the-deep-learners/TensorFlow-LiveLessons
notebooks/object-detection.ipynb
1
380399
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Object Detection" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Based on Renu Khandelwal's YOLOv3 demo provided [here](https://medium.com/datadriveninvestor/object-detection-using-yolov3-using-keras-80bf35e61ce1)." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import os\n", "os.environ[\"CUDA_DEVICE_ORDER\"] = \"PCI_BUS_ID\"\n", "os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"0\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Load dependencies" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Using TensorFlow backend.\n" ] } ], "source": [ "import os\n", "\n", "import scipy.io\n", "import scipy.misc\n", "import numpy as np\n", "import pandas as pd\n", "import PIL\n", "import struct\n", "import cv2\n", "from numpy import expand_dims\n", "import tensorflow as tf\n", "from skimage.transform import resize\n", "from keras import backend as K\n", "from keras.layers import Input, Lambda, Conv2D, BatchNormalization, LeakyReLU, ZeroPadding2D, UpSampling2D\n", "from keras.models import load_model, Model\n", "from keras.layers.merge import add, concatenate\n", "from keras.preprocessing.image import load_img\n", "from keras.preprocessing.image import img_to_array\n", "import matplotlib.pyplot as plt\n", "from matplotlib.pyplot import imshow\n", "from matplotlib.patches import Rectangle\n", "\n", "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "class WeightReader:\n", " def __init__(self, weight_file):\n", " with open(weight_file, 'rb') as w_f:\n", " major, = struct.unpack('i', w_f.read(4))\n", " minor, = struct.unpack('i', w_f.read(4))\n", " revision, = struct.unpack('i', w_f.read(4))\n", "\n", " if (major*10 + minor) >= 2 and major < 1000 and minor < 1000:\n", " w_f.read(8)\n", " else:\n", " w_f.read(4)\n", "\n", " transpose = (major > 1000) or (minor > 1000)\n", " \n", " binary = w_f.read()\n", "\n", " self.offset = 0\n", " self.all_weights = np.frombuffer(binary, dtype='float32')\n", " \n", " def read_bytes(self, size):\n", " self.offset = self.offset + size\n", " return self.all_weights[self.offset-size:self.offset]\n", "\n", " def load_weights(self, model):\n", " for i in range(106):\n", " try:\n", " conv_layer = model.get_layer('conv_' + str(i))\n", " print(\"loading weights of convolution #\" + str(i))\n", "\n", " if i not in [81, 93, 105]:\n", " norm_layer = model.get_layer('bnorm_' + str(i))\n", "\n", " size = np.prod(norm_layer.get_weights()[0].shape)\n", "\n", " beta = self.read_bytes(size) # bias\n", " gamma = self.read_bytes(size) # scale\n", " mean = self.read_bytes(size) # mean\n", " var = self.read_bytes(size) # variance \n", "\n", " weights = norm_layer.set_weights([gamma, beta, mean, var]) \n", "\n", " if len(conv_layer.get_weights()) > 1:\n", " bias = self.read_bytes(np.prod(conv_layer.get_weights()[1].shape))\n", " kernel = self.read_bytes(np.prod(conv_layer.get_weights()[0].shape))\n", " \n", " kernel = kernel.reshape(list(reversed(conv_layer.get_weights()[0].shape)))\n", " kernel = kernel.transpose([2,3,1,0])\n", " conv_layer.set_weights([kernel, bias])\n", " else:\n", " kernel = self.read_bytes(np.prod(conv_layer.get_weights()[0].shape))\n", " kernel = kernel.reshape(list(reversed(conv_layer.get_weights()[0].shape)))\n", " kernel = kernel.transpose([2,3,1,0])\n", " conv_layer.set_weights([kernel])\n", " except ValueError:\n", " print(\"no convolution #\" + str(i)) \n", " \n", " def reset(self):\n", " self.offset = 0\n" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def _conv_block(inp, convs, skip=True):\n", " x = inp\n", " count = 0\n", " \n", " for conv in convs:\n", " if count == (len(convs) - 2) and skip:\n", " skip_connection = x\n", " count += 1\n", " \n", " if conv['stride'] > 1: x = ZeroPadding2D(((1,0),(1,0)))(x) # peculiar padding as darknet prefer left and top\n", " x = Conv2D(conv['filter'], \n", " conv['kernel'], \n", " strides=conv['stride'], \n", " padding='valid' if conv['stride'] > 1 else 'same', # peculiar padding as darknet prefer left and top\n", " name='conv_' + str(conv['layer_idx']), \n", " use_bias=False if conv['bnorm'] else True)(x)\n", " if conv['bnorm']: x = BatchNormalization(epsilon=0.001, name='bnorm_' + str(conv['layer_idx']))(x)\n", " if conv['leaky']: x = LeakyReLU(alpha=0.1, name='leaky_' + str(conv['layer_idx']))(x)\n", "\n", " return add([skip_connection, x]) if skip else x" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Design model architecture" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def make_yolov3_model():\n", " input_image = Input(shape=(None, None, 3))\n", "\n", " # Layer 0 => 4\n", " x = _conv_block(input_image, [{'filter': 32, 'kernel': 3, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 0},\n", " {'filter': 64, 'kernel': 3, 'stride': 2, 'bnorm': True, 'leaky': True, 'layer_idx': 1},\n", " {'filter': 32, 'kernel': 1, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 2},\n", " {'filter': 64, 'kernel': 3, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 3}])\n", "\n", " # Layer 5 => 8\n", " x = _conv_block(x, [{'filter': 128, 'kernel': 3, 'stride': 2, 'bnorm': True, 'leaky': True, 'layer_idx': 5},\n", " {'filter': 64, 'kernel': 1, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 6},\n", " {'filter': 128, 'kernel': 3, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 7}])\n", "\n", " # Layer 9 => 11\n", " x = _conv_block(x, [{'filter': 64, 'kernel': 1, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 9},\n", " {'filter': 128, 'kernel': 3, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 10}])\n", "\n", " # Layer 12 => 15\n", " x = _conv_block(x, [{'filter': 256, 'kernel': 3, 'stride': 2, 'bnorm': True, 'leaky': True, 'layer_idx': 12},\n", " {'filter': 128, 'kernel': 1, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 13},\n", " {'filter': 256, 'kernel': 3, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 14}])\n", "\n", " # Layer 16 => 36\n", " for i in range(7):\n", " x = _conv_block(x, [{'filter': 128, 'kernel': 1, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 16+i*3},\n", " {'filter': 256, 'kernel': 3, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 17+i*3}])\n", " \n", " skip_36 = x\n", " \n", " # Layer 37 => 40\n", " x = _conv_block(x, [{'filter': 512, 'kernel': 3, 'stride': 2, 'bnorm': True, 'leaky': True, 'layer_idx': 37},\n", " {'filter': 256, 'kernel': 1, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 38},\n", " {'filter': 512, 'kernel': 3, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 39}])\n", "\n", " # Layer 41 => 61\n", " for i in range(7):\n", " x = _conv_block(x, [{'filter': 256, 'kernel': 1, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 41+i*3},\n", " {'filter': 512, 'kernel': 3, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 42+i*3}])\n", " \n", " skip_61 = x\n", " \n", " # Layer 62 => 65\n", " x = _conv_block(x, [{'filter': 1024, 'kernel': 3, 'stride': 2, 'bnorm': True, 'leaky': True, 'layer_idx': 62},\n", " {'filter': 512, 'kernel': 1, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 63},\n", " {'filter': 1024, 'kernel': 3, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 64}])\n", "\n", " # Layer 66 => 74\n", " for i in range(3):\n", " x = _conv_block(x, [{'filter': 512, 'kernel': 1, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 66+i*3},\n", " {'filter': 1024, 'kernel': 3, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 67+i*3}])\n", " \n", " # Layer 75 => 79\n", " x = _conv_block(x, [{'filter': 512, 'kernel': 1, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 75},\n", " {'filter': 1024, 'kernel': 3, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 76},\n", " {'filter': 512, 'kernel': 1, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 77},\n", " {'filter': 1024, 'kernel': 3, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 78},\n", " {'filter': 512, 'kernel': 1, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 79}], skip=False)\n", "\n", " # Layer 80 => 82\n", " yolo_82 = _conv_block(x, [{'filter': 1024, 'kernel': 3, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 80},\n", " {'filter': 255, 'kernel': 1, 'stride': 1, 'bnorm': False, 'leaky': False, 'layer_idx': 81}], skip=False)\n", "\n", " # Layer 83 => 86\n", " x = _conv_block(x, [{'filter': 256, 'kernel': 1, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 84}], skip=False)\n", " x = UpSampling2D(2)(x)\n", " x = concatenate([x, skip_61])\n", "\n", " # Layer 87 => 91\n", " x = _conv_block(x, [{'filter': 256, 'kernel': 1, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 87},\n", " {'filter': 512, 'kernel': 3, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 88},\n", " {'filter': 256, 'kernel': 1, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 89},\n", " {'filter': 512, 'kernel': 3, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 90},\n", " {'filter': 256, 'kernel': 1, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 91}], skip=False)\n", "\n", " # Layer 92 => 94\n", " yolo_94 = _conv_block(x, [{'filter': 512, 'kernel': 3, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 92},\n", " {'filter': 255, 'kernel': 1, 'stride': 1, 'bnorm': False, 'leaky': False, 'layer_idx': 93}], skip=False)\n", "\n", " # Layer 95 => 98\n", " x = _conv_block(x, [{'filter': 128, 'kernel': 1, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 96}], skip=False)\n", " x = UpSampling2D(2)(x)\n", " x = concatenate([x, skip_36])\n", "\n", " # Layer 99 => 106\n", " yolo_106 = _conv_block(x, [{'filter': 128, 'kernel': 1, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 99},\n", " {'filter': 256, 'kernel': 3, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 100},\n", " {'filter': 128, 'kernel': 1, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 101},\n", " {'filter': 256, 'kernel': 3, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 102},\n", " {'filter': 128, 'kernel': 1, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 103},\n", " {'filter': 256, 'kernel': 3, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 104},\n", " {'filter': 255, 'kernel': 1, 'stride': 1, 'bnorm': False, 'leaky': False, 'layer_idx': 105}], skip=False)\n", "\n", " model = Model(input_image, [yolo_82, yolo_94, yolo_106]) \n", " return model" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "--2019-09-24 23:42:49-- https://pjreddie.com/media/files/yolov3.weights\n", "Resolving pjreddie.com (pjreddie.com)... 128.208.4.108\n", "Connecting to pjreddie.com (pjreddie.com)|128.208.4.108|:443... connected.\n", "HTTP request sent, awaiting response... 200 OK\n", "Length: 248007048 (237M) [application/octet-stream]\n", "Saving to: ‘yolov3.weights’\n", "\n", "yolov3.weights 100%[===================>] 236.52M 39.8MB/s in 6.4s \n", "\n", "2019-09-24 23:42:56 (36.9 MB/s) - ‘yolov3.weights’ saved [248007048/248007048]\n", "\n" ] } ], "source": [ "## from https://github.com/ultralytics/yolov3/blob/master/weights/download_yolov3_weights.sh: \n", "# ! wget -c https://pjreddie.com/media/files/yolov3.weights " ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "loading weights of convolution #0\n", "loading weights of convolution #1\n", "loading weights of convolution #2\n", "loading weights of convolution #3\n", "no convolution 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weights of convolution #90\n", "loading weights of convolution #91\n", "loading weights of convolution #92\n", "loading weights of convolution #93\n", "no convolution #94\n", "no convolution #95\n", "loading weights of convolution #96\n", "no convolution #97\n", "no convolution #98\n", "loading weights of convolution #99\n", "loading weights of convolution #100\n", "loading weights of convolution #101\n", "loading weights of convolution #102\n", "loading weights of convolution #103\n", "loading weights of convolution #104\n", "loading weights of convolution #105\n" ] } ], "source": [ "net_h, net_w = 416, 416\n", "obj_thresh, nms_thresh = 0.5, 0.45\n", "anchors = [[116,90, 156,198, 373,326], [30,61, 62,45, 59,119], [10,13, 16,30, 33,23]]\n", "labels = [\"person\", \"bicycle\", \"car\", \"motorbike\", \"aeroplane\", \"bus\", \"train\", \"truck\", \\\n", " \"boat\", \"traffic light\", \"fire hydrant\", \"stop sign\", \"parking meter\", \"bench\", \\\n", " \"bird\", \"cat\", \"dog\", \"horse\", \"sheep\", \"cow\", \"elephant\", \"bear\", \"zebra\", \"giraffe\", \\\n", " \"backpack\", \"umbrella\", \"handbag\", \"tie\", \"suitcase\", \"frisbee\", \"skis\", \"snowboard\", \\\n", " \"sports ball\", \"kite\", \"baseball bat\", \"baseball glove\", \"skateboard\", \"surfboard\", \\\n", " \"tennis racket\", \"bottle\", \"wine glass\", \"cup\", \"fork\", \"knife\", \"spoon\", \"bowl\", \"banana\", \\\n", " \"apple\", \"sandwich\", \"orange\", \"broccoli\", \"carrot\", \"hot dog\", \"pizza\", \"donut\", \"cake\", \\\n", " \"chair\", \"sofa\", \"pottedplant\", \"bed\", \"diningtable\", \"toilet\", \"tvmonitor\", \"laptop\", \"mouse\", \\\n", " \"remote\", \"keyboard\", \"cell phone\", \"microwave\", \"oven\", \"toaster\", \"sink\", \"refrigerator\", \\\n", " \"book\", \"clock\", \"vase\", \"scissors\", \"teddy bear\", \"hair drier\", \"toothbrush\"]\n", "\n", "# make the yolov3 model to predict 80 classes on COCO\n", "\n", "yolov3 = make_yolov3_model()\n", "\n", "# load the weights trained on COCO into the model\n", "weight_reader = WeightReader('yolov3.weights')\n", "weight_reader.load_weights(yolov3)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Save model" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": true }, "outputs": [], "source": [ "yolov3.save('yolov3.h5') # can be loaded with: yolov3 = load_model('yolov3.h5')" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from numpy import expand_dims\n", "def load_image_pixels(filename, shape):\n", " # load the image to get its shape\n", " image = load_img(filename)\n", " width, height = image.size\n", " # load the image with the required size\n", " image = load_img(filename, target_size=shape)\n", " # convert to numpy array\n", " image = img_to_array(image)\n", " # scale pixel values to [0, 1]\n", " image = image.astype('float32')\n", " image /= 255.0\n", " # add a dimension so that we have one sample\n", " image = expand_dims(image, 0)\n", " return image, width, height" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "--2019-09-24 23:48:09-- https://raw.githubusercontent.com/arshren/YOLOV3/master/eagle.png\n", "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 151.101.0.133, 151.101.64.133, 151.101.128.133, ...\n", "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|151.101.0.133|:443... connected.\n", "HTTP request sent, awaiting response... 200 OK\n", "Length: 940917 (919K) [image/png]\n", "Saving to: ‘eagle.png’\n", "\n", "eagle.png 100%[===================>] 918.86K --.-KB/s in 0.06s \n", "\n", "2019-09-24 23:48:10 (15.9 MB/s) - ‘eagle.png’ saved [940917/940917]\n", "\n" ] } ], "source": [ "# ! wget -c https://raw.githubusercontent.com/arshren/YOLOV3/master/eagle.png" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# define the expected input shape for the model\n", "input_w, input_h = 416, 416\n", "# define our new photo\n", "photo_filename = 'eagle.png'\n", "# load and prepare image\n", "image, image_w, image_h = load_image_pixels(photo_filename, (input_w, input_h))" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": true }, "outputs": [], "source": [ "class BoundBox:\n", " def __init__(self, xmin, ymin, xmax, ymax, objness = None, classes = None):\n", " self.xmin = xmin\n", " self.ymin = ymin\n", " self.xmax = xmax\n", " self.ymax = ymax\n", " \n", " self.objness = objness\n", " self.classes = classes\n", "\n", " self.label = -1\n", " self.score = -1\n", "\n", " def get_label(self):\n", " if self.label == -1:\n", " self.label = np.argmax(self.classes)\n", " \n", " return self.label\n", " \n", " def get_score(self):\n", " if self.score == -1:\n", " self.score = self.classes[self.get_label()]\n", " \n", " return self.score\n", "\n", "def _sigmoid(x):\n", " return 1. / (1. + np.exp(-x))\n", "\n", "def _interval_overlap(interval_a, interval_b):\n", " x1, x2 = interval_a\n", " x3, x4 = interval_b\n", "\n", " if x3 < x1:\n", " if x4 < x1:\n", " return 0\n", " else:\n", " return min(x2,x4) - x1\n", " else:\n", " if x2 < x3:\n", " return 0\n", " else:\n", " return min(x2,x4) - x3 \n", "def bbox_iou(box1, box2):\n", " intersect_w = _interval_overlap([box1.xmin, box1.xmax], [box2.xmin, box2.xmax])\n", " intersect_h = _interval_overlap([box1.ymin, box1.ymax], [box2.ymin, box2.ymax])\n", " \n", " intersect = intersect_w * intersect_h\n", "\n", " w1, h1 = box1.xmax-box1.xmin, box1.ymax-box1.ymin\n", " w2, h2 = box2.xmax-box2.xmin, box2.ymax-box2.ymin\n", " \n", " union = w1*h1 + w2*h2 - intersect\n", " \n", " return float(intersect) / union\n", "\n", "def do_nms(boxes, nms_thresh):\n", " if len(boxes) > 0:\n", " nb_class = len(boxes[0].classes)\n", " else:\n", " return\n", " \n", " for c in range(nb_class):\n", " sorted_indices = np.argsort([-box.classes[c] for box in boxes])\n", "\n", " for i in range(len(sorted_indices)):\n", " index_i = sorted_indices[i]\n", "\n", " if boxes[index_i].classes[c] == 0: continue\n", "\n", " for j in range(i+1, len(sorted_indices)):\n", " index_j = sorted_indices[j]\n", "\n", " if bbox_iou(boxes[index_i], boxes[index_j]) >= nms_thresh:\n", " boxes[index_j].classes[c] = 0" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#decode_netout() that will take each one of the NumPy arrays, one at a time, \n", "#and decode the candidate bounding boxes and class predictions\n", "def decode_netout(netout, anchors, obj_thresh, net_h, net_w):\n", " grid_h, grid_w = netout.shape[:2]\n", " nb_box = 3\n", " netout = netout.reshape((grid_h, grid_w, nb_box, -1))\n", " nb_class = netout.shape[-1] - 5\n", "\n", " boxes = []\n", "\n", " netout[..., :2] = _sigmoid(netout[..., :2])\n", " netout[..., 4:] = _sigmoid(netout[..., 4:])\n", " netout[..., 5:] = netout[..., 4][..., np.newaxis] * netout[..., 5:]\n", " netout[..., 5:] *= netout[..., 5:] > obj_thresh\n", "\n", " for i in range(grid_h*grid_w):\n", " row = i / grid_w\n", " col = i % grid_w\n", " \n", " for b in range(nb_box):\n", " # 4th element is objectness score\n", " objectness = netout[int(row)][int(col)][b][4]\n", " #objectness = netout[..., :4]\n", " \n", " if(objectness.all() <= obj_thresh): continue\n", " \n", " # first 4 elements are x, y, w, and h\n", " x, y, w, h = netout[int(row)][int(col)][b][:4]\n", "\n", " x = (col + x) / grid_w # center position, unit: image width\n", " y = (row + y) / grid_h # center position, unit: image height\n", " w = anchors[2 * b + 0] * np.exp(w) / net_w # unit: image width\n", " h = anchors[2 * b + 1] * np.exp(h) / net_h # unit: image height \n", " \n", " # last elements are class probabilities\n", " classes = netout[int(row)][col][b][5:]\n", " \n", " box = BoundBox(x-w/2, y-h/2, x+w/2, y+h/2, objectness, classes)\n", " #box = BoundBox(x-w/2, y-h/2, x+w/2, y+h/2, None, classes)\n", "\n", " boxes.append(box)\n", "\n", " return boxes" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# bounding boxes will be stretched back into the shape of the original image\n", "#will allow plotting the original image and draw the bounding boxes, hopefully detecting real objects.\n", "# correct the sizes of the bounding boxes for the shape of the image\n", "#correct_yolo_boxes(boxes, image_h, image_w, input_h, input_w)\n", "def correct_yolo_boxes(boxes, image_h, image_w, net_h, net_w):\n", " if (float(net_w)/image_w) < (float(net_h)/image_h):\n", " new_w = net_w\n", " new_h = (image_h*net_w)/image_w\n", " else:\n", " new_h = net_w\n", " new_w = (image_w*net_h)/image_h\n", " \n", " for i in range(len(boxes)):\n", " x_offset, x_scale = (net_w - new_w)/2./net_w, float(new_w)/net_w\n", " y_offset, y_scale = (net_h - new_h)/2./net_h, float(new_h)/net_h\n", " \n", " boxes[i].xmin = int((boxes[i].xmin - x_offset) / x_scale * image_w)\n", " boxes[i].xmax = int((boxes[i].xmax - x_offset) / x_scale * image_w)\n", " boxes[i].ymin = int((boxes[i].ymin - y_offset) / y_scale * image_h)\n", " boxes[i].ymax = int((boxes[i].ymax - y_offset) / y_scale * image_h)" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# suppress non-maximal boxes\n", "#do_nms(boxes, 0.5)" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from matplotlib.patches import Rectangle\n", "def draw_boxes(filename, v_boxes, v_labels, v_scores):\n", " # load the image\n", " data = plt.imread(filename)\n", " # plot the image\n", " plt.imshow(data)\n", " # get the context for drawing boxes\n", " ax = plt.gca()\n", " # plot each box\n", " for i in range(len(v_boxes)):\n", " box = v_boxes[i]\n", " # get coordinates\n", " y1, x1, y2, x2 = box.ymin, box.xmin, box.ymax, box.xmax\n", " # calculate width and height of the box\n", " width, height = x2 - x1, y2 - y1\n", " # create the shape\n", " rect = Rectangle((x1, y1), width, height, fill=False, color='red')\n", " # draw the box\n", " ax.add_patch(rect)\n", " # draw text and score in top left corner\n", " label = \"%s (%.3f)\" % (v_labels[i], v_scores[i])\n", " plt.text(x1, y1, label, color='red')\n", " # show the plot\n", " plt.show()" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# get all of the results above a threshold\n", "# takes the list of boxes, known labels, \n", "#and our classification threshold as arguments and returns parallel lists of boxes, labels, and scores.\n", "def get_boxes(boxes, labels, thresh):\n", " v_boxes, v_labels, v_scores = list(), list(), list()\n", " # enumerate all boxes\n", " for box in boxes:\n", " # enumerate all possible labels\n", " for i in range(len(labels)):\n", " # check if the threshold for this label is high enough\n", " if box.classes[i] > thresh:\n", " v_boxes.append(box)\n", " v_labels.append(labels[i])\n", " v_scores.append(box.classes[i]*100)\n", " # don't break, many labels may trigger for one box\n", " return v_boxes, v_labels, v_scores" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# https://raw.githubusercontent.com/arshren/YOLOV3/master/eagle.png" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[(1, 13, 13, 255), (1, 26, 26, 255), (1, 52, 52, 255)]\n", "dog 99.7451424599\n" ] }, { "data": { "image/png": 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MY4XJeJ7NpuGrX/0qX/nql5inmqN7dzg9PUXOCrwv6Psea7sD7S37HQq2z7gRHDAT02od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\nThEKhSiVSoS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FGo0GoVDIWO0ajQaVWhZVk/yUzQCGEAKHwyFLY0Ih5ubmjFXIbpc5oHK5\nbJTQWFUXwWCQxcVFhNOGRccrNgM4zYHQ5GFpEizZbLKWzC40o011syy3qZQ1FLUi3bcUmK3HtQBW\nrlwJpipjIzNsAZILcrJ1dXUZUlXxeNzArjZRNPPz80SjUbLZrCEwWSgUcDgcBAIBbDabwfvSRP03\nVyGnSwKU6zUTFmuNSlk1tMWFEFhtDQKBiMxXer0GqVIw6MfptPM3//zXzMxOsmf3PTy1+xme3nMA\nu9/LQw89xPPHJhHiFBHLkzAnN6W46nWVkNPDbl0ltkmGu3r1ah599FE8Hg/PP//8qzKmz3jC6Zwm\ne4BpTdPeJoToBX6EpMjbB/y+pmk1IYQd+B5wPpAEfk/TtHH9M24E3geowJ9pmrbrpe7ZaDTwmmys\nOG8dyYkZKsksSRR+cNtPsfk8JKdn+ecv3UCjoRgiHNVUTgYwPA5j5ZuemMLlNhNu6TIQICaTiVQq\nRWx5N616cEbTpERv011qTkLhsBjuRbO0pFk60uSOVBQNm81LKBRiJjGGEMJIoIZCISNooKoqdrvd\nYFFuPjmbe6ImmHfp+UZDlhnB8Qp4l8sFDTsmS934O1VVyc0VsPklRXo9X8IeMBFzLCdZyGFr1IhE\nIgwMDBCPxyV5rP4wAKkmu3r1ahYXFzGZTExOTrJ27VqGh4cJh8OGfHEwGDRW6SbFfBMo3pyoTQ/B\nZrMZ7nVTKrm1tZVUsogJG3ZHlVa/5OCcn8sSiXQYApVNbkqz2YyiFrHZTZjNJmIxCUu78p2XcMlb\nd6A0Cvz2297EsWPHiERCLN9wPp/49N/J/2mj6Uo2ANOSagGp4mpTAc1KeTFhRG/D4TCViqSIj0aj\nCCHYuHEj/PCRM50up7SXkxb4MHB4yc+fA76oaVofkEZOJPT3tKZpK4Av6tchhFgLXAOsQzIuf02f\nxKc0VVXRHFbMwoTJKgdhMV/g2Gya7q4VBNta0JATqLW1VWoN9PYQiUVJLixit7nxWCxEYwHy+SIj\nI2NkMhlJI76YINLTIasKHBbSuSyVSsWIto2OjuJ0OqnUazhUuX+ZzSSNFQZkqL0ZDGlGU5PJJAFf\nFIEJrYGRMG0GOhqNhgGlevPOnbLqW9NAE3i9XgMxUi6XJUVfvY6iKCQSCarVKsVikXCoBTT5kKjX\nwOcNYzZJNZx8tYzT7sSEExCIhp1MWUZM7UIGMMLhMIuLizz33HN4PB68NlmOk0wmmUskjFRBPB5H\n0zTWrFljFGcuFQnJZrNYrRKlsbCwYFA1BINBI63g8XiYmpoinU5jMpmIx7oo5KsEAj6i0QgdHR1o\nmkY8LotjTeY6fr+XQMCHotQolUp0drazdeuFHD16lFgshsfj4dDAYe695wHGxo8Si8a5+h1v4c1v\n2UEyPcdzT93PVTvW884rLuGaK95I2GnD63Hok82ku5UNHYZnI+Coc8dt38LhcLBq1SqOHTvG+vXr\nmZiYMHKby5YtexlT5dR2pszLHcBvATcBH9Wp8a4ArtMv+XfgM0gdgav1Y4Bbga/q118N/EjnsRwT\nQgwjuSyfONV9zWYzlXSO8fFxenp6WFhY4MjRSfKFAk7TPDuv3MKG9RcwOjpKLpvHZK6zWMhKIK/F\njtNl5fEnduN2u1nWuwZrh5VSWUa0ent7sSvgiEj3xRn0SY1uk4nFxUXWXNDPzMSMTGBbJSi4I9xK\nIpHA7/czPj7OihUr0DSNmZkZ4vG4FOtwOIxwvdksteSaAQNN0/C6WzCbpcs5cOAAVqvVgHAlk0lc\njiBqo4zQHEQiUvUnFouRyWSwmOWqWi6pmM1OvF4rCwsLKA05oWxWL6FwGJcrSC6Xo1YFT9hp1PaZ\nTCYDj9iMKnZ3dxs5Jp9PRjYtegqjra2NoaEhWltb0TRNBmD06GgzaR6LyUR5JBKRRblBLz5Fwe/3\nk06npRCkXm4U7mqjmsoRDHnwer3kcjkq+Tp+v59CoWAk1lVVpWrxU88UZKqlnOXhhx8kl8vx2GOP\nMTQ0xCWXXEL/5vOIx+Mkk0nWbeygNepDddgYHBzEbJ5nJptF00q84bINmBDYFDt/8scf46o/vBZL\nRaHacPFPn3wvLcu9lJQag4ODbNy4Ea/Xy549e074rppomVdqZ+pSfgn4BJLvGSAMZDRNazrLS2nL\nDUpzTdMUIURWv74deHLJZ56W6rwLePf1//iixnwVIFmEr03D1+5g7Rl24lQW0d+3nMG1zXudv+Tc\n5pNd+Dqw/iXHtZhUWF25ciWjo6NGpK69vZ2/XYgQAAAGiUlEQVRkUtLwNVmIJycnWbVqFWNjY4yM\njOD3+yWFRCjEo48+yr5Uiu3btxtpkQsukAB7zWXj3vse5s1vu4psXWI7m3vlJv15rSYjh13hFsZy\nJZxOJxaLDNFv3bqVkZERrr76amMi79271/AMarUaK9u7MZXrjNrtJA8XEKKB2QpognpN4fs//RL/\n+dOvEo1G6Vu+gZniGH3BSwmFQrS3txughVwuZzxMhBBMT0+/Kt/5mRDBvg2Y1zRtrxDisubpk1yq\nneZ3Z0x1DnwToLMton39o++gXq+zqn8LExPjfPhj/4uuzj76ukN88wdfo1RUGRrYJ0tR1CLz8/OG\nHpzX6yXS0kG0u1PW0JWzKHWz4ca5XC4Utcj09DRBm6QXt9lspGtSbNFUrjM1NUWoM07Q5jKCHM28\nVjOh26TpazQaxkqGqWrkzsxmM5pqM2gPVFVFaRSNPY/FJMP8FpuK3eozBt1SVITJZJJa3pY6JpxY\nrTIhn0qlKJUk7cTRo0fp6+szQLjhcNiQoJqdnTVycOFwmGRSuseZTIZwOCyRM243c3NzBsayXC7T\n2tpKtVol3NXG+MFByuUyGzZskKtTpUJPT4+Bdf3FvT9n+6UXs3HjRqNqvhkgOnbsGMVUErPLgc3q\nxe8SHH12D73rNxL1ehkbG8PlcmGz2Zibk7nRJjFtpVIhHA4zNjaGoih4PB7S6bQRQa7VakxMTGCx\nWDh6dEx6GHWbwWomq/OrhFrcMtFvsaNpGoXyNJs2baKhw8WaQp6PPfYYO3fu5ODBg7hcLvL5lyeP\n9VJ22no4IcQ/AL+PZMF0ILUxb0eq4cT0VWw78BlN096iMyx/RtO0J4QQFiCBXERu0CfUP+ifa1z3\nEvfOAy8u7X39Wwvw6tTsn132m96vbk3TIqe7+CWtKZZwJi/gMuBu/fgnwDX68deBD+nH1wNf14+v\nQTI1gwyWPIeU3uoFRgHzae635+W07/XyOtev19fr1ezXK1knPwn8SAjxd8B+4Nv6+W8D39eDIil9\n0qFp2kEhxI+BQ8jV8npN016+lu05O2evYzurKRaEEHu0V1jSfjbauX69vuzV7NfZXp7zzde6Af+P\n7Fy/Xl/2qvXrrF7hztk5+02zs32FO2fn7DfKzk24c3bOfo121k44IcRbhRCDQohhIcQNr3V7TmdC\niO8IIeaFEANLzoWEEPcLIYb096B+Xggh/kXv2/NCiM1L/uY9+vVDQoj3vBZ9WWpCiE4hxENCiMNC\niINCiA/r51+3fRNCOIQQTwshntP79Fn9fK8Q4im9fbcIIWz6ebv+87D++54ln3Wjfn5QCPGW0978\ntc5xnCLvYQZGgGWADZm/W/tat+s0bb4EifIaWHLu88AN+vENwOf046uAnyHRNxcCT+nnQ8j8ZAgI\n6sfB17hfcWCzfuwFjiIRbq/bvult8+jHVuApva0/5sTc8p/oxx/ixNzyLfrxWk7MLY9wutzyaz1Q\nT/GFbAd2Lfn5RuDG17pdZ9DunhdMuEEgvmTgDurH3wCufeF1wLXAN5acP+G6s+EF3Am8+Telb4AL\nWV62DYkmsbxwDAK7gO36sUW/TrxwXC697lSvs9WlNADQup0U6Pw6sKimabMA+nurfv5U/Tur+627\nUpuQK8Lrum9CCLMQ4llgHrgfuTqdESAfWArIf1l9Olsn3BkBnV/H9ooA3q+FCSE8wG3ARzRNy73U\npSc5d9b1TdM0VdO0fqADWSa25mSX6e+vWp/O1gk3BXQu+bkDmHmN2vJKbE4IEQfQ3+f186fq31nZ\nbyGEFTnZfqhp2n/qp38j+qZpWgZ4GLmHC+iAezixfUbb9d/7kbDFl92ns3XCPQP06VEjG3Kjetdr\n3KZfxe4CmtG49yD3P83zf6BH9C4EsrpbtgvYKYQI6lG/nfq518z04uFvA4c1TftfS371uu2bECIi\nhAjox07gTUg2g4eAd+qXvbBPzb6+E3hQk5u2u4Br9ChmL9AHPP2SN3+tN60vsZm9ChkRGwH+6rVu\nzxm09/8As0Ad+eR7H9LPfwAY0t9D+rUC+Fe9bweAC5Z8zh8Bw/rrD8+Cfl2EdJOeB57VX1e9nvsG\nbEQC7p8HBoD/qZ9fpk+YYWQ1jF0/79B/HtZ/v2zJZ/2V3tdB4MrT3fsctOucnbNfo52tLuU5O2e/\nkXZuwp2zc/ZrtHMT7pyds1+jnZtw5+yc/Rrt3IQ7Z+fs12jnJtw5O2e/Rjs34c7ZOfs12v8Fi85V\njjQTgRcAAAAASUVORK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f63583ee5c0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# define the expected input shape for the model\n", "input_w, input_h = 416, 416\n", "# define our new photo\n", "# photo_filename = 'eagle.png'\n", "photo_filename = 'yolov3-model/oboe-book-small.png'\n", "# load and prepare image\n", "image, image_w, image_h = load_image_pixels(photo_filename, (net_w, net_w))\n", "\n", "\n", "# make prediction\n", "yolos = yolov3.predict(image)\n", "# summarize the shape of the list of arrays\n", "print([a.shape for a in yolos])\n", "\n", "# define the anchors\n", "anchors = [[116,90, 156,198, 373,326], [30,61, 62,45, 59,119], [10,13, 