Covid 19 High Risk States in India.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## High risk states in India to defend COVID 19"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dataset\n",
    "\n",
    "#### Indian Population Dataset based on 2011 Census \n",
    "Main Source [Wikipedia](https://en.wikipedia.org/wiki/List_of_states_and_union_territories_of_India_by_population)<br/>\n",
    "Alternate CSV Source [Kaggle](https://www.kaggle.com/sudalairajkumar/covid19-in-india/?select=population_india_census2011.csv)\n",
    "\n",
    "#### Hospital Bed Dataset \n",
    "Main Source [Ministry of Health and Family Welfare](https://pib.gov.in/PressReleasePage.aspx?PRID=1539877)\n",
    "<br/>\n",
    "Alternate CSV Source [Kaggle](https://www.kaggle.com/sudalairajkumar/covid19-in-india/?select=HospitalBedsIndia.csv)\n",
    "\n",
    "#### Covid 19 State-wise data  \n",
    "Main Source [api.covid19india.org](https://api.covid19india.org/csv/latest/state_wise.csv)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "High risk states will be those with lower number of infrastructures(Isolation wards) and high population. We now calculate the ratio of number of beds to population and find the high risk states in India.\n",
    "**A scatter plot will be plotted and we will show high risk states in red and least risk states in white-yellow shade.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Analysis on older dataset "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dataset which was released about 2 months before"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Importing dataset for analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Population Dataset\n",
    "populationData = pd.read_csv(\"population_india_census2011.csv\")\n",
    "# Hospital Bed Dataset\n",
    "hospitalData = pd.read_csv(\"HospitalBedsIndia.csv\")\n",
    "# Covid 19 cases\n",
    "covidData = pd.read_csv(\"state_wise.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Population Dataset**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>State</th>\n",
       "      <th>Population</th>\n",
       "      <th>Rural</th>\n",
       "      <th>Urban</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Uttar Pradesh</td>\n",
       "      <td>199812341</td>\n",
       "      <td>155317278</td>\n",
       "      <td>44495063</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Maharashtra</td>\n",
       "      <td>112374333</td>\n",
       "      <td>61556074</td>\n",
       "      <td>50818259</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Bihar</td>\n",
       "      <td>104099452</td>\n",
       "      <td>92341436</td>\n",
       "      <td>11758016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>West Bengal</td>\n",
       "      <td>91276115</td>\n",
       "      <td>62183113</td>\n",
       "      <td>29093002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Madhya Pradesh</td>\n",
       "      <td>72626809</td>\n",
       "      <td>52557404</td>\n",
       "      <td>20069405</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "            State  Population      Rural     Urban\n",
       "0   Uttar Pradesh   199812341  155317278  44495063\n",
       "1     Maharashtra   112374333   61556074  50818259\n",
       "2           Bihar   104099452   92341436  11758016\n",
       "3     West Bengal    91276115   62183113  29093002\n",
       "4  Madhya Pradesh    72626809   52557404  20069405"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "populationData.columns = [\"Sno\", \"State\", \"Population\", \"Rural\", \"Urban\", \"Area\", \"Density\", \"Gender\"]\n",
    "censusData = populationData[[\"State\", \"Population\", \"Rural\", \"Urban\"]]\n",
    "censusData.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Hospital Infrastructures** - Number of beds available"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>State</th>\n",
       "      <th>NumPublicBeds</th>\n",
       "      <th>NumRuralBeds</th>\n",
       "      <th>NumUrbanBeds</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Andaman &amp; Nicobar Islands</td>\n",
       "      <td>1246</td>\n",
       "      <td>575</td>\n",
       "      <td>500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Andhra Pradesh</td>\n",
       "      <td>60799</td>\n",
       "      <td>6480</td>\n",
       "      <td>16658</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Arunachal Pradesh</td>\n",
       "      <td>2320</td>\n",
       "      <td>2136</td>\n",
       "      <td>268</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Assam</td>\n",
       "      <td>19115</td>\n",
       "      <td>10944</td>\n",
       "      <td>6198</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Bihar</td>\n",
       "      <td>17796</td>\n",
       "      <td>6083</td>\n",
       "      <td>5936</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "                       State  NumPublicBeds  NumRuralBeds  NumUrbanBeds\n",
       "0  Andaman & Nicobar Islands           1246           575           500\n",
       "1             Andhra Pradesh          60799          6480         16658\n",
       "2          Arunachal Pradesh           2320          2136           268\n",
       "3                      Assam          19115         10944          6198\n",
       "4                      Bihar          17796          6083          5936"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hospitalData.columns = ['Sno', 'State', 'NumPrimaryHealthCenters',\n",
    "       'NumCommunityHealthCenters', 'NumSubDistrictHospitals',\n",
    "       'NumDistrictHospitals', 'TotalPublicHealthFacilities',\n",
    "       'NumPublicBeds', 'NumRuralHospitals', 'NumRuralBeds',\n",
    "       'NumUrbanHospitals', 'NumUrbanBeds']\n",
    "bedsData = hospitalData[[\"State\", \"NumPublicBeds\", \"NumRuralBeds\", \"NumUrbanBeds\"]]\n",
    "bedsData.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Covid 19 Data**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>State</th>\n",
       "      <th>Confirmed</th>\n",
       "      <th>Recovered</th>\n",
       "      <th>Deaths</th>\n",
       "      <th>Active</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Total</td>\n",
       "      <td>88506</td>\n",
       "      <td>33133</td>\n",
       "      <td>2790</td>\n",
       "      <td>52578</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Maharashtra</td>\n",
       "      <td>29100</td>\n",
       "      <td>6564</td>\n",
       "      <td>1068</td>\n",
       "      <td>21468</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Tamil Nadu</td>\n",
       "      <td>10585</td>\n",
       "      <td>3538</td>\n",
       "      <td>74</td>\n",
       "      <td>6973</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Gujarat</td>\n",
       "      <td>10989</td>\n",
       "      <td>4308</td>\n",
       "      <td>625</td>\n",
       "      <td>6056</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Delhi</td>\n",
       "      <td>9333</td>\n",
       "      <td>3926</td>\n",
       "      <td>129</td>\n",
       "      <td>5278</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "         State  Confirmed  Recovered  Deaths  Active\n",
       "0        Total      88506      33133    2790   52578\n",
       "1  Maharashtra      29100       6564    1068   21468\n",
       "2   Tamil Nadu      10585       3538      74    6973\n",
       "3      Gujarat      10989       4308     625    6056\n",
       "4        Delhi       9333       3926     129    5278"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "covidData = covidData[[\"State\", \"Confirmed\", \"Recovered\", \"Deaths\", \"Active\"]]\n",
    "covidData.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Merging Data**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>State</th>\n",
       "      <th>Population</th>\n",
       "      <th>Rural</th>\n",
       "      <th>Urban</th>\n",
       "      <th>Confirmed</th>\n",
       "      <th>Recovered</th>\n",
       "      <th>Deaths</th>\n",
       "      <th>Active</th>\n",
       "      <th>NumPublicBeds</th>\n",
       "      <th>NumRuralBeds</th>\n",
       "      <th>NumUrbanBeds</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Uttar Pradesh</td>\n",
       "      <td>199812341</td>\n",
       "      <td>155317278</td>\n",
       "      <td>44495063</td>\n",
       "      <td>4057</td>\n",
       "      <td>2165</td>\n",
       "      <td>95</td>\n",
       "      <td>1797</td>\n",
       "      <td>58310</td>\n",
       "      <td>39104</td>\n",
       "      <td>37156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Maharashtra</td>\n",
       "      <td>112374333</td>\n",
       "      <td>61556074</td>\n",
       "      <td>50818259</td>\n",
       "      <td>29100</td>\n",
       "      <td>6564</td>\n",
       "      <td>1068</td>\n",
       "      <td>21468</td>\n",
       "      <td>68998</td>\n",
       "      <td>12398</td>\n",
       "      <td>39048</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Bihar</td>\n",
       "      <td>104099452</td>\n",
       "      <td>92341436</td>\n",
       "      <td>11758016</td>\n",
       "      <td>1145</td>\n",
       "      <td>440</td>\n",
       "      <td>7</td>\n",
       "      <td>698</td>\n",
       "      <td>17796</td>\n",
       "      <td>6083</td>\n",
       "      <td>5936</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>West Bengal</td>\n",
       "      <td>91276115</td>\n",
       "      <td>62183113</td>\n",
       "      <td>29093002</td>\n",
       "      <td>2576</td>\n",
