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@samuelcolvin
Created February 21, 2015 12:24
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Buy Rate Equations.ipynb
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{
"metadata": {
"name": "",
"signature": "sha256:2f93a31ae92ed4ed19e9c134bbddcefe9b401e225408538c6a94fea01c13fd83"
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"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "code",
"collapsed": false,
"input": [
"%matplotlib inline\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 8
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# here we setup some constants to be reused in the calcs below\n",
"# this sets the amount of random variable in the rate of sales\n",
"randomness = 0.8\n",
"# average rate at which units are sold per day\n",
"average_sell_rate = 10\n",
"#the overall drift of the sell rate over the duration of the simulation\n",
"long_term_drift = 10\n",
"# this is the ideal amount of stock to keep, eg. how full the bucket should be\n",
"optimal_stock = 1000\n",
"# weeks between calculating buy rate and applying it\n",
"buy_delay = 5\n",
"# number of days over which to run the simulation\n",
"days = 365"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 9
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# set up the \"x\" values for days and the random data to be used below\n",
"x = np.arange(days)\n",
"random_data = np.cumsum(np.random.rand(days)-0.5)\n",
"# create some sell rate data and plot it\n",
"sell_rate = average_sell_rate + random_data * randomness + np.linspace(0, long_term_drift, days)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 10
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# this is just a utility function to apply a given buy equation and plot it together \n",
"# with sell rate and seperately plot held stock\n",
"def apply_buy_equation(buy_rate_equ):\n",
" stock = []\n",
" buy_rate = []\n",
" current_buy_rate = None\n",
" current_stock = optimal_stock\n",
" for day in range(days):\n",
" if day % 7 == 0 and day != 0:\n",
" week_sales = np.average(sell_rate[day-7:day])\n",
" to_buy = buy_rate_equ(week_sales, current_stock)\n",
" buy_rate.append(to_buy)\n",