16,30, 33,23]]\n", "# define the probability threshold for detected objects\n", "class_threshold = 0.6\n", "boxes = list()\n", "\n", "for i in range(len(yolos)):\n", " # decode the output of the network\n", " boxes += decode_netout(yolos[i][0], anchors[i], obj_thresh, net_h, net_w)\n", "\n", " # correct the sizes of the bounding boxes\n", "correct_yolo_boxes(boxes, image_h, image_w, net_h, net_w)\n", "\n", "# suppress non-maximal boxes\n", "do_nms(boxes, nms_thresh)\n", "\n", "# get the details of the detected objects\n", "v_boxes, v_labels, v_scores = get_boxes(boxes, labels, class_threshold)\n", "# summarize what we found\n", "for i in range(len(v_boxes)):\n", " print(v_labels[i], v_scores[i])\n", "# draw what we found\n", "draw_boxes(photo_filename, v_boxes, v_labels, v_scores)" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<matplotlib.image.AxesImage at 0x7f6324078d68>" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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dOhQIFpX20RUVx8OW5lU542epWlOHW6zfAufRGwo9tksBiy7U0MFxsHpt2/J7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\nzYNn76e7N8QNXc5urNHuBAy+NiLJpry8c83YSk//7BnfPyeE+Engq8B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zv3YWs1CnOj9L\nFEVobn49nD9/H5ubmxiGQZaq3Lp1g1KpxFJjluVmi2eefZLf/v0OcZSc7Iqac03eub2NFLs8fP4C\niprStwNGvo/vO7jTkI2zM8ShREFKKJfLSGKB+fkKWaJ+27X1/az3dcElaUqtICJJCY5rIwcRGhDE\nAYIgUKlUiFTx2+RFSeoSRBZy6b3E0yRJGFsdoji/q973xDpvv3yF02cX2R4fIQlFVs8+yDuvv8jR\nzh4dK6AzsHBthVj3aJoyVcXASnwqWoHenQ6nV3XqpsLR0RHLzRKTTp8gmlAoFJhpLBNaDomp5348\nIUOYTChqGodWfs6cb5jEnQFVMSENJVLbRwtCjAJkSoosC2wfZizKCq7bpdWaw7ImFOtzOI7DysoK\nQRDQaDTY3NykFYf81m/+Ogunn2TkO4SBQOYG0DDxgwmiFDFXrROLeSbeaAyLC0t4boJcLCCJ0okd\nKE5iJpMJRUlFNzL6jsVgMMAwDCaKgpq9pz7xSSiSOyRUWaPf72OpFoVul8PCIWEY0tveJ5T0HGwk\n6djDMVmWUTXKeGGEGIa5RtALTxKOhOyQg77F+XMPkaYpc3NzdNrjE3lcHMd0Ozn6fdC3sScijh1T\nLCkUJZWNhWXeuP4O/sjiZ37mZ5hOHXqdMdPplAvrZ1DUlG+8+Q7dzoS11ZSPfPg0M3MCTX2N4XBI\nGIbous6t7U32726RKn8zRMn3dcEpUt6WDQIXHO9k7KjrOmotv3vLyXs5YgAFU0LWCyeHfVmWcbxh\n7sSWiiw1ikzaDufuWyGKdVozdV5//XUMw2Dv5t0851pRKFdFWvNLPPHEE3R7Oxze3eLSfXUkUUOW\nFxBLChVFRxRTFDWlWV9AlJsnTmJVT4ljGcMwcIcTdF1HEATq9Tr9/m1kWWFxcYXJKEKWHUqlAk4/\nRFYTKppC4OVjkV53SlXPEMUe5+4/h1bIndavvvoqZ86cwbIslLLJl7/yZf7dX/plfvs3fjf/PEIL\nT5AJDn2WVmZolvJAS0VX6PU7tOZaJ9YbQQwJwvec9UGQQ3XsxCYexATh+CRTrVgs44uFnM2fHDex\nQgXDt3BlGTGICcMwR4erKrquI6YxSqGIG+cBiwPHyo22loViSMhayuL8Oloq5FpTMe+ADsdtrrzz\nWs6E8SV0WUYSCsw0l9neahOGIaZpsrJ0hvs3lvOmR2/Eq1ducHR0xGpzjbHaYbnZonWxxn7XYzgc\ncrC/SbFYJIoirCMbYWmGci2jYx+CndPQDvcHdO51Ob26yvm1Dba2tji8fPP7vqbf1wUXJxHR2EZT\nFIIkOYkNVlX122IMHcfBMAyMQt6SDtN8bjTxJ0hhvq141yC5venw5I9/kpE3JXUDNjfv0mg0mEwm\nJFmKF0Y89eFHWFlZwXVdxuMxvb7Aww8/TGIklEol9FhnOp0ytft5LJMQMraOKJfLJ3dHy7LQy7la\nRJIk5JKAMxwTeB5z1Qb1hRbjkUe6uoDvb1IUIwolyHSdKIooVRMEISMMRPqWTqWicvnyZT7yqY8g\nCAIXLlxg7tQKiqLQMAzKP/p3eO7FrzN99+k/N3PSvBBEm8kkZRqJaF7+BEnihEq5SJZldHoDPNc7\nge6Wy2WKssa9g93cbZCkxJFCIIgIxJhmgkCBJAIlBVHMCKIg/wyPt3CaplEzc0yCpNdOch5sy2F5\n8TTlqkaiyVjJBCkUwQtJjsXQyrHLoCAlXL95mWF7k09/8meZnVmmVakhiiIzMzMcHBzgui6CILB1\ndMAZWWan2+bN16/leRJhj3Pn7jtpEjWLZQKrT1ip8NVXXuLuwS4gs3nbptYYoahj0qUlDKNAqk14\n9NITWMkE27bxsh+QlvLf6Eqzk+F3vV5HVdWT7eO7wYGO4+Qzt0qFDJ8kc0mchIl1hDXxyUSZFb9E\n3auixxBnKfogpVBv8o2v/xaL932I5577IjNlAwyVhzbWufjIJXZ3d3HGY4Ig4GOfeIo3XrvK0a17\nVCTtxHRq2zZ+weD65Vs0Gg1EMTeV7vU7hL6Im3S4dOEMhYJApdRCUypUq1U6nQ5H23t0u13GXszD\nFytcvTzCMHRkGdSigkAx37KZNtZIYDyZsLSyhuu6tFotmssLfPVPv8jFDzxCpVLhj5//Opeeepb4\ntMTW1hahxMkNyvFtBu4UEw1JKCAruQvdseNjE6mPIOb/Htu28UMoyyVINSyrc2LyTZIEURRPzs9p\nEICm4Xnee3Iv8veVJAmPBCnRSOzxSUhlpVJh9dT8iVJoYk/x3CGZO0ErFvNdiZo3OkyjzjjYZ2dn\nn6+/9Cc8eulZFhfWSY9vt5PJhK2tLT7ykY/k9qlRn0qlgmEYnDt3DkVRmCnrx/O4mO3tW6iJj2rq\nJ2Js0zRR9QiZZdaaNQRB4Hd+53dQyw26nQHlqkwoqCdb3e93va8LLjnmH2qaRlZQcwUJIPkx7mgE\npkatVqNSqeQsRRF6jkV/dDdnniQCs8Mqiwc16kUTc2GGc//Jgwwsh9G9VzmYaEiHPS6ePkdn5y5C\nVuDpT3yKvd17rK62eLuzhxgEvPTSS5w9e5YaKbv7twkmOTKhUqmQqAkjN2X/MMBsJSy16gSFiygV\nBevgHu1phj5KsZr7qIlC+1oby7KYnZ2lUqlQm8vD4R97okGn08k1na5AqZJS0XXStMR8FdoHAaIc\nnqgqSqUSzzzzDN3pmIODAz71yR/icOqfqGR69oRiSUHTNQx/GQHh2CWvYVkW/d6UIDoAYGlhA4As\ncxiO2wgTAbGaD+wFQSBTZbIsIYL3zKihBGKMkRZQE41xd0Iki2iamqs2woAksjDNBDFyMI065XLe\n3FJVlUJRolCQkFOVTG3iOA6qqnLq1KmTs3coapQin1Ap0263eUv6OqkwZr61xvnVda41rxGGIWeX\n15ivNlDKJndv7zEajZivF1FVleFwyJ39HQ73+uy9fYu5RR3B0DgavYeiTxOZfm+MpsmIokPrvkXu\n3N4FLY+5OtVa5uM/+XH+wTf/SkfZX7ne1wUnKSqCWSHR82m55McnImO1VsqfbLKOJMQE5LrJw84h\nk8kEN4vJIoGCLYM7RSy2csydLzAajdi3Zc6cOcPOnWtk+ISex5NPPsmd29eYOTXDrW+8TpZlFGfr\nPFxdxx9MqM3PUqrkSgXIz1iToc/y8jL2XEaU2uxJy/gzi8yUdZaXzjPqj6nNNji4+hxaNMb1Jiwv\nL1Eq5T+/67rs7bWRJIlSWSXDxzB0BgPrBO+nGwKr6xU63SmN5Yh79+5hmiYHBwc88MAD1M8/ymd/\n73cozs5SKpXIMpFGo0Gc5hexpmr0+j0kIX/KvAvODYLgRLnzrr+tUqngui5H3X2E4zOeY8ckCWT4\nuYJfKgH5EwKNkxTWKCKPEstkkjRPUM3cAEkWkKQJmaCRJCatcj4cLxslQrFMkuZ5DgtnTqEp5Xye\nVy6TJAk7tSL3tq4TRe+l0hZMnVOnTnHp0iXOrZzi7NmzlCsak7HHzs4OmqbxyuVrNAslNo8OuHjx\nIg88uMCw7eI42zhTn+EoRpIU7DBiMI3Y67xD4apGpaYzN19Dy0QSQ8QhQBBERqP/H5CX4V1ysoAY\nJkymNqmh0FhZoCgo+VlBkPBcG9sfYkUemRswGR3nhEklurGPokcUFJ9wPML7zbeQHtdYWVnh3rWb\njMdjNk4vMY5SyuUyggy7l/MzQLlcZm1tjTe+/g3m5+dB8CnPNNgwDTZv3CJJkmNDKUy8jOt7Uz7+\nHz3C1ee+ystOl7Ki42UighhTFWExSjAykfbhEFHKt0SKllBUUlorGxwOcz+vYRg0Z0qMR17uBlfz\nAA/LajMe5q/dvrlzLEeSuXr9NbwMgsGAgTulUW8QuRZJnJCl+bxuNB4RBO3880w1ptMpcRJQLubN\npanTPzH3RlGEE3qkqUPggRvnBaFp9W/zG37rkiSJwHZP/pwkCSVZwpZsiscyt7lKnXNLaywvL1Ms\n5ltmK4IkqaDFcX4sSFTMkkyppCP7MgMpPcmhGwwGrK+vUywWyVKB86vrvLj3IrZtU6vnBt6LD55h\nebaKaOo8/5X8nPaJT3wCXddJkjGF2iyt8ix3um+CEFLUZLxUIk4AMWAyyZNrG0WNhIzAg9v7N7i2\ndeVv5Hp+Xxdcegwbdd2IeBxSmq/QarXQdZ2ioFAY+bhdl9j30HSRUqZBMoeayhhlA0nUiNIMRZfY\ndo84p66QTl2EsYxtjHjjjTcolhROzS1SWD/D1s51ikqWX4xxzPr6OtGkz5Of/CCdTodX/vRFFhcX\n8TyPLMuwxjmYdGa5gbV/BM0C/+hX/zGxmLeQ6w2T4WDKu2aJmTTl2ftFDEGg2+kzOzubF1SlyNWr\nV9E0jdbyAkdHR5imSb1h4joJk7FPpapz5swZIkHgzp076JUWr7/4En/63Mt8+id+nIODAwI5L9bJ\nZHIyN3q3YdDpbhP44onDIkkSNKWc07emU3w/I8lCbNum1+vhOTGFQhFJkqjKJqlUwCyUEcUxwcQ+\n6WjackgQBAwmFpMhKApkWYxeAEkroOl5Wmmapif06XwQHRMqAmmQnKhiHMeBNKbdnrAyUyVJEtrW\nENM0ORxMiZ0uF1wXazpAkYp4/pharcb+oIsoiqyvr/PBhx5huDzk4OCAs+dXudu+w2B0iOv6jMdj\nllfOIiBSL+QICMSAOExQNRXTVKhoBSqlWWqNAnPzNV69cpn24fAv5lT8f1zv64LL0pTEcqnU65hn\napQqZZrNZg4enQbIm/vQzVBljVJcwM8UIscly2ZIHJtUlXAzAUEQ0CtlUlkiKmiwCAVF45lnn8y/\nFxJBNEbNAu7e3uHs2QepLbRYXFykf7DFO1dfQwlTDMNgOBzS7XYhzSVMa6fmubffYXZsZsi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7cYDPpMpxOa9Qax7RFe28N4fpvSH+4SpCWqjXnSB8v81Gd/gdnmDFrBIEagGKwyvrPJ4Pld4ps9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\nvMOdt/P2S7TzDnfeztsv0f4v50kM+5PxObcAAAAASUVORK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f63600729e8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.imshow(plt.imread(photo_filename))" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "dog 99.7451424599\n" ] }, { "data": { "image/png": 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MY4XJeJ7NpuGrX/0qX/nql5inmqN7dzg9PUXOCrwv6Psea7sD7S37HQq2z7gRHDAT02od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\njjQTgRcAAAAASUVORK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f6358557a58>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# get the details of the detected objects\n", "v_boxes, v_labels, v_scores = get_boxes(boxes, labels, class_threshold)\n", "# summarize what we found\n", "for i in range(len(v_boxes)):\n", " print(v_labels[i], v_scores[i])\n", "# draw what we found\n", "draw_boxes(photo_filename, v_boxes, v_labels, v_scores)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.2" } }, "nbformat": 4, "nbformat_minor": 2 }
mit
arcyfelix/Courses
17-09-17-Python-for-Financial-Analysis-and-Algorithmic-Trading/05-Pandas-with-Time-Series/01 - Datetime Index.ipynb
2
8843
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "___\n", "\n", "<a href='http://www.pieriandata.com'> <img src='../Pierian_Data_Logo.png' /></a>\n", "___\n", "<center>*Copyright Pierian Data 2017*</center>\n", "<center>*For more information, visit us at www.pieriandata.com*</center>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Introduction to Time Series with Pandas\n", "\n", "A lot of our financial data will have a datatime index, so let's learn how to deal with this sort of data with pandas!" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import numpy as np\n", "import pandas as pd\n", "import matplotlib.pyplot as plt" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from datetime import datetime" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# To illustrate the order of arguments\n", "my_year = 2017\n", "my_month = 1\n", "my_day = 2\n", "my_hour = 13\n", "my_minute = 30\n", "my_second = 15" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "# January 2nd, 2017\n", "my_date = datetime(my_year,my_month, my_day)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "datetime.datetime(2017, 1, 2, 0, 0)" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Defaults to 0:00\n", "my_date " ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# January 2nd, 2017 at 13:30:15\n", "my_date_time = datetime(my_year, my_month, my_day, my_hour, my_minute, my_second)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "datetime.datetime(2017, 1, 2, 13, 30, 15)" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "my_date_time" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You can grab any part of the datetime object you want" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "2" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "my_date.day" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "13" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "my_date_time.hour" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Pandas with Datetime Index\n", "\n", "You'll usually deal with time series as an index when working with pandas dataframes obtained from some sort of financial API. Fortunately pandas has a lot of functions and methods to work with time series!" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[datetime.datetime(2016, 1, 1, 0, 0), datetime.datetime(2016, 1, 2, 0, 0)]" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Create an example datetime list/array\n", "first_two = [datetime(2016, 1, 1), datetime(2016, 1, 2)]\n", "first_two" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "DatetimeIndex(['2016-01-01', '2016-01-02'], dtype='datetime64[ns]', freq=None)" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Converted to an index\n", "dt_ind = pd.DatetimeIndex(first_two)\n", "dt_ind" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[-0.55141642 -0.13534547]\n", " [-0.52273663 -1.3083908 ]]\n" ] } ], "source": [ "# Attached to some random data\n", "data = np.random.randn(2, 2)\n", "print(data)\n", "cols = ['A','B']" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "df = pd.DataFrame(data,dt_ind,cols)" ] }, { "cell_type": "code", "execution_count": 14, "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>A</th>\n", " <th>B</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>2016-01-01</th>\n", " <td>-0.551416</td>\n", " <td>-0.135345</td>\n", " </tr>\n", " <tr>\n", " <th>2016-01-02</th>\n", " <td>-0.522737</td>\n", " <td>-1.308391</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " A B\n", "2016-01-01 -0.551416 -0.135345\n", "2016-01-02 -0.522737 -1.308391" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "DatetimeIndex(['2016-01-01', '2016-01-02'], dtype='datetime64[ns]', freq=None)" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.index" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "1" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Latest Date Location\n", "df.index.argmax()" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Timestamp('2016-01-02 00:00:00')" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.index.max()" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Earliest Date Index Location\n", "df.index.argmin()" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Timestamp('2016-01-01 00:00:00')" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.index.min()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Great, let's move on!" ] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.1" } }, "nbformat": 4, "nbformat_minor": 1 }
apache-2.0
harmsm/pythonic-science
chapters/00_inductive-python/05_lists.ipynb
1
12685
{ "cells": [ { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Lists\n", "Lists are objects that let you hold on to multiple values at once in a sane and organized fashion." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "### Introduction\n", "\n", "Lists are ordered collections of objects. Objects in lists can be of any type. You can also add and remove list entries. They are indicated by \"`[`\" and \"`]`\" brackets.\n", "\n", "```\n", "my_list = [1,2,3]\n", "```" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "### Indexing\n", "\n", "\n", "#### Predict what this code does." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "scrolled": true, "slideshow": { "slide_type": "-" } }, "outputs": [], "source": [ "some_list = [10,20,30]\n", "print(some_list[2])" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "slideshow": { "slide_type": "-" } }, "outputs": [], "source": [ "some_list = [10,20,30]\n", "print(some_list[0])" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "slideshow": { "slide_type": "-" } }, "outputs": [], "source": [ "some_list = [10,20,30]\n", "print(some_list[-1])" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "#### Summarize\n", "\n", "How do you access elements in a list?" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "#### Predict what this code does." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "slideshow": { "slide_type": "-" } }, "outputs": [], "source": [ "some_list = [10,20,30,40]\n", "print(some_list[1:3])" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "slideshow": { "slide_type": "-" } }, "outputs": [], "source": [ "some_list = [10,20,30]\n", "print(some_list[:3])" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "#### Summarize\n", "What does the \"`:`\" symbol do?" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Modify\n", "Change the cell below so it prints the second through fourth elements in the list." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "some_list = [0,10,20,30,40,50,60,70]\n", "print(some_list[2:4])" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "### Setting values in lists\n", "\n", "#### Predict what this code does." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "slideshow": { "slide_type": "-" } }, "outputs": [], "source": [ "some_list = [10,20,30]\n", "some_list[0] = 50\n", "print(some_list)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "#### Predict what this code does." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "slideshow": { "slide_type": "-" } }, "outputs": [], "source": [ "some_list = []\n", "for i in range(5):\n", " some_list.append(i)\n", "print(some_list)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "#### Predict what this code does." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "slideshow": { "slide_type": "-" } }, "outputs": [], "source": [ "some_list = [1,2,3]\n", "some_list.insert(2,5)\n", "print(some_list)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "slideshow": { "slide_type": "-" } }, "outputs": [], "source": [ "some_list = [10,20,30]\n", "some_list.pop(1)\n", "print(some_list)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "slideshow": { "slide_type": "fragment" } }, "outputs": [], "source": [ "some_list = [10,20,30]\n", "some_list.remove(30)\n", "print(some_list)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "#### Summarize\n", "\n", "How can you change entries in a list?" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "#### Implement\n", "\n", "Write a program that creates a list with all integers from 0 to 9 and then replaces the 5 with the number 423.\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "slideshow": { "slide_type": "fragment" } }, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "### Miscellaneous List Stuff" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "slideshow": { "slide_type": "-" } }, "outputs": [], "source": [ "# You can put anything in a list\n", "some_list = [\"test\",1,1.52323,print]" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "slideshow": { "slide_type": "fragment" } }, "outputs": [], "source": [ "# You can even put a list in a list\n", "some_list = [[1,2,3],[4,5,6],[7,8,9]] # a list of three lists!" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "slideshow": { "slide_type": "fragment" } }, "outputs": [], "source": [ "# You can get the length of a list with len(some_list)\n", "some_list = [10,20,30]\n", "print(len(some_list))" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "### Copying lists\n", "\n", "(a confusing point for python programmers)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "#### Predict what this code does." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "slideshow": { "slide_type": "-" } }, "outputs": [], "source": [ "some_list = [10,20,30]\n", "\n", "another_list = some_list\n", "some_list[0] = 50\n", "\n", "print(some_list)\n", "print(another_list)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "#### Predict what this code does." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "scrolled": false, "slideshow": { "slide_type": "-" } }, "outputs": [], "source": [ "import copy\n", "\n", "some_list = [10,20,30]\n", "\n", "another_list = copy.deepcopy(some_list)\n", "some_list[0] = 50\n", "\n", "print(some_list)\n", "print(another_list)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "#### Think about it for a moment. What might be going on?" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "<h3><font color=\"red\">DANGER</font></h3>\n", "\n", "The previous cells demonstrate a common (and confusing) python gotcha when dealing with lists." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "#### In the first case:\n", "\n", "The statement\n", "\n", "```python\n", " another_list = some_list\n", "```\n", "\n", "says that `another_list` **is** `some_list`. These are now two labels for the same underlying object. It does **not** create a new object. If I change `some_list`, it changes `another_list` because they are the same thing. In programming terms, `another_list` and `some_list` are both references to the same object. " ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "I can write:\n", "\n", "```\n", "Mary is also known as Jane.\n", "```\n", "\n", "I can then use \"Mary\" or \"Jane\" and it will be understood both labels point to the same person. If I say, \"Jane has red hair\", it implies that \"Mary\" also has red hair because they are the same person. " ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "#### In the second case:\n", "\n", "The statement\n", "\n", "```python\n", " another_list = copy.deepcopy(some_list)\n", "```\n", "\n", "says to make a copy of `some_list` and call it `another_list`. These are now independent. I can modify one without modifiying another. (In programming terms, `another_list` and `some_list` now refer to different objects). " ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "In our Mary and Jane analogy, the sentence would be:\n", "\n", "```\n", "I cloned Mary and named the clone Jane.\n", "```\n", "\n", "Now the two labels point to to different people. If I say \"Jane has red hair\" it does not imply that \"Mary has red hair\" (Mary may have dyed her hair blue). " ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "### Summary\n", "\n", "+ Python lists start at `0` and count to `N-1`. \n", "+ Negative numbers (starting at `-1`) count from the right side.\n", "+ \"`:`\" lets you *slice* lists. \n", " + Using `some_list[i:j+1]` returns the $i^{th}$ through the $j^{th}$ entries in the list. \n", " + `some_list[:3]` gives the $0^{th}$ through the $2^{nd}$ entries.\n", " + `some_list[1:-1]` gives the $1^{st}$ through the last entries.\n", " + `some_list[:]` gives the whole list" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "+ Change $i^{th}$ entry to $x$ by `some_list[i] = x`\n", "+ Append $x$ to the end by `some_list.append(x)`\n", "+ Insert $x$ at the $i^{th}$ position by `some_list.insert(i,x)`\n", "+ Remove the $i^{th}$ entry by `some_list.pop(i)`\n", "+ Remove the first entry with value $x$ by `some_list.remove(x)`" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "If you want to copy a list, do:\n", " \n", "```python\n", "import copy\n", "\n", "some_list = [1,2,3]\n", "list_copy = copy.deepcopy(some_list)\n", "```\n", " " ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.3" } }, "nbformat": 4, "nbformat_minor": 1 }
unlicense
marcino239/notebooks
torch_linear_regression_with_nn.ipynb
1
56844
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "There are few packages which can help with implementation of RNNs and their need for high performance calculations. I like caffe the most but it can be chalenging especially when it comes to adding new code as you need to deal with C++ and Cuda. There is also Theano, but I am not a great fun with heavy computational tree optimisation especially during evaluation stage. There is also Torch based on Lua which is ... well I don't know what Torch can do at this stage ...\n", " \n", "Hence this post will be about implementing linear regression using Cuda Tensors and Torch 7. The example is loosely based on Torch7 and iTorch demos. " ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "require 'cutorch';\n", "require 'cunn';\n", "require 'optim';\n", "\n", "torch.setdefaulttensortype( 'torch.FloatTensor' )\n", "logger = optim.Logger( paths.concat('.', 'train.log') )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For this exercise we will use fairly large table" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [], "source": [ "x_len = 1000000\n", "x_width = 2\n", "\n", "X = torch.CudaTensor( x_len, x_width ):normal()\n", "A = torch.CudaTensor{ {1}, {2} }\n", "Y = torch.mm( X, A ) + torch.CudaTensor( x_len, 1 ):normal( 3.0, 1.0 )" ] }, { "cell_type": "markdown", "metadata": { "collapsed": false }, "source": [ "Let's define linear layer to express our regression. NN package will take care of gradient derivation as well as forward and backward passes" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [], "source": [ "lin_layer = nn.Linear( (#X)[2], (#Y)[2] )\n", "model = nn.Sequential()\n", "model:add( lin_layer )\n", "model:cuda()\n", "criterion = nn.MSECriterion()\n", "criterion:cuda()\n", "params, dl_dparams = model:getParameters()" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [], "source": [ "sgd_params = {\n", " learningRate = 1e-3,\n", " learningRateDecay = 1e-4,\n", " weightDecay = 0,\n", " momentum = 0\n", "}\n", "epochs = 100\n", "batch_size = 50000" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [], "source": [ "function train( X, Y )\n", " \n", " local current_loss = 0\n", "\n", " -- mini input / target\n", " local inputs = torch.CudaTensor( batch_size, x_width )\n", " local targets = torch.CudaTensor( batch_size )\n", " \n", " -- we won't use shuffle over here as for loop is too slow in lua\n", " -- instead we will start from a random offset\n", " local offset = math.floor( torch.uniform( 0, batch_size-1 ) )\n", " \n", " -- for each mini batch\n", " for t = 1,(#X)[1], batch_size do\n", "\n", " local x_start = t + offset\n", " local x_end = math.min( t + offset + batch_size - 1, (#X)[1] )\n", " \n", " inputs[ {{1, x_end - x_start + 1}} ] = X[ {{x_start, x_end}} ]:clone()\n", " targets[ {{1, x_end - x_start + 1}} ] = Y[ {{x_start, x_end}} ]:clone()\n", " \n", " -- eval function to minimise \n", " feval = function( params_new )\n", " -- clean up \n", " collectgarbage()\n", "\n", " if params ~= params_new then\n", " params:copy( params_new )\n", " end\n", "\n", " -- reset gradients (gradients are always accumulated, to accomodate batch methods)\n", " dl_dparams:zero()\n", "\n", " -- evaluate the loss function and its derivative wrt x\n", " local outputs = model:forward( inputs )\n", " local loss = criterion:forward( outputs, targets )\n", " local backprop = criterion:backward( outputs, targets )\n", " model:backward( inputs, backprop )\n", "\n", " -- return loss and dloss/dparams\n", " return loss, dl_dparams\n", " end\n", "\n", " -- run SGD\n", " _, fs = optim.sgd( feval, params, sgd_params )\n", " current_loss = current_loss + fs[1]\n", " end\n", " \n", " current_loss = current_loss / batch_size\n", " logger:add{['training_error'] = current_loss }\n", " \n", " return current_loss\n", "end" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "Final loss = 0.00040390299797058\t\n", "Time per epoch = 0.15690538883209[s]\t" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "time = sys.clock()\n", "local cumm_loss = 0.\n", "for i = 1, epochs do\n", " cumm_loss = train( X, Y )\n", "end\n", "\n", "print( 'Final loss = ' .. cumm_loss )\n", "\n", "-- time taken\n", "time = sys.clock() - time\n", "print( \"Time per epoch = \" .. (time / epochs) .. '[s]')\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's take a look at recovered parameters. They should be close to matrix A + mean of noise ( 3 ):\n", "```\n", "1 \n", "2 \n", "3\n", "```" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "\n" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" }, { "data": { "text/plain": [ " 0.9919\n", " 1.9577\n", " 2.9063\n", "[torch.CudaTensor of size 3]\n", "\n" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "print( params )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Not bad. 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" Bokeh.load_models(all_models);\n", " var model = Bokeh.Collections(modeltype).get(modelid);\n", " $(\"#30478146-1415-43bc-c354-2f11b7f63741\").html(''); // clear any previous plot in window_id\n", " var view = new model.default_view({model: model, el: \"#30478146-1415-43bc-c354-2f11b7f63741\"});\n", " });\n", " }\n", "});\n", "</script>\n", "<div class=\"plotdiv\" id=\"6761a806-b16f-4d65-cdfb-ac8a32d052d3\"></div>\n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "Plot = require 'itorch.Plot'\n", "\n", "for name, list in pairs( logger.symbols ) do\n", " y = torch.Tensor( list )\n", " x = torch.linspace( 1, #list, #list )\n", " plot = Plot():line( x, y ,'blue', name ):legend(true):title('MSE'):draw()\n", "end\n", "plot:redraw()" ] } ], "metadata": { "kernelspec": { "display_name": "iTorch", "language": "lua", "name": "itorch" }, "language_info": { "name": "lua", "version": "20100" } }, "nbformat": 4, "nbformat_minor": 0 }
gpl-2.0
relopezbriega/mi-python-blog
content/notebooks/Blogen5min-en.ipynb
1
8962