       "      <td>892</td>\n",
       "      <td>232</td>\n",
       "      <td>1452</td>\n",
       "      <td>51163</td>\n",
       "      <td>19684</td>\n",
       "      <td>58882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Madhya Pradesh</td>\n",
       "      <td>72626809</td>\n",
       "      <td>52557404</td>\n",
       "      <td>20069405</td>\n",
       "      <td>4595</td>\n",
       "      <td>2283</td>\n",
       "      <td>239</td>\n",
       "      <td>2073</td>\n",
       "      <td>38140</td>\n",
       "      <td>10020</td>\n",
       "      <td>18819</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            State  Population      Rural     Urban  Confirmed  Recovered  \\\n",
       "0   Uttar Pradesh   199812341  155317278  44495063       4057       2165   \n",
       "1     Maharashtra   112374333   61556074  50818259      29100       6564   \n",
       "2           Bihar   104099452   92341436  11758016       1145        440   \n",
       "3     West Bengal    91276115   62183113  29093002       2576        892   \n",
       "4  Madhya Pradesh    72626809   52557404  20069405       4595       2283   \n",
       "\n",
       "   Deaths  Active  NumPublicBeds  NumRuralBeds  NumUrbanBeds  \n",
       "0      95    1797          58310         39104         37156  \n",
       "1    1068   21468          68998         12398         39048  \n",
       "2       7     698          17796          6083          5936  \n",
       "3     232    1452          51163         19684         58882  \n",
       "4     239    2073          38140         10020         18819  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Merging Data\n",
    "census_covid = pd.merge(censusData, covidData, how=\"inner\", on=\"State\") \n",
    "beds_census_covid = pd.merge(census_covid, bedsData, how=\"inner\", on=\"State\")\n",
    "data = beds_census_covid\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Calculating ratio between Number of beds to Population**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>State</th>\n",
       "      <th>Population</th>\n",
       "      <th>Rural</th>\n",
       "      <th>Urban</th>\n",
       "      <th>Confirmed</th>\n",
       "      <th>Recovered</th>\n",
       "      <th>Deaths</th>\n",
       "      <th>Active</th>\n",
       "      <th>NumPublicBeds</th>\n",
       "      <th>NumRuralBeds</th>\n",
       "      <th>NumUrbanBeds</th>\n",
       "      <th>TotalBeds</th>\n",
       "      <th>TotalBeds_Pop_Ratio</th>\n",
       "      <th>RemainingBeds</th>\n",
       "      <th>RemainingBeds_Pop_Ratio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Uttar Pradesh</td>\n",
       "      <td>199.812341</td>\n",
       "      <td>155317278</td>\n",
       "      <td>44495063</td>\n",
       "      <td>4057</td>\n",
       "      <td>2165</td>\n",
       "      <td>95</td>\n",
       "      <td>1797</td>\n",
       "      <td>58310</td>\n",
       "      <td>39104</td>\n",
       "      <td>37156</td>\n",
       "      <td>134570</td>\n",
       "      <td>0.000673</td>\n",
       "      <td>132.773</td>\n",
       "      <td>0.000664</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Maharashtra</td>\n",
       "      <td>112.374333</td>\n",
       "      <td>61556074</td>\n",
       "      <td>50818259</td>\n",
       "      <td>29100</td>\n",
       "      <td>6564</td>\n",
       "      <td>1068</td>\n",
       "      <td>21468</td>\n",
       "      <td>68998</td>\n",
       "      <td>12398</td>\n",
       "      <td>39048</td>\n",
       "      <td>120444</td>\n",
       "      <td>0.001072</td>\n",
       "      <td>98.976</td>\n",
       "      <td>0.000881</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Bihar</td>\n",
       "      <td>104.099452</td>\n",
       "      <td>92341436</td>\n",
       "      <td>11758016</td>\n",
       "      <td>1145</td>\n",
       "      <td>440</td>\n",
       "      <td>7</td>\n",
       "      <td>698</td>\n",
       "      <td>17796</td>\n",
       "      <td>6083</td>\n",
       "      <td>5936</td>\n",
       "      <td>29815</td>\n",
       "      <td>0.000286</td>\n",
       "      <td>29.117</td>\n",
       "      <td>0.000280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>West Bengal</td>\n",
       "      <td>91.276115</td>\n",
       "      <td>62183113</td>\n",
       "      <td>29093002</td>\n",
       "      <td>2576</td>\n",
       "      <td>892</td>\n",
       "      <td>232</td>\n",
       "      <td>1452</td>\n",
       "      <td>51163</td>\n",
       "      <td>19684</td>\n",
       "      <td>58882</td>\n",
       "      <td>129729</td>\n",
       "      <td>0.001421</td>\n",
       "      <td>128.277</td>\n",
       "      <td>0.001405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Madhya Pradesh</td>\n",
       "      <td>72.626809</td>\n",
       "      <td>52557404</td>\n",
       "      <td>20069405</td>\n",
       "      <td>4595</td>\n",
       "      <td>2283</td>\n",
       "      <td>239</td>\n",
       "      <td>2073</td>\n",
       "      <td>38140</td>\n",
       "      <td>10020</td>\n",
       "      <td>18819</td>\n",
       "      <td>66979</td>\n",
       "      <td>0.000922</td>\n",
       "      <td>64.906</td>\n",
       "      <td>0.000894</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            State  Population      Rural     Urban  Confirmed  Recovered  \\\n",
       "0   Uttar Pradesh  199.812341  155317278  44495063       4057       2165   \n",
       "1     Maharashtra  112.374333   61556074  50818259      29100       6564   \n",
       "2           Bihar  104.099452   92341436  11758016       1145        440   \n",
       "3     West Bengal   91.276115   62183113  29093002       2576        892   \n",
       "4  Madhya Pradesh   72.626809   52557404  20069405       4595       2283   \n",
       "\n",
       "   Deaths  Active  NumPublicBeds  NumRuralBeds  NumUrbanBeds  TotalBeds  \\\n",
       "0      95    1797          58310         39104         37156     134570   \n",
       "1    1068   21468          68998         12398         39048     120444   \n",
       "2       7     698          17796          6083          5936      29815   \n",
       "3     232    1452          51163         19684         58882     129729   \n",
       "4     239    2073          38140         10020         18819      66979   \n",
       "\n",
       "   TotalBeds_Pop_Ratio  RemainingBeds  RemainingBeds_Pop_Ratio  \n",
       "0             0.000673        132.773                 0.000664  \n",
       "1             0.001072         98.976                 0.000881  \n",
       "2             0.000286         29.117                 0.000280  \n",
       "3             0.001421        128.277                 0.001405  \n",
       "4             0.000922         64.906                 0.000894  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# # For Calculation based on Urban / Rural Data. Here In this case we don't do for now\n",
    "# data[\"RuralBeds_Pop_ratio\"] = data[\"NumRuralBeds\"] / data[\"Rural\"]\n",
    "# data[\"UrbanBeds_Pop_ratio\"] = data[\"NumUrbanBeds\"] / data[\"Urban\"]\n",
    "# State wise data\n",
    "data[\"TotalBeds\"] = data[\"NumPublicBeds\"] + data[\"NumRuralBeds\"] + data[\"NumUrbanBeds\"]\n",
    "data[\"TotalBeds_Pop_Ratio\"] = data[\"TotalBeds\"] / data[\"Population\"]\n",
    "data[\"RemainingBeds\"] = data[\"TotalBeds\"] - data[\"Active\"]\n",
    "# For Scatter Plot\n",
    "data[\"RemainingBeds_Pop_Ratio\"] = data[\"RemainingBeds\"] / data[\"Population\"]\n",
    "\n",
    "# For better scale in scatter plot\n",
    "data[\"Population\"] = data[\"Population\"]/1000000 # In Million\n",
    "data[\"RemainingBeds\"] = data[\"RemainingBeds\"] / 1000 # In 1000\n",
    "\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib notebook"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotting Scatter Plot\n",
    "\n",
    "### Interactivity is included. Click on plots to know details of a state"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support.' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            if (mpl.ratio != 1) {\n",
       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