" try:\n",
" used_buy_rate = buy_rate[-buy_delay]\n",
" except IndexError:\n",
" # cope first few weeks when we don't have old enough buy information\n",
" used_buy_rate = buy_rate[0]\n",
" current_stock += to_buy * 7\n",
" stock.append(current_stock)\n",
" current_stock -= sell_rate[day]\n",
" \n",
" plt.figure(1, figsize=(12, 8))\n",
" plt.subplot(211)\n",
" plt.plot(x, sell_rate, label='Sell Rate')\n",
" plt.hold(True)\n",
" plt.plot(range(7, days, 7), buy_rate, label='Buy Rate')\n",
" plt.title('Sell Rate and Buy Rate')\n",
" plt.ylim(0, (average_sell_rate + long_term_drift) * 1.5)\n",
" plt.legend(loc='upper left')\n",
"\n",
" plt.subplot(212)\n",
" plt.plot(x, stock)\n",
" plt\n",
" plt.title('Stock Held')\n",
" plt.ylim(0, optimal_stock * 2)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 11
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# really simple equation where we just buy at the original average sell rate, obviously this goes wrong as the sell rate\n",
"# increases over time.\n",
"def simple_buy_equation(week_sales, current_stock):\n",
" return average_sell_rate\n",
"apply_buy_equation(simple_buy_equation);"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
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OiGD7yy9Rk3ziiRG+e/eOuuq8vOLBu1mzqMNOJKBVq9i2YEG8tmfPqMtODeIQ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\nwH3WWdHSfOxYuOKK0qt87NgBJ58Mjz++7z6PJEm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emaxDXdjw4VEWT5K0dzJtL8IdaEkq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\nv75bfagSIwYAAAAASUVORK5CYII=\n",
"text": [
"<matplotlib.figure.Figure at 0x7face3678d10>"
]
}
],
"prompt_number": 12
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# buy rate based on the previous sell rate, ok but always trying to catch up.\n",
"def reactive_buy_equation(week_sales, current_stock):\n",
" return week_sales\n",
"apply_buy_equation(reactive_buy_equation);"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": 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Rkh4lb+p4DhyAN96AuDh4+GHTBzkrJ0/C1KkwZAjYadk2KYQKW4BuDvwNbMW0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\n+tfrj53FLl/HIiIiIiIFQyUcN2jnTnO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VFXYwrVvXuSefzAxGFRXW6ebOTT/3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\n1ce+VMo6er6LUILmzrXgl32qOMrMmbYjyre0JahvX9vJxlFWZgE06qK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"text": [