{ "cells": [ { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "<center><h1>A Blog in 5 minutes</h1></center>\n", "</br>\n", "<center><h2>using Pelican and Github Pages</h2></center>\n", "</br>\n", "</br>\n", "<center><img alt=\"SciPyLA\" title=\"SciPyLA\" src=\"https://conf.scipyla.org/scipyla2016/site_media/static/img/logo2-scipyla-path.svg\" width=\"200px\" height=\"120px\"></center> \n" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# What is Pelican?\n", "\n", "\n", "[Pelican](https://docs.getpelican.com/en/3.6.3/) is a static web site generator, written in [Python](https://www.python.org/), that requires no database or server-side logic. Some of the features include:\n", "\n", "* Write your content in [reStructuredText](https://docutils.sourceforge.net/rst.html), [Markdown](https://daringfireball.net/projects/markdown/), or [AsciiDoc](https://www.methods.co.nz/asciidoc/) formats.\n", "* it has [Themes](https://github.com/getpelican/pelican-themes) that can be customized via [Jinja](https://jinja.pocoo.org/) templates.\n", "* We can publish our content in multiple languages. \n", "* Supports <a href=\"https://es.wikipedia.org/wiki/Atom_(formato_de_redifusi%C3%B3n)\">Atom</a> / [RSS](https://es.wikipedia.org/wiki/RSS) feeds.\n", "* Completely static output is easy to host anywhere-\n", "\n", "Install [Pelican]([Pelican](https://docs.getpelican.com/en/3.6.3/)) is very easy, just run the following command:\n", "\n", "```sudo pip install pelican```" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# What are Github Pages?\n", "\n", "\n", "[Github Pages](https://pages.github.com/) is designed to host your web sites directly from a [Github](https://github.com/) repository. They have an [URL](https://es.wikipedia.org/wiki/Localizador_de_recursos_uniforme) **username.github.io**, where *username* is your username on [Github](https://github.com/).\n", "\n", "<center><img src=\"https://moduslaborandi.net/wp-content/uploads/2015/05/github.pages.jpg\" title=\"github pages\" width=\"400px\" height=\"320px\"></center>\n" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Creating the repository\n", "\n", "In order to use [Github Pages](https://pages.github.com/), we have to create a new repository with the name ***username*`.github.io`** in [Github](https://github.com/). (This will be our site internet address).\n", "\n", "<img src=\"https://relopezbriega.com.ar/wp-content/uploads/2015/10/Selection_216.png\" title=\"Creando repositorio github pages\">" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Cloning Github Pages and blog_template repositories\n", "\n", "Once we have the repository created, we can clone it.\n", "\n", "`git clone https://github.com/usuarioGithub/usuarioGithub.github.io.git`\n", "\n", "Additionally, we can clone the blog_template repository, this repository has the initial setup to make it easier to create the site. \n", "\n", "`git clone https://github.com/blogen5minutos/blog_template.git`\n", "\n", "If you prefer not to use the blog_template, you can start your own [Pelican](https://docs.getpelican.com/en/3.6.3/) project with the command:\n", "\n", "`pelican-quickstart`" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Editing pelicanconf.py and publishconf.py files\n", "\n", "The main files with the [Pelican](https://docs.getpelican.com/en/3.6.3/) configurations are ***pelicanconf.py*** and ***publishconf.py***, in the last one we should include the configurations for the final publishing of the site.\n", "\n", "If you are using the `blog_template` repository; you should change the variables SITENAME and SITESUBTITLE in the *pelicanconf.py* file and the variable SITEURL in the *publishconf.py* file." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Creating the content with Markdown\n", "\n", "To create the site articles, we should create a [Markdown](https://daringfireball.net/projects/markdown/) or [reStructuredText](https://docutils.sourceforge.net/rst.html) file in the content folder. For example, using [Markdown](https://daringfireball.net/projects/markdown/), we can create the following file:\n", "\n", "`Title: My Blog`\n", "\n", "`Date: 2016-05-18`\n", "\n", "`Category: Python`\n", "\n", "`Tags: python`\n", "\n", "`Author: Raul E. Lopez Briega`\n", "\n", "`Test article to show how easy is to create a blog in internet with [github pages](https://pages.github.com/).`\n", "\n", "\n", "For more information, visit the [Pelican documentation](https://docs.getpelican.com/en/3.6.3/content.html). " ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Creating articles with Jupyter notebooks\n", "\n", "Other way to create articles, is using [Jupyter notebooks](https://jupyter.org/). We will need to install the plugin [liquid_tags](https://github.com/getpelican/pelican-plugins/tree/master/liquid_tags).\n", "\n", "Once the plugin is enable in the *pelicanconf.py* file; we should save ours *notebooks* in the subfolder `notebooks` inside the `content` folder and then write a new [Markdown](https://daringfireball.net/projects/markdown/) file, using the following syntax in the end:\n", "\n", "`{% notebook filename.ipynb [ cells[start:end] ]%}`\n", "\n", "For example:\n", "\n", "`Title: Batman, ecuaciones y python`\n", "\n", "`Date: 2016-05-19`\n", "\n", "`Category: Python`\n", "\n", "`Tags: python, matematica, programacion, batman`\n", "\n", "`Author: Raul E. Lopez Briega`\n", "\n", "`{% notebook Batman.ipynb cells[2:] %}`\n" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Creating the site\n", "\n", "Once we have configurated the [Pelican](https://docs.getpelican.com/en/3.6.3/) project and the articles are ready; we can create the site with the following command:\n", "\n", "`make html`\n", "\n", "This command will create a new folder named **output** with all the content for running our site. We can test the results with the command:\n", "\n", "`make serve`\n", "\n", "and then going to [localhost:8000](https://localhost:8000/) to see the site.\n", "\n", "If everything is fine, we can create the final version with the command:\n", "\n", "`make publish`" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Publishing the site\n", "\n", "To publish our site, we have to copy the content of **output** folder into the ***username*`.github.io`** folder and then committing and pushing the changes.\n", "\n", "`git add --all`\n", "\n", "`git commit -m \"publicando blog\"`\n", "\n", "`git push -u origin master`\n", "\n", "Congratulations, the site is ready! we can visit the site at https://username.github.io." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "<center><h1>Thanks!</h1></center>\n", "<center><h3>Raul E. Lopez Briega</h3></center>\n", "\n", "\n", "[https://relopezbriega.github.io/](https://relopezbriega.github.io/)\n", "\n", "\n", "\n", "Twitter: [@relopezbriega](https://twitter.com/relopezbriega)\n", "\n", "Slides: [https://relopezbriega.github.io/blogen5min-en.html](https://relopezbriega.github.io/blogen5min-en.html])\n", "\n", "Demo: [https://blogen5minutos.github.io/](https://blogen5minutos.github.io/)\n", "\n" ] } ], "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": "3.5.1+" } }, "nbformat": 4, "nbformat_minor": 0 }
gpl-2.0
tylere/docker-tmpnb-ee
notebooks/2 - Earth Engine API Examples/0 - Authenticate to Earth Engine.ipynb
1
3641
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# How to authenticate this server for accessing Earth Engine\n", "\n", "## Step 1 - Click on the following link\n", "[Link to generate an authentication code for accessing Earth Engine](https://accounts.google.com/o/oauth2/auth?scope=https%3A%2F%2Fwww.googleapi\n", "s.com%2Fauth%2Fearthengine.readonly&redirect_uri=urn%3Aietf%3Awg%3Aoauth%3A2.0%3Aoob&response_type=code&clie\n", "nt_id=517222506229-vsmmajv00ul0bs7p89v5m89qs8eb9359.apps.googleusercontent.com)\n", " \n", "## Step 2 - Click on Accept\n", "\n", "## Step 3 - Copy the authentication code that is returned\n", "\n", "## Step 4 - Paste the authentication code below, then run the code by pressing the play button" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "authentication_code ='PASTE_YOUR_CODE_HERE'\n", "\n", "import ee\n", "import errno\n", "import json\n", "import os\n", "import urllib\n", "import urllib2\n", "\n", "from ee.oauthinfo import OAuthInfo\n", "\n", "# Try to initialize Earth Engine, and if unsuccessful try to get a credentials file\n", "# using the authentication code provided above.\n", "try:\n", " ee.Initialize()\n", "except:\n", " token_request_params = {\n", " 'code': authentication_code,\n", " 'client_id': OAuthInfo.CLIENT_ID,\n", " 'client_secret': OAuthInfo.CLIENT_SECRET,\n", " 'redirect_uri': 'urn:ietf:wg:oauth:2.0:oob',\n", " 'grant_type': 'authorization_code'\n", " }\n", " refresh_token = None\n", " try:\n", " response = urllib2.urlopen('https://accounts.google.com/o/oauth2/token',\n", " urllib.urlencode(token_request_params)).read()\n", " tokens = json.loads(response)\n", " refresh_token = tokens['refresh_token']\n", " except urllib2.HTTPError, e:\n", " raise Exception('Problem requesting tokens. Please try again. %s %s' %\n", " (e, e.read()))\n", "\n", " ### Write refresh token to filesystem for later use\n", " credentials_path = OAuthInfo.credentials_path()\n", " dirname = os.path.dirname(credentials_path)\n", " try:\n", " os.makedirs(dirname)\n", " except OSError, e:\n", " if e.errno != errno.EEXIST:\n", " raise Exception('Error creating %s: %s' % (dirname, e))\n", "\n", " json.dump({'refresh_token': refresh_token}, open(credentials_path, 'w'))\n", "\n", " print '\\nSuccessfully saved authorization to %s' % credentials_path\n", " \n", "# Try to authenticate to Earth Engine.\n", "try:\n", " ee.Initialize()\n", " print '\\nSuccessfully authenticated to Earth Engine!'\n", "except:\n", " print '\\nOops. Something went wrong!'" ] }, { "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 }
apache-2.0
vincent-noel/libSigNetSim
notebooks/BIOMD0000000003.ipynb
1
990
{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "%matplotlib notebook\n", "from libsignetsim import CombineArchive" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "archive = CombineArchive()\n", "archive.readArchive(\"combine_archives/BIOMD0000000003.sedx\")\n", "sedml_doc = archive.runMasterSedml()\n", "sedml_doc.showFigures()" ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.12" } }, "nbformat": 4, "nbformat_minor": 2 }
gpl-3.0
azhurb/deep-learning
sentiment_network/Sentiment Classification - How to Best Frame a Problem for a Neural Network (Project 4).ipynb
1
327360
{ "cells": [ { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "# Sentiment Classification & How To \"Frame Problems\" for a Neural Network\n", "\n", "by Andrew Trask\n", "\n", "- **Twitter**: @iamtrask\n", "- **Blog**: http://iamtrask.github.io" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "### What You Should Already Know\n", "\n", "- neural networks, forward and back-propagation\n", "- stochastic gradient descent\n", "- mean squared error\n", "- and train/test splits\n", "\n", "### Where to Get Help if You Need it\n", "- Re-watch previous Udacity Lectures\n", "- Leverage the recommended Course Reading Material - [Grokking Deep Learning](https://www.manning.com/books/grokking-deep-learning) (40% Off: **traskud17**)\n", "- Shoot me a tweet @iamtrask\n", "\n", "\n", "### Tutorial Outline:\n", "\n", "- Intro: The Importance of \"Framing a Problem\"\n", "\n", "\n", "- Curate a Dataset\n", "- Developing a \"Predictive Theory\"\n", "- **PROJECT 1**: Quick Theory Validation\n", "\n", "\n", "- Transforming Text to Numbers\n", "- **PROJECT 2**: Creating the Input/Output Data\n", "\n", "\n", "- Putting it all together in a Neural Network\n", "- **PROJECT 3**: Building our Neural Network\n", "\n", "\n", "- Understanding Neural Noise\n", "- **PROJECT 4**: Making Learning Faster by Reducing Noise\n", "\n", "\n", "- Analyzing Inefficiencies in our Network\n", "- **PROJECT 5**: Making our Network Train and Run Faster\n", "\n", "\n", "- Further Noise Reduction\n", "- **PROJECT 6**: Reducing Noise by Strategically Reducing the Vocabulary\n", "\n", "\n", "- Analysis: What's going on in the weights?" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true, "nbpresent": { "id": "56bb3cba-260c-4ebe-9ed6-b995b4c72aa3" } }, "source": [ "# Lesson: Curate a Dataset" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false, "deletable": true, "editable": true, "nbpresent": { "id": "eba2b193-0419-431e-8db9-60f34dd3fe83" } }, "outputs": [], "source": [ "def pretty_print_review_and_label(i):\n", " print(labels[i] + \"\\t:\\t\" + reviews[i][:80] + \"...\")\n", "\n", "g = open('reviews.txt','r') # What we know!\n", "reviews = list(map(lambda x:x[:-1],g.readlines()))\n", "g.close()\n", "\n", "g = open('labels.txt','r') # What we WANT to know!\n", "labels = list(map(lambda x:x[:-1].upper(),g.readlines()))\n", "g.close()" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "text/plain": [ "25000" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "len(reviews)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false, "deletable": true, "editable": true, "nbpresent": { "id": "bb95574b-21a0-4213-ae50-34363cf4f87f" } }, "outputs": [ { "data": { "text/plain": [ "'bromwell high is a cartoon comedy . it ran at the same time as some other programs about school life such as teachers . my years in the teaching profession lead me to believe that bromwell high s satire is much closer to reality than is teachers . the scramble to survive financially the insightful students who can see right through their pathetic teachers pomp the pettiness of the whole situation all remind me of the schools i knew and their students . when i saw the episode in which a student repeatedly tried to burn down the school i immediately recalled . . . . . . . . . at . . . . . . . . . . high . a classic line inspector i m here to sack one of your teachers . student welcome to bromwell high . i expect that many adults of my age think that bromwell high is far fetched . what a pity that it isn t '" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "reviews[0]" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false, "deletable": true, "editable": true, "nbpresent": { "id": "e0408810-c424-4ed4-afb9-1735e9ddbd0a" } }, "outputs": [ { "data": { "text/plain": [ "'POSITIVE'" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "labels[0]" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "# Lesson: Develop a Predictive Theory" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false, "deletable": true, "editable": true, "nbpresent": { "id": "e67a709f-234f-4493-bae6-4fb192141ee0" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "labels.txt \t : \t reviews.txt\n", "\n", "NEGATIVE\t:\tthis movie is terrible but it has some good effects . ...\n", "POSITIVE\t:\tadrian pasdar is excellent is this film . he makes a fascinating woman . ...\n", "NEGATIVE\t:\tcomment this movie is impossible . is terrible very improbable bad interpretat...\n", "POSITIVE\t:\texcellent episode movie ala pulp fiction . days suicides . it doesnt get more...\n", "NEGATIVE\t:\tif you haven t seen this it s terrible . it is pure trash . i saw this about ...\n", "POSITIVE\t:\tthis schiffer guy is a real genius the movie is of excellent quality and both e...\n" ] } ], "source": [ "print(\"labels.txt \\t : \\t reviews.txt\\n\")\n", "pretty_print_review_and_label(2137)\n", "pretty_print_review_and_label(12816)\n", "pretty_print_review_and_label(6267)\n", "pretty_print_review_and_label(21934)\n", "pretty_print_review_and_label(5297)\n", "pretty_print_review_and_label(4998)" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "# Project 1: Quick Theory Validation" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "from collections import Counter\n", "import numpy as np" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "positive_counts = Counter()\n", "negative_counts = Counter()\n", "total_counts = Counter()" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "for i in range(len(reviews)):\n", " if(labels[i] == 'POSITIVE'):\n", " for word in reviews[i].split(\" \"):\n", " positive_counts[word] += 1\n", " total_counts[word] += 1\n", " else:\n", " for word in reviews[i].split(\" \"):\n", " negative_counts[word] += 1\n", " total_counts[word] += 1" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "text/plain": [ "[('', 550468),\n", " ('the', 173324),\n", " ('.', 159654),\n", " ('and', 89722),\n", " ('a', 83688),\n", " ('of', 76855),\n", " ('to', 66746),\n", " ('is', 57245),\n", " ('in', 50215),\n", " ('br', 49235),\n", " 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321),\n", " ('imagine', 321),\n", " ('kept', 320),\n", " ('office', 320),\n", " ('uses', 319),\n", " ('pure', 318),\n", " ('wait', 318),\n", " ('stunning', 318),\n", " ('review', 317),\n", " ('previous', 317),\n", " ('copy', 317),\n", " ('seriously', 317),\n", " ('reading', 316),\n", " ('create', 316),\n", " ('hot', 316),\n", " ('created', 316),\n", " ('magic', 316),\n", " ('somehow', 316),\n", " ('stay', 315),\n", " ('attempt', 315),\n", " ('escape', 315),\n", " ('crazy', 315),\n", " ('air', 315),\n", " ('frank', 315),\n", " ('hands', 314),\n", " ('filled', 313),\n", " ('expected', 312),\n", " ('average', 312),\n", " ('surprisingly', 312),\n", " ('complex', 311),\n", " ('quickly', 310),\n", " ('successful', 310),\n", " ('studio', 310),\n", " ('plus', 309),\n", " ('male', 309),\n", " ('co', 307),\n", " ('images', 306),\n", " ('casting', 306),\n", " ('following', 306),\n", " ('minute', 306),\n", " ('exciting', 306),\n", " ('members', 305),\n", " ('follows', 305),\n", " ('themes', 305),\n", " ('german', 305),\n", " ('reasons', 305),\n", " ('e', 305),\n", " ('touch', 304),\n", " ('edge', 304),\n", " ('free', 304),\n", " ('cute', 304),\n", " ('genius', 304),\n", " ('outside', 303),\n", " ('reviews', 302),\n", " ('admit', 302),\n", " ('ok', 302),\n", " ('younger', 302),\n", " ('fighting', 301),\n", " ('odd', 301),\n", " ('master', 301),\n", " ('recent', 300),\n", " ('thanks', 300),\n", " ('break', 300),\n", " ('comment', 300),\n", " ('apart', 299),\n", " ('emotions', 298),\n", " ('lovely', 298),\n", " ('begin', 298),\n", " ('doctor', 297),\n", " ('party', 297),\n", " ('italian', 297),\n", " ('la', 296),\n", " ('missed', 296),\n", " ...]" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "positive_counts.most_common()" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "pos_neg_ratios = Counter()\n", "\n", "for term,cnt in list(total_counts.most_common()):\n", " if(cnt > 100):\n", " pos_neg_ratio = positive_counts[term] / float(negative_counts[term]+1)\n", " pos_neg_ratios[term] = pos_neg_ratio\n", "\n", "for word,ratio in pos_neg_ratios.most_common():\n", " if(ratio > 1):\n", " pos_neg_ratios[word] = np.log(ratio)\n", " else:\n", " pos_neg_ratios[word] = -np.log((1 / (ratio+0.01)))" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "text/plain": [ "[('edie', 4.6913478822291435),\n", " ('paulie', 4.0775374439057197),\n", " ('felix', 3.1527360223636558),\n", " ('polanski', 2.8233610476132043),\n", " ('matthau', 2.8067217286092401),\n", " ('victoria', 2.6810215287142909),\n", " ('mildred', 2.6026896854443837),\n", " ('gandhi', 2.5389738710582761),\n", " ('flawless', 2.451005098112319),\n", " ('superbly', 2.2600254785752498),\n", " ('perfection', 2.1594842493533721),\n", " ('astaire', 2.1400661634962708),\n", 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0.47000362924573563),\n", " ('discovery', 0.47000362924573563),\n", " ('vicious', 0.47000362924573563),\n", " ('genuinely', 0.46871413841586385),\n", " ('hatred', 0.46734051182625186),\n", " ('mistaken', 0.46702300110759781),\n", " ('dream', 0.46608972992459924),\n", " ('challenge', 0.46608972992459924),\n", " ('crisis', 0.46575733836428446),\n", " ('photographed', 0.46488852857896512),\n", " ('critics', 0.46430560813109778),\n", " ('bird', 0.46430560813109778),\n", " ('machines', 0.46430560813109778),\n", " ('born', 0.46411383518967209),\n", " ('detective', 0.4636633473511525),\n", " ('higher', 0.46328467899699055),\n", " ('remains', 0.46262352194811296),\n", " ('inevitable', 0.46262352194811296),\n", " ('soviet', 0.4618180446592961),\n", " ('ryan', 0.46134556650262099),\n", " ('african', 0.46112595521371813),\n", " ('smaller', 0.46081520319132935),\n", " ('techniques', 0.46052488529119184),\n", " ('information', 0.46034171833399862),\n", " ('deserved', 0.45999798712841444),\n", " ('lynch', 0.45953232937844013),\n", " ('spielberg', 0.45953232937844013),\n", " ('cynical', 0.45953232937844013),\n", " ('tour', 0.45953232937844013),\n", " ('francisco', 0.45953232937844013),\n", " ('struggle', 0.45911782160048453),\n", " ('language', 0.45902121257712653),\n", " ('visual', 0.45823514408822852),\n", " ('warner', 0.45724137763188427),\n", " ('social', 0.45720078250735313),\n", " ('reality', 0.45719346885019546),\n", " ('hidden', 0.45675840249571492),\n", " ('breaking', 0.45601738727099561),\n", " ('sometimes', 0.45563021171182794),\n", " ('modern', 0.45500247579345005),\n", " ('surfing', 0.45425527227759638),\n", " ('popular', 0.45410691533051023),\n", " ('surprised', 0.4534409399850382),\n", " ('follows', 0.45245361754408348),\n", " ('keeps', 0.45234869400701483),\n", " ('john', 0.4520909494482197),\n", " ('mixed', 0.45198512374305722),\n", " ('defeat', 0.45198512374305722),\n", " ('justice', 0.45142724367280018),\n", " ('treasure', 0.45083371313801535),\n", " ('presents', 0.44973793178615257),\n", " ('years', 0.44919197032104968),\n", " ('chief', 0.44895022004790319),\n", " ('shadows', 0.44802472252696035),\n", " ('closely', 0.44701411102103689),\n", " ('segments', 0.44701411102103689),\n", " ('lose', 0.44658335503763702),\n", " ('caine', 0.44628710262841953),\n", " ('caught', 0.44610275383999071),\n", " ('hamlet', 0.44558510189758965),\n", " ('chinese', 0.44507424620321018),\n", " ('welcome', 0.44438052435783792),\n", " ('birth', 0.44368632092836219),\n", " ('represents', 0.44320543609101143),\n", " ('puts', 0.44279106572085081),\n", " ('visuals', 0.44183275227903923),\n", " ('fame', 0.44183275227903923),\n", " ('closer', 0.44183275227903923),\n", " ('web', 0.44183275227903923),\n", " ('criminal', 0.4412745608048752),\n", " ('minor', 0.4409224199448939),\n", " ('jon', 0.44086703515908027),\n", " ('liked', 0.44074991514020723),\n", " ('restaurant', 0.44031183943833246),\n", " ('de', 0.43983275161237217),\n", " ('flaws', 0.43983275161237217),\n", " ('searching', 0.4393666597838457),\n", " ('rap', 0.43891304217570443),\n", " ('light', 0.43884433018199892),\n", " ('elizabeth', 0.43872232986464682),\n", " ('marry', 0.43861731542506488),\n", " ('learned', 0.43825493093115531),\n", " ('controversial', 0.43825493093115531),\n", " ('oz', 0.43825493093115531),\n", " ('slowly', 0.43785660389939979),\n", " ('comedic', 0.43721380642274466),\n", " ('wayne', 0.43721380642274466),\n", " ('thrilling', 0.43721380642274466),\n", " ('bridge', 0.43721380642274466),\n", " ('married', 0.43658501682196887),\n", " ('nazi', 0.4361020775700542),\n", " ('murder', 0.4353180712578455),\n", " ('physical', 0.4353180712578455),\n", " ('johnny', 0.43483971678806865),\n", " ('michelle', 0.43445264498141672),\n", " ('wallace', 0.43403848055222038),\n", " ('comedies', 0.43395706390247063),\n", " ('silent', 0.43395706390247063),\n", " ('played', 0.43387244114515305),\n", " ('international', 0.43363598507486073),\n", " ('vision', 0.43286408229627887),\n", " ('intelligent', 0.43196704885367099),\n", " ('shop', 0.43078291609245434),\n", " ('also', 0.43036720209769169),\n", " ('levels', 0.4302451371066513),\n", " ('miss', 0.43006426712153217),\n", " ('movement', 0.4295626596872249),\n", " ...]" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# words most frequently seen in a review with a \"POSITIVE\" label\n", "pos_neg_ratios.most_common()" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false, "deletable": true, "editable": true, "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "[('boll', -4.0778152602708904),\n", " ('uwe', -3.9218753018711578),\n", " ('seagal', -3.3202501058581921),\n", " ('unwatchable', -3.0269848170580955),\n", " ('stinker', -2.9876839403711624),\n", " ('mst', -2.7753833211707968),\n", " ('incoherent', -2.7641396677532537),\n", " ('unfunny', -2.5545257844967644),\n", " ('waste', -2.4907515123361046),\n", " ('blah', -2.4475792789485005),\n", " ('horrid', -2.3715779644809971),\n", " ('pointless', -2.3451073877136341),\n", " ('atrocious', -2.3187369339642556),\n", " ('redeeming', -2.2667790015910296),\n", " ('prom', -2.2601040980178784),\n", " ('drivel', -2.2476029585766928),\n", " ('lousy', -2.2118080125207054),\n", " ('worst', -2.1930856334332267),\n", " ('laughable', -2.172468615469592),\n", " ('awful', -2.1385076866397488),\n", " ('poorly', -2.1326133844207011),\n", " ('wasting', -2.1178155545614512),\n", " ('remotely', -2.111046881095167),\n", " ('existent', -2.0024805005437076),\n", " ('boredom', -1.9241486572738005),\n", " ('miserably', -1.9216610938019989),\n", " ('sucks', -1.9166645809588516),\n", " ('uninspired', -1.9131499212248517),\n", " ('lame', -1.9117232884159072),\n", " ('insult', -1.9085323769376259)]" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# words most frequently seen in a review with a \"NEGATIVE\" label\n", "list(reversed(pos_neg_ratios.most_common()))[0:30]" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "# Transforming Text into Numbers" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "image/png": 