{
 "metadata": {
  "name": ""
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Code by [Scott B Miles](http://resilscience.com/?page_id=2)."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import numpy as np\n",
      "from pandas import *\n",
      "from __future__ import division\n",
      "from scipy.integrate import simps, trapz\n",
      "import scipy.stats as stats\n",
      "from matplotlib.font_manager import FontProperties"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pandas.set_option('display.height', 500)\n",
      "pandas.set_option('display.max_rows', 500)\n",
      "pandas.set_option('display.max_columns', 500)\n",
      "pandas.set_option('display.width', 500)\n",
      "pandas.set_option('display.mpl_style', False)\n",
      "rcParams['figure.figsize'] = 25, 25"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "t_run = 30\n",
      "t_dt = linspace(1,t_run,t_run)\n",
      "\n",
      "# Function/data for defining Q(t) -- \"Quantity Resilience\"\n",
      "\n",
      "function_supply = \"Binomial\"\n",
      "function_demand = \"Binomial\"\n",
      "\n",
      "# Q_supply(t) defined by binomial function\n",
      "if function_supply == \"Binomial\":\n",
      "    cdf = stats.binom.cdf\n",
      "    mu_supply = 90\n",
      "    sigma_supply = 0.0045 \n",
      "    supply = Series(cdf(t_dt,mu_supply, sigma_supply), index=t_dt, name='Supply')\n",
      "    print \"Binomial supply quantity curve\"\n",
      "    print \"Mu = \" + str(mu_supply) + \" Sigma = \" + str(sigma_supply)\n",
      "    print\n",
      "elif function_supply == \"Empirical\":\n",
      "    supply = read_excel('CanterburyHotels.xlsx', 'data_norm', parse_cols=[0,1], index_col=0, na_values=['..']) \n",
      "    #supply.index = t_dt\n",
      "    print \"Empirical supply quantity curve: \" + 'CanterburyHotels.xlsx'\n",
      "else:\n",
      "    print \"Supply quantity curve undefined\"\n",
      "    print\n",
      "    \n",
      "# Q_demand(t) defined by binomial function\n",
      "if function_demand == \"Binomial\":\n",
      "    cdf = stats.binom.cdf\n",
      "    mu_demand = 30\n",
      "    sigma_demand = 0.125\n",
      "    demand = Series(cdf(t_dt,mu_demand, sigma_demand), index=t_dt, name='Demand')\n",
      "    print \"Binomial demand quantity curve\"\n",
      "    print \"Mu = \" + str(mu_demand) + \" Sigma = \" + str(sigma_demand)\n",
      "    print\n",
      "elif function_supply == \"Empirical\":\n",
      "    demand = read_excel('CanterburyHotels.xlsx', 'data_norm', parse_cols=[0,2], index_col=0, na_values=['..'])\n",
      "    #demand.index = t_dt\n",
      "    print \"Empirical demand quantity curve: \" + 'CanterburyHotels.xlsx'\n",
      "else:\n",
      "    print \"Demand quantity curve undefined\"\n",
      "\n",