       "            }\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        fig.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option)\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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xx+HWq1G48aN5QfmjSlrEkhpplzpLCsAlhx4Af0QV5UAePv2bdSrVw8BAQFYtGgRfv31V+zcuVO+6lve82rlBUBDa7SZMwA+9dRTaNiwocHf886dO3WuYBoKLRXpK8Zm/ZfuC1UJgADQqFEjk69oLV++HEKIMieKffvttxBC9xlAAPjpp5/kPpefn4+6devi6aef1vv5qjwDuGTJEpPeB1B+AASK/+fyzJkz2LdvH9LT05GTkwO1Wo3BgwebfB6q2RgAySzMEQAregtYyQBYUlxcHFxcXDB8+HC9epKTk+Hv74+AgABcvXq1zDqM+eGHH3QGobS0NIPBSHvLtOSVPm19pfefN2+ewfChvdVWkRmIWgcOHIAQZS+UC1SvAFiVW8Da5zVLX+nTTi4oL6wY+x0YuwV8/fp1s94Cfvvtt2FnZ2fwOcbSDIWWivQVUwNgnz590KhRo0rdAgbuLety8ODBcmu6fPky7OzsdG5Vl9arVy+dWcBaOTk5cHd3R2hoKDZt2gQh9J+lBKo2CzguLq7c96BlSgAsbevWrRBCYPny5Sb/DNVsDIBkFuYIgP/3f/9XoUkgSgTAu3fvyrd9tDQaDXx8fHSuzJSsJz4+Hr6+vmjdurXOciyG3Lp1S2/w087W0z5flJOTAyEExo8fr7Of9qpV6Vumb7/9tl6Y0c4kLn3768KFC7Czs8OwYcP06pAkSa4/MzNT7wrVnTt3oFary50BW50CoPZ2W2UmgWzZsgVCCOzZs0feJz8/Hx06dDBpMDb2O9BOAnnjjTd0tk+cOBFCmG8SyJ49eyCEwAcffKB3jMLCQp1ZvIZCi6l9BTA9AFZlEoi2JldXV7Ru3Vrv9r/29ZLPwmpnhkdFRentq/39jBkzxuC5XnnlFTg6OuKpp56Cq6urzoLRWpVdB/Cll14q832WVtEAmJOTg06dOsHPzw937typ0Lmo5mIAJLMwRwC8fPkyvLy8UL9+fZ1lYLQDbMnlRpQKgNHR0fD29sabb76Jzz//HFFRUXjyySchhMC6deuM1nPq1Cl4e3ujc+fOZa7yv3DhQgQFBWHixIlYvnw55s2bh5YtW8LDw0PnNnLr1q3h6+uLJUuWYM2aNTh9+jQAoHv37nBxccHkyZMRFRWFQYMGycvmlAwz2itcTz/9NL7//nusWbNGHsC0g1C3bt3w2WefYenSpZg4cSKCgoLkz2jjxo1o3LgxwsPDERUVhc8//xxdunRBnTp1jC6Sq1WdAiAAeTmW4cOHY8mSJSYvA5OWloa6devC398f8+fPx4IFC9CxY0f58y5vMC7rd6BdBmbIkCFYsmSJ/HdTloHZvn27zjIwU6ZMMboMzJgxYyCEQL9+/bBw4UIsXrwY48ePR6NGjXQ+T2OhxZS+ApgeALVhz8nJCZMmTarwMjBA8e1i7ZqE48ePx4oVK7BkyRIMHz4cDg4OOsE6KysLjz32mPzMY1RUFKKiojBw4EAIof9NICVpQ7W27xhiyjeBrFixAlOmTJHfY8+ePU0OZdOnT8f06dPx0ksvQYjiWe/abSUNHjwY48ePx/LlyzF37ly0atUKjo6O2LVrl0nnodqBAZDMwlwLQUdHR8trkN1333349NNP8fnnn0MI3Qf8lQqAaWlpGDt2LIKDg+Hq6gpPT088/PDDOlcsjNVz5MgRuLu7o3v37kZvu504cQJDhw5F06ZN5YV5+/fvj+PHj+vsd/DgQXTu3BkODg46QeXKlSsIDQ2Fl5cXPD09MXjwYPm2YOkgNX36dDRu3BhqtVovFK1fvx6PPfYYXF1d4erqiuDgYIwdO1a+3ZeQkIBXX30V999/P5ycnODt7Y2ePXuaNKBUtwCYm5uLcePGoV69enB1da3QQtAHDhzAI488Ii9sPHHiRPl7c00JK8Z+B4WFhZg6dSoCAgJQp04dNGnSpEILQa9fv14e5Fu3bm10IWig+JZ1586d4ezsDHd3dzzwwAOYOHGizjdplHXVqry+ApgeAAHg0qVLGDBgAFxcXFC/fn2MHz/e5IWgteLi4vD666+jWbNmcHBwgLu7Ox599FF88cUXep9hfn4+Fi5ciM6dO8PV1RUuLi7o1KkTFi1aVOYalEVFRfDz84MQumsZlmTKdwG7uLigWbNmFf4uYMDwIt3aVtKcOXMQHBwsB+MBAwaUOfmFaicGQKr2xo8fDycnpwp/Py8REREZxgBI1UrpK2VpaWnw9vYu88FtIiIiqhgGQKpW2rdvj/Hjx2PZsmWYOnUqmjZtCnt7+3LXWCMiIiLTMQBStfLBBx8gKCgIzs7OcHFxwWOPPYadO3dauywiIqIahQGQiIiIqJZhACQiIiKqZRgAiYiIiGoZBkAiIiKiWoYBsAo0Gg2Sk5ORkZGBzMxMNjY2NjY2NhtoGRkZSE5OrtBC3DUNA2AVaL89gI2NjY2Njc32WnJysrWjhNUwAFZBRkaG3IGs/X8zbGxsbGxsbKY17QWcjIwMa0cJq2EArILMzEwIIZCZmWntUoiIiMhEHL8ZAKuEHYiIiMj2cPxmAKwSdiAiIiLbw/GbAbBK2IGIiIhsD8dvBQNgQkICvvvuO0ybNg3//e9/MX/+fPz555/Izc1VqgSzYweiGk2TDeTFArlngIJkQKq9yyUQUc3C8VuBALh69Wp06dIFKpUKvr6+6NSpEx599FG0atUKDg4O8PDwwFtvvYWkpCRLl2J27EBUI0lFQPYh4PYP/7Yfi//MWA8UXLN2dUREVcbx28IBsEOHDnjooYewZMkSXL58We/1vLw87N69G2PGjEH9+vXxyy+/WLIcs2MHohope3+J8Fe6/QgUplq7QiKiKuH4beEA+Pvvv5u8b1paGo4fP27BasyPHYhqnKLbZYS/fwNg1m5rV0lEVCUcvzkJpErYgajGyYm5d8u3rCblW7tSIqJK4/itYABMSUnBpk2bsGzZMixbtgybNm1CSkqKUqe3CHYgqnHuHjUtAGqyrV0pEVGlcfxWIABmZ2dj+PDhsLOzg729PRo2bIiGDRvC3t4ednZ2GDFiBO7evVuhY+7duxf9+/eHn58fhBDYuHGj0X3HjBkDIQQWLlyosz09PR3Dhg2Du7s7PD098eqrryIrK6tCdbADUY2Te6788Hf7p+KJIkRENorjtwIBcPTo0QgKCsLvv/+OoqJ7g0ZRURF27NiBFi1a4LXXXqvQMbdt24bJkydjw4YNZQbADRs2oH379mjUqJFeAOzbty/at2+Pw4cPY9++fWjevDmGDh1aoTrYgajG0eSWcwXwR+DuEWtXSURUJRy/FQiAXl5eOHDggNHX9+/fDy8vr0of31gAvHLlCho3bowzZ87A399fJwCeO3cOQggcO3ZM3rZ9+3aoVCpcvXrV5HOzA1GNlHveePjL2ABocqxdIRFRlXD8ViAAenh46ASt0o4ePQoPD49KH99QANRoNOjZsycWLVoEAHoB8Ouvv9YLnYWFhbCzs8OGDRtMPjc7ENVY+QlA5mbd8Je9D9BU7HENIqLqiOO3AgFw2LBh6NixI06cOKH32okTJ9C5c2cMHz680sc3FABnzZqFJ598EpIkAdAPgDNnzkSLFi30jtWgQQNERUUZPVdeXh4yMzPllpycXOs7ENVgklS8LExhavGtYSKiGoIBUIEAeOvWLfTt2xcqlQre3t4IDg5GcHAwvL29oVar0a9fP9y+fbvSxy8dAI8fPw4fHx+dW7nmCoAREREQQui12tyBiIiIbA0DoILLwJw7dw4rV67ErFmzMGvWLKxcuRLnz5+v8nFLB8CFCxdCpVLBzs5ObkIIqNVq+Pv7A6j8LWBeASQiIrJ9thoAExMTIYRAdHR0lY9l8wtBlw6AaWlpOH36tE5r1KgRJk2ahNjYWAD3JoGU/OaRHTt2cBIIERFRNRMSEoLx48frbf/mm2/g6ekp/z0sLAwDBw7U2cdYYDLn+B0SEiLfEXR0dESrVq2wZMmSKh/XEJsLgPn5+fj5558RHh6Ol156CS+99BLCw8Pxyy+/ID+/4t8okJWVhejoaERHR0MIgQULFiA6OhqXLl0yuH/pW8BA8TIwHTt2xJEjR7B//34EBQVxGRgiIqJqproEwIKCAqP1vf7660hJScHFixflx8V+/PFHg/tXJvdo2VQAjI+PR2BgIJycnBASEoIhQ4ZgyJAhCAkJgZOTE5o3b474+PgKHXP37t0Gn8ULCwszuL+hAJieno6hQ4fCzc0NHh4eGDVqFBeCJiIiqmZMCYCGntE3lBVCQkIAAH/++SeEEPD29oaHhwe6d++Ov//+W+f4QghERUXh2WefhYuLCyIiIkyuLygoCC+99JJcW/v27bFixQo0a9YMKpUKQPHyc48++ig8PT3h7e2NZ555BhcuXNA5zpEjR9ChQwc4Ojqic+fO8vrHJQPg6dOn0bdvX7i6uqJhw4YYMWIEUlNT5dfXrl2Ltm3bwsnJCd7e3ujduzeys7MtHwCfeOIJDBw40GBIyszMxMCBA9GnTx9Ll2ERDIBERESWZUoAzMrKwpAhQ9C3b1+kpKQgJSUF+fn5OHr0KIQQ2LVrF1JSUpCeng4A2LJli7we8Llz5zB69Gj4+Pjgzp078vGFEGjYsCFWrlyJixcvGr3LaKi+du3a4bnnngNQHABdXV3Rt29fnDhxAjExMQCAdevWYf369YiPj0d0dDSeffZZPPDAA9BoNPJ7atCgAYYNG4YzZ87g119/RWBgoE4AvH37Nho0aIAPPvgA58+fx4kTJ/Dkk0+iZ8+eAIBr167B3t4eCxYsQGJiIk6dOoUlS5YgKyvL8gHQ2dkZp0+fNvr6qVOn4OzsbOkyLIIBkIiIyPwkqQjSzdOQkvch5NEuGDdunN4+5rwFrNFo4O7ujl9//VXeRwiB8PDwcmstGQCLioqwatUqCCGwePFiAMUBsE6dOrh582aZx0lNTYUQQs5My5cvR7169ZCbe28ZrqVLl+q8n+nTp+tdRNNOUP3nn3/w999/QwiBpKQkvfNZPAD6+fnpfKClbdmyBX5+fpYuwyIYAImIiMxLurwH0m8vQ1rbD9Lafghp7Y1xg9pCuq4b4qoSAOPj4yGEQGBgIDw8PODq6gqVSqUzeUMIgdWrV5dbb0hICOrUqQNXV1fUqVMHTk5OmDBhgnwlLyIiAs2bN9f7ubi4OLz00ksICAiAu7s7XF1dIYTA1q1bAQDh4eHylTytkydP6ryfF154QT53ySaEwLZt21BUVITevXvD3d0dL7zwAr788kvcunWr+P2V+86q6KOPPkLdunWxYMECxMTE4Pr167h+/TpiYmKwYMECeHt7G72vXt0xABIREZmPdOlPOfhp27OdG2Jkj8aQ1j0D6ca9ILdw4UI0bdpU/ntFAmDv3r0hhMDatWtx5swZxMfHo379+jrzBYx91WxpISEhGDlyJOLj43HlyhU5+GlpnwEsrWXLlujTpw927dqFc+fO4cyZMzrnNCUA9u3bF8899xzi4+P1WnZ2dvFnKknYv38/Pv74YzzwwANo0KABEhISlJkFPHv2bPj5+UGlUkGtVkOtVkOlUsHPzw9z5sxRogSLYAAkIiIyD0lTAGnzEL0A+N6zAWjn7w5p7dOQdrwpf8vXyy+/jCeeeEL++ddffx39+/fXOebVq1f1ln0DADc3N53x+/LlyxBCVDoAGnpGUctQAExLS4MQAn/99Ze8bd++fTrnNHQLeNmyZToB8MMPP0TLli1RWFhYbp1A8S3qxo0bY/78+cquA3jx4kUcPHgQBw8eREJCgpKntggGQCIiIvOQrh7WC3/S2n64sDgETnXU+L9+/jg571GcP7oT8+fPh729PbZv3y7//MyZM9G0aVPExsYiNTUVBQUFKCwshLOzM2bMmIHr168jIyMDQPEkDSEEjh49isOHD+Pxxx+Hs7OzYgFQo9GgXr16GDFiBOLj4/G///0PXbp00TlnVlYW6tevjxEjRuDs2bPYunUrmjdvrhMAr169igYNGuCFF17A0aNHceHCBfz+++8YOSXEBtMAACAASURBVHIkioqKcPjwYcycORPHjh3DpUuX8Msvv8DBwQHbtm2z/YWgrYkBkIiIyDykC78ZDIDS2n448mlXPNmuHhp4OMDTww0PP/ywXji7efMmnnzySfnq3u7duwEAK1asQJMmTaBWq+VlYP766y8IIeDk5ISgoCCsXbtWb8k4SwZAANi5cydatWoFR0dHtGvXDnv27NE756FDh9C+fXs4ODigQ4cOWL9+vd4t7bi4OISGhsLLywvOzs4IDg5GeHg4JEnCuXPn8NRTT6FBgwZwdHREixYt8MUXXxS/v3LfmRmcPXsWb731Fjp06ABfX1/4+vqiQ4cOeOutt3D27FklSrAIBkAiIiLzkK4cMBoAddqtiq0dbAjHbwUC4LZt2+Dg4IBHHnkEERERiIqKQlRUFCIiItCtWzc4Ojri999/t3QZFsEOREREZB5SUT6kTS+UEf6ehvT76/IzgFXB8VuBANiuXTt89NFHRl+PiIjAAw88YOkyLIIdiKga0OQBWceBG98DKcuAm6uB7JOAZPhrm4io+pIubjce/tY+DenaUbOch+O3AgHQyckJsbGxRl+PjY2Fk5OTpcuwCHYgIisrugPcWAlci9RvN38sDodEZFOkhB2QNr+oGwB/exnS1cNmOwfHbwUCYHBwMObPn2/09fnz56Nly5aWLsMi2IGIrCxtHXDtc8MB8NrnwK0d1q6QiCpB0hRAunYUUuJOSDeiIUlFZj0+x28FAuAvv/wCe3t7PPvss4iMjMRPP/2En376CZGRkRgwYAAcHBywbt06S5dhEexAZFWSpvg2pxmeh7FJBWlGgl/J9gVQdNfalRJRNcPxW6FZwAcOHMCLL76Ipk2bwsHBAQ4ODmjatClefPFFHDx4UIkSLIIdiKyi6BaQvR/I+AnIWANkbgByY2rfM293T5sQACOB3ERrV0pE1QzHb4UCYE3FDkSKK0wBMn6+F/7k9hNwZxsg5Vu7QuXcPWtaAMy7ZO1Kiaia4fjNAFgl7ECkKKkIyFxfKviVCoE5x8s/Tk1RmGlCAFzMiSBEpIfjdzUIgOfOnUNAQIC1y6gUdiBSVH5SGeFP234BJNO+E7JGSP+tjEkgkUDmXmtXSETVEMfvahAAT548CbVabe0yKoUdiBSVe9LArV8DrSjD2pUqR5MHpP50b9ZvyT/TN9WuMExEJuP4rUAAnDBhQpltxIgRDIBEpsg9bVoA1GRZu1JlSUVAzj9A+ubitf/SfwNyE2rv7GgiKhfHbwUCoFqtRqdOndCjRw+D7cEHH2QAJDJF0a3yw9+drQw+RETl4PitQABs0aIFVq1aZfT16OhoBkAiU2XvLvsqYH6StSskIqr2OH4rEACHDRuG8PBwo6+fPHkSKpXK0mVYBDsQKU7KB7J23Zv1K7c1QN55a1dHRGQTOH4rEABTUlKQlFQzr0qwA5FVSBJQeAPIOQbcPVj8bKAm29pVERHZDI7f1WAWcGXs3bsX/fv3h5+fH4QQ2Lhxo/xaQUEBJk6ciLZt28LFxQV+fn54+eWXcfXqVZ1jpKenY9iwYXB3d4enpydeffVVZGVV7OF5diAiIiLbw/FbwQD4559/Gn1t2bJlFTrWtm3bMHnyZGzYsEEvAGZkZOCJJ57Azz//jNjYWBw6dAgPPfQQOnfurHOMvn37on379jh8+DD27duH5s2bY+jQoRWqgx2IiIjI9nD8VjAAOjg44P3330dBwb3vK01NTUX//v3h5eVV6eOWDoCGHD16FEIIXLpU/JVQ586dgxACx44dk/fZvn07VCqV3pXCsrADERER2R6O3woGwAMHDuD+++9H+/btcfbsWfz222/w8fFB9+7dq/SMoCkBcOfOnVCpVPIv+uuvv9YLnYWFhbCzs8OGDRtMPjc7EBERke3h+K3wM4BZWVkYPnw4HB0dUadOHcyePRtSFdcsKy8A5ubmolOnThg2bJi8bebMmWjRooXevg0aNEBUVJTRY+Xl5SEzM1NuycnJtb4DERER2RoGQIUD4N9//42WLVvi/vvvh7OzM0aNGoXs7KrNXiwrABYUFODZZ59Fx44ddX7JlQ2AEREREELotdrcgYiIiGwNA6CCAfDTTz+Fg4MD/u///g+5ubk4ffo0OnTogMDAQBw8eLDSxzUWAAsKCjBo0CC0a9cOaWlpOq9V9hYwrwASERHZPgZABQOgr68vtm3bprOtoKAA77//PhwcHCp9XEMBUBv+2rRpg5s3b+r9jHYSyPHjx+VtO3bs4CQQIiKiWoDjt4IBMDU11ehre/bsqdCxsrKyEB0djejoaAghsGDBAkRHR+PSpUsoKCjAgAEDcN999+HkyZNISUmRW35+vnyMvn37omPHjjhy5Aj279+PoKAgLgNDRERUC3D8ttGFoHfv3m3wWbywsDAkJiYafE0Igd27d8vHSE9Px9ChQ+Hm5gYPDw+MGjWKC0ETERHVAhy/LRwAx4wZg+TkZJP2/emnn7B69WpLlmN27EBERES2h+O3hQPglClT4OHhgX79+iEqKgpHjx7FlStXkJaWhvj4eGzevBn/+c9/0KRJEzz88MOIiYmxZDlmxw5ERERkezh+K3AL+Pr165gxYwbatm0LtVqt0zw9PfH8889j+/btli7DItiBiIiIbA/Hb4WfAbx16xZOnjyJQ4cOIT4+vsqLQFsbOxAREZHt4fhto5NAqgt2ICIiItvD8ZsBsErYgYiIiGwPx28GwCphByIiIrI9HL8ZAKuEHYiIiMj2cPxmAKwSdiAiIiLbw/FbwQCYk5ODu3fvyn9PSkrCwoULsWPHDqVKMDt2ICIiItvD8VvBAPjkk09i6dKlAIDbt2/Dx8cH9913H5ycnBAVFaVUGWbFDkRERGR7OH4rGADr1auHM2fOAABWrFiBdu3aQaPR4JdffkFwcLBSZZgVOxAREZHt4fitYAB0dnbGpUuXAACDBw/GJ598AgC4fPkynJ2dlSrDrNiBiIiIbA/HbwUD4AMPPIDIyEhcvnwZHh4eOHjwIADg+PHj8PHxUaoMs2IHIiIisj0cvxUMgGvXrkWdOnWgVqvx5JNPyttnzZqFvn37KlWGWbEDERER2R6O3wovA5OSkoITJ05Ao9HI244cOYLz588rWYbZsAMRERHZHo7fXAewStiBiIiIbA/HbwsHwNDQUJObLWIHIiIisj0cvy0cAEeOHCm3sLAweHh4oEmTJnLoa9q0KTw8PDBy5EhLlmEx7EBERES2h+O3greAJ06ciNdeew1FRUXytqKiIrzxxht4//33lSrDrNiBiIiIbA/HbwUDYP369REbG6u3PTY2Ft7e3kqVYVbsQERERLaH47eCAdDLywubNm3S275p0yZ4eXkpVYZZsQMRERHZHo7fCgbACRMmoF69epg/fz727duHffv2Yd68eahfvz4mTJigVBlmxQ5ERERkezh+KxgANRoN5syZg0aNGkGlUkGlUqFRo0aYM2eOznOBpti7dy/69+8PPz8/CCGwceNGndclScJHH30EX19fODk5oXfv3oiLi9PZJz09HcOGDYO7uzs8PT3x6quvIisrq0J1sAMRERHZHo7fVloHMDMzs0of+rZt2zB58mRs2LDBYACcPXs2PD09sWnTJsTExGDAgAEICAhAbm6uvE/fvn3Rvn17HD58GPv27UPz5s0xdOjQCr+P2t6BiKhqJEkD6fZJSJd+gpT0I6S0w5A0BdYui6hG4/itcAAsLCzEzp07sWzZMty5cwcAcPXq1QpfeSupdACUJAm+vr6YO3euvC0jIwOOjo5Ys2YNAODcuXMQQuDYsWPyPtu3b4dKpcLVq1dNPjc7EBFVhZR7HdKpKZCOjYF0/K3idmwMpOh3IWVdsHZ5RDUWx28FA2BSUhKCg4Ph4uICOzs7XLx4EQAwbtw4jBkzptLHLR0AL168CCEEoqOjdfbr3r07xo0bBwD4+uuv9SaeFBYWws7ODhs2bDD53OxARFRZUlEupJOTIB37N/TptDch/f0OpLxUa5dJVCNx/FYwAA4cOBAjRoxAfn4+3Nzc5AC4e/duNG/evNLHLR0ADxw4ACEErl27prPf4MGDMWTIEADAzJkz0aJFC71jNWjQAFFRUUbPlZeXJ9++zszMRHJycq3vQERUOdKNPQaCX8n2FqTLa61dJlGNxACoYAD09vaW1wEsGQATExPh7Oxc6eMqGQAjIiIghNBrtbkDEVHlSLELygmAYyCdnGTtMolqJAZAhdcBPHv2LADdALhv3z40bNiw0sdV8hYwrwASkblIZ2eVHwBP2OYSWUTVHQOgggFwyJAheP311wEUB8CEhARkZWWhV69eVfouYGOTQObNmydvy8zMNDgJ5Pjx4/I+O3bs4CQQIlKMlPCdkef/SjwHeP4za5dJVCNx/FYwACYnJ6N169Zo1aoV7O3t8cgjj6BevXpo2bIlbty4UaFjZWVlITo6GtHR0RBCYMGCBYiOjsalS5cAFC8D4+Xlhc2bN+PUqVMYOHCgwWVgOnbsiCNHjmD//v0ICgriMjBEpBgpO6n8K4DpR61dJlGNxPHbCsvArFq1Cv/5z3/w1ltvYcWKFcjJyanwcXbv3m3wWbywsDAA9xaC9vHxgaOjI3r37o1//vlH5xjp6ekYOnQo3Nzc4OHhgVGjRnEhaCJSlJS88d7VvtLh78KXkCSNtUskqpE4fltpIeiagh2IiKpCkqTihZ9PT70X/GI+gHR9F8MfkQVx/FY4AMbGxmLs2LHo1asXevXqhbFjx+L8+fNKlmBW7EBEZA6SJEEqzIJUkMngR6QAjt8KBsB169bJz/5NmDABEyZMQNeuXWFvb49169YpVYZZsQMRERHZHo7fCgbAwMBAfPTRR3rbP/74YwQGBipVhlmxAxEREdkejt8KBkBnZ2fEx8frbY+Li6vSQtDWxA5ERERkezh+KxgA+/Xrh5UrV+ptX7lyJfr06aNUGWbFDkRERGR7OH5bOABu3rxZbkuXLkWDBg0wduxYrFq1CqtWrcLYsWPRsGFDLF261JJlWAw7EBERke3h+G3hAKhSqUxqarXakmVYDDsQERGR7eH4zXUAq4QdiIiIyPZw/GYArBJ2ICIiItvD8ZsBsErYgYiIiGwPx28GwCphByIiIrI9HL8ZAKuEHYiIiMj2cPy2cACcMGECsrOzAQB79+5FYWGhJU+nOHYgIiIi28Px28IB0N7eHtevXwcAqNVq3Lhxw5KnUxw7EBERke3h+G3hANi8eXN8+OGH2LNnD1QqFTZt2oS9e/cabLaIHYiIiMj2cPy2cADcuHEjfHx85MWeuRA0ERERWRvHb4UmgWRlZUGlUiEuLg4ZGRkGmy1iByI9kgQU3gIKrgOau9auhoiIDOD4reAs4D179nASCNVseQnArbVA6lf3WuYfQBH7BxFRdcLxW+FlYIqKirBu3TpMnz4d06dPx/r161FUVKRkCWbFDkSy3Fjd4Ce3r4G0VUDRHWtXaJAkSZAkjbXLICJSFMdvBQNgfHw8WrRoARcXF3Ts2BEdO3aEi4sLWrZsiQsXLihVhlmxAxEAQJMPpH5jJAD+GwIz/7R2lTqk3EuQbvwMKWkWpKQZkK5+CSkrGpIkWbs0IiKL4/itYADs168f+vbti/T0dHlbWloa+vbti6efflqpMsyKHYgAALnnywh/JUKgJs/alQIApKyTxaEvaea/f5ZoNzcwBBJRjcfxW8EA6OLiglOnTultP3nyJFxdXZUqw6zYgQgAkH0USF1ZfggsvGXtSiEVZhoOfiVb1klrl0lEZFEcvxUMgHXr1sWBAwf0tu/fvx9169Y1+/mKioowZcoUNGvWDE5OTggMDMS0adN0rm5IkoSPPvoIvr6+cHJyQu/evREXF2fyOdiBCABwN6b4Cl95AbAazAqWbu8pJwDOhHR1hbXLJCKyKI7fCgbAl19+GW3atMHhw4f/ffBcwqFDh9C2bVuEhYWZ/XwzZ85EvXr18NtvvyExMRFr166Fm5sbIiMj5X1mz54NT09PbNq0CTExMRgwYAACAgKQm5tr0jnYgQhA8QSP8m7/3v7V2lUCwL/P/ZVx9U8bAnkbmIhqMI7fCgbA27dvY8CAAVCpVHBwcICDgwPUajUGDRpkkXUAn3nmGbz66qs625577jkMHz4cQPHVP19fX8ydO1d+PSMjA46OjlizZo1J52AHItmd/WUHwIJr1q4QACDdXFf+LeCkT61dJhGRRXH8VngZGKB4NvCWLVuwZcsWxMfHW+w8M2fOhL+/P/755x8Axc8aNmzYEKtXrwYAXLx4EUIIREdH6/xc9+7dMW7cOJPOwQ5UDUgSIBUAkpWXE5I0QNbBEreC//0zbRWQd8m6tZVwbwKIsTYL0s311i6TiMiiOH5bIQAqRaPRYNKkSVCpVLC3t4dKpcKsWbPk1w8cOAAhBK5d070yM3jwYAwZMsTgMfPy8pCZmSm35OTkWt+BrEbSAPmxQNZW4M664nZ3L1B43bp1ae4COeeAuyeBvETrB9NSJE0BpOTIMq4CzoSUd8XaZRIRWRQDYA0OgGvWrMF9992HNWvW4NSpU/j+++/h7e2Nb7/9FkDlAmBERASEEHqtNncgq5A0xWFPG/zktr74z/wEa1dYrUkFqf+GwJJLwcwsvvqXfcba5RERWRwDYA0OgPfddx8WL16ss2369Olo2bIlgMrdAuYVwGoiP85A+CsVBDU51q6yWpOkQkhZp4rX/buxFlLGfkhFWdYui4hIEQyANTgAent7IyoqSmfbrFmzEBQUBODeJJB58+bJr2dmZnISiC3I2l5OAFwH5J2zdpVERFRNcfxWKAAWFhZi6tSpSE5OVuJ0AICwsDA0btxYXgZmw4YNqF+/PiZOnCjvM3v2bHh5eWHz5s04deoUBg4cyGVgqjtJU374u7MOyDls7UqJiKia4vit4BVANzc3JCYmKnU63LlzB+PHj0fTpk3lhaAnT56M/Px8eR/tQtA+Pj5wdHRE79695VnDpmAHsgJJuvesX1m3gHOOWbtSIiKqpjh+KxgABwwYIE/AqCnYgazk7sHyQ2ABZ7ISEZFhHL8VDIBLly6Fr68v3nvvPfz444/YvHmzTrNF7EBWUpRWRgBcD2TtKL5VTEREZADHbwUDoEqlMtrUarVSZZgVO5AVFSQDdzaUWP7l30CY/Ue1+M5dIiKqvjh+1+BZwEpgB7IyKb94SZicY0Du30DhteJnBImIiMrA8dtKAdDUWbbVHTsQERGR7eH4rWAALCoqwrRp09CoUSPY2dnh4sWLAIApU6bgq6++UqoMs2IHIiIisj0cvxUMgFOnTkVgYCBWr14NZ2dnOQD+9NNPeOSRR5Qqw6zYgYiIiGwPx28FA+D999+PXbt2ASheE1AbAM+fPw8vLy+lyjArdiAiIiLbw/FbwQDo5OSEpKQkALoB8OzZs3B1dVWqDLNiByIiIrI9HL8VDICdOnXCqlWrAOgGwKlTp+Kxxx5TqgyzYgciIiKyPRy/FQyAmzZtgqenJ2bPng0XFxfMnTsXr732GhwcHPDHH38oVYZZsQOR0iRJgpSTAOn2AUgZhyEVpFu7JCIim8PxW+FlYP766y888cQTaNCgAZydnfHoo49ix44dSpZgVuxApCQpLwXS5c8hXYyAdPGTf/+MgHT9Z0iaPGuXR0RkMzh+cyHoKmEHIqVIBbcgJczSDX5y+wTS1W8hcRFsIiKTcPy2QgA8duwYvv/+e3z//fc4fvy40qc3K3YgUoqU+huki1MNhL8SLeeitcskIrIJHL8VDIDJycl47LHHoFKpULduXdStWxcqlQqPPvookpOTlSrDrNiBSClSwsyyw9/FqZBubjL/eYtyIKUfgXRjN6SMM5AkjdnPQUSkNI7fCgbAp556Cg8//DBiY2PlbbGxsejatSueeuoppcowK3YgUoIkacoJf/+2lB/Ne86rv0L6+x1Ix9+812L+CynjrNnOQ0RkDRy/FV4H8MSJE3rbjx8/DmdnZ6XKMCt2IFKKlDSv/CuAqdvMd74rm3SDn9zegnT8bUh34sx2LiIipXH8VjAABgUF4ciRI3rbjxw5gvvvv1+pMsyKHYiUIt3abWQCSImWf9085yrILA55BgPgvyHw/FyznIuIyBo4fiu8DuBDDz2EY8eOyduOHTuGRx55BBs3blSqDLNiByKlSJrcf5eAMRICU7ea71w39vx7pc9YAPy35XMNQiKyTRy/LRwAvby85AkfdevWhYODA9RqNRwcHHT+uW7dupYsw2LYgUhJUtFdSDc26M4GTvyseFFoMy4BU/zs39jyA2B2ktnOSUSkJI7fFg6A3377rcnNFrEDkTVIRXch5SZByr0CSSoy//FTD5Qf/o6/Cakgw+znJiJSAsdvLgRdJexAVBNJRTn6s3912tuQ4hZbu0wiokrj+G2FAHjjxg2cPn0aMTExOs0WsQNRTSXd3Gs8/J0YDynnqrVLJCKqNI7fCgbA48ePo02bNlCr1VCpVDpNrVZb5JxXrlzB8OHD4e3tDScnJ7Rt21ZnEookSfjoo4/g6+sLJycn9O7dG3Fxpi9vwQ5ENZmUdhhSzAe6ATB2HqScK9YujYioSjh+KxgA27Vrh9DQUBw+fBiJiYlISkrSaeZ269Yt+Pv7Y+TIkThy5AgSEhKwY8cOXLhwQd5n9uzZ8PT0xKZNmxATE4MBAwYgICAAubm5Jp2DHYhqOknSQMpOhJR5FlLuDWuXQ0RkFhy/FQyAbm5uiI+PV+p0mDRpEh577DGjr0uSBF9fX8yde289s4yMDDg6OmLNmjUmnYMdiIiIyPZw/FYwAA4cOBDr1q1T6nRo1aoVwsPD8cILL6BBgwbo0KEDvvzyS/n1ixcvQgiB6OhonZ/r3r07xo0bZ9I52IGIiIhsD8dvBQNgamoqnn76aXzyySdYt24dNm/erNPMzdHREY6Ojvjggw9w4sQJLF++HE5OTvKSMwcOHIAQAteuXdP5ucGDB2PIkCEGj5mXl4fMzEy5JScn1/oOREREZGsYABUMgFu2bIGnp6feBBBLTQKpU6cOunbtqrPtnXfewSOPPAKgcgEwIiICQgi9Vps7EBERka1hAFQwAPr7+2Ps2LG4ft0831danqZNm2L06NE626KiotCoUSMAlbsFzCuAREREto8BUOFJICVn4Fra0KFD9SaBhIeHy1cFtZNA5s2bJ7+emZnJSSBEREQ1HMdvBQPgK6+8ghUrVih1Ohw9ehT29vaYOXMm4uPj8cMPP8DFxQWrV6+W95k9eza8vLywefNmnDp1CgMHDuQyMERERDUcx28FA+CMGTNQv359hIWFYd68eYiMjNRplvDrr7+ibdu2cHR0RHBwsM4sYODeQtA+Pj5wdHRE79698c8//5h8fHYgIiIi28PxW8EA2KxZM6MtICBAqTLMih2IiIjI9nD8tsJ3Adck7EBERES2h+M3A2CVsAMRERHZHo7fCgbAUaNGldlsETsQERGR7eH4rWAAHDRokE575pln4O/vD09PT4SGhipVhlmxAxEREdkejt9WvgWs0WjwxhtvYM6cOdYso9LYgYiIiGwPx+9q8AxgbGwsfH19rV1GpbADERER2R6O39UgAG7duhX169e3dhmVwg5ERERkezh+KxgAJ0yYoNPCw8Px4osvws3NDWPHjlWqDLNiByIiIrI9HL8VDIA9evTQab169cKLL76I5cuXo7CwUKkyzIodiIiIyPZw/K4Gt4BtGTsQERGR7eH4zQBYJexAREREtofjtwIBsEePHujZs2eZrVevXpYuwyLYgYiIiGwPx28FAmB4eLjRNnr0aDg7O0OtVlu6DItgByIiIrI9HL+tdAu4sLAQixYtQoMGDdC8eXOsWbPGGmVUGTsQERGR7eH4bYUAuHr1agQGBsLPzw9Lliyx2RnAADsQERGRLeL4rWAA3L59O9q3bw8PDw9MmzYN2dnZSp3aYtiBiIiIbA/HbwUC4JEjR9CjRw84OTkhPDwcqamplj6lYtiBiIiIbA/HbwUCoEqlgouLC8LDwxEZGWm02SJ2ICIiItvD8VuBAOjv749mzZqV2QICAixdhkWwAxEREdkejt9cCLpK2IGIiIhsD8dvBsAqYQciIiKyPRy/GQCrhB2IiIjI9nD8riUB8NNPP4UQAuPHj5e35ebm4u2334a3tzdcXV3x3HPP4fr16xU6bq3pQFIBkH8BuLsHyN4J5BwGCm8AkmTtyoiIiCqs1ozfZajxAfDo0aNo1qwZ2rVrpxMA33zzTTRp0gT/+9//cPz4cTzyyCPo1q1bhY5dKzqQJgvI3gZkbSjRNhb/mXucIZCIiGxOrRi/y1GjA2BWVhaCgoKwc+dOhISEyAEwIyMDderUwdq1a+V9z58/DyEEDh06ZPLxa3wHkqTiK37awGeo5cdbu0oiIqIKqfHjtwkUC4CZmZkG2507d5Cfn2+Rc77yyisIDw8HAJ0A+L///Q9CCNy+fVtn/6ZNm2LBggVGj5eXl6dTe3Jycs3uQIU3jAc/bcvexquARERkUxgAFQyAKpUKarXaaGvatCk+/vhjaDQas5xvzZo1aNu2LXJzcwHoBsAffvgBDg4Oej/TpUsXTJw40egxIyIiIITQazW2A+WdKfvqn7ZpsqxdKRERkckYABUMgN999x3uu+8+TJkyBVu2bMGWLVswZcoUNGnSBMuXL8eMGTPg5eWFmTNnVvlcly9fRsOGDRETEyNvM0cArHVXAKtZAJQkCVJRFqSiHEXOR0RENRMDoIIBsFevXvj555/1tv/888/o1asXAOD7779Hy5Ytq3yujRs3QggBOzs7uQkhoFKpYGdnh127dlXqFnBpNb4DFaaYcAt4KyCZ56qtMZJUBOn2PkiX5kNKmFbcrqyAlH3eouclIqKaqcaP3yZQLAA6OTkhLi5Ob3tcXBycnZ0BAAkJCfI/V8WdO3dw+vRpnfbggw9ixIgRYIdOfQAAIABJREFUOH36tDwJZN26dfLPxMbGchJIaZIEZP9eziSQWAuXUATp2ipICdPvhb+Eaff+nnHQoucnIqKap8aP3yZQLAAGBQVh0qRJetsnTZqEFi1aAACOHTuGRo0aWeT8JW8BA8XLwDRt2hR//vknjh8/jq5du6Jr164VOmat6EBFmUDWb4aXgck5bPmrf5lHSwU/A60g3TznkjSQ7l6BlJUEqSjXLMckIqLqp1aM3+VQLABu3rwZDg4OaNeuHUaPHo3Ro0ejffv2cHR0xK+//goAiIqKwoQJEyxy/tIBULsQdN26deHi4oLQ0FCkpKRU6Ji1pgNp8oD880D2H8Wzfu/+BRReUWT2r3R5STkBcDqk9F1VO4ckQUr5E9LRCZD2hxW3g69DuvA9pEI+b0hEVNPUmvG7DIquA5iQkIBJkyYhNDQUoaGh+O9//4vExEQlSzArdiDLkiSp/Kt/CdMgpayp2nmS1t4LfjptFKQTU3g1kIiohuH4XcMXgrY0diDLkxJnlX8F8Mb6yh//7lUj4U/bRkK6vMV8b4hqrcTERAghEB0dDQDYvXu3wcloRGR5HL8VDoC3b9/Gjh07sGrVKnz33Xc6zRaxA1medGMjpIQZZYfA7LOVP37Cj8VX+soKgUct81gC1SxhYWE664N6e3vjqaeekpejKioqQkpKCgoLCwEwABJZE8dvBQPgli1b4O7uDpVKBU9PT3h5ecmtbt26SpVhVuxAliflXy8jAM6AlLwEklRU+eOfW1TOFcB/m4Unu5DtCwsLQ9++fZGSkoKUlBRER0fjmWeeQZMmTQzub6kAaKlvViKqSTh+KzwLePz48bh7965Sp7Q4diDzkqQiSPk3iptUeG/73QuQEufcC33aQJi8DFJh1T57Ke7L8q8AHnyjiu+MaoOwsDAMHDhQZ9u+ffsghMDNmzeN3gLetWsXOnfuDGdnZ3Tt2hWxsfeWVrpw4QIGDBiAhg0bwtXVFQ8++CB27typcw5/f39MmzYNL7/8Mtzd3REWFmbx90pk6zh+KxgAXVxccPHiRaVOpwh2IPOQJE3xQs9Jn0G6GFHcEj+FlP6nHAQlTT6kOycgpW6FlPY7pJyLkMwwC1m6FVPO1b9RkOK/qfJ5qOYrHQCzsrIwZswYNG/eHBqNxmgAfPjhh7Fnzx6cPXsWjz/+OLp16yYf4+TJk1i2bBlOnz6NuLg4TJkyBU5OTrh06ZK8j7+/Pzw8PDBv3jxcuHABFy5cUO5NE9kojt8KBsDQ0FCD3wRiy9iBqk6SJEg31t8LfjrtE0jXVlfpFm/559dAiplm5CrgKEgH34CUU7Hlgah2CgsLg52dHVxdXeHq6gohBPz8/PD3338DMD4JZNeue8sYbd26FUII+TvMDWnTpg2++OIL+e/+/v4YNGiQhd4VUc3E8VvBAPjVV1+hadOmiIiIwLp167B582adZovYgapOuhtvJPyVaFmnLFtDYTakM/Puhb4D/4bBo+GQ7vBqChmWn30XZ37ZhmNLf0T89r145ZVX8MQTTyA+Ph7x8fE4evQoRo4ciYYNGyIpKcloALx586Z8zBMnTkAIIV/hy8rKwnvvvYfg4GB4enrC1dUVarUa//nPf+Sf8ff3x4wZM5R980Q2juO3ggFQpVIZbWq1WqkyzIodqOqklJ8gXZxaRgD8BNLVlcrUkp0MKfk3SJc2QUqP5sQPMkiSJByY+xVmurbHJ6KF3B50bojeDz+qs29RURFcXV0xefJkk5aBiY6OhhBCXh91zJgxCAwMxIYNG3Dq1CnEx8ejffv2Oova+/v7Y+HChZZ/40Q1CMdvrgNYJexAVSddXlz+FcCkudYuk0i2b/ZyneCnbe2FB4KFGy7uPCDvq9Fo4O7ujnfffbdSAbBt27aYNm2a/HpWVhY8PT0ZAImqiOM3A2CVsANVnXT16/ID4OUvyj8QkQJyM+5ghtMDRgNgc+GKOW2Ll4I5d+4c3n77bahUKuzevbtSATA0NBQdOnRAdHQ0Tp48iWeffRbu7u4MgERVxPHbwgEwMjJSfpg5MjKyzGaL2IGqTso8Wk4A/ATSrb3WLpMIABD97QaD4U8bAEsuBO3u7o4uXbpg3bp1AEz7JpDSATAxMRE9e/aEs7MzmjRpgsWLF+t9rzkDIFHFcfy2cABs1qwZ0tLS5H821gICAixZhsWwA1WdpMmDdGmhkecApxYvDVOUbe0yiQAA+z9bgal2rYyGQG1L+uuYtUslojJw/OYt4CphBzIPqfA2pOSl90KfNgxe/hxSfqq1yyOSnV7zW7nh7xPRArcSLlu7VCIqA8dvBsAqYQeqPKkoF1LedfmbPCRJgpSbBOnW7uJmpoWeicypICcXn3p0Mhr8ptoF45vuw6xdJhGVg+O3ggGwqKgIX331FYYOHYrevXujZ8+eOs0WsQNVnFRwC9KVHyCd/Q+ks+8Wt8QvIGVzvT2yDSdWrjMa/mY4tcXVY5Zdt5KIqo7jt4IBcOzYsXB1dcWQIUMwfvx4hIeH6zRbxA5UMVLBLUixEZDOvn8v/J19F9LZ94rbndPWLpHIJKd/2oqF/j10AuCXXZ7HlaMx1i6NiEzA8VvBAFivXj1s3bpVqdMpgh2oYqTL3/4b9t413M5/CElTaO0yiUwiaTRIPnwScVv34ObZeGuXQ0QVwPFbwQDo5+eHf/75R6nTKYIdyHRSYVbZ4U/b0vdbu1QiIqrhOH4rGADnzZuHt99+u0Y92M8OZDop51L54e/MBEgXP7d2qUREVMNx/FYwAA4aNAienp4ICAhA//79ERoaqtNsUbXsQJIG0NwFpFygGoVtKe+GaQHw7H+rfK7SC+X+f3t3HhdVubgB/CACggi4I9uIKGqaW4rihtdMTU20e3OpFO0X5VbmLc3bhmnuJpqaaeqYqVGZSyXu4o5bgRuKimiouANuLDLz/P54X6ZGkEVgFs7z/Xyezydmzsy8p/fIeTgz50xJCgkJQXBw8BPvDwsLQ9OmTUvltQui1Wrh6upqltcmIrImFrn/NjGTFcAhQ4bkG2tkURuQXgc8Og+k7wDSN4lk7AYeJVlEEdTr9dCfGidLXj4F8MS4p3r+fxYzFkAiIsqPRe2/zYTXASwGi9mA9Dog88jfxe/xZJ0x7/gkfcL8fxzpy6v8vQf9udlP9dylXQCzs7Oh0+lYAImIygCL2X+bUZktgFOmTEHLli3h7OyM6tWrIzg4GGfOGBeh9PR0jBgxAlWqVEHFihXx8ssv49q1a4V+DYvZgB4lPbn85USXat4xAtA/uAj98dGi7D2eE+9Bf+xd6O8cfqrnfrwAvvPOOxg7diwqV66MmjVrIiwszGj5L7/8Eo0bN4aTkxO8vLwwfPhw3Lt3z3B/TpnasGEDGjZsCFtbWyQmJuYqgIcPH0a1atUwbdo0AH8XwBUrVkCj0cDFxQX9+/fH3bt3DY/ZtGkT2rVrB1dXV1SpUgU9e/bE+fN/Xwcx5ztjf/nlF3Tq1AmOjo5o0qQJDhw4YLQOWq0W3t7ecHR0RJ8+fTBr1iwWQCKiQrCY/bcZlWoBbN68Oe7cuQMAaNasGZo3b/7ElLRu3bpBq9Xi5MmTiI2NRY8ePeDj44P79//+Xtlhw4bB29sbO3bswNGjR9GmTRu0bdu20K9hMRtQxv4CCuBmIOukecco6a+sE0Xv+GhR+o6PFj8fexf6C4uh1+ue6nkfL4AuLi6YMGECzp49i++++w42NjbYunWrYfnw8HDs3LkTiYmJ2LFjB+rXr4/hw4cb7tdqtbCzs0Pbtm2xf/9+nDlzBg8ePDB6nR07dsDV1RWLFi0yPC4sLAzOzs54+eWXceLECezZswfu7u746KOPDMusWbMGv/zyC86dO4eYmBi89NJLePbZZ6HTiXXPKYANGjTA77//jvj4ePznP/+BRqPBo0fiMjkHDx5EuXLlMH36dMTHx2Pu3Llwc3NjASQiKgSL2X+bUakWwAkTJuDBgweG/84vpe3GjRtQFAW7d+8GAKSmpsLOzg4///yzYZnTp09DURRER0cX6jktZgNK31rwEcCMwq1TadPr9dDfjob+zBd/F79Tn0B/fSv0+uyiPVfWQ+ivn4X+ViJCBg82KoDt27c3WrZVq1b48MMPn/hcP//8M6pWrWr4WavVQlEUxMbGGi2XUwDXrl0LZ2dnREREGN0fFhYGJycnoyN+Y8eORevWrZ/42jdv3oSiKDhxQlwIO6cALlmyxLDMqVOnoCgKTp8+DQAYOHAgevToYfQ8/fv3ZwEkIioEi9l/m1GZfQv4cefOnTPaye7YsQOKoiAlJcVoOR8fH8yenffn0DIyMpCWlmZIUlKSZWxA/zzx40nJPGLeMT5Gr9dDn5UKfeadIh/102c+gG7XV9DNfwG68A7QhXfA4EAfBHdqBb1ej6CgIIwYMcLoMb1798bQoUMNP2/btg2dO3eGh4cHnJ2dUaFCBSiKYviDRavVwt7ePtdli0JCQuDu7g5bW1usW7cu19jCwsLwzDPPGN02e/Zs+Pr6Gn4+e/YsBgwYAF9fX1SqVAkVK1aEoiiGC6XnFMDDh/9+O/zOnTtGf8A0a9YMn3/+udHrzJkzhwWQiKgQWABVUgB1Oh169uyJdu3aGW5btWoV7O3tcy3bqlUrjBuX95moYWFhUBQlV8y+AWXFibd58yuA2ZfNO8YSon+UAd3qt6CbE2Qof7rwDhjcqgZ6N64K3a55eZ4EEhwcjJCQEACiYDk4OOC9995DdHQ04uPjsXTpUqM/CJ50QkVISAjatWuHRo0a4aWXXkJWVpbR/XmdBBIeHg6NRmP4uX79+ujatSu2b9+OuLg4nDx5EoqiGAplTgGMiYkxPCYlJQWKoiAqKgoACyARUXGwAJqwAGZnZ2PmzJlo1aoVatasicqVKxulNA0bNgwajQZJSUmG256mAFrsEUDdg3zeBt4MZOwCivj2qqXS//EjdOEdjcqfUQEM74CgtgH5FsA1a9bAzs7O8Jk7AJg0aVKhC2BwcDCuX7+Ohg0bok+fPkYlsKACeOvWLSiKgj179hju37t3b5ELYF5vAQ8YMIAFkIioEFgATVgAP/30U9SqVQuzZs1ChQoVMGnSJPzf//0fqlatirlz55ba644cORJeXl64cOGC0e1P8xbw4yxqA9Kl/OOt4M1/HxHM2AvoH5p7dCVGpx2YfwGc0wlBTf3yLYCxsbFQFAVz5sxBQkICVqxYAU9PzyIVQABITk5GgwYN8O9//9twckZBBVCn06Fq1ap4/fXXce7cOezYsQOtWrUqcgGMjo5GuXLlMHPmTJw9exbz5s3jSSBERIVkUftvMzFZAaxTpw5+//13AICzs7Phshdz587FwIEDS/z19Ho9Ro4cCQ8PD5w9ezbX/TkngaxZs8Zw25kzZ6zzJJAceh2QnQxknRbX/su+ZXwRaF0GkJ4ApJ8FHt023ziLQTe3U67ypwvvgNdb1sDLTeQRwEZe+RZAQHwur1atWnB0dES3bt2wYsWKIhdAALh69Sr8/f3Rr18/ZGdnF+ot4G3btqFhw4ZwcHBAkyZNsGvXriIXQABYunQpvLy84OjoiJdeeomXgSEiKiSL23+bgckKoJOTEy5dugQAcHd3xx9//AEASEhIgIuLS4m/3vDhw+Hq6opdu3YhOTnZkIcP/z4aNmzYMPj4+GDnzp04evQoAgMDERgYWOjXsJoNSJ8N3N0PXPsGuLbg79xeCzwy//UBi0K3sGeeBbBrg8oY0b6W+GzgxjBzD5OIiCyY1ey/S5HJCqC/vz8OHjwIAGjXrh2mTp0KAIiIiED16tVL/PXyOllDURRotVrDMjkXgq5cuTKcnJzQt29fJCcnF/o1rGYDSt1mXPwM+Rq4vgzIvl/wc1gIXdQc6Ob8fRTw1uQ22PDmM3Aob4Nf3mgIXXgH6M/tNvcwiYjIglnN/rsUmawAfvjhh5g8eTIAUfrKly+PunXrwt7ePt/rs1kyq9iAsq4/ofz9owTe3WfuURaaPvUqdAu6Gc4CDn62Kjxd7fG/Lt7IDu8I3cr/g173yNzDJCIiC2YV++9SZrbLwBw4cABffvklfv31V3MNodisYgNK2yNKXn4l8Ppi488KWjj99TPQLfmPOAo4p9Pfl4T5eTT0D1MKfgIiIlI1q9h/lzJVXAewtFjFBpSyuYAjgDJWdpkYvV4H/YVo6KO10B/+HvrruU/0ISIiyotV7L9LmUkL4JUrV/Djjz9i3rx5mDt3rlGskVVsQGXwCCAREVFxWMX+u5SZrADmfLWWs7MzNBoNateubcg/vybLmljFBpR1o0x9BpCIiKi4rGL/XcpMVgC9vLzwxRdfGH37grWzmg0ov7OAb1jXWcBERETFZTX771JksgJYpUoVw8Wfywqr2YD0OuDugTyuA7jO6q4DSEREVFxWs/8uRSYrgGPHjjVc+6+sMPkGpH8EZF8FMo8B6X8CmaeA7CKc9arLBNIvAOnngEd3Sm+cREREFowF0IQFMDs7G927d0dQUBBGjRqFMWPGGMUamWwD0uvEV7vlfL9v+ibgYSTw4Hfg/nogPVqUQyIiIioQC6AJC+CkSZNgY2ODBg0aICgoCJ06dTLkX//6l6mGUaJKdQPS6wF9KqBPBLIPA1lRxgUwpwQ+3Ajc3yBKIBERERWIBdCEBdDNzc3oa9jKglLbgPRZgP4EoD8E6A4BuoN/J3NH7hL44DdxJFDHz/MREREVhAXQhAWwZs2aOHu2bF2st1Q2IL0e0J8U5e/x5JTAjG15HwXMPF1y4yAiIiqjWABNWACnTJmCd955x1QvZxKlUwBT8y5//yyB2XtzHwW8vwHIOFFy4yAiIiqjWABNWAD79OkDFxcX+Pr6olevXujbt69RrFHpFMDE/AtgTnIVwPVA1sWSGwcREVEZxQJowgI4ZMiQfGONSqcAJhSyAG5+7DOAv/FMYCIiokJgATTxdwGXNaVTAJP/PvEjex/waK9I9gFxm/4QoIv+x+f/5NG/R5dLbgxERERlGAugiQvgo0ePsG3bNnzzzTe4e/cuAODKlSu4d++eKYdRYkqnAGYBj/YDWbvyyG4gOxrI2gPcXSuuA/hwD5B9s+Ren4iIqIxjATRhAbx48SIaNGgAJycn2NraIiEhAQDw7rvv4u233zbVMEpUqWxA2ZefUP7+kYytQNoaICVSXCSaiIiICo0F0IQFMDg4GK+//joyMzPh7OxsKIBRUVGoW7euqYZRokplA8raB2RGFVAAt4vP/F1fCDw4XnKvTUREpAIsgCYsgFWqVMGZM2cAwKgAJiYmwtHR0VTDKFElvgHpsoHMAo7+ZUYBmTuBe+uBm0uAm6vEtQOJiIioUFgATfxNIKdOnQJgXAD37t2LGjVqmGoYJarEN6DsqwW//ZtTANN+Am5rxVFAXWbJvD4REZEKsACasAD269cPoaGhAEQBvHDhAu7du4fOnTvzMjA5so7II4B75Nm/e/IugBk7gJSVwO3logDy8i9ERESFxgJowgKYlJSEZ555Bg0bNkT58uXRpk0bVK1aFfXr18f169dNNYwSVaIbkD5blD79IQBH/47+sLg9a5coh5lRwP1fgTvfA7eWAXfWF/+1iYiIVIQF0AyXgVm5ciXGjh2L4cOH49tvv8XDhw9NOYRc5s+fD41GAwcHBwQEBODQoUOFfmzJFsBUQH9ExKgAyp8f7RMFMGOnKH93vgeufwNk8Ns/iIiIioIF0EIuBG2uEhgREQF7e3ssW7YMp06dQmhoKNzc3Ap9RLLkNiA9gFOA/h/F7/ESqD8sjgLeXSvK381vgQexxXxdIiIi9WEBNHMBzMjIwKxZs1CzZk2zvH5AQABGjhxp+Fmn08HDwwNTp04t1ONLbgN6gDyL3+PJ2gPciQDu7gQepRTzNYmIiNSJBdAEBTAjIwPjx4/Hc889h8DAQKxbtw4AsGzZMtSqVQteXl6YNm1aaQ8jl8zMTNja2hrGk2Pw4MHo3bt3no/JyMhAWlqaIUlJSSW0AaWiwPKnPwLo/irm6xARERELoAkK4Lhx4+Dq6op///vfqFWrFsqXL4/Q0FA8++yz+OGHH5CdnV3aQ8jTlStXoCgKDhw4YHT72LFjERAQkOdjwsLCoChKrhR/A7qPQh0BxJ1ivg4RERGxAJqgAPr6+mLDhg0AgBMnTsDGxgZDhw6F3swXL36aAlh6RwD1AE4g//IXA4Bf+0ZERFRcLIAmKIB2dna4fPmy4ecKFSrg+HHzf33Z07wF/LiS3YBSkH8BvFECr0FEREQsgCYogOXKlcONG3+Xl5yLQFuCgIAAjBo1yvCzTqeDp6enGU4CyXEHQCxyH/lj+SMiIiopLIAmKIA2Njbo0aMH+vbti759+6J8+fLo2rWr4eecmENERAQcHBywfPlyxMXF4a233oKbmxuuXbtWqMeXzgakhzgp5AbEUUG+7UtERFSSWABNUACHDBlSqJjLvHnz4OPjA3t7ewQEBODgwYOFfiw3ICIiIuvD/beFXAjaWnEDIiIisj7cf7MAFgs3ICIiIuvD/TcLYLFwAyIiIrI+3H+zABYLNyAiIiLrw/03C2CxpKamQlEUJCUlGV0gmmEYhmEYy03OFzmkpqaau0qYDQtgMeRsQAzDMAzDWF+SkpLMXSXMhgWwGHQ6HZKSkpCamloqf5mU5SOLZX0dy/r6qWEdy/r6qWEduX7Wn9Jax9TUVCQlJUGnU++1dlkALVBaWtn/bEJZX8eyvn5A2V/Hsr5+QNlfR66f9VPDOpoLC6AFUsMGX9bXsayvH1D217Gsrx9Q9teR62f91LCO5sICaIHUsMGX9XUs6+sHlP11LOvrB5T9deT6WT81rKO5sABaoIyMDISFhSEjI8PcQyk1ZX0dy/r6AWV/Hcv6+gFlfx25ftZPDetoLiyARERERCrDAkhERESkMiyARERERCrDAkhERESkMiyARERERCrDAmiB5s+fD41GAwcHBwQEBODQoUPmHtJTmTJlClq2bAlnZ2dUr14dwcHBOHPmjNEyQUFBub6a5+233zbTiIsuLCws1/jr169vuD89PR0jRoxAlSpVULFiRbz88su4du2aGUdcNBqNJs+vTxoxYgQA65y/3bt3o1evXqhVqxYURcG6deuM7tfr9fj000/h7u6OChUq4Pnnn8fZs2eNlrl9+zZeffVVVKpUCa6urnjjjTdw7949U67GE+W3fllZWRg3bhwaN24MJycn1KpVC4MGDcKVK1eMniOveZ86daqpVyVPBc1fSEhIrrF369bNaBlLnj+g4HV80teazZgxw7CMJc9hYfYNhfndeenSJfTo0QOOjo6oXr06PvjgAzx69MiUq2LVWAAtTEREBOzt7bFs2TKcOnUKoaGhcHNzw/Xr1809tCLr1q0btFotTp48idjYWPTo0QM+Pj64f/++YZmgoCCEhoYiOTnZEGu63lNYWBgaNWpkNP6bN28a7h82bBi8vb2xY8cOHD16FG3atEHbtm3NOOKiuXHjhtG6bdu2DYqiICoqCoB1zl9kZCQ+/vhjrF27Ns+d67Rp0+Dq6or169fj2LFj6N27N3x9fZGenm5Ypnv37mjatCkOHjyIvXv3om7duhg4cKCpVyVP+a1famoqunTpgh9//BFnzpxBdHQ0AgIC8Nxzzxk9h0ajwcSJE43m9Z//bs2poPkLCQlB9+7djcZ+584do2Usef6Agtfxn+uWnJyMZcuWwcbGBgkJCYZlLHkOC7NvKOh3Z3Z2Nho3bowuXbogJiYGkZGRqFatGv73v/+ZY5WsEgughQkICMDIkSMNP+t0Onh4eFjMX27FcePGDSiKgt27dxtuCwoKwujRo804quIJCwtD06ZN87wvNTUVdnZ2+Pnnnw23nT59GoqiIDo62lRDLFGjR4+Gn58f9Ho9AOufv8d3rnq9Hu7u7pg5c6bhttTUVDg4OOCHH34AAMTFxUFRFBw5csSwzKZNm2BjY5PrSJq55VUeHnf48GEoioJLly4ZbtNoNAgPDy/t4RXbkwpgcHDwEx9jTfMHFG4Og4OD0blzZ6PbrGUOgdz7hsL87oyMjES5cuWMjgouXLgQLi4uyMzMNO0KWCkWQAuSmZkJW1vbXP/YBw8ejN69e5tpVCXn3LlzUBQFJ06cMNwWFBSEatWqoWrVqmjUqBHGjx+PBw8emHGURRMWFmZ4K83X1xevvvqqYUe6Y8cOKIqClJQUo8f4+Phg9uzZ5hhusWRmZqJq1aqYPHmy4TZrn7/Hd64JCQlQFAUxMTFGy3Xs2BHvvvsuAGDp0qVwc3Mzuv/Ro0ewtbXF2rVrS3/QRVCY8rBt2zbY2NgYHbnVaDSoWbMmqlSpgmbNmmHGjBkW+dbakwqgq6srqlevDn9/fwwbNgy3bt0y3G9N8wcUPIfXrl1D+fLlsWrVKqPbrWUOgdz7hsL87vz0009z/fF94cIFKIqCP//80zQDt3IsgBbkypUrUBQFBw4cMLp97NixCAgIMNOoSoZOp0PPnj3Rrl07o9sXLVqEzZs34/jx41i5ciU8PT3Rt29fM42y6CIjI/HTTz/h2LFj2Lx5MwIDA+Hj44O7d+9i1apVsLe3z/WYVq1aYdy4cWYYbfH8+OOPsLW1NTpKYu3z9/jOdf/+/VAUBVevXjVa7pVXXkG/fv0AAJMnT4a/v3+u56pevTq+/vrr0h1wERVUHtLT09GiRQu8+uqrRrd/+eWXiIqKwrFjx7Bw4UK4ublhzJgxpT3cIstr/X744Qds2LABx48fx7p169CwYUO0atUK2dnZAKxr/oCC53D69OmoXLmy0UcUAOuZw7z2DYX53RkaGoquXbsa3f/gwQMoioLIyMjSHXQZwQJoQcpyARw2bBg0Gg2SkpLyXS7nL7/z58+baGQlKyUlBS4uLliyZEmZK4Bdu3ZFr1698l3G2uZPzQUwKysLL730Epo3b17g5zaXLl2K8uXLW9zXcRUEWqDmAAAfV0lEQVTmCGfOUd3t27cDsK75Awpex/r162PUqFEFPo+lzmFe+wYWQNNgAbQgZfUt4JEjR8LLywsXLlwocNn79+9DURRs3rzZBCMrHS1btsT48ePL1FvAFy9eRLly5bB+/fp8l7O2+VPrW8BZWVno06cPmjRpYvT26JOcPHkSiqLkOlPT3ApTAAGgWrVq+OabbwBY1/wB+a/jnj17oCgKYmNjC3weS5zDJ+0b+BawabAAWpiAgACjv+Z0Oh08PT2t8iQQvV6PkSNHwsPDI9dlNJ5k3759UBQFx44dK+XRlY579+6hcuXKmDt3ruGDzGvWrDHcf+bMGas8CSQsLAzu7u4FfobI2ubvSSeBzJo1y3BbWlpanieBHD161LDMli1bLPIkgrzKQ075a9SoEW7cuFGo51m5ciXKlSuX62xacytMAUxKSoKNjQ02bNgAwLrmD8h/HUNCQnKdwf0kljSHBe0bCvO7M+ckkH9eIWPRokVwcXGxuKOclooF0MJERETAwcEBy5cvR1xcHN566y24ublZ1bXjcgwfPhyurq7YtWuX0aUIHj58CAA4f/48Jk6ciKNHjyIxMREbNmxAnTp10LFjRzOPvPDef/997Nq1C4mJidi/fz+6dOmCatWqGXasw4YNg4+PD3bu3ImjR48iMDAQgYGBZh510eh0Ovj4+ODDDz80ut1a5+/evXuIiYlBTEwMFEXB7NmzERMTYzh5Z9q0aXBzczN8jiw4ODjPy8A0b94chw4dwr59+1CvXj2LuYxIfuuXlZWF3r17w8vLC7GxsUb/LnPOnDxw4ADCw8MRGxuLhIQErFy5EtWrV8fgwYPNvGZCfut37949fPDBB4iOjkZiYiK2b9+OFi1aoF69ekalwJLnDyh4GwXEHyZOTk5YuHBhrsdb+hwWtG8ACv7dmXMZmK5duyI2NhabN29G9erVeRmYImABtEDz5s2Dj48P7O3tERAQgIMHD5p7SE/lSRcr1Wq1AIC//voLHTt2RJUqVeDg4IC6deti7NixFn8duX/q378/atWqBXt7e3h6eqJ///5Gn3/LuZhp5cqV4eTkhL59+yI5OdmMIy66LVu2QFEUxMfHG91urfMXFRWV53YZEhIC4O8LQdesWRMODg54/vnnc6377du3MXDgQDg7O8PFxQVDhw61mAsJ57d+iYmJT/x3mXNtxz/++AOtW7eGq6srKlSogIYNG2LKlCkWc1Qlv/V7+PAhunbtiurVq8POzg4ajQahoaG5/oC25PkDCt5GAXG0y9HREampqbkeb+lzWNC+ASjc786LFy/ixRdfhKOjI6pVq4b333/fYs90tkQsgEREREQqwwJIREREpDIsgEREREQqwwJIREREpDIsgEREREQqwwJIREREpDIsgEREREQqwwJIREREpDIsgEREREQqwwJIRBYj5xsQHv8SeHM9T0GCgoIwevToUn2NvDy+flqtFq6urob7w8LC0LRpU8PPISEhCA4ONvk4ichysQASEUJCQgxfx2RnZwc/Pz98/vnnJv9apacpbnmVsMzMTCQnJ0Ov15f0EI3cvn0bd+/eLdXXyMvj61dQAUxNTS31MkxE1oUFkIgQEhKC7t27Izk5GRcvXsTXX38NGxsbTJkyxaTjKKkCqDYFFUAiosexABJRnm8RvvDCC2jTpo3h5zVr1uCZZ56Bvb09NBoNZs2aZbS8RqPBxIkTMWDAADg5OcHDwwPz58833J+YmAhFURATE2O4LSUlBYqiICoqCkDuAnjr1i0MGDAAHh4ecHR0ROPGjbF69WqjcT/+hfKJiYl5FsnCjH/y5MkYOnQonJ2d4e3tjUWLFuX7/+3x8vm0zzFq1CiMHj0abm5uqFGjBhYvXoz79+9jyJAhcHZ2hp+fHyIjIw2PKe5bwBkZGXjnnXdQvXp1ODg4oF27djh8+HCu59++fTuee+45ODo6IjAwEGfOnMl3XYjIerAAElGeBbB3795o0aIFAODo0aMoV64cJk6ciPj4eGi1Wjg6OkKr1RqW12g0qFSpEqZOnYr4+Hh89dVXsLW1xdatWwE8XQG8fPkyZs6ciZiYGCQkJBie89ChQwDEW5uBgYEIDQ1FcnIykpOTkZ2dnet5Cjv+KlWqYMGCBTh37hymTp2KcuXK5Vt68iqAT/MclSpVwqRJk3D27FlMmjQJtra2ePHFF7F48WKcPXsWw4cPR9WqVfHgwYM8/z8VtQC+++678PDwQGRkJE6dOoWQkBBUrlwZt2/fNnr+1