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id8YZZzAf0/mkzF6r5Ox2SvChtsiRRx7Z2E3gGLj5QB7SSC/iAkEhYBgDBVYJ\nVhiVSUggUwTGox0Em8OBOG2b0rSftCIjaRErLj39zQ7QJ5xwQnGFqqSBQ6AWRITtokeNGtX58Gb7\n1ltvxXbGpk2b7Nuvn2fMmDF222m3MiKc2U9Lfj4nnXSSrZP6kSRpcMry04Xr8X+vW7euUwd5qI9z\nWSSqzThook9WYVrmiCOOyJo9dz6HI+0oQpxpuGlvxxAQxOmfFAvyhadk4vKed955lhQQK8YREkzq\nRQmYMwWEU/AzzzxjV+0wFZN0eimvHo6MlEmy8urYxvzr1683gZ9ZpEWuje1Vm0pCoN/OLb6zJ86q\nfIKmjfjss88+Qzt27Bih3vXXXz8irZ+ffK+//vqIfH6acBnOOTRJGl9/0vvi57/66qtj9XT1+Xm7\nOauS3i87fExdaQXHsiASZ9pshaZ37TjxxBMLKzeYaqqFg2bSBtEPOF1mkbCDatIycIK95557bP8H\nFhPrRBoMKKn1oP5rr712WDlZ25JU9yTpsuKSpGylGY5AW5zdh7dKv/qNQKXLd++///5gLIqWgITY\neAgrV67sJMBycdVVV3V+Rx2Q7+STTzavvfaaDVYVlaZXGeRJkiaqbHfuuuuuc4cjvi+44AJz8MEH\nG6YSegkWFNJ3E+oaO3asmTVrVrdkw65t3brV7L///sPOVfXjiSeeKKzqCRMmGO6BJghv71k3dOs1\nJdOt/VgPsJDwwZLBPbZ48WLruEyYeO4L7iesjGHB2vHzn//cbjgYrIQxfDDNH3/88eGklf12vjFl\nTwlV1sCaVIxFDqsqy+YlQiAPApVPzQRvwyawYFifi127dhl+O/GJClMubJLlhMiMwRLZjq9GYBVw\nl+xAdMcdd3R+Rx2QPmB99hM3gCdJE1W2OxdYMjp1BJYUd9p+sz9KEvHb5WPFYBtYkzpFgE3ctFQn\nkXdAfsz0dRGmnoqQ8ePH26mBIsoqswxHJLJMXaSZkunVBqaDcCTFj4T/B+7TuXPnRpIQyrrooovM\nggULbFrilMybN69WJMS115GRqlYLOT3a/M09EyzJ7tv0W5uxHPi29dsEE57aCAbEYSoQfyPolM4n\nICf2ejhfVJwOf5qHKRpf/DJJFyVJ0oT18Mvx8wcEwr9kj8NTLK5tXIyammGKyS8zjBX5aadLg25J\n5aabbhriU6U4vflmesbHI6temOUxF9dVmBbJO3WQN39dsSlDL6a+wFxSPAJM7TKlJxECeRGo1CKC\nVWPvvfcOxqE/Svi3e8tnu3AnweAbOa3xmc98xiWxVhGmH6KEuAa9JEmabmWceuqpIy5/9KMfHXau\nm0MuCZlecoI1JIwN+6Scc845Lon5yU9+0jnudYCJHetBXsHR1Dmdpv3262Z6BjM/02+9cPHzNekY\nSwafPFMGzpLSpHZXqSsWHzCXZaTYXnAO4XWakiu2hSqtnwhUSkQOOOCAxG198803O2nDA7q7sPvu\nu7tD++1IzLCTwY9wuvB1fidJE5XPnQuTBs5DHHyJ08+lCSwE7tAwUEcN9L4vyttvv91Jn+QgrE+S\nPGWmefnll60/TJNjgcThw2CIpF0Z45dHGUlXyfj5Bv1YZKT4O4AXBlbLSIRAEQhUSkSKaIDKEAJ1\nR8C9PUYFHUuje1zgsjRlDGpaR0YcIRxUHIpqN4sIiKkkEQJFINAYIsKOnU6effZZdzjs27cgcKHK\nN34ccMMStoBEWU38PL5VBsfUYB6u6+fLX/6yn73rMasi4qauumbUxVQIMJUCAclLQjQlkwr2yMTO\nGiUyEglP4pPEnwFLh2fijEooBGIQqHT5boxOkadZVuiEFR+ssggvfw1iI7gkBj+ScePGdX73+wAf\nDAKY+eITKPTrRUQOOuigTnbyQmSKIlcszQwTt05lKQ5YNfH5z38+RY7eSXvh0ruEeqQoijz0Y0qG\nOl544QXrW8W9i7hluhxDpCZOnMih4X+R+4f/v6YNRrSDtvLJSw4tGAP4B2sIS78lQqAoBBpDRIgN\nwuANCUH+8R//0Xzta1/rkBGisfrLfX0nzqLASlNOOLYHEVD9eCBJ9INI4dCL7wTt/tSnPmW3UHcE\nizKJZukwCVbNpDKXFkFEnC5psCkzLVYerD1VCo6RRYY2Z0omj4NrHBZMGXEPEQti586dlmiwpPvs\ns8/ukGRXLwM3eiAsm9+4caO9F8k3efJku1UAG+01QRwZof1NI1JV48u9vWzZMhMEsqtaFdXfJgTy\nLrtJm99f/hq1jDYYVDvLUQOch0VXDS9/5XrUJyAsI5aC+unilrkmSePrT3pf/Py9jmmnL1HLd7ke\nri+uXPKnkbovc03TFj8tkT6rXJZMZFGWjBYlZSzVXb16dScaKnjlqYP2EqU1WPE0FAzwFnvONUEC\nC2OhfdWENufVkb4merFECBSJQGN8RIIB2EYO9QN8cS4sWE0ef/zxwqYwwuUn/R2EkY9NSqCzpNMP\nOISRvptgNVm7dm23JCOusfqCN+G2Ccu83RRCv9uG1QAp6i2b8opcJfPwww+bQw891FxzzTVm/vz5\n1sLB1JqzemTBC+sCZnqCm7HL7vbt263ObHxX9yWzbn8a50ycpf2Dlmf58uWt2ihz0Pqvru1tFBEB\nRBwyIRphQgIBYcAm9kYdpguIrxFYM+zUiut8YoGge1wkV5cu/E16nF/D5AYCQptfeumlxMTGlc0A\nwlx/mx7CDHyQK8Km91scjuBalDAVUkR56Mauu46AbN682ZQxjQKhYaM79MbPhH4ocmO9onD1yxEZ\n8dHofgwx5l4q497pXrOuth2BUZhX2t5ItS8aAfxLcDokxHcbhEEPosrbeT8Fp9Sse8bE6VmUoytv\nsJB2wraff/75fQ3HTX+ce+651odk0aJFfa07Dte480X79cTV0+TzbCOBXxlkUyIEikSgcRaRIhs/\n6GXhZIg5vQ3CoLdixYq+e/M7wpBlz5g43IuakoFoQkLoY8hmkTrG6e6fJ+omTrsE2psyZUqtrW9g\ng0WH/pREI4C18cwzz4y+qLNCIAcCIiI5wGt6VgYKTK1uWqHJ7fnwhz9sp71Gjx5tBxMGlDIHFd6g\nHQkpGre8UzLoxhYFrLYqcvVOlnYywLM5GtOI6FT3e01kJLqX3f9SHn+i6JJ1VggYo6mZAb8L2mJu\nZQrikUcesYOe36XuAerOFTGFgsWCwb4op1SnG995yQ16YX1AcGDutxXEVhzzB2fZSy65xE6dlYFd\nTLWZTufth0yV1jgT1jUCLOLcLBECRSMgIlI0og0rz00D5H0Lr7rZrAZZuHBhzy3peSP3I9yyKiWN\nQyh4IWnyJMWmiLJxSiUQWd1IiMOgaWSkCOLq2t7Ub8gtOEDOyrjvm4qL9C4OARGR4rBsbEm87SBN\ndULDGoIzJKtB0gqDPyTMCZFr497WITFYGMp6GOd9C8e69fTTT9eWhDiMm6In+tLn9HedLEsOx359\nQx5vueWWvjuB96t9qqd6BEREqu+DyjVwVhH8CeIG4cqVjFHAva3hkFnE/DXlgYMvzm+gzLfjvCTE\nrVBpylsrlps99tjDLF261Ie6lseDTkZmzpzZqMi5tbyJpFRXBEREusIzOBcJQMVg3u+lr3kR5iGJ\nlDmgYXHxY9MUQXj8duedknFk7N577+05NeXXW+Vx03Qu2xpWZV90q9u9pDCdOchWoW4Y6Vp+BERE\n8mPYmhJY1TB16tTGxBUp2wrgrCNh4oHVwZe8lpK81pB+kDG/vUUdu/7DAtWEQS4vYSwKt36WAwln\nX6EyiX4/26O66omAiEg9+6USrXjrg4w04c26bF0ZdCAiSaaq0CWrA2xeEkJ+yGNTBvPwjc0UDRF+\nm7IaY9DICM8DNhRlqb9ECJSFgIhIWcg2tNwmrGqAILBENdhorZQBLO9gQ/4kDrB56+EWYyBnx9ym\nRscFA1YuNWnVVhH91oTHgyP7/r3cBL2lY/MQEBFpXp+VrjHmWCJy4i+SxCJQukJeBZCQE044wXzs\nYx8rZZUPD9+iV8a4KR6vGZ0onuFpHz9Nr+OmW0Nc+5oYowIyktRi5trZtO+2xBhqGu6DqK+IyCD2\neoI2MzisXLmyVmTEWUIOOuggc8455xSySsaHgoE9r7+HX16347ADbJZ66aM27BXU1Ddv7kcISd3I\nerf7Ls01LFV1fBlJ0walbQYCIiLN6KdKtHTTNHXwGWGwYp+LSZMmdSwhRRIHyspjnUjTQVGmfdqX\nxs+EQZCYJ02a0uiGEb4Is2fPbtzOrm0lIzgSX3755Zli83TrZ10TAlEI/On/CyTqgs4JgQMPPNDs\nt99+ZtasWea3v/2t+fjHP14JKFgP2MX1i1/8ornqqqs6OvDGtm3bNvN///d/mVddMJC88sorfSMh\nKI9jKRYQX/baay/rK0Gb+KAX6SAtfH79618b0jj55je/af7nf/7Hhkx355r+vX79evP3f//3jWrG\nO9/5TsOHe4h+a4vcdttt1g+LiMUSIVA2ArKIlI1wC8rnbf2iiy6yLVmwYEHfBm0GYJww33jjDbNk\nyZLYeknHwJ3WRJ41X54uzWp5ccTE1X3TTTdZX5nzzjvPnWr0N32BRarJjpFZ+7ZuHce91iZrW93w\nlT4jEdDuuyMx0ZkQAgzwzBWzTJQPvgkMHGUJD0Ic5XjDZGkncQy6TZsQgpsPA0FScfqnJS9Jy49K\nR51Z35pxoAUD93nttdfMe97zntrvZhuFQ9Q5+s/tnBx1vQnn6Js092Dd2sT/HYJlatq0aaVtZVC3\ndkuf6hEQEam+DxqjAdYJpgsQBlQCaTGXXJRgeYHk8Da2a9cu+3ZMfIkkwa7cQM1A4B6ocXpRD8Lg\n108pyp8DQrNlyxa7dLefRKpsrPD/2bp1a9nVlFp+k8nInDlz7P/zihUrzNlnn10qTipcCPgIiIj4\naOi4JwIM+GyOh2PlhAkTrEMb88gQCEhJLxIQrgDigPWDMnBYZKvxZ555xsyfPz8TUWAgYKB2Fo+o\n+pwFJXytzN+0E92KEAgNb6xtE1YAvfrqq41vliMjaf8Xqm7422+/bZ3BV61aZadD2fYh7v+oal1V\nf7sQeEe7mqPW9AsBCAkWEj5YGB588EGzePFi+yBjOmX//fe30yoQi7BANNiqnp1iCUrGZ+HChcOi\nN+YZuLES8ABFL99ikKfMcBvS/EaXrFMyUfVgNRg7dmzUpUafmzhxoiWhjW7EH5SHjHD/QXqTWPTq\n1macwlk102+rYd1wkD79QUBEpD84t7oWBns/RDcPYCwmzz//fGS7cXxl+qWbhcC9VXZLE1n4H07y\nAOWNFPLBChWmlLKW1a2eJNewYBRZN9NWWA8k9UaA/4umkhFeDrB8SoRAPxAQEekHygNWh7NC5B18\nsSJgTcj6VsabKGWsW7fOnHTSSZX0QlVWmEoam7NSCCPTAm0SR0a4F7Pex/3GAxKydu3afler+gYY\nAfmIDHDn173pzqqRda7dzW+zHwvHWcvJilPRUzJZ9WhKviZOYSTB1hFzdz8myVNVGv7nmGJta19U\nhavq7Y6AiEh3fHS1YgR4iLuVOmlUwSSOuLdQymEg6OdgUNQqmTTtVtp6IuDuw37ef1mQWLNmzTC/\nqixlKI8QSIuAiEhaxJS+7whgsnfEIknlTIfw4HcPf5fHvZmmKcvlTfutKZm0iJlc03Dpa+t/Dnc/\n1pWMfPnLXy7Ul6n/CKvGpiIgItLUnhsgvTET80nyAHcEIM607AgK6coS9CxylUxZetatXCxIrJxp\nszgy0g8ynBZHR9TT5lN6IZAXATmr5kVQ+TsIMAC/8MILZseOHZ1lmG6ZLoncsl53zMqPI488MpEp\nmAe4s3R0KvQO8P9IujIGkoIjLeVhbYkjLV7xqQ6LXiUTrnyfffYxjz76aPh043/7m/41vjFdGsC9\nzP0KGSli8Of/7vvf/755/fXX7Z43xAPx/++ozxE8/gf5vxs3bpysH136SJf6i4D2mukv3q2rjYcp\nMURY7bBz5077wDv66KPN+PHj7RJdGuxWz5CWwYaPe2i++OKLNt/06dPN5MmTh8USiQLLWTz8azyI\nebBneaijE0TEvan65WY5jtIvSznd8lDH3Llzbdj9bumado0AWgixaQZBuGe5d7Pet+7/jii7BLjj\n/w6Suvfee1v4wv93nGRJ/fbt220Yd/5f+Z875ZRTbPyfogn5IPSh2lgMAiIixeA4UKXwAGU/imuu\nuca2m4fgGWeckemBSgGQgU2bNplFixZZUsIge/7550daKsIPbx7kSB4ikYfI2Mr/8KcIXfzy4o7B\ngDgsELqsA1lc2VWeZ8sABlJ2e87Tn1W2IW3d9GVSSx5lP/zww+aWW26x/zNJyXucTtw7Tz75pGF3\na/4HL7zwQjNjxoyBwT4OF52vAIEhiRBIgcDq1auHgkFiKCAfQxwXLd/5znds2dQR7DA7FAy2I6oI\nHtz2PN/BNMiI61lOUA9155G8+ZPUTR18Dj/88KFvfOMbSbI0Jg197vrUtbMfmNYBoF7tDIj/UDCt\nYj9l/N+BexBJdSgYgux31P9dHXCSDu1EwLSzWWpV0QjwoAoCHdkHYa+HZhF1Uwdkh4cvD+Gw3HPP\nPZEkJZwu7W/qzfIQLgsTyvU/rj3XXnvtEJ+2CPcXfR0lfvuLIp5R9VR9Luoeor38H0DSyiAg4TZT\nX2AVsfXxPyYRAv1AQESkHyg3vA4eSLwpYaHotzgLDIOuIwg8sDlm8CpDKDfNgEfaNOm76Uzd/sAb\nl9a9Icddb9p5+pc38l4Czknw6VVOXa/7ZCTq3u+X3ugBMYSUuP+7ftWtegYPAfmIVDAd1pQqmb/G\nDwR/kAceeCCzD0je9jKX/elPf9r87ne/M8GAZT7+8Y/bIsv0yaBs2p/EkTCPgyr1BINrB6I0q3hY\nIkwAKueU2CmkgQfsvrxkyZLUbQF7J+ARWA7cz8Z+06b77rvPOoBX2b/c/3PmzDE4lFf5/9/YjpTi\niREQEUkM1WAl5CE0ZcoU22j2najao54B+4tf/KLdN+app57qEIQ8JKBXj4JBYKHoOjimrd+V6erO\nM3h+6UtfMmyA1/TNyXDAhPBu3rzZwZLp2yd1OPMmIZGZKio50xVXXGG+/e1vm3vvvdcccMABJdfW\nu3hWMy1YsMCu0moqpr1bqRRVIiAiUiX6Na3bkZDDDjusNoMcOkGGeEivXLly2EMxLRlICzvlR1kq\nGPiQXm/h5HdS5ABJ/RAZLCq9dHD11/GbvYDOPvtsc9pppxWmXpjwRfVfYZUVWBD398svv2w3naMN\ndelXyOIll1wy7P+uwGarqAFHQERkwG+AcPPrSELCOjoygrUCcoLODMplvq1FxRuJI0A+8UD3MqdO\nwAJpqlUErKZOnWotT2Va3ei/wNfBYlUkGbQFFvTHJyFlYpFVXZGRrMgpXy8ERER6ITRg1+v+MHTd\n4fRkmgZhoOHtscwHOGQH0gPh8UmIP8ihS5nEg/J9QSfq86er/Ot1P8Y3ZP78+YVaQ3q1mT6ExDqp\ng7WE6Y+77rrLbNy4sdR72LU56zcxR4j3U3c9s7ZP+apBQESkGtxrWSsPmauvvrr0t9OiGn/ccceZ\nYEmxmTdvni3SJwdF1REuh0EMB8K//Mu/NKNHj7aXqx7IGMTQyZGysM51/V0XvX0iWYW1xFmFmkIm\nCTxHGPmHHnqorreW9GoYAiIiDeuwstR1b9ZVeumnbVuUzmWQkfAbNKGxISFVExAfL0gZUxxNCY/O\n4A9+WCbKnFLzMUpyHO7rsvuY+qjjuuuuM+edd14SFStPg85HHXWUXVHTFJ0rB00KdEVARKQrPINz\nEYfBIG5Ax7rQlJaHTcWQEySvkx+Exon/luwTnX5MBzkden2jC2QEsznh9ussTRrIfGtJnhVOcf3B\nyif2immadcFZcfjO+78Wh43ODw4CIiKD09exLXUPFef8GZuwphcgUWz45awBPllIqrI/4JAnys8j\niuSQD7+UOjyMb731VvOv//qv5j//8z9rZWXw+wASwrLwOq3I8vXrdkz/+zFfou6RbvnD1yivyaue\nmu4oHe4P/a4QgcGL4aYWhxEggmI/wkeH6y3qN1EgAyIwLAKkH6Eyqp6AdA2L0JkkemRcmUT7pLwq\nBd1oA1FwwaJqfeKwIHoqWwUkwTuujLqcB3P34R5IK2BRRbTitHrGpafNwdCVO6pwsCOwLYey7rzz\nzrjqBup8EECugwm49FvoB+rlE+yUXnr1/W9h6U0qpoLgja3TEeF/jm7Xiqm9f6W0JVQ4+3H4D3UG\nOn8w5qHpBg03aKdBmTzdhPp6pemWP+u1qHoZ4OpGRtCTcOFtISHh/grfX+Hr4d9uEAeXJgv3Gp88\n4p6nfA+SuIGeb4iHL1UTEXRx/XLiiSf6qpVy/CcBCJIGIzBq1CjjPg8++GDqlhAcjDDOTZe5c+fa\n5Y9+O1599VU7TcFUDYIp3X3SLPN1JnS/7PAx5VE2dTH90A9BLz7hKQJiiuD8iM/Ihg0b+qFK1zrc\ndMybb75pA3Wlwb5rwTW6yNScu7fcfcC9wIc+CstXvvIVEwzgtV6qG9Y56vdll11mFi5cmPmeJ6w/\nAdwQ/ocl9UHA9ccTTzxhsowtqVpSCr1pQaGODQZgjjAXdrvW76ajn/uEWXUvXdryVuba+bd/+7dD\nX/va16xlwllDirBSpC2DusG2TElSh9s0zbcUlalTVNlgh3Um71tzVNlNOedbS9x9WTeLVR4ssUZm\nmdoNticY2meffezzi+9BE/fc5jvts7sfWIX7h99liSwiwV0wqPLkk0+awFze+Lcy+o83T1aL8NbN\nG6lbEureTrP2MeVSRhpxdePIWoagE2/gfLoJIdOJTcGSbKwjZekTpQNWEFaEsKQYJ9qmRn6Nalva\nc/QT9xAfjm+77Tbjr8RKW17d0hOef8WKFanVCgZfs2PHDptv9uzZw/LzBu4svV/4whfstRtuuKFz\njmsXX3yx2bp167B8/g+sLYcffviwPJx76623/GTDjimPcl3dY8aMsdYA8rhzfIfL4HdYP9JxLqzj\n9OnTbVl+xWeeeaY95ywPfvspBwmm8YbpgJ5hCadZt27dsCSbNm0y4Om3BX1cvX5i7tFzzjnHnqKf\nHn/8cf9yscdFMhx8KXxrQaCptSYEjRhWTRA0q/MWDxMOX/fL4DgsMDPfmYZ64ury8ybVjzy+Dml9\nRHC+8tuIbmeddVYs6/XrAgvyMy/n2gVGYR0oz10Pfydl18zZZ3mT8TGt0zFvmzjehoU30iwWiqz5\nXP3M/6e1pri8Ud95ysMqEgyC1jKRBYsofaLOoaOri/urzLqi6m/CuWAH6SE+bRH6nGcQ32nEf+7x\nzPOFZ5h7rvEs9Z+H7rz7DuflGdotPc/TKAdMynFlhr+vv/76Ydf8MYuywunDv/3nd5Jnt99+ynJy\n0UUXdeqKsiJRj6ub674Vw7/m0vjf4ByWgHx0yivTV+SPLQxrkOJ32o4nPSA5EHwAwh0QvsnodD+v\nK8P/DudJqx9N9/9J/Juo17UsnR2uy2+Lf8wN7CTJzezSxn1TdtEDBVjTh/QpOkb1Fee4RhrSkqco\nYbCNahMkJe2DsigSQTlp6w7jQZucWT98LelvymCKhH7nO295fr2U7QgIpvqisPPraMsxDrtF41P1\n/x1twvE9qYQH73C+8DjgPwfDx+EBshsJcXl5BvmDNMdRzyqXPvztP7P853c4nf/b1Zfk2R1uv8Mn\nfD5MqHyiwrETn1D4OoWPw2MdOvtpwvW58vN+F0JEsnR8eMB2Het3qg8kDU16s1CGL1n08/UId07c\ntayd7Zfnd3rUMXUgSW5mH4PwcZz1IJwu6W/6z/8niNK92znyunsgaZ1R6brNV6d5+KdJG6VH+Bx4\nRxGkcLqo33nyRpWHHryRQ9qwIHGcpb3oxXJhLB/0Ld9ZyonSsc3nwKooqcv/HfdQGl8k//kfJhJg\nEx5wSeMGQcaB8PPPDfLhfP6zu9s1Xx/6x88XtoZw3T2rwlYUP1/4mnt2u76nHPdBN1/CurprYWLg\n10can0z59fljjI8l7fCxDBM0yvTzhuvjehGS+z8iDJivaLdrKO830L0du44BENfZrqGU7a7z7dcV\nvllcJ3TTods1Xze/nrDe/jU/T5rO9vOF20U7/DbTTl/8a7QnqfD2wqBdhKCj/w/g65TmmDJcv2XV\ni4dh3AMRqwSDZy9hoM5KGrqVTZlJ6vfLIH1ea4pfXviY+4BBBEJCX/FmC6FwOIa/Sct9A4nhQ1qm\n98rUMaxzk39D1MC4CKnT/x33APdCUvFfWsIvnJQRfjaHx4KwRcVd98v1Le1OL38M4bnrhOe1e1ZF\n5eOcu863q88vj+dXWPxne/j57JcXvhZuv1+u30YfOx8TdHHkzD/v6+7KJJ3//A7r4hOVKGxcOXm+\nczurfutb3wra9nsJlDSzZs1yP63zYNBRnd+3335755iDYIVD5zfLDRcsWND5zUZme++9d+c3B34Y\n5HBdV155pY3W6DK88sor9jCPfq6sJN84JLllaKRftmyZGTdunM1KO+644w4TdLb9HdzEsY4/4Xad\ndNJJJrjZbD7+sNlUERLcnDYaad6ycNKiz2lTXqEMygo7gqUpF4yfeeaZyCxu2Wiv5bUBYejpCBpZ\nQY+TwcBty8XZNIk4p1Snd5I8adMcf/zxNqz/5s2brTMc/4PsIxInu+++u11miW7gtHTpUrtzbpk6\nxunSxPMBYTN77rlnbtXr9n/HMy7Ns+mFF17oYLDvvvt2jqMOgsF8xFjgnq3h9H65RFsOy+TJkzun\neF7TH3xYouokKl/UOdLzvAoGYPvZvn27LQKHUJxicSb1xwRXft7vY445plPEf/zHf3SOn3322c7x\nJz7xCesQzYnXXnutcz4gXCOw9J1SSfiTn/ykk56D/fbbr/P7xRdf7BwXefCOvIVl6Xgajhx55JF2\nt1dICOI6jRvPJzT2YvDHv1mCNzh3uvP90ksvdY7dQR79XBlJvpN2tmtruLNdHVHt+sAHPuAu1+6b\nOCRhEkL/BSza7LHHHubggw+O1JkYHzy4gjfyYf1KWZQJscwiYfIaLoMVLQyicSthul0Ll5XlNwM2\ndVNP3IZqEKXAEhKrY5Z6k+RxusVhk6QMpemOQFEvAHX7vyNU/apVq7o33rvKxpFOeE50kwMOOKDb\n5WHX3BjCyZNPPnnYtagfkJCwjB8/Pnyq8xI54kJwgjICK4K54IILoi4Xfs5vF89LXoIhZt/73vc6\ndbGNgpPA4uEO7bPWrcLpnAwddCOUP/vZz0Kpi/mZm4hk6XhHRGgCb/v33XffsMHMt5S4ZobfkllW\nlUTy6pekDtIU1dlJ25VUr7h0WA1YdpdXIBK+8A+ZZNM1SCgC4eANYuLEiZ1iKDMrEekU0uXAEYHw\ngJskcFmXYlNdou6ofWrQASIS1i1V4UrcegTq9n+HtS+NhF9e0uStU1pISDDV1nmJdrph2R47dqxh\nFsAfg9z1PN+Mn7zo3X///bYYLCG8gC1evNj+xirsP0/z1NWvvH/Sr4ri6qEjwzclb8uS8hHoZT1I\nooFvpeKfLwkJCZfrLGPuvF+mO1f0N29w4YiXZU3JxOkejjfi4ny483H5dF4I+P8jTfq/cz2H1bQM\n8csNnEU70yZu+iT8HfUMxGoVlvAY5a7z4uWIBnWTjjq+/OUvR1r1Xb6838QFcoIlhLY68adlOMd0\nqhMITBiD8G9077fkJiJ5O56gR2HhXNgC4ltRSO/m48J5w7/z6hcuL+53Ezo7TveizvMGkFXy5M1S\nJ29wWB6cv0jZUzJxOjq/keXLl1v/kbRvlnHl6vzgIJDnfydP3jwI77XXXp3s3aYCOokSHhxxxBGd\nlElfaCEjzn+PzFE+ZlHnSEvAQCcXXnjhMP8LXrIdSXFpivomAJoTLCG+fv60DGkOOuggl9RgPUGv\nrJJmmixNHbmJSJaOdwoSzc2Zl7gRcKRBYJXOzOTSQkT8myXKx4JpDRcxDmchJI9+ru4k30V2dpL6\n8qZhXjYc8S9vmf4/Zdqy8uRNW5dLj+UBX4x+Tsm4ut238wc577zzrC6OGLnr+hYCvRDI87+TJ28v\nvbpdf9/73te5/OMf/7hznPfAd+TEZ8ONA5TLy60fNZWoq05cBFF+48fn5+PY+fa59FHfLKZwgzzT\nzZ/61KeikkWe86f2IxOETrrpGXfa6Rc1LYP/iHshZ2xFL//Zzzjsj53hKKtEq3biynG/C/sOzDK5\nhKU+gTKdj7+cNWj0sNgSQSM6dYWXDJEvvO6a374EJshOPdTpL/UML99lyRKSVT90de3y20SZcdf8\n8/7yXadHcJN0ykQvJ36+cJtJQ/1OFzDwxZ3nO6ynny587JZlhs+n/e0v7UIH+oG+TSqk9dtHGZSZ\nVVgemWZZcvDgGPrGN76Rtbpc+aKW87Jcl/P9FnAAO+4Lt0QXHMMfAqGRJvBRqETPfuNSdH0O37zl\n1u3/jvs2sOYlbpb/P8+zMiz+czvueeA/+xhrnPjPUz9N+Nh/BpM/fL3bb1dfeNzplsevD12j0ro0\nfvtJFyU+hq4sfzmvnydcnksf/ga7sPjjlj/mhtPl+R3dwpQlZul4n1TQUDd4+f9gYVCS3izhzsii\nn58nPMDHXcva2X55eYiIu6nczdytG4t6IEb9M6AHDxf6kutRH66Rxunsf4fx7taO8DUCbKXZYI3B\nl4G/34N/N8LRL32oB7yIawH+fDuiAS5RH9Jz70BQyJMnIFq47wbhN5imIcpxmNTt/y5tu8LPcvf8\nd+31n6VpiQhlxz1b3HMm6hnj1+nSue8w4XBEhG9/oHbp+WaMYyxy5yjDlygd3bM7rIufzx2DmSvb\nfXcjCnH3jMvLOOTaFVdHuJ9curzfhRCRtB2PtcI1nm//puh2jVopbAYAACaLSURBVMaGO8gvh2M6\nNwxWWv2oxycHvn69rmXpbL+utESk282MrnGS9sERVw5YR+kQ7pekv6P6L67uqPNpIjz6Az549Euo\nCwtEN0E3yEoZgjXDEQmIB7976ROnB21xAdEgJRCVrGXF1dGm8/QpOOWVuv3fpX0BoP3+cyM8gPrP\n+bRExGHLs9ivg2cQ5CDqGevycI363POKZzO6cN6d49sfYxizfMLh8lBmHIHhWjgf5VIX4ref83Hi\nt89/oY9LT51hndA3PMa5/PSLa3dcP7i0eb7jW5ih1KQd74MHCGEJW0vCLA0w/TQA1Q1MV35S/UhP\nea4Dwp3U7Rp503a2X17UPwn1O11oty/dbmY/XfiYgS6NKTWc3/+NDn6fOl3TflMGZeURBlgG1iQS\nJh/h30nKSJPGTX8kzZM2fa9yaR+DYFmEwREc7iusJpJoBPi/KIKs1en/DkILGUkj/mAbfq6lKacf\naf1nMAP+oIhPsBxJKqPthRKRMhRUmeUhwIBU5Fs3/6w+qUpKRMgTJntZW530IR9FOnwLSdb64/Ll\nsXCga56Bi7oJvw1BSDtYxLWn23n0hRByf0Xh3C3vIFxLQ5aT4FGH/7ssfY1VwU1rJHmbT4JF1jT+\nixTPI//ll5dDpyfPF9IOgtA/7hkOJmXKKAoPKpMMIAJXXHGFjXzKio0iBe/05557zgZ5Y437L37x\ni2HF/8Vf/IUhWixLnj/ykY8MW/I2LGHKHxs2bLDr93utBHDxQ4KBeUQNxPLgfJEhy6MCl42ouMeJ\nrGWAybnnnmumT59u5s+fX2i7eqhsWJIcvOkaljWyZYPk9wjcfPPNNvzAjTfeWCgkVf3f8f9EAL6A\n8KZuDytSXETSgFCVGnujm3K+Ht3ScS2wDGSKl9Sr3Lpd9zEpvc1lshyVXW8E2KiqqA24qm4p0wKz\nZ8+2/gq9dOn1lt7req/y/etYnPJYM/yy0lpV8N3ACpJ0qsqvq6hjdOYe41MUDkXpVkU5YMAqrdGj\nR7cGjyz+IT72zhpR9lu3X2fUse8bEpCCjjXAP677FFJUu7Kc861V/bAAaWomSy+1JA8PRQYqBoum\nC235q7/6K/uQh0jEkYm48377KauIKSvqoqwihfKStIE5ewb/ItpRhP7oU/RUYBF69aMM+oA+4+P6\nAyyqJIhFtjtvW/B1cYN9UVO0WduHH4TvF+H0wsEzyn8vaz11z0c/0HampPxpqrL01tRMgPYgC9Mz\nSNFm4n5j+vDDD5tbbrllWKRDoqU6IQCQm26JmpJx6dx3t+kblybu2wUpK3O/mG6b5tGnRHRcu3Zt\np81xuvbzPHqxWRtTZ20OY8+9409TRG1uyLTVI488MmxH8X72RVF1cR9OnTp1WHuLKlvlDA4CIiKD\n09eRLXXzu8GbWq0GrUhlu5w89NBDrQ/EaaedFpkKchBMRdldKknAXjO9CEmWsO/gSV39GGij/Ebq\nSkJcpzgy0vT7zbXHffukN8m9xT0CQSFfr/vQ1VHH79NPP90cffTR5tJLL62jetKpIQiIiDSko8pU\nk8GBEL9NfZhgDbnmmmvM5s2bY2EKk4okb60UFs4XW0FwIYoYdEtfxDWf+OAEedddd5mNGzfWmlTW\nnSwl6Rf6Opgm6yTNYv2iv9gjhNDgTRT+N7CGtI1UNrEvmq6ziEjTe7AA/RnMeIvDnNy0tzPeLI86\n6iizcOFCc/zxx0eiQfuQbm2LG1gon/y9LBw8lKNM8JEKFXwSHbH2/PM//7N5+umne+pacPWZimP3\n0MCHpTGracCYAddJEX1NmZSzZs0au+rEld2Ub1lDmtJT9ddTRKT+fdQXDdnxeMuWLY17O0uidxqr\nhgObPE7+93//13z0ox+NtDK4ASrLG7ErP++3G9BuvfVWEzc1lbeOovND7sDs3nvvjSWQRdeZtjz/\nHsDHqBcZTVs+6fEVWbRoUe2tWOG2Ob27WSHDefRbCMQhICISh8yAnWcww7IwZ84cU3RckbKgZKDA\nNMx3nLWDa3lJAthE+ZcwmHKtjAEqDWZMdbCV+tKlS9Nkqzytm1Kry1QS/ek7mea9b5ICjGUhWHnS\nGOsQ1kMsWk215CTtF6XrHwIiIv3DuvY18YDBVIwJuurBtRdYSawADCxIHEnpVYd/nfooD1z4JmDb\nu9/9bhPEg6hsSgb93KDQjYz57ajbcdXmfXBzksTJ1KUt8pv7CdLTBIsW/wdTpkyxLwBN9Skrsu9U\nVjEIiIgUg2NrSuEt9ZJLLqn1Ekv3MAwCIHVddlyENcTvWEdseGv2fQTi/Ev8vGUdVz2Q520XfdRP\nh8cq+6obVi4CLkt6ua/rKjNnzrTWt6Y62NYV10HXS0Rk0O+AiPbXeVWDIyF77rlnV3+WokkIMFE3\nUzS9pq78t+yyfAvQx1lDmr5qoUwyRZ8V7WQK9mXIv//7v9upUVbS1NEiWefnQhn9oTL7h4CISP+w\nblRN7qGzePHi2jwUHQkByG7BupzloogpGddplEn9DBBpSE54ICzS/E8fsV9P0/dxcVYR3z/D4Z7l\nu19EMItucXnc/bV169ZaWiTd86Db/11c23ReCPRCQESkF0IDfJ2HT10iYfKg/vSnP232228/u8rA\nRUmN6p40RCEqf/gclgfqc8QGcoE+Wd5ayecPuP4UT7jebr/Rgby01enVLX3drxGQrtsS7G76hzHt\nl5NpN53SXEN/xPWjmx6tg88I9xk+IYhIiIVBf0pA4E//XyAllKsiW4BAsNmRHfhZTbPvvvsaBosq\n5MEHH7R+BGeccYYdrN75znfGqlEGCWGA2GuvvTp1Uj9Levnupksng3cAocEq4j7btm0zP/7xjy2x\n+fWvfz2sHi/biMNvf/vbxr09j7jYwBPvete7zLPPPmu453oJg+Mrr7xiMWMQZ/oLUuYw7ZW/TtfD\nJATdDjzwQPu/NmvWLPPb3/7WfPzjH69EZf6XWA7O0vXbb789cvl6JYqp0tYhIItI67q0+AZhETjz\nzDPN/vvvb4gG6d7ciq9peIkMOCwnfuyxx6wVhCWO3awQUQ/14SWm+8WDuJvFomjSQ3t9f4Zu0zhY\nq5ocDTfcE/QdlgzfWuSn8Z1My/S78ess+7jX/cp1rIDIggULci9DT9oe7sOvfvWrlnxcd911PX2i\nkpardEIgFoGydtNTue1CgF1fb7rpJrsjIzupBgNGaQ10dQWEZ2jGjBmdHWw5HwzUsfUm2ZU2NrN3\ngXqSlpU0nVd84kMwpnz3QS8n7HjaDQuXrknffptcH0S1vUltitOVvk36P8T/Hf8LZf/foWvgjG3r\nmjZtWmL94tqo80IgKQImaUKlEwIgwMOTB2LAbO13kYMhZbuHLg/CqEHeDVDh3ohKG06T5Dc6pGkT\n6fn0Q9CLdr700ksW/37U2c86zj333KGrrrrKtjFNH/RTxyLqynLPcN/7/3dF3e+0h7L5v4MI8lm/\nfn0RzVQZQiAxAn8SayrRBSEQgQDTMjfeeGPHhE6ERT5M2TBVkVYwuRMumjKYiti+fbuN2Eicgiin\nQ3wsOE9dmJARTNjkzSvognSb/gnXAR7o4XQJXy/yN3rR9t/97ncmIGpFFl2Lsg4//HDzZ3/2Z7aN\nafqgFsonVKLXdExcMdz37v+OKTn8R/DZYouDLP936IFTLHFBmOrC52bJkiV248i4PZvidNN5IZAX\nAfmI5EVQ+Q3BmPDjCN6k7H41DJJjx461PgzAwxJTHnY7duywaO3atcume/755+3vyZMnm1NOOcVM\nmjQplUMcxIEHdPCGGUlabOEJ//Aw7+YP0qsY8kcRp175slyHuL366qtdg7llKbfqPGCIL0Rbg2Vl\nJSFx/cL/HRF+2eiQD5sIsqpswoQJnSzjx483r7/+uv0d9393xBFH9M3vq6OYDoSAh4CIiAeGDvMj\ngGUgMKvbFR08+BCsHOyF4j8gJ06caK0YWBTyyDe/+U3zvve9L5UVw6/P6ZuXRFAOA00/3uSxPiFt\nC7HdZiJSNAnx72GO3X3MSqpu/3cQk7333rsv92lYR/0WAnEIiIjEIaPztUfAPdyxikB+0pIJ8vMA\nL4o8YKGBWKFPmdJWIkJfYDkLJpbLhK/vZbv7NC/p7rviqlAI9AkB+Yj0CWhVUzwCTMm4gR8Swhs1\ng1kSyeIP0qtcCA2ESJINgbIJXDat8uVy95lISD4clbvdCIiItLt/W9u6KJ8MyAhvn+4NNK7x5GVg\nKGNwcIQorm6dHxwEnIWsjPtscFBUSwcBARGRQejllrURohG3SsZNs7g3Ub/pWEscgSnz7RvdepEh\nXy8d/x4BMGvLoO1ISJn3me4bIdAWBERE2tKTA9QONyUT12Rn7YB0OGGQ45PWj8TlT/NN/ZCepNNE\nacpuc1r6FSfmpotISNN7UPr3G4F39LtC1ddeBBh48ZFgWa5bKhjVWre0Fw9+9tVI8xbsLBpR5frn\neBN10yR/+qd/av76r/+6MKdUv564YywzSXWNKyPuPMuhN27cGHe5seeDwFqN1d0pLhLikNC3EEiO\ngIhIcqyUMgIBrAxPPvmkDUrmYhkcdthhNobI3LlzI3IYu7SXJb2LFy82q1atMkE0Rxugi03t3NRK\nVEbqipuSiUrPOVZh/OY3v4m7XOp54pIwMHVrUxYFxo0bZ/HOkrfOeYh3wb3QVBEJaWrPSe+qEdDy\n3ap7oKH1E5VxxYoV1voxffp0Q1CyD3/4w5mWrrrATOzwOXr0aDN//vzI4GZpLQykd0HKIDFYbIom\nBb26j3qRNFafXmXSjjYucyXKJ4Ht2PG1aSIS0rQek751QkBEpE690QBdIA3BnhdW0zjCkKcZPsG5\n9dZbO4NSGhLipojC/iBx5/PomyRvGt2TlEcawnsTkjvcxqT565gOa9dTTz3Vd7KYFwuRkLwIKv+g\nIyAiMuh3QML282ZPJE/8P3yCkDB76mQM3uynseeeexq2vGfgTWJVSGL5KIMY9Gpg0XWCCb4i8+bN\n61V1I64zmLPfEA6rTRKRkCb1lnStKwJaNVPXnqmRXlgpePNm/h5n1H6Yzqlv8+bNZurUqdZcn2T/\nEQYFpNf0C2VDDLCQ9EuKXNIL2WJPkfvuu8+2o19tKLOeBx980DDF1yThHoIca4luk3pNutYRAVlE\n6tgrNdKJN++VK1faHXGrmgaAYFx00UXWOnL33XdHPvgZFJw/SFL4KJdBJImlJWmZ3dJlfXsmn7+i\nBFKDzk2dyojCCIvXwoULTVN2fi3awhWFic4JgUFBQERkUHo6ZTuxFpx//vnm5z//ufn617/et8E6\nTk30mTNnjnnzzTfN2rVrO2Qkr99HkqmcOJ2ynE8ygIWJRxzBYgt4lkmzPXyTxfkdYQFrgiTpwya0\nQzoKgbogICJSl56okR4M7lOmTLEa+YN+HVTEQvPyyy9bMoKefHpNxfTSOy+Z6VW+f526ID++zgxs\nvsQRDz8Nx5SDVaRXgLdwvrr9Pv30083RRx/diN2ERULqdvdInzYgICLShl4suA0sowxbHgquIldx\nkJFvf/vb5t577zUHHHBArrL8zAwySUmAny/t8Te/+U2bhaXKSJ4pL7BAmmoVAXP8gPA9qruvhUiI\nvdX0RwgUjoCISOGQNrtAzP0EJqubJcRHFVM+JIRAZUmcWP28vY6L9htx1ha/Xucsm4eAuPKabhVh\npQxEhBVZdRaRkDr3jnRrOgIiIk3vwQL1Z4A/99xz7UqMfjlwZlWfAZ7pozIGsTx+I2HiQeAxfxrG\nb29Rg9vNN99snnnmmcJJma9rGcfLly83ixYtsqujyii/qDKL6qei9FE5QqBtCIiItK1HM7aHAZRp\nCSwNTVm5gPWCN+o1a9bkmt6IgswRil5WC5fOldGNeLg07pu8YX8Rdy3NN+UcddRR1pn3vPPOS5O1\nsrRl9l2RjRIJKRJNlSUEohEQEYnGZeDO4heCLF26tFFtL/utmoHI9xuBOPhBt9IQjyhgGZCLiEVB\nOeiJr0WcBSaq/irOQZzKsmYV2R6RkCLRVFlCIB4BEZF4bAbmCg/cpjgMRnUKVhEsAWVYAyAezz33\nnHn3u99t98FxMTyi9Mh6rqgBj8Bzl1xySe3DpEN633777VpPJRXVJ1nvCeUTAoOEgIjIIPV2TFub\ntHwyqglFEiksC1HBw/L4jUTp7J8raoqGMv3lzXVchVJ3/egLrEq9puT8/tOxEBAC+RAQEcmHX+Nz\nFzmIVwkGZOrUU09NbRUJEw9/GibcnjIHKYgOUoSTsBvs6xCIzsfQ6VXXFVlFEkK/3ToWAkKgOwLa\na6Y7PrFXDz/8cDNq1Cj7YRdUX9x5vjdt2uRf6nrMfht+3q6JC7p4xx13mLlz59Y+hkOv5hICnhUY\nvQTi5X8Y+Hn7dZ9uVgSukY78DFpFCnr4vid5yiamyGGHHWZ1hWhVLWAFUXSB6LphXJWuIiFVIa96\nhYAxtSUiDO5uUGbQlxSPAA/fZcuW2UGi+NL7W6Jb6QNJ8MUnHRw7wuG+swyK5MWC4awYfn15jh3J\nyVOGywsZYZdkLDw49FYlECFW9Oyxxx61jU0jElLV3aF6hcDvEXiHgBhcBNavX29mzJhRyHRAHVD8\nxCc+YYmVrwuDexnCyhRHRoqYTnE6QhwYvItY+cIuyd/5znfMrFmzzCOPPGKIN1Kkrk7nqG8GdzYo\n/PznP2/uueee1FNmUWWWcU4kpAxUVaYQSIdAbS0i6ZrR/9QvvfSSGRoash8e9E2URx991L6tNlH3\nsM4EY/vYxz5mp8KctaMsEuLqdoN6kdMfzkLDAFmEgMHGjRvNIYccYvelgYwUVXacfqzewQpCkDUc\nP8tYzRRXd5rzIiFp0FJaIVAiAsFgWit5/vnnh4LmRn6Cee8Rut55551DnPfzcG7Hjh2RaV26s846\ny16//vrrO3ldBpeGb/Thc+KJJ9p0lI34dbpzcfkff/zxTn7KpCzOheWBBx7o6EK6KEnT3qj8/rlg\nIB0K/BL8U40/rqJNwSqbocDyUCh2RZeHcgEpGJo2bdoQGF177bWF9j0YrF69eiggPPbDcZ0FfcFD\nIgSEQPUIRI92FeqVlIhANBw58ImDO95nn32GXn/99WEtYRB31yEi4fwusUvDt09U+O1IR1IicvXV\nV3fq9Mv1y3L1diMiWdrryo365iHMgNQ2YaANppwqaRbkgQGuKCmDjKAbfX/55Zfb+/LYY48dCqZO\nMg3KjnxQFvcS2NedgNB+kRBQkAiB+iDQWB8RfBueeOKJYDyPlmDgNieffLJ57bXXDNEvw3L//feH\nT0X+vuqqqyLPJz153XXXxSa94IILzMEHH2yOPPLI2DTuQt72unLc91tvvWUmTpzoflbyPX36dOP6\nIfiXKEQHtpMPCGglYeqZBmGahumVYGDO3R6Cp+GHUkRZvjL4n+DMOn/+fIOfEFN0AWG2SbgnwBAZ\nP378sP+drVu3ml27dplXXnnFvPjii2bLli0mIB922fRll11WuJ5WiYL/aDqmYEBVnBAoAIHa+Ygw\nKDMoBZaHTvMC64M9h18GwjJXn4SQljx8AqtCJx9kxP/dufCHA8oNLDCdvOHr7jcPaVd+Fn+QOP0o\nn71deklR7fXrYbB2A45/vqrj4C21kKoDS5gdKAspLEMhzsm0CL8RCEhRS3qjmgJhwqGVsP7U89RT\nTxmWQSMQjsWLF5sFCxZ0Pq+++qq9dsoppxhWtfE/we7H+IAUTZZsRQX/cc7Fro8KLl7FCQEhkBWB\n4GFSS2EKJGiT/TAN4kvwsOxcY+ojLHF5/fOUzTRQlLh6+Xa+JOF0aaZmwnnDegQPfZskbmoma3vD\n9fq/b7rppiE+VQrYOqzj+iKtfkxnMEVQtWD+L2pqpahyqsakyvrxhWqbP1SVeKpuIVAkArWziAQD\nU0954YUXOmmi3uonT57cuU4Qpbi37SRTIuxjkkeI9hmWj370o8NOMU3STYpqr18H5nWsB3UR97Zd\nF33y6oG1gamaIoKfuSW9eXUa1Pwu3ksTrDaD2kdq92Aj0EgiArlwgh+IC3zmvsMDbBQRYVomiey+\n++5JksWm2XvvvUdcC/usROnnZyqivX55HLPpWJRu4XT9+v2lL33J4IPQNoGMuCmBrG0reklvVj2a\nmE8kpIm9Jp0HDYFGEpFB66Q6t9ePgOuIYNJv56hK+5xz8Q033NA6QuJ8EvL4jVBGsNqlzrdC7XQT\nCaldl0ghIRCJQCOJiG/N8J1NgzmrjlOpf1zlmz9OoWEJW0B66VdGewm53WtKKKx32b8hI6xSwjrS\nNmFagE84BH2adrqpnjR5BjWtSMig9rza3UQEGrl894gjjrAbaAE4vgVJfD2q6hyiS5500knDqn/2\n2Wc7v5lG6kVEymjvhAkTrBWio4gOSkfA9xvptstvN0XKWtJLnVhsmB6DEG7fvt1s27ZtmCqQV+4b\npivHjRtnfWCGJajJD5GQmnSE1BACCRFohEVk586dw5pzzDHHdH4Ti8Pf/Za3/IsvvrjjN1L1hnnE\nEfH1YykuOjs555xz3GHsd1ntZYlm24SBlAGzzpLHbwSrCrEwigjTThmEY585c6YN/37mmWeaFStW\nWOjGjBljd2VmZ2b3YdkuAvnnHFNwOHMTNt4N/jZBhX+cHnJMrbATVLUQSIlAIywivKHx0GOKglgi\nZ5xxho1t4Jw4Gdj9wd3HgAdm1dJNPxe3oZuOZbSXYFXEicgrrFBieqxICTvzpikbcsVbe90Fnw8G\nTawQzockqc6kdzsJJ83jpyMv8XUWLlzYCUgWhHzvGQsEAuULRCZYWmwee+wxax1Br9mzZ9vYJH66\nfh2LhPQLadUjBApGoMi1wEWWxV4sQVOHfQIi0qkiICcjQrSH0xOvwxc/fodflp+GY78cYntECfld\nunA97jzf4RDx/rVwvrg4ItSfpb1RertzxFQI3hrdz9Z8VxniPQuIgb9Q5vDqafdKcTFW6HdiyBQZ\nV4N2VLnXjOKEZLn7lEcI1AOB2k7N4FcRDNSxsS7wq1i3bp1NE+wZE4zvfxQiofKWniUK6h9LKeaI\nMOa8fWLNcYK+AdFKpV/R7XWm6zwrOVx76vS9atUqc+CBB9ZJpa664DdCX6R1YiUfH2cF6FYJlgsc\ngKdOnWqj6bL65tJLL+1pAelWZvgauhCldfPmzTZ0/DXXXGOnbfpxf7k63D0d1k2/hYAQqDcCo+BD\n9VZR2pWFAL4BbNde123a07Z7w4YNJtiAzQ6GafPWIT1kJK0Ta68pGq5DyPfff3/ry9HPwRrfkc9/\n/vMmsL5Y4lMGxpAQ2gQRkggBIdBMBGprEWkmnM3SGufD5cuXN0vpLto+99xz1uehS5JaX8rixNpt\nSS99ixVkzpw5dk+YfpIQgMbqgvVlzZo11iG2CAdbvwNFQnw0dCwEmouALCLN7bvcmjMw4BgazK8X\naqbPrVjGAljaysZtaZ0/M1ZXWjamW+ibpO1w0zM+0bjiiivMypUra4EHbYEMvfnmm2bt2rWFWC9E\nQkq7/VSwEOg7ArKI9B3y+lSIOZupjGXLltVHqYyasAx19OjRiQfvjNX0JRuEgk9SvxHSMtjzQSAh\nrCjDGpGUzJTZMO4zdvjFT2rKlCkdPbPWKRKSFTnlEwL1REAWkXr2S9+0YrDDfM+g1eR59g984APm\nkksuMTNmzOgbdv2oiP5J6jdCWhyjISFFWR6KbqMjSVn1EwkpukdUnhCoHgFZRKrvg0o1wMdg4sSJ\n5u67765UjzyVYw3B55qYJgzG/idPuXXIm8ZvhGBuTMd8/etfry2pvPHGG81+++1nzj///NTwioSk\nhkwZhEAjEJBFpBHdVK6SDNxYRfj2/QzKrbW40g899FC7ZJTlo2GhTb7gE1OH6QpfpyTHvfxGGKSx\nnNRlOqZbm5hCYorm2GOPNfPmzeuWtHNNJKQDhQ6EQOsQEBFpXZdmaxAm87ffftvO5WcroZpcLBFl\nVQZOqkmEQZDB2pekUx9+niqOne5YSXzhPMuwcQhtylJsiAXh4em7cHv8tnEsEhJGRL+FQLsQEBFp\nV39mbg2DGQPyrbfeWlmI7rTKF2UFoJzwjsi9Bse0uhaZHiuPT54IVrZlyxa7RLfIesoui+XFixYt\n6hr3JdzWsnVS+UJACPQfARGR/mNe2xp56DNF04QlsGVbAcJTOiwNrtO0FeTJORejW1OXYGMV4Z4j\n5khY6IM6E8KwvvotBIRANgRERLLh1tpcTHXcddddZuPGjZ2Bro6NZQBjOSjOj/0QfDQY7H3xrRL+\n+X4do9MXv/hFs++++yb2teiXbknrceQ3vGpLJCQpgkonBJqPgIhI8/uw8BbkXWJZuEKhAuuiX3hK\np9+OsBARrCHoccABB4RQas7P008/3e6B46wiIiHN6TtpKgSKQEBEpAgUW1hGXQZ7H1qmY9hMra5x\nMtAv7Ahb5pQOfYT0yyrk90WRxxAP9sNhwzyRkCKRVVlCoBkIiIg0o58q0ZKBbv369TZIVtVLXhnk\nWfKJZA2GVQWIUVM6Rfk9QHKa4M+TBHeWYF9wwQXm4osvTpJcaYSAEGgRAu9oUVvUlIIR4E0bnxH8\nMapcTcNbMg6N06dPt/FCnJNmwc0tpTgcXMNOrrTHlyxTOuw0DDmsmiD67chzPG3aNLsXTZ4ylFcI\nCIFmIiCLSDP7ra9aOyJA5NJrr712xMBaljJYQb761a+a22+/3Vx33XWNiZGRFo+oKZ1ejrBYq3bf\nfffGOqmGMcLPBcIbdggOp9NvISAE2oeAiEj7+rSUFjFY4p9BCPG5c+faEN1lWiYI2059+++/v7XK\nhK0KpTSyRoWGHWFRzZ/SYSpjyZIlw87VSP1MqrRpqikTAMokBAYUARGRAe34rM3GOrJgwQLz/PPP\nW0LCioeiSAJkZ/Xq1TbIFfotXLjQHH/88VlVbV0+N6Xz29/+1hxzzDGNjR0S1zEzZ860EWKbEh02\nrh06LwSEQDoEtOldOrwGPjVv5Q899JANzb19+3a7fJQBhCiZOGamFcgH1g/KwFfikUcesQSEFRQi\nIcPRBHs+7373uw0+FQiWk7bIhAkTDPeURAgIgcFCQBaRwervwlsLkWBlzaOPPmoee+wxM3r0aDud\ncvTRR9u62NnXF3aI3bVrl3nllVfMiy++aEOTM6ieeuqp5oQTTijMuuLX2bZjSB8B55YuXdqqpuGA\nu3jx4saFqm9VJ6gxQqACBLRqpgLQ21QlfiLseut2vuUNHbKxY8cOSziYxvFl7NixZsyYMeaUU04x\nn/vc51rl4+C3s8xjiBzWg7YJFjGJEBACg4eAiMjg9XmpLW7TktJSgVLhIxDAWRXfI4kQEAKDhYB8\nRAarv9VaIVBbBHB6zuJnVNsGSTEhIAQSISAikggmJRICQkAICAEhIATKQEBEpAxUVaYQEAKpEZA1\nJDVkyiAEWoGAiEgrulGNEALNR4Coqm5ZcvNboxYIASGQFAERkaRIKZ0QqAkChHZXvI2adIbUEAJC\nIDcCIiK5IVQBQqC/CIwbN85s27atv5X2oTZWzBxyyCF9qElVCAEhUCcERETq1BvSRQgkQIAN8Vat\nWpUgZbOSEOSOGDMSISAEBgsBEZHB6m+1tgUIEEQOy0GbwrvTLUTaPfLII1vQQ2qCEBACaRAQEUmD\nltIKgZogMGnSJLNu3bqaaJNfDUjVzp07DQHxJEJACAwWAiIig9Xfam1LEJg8ebLdeLAlzTGbNm0y\n06dPb0tz1A4hIARSICAikgIsJRUCdUGAnYmxIrQl9saiRYsM5EoiBITA4CEgIjJ4fa4WtwQBLAhf\n+cpXGt+a7373u3ZaBnIlEQJCYPAQGDUUyOA1Wy0WAs1HAGvIhz70IfODH/zA4MDaVDn99NPN0Ucf\nbS699NKmNkF6CwEhkAMBWURygKesQqBKBNgkbuLEiebuu++uUo1cdWMNIX7I+eefn6scZRYCQqC5\nCMgi0ty+k+ZCwPqJEFeE8OgQk6aJrCFN6zHpKwSKR0AWkeIxVYlCoG8IsNz12muvbeS0xvLly80b\nb7wha0jf7hZVJATqiYAsIvXsF2klBBIj8Ktf/cpgFbnuuuvMeeedlzhflQmdf8uaNWusn0uVuqhu\nISAEqkVARKRa/FW7ECgEAXwtpk6dap566qlGBAU77rjjzLHHHmvmzZtXSPtViBAQAs1FQFMzze07\naS4EOgiwembu3LnmzDPPNFhI6ixXXHGFVU8kpM69JN2EQP8QkEWkf1irJiFQOgIM8i+//LJZu3Zt\nLZf0ot/69evNxo0ba6lf6R2kCoSAEBiBgCwiIyDRCSHQXARuvPFGc9hhh5kpU6bUzjICCVm5cqV5\n4IEHREKae4tJcyFQOAIiIoVDqgKFQLUI+GSkDjv0MlXkLDVN8WGptgdVuxAYLARERAarv9XaAUEA\nMoIzKE6hGzZsqKzVrI7BOuOmi7S7bmVdoYqFQG0REBGpbddIMSGQDwGcQe+9915z7rnnmpkzZ/Z9\nqoY4ITjRQoiwhDQ5DH2+nlBuISAEuiEgItINHV0TAg1HgI3k2IsGIdbIzTffXHqLWEqMJYYddYkT\notUxpUOuCoRAoxHQqplGd5+UFwLJEYAgLFiwwEYz/exnP2sjmhZppXj44YfNihUr7N4xTQqulhxB\npRQCQqAMBEREykBVZQqBGiMAIbnjjjvMsmXLzIwZM8wpp5xiJk2alGnqhLIef/xxc/vtt5vRo0eb\nOXPmmE9+8pOZyqoxZFJNCAiBEhEQESkRXBUtBOqMAI6kTz75pHnkkUfMqlWrrC8HS3/HjBljxo8f\nb3bbbbcR6rNT7q5du8yWLVtsnkMOOcRMmzbNnHHGGY2I6DqiQTohBIRA5QiIiFTeBVJACNQDAawb\nW7dutUTjmWeeiVRq7NixHaJy4IEHNnLH38iG6aQQEAKVISAiUhn0qlgICAEhIASEgBDQqhndA0JA\nCAgBISAEhEBlCIiIVAa9KhYCQkAICAEhIARERHQPCAEhIASEgBAQApUhICJSGfSqWAgIASEgBISA\nEBAR0T0gBISAEBACQkAIVIaAiEhl0KtiISAEhIAQEAJCQERE94AQEAJCQAgIASFQGQIiIpVBr4qF\ngBAQAkJACAgBERHdA0JACAgBISAEhEBlCIiIVAa9KhYCQkAICAEhIARERHQPCAEhIASEgBAQApUh\nICJSGfSqWAgIASEgBISAEBAR0T0gBISAEBACQkAIVIaAiEhl0KtiISAEhIAQEAJC4P8Di13nEo+f\nAH0AAAAASUVORK5CYII=\n", "text/plain": [ "<IPython.core.display.Image object>" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from IPython.display import Image\n", "\n", "review = \"This was a horrible, terrible movie.\"\n", "\n", "Image(filename='sentiment_network.png')" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "image/png": 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4nb799ttmyZIlLeslHQN9WpN91nx5boSslh0hMlL33XffbX19Jk2aJKcq/U1f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\n", "text/plain": [ "<IPython.core.display.Image object>" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "review = \"The movie was excellent\"\n", "\n", "Image(filename='sentiment_network_pos.png')" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true, "deletable": true, "editable": true }, "source": [ "# Project 2: Creating the Input/Output Data" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "74074\n" ] } ], "source": [ "vocab = set(total_counts.keys())\n", "vocab_size = len(vocab)\n", "print(vocab_size)" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "text/plain": [ "['',\n", " 'touissant',\n", " 'muncey',\n", " 'fullscreen',\n", " 'manifesting',\n", " 'overplaying',\n", " 'tents',\n", " 'landscapes',\n", " 'silicone',\n", " 'lalanne',\n", " 'unobserved',\n", " 'godly',\n", " 'jianxiang',\n", " 'nachoo',\n", " 'acoustics',\n", " 'phillimines',\n", " 'pummeled',\n", " 'ejaculation',\n", " 'gadi',\n", " 'shugoro',\n", " 'substitute',\n", " 'turtles',\n", " 'shoddily',\n", " 'polanski',\n", " 'syafie',\n", " 'definative',\n", " 'gauteng',\n", " 'nonstop',\n", " 'buccaneering',\n", " 'weary',\n", " 'kji',\n", " 'arrrgghhh',\n", " 'agamemnon',\n", " 'seaward',\n", " 'triste',\n", " 'mortimer',\n", " 'indict',\n", " 'corruption',\n", " 'conjunction',\n", " 'cineastes',\n", " 'hirjee',\n", " 'profiles',\n", " 'channelling',\n", " 'kayak',\n", " 'gv',\n", " 'hershman',\n", " 'decisionsin',\n", " 'hemmingway',\n", " 'synch',\n", " 'walt',\n", " 'nob',\n", " 'cimarron',\n", " 'heisler',\n", " 'huxtables',\n", " 'juscar',\n", " 'funt',\n", " 'deterrent',\n", " 'scarynot',\n", " 'differentiation',\n", " 'rewatchability',\n", " 'vouch',\n", " 'dardino',\n", " 'clerking',\n", " 'matheisen',\n", " 'expeditiously',\n", " 'radivoje',\n", " 'kohara',\n", " 'adulterated',\n", " 'intros',\n", " 'dunn',\n", " 'construe',\n", " 'peacefulness',\n", " 'charter',\n", " 'bang',\n", " 'sergi',\n", " 'physically',\n", " 'gingernuts',\n", " 'definently',\n", " 'brasseur',\n", " 'ivay',\n", " 'ranged',\n", " 'reaming',\n", " 'leapt',\n", " 'shaft',\n", " 'tradeoff',\n", " 'segueing',\n", " 'hurting',\n", " 'goose',\n", " 'bonde',\n", " 'culled',\n", " 'scribbles',\n", " 'loris',\n", " 'purrrrrrrrrrrrrrrr',\n", " 'cameras',\n", " 'goading',\n", " 'mischievous',\n", " 'gratitude',\n", " 'indebtedness',\n", " 'submarines',\n", " 'glamor',\n", " 'appease',\n", " 'romanticism',\n", " 'hll',\n", " 'louda',\n", " 'foodstuffs',\n", " 'preyer',\n", " 'pentameter',\n", " 'sourly',\n", " 'takers',\n", " 'hypersensitive',\n", " 'dismantle',\n", " 'gearheads',\n", " 'dza',\n", " 'mou',\n", " 'snails',\n", " 'steely',\n", " 'effected',\n", " 'feather',\n", " 'strangest',\n", " 'woodlands',\n", " 'ingratiate',\n", " 'okey',\n", " 'sandbag',\n", " 'clowned',\n", " 'blackploitation',\n", " 'chaptered',\n", " 'macbeth',\n", " 'sporadically',\n", " 'verve',\n", " 'mbongeni',\n", " 'malte',\n", " 'fomenting',\n", " 'bruhls',\n", " 'amontillado',\n", " 'larded',\n", " 'vfcc',\n", " 'deewana',\n", " 'guptil',\n", " 'larp',\n", " 'demanding',\n", " 'resembled',\n", " 'extraterrestrial',\n", " 'harni',\n", " 'lynched',\n", " 'jbl',\n", " 'alleging',\n", " 'champmathieu',\n", " 'determination',\n", " 'patron',\n", " 'pertain',\n", " 'unimpressively',\n", " 'limousines',\n", " 'heterogeneity',\n", " 'hiller',\n", " 'snippers',\n", " 'tc',\n", " 'puppetry',\n", " 'nordham',\n", " 'detritus',\n", " 'frogballs',\n", " 'beheadings',\n", " 'gourds',\n", " 'pagemaster',\n", " 'coveys',\n", " 'lucian',\n", " 'fragrant',\n", " 'afflicted',\n", " 'mahnaz',\n", " 'airline',\n", " 'sega',\n", " 'glower',\n", " 'musican',\n", " 'kudisch',\n", " 'snarls',\n", " 'theonly',\n", " 'mindbender',\n", " 'ebersole',\n", " 'crummiest',\n", " 'doorpost',\n", " 'brochures',\n", " 'gioconda',\n", " 'maine',\n", " 'mandelbaum',\n", " 'alexandria',\n", " 'afortunately',\n", " 'ungallant',\n", " 'debilitating',\n", " 'troupe',\n", " 'lactating',\n", " 'constanly',\n", " 'revere',\n", " 'inarticulate',\n", " 'neither',\n", " 'shimmer',\n", " 'metcalfe',\n", " 'casanova',\n", " 'umilak',\n", " 'vfx',\n", " 'terrorize',\n", " 'latches',\n", " 'machinist',\n", " 'gladly',\n", " 'deceived',\n", " 'vntoarea',\n", " 'cleaning',\n", " 'unchallenged',\n", " 'fiefdoms',\n", " 'percent',\n", " 'schoolroom',\n", " 'microscopically',\n", " 'li',\n", " 'faw',\n", " 'planning',\n", " 'bodysuit',\n", " 'transition',\n", " 'crumpled',\n", " 'sagr',\n", " 'deteriorated',\n", " 