"<matplotlib.figure.Figure at 0x7face34b1e50>"
]
}
],
"prompt_number": 13
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# buy rate based on the difference between current stock and optimal_stock seems to work well\n",
"def corrective_buy_equation(week_sales, current_stock):\n",
" return average_sell_rate + (optimal_stock - week_sales/2 - current_stock) * 0.1\n",
"apply_buy_equation(corrective_buy_equation);"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": 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WoLsBfwE7MW3sAKYAm4HvgQaojZ2IiNQwOTmm33FCArRrZ1q35QkNNTO/zs6m\nBjdv26+/wqRJsG3b+QVCLBYLvxz6hdf+eo3E9ERevOVF7ml1D9a1rCv+S4lcw6pagL4aCtAiIlJl\npKWZjg5eXqZv8e23l/5YTzxhuj/Urm0WEfn8c9O7OTbWlEAMGgSnTpkFQ1591czutm1rboK7/XbI\nseTw8/6fef2v18m2ZPNi9xcZ0nJIka3cROTyFKBFRETKwRNPmBv4Bg40ZQuzZ5tAXVLr1pmZ5d27\nTfeHpUvhwQfNDW7btplFUT75xOy7cSM89RTsPxpPi24HeOC5/RyIOcCSg0uwt7bnpVteYkCzAQrO\nIldJAVpERCrdli2mS8Rjj1X2SK5ecrKp333zTTMj7O0NK1eapac//dQsYvLAA9Chw+WPlZICHTvC\nv/4FQ4acf//gQejdN5tkm6N88v0BTmXs50D0AfbHmOeE1CSu82pGG7/mNPduzk31b6JPYB/1IxYp\nIwrQIiJSqTIyTJg8e9YsRDFxYmWPqHTyZoKnTYOuXc1zx47nt7/6KixaBL17w/z5sHYtBAae337o\nEEydClOmWAi4LoHTSad5cuppEnIiGTLmNJFJkZxOOs3ppNNEJEYQFhuOh60Pbes1o3nt5jTzzn2u\n3Sy/T7OIlA8FaBERKXfz55uV8AIDTTuzOXPg5Elo396UOWzfbsJnt25m0Y+Cs63XgrxlpgE++wya\nX9ho9QL//rdZkKNZ+zgcWi8n1f9XNocdxsH7NImcxraWLS74khrtyx3d/Kjv4Yuviy9+rn7m2cWP\nJl5NcLZzLv8vJyIXUYAWEZFy9fff5oa3xo1N+ULfvnD99eYmt7/+MjOxf/8NjRqZUo5+/Uzd76UW\n/bhaFgvMnAm33GK6WlyN6Giz0Eb37ib8X67t2qGYQyw5uIQF25ewO2YLDSy3YDnQnxF92jByoC/Z\nCb78utiZAwfgkUegdeurG5+IlD0FaBERKTcZGWYVvGefNSvTLVpkev/++9/FLyn92Wfw8ceweFkK\nWY4naOjREAcbhzId11dfmbrilBQT2L/4wiyYUVKnT5tfCPr3N0s9F/WdsnKy2HBiA0sOLmHJwSXE\np8XT/7r+DGg2gD6BfXCydbr4QyJSpSlAi4hIuXn1Vdi8GZYsMW3dPv8c/vlPsLEpev/M7ExWhP3O\n8/O/Y1f6Uvw8vIjOiMDXxZcgzyCaeDWhiVeT/J+DvIJwsXMp0ZjCwuCmm2D1alNSMmCAmYWeMsWU\nmnh7m+2SSj7fAAAgAElEQVRBQZc+TnS0qXUeORJefNFCenYaKZkpJGcmk5KZws4zO1lycAnLDi0j\nwD2AAU0HMKDpADrV66QuGCLXOAVoEREpF7t3m9KNbdugfv3i98ux5LDu+Drm757Pwr0LaerdlHtb\n34vLsWE8+2hd/lyThV2d4xyOCSMs7jCHYw+z82QYJ1MOc+xcOG72bvlh2tfZl/Rz7jhbu9O4njt1\n3d1xd3DH3d6djER33n7VnRVL3XhzujUPP2zOHxWdyc294jhzLpZufWNJyo5l5+EYGjaP5ZZbY8Ex\nlti0WOJS40jJTMl/HDuVgsUmGSv7FFIzU7G3scfJ1gknWyecbZ0J9AxkQNMB9G/anwD3gIq56CJS\nIRSgRUSkVMLDzY1/a9ZAVJTpIDF2rNm2Zw/cdx9MmGB6Fl/IYrGw88xOvtv1HfN3z8fN3o2RbUYy\nvPVwGns2zt/v009NrXKXLvDjj2Z1vSZNzIyxrS389HMOPoGRHI41wXrT7ii+XZiAk0cCKTkJNG+f\ngIt3AglpCRw6kYDFLoEs6yScbJ1wtXMlKSOJlMwUPOw98bD3oo6rF16OXrjZenFsnzcHd3gxZZIX\nfh5eeDh44GzrjJOtE2tWOTPzfSdWr3DC280JRxtHreYnUoMoQIuISIlkZsLTT8Pcuaafcf/+pvZ3\n4kQ4fNjs4+FhShtee63wTXXhceHM3zWf73Z/R3JGMve2vpcRbUbQpm6bIs9lsZh2cADXXWdqqVu1\nMt07OnaEf/wDHBxM14sXXjCB/dNP4Y47zBLX48aBp6fZvm8fhIRALescEtMTScxIxNXOFVd712JL\nKu67z4zh66/P1zdv2wa33Qb/+58p9RCRmkcBWkRErlhsLNx5J7i6mgDt4XF+W06O2Q6mjjgvcKZl\npbFo3yI+3/o5e6L2MKzlMEa0GcHNATeXuBb47bfNjYh//gn29pCaCgkJ8Ntv8NxzMGyYWb46T1aW\nmbmeN8/cmNiwYcm+b1IS9Opl6qZvvNF0D5k1y9wEedddJTuWiFQfCtAiInJFYmNNx4nu3eG99y7f\nrm3v2b18vuVz5u6aSwffDkzoOIFBzQdhZ213VeOwWIrudpGRYW5OvNy4SuPUKbNM9saNppxk8OCy\nP4eIXDsUoEVE5LKys03P5JtvhnfeKb4FXUpmCgv3LGTW1lkciTvCfe3vY3zH8QR6Bhb9ARGRa9DV\nBOhimhCJiEh18/nnJjS//XbR4Xn76e18vuVz/rvnv9xc/2ae6fIM/9f0/7Cppf9ViIgUpBloEZFr\n2JkzJgz7+Fy8be9eqF3bbDtzBtq0MQuhtG1rtqdlpbHrzC42ntzInJ1zOJ10mvEdxnN/h/vVsk1E\nqj2VcIhItWGxwDffwMCB5ia2mi47G5Ytg99/N6vsJSRAZCTce6/pSjF4sKkZ/uMP0+EiT2ioWZo6\nJ8f0bw4/lsGYp/bQqX8ooadCCY0MZd/ZfTSr3YxOfp0Y0mIItwbdqjZuIlJjKECLSLXx0UemlVnn\nzrBihekTfC3KyYGnnjK/BDz33KWXmP7tN9MJ48YbC78fG2tu9nNxgaFDzY11bm7g7g7vvw87d5ou\nGrGx8PLLsGoVNGsG0bFZtO+zj0H/DCWrTijrjoRyJGU3jT0a07le5/xHu7rtcLR1LN8LISJSRSlA\ni8g17dAhEzJdXMxM69q18PjjYGcHvXvD7beb5ZqvFRaL6ae8ZYv5DmCCrr//xfvGxECLFuYXhW3b\nzpdiZGWZ792uHcyYUfQ5YhPSicw4xN6ze5m3Yi8rd+7Fp9Vejp4Lw4MG3NH++vyw3N63fYmXyxYR\nqc4UoEXkmpWRYVqK9ekDDRqYGdc2beDcOTMbfegQrFtnwqWra2WP9sq88w589x2sXm3G/Oabpn/x\nf/5zceu0hx82JRhubiZw/+9/Zrb6oYfg6DELP/ycQlJWPGeSz7Dv7D72nt3L3ui97D27l2Pxx2js\n2ZgWtVvQsk5LDqxtyU+zWvLm08148nHHYrtsiIhIDQ3Qe6L24GznTCOPRuU7IhEhJgYmTzYt0B54\n4Mo/d+6cWcJ51y5ThztunCkxsMlt6mCxmOMeOQI//1x8W7UJEyA1zcIHn8VwKvEUkYmRnEo8xemk\n03g7eRPkGUSgZyAB7gGFOkYcP27KHurXN69TU83sto+PmdktrYQE8x2cnc3rM2dg/Xpz3NRUGDUK\nNm82vxDkWb/eLJHdotNZ7nt2D1HZB9h2IJo5P8QzZGQ8KdnxrPk7nnMZ8dRyiifTJh6LfTy2tWzx\ncPCgtlNtWtRpQcvaLWlZxzyu876uUD9miwXOni36hkIRESmsRgboqaunEpcWx0e3f3T5nUWk1JYv\nh/HjYdAgE3Jnzrz06m1btpgAFxAA998PUVFw992mI8R335nFLAICzAzrunUQEWFqgJ3cU9hxegeH\nYg9xKvFUoUfEuVOciIvE0caZxrXrUc/VPOo61yUmNYawuDDC48I5k3QGL5v62CYFkRkVSPyRQGzO\nBfHMhEDOHQvis49cad3azGr/8AN07Xp+PFdqxQoYM8asbnfddXD6NKSlmd7KkZGwZw/8+it06BLD\nnrN72BO1xzzn/pyQnIFNTCv639SC3xb5MPBWD7p19sDDwTx2h3oQtseD5yZ54OPmjr2N/dX/IYqI\nyEVqZIA+Gn+UzrM6c2LyCd0EI1IOMjPhySdNaP7mG7MU8tatcNttMGQIPPNM4brkmBh48EHYsMHM\nhE6aZEoWdu4sXHqRkwMbQlP415c7yK4bSt32W9h2ZgthsWG0rNOS5rWb4+/qTz3Xevi5+uWH5bSz\nfvTu4ciXX8IddxQ95l1707ll4DGGTAjD1icc+7rh7DgRxsYD4WS5huPkYIuTnQPZGTbERNtglWOL\nJdsGd1cb6tezwdHeFptaNtjUssG2lvnZztou/3HqpB2bN9hxW29b/P3sSIi1w8vNDm8PO+xsbIk4\nF8HuqD3si95LalYqreq0Mg8f89yyTkv8XOrx1FNWfPKJuV4faQ5ARKRS1MgADXDb3NsY224sI9qM\nKMchidQsmZmm9GL4cHBwgG+/BU/P89vPnDGz0J99BgsXQo8e5v1Ro8wNc//+N6xcCSNGwKJF0KWH\nmVkOPRXKlsgtbIk8H5Y7+XWiU71OdPLrRGuf1pedbd2wwbS3GzUKnn8e6tQ5v81iMcG6Tx8T/C9k\nsViIS4sjIzuDzOxMDhzKwtouCxe3TD74KItFP2dR1y+ToXdncceATNIyskjLzMTGLpOM7Ayi4zJ4\n9vkM/vlYBv4NzDEysjMKPXxdfGnlY4Kyv6t/3n+cixgLzJ9vfhGx1wSziEilqLEBeuGehXwa+imr\nxq4qxyGJ1ByffGK6X9jYmNnR998vvv3aypUmZL/4oqkxfuppCyvWn+Jw4k52nNnBtsgd7IrawdH4\no6UKy8WJioJp02DePOjYEb74AoKCTFh/8UXYsaN0re+yskzv5MmTIS7OlHY4OZnV+9q3NzPqjRsX\n3RFDRESuPTU2QKdnpRPwfgDrx6+niVeTchyWSPV34ICpCd6wofCCHEVJz0pnX/Q+lv69g2+W7yAs\neQdu1+3A3q4W7Xzb0a5uO9rWbUu7uu1oUadFoRvdykpqKnzwASxYYMJ0r16m3OTmm6/uuNnZ5kbD\ntm1N3fbo0abeuWdP+PprE6pFROTaV9UC9FfA/wFRQJvc96YBDwBnc19PAZYX8dkSt7F78rcnsbex\nZ3rv6aUarEhNZbGYmuYdO8yNfD/8YLpdPPpo0fsnpify7Y5v+Wr7V+w9u5dAz0Da1TVhuYlrO7oE\ntsPXxbfYsoXy+g4jR5rZ57ffNrPnZS0nx3QHUUs4EZHqpaoF6O5AEvAt5wP0VCAReO8yny1xgN57\ndi99vu3D8cnHC7WvEpFLe/ZZ+P576NYNGjY0j/HjTdu3gvZH7+ffm//NvF3z6B3Ym4c7P0yXgC44\n2DhUzsAvkJRkZqHvv18hV0RErtzVBOjySJxrgEZFvF8u/2trWacljT0b8+uhXxnYbGB5nEKk2lm1\nyqyMt2MH1K598fbsnGx+OfQLMzfPZOeZnUzoOIGdD++kvlv9ih/sZbi4mOAvIiJSUSpyyvYxYAwQ\nCjwJxJfVgR/o8ABfbP1CAVqqvP37TY1tq1YXb9u61YTZgotvlJWsLBOaf/8dwsJMje+cOReH55iU\nGL7c9iWf/P0Jfq5+PHr9owxtOVS9iEVERAoor7/wbAQs4XwJhw/n659fA/yAouaMLFOnTs1/ERwc\nTHBw8GVPlpSRRMD7Aex+eDf+bv6lH7VICaxeDX5+ZmW9KykdSE83q98lJ8P27eDtfX7bvHmm+0NO\njjneY49B69ZmUZLbb7+6leWysuCee0xwvusuE96bNCm8Et/WyK3M3DyTn/b/xKBmg3j0hkfpXK9z\n6U8qIiJSxYSEhBASEpL/+pVXXoEqVAMNFwfoK91W4hroPP9Y8g8aejTk+e7Pl+rzIpeSmWlCct4S\n1EuWmBvubGygUSOzkl7ess7FeeUV2LbNtFw