Effect of Vaccination

Effect of Vaccination.ipynb

{
  "cells": [
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "<div style=\"width: 100%; overflow: hidden;\">\n    <div style=\"width: 150px; float: left;\"> <img src=\"https://raw.githubusercontent.com/DataForScience/Networks/master/data/D4Sci_logo_ball.png\" alt=\"Data For Science, Inc\" align=\"left\" border=\"0\" width=150px> </div>\n    <div style=\"float: left; margin-left: 10px;\"> <h1>Effect of Vaccination on Disease Prevalence</h1>\n        <p>Bruno Gonçalves<br/>\n        <a href=\"http://www.data4sci.com/\">www.data4sci.com</a><br/>\n            @bgoncalves, @data4sci</p></div>\n</div>"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "import numpy as np\nimport pandas as pd\n\nimport matplotlib\nimport matplotlib.pyplot as plt \n\nimport requests\n\nimport watermark\n\n%load_ext watermark\n%matplotlib inline",
      "execution_count": 1,
      "outputs": []
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "We start by print out the versions of the libraries we're using for future reference"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "%watermark -n -v -m -g -iv",
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": "Python implementation: CPython\nPython version       : 3.10.9\nIPython version      : 8.10.0\n\nCompiler    : Clang 14.0.6 \nOS          : Darwin\nRelease     : 22.5.0\nMachine     : x86_64\nProcessor   : i386\nCPU cores   : 16\nArchitecture: 64bit\n\nGit hash: dbde4540ea5e768f764225ff2efd14962858a1ad\n\npandas    : 1.5.3\nrequests  : 2.28.1\nmatplotlib: 3.7.0\nnumpy     : 1.23.5\nwatermark : 2.4.2\n\n",
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "# Code"
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "We start by downloading the [data](https://graphics.wsj.com/infectious-diseases-and-vaccines/data/datum.json) used by the [WSJ visualization](http://graphics.wsj.com/infectious-diseases-and-vaccines/). You can find it at:"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "url = 'https://graphics.wsj.com/infectious-diseases-and-vaccines/data/datum.json'",
      "execution_count": 3,
      "outputs": []
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "And download it with a simple request"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "req = requests.get(url)",
      "execution_count": 4,
      "outputs": []
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "We can check that it all went well"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "req.status_code",
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 5,
          "data": {
            "text/plain": "200"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "And convert the raw data into a JSON object"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "raw_data = req.json()",
      "execution_count": 6,
      "outputs": []
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "Get a quick dictionary mapping each dataset to its location in the JSON array. In total, there are 7 different datasets"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "data_dict = {data['id']: i for i, data in enumerate(raw_data)}\ndata_dict",
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 7,
          "data": {
            "text/plain": "{'measles': 0,\n 'hepatitisA': 1,\n 'mumps': 2,\n 'pertussis': 3,\n 'polio': 4,\n 'rubella': 5,\n 'smallpox': 6}"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "For simplicity, we'll visualize just one of the datasets, the one for measles. You can try out the others by changing your selection here."
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "selected_dataset = raw_data[data_dict[\"measles\"]]['data']",
      "execution_count": 8,
      "outputs": []
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "We can now generate a pandas DataFrame from the selected dataset"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "data = pd.DataFrame(selected_dataset['values']['data'], \n                    columns=['Year', 'State', 'value'])\ndata",
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 9,
          "data": {
