Mixing example plots.ipynb

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 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%pylab inline\n",
      "import matplotlib.pyplot as plt\n",
      "import numpy as np"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Populating the interactive namespace from numpy and matplotlib\n"
       ]
      }
     ],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "no_death_mixing = np.load('./model/no_death_outs_1/MixingAnalysisOnlypipe_out_Thu_Mar__1_11_07_26_2018/mixing_analysis.npy')\n",
      "full_death_mixing = np.load('./model/full_death_outs_1/MixingAnalysisOnlypipe_out_Sun_Feb_18_19_17_09_2018/mixing_analysis.npy')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "no_death_mixing.shape"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 6,
       "text": [
        "(100, 7, 100)"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "no_death_mixing = no_death_mixing[np.argsort(no_death_mixing[:, 0, 0])]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 11
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "full_death_mixing = full_death_mixing[np.argsort(full_death_mixing[:, 0, 0])]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 13
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "distance_to_tumor_COM, distance_to_cluster_COM, p_sgas, n_sgas, p_pgas, n_pgas =  no_death_mixing[0]\n",
      "distance_to_COM = distance_to_tumor_COM[0]\n",
      "plt.scatter(np.argsort(distance_to_cluster_COM), p_pgas, label='No turnover (distance: {:3g})'.format(distance_to_COM))\n",
      "\n",
      "\n",
      "distance_to_tumor_COM, distance_to_cluster_COM, p_sgas, n_sgas, p_pgas, n_pgas =  full_death_mixing[93]\n",
      "distance_to_COM = distance_to_tumor_COM[0]\n",
      "plt.scatter(np.argsort(distance_to_cluster_COM), p_pgas, c='r', label='Turnover (distance: {:3g})'.format(distance_to_COM))\n",
      "plt.xlabel('Ranked Distance to COM of cluster')\n",
      "plt.ylabel('% of private GAS')\n",
      "\n",
      "plt.legend(loc=(0.1, 1))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 54,
       "text": [
        "<matplotlib.legend.Legend at 0x7fd8c4cdbf90>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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LCy+8EOlQTDWcf/753HDDDf66j9piPQBWjyWOGmaJw5jYYYmjeqyOwxhjjCcR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\njDHGeGKJwxhjjCeWOIwxxnjy/xjTNwSOrRiUAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7fd8c4db0510>"
       ]
      }
     ],
     "prompt_number": 54
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig = plt.figure(figsize=(14, 6))\n",
      "ax = fig.add_subplot(121)\n",
      "\n",
      "distance_to_tumor_COM, distance_to_cluster_COM, p_sgas, n_sgas, p_pgas, n_pgas =  no_death_mixing[32]\n",
      "distance_to_COM = distance_to_tumor_COM[0]\n",
      "plt.scatter(np.argsort(distance_to_cluster_COM), p_pgas, label='No turnover (distance: {:3g})'.format(distance_to_COM))\n",
      "\n",
      "\n",
      "distance_to_tumor_COM, distance_to_cluster_COM, p_sgas, n_sgas, p_pgas, n_pgas =  full_death_mixing[94]\n",
      "distance_to_COM = distance_to_tumor_COM[0]\n",
      "ax.scatter(np.argsort(distance_to_cluster_COM), p_pgas, c='r', label='Turnover (distance: {:3g})'.format(distance_to_COM))\n",
      "ax.set_xlabel('Ranked Distance to COM of cluster')\n",
      "ax.set_ylabel('% of private GAS')\n",
      "\n",
      "ax.legend(loc=(0.1, 1))\n",
      "\n",
      "\n",
      "\n",
      "ax = fig.add_subplot(122)\n",
      "\n",
      "distance_to_tumor_COM, distance_to_cluster_COM, p_sgas, n_sgas, p_pgas, n_pgas =  no_death_mixing[90]\n",
      "distance_to_COM = distance_to_tumor_COM[0]\n",
