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@anaderi
Created August 5, 2013 23:30
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{
"metadata": {
"name": "demo_ROC"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": "gets prediction, signal - 1D arrays, returns ROC\n\n*NB*: ROC is a function object"
},
{
"cell_type": "code",
"collapsed": false,
"input": "def calc_ROC_cols(prediction, signal):\n mn_hash = {}\n total_B = 0\n total_S = 0\n for i in xrange(len(prediction)):\n key = prediction[i]\n add_S = signal[i]\n add_B = 1 - add_S\n total_B += add_B\n total_S += add_S\n if key not in mn_hash:\n mn_hash[key] = {\n \"B\": add_B,\n \"S\": add_S,\n }\n else:\n mn_hash[key][\"B\"] += add_B\n mn_hash[key][\"S\"] += add_S\n\n sensitivity, specificity, ROC = _calc_ROC(mn_hash, total_B, total_S)\n return ROC",
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 3
},
{
"cell_type": "code",
"collapsed": false,
"input": "def _calc_ROC(hash_mn, total_B, total_S, xmax=1):\n '''aux method'''\n running_B = 0\n running_S = 0\n sensitivity = zeros(len(hash_mn.keys()))\n specificity = zeros(len(hash_mn.keys()))\n ROC = {}\n for i, key in enumerate(sorted(hash_mn.keys())):\n running_B += hash_mn[key][\"B\"]\n running_S += hash_mn[key][\"S\"]\n TPR = float((total_S - running_S) / float(total_S))\n TNR = float(running_B / float(total_B))\n hash_mn[key][\"TPR\"] = TPR\n hash_mn[key][\"TNR\"] = TNR\n sensitivity[i] = TPR\n specificity[i] = TNR\n ROC[TPR] = TNR\n ROC[0.0] = 1.0\n ROC[1.0] = 0.0\n\n ROC1d = scipy.interpolate.interp1d(*zip(*sorted(ROC.items())))\n return sensitivity, specificity, ROC1d",
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 10
},
{
"cell_type": "code",
"collapsed": false,
"input": "def plot_ROC(ROCs, xlim=(0.0, 1.0), ylim=(0.0, 1.0)):\n '''\n ROCs - list of hashes:\n {\n 'ROC': ROC, # 1d ROC function\n 'name': name, # name of formula for display\n }\n '''\n colors = ['c', 'y', 'r', 'b', 'm', 'g', 'k']\n\n fig = figure(figsize=(8, 6))\n ax = fig.add_subplot(\n 111, title='ROC curve',\n xlabel='Signal sensitivity', ylabel='Bg rejection eff (specificity)')\n points_x = linspace(xlim[0], xlim[1], 500)\n ymin, ymax = ylim\n colors_iter = itertools.cycle(colors)\n plot_handle_list = []\n legend_names = []\n for R in ROCs:\n plot_handle, = ax.plot(\n points_x, R[\"ROC\"](points_x),\n colors_iter.next(), linewidth=2)\n ymin = max(ymin, R[\"ROC\"](xlim[1]))\n ymax = min(ymax, R[\"ROC\"](xlim[0]))\n plot_handle_list.append(plot_handle)\n legend_names.append(\"%s\" % R[\"name\"])\n height = ymax - ymin\n matplotlib.pyplot.ylim(ymin - 0.05 * height, ymax + height * 0.05)\n matplotlib.pyplot.xlim(xlim)\n legend(plot_handle_list, legend_names, loc='lower left')",
