toy_example.ipynb

{
 "metadata": {
  "name": ""
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# we'll use R to plot the maps\n",
      "%load_ext rmagic\n",
      "%R library(maptools)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "Loading required package: foreign\n",
        "Loading required package: sp\n",
        "Loading required package: grid\n",
        "Loading required package: lattice\n",
        "Checking rgeos availability: TRUE\n"
       ]
      }
     ],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import pysal\n",
      "import numpy as np\n",
      "import random\n",
      "import acs_regionalization as ACS"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# read in estimates and standared errors\n",
      "ests = np.genfromtxt('example_data/99999_a225_p9_pop9999_k3_j0_est.csv', delimiter=',')\n",
      "sdes = np.genfromtxt('example_data/99999_a225_p9_pop9999_k3_j0_stdErr.csv', delimiter=',')\n",
      "# build the spatial connectivity\n",
      "shp = pysal.open('example_data/99999_225.shp')    \n",
      "w = pysal.rook_from_shapefile('example_data/99999_225.shp')\n",
      "# convert standard error to margin of error\n",
      "moes = sdes * 1.645"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# take a look at the map\n",
      "%R shp = readShapePoly('example_data/99999_225.shp')\n",
      "#%R plot(shp, col=shp$region)\n",
      "%R plot(shp)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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e+c53mmVqkN5/UVGR5OTkmOUODAzIyZMnpaSkxCxTg/7zn//IzTffLFlZWWa5vb29on9u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\n"
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# since this is a \"toy\" example, we built the data with a \"true\" map in mind\n",
      "# these are the regions in the true map\n",
      "%R plot(shp, col=shp$region)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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Y52eU3un3c/1Opm7JtsgM85M2P1pXRjKSMz+d5kfrGpIhaUo1q2ba2bISqM+a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\n"
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# set some seeds to get the same solution each time\n",
      "start_seed = 5009\n",
      "random.seed(start_seed)\n",
      "np.random.seed(start_seed)\n",
      "# run the regionalization algorithm\n",