      "recovery = concat([supply, demand], axis=1)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Binomial supply quantity curve\n",
        "Mu = 90 Sigma = 0.0045\n",
        "\n",
        "Binomial demand quantity curve\n",
        "Mu = 30 Sigma = 0.125\n",
        "\n"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Calculate adaptation resilience and speed resilience using the respective two formulas:\n",
      "\n",
      "  $A(t) =\\frac{d^2Q(t)}{dt^2}$\n",
      "\n",
      "  $V(t) = \\frac{dQ(t)}{dt}$"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Central finite difference function to estimate derivatives\n",
      "def deriv(x, y):\n",
      "    \"Finite difference derivative of the function f\"\n",
      "    n = len(y)\n",
      "    d = zeros(n,'d') # assume double\n",
      "    # Use centered differences for the interior points, one-sided differences for the ends\n",
      "    for i in range(1,n-1):\n",
      "        d[i] = (y[i+1]-y[i])/(x[i+1]-x[i])\n",
      "    d[0] = (y[1]-y[0])/(x[1]-x[0])\n",
      "    d[n-1] = (y[n-1]-y[n-2])/(x[n-1]-x[n-2])\n",
      "    return d\n",
      "\n",
      "# Calculate the first derivative of Q(t) to get V(t) -- \"Speed Resilience\"\n",
      "recovery['Supply Speed'] = deriv(recovery.index, recovery['Supply'])\n",
      "recovery['Demand Speed'] = deriv(recovery.index, recovery['Demand'])\n",
      "\n",
      "# Calculate the second derivative of Q(t) to get A(t) -- \"Adaptation Resilience\"\n",
      "recovery['Supply Adaptation'] = deriv(recovery.index, recovery['Supply Speed'])\n",
      "recovery['Demand Adaptation'] = deriv(recovery.index, recovery['Demand Speed'])  "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 4
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Calculate Time and Sufficiency resilience using the following formulas:\n",
      "    \n",
      "$R(t) = \\frac{1}{t} \\int_{t_0}^t Q(t)\\mathrm{d}t$\n",
      "\n",
      "$S(t) = \\frac{1}{2} [R_{s}(t) + R_{d}(t) - (R_{s}(t) + R_{d}(t)) \\vert R_{supply}(t) - R_{d}(t) \\vert]$"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "recovery['Demand Efficiency'] = recovery['Supply'] * 0.0\n",
      "recovery['Supply Efficiency'] = recovery['Supply'] * 0.0\n",
      "recovery['Demand Supply Ratio'] = recovery['Supply'] * 0.0\n",
      "recovery['Sufficiency'] = recovery['Supply'] * 0.0\n",
      "\n",
      "#recovery['Demand Supply Ratio'] = recovery['Supply'] / recovery['Demand']\n",
      "for i in (recovery.index):\n",
      "    recovery['Demand Efficiency'][i] = simps(recovery['Demand'][0:i], dx=1, even='first') / i\n",
      "    recovery['Supply Efficiency'][i] = simps(recovery['Supply'][0:i], dx=1, even='first') / i\n",
      "    \n",
      "recovery['Sufficiency'] = 0.5*(recovery['Supply Efficiency'] + recovery['Demand Efficiency'] - \n",