'goykiba',\n", " 'dynasty',\n", " 'steered',\n", " 'orchestration',\n", " 'redblock',\n", " 'wannabe',\n", " 'moretti',\n", " 'gring',\n", " 'jayston',\n", " 'natali',\n", " 'ayatollahs',\n", " 'candians',\n", " 'allan',\n", " 'reabsorbed',\n", " 'equipe',\n", " 'aberystwyth',\n", " 'chaparones',\n", " 'financiers',\n", " 'saws',\n", " 'sayuri',\n", " 'pip',\n", " 'duilio',\n", " 'significantly',\n", " 'norwegia',\n", " 'colonel',\n", " 'winged',\n", " 'netherworld',\n", " 'myspace',\n", " 'autopsied',\n", " 'courtesy',\n", " 'mouths',\n", " 'luxemburg',\n", " 'malevolence',\n", " 'slinging',\n", " 'bilardo',\n", " 'loveliness',\n", " 'shaping',\n", " 'bolha',\n", " 'mysteriosity',\n", " 'households',\n", " 'maximimum',\n", " 'sssr',\n", " 'aleck',\n", " 'biroc',\n", " 'airfield',\n", " 'coral',\n", " 'coped',\n", " 'distilled',\n", " 'talks',\n", " 'nineteenth',\n", " 'kennedys',\n", " 'pointblank',\n", " 'cleverness',\n", " 'wrinkle',\n", " 'ninga',\n", " 'doen',\n", " 'ramya',\n", " 'fastforwarding',\n", " 'blackguard',\n", " 'orry',\n", " 'gravitas',\n", " 'disastrously',\n", " 'snuff',\n", " 'schiff',\n", " 'aimlessness',\n", " 'paton',\n", " 'boars',\n", " 'retaining',\n", " 'tinting',\n", " 'nomad',\n", " 'insult',\n", " 'despondently',\n", " 'cindy',\n", " 'bobbies',\n", " 'diffident',\n", " 'super',\n", " 'eloquent',\n", " 'anchored',\n", " 'refuting',\n", " 'chastise',\n", " 'blatantly',\n", " 'narrate',\n", " 'leveling',\n", " 'caballo',\n", " 'weightwatchers',\n", " 'excruciatingly',\n", " 'whitt',\n", " 'concerning',\n", " 'kramer',\n", " 'farrakhan',\n", " 'sportsman',\n", " 'where',\n", " 'hauptmann',\n", " 'stationmaster',\n", " 'ugghh',\n", " 'swamps',\n", " 'nadja',\n", " 'hologram',\n", " 'whack',\n", " 'riffraff',\n", " 'withstood',\n", " 'confesses',\n", " 'weeds',\n", " 'discoverer',\n", " 'dewet',\n", " 'scrivener',\n", " 'embezzled',\n", " 'homepages',\n", " 'finding',\n", " 'compliance',\n", " 'frocked',\n", " 'unlockables',\n", " 'emek',\n", " 'latrines',\n", " 'detectives',\n", " 'braindeads',\n", " 'elongate',\n", " 'inadvisable',\n", " 'ivanova',\n", " 'images',\n", " 'absolutly',\n", " 'southerrners',\n", " 'maaan',\n", " 'perceptible',\n", " 'chit',\n", " 'mccathy',\n", " 'gouden',\n", " 'harrods',\n", " 'persuaders',\n", " 'obligatory',\n", " 'worth',\n", " 'counters',\n", " 'firms',\n", " 'disentangling',\n", " 'ryecart',\n", " 'analyze',\n", " 'caalling',\n", " 'platitudes',\n", " 'wow',\n", " 'cynthia',\n", " 'les',\n", " 'isolationist',\n", " 'drawback',\n", " 'wormwood',\n", " 'preens',\n", " 'rebelled',\n", " 'reviews',\n", " 'miser',\n", " 'burglary',\n", " 'farnel',\n", " 'groupthink',\n", " 'revivalist',\n", " 'snips',\n", " 'ruffin',\n", " 'dramatisations',\n", " 'ascendant',\n", " 'distraction',\n", " 'herapheri',\n", " 'neous',\n", " 'lampio',\n", " 'sophomore',\n", " 'gangfights',\n", " 'broadways',\n", " 'ingredients',\n", " 'afv',\n", " 'flavored',\n", " 'cauldrons',\n", " 'philosophy',\n", " 'unhelpful',\n", " 'bladed',\n", " 'wgbh',\n", " 'haje',\n", " 'thriteen',\n", " 'birkina',\n", " 'strangulations',\n", " 'neagle',\n", " 'precipice',\n", " 'syllable',\n", " 'countermeasures',\n", " 'punctures',\n", " 'holt',\n", " 'ter',\n", " 'rivire',\n", " 'mordantly',\n", " 'operas',\n", " 'insurgents',\n", " 'stepdaughter',\n", " 'vibrators',\n", " 'lepus',\n", " 'mmhm',\n", " 'doodle',\n", " 'bookcase',\n", " 'recreate',\n", " 'nakadai',\n", " 'jorge',\n", " 'mandatory',\n", " 'agendas',\n", " 'reception',\n", " 'plexiglas',\n", " 'bubba',\n", " 'gender',\n", " 'jules',\n", " 'werecat',\n", " 'overdressed',\n", " 'foremost',\n", " 'eggbeater',\n", " 'horrify',\n", " 'tailored',\n", " 'cleaners',\n", " 'lansbury',\n", " 'alfie',\n", " 'mistry',\n", " 'whiile',\n", " 'nah',\n", " 'popularised',\n", " 'weill',\n", " 'gariazzo',\n", " 'rojar',\n", " 'skewing',\n", " 'benetakos',\n", " 'deathrow',\n", " 'entwistle',\n", " 'cebuano',\n", " 'hana',\n", " 'pragmatist',\n", " 'ethnographer',\n", " 'matchbox',\n", " 'tulkinghorn',\n", " 'essandoh',\n", " 'juvie',\n", " 'heartening',\n", " 'wearing',\n", " 'pixar',\n", " 'panties',\n", " 'ills',\n", " 'nihilists',\n", " 'mulher',\n", " 'pleasantness',\n", " 'distatefull',\n", " 'unisten',\n", " 'career',\n", " 'dependence',\n", " 'vanquished',\n", " 'hamatova',\n", " 'bolsters',\n", " 'timesfunny',\n", " 'millenia',\n", " 'doe',\n", " 'antisocial',\n", " 'hound',\n", " 'illumination',\n", " 'changings',\n", " 'compendium',\n", " 'homemade',\n", " 'helvard',\n", " 'cryptozoology',\n", " 'earthquake',\n", " 'hahahahaha',\n", " 'wasted',\n", " 'steveday',\n", " 'detaining',\n", " 'logothethis',\n", " 'lumped',\n", " 'maltz',\n", " 'constructions',\n", " 'booger',\n", " 'radicalism',\n", " 'rosalba',\n", " 'riverdance',\n", " 'arctic',\n", " 'recieves',\n", " 'parentingwhere',\n", " 'kam',\n", " 'doppleganger',\n", " 'collyer',\n", " 'unscripted',\n", " 'wan',\n", " 'spaak',\n", " 'shinning',\n", " 'maclhuen',\n", " 'incidental',\n", " 'ineptly',\n", " 'flane',\n", " 'invent',\n", " 'straightness',\n", " 'chirping',\n", " 'concorde',\n", " 'bracy',\n", " 'recording',\n", " 'burnout',\n", " 'exaggerated',\n", " 'ainley',\n", " 'rokkuchan',\n", " 'unassuming',\n", " 'favors',\n", " 'colleen',\n", " 'assaulting',\n", " 'deprecation',\n", " 'swallowing',\n", " 'feodor',\n", " 'ven',\n", " 'blammo',\n", " 'xine',\n", " 'overacts',\n", " 'personified',\n", " 'logophobia',\n", " 'mnard',\n", " 'confuse',\n", " 'antagonizing',\n", " 'arthurian',\n", " 'manish',\n", " 'hickish',\n", " 'joxs',\n", " 'extrapolation',\n", " 'fattish',\n", " 'magnificent',\n", " 'temperment',\n", " 'popularizing',\n", " 'vilification',\n", " 'vandyke',\n", " 'trifunovic',\n", " 'contemporary',\n", " 'colorlessly',\n", " 'pomerantz',\n", " 'boulevardier',\n", " 'barzell',\n", " 'kafi',\n", " 'mutiracial',\n", " 'playgroung',\n", " 'looping',\n", " 'danver',\n", " 'leisurely',\n", " 'exaggeratedly',\n", " 'willow',\n", " 'cardinale',\n", " 'bumpkins',\n", " 'neidhart',\n", " 'abstractions',\n", " 'monocle',\n", " 'movieits',\n", " 'epochs',\n", " 'axe',\n", " 'ovies',\n", " 'subtitles',\n", " 'reincarnate',\n", " 'shrewsbury',\n", " 'winding',\n", " 'imprisoning',\n", " 'guerrilla',\n", " 'imprezza',\n", " 'dicker',\n", " 'figgy',\n", " 'repoman',\n", " 'milimeters',\n", " 'heretics',\n", " 'heroo',\n", " 'modernity',\n", " 'lugging',\n", " 'sindhoor',\n", " 'mizu',\n", " 'postmortem',\n", " 'greeting',\n", " 'prophetic',\n", " 'freuchen',\n", " 'mummies',\n", " 'napoli',\n", " 'seiing',\n", " 'blurred',\n", " 'canadians',\n", " 'alignment',\n", " 'junction',\n", " 'anniversary',\n", " 'yearn',\n", " 'howling',\n", " 'nearing',\n", " 'irrevocably',\n", " 'witches',\n", " 'budding',\n", " 'kardasian',\n", " 'glb',\n", " 'hately',\n", " 'neno',\n", " 'baston',\n", " 'combined',\n", " 'boar',\n", " 'vohrer',\n", " 'demille',\n", " 'crustacean',\n", " 'unsub',\n", " 'saxophonists',\n", " 'lemma',\n", " 'journalist',\n", " 'fking',\n", " 'chimera',\n", " 'strum',\n", " 'unmoored',\n", " 'crossbeams',\n", " 'boyish',\n", " 'thundercleese',\n", " 'disslikes',\n", " 'cartwright',\n", " 'hounding',\n", " 'ideologist',\n", " 'putner',\n", " 'whap',\n", " 'glides',\n", " 'deftly',\n", " 'taunt',\n", " 'tres',\n", " 'voiceless',\n", " 'apon',\n", " 'codenamedragonfly',\n", " 'devo',\n", " 'reconstituirea',\n", " 'promotes',\n", " 'voorhees',\n", " 'abhi',\n", " 'smooth',\n", " 'geyser',\n", " 'enterntainment',\n", " 'vocation',\n", " 'sweat',\n", " 'midpoint',\n", " 'calico',\n", " 'refried',\n", " 'shakers',\n", " 'normality',\n", " 'grotesquery',\n", " 'flabbergastingly',\n", " 'taz',\n", " 'miyako',\n", " 'plinplin',\n", " 'period',\n", " 'dernier',\n", " 'coherrent',\n", " 'providing',\n", " 'arena',\n", " 'sussanah',\n", " 'categorizing',\n", " 'chases',\n", " 'outsized',\n", " 'hooligans',\n", " 'hassle',\n", " 'suggestion',\n", " 'klutz',\n", " 'boobage',\n", " 'choreographing',\n", " 'shi',\n", " 'gallantry',\n", " 'providence',\n", " 'banton',\n", " 'scrimm',\n", " 'bonhomie',\n", " 'tagged',\n", " 'envoked',\n", " 'dahl',\n", " 'bullsh',\n", " 'fagan',\n", " 'silhouette',\n", " 'pitted',\n", " 'hedgehog',\n", " 'quarrels',\n", " 'precarious',\n", " 'hepton',\n", " 'profundo',\n", " 'mileu',\n", " 'set',\n", " 'pinto',\n", " 'stradling',\n", " 'acrap',\n", " 'rexs',\n", " 'onecharacter',\n", " 'frenetically',\n", " 'bingo',\n", " 'backyard',\n", " 'backup',\n", " 'gems',\n", " 'continual',\n", " 'sparkly',\n", " 'crewmember',\n", " 'promo',\n", " 'robin',\n", " 'acceptance',\n", " 'mcneill',\n", " 'chalta',\n", " 'into',\n", " 'deciding',\n", " 'channeling',\n", " 'trampy',\n", " 'intermitable',\n", " 'rashness',\n", " 'afresh',\n", " 'alaskan',\n", " 'cq',\n", " 'franciscus',\n", " 'bodybut',\n", " 'dodesukaden',\n", " 'simpleton',\n", " 'wasabi',\n", " 'embraceable',\n", " 'rake',\n", " 'anchorwoman',\n", " 'natassja',\n", " 'pino',\n", " 'reminisces',\n", " 'pulcherie',\n", " 'jarring',\n", " 'tilted',\n", " 'accomplishment',\n", " 'brighten',\n", " 'delli',\n", " 'kaoru',\n", " 'quizzes',\n", " 'crawling',\n", " 'wikipedia',\n", " 'solder',\n", " 'unheated',\n", " 'windowless',\n", " 'klangs',\n", " 'verhoven',\n", " 'archetypal',\n", " 'stickler',\n", " 'realisticly',\n", " 'henleys',\n", " 'hangout',\n", " 'monolithic',\n", " 'conspir',\n", " 'hardbitten',\n", " 'erman',\n", " 'mim',\n", " 'enlarged',\n", " 'dragnet',\n", " 'yours',\n", " 'caudillos',\n", " 'border',\n", " 'conviction',\n", " 'bewildered',\n", " 'endeavour',\n", " 'reds',\n", " 'ever',\n", " 'culminates',\n", " 'achieve',\n", " 'emanuele',\n", " 'tisserand',\n", " 'deploy',\n", " 'shipbuilding',\n", " 'kkk',\n", " 'takeovers',\n", " 'celler',\n", " 'opposition',\n", " 'kahn',\n", " 'misinforms',\n", " 'pumping',\n", " 'caetano',\n", " 'rajasthani',\n", " 'white',\n", " 'bevilaqua',\n", " 'volleyball',\n", " 'engel',\n", " 'sarcastic',\n", " 'notably',\n", " 'expcept',\n", " 'bhiku',\n", " 'iyer',\n", " 'goivernment',\n", " 'stars',\n", " 'immediate',\n", " 'debauchery',\n", " 'viel',\n", " 'writhed',\n", " 'thesis',\n", " 'cynical',\n", " 'razed',\n", " 'stephani',\n", " 'teamed',\n", " 'prettier',\n", " 'cornball',\n", " 'britains',\n", " 'inconclusive',\n", " 'unsuitably',\n", " 'explicit',\n", " 'scratchy',\n", " 'paraday',\n", " 'obstructs',\n", " 'fraculater',\n", " 'altron',\n", " 'criminey',\n", " 'euphemism',\n", " 'knowable',\n", " 'cheaper',\n", " 'bdus',\n", " 'foulkrod',\n", " 'lated',\n", " 'regimens',\n", " 'nikolaev',\n", " 'sylvio',\n", " 'amish',\n", " 'stillman',\n", " 'bie',\n", " 'earthlings',\n", " 'jurado',\n", " 'novodny',\n", " 'somnambulistic',\n", " 'underground',\n", " 'bluest',\n", " 'souvenir',\n", " 'plural',\n", " 'anthropophagus',\n", " 'plucking',\n", " 'cpr',\n", " 'summons',\n", " 'whelk',\n", " 'clocked',\n", " 'exult',\n", " 'shun',\n", " 'swells',\n", " 'pulpit',\n", " 'wednesdays',\n", " 'aeroplane',\n", " 'mozes',\n", " 'justified',\n", " 'morrisette',\n", " 'samu',\n", " 'rhythymed',\n", " 'berth',\n", " 'wainwright',\n", " 'shainin',\n", " 'trude',\n", " 'undress',\n", " 'halts',\n", " 'nascar',\n", " 'haavard',\n", " 'reignite',\n", " 'damsel',\n", " 'location',\n", " 'applause',\n", " 'sham',\n", " 'wwaste',\n", " 'outshine',\n", " 'romasantathe',\n", " 'computerized',\n", " 'crooks',\n", " 'loffe',\n", " 'scathed',\n", " 'vemork',\n", " 'denman',\n", " 'dainty',\n", " 'hassett',\n", " 'broflofski',\n", " 'sanctimonious',\n", " 'overlay',\n", " 'shakespearian',\n", " 'karma',\n", " 'attainment',\n", " 'leontine',\n", " 'prominent',\n", " 'tapped',\n", " 'establishments',\n", " 'neutralized',\n", " 'monicker',\n", " 'behave',\n", " 'hades',\n", " 'unafraid',\n", " 'theda',\n", " 'stealthily',\n", " 'smuggle',\n", " 'weber',\n", " 'schizophreniac',\n", " 'lenient',\n", " 'embellish',\n", " 'brie',\n", " 'clytemenstra',\n", " 'transfused',\n", " 'incipient',\n", " 'riddled',\n", " 'ii',\n", " 'olympian',\n", " 'azam',\n", " 'nothing',\n", " 'sticking',\n", " 'powerhouse',\n", " 'toffee',\n", " 'admonish',\n", " 'hornburg',\n", " 'mein',\n", " 'pathologize',\n", " 'agonizes',\n", " 'moderators',\n", " 'trot',\n", " 'montmarte',\n", " 'denotes',\n", " 'axed',\n", " 'manichaean',\n", " 'stimulate',\n", " 'artem',\n", " 'padayappa',\n", " 'tens',\n", " 'dapper',\n", " 'fibers',\n", " 'qaida',\n", " 'precedence',\n", " 'value',\n", " 'rapeing',\n", " 'farse',\n", " 'drinks',\n", " 'covenant',\n", " 'register',\n", " 'shaggy',\n", " 'wqasn',\n", " 'committees',\n", " 'lice',\n", " 'distractive',\n", " 'beauticin',\n", " 'glammier',\n", " 'robotic',\n", " 'gertrude',\n", " 'chuke',\n", " 'abodes',\n", " 'bewitchment',\n", " 'pettyjohn',\n", " 'patronage',\n", " 'browning',\n", " 'formally',\n", " 'objectionable',\n", " 'output',\n", " 'vivacious',\n", " 'redman',\n", " 'fallacious',\n", " 'baptized',\n", " 'lykis',\n", " 'wil',\n", " 'freudians',\n", " 'psychokinetic',\n", " 'marathan',\n", " 'kmart',\n", " 'uprightness',\n", " 'dearable',\n", " 'caps',\n", " 'guilty',\n", " 'glitxy',\n", " 'ripped',\n", " 'recovers',\n", " 'braces',\n", " 'mufti',\n", " 'unfourtunatly',\n", " 'characteratures',\n", " 'giorgos',\n", " 'viewership',\n", " 'perked',\n", " 'milieux',\n", " 'flicking',\n", " 'inmates',\n", " 'bicker',\n", " 'impropriety',\n", " 'scalpels',\n", " 'gobsmacked',\n", " 'mybluray',\n", " 'gills',\n", " 'alexandra',\n", " 'wingfield',\n", " 'goldin',\n", " 'innocous',\n", " 'fanbase',\n", " 'punt',\n", " 'humanist',\n", " 'tragicomedies',\n", " 'gads',\n", " 'freakiness',\n", " 'mishandle',\n", " 'purer',\n", " 'shittier',\n", " 'brownings',\n", " 'bibbity',\n", " 'freddys',\n", " 'houseman',\n", " 'adamant',\n", " 'betrays',\n", " 'doncha',\n", " 'guadalajara',\n", " 'castings',\n", " 'carlos',\n", " 'amfortas',\n", " ...]" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "list(vocab)" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "text/plain": [ "array([[ 0., 0., 0., ..., 0., 0., 0.]])" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import numpy as np\n", "\n", "layer_0 = np.zeros((1,vocab_size))\n", "layer_0" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "image/png": 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WU0gI3cKUzJIlS+xKGkiJpFkIXHzxxeakk04apjTnRo0aZX75y18OO1/kDzZmpI4bbrih\nyGJVVsUIiIhU3AF1qJ43aqJvHnTQQTb2xL/8y7/UQS3pkAAB+u355583//RP/5Qgdb2SQJxwlL7m\nmmvqpZi0qRwBNlaEbJRJaipvpBToIKCpmQ4Ug3vgpmUgJE5uuukmw5u2pN4IELWUnW+bOr3BPYej\nNFOCmgqs973ma4f147/+67/MunXr/NOFHVPuySefbF5//XW7G3RhBaugWiIgi0gtu6W/SjElw4oZ\nSbMQcNaQppIQ0IZ8XH755eYrX/lKs8CXtkJACBSGgIhIYVA2uyDIiIKaNasP77jjDjN37txmKR2h\n7WWXXWZX/MhXJAIcnRICA4CAiMgAdHLSJhJw6p577kmaXOkqRIBBe9myZXZDuQrVKKRqrCIQ4fXr\n1xdSXpMKcc6XOO5yjAMozpjuw2+uRQnX+OBH4RxFx4wZMyLpgw8+aH1xXJl8f+ELX7D1jUj8hxOU\niY+Gnwd/njhdyIYOUfVzLao80ob9QEhHnUzLIOPHj7e/nXOqj5dNEPqDfocffvgwvWkrPidhYfqH\nuigTjMLYuzrD+fS7eARERIrHtNElYubHVC6pNwIM2oTmb4tfBffdo48+Wm/QS9Tue9/7nh10ia8y\nNDTU+UyaNMlccMEFlkjEVf+pT33KXtq1a5fZvn37sGSQAwLdsTTflbtjxw7zi1/8wtYX5ePBoH3s\nscdaYvjAAw908n3mM5+xU7iUmUYY6HGEJ9gePh9Oj3nz5plbbrnF1gUBQXbbbTd7/fHHH7e/Xfor\nr7zS/o77Q36IxO23326thK4O19Z99tkn1p+FjT8h9fw/uXzUz/8YZUr6gEAAvEQIjEDgBz/4wYhz\nOlEfBIKH5lBgvaqPQjk1CZxVh4LHXc5Smpc9GGhtu2n7nXfeGdmAYFWUTXP99dcPu37iiScOBQPs\nULCZ4LDz7gflcT0YjN2pYd/k43pAYIadp9xgr6IR510iVy/fvlx00UW2PP8cZVPWWWed5Z/uHLu2\nhdsQEAHbZvDxxeEVxoryu+ns2upj4eqIyxdXl6+PjotBQBaRPpC9JlaBqVxSXwQee+wxc+SRR9ZX\nwZSaYdnhLRwH3EGUYDA0s2bNimw6/RwM8tZ6EE7AGz/XooR4QLNnzzZ777131GWbj/xYPZxgIXni\niSdsXBqsE1HCcmvyJRHKxhKC9SNKXNvuu+++qMuJzm3atMncf//9XXUGI3RevHjxiDKJwRPV1nHj\nxhksKdu2bRuRRyeKRUBEpFg8VZoQKB0Bt8y6bWSRwZiYKIMowRt912Yfc8wxdiBl0PUFzKKIBj4P\nDLxEr40T8pHfXzH3zDPP2OSTJ0+Oy2YJMPmSCGWTlkE9Tm677bYRU0pxaaPOv/rqq/Z0N51dW92U\nj1/OwQcf7P8cccw0lqRcBEREysVXpQuBwhEg5sbEiRMLL7fqAhkQwj4OVevUr/r32GOPrlXtvvvu\n9jp+IL7stdde/s/OsUvHfeI7nIaPsVb8/Oc/7+Rj0MUKEEVuOomCgwMOOMD/GXv87LPPJk4bW0iP\nC/jXIFFWDT/rEUccYXbu3Omfsse98o3IoBOFIyAiUjikKlAIlIsAVoOxY8eWW0kFpfPWLDN4d+Ad\nweie6o9XAz+HjgNmMJsfeRzlsPrHEnQkBMpHQESkfIxVgxAoHIG4ZZKFV6QC+4LA22+/3bUeZylK\n2u/OgvLaa691LTd88S/+4i/slI5bxRK+7n7/6Ec/coddv0ePHm16pWVVTXgZb9dCQxc/8IEP2DO9\ndH7hhRcM+kjqh4CISP36RBoJgYFE4P3vf79ZtWrVQLYdZ8tugq8FUyZJHZQ/8pGP2OK2bNkSWyzL\ndJmq8ZfjHnLIITZ9N18diANTOkkE3xfSkidOVqxYYR1xs06ROB8PHLjjxOncyxcnLr/Ol4uAiEi5\n+Kp0ISAEEiLAjryDKgzWccHCcDyFqMStPInCDB8PVopcd911Juzg6tK7FSSXXHKJO2XOOOMM61xK\nyP04CwOrcZIKQdAgUHF5IEOsmPnEJz6RtMgR6SBnEAxiiHTTGT0+97nPjcivE9UjICJSfR9IAyEg\nBAYcgSBGiB1IsU74gylTFmeeeaYlFXHLe+Og+7d/+zcTxPqw5MInOQz+EARIShCPY8SKFojB97//\nfUOgNEiQE6wK5MO5NW7JsEvrviFE1A2RIi91O6HsKVOm2OkSdPXFTS3h7JpE2O4Ax12WgPs6u7ZS\nDnpktbok0UFpsiMgIpIdO+UUAkKgQATYBbotkWLTwsKqmZdeesnsu+++NgqpW91CdE/IAktc0wqD\nLo6oWFIeeuihzuoZLAMIMT6iyA1Ow88995whqiskyOlCuHV8SNI6txKdlKXE++23n7WOuPKI+Iol\ng3aHCYKLL0JU2fD0URQOrq3EBCFKqquDtrJ8mPYoSmoUcvU4N4q4aPVQRVoIASHQCwH2mPnqV79q\neGO89NJLeyVv1HWCmS1YsMAOmo1SPIeyWBkY4CEbUaQgR9HKKgQag8A7GqOpFK09AgwkPFgJMMQy\nzDfeeMOEneV44yW2AW+AEyZMsMcf+tCHat+2KhWEfAQh960KvPmxb0cb92XxpySqxFt1CwEh0F8E\nRET6i3fratuwYYPdwh2PdZbGYc7Fix2TLoNmOPonUUEJyMXcLUsL2cb+6aefthtOnXLKKTb/IDst\nuhvE4cRvcPTJGgP2m2++6ZK25puYF0cffXRr2qOGCAEhkAwBTc0kw0mpPAR4Q7/77rutGR3ywe6V\nJ5xwQub5fcpjLpxlfCwbxKntsssuy1yep2qjDn3y8d73vrdr+5kDh5C0ibSdfvrp5uyzzzannXZa\no/otj7KamsmDnvK2BQE5q7alJ/vQDgjDzTffbIj3wJ4Ua9asMZs3bzZs4Z7HyZDBlMEHhzq36RkD\nMc5s1NlmwUGTNrt2Y/ng0wtPVgd85zvfaRU0kFDCcEuEgBAYLARERAarvzO3loGSDbQcAYE0+NMF\nmQsOZWQAvvHGG+30DdEmIT3Lly8PpWr2T0c8+Ka9ScmH32qICCsB2iJggXWtFwFrS3tdO1ihwnqB\ntjmqYt079NBDXTP1LQS6IqCpma7w6CIIEIyIYEHEHcD60U9hgOIh/cEPftAsWrSosVMRtMNJEQQO\nSwp+OFik2iDcYz/72c/MTTfd1IbmDHwb6E/2xeGlQiIEeiEgItILoQG+zrQIAYeQr3/965W9raIH\nfig4aBINMuwAW8cuQme30gX9iiAf4Xbyxrlw4UJz/PHHhy817jdTcY888oh5z3ve04j+bRzAfVaY\ne5MAYmXc931uiqrrAwKamukDyE2swpEQggGtXbu2MhICdviQLF261EZNPO644wzWgDoK5mgsH3w4\ndlMuZT2MP/vZz9oVS3XEIo1ODz/8sCUf3GtMzfjWozTlKG09EHD9V9Z9X49WSosiEZBFpEg0W1KW\nT0LqZlpl0GJvDDYBq4NlJM1Kl6JvD/qJpb0sh26ybwVvz/Pnzx+2WobBTANZ0XdMf8rDyZyAe2n2\nxumPZqqlrgiIiNS1ZyrSq84kxEFSNRnBIuOCb/VaZut0LuMbEkQk0t/85jfWYlRGHWWXSV9ec801\nkb4uIiNlo198+Tw/cDCn75pMjotHRiV2Q0BEpBs6A3ht5syZhtUqrIqps+AMRyA0po36EUvDmZvB\nhAdtP+rshj9kCB34oA9LqZtmQWDQYiVW2Britxvc64C3r5OO4xGAWN5yyy3WYhmfSleEwHAERESG\n4zHQv4gRctddd5mNGzdWPtAm6QgCYBEqHv+RMsQnH3Ua5MODM8ubWVHUlH5zfQWZZAuAXqQX0sXb\nddXkz+mt73gEeJEhQvIgBaWLR0NXkiIgIpIUqZan42HPmycrPerge5EEbmcGvvfeewtZOUJ5Za90\nSdKubmkgIVGkCFJ2yCGHNGZennZMnTo1sQlfZKTbXVGPa0wVMlXZtoi/9UC33VqIiLS7fxO3joGM\nfT6atqMre92ce+65lkBkeWP2yUfU3jiJASw5odMzioRQtVulc+utt9b+bTSrriIjJd9kOYvHModV\nriwLZU71lL3GCIiI1Lhz+qWacxhsmmnf4ZOWRDEQstIEqTP5cO1DX4hIL0uVI2V1WVHk9Pe/aQex\naViqm2VFFmQEwilHSB/VehyztP4LX/hCIdbJerRIWvQLARGRfiFd43qilk/WWN0RqjE48RBkWiXO\nKkKaOqx0GaF8jxOQECTJwEvab33rW+bKK6+szfJmv3mOhOy333653prTYOLXr+PyEHD/g47gl1eT\nSm4jAu9oY6PUpuQIYA1BmuxchqWAHXvZEdifWvLJB/4vvSwKyVHrT0r0T/P2zyDwD//wD+Zd73qX\nJWZ1sow4EgJyONbmEUgZZIRPEoKWpy7lTYbAgw8+aP8Hk6VWKiEQQiDYcKmv8sADDwwFKtjPWWed\n1de6i6jM1592+OLaxXewk6h/qedxYKru4HLnnXf2TF9UgmnTpg2tXr26qOIqKyfYiXYoGJSG+Haf\nwAJSmT55K6YNafQnvS/0KfdhHfo2sFQNBZv0DV1++eW+irmPA+I1RNmS6hHgf099UX0/NFUDhXgP\nntaDKrxRrlq1ykyaNKkVELBPCdMvOHTyiZumqXtj3cqYpPrTj6xW8AULV0BObBRaIl1ikahCsLgx\nbcYKmSw+Id10xhrCB8uRpDoE8E1i5+SmWRyrQ0w1hxEQEQkjMkC/n3zySTNjxozGDth+V0E8cJR7\n7LHH/NONOoYsOBKSRnGmZBiQwwImlLdt2zYbOIzjfgnkiJgShOMn2Jo/ZVakDm7qSmSkSFTTlcX/\nHJtSSoRAVgRERFIid8YZZzAf0/mkzF6r5Ox2SvChtsiRRx7Z2E3gGLj5QB7SSC/iAkEhYBgDBVYJ\nVhiVSUggUwTGox0Em8OBOG2b0rSftCIjaRErLj39zQ7QJ5xwQnGFqqSBQ6AWRITtokeNGtX58Gb7\n1ltvxXbGpk2b7Nuvn2fMmDF222m3MiKc2U9Lfj4nnXSSrZP6kSRpcMry04Xr8X+vW7euUwd5qI9z\nWSSqzThook9WYVrmiCOOyJo9dz6HI+0oQpxpuGlvxxAQxOmfFAvyhadk4vKed955lhQQK8YREkzq\nRQmYMwWEU/AzzzxjV+0wFZN0eimvHo6MlEmy8urYxvzr1683gZ9ZpEWuje1Vm0pCoN/OLb6zJ86q\nfIKmjfjss88+Qzt27Bih3vXXXz8irZ+ffK+//vqIfH6acBnOOTRJGl9/0vvi57/66qtj9XT1+Xm7\nOauS3i87fExdaQXHsiASZ9pshaZ37TjxxBMLKzeYaqqFg2bSBtEPOF1mkbCDatIycIK95557bP8H\nFhPrRBoMKKn1oP5rr712WDlZ25JU9yTpsuKSpGylGY5AW5zdh7dKv/qNQKXLd++///5gLIqWgITY\neAgrV67sJMBycdVVV3V+Rx2Q7+STTzavvfaaDVYVlaZXGeRJkiaqbHfuuuuuc4cjvi+44AJz8MEH\nG6YSegkWFNJ3E+oaO3asmTVrVrdkw65t3brV7L///sPOVfXjiSeeKKzqCRMmGO6BJghv71k3dOs1\nJdOt/VgPsJDwwZLBPbZ48WLruEyYeO4L7iesjGHB2vHzn//cbjgYrIQxfDDNH3/88eGklf12vjFl\nTwlV1sCaVIxFDqsqy+YlQiAPApVPzQRvwyawYFifi127dhl+O/GJClMubJLlhMiMwRLZjq9GYBVw\nl+xAdMcdd3R+Rx2QPmB99hM3gCdJE1W2OxdYMjp1BJYUd9p+sz9KEvHb5WPFYBtYkzpFgE3ctFQn\nkXdAfsz0dRGmnoqQ8ePH26mBIsoqswxHJLJMXaSZkunVBqaDcCTFj4T/B+7TuXPnRpIQyrrooovM\nggULbFrilMybN69WJMS115GRqlYLOT3a/M09EyzJ7tv0W5uxHPi29dsEE57aCAbEYSoQfyPolM4n\nICf2ejhfVJwOf5qHKRpf/DJJFyVJ0oT18Mvx8wcEwr9kj8NTLK5tXIyammGKyS8zjBX5aadLg25J\n5aabbhriU6U4vflmesbHI6temOUxF9dVmBbJO3WQN39dsSlDL6a+wFxSPAJM7TKlJxECeRGo1CKC\nVWPvvfcOxqE/Svi3e8tnu3AnweAbOa3xmc98xiWxVhGmH6KEuAa9JEmabmWceuqpIy5/9KMfHXau\nm0MuCZlecoI1JIwN+6Scc845Lon5yU9+0jnudYCJHetBXsHR1Dmdpv3262Z6BjM/02+9cPHzNekY\nSwafPFMGzpLSpHZXqSsWHzCXZaTYXnAO4XWakiu2hSqtnwhUSkQOOOCAxG198803O2nDA7q7sPvu\nu7tD++1IzLCTwY9wuvB1fidJE5XPnQuTBs5DHHyJ08+lCSwE7tAwUEcN9L4vyttvv91Jn+QgrE+S\nPGWmefnll60/TJNjgcThw2CIpF0Z45dHGUlXyfj5Bv1YZKT4O4AXBlbLSIRAEQhUSkSKaIDKEAJ1\nR8C9PUYFHUuje1zgsjRlDGpaR0YcIRxUHIpqN4sIiKkkEQJFINAYIsKOnU6effZZdzjs27cgcKHK\nN34ccMMStoBEWU38PL5VBsfUYB6u6+fLX/6yn73rMasi4qauumbUxVQIMJUCAclLQjQlkwr2yMTO\nGiUyEglP4pPEnwFLh2fijEooBGIQqHT5boxOkadZVuiEFR+ssggvfw1iI7gkBj+ScePGdX73+wAf\nDAKY+eITKPTrRUQOOuigTnbyQmSKIlcszQwTt05lKQ5YNfH5z38+RY7eSXvh0ruEeqQoijz0Y0qG\nOl544QXrW8W9i7hluhxDpCZOnMih4X+R+4f/v6YNRrSDtvLJSw4tGAP4B2sIS78lQqAoBBpDRIgN\nwuANCUH+8R//0Xzta1/rkBGisfrLfX0nzqLASlNOOLYHEVD9eCBJ9INI4dCL7wTt/tSnPmW3UHcE\nizKJZukwCVbNpDKXFkFEnC5psCkzLVYerD1VCo6RRYY2Z0omj4NrHBZMGXEPEQti586dlmiwpPvs\ns8/ukGRXLwM3eiAsm9+4caO9F8k3efJku1UAG+01QRwZof1NI1JV48u9vWzZMhMEsqtaFdXfJgTy\nLrtJm99f/hq1jDYYVDvLUQOch0VXDS9/5XrUJyAsI5aC+unilrkmSePrT3pf/Py9jmmnL1HLd7ke\nri+uXPKnkbovc03TFj8tkT6rXJZMZFGWjBYlZSzVXb16dScaKnjlqYP2EqU1WPE0FAzwFnvONUEC\nC2OhfdWENufVkb4merFECBSJQGN8RIIB2EYO9QN8cS4sWE0ef/zxwqYwwuUn/R2EkY9NSqCzpNMP\nOISRvptgNVm7dm23JCOusfqCN+G2Ccu83RRCv9uG1QAp6i2b8opcJfPwww+bQw891FxzzTVm/vz5\n1sLB1JqzemTBC+sCZnqCm7HL7vbt263ObHxX9yWzbn8a50ycpf2Dlmf58uWt2ihz0Pqvru1tFBEB\nRBwyIRphQgIBYcAm9kYdpguIrxFYM+zUiut8YoGge1wkV5cu/E16nF/D5AYCQptfeumlxMTGlc0A\nwlx/mx7CDHyQK8Km91scjuBalDAVUkR56Mauu46AbN682ZQxjQKhYaM79MbPhH4ocmO9onD1yxEZ\n8dHofgwx5l4q497pXrOuth2BUZhX2t5ItS8aAfxLcDokxHcbhEEPosrbeT8Fp9Sse8bE6VmUoytv\nsJB2wraff/75fQ3HTX+ce+651odk0aJFfa07Dte480X79cTV0+TzbCOBXxlkUyIEikSgcRaRIhs/\n6GXhZIg5vQ3CoLdixYq+e/M7wpBlz5g43IuakoFoQkLoY8hmkTrG6e6fJ+omTrsE2psyZUqtrW9g\ng0WH/pREI4C18cwzz4y+qLNCIAcCIiI5wGt6VgYKTK1uWqHJ7fnwhz9sp71Gjx5tBxMGlDIHFd6g\nHQkpGre8UzLoxhYFrLYqcvVOlnYywLM5GtOI6FT3e01kJLqX3f9SHn+i6JJ1VggYo6mZAb8L2mJu\nZQrikUcesYOe36XuAerOFTGFgsWCwb4op1SnG995yQ16YX1AcGDutxXEVhzzB2fZSy65xE6dlYFd\nTLWZTufth0yV1jgT1jUCLOLcLBECRSMgIlI0og0rz00D5H0Lr7rZrAZZuHBhzy3peSP3I9yyKiWN\nQyh4IWnyJMWmiLJxSiUQWd1IiMOgaWSkCOLq2t7Ub8gtOEDOyrjvm4qL9C4OARGR4rBsbEm87SBN\ndULDGoIzJKtB0gqDPyTMCZFr497WITFYGMp6GOd9C8e69fTTT9eWhDiMm6In+tLn9HedLEsOx359\nQx5vueWWvjuB96t9qqd6BEREqu+DyjVwVhH8CeIG4cqVjFHAva3hkFnE/DXlgYMvzm+gzLfjvCTE\nrVBpylsrlps99tjDLF261Ie6lseDTkZmzpzZqMi5tbyJpFRXBEREusIzOBcJQMVg3u+lr3kR5iGJ\nlDmgYXHxY9MUQXj8duedknFk7N577+05NeXXW+Vx03Qu2xpWZV90q9u9pDCdOchWoW4Y6Vp+BERE\n8mPYmhJY1TB16tTGxBUp2wrgrCNh4oHVwZe8lpK81pB+kDG/vUUdu/7DAtWEQS4vYSwKt36WAwln\nX6EyiX4/26O66omAiEg9+6USrXjrg4w04c26bF0ZdCAiSaaq0CWrA2xeEkJ+yGNTBvPwjc0UDRF+\nm7IaY9DICM8DNhRlqb9ECJSFgIhIWcg2tNwmrGqAILBENdhorZQBLO9gQ/4kDrB56+EWYyBnx9ym\nRscFA1YuNWnVVhH91oTHgyP7/r3cBL2lY/MQEBFpXp+VrjHmWCJy4i+SxCJQukJeBZCQE044wXzs\nYx8rZZUPD9+iV8a4KR6vGZ0onuFpHz9Nr+OmW0Nc+5oYowIyktRi5trZtO+2xBhqGu6DqK+IyCD2\neoI2MzisXLmyVmTEWUIOOuggc8455xSySsaHgoE9r7+HX16347ADbJZ66aM27BXU1Ddv7kcISd3I\nerf7Ls01LFV1fBlJ0walbQYCIiLN6KdKtHTTNHXwGWGwYp+LSZMmdSwhRRIHyspjnUjTQVGmfdqX\nxs+EQZCYJ02a0uiGEb4Is2fPbtzOrm0lIzgSX3755Zli83TrZ10TAlEI/On/CyTqgs4JgQMPPNDs\nt99+ZtasWea3v/2t+fjHP14JKFgP2MX1i1/8ornqqqs6OvDGtm3bNvN///d/mVddMJC88sorfSMh\nKI9jKRYQX/baay/rK0Gb+KAX6SAtfH79618b0jj55je/af7nf/7Hhkx355r+vX79evP3f//3jWrG\nO9/5TsOHe4h+a4vcdttt1g+LiMUSIVA2ArKIlI1wC8rnbf2iiy6yLVmwYEHfBm0GYJww33jjDbNk\nyZLYeknHwJ3WRJ41X54uzWp5ccTE1X3TTTdZX5nzzjvPnWr0N32BRarJjpFZ+7ZuHce91iZrW93w\nlT4jEdDuuyMx0ZkQAgzwzBWzTJQPvgkMHGUJD0Ic5XjDZGkncQy6TZsQgpsPA0FScfqnJS9Jy49K\nR51Z35pxoAUD93nttdfMe97zntrvZhuFQ9Q5+s/tnBx1vQnn6Js092Dd2sT/HYJlatq0aaVtZVC3\ndkuf6hEQEam+DxqjAdYJpgsQBlQCaTGXXJRgeYHk8Da2a9cu+3ZMfIkkwa7cQM1A4B6ocXpRD8Lg\n108pyp8DQrNlyxa7dLefRKpsrPD/2bp1a9nVlFp+k8nInDlz7P/zihUrzNlnn10qTipcCPgIiIj4\naOi4JwIM+GyOh2PlhAkTrEMb88gQCEhJLxIQrgDigPWDMnBYZKvxZ555xsyfPz8TUWAgYKB2Fo+o\n+pwFJXytzN+0E92KEAgNb6xtE1YAvfrqq41vliMjaf8Xqm7422+/bZ3BV61aZadD2fYh7v+oal1V\nf7sQeEe7mqPW9AsBCAkWEj5YGB588EGzePFi+yBjOmX//fe30yoQi7BANNiqnp1iCUrGZ+HChcOi\nN+YZuLES8ABFL99ikKfMcBvS/EaXrFMyUfVgNRg7dmzUpUafmzhxoiWhjW7EH5SHjHD/QXqTWPTq\n1macwlk102+rYd1wkD79QUBEpD84t7oWBns/RDcPYCwmzz//fGS7cXxl+qWbhcC9VXZLE1n4H07y\nAOWNFPLBChWmlLKW1a2eJNewYBRZN9NWWA8k9UaA/4umkhFeDrB8SoRAPxAQEekHygNWh7NC5B18\nsSJgTcj6VsabKGWsW7fOnHTSSZX0QlVWmEoam7NSCCPTAm0SR0a4F7Pex/3GAxKydu3afler+gYY\nAfmIDHDn173pzqqRda7dzW+zHwvHWcvJilPRUzJZ9WhKviZOYSTB1hFzdz8myVNVGv7nmGJta19U\nhavq7Y6AiEh3fHS1YgR4iLuVOmlUwSSOuLdQymEg6OdgUNQqmTTtVtp6IuDuw37ef1mQWLNmzTC/\nqixlKI8QSIuAiEhaxJS+7whgsnfEIknlTIfw4HcPf5fHvZmmKcvlTfutKZm0iJlc03Dpa+t/Dnc/\n1pWMfPnLXy7Ul6n/CKvGpiIgItLUnhsgvTET80nyAHcEIM607AgK6coS9CxylUxZetatXCxIrJxp\nszgy0g8ynBZHR9TT5lN6IZAXATmr5kVQ+TsIMAC/8MILZseOHZ1lmG6ZLoncsl53zMqPI488MpEp\nmAe4s3R0KvQO8P9IujIGkoIjLeVhbYkjLV7xqQ6LXiUTrnyfffYxjz76aPh043/7m/41vjFdGsC9\nzP0KGSli8Of/7vvf/755/fXX7Z43xAPx/++ozxE8/gf5vxs3bpysH136SJf6i4D2mukv3q2rjYcp\nMURY7bBz5077wDv66KPN+PHj7RJdGuxWz5CWwYaPe2i++OKLNt/06dPN5MmTh8USiQLLWTz8azyI\nebBneaijE0TEvan65WY5jtIvSznd8lDH3Llzbdj9bumado0AWgixaQZBuGe5d7Pet+7/jii7BLjj\n/w6Suvfee1v4wv93nGRJ/fbt220Yd/5f+Z875ZRTbPyfogn5IPSh2lgMAiIixeA4UKXwAGU/imuu\nuca2m4fgGWeckemBSgGQgU2bNplFixZZUsIge/7550daKsIPbx7kSB4ikYfI2Mr/8KcIXfzy4o7B\ngDgsELqsA1lc2VWeZ8sABlJ2e87Tn1W2IW3d9GVSSx5lP/zww+aWW26x/zNJyXucTtw7Tz75pGF3\na/4HL7zwQjNjxoyBwT4OF52vAIEhiRBIgcDq1auHgkFiKCAfQxwXLd/5znds2dQR7DA7FAy2I6oI\nHtz2PN/BNMiI61lOUA9155G8+ZPUTR18Dj/88KFvfOMbSbI0Jg197vrUtbMfmNYBoF7tDIj/UDCt\nYj9l/N+BexBJdSgYgux31P9dHXCSDu1EwLSzWWpV0QjwoAoCHdkHYa+HZhF1Uwdkh4cvD+Gw3HPP\nPZEkJZwu7W/qzfIQLgsTyvU/rj3XXnvtEJ+2CPcXfR0lfvuLIp5R9VR9Luoeor38H0DSyiAg4TZT\nX2AVsfXxPyYRAv1AQESkHyg3vA4eSLwpYaHotzgLDIOuIwg8sDlm8CpDKDfNgEfaNOm76Uzd/sAb\nl9a9Icddb9p5+pc38l4Czknw6VVOXa/7ZCTq3u+X3ugBMYSUuP+7ftWtegYPAfmIVDAd1pQqmb/G\nDwR/kAceeCCzD0je9jKX/elPf9r87ne/M8GAZT7+8Y/bIsv0yaBs2p/EkTCPgyr1BINrB6I0q3hY\nIkwAKueU2CmkgQfsvrxkyZLUbQF7J+ARWA7cz8Z+06b77rvPOoBX2b/c/3PmzDE4lFf5/9/YjpTi\niREQEUkM1WAl5CE0ZcoU22j2najao54B+4tf/KLdN+app57qEIQ8JKBXj4JBYKHoOjimrd+V6erO\nM3h+6UtfMmyA1/TNyXDAhPBu3rzZwZLp2yd1OPMmIZGZKio50xVXXGG+/e1vm3vvvdcccMABJdfW\nu3hWMy1YsMCu0moqpr1bqRRVIiAiUiX6Na3bkZDDDjusNoMcOkGGeEivXLly2EMxLRlICzvlR1kq\nGPiQXm/h5HdS5ABJ/RAZLCq9dHD11/GbvYDOPvtsc9pppxWmXpjwRfVfYZUVWBD398svv2w3naMN\ndelXyOIll1wy7P+uwGarqAFHQERkwG+AcPPrSELCOjoygrUCcoLODMplvq1FxRuJI0A+8UD3MqdO\nwAJpqlUErKZOnWotT2Va3ei/wNfBYlUkGbQFFvTHJyFlYpFVXZGRrMgpXy8ERER6ITRg1+v+MHTd\n4fRkmgZhoOHtscwHOGQH0gPh8UmIP8ihS5nEg/J9QSfq86er/Ot1P8Y3ZP78+YVaQ3q1mT6ExDqp\ng7WE6Y+77rrLbNy4sdR72LU56zcxR4j3U3c9s7ZP+apBQESkGtxrWSsPmauvvrr0t9OiGn/ccceZ\nYEmxmTdvni3SJwdF1REuh0EMB8K//Mu/NKNHj7aXqx7IGMTQyZGysM51/V0XvX0iWYW1xFmFmkIm\nCTxHGPmHHnqorreW9GoYAiIiDeuwstR1b9ZVeumnbVuUzmWQkfAbNKGxISFVExAfL0gZUxxNCY/O\n4A9+WCbKnFLzMUpyHO7rsvuY+qjjuuuuM+edd14SFStPg85HHXWUXVHTFJ0rB00KdEVARKQrPINz\nEYfBIG5Ax7rQlJaHTcWQEySvkx+Exon/luwTnX5MBzkden2jC2QEsznh9ussTRrIfGtJnhVOcf3B\nyif2immadcFZcfjO+78Wh43ODw4CIiKD09exLXUPFef8GZuwphcgUWz45awBPllIqrI/4JAnys8j\niuSQD7+UOjyMb731VvOv//qv5j//8z9rZWXw+wASwrLwOq3I8vXrdkz/+zFfou6RbvnD1yivyaue\nmu4oHe4P/a4QgcGL4aYWhxEggmI/wkeH6y3qN1EgAyIwLAKkH6Eyqp6AdA2L0JkkemRcmUT7pLwq\nBd1oA1FwwaJqfeKwIHoqWwUkwTuujLqcB3P34R5IK2BRRbTitHrGpafNwdCVO6pwsCOwLYey7rzz\nzrjqBup8EECugwm49FvoB+rlE+yUXnr1/W9h6U0qpoLgja3TEeF/jm7Xiqm9f6W0JVQ4+3H4D3UG\nOn8w5qHpBg03aKdBmTzdhPp6pemWP+u1qHoZ4OpGRtCTcOFtISHh/grfX+Hr4d9uEAeXJgv3Gp88\n4p6nfA+SuIGeb4iHL1UTEXRx/XLiiSf6qpVy/CcBCJIGIzBq1CjjPg8++GDqlhAcjDDOTZe5c+fa\n5Y9+O1599VU7TcFUDYIp3X3SLPN1JnS/7PAx5VE2dTH90A9BLz7hKQJiiuD8iM/Ihg0b+qFK1zrc\ndMybb75pA3Wlwb5rwTW6yNScu7fcfcC9wIc+CstXvvIVEwzgtV6qG9Y56vdll11mFi5cmPmeJ6w/\nAdwQ/ocl9UHA9ccTTzxhsowtqVpSCr1pQaGODQZgjjAXdrvW76ajn/uEWXUvXdryVuba+bd/+7dD\nX/va16xlwllDirBSpC2DusG2TElSh9s0zbcUlalTVNlgh3Um71tzVNlNOedbS9x9WTeLVR4ssUZm\nmdoNticY2meffezzi+9BE/fc5jvts7sfWIX7h99liSwiwV0wqPLkk0+awFze+Lcy+o83T1aL8NbN\nG6lbEureTrP2MeVSRhpxdePIWoagE2/gfLoJIdOJTcGSbKwjZekTpQNWEFaEsKQYJ9qmRn6Nalva\nc/QT9xAfjm+77Tbjr8RKW17d0hOef8WKFanVCgZfs2PHDptv9uzZw/LzBu4svV/4whfstRtuuKFz\njmsXX3yx2bp167B8/g+sLYcffviwPJx76623/GTDjimPcl3dY8aMsdYA8rhzfIfL4HdYP9JxLqzj\n9OnTbVl+xWeeeaY95ywPfvspBwmm8YbpgJ5hCadZt27dsCSbNm0y4Om3BX1cvX5i7tFzzjnHnqKf\nHn/8cf9yscdFMhx8KXxrQaCptSYEjRhWTRA0q/MWDxMOX/fL4DgsMDPfmYZ64ury8ybVjzy+Dml9\nRHC+8tuIbmeddVYs6/XrAgvyMy/n2gVGYR0oz10Pfydl18zZZ3mT8TGt0zFvmzjehoU30iwWiqz5\nXP3M/6e1pri8Ud95ysMqEgyC1jKRBYsofaLOoaOri/urzLqi6m/CuWAH6SE+bRH6nGcQ32nEf+7x\nzPOFZ5h7rvEs9Z+H7rz7DuflGdotPc/TKAdMynFlhr+vv/76Ydf8MYuywunDv/3nd5Jnt99+ynJy\n0UUXdeqKsiJRj6ub674Vw7/m0vjf4ByWgHx0yivTV+SPLQxrkOJ32o4nPSA5EHwAwh0QvsnodD+v\nK8P/DudJqx9N9/9J/Juo17UsnR2uy2+Lf8wN7CTJzezSxn1TdtEDBVjTh/QpOkb1Fee4RhrSkqco\nYbCNahMkJe2DsigSQTlp6w7jQZucWT98LelvymCKhH7nO295fr2U7QgIpvqisPPraMsxDrtF41P1\n/x1twvE9qYQH73C+8DjgPwfDx+EBshsJcXl5BvmDNMdRzyqXPvztP7P853c4nf/b1Zfk2R1uv8Mn\nfD5MqHyiwrETn1D4OoWPw2MdOvtpwvW58vN+F0JEsnR8eMB2Het3qg8kDU16s1CGL1n08/UId07c\ntayd7Zfnd3rUMXUgSW5mH4PwcZz1IJwu6W/6z/8niNK92znyunsgaZ1R6brNV6d5+KdJG6VH+Bx4\nRxGkcLqo33nyRpWHHryRQ9qwIHGcpb3oxXJhLB/0Ld9ZyonSsc3nwKooqcv/HfdQGl8k//kfJhJg\nEx5wSeMGQcaB8PPPDfLhfP6zu9s1Xx/6x88XtoZw3T2rwlYUP1/4mnt2u76nHPdBN1/CurprYWLg\n10can0z59fljjI8l7fCxDBM0yvTzhuvjehGS+z8iDJivaLdrKO830L0du44BENfZrqGU7a7z7dcV\nvllcJ3TTods1Xze/nrDe/jU/T5rO9vOF20U7/DbTTl/8a7QnqfD2wqBdhKCj/w/g65TmmDJcv2XV\ni4dh3AMRqwSDZy9hoM5KGrqVTZlJ6vfLIH1ea4pfXviY+4BBBEJCX/FmC6FwOIa/Sct9A4nhQ1qm\n98rUMaxzk39D1MC4CKnT/x33APdCUvFfWsIvnJQRfjaHx4KwRcVd98v1Le1OL38M4bnrhOe1e1ZF\n5eOcu863q88vj+dXWPxne/j57JcXvhZuv1+u30YfOx8TdHHkzD/v6+7KJJ3//A7r4hOVKGxcOXm+\nczurfutb3wra9nsJlDSzZs1yP63zYNBRnd+3335755iDYIVD5zfLDRcsWND5zUZme++9d+c3B34Y\n5HBdV155pY3W6DK88sor9jCPfq6sJN84JLllaKRftmyZGTdunM1KO+644w4TdLb9HdzEsY4/4Xad\ndNJJJrjZbD7+sNlUERLcnDYaad6ycNKiz2lTXqEMygo7gqUpF4yfeeaZyCxu2Wiv5bUBYejpCBpZ\nQY+TwcBty8XZNIk4p1Snd5I8adMcf/zxNqz/5s2brTMc/4PsIxInu+++u11miW7gtHTpUrtzbpk6\nxunSxPMBYTN77rlnbtXr9n/HMy7Ns+mFF17oYLDvvvt2jqMOgsF8xFjgnq3h9H65RFsOy+TJkzun\neF7TH3xYouokKl/UOdLzvAoGYPvZvn27LQKHUJxicSb1xwRXft7vY445plPEf/zHf3SOn3322c7x\nJz7xCesQzYnXXnutcz4gXCOw9J1SSfiTn/ykk56D/fbbr/P7xRdf7BwXefCOvIVl6Xgajhx55JF2\nt1dICOI6jRvPJzT2YvDHv1mCNzh3uvP90ksvdY7dQR79XBlJvpN2tmtruLNdHVHt+sAHPuAu1+6b\nOCRhEkL/BSza7LHHHubggw+O1JkYHzy4gjfyYf1KWZQJscwiYfIaLoMVLQyicSthul0Ll5XlNwM2\ndVNP3IZqEKXAEhKrY5Z6k+RxusVhk6QMpemOQFEvAHX7vyNU/apVq7o33rvKxpFOeE50kwMOOKDb\n5WHX3BjCyZNPPnnYtagfkJCwjB8/Pnyq8xI54kJwgjICK4K54IILoi4Xfs5vF89LXoIhZt/73vc6\ndbGNgpPA4uEO7bPWrcLpnAwddCOUP/vZz0Kpi/mZm4hk6XhHRGgCb/v33XffsMHMt5S4ZobfkllW\nlUTy6pekDtIU1dlJ25VUr7h0WA1YdpdXIBK+8A+ZZNM1SCgC4eANYuLEiZ1iKDMrEekU0uXAEYHw\ngJskcFmXYlNdou6ofWrQASIS1i1V4UrcegTq9n+HtS+NhF9e0uStU1pISDDV1nmJdrph2R47dqxh\nFsAfg9z1PN+Mn7zo3X///bYYLCG8gC1evNj+xirsP0/z1NWvvH/Sr4ri6qEjwzclb8uS8hHoZT1I\nooFvpeKfLwkJCZfrLGPuvF+mO1f0N29w4YiXZU3JxOkejjfi4ny483H5dF4I+P8jTfq/cz2H1bQM\n8csNnEU70yZu+iT8HfUMxGoVlvAY5a7z4uWIBnWTjjq+/OUvR1r1Xb6838QFcoIlhLY68adlOMd0\nqhMITBiD8G9077fkJiJ5O56gR2HhXNgC4ltRSO/m48J5w7/z6hcuL+53Ezo7TveizvMGkFXy5M1S\nJ29wWB6cv0jZUzJxOjq/keXLl1v/kbRvlnHl6vzgIJDnfydP3jwI77XXXp3s3aYCOokSHhxxxBGd\nlElfaCEjzn+PzFE+ZlHnSEvAQCcXXnjhMP8LXrIdSXFpivomAJoTLCG+fv60DGkOOuggl9RgPUGv\nrJJmmixNHbmJSJaOdwoSzc2Zl7gRcKRBYJXOzOTSQkT8myXKx4JpDRcxDmchJI9+ru4k30V2dpL6\n8qZhXjYc8S9vmf4/Zdqy8uRNW5dLj+UBX4x+Tsm4ut238wc577zzrC6OGLnr+hYCvRDI87+TJ28v\nvbpdf9/73te5/OMf/7hznPfAd+TEZ8ONA5TLy60fNZWoq05cBFF+48fn5+PY+fa59FHfLKZwgzzT\nzZ/61KeikkWe86f2IxOETrrpGXfa6Rc1LYP/iHshZ2xFL//Zzzjsj53hKKtEq3biynG/C/sOzDK5\nhKU+gTKdj7+cNWj0sNgSQSM6dYWXDJEvvO6a374EJshOPdTpL/UML99lyRKSVT90de3y20SZcdf8\n8/7yXadHcJN0ykQvJ36+cJtJQ/1OFzDwxZ3nO6ynny587JZlhs+n/e0v7UIH+oG+TSqk9dtHGZSZ\nVVgemWZZcvDgGPrGN76Rtbpc+aKW87Jcl/P9FnAAO+4Lt0QXHMMfAqGRJvBRqETPfuNSdH0O37zl\n1u3/jvs2sOYlbpb/P8+zMiz+czvueeA/+xhrnPjPUz9N+Nh/BpM/fL3bb1dfeNzplsevD12j0ro0\nfvtJFyU+hq4sfzmvnydcnksf/ga7sPjjlj/mhtPl+R3dwpQlZul4n1TQUDd4+f9gYVCS3izhzsii\nn58nPMDHXcva2X55eYiIu6nczdytG4t6IEb9M6AHDxf6kutRH66Rxunsf4fx7taO8DUCbKXZYI3B\nl4G/34N/N8LRL32oB7yIawH+fDuiAS5RH9Jz70BQyJMnIFq47wbhN5imIcpxmNTt/y5tu8LPcvf8\nd+31n6VpiQhlxz1b3HMm6hnj1+nSue8w4XBEhG9/oHbp+WaMYyxy5yjDlygd3bM7rIufzx2DmSvb\nfXcjCnH3jMvLOOTaFVdHuJ9curzfhRCRtB2PtcI1nm//puh2jVopbAYAACaLSURBVMaGO8gvh2M6\nNwxWWv2oxycHvn69rmXpbL+utESk282MrnGS9sERVw5YR+kQ7pekv6P6L67uqPNpIjz6Az549Euo\nCwtEN0E3yEoZgjXDEQmIB7976ROnB21xAdEgJRCVrGXF1dGm8/QpOOWVuv3fpX0BoP3+cyM8gPrP\n+bRExGHLs9ivg2cQ5CDqGevycI363POKZzO6cN6d49sfYxizfMLh8lBmHIHhWjgf5VIX4ref83Hi\nt89/oY9LT51hndA3PMa5/PSLa3dcP7i0eb7jW5ih1KQd74MHCGEJW0vCLA0w/TQA1Q1MV35S/UhP\nea4Dwp3U7Rp503a2X17UPwn1O11oty/dbmY/XfiYgS6NKTWc3/+NDn6fOl3TflMGZeURBlgG1iQS\nJh/h30nKSJPGTX8kzZM2fa9yaR+DYFmEwREc7iusJpJoBPi/KIKs1en/DkILGUkj/mAbfq6lKacf\naf1nMAP+oIhPsBxJKqPthRKRMhRUmeUhwIBU5Fs3/6w+qUpKRMgTJntZW530IR9FOnwLSdb64/Ll\nsXCga56Bi7oJvw1BSDtYxLWn23n0hRByf0Xh3C3vIFxLQ5aT4FGH/7ssfY1VwU1rJHmbT4JF1jT+\nixTPI//ll5dDpyfPF9IOgtA/7hkOJmXKKAoPKpMMIAJXXHGFjXzKio0iBe/05557zgZ5Y437L37x\ni2HF/8Vf/IUhWixLnj/ykY8MW/I2LGHKHxs2bLDr93utBHDxQ4KBeUQNxPLgfJEhy6MCl42ouMeJ\nrGWAybnnnmumT59u5s+fX2i7eqhsWJIcvOkaljWyZYPk9wjcfPPNNvzAjTfeWCgkVf3f8f9EAL6A\n8KZuDytSXETSgFCVGnujm3K+Ht3ScS2wDGSKl9Sr3Lpd9zEpvc1lshyVXW8E2KiqqA24qm4p0wKz\nZ8+2/gq9dOn1lt7req/y/etYnPJYM/yy0lpV8N3ACpJ0qsqvq6hjdOYe41MUDkXpVkU5YMAqrdGj\nR7cGjyz+IT72zhpR9lu3X2fUse8bEpCCjjXAP677FFJUu7Kc861V/bAAaWomSy+1JA8PRQYqBoum\nC235q7/6K/uQh0jEkYm48377KauIKSvqoqwihfKStIE5ewb/ItpRhP7oU/RUYBF69aMM+oA+4+P6\nAyyqJIhFtjtvW/B1cYN9UVO0WduHH4TvF+H0wsEzyn8vaz11z0c/0HampPxpqrL01tRMgPYgC9Mz\nSNFm4n5j+vDDD5tbbrllWKRDoqU6IQCQm26JmpJx6dx3t+kblybu2wUpK3O/mG6b5tGnRHRcu3Zt\np81xuvbzPHqxWRtTZ20OY8+9409TRG1uyLTVI488MmxH8X72RVF1cR9OnTp1WHuLKlvlDA4CIiKD\n09eRLXXzu8GbWq0GrUhlu5w89NBDrQ/EaaedFpkKchBMRdldKknAXjO9CEmWsO/gSV39GGij/Ebq\nSkJcpzgy0vT7zbXHffukN8m9xT0CQSFfr/vQ1VHH79NPP90cffTR5tJLL62jetKpIQiIiDSko8pU\nk8GBEL9NfZhgDbnmmmvM5s2bY2EKk4okb60UFs4XW0FwIYoYdEtfxDWf+OAEedddd5mNGzfWmlTW\nnSwl6Rf6Opgm6yTNYv2iv9gjhNDgTRT+N7CGtI1UNrEvmq6ziEjTe7AA/RnMeIvDnNy0tzPeLI86\n6iizcOFCc/zxx0eiQfuQbm2LG1gon/y9LBw8lKNM8JEKFXwSHbH2/PM//7N5+umne+pacPWZimP3\n0MCHpTGracCYAddJEX1NmZSzZs0au+rEld2Ub1lDmtJT9ddTRKT+fdQXDdnxeMuWLY17O0uidxqr\nhgObPE7+93//13z0ox+NtDK4ASrLG7ErP++3G9BuvfVWEzc1lbeOovND7sDs3nvvjSWQRdeZtjz/\nHsDHqBcZTVs+6fEVWbRoUe2tWOG2Ob27WSHDefRbCMQhICISh8yAnWcww7IwZ84cU3RckbKgZKDA\nNMx3nLWDa3lJAthE+ZcwmHKtjAEqDWZMdbCV+tKlS9Nkqzytm1Kry1QS/ek7mea9b5ICjGUhWHnS\nGOsQ1kMsWk215CTtF6XrHwIiIv3DuvY18YDBVIwJuurBtRdYSawADCxIHEnpVYd/nfooD1z4JmDb\nu9/9bhPEg6hsSgb93KDQjYz57ajbcdXmfXBzksTJ1KUt8pv7CdLTBIsW/wdTpkyxLwBN9Skrsu9U\nVjEIiIgUg2NrSuEt9ZJLLqn1Ekv3MAwCIHVddlyENcTvWEdseGv2fQTi/Ev8vGUdVz2Q520XfdRP\nh8cq+6obVi4CLkt6ua/rKjNnzrTWt6Y62NYV10HXS0Rk0O+AiPbXeVWDIyF77rlnV3+WokkIMFE3\nUzS9pq78t+yyfAvQx1lDmr5qoUwyRZ8V7WQK9mXIv//7v9upUVbS1NEiWefnQhn9oTL7h4CISP+w\nblRN7qGzePHi2jwUHQkByG7BupzloogpGddplEn9DBBpSE54ICzS/E8fsV9P0/dxcVYR3z/D4Z7l\nu19EMItucXnc/bV169ZaWiTd86Db/11c23ReCPRCQESkF0IDfJ2HT10iYfKg/vSnP232228/u8rA\nRUmN6p40RCEqf/gclgfqc8QGcoE+Wd5ayecPuP4UT7jebr/Rgby01enVLX3drxGQrtsS7G76hzHt\nl5NpN53SXEN/xPWjmx6tg88I9xk+IYhIiIVBf0pA4E//XyAllKsiW4BAsNmRHfhZTbPvvvsaBosq\n5MEHH7R+BGeccYYdrN75znfGqlEGCWGA2GuvvTp1Uj9Levnupksng3cAocEq4j7btm0zP/7xjy2x\n+fWvfz2sHi/biMNvf/vbxr09j7jYwBPvete7zLPPPmu453oJg+Mrr7xiMWMQZ/oLUuYw7ZW/TtfD\nJATdDjzwQPu/NmvWLPPb3/7WfPzjH69EZf6XWA7O0vXbb789cvl6JYqp0tYhIItI67q0+AZhETjz\nzDPN/vvvb4gG6d7ciq9peIkMOCwnfuyxx6wVhCWO3awQUQ/14SWm+8WDuJvFomjSQ3t9f4Zu0zhY\nq5ocDTfcE/QdlgzfWuSn8Z1My/S78ess+7jX/cp1rIDIggULci9DT9oe7sOvfvWrlnxcd911PX2i\nkpardEIgFoGydtNTue1CgF1fb7rpJrsjIzupBgNGaQ10dQWEZ2jGjBmdHWw5HwzUsfUm2ZU2NrN3\ngXqSlpU0nVd84kMwpnz3QS8n7HjaDQuXrknffptcH0S1vUltitOVvk36P8T/Hf8LZf/foWvgjG3r\nmjZtWmL94tqo80IgKQImaUKlEwIgwMOTB2LAbO13kYMhZbuHLg/CqEHeDVDh3ohKG06T5Dc6pGkT\n6fn0Q9CLdr700ksW/37U2c86zj333KGrrrrKtjFNH/RTxyLqynLPcN/7/3dF3e+0h7L5v4MI8lm/\nfn0RzVQZQiAxAn8SayrRBSEQgQDTMjfeeGPHhE6ERT5M2TBVkVYwuRMumjKYiti+fbuN2Eicgiin\nQ3wsOE9dmJARTNjkzSvognSb/gnXAR7o4XQJXy/yN3rR9t/97ncmIGpFFl2Lsg4//HDzZ3/2Z7aN\nafqgFsonVKLXdExcMdz37v+OKTn8R/DZYouDLP936IFTLHFBmOrC52bJkiV248i4PZvidNN5IZAX\nAfmI5EVQ+Q3BmPDjCN6k7H41DJJjx461PgzAwxJTHnY7duywaO3atcume/755+3vyZMnm1NOOcVM\nmjQplUMcxIEHdPCGGUlabOEJ//Aw7+YP0qsY8kcRp175slyHuL366qtdg7llKbfqPGCIL0Rbg2Vl\nJSFx/cL/HRF+2eiQD5sIsqpswoQJnSzjx483r7/+uv0d9393xBFH9M3vq6OYDoSAh4CIiAeGDvMj\ngGUgMKvbFR08+BCsHOyF4j8gJ06caK0YWBTyyDe/+U3zvve9L5UVw6/P6ZuXRFAOA00/3uSxPiFt\nC7HdZiJSNAnx72GO3X3MSqpu/3cQk7333rsv92lYR/0WAnEIiIjEIaPztUfAPdyxikB+0pIJ8vMA\nL4o8YKGBWKFPmdJWIkJfYDkLJpbLhK/vZbv7NC/p7rviqlAI9AkB+Yj0CWhVUzwCTMm4gR8Swhs1\ng1kSyeIP0qtcCA2ESJINgbIJXDat8uVy95lISD4clbvdCIiItLt/W9u6KJ8MyAhvn+4NNK7x5GVg\nKGNwcIQorm6dHxwEnIWsjPtscFBUSwcBARGRQejllrURohG3SsZNs7g3Ub/pWEscgSnz7RvdepEh\nXy8d/x4BMGvLoO1ISJn3me4bIdAWBERE2tKTA9QONyUT12Rn7YB0OGGQ45PWj8TlT/NN/ZCepNNE\nacpuc1r6FSfmpotISNN7UPr3G4F39LtC1ddeBBh48ZFgWa5bKhjVWre0Fw9+9tVI8xbsLBpR5frn\neBN10yR/+qd/av76r/+6MKdUv564YywzSXWNKyPuPMuhN27cGHe5seeDwFqN1d0pLhLikNC3EEiO\ngIhIcqyUMgIBrAxPPvmkDUrmYhkcdthhNobI3LlzI3IYu7SXJb2LFy82q1atMkE0Rxugi03t3NRK\nVEbqipuSiUrPOVZh/OY3v4m7XOp54pIwMHVrUxYFxo0bZ/HOkrfOeYh3wb3QVBEJaWrPSe+qEdDy\n3ap7oKH1E5VxxYoV1voxffp0Q1CyD3/4w5mWrrrATOzwOXr0aDN//vzI4GZpLQykd0HKIDFYbIom\nBb26j3qRNFafXmXSjjYucyXKJ4Ht2PG1aSIS0rQek751QkBEpE690QBdIA3BnhdW0zjCkKcZPsG5\n9dZbO4NSGhLipojC/iBx5/PomyRvGt2TlEcawnsTkjvcxqT565gOa9dTTz3Vd7KYFwuRkLwIKv+g\nIyAiMuh3QML282ZPJE/8P3yCkDB76mQM3uynseeeexq2vGfgTWJVSGL5KIMY9Gpg0XWCCb4i8+bN\n61V1I64zmLPfEA6rTRKRkCb1lnStKwJaNVPXnqmRXlgpePNm/h5n1H6Yzqlv8+bNZurUqdZcn2T/\nEQYFpNf0C2VDDLCQ9EuKXNIL2WJPkfvuu8+2o19tKLOeBx980DDF1yThHoIca4luk3pNutYRAVlE\n6tgrNdKJN++VK1faHXGrmgaAYFx00UXWOnL33XdHPvgZFJw/SFL4KJdBJImlJWmZ3dJlfXsmn7+i\nBFKDzk2dyojCCIvXwoULTVN2fi3awhWFic4JgUFBQERkUHo6ZTuxFpx//vnm5z//ufn617/et8E6\nTk30mTNnjnnzzTfN2rVrO2Qkr99HkqmcOJ2ynE8ygIWJRxzBYgt4lkmzPXyTxfkdYQFrgiTpwya0\nQzoKgbogICJSl56okR4M7lOmTLEa+YN+HVTEQvPyyy9bMoKefHpNxfTSOy+Z6VW+f526ID++zgxs\nvsQRDz8Nx5SDVaRXgLdwvrr9Pv30083RRx/diN2ERULqdvdInzYgICLShl4suA0sowxbHgquIldx\nkJFvf/vb5t577zUHHHBArrL8zAwySUmAny/t8Te/+U2bhaXKSJ4pL7BAmmoVAXP8gPA9qruvhUiI\nvdX0RwgUjoCISOGQNrtAzP0EJqubJcRHFVM+JIRAZUmcWP28vY6L9htx1ha/Xucsm4eAuPKabhVh\npQxEhBVZdRaRkDr3jnRrOgIiIk3vwQL1Z4A/99xz7UqMfjlwZlWfAZ7pozIGsTx+I2HiQeAxfxrG\nb29Rg9vNN99snnnmmcJJma9rGcfLly83ixYtsqujyii/qDKL6qei9FE5QqBtCIiItK1HM7aHAZRp\nCSwNTVm5gPWCN+o1a9bkmt6IgswRil5WC5fOldGNeLg07pu8YX8Rdy3NN+UcddRR1pn3vPPOS5O1\nsrRl9l2RjRIJKRJNlSUEohEQEYnGZeDO4heCLF26tFFtL/utmoHI9xuBOPhBt9IQjyhgGZCLiEVB\nOeiJr0WcBSaq/irOQZzKsmYV2R6RkCLRVFlCIB4BEZF4bAbmCg/cpjgMRnUKVhEsAWVYAyAezz33\nnHn3u99t98FxMTyi9Mh6rqgBj8Bzl1xySe3DpEN633777VpPJRXVJ1nvCeUTAoOEgIjIIPV2TFub\ntHwyqglFEiksC1HBw/L4jUTp7J8raoqGMv3lzXVchVJ3/egLrEq9puT8/tOxEBAC+RAQEcmHX+Nz\nFzmIVwkGZOrUU09NbRUJEw9/GibcnjIHKYgOUoSTsBvs6xCIzsfQ6VXXFVlFEkK/3ToWAkKgOwLa\na6Y7PrFXDz/8cDNq1Cj7YRdUX9x5vjdt2uRf6nrMfht+3q6JC7p4xx13mLlz59Y+hkOv5hICnhUY\nvQTi5X8Y+Hn7dZ9uVgSukY78DFpFCnr4vid5yiamyGGHHWZ1hWhVLWAFUXSB6LphXJWuIiFVIa96\nhYAxtSUiDO5uUGbQlxSPAA/fZcuW2UGi+NL7W6Jb6QNJ8MUnHRw7wuG+swyK5MWC4awYfn15jh3J\nyVOGywsZYZdkLDw49FYlECFW9Oyxxx61jU0jElLV3aF6hcDvEXiHgBhcBNavX29mzJhRyHRAHVD8\nxCc+YYmVrwuDexnCyhRHRoqYTnE6QhwYvItY+cIuyd/5znfMrFmzzCOPPGKIN1Kkrk7nqG8GdzYo\n/PznP2/uueee1FNmUWWWcU4kpAxUVaYQSIdAbS0i6ZrR/9QvvfSSGRoash8e9E2URx991L6tNlH3\nsM4EY/vYxz5mp8KctaMsEuLqdoN6kdMfzkLDAFmEgMHGjRvNIYccYvelgYwUVXacfqzewQpCkDUc\nP8tYzRRXd5rzIiFp0FJaIVAiAsFgWit5/vnnh4LmRn6Cee8Rut55551DnPfzcG7Hjh2RaV26s846\ny16//vrrO3ldBpeGb/Thc+KJJ9p0lI34dbpzcfkff/zxTn7KpCzOheWBBx7o6EK6KEnT3qj8/rlg\nIB0K/BL8U40/rqJNwSqbocDyUCh2RZeHcgEpGJo2bdoQGF177bWF9j0YrF69eiggPPbDcZ0FfcFD\nIgSEQPUIRI92FeqVlIhANBw58ImDO95nn32GXn/99WEtYRB31yEi4fwusUvDt09U+O1IR1IicvXV\nV3fq9Mv1y3L1diMiWdrryo365iHMgNQ2YaANppwqaRbkgQGuKCmDjKAbfX/55Zfb+/LYY48dCqZO\nMg3KjnxQFvcS2NedgNB+kRBQkAiB+iDQWB8RfBueeOKJYDyPlmDgNieffLJ57bXXDNEvw3L//feH\nT0X+vuqqqyLPJz153XXXxSa94IILzMEHH2yOPPLI2DTuQt72unLc91tvvWUmTpzoflbyPX36dOP6\nIfiXKEQHtpMPCGglYeqZBmGahumVYGDO3R6Cp+GHUkRZvjL4n+DMOn/+fIOfEFN0AWG2SbgnwBAZ\nP378sP+drVu3ml27dplXXnnFvPjii2bLli0mIB922fRll11WuJ5WiYL/aDqmYEBVnBAoAIHa+Ygw\nKDMoBZaHTvMC64M9h18GwjJXn4SQljx8AqtCJx9kxP/dufCHA8oNLDCdvOHr7jcPaVd+Fn+QOP0o\nn71deklR7fXrYbB2A45/vqrj4C21kKoDS5gdKAspLEMhzsm0CL8RCEhRS3qjmgJhwqGVsP7U89RT\nTxmWQSMQjsWLF5sFCxZ0Pq+++qq9dsoppxhWtfE/we7H+IAUTZZsRQX/cc7Fro8KLl7FCQEhkBWB\n4GFSS2EKJGiT/TAN4kvwsOxcY+ojLHF5/fOUzTRQlLh6+Xa+JOF0aaZmwnnDegQPfZskbmoma3vD\n9fq/b7rppiE+VQrYOqzj+iKtfkxnMEVQtWD+L2pqpahyqsakyvrxhWqbP1SVeKpuIVAkArWziAQD\nU0954YUXOmmi3uonT57cuU4Qpbi37SRTIuxjkkeI9hmWj370o8NOMU3STYpqr18H5nWsB3UR97Zd\nF33y6oG1gamaIoKfuSW9eXUa1Pwu3ksTrDaD2kdq92Aj0EgiArlwgh+IC3zmvsMDbBQRYVomiey+\n++5JksWm2XvvvUdcC/usROnnZyqivX55HLPpWJRu4XT9+v2lL33J4IPQNoGMuCmBrG0reklvVj2a\nmE8kpIm9Jp0HDYFGEpFB66Q6t9ePgOuIYNJv56hK+5xz8Q033NA6QuJ8EvL4jVBGsNqlzrdC7XQT\nCaldl0ghIRCJQCOJiG/N8J1NgzmrjlOpf1zlmz9OoWEJW0B66VdGewm53WtKKKx32b8hI6xSwjrS\nNmFagE84BH2adrqpnjR5BjWtSMig9rza3UQEGrl894gjjrAbaAE4vgVJfD2q6hyiS5500knDqn/2\n2Wc7v5lG6kVEymjvhAkTrBWio4gOSkfA9xvptstvN0XKWtJLnVhsmB6DEG7fvt1s27ZtmCqQV+4b\npivHjRtnfWCGJajJD5GQmnSE1BACCRFohEVk586dw5pzzDHHdH4Ti8Pf/Za3/IsvvrjjN1L1hnnE\nEfH1YykuOjs555xz3GHsd1ntZYlm24SBlAGzzpLHbwSrCrEwigjTThmEY585c6YN/37mmWeaFStW\nWOjGjBljd2VmZ2b3YdkuAvnnHFNwOHMTNt4N/jZBhX+cHnJMrbATVLUQSIlAIywivKHx0GOKglgi\nZ5xxho1t4Jw4Gdj9wd3HgAdm1dJNPxe3oZuOZbSXYFXEicgrrFBieqxICTvzpikbcsVbe90Fnw8G\nTawQzockqc6kdzsJJ83jpyMv8XUWLlzYCUgWhHzvGQsEAuULRCZYWmwee+wxax1Br9mzZ9vYJH66\nfh2LhPQLadUjBApGoMi1wEWWxV4sQVOHfQIi0qkiICcjQrSH0xOvwxc/fodflp+GY78cYntECfld\nunA97jzf4RDx/rVwvrg4ItSfpb1RertzxFQI3hrdz9Z8VxniPQuIgb9Q5vDqafdKcTFW6HdiyBQZ\nV4N2VLnXjOKEZLn7lEcI1AOB2k7N4FcRDNSxsS7wq1i3bp1NE+wZE4zvfxQiofKWniUK6h9LKeaI\nMOa8fWLNcYK+AdFKpV/R7XWm6zwrOVx76vS9atUqc+CBB9ZJpa664DdCX6R1YiUfH2cF6FYJlgsc\ngKdOnWqj6bL65tJLL+1pAelWZvgauhCldfPmzTZ0/DXXXGOnbfpxf7k63D0d1k2/hYAQqDcCo+BD\n9VZR2pWFAL4BbNde123a07Z7w4YNJtiAzQ6GafPWIT1kJK0Ta68pGq5DyPfff3/ry9HPwRrfkc9/\n/vMmsL5Y4lMGxpAQ2gQRkggBIdBMBGprEWkmnM3SGufD5cuXN0vpLto+99xz1uehS5JaX8rixNpt\nSS99ixVkzpw5dk+YfpIQgMbqgvVlzZo11iG2CAdbvwNFQnw0dCwEmouALCLN7bvcmjMw4BgazK8X\naqbPrVjGAljaysZtaZ0/M1ZXWjamW+ibpO1w0zM+0bjiiivMypUra4EHbYEMvfnmm2bt2rWFWC9E\nQkq7/VSwEOg7ArKI9B3y+lSIOZupjGXLltVHqYyasAx19OjRiQfvjNX0JRuEgk9SvxHSMtjzQSAh\nrCjDGpGUzJTZMO4zdvjFT2rKlCkdPbPWKRKSFTnlEwL1REAWkXr2S9+0YrDDfM+g1eR59g984APm\nkksuMTNmzOgbdv2oiP5J6jdCWhyjISFFWR6KbqMjSVn1EwkpukdUnhCoHgFZRKrvg0o1wMdg4sSJ\n5u67765UjzyVYw3B55qYJgzG/idPuXXIm8ZvhGBuTMd8/etfry2pvPHGG81+++1nzj///NTwioSk\nhkwZhEAjEJBFpBHdVK6SDNxYRfj2/QzKrbW40g899FC7ZJTlo2GhTb7gE1OH6QpfpyTHvfxGGKSx\nnNRlOqZbm5hCYorm2GOPNfPmzeuWtHNNJKQDhQ6EQOsQEBFpXZdmaxAm87ffftvO5WcroZpcLBFl\nVQZOqkmEQZDB2pekUx9+niqOne5YSXzhPMuwcQhtylJsiAXh4em7cHv8tnEsEhJGRL+FQLsQEBFp\nV39mbg2DGQPyrbfeWlmI7rTKF2UFoJzwjsi9Bse0uhaZHiuPT54IVrZlyxa7RLfIesoui+XFixYt\n6hr3JdzWsnVS+UJACPQfARGR/mNe2xp56DNF04QlsGVbAcJTOiwNrtO0FeTJORejW1OXYGMV4Z4j\n5khY6IM6E8KwvvotBIRANgRERLLh1tpcTHXcddddZuPGjZ2Bro6NZQBjOSjOj/0QfDQY7H3xrRL+\n+X4do9MXv/hFs++++yb2teiXbknrceQ3vGpLJCQpgkonBJqPgIhI8/uw8BbkXWJZuEKhAuuiX3hK\np9+OsBARrCHoccABB4RQas7P008/3e6B46wiIiHN6TtpKgSKQEBEpAgUW1hGXQZ7H1qmY9hMra5x\nMtAv7Ahb5pQOfYT0yyrk90WRxxAP9sNhwzyRkCKRVVlCoBkIiIg0o58q0ZKBbv369TZIVtVLXhnk\nWfKJZA2GVQWIUVM6Rfk9QHKa4M+TBHeWYF9wwQXm4osvTpJcaYSAEGgRAu9oUVvUlIIR4E0bnxH8\nMapcTcNbMg6N06dPt/FCnJNmwc0tpTgcXMNOrrTHlyxTOuw0DDmsmiD67chzPG3aNLsXTZ4ylFcI\nCIFmIiCLSDP7ra9aOyJA5NJrr712xMBaljJYQb761a+a22+/3Vx33XWNiZGRFo+oKZ1ejrBYq3bf\nfffGOqmGMcLPBcIbdggOp9NvISAE2oeAiEj7+rSUFjFY4p9BCPG5c+faEN1lWiYI2059+++/v7XK\nhK0KpTSyRoWGHWFRzZ/SYSpjyZIlw87VSP1MqrRpqikTAMokBAYUARGRAe34rM3GOrJgwQLz/PPP\nW0LCioeiSAJkZ/Xq1TbIFfotXLjQHH/88VlVbV0+N6Xz29/+1hxzzDGNjR0S1zEzZ860EWKbEh02\nrh06LwSEQDoEtOldOrwGPjVv5Q899JANzb19+3a7fJQBhCiZOGamFcgH1g/KwFfikUcesQSEFRQi\nIcPRBHs+7373uw0+FQiWk7bIhAkTDPeURAgIgcFCQBaRwervwlsLkWBlzaOPPmoee+wxM3r0aDud\ncvTRR9u62NnXF3aI3bVrl3nllVfMiy++aEOTM6ieeuqp5oQTTijMuuLX2bZjSB8B55YuXdqqpuGA\nu3jx4saFqm9VJ6gxQqACBLRqpgLQ21QlfiLseut2vuUNHbKxY8cOSziYxvFl7NixZsyYMeaUU04x\nn/vc51rl4+C3s8xjiBzWg7YJFjGJEBACg4eAiMjg9XmpLW7TktJSgVLhIxDAWRXfI4kQEAKDhYB8\nRAarv9VaIVBbBHB6zuJnVNsGSTEhIAQSISAikggmJRICQkAICAEhIATKQEBEpAxUVaYQEAKpEZA1\nJDVkyiAEWoGAiEgrulGNEALNR4Coqm5ZcvNboxYIASGQFAERkaRIKZ0QqAkChHZXvI2adIbUEAJC\nIDcCIiK5IVQBQqC/CIwbN85s27atv5X2oTZWzBxyyCF9qElVCAEhUCcERETq1BvSRQgkQIAN8Vat\nWpUgZbOSEOSOGDMSISAEBgsBEZHB6m+1tgUIEEQOy0GbwrvTLUTaPfLII1vQQ2qCEBACaRAQEUmD\nltIKgZogMGnSJLNu3bqaaJNfDUjVzp07DQHxJEJACAwWAiIig9Xfam1LEJg8ebLdeLAlzTGbNm0y\n06dPb0tz1A4hIARSICAikgIsJRUCdUGAnYmxIrQl9saiRYsM5EoiBITA4CEgIjJ4fa4WtwQBLAhf\n+cpXGt+a7373u3ZaBnIlEQJCYPAQGDUUyOA1Wy0WAs1HAGvIhz70IfODH/zA4MDaVDn99NPN0Ucf\nbS699NKmNkF6CwEhkAMBWURygKesQqBKBNgkbuLEiebuu++uUo1cdWMNIX7I+eefn6scZRYCQqC5\nCMgi0ty+k+ZCwPqJEFeE8OgQk6aJrCFN6zHpKwSKR0AWkeIxVYlCoG8IsNz12muvbeS0xvLly80b\nb7wha0jf7hZVJATqiYAsIvXsF2klBBIj8Ktf/cpgFbnuuuvMeeedlzhflQmdf8uaNWusn0uVuqhu\nISAEqkVARKRa/FW7ECgEAXwtpk6dap566qlGBAU77rjjzLHHHmvmzZtXSPtViBAQAs1FQFMzze07\naS4EOgiwembu3LnmzDPPNFhI6ixXXHGFVU8kpM69JN2EQP8QkEWkf1irJiFQOgIM8i+//LJZu3Zt\nLZf0ot/69evNxo0ba6lf6R2kCoSAEBiBgCwiIyDRCSHQXARuvPFGc9hhh5kpU6bUzjICCVm5cqV5\n4IEHREKae4tJcyFQOAIiIoVDqgKFQLUI+GSkDjv0MlXkLDVN8WGptgdVuxAYLARERAarv9XaAUEA\nMoIzKE6hGzZsqKzVrI7BOuOmi7S7bmVdoYqFQG0REBGpbddIMSGQDwGcQe+9915z7rnnmpkzZ/Z9\nqoY4ITjRQoiwhDQ5DH2+nlBuISAEuiEgItINHV0TAg1HgI3k2IsGIdbIzTffXHqLWEqMJYYddYkT\notUxpUOuCoRAoxHQqplGd5+UFwLJEYAgLFiwwEYz/exnP2sjmhZppXj44YfNihUr7N4xTQqulhxB\npRQCQqAMBEREykBVZQqBGiMAIbnjjjvMsmXLzIwZM8wpp5xiJk2alGnqhLIef/xxc/vtt5vRo0eb\nOXPmmE9+8pOZyqoxZFJNCAiBEhEQESkRXBUtBOqMAI6kTz75pHnkkUfMqlWrrC8HS3/HjBljxo8f\nb3bbbbcR6rNT7q5du8yWLVtsnkMOOcRMmzbNnHHGGY2I6DqiQTohBIRA5QiIiFTeBVJACNQDAawb\nW7dutUTjmWeeiVRq7NixHaJy4IEHNnLH38iG6aQQEAKVISAiUhn0qlgICAEhIASEgBDQqhndA0JA\nCAgBISAEhEBlCIiIVAa9KhYCQkAICAEhIARERHQPCAEhIASEgBAQApUhICJSGfSqWAgIASEgBISA\nEBAR0T0gBISAEBACQkAIVIaAiEhl0KtiISAEhIAQEAJCQERE94AQEAJCQAgIASFQGQIiIpVBr4qF\ngBAQAkJACAgBERHdA0JACAgBISAEhEBlCIiIVAa9KhYCQkAICAEhIARERHQPCAEhIASEgBAQApUh\nICJSGfSqWAgIASEgBISAEBAR0T0gBISAEBACQkAIVIaAiEhl0KtiISAEhIAQEAJC4P8Di13nEo+f\nAH0AAAAASUVORK5CYII=\n", "text/plain": [ "<IPython.core.display.Image object>" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from IPython.display import Image\n", "Image(filename='sentiment_network.png')" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "text/plain": [ "{'': 0,\n", " 'touissant': 1,\n", " 'muncey': 2,\n", " 'fullscreen': 3,\n", " 'manifesting': 4,\n", " 'overplaying': 5,\n", " 'tents': 6,\n", " 'landscapes': 7,\n", " 'silicone': 8,\n", " 'lalanne': 9,\n", " 'unobserved': 10,\n", " 'godly': 11,\n", " 'jianxiang': 12,\n", " 'nachoo': 13,\n", " 'acoustics': 14,\n", " 'phillimines': 15,\n", " 'pummeled': 16,\n", " 'ejaculation': 17,\n", " 'gadi': 18,\n", " 'shugoro': 19,\n", " 'substitute': 20,\n", " 'turtles': 21,\n", " 'shoddily': 22,\n", " 'polanski': 23,\n", " 'syafie': 24,\n", " 'definative': 25,\n", " 'gauteng': 26,\n", " 'nonstop': 27,\n", " 'buccaneering': 28,\n", " 'weary': 29,\n", " 'kji': 30,\n", " 'arrrgghhh': 31,\n", " 'agamemnon': 32,\n", " 'seaward': 33,\n", " 'triste': 34,\n", " 'mortimer': 35,\n", " 'indict': 36,\n", " 'corruption': 37,\n", " 'conjunction': 38,\n", " 'cineastes': 39,\n", " 'hirjee': 40,\n", " 'profiles': 41,\n", " 'channelling': 42,\n", " 'kayak': 43,\n", " 'gv': 44,\n", " 'hershman': 45,\n", " 'decisionsin': 46,\n", " 'hemmingway': 47,\n", " 'synch': 48,\n", " 'walt': 49,\n", " 'nob': 50,\n", " 'cimarron': 51,\n", " 'heisler': 52,\n", " 'huxtables': 53,\n", " 'juscar': 54,\n", " 'funt': 55,\n", " 'deterrent': 56,\n", " 'scarynot': 57,\n", " 'differentiation': 58,\n", " 'rewatchability': 59,\n", " 'vouch': 60,\n", " 'dardino': 61,\n", " 'clerking': 62,\n", " 'matheisen': 63,\n", " 'expeditiously': 64,\n", " 'radivoje': 65,\n", " 'kohara': 66,\n", " 'adulterated': 67,\n", " 'intros': 68,\n", " 'dunn': 69,\n", " 'construe': 70,\n", " 'peacefulness': 71,\n", " 'charter': 72,\n", " 'bang': 73,\n", " 'sergi': 74,\n", " 'physically': 75,\n", " 'gingernuts': 76,\n", " 'definently': 77,\n", " 'brasseur': 78,\n", " 'ivay': 79,\n", " 'ranged': 80,\n", " 'reaming': 81,\n", " 'leapt': 82,\n", " 'shaft': 83,\n", " 'tradeoff': 84,\n", " 'segueing': 85,\n", " 'hurting': 86,\n", " 'goose': 87,\n", " 'bonde': 88,\n", " 'culled': 89,\n", " 'scribbles': 90,\n", " 'loris': 91,\n", " 'purrrrrrrrrrrrrrrr': 92,\n", " 'cameras': 93,\n", " 'goading': 94,\n", " 'mischievous': 95,\n", " 'gratitude': 96,\n", " 'indebtedness': 97,\n", " 'submarines': 98,\n", " 'glamor': 99,\n", " 'appease': 100,\n", " 'romanticism': 101,\n", " 'hll': 102,\n", " 'louda': 103,\n", " 'foodstuffs': 104,\n", " 'preyer': 105,\n", " 'pentameter': 106,\n", " 'sourly': 107,\n", " 'takers': 108,\n", " 'hypersensitive': 109,\n", " 'dismantle': 110,\n", " 'gearheads': 111,\n", " 'dza': 112,\n", " 'mou': 113,\n", " 'snails': 114,\n", " 'steely': 115,\n", " 'effected': 116,\n", " 'feather': 117,\n", " 'strangest': 118,\n", " 'woodlands': 119,\n", " 'ingratiate': 120,\n", " 'okey': 121,\n", " 'sandbag': 122,\n", " 'clowned': 123,\n", " 'blackploitation': 124,\n", " 'chaptered': 125,\n", " 'macbeth': 126,\n", " 'sporadically': 127,\n", " 'verve': 128,\n", " 'mbongeni': 129,\n", " 'malte': 130,\n", " 'fomenting': 131,\n", " 'bruhls': 132,\n", " 'amontillado': 133,\n", " 'larded': 134,\n", " 'vfcc': 135,\n", " 'deewana': 136,\n", " 'guptil': 137,\n", " 'larp': 138,\n", " 'demanding': 139,\n", " 'resembled': 140,\n", " 'extraterrestrial': 141,\n", " 'harni': 142,\n", " 'lynched': 143,\n", " 'jbl': 144,\n", " 'alleging': 145,\n", " 'champmathieu': 146,\n", " 'determination': 147,\n", " 'patron': 148,\n", " 'pertain': 149,\n", " 'unimpressively': 150,\n", " 'limousines': 151,\n", " 'heterogeneity': 152,\n", " 'hiller': 153,\n", " 'snippers': 154,\n", " 'tc': 155,\n", " 'puppetry': 156,\n", " 'nordham': 157,\n", " 'detritus': 158,\n", " 'frogballs': 159,\n", " 'beheadings': 160,\n", " 'gourds': 161,\n", " 'pagemaster': 162,\n", " 'coveys': 163,\n", " 'lucian': 164,\n", " 'fragrant': 165,\n", " 'afflicted': 166,\n", " 'mahnaz': 167,\n", " 'airline': 168,\n", " 'sega': 169,\n", " 'glower': 170,\n", " 'musican': 171,\n", " 'kudisch': 172,\n", " 'snarls': 173,\n", " 'theonly': 174,\n", " 'mindbender': 175,\n", " 'ebersole': 176,\n", " 'crummiest': 177,\n", " 'doorpost': 178,\n", " 'brochures': 179,\n", " 'gioconda': 180,\n", " 'maine': 181,\n", " 'mandelbaum': 182,\n", " 'alexandria': 183,\n", " 'afortunately': 184,\n", " 'ungallant': 185,\n", " 'debilitating': 186,\n", " 'troupe': 187,\n", " 'lactating': 188,\n", " 'constanly': 189,\n", " 'revere': 190,\n", " 'inarticulate': 191,\n", " 'neither': 192,\n", " 'shimmer': 193,\n", " 'metcalfe': 194,\n", " 'casanova': 195,\n", " 'umilak': 196,\n", " 'vfx': 197,\n", " 'terrorize': 198,\n", " 'latches': 199,\n", " 'machinist': 200,\n", " 'gladly': 201,\n", " 'deceived': 202,\n", " 'vntoarea': 203,\n", " 'cleaning': 204,\n", " 'unchallenged': 205,\n", " 'fiefdoms': 206,\n", " 'percent': 207,\n", " 'schoolroom': 208,\n", " 'microscopically': 209,\n", " 'li': 210,\n", " 'faw': 211,\n", " 'planning': 212,\n", " 'bodysuit': 213,\n", " 'transition': 214,\n", " 'crumpled': 215,\n", " 'sagr': 216,\n", " 'deteriorated': 217,\n", " 'goykiba': 218,\n", " 'dynasty': 219,\n", " 'steered': 220,\n", " 'orchestration': 221,\n", " 'redblock': 222,\n", " 'wannabe': 223,\n", " 'moretti': 224,\n", " 'gring': 225,\n", " 'jayston': 226,\n", " 'natali': 227,\n", " 'ayatollahs': 228,\n", " 'candians': 229,\n", " 'allan': 230,\n", " 'reabsorbed': 231,\n", " 'equipe': 232,\n", " 'aberystwyth': 233,\n", " 'chaparones': 234,\n", " 'financiers': 235,\n", " 'saws': 236,\n", " 'sayuri': 237,\n", " 'pip': 238,\n", " 'duilio': 239,\n", " 'significantly': 240,\n", " 'norwegia': 241,\n", " 'colonel': 242,\n", " 'winged': 243,\n", " 'netherworld': 244,\n", " 'myspace': 245,\n", " 'autopsied': 246,\n", " 'courtesy': 247,\n", " 'mouths': 248,\n", " 'luxemburg': 249,\n", " 'malevolence': 250,\n", " 'slinging': 251,\n", " 'bilardo': 252,\n", " 'loveliness': 253,\n", " 'shaping': 254,\n", " 'bolha': 255,\n", " 'mysteriosity': 256,\n", " 'households': 257,\n", " 'maximimum': 258,\n", " 'sssr': 259,\n", " 'aleck': 260,\n", " 'biroc': 261,\n", " 'airfield': 262,\n", " 'coral': 263,\n", " 'coped': 264,\n", " 'distilled': 265,\n", " 'talks': 266,\n", " 'nineteenth': 267,\n", " 'kennedys': 268,\n", " 'pointblank': 269,\n", " 'cleverness': 270,\n", " 'wrinkle': 271,\n", " 'ninga': 272,\n", " 'doen': 273,\n", " 'ramya': 274,\n", " 'fastforwarding': 275,\n", " 'blackguard': 276,\n", " 'orry': 277,\n", " 'gravitas': 278,\n", " 'disastrously': 279,\n", " 'snuff': 280,\n", " 'schiff': 281,\n", " 'aimlessness': 282,\n", " 'paton': 283,\n", " 'boars': 284,\n", " 'retaining': 285,\n", " 'tinting': 286,\n", " 'nomad': 287,\n", " 'insult': 288,\n", " 'despondently': 289,\n", " 'cindy': 290,\n", " 'bobbies': 291,\n", " 'diffident': 292,\n", " 'super': 293,\n", " 'eloquent': 294,\n", " 'anchored': 295,\n", " 'refuting': 296,\n", " 'chastise': 297,\n", " 'blatantly': 298,\n", " 'narrate': 299,\n", " 'leveling': 300,\n", " 'caballo': 301,\n", " 'weightwatchers': 302,\n", " 'excruciatingly': 303,\n", " 'whitt': 304,\n", " 'concerning': 305,\n", " 'kramer': 306,\n", " 'farrakhan': 307,\n", " 'sportsman': 308,\n", " 'where': 309,\n", " 'hauptmann': 310,\n", " 'stationmaster': 311,\n", " 'ugghh': 312,\n", " 'swamps': 313,\n", " 'nadja': 314,\n", " 'hologram': 315,\n", " 'whack': 316,\n", " 'riffraff': 317,\n", " 'withstood': 318,\n", " 'confesses': 319,\n", " 'weeds': 320,\n", " 'discoverer': 321,\n", " 'dewet': 322,\n", " 'scrivener': 323,\n", " 'embezzled': 324,\n", " 'homepages': 325,\n", " 'finding': 326,\n", " 'compliance': 327,\n", " 'frocked': 328,\n", " 'unlockables': 329,\n", " 'emek': 330,\n", " 'latrines': 331,\n", " 'detectives': 332,\n", " 'braindeads': 333,\n", " 'elongate': 334,\n", " 'inadvisable': 335,\n", " 'ivanova': 336,\n", " 'images': 337,\n", " 'absolutly': 338,\n", " 'southerrners': 339,\n", " 'maaan': 340,\n", " 'perceptible': 341,\n", " 'chit': 342,\n", " 'mccathy': 343,\n", " 'gouden': 344,\n", " 'harrods': 345,\n", " 'persuaders': 346,\n", " 'obligatory': 347,\n", " 'worth': 348,\n", " 'counters': 349,\n", " 'firms': 350,\n", " 'disentangling': 351,\n", " 'ryecart': 352,\n", " 'analyze': 353,\n", " 'caalling': 354,\n", " 'platitudes': 355,\n", " 'wow': 356,\n", " 'cynthia': 357,\n", " 'les': 358,\n", " 'isolationist': 359,\n", " 'drawback': 360,\n", " 'wormwood': 361,\n", " 'preens': 362,\n", " 'rebelled': 363,\n", " 'reviews': 364,\n", " 'miser': 365,\n", " 'burglary': 366,\n", " 'farnel': 367,\n", " 'groupthink': 368,\n", " 'revivalist': 369,\n", " 'snips': 370,\n", " 'ruffin': 371,\n", " 'dramatisations': 372,\n", " 'ascendant': 373,\n", " 'distraction': 374,\n", " 'herapheri': 375,\n", " 'neous': 376,\n", " 'lampio': 377,\n", " 'sophomore': 378,\n", " 'gangfights': 379,\n", " 'broadways': 380,\n", " 'ingredients': 381,\n", " 'afv': 382,\n", " 'flavored': 383,\n", " 'cauldrons': 384,\n", " 'philosophy': 385,\n", " 'unhelpful': 386,\n", " 'bladed': 387,\n", " 'wgbh': 388,\n", " 'haje': 389,\n", " 'thriteen': 390,\n", " 'birkina': 391,\n", " 'strangulations': 392,\n", " 'neagle': 393,\n", " 'precipice': 394,\n", " 'syllable': 395,\n", " 'countermeasures': 396,\n", " 'punctures': 397,\n", " 'holt': 398,\n", " 'ter': 399,\n", " 'rivire': 400,\n", " 'mordantly': 401,\n", " 'operas': 402,\n", " 'insurgents': 403,\n", " 'stepdaughter': 404,\n", " 'vibrators': 405,\n", " 'lepus': 406,\n", " 'mmhm': 407,\n", " 'doodle': 408,\n", " 'bookcase': 409,\n", " 'recreate': 410,\n", " 'nakadai': 411,\n", " 'jorge': 412,\n", " 'mandatory': 413,\n", " 'agendas': 414,\n", " 'reception': 415,\n", " 'plexiglas': 416,\n", " 'bubba': 417,\n", " 'gender': 418,\n", " 'jules': 419,\n", " 'werecat': 420,\n", " 'overdressed': 421,\n", " 'foremost': 422,\n", " 'eggbeater': 423,\n", " 'horrify': 424,\n", " 'tailored': 425,\n", " 'cleaners': 426,\n", " 'lansbury': 427,\n", " 'alfie': 428,\n", " 'mistry': 429,\n", " 'whiile': 430,\n", " 'nah': 431,\n", " 'popularised': 432,\n", " 'weill': 433,\n", " 'gariazzo': 434,\n", " 'rojar': 435,\n", " 'skewing': 436,\n", " 'benetakos': 437,\n", " 'deathrow': 438,\n", " 'entwistle': 439,\n", " 'cebuano': 440,\n", " 'hana': 441,\n", " 'pragmatist': 442,\n", " 'ethnographer': 443,\n", " 'matchbox': 444,\n", " 'tulkinghorn': 445,\n", " 'essandoh': 446,\n", " 'juvie': 447,\n", " 'heartening': 448,\n", " 'wearing': 449,\n", " 'pixar': 450,\n", " 'panties': 451,\n", " 'ills': 452,\n", " 'nihilists': 453,\n", " 'mulher': 454,\n", " 'pleasantness': 455,\n", " 'distatefull': 456,\n", " 'unisten': 457,\n", " 'career': 458,\n", " 'dependence': 459,\n", " 'vanquished': 460,\n", " 'hamatova': 461,\n", " 'bolsters': 462,\n", " 'timesfunny': 463,\n", " 'millenia': 464,\n", " 'doe': 465,\n", " 'antisocial': 466,\n", " 'hound': 467,\n", " 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906,\n", " 'montmarte': 907,\n", " 'denotes': 908,\n", " 'axed': 909,\n", " 'manichaean': 910,\n", " 'stimulate': 911,\n", " 'artem': 912,\n", " 'padayappa': 913,\n", " 'tens': 914,\n", " 'dapper': 915,\n", " 'fibers': 916,\n", " 'qaida': 917,\n", " 'precedence': 918,\n", " 'value': 919,\n", " 'rapeing': 920,\n", " 'farse': 921,\n", " 'drinks': 922,\n", " 'covenant': 923,\n", " 'register': 924,\n", " 'shaggy': 925,\n", " 'wqasn': 926,\n", " 'committees': 927,\n", " 'lice': 928,\n", " 'distractive': 929,\n", " 'beauticin': 930,\n", " 'glammier': 931,\n", " 'robotic': 932,\n", " 'gertrude': 933,\n", " 'chuke': 934,\n", " 'abodes': 935,\n", " 'bewitchment': 936,\n", " 'pettyjohn': 937,\n", " 'patronage': 938,\n", " 'browning': 939,\n", " 'formally': 940,\n", " 'objectionable': 941,\n", " 'output': 942,\n", " 'vivacious': 943,\n", " 'redman': 944,\n", " 'fallacious': 945,\n", " 'baptized': 946,\n", " 'lykis': 947,\n", " 'wil': 948,\n", " 'freudians': 949,\n", " 'psychokinetic': 950,\n", " 'marathan': 951,\n", " 'kmart': 952,\n", " 'uprightness': 953,\n", " 'dearable': 954,\n", " 'caps': 955,\n", " 'guilty': 956,\n", " 'glitxy': 957,\n", " 'ripped': 958,\n", " 'recovers': 959,\n", " 'braces': 960,\n", " 'mufti': 961,\n", " 'unfourtunatly': 962,\n", " 'characteratures': 963,\n", " 'giorgos': 964,\n", " 'viewership': 965,\n", " 'perked': 966,\n", " 'milieux': 967,\n", " 'flicking': 968,\n", " 'inmates': 969,\n", " 'bicker': 970,\n", " 'impropriety': 971,\n", " 'scalpels': 972,\n", " 'gobsmacked': 973,\n", " 'mybluray': 974,\n", " 'gills': 975,\n", " 'alexandra': 976,\n", " 'wingfield': 977,\n", " 'goldin': 978,\n", " 'innocous': 979,\n", " 'fanbase': 980,\n", " 'punt': 981,\n", " 'humanist': 982,\n", " 'tragicomedies': 983,\n", " 'gads': 984,\n", " 'freakiness': 985,\n", " 'mishandle': 986,\n", " 'purer': 987,\n", " 'shittier': 988,\n", " 'brownings': 989,\n", " 'bibbity': 990,\n", " 'freddys': 991,\n", " 'houseman': 992,\n", " 'adamant': 993,\n", " 'betrays': 994,\n", " 'doncha': 995,\n", " 'guadalajara': 996,\n", " 'castings': 997,\n", " 'carlos': 998,\n", " 'amfortas': 999,\n", " ...}" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "word2index = {}\n", "\n", "for i,word in enumerate(vocab):\n", " word2index[word] = i\n", "word2index" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "def update_input_layer(review):\n", " \n", " global layer_0\n", " \n", " # clear out previous state, reset the layer to be all 0s\n", " layer_0 *= 0\n", " for word in review.split(\" \"):\n", " layer_0[0][word2index[word]] += 1\n", "\n", "update_input_layer(reviews[0])" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "text/plain": [ "array([[ 18., 0., 0., ..., 0., 0., 0.]])" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "layer_0" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "def get_target_for_label(label):\n", " if(label == 'POSITIVE'):\n", " return 1\n", " else:\n", " return 0" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "text/plain": [ "'POSITIVE'" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "labels[0]" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "text/plain": [ "1" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "get_target_for_label(labels[0])" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "text/plain": [ "'NEGATIVE'" ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ "labels[1]" ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "text/plain": [ "0" ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" } ], "source": [ "get_target_for_label(labels[1])" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "# Project 3: Building a Neural Network" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true, "deletable": true, "editable": true }, "source": [ "- Start with your neural network from the last chapter\n", "- 3 layer neural network\n", "- no non-linearity in hidden layer\n", "- use our functions to create the training data\n", "- create a \"pre_process_data\" function to create vocabulary for our training data generating functions\n", "- modify \"train\" to train over the entire corpus" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "### Where to Get Help if You Need it\n", "- Re-watch previous week's Udacity Lectures\n", "- Chapters 3-5 - [Grokking Deep Learning](https://www.manning.com/books/grokking-deep-learning) - (40% Off: **traskud17**)" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "import time\n", "import sys\n", "import numpy as np\n", "\n", "# Let's tweak our network from before to model these phenomena\n", "class SentimentNetwork:\n", " def __init__(self, reviews,labels,hidden_nodes = 10, learning_rate = 0.1):\n", " \n", " # set our random number generator \n", " np.random.seed(1)\n", " \n", " self.pre_process_data(reviews, labels)\n", " \n", " self.init_network(len(self.review_vocab),hidden_nodes, 1, learning_rate)\n", " \n", " \n", " def pre_process_data(self, reviews, labels):\n", " \n", " review_vocab = set()\n", " for review in reviews:\n", " for word in review.split(\" \"):\n", " review_vocab.add(word)\n", " self.review_vocab = list(review_vocab)\n", " \n", " label_vocab = set()\n", " for label in labels:\n", " label_vocab.add(label)\n", " \n", " self.label_vocab = list(label_vocab)\n", " \n", " self.review_vocab_size = len(self.review_vocab)\n", " self.label_vocab_size = len(self.label_vocab)\n", " \n", " self.word2index = {}\n", " for i, word in enumerate(self.review_vocab):\n", " self.word2index[word] = i\n", " \n", " self.label2index = {}\n", " for i, label in enumerate(self.label_vocab):\n", " self.label2index[label] = i\n", " \n", " \n", " def init_network(self, input_nodes, hidden_nodes, output_nodes, learning_rate):\n", " # Set number of nodes in input, hidden and output layers.\n", " self.input_nodes = input_nodes\n", " self.hidden_nodes = hidden_nodes\n", " self.output_nodes = output_nodes\n", "\n", " # Initialize weights\n", " self.weights_0_1 = np.zeros((self.input_nodes,self.hidden_nodes))\n", " \n", " self.weights_1_2 = np.random.normal(0.0, self.output_nodes**-0.5, \n", " (self.hidden_nodes, self.output_nodes))\n", " \n", " self.learning_rate = learning_rate\n", " \n", " self.layer_0 = np.zeros((1,input_nodes))\n", " \n", " \n", " def update_input_layer(self,review):\n", "\n", " # clear out previous state, reset the layer to be all 0s\n", " self.layer_0 *= 0\n", " for word in review.split(\" \"):\n", " if(word in self.word2index.keys()):\n", " self.layer_0[0][self.word2index[word]] += 1\n", " \n", " def get_target_for_label(self,label):\n", " if(label == 'POSITIVE'):\n", " return 1\n", " else:\n", " return 0\n", " \n", " def sigmoid(self,x):\n", " return 1 / (1 + np.exp(-x))\n", " \n", " \n", " def sigmoid_output_2_derivative(self,output):\n", " return output * (1 - output)\n", " \n", " def train(self, training_reviews, training_labels):\n", " \n", " assert(len(training_reviews) == len(training_labels))\n", " \n", " correct_so_far = 0\n", " \n", " start = time.time()\n", " \n", " for i in range(len(training_reviews)):\n", " \n", " review = training_reviews[i]\n", " label = training_labels[i]\n", " \n", " #### Implement the forward pass here ####\n", " ### Forward pass ###\n", "\n", " # Input Layer\n", " self.update_input_layer(review)\n", "\n", " # Hidden layer\n", " layer_1 = self.layer_0.dot(self.weights_0_1)\n", "\n", " # Output layer\n", " layer_2 = self.sigmoid(layer_1.dot(self.weights_1_2))\n", "\n", " #### Implement the backward pass here ####\n", " ### Backward pass ###\n", "\n", " # TODO: Output error\n", " layer_2_error = layer_2 - self.get_target_for_label(label) # Output layer error is the difference between desired target and actual output.\n", " layer_2_delta = layer_2_error * self.sigmoid_output_2_derivative(layer_2)\n", "\n", " # TODO: Backpropagated error\n", " layer_1_error = layer_2_delta.dot(self.weights_1_2.T) # errors propagated to the hidden layer\n", " layer_1_delta = layer_1_error # hidden layer gradients - no nonlinearity so it's the same as the error\n", "\n", " # TODO: Update the weights\n", " self.weights_1_2 -= layer_1.T.dot(layer_2_delta) * self.learning_rate # update hidden-to-output weights with gradient descent step\n", " self.weights_0_1 -= self.layer_0.T.dot(layer_1_delta) * self.learning_rate # update input-to-hidden weights with gradient descent step\n", "\n", " if(np.abs(layer_2_error) < 0.5):\n", " correct_so_far += 1\n", " \n", " reviews_per_second = i / float(time.time() - start)\n", " \n", " sys.stdout.write(\"\\rProgress:\" + str(100 * i/float(len(training_reviews)))[:4] + \"% Speed(reviews/sec):\" + str(reviews_per_second)[0:5] + \" #Correct:\" + str(correct_so_far) + \" #Trained:\" + str(i+1) + \" Training Accuracy:\" + str(correct_so_far * 100 / float(i+1))[:4] + \"%\")\n", " if(i % 2500 == 0):\n", " print(\"\")\n", " \n", " def test(self, testing_reviews, testing_labels):\n", " \n", " correct = 0\n", " \n", " start = time.time()\n", " \n", " for i in range(len(testing_reviews)):\n", " pred = self.run(testing_reviews[i])\n", " if(pred == testing_labels[i]):\n", " correct += 1\n", " \n", " reviews_per_second = i / float(time.time() - start)\n", " \n", " sys.stdout.write(\"\\rProgress:\" + str(100 * i/float(len(testing_reviews)))[:4] \\\n", " + \"% Speed(reviews/sec):\" + str(reviews_per_second)[0:5] \\\n", " + \"% #Correct:\" + str(correct) + \" #Tested:\" + str(i+1) + \" Testing Accuracy:\" + str(correct * 100 / float(i+1))[:4] + \"%\")\n", " \n", " def run(self, review):\n", " \n", " # Input Layer\n", " self.update_input_layer(review.lower())\n", "\n", " # Hidden layer\n", " layer_1 = self.layer_0.dot(self.weights_0_1)\n", "\n", " # Output layer\n", " layer_2 = self.sigmoid(layer_1.dot(self.weights_1_2))\n", " \n", " if(layer_2[0] > 0.5):\n", " return \"POSITIVE\"\n", " else:\n", " return \"NEGATIVE\"\n", " " ] }, { "cell_type": "code", "execution_count": 87, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "mlp = SentimentNetwork(reviews[:-1000],labels[:-1000], learning_rate=0.1)" ] }, { "cell_type": "code", "execution_count": 61, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Progress:99.9% Speed(reviews/sec):587.5% #Correct:500 #Tested:1000 Testing Accuracy:50.0%" ] } ], "source": [ "# evaluate our model before training (just to show how horrible it is)\n", "mlp.test(reviews[-1000:],labels[-1000:])" ] }, { "cell_type": "code", "execution_count": 62, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Progress:0.0% Speed(reviews/sec):0.0 #Correct:0 #Trained:1 Training Accuracy:0.0%\n", "Progress:10.4% Speed(reviews/sec):89.58 #Correct:1250 #Trained:2501 Training Accuracy:49.9%\n", "Progress:20.8% Speed(reviews/sec):95.03 #Correct:2500 #Trained:5001 Training Accuracy:49.9%\n", "Progress:27.4% Speed(reviews/sec):95.46 #Correct:3295 #Trained:6592 Training Accuracy:49.9%" ] }, { "ename": "KeyboardInterrupt", "evalue": "", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", "\u001b[0;32m<ipython-input-62-d0f5d85ad402>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;31m# train the network\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mmlp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mreviews\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1000\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mlabels\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1000\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;32m<ipython-input-59-6334c4ec4642>\u001b[0m in \u001b[0;36mtrain\u001b[0;34m(self, training_reviews, training_labels)\u001b[0m\n\u001b[1;32m 117\u001b[0m \u001b[0;31m# TODO: Update the weights\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 118\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mweights_1_2\u001b[0m \u001b[0;34m-=\u001b[0m \u001b[0mlayer_1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mT\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlayer_2_delta\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlearning_rate\u001b[0m \u001b[0;31m# update hidden-to-output weights with gradient descent step\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 119\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mweights_0_1\u001b[0m \u001b[0;34m-=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlayer_0\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mT\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlayer_1_delta\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlearning_rate\u001b[0m \u001b[0;31m# update input-to-hidden weights with gradient descent step\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 120\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 121\u001b[0m \u001b[0;32mif\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mabs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlayer_2_error\u001b[0m\u001b[0;34m)\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[0m\n", "\u001b[0;31mKeyboardInterrupt\u001b[0m: " ] } ], "source": [ "# train the network\n", "mlp.train(reviews[:-1000],labels[:-1000])" ] }, { "cell_type": "code", "execution_count": 63, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "mlp = SentimentNetwork(reviews[:-1000],labels[:-1000], learning_rate=0.01)" ] }, { "cell_type": "code", "execution_count": 64, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Progress:0.0% Speed(reviews/sec):0.0 #Correct:0 #Trained:1 Training Accuracy:0.0%\n", "Progress:10.4% Speed(reviews/sec):96.39 #Correct:1247 #Trained:2501 Training Accuracy:49.8%\n", "Progress:20.8% Speed(reviews/sec):99.31 #Correct:2497 #Trained:5001 Training Accuracy:49.9%\n", "Progress:22.8% Speed(reviews/sec):99.02 #Correct:2735 #Trained:5476 Training Accuracy:49.9%" ] }, { "ename": "KeyboardInterrupt", "evalue": "", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", "\u001b[0;32m<ipython-input-64-d0f5d85ad402>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;31m# train the network\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mmlp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mreviews\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1000\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mlabels\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1000\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;32m<ipython-input-59-6334c4ec4642>\u001b[0m in \u001b[0;36mtrain\u001b[0;34m(self, training_reviews, training_labels)\u001b[0m\n\u001b[1;32m 117\u001b[0m \u001b[0;31m# TODO: Update the weights\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 118\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mweights_1_2\u001b[0m \u001b[0;34m-=\u001b[0m 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120\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 121\u001b[0m \u001b[0;32mif\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mabs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlayer_2_error\u001b[0m\u001b[0;34m)\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[0m\n", "\u001b[0;31mKeyboardInterrupt\u001b[0m: " ] } ], "source": [ "# train the network\n", "mlp.train(reviews[:-1000],labels[:-1000])" ] }, { "cell_type": "code", "execution_count": 65, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "mlp = SentimentNetwork(reviews[:-1000],labels[:-1000], learning_rate=0.001)" ] }, { "cell_type": "code", "execution_count": 66, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Progress:0.0% Speed(reviews/sec):0.0 #Correct:0 #Trained:1 Training Accuracy:0.0%\n", "Progress:10.4% Speed(reviews/sec):98.77 #Correct:1267 #Trained:2501 Training Accuracy:50.6%\n", "Progress:20.8% Speed(reviews/sec):98.79 #Correct:2640 #Trained:5001 Training Accuracy:52.7%\n", "Progress:31.2% Speed(reviews/sec):98.58 #Correct:4109 #Trained:7501 Training Accuracy:54.7%\n", "Progress:41.6% Speed(reviews/sec):93.78 #Correct:5638 #Trained:10001 Training Accuracy:56.3%\n", "Progress:52.0% Speed(reviews/sec):91.76 #Correct:7246 #Trained:12501 Training Accuracy:57.9%\n", "Progress:62.5% Speed(reviews/sec):92.42 #Correct:8841 #Trained:15001 Training Accuracy:58.9%\n", "Progress:69.4% Speed(reviews/sec):92.58 #Correct:9934 #Trained:16668 Training Accuracy:59.5%" ] }, { "ename": "KeyboardInterrupt", "evalue": "", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", "\u001b[0;32m<ipython-input-66-d0f5d85ad402>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;31m# train the network\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mmlp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mreviews\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1000\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mlabels\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1000\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;32m<ipython-input-59-6334c4ec4642>\u001b[0m in \u001b[0;36mtrain\u001b[0;34m(self, training_reviews, training_labels)\u001b[0m\n\u001b[1;32m 117\u001b[0m \u001b[0;31m# TODO: Update the weights\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 118\u001b[0m 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with gradient descent step\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 120\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 121\u001b[0m \u001b[0;32mif\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mabs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlayer_2_error\u001b[0m\u001b[0;34m)\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[0m\n", "\u001b[0;31mKeyboardInterrupt\u001b[0m: " ] } ], "source": [ "# train the network\n", "mlp.train(reviews[:-1000],labels[:-1000])" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "# Understanding Neural Noise" ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "image/png": 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id8YZZzAf0/mkzF6r5Ox2SvChtsiRRx7Z2E3gGLj5QB7SSC/iAkEhYBgDBVYJ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\nAH0AAAAASUVORK5CYII=\n", "text/plain": [ "<IPython.core.display.Image object>" ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from IPython.display import Image\n", "Image(filename='sentiment_network.png')" ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "def update_input_layer(review):\n", " \n", " global layer_0\n", " \n", " # clear out previous state, reset the layer to be all 0s\n", " layer_0 *= 0\n", " for word in review.split(\" \"):\n", " layer_0[0][word2index[word]] += 1\n", "\n", "update_input_layer(reviews[0])" ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "text/plain": [ "array([[ 18., 0., 0., ..., 0., 0., 0.]])" ] }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" } ], "source": [ "layer_0" ] }, { "cell_type": "code", "execution_count": 35, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "review_counter = Counter()" ] }, { "cell_type": "code", "execution_count": 36, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "for word in reviews[0].split(\" \"):\n", " review_counter[word] += 1" ] }, { "cell_type": "code", "execution_count": 37, "metadata": { "collapsed": false, "deletable": true, "editable": true, "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "[('.', 27),\n", " ('', 18),\n", " ('the', 9),\n", " ('to', 6),\n", " ('high', 5),\n", " ('i', 5),\n", " ('bromwell', 4),\n", " ('is', 4),\n", " ('a', 4),\n", " ('teachers', 4),\n", " ('that', 4),\n", " ('of', 4),\n", " ('it', 2),\n", " ('at', 2),\n", " ('as', 2),\n", " ('school', 2),\n", " ('my', 2),\n", " ('in', 2),\n", " ('me', 2),\n", " ('students', 2),\n", " ('their', 2),\n", " ('student', 2),\n", " ('cartoon', 1),\n", " ('comedy', 1),\n", " ('ran', 1),\n", " ('same', 1),\n", " ('time', 1),\n", " ('some', 1),\n", " ('other', 1),\n", " ('programs', 1),\n", " ('about', 1),\n", " ('life', 1),\n", " ('such', 1),\n", " ('years', 1),\n", " ('teaching', 1),\n", " ('profession', 1),\n", " ('lead', 1),\n", " ('believe', 1),\n", " ('s', 1),\n", " ('satire', 1),\n", " ('much', 1),\n", " ('closer', 1),\n", " ('reality', 1),\n", " ('than', 1),\n", " ('scramble', 1),\n", " ('survive', 1),\n", " ('financially', 1),\n", " ('insightful', 1),\n", " ('who', 1),\n", " ('can', 1),\n", " ('see', 1),\n", " ('right', 1),\n", " ('through', 1),\n", " ('pathetic', 1),\n", " ('pomp', 1),\n", " ('pettiness', 1),\n", " ('whole', 1),\n", " ('situation', 1),\n", " ('all', 1),\n", " ('remind', 1),\n", " ('schools', 1),\n", " ('knew', 1),\n", " ('and', 1),\n", " ('when', 1),\n", " ('saw', 1),\n", " ('episode', 1),\n", " ('which', 1),\n", " ('repeatedly', 1),\n", " ('tried', 1),\n", " ('burn', 1),\n", " ('down', 1),\n", " ('immediately', 1),\n", " ('recalled', 1),\n", " ('classic', 1),\n", " ('line', 1),\n", " ('inspector', 1),\n", " ('m', 1),\n", " ('here', 1),\n", " ('sack', 1),\n", " ('one', 1),\n", " ('your', 1),\n", " ('welcome', 1),\n", " ('expect', 1),\n", " ('many', 1),\n", " ('adults', 1),\n", " ('age', 1),\n", " ('think', 1),\n", " ('far', 1),\n", " ('fetched', 1),\n", " ('what', 1),\n", " ('pity', 1),\n", " ('isn', 1),\n", " ('t', 1)]" ] }, "execution_count": 37, "metadata": {}, "output_type": "execute_result" } ], "source": [ "review_counter.most_common()" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "# Project 4: Reducing Noise in our Input Data" ] }, { "cell_type": "code", "execution_count": 38, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "import time\n", "import sys\n", "import numpy as np\n", "\n", "# Let's tweak our network from before to model these phenomena\n", "class SentimentNetwork:\n", " def __init__(self, reviews,labels,hidden_nodes = 10, learning_rate = 0.1):\n", " \n", " # set our random number generator \n", " np.random.seed(1)\n", " \n", " self.pre_process_data(reviews, labels)\n", " \n", " self.init_network(len(self.review_vocab),hidden_nodes, 1, learning_rate)\n", " \n", " \n", " def pre_process_data(self, reviews, labels):\n", " \n", " review_vocab = set()\n", " for review in reviews:\n", " for word in review.split(\" \"):\n", " review_vocab.add(word)\n", " self.review_vocab = list(review_vocab)\n", " \n", " label_vocab = set()\n", " for label in labels:\n", " label_vocab.add(label)\n", " \n", " self.label_vocab = list(label_vocab)\n", " \n", " self.review_vocab_size = len(self.review_vocab)\n", " self.label_vocab_size = len(self.label_vocab)\n", " \n", " self.word2index = {}\n", " for i, word in enumerate(self.review_vocab):\n", " self.word2index[word] = i\n", " \n", " self.label2index = {}\n", " for i, label in enumerate(self.label_vocab):\n", " self.label2index[label] = i\n", " \n", " \n", " def init_network(self, input_nodes, hidden_nodes, output_nodes, learning_rate):\n", " # Set number of nodes in input, hidden and output layers.\n", " self.input_nodes = input_nodes\n", " self.hidden_nodes = hidden_nodes\n", " self.output_nodes = output_nodes\n", "\n", " # Initialize weights\n", " self.weights_0_1 = np.zeros((self.input_nodes,self.hidden_nodes))\n", " \n", " self.weights_1_2 = np.random.normal(0.0, self.output_nodes**-0.5, \n", " (self.hidden_nodes, self.output_nodes))\n", " \n", " self.learning_rate = learning_rate\n", " \n", " self.layer_0 = np.zeros((1,input_nodes))\n", " \n", " \n", " def update_input_layer(self,review):\n", "\n", " # clear out previous state, reset the layer to be all 0s\n", " self.layer_0 *= 0\n", " for word in review.split(\" \"):\n", " if(word in self.word2index.keys()):\n", " self.layer_0[0][self.word2index[word]] = 1\n", " \n", " def get_target_for_label(self,label):\n", " if(label == 'POSITIVE'):\n", " return 1\n", " else:\n", " return 0\n", " \n", " def sigmoid(self,x):\n", " return 1 / (1 + np.exp(-x))\n", " \n", " \n", " def sigmoid_output_2_derivative(self,output):\n", " return output * (1 - output)\n", " \n", " def train(self, training_reviews, training_labels):\n", " \n", " assert(len(training_reviews) == len(training_labels))\n", " \n", " correct_so_far = 0\n", " \n", " start = time.time()\n", " \n", " for i in range(len(training_reviews)):\n", " \n", " review = training_reviews[i]\n", " label = training_labels[i]\n", " \n", " #### Implement the forward pass here ####\n", " ### Forward pass ###\n", "\n", " # Input Layer\n", " self.update_input_layer(review)\n", "\n", " # Hidden layer\n", " layer_1 = self.layer_0.dot(self.weights_0_1)\n", "\n", " # Output layer\n", " layer_2 = self.sigmoid(layer_1.dot(self.weights_1_2))\n", "\n", " #### Implement the backward pass here ####\n", " ### Backward pass ###\n", "\n", " # TODO: Output error\n", " layer_2_error = layer_2 - self.get_target_for_label(label) # Output layer error is the difference between desired target and actual output.\n", " layer_2_delta = layer_2_error * self.sigmoid_output_2_derivative(layer_2)\n", "\n", " # TODO: Backpropagated error\n", " layer_1_error = layer_2_delta.dot(self.weights_1_2.T) # errors propagated to the hidden layer\n", " layer_1_delta = layer_1_error # hidden layer gradients - no nonlinearity so it's the same as the error\n", "\n", " # TODO: Update the weights\n", " self.weights_1_2 -= layer_1.T.dot(layer_2_delta) * self.learning_rate # update hidden-to-output weights with gradient descent step\n", " self.weights_0_1 -= self.layer_0.T.dot(layer_1_delta) * self.learning_rate # update input-to-hidden weights with gradient descent step\n", "\n", " if(np.abs(layer_2_error) < 0.5):\n", " correct_so_far += 1\n", " \n", " reviews_per_second = i / float(time.time() - start)\n", " \n", " sys.stdout.write(\"\\rProgress:\" + str(100 * i/float(len(training_reviews)))[:4] + \"% Speed(reviews/sec):\" + str(reviews_per_second)[0:5] + \" #Correct:\" + str(correct_so_far) + \" #Trained:\" + str(i+1) + \" Training Accuracy:\" + str(correct_so_far * 100 / float(i+1))[:4] + \"%\")\n", " if(i % 2500 == 0):\n", " print(\"\")\n", " \n", " def test(self, testing_reviews, testing_labels):\n", " \n", " correct = 0\n", " \n", " start = time.time()\n", " \n", " for i in range(len(testing_reviews)):\n", " pred = self.run(testing_reviews[i])\n", " if(pred == testing_labels[i]):\n", " correct += 1\n", " \n", " reviews_per_second = i / float(time.time() - start)\n", " \n", " sys.stdout.write(\"\\rProgress:\" + str(100 * i/float(len(testing_reviews)))[:4] \\\n", " + \"% Speed(reviews/sec):\" + str(reviews_per_second)[0:5] \\\n", " + \"% #Correct:\" + str(correct) + \" #Tested:\" + str(i+1) + \" Testing Accuracy:\" + str(correct * 100 / float(i+1))[:4] + \"%\")\n", " \n", " def run(self, review):\n", " \n", " # Input Layer\n", " self.update_input_layer(review.lower())\n", "\n", " # Hidden layer\n", " layer_1 = self.layer_0.dot(self.weights_0_1)\n", "\n", " # Output layer\n", " layer_2 = self.sigmoid(layer_1.dot(self.weights_1_2))\n", " \n", " if(layer_2[0] > 0.5):\n", " return \"POSITIVE\"\n", " else:\n", " return \"NEGATIVE\"\n", " " ] }, { "cell_type": "code", "execution_count": 40, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "mlp = SentimentNetwork(reviews[:-1000],labels[:-1000], learning_rate=0.1)" ] }, { "cell_type": "code", "execution_count": 41, "metadata": { "collapsed": false, "deletable": true, "editable": true, "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Progress:0.0% Speed(reviews/sec):0.0 #Correct:0 #Trained:1 Training Accuracy:0.0%\n", "Progress:10.4% Speed(reviews/sec):107.3 #Correct:1816 #Trained:2501 Training Accuracy:72.6%\n", "Progress:20.8% Speed(reviews/sec):118.3 #Correct:3796 #Trained:5001 Training Accuracy:75.9%\n", "Progress:31.2% Speed(reviews/sec):127.0 #Correct:5884 #Trained:7501 Training Accuracy:78.4%\n", "Progress:41.6% Speed(reviews/sec):131.7 #Correct:8021 #Trained:10001 Training Accuracy:80.2%\n", "Progress:52.0% Speed(reviews/sec):134.7 #Correct:10158 #Trained:12501 Training Accuracy:81.2%\n", "Progress:62.5% Speed(reviews/sec):136.8 #Correct:12287 #Trained:15001 Training Accuracy:81.9%\n", "Progress:72.9% Speed(reviews/sec):138.3 #Correct:14398 #Trained:17501 Training Accuracy:82.2%\n", "Progress:83.3% Speed(reviews/sec):139.3 #Correct:16572 #Trained:20001 Training Accuracy:82.8%\n", "Progress:93.7% Speed(reviews/sec):140.2 #Correct:18755 #Trained:22501 Training Accuracy:83.3%\n", "Progress:99.9% Speed(reviews/sec):140.7 #Correct:20077 #Trained:24000 Training Accuracy:83.6%" ] } ], "source": [ "mlp.train(reviews[:-1000],labels[:-1000])" ] }, { "cell_type": "code", "execution_count": 42, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Progress:99.9% Speed(reviews/sec):540.6% #Correct:859 #Tested:1000 Testing Accuracy:85.9%" ] } ], "source": [ "# evaluate our model before training (just to show how horrible it is)\n", "mlp.test(reviews[-1000:],labels[-1000:])" ] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.1" } }, "nbformat": 4, "nbformat_minor": 1 }