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 21
},
{
"cell_type": "heading",
"level": 3,
"metadata": {},
"source": "example"
},
{
"cell_type": "code",
"collapsed": false,
"input": "import pandas\nimport scipy\nimport scipy.interpolate\nimport itertools",
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 19
},
{
"cell_type": "markdown",
"metadata": {},
"source": "read file by pandas"
},
{
"cell_type": "code",
"collapsed": false,
"input": "d = pandas.read_csv(\"test_ROC.t\", sep='\\t')",
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": "calculate ROC function"
},
{
"cell_type": "code",
"collapsed": false,
"input": "ROC = calc_ROC_cols(d.is_signal, d.MN)",
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 17
},
{
"cell_type": "markdown",
"metadata": {},
"source": "and plot it"
},
{
"cell_type": "code",
"collapsed": false,
"input": "\nplot_ROC([{'ROC': ROC, 'name': 'test'},])",
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
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btpDf0mJ0PBGRHuu2nefMmcOFF17Inj17uOuuu5g0aRJ33nlntyf28PDg2Wef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\n6dJS7isqYr/NRoC7Ow8lJ3NTXBweQ3A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zurq68s8//7TY8ZtiMBjIyMhg27ZtT/T42NhY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\nAAAASUVORK5CYII=\n",
"text": "<matplotlib.figure.Figure at 0x105ce6d10>"
}
],
"prompt_number": 22
},
{
"cell_type": "markdown",
"metadata": {},
"source": "..or with limits"
},
{
"cell_type": "code",
"collapsed": false,
"input": "plot_ROC([{'ROC': ROC, 'name': 'test'},], xlim=(0, 0.4))",
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": 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G8FZKCkEePd76QURkwLPbzXAWL17M4sWLux+np6d3b/z7bkyya9cuRo4cSXx8\nPABLlixh06ZNx4R8ZGQkOTk5ADQ0NBASEnJcwIuczHh/f7anpTE3O5uP6+uZl53N5okTGebp6ejR\nREQGhB4TddmyZd1/rqmpoaSkhIkTJ/b4xqWlpcTGxnY/jomJ6S65OeKaa67hrLPOIioqisbGRl56\n6aVTGF0ERvn5sX3SJM7KyuKzxkbmZGXxbmoqw728HD2aiIjD9Rjy6enpvPbaa3R1dTFlyhTCwsKY\nNWsWjzzyyPe+rje3H12zZg1paWlkZGSQn5/P/Pnzyc7OJjAw8Ljnrlq16piZ0tPTe3x/GRzifXz4\ncNIk5mZnk9PcTHpWFltTU4ny9nb0aCIivZKRkUFGRka/v2+PIV9XV0dQUBBPPfUUP//5z1m9ejUp\nKSk9vnF0dDTFxcXdj4uLi49bsPfJJ5+wYsUKAJKSkkhISGDfvn1MnTr1uPc7OuRFviva25sP0tKY\nn51NbnMzs7Oy2JaaygjrVSEiIgPZdw9eV69e3S/v2+PqerPZTFlZGS+99BKLFi0CeneUPnXqVPbv\n309RUREdHR28+OKLXHDBBcc8Z8yYMWzduhWAiooK9u3bR2Ji4g/ZDxHCvbx4Py2NyQEB5Le2Mjsz\nk3zdvElEBrEeQ/7uu+9m4cKFJCUlMX36dPLz8xk1alSPb+zh4cFjjz3GwoULGTduHJdddhljx47l\n8ccf5/HHHwfgjjvu4PPPPyc1NZV58+axbt06hg0b1ve9kkErxNOTbWlpnBYUxMH2ds7IzGRPc7Oj\nxxIRcYhe3bve0XQJnZyqJrOZ83NzyairI8zTk3dTU0kNCHD0WCIivWLzm+GsWrWKioqKk76wrKyM\nlStX9nkAEVsIcHfnzZQUFg4bRmVnJ3Oysvi8sdHRY4mI2NVJF95NnTqVJUuW0NHRweTJk4mMjMQw\nDMrLy/nyyy/x9vbm97//vT1nFTklfu7ubJowgct272ZTVRVzs7J4a+JEZg0Z4ujRRETsosfT9cXF\nxXz88cccPHgQgBEjRjBr1qxe39q2P+h0vfRFp8XCz/bs4aXKSvzc3Hg9JYWzgoMdPZaIyEn1V+7p\nM3kZFMyGwdX79rG+vBwfNzfuT0jgf6Kj8XbrVduyiIhdKeRFTpHFMLhu/37+dugQAAk+PjyQmMil\nYWG9uixURMReFPIiP4BhGGyuqeEPBQXkWS+tmxYYyLqkJNKHDnXwdCIi37L56vrly5cD6H7y4lJM\nJhPnhIQijXGlAAAgAElEQVSQNXUqTyYnE+Hl1X3P+0U5OeQ2NTl6RBGRfnPSI/kJEyaQm5vL5MmT\nyczMtPdcx9CRvNhKk9nMI8XFrCsupslsxgRcGRHBPfHxxOqWuCLiIDY/Xf+HP/yBJ598kqamJnx9\nfY/beENDQ5833lsKebG1wx0d3HfgAH87dIguw8DbzY0boqO5PS6OYFXXioid2Tzk29ra8PHx4cIL\nL2TTpk193lBfKOTFXvJbW1lRWMiLhw8DEOzhwR0jRnBddDQ+WokvInZi88/kTz/9dIAT1r6KuKok\nX19eGDeOz6ZM4ayhQ6nt6uIP+fmM3rmT9eXlmPXLpog4kZPe8a69vZ3nn3+eTz75hFdfffWY3yhM\nJhMXXXSRXQYUcYSpgYFsTU1lS20ty/PzyWluZtnevTxcXMyDiYmcM2yYLrsTkQHvpKfrP/zwQ55/\n/nlefvnl4ypiAf75z3/afLgjdLpeHMliGDxfUcGdhYUcbG8HIH3oUNYlJjItKMjB04mIK7LbdfJP\nPfUUv/zlL/u8ob5QyMtA0Gax8H+lpdx/4AC1XV0AXBIWxprEREZ+Z3GqiEhf2C3km5ub+eMf/8jB\ngwd58skn2b9/P/v27eO8887r88Z7SyEvA0ltZydri4v5c0kJbRYLHiYT10ZFcfeIEQz38nL0eCLi\nAuwW8pdeeilTpkzh2WefJS8vj+bmZk4//XSys7P7vPHeUsjLQFTc1sbKoiLWl5dj4dt62z/ExnJL\nbCwB7u6OHk9EnJjNV9cfkZ+fz/Lly/GyHqH4+/v3eaMiriDWx4d/jBlD9rRpnBcSQpPZzMqiIkbu\n3MnfSkvptFgcPaKIDHI9hry3tzetra3dj/Pz8/H29rbpUCLOZIK/P6+npPBBWhozgoKo6Ojgt/v3\nM/6zz/h3ZaXOQomIw/R4uv6dd97h/vvvZ/fu3cyfP5+PP/6YZ555hjlz5thrRp2uF6dhGAavVlVx\ne0EB+62/HM8ICmJdYiKzVYAjIr1k1xa6qqoqPv30UwBmzpxJaGhonzd8KhTy4mw6LRaeKitj9YED\nVHR0AHBeSAgPJCYyQR95iUgPVDUr4gSazGb+WFzM/1oLcNywFuAkJBCjj71E5CQU8iJOpKKjg3sP\nHOBxawGOz5ECnBEjGOpx0htPisggpZAXcULftLayoqCAlyorgW8LcFaMGMH/qABHRI5i15A3m81U\nVFTQZb3LF0BcXFyfN95bCnlxNZ81NHBrQQEZdXUAxHl7c29CAleEh+Oue+KLDHp2C/lHH32U1atX\nM3z4cNyPusFHbm5unzfeWwp5cUWGYbC5poblBQXkNjcDMNHfn7VJSSwMDlYBjsggZreQT0pKYteu\nXYSEhPR5Yz+UQl5cmdkw+FdFBXcVFlJsLcCZM3Qo65KSmKqqZ5FByW53vIuLiyNITVsiNuNuMnFl\nRARfz5jB/yYlEezhwft1dUz74guW7N5N/lE3oxIRORU9HslfddVVfP311yxatKj71rYmk4lbbrnF\nLgMe2Z6O5GWwqO3s5IGDB/lLaSnt1gKcX0dFcZcKcEQGDbseyc+bN4+Ojg6amppobGyksbGxzxsW\nkRML9vRkXVIS+6dPZ1lEBGbD4LHSUpJ27uTeoiKazWZHjygiTqLXl9AdCfZAB3xGqCN5Gcxym5q4\nraCAt2pqAIjw8mJlfDxXR0TgqcvuRFyS3Rbe5ebm8vOf/5zq6moAwsLCWL9+PRMmTOjzxntLIS8C\nGXV1LM/PZ5f1F+7Rvr48kJjI4tBQrcQXcTF2C/nTTjuNNWvWdBfSZGRkcMcdd/DJJ5/0eeO9pZAX\n+ZZhGPy7spI7Cgv5xrogb6a1AOcMFeCIuAy7hXxqairZ2dk9fs2WFPIix+q0WHiyrIzVRUUc7uwE\n4PyQEB5MTGScCnBEnJ7dQv7HP/4xU6ZMYenSpRiGwfPPP88XX3zBf/7znz5vvLcU8iIn1tjVxcMl\nJTxUXEyztQBnWUQEq1WAI+LU7BbyNTU1rFy5ko8//hiAM844g1WrVhEcHNznjfeWQl7k+1V0dHBP\nURFPlJV1F+DcFBPD8rg4FeCIOCEV1