      "plt.scatter(np.argsort(distance_to_cluster_COM), p_pgas, label='No turnover (distance: {:3g})'.format(distance_to_COM))\n",
      "\n",
      "\n",
      "distance_to_tumor_COM, distance_to_cluster_COM, p_sgas, n_sgas, p_pgas, n_pgas =  full_death_mixing[96]\n",
      "distance_to_COM = distance_to_tumor_COM[0]\n",
      "ax.scatter(np.argsort(distance_to_cluster_COM), p_pgas, c='r', label='Turnover (distance: {:3g})'.format(distance_to_COM))\n",
      "ax.set_xlabel('Ranked Distance to COM of cluster')\n",
      "ax.set_ylabel('% of private GAS')\n",
      "\n",
      "ax.legend(loc=(0.1, 1))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 76,
       "text": [
        "<matplotlib.legend.Legend at 0x7fd8c4820510>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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bLCz+XIrKU9nZ2da9UycbVLOmvSVZv/Bw63vhhZabm+vZp0Pz5vZVgTz1oGRj\nj3svemzCBKsdFmZNIiKsZZMm9vPPPxfqqyTIU5U/T1166aVWo0YNCwkJsb59+3q1bdq0yerWrWsh\nISFWo0YNmzZtmpmZ7d+/30JCQmzfvn2efYcNG2adO3cuso+TyXnbt2+3mjVr2i+//FKorWHDhrZk\nyZJC29955x3r0qWLz+dpZuSaCr5wpcmPPv/8c9X//Xe9lJWlIZLmHDmiN958U0ePHi3xMaZNm6av\n33pLWzMzte3IEfX45RfdPHRo+QVdhDFjxig2NlY1atTwbPvb3/6mmJgYRUdHq0+fPlq1apUn3htu\nuEHt2rVTWFiYHn/8cX355Zfavn27EhMTlZWVpWXLlkmS3nnnHZ1//vk69dRTtXTpUh08eFB33323\nqlWrpubNm+v666/XjBkzPH2ed9556t27tyR5xXLM/v37FRkZ6VmfM2eOzj77bPXr10/VqlXTnXfe\nqZiYmCKfY2hoqNLS0rRu3TqZmVq3bl3kZfljBg4c6GkfNGiQ4uPjvYasJCQkaOjQoXLOaejQodq2\nbZv27t2rrVu36osvvtArr7ziGb9/3nnnSZJef/11jR49Wh07dpRzTtddd50kacWKFZLyPhmdOHFi\nsTEdc+DAAV133XV66KGHVKtWLUlS27ZttXr1au3YsUOfffaZli1bpr///e9FPj4nJ0e33HKLXnzx\nxSLbjxw5ojp16njW69Spo4MHD54wruO9/vrr+uGHH3THHXd4bY+MjNT+/ftP+ngATt6qVau0Y8MG\nvZ2eriGSZh09qm+XLtWvv/5a4mMkJyfr1See0E+Zmdp65IhGb9umq//yl/ILugjkqcKCNU998skn\nOnz4sObMmaOLLrrIqy0+Pl779u3Tnj179NBDD+n000+XlJdnunTpoocffliZmZn69ttv9cEHH+jI\nkSNF9lHSnJeVlaUhQ4boxhtvVEJCgs+4CyJPVQ0UTX6Uk5OjmgXWw5R3ObrgELtRN9+sKyIi9IGk\nx0NCNLdmTQ0cONDTvvKbb3TVkSOqI8lJujEry3MJ2V+OXcIu6NgldEmKiIjQoUOHJEnbt29XXFyc\npy0yMlLR0dHatm2bnHO68sorPTdjTps2TUOGDJEkbdmyRZs3b1a9evVUr149RUdH6x//+IdnyISU\nN9zCl+joaK//vG/fvt3rMc65Ip+LJF188cUaOXKkRo0apYYNG2r06NE6fPhwsX1NmTJFHTp08MT6\n3//+1+v3SJAVAAAgAElEQVQ+nIYNG3qdHzPToUOH9Ntvv+nUU0/1DCEpaPPmzXriiSe8zsHOnTuL\nHK5ZnCNHjqhPnz6eYRfHNGjQQK1atZIkNWvWTE888YTefffdIo/x3HPPqXPnzurYsWOR7REREUpL\nS/Osp6Wlef0noCTee+89Pfjgg5o/f77q1q3r1Xbw4MFC2wCUj5ycHNVwTscGIVWXFOqc11DyUWPH\n6vqICM2S9IKkF2vV0tAbbvC0f//99+qbna3Y/PURZlq5YYOfnkEe8lRhwZqnJKlatWq69NJLNXv2\nbM2fP79Q+7Ehlf369fNsmzFjhv773/+qadOmuv3223XNNdcUe65KkvNyc3M1ePBgRUVFlegDyYLI\nU1UDRZMf9ejRQ+tr1tQj1appkaSkmjXV75JLPJ/+S9KExx7TdY8+qjfOP1/rBwzQl8uXq3Hjxp72\n5q1aaWF4uI6NEF/gnJoXGMv9R9WqVcvrk5odO3YU2qe4Mb1FadSokTZv3uxZP3jwoPbt2+d5TklJ\nSZo1a5ZSUlL0/fffa8CAAZLyEk3Lli21d+9e7d27V/v27dOBAwf04YcfljiO9u3be02kERsbq61b\nt3rWzcwzdroot912m7777jutXbtWP/74o5555pki+920aZNGjx6tV1991RNrq1atjg1/8alp06ba\ns2ePJ3kf3/bggw96nYNDhw55FdG+ZGRkqH///mrRooVn/L0vxcW7aNEivfvuu4qNjVVsbKyWL1+u\n22+/XX/7298k5Y1JX716tWf/VatWqU2bNiWKUcqbbvfmm2/WJ598otatWxdqX79+vc4666wSHw9A\n6XXo0EHVGjbU30JD9YWkETVq6LRWrdSiRQvPPiNGjdKDL7+st3v00JI+fTQvOVlt27b1tDdv3lxf\nhYbqWCZZIKl5bKzKCnnq/1T0PHW87Oxsz31Lx8vKytKuXbs8cZx22mmaM2eOdu3apSVLlmjXrl3q\n3Llzifsq+NzNTMOGDVNaWpreffddz31WJUWeqhoomvwoOjpaX65YoXWXXaYJZ52lljfdpKmzZnnt\n45zTLWPGaPZXX2nqrFlq2bKlV/uIESNUvXNnta1VS92jovRkvXp6scD0z39Uhw4dNHfuXO3fv187\nduzQ888//4eOl5SUpEmTJmnt2rXKyMjQPffcowsuuECNGjWSJHXq1EmRkZEaMWKEevfu7Skgu3Xr\nprCwMD3zzDPKyMhQTk6O1q5de1JX1Xr37q3k5GTP+rHhGLNnz1Z2draeeeaZYmdlW7FihVasWKGc\nnByFh4crLCzM8ybaoEEDr6Eqhw4dUkhIiE499VTl5OTo9ddf14YSfqrapEkTXXTRRbr55pt14MAB\nZWdna/HixZKkG2+8US+++KJn+MShQ4c0Z86cEg3nzMrK0uWXX666det6zWR3THJysicRb9myRffe\ne6/+UszwmbffftszM97q1at19tln66GHHvLciH3ttdfq6aef1o4dO/Tbb79p4sSJGjZsmFcs6enp\nMjNlZmZ63VT+2WefaejQofrggw+KnF0vPT1dq1atKjRkA0D5CAsL02dLl+rQwIF6sH17hSUlac4X\nXxT6T+TV116rDxct0vTZs9WpUyevtn79+umcfv3UplYtXVinjm6KjNTkd94psxjJU3kqep5av369\nFixYoIyMDGVlZWnq1KlatmyZZ2KM999/X7/88oskaffu3Ro7dqw6d+7sueK1fv16HT58WJmZmZo6\ndaqSk5M1ZsyYIvs6Uc4bMWKEfv31V3344YeqXr16ocdnZmZ6Zp/NyMgoNDnKl19+qUsvvfSEzxkV\nG0WTn8XFxWnaRx/py1Wr9MTEiapZs+aJH1RAaGioZi9apCmff64H3ntPP2zc6LnkfLKK+gTsuuuu\nU+vWrRUXF6fevXsXmt61qMf4+iStV69eGjdunP7yl7+ocePG+u233wrNIJSUlKSFCxd6hjxIeZfq\nP/nkEy1fvlzx8fGqX7++Ro4ceVL3ypx77rmqWbOmvv/+e0l5M/HMnDlTY8eOVUxMjH777Td16dKl\nyMfu379fw4cPV3R0tJo3b67GjRt7hrfdcMMNWrVqlU455RRdeeWVateunW655Rade+65atSokX7+\n+Wd17drVZ2wFz9lbb70lM1PLli3VsGFDz1WhLl266OWXX9aoUaNUr149tW7d2uvc3XjjjbrtttuK\nPP7ixYu1YMECzZs3z+t7Kb755htJ0rfffquuXbuqdu3auuCCC9SpUyfPJ5RS3uv21FNPSZKioqJU\nv359zxIWFqY6dep4huCNHj1avXr1Ups2bTwzGBUsmnr27KlatWppxYoVGj58uCIiIjzfc/Hwww8r\nLS1NvXr18sTYv39/z2M/+OAD/fnPfy52TD+AsnfqqafqX9Om6avVq/XS5Mle9yyWhHNO/3r7bb33\n1Ve6+513tHbjRnXr1q1UsZCnKm+eys3N1bhx49SgQQM1aNBAr7zyit59913PVcutW7fqz3/+syIj\nI3X22WerRo0amlXgg+Z58+YpPj5ep5xyit544w19+umnntldc3NzFRkZWaKc9+uvv2rSpElauXKl\n6tev78lFBftKSEhQrVq1tHv3bl100UWKiIjQ9u3bJeXNoPjLL7+ob9++Ps8nKj5XkkuzOHnOOePc\nBt68efM0efJkvVOGn3LCfzp37qx///vfpf5goKScczKzko/nASoB8lRwIE9VbGPGjFHbtm11Q4H7\n+opDrqnYKJrKCckIqDhIZKiKyFOAf5FrKjaG5wEAAACADwEvmpxzk5xzu5xza4ppH+ycW52/fO2c\na+fvGAEAVRd5CgAQ8KJJ0mRJvXy0/yrpAjM7S9Ijkl73S1QAAOQhTwFAFVd4XkU/M7OvnXNxPtqX\nFVhdJqlxcfsCAFDWyFMAgGC40nQybpA0L9BBAABQDPIUAFRCAb/SVFLOuR6Shkk6r5j2oJoCqEaN\nGif1jeQAAif/7zWo3kMquqo4Q9SJ8lT+PkHze0aeAvyLXBNcTjZPVYgrTc659pJek9TPzPYVt5+Z\nBc2Snp5+wn0efPDBgMd5MktFi7cixky8gYm5JH+vwRRvsC9VUUnzlBQ8uYo8FRxLRYuZeEu/lDTX\nBFPMlTFes9LlqWApmlz+UrjBudMkvSfpGjPb6NeoAADIQ54CgCos4MPznHPTJCVKOsU5t0XSg5LC\nJJmZvSbpAUn1JL3k8sYRZJlZ50DFCwCoWshTAICAF01mNvgE7TdKutFP4fhVYmJioEM4KRUtXqni\nxUy85a+ixVzR4q2MyFMVR0WLV6p4MRNv+atoMVe0eEvLlXZcX7BxzllleS4AUJE552RVcCKIkiBX\nAUDglSZPBcs9TQAAAAAQlCiaAAAAAMAHiiYAAAAA8IGiCQAAAAB8oGgCAAAAAB8omgAAAADAB4om\nAAAAAPCBogkAAAAAfKBoAgAAAAAfKJoAAAAAwAeKJgAAAADwgaIJAAAAAHygaAIAAAAAHyiaAAAA\nAMAHiiYAAAAA8IGiCQAAAAB8oGgCAAAAAB8omgAAAADAB4omAAAAAPCBogkAAAAAfKBoAgAAAAAf\nKJoAAAAAwAeKJgAAAADwgaIJAAAAAHygaAIAAAAAHyiaAAAAAMAHiiYAAAAA8IGiCQAAAAB8oGgC\nAAAAAB8omgAAAADAB4omAAAAAPCBogkAAAAAfKBoAgAAAAAfKJoAAAAAwAeKJgAAAADwgaIJAAAA\nAHygaAIAAAAAHyiaAAAAAMCHgBdNzrlJzrldzrk1PvZ5zjn3s3NulXOugz/jAwBUbeQpAEDAiyZJ\nkyX1Kq7ROXeppAQzO13STZJe8Vdg5Wn9+vWaOnWq1q9fX679pKamasWKFUpNTS3Xfiqikpybsjp/\nwdaXP48TLP34sy/+7iod8hQAVHVmFvBFUpykNcW0vSLpqgLr6yU1KGI/qyhuu+VWC5eshUIsXLJb\nb7m1XPqZNm2GhYfXszp1Olp4eD2bNm1GufRTEZXk3JTV+Qu2vvx5nGDpx5998Xdnlv9+HPDcUpZL\nWeQpq0C5yl95CgACoTR5KuCJyE6cjGZL+lOB9c8ldSxivzI7keVp3bp1Fi7Zasks/99wydatW1em\n/ezevdvCw+uZtNryulpt4eH1bPfu3WXaT0VUknNTVucv2Pry53GCpR9/9sXfXZ4qWDSVKE9ZBclV\n/spTABAopclT1cv+2lXgjB8/3vNzYmKiEhMTAxZLcZYvX67GClF75UqS2ktqpBAtX75cZ5xxRpn1\nk5KSorCweB092j5/S3uFhsYpJSVFMTExZdZPRVSSc1NW5y/Y+vLncYKlH3/2VVX/7pKTk5WcnBzo\nMCqMYM9V/spTAOAvZZKnTrbKKo9FJzfsYYMq8PA8rjQFXrBd/Qm2K0SV8apMZXxOwUxV70pTifKU\nVZBcxZUmAJVdafJUwBNRXtyKl/RDMW29Jc3N/7mrpGXF7FdmJ7K83Zo/VjzBT/c0RUWdXWXvrShO\nSc5NWZ2/YOvLn8cJln782Rd/d5W2aPrDecoqUK7yV54CgEAoTZ5yeY8LHOfcNEmJkk6RtEvSg5LC\nlPdkXsvf5wVJl0g6LGmYma0s4jgW6OdyMtavX6/ly5erc+fO5TrcITU1VSkpKYqPj6/Uw4NKoyTn\npqzOX7D15c/jBEs//uyrqv/dOedkZi7QcZSVsspT+ftVmFzlrzwFAP5WmjwV8KKprFSkRAQAlVll\nK5rKErkKAAKvNHkqGL6nCQAAAACCFkUTAAAAAPhA0QQAAAAAPlA0AQAAAIAPFE0AAAAA4ANFEwAA\nAAD4QNEEAAAAAD5QNAEAAACADxRNAAAAAOADRRMAAAAA+EDRBAAAAAA+UDQBAAAAgA8UTQAAAADg\nA0UTAAAAAPhA0QQAAAAAPlA0AQAAAIAPFE0AAAAA4ANFEwAAAAD4QNEEAAAAAD5QNAEAAACADxRN\nAAAAAOADRRMAAAAA+EDRBAAAAAA+UDQBAAAAgA8UTQAAAADgA0UTAAAAAPhA0QQAAAAAPlA0AQAA\nAIAPFE0AAAAA4ANFEwAAAAD4QNEEAAAAAD5QNAEAAACADxRNAAAAAOADRRMAAAAA+EDRBAAAAAA+\nUDQBAAAAgA8UTQAAAADgA0UTAAAAAPgQ8KLJOXeJc26Dc+4n59zdRbQ3dc4tcs6tdM6tcs5dGog4\nAQBVE3kKAODMLHCdOxci6SdJF0raLmmFpEFmtqHAPq9KWmlmrzrnzpD0iZk1K+JYFsjnAgDI45yT\