      "                              (recovery['Supply Efficiency'] + recovery['Demand Efficiency']) *\n",
      "                              abs(recovery['Supply Efficiency'] - recovery['Demand Efficiency']))\n",
      "\n",
      "recovery.fillna(0, inplace=True)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "t_supply_adapt_max = int(recovery.index[argmax(recovery['Supply Adaptation'])])\n",
      "t_demand_adapt_max = int(recovery.index[argmax(recovery['Demand Adaptation'])])\n",
      "\n",
      "t_supply_speed_max = int(recovery.index[argmax(recovery['Supply Speed'])])\n",
      "t_demand_speed_max = int(recovery.index[argmax(recovery['Demand Speed'])])\n",
      "\n",
      "# Fraction defining \"recovered\" 1 = 100%\n",
      "recovered = 0.99\n",
      "\n",
      "if len(recovery['Supply'][recovery['Supply'] > recovered]) == 0:\n",
      "       t_supply_rec = t_run\n",
      "else:\n",
      "       t_supply_rec = int(min(recovery['Supply'][recovery['Supply'] > recovered].index))\n",
      "\n",
      "if len(recovery['Demand'][recovery['Demand'] > recovered]) == 0:\n",
      "       t_demand_rec = t_run\n",
      "else:\n",
      "       t_demand_rec = int(min(recovery['Demand'][recovery['Demand'] > recovered].index))\n",
      "\n",
      "supply_demand_diff = abs(recovery['Demand'] - recovery['Supply'])\n",
      "\n",
      "if len(supply_demand_diff[supply_demand_diff > recovered]) == 0:\n",
      "       t_sufficiency = t_run\n",
      "else:\n",
      "       t_sufficiency = int(min(supply_demand_diff[supply_demand_diff > recovered].index))\n",
      "    \n",
      "print \"Maximum supply adaptation time = \", t_supply_adapt_max, \"days\"\n",
      "print \"Maximum demand adaptation time = \", t_demand_adapt_max, \"days\"    \n",
      "print \"Maximum supply speed time = \", t_supply_speed_max, \"days\"\n",
      "print \"Maximum demand speed time = \", t_demand_speed_max, \"days\"\n",
      "print \"Supply quantity recovery time = \", t_supply_rec, \"days\"\n",
      "print \"Demand quantity recovery time = \", t_demand_rec, \"days\""
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Maximum supply adaptation time =  2 days\n",
        "Maximum demand adaptation time =  2 days\n",
        "Maximum supply speed time =  2 days\n",
        "Maximum demand speed time =  3 days\n",
        "Supply quantity recovery time =  2 days\n",
        "Demand quantity recovery time =  8 days\n"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "r_supply_adapt = max(recovery['Supply Adaptation']) - min(recovery['Supply Adaptation'])\n",
      "r_demand_adapt = max(recovery['Demand Adaptation']) - min(recovery['Demand Adaptation'])\n",
      "r_supply_speed = max(recovery['Supply Speed'])\n",
      "r_demand_speed = max(recovery['Demand Speed'])\n",
      "r_supply_quant = recovery['Supply'][-1] \n",