IjIcfa3tLCisJCXrQU4w44qwPHWSnwRp2HzkL/xxhv585//\nzPnnn3/Cjb/22mt93nhvKeRFTs0uawHOB9YCnBE+PtyXkMDlw4fjppX4IgOezUP+iy++YMqUKWRk\nZJxw42eeeWafN95bCnmRU2cYBm9bC3C+shbgpAYEsDYxkQUqwBEZ0Ox2uv5Pf/oTN910U49fsyWF\nvMgPZzYMnrMW4JRYC3DmBgezNjGRKSrAERmQ7BbykyZNIjMz85ivpaWlkZWV1eeN95ZCXqTvWs1m\nHi0t5YGDB6nr6gJgyfDh3J+QQKKvr4OnE5Gj2TzkN27cyIYNG/jwww8544wzur/e2NiIu7s727Zt\n6/PGez2kQl6k39RYC3AetRbgeJpM/CYqijtHjCBMBTgiA4LNQ/7AgQMUFhZy2223sXbt2u6NBQUF\nMXHiRDzseFmOQl6k/x1sa+PuoiKeLS/HAALd3bk1Lo6bY2Lwd3d39Hgig5rdTtcXFBQQGRmJr/V0\nXmtrKxUVFcTHx/d5472lkBexnRxrAc7bRxXgrIqP5+rISDy0OE/EIewW8lOnTuWTTz7p7pJvb29n\n1qxZfP75533eeG8p5EVs7/3aWpYXFPCZtQAn2c+PBxIS+LEKcETszm598l1dXd0BD+Dt7U2n9V7Z\nIuI65gQHs3PyZF4cN44kX1/2tbRwUV4eP8rM5OP6ekePJyI/QI8hHxoayqZNm7ofb9q0idDQUJsO\nJSKOYTKZuHT4cHZPm8Zjo0YR5unJJw0N/Cgzkwtzc9ltvd5eRJxDj6frv/nmG6644goOHToEQExM\nDM899xwjR460y4Cg0/UijtLY1cVDxcU8XFLSXYBzVWQkq+LjiVYBjojN2P3e9U1NTRiGQaADbp6h\nkBdxrPKjCnDMhoHvUQU4Q1SAI9Lv7PaZfHl5OVdffTU/+clPCAwMZPfu3Tz99NN93rCIOI8ILy/+\nOno0u6dN4+KwMFotFh44eJDETz/lkeJi2i0WR48oIifQY8gvW7aMBQsWdJ+uHzVqFI888ojNBxOR\ngWe0nx//Hj+eHZMnM3vIEGq6urglP58xu3bxfEUFFp1xExlQegz5qqoqLrvsMtytN8fw9PS0641w\nRGTgmRkUREZaGq+npDDe35+itjZ+tmcPU774gnes19uLiOP1GPIBAQFUV1d3P/70008ZMmSITYcS\nkYHPZDJxXkgI2VOn8nRyMtHe3mQ1NbEwJ4f52dl8ab3eXkQcp8eFd1988QXXX389eXl5jB8/nsrK\nSv7973+Tmppqrxm18E7ECbSazfzFWoBTby3AuXz4cO5LSCBBBTgip8Suq+s7OzvZt28fAMnJyXh6\nevZ5w6dCIS/iPKqPFOCUlNBhGHiaTPw2Opo7R4wg1M7/dog4K5uH/LZt25g7dy6vvPLKMRs7cnvL\nkJAQfvSjH3V/Vm9LCnkR53OgrY27Cgv5V0UFBhBkLcC5SQU4Ij2yecivXLmS1atXs2zZshPet7q6\nuprW1lbefffdPg/R45AKeRGnlW0twNlsXZAX6eXF6vh4fqECHJGTsvvNcE7kqquu4h//+Eefh+iJ\nQl7E+b1XW8utBQV8YV2QN8bPjwcSE7kwJEQFOCLfYbeQLy8vZ8WKFZSWlrJ582Z2797Njh07uPrq\nq/u88d5SyIu4Both8HJlJXcUFFDQ1gbA6UFBrEtKYpau2hHpZrc73vXlZjibN29mzJgxjBo1irVr\n1x73/YceeohJkyYxadIkUlJS8PDwoK6u7hR3QUSchZvJxGXDh7Nn+nT+MnIkoUcV4Pz4q6/YowIc\nkX7Vqz75zz//nEmTJpGZmQlAWloaWVlZ3/vGZrOZ5ORktm7dSnR0NNOmTWPjxo2MHTv2hM9/4403\n+NOf/sTWrVuPH1JH8iIuqeFIAU5xMS0WC27A1dYCnCgV4MggZrcj+R96M5xdu3YxcuRI4uPj8fT0\nZMmSJcdU1n7Xhg0b+OlPf9rLsUXEFQR5eHBPQgLfzJjBtVFRmEwmniwrY+TOnawoKOi+3l5Efpge\n70/78MMPc/7551NQUMDpp5/efTOcnpSWlhIbG9v9OCYmhp07d57wuS0tLWzZsoW//vWvJ32/VatW\ndf85PT2d9PT0HmcQEecQ6e3N30eP5uaYGO4oKODVqirWHDzI42Vl3DViBL+OisLbrcdjEhGnlZGR\nQUZGRr+/7/eGvNlsZvv27Wzfvp29e/diGAbJycl4eXn1+Manslr29ddf50c/+hFDhw496XOODnkR\ncU3Jfn68MmECO+rrubWggI/q67npm2/4c0kJ9yUksGT4cNy0El9c0HcPXlevXt0v7/u9vxq7u7uz\nYcMGPDw8mDBhAikpKb0KeIDo6GiKi4u7HxcXFxMTE3PC577wwgs6VS8i3U4bMoTtaWm8NmEC4/z8\nKGxr44o9e5j2xRdsra119HgiTqPHhXc333wznZ2dXHbZZfj7+2MYBiaTicmTJ3/vG3d1dZGcnMy2\nbduIiopi+vTpJ1x4V19fT2JiIiUlJfie5P7WWngnMnh1GQbry8u5u7CQQx0dAMwPDmZtYiKTAgMd\nPJ2