            "text/plain": "      Year  State       value\n0     1928      0         NaN\n1     1928      1  334.962121\n2     1928      2  481.808338\n3     1928      3  200.710901\n4     1928      4   69.189746\n...    ...    ...         ...\n4329  2012     46    0.000000\n4330  2012     47    0.000000\n4331  2012     48    0.000000\n4332  2012     49    0.000000\n4333  2012     50    0.000000\n\n[4334 rows x 3 columns]",
            "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>Year</th>\n      <th>State</th>\n      <th>value</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1928</td>\n      <td>0</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1928</td>\n      <td>1</td>\n      <td>334.962121</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>1928</td>\n      <td>2</td>\n      <td>481.808338</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>1928</td>\n      <td>3</td>\n      <td>200.710901</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>1928</td>\n      <td>4</td>\n      <td>69.189746</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>4329</th>\n      <td>2012</td>\n      <td>46</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>4330</th>\n      <td>2012</td>\n      <td>47</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>4331</th>\n      <td>2012</td>\n      <td>48</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>4332</th>\n      <td>2012</td>\n      <td>49</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>4333</th>\n      <td>2012</td>\n      <td>50</td>\n      <td>0.000000</td>\n    </tr>\n  </tbody>\n</table>\n<p>4334 rows × 3 columns</p>\n</div>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "We also have information about when the vaccine became available"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "vaccine_year = selected_dataset['chart_options']['vaccine_year']\nvaccine_year",
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 10,
          "data": {
            "text/plain": "1963"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "Finally, we use a pivot table to put our dataset into the final form that we can visualize using a heatmap, with states as rows and years as columns"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "img = pd.pivot_table(data, index='State', columns='Year', values='value')\nplt.imshow(img)",
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 11,
          "data": {
            "text/plain": "<matplotlib.image.AxesImage at 0x7fb3b842e530>"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 640x480 with 1 Axes>",
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "The column for the vaccine year is then given by:"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "vaccine_pos = vaccine_year - data.Year.min()",
      "execution_count": 12,
      "outputs": []
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "Which we can easily add to our figure. We also make the figure larger for the sake of ease of visualization"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "# Create a figure and axis with a pre defined size\nfig, ax = plt.subplots(1, figsize=(16, 10))\n\n# Generate a histogram of the data\nim = ax.imshow(img)\n\n# Add the vertical line indicating when vaccines became available\nax.axvline(x=vaccine_pos, linewidth=3, color='red')\nax.text(x=vaccine_pos, y=-1, s='Vaccine introduced', fontsize=14)",
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 13,
          "data": {
            "text/plain": "Text(35, -1, 'Vaccine introduced')"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 1600x1000 with 1 Axes>",
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "A heatmap isn't very helpful without a color bar to identify which value is which, so we also add one"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "# Create a figure and axis with a pre defined size\nfig, ax = plt.subplots(1, figsize=(16, 10))\n\n# Generate a histogram of the data\nim = ax.imshow(img)\n\n# Add the vertical line indicating when vaccines became available\nax.axvline(x=vaccine_pos, linewidth=3, color='red')\nax.text(x=vaccine_pos, y=-1, s='Vaccine introduced', fontsize=14)\n\n# Add the color bar\nfig.colorbar(im, orientation='horizontal')",
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 14,
          "data": {
            "text/plain": "<matplotlib.colorbar.Colorbar at 0x7fb3c8417160>"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 1600x1000 with 2 Axes>",