      "r_demand_quant = recovery['Demand'][-1]\n",
      "r_supply = recovery['Supply Efficiency'][-1]\n",
      "r_demand = recovery['Demand Efficiency'][-1]\n",
      "r_sufficiency = recovery['Sufficiency'][-1]\n",
      "\n",
      "print \"Supply adaptation resilience = \", r_supply_adapt\n",
      "print \"Demand adaptation resilience = \", r_demand_adapt\n",
      "print \"Supply speed resilience = \", r_supply_speed\n",
      "print \"Demand speed resilience = \", r_demand_speed\n",
      "print \"Supply quantity resilience = \", r_supply_quant\n",
      "print \"Demand quantity resilience = \", r_demand_quant\n",
      "print \"Supply efficiency resilience = \", r_supply\n",
      "print \"Demand efficiency resilience = \", r_demand\n",
      "print \"Sufficiency resilience = \", r_sufficiency"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Supply adaptation resilience =  0.101834969431\n",
        "Demand adaptation resilience =  0.224120970033\n",
        "Supply speed resilience =  0.054532927417\n",
        "Demand speed resilience =  0.21551301576\n",
        "Supply quantity resilience =  1.0\n",
        "Demand quantity resilience =  1.0\n",
        "Supply efficiency resilience =  0.965596430183\n",
        "Demand efficiency resilience =  0.889789144955\n",
        "Sufficiency resilience =  0.857366915817\n"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "subplot(511, axisbg='.97')\n",
      "plot(recovery.index, recovery['Supply Adaptation'], label=r'$A_{s}(t)$', color='k')\n",
      "plot(recovery.index, recovery['Demand Adaptation'], label=r'$A_{d}(t)$', color='k', linestyle='--')\n",
      "tick_params(axis='both', which='major', labelsize=20)\n",
      "ylabel('Adaptation', fontsize=25, family='serif')\n",
      "xlim([1,t_run])\n",
      "legend(loc='best', fontsize=20)\n",
      "\n",
      "\n",
      "subplot(512, axisbg='.97')\n",
      "plot(recovery.index, recovery['Supply Speed'], label=r'$V_{s}(t)$', color='k')\n",
      "plot(recovery.index, recovery['Demand Speed'], label=r'$V_{d}(t)$', color='k', linestyle='--')\n",
      "tick_params(axis='both', which='major', labelsize=20)\n",
      "ylabel('Speed', fontsize=25, family='serif')\n",
      "xlim([1,t_run])\n",
      "legend(loc='best', fontsize=20)\n",
      "\n",
      "subplot(513, axisbg='.97')\n",
      "plot(recovery.index, recovery['Supply'], label=r'$Q_{s}(t)$', color='k')\n",
      "plot(recovery.index, recovery['Demand'], label=r'$Q_{d}(t)$', color='k', linestyle='--')\n",
      "tick_params(axis='both', which='major', labelsize=20)\n",
      "ylabel('Quantity', fontsize=25, family='serif')\n",
      "xlim([1,t_run])\n",
      "legend(loc='best', fontsize=20)\n",
      "\n",
      "subplot(514, axisbg='.97')\n",
      "plot(recovery.index, recovery['Supply Efficiency'], label=r'$R_{s}(t)$', color='k')\n",