IbdjtOvn09PQTnnp///33e3zzt99+m5tuugmz2czVV1/N7bffzuOPPw7AtddeC8D69evZsmUL\nGzZsOPmQCnmRQa/lSAHOgQM0mM0AXBEezn0JCcT7+Dh4OpH+NSDueGcvCnkROaK6s5P7Dxzg/0pL\n6TAMvKwFOCtUgCMuRCEvIoNakbUA5/mjCnBui4vjxpgY/FSAI05OIS8iAmRZC3C2WAtwory8WJ2Q\nwLKICBXgiNNSyIuIHGVbbS235ufzZVMTAGP9/HgwMZHzVYAjTshuIX+kT/5oQ4YMISUlheHDh/d5\ngN5QyItIb1gMgxcPH2ZFYSGF1gKcHw0ZwtrERE5XAY44EbuF/KJFi9ixYwdz5swBvr0rz+TJkyks\nLOTuu+/m5z//eZ+H6HFIhbyInIJ2i4XHDx3i3gMHqOrsBGBxaChrEhMZ4+fn4OlEema3kF+wYAHP\nPfcc4eHhAFRUVLB06VI2btzI7NmzycvL6/MQPQ6pkBeRH6Chq4t1xcX8sbiYVosFd5OJqyMiWBUf\nT6QKcGQAs1tBTXFxcXfAAwwfPpzi4mJCQkJ6ffc7ERFHCPLw4D5rAc6vIiMBeMJagHNXYSENKsAR\nF9djyM+ZM4dFixaxfv16nnnmGS644ALS09Npbm7+3nvNi4gMFFHe3jyenMxX06bx49BQWiwW7jtw\ngKSdO/lLSQkdFoujRxSxiR5P11ssFl599VU++ugjTCYTs2bN4uKLL7bralWdrheR/vSJtQDn4/p6\nABJ9fLg/MZFLw8JUgCMDgkMuoausrCQ0NNTul6Mo5EWkvxmGwWvV1dxWUMDelhYApgQGsjYxkbnB\nwQ6eTgY7m38mv2PHDtLT07nooovIzMzsbqELDw/n7bff7vOGRUQcyWQycWFoKLnTpvFkcjJRXl58\n0djIvOxszs7JIct6vb2IMzvpkfyUKVN44IEHqK+v55prrmHz5s3MnDmTvXv3smTJErKysuw3pI7k\nRcTGWsxm/lRSwtqDB2kwmzHxbQHOvSrAEQew+en6tLS07iAfO3Yse/bs6f7epEmTyMzM7PPGe0sh\nLyL2UnVUAU6ntQDnuuho7hgxghAV4Iid2Px0/dGfu/vot1gRGSRCPT15ZORI9k2fzhXh4XQYBn8s\nKSFp504ePHiQVmvNrYgzOOmRvLu7O37WO0O1trbi6+vb/b3W1la67Hh9qY7kRcRRMhsbWV5QwLu1\ntQBEe3uzOj6eZRERuGslvtiICmpEROzo3ZoalhcUkGldkDfOWoBzngpwxAYU8iIidmYxDF6wFuAU\nWQtwzhgyhHVJScwMCnLwdOJKFPIiIg7SbrHwt0OHuO/AAaqtBTgXWQtwklWAI/1AIS8i4mD1XV2s\nO3iQR0pKugtwfhkZycoRI1SAI32ikBcRGSBK29tZVVTEP8rKsAB+bm78LjaWP8TGEujh4ejxxAkp\n5EVEBpg9zc3cXljIpqoqAMI8Pbk7Pp5fRUbi5dZjH5hIN4W8iMgA9XF9Pbfm5/NJQwMASb6+3J+Q\nwCUqwJFeUsiLiAxghmGwyVqAs89agDPVWoBzlgpwpAcKeRERJ9BlGPyjrIxVRUWUdXQAcPawYaxN\nTGRiQICDp5OBSiEvIuJEmo8qwGm0FuD8zFqAM0K3DpfvUMiLiDihyo4O7j94kL8eVYBzfUwMd8TF\nMUwFOGKlkBcRcWIFra3cWVjIxsOHARji4cHtcXHcEB2Nr7u7g6cTR1PIi4i4gC+tBThbrQU4Md7e\n3BMfz89VgDOoKeRFRFzIO9YCnCxrAc54f38eTExk0bBhKsAZhBTyIiIuxmIYbDh8mDsLCzlgLcCZ\nbS3AmaECnEFFIS8i4qLaLRb+WlrKfQcOUNPVBcDFYWGsSUhgtApwBgWFvIiIi6s7qgCnzVqA86vI\nSO6OjyfCy8vR44kNKeRFRAaJ0vZ2VhYV8U9rAY6/uzu/i4nh9yrAcVkKeRGRQWZ3czO3FxTwWnU1\nAMOPKsDxVAGOS1HIi4gMUh/W1bG8oIAd1gKckUcV4GglvmtQyIuIDGKGYfDfqipuLyzsLsCZFhjI\nuqQk0ocOdfB00lcKeRERocsweNpagFNuLcA5Z9gwHlQBjlNTyIuISLdms5lHSkpYd1QBzs8jIrgn\nPp44FeA4HYW8iIgcp7Kjg3sPHODvhw7RaRh4u7lxfXQ0t6sAx6ko5EVE5KTyrQU4L1gLcIZaC3Cu\nVwGOU1DIi4hIjz5vbGR5fj7v1dUBEOvtzT0JCSwND1cBzgCmkBcRkV4xDIMttbUsz88np7kZgBRr\nAc45KsAZkBTyIiJySiyGwfMVFdxZWMjB9nYAzhw6lHWJiUxXAc6AopAXEZEfpO2oApxaawHOJWFh\n3J+QwCgV4AwICnkREemTuq4uHjx4kD9bC3A8jirACVcBjkMp5EVEpF+UtLezsrCQZ8rLsQAB7u78\nPjaWW2JiVIDjIAp5ERHpV181N3NHQQGvH1WAszI+nmtUgGN3CnkREbGJ7XV13FpQwE5rAc4oX1/W\nJCZycWioVuLbiUJeRERsxjAMXq2q4vaCAva3tgIw3VqAc6YKcGxOIS