q1bY9euXTh16hQ6dOiAtm3bPnE9iMi6sAASkVFB0Ov12LZtGxwcHPDBBx8AAF599VW88MILRo8ZO3YsnnnmGcPPGo0G3bt3N1qmf//+ePHFFwE8XQHMS8+ePfH+++8bfs7rLeDHn6ew43/99dcNP+v1etSoUQMLFy584ljyKoBP8xzt27c3/JydnY2KFSti0KBBhtuSk5OhKAqio6PzXL+iFMD79+/Dzs4Oq1atMtyflZUFDw8PzJgxw+j5t2/fblhm48aNUBQF6enpT1wXIrIeLIBEhJCQENja2qJixYqwt7dH+fLlMXjwYNy/fx8A0Lx5c0yYMMHoMevXr4ednR2ys7MBiPLz+eefGy0zZ84c1K5dG8DTFcDs7GxMnDgRjRs3RuXKlVGxYkWUL18er7zyiuE5ClMACzv+nAKUo0mTJrnW6Z/yKoBP8xwjRowwus3Hx8foefR6PRRFwYYNG/Jcv6IUwGPHjkFRFFy8eNHoNfv06YOhQ4caPf+NGzcM9//5559QFAWXLl164roQkfVgASQihISEoEuXLjh37hwuXbqU6+zfkiiAly5dgqIo+PPPPw3337hxI98COHXqVFStWhXff/89YmNjce7cOfTs2dPo7cySLIDh4eFGyzRt2hRhYWFP/P+WVwEs7nM86XkURcG6devyXL/SKoD/PBIbExNj+IwlEVk/FkAiKvA6cU96C7VRo0aGnzUajeHt3hwDBgww3Pbw4UMoioKNGzca7t+6dWu+BbBXr1544403DMvrdDrUq1fPaKwvvPACRo0aZfS6hX0L+PHxq6EA3r9/H/b29rneAvb09MTMmTPzfH6ABZCorGEBJKICC+Aff/xhdBLF8uXL8zyJwsXFBdOnT0d8fDzmz58PW1tbbN682bBMmzZt0KFDB8TFxWHXrl0ICAjItwCOGTMG3t7e2L9/P+Li4vDmm2/CxcXFaKyhoaFo1aoVEhMTcfPmTeh0ulzPU9jxq6EAAsDo0aPh4eGBTZs2GZ0EcufOnTyfH2ABJCprWACJqFDfFJFzGRU7Ozv4+PgYjhblyHkL+JVXXoGTkxPc3d0xd+5co2Xi4uIQGBgIR0dHNGvWrMAjgLdv30ZwcDCcnZ1Ro0YNfPLJJxg8eLDRWOPj49GmTRs4OjoW6jIw+Y1fLQUwPT0d77zzDqpVq5bvZWBYAInKLhZAIioReZUWIiKyTCyARFQiWACJiKwHCyARlQgWQCIi68ECSERERKQyLIBEREREKsMCSERERKQyLIBEREREKsMCSERERKQyLIBEREREKsMCSERERKQyLIBEREREKsMCSERERKQyLIBEREREKsMCSERERKQyLIDFlJ6ejrS0NIZhGIZhrCjp6enmrhBmxQJYDOnp6XB3rwpFURiGYRiGsaK4u7urugSyABZDWloaFEVB0l+/Iy01CmmpO0RScrJN5M5WmS0itzeJ3IqU2Shy8zeZX0VurJdZJ3J9rcwakWtrkJb8s8yPIlcjZFaLXFkls1Lk8gqRpO9ktCJ/LZNZKnLpW5nFIhdlEr+RWYi0xK9l5otcmCeS8JXMHJHz4TKzRc7NEjk7U2aGSPx0kTPTZKaKnJ4s8wXSTn+B1LhJSD31ucwEkZNhIic+lflE5PjHIsc+khkvEpuTD0Vixor8+YHM+zJjRP54T2Y0Uo+8I3J4lMihkSIHh4tEDxPZ/7bIvrdE9oaK7HlTZNcbIlFDRXYOEdkRIjNIZNvrIltfQ+qWgSKbB4hE9hfZ2E/k91dEfvuPyK8vi6zvK7Kuj8ja3iK/vCSyppfIzz1EfnpR5MfuIhHdkLq6q8gqmZUviHwvs6KLyHcyWpllMkufF1ki821nkcUyi/4l8o3Mwk5IXdgJKV93QsqCIJH5MvM6isyVmdNBJFxmdnuRL2VmtROZITO9rXGmBYpMlZnSRuSLNkiZ2FpkgsxnASKftBL5SGZ8S5EPZcY+J/K+zBiZ0TLvtBAZ2VxkuMzbzZDydjPceasZ7rzZVOQNmSFNRAY/KzJI5rXGIgNl+jcS6Sfzn2dEXm4o0kemdwORXjI96ot0r4/bXf1xu6s/br0gcrNLPdzsUg83Osv8S+R6UF1cD6qLax1Fktv7Ibm9H662lQmsg6uBdXCltcjlViJJLWWeE/mrhS/+auGLS818cbFJbVxsUhuJz8o00og0FLnQQCTB3wcJ/j44X1fGzxvn/bxxro7IWV8vnPX1QrxGxtsT8d6eOOMlctpDJM7dA3HuHjhV0wMna3riZE1PnKghcry6SGw1kZiqXoip6oU/qxjnj8re+KOyN47IHHYTOeTqg0OuPjgoE+0isr+SBvsrabDPWWRvRQ32VKyNPRVrY7eTSJSjyM4KvthZwRc7HGTsRbbLbLWrg612dbClvMhmW5FN5UQiZTbaiPym+OE3xQ+/yqxX/LBWZs1j+UkmQma1zEqZFTLLZZbJLJFZLLNI5huZr2XmK374SmaOzGyZWTIzZKbLTJWZLDNJZqLMBJnPZD6R+UhmvOKHMYovFEVBWlqauauE2bAAFkNOAUxLjQL0RwD9IRHdQZkDItn7ZfaJPNojkrVbZpdI5k6ZHSIZ22S2iqRvkdkk8jASeLBR5jeR+7/KrBe5t05mrcjdNSJpP8v8KJIaIfODSMoqmZUid74Xuf2dzHLgllZmqcjNJSI3vpVZJHL9G5mFItcWiCTPl5kncnWuyJU5MuEil7+UmQVcngV90kzo/5ouM03k0lSRi5NlvhBJnCRyYaLMBJGEMJnPRM5/InLuY5mPRM6OF4n/UGYc9Kc/EIl7X+TUf0VOvidyYrTIsXdFYkeJxIwU+XOEyNFhIkfeFjn8lsihUJGD/ydy4A2R/UOh3xcisnewyO5BIrteE4l6VWTnQJEd/UW29RPZ+orIln+LbH5ZJLKvyMZgkd97i/z2ksivvaBf31NknczaHiK/yKx5UeRnmR+7i0TI/NBNZLXMqq4iK2VWvCDynczyLtAv7wKdtgt0y54XWSqzpLPIYplF/xL5RmZhJ5GvZRYEicyT+aqjyFyZOR1EwmVmtxeZ1R66Ge1EpslMaSvyRaDIRJkJbUQ+k/mktchHMuMDRMbJfNBK5L8tRd6Tefc56N59DtmjnkP2iBYiw2Teai7yZjOR/5MZ2lQkRGZQE5HXZAY+K9K/scgrMv9uJNJXJvgZkZeewaOeDfGoZ0Nk9RDJ7N4Amd0bIKOrzAsi6c/XR/rz9fGws8iDTv540Mkf9zvKdKiH+x3q4V47kbuBdXE3sC7S2sgEiKS28kNqKz+kPOeHO83r4E7zOrjdTKaJL2438cWtxiI3G4ncaFgbNxrWxvX6Mv4aXPfX4FpdkWQ/HyT7+eCqr4zGG1c13rjiI3LZSyTJwwtJHl74q5YXLtXyxqVa3rjoLpJYUyShhsj56j44X90H56qJnJWJr6pBfFUNTlcRiasscsqtNk651cZJmROuIsdcfHHMxRexlURinH3xp3Md/OlcB39UFDniJHLY0Q+HHf1wqILIQQeRaJn99nWx374u9tmJ7C0vsse2HvbY1sNumV3lRHba+GOnjT92KCLbFH9skdkkEynzu8yvMutl1sqskflJJkJmtcxKmRUy38loZZYq/vhWZpHMQpkFMvNk5sqEy3wpM1Nmhsw0mSkyX8hMlJmg+GO84scCaO4BWDMWQC0LIAsgCyALIAsgCyALoBViASwGFkAtCyALIAsgCyALIAsgC6AVYgEsBhZALQsgCyALIAsgCyALIAugFWIBLAYWQC0LIAsgCyALIAsgCyALoBViASwGFkAtCyALIAsgCyALIAsgC6AVYgEsBhZALQsgCyALIAsgCyALIAugFWIBLAYWQC0LIAsgCyALIAsgCyALoBViASwGFkAtCyALIAsgCyALIAsgC6AVYgEsBhZALQsgCyALIAsgCyALIAugFWIBLAYWQC0LIAsgCyALIAsgCyALoBViASwGFkAtCyALIAsgCyALIAsgC6AVYgEsBhZALQsgCyALIAsgCyALIAugFWIBLAYWQC0LIAsgCyALIAsgCyALoBViASwGFkAtCyALIAsgCyALIAsgC6AVYgEsBhZALQsgCyALIAsgCyALIAugFWIBLAYWQC0LIAsgCyALIAsgCyALoBViASwGFkAtCyALIAsgCyALIAsgC6AVYgEsBhZALQsgCyALIAsgCyALIAugFWIBLAYWQC0LIAsgCyALIAsgCyALoBViASwGFkAtCyALIAsgCyALIAsgC6AVYgEsBhZALQsgCyALIAsgCyALIAugFWIBLIacApj01+9IS41CWuoOkZScbBO5s1Vmi8jtTSK3ImU2itz8TeZXkRvrZdaJXF8rs0bk2hqkJf8s86PI1QiZ1SJXVsmsFLm8QiTpOxmtyF/LZJaKXPpWZrHIRZnEb2QWIi3xa5n5IhfmiSR8JTNH5Hy4zGyRc7NEzs6UmSESP13kzDSZqSKnJ8t8gbTTXyA1bhJST30uM0HkZJjIiU9lPhE5/rHIsY9kxovE5uRDkZixIn9+IPO+zBiRP96TGY3UI++IHB4lcmikyMHhItHDRPa/LbLvLZG9oSJ73hTZ9YZI1FCRnUNEdoTIDBLZ9rrI1teQumWgyOYBIpH9RTb2E/n9FZHf/iPy68si6/uKrOsjsra3yC8viazpJfJzD5GfXhT5sbtIRDekru4qskpm5Qsi38us6CLynYxWZpnM0udFlsh821lkscyif4l8I7OwE1IXdkLK152QsiBIZL7MvI4ic2XmdBAJl5ndXuRLmVntRGbITG9rnGmBIlNlprQR+aINUia2Fpkg81mAyCetRD6SGd9S5EOZsc+JvC8zRma0zDstREY2Fxku83YzpLzdDHfeaoY7bzYVeUNmSBORwc+KDJJ5rbHIQJn+jUT6yfznGZGXG4r0kendQKSXTI/6It3r43ZXf9zu6o9bL4jc7FIPN7vUw43OMv8SuR5UF9eD6uJaR5Hk9n5Ibu+Hq21lAuvgamAdXGktcrmVSFJLmedE/mrhi79a+OJSM19cbFIbF5vURuKzMo00Ig1FLjQQSfD3QYK/D87XlfHzxnk/b5yrI3LW1wtnfb0Qr5Hx9kS8tyfOeImc9hCJc/dAnLsHTtX0wMmanjhZ0xMnaogcry4SW00kpqoXYqp64c8qxvmjsjf+qOyNIzKH3UQOufrgkKsPDspEu4jsr6TB/koa7HMW2VtRgz0Va2NPxdrY7SQS5Siys4IvdlbwxQ4HGXuR7TJb7epgq10dbCkvstlWZFM5kUiZjTYivyl++E3xw68y6xU/rJVZ81h+komQWS2zUmaFzHKZZTJLZBbLLJL5RuZrmfmKH76SmSMzW2aWzAyZ6TJTZSbLTJKZKDNB5jOZT2Q+khmv+GGM4ssCaO4BWLPU1FQ4OztDURSGYRiGYawozs7OSE1NNXeVMBsWwGIwHAFMSkJaWhpjxiQlJXEuLCScC8sK58NywrmwnOTMRVoajwDSU0hLS1P9BmQpOBeWg3NhWTgfloNzYTk4FyyAxcINyHJwLiwH58KycD4sB+fCcnAuWACLhRuQ5eBcWA7OhWXhfFgOzoXl4FywABZLRkYGwsLCkJGRYe6hqB7nwnJwLiwL58NycC4sB+eCBZCIiIhIdVgAiYiIiFSGBZCIiIhIZVgAiYiIiFSGBbAA8+fPh0ajgYODAwICAnDo0KF8l//pp59Qv359ODg4oHHjxti4caOJRlr2FWUuFi9ejPbt28PNzQ1ubm54/vnnC5w7Kryi/rvI8cMPP0BRFAQHB5fyCNWjqHORkpKCESNGwN3dHfb29qhXrx5/T5Wgos5HeHg4/P39UaFCBXh5eeG9995Denq6iUZbdu3evRu9evVCrVq1oCgK1q1bV+BjoqKi0Lx5c9jb28PPzw9arbb0B2pGLID5iIiIgL29PZYtW4ZTp04hNDQUbm5uuH79ep7L79+/H7a2tpgxYwbi4uLwySefwM7ODidOnDDxyMueos7Fq6++igULFiAmJganT5/GkCFD4OrqisuXL5t45GVPUeciR2JiIjw9PdGhQwcWwBJS1LnIzMxEy5Yt0aNHD+zbtw+JiYnYtWsXYmNjTTzysqmo87Fq1So4ODhg1apVSExMxJYtW1CrVi2MGTPGxCMveyIjI/Hxxx9j7dq1hSqAFy5cgJOTE/773/8iLi4O8+bNg62tLTZv3myiEZseC2A+AgICMHLkSMPPOp0OHh4emDp1ap7L9+vXDz179jS6rXXr1nj77bdLdZxqUNS5eFx2djYqVaqE7777rrSGqBpPMxfZ2dlo27YtlixZgpCQEBbAElLUuVi4cCHq1KmDrKwsUw1RVYo6HyNHjkTnzp2Nbvvvf/+Ldu3aleo41aYwBXDcuHFo1KiR0W39+/dHt27dSnNoZsUC+ASZmZmwtbXNtdEMHjwYvXv3zvMx3t7eCA8PN7rts88+Q5MmTUptnGrwNHPxuLt376JChQr47bffSmOIqvG0c/HZZ5+hT58+AMACWEKeZi5efPFFvPbaawgNDUWNGjXQqFEjTJ48GdnZ2aYYcpn2NPOxatUquLq6Gt4mTkhIQIMGDTB58uRSH6+aFKYAdujQAaNHjza6bdmyZXBxcSnNoZkVC+ATXLlyBYqi4MCBA0a3jx07FgEBAXk+xs7ODqtXrza6bcGCBahRo0apjVMNnmYuHjd8+HDUqVOHn60ppqeZi71798LT0xM3b94EwAJYUp5mLnI+n/zGG2/g6NGjiIiIQJUqVTBhwgRTDLlMe9rfU3PnzoWdnR3Kly8PRVEwbNiw0h6q6hSmANarVw9Tpkwxum3jxo1QFAUPHz4szeGZDQvgE7AAWo7iFsCpU6eicuXKOHbsWGkNUTWKOhd3795F7dq1ERkZabiNBbBkPM2/i3r16sHb29voiN+XX34Jd3f3Uh2rGjzNfERFRaFmzZr49ttvcfz4caxduxbe3t6YOHGiKYasGiyAeWMBfAK+BWw5ivMW8MyZM+Hq6oojR46U5hBVo6hzERMTA0VRYGtra4iNjQ1sbGxga2uL8+fPm2roZc7T/Lvo2LEjnn/+eaPbIiMjoSgKMjMzS22savA089G+fXt88MEHRrd9//33cHR0hE6nK7Wxqg3fAs4bC2A+AgICMGrUKMPPOp0Onp6e+Z4E0qtXL6PbAgMDeRJICSjqXADA9OnT4eLigujoaFMMUTWKMhfp6ek4ceKEUYKDg9G5c2ecOHGCpaOYivrv4n//+x80Go1RuZgzZw5q1apV6mNVg6LOR4sWLTBu3Dij21avXg1HR0d+LrMEFfYkkMaNGxvdNnDgQJ4EolYRERFwcHDA8uXLERcXh7feegtubm64du0aAGDQoEEYP368Yfn9+/ejfPnymDVrFk6fPo2wsDBeBqaEFHUupk2bBnt7e6xZswbJycmG3Lt3z1yrUGYUdS4ex7eAS05R5+Kvv/5CpUqVMGrUKMTHx+P3339HjRo18MUXX5hrFcqUos5HWFgYKlWqhB9++AEXLlzA1q1b4efnh379+plrFcqMe/fuISYmxvAuxOzZsxETE4NLly4BAMaPH49BgwYZls+5DMzYsWNx+vRpLFiwgJeBUbt58+bBx8cH9vb2CAgIwMGDBw33BQUFISQkxGj5n376Cf7+/rC3t0ejRo14gdUSVJS50Gg0UBQlV8LCwkw/8DKoqP8u/okFsGQVdS4OHDiA1q1bw8HBAXXq1OFZwCWsKPPx6NEjTJgwAX5+fqhQoQK8vb0xYsQIpKSkmGHkZUtUVFSe+4Cc//8hISEICgrK9ZhmzZrB3t4ederU4YWgiYiIiKhsYQEkIiIiUhkWQCIiIiKVYQEkIiIiUhkWQCIiIiKVYQEkIiIiUhkWQCIiIiKVYQEkIiIiUhkWQCIiIiKVYQEkIiIiUhkWQCIiIiKVYQEkIiIiUhkWQCIiIiKVYQEkIiIiUhkWQCIiIiKVYQEkIiIiUhkWQCIiIiKVYQEkIiIiUhkWQCIiIiKVYQEkIiIiUhkWQCIiIiKVYQEkIiIiUhkWQCIiIiKVYQEkIiIiUhkWQCIiIiKVYQEkIiIiUhkWQCIiIiKVYQEkIiIiUhkWQCIiIiKVYQEkIiIiUhkWQCIiIiKVYQEkIiIiUhkWQCIiIiKVYQEkIiIiUhkWQCIiIiKVYQEkIiIiUhkWQCIiIiKVYQEkIiIiUhkWQCIiIiKVYQEkIiIiUhkWQCIiIiKVYQEkIiIiUhkWQCIiIiKVYQEkIiIiUhkWQCIiIiKVYQEkIiIiUhkWQCIiIiKVYQEkIiIiUhkWQCIiIiKVYQEkIiIiUhkWQCIiIiKVYQEkIiIiUhkWQCIiIiKVYQEkIiIiUhkWQCIiIiKVYQEkIiIiUhkWQCIiIiKVYQEkIiIiUhkWQCIiIiKVYQEkIiIiUhkWQCIiIiKVYQEkIiIiUhkWQCIiIiKV+X9dcDJ5ikvlkQAAAABJRU5ErkJggg==\" width=\"640\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.colors as col\n",
    "import matplotlib.cm as cm\n",
    "\n",
    "# plt.figure(figsize=(8, 7))\n",
    "# Color Map\n",
    "colorMap = \"YlOrRd\"\n",
    "# Threashold where we consider if less than threshold its high risk\n",
    "threshold = 0.0008\n",
    "\n",
    "# Scatter Plot with color shade as the ratio of decreasing amount of beds\n",
    "fig, ax = plt.subplots()\n",
    "ax.scatter(data[\"Population\"], data[\"RemainingBeds\"], c=1/data[\"RemainingBeds_Pop_Ratio\"], cmap=colorMap, picker=5)\n",
    "\n",
    "# Setting OFF Grid\n",
    "plt.grid(False)\n",
    "\n",
    "# Setting the Axes and background color\n",
    "# ax = plt.gca()\n",
    "# ax.set_ylim([0, 0.01])\n",
    "ax.set_facecolor('white')\n",
    "\n",
    "n = data[\"State\"].values\n",
    "for i, txt in enumerate(n):\n",
    "    if  data[\"RemainingBeds_Pop_Ratio\"][i] < threshold:\n",
    "        ax.annotate(txt, (data[\"Population\"][i], data[\"RemainingBeds\"][i]))\n",
    "\n",
    "\n",
    "# Setting XLabel and YLabel\n",
    "plt.xlabel(\"Population in million\")\n",
    "plt.ylabel(\"Remaining Number of beds (x1000)\")\n",