      "plot(recovery.index, recovery['Demand Efficiency'], label=r'$R_{d}(t)$', color='k', linestyle='--')\n",
      "tick_params(axis='both', which='major', labelsize=20)\n",
      "ylabel('Efficiency', fontsize=25, family='serif')\n",
      "xlim([1,t_run])\n",
      "legend(loc='best', fontsize=20)\n",
      "\n",
      "\n",
      "subplot(515, axisbg='.97')\n",
      "plot(recovery.index, recovery['Sufficiency'], label=r'$S(t)$', color='k')\n",
      "tick_params(axis='both', which='major', labelsize=20)\n",
      "ylabel('Sufficiency', fontsize=25, family='serif')\n",
      "xlabel('Time', fontsize=25)\n",
      "xlim([1,t_run])\n",
      "legend(loc='best', fontsize=20)\n"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "-"
      }
     },
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 8,
       "text": [
        "<matplotlib.legend.Legend at 0x825bd90>"
       ]
      },
      {
       "metadata": {},
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aLSoqKhoN2nXHCoXC6Gu7ubnpheze3t7w9fUVjXl7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\nlajVapw/fx4//PADTp48iaKiIly9elVUV1tbiwMHDmD48OHo2bMnb9JKRER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HNfA6dSqfS9BFFhXT0icWMMEz07lUqF4uJiIamu\nfulKuj958qRRc1pZWcHDwwPu7u7CR3d3d3Tu3FnjnJOTEyQSCWOYSMQYv0TixhgmIqNPzOfk5CAq\nKgpubm5Yvny5Rt+nn36KPXv2YPPmzVi+fDmsrKyaPP/169dx8eJFeHl5YdKkSc1aq62tLcrKymBj\nY9Oseah1lZWVwdbWVt/LICIiIj1RqVQoKirSSKzXtcv96dOnjZrT2tpaK+Hu4eEhvNRtBwcHbuIg\nIiIiIhIBo0/Mx8bGQqFQYPLkyVo/xDg7OyMwMBBnz55FQkKCsPO9Kf75z38CAObPn9/sH5JcXFxw\n7949AL/9cMYfugyLSqVCWVkZCgoK4Ovrq+/liAZ3CBCJG2OYjE15eTmys7ORlZWF7Oxs5OTkaCXb\nHzx4gLKyskbNZ2Njo5Vcr5l0Vx936NChVf7txxgmEi/GL5G4MYaJyOgT81lZWQAAqVSqs9/HxwcA\nkJ2d3eS5y8rKsG/fPpiZmWHBggXPvEY1ExMT+Pr6Ii8vDw8fPmz2fNTybG1t4evryzqsREREIlRV\nVYWcnBxkZWVpvdTJ+Nzc3EbNZWtrW2/CXd22t7fnZgsiIiIiIiNk9Il5dcLdxcVFZ7+rqyuA/yTw\nmyIqKgpFRUWYPHkyPD09n32RNZiYmMDNza1F5iIyBKyrRyRujGESC6VSiYcPH+pMuqtfDx48aPBZ\nMWZmZujcuTO8vLzg5eVVb8JdDBjDROLF+CUSN8YwERl9Yr41qcvYvPnmm3peCREREVH7pVKpkJeX\nV2/SPScnBwqFot55JBIJPDw8hKS7rlenTp1gamraRndGRERERETtlcEn5n19fXH69Gl07969VeZX\n72TPy8vT2a/+c2UvL68mzXvjxg3ExcWhS5cumDhxYoPjFy5cKJTNcXBwgL+/v/CbU5lMBgBss90u\n2+pzhrIettlmu+ltNUNZD9vtqz1q1CgUFRXhX//6F3Jzc+Ho6IisrCzEx8cjNzcXpaWlyM7ORnl5\nORri4OAAX19feHp6QiKRwNXVFaNHj4aXlxeys7Ph4uKC4ODgOtdz9+5ddO7c2aA+P2yzzTbbbLPN\ntjjbo0ePNqj1sM0223W3ZTIZ9u7dC+C3sudBQUFoCRJVQ3+vq2cmJibYtWsX5syZ0yrz79+/H7Nn\nz8bcuXOxfft2rf7g4GCcPXsWP//8M4YPH97oed955x18+eWXWLNmDVatWlXv2JiYGAQEBDR57URE\nRERi9+TJk3p3umdlZaG0tLTBeRwdHevd6e7p6QkrK6s2uCMiIiIiImrP5HI5QkJCmj2PKBLzANC9\ne3csXrwYc+fOhaOjY4vNf//+fUilUjg5OSEnJ0fjoZ35+flwd3eHs7MzMjIyYGlp2ag5y8vL0blz\nZ5SWliI1NbXB+vJMzJMxk8lYV49IzBjD1JDy8nJkZGQgNTUVaWlpGq/09HQUFBQ0OIetrW29SXcv\nLy/Y2dm1wd20P4xhIvFi/BKJG2OYSLxaKjFv8KVsAGDPnj2IjY1FREQEIiIi8Oqrr2LJkiUYNGhQ\ns+f28PBAeHg4IiMjsWHDBkRERAh9H374IZRKJRYtWiQk5RUKBe7evQsLCwv4+vrqnPPAgQMoLCzE\nlClTWuyhr0RERESGSKlUIicnB+np6VrJ99TUVNy/f7/e6y0tLeHp6Vlv0t3R0RESiaSN7oiIiIiI\niKj1iWLHfGJiIvr06YPCwkLs3LkTmzdvxp07d/D888/jrbfeQnh4OCwsLJ75PTIyMhAaGorbt28j\nKCgI/fr1Q1xcHC5fvoyAgAAcO3YMDg4OAIC0tDT4+vrCx8cHqampOucbNWoU5HI5jh49ikmTJjX4\n/twxT0RERIZKpVLh8ePHGsn29PR0jV3vVVVVdV5vamqKLl26oGvXrpBKpRovHx8fuLm5MelORERE\nRESiYTSlbHRRqVT48ccf8fXXX+PYsWNwdHTE/PnzsWjRIuEBqk1VVFSEiIgIyOVypKSkwM/PD8HB\nwVi9ejXMzc2FcerEvFQqxb1797TmuXXrFvz8/NClSxekpaU16gdNJuaJiIhIn54+faqRbFfvfFef\nKy4urvf6Tp06wcfHRyP5rj729PSEmZko/kiTiIiIiIioQUadmK8pOzsbf/rTn7B//36YmpoiLCwM\nS5YsQWhoqL6X1mhMzJMxY109InFjDIuDUqlEVlaWVo139e73Bw8e1Hu9vb29sMO9dvLdx8cHNjY2\nbXQn1NIYw0TixfglEjfGMJF4GVWNeV1UKhVOnDiBr776CtHR0QB++6EzOjoax44dQ7du3fDee+9h\n0aJFel4pERERUetSqVTIy8vTWWomNTUVmZmZUCgUdV5vZmYGHx8frVIz6uS7s7Mzy80QERERERG1\nIIPfMR8cHIydO3cKJWry8/Oxbds2bNmyBWlpacI4Hx8fvPnmm1iwYAFyc3OxdetWbN++HQMHDsSe\nPXvg7e2tpztoGHfMExERUUNUKhUePHiAu3fvIjk5GcnJybh3756QfH/y5Em917u7u+ssNePj4wNP\nT0+Ympq20Z0QERERERGJl9GUslE//LW4uBhfffUVDhw4gIqKCgCARCLB+PHjsWTJEkyePFlrJ9f9\n+/fx+9//HlKpFNu2bdPH8huFiXkiIiJSKy0tFZLv6o937tzBnTt3UFJSUud1Dg4OOne7S6VSeHt7\nw9raug3vgoiIiIiIqH0yqlI2L774osaDVp2cnDB37lwsXrwY3bp1q/M6Dw8PfPTRR1iwYEFbLJOI\nngHr6hGJG2P42SiVSqSnp2sk3e/cuYPk5GTk5OTUeZ2TkxN69OiBnj17okePHujWrRu6du2Krl27\nwsnJqQ3vgNoLxjCReDF+icSNMUxEokjMq5PygwcPxpIlS/Dqq6/CysqqUdfGx8fj0aNHrbk8IiIi\nIi3quu81k+7q43v37qGyslLndRYWFujWrRt69OghJOG7d++Onj17wsXFpY3vgoiIiIiIiFqDKErZ\nhIeHY+nSpRg2bFiTrn399dexd+9e9OrVC7du3WqlFTYfS9kQERGJV1lZGVJSUjRKz6iPCwoK6rzO\n09NTY/e7+tjb25v13omIiIiIiAyUUZWyWblyJfz8/Jp83dy5czFu3Dh06dKlFVZFRERExqK6uhqZ\nmZlaiffk5GRkZmairn0O9vb2OpPv3bt3h62tbRvfBRERERERERkKg0/MnzlzBl27dn2ma8eMGdOy\niyGiFse6ekTi1t5iuKCgQKvm+507d3D37l2Ul5frvMbMzAxdu3bVKDmjTsJ36tRJ6+H0RIakvcUw\nkTFh/BKJG2OYiAw+MT9mzBjcvHkTCoUCAODr6ws7Ozuhv7KyEjKZDGPHjtXXEomIiEhEVCoVMjIy\nkJiYiNu3b2sk4nNzc+u8zt3dXSvx3rNnT0ilUpibm7fhHRAREREREZHYGXyN+aysLPj4+AjtkydP\nYvz48UK7tLQUHTp0wIwZM/DFF1+gc+fO+lhms7DGPBERUesoLCxEYmIibty4gcTERCQmJuLmzZso\nLi7WOd7GxkZIutcuPePg4NDGqyciIiIiIiJDYzQ15r/77juoVCp07NgRf/zjHzFy5EiNfltbW6xZ\nswaffvopoqOjcf36dXh7e+tptURERKQPlZWVSEpKEpLwN27cwPXr15Gdna1zvJubG/z8/NC7d2/0\n6tVLSMJ7enqy9AwRERERERG1OoPfMT9u3DhkZmYiISEB1tbWdY67f/8+Ro4cidDQUHz99ddtuMLm\n4455Mmasq0ckbm0dwyqVCpmZmRo74G/cuIGkpCSh7F1N1tbW6N27N/r27Qs/Pz/069cPfn5+cHNz\na7M1Exkyfh8mEi/GL5G4MYaJxMtodsynp6fjpZdeqjcpDwAeHh6YPn06Tpw48UzvU1hYiJUrV0Iu\nlyMlJQV+fn4ICgrC6tWrYWFh0aS5Ll++jE2bNiEhIQF3796Fo6Mj+vXrh3feeQdhYWHPtD4iIiJj\nU1RUpLUD/ubNmygqKtIaK5FI0K1bN/Tt21dIwvft2xe+vr4wNTXVw+qJiIiIiIiI6mbwO+ZtbW1x\n4sQJvPDCCw2OjY2NxZQpU1BaWtqk90hPT0doaCiSkpIQHByM/v37Qy6X4/LlywgICMDRo0fh6OjY\nqLk+++wzLFu2DK6urhg1ahR8fX2Rl5eHq1evYuzYsdi4caPWNdwxT0RExqyqqgrJycm4fv26kIRP\nTExEZmamzvEuLi4ayfe+ffuid+/esLW1beOVExERERERkbExmh3zHh4eePToUaPG5ufnw93dvcnv\nsWLFCiQlJWHdunVYsWKFcH7BggXYsWMHPv/8c6xdu7bBeY4cOYIPPvgA4eHh2LZtG+zs7DT6df2J\nPRERkbFQqVTIysrSWYamqqpKa7yVlZVGGRr1x06dOrEOPBEREREREYmawe+Ynzp1KlQqFf797383\naqxEIsGRI0caPX9OTg6kUimcnZ1x//59jR/0Hz9+DHd3dzg6OiIjIwNWVlZ1zqNQKODn54fCwkKk\npKRoJeXrwx3zZMxYV49I3OqK4eLiYo0yNOrjwsJCnfP4+vpq7IDv27cvunXrxjI0RK2M34eJxIvx\nSyRujGEi8TKaHfNTpkzBG2+8gTlz5mDNmjXw9fXVGnPv3j2sXbsWR48exbZt25o0f2xsLBQKBSZP\nnqy1+87Z2RmBgYE4e/YsEhISMGLEiDrnOXfuHO7cuYPFixfD2toaFy9exMWLF9GhQwcMGDAAAwYM\naNK6iIiIxEChUODmzZsaO+ATExORkZGhc3zHjh01EvB+fn7o06dPk36hTURERERERCR2Bp+Ynz9/\nPvbv3499+/Zh//798PPzQ+fOndGxY0fk5+cjJycHiYmJUKlUCA4Oxrx585o0f1ZWFgBAKpXq7Pfx\n8QEAZGdn1ztPcnIyAKBDhw4IDg7G+fPnNfpffvll/POf/4Szs3OT1kfU3nGHAJF4KBQK3L59G1eu\nXEF8fDyuXLmCmzdvorKyUmuspaUlevfuDT8/P/j5+aFfv37w8/ODu7s7y9AQGRB+HyYSL8Yvkbgx\nhonI4BPzEokEe/fuxZw5cxAbG4vr16/j+vXrWuPGjRuHXbt2NXl+dcLdxcVFZ7+rqyuA/yTw63Ln\nzh0Avz381dvbG5999hlmzpyJ06dP48svv8ShQ4cgkUhw4MCBJq+RiIioralUKqSlpeHy5ctCEv6X\nX37B06dPtcZKpVKNHfB9+/ZF9+7dYWZm8P/MICIiIiIiItILUfzE7OnpiZiYGERGRuLgwYO4desW\n0tPTIZVK0adPH0yfPh0zZ87U6xrVD3ZVKpXYuXMnRo0aBQB4/fXXMXHiRLi7u+PgwYP45ZdfdJa1\nWbhwobA738HBAf7+/sJvT2UyGQCwzXa7bH/55Zf8/51ttg2g/fDhQ+zevRtJSUnIy8tDfHw88vPz\nUVunTp0QGBiIIUOGwNzcHGVlZXj//fc15nvuuef0fj9ss81249rqc4ayHrbZZpvxyzbbxtKuHcv6\nXg/bbLNdf7zu3bsXwG/VVYKCgtASDP7hr63tk08+wbJly7B27VpERERo9c+dOxe7d+9GVFQUpk+f\nXuc8f/nLX/CnP/0J7u7uyMnJ0eoPCgrCuXPnsHPnTrz++usafXz4KxkzmYwPvCFqa8XFxUhISMCV\nK1eEsjSZmZla41xdXTFkyBAMHjwYQ4YMwaBBg4S/JFNjDBOJG2OYSLwYv0TixhgmEi+jefhra+vS\npQsAIDU1VWd/eno6AMDLy6tR8+h6OC0A9OjRA+fOnUNhYeGzLpWoXeI/RIhaV0VFBa5duyaUo4mP\nj0dSUhJq/17ezs4OgwYNwuDBg4VEvLe3d4P14BnDROLGGCYSL8YvkbgxhomoXSXmExMT4e/vD6VS\n2ehrgoKCYG5ujuPHj6O6uhomJiZCX35+Ps6fPw9XV1cMHDiw3nmCg4NhYWGBCxcu4NGjR3BzcxP6\nqqurcfToUQBA7969m3hXREREjaNUKpGcnCwk4C9fvozr16+jqqpKY5y5uTn69euHoUOHCkn4nj17\nwtTUVE8rJyIiIiIiIjIu7SoxL5FItHYANsTDwwPh4eGIjIzEhg0bNMrZfPjhh1AqlVi0aBEsLS0B\n/FZL/u7du7CwsNDYHe/m5oY5c+Zg27Zt+OKLL7B27VqYm5tDpVJh9+7dePjwIby8vFiyhqgW/vke\n0bNRqVTIyMjQ2Al/9epVlJaWaoyTSCR47rnnhAT84MGD0b9/f+H7WnMxhonEjTFMJF6MXyJxYwwT\nkSgS8/fu3cMnn3yCCxcuICMjA8XFxTrHqVSqBv/kXpf169cjPj4eq1atQmxsLPr164e4uDhcvnwZ\nAQEBWLp0qTA2KysLffr0gY+Pj1b5m7Vr1yI+Ph6ffPIJduzYgalTpyImJgapqamwtrbG9u3bYWtr\n2+T1ERERqR/IWjMR/+jRI61xXbp0EZLwQ4YMwcCBA9GhQwc9rJiIiIiIiIiI6mLwD39NSkrCoEGD\nUFZW1qjxEomkSaVs1IqKihAREQG5XI6UlBT4+fkhODgYq1evhrm5uTAuLS0Nvr6+kEqluHfvntY8\nZWVl+POf/wyZTIbExER4eHhg8ODBWLduHbp166bzvfnwVyIiqqm0tBS//PKLxsNZ09LStMY5Oztr\nPJx18ODB6NSpU9svmIiIiIiIiMhItNTDXw0+MT916lQcPXoUkyZNwuTJk+Hm5gZHR0edO+NTU1Mx\nf/58VFdX62Glz46JeSIi41VVVYXExEQhAX/lyhXcunVL63uZjY0NBgwYICTghw4dCqlU+kx/KUZE\nREREREREz6alEvMGX8rm6tWrGDp0qPDw1Pq4uLi0wYqIqCWxrh4ZmydPnuDChQuQyWSQyWRISEhA\nRUWFxhgzMzP0799fSMIPHjwYvXv3hpmZ4X3bZgwTiRtjmEi8GL9E4sYYJiLD+wm/lsrKSrz77ruN\nGuvr64szZ8608oqIiIgar3Yi/sqVK1AoFBpjevTooVGOxt/fH9bW1npaMRERERERERG1NoNPzAcE\nBMDBwaFRY21sbDBmzJjWXRARtSjuEKD2pqFEvImJCQYNGoTRo0dj9OjRGD58OJycnPS44uZhDBOJ\nG2OYSLwYv0TixhgmIoNPzIeHh2PPnj2YOHFig2OTk5MRGhqq86GsREREraGpifiRI0c2+hfORERE\nRERERNQ+GXxifubMmcjNzcVrr72GDz74AAMGDKjzQXdVVVVIS0tr2wUSUbOwrh6JDRPxmhjDROLG\nGCYSL8YvkbgxhonI4BPzXbt2hUQiQVZWFr777juYmprC1dUVlpaWWmMrKyvrTNoTERE9CybiiYiI\niIiIiKilSVQqlUrfi6iPiYlJk8ZLJBIolcpWWk3riImJQUBAgL6XQUREaFwifsCAAUzEExERERER\nERkhuVyOkJCQZs9j8DvmAeB//ud/0Llz5wbHZWdnY+XKlW2wIiIiai+4I56IiIiIiIiI2pooEvO/\n+93v0KdPnwbHJSYmMjFPJDKsq0dtjYn4lsUYJhI3xjCReDF+icSNMUxEBp+YX7VqFVxdXRs1tkuX\nLtixY0crr4iIiMSEiXgiIiIiIiIiMjQGX2PeGLDGPBFRy2GNeCIiIiIiIiJqLUZVY15NpVIhIyMD\nd+7cwbBhw9ChQweUlZXB2tpa30sjIiI94Y54IiIiIiIiIhIbE30voLH279+Pzp07o2vXrpgwYQKy\nsrKE8/3798fu3bubNX9hYSHefvttDBo0CA4ODhg5ciRWrFiBysrKRs8hlUphYmKi8+Xh4dGs9RG1\nVzKZTN9LIJFRqVS4fv06Pv30UwQHB8Pd3R2TJ0/Gp59+igsXLqC6uhqDBg3Cu+++i0OHDiEnJwdy\nuRwff/wxwsLCmJRvYYxhInFjDBOJF+OXSNwYw0Qkih3zb7/9NjZt2iS0a1bf6dKlC+7cuYO5c+fi\n+vXr+Otf/9rk+dPT0xEaGoqkpCQEBwdjzJgxQhJHJpPh6NGjcHR0bNRcjo6OePfdd7XO29nZNXld\nRET0m6dPn+Ls2bM4efIkTp48iezsbKGPO+KJiIiIiIiISGwMvsZ8XFwcAgMDYW5ujqCgILz44ot4\n++23ce3aNfTp0wcAkJGRgRUrViAyMhKxsbF44YUXmvQes2fPxv79+7Fu3TqsWLFCOL9gwQLs2LED\nERERWLt2bYPzqHfM37t3r0nvzxrzRETa0tPTER0djZMnT+LcuXMoLy8X+tzd3TFhwgSEhYVhzJgx\nTMQTERERERERUZtoqRrzBp+Yf/XVV3Hs2DGkpaWhY8eOAABzc3P8+uuvQmJebdiwYfD09MThw4cb\nPX9OTg6kUimcnZ1x//59SCQSoe/x48dwd3eHo6MjMjIyYGVlVe9cTMwTET07hUKBCxcuCMn4mzdv\navQPHjwYYWFhCAsLw4ABA2BiIppqbERERERERETUThjNw19v376NadOmCUn5+oSHh2PLli1Nmj82\nNhYKhQKTJ0/WSMoDgLOzMwIDA3H27FkkJCRgxIgRDc5XXl6Offv2ISsrCx4eHujVqxeGDx/epDUR\nGROZTIbRo0frexmkJ/n5+Th9+jSio6Pxww8/oKCgQOizt7dHSEgIwsLCMH78eLi7u+txpVQXxjCR\nuDGGicSL8UskboxhIjL4xPzdu3fxySefNGrs8OHDsXr16ibNr36IrFQq1dnv4+MDABr1jOsikUjw\n4MEDzJkzR+P8oEGDsGnTJjz//PNNWhsRUXujUqlw48YNoVb8xYsXUV1dLfR3795d2BUfEBAACwsL\nPa6WiIiIiIiIiKh1GHxivlOnTrh06RLGjx/f4NgbN24IifTGUifcXVxcdPa7uroC+E8Cvz5z587F\n6NGj4efnB1NTUxw5cgQHDx7EyZMnERoail9//RXe3t5NWh9Re8cdAu1fWVkZzp49i+joaJw4cULj\n66m5uTleeOEFIRnfvXt3Pa6UngVjmEjcGMNE4sX4JRI3xjARGXxivn///ti+fTsiIiLqHVdUVIS/\n/e1v6NevXxutTNuqVas02vPmzcO8efMwduxYnDlzBsuWLUNkZKTOaxcuXCj8UsHBwQH+/v7CF2mZ\nTAYAbLPNNtuiaUulUkRHRyMyMhIJCQmoqqqCmpOTE6ZMmYLQ0FBYWVnB1tZW7+tlm2222WabbbbZ\nZpttttlmm2222dbVlslk2Lt3L4DfqqsEBQWhJRj8w1+//fZbvPbaa5gwYQIWLlyIKVOmwMbGRnj4\nq1KpxKFDh7Bhwwb8+uuvOHz4MKZOndro+T/55BMsW7YMa9eu1Zn8nzt3Lnbv3o2oqChMnz79me7h\nyJEjePnll9G9e3ckJydr9fPhr2TMZDLW1WsPlEolLl68iJMnTyI6OhqJiYka/YMGDUJYWBhCQ0Mx\naNAgPri1HWEME4kbY5hIvBi/ROLGGCYSL6N5+OvMmTPx/fff49ChQzh9+jRcXFygVCoxa9YsFBUV\n4f6zS5AkAAAgAElEQVT9+6ioqAAAvPLKK01KygNAly5dAACpqak6+9PT0wEAXl5ez3wP/v7+AIDc\n3NxnnoOIyNA8fvwYP/zwA06ePIkffvgBjx8/Fvrs7OwQHByMiRMnYvz48fDw8NDjSomIiIiIiIiI\nDIvBJ+YBYNu2bejQoQN27dolJLd//fVXoV8ikWD+/Pn47LPPmjx3UFAQzM3Ncfz4cVRXV2vs4szP\nz8f58+fh6uqKgQMHPvP6//nPfwKAXsvsEBkq7hAQD5VKhZs3bwq74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       "text": [
        "<matplotlib.figure.Figure at 0x81459b0>"
       ]
      }
     ],
     "prompt_number": 8
    }
   ],
   "metadata": {}
  }
 ]
}