nmblAx1EWyjJP5e9LrgKAACtNngr0labOkn42s81mliVphqT+x+2TKykq/+e6krb5MT4AQNVGngIA\nqHqA+28saWuB9d+Ul6AKmiDpU+fcbZIiJF3kp9gAACBPAQACfqWpJJIkTTazppIuk/RWgOMBAKAg\n8hQAVHKBvtK0TdJpBdabqPCwhuGSekmSmS1zztV0zp1qZnuOP9j48eM9PycmJioxMbGs4wUAHCc5\nOVnJycmBDqO8lGmekshVAOBvZZGnAj0RRDVJ/1XeDbY7JC2XlGRm6wvsM1fSO2Y2Nf8G28/MrEkR\nx+LmWgAIApVsIogyy1P5+5KrACDAKtxEEGaWI+kWSZ9K+lHSDDNb75yb4Jzrk7/bWEk3OudWSXpb\n0tDARAsAqGrIUwAAKcBXmsoSn94BQHCoTFeayhq5CgACr8JdaQIAAACAYEfRBAAAAAA+UDQBAAAA\ngA8UTQAAAADgA0UTAAAAAPhA0QQAAAAAPlA0AQAAAIAPFE0AAAAA4ANFEwAAAAD4QNEEAAAAAD5Q\nNAEAAACADxRNAAAAAOADRRMAAAAA+EDRBAAAAAA+UDQBAAAAgA8UTQAAAADgA0UTAAAAAPhA0QQA\nAAAAPlA0AQAAAIAPFE0AAAAA4ANFEwAAAAD4QNEEAAAAAD5QNAE+pKamasWKFUpNTQ10KFUarwMA\nAAgkiiagGDOnT1fruDiNvPhitY6L08zp0wMdUpXE6wAAAALNmVmgYygTzjmrLM8FgZeamqrWcXH6\n4uhRtZe0RlKP8HBt2LxZMTExgQ6vyuB1qJicczIzF+g4ghG5CgACrzR5iitNQBFSUlIUHxam9vnr\n7SXFhYYqJSUlgFFVPbwOAAAgGFA0AUWIj49XSmam1uSvr5G0OStL8fHxAYyq6uF1AAAAwaDYosk5\nd6Nz7vT8n51zbrJzLs05t8Y519F/IQL+FxMTo5cmTVKP8HB1jIpSj/BwvTRpEkPC/IzXAb6QpwAA\n/lLsPU3OubWSzjazLOfcYEl3SvqzpLMlPWhm5/svzBNjnDjKQ2pqat4Qsfh4/qMeQLwOFYu/7mmq\naHlKIlcBQDAoTZ7yVTStMrMO+T9Pk/SNmf0zf32lmQXVp3gkIgAIDn4smipUnpLIVQAQDMp6Iohc\n51ysc66mpAslfV6gLbw0AQIAUIbIUwAAv6juo22cpG8lVZP0sZn9KEnOue6SfvVDbAAA+EKeAgD4\nhc/vaXLOVZcUaWb7CmyLkFTNzA76Ib4SY8gDAAQHf35PU0XKUxK5CgCCQZl/T5OZZR9LRPkzE10o\n6XlJv5Q+TAAAygZ5CgDgDyf8nibnXFfn3HOSNkv6SNJXklqXd2AAAJQEeQoAUN58zZ73/yRdIWmL\npOmSPpD0rZk18194JceQBwAIDn6cPa9C5SmJXAUAwaA0ecrXRBA3SPpJ0suSZptZhnOOd3oAQLAg\nTwEA/MLX8LxYSY9I6itpo3Pu35LC82+6BQAg0MhTAAC/8Dl7nmcn52pI6iMpSdL5khaa2eByju2k\nMOQBAIKDP2fPK9Bn0OcpiVwFAMGgzGfPO8bMMszsPTMbKKmFpPmlCbAozrlLnHMbnHM/OefuLmaf\nK51zPzrnfnDOvVVWfQMAKgfyFACgPPmaCKKJpHgz+zp//Q5JtfObp5nZH57O1TkXorzx6BdK2i5p\nhaRBZrahwD4tJM2U1MPM0pxzp5rZniKOxad3ABAE/DgRRIXKU/n7kqsAIMDK+krTPyTVLbB+k6TD\nkkzShJMPr0idJf1sZpvNLEvSDEn9j9vnRkkvmlmaJBWXiAAAVQ55CgDgF76KplZmNqfA+hEze9rM\nHpZ0Whn131jS1gLrv+VvK6ilpFbOua+dc0udc73KqG8EQGpqqlasWKHU1NRAh4JKJph+t4IplkqO\nPAUA8AtfMwzVPG79wgI/n1oOsRSnuvLGp1+gvCT4lXOu7bFP9AoaP3685+fExEQlJib6KUSUxMzp\n0zV6+HDFh4UpJTNTL02apKuSkgIdFiqBYPrdCqZY/CU5OVnJycmB6LrC5SmJXAUA/lYWecrXPU3f\nSLrGzH46bntrSW+aWec/1HPesbpKGm9ml+Sv/68kM7MnCuzzsqRlZjY1f/1zSXeb2XfHHYtx4kEs\nNTVVrePi9MXRo2ovaY2kHuHh2rB5s2JiYgIdHiqwYPrdCqZYAsmP9zRVqDyV30auAoAAK+t7mh6U\nNMc5N9Q51y5/uU7Sx/ltZWGFpBbOuTjnXJikQfnHL+hDST0kyTl3qqTTJf1aRv3DT1JSUhQfFqb2\n+evtJcWFhiolJSWAUaEyCKbfrWCKpYogTwEA/KLYosnM5ksaoLzhDlPylx6SBpjZvLLo3MxyJN0i\n6VNJP0qaYWbrnXMTnHN98vdZIOl359yPkhZKGmtm+8qif/hPfHy8UjIztSZ/fY2kzVlZio+PD2BU\nqAyC6XcrmGKpCshTAAB/KdGX21YEDHkIfsfu9YgLDdXmrKwqca8H/COYfreCKZZACcSX21YU5CoA\nCLzS5CmKJvhVampq3hCm+PgqdY8Hyl8w/W4FUyyBQNFUPHIVAAQeRVMleS4AUJFRNBWPXAUAgVfW\nE0EAAAAAQJV3wqLJOdfSObfQObc2f729c+7+8g8NAIATI08BAMpbSa40vS7pHklZkmRma5Q35SoA\nAMGAPAUAKFclKZoizGz5cduyyyMYAABKgTwFAChXJSma9jjnEiSZJDnnBkraUa5RAQBQcuQpAEC5\nOuHsec655pJek/QnSfskbZI0xMw2l394JceMRAAQHPw9e15FyVMSuQoAgkFp8lT1EuxjZnaRc66W\npBAzO+ica1a6EAEAKHPkKaCS8df33VX179VDyZVkeN57kmRmh83sYP62d8svJAAoH6mpqVqxYoVS\nU1MDHQrKFnkKqERmTp+u1nFxGnnxxWodF6eZ06dX6H5QORQ7PM8511pSG0lPSrqrQFOUpLvMrE35\nh1dyDHkA4MvM6dM1evhwxYeFKSUzUy9NmqSrkpICHVal5K/heRUtT0nkKuBEUlNT1TouTl8cPar2\nktZI6hEerg2bN5fplSB/9YPgVNbD81pJ6iOprqS+BbYflHTjyYcHAIGRmpqq0cOH5yXHo0fzkuPw\n4ep50UUkx4qNPAVUMikpKYoPC1P7o0clSe0lxYWGKiUlpUzfr/3VDyqPYosmM/tI0kfOuW5m9h8/\nxgQAZYrkWDmRp4DKJz4+XimZmVojea4Abc7KUnx8fIXsB5VHSSaC+N45d7PyhkDUPLbRzK4vt6gA\noAyRHCs98hRQScTExOilSZPUY/hwxYWGanNWll6aNKnMP+DyVz+oPEoy5fgsSRskDZb0kKQhktab\n2e3lH17JMU4cgC/H7mkqmBy5p6l8BGDK8QqRpyRyFVBSzJ6H8lSaPFWSoul7MzvbObfGzNo750Il\nLTazrn8k2LJGIgJwIiRH/whA0VQh8pRErgKAYFBe39OUlf/vfudcW0k7JdU/2eAAINBiYmIolion\n8hQAoFyVpGh6zTkXLekBSR9Lqp3/MwAAwYA8BQAoVyUZnlfNzHL8FE+pMeQBAIJDAIbnVYg8JZGr\nACAYlCZPhZRgn03Oudeccxc65/yWBAEAKCHyFACgXJWkaGot6XNJN0tKcc694Jw7r3zDAgCgxMhT\nAIBydcLheV47540Z/6ekIWZWrdyiKgWGPABAcPD38Lzj+g7aPCWRqwAgGJTX8Dw557o7516S9J3y\nvjjwylLEBwBAuSBPAQDKU0kmgkiR9L2kdyR9bGaH/RDXSePTOwAIDgGYCCJFFSBPSeQqAAgG5fXl\ntlFmlvaHIvMDEhEABIcAFE0VIk9J5CoACAZl+uW2zrm/m9mTkh51zhV6hzez20oRIwAAZYI8BQDw\nF19fbrs+/99v/REIAAAniTwFAPCLYosmM5vtnKsmqZ2ZjfVjTAAAnBB5CgDgLz5nz8v/hvX/8VMs\nAACcFPIUAMAffA3PO2aVc+5jSbMkeWYkMrP3yy0qAABKjjwFAChXJSmaakr6XVLPAttMEskIABAM\nyFMAgHJ1winHKwqmcQWA4ODvKccrEnIVAF9SU1OVkpKi+Ph4xcTEBDqcSqs0ecrnPU35B23unJvt\nnEt1zu12zn3knGtW+jABACg75CkAlcHM6dPVOi5OIy++WK3j4jRz+vRAh4QCSvLltsskvSjp2Cs3\nSNKtZtalnGM7KXx6BwDBIQBfblsh8pRErgJQtNTUVLWOi9MXR4+qvaQ1knqEh2vD5s1ccSoH5XKl\nSVKEmf3bzLLzl7eUN34cAIBgQJ4CUKGlpKQoPixM7fPX20uKCw1VSkpKAKNCQSUpmuY55/7XORfv\nnItzzv1d0ifOuXrOuXrlHSAAACdAngJQocXHxyslM1Nr8tfXSNqclaX4+PgARoWCSjI8b5OPZjOz\n5mUbUukw5AEAgkMAhudViDwlkasAFG/m9OkaPXy44kJDtTkrSy9NmqSrkpICHValVJo8xex5AIAy\nxex5xSNXAfCF2fP8g6KpkjwXAKjIKJqKR64CgMArr4kgypVz7hLn3Abn3E/Oubt97PdX51yuc66j\nP+MDAFRt5CkAQLFFk3Puf/L/rVFenTvnQiS9IKmXpDaSkpxzrYvYr7ak2yQtK69YAAAVC3kKAOAv\nvq40PZf/73/Ksf/Okn42s81mliVphqT+Rez3sKTHJWWUYywAgIqFPAUA8IvqPtqynHOvSWrsnHvu\n+EYzu60M+m8saWuB9d+Ul6A8nHNnS2piZvPyp5EFAEAiTwEA/MRX0dRH0kXKG5LwnX/C8eacc5Ke\nkTS04Obi9h8/frzn58TERCUmJpZXaACAfMnJyUpOTg5E1xUuT0nkKgDwt7LIUyX5nqazzGz1H+ql\n+GN3lTTezC7JX/9f5X2nxhP561GSfpF0SHlJqKGk3yX1M7OVxx2LGYkAIAgE4HuaKkSeyt+fXAUA\nAVZes+f97pz7wDm3O395zznXpJQxHm+FpBb53+AeJmmQpI+PNZpZmpnVN7PmZtZMeTfY9i0qEQEA\nqizyFACgXJWkaJqsvATRKH+Znb/tDzOzHEm3SPpU0o+SZpjZeufcBOdcn6IeohMMewAAVDnkKQBA\nuSrJ8LzVZnbWcdtWmVmHco3sJDHkAQCCQwCG51WIPCWRqwAgGJTX8Lw9zrmrnXPV8perlTdeGwCA\nYECeAgCUq5IUTddLulLSTkk7JA2UNKw8gwIA4CSQpwAA5eqEw/MqCoY8AEBw8PfwvIqEXAUAgVde\nw/MAAAAAoMqiaAIAAAAAHyiaAAAAAMCHEhdNzrmuzrn5zrlk59xfyjMoAABOFnkKAFBeip0IwjnX\n0Mx2Flh/R9JQ5X1p3zdm1s4/IZYMN9cCQHDw10QQFS1PSeQqAAgGpclT1X20veKcWynpSTNLl7Rf\nedO45kpKK32YAACUCfIUAMAvih2eZ2Z/kfS9pDnOuWsljZFUQ9Ipkhj2AAAIKPIUAMBfTvg9Tc65\napJGS+oj6VEz+8ofgZ0shjwAQHDw9/c0VZQ8JZGrACAYlOn3NDnn+jnnvpA0X9JaSVdJ6u+cm+Gc\nS/hjoQIA8MeQpwAA/uJrIog1kjpLCpe0wMw6528/XdLDZjbIb1GWAJ/eAUBw8ONEEBUqT0nkKgAI\nBmU9EcQBSQMkRUjafWyjmf0sKegSEQCgyiFPAQD8wtf3NF2uvJtpq0sa7J9wAAAoMfIUAMAvTjgR\nRKMkd+gAAB28SURBVEXBkAcACA7+ngiiIiFXAUDglelEEAAAAAAAiiYAAAAA8ImiCQAAAAB8oGgC\nAAAAAB8omgAAAADAB4omVEipqalasWKFUlNTAx1K0OHcAAAAlC2KJlQ4M6dPV+v/3969R8lxlnce\n//1ky6aNM8ICQYLBGkLYtUNskIwMBC+WDpg4VwgJYAdvIFFYfAyGLEkWwyEHh92E4A0bzLLOMVnF\nOIAsAwFz2RiMgwQLBEb4IjuWHMxllHDzzHJxwAgs8LN/1DtSa9xd0z1TXfVW9/dzTp/py1tVT711\neeateqtq/XpdcPbZOnn9el1z9dVNh5QN6gZAG+V0sKfOWHKa7zq1bb7bFu+gxnW+RoVGU8vltMJX\nFUvZeObn53Xh1q3aeeCAbrz7bu08cEAXbt2axfw3jboB0EY5HeypM5ac5rtObZvvtsU7qHGdr5GK\niLF4FbMyWXZs3x5rO53YuGZNrO10Ysf27a2PZanxzMzMxMY1ayKkQ68NU1MxMzNTxWy0GnWDXKT9\nceN5IcfXJOaqMnNzc7G204k9aZ+1R4q1nU7Mzc2NdSw5zXed2jbfbYt3UOM6X8NYTp7iTFNL5XRW\noapYBhnP9PS0Zu+9V7emz7dK2n/woKanpyubn5yVnYWb9LqpSk5nb4FxNzs7q+ljjtFp6fNpktav\nXq3Z2dmhx7XSbXeYWAaZVlmZqqe1lFz2a1Uu7zq0Ld5Bjet8jRqNppbKaYWvKpZBxrNu3Tpdvm2b\ntnQ62jg1pS2dji7ftk3r1q1b+YxkbqlT6VXXTS5Jtk50VwDqVdXBniq23UFjGWRaS5WpclpLyWm/\n1raDe22Ld1DjOl8jN+ypqVxfmrAuDzmdWq0qlmHGMzc3FzMzMxNzKrnuusmp62ddctqm2k50zyNX\nDWFhf7NhampZ+5sqt92lYhlkWoPGU8W06qybqqx0edetbfEOalzna1DLyVONJ5CqXpOYiHJa4auK\nJad5ykmd1yvlmGTrwDVh1aHRRK4a1lIHe8p+r3rbXem0holn1POV636tbQc+2xbvoMZ1vgZBo2kC\n5bTCVxVLTvOUizobMrkm2VGb1MbiKNBoIldVaakz37ndwKGJ3hejHAcwjmg0AWOsrrNwk5xkOdNZ\nDRpN5KqqVNXVrUqDTCun3hfs14D7W06ecjFc+9mOcZkXoJ/5+fnihhnT0yO9+cU1V1+tC7du1frV\nq7X/4EFdvm2bnnfeeSObXk7qquNxZlsR4abjyBG5aji7d+/WBWefrRvvvvvQdxunpnTFDTdo06ZN\nR5Stc9sdZFpVxVPFeNivAUdaTp6i0YSBjOMON7d5mtR4cptvrByNpv7IVcOZn5/XyevXa+eBAzpN\nxV2+tnQ6umP/fvYXAJZtOXmKW45jSTndrrQquc1TbvFIxS3MN23aNPIzWrnNN4B8TPJjJgDkhTNN\nKDWOR/lym6fc4qnLpM73gnE+w8aZpv7IVcszztsLgPpxpgmVy+khulXJbZ5yi6cukzrfEmfYgGHV\nceYbAMrQaEKpcXxqdG7zlFs8dZnU+Z6fn9eFW7dq54EDuvHuu7XzwAFduHWr5ufnmw4NAAD0QaMJ\npcaxP3lu85RbPHWZ1Pme5DNsAAC0Fdc0YSDj2J88t3nKLZ66TNp8T8K1XFzT1B+5CgCa18pbjts+\nR9KbVJz12hYRb1j0+3+W9HuSDkqal/S7EfGvPcZDIgLQCuP+HKxxazRVladSWXIVADSsdY0m26sk\nfV7S0yR9TdJuSedGxB1dZc6S9NmI+IHtCyRtjohze4yLRASgNcb5DNs4NZqqzFOpLLkKlRrnfQkw\nKm28e94Zku6MiP0RcVDSDknP7C4QER+PiB+kj5+RdGLNMQJA5bgbWGuQp5At7sQJ1KfpRtOJkrq7\nMHxF5clmq6TrRhoRAACHkaeQJe7ECdTr6KYDGJTt8yWdLumsfmUuueSSQ+83b96szZs3jzwuAJh0\nu3bt0q5du5oOo3GD5CmJXIVqHLoT54EDko68EydnsEG3zSNVkaeavqbpSZIuiYhz0ueLJUWPi2yf\nLukySU+NiG/2GRf9xAEgA2N2TVNleSqVI1ehEpNwJ04sz8LNhqaPOUaz9947djcbqkIbbwRxlKR/\nVnGB7dclzUg6LyL2dZXZIOndkn4hIr5YMi4SEQBkYMwaTZXlqVSWXIXKjPudODE8GtODad2NICLi\nx5JeKul6SbdL2hER+2z/ie1fScUulfRASe+2fbPtaxsKFwAwYchT5ebn57V79+7S62gGKVOXnGKp\nwvPOO0937N+vK264QXfs3z9xDaaclmcu20KVD1DPqX6zEBFj8SpmBQDQtLQ/bjwv5Pgap1y1Y/v2\nWNvpxMY1a2JtpxM7tm9fVpm65BQLVi6n5ZnTtjA3NxdrO53YI0VIsUeKtZ1OzM3NDTWenOp3FJaT\npxp/uG1V6PIAAHkYp+55VRuXXDVIF6CcugnlFMu4q+MGBDktzxy3hZV228ypfkeldd3zAABA+wzS\nBajKbkIrlVMs46yu50bltDxz3BZW2m0zp/rNCY0mAAAwlOnpac3ee69uTZ9vlbT/4EFNT08PVaYu\nOcWSq5Vev1Lnc6NyWp5NbAuDLKuVPEA9p/rNCY0mAAAwlHXr1unybdu0pdPRxqkpbel0dPm2bUf8\ngzZImZzinWRVnCGq8+xETsuz7m2hjrN5OdVvTrimCTwADcvCeoN+uKapv3HLVYPsB3LaV+QUSy6q\nun6lietgclqedWwLdddxTvVbteXkqaNHFQxWroqVdalx8AC0ybTSdYv1BoBUHJFeah8ySJm65BRL\nLg6dITpwQNKRZ4iGqatDZycW3YBglPWd0/KsY1uoalkNKqf6zQFnmjJVxT+lS41jEu6OUoe2HWld\n6brFeoOlcKapv3HLVWi/qvfpOeW7cUP+rc6y8tSw9yjP9aUxevZFFffYH2QcMzMzsXHNmohUJqTY\nMDUVMzMzo5itsZTTsxkGUcW6xXqDpYjnNE1ErsL4WMhTG6amGs9TKMeyqsZy8hQ3gshQFRdTDjIO\n7o6yMoPcKajOuwkNoop1i/UGAMbLoLeoXukd9rByK72dOJaPRlOGqvindJBxcHeUlcnx2QxLqWLd\nYr0BgPGz1C2q63oGE5a2ktuJY/m4pilTK32a8zDjoP/x8uT4FPBBVLFuSaw36I9rmvobt1yFyZBj\nLhsEeQr9LCdP0WjKWB13z8PKDNIAqaqRUiXWC4wSjab+xjFXYfzt3r1bF5x9tm68++5D322cmtIV\nN9ygTZs2NRhZf9zlFWVoNI3JvKBd2nb3PGDUaDT1R65CG7XtTFPb4kX9eE4T0IC2PacEAIBhNPEM\nppWo+3lGmAycaQIAVIozTf2Rq9Bmbek1wZkmLIUzTQBQg7b84wAAVWpLr4m2nRlDO3CmCQCGwMXF\nS+NMU3/kKqA+HOBCP9wIYkzmBUCe6PIxGBpN/ZGrAKB5y8lTPNwWAAaU28OKAQBAPWg0AcCApqen\nNXvvvbo1fb5V0v6DBzU9Pd1gVAAAYNRoNAHAgA5dXNzpaOPUlLZ0OlxcDADABOCaJgAYEhcXl+Oa\npv7IVQDQPG4EMSbzAgBtRqOpP3IVADSPG0EAAAAAQMVoNAEAAABACRpNAAAAAFCCRhMAAAAAlKDR\nBAAAAAAlaDQBAAAAQAkaTQAAAABQgkYTAAAAAJSg0QQAAAAAJWg0AQAAAEAJGk0AAAAAUIJGEwAA\nAACUoNEEAAAAACVoNAEAAABACRpNAAAAAFCCRhMAAAAAlGi80WT7HNt32P687Vf2+P0Y2zts32n7\nH22f1EScAIDJRJ4CADTaaLK9StJbJP2CpMdKOs/2yYuKbZX0rYh4jKQ3Sbq03iibMz8/r927d2t+\nfn5FZeqaTm5l9u3bp6uuukr79u3rW6YKVSyDuqdV13pT1TKoa1uoc1p1rjdYPvJUubr26bnll5zK\n5BRLbmVyiqWNZchTi0REYy9JT5J0XdfniyW9clGZD0t6Ynp/lKT5PuOKcbJ9+47odNbGmjUbo9NZ\nG9u371hWmbqmk1uZl730ouhI8TNaFR0pLnrpRYNWyVCqWAZ1T6uu9aaqZVDXtlDntOpcb5qQ9seN\n5peqXlXmqRizXFXXPj23/JJTmZxiya1MTrG0sQx5qsf+e9gBqnxJ+g1Jb+36fL6kNy8qc5ukh3d9\nvlPS2h7jqrAqmzU3NxedztqQ9oQUIe2JTmdtzM3NDVWmrunkVmbv3r3RkWJPUSD2SNGRYu/evYMv\nhIrqL7dp1bXeVLUM6toW6pxWnetNU8as0VRZnooxylV17dNzyy85lckpltzK5BRLG8uQp3q/Gr+m\naRnc74dLLrnk0GvXrl01hlSt2dlZHXPMtKTT0jenafXq9ZqdnR2qTF3Tya3MzMyMTtSqrhLSw7VK\nMzMzS1fKEKpYBnVPq671pqplUNe2UOe06lxv6rJr164j9r/on6ek8chVde3Tc8svOZXJKZbcyuQU\nSxvLkKf6GLaVVeVLRbeHD3d97tXt4Tod2e1hrs+4Kmx/NiunI95tLMOZptGOhzNNnGlaisbrTFNl\neSrGKFfldMR7UsvkFEtuZXKKpY1lyFN99t/DDlDlKyWXL0haL+kYSbdIOmVRmQslXZ7enytpR59x\nVVqZTVvoSzo1tSE6nfL+qGVl6ppObmUuStfTPLqma5pWsgzqnlZd601Vy6CubaHOadW53jRhzBpN\nleWpGLNcVdc+Pbf8klOZnGLJrUxOsbSxDHnq/i8XwzXH9jmSLlNxJ79tEfHntv9E0u6I+JDtYyW9\nXdIGSd+UdG5EzPYYTzQ9L1Wbn5/X7OyspqentW7dumWXqWs6uZXZt2+fZmZmdMYZZ+iUU07pWaYK\nVSyDuqdV13pT1TKoa1uoc1p1rjd1s62IKO2i1iZV5ak0rrHKVXXt03PLLzmVySmW3MrkFEsby5Cn\nFg0zLjvvcUtEANBW49ZoqhK5CgCat5w81cYbQQAAAABAbWg0AQAAAEAJGk0AAAAAUIJGEwAAAACU\noNEEAAAAACVoNAEAAABACRpNAAAAAFCCRhMAAAAAlKDRBAAAAAAlaDQBAAAAQAkaTQAAAABQgkYT\nAAAAAJSg0QQAAAAAJWg0AQAAAEAJGk0AAAAAUIJGEwAAAACUoNEEAAAAACVoNAEAAABACRpNAAAA\nAFCCRhMAAAAAlKDRBAAAAAAlaDQBAAAAQAkaTQAAAABQgkYTAAAAAJSg0QQAAAAAJWg0AQAAAEAJ\nGk0AAAAAUIJGEwAAAACUoNEEAAAAACVoNAEAAABACRpNAAAAAFCCRhMAAAAAlKDRBAAAAAAlaDQB\nAAAAQAkaTQAAAABQgkYTAAAAAJSg0QQAAAAAJWg0AQAAAEAJGk0AAAAAUKKxRpPtE2xfb/ufbX/E\n9poeZR5n+9O2b7N9i+3nNhHrqOzatavpEIbStnil9sVMvKPXtpjbFu84IU+1b/1rW7xS+2Im3tFr\nW8xti3e5mjzTdLGkGyLi30v6mKRX9Shzj6T/GBGnSvpFSW+yPVVjjCPVtpWsbfFK7YuZeEevbTG3\nLd4xQ55q2frXtnil9sVMvKPXtpjbFu9yNdloeqakq9L7qyQ9a3GBiPhCRHwxvf+6pDlJ62qLEAAw\nychTAABJzTaaHhoRd0lSRHxD0kPLCts+Q9LqheQEAMCIkacAAJIkR8ToRm5/VNLDur+SFJJeI+lt\nEbG2q+w3I+LBfcbzU5J2qugCsbtPmdHNCABgKBHhpmMYRJ15KpUjVwFABobNU0ePKhBJioiz+/1m\n+y7bD4uIu2z/pIouDb3K/YSkD0l6VVkiakuCBgDko848laZHrgKAFmqye94HJL0wvX+BpPcvLmB7\ntaRrJV0VEe+rLzQAAMhTAIDCSLvnlU7YXivpXZIeKWm/pOdGxHdsny7pxRHxn2w/X9LfSLpdh7tM\nvDAibm0kaADAxCBPAQAWNNZoAgAAAIA2aLJ7XuVsv9b2V2zflF7nNB1TL7bPsX2H7c/bfmXT8QzC\n9qztPbZvtj3TdDyL2d6Wrj+4teu7JR9M2aQ+MWe7Dtt+hO2P2b49PcjzZen7LOu5R7wXpe9zruNj\nbX82bWe32X5t+n7a9mfSPuNq2yO9HnVQJfFeaftL6fubbJ/WdKy5yHn960aeGo225Sry1GiRp0av\nyjw1VmeaUkV8NyL+R9Ox9GN7laTPS3qapK9J2i3p3Ii4o9HAlmD7S5JOj4hvNx1LL7bPlPQ9SX8b\nEael794g6ZsRcWlK+idExMVNxtmtT8zZrsMuLoT/yYi4xfbxkm5U8Ryb31GG9VwS7/OUaR1Lku3j\nIuL7to+S9ClJL5f0CknviYh32/4rSbdExBWNBpr0ifcCSR+MiPc2G11+ct7GF5CnRqdtuYo8NVrk\nqXpUlafG6kxTkvudic6QdGdE7I+Ig5J2qNhAcmdlvL5ExCclLU6USz6Yskl9YpYyXYcj4hsRcUt6\n/z1J+yQ9QpnWc594T0w/Z1nHkhQR309vj1Vxh9OQtEXS36Xvr5L06w2E1lOPeO9Ln7Ot4wzkXjfk\nqRFpW64iT40WeaoeVeWprHcuy/QS27fY/t+5nH5d5ERJ/9r1+Ss6vIHkLCR9xPZu2y9qOpgBDfVg\nyozkvg7L9rSkx0v6jKSH5V7PXfF+Nn2VbR3bXmX7ZknfkPRRSV+U9J2IWNjJf0XSw5uKb7HF8Xbd\ncvu/pTp+o4s7zOGwbNe/hDxVrzbmqtzXYfLUCE1qnmpdo8n2R23f2vW6Lf39VUmXS3p0RDxeRcVk\neVqzpZ4SEU+Q9EsqNuQzmw5oGdrQFzX7dTh1IXiPpJenI2OL6zWreu4Rb9Z1HBH3RcQGFUdHz5B0\ncsMhlVocr+2flXRxRJwiaZOkB0tqxTUxVSFPNWYc8pSU2T60h+zXYfLUaE1qnsriIq1hlD2IcJG/\nlvTBUcayTF+VdFLX50ek77IWEV9Pf+dtv0/FRvLJZqNa0kAPpsxJRMx3fcxuHU4Xdr5H0tsjYuGZ\nNdnWc694c6/jBRHxb7Z3SXqypAfZXpWO4mW5z+iK95yFfvgRcdD2lZL+oNHgakaeakZL85SU8T60\nl9z3oeSp+kxanmrdmaYyaUNY8GxJ/9RULCV2S/oZ2+ttHyPpXBUPUMyW7ePSURDZfqCkZyjPurWO\n7J+65IMpM3BEzC1Yh/9G0t6IuKzru5zr+X7x5lzHth+y0A3DdkfS2ZL2Stop6TmpWDZ13CfeOxbq\n2LZVXDuQTR03Lef1rwt5arTalqvIU6NFnhqhKvPUuN09729V9Ae9T9KsiocP3tVoUD24uHXkZSoa\nrdsi4s8bDqmU7UdJep+K09lHS3pnbjHb3i5ps4pTrHdJeq2kayW9W4seTNlUjIv1iXmLMl2HbT9F\n0ick3aZiXQhJr5Y0ox4PAG0qzgUl8f6W8q3jU1VcQLsqva6JiD9N2+AOSSdIulnS+ekC/UaVxPsP\nkh6i4h+tWyRd0HUh7kQjT41GG/KU1L5cRZ4aLfLU6FWZp8aq0QQAAAAAVRur7nkAAAAAUDUaTQAA\nAABQgkYTAAAAAJSg0QQAAAAAJWg0AQAAAEAJGk0AAAAAUIJGE5bF9o9t32T7Ntvvtz21gnHttL1x\nmcOeZft+T8pO33/H9o2277C9y/Yvd/3+YtvnLzHeJy8nppWy/Tjbv7iM4c6w/XHb+9J8v9X2A9Jv\nz7K9x/bt6e8zu4Z7m+170gMhF757k+37bK8dYvq/aXtvevbBIOWvtP3sYeYxDbfe9nnDDgdgspCn\nRoc8teRw5KkxRKMJy3VPRGyMiFMlfVvSSxqMpd/Dxj4REadHxMmSXi7pLba3SFJEXBER7ygZ52ZJ\nP19tmAN7vKRfGmYA2w9V8eC+P4qIUyLidEkflvQTth8n6VJJvxoRj5X0TEl/Yfvn0uAh6c70/cLT\nsbdI+sqQcW+V9HsR8bQhhxvWo1Q8+G9gto8aUSwA8kWeGh3yVDny1Bii0YQq/KOkEyXJ9gNt32D7\nc+lI0a+l79enoztvtf1Ptj9s+9jukbhwpe3Xpc9n2/50Gtc1to9L35+TjlJ9TtJAR4AiYo+k10l6\naRrHa22/Ir1/WTqydYvt7bbXS7pA0u+no5RPsf0rtj+Tjoxdb3td13i2paOQX7B9Udf8/Haqg5tt\nX5W+e4jt99j+bHodkfBsr05xPjdN+zm2T7D9vjSuT7t4uvViL5H0toiY6Zrn90bEvKQ/kPRnEfEv\n6ftZSa+X9Eddw++Q9Lz0frOkT0n6Ua+6tH2e7VvT6/Xpuz+WdKakbbbf0GOYV6byN9v+sx6/f3nh\naKHt023vTO/PSsPclOr+gSn2M9N3L7e9yvalqT5vsf2irmE/Yfv9km7vNS8AJgZ5ijxFnsLKRAQv\nXkO/JH03/T1KxZGjZ6TPqyQdn94/WNKd6f16SfdKOjV9vkbSb6X3OyU9UdJ2Sa/qGvbjkjrp83+R\n9BpJx0r6F0k/3TWeD/SI76zF30t6nKTb0/vXSnpFev9VSavT+6nFv6fPa7reb5X037vKfVLS0Snm\n/5fq5LGS7pB0Qir3oPT3nZJ+Pr1/pKS9PWJ/gaQ3d31+s6Q/Tu+3SLq5xzB/p+IIXa9ldeNCvXd9\nd5qkz6X3V0r6DUmflvQgSW+V9B8kfUnS2kXD/ZSk/ZLWpmX9D5J+rWs5bugx/XNSHR27qC6ulPTs\n9P7QtCSdLulj6f0HJD05vT8uTfOIZSvpRZJend4fI2m3ivXtLEnflXRS09sLL1686n+JPEWeIk/x\nqvB1tIDl6di+SdIjJO2V9NH0/SpJr7f9VEn3SXq4i1PykvTliLgtvb9R0nTX+K6QdE1EvD59fpKk\nn5X0KduWtFrFkcKTJX0pIr6Uyr1Dxc5oEO7z/R5J221fK+naPmUeaftdKnbGqyV9ueu3/xMRP5L0\nTdt3SXqYiqTx7oj4tiRFxHdS