    "plt.title(\"High risk states in India to defend COVID 19\")\n",
    "\n",
    "# Initializing the text box\n",
    "text = plt.text(150, 30, \"\")\n",
    "\n",
    "# Interactive Pick Event Listner Function\n",
    "def interactive(event):\n",
    "#     plt.title(data.iloc[event.ind[0]][\"State\"])\n",
    "    # Setting the data inside text box\n",
    "    text.set_text(\"State: {}\\nRemaining Beds(x1000): {}\\nPopulation(million): {}\".format(data.iloc[event.ind[0]][\"State\"], data.iloc[event.ind[0]][\"TotalBeds\"], data.iloc[event.ind[0]][\"Population\"]))\n",
    "\n",
    "    \n",
    "# Horizontal colormap for the data\n",
    "cpick = cm.ScalarMappable(cmap=colorMap, norm=col.Normalize(vmin=0, vmax=1.0))\n",
    "cpick.set_array([])\n",
    "cbar = plt.colorbar(cpick, orientation=\"horizontal\")\n",
    "\n",
    "# Binding Event Listner to the figure\n",
    "plt.gcf().canvas.mpl_connect('pick_event', interactive);\n",
    "\n",
    "# Show\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**State which needs more care**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Bihar, is where more care has to be taken. \n",
      "State has active cases and have 29815 beds available in total for 104.099452 million people.\n"
     ]
    }
   ],
   "source": [
    "morecare = data[data.RemainingBeds_Pop_Ratio == data.RemainingBeds_Pop_Ratio.min()]\n",
    "print(\"{}, is where more care has to be taken. \\nState has active cases and have {} beds available in total for {} million people.\".format(morecare[\"State\"].values[0], morecare[\"TotalBeds\"].values[0], morecare[\"Population\"].values[0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Analysis on relatively newer dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "_Assumption are taken based on the availability of dataset_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dataset released recently"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Covid 19 cases\n",
    "covidData = pd.read_csv(\"state_wise_newer.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>State</th>\n",
       "      <th>Population</th>\n",
       "      <th>Rural</th>\n",
       "      <th>Urban</th>\n",
       "      <th>Confirmed</th>\n",
       "      <th>Recovered</th>\n",
       "      <th>Deaths</th>\n",
       "      <th>Active</th>\n",
       "      <th>Last_Updated_Time</th>\n",
       "      <th>Migrated_Other</th>\n",
       "      <th>State_code</th>\n",
       "      <th>Delta_Confirmed</th>\n",
       "      <th>Delta_Recovered</th>\n",
       "      <th>Delta_Deaths</th>\n",
       "      <th>State_Notes</th>\n",
       "      <th>NumPublicBeds</th>\n",
       "      <th>NumRuralBeds</th>\n",
       "      <th>NumUrbanBeds</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Uttar Pradesh</td>\n",
       "      <td>199812341</td>\n",
       "      <td>155317278</td>\n",
       "      <td>44495063</td>\n",
       "      <td>509903</td>\n",
       "      <td>479182</td>\n",
       "      <td>7354</td>\n",
       "      <td>23367</td>\n",
       "      <td>14/11/2020 23:53:22</td>\n",
       "      <td>0</td>\n",
       "      <td>UP</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>58310</td>\n",
       "      <td>39104</td>\n",
       "      <td>37156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Maharashtra</td>\n",
       "      <td>112374333</td>\n",
       "      <td>61556074</td>\n",
       "      <td>50818259</td>\n",
       "      <td>1744698</td>\n",
       "      <td>1612314</td>\n",
       "      <td>45914</td>\n",
       "      <td>85503</td>\n",
       "      <td>14/11/2020 21:47:25</td>\n",
       "      <td>967</td>\n",
       "      <td>MH</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>[Sep 9] :239 cases have been removed from the ...</td>\n",
       "      <td>68998</td>\n",
       "      <td>12398</td>\n",
       "      <td>39048</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Bihar</td>\n",
       "      <td>104099452</td>\n",
       "      <td>92341436</td>\n",
       "      <td>11758016</td>\n",
       "      <td>226916</td>\n",
       "      <td>219864</td>\n",
       "      <td>1184</td>\n",
       "      <td>5867</td>\n",
       "      <td>15/11/2020 18:54:26</td>\n",
       "      <td>1</td>\n",
       "      <td>BR</td>\n",
       "      <td>247</td>\n",
       "      <td>614</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17796</td>\n",
       "      <td>6083</td>\n",
       "      <td>5936</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>West Bengal</td>\n",
       "      <td>91276115</td>\n",
       "      <td>62183113</td>\n",
       "      <td>29093002</td>\n",
       "      <td>428498</td>\n",
       "      <td>390096</td>\n",
       "      <td>7610</td>\n",
       "      <td>30792</td>\n",
       "      <td>14/11/2020 21:15:25</td>\n",
       "      <td>0</td>\n",
       "      <td>WB</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>51163</td>\n",
       "      <td>19684</td>\n",
       "      <td>58882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Madhya Pradesh</td>\n",
       "      <td>72626809</td>\n",
       "      <td>52557404</td>\n",
       "      <td>20069405</td>\n",
       "      <td>183057</td>\n",
       "      <td>170969</td>\n",
       "      <td>3083</td>\n",
       "      <td>9005</td>\n",
       "      <td>14/11/2020 23:53:24</td>\n",
       "      <td>0</td>\n",
       "      <td>MP</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>[Oct 14]: 4469 confirmed cases and 4469 recove...</td>\n",
       "      <td>38140</td>\n",
       "      <td>10020</td>\n",
       "      <td>18819</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            State  Population      Rural     Urban  Confirmed  Recovered  \\\n",
       "0   Uttar Pradesh   199812341  155317278  44495063     509903     479182   \n",
       "1     Maharashtra   112374333   61556074  50818259    1744698    1612314   \n",
       "2           Bihar   104099452   92341436  11758016     226916     219864   \n",
       "3     West Bengal    91276115   62183113  29093002     428498     390096   \n",
       "4  Madhya Pradesh    72626809   52557404  20069405     183057     170969   \n",
       "\n",
       "   Deaths  Active    Last_Updated_Time  Migrated_Other State_code  \\\n",
       "0    7354   23367  14/11/2020 23:53:22               0         UP   \n",
       "1   45914   85503  14/11/2020 21:47:25             967         MH   \n",
       "2    1184    5867  15/11/2020 18:54:26               1         BR   \n",
       "3    7610   30792  14/11/2020 21:15:25               0         WB   \n",
       "4    3083    9005  14/11/2020 23:53:24               0         MP   \n",
       "\n",
       "   Delta_Confirmed  Delta_Recovered  Delta_Deaths  \\\n",
       "0                0                0             0   \n",
       "1                0                0             0   \n",
       "2              247              614             5   \n",
       "3                0                0             0   \n",
       "4                0                0             0   \n",
       "\n",
       "                                         State_Notes  NumPublicBeds  \\\n",
       "0                                                NaN          58310   \n",
       "1  [Sep 9] :239 cases have been removed from the ...          68998   \n",
       "2                                                NaN          17796   \n",
       "3                                                NaN          51163   \n",
       "4  [Oct 14]: 4469 confirmed cases and 4469 recove...          38140   \n",
       "\n",
       "   NumRuralBeds  NumUrbanBeds  \n",
       "0         39104         37156  \n",
       "1         12398         39048  \n",
       "2          6083          5936  \n",
       "3         19684         58882  \n",
       "4         10020         18819  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Merging Data\n",
    "census_covid = pd.merge(censusData, covidData, how=\"inner\", on=\"State\") \n",
    "beds_census_covid = pd.merge(census_covid, bedsData, how=\"inner\", on=\"State\")\n",
    "data = beds_census_covid\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>State</th>\n",
       "      <th>Population</th>\n",
       "      <th>Rural</th>\n",
       "      <th>Urban</th>\n",
       "      <th>Confirmed</th>\n",
       "      <th>Recovered</th>\n",
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       "      <th>Last_Updated_Time</th>\n",
       "      <th>Migrated_Other</th>\n",
       "      <th>...</th>\n",
       "      <th>Delta_Recovered</th>\n",
       "      <th>Delta_Deaths</th>\n",
       "      <th>State_Notes</th>\n",
       "      <th>NumPublicBeds</th>\n",
       "      <th>NumRuralBeds</th>\n",
       "      <th>NumUrbanBeds</th>\n",
       "      <th>TotalBeds</th>\n",
       "      <th>TotalBeds_Pop_Ratio</th>\n",
       "      <th>RemainingBeds</th>\n",
       "      <th>RemainingBeds_Pop_Ratio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Uttar Pradesh</td>\n",
       "      <td>199.812341</td>\n",
       "      <td>155317278</td>\n",
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       "      <td>509903</td>\n",
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       "      <td>14/11/2020 23:53:22</td>\n",
       "      <td>0</td>\n",
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       "      <td>58310</td>\n",
       "      <td>39104</td>\n",
       "      <td>37156</td>\n",
       "      <td>134570</td>\n",
       "      <td>0.000673</td>\n",
       "      <td>111.203</td>\n",