mit
VCG/gp
ipy_train/train_RGBA.ipynb
1
450240
{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/d/nolearn/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", "Using gpu device 0: GeForce GTX TITAN (CNMeM is disabled, CuDNN 4007)\n", "/home/d/nolearn/local/lib/python2.7/site-packages/theano/tensor/signal/downsample.py:6: UserWarning: downsample module has been moved to the theano.tensor.signal.pool module.\n", " \"downsample module has been moved to the theano.tensor.signal.pool module.\")\n" ] } ], "source": [ "%load_ext autoreload\n", "%autoreload 2\n", "\n", "import cPickle as pickle\n", "import os; import sys; sys.path.append('..')\n", "import gp\n", "import gp.nets as nets\n", "\n", "from nolearn.lasagne.visualize import plot_loss\n", "from nolearn.lasagne.visualize import plot_conv_weights\n", "from nolearn.lasagne.visualize import plot_conv_activity\n", "from nolearn.lasagne.visualize import plot_occlusion\n", "\n", "from matplotlib.pyplot import imshow\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [], "source": [ "PATCH_PATH = ('cylinder2_rgba_small')" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Loaded /home/d/patches//cylinder2_rgba_small/ in 0.16304898262 seconds.\n" ] } ], "source": [ "X_train, y_train, X_test, y_test = gp.Patch.load_rgba(PATCH_PATH)" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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hhRdekOY15xTYH0kmk6hUKnA6nahUKgiFQshkMuh2uxLcL168iPF4jB/7sR9DIBBAMBiU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\nNRaLyVAfAxmVWcDNoBQKhWAYhqwuZt+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"text/plain": [ "<matplotlib.figure.Figure at 0x7f1a5e486f90>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "gp.Util.view_rgba(X_train[100], y_train[100])" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CNN configuration: \n", " Our CNN with image, prob, merged_array and border overlap as RGBA.\n", "\n", " This includes dropout.\n", " \n" ] } ], "source": [ "cnn = nets.RGBANet()" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "# Neural Network with 7134066 learnable parameters\n", "\n", "## Layer information\n", "\n", " # name size\n", "--- -------- --------\n", " 0 input 4x75x75\n", " 1 conv1 64x73x73\n", " 2 pool1 64x36x36\n", " 3 dropout1 64x36x36\n", " 4 conv2 48x34x34\n", " 5 pool2 48x17x17\n", " 6 dropout2 48x17x17\n", " 7 hidden3 512\n", " 8 dropout3 512\n", " 9 output 2\n", "\n", " epoch train loss valid loss train/val valid acc dur\n", "------- ------------ ------------ ----------- ----------- ------\n", " 1 \u001b[36m0.29179\u001b[0m \u001b[32m0.21075\u001b[0m 1.38452 0.91803 25.29s\n", " 2 \u001b[36m0.19556\u001b[0m \u001b[32m0.17478\u001b[0m 1.11886 0.93354 25.28s\n", " 3 \u001b[36m0.16524\u001b[0m \u001b[32m0.14738\u001b[0m 1.12112 0.94464 25.27s\n", " 4 \u001b[36m0.13552\u001b[0m \u001b[32m0.12874\u001b[0m 1.05263 0.95317 25.35s\n", " 5 \u001b[36m0.10431\u001b[0m \u001b[32m0.09559\u001b[0m 1.09123 0.96613 25.33s\n", " 6 \u001b[36m0.07986\u001b[0m \u001b[32m0.07920\u001b[0m 1.00832 0.97178 25.30s\n", " 7 \u001b[36m0.06338\u001b[0m \u001b[32m0.06350\u001b[0m 0.99820 0.98082 25.37s\n", " 8 \u001b[36m0.04695\u001b[0m 0.08050 0.58320 0.97457 25.48s\n", " 9 \u001b[36m0.03873\u001b[0m \u001b[32m0.04434\u001b[0m 0.87349 0.98740 25.45s\n", " 10 \u001b[36m0.03205\u001b[0m \u001b[32m0.04062\u001b[0m 0.78901 0.98731 25.45s\n", " 11 \u001b[36m0.02481\u001b[0m \u001b[32m0.03864\u001b[0m 0.64205 0.98983 25.46s\n", " 12 \u001b[36m0.01947\u001b[0m \u001b[32m0.03795\u001b[0m 0.51303 0.99072 25.46s\n", " 13 \u001b[36m0.01929\u001b[0m \u001b[32m0.03571\u001b[0m 0.54017 0.99018 25.46s\n", " 14 \u001b[36m0.01839\u001b[0m 0.04099 0.44851 0.99097 25.44s\n", " 15 \u001b[36m0.01478\u001b[0m 0.03803 0.38876 0.99126 25.44s\n", " 16 \u001b[36m0.01165\u001b[0m \u001b[32m0.03516\u001b[0m 0.33130 0.99234 25.44s\n", " 17 \u001b[36m0.01028\u001b[0m 0.04292 0.23958 0.99126 25.45s\n", " 18 \u001b[36m0.00873\u001b[0m 0.04098 0.21296 0.99126 25.45s\n", " 19 0.00897 0.04412 0.20324 0.99171 25.46s\n", " 20 0.01151 0.04099 0.28085 0.99153 25.45s\n", " 21 \u001b[36m0.00849\u001b[0m 0.04151 0.20444 0.99162 25.45s\n", " 22 \u001b[36m0.00770\u001b[0m 0.04296 0.17931 0.99132 25.45s\n", " 23 \u001b[36m0.00739\u001b[0m 0.04930 0.14983 0.99180 25.45s\n", " 24 \u001b[36m0.00579\u001b[0m 0.05017 0.11535 0.99097 25.44s\n", " 25 \u001b[36m0.00519\u001b[0m 0.05995 0.08655 0.99090 25.44s\n", " 26 0.00615 0.04067 0.15125 0.99207 25.45s\n", " 27 0.00753 0.04245 0.17739 0.99315 25.45s\n", " 28 0.00667 0.04175 0.15974 0.99279 25.43s\n", " 29 \u001b[36m0.00436\u001b[0m 0.04009 0.10883 0.99279 25.43s\n", " 30 \u001b[36m0.00411\u001b[0m 0.05633 0.07288 0.99189 25.43s\n", " 31 0.00551 0.04092 0.13460 0.99369 25.43s\n", " 32 0.00431 0.04565 0.09440 0.99171 25.44s\n", " 33 \u001b[36m0.00282\u001b[0m 0.05117 0.05513 0.99153 25.44s\n", " 34 0.00537 0.03950 0.13588 0.99261 25.45s\n", " 35 0.00331 0.05039 0.06562 0.99243 25.44s\n" ] } ], "source": [ "cnn = cnn.fit(X_train, y_train)" ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " 36 \u001b[36m0.00240\u001b[0m 0.04912 0.04885 0.99189 25.31s\n", " 37 0.00263 0.05617 0.04687 0.99162 25.25s\n", " 38 0.00337 0.04676 0.07214 0.99225 25.36s\n", " 39 0.00368 0.04230 0.08706 0.99225 25.45s\n", " 40 0.00460 0.05154 0.08920 0.99171 25.46s\n", " 41 \u001b[36m0.00199\u001b[0m 0.05178 0.03845 0.99153 25.46s\n", " 42 \u001b[36m0.00164\u001b[0m 0.05414 0.03024 0.99117 25.46s\n", " 43 \u001b[36m0.00164\u001b[0m 0.04746 0.03447 0.99279 25.45s\n", " 44 0.00257 0.07061 0.03645 0.99016 25.45s\n", " 45 0.00272 0.04788 0.05684 0.99261 25.47s\n", " 46 0.00322 0.05044 0.06374 0.99225 25.45s\n", " 47 \u001b[36m0.00142\u001b[0m 0.04915 0.02895 0.99189 25.46s\n", " 48 0.00497 0.06113 0.08135 0.99180 25.45s\n", " 49 0.00319 0.04843 0.06594 0.99279 25.45s\n", " 50 0.00309 0.04497 0.06868 0.99243 25.46s\n", " 51 0.00301 0.05209 0.05770 0.99189 25.45s\n", " 52 0.00226 0.04459 0.05068 0.99297 25.46s\n", " 53 0.00458 0.04665 0.09811 0.99225 25.46s\n", " 54 0.00341 0.04442 0.07682 0.99261 25.46s\n", " 55 0.00181 0.04980 0.03627 0.99279 25.46s\n", " 56 0.00203 0.04549 0.04451 0.99099 25.46s\n", " 57 \u001b[36m0.00142\u001b[0m 0.04496 0.03160 0.99261 25.47s\n", " 58 \u001b[36m0.00091\u001b[0m 0.05111 0.01786 0.99225 25.46s\n", " 59 0.00128 0.04837 0.02654 0.99279 25.46s\n", " 60 0.00192 0.04707 0.04077 0.99351 25.46s\n", " 61 0.00229 0.04862 0.04715 0.99207 25.45s\n", " 62 0.00318 0.05552 0.05724 0.99333 25.50s\n", " 63 0.00196 0.05522 0.03553 0.99279 25.46s\n", " 64 0.00242 0.05030 0.04808 0.99261 25.46s\n", " 65 0.00189 0.03927 0.04824 0.99297 25.47s\n", " 66 0.00178 0.05818 0.03064 0.99261 25.46s\n", "Early stopping.\n", "Best valid loss was 0.035157 at epoch 16.\n", "Loaded parameters to layer 'conv1' (shape 64x4x3x3).\n", "Loaded parameters to layer 'conv1' (shape 64).\n", "Loaded parameters to layer 'conv2' (shape 48x64x3x3).\n", "Loaded parameters to layer 'conv2' (shape 48).\n", "Loaded parameters to layer 'hidden3' (shape 13872x512).\n", "Loaded parameters to layer 'hidden3' (shape 512).\n", "Loaded parameters to layer 'output' (shape 512x2).\n", "Loaded parameters to layer 'output' (shape 2).\n" ] } ], "source": [ "cnn = cnn.fit(X_train, y_train)" ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "collapsed": true }, "outputs": [], "source": [ "test_accuracy = cnn.score(X_test, y_test)" ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "0.9254780652418447" ] }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" } ], "source": [ "test_accuracy" ] }, { "cell_type": "code", "execution_count": 35, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<module 'matplotlib.pyplot' from '/home/d/nolearn/local/lib/python2.7/site-packages/matplotlib/pyplot.pyc'>" ] }, "execution_count": 35, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7f19a00ed4d0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot_loss(cnn)" ] }, { "cell_type": "code", "execution_count": 46, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/d/nolearn/local/lib/python2.7/site-packages/matplotlib/pyplot.py:516: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n", " max_open_warning, RuntimeWarning)\n" ] }, { "data": { "text/plain": [ "<module 'matplotlib.pyplot' from '/home/d/nolearn/local/lib/python2.7/site-packages/matplotlib/pyplot.pyc'>" ] }, "execution_count": 46, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7f1a4afcb6d0>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7f1a505cd8d0>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7f1a519a8350>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7f1a50108450>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7f1a4e7ac190>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7f1a4cf05b10>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7f1a4a148c90>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7f1a4898a750>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7f1a4720be10>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7f1a45a4f8d0>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7f1a442c0610>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7f1a42b020d0>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7f1a41339b50>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7f1a3fba8750>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7f1a3e3eacd0>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7f1a3cc29fd0>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7f1a3b4fb310>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7f1a39d34d90>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7f1a38575910>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7f1a36dba490>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7f1a3569d610>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7f1a33edf110>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7f1a32713c50>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7f1a30f587d0>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7f1a2f79b350>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7f1a2e096e90>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7f1a2c8d9950>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7f1a2b11b4d0>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7f1a29961050>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7f1a28196b90>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7f1a269da710>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7f1a2521c290>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7f1a23aedd90>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7f1a2232ff90>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7f1a20b7d2d0>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7f1a1f3c05d0>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7f1a1dc018d0>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7f1a1c443bd0>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7f1a1ac85ed0>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7f1a196101d0>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7f1a17e52350>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7f1a16692650>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7f1a14ed7950>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7f1a13718c50>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7f1a11f58f50>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7f1a10729290>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7f1a0ef6b590>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7f1a0d7ad890>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7f1a0bff0b90>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7f1a0aa23d10>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7f1a092665d0>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7f1a07a2b150>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7f1a062e1c90>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7f1a04b23810>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7f1a032e8390>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7f1a01b9eed0>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7f1a003e0a50>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7f19fec245d0>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7f19fd3e8150>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7f19fbc9dc90>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7f19fa4e0810>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7f19f8e6e190>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7f19f76ae590>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7f19f5ef1890>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot_conv_weights(cnn.layers_['conv2'])" ] }, { "cell_type": "code", "execution_count": 48, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# store CNN\n", "sys.setrecursionlimit(1000000000)\n", "with open(os.path.expanduser('~/Projects/gp/nets/RGBA.p'), 'wb') as f:\n", " pickle.dump(cnn, f, -1)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true }, "outputs": [], "source": [ "with open(os.path.expanduser('~/Projects/gp/nets/RGBA.p'), 'rb') as f:\n", " net = pickle.load(f)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from sklearn.metrics import classification_report, accuracy_score, roc_curve, auc, precision_recall_fscore_support, f1_score, precision_recall_curve, average_precision_score, zero_one_loss\n" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Precision/Recall:\n", " precision recall f1-score support\n", "\n", " 0 0.90 0.96 0.93 3556\n", " 1 0.96 0.89 0.92 3556\n", "\n", "avg / total 0.93 0.93 0.93 7112\n", "\n" ] } ], "source": [ "test_prediction = net.predict(X_test)\n", "test_prediction_prob = net.predict_proba(X_test)\n", "print\n", "print 'Precision/Recall:'\n", "print classification_report(y_test, test_prediction)" ] }, { "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 }
mit
crawfordsm/unam_telescope_lab
night_sky/night_sky.ipynb
1
981
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Observe the Night Sky" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## I. Set up" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## II. Identify the bright objects in the sky " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## III. Constellations" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## IV. Bright Stars" ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.10" } }, "nbformat": 4, "nbformat_minor": 0 }
bsd-3-clause
jalabort/alabortcvpr2015
notebooks/Exp4/Build-Models-view2.ipynb
1
12430
{ "metadata": { "name": "", "signature": "sha256:e7031b459456dd9415d56f594fe85a4bc3d3d11f8d97e2be09436a145f1f4b8f" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "%matplotlib inline\n", "%pylab inline" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "heading", "level": 1, "metadata": {}, "source": [ "Load training data" ] }, { "cell_type": "code", "collapsed": false, "input": [ "import menpo.io as mio\n", "from menpo.landmark import labeller, streetscene_car_view_2\n", "from menpofast.utils import convert_from_menpo\n", "\n", "path = '/data/'\n", "group = 'streetscene_car_view_2'\n", "\n", "training_images = []\n", "for i in mio.import_images(path + 'PhD/DataBases/cars/cmu_car_data1/view2/',\n", " verbose=True, max_images=None):\n", " \n", " # convert the image from menpo Image to menpofast Image (channels at front)\n", " i = convert_from_menpo(i)\n", " \n", " labeller(i, 'PTS', eval(group))\n", " i.crop_to_landmarks_proportion_inplace(1.5, group=group)\n", " i = i.rescale_landmarks_to_diagonal_range(200, group=group)\n", " \n", " if i.n_channels == 3:\n", " i = i.as_greyscale(mode='average')\n", " training_images.append(i)" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "training_images = training_images[::2]" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "from menpo.visualize import visualize_images\n", "\n", "visualize_images(training_images)" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "heading", "level": 1, "metadata": {}, "source": [ "Building options" ] }, { "cell_type": "code", "collapsed": false, "input": [ "from menpofast.feature import no_op, fast_dsift\n", "\n", "parts_shape = (31, 31)\n", "features = fast_dsift\n", "diagonal = 200\n", "normalize_parts = False\n", "covariance = 2\n", "scales = (1, .5)\n", "max_shape_components = 25\n", "max_appearance_components = 500" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "img = training_images[0].copy()\n", "img = img.rescale_to_diagonal(diagonal)\n", "img = img.as_masked()\n", "img.build_mask_around_landmarks(parts_shape, group=group)\n", "img.view_widget()" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "img2 = img.copy()\n", "img2 = img2.rescale(0.5)\n", "img2 = img2.as_masked()\n", "img2.build_mask_around_landmarks(parts_shape, group=group)\n", "img2.view_widget()" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "heading", "level": 1, "metadata": {}, "source": [ "Active Appearance Models" ] }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Parts" ] }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "Build" ] }, { "cell_type": "code", "collapsed": false, "input": [ "from alabortcvpr2015.aam import PartsAAMBuilder\n", "\n", "parts_aam = PartsAAMBuilder(\n", " parts_shape=parts_shape,\n", " features=features,\n", " diagonal=diagonal,\n", " normalize_parts=normalize_parts,\n", " scales=scales,\n", " max_shape_components=max_shape_components,\n", " max_appearance_components=max_appearance_components).build(training_images,\n", " group=group,\n", " verbose=True)" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "from menpofast.image import Image\n", "\n", "Image(parts_aam.appearance_models[1].mean().pixels[8, 0]).view()" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "Save" ] }, { "cell_type": "code", "collapsed": false, "input": [ "from alabortcvpr2015.utils import pickle_dump\n", "\n", "pickle_dump(parts_aam, path + 'PhD/Models/parts_aam_view2_fast_dsift')" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Global" ] }, { "cell_type": "code", "collapsed": false, "input": [ "from alabortcvpr2015.aam import GlobalAAMBuilder\n", "\n", "global_aam = GlobalAAMBuilder(\n", " features=features,\n", " diagonal=diagonal,\n", " scales=scales,\n", " max_shape_components=max_shape_components,\n", " max_appearance_components=max_appearance_components).build(training_images,\n", " group=group,\n", " verbose=True)" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "global_aam.appearance_models[1].mean().view()" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "Save" ] }, { "cell_type": "code", "collapsed": false, "input": [ "from alabortcvpr2015.utils import pickle_dump\n", "\n", "pickle_dump(global_aam, path + 'PhD/Models/global_aam_view2_fast_dsift')" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "heading", "level": 1, "metadata": {}, "source": [ "Constrained Local Models" ] }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "Build" ] }, { "cell_type": "code", "collapsed": false, "input": [ "from alabortcvpr2015.clm import CLMBuilder\n", "\n", "clm = CLMBuilder(\n", " parts_shape=parts_shape,\n", " features=features,\n", " diagonal=diagonal,\n", " normalize_parts=normalize_parts,\n", " covariance=covariance,\n", " scales=scales,\n", " max_shape_components=max_shape_components).build(training_images,\n", " group=group,\n", " verbose=True)" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "clm.parts_filters()[1][8].view()" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "Save" ] }, { "cell_type": "code", "collapsed": false, "input": [ "from alabortcvpr2015.utils import pickle_dump\n", "\n", "pickle_dump(clm, path + 'PhD/Models/clm_view2_fast_dsift')" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "heading", "level": 1, "metadata": {}, "source": [ "Unified" ] }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Parts" ] }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "Build" ] }, { "cell_type": "code", "collapsed": false, "input": [ "from alabortcvpr2015.unified import PartsUnifiedBuilder\n", "\n", "parts_unified = PartsUnifiedBuilder(\n", " parts_shape=parts_shape,\n", " features=features,\n", " diagonal=diagonal,\n", " normalize_parts=normalize_parts,\n", " covariance=covariance,\n", " scales=scales,\n", " max_shape_components=max_shape_components,\n", " max_appearance_components=max_appearance_components).build(training_images,\n", " group=group,\n", " verbose=True)" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "from menpofast.image import Image\n", "\n", "Image(parts_unified.appearance_models[1].mean().pixels[8, 0]).view()" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "parts_unified.parts_filters()[1][8].view()" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "Save" ] }, { "cell_type": "code", "collapsed": false, "input": [ "from alabortcvpr2015.utils import pickle_dump\n", "\n", "pickle_dump(parts_unified, path + 'PhD/Models/parts_unified_view2_fast_dsift')" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Global" ] }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "Build" ] }, { "cell_type": "code", "collapsed": false, "input": [ "from menpofast.feature import no_op, fast_dsift, fast_daisy\n", "from alabortcvpr2015.unified import GlobalUnifiedBuilder\n", "\n", "global_unified = GlobalUnifiedBuilder(\n", " parts_shape=parts_shape,\n", " features=features,\n", " diagonal=diagonal,\n", " normalize_parts=normalize_parts,\n", " covariance=covariance,\n", " scales=scales,\n", " max_shape_components=max_shape_components,\n", " max_appearance_components=max_appearance_components).build(training_images,\n", " group=group,\n", " verbose=True)" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "global_unified.appearance_models[1].mean().view()" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "parts_unified.parts_filters()[1][8].view()" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "Save" ] }, { "cell_type": "code", "collapsed": false, "input": [ "from alabortcvpr2015.utils import pickle_dump\n", "\n", "pickle_dump(global_unified, path + 'PhD/Models/global_unified_view2_fast_dsift')" ], "language": "python", "metadata": {}, "outputs": [] } ], "metadata": {} } ] }
bsd-2-clause
rsignell-usgs/notebook
UGRID/plot_mesh.ipynb
1
175761
{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# plot a ugrid mesh" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import matplotlib.tri as tri\n", "import pyugrid" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#url = 'http://www.smast.umassd.edu:8080/thredds/dodsC/fvcom/mwra/fvcom'\n", "url = 'http://geoport.whoi.edu/thredds/dodsC/usgs/vault0/models/tides/vdatum_fl_sab/adcirc54.ncml'" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [], "source": [ "ug = pyugrid.UGrid.from_ncfile(url)\n", "lon = ug.nodes[:,0]\n", "lat = ug.nodes[:,1]\n", "nv = ug.faces\n", "triang = tri.Triangulation(lon,lat,triangles=nv)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import cartopy.crs as ccrs\n", "import matplotlib.pyplot as plt\n", "from cartopy.mpl.gridliner import LONGITUDE_FORMATTER, LATITUDE_FORMATTER\n", "\n", "\n", "def make_map(projection=ccrs.PlateCarree()):\n", " fig, ax = plt.subplots(figsize=(8, 6),\n", " subplot_kw=dict(projection=projection))\n", " ax.coastlines(resolution='10m')\n", " gl = ax.gridlines(draw_labels=True)\n", " gl.xlabels_top = gl.ylabels_right = False\n", " gl.xformatter = LONGITUDE_FORMATTER\n", " gl.yformatter = LATITUDE_FORMATTER\n", " return fig, ax" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[<matplotlib.lines.Line2D at 0x7fe7d82ae890>,\n", " <matplotlib.lines.Line2D at 0x7fe7d81a1810>]" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" }, { "data": { 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Wr6fkOHs2zwUwXGDUKKo369UDxo83pEILSQ6WWtKChReET2w2vA+gLIDMYJbx9+J2SC8G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9YHDxJUT5wgEHTtqqpTv/ceGX337py4x4/z2K+/5j1fucLtx46xbxYL+dy+fWqyN2xIwL17V4hK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\n8AXQBEBnAB0BmNnZAVrhpodatYB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"text/plain": [ "<matplotlib.figure.Figure at 0x7fe7d82ae850>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#geodetic = ccrs.Geodetic(globe=ccrs.Globe(datum='WGS84'))\n", "#fig, ax = make_map(projection=geodetic)\n", "fig, ax = make_map()\n", "\n", "kw = dict(marker='.', linestyle='-', alpha=0.85, color='darkgray')\n", "kw = dict(linestyle='-',color='red')\n", "ax.triplot(triang, **kw) # or lon, lat, triangules;\n", "#ax.set_extent([-84, -78, 25, 32])" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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Yr1TiUXIyGnXsiKbNm6Np06bw9PTM08/h8uXLWLp0KaZPn/5Oz42JiUH9+vWR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\nWkFtMlj282HQRj4FkHs0ghiRN6/UCgqSnu3by2OPPCLTAXkIkFKA+IE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AzDCDcMwzQM44h1/Rjt2F0dl7f3bR2bYD3bz8u1RQ3DWGfN1aOGYdTLCmO6U2PNDvjCMIynDMP4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\ngQ+yOfiIuQ984AMf+MAH2Rz+D64QwOvkOgjmAAAAAElFTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7fe7d8117ad0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import mplleaflet\n", "#geodetic = ccrs.Geodetic(globe=ccrs.Globe(datum='WGS84'))\n", "#fig, ax = make_map(projection=geodetic)\n", "fig, ax = make_map()\n", "\n", "kw = dict(marker='.', linestyle='-', alpha=0.85, color='darkgray')\n", "kw = dict(linestyle='-',color='red')\n", "ax.triplot(triang, **kw) # or lon, lat, triangules;\n", "ax.set_extent([-87.5, -82.5, 29.4, 31])" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from cartopy.io.img_tiles import MapQuestOpenAerial, MapQuestOSM, OSM\n", "geodetic = ccrs.Geodetic(globe=ccrs.Globe(datum='WGS84'))\n", "\n", "fig = plt.figure(figsize=(12,8))\n", "tiler = MapQuestOpenAerial()\n", "ax = plt.axes(projection=tiler.crs)\n", "\n", "bbox=[-71, -69.3, 42, 42.8]\n", "#ax = plt.axes(projection=ccrs.PlateCarree())\n", "ax.set_extent(bbox)\n", "ax.add_image(tiler, 8)\n", "\n", "#ax.coastlines()\n", "kw = dict(marker='.', linestyle='-', alpha=0.85, color='darkgray', transform=geodetic)\n", "ax.triplot(triang, **kw) # or lon, lat, triangules\n", "#ax.set_extent()\n", "gl = ax.gridlines(draw_labels=True)\n", "gl.xlabels_top = False\n", "gl.ylabels_right = False\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "gist_id": "", "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