8iIjbXabHwdHk5q4qKqLAW\n4CwKCeGBhARSVIBjMwp5ERGxmyazmT8WF/O/xcU0WQtwrrQW4MSqAKffKeRFRMTuDh9VgNNlLcC5\nwVqAE6wCnH6jkBcREYf5prWVFQUFvFRZCUCwhwd3jBjBddHR+Gglfp8p5EVExOE+a2hgeUEB7x9V\ngHNvQgI/UwFOnyjkRURkQDAMg801NSwvKCD3qAKctYmJnK0CnB9EIS8iIgOK+agCnGJrAc6coUNZ\nm5jINBXgnBKFvIiIDEhtFguPlZay5qgCnEvDwrg/MZGRvr4Ons459Ffu2XR1xObNmxkzZgyjRo1i\n7dq1J3xORkYGkyZNYsKECaSnp9tyHBERsQMfNzd+HxtL/owZ3Bobi7ebGy9VVjJ21y6u37+fw9br\n7cX2bHYkbzabSU5OZuvWrURHRzNt2jQ2btzI2LFju59TV1fHrFmz2LJlCzExMVRVVREaGnr8kDqS\nFxFxWsVtbawsKuKZ8nIMvi3A+UNsLLfExhLg7u7o8QakAX8kv2vXLkaOHEl8fDyenp4sWbKETZs2\nHfOcDRs2cPHFFxMTEwNwwoAXERHnFuvjwz/GjCFn2jQWhYTQZDazsqiIkTt38rfSUjotFkeP6LJs\nFvKlpaXExsZ2P46JiaG0tPSY5+zfv5+amhrmzJnD1KlTee6552w1joiIONgEf3/eSEkhIy2N6YGB\nVHR08Nv9+5nw2We8UlmpM7Y2YLOi4N5cMtHZ2cmXX37Jtm3baGlp4bTTTmPmzJmMGjXquOeuWrWq\n+8/p6en6/F5ExEmdOXQon06ezCtVVdxRUMDXra38JC+PGUFBrEtMZPYgLMDJyMggIyOj39/XZiEf\nHR1NcXFx9+Pi4uLu0/JHxMbGEhoaiq+vL76+vsyePZvs7OweQ15ERJybyWTiJ2FhXBgSwpNlZawu\nKmJnQwNnZmVxXkgIDyYmMt7f39Fj2s13D15Xr17dL+9rs9P1U6dOZf/+/RQVFdHR0cGLL77IBRdc\ncMxzLrzwQj766CPMZjMtLS3s3LmTcePG2WokEREZYDzd3PhtdDT5M2eyKj4ef3d33qiuZuJnn3HV\n3r2UWK+3lx/GZiHv4eHBY489xsKFCxk3bhyXXXYZY8eO5fHHH+fxxx8HYMyYMZx99tlMnDiRGTNm\ncM011yjkRUQGoQB3d1bGx5M/Ywa/jYrCzWTin+XljNq5k9sKCqizXm8vp0Y3wxERkQFnf0sLKwoL\nefmoApwVI0bwP4OkAEd3vBMREZe3q6GBWwsK+MBagBPn7c19CQlc7uIFOAp5EREZFAzD4G1rAc5X\n1gKcif7+rE1KYmFwsEsW4CjkRURkUDEbBs9VVHBXYWH3gryzhg5lbVISUwMDHTxd/1LIi4jIoNRq\nNn9bgHPwYPeCvMuGD+f+hASSXKQARyEvIiKDWk1nJw8ePMhfSktpt1jwNJn4dVQUd40YQZiXl6PH\n6xOFvIiICHCwrY27i4p41lqAE3hUAY6/kxbgKORFRESOktPUxO0FBbxVUwNAhJcXq+LjuSoiAk8n\nu+xOIS8iInICGXV13Jqfz2eNjQCM9vXlgcREFoeGOs1KfIW8iIjISRiGwcuVldxRWEh+aysAM60F\nOGc4QQGOQl5ERKQHHRZLdwFOZWcnAOdbC3DGDeACHIW8iIhILzV2dfFwSQkPFRfTbDbjBiyLiGB1\nQgIx3t6OHu84CnkREZFTVN7RwT1FRTxRVobZMPBxc+OmmBiWx8Ux1MNm7eunTCEvIiLyA31tLcD5\nt7UAZ5iHB3eOGMFvo6PxHgAr8RXyIiIifbSzoYFb8/PZXl8PwAgfn28LcIYPx82BK/EV8iIiIv3A\nMAzeshbg5FkLcFIDAlibmMgCBxXgKORFRET6kdkweLa8nLuLiroLcOYGB7M2MZEpdi7AUciLiIjY\nQKvZzKPWApx6awHOT4cP576EBBLtVICjkBcREbGhms5O1hw8yKMlJXQYBp4mE7+JiuJOOxTgKORF\nRETs4EBbG3cXFvJcRUV3Ac7yuDhuiomxWQGOQl5ERMSOspuauK2ggM3WApzIIwU4kZF49PPiPIW8\niIiIA7xXW8utBQV8YS3ASfbz44GEBH7cjwU4CnkREREHsRwpwCkooKCtDYDTg4JYl5TErCFD+vz+\nCnlg2LBh1NbWOmCigSU4OJga6+kjERGxnw6LhSfKyrjnqAKcC6wFOGP7UICjkP+erw82+jmIiDhW\nQ1cXDxUX83BxMS0WC27AVZGRrIqPJ/oHFOAo5L/n64ONfg4iIgNDeUcHq4uKeNJagON7VAHOkFMo\nwFHIf8/XBxv9HEREBpZ91gKcV6wFOCGentw5YgS/iYrqVQGOQv57vj7Y6OcgIjIwfWotwPnQWoAT\nby3A+WkPBTgK+e/5+mCjn4OIyMBlGAZvVFdzW0EBu1taAJhkLcCZP2zYCV/TX/+uO74010XFx8fz\n3nvv9ek9nnnmGc4444x+mkhERBzBZDJxfmgo2dOm8XRyMtHe3mQ2NbEgJ4cF2dl8ab3e3hYU8jai\no2sRETmah8nEVZGRfD19Og8kJjLEw4N3a2uZ8sUXXLF7N4Wtrf2+TYW8DSxdupSDBw9y/vnnExgY\nyEMPPcSnn37K6aefTnBwMGlpaXzwwQfdz3/mmWdISkoiKCiIxMRENmzYwN69e/n1r3/Njh07CAwM\nZNhJTumIiIhz8XN357a4OPJnzOCWmBi8TCY2HD5M8q5d3PTNN1RZr7fvD/pM3kYSEhJ4+umnOeus\nsygtLSU1NZV//etfnH322WzdupUlS5awb98+fHx8iIqK4vPPP2fUqFFUVFRQXV3NuHHjWL9+PU89\n9RQffvjh925rIP8cRETk+xW1tXFXYSHPWwtwgtzdaZg9u1/+Xe/9RXtOxpSR0W/vZaSn9+n1//rX\nvzj33HM5++yzAZg3bx5Tp07lzTff5Cc/+Qlubm7k5uYSExNDeHg44eHh325XwS0i4vLifXx4buxY\nfhcby20FBWzpxzuY6nS9HRw4cICXX36Z4ODg7v8+/vhjysvL8fPz48UXX+Tvf/87UVFRnHfeeezb\nt8/RI4uIiJ2lBQSweeJEtqam9tt7uuyRfF+Pvvvq6CaiuLg4li5dyhNPPHHC5y5YsIAFCxbQ3t7O\nihUruOaaa9i+fXu/tRmJiIjzmBsc3G/vpSN5GwkPDyc/Px+AK664gtdff5133nkHs9lMW1sbGRkZ\nlJaWcvjwYTZt2kRzczOenp74+/vj7u7e/R4lJSV09uMiDBERGTwU8jZy++23c9999xEcHMzLL7/M\npk2bWLNmDcOHDycuLo6HH34YwzCwWCw88sgjREdHExISwocffsjf/vY3AObOncv48eOJiIhg+PDh\nDt4jERFxNlpd7wL0cxARcS26452IiIh8L4W8iIiIi1LIi4iIuCiFvIiIiIty6uvkg4ODdS053/4c\nREREvsupV9eLiIi4Iq2udyEZ/Xif/YHIlffPlfcNtH/OTvsnCvkBwNX/h+rK++fK+wbaP2en/ROF\nvIiIiItSyIuIiLgop1l4JyIiMpj0Rzw7xSV0TvB7iIiIyICj0/UiIiIuSiEvIiLiohwa8ps3b2bM\nmDGMGjWKtWvXnvA5N9xwA6NGjSI1NZXMzMxTeq2j9WX/4uPjmThxIpMmTWL69On2GvmU9LR/e/fu\n5bTTTsPHx4eHH374lF47EPRl/1zh7+/5558nNTWViRMnMmvWLHJycnr92oGgL/s30P/+etq3TZs2\nkQp0m8wAAAqdSURBVJqayqRJk5gyZQrvvfder187EPRl/wb63x30/u/gs88+w8PDg1deeeWUX9vN\ncJCuri4jKSnJKCwsNDo6OozU1FRj9+7dxzznzTffNM455xzDMAzj008/NWbMmNHr1zpaX/bPMAwj\nPj7eqK6utuvMp6I3+3f48GHjs88+M1asWGE89NBDp/RaR+vL/hmGa/z9ffLJJ0ZdXZ1hGIbx9ttv\nu9z//062f4YxsP/+erNvTU1N3X/OyckxkpKSev1aR+vL/hnGwP67M4ze/x10dXUZc+bMMRYtWmT8\n+9//PqXXHs1hR/K7du1i5MiRxMfH4+npyZIlS9i0adMxz3nttde48sorAZgxYwZ1dXWUl5f36rWO\n9kP3r6Kiovv7xgBecNib/QsLC2Pq1Kl4enqe8msdrS/7d4Sz//2ddtppDBky5P+1d68xTZ1hAMf/\ngEwcgoSICVATkWhFlLYDxOgIGtEhhgDemCNECCHxxnBRE3bJ5hI102hC9AtqnBJvm5E4mBoNxsuE\nIWy1y4ibeJkSFIgygwiJhs53H0jPxGktVG2tz+8T7TnP6fuch+bpac85L9D7/3nr1i2HY13Nmfxs\n3LV+juTm7++v/d3V1cXw4cMdjnU1Z/KzcdfageM12LZtG/PnzyckJKTfsU9yWZO/ffs2I0eO1B7r\ndDpu377t0DotLS0vjHU1Z/KD3ssGk5OTiYuLY+fOna9n0P3gSH6vIvZ1cXaMnla/Xbt2kZqaOqBY\nV3AmP3Dv+jma2w8//EBUVBSzZ89m69at/Yp1JWfyA/euHTjeGyoqKli6dCnw32XkA6mfyy6hc/Ta\nd3f+RGaPs/lVV1cTFhbG3bt3mTlzJuPGjSMxMfFlDtEpzty74E2474GzY6ypqSE0NNQj6nfmzBm+\n/fZbampq+h3rKs7kB+5dP0dzy8jIICMjg/Pnz5OTk8Ply5df8chejoHm19jYCLh37cCx/FauXMk3\n33yjTVJj6xMDee+57Eg+PDyc5uZm7XFzczM6nc7uOrdu3UKn0zkU62oDzS88PByAsLAwoPcr4czM\nTOrr61/DqB3nTA08pX72hIaGAm9+/X7//XcKCgqorKzUpjT2pPo9Kz9w7/r1d/8nJiZitVq5d+8e\nOp3OY2pnY8vv77//Bty7duBYfmazmQ8//JCIiAjKy8tZtmwZlZWVA3vvvdQzCvqhp6dHjR49Wt24\ncUM9evTohSem1dbWaifGOBLras7k193drTo7O5VSvSeYTJkyRZ08efL1JvAC/anBV1991efENE+p\nn83T+XlK/ZqamlRkZKSqra3td6yrOZOfu9fPkdyuXbumHj9+rJRSymw2q9GjRzsc62rO5OfutVOq\n/zXIzc1V5eXlA4pVqvdrAJc5fvy4Gjt2rIqMjFQbNmxQSilVWlqqSktLtXWWL1+uIiMjVUxMjDKb\nzXZj3c1A87t+/boyGAzKYDCo6OjoNza/1tZWpdPpVGBgoAoKClIjR45UDx48eG6suxlofp5Sv/z8\nfBUcHKyMRqMyGo0qPj7ebqy7GWh+b0L9XpTbxo0bVXR0tDIajer9999X9fX1dmPdzUDzexNqp5Rj\nvcHmySb/vFh73oh71wshhBCi/+SOd0IIIYSHkiYvhBBCeChp8kIIIYSHkiYvhBBCeChp8kK4yPr1\n65kwYYI20cYvv/wCQEFBAX/++edLf72hQ4e+9G2+yNSpUwFoamri4MGD2vNms5mioiK7sdu3b2fv\n3r0A7Nmzh9bW1lc3UCE8lJxdL4QL1NbWsmrVKs6dO4evry/37t3j0aNH2o08XoWAgAAePHjwyrZv\nz9mzZ9myZQs//vjjgOKnT5/O5s2biY2NfckjE8KzyZG8EC7Q1tbG8OHDtcltgoODtQY/bdo0zGYz\n0HtPdb1eT0JCAgUFBRQWFgKQm5tLUVERU6dOJTIyUpuKsquri+TkZGJjY4mJiaGystLuOLq7u5kz\nZw5Go5GJEydy6NAhoPdIe9q0acTFxZGSkkJbW5s2tuLiYhISEtDr9VRXVwNw6dIlEhISMJlMGAwG\nrl+/Dvz37UFxcTHnz5/HZDJRUlLC2bNnSUtLQylFREQE9+/f18Y0duxY7ty5w9q1a9myZQvl5eX8\n+uuvZGdnYzKZOH78OJmZmdr6VVVVzJ0714lqCOG5pMkL4QKzZs2iubkZvV7P8uXL+emnn7RlXl5e\neHl50dLSwrp166irq6OmpobGxsY+965ua2ujpqaGo0ePUlxcDMCQIUM4cuQIZrOZ06dPs2rVKrvj\nOHHiBOHh4fz22280NDSQkpJCT08PhYWFWnPNy8vj888/18b2zz//UFdXR0lJCV9//TUApaWlFBUV\nYbFYMJvN2u2ZbePduHEjiYmJWCwWVq5c2SfX9PR0jhw5AkBdXR2jRo1ixIgR2n6YN28ecXFxHDhw\nAIvFQmpqKpcvX9ZuY7p7927y8/OdqocQnkqavBAu4O/vj9lsZseOHYSEhJCVlUVZWZm2XClFfX09\nSUlJBAUFMWjQIBYsWNBnooqMjAwAoqKitCmKHz9+zKefforBYGDmzJm0tLRw586d544jJiaGqqoq\niouLqa6uJjAwkMbGRi5dukRycjImk4n169f3menKdtT83nvvcfPmTQCmTJnChg0b2LRpEzdv3sTP\nz6/P69j7VTArK4vvv/8egO+++46srKxnrvfkNnJycti7dy8dHR1cuHCB2bNnP3f7QrzNXDYLnRBv\nO29vb5KSkkhKSmLixImUlZWxePFibfnTM0493Sjfeeed/y3bv38/7e3tXLx4ER8fHyIiInj48OFz\nxzBmzBgsFgvHjh3jiy++YMaMGWRmZhIdHc3PP//8zJjBgwcD4OPjg9VqBWDRokVMnjyZo0ePkpqa\nyvbt25k+fbpD+2Hy5Mlcu3aN9vZ2Kioq+PLLL5+53pP7Iy8vj7S0NPz8/Fi4cCHe3nK8IsSzyDtD\nCBe4cuUKV69e1R5bLBZGjRqlPfby8iI+Pp5z587R0dGB1WqlvLz8hVNNdnZ2MmLECHx8fDhz5gxN\nTU12129tbcXPz4/s7GxWr16NxWJBr9dz9+5dLly4AEBPTw9//PGH3e389ddfREREUFhYSHp6Og0N\nDX2W2zvpz8vLi8zMTD755BPGjx/fZzY424eXgIAAOjs7tedDQ0MJCwtj3bp15OXl2R2bEG8zOZIX\nwgW6urooLCyko6ODQYMGMWbMGHbs2NFnnbCwMD777DMmTZpEcHAw48aNY9iwYdryJxu+7e/s7GzS\n0tKIiYkhLi6OqKioZ65v09DQwJo1a/D29sbX15fS0lJ8fX05fPgwH3/8Mffv38dqtWoN+Gm2bR46\ndIh9+/bh6+tLaGhon9/wAQwGAz4+PhiNRnJzczGZTH3Gk5WVRXx8fJ+fLJ6Mz83NZcmSJbz77rvU\n1tYyePBgPvroI9rb29Hr9Q7scSHeTnIJnRBurLu7G39/f6xWK3PnziU/P5/09HRXD8strFixgtjY\nWDmSF8IOafJCuLE1a9Zw6tQpHj58yAcffEBJSYmrh+QWYmNjCQgIoKqqSrsMUQjxf9LkhRBCCA8l\nJ94JIYQQHkqavBBCCOGhpMkLIYQQHkqavBBCCOGhpMkLIYQQHkqavBBCCOGh/gURdTYJVKttFwAA\nAABJRU5ErkJggg==\n",
"text": "<matplotlib.figure.Figure at 0x105cbde50>"
}
],
"prompt_number": 23
},
{
"cell_type": "code",
"collapsed": false,
"input": "",
"language": "python",
"metadata": {},
"outputs": []
}
],
"metadata": {}
}
]
}
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