7dAgWLzbHXLwYHn4Y/vjDdL9YuhQ+/NDc2Ne0qVkG\n++uvzWIk9eqdX5a6oFdfNcF80CDo0uX8+1lZMH5CJkfOnub1jyKITj9FxLkIs7pfYgQRiRGcSDhB\nenY6D3d+mPEdxlPHuc7FJxAREalmqtpNhHBxSPYDInN/ngxcDxS1+kmpA/TfEX8z/MfhHHrsELWs\nal3+AyJX6OxZaNPGPPv5mU4P776bu0hIF7jvPnMj2/ffX3wDXloa3HKLWe45OdnUHPv4mM+NHWse\nLVrAggUWmnY4y5G4I4THhec/jsQfYXdEOGfTTmFlsYEse5zsHPD2sMfB1h57a3uy0hwIO2iPf117\nIk/Y0zTQgQaBGRyJjuBg5CmybGPwdfOhvps//m7+1HOpZ55d6+Hvat5r6t1UN+GKiEiNUtUC9Hyg\nB1AbOIPpwBEMtAcswBHgH7nbLlTqAG2xWGj/WXvev+19ejXuVapjiBRl/HhwdzeheetWM5Pcs6cp\nuQATknv2NN0s3n4bNm+G2bPNghvvv29ef/ghWNtmku54jMOxh9lw4DBvfxGOR+Nw0h3DyXAOx97G\nnkDPQPPwCKSxZ+P81/Vc65Gdk82xiHSefCadTNL4cGY66dlpjBybztB70uneM42Tkem8MDUdOxsb\nYo768/g4f155ui62NsWshiIiIlJDVbUAfTVKHaABPt70MRtObuC7Id+V4ZCkqkhKMot9dOxYce3K\nNm40yy3v2wdubsXvFxMDt94KDQMzCNlxhEYdDxNvdZhT6Ye58fbDRKQe4uS5k9RzrUcTryYEeQZx\n7lgT/je7MYu/CaRTYGPcHdyvaEwZGfB//2cW9EhJMTPb69advybR0bBnj1lQxK7s1y8RERGpFhSg\nc8WlxtH4w8aEPR6Gt5P35T8glSY729wwZ2trft6719zcdmEJBMCRI/DCC6Y22MUFevSAzz67dKAt\nC6mp0LkzvPSSWbK6KJnZmawIW8G8XfNYf3wjJ+IjqOcSQEvfJkTubUJjt+t4cEgTmng1oZFHo4u6\nWaSlgUMp2iknJJjZ7VatTHB3v7LsLSIiIrkUoAsYtWgUN/jfwOM3lsOSZHJFcnLMEsv79pnA++KL\nhTtOgKkjnj/flEcsWwaRkeDhAU88Yd6ztTX7LVgAjzwCkyaZZwcH06Fi507466/Shc8rNXEiREXB\nd98VnvG2WCxsitjEvJ3zWLBnAdd5X8fINiPpG9iXRh6NsLW2Lb9BiYiISJlQgC4g5GgIjy17jJ0P\n7azQJYXFSE2FUaPMDXejR5sOEj/+aEJx3rLL8fFw553mJry8lfDuugvWr4dp0+D4cfjkE9MP+eYu\nOXz3v0gc64UTFheWf3Pdsk1hpNofoa63IwFuAQS4B9DArQEOGQE09Q2gZb0GBLgH4OngeUX/HFgs\nhUPyDz+YML9jB3h6mvcOxRxi3q55zN05F+ta1oxqM4oRbUYQ5BVUPhdTREREyo0CdOED0HRmU+bd\nNY8b/G8oo2HJlcrrSDF3LtjnViv8+adp0ZaRAV9+aWaNZ8woXBaRlZPF5ojNbI74m1Xbwvk9NIxM\nl3BqeR3By9mdQM9AgryCCPQwN9X5OQTxyMjG3DchjS79TnA84Th7Tpzgo29PYOV+HL9mJ4hKP04O\n2TTwCDAh2y0AXxdffJx9Cj1CfvXhvde9mTPbhm7dzGIjI0ea1nT1rjvDgj0LmLdrHsfijzG89XBG\ntR1FJ79O+gVNRETkGqYAfYE3175JeFw4swbMKoMhlY+sLNPbd+jQ8zOc5eHMGdM27XJZb+9eaN68\n6BrkK7VmDYwYYY7l6lr0PseOmR7KjzwCJ84d57fDv/Fb2G+sOrKKBu4N6BLQhSZeTahrG8SRbYFM\nHNMYV3uXIo914AB07w6//ALt20Pv3qYeuEULU37h7g5RCef4etEJrD2Pc+LcCaKSowo9TiVEcTAi\nCiunWCypnlin+kCyD907+WDnmsDGkxsZ0GwAo9qMondgb7V6ExERqSYUoC8QmRhJq09acXzycVzs\nig5f5Sk1Fb75BsLDITjYrCzn51d4oY2ZM+HNN80CHe+/b4JnWfvsM1Nr3LKlaa/Wt2/R+82aZRby\nGDsWPv8crEvR8Sw1FW64AaZONb8UFCUlM4WQoyGsCFvBb2G/EZMSQ9+gvtwWdBt9A/vi5+pX4vP+\n9