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "Unfortunately, the default color bar is a bit too large for our purpose and is causing our main figure to be shrunk. We manually set the color bar size into something more manageable"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "# Create a figure and axis with a pre defined size\nfig, ax = plt.subplots(1, figsize=(16, 10))\n\n# Generate a histogram of the data\nim = ax.imshow(img)\n\n# Add the vertical line indicating when vaccines became available\nax.axvline(x=vaccine_pos, linewidth=3, color='red')\nax.text(x=vaccine_pos, y=-1, s='Vaccine introduced', fontsize=14)\n\n# Set the size of the axis for the color bar\ncax = fig.add_axes([0.4, 0.05, 0.22, 0.02])\n\n# Add the color bar\nfig.colorbar(im, cax=cax, orientation='horizontal')",
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 15,
          "data": {
            "text/plain": "<matplotlib.colorbar.Colorbar at 0x7fb3a84f79a0>"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 1600x1000 with 2 Axes>",
            "image/png": 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DAwMxMDAQv/mbv/mM2RNOOCH++I//ON773vfGGWecEW9605ti6tSp8U//9E9x3333xetf//p461vfOnb9E088Mf77f//v8cEPfnDs+h0dHfH3f//3cfrpp4/7q85P+q3f+q347Gc/G7/2a78WK1eujNmzZ8c3v/nN2LZtWzzvec8b+0MhT/rN3/zNuPnmm+NLX/pSLFmyJP7Tf/pP0d/fH2vWrIl/+Zd/ic985jPxhje8ISIiPvnJT8b9998f73//+8fu+4GBgVi7dm3ccccd8cADD8QTTzwx9lel/+zP/ixe8pKXxNvf/vb46le/GkuWLBn7i9cve9nLDvjuRwCAI6oEAJhk3v72t5cRUc6dO7ccGRnZ5/PDw8PlRz/60XLJkiVld3d3eeyxx5aXXXZZuWPHjnLRokXlokWL9sns3bu3/NM//dPy+c9/ftnb21tOmzatPPXUU8v3vve95datW8ddd926deV73vOe8vjjjy+7urrKmTNnlmeffXZ59dVXj7teRJTnnXfeuMsuvvjiMiLK1atX77OGD37wg2VElP/+7/++z+dWrlxZvu51rytnz55ddnZ2lvPmzSvPPffc8sMf/nC5Zs2aZzpk5erVq8uIKC+++OJxl5933nnlgV4SHmitn/vc58rTTz+97O7uLiNi3PE80PH9aV/72tfK8847r+zr6yu7u7vL008/vfz4xz++3/uyLMvy05/+dHnqqaeWXV1d5THHHFO+733vK3fv3r3f41uWZflv//Zv5dlnn112d3eXRx11VPm2t72tXLdu3QFva7PZLP/qr/6qPOecc8qpU6eWU6ZMKZcsWVK+613v2ufY7t69u7zqqqvKM888s5w6dWrZ29tbLl68uHzDG95Qfv7zn9/nNtx9993la1/72nLatGllX19feeGFF5Z333330+4DAIAjqSjL/fxMBAAAAADwM8/vHAQAAACAijIcBAAAAICKMhwEAAAAgIoyHAQAAACAijIcBAAAAICK6mj3Ap6q2WzG448/Hn19fVEURbuXAwAAAACTSlmWsWPHjliwYEHUak//3sBn3XDw8ccfj4ULF7Z7GQAAAAAwqa1duzaOOeaYp73Os2442NfXFxE/WXx/f3+bVwPA4fKaN12Tzk79wROp3GNvODbdOf+Wbens6EB3Krdrbi4XEdGzbTSV6964J905krydERHD/eNfkgwM7YoV//rH4y5b9urfi8HuqeMum/Lo7nRncf9DqVztqJnpzsaGzalcOTKc7mxJrZ7Llc18Z1nms23wtcHPp3KvH3h7vjT70zUVObYR+ePbSmdVtLR3K2Ky7d3Jdp+247w72c4Nk+0+5fAYjZH4Vvzz2Jzt6TzrhoNP/ihxf3+/4SDAz7COzp58tpYbQtW7W+is5wdf0ZHr7ejMd3Z05IaDHfX8UKdM3s6IiGbn+Jcknc3ReOqrgM7O7n32TUe9ke4siq5Urpbcfz/p7EzlyqJNQ50iORyMFoaDMbkGWNnXqx3JvRAR+eFgRY5tRP74+v+PZ9bS3q2IybZ3J9t92o7z7mQ7N0y2+5TD5P8+7R/Mr+w7bH+Q5JOf/GQsXrw4enp64swzz4xvfvObh6sKAAAAAEg4LMPBG264IS699NK4/PLL47vf/W687GUviwsvvDDWrFlzOOoAAAAAgITDMhy8+uqr453vfGf82q/9WjznOc+Ja665JhYuXBif+tSnDkcdAAAAAJBwyIeDw8PDcccdd8QFF1ww7vILLrggbr311kNdBwAAAAAkHfI/SLJp06ZoNBoxd+7ccZfPnTs31q1bt8/1h4aGYmhoaOzj7du3H+olAQAAAAD7cdj+IMlT/xpKWZb7/QspV155ZQwMDIz9W7hw4eFaEgAAAADwUw75cHDWrFlRr9f3eZfghg0b9nk3YUTEBz7wgRgcHBz7t3bt2kO9JAAAAABgPw75cLCrqyvOPPPMWLly5bjLV65cGS9+8Yv3uX53d3f09/eP+wcAAAAAHH6H/HcORkRcdtll8ba3vS3OOuusOPfcc+Mv//IvY82aNfGud73rcNQBAAAAAAmHZTj4S7/0S7F58+b4oz/6o3jiiSfitNNOi3/+53+ORYsWHY46AAAAACDhsAwHIyIuueSSuOSSSw7XlwcAAAAAWnTY/loxAAAAAPDsVpRlWbZ7ET9t+/btMTAwEK868dLoqHdPOF8+sSHVW3R1pnIRETFrZirWuP/H6cqie+LHJiJi9MXPTXd2P7QxnR1d82g6m3b26alY/cEn0pXl3r3pbG32UbnOTVvSnVErUrGiry9dOfpY/vjWp01NZ7OaQ0OpXO2E/K9RaD7wcCrXynmsOHZBOluueTyVq82Zle5sPL4ulavPyj3OIiKiuyufHR5JxZqD29OV5fBwKlcUufNCq5pPOUYD5d74cvNr4y77f2qvj8GiZ9xl5bm5c31ERO32e3PBZgsvncpmLjY6mu9sRXI/FB3581E5ktu7k83K5op0dmlt2SFcCe1mLzx7Ze+bVu6XVvZDO7RjD06mY+QxSjuNliNxU3wtBgcHn/GP/3rnIAAAAABUlOEgAAAAAFSU4SAAAAAAVJThIAAAAABUlOEgAAAAAFSU4SAAAAAAVJThIAAAAABUlOEgAAAAAFSU4SAAAAAAVJThIAAAAABUlOEgAAAAAFSU4SAAAAAAVJThIAAAAABUVEe7F3AgjYHeKDp6JpwrHhlO9ZV7h1K5iIgY3J7PJtWPnp/KFRt3pzub6zems1GWuVxRpCvrg3tywaEW9sLISD7bbKZiRX9furLckzxGLdzOorOF005XZy43OpqurM+YnsrtWjSQ7ux9OHmMTliY7oz1W9LRXa9+bio37ZYH0p21vmmp3Mixs9Od9e/en84W06amcunHaEQUXV3JYP68G41GOlp7yuO7VjYi9u57nVox/nrl7femO7OKev57q2WZPL4tnMdaknz+LkdbeD6siKW1Ze1ews+0lc0Vqdyku19aOGevbHzpEC7k4LTj+Gb3wmTrbOXYTrZjNJn20WS7nVSXdw4CAAAAQEUZDgIAAABARRkOAgAAAEBFGQ4CAAAAQEUZDgIAAABARRkOAgAAAEBFGQ4CAAAAQEUZDgIAAABARRkOAgAAAEBFGQ4CAAAAQEUZDgIAAABARRkOAgAAAEBFGQ4CAAAAQEUZDgIAAABARXW0ewEH0vHopuiodU0412iWucLnn5zLRURZL3LBb9+d7syqbduRzpY93fnsmaekcjuO7Ul3zvjG/blgvZ7uLBYdk87uPWYglRvpy6932rceTOV2nLMo3Tl15c50tnHi0alcx9pN6c49z5mfynVtH0l3FkfPS+UefNOMdOfxX85/r6h7W+62jp6a30ebzuhN5QYeyt8vQ68+I50dmZY7vp27munOqfdvTuVG5venO+vbh9PZ0enjn2NGhndGfGv8dUbOOilGuqaNu6xz8+50Zzy4NhXLPkYjIspHHs0Fi+RrjYiIIv/4LpLPiWWj0UJn7raWo6PpTn72LK0tS+VWNlcc4pU8s+xaJ2vvkVaV29mKdhyjdnS28vieTOcUyPDOQQAAAACoKMNBAAAAAKgow0EAAAAAqCjDQQAAAACoKMNBAAAAAKgow0EAAAAAqCjDQQAAAACoKMNBAAAAAKgow0EAAAAAqCjDQQAAAACoKMNBAAAAAKgow0EAAAAAqCjDQQAAAACoqI52L+BAGtsGoyg6J5wr6rl5586FU1K5iIgpj+5O5TrmzU13NmZMTeWKRpnuLOfPTGd3z+tO5aZ/5a50ZzFvTiq34RVHpztnf2dLOtv9/TWp3LpfW5LunHbvjFSuc3sj3VkcMz+dbfTkTlm1zfn7pXP7UalcfeuudGds3JyKHfsvA+nKYvdQOlsvilywI//9qVnfy513O9dsSneWffnniWJHbr2RPbYRUe7M7cGuXXvynUP5fdT54+HxHzf37nudu1ZHZ61n3GVFT88+1ztYZSN3LivXPJbvHBlNBvPP31Hmz9llM5ltZe/ml9setXoulz227eqsiKW1Ze1eAvuT3fMR9j1j2vH4dk5hsvDOQQAAAACoKMNBAAAAAKgow0EAAAAAqCjDQQAAAACoKMNBAAAAAKgow0EAAAAAqCjDQQAAAACoKMNBAAAAAKgow0EAAAAAqCjDQQAAAACoKMNBAAAAAKgow0EAAAAAqCjDQQAAAACoKMNBAAAAAKiojnYv4EDq0weiXuuacK65bTDVN3hcPZWLiJi6pswFa/nZbPnd+1K5R99/drozkjczIuKYq2/PBbs6051bz16Qys340Z50Z7FtRzq7+8zjUrmjb9qV7tzw8tmp3Iz79qY7y0efSGe3npdb79zv9aY798zPZbu786fXjt6Jn/siIrad2JPunLU1v+/3zp+Syk351v3pztrcWanc5vMXpjtnfuOBdHb4ucemch3b8o+1GJiWihWbt6Urm4tz592IiNq28eeyYnR3xM7x1ynmHBVFx1P229bc835ERHHM/FSulfNYLfm81hxOV0Y0Gy2Ec4p6/nVV2Tjy621FUStSubLM5X4SbuazPK2VzRVHvHNpbdkR75x02nAe4/Bq5bHWjseMcwM/67xzEAAAAAAqynAQAAAAACrKcBAAAAAAKspwEAAAAAAqynAQAAAAACrKcBAAAAAAKspwEAAAAAAqynAQAAAAACrKcBAAAAAAKspwEAAAAAAqynAQAAAAACrKcBAAAAAAKspwEAAAAAAqynAQAAAAACqqo90LOJA9px0dHR09E84VjWNSfUd/+u5ULiJi+2tOTeUGduxNd9ZPPC6VW3TDY+nO+y9ZkM7W58xK5faeMj/dOfDP96Rym/6f09Kds7f1pbO9awZTueG509KdM+7L7cHaSCPdWfRO/HH9pDm3bU3ltl1wcrpz+o33p3Ib33BKunP2P+cep3Nu3J7uXPPLx6Wzx/zZ91K5YvpAunP1L81L5Y77//0g3VkuPjqdHe2pp3LF1K50Z23PaC7Y1Znu3Dsr//ie+ujGcR8XjT37XKfYtSeKpxzKsp47tj/5gkUq1hweyXc2k+fP2pG/nRERUZb5bFLRkdyDtfztLIeG8tnR5GOtFen90Mx3tmEvtMPS2rJ2LwE4TFY2V6Sz2XNDK51wJHnnIAAAAABUlOEgAAAAAFSU4SAAAAAAVJThIAAAAABUlOEgAAAAAFSU4SAAAAAAVJThIAAAAABUlOEgAAAAAFSU4SAAAAAAVJThIAAAAABUlOEgAAAAAFSU4SAAAAAAVJThIAAAAABUVEe7F3Ag9d2jUe8YnXCu47sPpPqKKVNSuYiIsl6kcs2H1qQ7i67OVO7R33heuvOkj92XzpZHzUjlutduy3cmczPv3ZXujLVPpKONU49L5bp++Gi6s+jM7aOypyvd2dw2mM7G4gWpWP9Xv5vvXJjrnPPvj+U7a7nv25TDI+nKY1fk19sYGkrlyul96c5j//j2VK6YNTPdWT6Uf6z1rutN5ZpbtqU7a/3TUrnG9p3pzt6Nm9PZ0b3j99FouXff62zcFKNFz7jLilruOTgiotywMZUrOnLnzoiIstnIBbO5FhUduZeK5ejEX8MxAW3aD1WwsrniiHcurS1LZ9ux3la0cluzWjlG2fW6Xw6vdhzfyXaf8uyU3UdH4jHqnYMAAAAAUFGGgwAAAABQUYaDAAAAAFBRhoMAAAAAUFGGgwAAAABQUYaDAAAAAFBRhoMAAAAAUFGGgwAAAABQUYaDAAAAAFBRhoMAAAAAUFGGgwAAAABQUYaDAAAAAFBRhoMAAAAAUFGGgwAAAABQUR3tXsCBdD22JTpq3RMPzpie6msObk/lIiKGBopUrhwdSXcWnbm7rpE4pE8qj5mbzo7296RywwOd6c5pu/fmgnc/mO5sDg2ls2uXTkvlFn18Z7pz5IUnp3Kd33so3Vl05zdhbe2GVG50eDjdGbXk91BG851ls5nKFfX893vKzVvT2aK3N5UbnZ7LRUQUjUY6mzaSP2fHntzzRG3a1HRl0dWV61xyXLozHl+fjnYcNXP8x43dEY895Trz50ZHfcq4y5obN6U7s+ejcriFvdAGtan5fTTZbuukUuTOCxERUZaHbh3PYiubK9q9hCOildu5tLbsEK7k4Ey2+6Udx6iVzsl2fLPrrdIxgie143x0sLxzEAAAAAAqynAQAAAAACrKcBAAAAAAKspwEAAAAAAqynAQAAAAACrKcBAAAAAAKspwEAAAAAAqynAQAAAAACrKcBAAAAAAKspwEAAAAAAqynAQAAAAACrKcBAAAAAAKspwEAAAAAAqqijLsmz3In7a9u3bY2BgIF7V/9boKLomnC+Hh1O9xeKFqVxERLF7byo3uubRfGfXxI9NRETz505Jd9a/e3862xwaSmezynPPSOU67l+b7xweyWdPXpTK1dZuSHc2tw3mOqcP5Ds3b0lna1Om5Dr35B6jERG1/mm5YDN/ai2zj5dmM90ZnZ3paLlnTypXOy5/3m0+nHucZvdQq4oZucdMc/3GQ7ySZ5Z9fonIPwdHRDz15chAuTdWDP39uMuWdf/nGCx6xl1W6+5Od7bluWlkNBdsNg7tQg5WUSRzLXz/uUyey55dL2kB+BmysrkilVtaW3aIVwIHb7QciZviazE4OBj9/f1Pe13vHAQAAACAijIcBAAAAICKMhwEAAAAgIoyHAQAAACAijIcBAAAAICKMhwEAAAAgIoyHAQAAACAijIcBAAAAICKMhwEAAAAgIoyHAQAAACAijIcBAAAAICKMhwEAAAAgIoyHAQAAACAijIcBAAAAICK6mj3Ag6ooyOiNvHllbt3p+qKDZtTuYiI5t6hXLDIz2Zr06amcjsX9KQ7p/6fvels1srminT2whN/N5VrDo+kO6PZTEfrW3amcmWjke6sdXfngi10ls0ync0qujrT2ebg9lSuvvDofOfj63KdM2ekO6OWPx81s/thx650Z9TruVytOPKdEVHuzp0/m3uP/Hk32tG5H2U5vO9lQ8NRFuPvw8ZQ8jk4IqJI7ofyyJ/H2iZ7W1t4qFXq+GZNtr3bhvVmX0MurS1Ld1ZFK6/PHV8OhVb2YJa9y8867xwEAAAAgIoyHAQAAACAiprwcPCWW26J173udbFgwYIoiiK++tWvjvt8WZaxfPnyWLBgQfT29sb5558f99xzz6FaLwAAAABwiEx4OLhr16543vOeF9dee+1+P3/VVVfF1VdfHddee22sWrUq5s2bF0uXLo0dO3a0vFgAAAAA4NCZ8F/8uPDCC+PCCy/c7+fKsoxrrrkmLr/88njjG98YERHXXXddzJ07N66//vr4jd/4jdZWCwAAAAAcMof0dw6uXr061q1bFxdccMHYZd3d3XHeeefFrbfeut/M0NBQbN++fdw/AAAAAODwO6TDwXXr1kVExNy5c8ddPnfu3LHPPdWVV14ZAwMDY/8WLlx4KJcEAAAAABzAYflrxUVRjPu4LMt9LnvSBz7wgRgcHBz7t3bt2sOxJAAAAADgKSb8Owefzrx58yLiJ+8gnD9//tjlGzZs2OfdhE/q7u6O7u7uQ7kMAAAAAOAgHNJ3Di5evDjmzZsXK1euHLtseHg4br755njxi198KKsAAAAAgBZN+J2DO3fujB//+MdjH69evTruuuuumDlzZhx77LFx6aWXxhVXXBFLliyJJUuWxBVXXBFTpkyJiy666JAuHAAAAABozYSHg7fffnu84hWvGPv4sssui4iIiy++OD73uc/F+9///tizZ09ccsklsXXr1jj77LPjxhtvjL6+vkO3agAAAACgZRMeDp5//vlRluUBP18URSxfvjyWL1/eyroAAAAAgMOsKJ9u0tcG27dvj4GBgRgcHIz+/v52LwfgiFtaf1Mqt/lXz0l33vnpy9LZrJ/ve0c6u/a3npfKTVmff8qrjeSzM756dyo38qKT052753alcn1fWpXurJ26JJ1t9I//42QDI7vi77/zsXGX/ecX/X4Mdk4dd1nHD1anO4d/7sRUrn7zd9OdUSR/3XOzke9sRa2eihWd+b95Vw4NpbPQVkWRyz27/neMNlvZXJHKLa0tO8QrObyytzNi8t1WaJfRciRuiq8d1HztkP5BEgAAAABg8jAcBAAAAICKMhwEAAAAgIoyHAQAAACAijIcBAAAAICKMhwEAAAAgIoyHAQAAACAijIcBAAAAICKMhwEAAAAgIoyHAQAAACAijIcBAAAAICKMhwEAAAAgIoyHAQAAACAijIcBAAAAICKKsqyLNu9iJ+2ffv2GBgYiPNrb4yOonPC+VpPd674xGNzuYho/uCBZLCR7oxaPRWrL1mcrtxy1qx0duBvb0tnszqOXpALNlq4X4oin80+FDsn/jhpuXN0NF3Z2Lotna1Nm5oM5h4vERExa3oqVj7yWL4zez56cG26sqjnj1G5+OhUrtndke7s2Lg9lSvrLXxPrJbPNvt7U7litJnuLO99MJWrTR/Id86emc7Wdu0Z9/FAY3d88ZFPjbvszYt+MwbrU8ZdtvVF89Od0+/alAtuy+2/iIjGxs25YCuvGVqRPH/WenvSlc09e5PByXWMWllvvb8/lWtsz+/dyWZlc0W7l3BELK0tS2fbcYxaWW87TKZj1Mpaq3K/eLxQNaPlSNwUX4vBwcHof4bXDt45CAAAAAAVZTgIAAAAABVlOAgAAAAAFWU4CAAAAAAVZTgIAAAAABVlOAgAAAAAFWU4CAAAAAAVZTgIAAAAABVlOAgAAAAAFWU4CAAAAAAVZTgIAAAAABVlOAgAAAAAFWU4CAAAAAAVZTgIAAAAABXV0e4FHFDZjIjmhGNFb0+ub/2WXK5N6rOPSuWK3XvTnTNvfDCdLRYtzAUbE98DT2rOGkjl9hw9Nd055cdb09ny0SdywcXJYxsRe4/pS+V673g43VmfNyedbczszwV/8EC6sz61N5Ubfd6SdGfHph254LEL0p2xZTAdLXYPpXJ7Fuf2X0TE1HsfTuWKebPTnY3pU/LZqZ2pXPePN6Q7m885IZUrBnelO4ttyb0bEUMnzh3/8XB3xCNPuc4xM2Koa9q4y6Z/d2O6s9mfe3zXW3guLTpzL73KoUa6M4oiny1zz8PlUO680Epn1Or5zmYLxzeZ7Tg6f84efTz5mqFNVjZXpHJLa8sO8UoOb+dkup1VUnQ8e/+X91Cyj55Z9jEa4fjys887BwEAAACgogwHAQAAAKCiDAcBAAAAoKIMBwEAAACgogwHAQAAAKCiDAcBAAAAoKIMBwEAAACgogwHAQAAAKCiDAcBAAAAoKIMBwEAAACgogwHAQAAAKCiDAcBAAAAoKIMBwEAAACgojravYADqU3pjVrRNeHc6EkLU32dD69P5SIiynNOy3U+tC7fOTKSyg0955h059CMznR2yle/k8rVp09Pdz72S4tSuaP//HvpzrIs09nhc5+TynXf8eN0Z+9wbh8V3RN/bD6puWlLOluv13OdnflTXXP6tFSudud96c5ixvRUrpX9V0ydks6WWwdTuWn35+7PiIjoyN2nzf7edGXtnofS2Y7kfdrcui3dWWs2U7ly7950ZxRFOtq56v7xH5f7rqPzrgejs+gZf2FX/nwUD+1IxZq9+X1UDg/ngi0c22jh3BC13OO0bLZwPkqe68tGI93Z0vFNGn38iSPe2S5La8t0Pgu1Zb3Zx1oL57FydDSdnWz3KU+vHffnyuaKdNb+40jyzkEAAAAAqCjDQQAAAACoKMNBAAAAAKgow0EAAAAAqCjDQQAAAACoKMNBAAAAAKgow0EAAAAAqCjDQQAAAACoKMNBAAAAAKgow0EAAAAAqCjDQQAAAACoKMNBAAAAAKgow0EAAAAAqCjDQQAAAACoqI52L+BAakfNiFqte8K5YtOOVF+5a3cqFxFR1opUrrk9t9aIiKjXc51d+Xlw//fWp7OjyVzRM/E98KR5t+Xu03I0u9qIcng4ne1etzPXOZJfb20ot95WHi/NPXvT2ezuLRuNfOdw8vh25E+v2T1YtnBso1nms2UuOzpjSrqy/shQKlfbM5LujCn59TZn9uWCW7elO9NaOKdEZ37fF095Xiua+z7ii1otitr46zVPODrdGd/7USpWFLnn/UmpmTt/Ft355+9WntfypS2cA5NqPT1HvLO5t4XnCX7mrGyuSOWW1pblS9vwWKuK7P3ZLq3so2y2lWOU7Wzp8QJHkHcOAgAAAEBFGQ4CAAAAQEUZDgIAAABARRkOAgAAAEBFGQ4CAAAAQEUZDgIAAABARRkOAgAAAEBFGQ4CAAAAQEUZDgIAAABARRkOAgAAAEBFGQ4CAAAAQEUZDgIAAABARRkOAgAAAEBFdbR7AQdSbt8RZTE04dzI6cen+kZOPCqVi4joveWHqVxxzPx0Z1Z992g6+8B/ya938QceSeUaGzelO5unLEjlOqZNzXced2I6u2PRtFSuZ0ZvujOKIhmcla7suPNH6ezuk2anct1rH093Nh/M7d1ty16Q7pzxlbtTuZEXnpzu7Hp8MJ3d8dzcfpj6z3elO4spU3K5LfnbObpxYzobyezK5op852S3cWPEnL8bd9END34iYnbuPPBssbS2rN1LePZrNtq9ggmpTc29bmju2ZvuLDqftS/hJ712nHedF372tLKPsvuhXfsoe1ur8lhrx16ADO8cBAAAAICKMhwEAAAAgIoyHAQAAACAijIcBAAAAICKMhwEAAAAgIoyHAQAAACAijIcBAAAAICKMhwEAAAAgIoyHAQAAACAijIcBAAAAICKMhwEAAAAgIoyHAQAAACAijIcBAAAAICKMhwEAAAAgIrqaPcCDqTo6Y2i1jXhXP3/3J3q6+jIH4qiry+VK9c8lu4sG81UrmPKienO4/5XOhq17u5cbvasdGexcXcqV+7K5SIiivseTme7pp+SynXc+aN0Zxx/bC730Jp0ZdlopLNT7lufyo220FmfOi2VO+rWdenOsif3eOles6WFzomfb5/U9+/3pXLrfvXn0p1zP/vdVG7o545Pd9aWLEhnG92578VduPA96c6yf2ouWBTpzmLLYDobvT3jPhxo7I4vPuUqb37hB2OwPmXcZaOz+9OV9fseyQU7W3jN0Jl7rJUjw+nOltTqqVjRlT+nlMNtuK1lmY42d+3KBVt4rJUjo+ksh8/S2rJ2L4GfASubK9q9hGe97GOtlWPr8c3POu8cBAAAAICKMhwEAAAAgIoyHAQAAACAijIcBAAAAICKMhwEAAAAgIoyHAQAAACAijIcBAAAAICKMhwEAAAAgIoyHAQAAACAijIcBAAAAICKMhwEAAAAgIoyHAQAAACAijIcBAAAAICKMhwEAAAAgIrqaPcCDqirI6LWOeFY0dV1GBbz9BobN6ZyRXd3vrRspmK1jdvSlcVDO9PZZiO33tFHH0t3Fk/UU7naQH+6s7k9f4w6b/5eKldMmZLuLB9JHt+iSHdGo5GOllu2pXJFPbcXIiKau/bkgtlcRJTJY1TbtTvd2RweSWfrMwZSublf+EG6s7l3byrX+a18Zzkyms7Wmrn7NN8YEfnT57PCaDm072WPPBqjxVOeO1fnO/JnowpJ7t1yT/4cGGWZz04mrdzOMrl7W