      "results = ACS.ACS_Regions(w=w,\n",
      "                          target_est_count=ests[:,3:],\n",
      "                          target_moe_count=moes[:,3:], \n",
      "                          target_th_all=0.15,\n",
      "                          count_est=ests[:,2],\n",
      "                          count_th_min=46000,\n",
      "                          base_solutions=100,\n",
      "                          pca=False,\n",
      "                          compactness=shp)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# we can plot the results\n",
      "# note that the shapes of the regions found by the algorithm match the \"true\" regions;\n",
      "# (the color arrangement is different simply because the algorthm assigned region IDs randomly)  \n",
      "rids = results.region_ids\n",
      "%R -i rids  # send the variable to R\n",
      "%R plot(shp, col=rids)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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A/hFAAAEEvAhQwF7YWRQBBBBAIO4CFHDcTwD7RwABBBDwIpD2smrMFz3+5pvK3nSTqcLo\nU08pGYY6s2OHXW61qrG9e3XmD//QLtMlnXnlFSX+8R9Vfeghs9zyiRM68+MfK2M8a/rgfn09+zf6\ndnKC2ayHgm69VHxRf5S3dT1aPKKvZL+sfDJnNuvr5df1ZulNDeeHzDKjoOOFPn0u/ydKye47wO7y\nawqrTyqf/9+msxYKx1ym7b0qFJ5TOp1xf541mzUMAxWLe8xnLRZfUybzLaVS9xnOWlRf3xGzvGYN\nooA93LlUKqXqokVSImG2etrlVWfOVPWCC8wywyBQMptVdfFis8woKNvertDNWT3nHLPccNIkpdwf\n61nTU9q0uPditbofq6vqgnrSh3RRwdY1n8prceliZd2P1TWgQY2lxsxnTSXSLnORK+CU1ah60/2E\nWqCCqWvo5ksaZ0Yv/QtUqZzn/sw1279UUTLZYj5rOj1T5fKF7s8Uw1kLamsbNcxrzigK2MN9O8eV\nT/kjH1Eiaffpv+y++eYuv1wTN20y21F1eFilb39bkz/6UbPMKCh49FHlrrhCrR0dZrljnZ1K7Npl\nPmvmru/pPb1XaoX7sbq2a7tK6YI+UrF1vSf9Lf1WsN5V0AKrUTXkCviC9AJ9JLCd9Rvpr+n3yjeo\nzf1YXXu02xXQpapWN1tFupzAfUu92xWl7f5TqR+6zOhMbTCctd/N+i8qlWxnTad/4GZ9p5tzteGs\nXe6b+puGec0ZZdcAzbl/pkYAAQQQQMCLAAXshZ1FEUAAAQTiLkABx/0EsH8EEEAAAS8CFLAXdhZF\nAAEEEIi7AAUc9xPA/hFAAAEEvAhQwF7YWRQBBBBAIO4CFHDcTwD7RwABBBDwIkABe2FnUQQQQACB\nuAtQwHE/AewfAQQQQMCLAAXshZ1FEUAAAQTiLkABx/0EsH8EEEAAAS8CFLAXdhZFAAEEEIi7AAUc\n9xPA/hFAAAEEvAhQwF7YWRQBBBBAIO4CFHDcTwD7RwABBBDwIkABe2FnUQQQQACBuAtQwHE/Aewf\nAQQQQMCLAAXshZ1FEUAAAQTiLpCOO4CP/Z/q61P6tdekRMJs+dLRo8qOjKi0a5dZZlipqHTihGlm\nNFzp+HHzWauDg4oMLPcfzVodOqkR97PL/VhdBY3pYOWgaWY021BlSCfczxn3Y3UVVNC+YK/5rKeC\nYe13P2n3Y3UVVVQY7nZxdvdKChUEg8aZcpm9LnPEOLesSqXPOFMus38cZh3VmTNDLjfeVyJ0V7wJ\nGr/7qYsXK5w1y3ThM11dys2dq8yECWa5VVfAo4cOaeLChWaZUdDIgQPKuf2nW1rMcktDQyq7PxPm\nzzfLjIJaf9yjucG5yqZyZrl95V4VqwWdn5tvlhkF9RS6NTvbrlQyY5bbU+p2HRRqbm6eWWYUdLBw\nQPPy85SQ3S/h9hX3qlcZ5UxnDVUo7FM+f6Hp/guFvUqnZ7s/rWa5YVhWsdjjZr3ALDMKKhQOKZOZ\nqVQqb5ZbrY7o0kvP086d95tlNmOQ3cfPZty9p5lnzJmj8uOPK5G0e/MJb7hBuWuu0cRNm8x2VR0e\nVnj11Zr2zDNmmVFQYv165bZsUWtHh1nuWGenCnfcoan3276gM5es0bauL2tFeYXZrNu1XT/KP6+7\nC18zy4yCVubeqe3Fe7TA/Vhdt+mLKuVK+nzhFqvIsznLM0t1f+H7anM/VtfHtEVfT17qCmOzVaTL\nCVxJLnGZtq+BdHqT+2a50f3ZYDhrv7LZ692snYaZcoW+zmVuVbm82jC3SzNnbjPMa84ouwZozv0z\nNQIIIIAAAl4EKGAv7CyKAAIIIBB3AQo47ieA/SOAAAIIeBGggL2wsygCCCCAQNwFKOC4nwD2jwAC\nCCDgRYAC9sLOoggggAACcReggON+Atg/AggggIAXAQrYCzuLIoAAAgjEXYACjvsJYP8IIIAAAl4E\nKGAv7CyKAAIIIBB3AQo47ieA/SOAAAIIeBGggL2wsygCCCCAQNwFKOC4nwD2jwACCCDgRYAC9sLO\noggggAACcReggON+Atg/AggggIAXAQrYCzuLIoAAAgjEXYACjvsJYP8IIIAAAl4EKGAv7CyKAAII\nIBB3AQo47ieA/SOAAAIIeBFIhO7ysnKMF50yf75Sv/mbpgLDTz2l1uXLlZkyxS43CDT8zDNqu+oq\nu0yXdOr559X69rcrPXmyWW5lcFCjr7yiyVdeaZYZBeXu26HLwyVqTbaa5R6rHNObwZtalVtllhkF\n7Si8oGXZy5VLZs1yD1QO6EhwWCtz/9EsMwp6tvCs3p1/t1Lux+p6vfy6Xq0OKJd7l1Xk2ZxC4Qnl\n8+81znxG6fRl7k+bWW4YVlQs/tDNutosMwoqFJ5XJrNEqdREs9xqtahLLw20c+cDZpnNGJRuxqGb\nfeaWiRNV/eAHpUTCbCu5M2eUuuIKpZctM8uUK+DUSy8p/aEP2WW6pPzRo0pfc43SCxbY5XZ3K3Pi\nhPms2ef26brDGzXN/VhdO7VTqUxKv1uwdd2V3qXfLX1QOfdjdT2sH2h2erb5rD9K7tAmt/+0YQH/\nD23XK1riCuN6q+27nNC9TJ91mbb3KpkcVKXyHvfn7YazVlxJvmY+ayZzROXyOvdnjuGsBU2d+pBh\nXnNGUcAe7tukmTNVfv/7lUja/ReAwj//szIXXqgJ69aZ7ag6PKzRc881zYyGK33jG8osXqzWjg6z\nWcc6O1WdM8d81swn79Al+g9a4X6srhMaUF/qmN7v3tQsr9tSX9SyynItcD9W16uu0makZuj9FdtZ\nP5u6WVdWr1Sb+7G6HtHDSiYvVbVqOav7EJqa6orSMlNuznvcnBe5rVvm9rtZZ7nPzZaZcpl/48p3\nsZvV8pt1l7LZJ61ufdPm2DVA0xIwOAIIIIAAAo0XoIAbb86KCCCAAAIIiALmECCAAAIIIOBBgAL2\ngM6SCCCAAAIIUMCcAQQQQAABBDwIUMAe0FkSAQQQQAABCpgzgAACCCCAgAcBCtgDOksigAACCCBA\nAXMGEEAAAQQQ8CBAAXtAZ0kEEEAAAQQoYM4AAggggAACHgQoYA/oLIkAAggggAAFzBlAAAEEEEDA\ngwAF7AGdJRFAAAEEEKCAOQMIIIAAAgh4EKCAPaCzJAIIIIAAAhQwZwABBBBAAAEPAhSwB3SWRAAB\nBBBAIA1B4wUGDhxQ9tlnpUTCbPHim28q8853auyZZ8wyVamo0NNjm+mmK3R3KzM0ZJpbdqZje/cq\nb7l/N2v5eK/61Ktn3I/VNagB7SrvMs2MZusvH9ebekPd7sfqGtGIXqm8bD7ricoJvaidSsnuLWhU\nowrDH7qtX2K1fZcTupdBn/un3f2PhguCfe7/DhnnllUuHzTOlEqlnnGYdVQnT0au8b4SobviTdD4\n3c+YP18z5s41Xbi7q0szzj9fLa2tZrmVclk9rtgWLFpklhkFHXFFOX3OHOXyebPcU4ODGjx5UvMW\nLjTLjIIO/eRVTalOVCqVMssdLY8prFY1ITfBLDMKGiwMqi07Rclkwiz3TGlEyTCh1pzduYqGGyoM\nqS03xfIzqE4VTunMpIxa5hrO6t4dz+wb0sQLp5iZRkEjhwaUO7dN6Ra7DyDVUqixw6c14YLJprOO\nHjmp3PTJSuXsZg1GAr19wQX68dPui0iMLzvRGCO+1a0vnDVLn/6jP3JvlHb/BeDOO+/U8uXLtWrV\nqrc6zi98/sjIiG699Vbd9olP/MLn/Cr/4vbbb9fatWu1bNmyX+Wv/9y/s2vXLj300EP6jPGsN954\no7rdN3aVf+6yv/L/mHcfPk4WTv7Kf//n/cVcLqcjxcM/71/9u/63KHegMPDvyvh//3Imk9HpYvTN\nyvaa+pG0Wq4bNgsNA/cN+EPS5L8bM8uMghKfSyh9ZZ9arrJ7DwgGQyVvTmny3SOms6Y+kVZ6U59y\nl9vNWj4YatajS03nbMYwO9Fm3D0zI4AAAggg4EmAAvYEz7IIIIAAAvEWoIDjff/ZPQIIIICAJwEK\n2BM8yyKAAAIIxFuAAo73/Wf3CCCAAAKeBChgT/AsiwACCCAQbwEKON73n90jgAACCHgSoIA9wbMs\nAggggEC8BSjgeN9/do8AAggg4EmAAvYEz7IIIIAAAvEWoIDjff/ZPQIIIICAJwEK2BM8yyKAAAII\nxFuAAo73/Wf3CCCAAAKeBChgT/AsiwACCCAQbwEKON73n90jgAACCHgSoIA9wbMsAggggEC8BSjg\neN9/do8AAggg4EmAAvYEz7IIIIAAAvEWoIDjff/ZPQIIIICAJ4FE6C5Pa8d22fPOO08zZsww3f/e\nvXvV3t6uCRMmmOVWq1V1d3dr/vz5ZplRUE9Pz9n95/N5s9yRkRH19/ebz7p79+6zMyaTdp9VK5WK\nIttsNmu2/yioVCopk8kokUiY5Y7nrNb7L5fLSkyWWi+ydR19s+gyc2amUdDI3qJys9NKt6bMcsNq\nqMKBsloW2u5/7FBJ2ZlppfJ2r4FqOdQl575dOx970Wz/zRiUbsahm33m6dOn6+Mf/7jpG+Xf//3f\na+nSpVq+fLkZTxAE+sIXvqCbbrrJLDMK+upXv6qOjg5ddNFFZrkHDx7Uvffeaz7rzTffrCNHjpjN\n+bOgXC6nYrH4s//X5J/jkRkNFhVlVO6WVzqdNs+M5pv0gbRafjuwHFWl/5rShFtsM/UXGaXfU1Xu\ncsNcFxV+JmM+a/ILOaX/U1mZi+y+q1VPSrOemGl6n5oxjAL2cNdaWlrU1tYmy29V0bee6E1typQp\nZjuKvlVGb+qWmdFwqVTKfNYoM3Idj1nNQGuCLL+l1sSOy8PxmHU8MqPNp6a6P+fY/QYgDEIlMwnT\nzGjORCqhhHv3tZw1GHSz5sdp1oztrNXTbtak3X2KTJvxsvudQjPunpkRQAABBBDwJEABe4JnWQQQ\nQACBeAtQwPG+/+weAQQQQMCTAAXsCZ5lEUAAAQTiLUABx/v+s3sEEEAAAU8CFLAneJZFAAEEEIi3\nAAUc7/vP7hFAAAEEPAlQwJ7gWRYBBBBAIN4CFHC87z+7RwABBBDwJEABe4JnWQQQQACBeAtQwPG+\n/+weAQQQQMCTAAXsCZ5lEUAAAQTiLUABx/v+s3sEEEAAAU8CFLAneJZFAAEEEIi3AAUc7/vP7hFA\nAAEEPAlQwJ7gWRYBBBBAIN4CFHC87z+7RwABBBDwJEABe4JnWQQQQACBeAuk4719P7vft2+fduzY\noUQiYTbAgQMHdOGFF+qFF14wywyCQEeOHDHNjIY7fPiw+vv7TXOPHz+u/fv3m2ZGs0ZzjsdVKpXM\nY8cjMxqyXC6bzzoemdGQYz+sSlPtXlcKpWJfoLGnU6YGhb1VtV6WcLluXqur4vZ/sKTM07bfq0Z7\nippw1M16ym7WsCD1HRmf15YVZyNyKOBGKP+cNSyLMoo/ffq0fvrTn6q1tfXnrPar/U/Rm2QYhnr+\n+ed/tYBf8LeKxaJ27typXC73C57x1v/ngYEBRQVkPWulUlEqlVIyafem9jPXTCbz1jf6S/5GlNtM\ns47H/oPDUuUpw7J0BRwmAttMdw+rrsxKzyWUbrWbtVoIFVbHYdZCWaVnk0rl7V4DwVBV4RS7Qv8l\nL4v/r/8VBezh9ixcuFA33XST6Zv6nXfeqeXLl2vVqlVmOxoZGdGtt96qrVu3mmVGQbfffrvWrl2r\nZcuWmeXu2rVLDz30kPmsN954o7q7uxX9NsDyij58FArua4DhFWVGH26sZ81ms2dzDUdVVL7j8S14\n4qakWq5zrWl0hUGo8ofSmnSrXebZ0T6XUfrKQC1X2eUGg6ESN+fcrLbFlvxEXunfKSl3ud2s5YPS\nrEdnGt2l5o2x+0jTvAZMjgACCCCAQMMFKOCGk7MgAggggAACEgXMKUAAAQQQQMCDAAXsAZ0lEUAA\nAQQQoIA5AwgggAACCHgQoIA9oLMkAggggAACFDBnAAEEEEAAAQ8CFLAHdJZEAAEEEECAAuYMIIAA\nAggg4EGAAvaAzpIIIIAAAghQwJwBBBBAAAEEPAhQwB7QWRIBBBBAAAEKmDOAAAIIIICABwEK2AM6\nSyKAAAIIIEABcwYQQAABBBDwIEABe0BnSQQQQAABBChgzgACCCCAAAIeBChgD+gsiQACCCCAAAXM\nGUAAAQQQQMCDQCJ0l4d1Y71ke3u7crmcqcGxY8c0bdo009zoaAwNDWnq1Kmms0aZra2tymazZrmF\nQkFjY2Pms/b09JydMZm0+6xaqVTOZqbTabP9R0FRbiqVUiKRMMsdz1nHY/+JFik3O2O2/yioNFBW\n9hzbzEJvWZkpSaXyKbNZwyBUeahiPmtxyM3amlIya/caqJaretuFb9NLT75itv9mDLJ9B2hGAQ8z\nz5s3T5/85CdN3yjvuusuLVu2TCtXrjTbUVRot956q770pS+ZZUZBX/nKV9TR0aGlS5ea5e7evVsP\nPfSQPvWpT5llRkE33XSTuru7Va1WTXPz+byiDw2WV/ShrlgsWkaezRqP3Ewmo3K5bD7rlC1p5X/b\n9jvFyQ+lNXW7beaZW7JKrw6UX2WXWz0ljdyc06S7AlPX0c+0KP07JWUvs5u10pPQnMfmmM7ZjGEU\nsKe7Fn2jsvxWFW0j+uYTfQOyuqI868yfzWadO56z/mxm/tkEAu5LWiJt9xuA6Ftl9AsFy8yziv86\nomluwhXkeMwaDWydG83KJbvfKYCJAAIIIIAAAnULUMB1U/FEBBBAAAEE7AQoYDtLkhBAAAEEEKhb\ngAKum4onIoAAAgggYCdAAdtZkoQAAggggEDdAhRw3VQ8EQEEEEAAATsBCtjOkiQEEEAAAQTqFqCA\n66biiQgggAACCNgJUMB2liQhgAACCCBQtwAFXDcVT0QAAQQQQMBOgAK2syQJAQQQQACBugUo4Lqp\neCICCCCAAAJ2AhSwnSVJCCCAAAII1C1AAddNxRMRQAABBBCwE6CA7SxJQgABBBBAoG4BCrhuKp6I\nAAIIIICAnQAFbGdJEgIIIIAAAnULUMB1U/FEBBBAAAEE7ATSdlEk1Svw+uuv64knnqj36XU9L8qc\nPXu2HnvssbqeX8+TgiBQT0+PaWa07sGDB3XgwAH19/fXM0ZdzxkYGFBkYLn/aOG+vj4lEom6Zqj3\nSWEYqlgsmueWSiXzzGjW8cgtl8vjMuvIw4GUTNV7K/7N54XuGcVjFY3dZ/tWObanotYLki43WsHm\nCt3WR/eVlLnPbv/RZCMHimrdk1C1x27WajHUsZ4+m403cYrtqWpiiEaOXq1WtX//ftMlJ0yYoCNH\njujkyZNmudGc4zFrPp9Xd3e3on9aXadOnRqXWbPZ7NmytJrzZzlRqUe2llcymTTP/Nl8URFbX+OR\nWT3tCvMN10RWl9t2tHXTTDdbcqJUPlhVpdVqUJcTbdsdKetZU7mkygfcrHYvV4VlaeJkhxDziwL2\ncAAuueQS/cEf/IGiN0yr684779Ty5cu1atUqq0iNjIycLcrNmzebZUZBt99+u97znvdo2bJlZrm7\ndu06Wz7Ws+7Zs0enT7t3deMrKvZCoWCamslkxuXDwnh8CIlmjb4FW1+TPpBSy3V2v7EIg1DVl6Up\nn7LLjPZ8+nMJpVcFarnK7j0gGAyV7Elp8qdsP9iNfCKt9PtLyl1uN2v5YKhJj06wvv1Nl2cn2nRb\nZ2AEEEAAAQT8CVDA/uxZGQEEEEAgxgIUcIxvPltHAAEEEPAnQAH7s2dlBBBAAIEYC1DAMb75bB0B\nBBBAwJ8ABezPnpURQAABBGIsQAHH+OazdQQQQAABfwIUsD97VkYAAQQQiLEABRzjm8/WEUAAAQT8\nCVDA/uxZGQEEEEAgxgIUcIxvPltHAAEEEPAnQAH7s2dlBBBAAIEYC1DAMb75bB0BBBBAwJ8ABezP\nnpURQAABBGIsQAHH+OazdQQQQAABfwIUsD97VkYAAQQQiLEABRzjm8/WEUAAAQT8CVDA/uxZGQEE\nEEAgxgLpGO/d29bL5bLGxsaUSCTMZogygyDQ6OioWWaUF+VaZkbDjces1WpVpVLJfNbIYDyuMAzN\nY8cjMxpyPHLHIzOateqOf3XE0NZFVd0RMM2MTMvu/1jnRnnl0HzWKDOsGBu4WYOgGt2yWF8J90Iw\nPK2xtqx783Pntiufz9T9/HqeeOzYcU2d2uZyc/U8va7nRKU2NHRa06a11fX8ep80NDSolpaJyuXs\nDEZHiyoUCuaz9h08psC9RFJJu18WFSoVVV1ma8Zu/5F9xX1YSqbTShp+sBt1mdEbxATjWUsuN+My\n7T6CSmei/bclNWnWxHqPYl3PGz0xqtbprXU9t94njR4fVWZyRhnD94Gq+7BYGCqp9ZyWeseo63nl\nobISrQmls3bf1yqlspZetEz/69Hn6prh1/VJdqK/rkLjsK9Fi1r1+OO9Sibt3n5uuCHUNdcMa9Mm\nu1s6PBzq6qsDvfDCSVOF9etDbdkyqI4Ou1k7Oyu644607r/fdtY1l2T1xa6yVgR2BbxdSf0on9fd\nBfe1wvBamWvRt4qBFhjW2m1KqZTL6fMu1/JansnqafctsM1w1o8516W3f1WbN282GzX6DciSJUu0\ne/dus8woaNOmTdq4caM2bNhgltvf36/rr79enZ2dZplR0Lp167R161atXr3aLLerq0vbtm0zy2vW\nILt3lWYVYG4EEEAAAQQ8CFDAHtBZEgEEEEAAAQqYM4AAAggggIAHAQrYAzpLIoAAAgggQAFzBhBA\nAAEEEPAgQAF7QGdJBBBAAAEEKGDOAAIIIIAAAh4EKGAP6CyJAAIIIIAABcwZQAABBBBAwIMABewB\nnSURQAABBBCggDkDCCCAAAIIeBCggD2gsyQCCCCAAAIUMGcAAQQQQAABDwIUsAd0lkQAAQQQQIAC\n5gwggAACCCDgQYAC9oDOkggggAACCFDAnAEEEEAAAQQ8CFDAHtBZEgEEEEAAAQqYM4AAAggggIAH\ngbSHNWO/ZFfXcd16q+1nn2efrWjy5KzeeCNh5hsEcnkl3XJLxiwzCtq1q6CHH87qhRfsZu3tTejH\nPx5zs7aazrr/0Ji+k8nq4aTd/dobhHquOKZbc3nTWY+4zL/N5pVP2Lm+WqlqT7moZC5nOmuvm/WO\nXItShqn7y4Gev/tu9fX1maWGYagjR464c3WLWWYUtGPHDs2YMUOvvfaaWW65XNbu3bvNZ40yH3zw\nQT377LNms46Njenw4cNmec0aRAF7uXMJJZMFGb5Pau7chMKwolTKtabhlUwmXGbRMFE655ykstlo\n1opZbioVKpezn3XCpJQyx0uuKOxKLa2qWlJJpYq2rmmXmSkVTWdNuVknuA8f1rMm3OFPFQums1bc\nrJPb2ty5sqv1qICjP5aZ0aFvb293r//ovNrOGuVZZkazTp061b1es6a5mUxGbe5exf2igD2cgIsv\nnqGbb+51JWz3pv7GG2WtXJnQpk12t3R4ONQjjyT1J39ilxlx/+QnodasCdTRYZfb2VlRT0/azWp7\nQ5++J9RvH5dWyO4b8HZVlMvk9ceB3QeQaNc/SCf1n92vLRYYfli4zc16SSanPy7afrD7bjrUjWWp\nzXDWYypr6Qc/qM2bN5sdgsB5/tM//ZM7V7YHK/pWuWrVKm3YsMFs1v7+fnV2dprPGn1bX7t2rVav\nXm02a1dXl7Zt22aW16xBdu8qzSrA3AgggAACCHgQoIA9oLMkAggggAACFDBnAAEEEEAAAQ8CFLAH\ndJZEAAEEEECAAuYMIIAAAggg4EGAAvaAzpIIIIAAAghQwJwBBBBAAAEEPAhQwB7QWRIBBBBAAAEK\nmDOAAAIIIICABwEK2AM6SyKAAAIIIEABcwYQQAABBBDwIEABe0BnSQQQQAABBChgzgACCCCAAAIe\nBChgD+gsiQACCCCAAAXMGUAAAQQQQMCDAAXsAZ0lEUAAAQQQoIA5AwgggAACCHgQoIA9oLMkAggg\ngAACaQgaLzA4OKZDh0IlEqHZ4idPVlUoJHXwYNUss1KRhocDl2n7OW14OJpVprMWi9LAQMV81tNn\n3KxO9KDs7lXJ5R0PnKthZnTTR4LKv84a/X82V8nNeLQ6DrO6zH6lNGhoUHZb7u7udmfgoM3mXUoY\nhjpz5oxpZjTc4OCgew0UTHPL5fLZXMv9R7MODw+bzxrtPXKN+5VwB8zunSXumnXuf9GiyTrnHNdu\nhte+fQW1t6fV2mr3mSoI3Jvv0aLmzs0bTiodOVLQtGkZtbSkzHKHhys6fbqiOXNsZ+19uaCpQUaZ\npN2sJ8ollcOq2rO2sx4vFnRONqtUwu4D07FS8WxFzs7mzO5VFHTMzdqeizITZrndLjM5u13nn3++\nWWb09tjT02OaGQ134MABzZw5071eW81mjQq4r6/PvQbmmGVGQUePHnWv12nK5+3O69jY2FnTBx54\nwHTWZguze7dutp17nHfu3Bl6/PFeJZN2bz433JDRNdcktGmT3Zvv8HCoq6/O6oUX7DIj9vXrW7Rl\nS6CODrvczs6k7rijVfffb3tj11zSoi92lbVCdr9Z2O5G/FG+VXcXbD+Erczl9a1ioAWGs97mCrLk\nivLzLtfyWp7J6AfFqtoMC/hjLmvpZz+rzZs3m40auN9ULFmyxL0