JMZd1aW+X6LFhUe/xdfmOv8wgvmxru6dc2Nd23amNfjxplfHF6fnkVYZAzHY6Kwco7ibEoUNrVs\nuOO6O3C2u8wKKSIiInLNUYAuwuD/DmZgs4Hc3+H+Mjneldq/H3r1guuvh06dTPnC0aOmtdjLL5ub\n4aKjzTLNISEmQN9xB3z8sQnZX3wBzz9vllrOc+5cyTtOzJxpgvlvv8Hu3fDAA7Bhg1npDsxS0s8+\na2ac9+2D//3P9DVOSYE6dUz3ibxZ3MsJD4chQy20bZfFv/+TQZYlk4zsDDKzM4lKjuKP8D/4Lew3\nNkVsoqNfR24Luo3bgm6jg1+HMln05tgxM4tf3DX68EOzJLa7u7kpMDTUBO6EBNMO7osvwNHxqoch\nIiIi1xAF6CIsPbiU6Wums378+jI53pVISDCzsM8+C/dfkNvDw817+/ebsDZkiKkDBrO8c9++pmb4\nnntM/fD778Pdd8P0d1J45ZN99A124l8vetKmiSepSfY8/LAJhj4+F4/j0CHTA/jvv6FxY/Pehx/C\n9wst/O+3BE7GRTFwxBl63B6FQ+0oavsnYOOYQmJqKvvDUsm0pBJ2IoVTUam0ap+Ks0cKqZmppGal\nkpqZSkZ2hgnIOZlkZGWQkp4J1pnY1LLBtpYttta22FnbYVvLFncHd3o26sltQbfRs3FP3OzLuffc\nFbBYTJlJ7dpmRlpERERqHgXoImTlZNHwg4asGLWCVj6tyuSYlzNkiJlFnjmz6O0Wi5ktPXnSBO2C\ni1wcPgzetXOIzNzPvD838dVvm4iy2wReB2lauwln49KJS42jlnMslmw7SPXCycqTji288HT0xMvB\nPHs6ePH1l7YENDtLg5Zn8mt9zySd4VTCWWrlOGCf5YOTxYduHXzwcfLBw8EDR1tHnGydcLRxzP/5\neJgjb7/hyNCBTtw71JE6no442jhib2NvgnItO8bfZ0vD+na8N8NGS6iLiIjINUMBuhgvrnqRlMwU\n3rvtvTI7ZnEWLzYzzzt2nO8+cTlRyVFsOrmJTRGb2HhyI6GnQvF28uZG/xu50f9G/LmRXi3b4+Vm\nmh1/9RU8+ZSFuvWT+eGXWIaOjuP/hsTStU8csamxxKXGseSPWA6EZTDpAR98XU2XiboudfFx9sHJ\nUoeQPxwJDTVjvZKbF48ehaeeMl003njD1ErHxJi64iNHzIx6aGj59mMWERERKWsK0MUIjwvnpi9u\n4sTkExetAFeWkpLMjXqzZ0PPnpfe93jCcd5Y8wbLw5YTlxrHDf43mMBc34TmOs51Lvn5hQvNDXDN\nm5va5VtuMcs4X3cdvP66WfRj9Wrw9S3DLwiEhcFNN8GKFfDcc6ZOundv6NMHAgLK9lwiIiIi5U0B\n+hL6fNuHBzs9yN2t7i7T4xb0yCOQmAjfflv8PtEp0byx5g2+2fEN/+j0D8a0G0NT76ZXXfbwySem\nHV6TJmY2eNmysg/PeWbPNt+1c2czI22jjm4iIiJyjbqaAF3tI9ADHR/gi61flFuAXrwYfv3V3AhY\nlKSMJN7f8D4fbvqQe1rdw+6Hd5eqXVtxHn7YzDi7uJiV/Mqzm8SYMRAXB/feq/AsIiIiNVe1n4FO\ny0oj4P0ANj+wmcaeja/oM4cOmZnWoCDT/q1Tp6IGapaofuQR+Pln0/WioIzsDGZtmcW/1vyLno16\n8lrP17Tks4iIiEgVcTUz0NW+bYKDjQMj24zk6+1fX9H+Fgs8+ii0bQsNG0L//hARUXiflBRT+zt9\nOsyfXzg8Z+dkM3fnXJrPbM6vh35l2ch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ZZ9ZM2fn8cw+K88/yBHXP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"text": [
"<matplotlib.figure.Figure at 0x7face34c4250>"
]
}
],
"prompt_number": 14
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 14
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 14
}
],
"metadata": {}
}
]
}
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