3n+rsj9srS2rN1LeNZb2VyRzrbj+Lay3qzJto8m2zFqx3rb0QlHkncOAgAAAEBFGQ4CAAAAQEUZDgIAAABARRkOAgAAAEBFGQ4CAAAAQEUZDgIAAABARRkOAgAAAEBFGQ4CAAAAQEUZDgIAAABARRkOAgAAAEBFGQ4CAAAAQEUZDgIAAABARRkOAgAAAEBFdbR7AQey4ZVHR72rZ8K5Od+aeCYiYu/CgVQuIqL78Z2pXNmTP/y1XUOp3OOvmJ3uXPD1x9LZkeNnpXIdO4bTnfXHNqdyw8fPTXfWRhrpbNEoU7mRKfl9tHdmVyo39eHcno+IqD2+MZ0dfPniVG76bfm9u+v0+alc547RdGfXmk2pXNmZ3wtbXjYvnT3q+jtzwTNOSnduPmNaKjf7b7+b7qzPz58bdv7cManc1JvvS3eWx+c6o8ydiyIiGlNz55SIiPpdD4z7uFbWInaPv05tSm/UivHP80VXZ7oz5s9JxRr3/Tjf2cLxbYuiSMXK0fw5MNtZdOX3XzmUe13VNsljNOn2H4fV0tqydi/hiKjK7WzFZDtGk2m9K5sr2r0EOCjeOQgAAAAAFWU4CAAAAAAVZTgIAAAAABVlOAgAAAAAFWU4CAAAAAAVZTgIAAAAABVlOAgAAAAAFWU4CAAAAAAVZTgIAAAAABVlOAgAAAAAFWU4CAAAAAAVZTgIAAAAABVlOAgAAAAAFWU4CAAAAAAV1dHuBRzIaE8RZXcx8eBj61N93avXpnIREY+87+dSuWP/5I50Z7PRSOW2/faMdOf867els10be1O53cf2pzunPrg3ldu5sDvdOfOW/D5qTu/LBbvr6c6+W1enckOnLUx3dt2/M52dtnZPKtdYtyHd2TNrIJXbvWhqurP+ndx66/Pnpjtn37oxnS2LxLk6IsrRZrpz9t99L5Urjpmf7mw++kQ6O+323G1tDg+nO+OBR1KxojP/0qA+MprOlk95XivLfZ/nykYjyuIpl7dwiMoHcufAoqMz3znSwoLboch+Hzn/+I6yzMWGhvKd7ZA8d0ZE+hgB/CxY2VxxxDuX1pYd8U44krxzEAAAAAAqynAQAAAAACpqQsPBK6+8Ml74whdGX19fzJkzJ97whjfE/fffP+46ZVnG8uXLY8GCBdHb2xvnn39+3HPPPYd00QAAAABA6yY0HLz55pvj3e9+d9x2222xcuXKGB0djQsuuCB27do1dp2rrroqrr766rj22mtj1apVMW/evFi6dGns2LHjkC8eAAAAAMib0G8d/8Y3vjHu489+9rMxZ86cuOOOO+LlL395lGUZ11xzTVx++eXxxje+MSIirrvuupg7d25cf/318Ru/8RuHbuUAAAAAQEta+p2Dg4ODERExc+bMiIhYvXp1rFu3Li644IKx63R3d8d5550Xt956636/xtDQUGzfvn3cPwAAAADg8EsPB8uyjMsuuyxe+tKXxmmnnRYREevWrYuIiLlz54677ty5c8c+91RXXnllDAwMjP1buHBhdkkAAAAAwASkh4O/9Vu/Fd///vfj7/7u7/b5XFEU4z4uy3Kfy570gQ98IAYHB8f+rV27NrskAAAAAGACJvQ7B5/027/92/GP//iPccstt8Qxxxwzdvm8efMi4ifvIJw/f/7Y5Rs2bNjn3YRP6u7uju7u7swyAAAAAIAWTOidg2VZxm/91m/FP/zDP8T//t//OxYvXjzu84sXL4558+bFypUrxy4bHh6Om2++OV784hcfmhUDAAAAAIfEhN45+O53vzuuv/76+NrXvhZ9fX1jv0dwYGAgent7oyiKuPTSS+OKK66IJUuWxJIlS+KKK66IKVOmxEUXXXRYbgAAAAAAkDOh4eCnPvWpiIg4//zzx13+2c9+Nt7xjndERMT73//+2LNnT1xyySWxdevWOPvss+PGG2+Mvr6+Q7JgAAAAAODQmNBwsCzLZ7xOURSxfPnyWL58eXZNAAAAAMARkPqDJEfCvG9tjY76xP9Qya7zTkn1TVv1SCoXEXHs/zuYyq37jTPTnXP/5/9J5U657Pvpzq1vfH4626zv/69VP5Oj/tcP052jzzkulZt56+Ppzi0vW5jOTnt0KBc8iKH9gRRTp6SzWWWjkc42enKnrNpZz0l3dmzemcoNHjeQ7pza1ZXKlXv2pjs3LD06nZ3z1dw5cOionnRn41WnpXJTbro33dk8Y0k6WzZzj9N6d24vREQ0B6amcsX23enOXaft/4+PHYzeb9w1/oL9nduaZUQx/vLixPx5t7Ynd94dfXhtujNq9VyumT93tqKo5Z6/a1PyPzHS2LEjF2zh+bAtWllvkbtf6i38JE9j+/Z0th1WNle0ewlHxNLasnYvYUKy98tku53t0Mqeb8fxnWyP0cm2XpioCf1BEgAAAADgZ4fhIAAAAABUlOEgAAAAAFSU4SAAAAAAVJThIAAAAABUlOEgAAAAAFSU4SAAAAAAVJThIAAAAABUlOEgAAAAAFSU4SAAAAAAVJThIAAAAABUlOEgAAAAAFSU4SAAAAAAVJThIAAAAABUVEe7F3Ago/09ER09E85NfWgw1Tf0nKNTuYiIru8+mMr1PLcv3Vmc+dxc7sdr053rXt5MZ0/+7btSubKnO9354C/1pnInf3JnunPmt9els4++fkEqd/Tf3J/u3P7yE1O5qWt3pztr3fn7tNmV+35G1/dWpztHzjgulTv6n55Idw6fuSSV634gv/9mf2dbOhvTc+ey+kj+nNLz8JZUbuR5J6Q7ux7emM6OLJqdytUGd6Q7y6Ny90vZ05XunHrfpnR29IXPGfdxc2RXxHfGX6f5gpOi2Tl13GW1rfnzUdlRT+XqR81MdzY25Y9RO5SNRirX2LkrX1rkzvVF8v6MyN/OiIiiVuSC9RbWOzycy42Opjsnm6W1Ze1eAvvhfjl8JtuxnWzrXdlc0e4lwGHlnYMAAAAAUFGGgwAAAABQUYaDAAAAAFBRhoMAAAAAUFGGgwAAAABQUYaDAAAAAFBRhoMAAAAAUFGGgwAAAABQUYaDAAAAAFBRhoMAAAAAUFGGgwAAAABQUYaDAAAAAFBRhoMAAAAAUFGGgwAAAABQUUVZlmW7F/HTtm/fHgMDA/HKnjdFR9E14Xzz+Selejse2ZDKRUSUu3bnco1GunPnBaelcn1352/nE6+Zn84u+H8fS+VGH3k03dmxcEEqt/s589KdU360MZ1trMnd1sZLTk93dmzdk8rtWDKQ7pz2T3els5F8zNQWH5uuLEZznc1NW/KdRZHKtXJOaUXR2ZHKlcMj6c7ajOmpXGN9/hxYnzsnnc32Fh25YxsRUXRN/Dk0IqKxY0e+s6Mzn62P/37lQLk3vrT3y+Mue1PP/xODRc+4y5p796Y7o1ZPxYp6LhcRUY4Mp7PwpKK7O5Urh4bypcnHS0ua+ee1lc0VqdzS2rJ0Z1Z2rRHtWS/PrJX7tB3se/jZNFqOxE3xtRgcHIz+/v6nva53DgIAAABARRkOAgAAAEBFGQ4CAAAAQEUZDgIAAABARRkOAgAAAEBFGQ4CAAAAQEUZDgIAAABARRkOAgAAAEBFGQ4CAAAAQEUZDgIAAABARRkOAgAAAEBFGQ4CAAAAQEUZDgIAAABARXW0ewEHUhsYiFqta8K54sEnUn3l6GgqFxERx8zLdf7ooXRl3y0PpHIjzzk23Tl/5YZ0dvThNalcfc7sdOfIMUelcj033Z3uHB0aSmfrs2alcrUfPprujFqRivU/lt8LjZH8Y60+c3qu88FH8p0D/alcbeqUdGdj89ZcZ/L4REQ0tw2ms7X+vlznzl3pzux6a1Py90srx6jomvjzWasaO3akci0doz1709mnno/KstznKmVZRhlPubxWT1cWnbmXQeXwcLpz0ilyzxNFPX+/tPSarCLasgebjSPf2YKltWXtXsJBm0xrnYxWNleks9n7xn367NSOvQCThXcOAgAAAEBFGQ4CAAAAQEUZDgIAAABARRkOAgAAAEBFGQ4CAAAAQEUZDgIAAABARRkOAgAAAEBFGQ4CAAAAQEUZDgIAAABARRkOAgAAAEBFGQ4CAAAAQEUZDgIAAABARRkOAgAAAEBFGQ4CAAAAQEV1tHsBBzK6fkNE0Tnh3MrmisOwGtplaW1ZOlus35DKNdONrWls3HjEO7OPl1bul1Z8Y8Ofp3JL629KdzZ3707litHRdGfRNfFzX0REuWNnurMVzZ27UrlaX1+6s9yzJ5er5b8nVrZwn7ZDrbs7lWvldtanTU1nG0/ZR2