2adLOiXNkyQdb/u4iPh+SdxnKh2pjIidttfaPj4ivlc6t8MJSe+V\ndK6kMyS9WL3ra5OknRHxLUmy/U5JT1WRNNRnmKdLujIifpjm4Ts9yvRbNp+S9JdpOu+NiK8errpD\nniHpVNvPSZ+nJD1G0kFJM5GOXAKYOOSpw8hT5CmsEN3zsFzfj4iNkk5SsSNZ6Cv+fEkPUXEkZ4Ok\nOUkPSL/9sGv4H0tHNNo/JWlLV1cIS7o+iv7oGyLi5yLiRV2/LcdGSft6fP/Lkt6Sft9tu9d28T9V\nHFU7TUWXiAd0/dZvvnrFaUlPTPO0ISJOWiIRSffvC99rvLdLekKf4Xv99gTdvyvAuyT9VxX13q//\nfb/pr9SPdHh/dKhuI+INKo6YdlT8Y/Lv+sRzUVedPjoibki/3TOCWAG0A3nqMPLUypGnJhyNJiyX\nJSkifqDi4tU/TDvxNZLmIuI+Fxezrl88TB/bJF0n6V1pPJ+R9BTbj5Yk28fZfoyKrgTrbT8qDVd2\nd5pD07N9mopuE2/pUe6kiPi4pItVHP05XsXp8u47LU1J+lp6/4IBpvkxSb/Z1f/5hPT99SrqayGu\nx/UYx+Jp/19J56fymyXN9zh69xZJv217U9e4fz31aX+jpItTH3jZnpb0Kkl/0T2CdKTr1ZL+qmT+\nZiQ9NR1FPEpF/e8qKS8VR3d/x3YnTf+EHmW+rKK7g1R0wViYh5+OiNsj4lIV3RlO1v3r5yOSLrR9\ndBrmMU7XFQCYaOSp8mmSpw4jT2FJdM/Dch06whMRt9jeo2LH9E5JH0yfP6cjj5j1OyoUaTx/aXuN\npLdHxPNtv1DS1emoXkh6TUTcafvFkv7e9j0qdtTH9xnvmbZvlPRASXdJemlE7OoukHZg73BxK1pL\nuiwi/s3F7WHf4+IC4YskXZI+f0tFopleYl722v5TSR+3/SNJN0v6XRWJ6H+l+jlK0ickXbhoHDtV\nJI+bVFxMeomkK9Mw96hHMoyIOdvnSnpjSkD3pXFfFxF7bL9SxXI5WkV3gD/s6oLSvSz/evG8LJrO\nN2xfrMMJ6EMR8aF+5dMwH0lJ93O2fyjp71X8Y9Bd/nUqLs69W0cmt99P/9T8WMURx+vScD+2fbOK\ni4ovSwn2ptSdZE7Ss3rFAmCikKfK54U8dXgY8hSW5PKzmwAAAAAw2eieBwAAAAAlaDQBAAAAQAka\nTQAAAABQgkYTAAAAAJSg0QQAAAAAJWg0AQAAAEAJGk0AAAAAUOL/A1HWWl9sOO+WAAAAAElFTkSu\nQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7fd8c49672d0>"
       ]
      }
     ],
     "prompt_number": 76
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig = plt.figure(figsize=(14, 6))\n",
      "ax = fig.add_subplot(121)\n",
      "\n",
      "distance_to_tumor_COM, distance_to_cluster_COM, p_sgas, n_sgas, p_pgas, n_pgas =  no_death_mixing[32]\n",
      "distance_to_COM = distance_to_tumor_COM[0]\n",
      "plt.scatter(np.argsort(distance_to_cluster_COM), n_pgas, label='No turnover (distance: {:3g})'.format(distance_to_COM))\n",
      "\n",
      "\n",
      "distance_to_tumor_COM, distance_to_cluster_COM, p_sgas, n_sgas, p_pgas, n_pgas =  full_death_mixing[94]\n",
      "distance_to_COM = distance_to_tumor_COM[0]\n",
      "ax.scatter(np.argsort(distance_to_cluster_COM), n_pgas, c='r', label='Turnover (distance: {:3g})'.format(distance_to_COM))\n",
      "ax.set_xlabel('Ranked Distance to COM of cluster')\n",
      "ax.set_ylabel('# of private GAS')\n",
      "\n",
      "ax.legend(loc=(0.1, 1))\n",
      "\n",
      "\n",
      "\n",
      "ax = fig.add_subplot(122)\n",
      "\n",
      "distance_to_tumor_COM, distance_to_cluster_COM, p_sgas, n_sgas, p_pgas, n_pgas =  no_death_mixing[90]\n",
      "distance_to_COM = distance_to_tumor_COM[0]\n",
      "plt.scatter(np.argsort(distance_to_cluster_COM), n_pgas, label='No turnover (distance: {:3g})'.format(distance_to_COM))\n",
      "\n",
      "\n",
      "distance_to_tumor_COM, distance_to_cluster_COM, p_sgas, n_sgas, p_pgas, n_pgas =  full_death_mixing[96]\n",
      "distance_to_COM = distance_to_tumor_COM[0]\n",
      "ax.scatter(np.argsort(distance_to_cluster_COM), n_pgas, c='r', label='Turnover (distance: {:3g})'.format(distance_to_COM))\n",
      "ax.set_xlabel('Ranked Distance to COM of cluster')\n",
      "ax.set_ylabel('# of private GAS')\n",
      "\n",
      "ax.legend(loc=(0.1, 1))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 79,
       "text": [
        "<matplotlib.legend.Legend at 0x7fd8c5881f90>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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PHs3XFhQUpDt27Mi3fPny5dqxY8dCn6eqMteU44lnhkrJnBkzsPDaNTwE4DlV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\nJEmd5GBIkiRJUif9f8PFm4TrABx4AAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7fd8c4415410>"
       ]
      }
     ],
     "prompt_number": 79
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}