GXjDLjII2bdqkjRs3asOGDWa5\n/f39uv7669XZ2WmWGQWtW7dOW7du1erVq81yu7q6tG3bNrO8Zg2yewdsVgHmRgABBBBAwIMABewB\nnSURQAABBBCggDkDCCCAAAIIeBCggD2gsyQCCCCAAAIUMGcAAQQQQAABDwIUsAd0lkQAAQQQQIAC\n5gwggAACCCDgQYAC9oDOkggggAACCFDAnAEEEEAAAQQ8CFDAHtBZEgEEEEAAAQqYM4AAAggggIAH\nAQrYAzpLIoAAAgggQAFzBhBAAAEEEPAgQAF7QGdJBBBAAAEEKGDOAAIIIIAAAh4EKGAP6CyJAAII\nIIAABcwZQAABBBBAwIMABewBnSURQAABBBCggDkDCCCAAAIIeBBIhO7ysG6sl5w3b7KuucaW/YEH\nRrVyZVbTpqXNbINAuvfeEW3cOMEsMwp6+OFRvetdWbW12c164kRFzz1X0vr1raazPrLduVazak2m\nzHIPB2W9WqnofbkWs8wo6JHCqFZn88ol7T5Xv1kuaX810FrjWR90s74v3yo7VWmXm7V//vl673vf\na+YavT1+5zvf0Qc+8AGzzCjo+9//vt797ndr6tSpZrkVd6YeeOABXXfddWaZUdAjjzziXq/v0uTJ\nk81yC4WCBgYG9OCDD5plNmOQ3TtgM+7e08yZTEbveMcZJRJ2A+zbl9AFF1S0eHHVLDQq4EcfTeqK\nK0pmmVHQq6+mtWRJoFmz7GY9fDjUnj0p81l3PJDWO46V1aqymcEkhRpIp/QO9yZkeXVGmaWScoah\nRTdrKpU0n/UHycTZTMsCPqqqZl16qTsDV5gJRAV87733mmZGw73xxhtatGiRe81eYDZrVMBPPvmk\n+awvv/yyli1bpunTp5vNGhXwjh07zPKaNYgC9nDn5s2bog9/uKCkexOyup5+Wlq6NKFNm+xu6fBw\nqG9+M6Hf/327zGi/990X6vLLA3V02OV2dla0c2fazWol+n9z/ud/C10Bh1ohu2+V21XR6XRWH3Zv\nmJbX37qi7KgEWiC7c3XMzXp+NGv0aczw+stUqI3u81eb4awvug9JS9eudWfA7hAEbt9//ud/bpoZ\nMT722GO67LLLtGHDBjPV/v5+3XPPPeazfu9739OKFSu0evVqs1m7urrcB/FXzfKaNcjuXaVZBZgb\nAQQQQAABDwIUsAd0lkQAAQQQQIAC5gwggAACCCDgQYAC9oDOkggggAACCFDAnAEEEEAAAQQ8CFDA\nHtBZEgEEEEAAAQqYM4AAAggggIAHAQrYAzpLIoAAAgggQAFzBhBAAAEEEPAgQAF7QGdJBBBAAAEE\nKGDOAAIIIIAAAh4EKGAP6CyJAAIIIIAABcwZQAABBBBAwIMABewBnSURQAABBBCggDkDCCCAAAII\neBCggD2gsyQCCCCAAAIUMGcAAQQQQAABDwIUsAd0lkQAAQQQQCANQeMFDh8e1j33VJRI2K29Z09F\nK1em9eKLgVlopSL19lZcpuGgbrpjxwKdPp00nfX48VDd3SWXmTLbfxTUfzLQGffPF1U1yx1zSXsr\nZZcZmmVGQScrgY4qoZOGuWMu63V3EKxnHQoCdSmltOGsBWfw8ssvuzPwoplrGIYaGBgwzYyG6+np\nca+B06a55XLZvbaOmWZGs/b29prPOjo6qpMnT0bxsb4S7oDZvgvEmrO+zc+fP1lr1tiyP/30mPL5\npGbNytY3RB3PqlRC7d8/pkWLWut4dv1P2b9/VO3tWbW02H3+O3my7F7QFV14YUv9g9TxzIM7xjQr\nyCiXtCv2Y+WSxqpVXZDL1zFB/U85WBjTnFxO6YTdL7YOlApKuKM633jWvW7WC/ItsptUet1l5ubP\n04IFC+pH+zeeGb09vv7667r44ov/jWe+tX/9xhtv6LzzztOECRPe2l/8Jc8ulUo6dOiQLrrool/y\nrLf+r/bv36/Zs2e79xe78zoyMuLeA9p13333vfWBfo3+ht074K8RynhvZeHCGfrGN3qVTNp9s/zr\nv85qz56y7rrLbvrhYenqq7N66im7zChp/foWbdkSqKPDLrezM6E77mjR/ffbZUZJay7J64tdZa0w\njN3uvvX9yJXP3QX3KwbDa6Uryb8rBlpg+K3yNveNuuRK/fMFu9+sRFtensnoX9z+21y+1fUxl7X0\n05/W5s2brSIVuG/qS5Ysca8B2xfBpk2btHHjRm3YsMFs1v7+fl1//fXms65bt05bt27V6tWrzWbt\n6urStm3bzPKaNcjyA2izGjA3AggggAACDReggBtOzoIIIIAAAgjI9D/B4IkAAggggAACdQrwDbhO\nKJ6GAAIIIICApQAFbKlJFgIIIIAAAnUKUMB1QvE0BBBAAAEELAUoYEtNshBAAAEEEKhTgAKuE4qn\nIYAAAgggYClAAVtqkoUAAggggECdAhRwnVA8DQEEEEAAAUsBCthSkywEEEAAAQTqFKCA64TiaQgg\ngAACCFgKUMCWmmQhgAACCCBQpwAFXCcUT0MAAQQQQMBSgAK21CQLAQQQQACBOgUo4DqheBoCCCCA\nAAKWAhSwpSZZCCCAAAII1ClAAdcJxdMQQAABBBCwFKCALTXJQgABBBBAoE6BROiuOp/L04wEVqxY\nqMsus2V/8slezZ8/UQsXTjSaUgqCUI8+elTve995ZplR0FNPHXP7n6Jp01rMcoeHy9qxo19r1842\ny4yCOr93TMtzUzUxlzPLPV4saNfQkNbMnGWWeXbW3mP6zekzlE+nzXIPj43qjdOndNW5trM+fuyI\n1rTPVlIJs1n3j5zRoUkTtWbNGrPMKOjBBx/Utddea5r5xBNPuNfAZZo+fbpZbqVS0WOPPeZer+8z\ny4yCOjs7z846ZcoUs9xisago76677jLLbMYgCtjDXRscHNTAwIDpyslkUtVq1TQzCkulUq6IA9Pc\n8Zp1PHLHIzPCHI/c8chk1vG5V4lEQuPx3Wc8Xq/jNet5552nlha7D+Gmb1INCqOAGwTNMggggAAC\nCNQK8N+AazV4jAACCCCAQIMEKOAGQbMMAggggAACtQIUcK0GjxFAAAEEEGiQAAXcIGiWQQABBBBA\noFaAAq7V4DECCCCAAAINEqCAGwTNMggggAACCNQKUMC1GjxGAAEEEECgQQIUcIOgWQYBBBBAAIFa\nAQq4VoPHCCCAAAIINEiAAm4QNMsggAACCCBQK0AB12rwGAEEEEAAgQYJUMANgmYZBBBAAAEEagUo\n4FoNHiOAAAIIINAgAQq4QdAsgwACCCCAQK0ABVyrwWMEEEAAAQQaJEABNwiaZRBAAAEEEKgVoIBr\nNXiMAAIIIIBAgwQo4AZBswwCCCCAAAK1AhRwrQaPEUAAAQQQaJAABdwgaJZBAAEEEECgVoACrtXg\nMQIIIIAAAg0SoIAbBM0yCCCAAAII1ApQwLUaPEYAAQQQQKBBAhRwg6BZBgEEEEAAgVoBCrhWg8cI\nIIAAAgg0SIACbhA0yyCAAAIIIFArQAHXavAYAQQQQACBBglQwA2CZhkEEEAAAQRqBSjgWg0eI4AA\nAggg0CABCrhB0CyDAAIIIIBArQAFXKvBYwQQQAABBBokQAE3CJplEEAAAQQQqBWggGs1eIwAAggg\ngECDBCjgBkGzDAIIIIAAArUCFHCtBo8RQAABBBBokAAF3CBolkEAAQQQQKBWgAKu1eAxAggggAAC\nDRKggBsEzTIIIIAAAgjUClDAtRo8RgABBBBAoEECFHCDoFkGAQQQQACBWgEKuFaDxwgggAACCDRI\ngAJuEDTLIIAAAgggUCtAAddq8BgBBBBAAIEGCVDADYJmGQQQQAABBGoFKOBaDR4jgAACCCDQIAEK\nuEHQLIMAAggggECtAAVcq8FjBBBAAAEEGiRAATcImmUQQAABBBCoFaCAazV4jAACCCCAQIMEKOAG\nQbMMAggggAACtQIUcK0GjxFAAAEEEGiQAAXcIGiWQQABBBBAoFaAAq7V4DECCCCAAAINEqCAGwTN\nMggggAACCNQKUMC1GjxGAAEEEECgQQIUcIOgWQYBBBBAAIFaAQq4VoPHCCCAAAIINEiAAm4QNMsg\ngAACCCBQK0AB12rwGAEEEEAAgQYJUMANgmYZBBBAAAEEagUo4FoNHiOAAAIIINAgAQq4QdAsgwAC\nCCCAQK0ABVyrwWMEEEAAAQQaJEABNwiaZRBAAAEEEKgVoIBrNXiMAAIIIIBAgwQo4AZBswwCCCCA\nAAK1AhRwrQaPEUAAAQQQaJAABdwgaJZBAAEEEECgVoACrtXgMQIIIIAAAg0SoIAbBM0yCCCAAAII\n1ApQwLUaPEYAAQQQQKBBAhRwg6BZBgEEEEAAgVoBCrhWg8cIIIAAAgg0SIACbhA0yyCAAAIIIFAr\nQAHXavAYAQQQQACBBglQwA2CZhkEEEAAAQRqBSjgWg0eI4AAAggg0CABCrhB0CyDAAIIIIBArQAF\nXKvBYwQQQAABBBokQAE3CJplEEAAAQQQqBWggGs1eIwAAggggECDBCjgBkGzDAIIIIAAArUCFHCt\nBo8RQAABBBBokAAF3CBolkEAAQQQQKBW4P8AHOlFp8YvgjcAAAAASUVORK5CYII=\n"
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}