       "      <td>0.000557</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Maharashtra</td>\n",
       "      <td>112.374333</td>\n",
       "      <td>61556074</td>\n",
       "      <td>50818259</td>\n",
       "      <td>1744698</td>\n",
       "      <td>1612314</td>\n",
       "      <td>45914</td>\n",
       "      <td>85503</td>\n",
       "      <td>14/11/2020 21:47:25</td>\n",
       "      <td>967</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>[Sep 9] :239 cases have been removed from the ...</td>\n",
       "      <td>68998</td>\n",
       "      <td>12398</td>\n",
       "      <td>39048</td>\n",
       "      <td>120444</td>\n",
       "      <td>0.001072</td>\n",
       "      <td>34.941</td>\n",
       "      <td>0.000311</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Bihar</td>\n",
       "      <td>104.099452</td>\n",
       "      <td>92341436</td>\n",
       "      <td>11758016</td>\n",
       "      <td>226916</td>\n",
       "      <td>219864</td>\n",
       "      <td>1184</td>\n",
       "      <td>5867</td>\n",
       "      <td>15/11/2020 18:54:26</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>614</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17796</td>\n",
       "      <td>6083</td>\n",
       "      <td>5936</td>\n",
       "      <td>29815</td>\n",
       "      <td>0.000286</td>\n",
       "      <td>23.948</td>\n",
       "      <td>0.000230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>West Bengal</td>\n",
       "      <td>91.276115</td>\n",
       "      <td>62183113</td>\n",
       "      <td>29093002</td>\n",
       "      <td>428498</td>\n",
       "      <td>390096</td>\n",
       "      <td>7610</td>\n",
       "      <td>30792</td>\n",
       "      <td>14/11/2020 21:15:25</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>51163</td>\n",
       "      <td>19684</td>\n",
       "      <td>58882</td>\n",
       "      <td>129729</td>\n",
       "      <td>0.001421</td>\n",
       "      <td>98.937</td>\n",
       "      <td>0.001084</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Madhya Pradesh</td>\n",
       "      <td>72.626809</td>\n",
       "      <td>52557404</td>\n",
       "      <td>20069405</td>\n",
       "      <td>183057</td>\n",
       "      <td>170969</td>\n",
       "      <td>3083</td>\n",
       "      <td>9005</td>\n",
       "      <td>14/11/2020 23:53:24</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>[Oct 14]: 4469 confirmed cases and 4469 recove...</td>\n",
       "      <td>38140</td>\n",
       "      <td>10020</td>\n",
       "      <td>18819</td>\n",
       "      <td>66979</td>\n",
       "      <td>0.000922</td>\n",
       "      <td>57.974</td>\n",
       "      <td>0.000798</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            State  Population      Rural     Urban  Confirmed  Recovered  \\\n",
       "0   Uttar Pradesh  199.812341  155317278  44495063     509903     479182   \n",
       "1     Maharashtra  112.374333   61556074  50818259    1744698    1612314   \n",
       "2           Bihar  104.099452   92341436  11758016     226916     219864   \n",
       "3     West Bengal   91.276115   62183113  29093002     428498     390096   \n",
       "4  Madhya Pradesh   72.626809   52557404  20069405     183057     170969   \n",
       "\n",
       "   Deaths  Active    Last_Updated_Time  Migrated_Other  ... Delta_Recovered  \\\n",
       "0    7354   23367  14/11/2020 23:53:22               0  ...               0   \n",
       "1   45914   85503  14/11/2020 21:47:25             967  ...               0   \n",
       "2    1184    5867  15/11/2020 18:54:26               1  ...             614   \n",
       "3    7610   30792  14/11/2020 21:15:25               0  ...               0   \n",
       "4    3083    9005  14/11/2020 23:53:24               0  ...               0   \n",
       "\n",
       "   Delta_Deaths                                        State_Notes  \\\n",
       "0             0                                                NaN   \n",
       "1             0  [Sep 9] :239 cases have been removed from the ...   \n",
       "2             5                                                NaN   \n",
       "3             0                                                NaN   \n",
       "4             0  [Oct 14]: 4469 confirmed cases and 4469 recove...   \n",
       "\n",
       "   NumPublicBeds NumRuralBeds  NumUrbanBeds  TotalBeds  TotalBeds_Pop_Ratio  \\\n",
       "0          58310        39104         37156     134570             0.000673   \n",
       "1          68998        12398         39048     120444             0.001072   \n",
       "2          17796         6083          5936      29815             0.000286   \n",
       "3          51163        19684         58882     129729             0.001421   \n",
       "4          38140        10020         18819      66979             0.000922   \n",
       "\n",
       "   RemainingBeds  RemainingBeds_Pop_Ratio  \n",
       "0        111.203                 0.000557  \n",
       "1         34.941                 0.000311  \n",
       "2         23.948                 0.000230  \n",
       "3         98.937                 0.001084  \n",
       "4         57.974                 0.000798  \n",
       "\n",
       "[5 rows x 22 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# # For Calculation based on Urban / Rural Data. Here In this case we don't do for now\n",
    "# data[\"RuralBeds_Pop_ratio\"] = data[\"NumRuralBeds\"] / data[\"Rural\"]\n",
    "# data[\"UrbanBeds_Pop_ratio\"] = data[\"NumUrbanBeds\"] / data[\"Urban\"]\n",
    "# State wise data\n",
    "data[\"TotalBeds\"] = data[\"NumPublicBeds\"] + data[\"NumRuralBeds\"] + data[\"NumUrbanBeds\"]\n",
    "data[\"TotalBeds_Pop_Ratio\"] = data[\"TotalBeds\"] / data[\"Population\"]\n",
    "data[\"RemainingBeds\"] = data[\"TotalBeds\"] - data[\"Active\"]\n",
    "# For Scatter Plot\n",
    "data[\"RemainingBeds_Pop_Ratio\"] = data[\"RemainingBeds\"] / data[\"Population\"]\n",
    "\n",
    "# For better scale in scatter plot\n",
    "data[\"Population\"] = data[\"Population\"]/1000000 # In Million\n",
    "data[\"RemainingBeds\"] = data[\"RemainingBeds\"] / 1000 # In 1000\n",
    "\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
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       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option)\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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\" width=\"640\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.colors as col\n",
    "import matplotlib.cm as cm\n",
    "\n",
    "# plt.figure(figsize=(8, 7))\n",
    "# Color Map\n",
    "colorMap = \"YlOrRd\"\n",
    "# Threashold where we consider if less than threshold its high risk\n",
    "threshold = 0.0008\n",
    "\n",
    "# Scatter Plot with color shade as the ratio of decreasing amount of beds\n",
    "fig, ax = plt.subplots()\n",
    "ax.scatter(data[\"Population\"], data[\"RemainingBeds\"], c=1/data[\"RemainingBeds_Pop_Ratio\"], cmap=colorMap, picker=5)\n",
    "\n",
    "# Setting OFF Grid\n",
    "plt.grid(False)\n",
    "\n",
    "# Setting the Axes and background color\n",
    "# ax = plt.gca()\n",
    "# ax.set_ylim([0, 0.01])\n",
    "ax.set_facecolor('white')\n",
    "\n",
    "n = data[\"State\"].values\n",
    "for i, txt in enumerate(n):\n",
    "    if  data[\"RemainingBeds_Pop_Ratio\"][i] < threshold:\n",
    "        ax.annotate(txt, (data[\"Population\"][i], data[\"RemainingBeds\"][i]))\n",
    "\n",
    "\n",
    "# Setting XLabel and YLabel\n",
    "plt.xlabel(\"Population in million\")\n",
    "plt.ylabel(\"Remaining Number of beds (x1000)\")\n",
    "plt.title(\"High risk states in India to defend COVID 19\")\n",
    "\n",
    "# Initializing the text box\n",
    "text = plt.text(150, 30, \"\")\n",
    "\n",
    "# Interactive Pick Event Listner Function\n",
    "def interactive(event):\n",
    "#     plt.title(data.iloc[event.ind[0]][\"State\"])\n",
    "    # Setting the data inside text box\n",
    "    text.set_text(\"State: {}\\nRemaining Beds(x1000): {}\\nPopulation(million): {}\".format(data.iloc[event.ind[0]][\"State\"], data.iloc[event.ind[0]][\"TotalBeds\"], data.iloc[event.ind[0]][\"Population\"]))\n",
    "\n",
    "    \n",
    "# Horizontal colormap for the data\n",
    "cpick = cm.ScalarMappable(cmap=colorMap, norm=col.Normalize(vmin=0, vmax=1.0))\n",
    "cpick.set_array([])\n",
    "cbar = plt.colorbar(cpick, orientation=\"horizontal\")\n",
    "\n",
    "# Binding Event Listner to the figure\n",
    "plt.gcf().canvas.mpl_connect('pick_event', interactive);\n",
    "\n",
    "# Show\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Kerala, is where more care has to be taken. \n",
      "State has active cases and have 77515 beds available in total for 33.406061 million people.\n"
     ]
    }
   ],
   "source": [
    "morecare = data[data.RemainingBeds_Pop_Ratio == data.RemainingBeds_Pop_Ratio.min()]\n",
    "print(\"{}, is where more care has to be taken. \\nState has active cases and have {} beds available in total for {} million people.\".format(morecare[\"State\"].values[0], morecare[\"TotalBeds\"].values[0], morecare[\"Population\"].values[0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Conclusion\n",
    "\n",
    "This analytics shows how severe is current condition and how drastically conditions change. Also analysis shows how critical is the days to come. Stay Home. Stay Safe. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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