W57zrKkdEoi/GXdyxckO98fH0qV3TkHqMREWWjkQs2k7lWlWUuV6/nOyfZY60tsvdLrYX7pQ17sJXX9e14rTLZXldx+FTp/0kn02NtsnU6N3AkeecgAAAAAFSU4SAAAAAAVJThIAAAAABUlOEgAAAAAFSU4SAAAAAAVJThIAAAAABUlOEgAAAAAFSU4SAAAAAAVJThIAAAAABUlOEgAAAAAFSU4SAAAAAAVJThIAAAAABUlOEgAAAAAFRUR7sXcCD1GdOjXnRNOHfOWz6e6pv+9R+mchERxVEzUrl1FyxId8759mAqVwyNpDsfeMesdPbED30vlav19aU7H/+101O57ldvTHfO+FBPOlt/8LFUbviM49Kdp7/3f6RyA69/Ubpz2q2r09mf/7kPpnLb33x2unPGnZtSud0n5M4LERFT78vtwWZfb7pzaN7UdLZze+68smde/vEydc3OVG7DC/vTnbPu3pPObnzBlFRuxn1D6c6eR7amckVRpDvXvmFuOrvgph3jLxjZFXHnU670/JMjOsfv1aEpnenO4tjc81rX2s3pzsZj61K5stlId0YL92m6smvir+GeVA7l9n3Rme+MspmPjo6mckV3d7qzlsw2dux45iv9jFjZXNHuJRy8Vh6jZXnke1vpTGrX/Tmp9lGbOEbws8E7BwEAAACgogwHAQAAAKCiDAcBAAAAoKIMBwEAAACgogwHAQAAAKCiDAcBAAAAoKIMBwEAAACgogwHAQAAAKCiDAcBAAAAoKIMBwEAAACgogwHAQAAAKCiDAcBAAAAoKIMBwEAAACgogwHAQAAAKCiOtq9gANZ/T+OjtqUngnnFv/y7am+Zir1E+Upi1K5Od8eTHc2e3J33Y/e05vufM4V69PZ5vBwKlefOyfduWdOmcrNuHZ6urNj7dp0dvcLT8h17hlNdx79N/encrtfeHy6s9yxI52tbexM5WY89Gi6s7FzVyrXM6073Vlu2pILrsvvhZ4fF+ls1HLfZ+pf05euHH0idz6at2ZmurO5dWs6O++u5H5oNNKdjeGRVK42dUq6c8GfPJTORvGUfVTu3fc6d90fUYx/bVDPN0Y0c8d3tGjh8dIOZe75MCIikre13LMn35lUjuRea7RLOTSUzjZayE4mS2vLjnjnyuaKI97Z0mN0MvYmtGMvtKKVfZS9re3obMVkO0bws847BwEAAACgogwHAQAAAKCiDAcBAAAAoKIMBwEAAACgogwHAQAAAKCiDAcBAAAAoKIMBwEAAACgogwHAQAAAKCiDAcBAAAAoKIMBwEAAACgogwHAQAAAKCiDAcBAAAAoKIMBwEAAACgojravYADWfTrD0RH0TnhXO2E43KFT2zI5SIiHng0l5s1I11ZH5n4sYmIOO6G7nRnMdpIZ8tGLjv62OPpzpk/ODaVG5peT3dO2TuUz979WC7YmX8Yb3vFialc1/YW9kJZprON9bnHaX3e3HRnx9QpueCGwXRnYyi3j4rnnJDuLIsina1vyt3Wxtzp6c7arIFccCS/d4dfsCidbXTnvhe3d0b+fDTzzq2pXNmZ72xMzz/HjPaO7x0a2hnxr18bf9krz4ih7mnjLhuZll9v95aRVK73nuT5OiLK5PNEY9u2dGcrio7c640om4d2Ic9i9em581FjW/55Iq2Wf7xEM3/+zFrZXHHEO3lm7pfDqx3Ht5XOpbVlh3AlB8cehEPPOwcBAAAAoKIMBwEAAACgogwHAQAAAKCiDAcBAAAAoKIMBwEAAACgogwHAQAAAKCiDAcBAAAAoKIMBwEAAACgogwHAQAAAKCiDAcBAAAAoKIMBwEAAACgogwHAQAAAKCiDAcBAAAAoKIMBwEAAACgojravYADKepFFEVidtlopPp2vOo5qVxExJSv3Z4LDm5Pd9anTU3lekdG0527T5qdzvas35jKNfcOpTuP+rfVuWBPd7qzLJvp7OgT61O52hknpzv77x9M5Yr1W9KdZVGks3HqialY44c/TlfWj56fCw4N5zvnz03lGj/4Ub5z9qx0thzNnXdrazekOxsbcueU+qz87ey894F0tnvatFSuZ9fudGczeT4q6vV0Z300/xxTf8q5obvc9/zf/W/fi+5i/Dm6p6Mz3VkmXzOMNnO5iIiiI/nSqyyPfGcLyhb2wmTT2L7zyJdmn0tb2LvtsLS27Ih3rmyumFSdrRyjdtzWdtynrZhMx6gdawV+dnjnIAAAAABUlOEgAAAAAFSU4SAAAAAAVJThIAAAAABUlOEgAAAAAFSU4SAAAAAAVJThIAAAAABUlOEgAAAAAFSU4SAAAAAAVJThIAAAAABUlOEgAAAAAFSU4SAAAAAAVJThIAAAAABUlOEgAAAAAFRUR7sXcCC1ubOjVuuecK6s11N9ff92byoXEVEctzCVKwe35zu7ulK5LWfOTncOfPnOdLaYPpDK1Y4/Nt1Zjozmgus3pjujyM/bO+bMSuXKR9enO4vOzlznnr3pzubwSDrbsTn3mGkkzwsREc3kfqgdNTPdObrmsVSuPvuodGdjQT4b338gFasN9KUrd/zS2alc/5dvT3dmz7sREcXC+alc7aE1+c6B/lwueV6IiCj35s8NjU2bn/LFyv0UlBEx/vLa1N50Z3PXnlSubDbSneVo8rmpKPKdzf0cy4PVHM7lavnzbq23Jxfc3545SM09ub3wk3B+P6RlX2+UbVhrRH4/tOPYtmBpbVkqt7K54hCv5OAsrb8pF2zhsZbVtmOUvE85vNpxv7RrD8KR4p2DAAAAAFBRhoMAAAAAUFETGg5+6lOfijPOOCP6+/ujv78/zj333Pj6178+9vmyLGP58uWxYMGC6O3tjfPPPz/uueeeQ75oAAAAAKB1ExoOHnPMMfGxj30sbr/99rj99tvjla98Zbz+9a8fGwBeddVVcfXVV8e1114bq1atinnz5sXSpUtjx44dh2XxAAAAAEDehIaDr3vd6+K1r31tnHTSSXHSSSfFRz/60Zg2bVrcdtttUZZlXHPNNXH55ZfHG9/4xjjttNPiuuuui927d8f1119/uNYPAAAAACSlf+dgo9GIL37xi7Fr164499xzY/Xq1bFu3bq44IILxq7T3d0d5513Xtx6660H/DpDQ0Oxffv2cf8AAAAAgMNvwsPBu+++O6ZNmxbd3d3xrne9K77yla/EqaeeGuvWrYuIiLlz5467/ty5c8c+tz9XXnllDAwMjP1buHDhRJcEAAAAACRMeDh48sknx1133RW33XZb/OZv/mZcfPHF8cMf/nDs80VRjLt+WZb7XPbTPvCBD8Tg4ODYv7Vr1050SQAAAABAQsdEA11dXXHiiSdGRMRZZ50Vq1atik984hPxe7/3exERsW7dupg/f/7Y9Tds2LDPuwl/Wnd3d3R3d090GQAAAABAi9K/c/BJZVnG0NBQLF68OObNmxcrV64c+9zw8HDcfPPN8eIXv7jVGgAAAADgEJvQOwf/4A/+IC688MJYuHBh7NixI774xS/GTTfdFN/4xjeiKIq49NJL44orroglS5bEkiVL4oorrogpU6bERRdddLjWDwAAAAAkTWg4uH79+njb294WTzzxRAwMDMQZZ5wR3/jGN2Lp0qUREfH+978/9uzZE5dcckls3bo1zj777Ljxxhujr6/vsCweAAAAAMib0HDwM5/5zNN+viiKWL58eSxfvryVNQEAAAAAR8CE/yDJkbLt5+ZFR2fPhHMDNz2U6hs6a0kqFxHRsXsklav35v8Qy44TB1K5GXdtTncOvuEF6ez0bz2Syj326pn5zgdHU7kpj69Pd9Zmz0hnH3/NglRu/sp16c6NLznwHwt6OlPX5Y5tRETPTXens2veclwqd+wN+V+vuvP5ufslynRldJw8/5mvtB+1DbvSnY/8Qu6cEhFx3D2549tcNC/dOdJbpHK1E45Ldzb7Jv6c9KRytJnKNV5wcrqz48eP54JdnenO0YWz0tmOcvyDpt7cE7Fl/HXqM6dHvdY7/sJaPd0Ztb3JXAudzUYuV7ZwUonc/ouI/G0t853NXflzWVatJ//4bu5N7qNWZPdRu0yi9S6tLatEZ0S0eF45slo6RkXuNcNk07Z9lNTKelc2VxzClRyc7HrbsVbIaPkPkgAAAAAAk5PhIAAAAABUlOEgAAAAAFSU4SAAAAAAVJThIAAAAABUlOEgAAAAAFSU4SAAAAAAVJThIAAAAABUlOEgAAAAAFSU4SAAAAAAVJThIAAAAABUlOEgAAAAAFSU4SAAAAAAVJThIAAAAABUVEe7F3AgnTub0dHZnHCuuW0w1VcfaqRyERH1zTtTuWJkNN057ZYNqdzOly9Jdw7864/S2Zg6JRWbd+uOdOXI9O5Urpg3O90ZO3eno1PX5/dg1lF//4NUrpg7K93ZLMt09ti/fSgXrOW/DzL1ttWp3O6zjkt3dt/541SumDkj3Xn8p5PHNiJ2veL0VG7Kw9vSnX1rh1O5xozcuSgiYvf83nR2qD+3B2fcm3t+iYhobNyYC2ZzEfGvP16Rzu53HXP+ZtxFN9z38YjZ48/RPz/wq4eu8yDVp01NZ5tDQ6lcOZzb8xERURz57wUX9Xo6WzaO/PNhc+/eI97ZDiub+cfo0tqyQ7iSw6+V25o12Y5R1qTbRy289syy/55ZO45RKybbemGivHMQAAAAACrKcBAAAAAAKspwEAAAAAAqynAQAAAAACrKcBAAAAAAKspwEAAAAAAqynAQAAAAACrKcBAAAAAAKspwEAAAAAAqynAQAAAAACrKcBAAAAAAKspwEAAAAAAqynAQAAAAACqqo90LOJBvfPnS6O/vb/cyYEKW1palswN3dKVy2160IN3Zf1+us9i6M91ZFEU6W+7anco1duxIdxb1eirX87+3pzubyVxtewu3s29aOtt7yw9zwSlT0p09G7ekcs3B/DHqfulp6ey0r+eOUW3G9HRnLFqYyw0NpytfM//d6Wxz2+C4jwfKvbHiKddZtvA3Y7DoGXdZffasdGdj/cZcrpVzSlfuvBtlme6MspHPZiuzJ7J2qeXO9RER0Uwe3xaeD7P7oZXXKVXRyjFa2XzqWevwq8x92obHSysm2z6abNqx77P3S2Ueo0x63jkIAAAAABVlOAgAAAAAFWU4CAAAAAAVZTgIAAAAABVlOAgAAAAAFWU4CAAAAAAVZTgIAAAAABVlOAgAAAAAFWU4CAAAAAAVZTgIAAAAABVlOAgAAAAAFWU4CAAAAAAVZTgIAAAAABVlOAgAAAAAFdXR7gUcyIX/+X9ER0fPhHNTfrgu1Vf2dKVyERHl1ImvMyKi7KynOxs9ubuua82mdOfI0TPT2WK0mcp1rNmQ7hw5fl6uc9POdGfH/FxnRMTgWfNTuYGbH0p3NhbNTeWK3u50Z9nI7YWIiOGzlqRyXat+lO4s5s/JBev5770UW7fngh35U/rIvOnpbP2J9bnOU45Jd3atzp0batP7053dD+RuZ0REefyxudwTG9OdRXf+eS3d2Zt7PoyIKHbvGf9xsxkx9JTrdHZFURt/u8o943NHQtHVwmuGoaFnvtJ+S4t0ZxQtfC+42cjlavnXOOnOVrSjsxXZ4zvZbmcLltaWVaKzHdpyO8vyyHe2STuO78rminR2Mq23lbVW5fFNdXnnIAAAAABUlOEgAAAAAFSU4SAAAAAAVJThIAAAAABUlOEgAAAAAFSU4SAAAAAAVJThIAAAAABUlOEgAAAAAFSU4SAAAAAAVJThIAAAAABUlOEgAAAAAFSU4SAAAAAAVJThIAAAAABUlOEgAAAAAFRUR7sXcCC9a7dHR31owrnh42en+roe2ZzKRUQU23encsMLpqc7u1ZvSOWaR/WnO8takc52/GhNrnPB3HTn8IyuVK5+52PpzsbQxPfsk7Yfe3wq179nb7qzY8NgKje86Kh0Z311Oho9P3w0FxzI7/sYGc3lhst0ZTkyksoVU3rTnZ2P5s+B5cIFuc51uf0XEVHuze37xoKZ6c7aDx5MZ4ue3PkoGo10Z5k8NxRF/lxfbtmWzhYd41+SFM36fq5Tj6L2lJcuHfmXMkVnLlu2cL9E9viW+XNKlPn1PvV+ORLK5hGvjKjtu98OWjN5fFu5T6MdB4lno5XNFens0tqyQ7iSg5R9rGUfZxyUduyFVjpb2ffA/nnnIAAAAABUlOEgAAAAAFSU4SAAAAAAVJThIAAAAABUlOEgAAAAAFSU4SAAAAAAVJThIAAAAABUlOEgAAAAAFSU4SAAAAAAVJThIAAAAABUlOEgAAAAAFSU4SAAAAAAVJThIAAAAABUVEe7F3Agxd6hKBKjy/p/rE71ldMHUrmIiMaWbalcfc2j6c5md3cqVwxMS3d2fPeBdLaxe3cqV+zcle6c8uiUXHCgP91ZbBtMZ4/+87tynbOPSnc2121I5TpbuF+a6WREWZapXOPRx9KdRfKxVjv+2HRnY01uvfU5+b1QJvdCREQxpTdZmrs/IyKi0UjFipH8DiyTnRERRVGkco0WHmvFnr2pXCu3s9aTe7xERDR37Bj3caMc2uc6jcHt0SjGX17U6+nOcnQ0F6zlO1MvbiIiyvz90oqy2cLjdDJptuf4prVy/myH5Dlw0t3OpJXNFens0tqyQ7iSI6ANj7V2HN9WOiebyXSMJtv9Muke30xq3jkIAAAAABVlOAgAAAAAFWU4CAAAAAAVZTgIAAAAABVlOAgAAAAAFWU4CAAAAAAVZTgIAAAAABVlOAgAAAAAFWU4CAAAAAAVZTgIAAAAABVlOAgAAAAAFWU4CAAAAAAVZTgIAAAAABVlOAgAAAAAFdXR7gUc0MhoRK0+4Vh51qmpumLdtlQuIqKjpyeVGzl2Vrqz85GNqVy5aWu6c/crnpvO7pk18fsyImLmF1alO7OKWn5mvveVZ6Sz9eFmKtexayTdOXTavFRu6vceT3fWyjKdbS6ck+sc3J7uLI4/NpXbfdxAurPnx7nHy96j+9Odvdt2pLPDJ81P5boeyp3HIiKiM/f0VVv9aLpyy7IXpLP9q/emcrX+U9KdwzNyz009j+SfJ3acPjud7b9j/Hmlo7E7Yu3463QcMz866lPGXdaYld/39TUbcsGhoXRnOTqayjX35juj2chny9xzU1sURT7bwnNTW7RyW7NaOUaT7fgmrWyuSOWW1pYd4pXw06pyfKtyOyPytzX7GG1XJxxJ3jkIAAAAABVlOAgAAAAAFWU4CAAAAAAVZTgIAAAAABVlOAgAAAAAFWU4CAAAAAAVZTgIAAAAABVlOAgAAAAAFWU4CAAAAAAVZTgIAAAAABVlOAgAAAAAFWU4CAAAAAAVZTgIAAAAABXV0e4FHMjQiXOj0dEz4Vx9z2iqr+ztTuUiIradcVQq1//QrnRnObU3ldt65ux0Z6OrSGdn/+uaVK55yonpzofeNCOVO/7PHkx3Trntx+nsul86JZWb982d6c7O7nouWG/h+wojucdoRMTIQO5x2tXZme6M4ZFUrHvT3nRlbaAvlet9aHO6s5ye64yI6Pz+w7nOebPSnbFxS67z2Pnpypn/+MN0NubknieKodz+i4jo+kHufFTMyq01ImLaN+5OZ5v18eejZnPfx1Bz6/Zo1obHXVZsyu2FiIhm8vHdinJk+JmvRE5ZtnsFE1NLPge3otk48p2TzMrminR2aW3ZIVwJh0yR/3+YdpxXqrKPWrmd2cdpVY4tZHjnIAAAAABUlOEgAAAAAFSU4SAAAAAAVJThIAAAAABUlOEgAAAAAFSU4SAAAAAAVJThIAAAAABUlOEgAAAAAFSU4SAAAAAAVJThIAAAAABUlOEgAAAAAFSU4SAAAAAAVJThIAAAAABUlOEgAAAAAFRUR7sXcCDD/R3R7Jz48vpWb0r1NR59PJWLiOg6bnoq9+M3T013nvDeu1O5gUceTXduvPjn0tmyP3db9xzbl+487o9WpXLlc5ekO8uu/ENqzmfuSOWGXn56urNn9eZUbnjhUenO+uPr09nuu1ancqOnHZ/urN1+byrXPPq56c7Ysi0Vqw0Npyv3vPSUdLbnoTWpXH37rnTn7rNy92nXv9ye7qx1d6ezzVm5c1lx+w/TnfW5c1K57WcvTHf23Z8/Z8fjG8Z9WDTr+1yl6KhHURt/ni2m96crGzOnpXLlHfekOyedsszlavvefwer6Ehma0W6sxwaSmfTmo0j3znJrGyuaPcSJqQd611aW3bEO1u5ne1Yb/o81oK23M4WtOM+bcfjZbKdU+BI8s5BAAAAAKgow0EAAAAAqCjDQQAAAACoKMNBAAAAAKgow0EAAAAAqCjDQQAAAACoKMNBAAAAAKgow0EAAAAAqCjDQQAAAACoKMNBAAAAAKgow0EAAAAAqCjDQQAAAACoKMNBAAAAAKgow0EAAAAAqKiOdi/gQAaP64h698SX1/u1tYdhNU9vyp2PpHIn3V7mS089KRXbe0x/unL2X69KZ5tFbg7d+0hnurM4+YRUrnnfQ+nOcmQ0nY2zTk3Fen74WLqyHB5O5Tp7uvKd9Ra+JzHnqFSs40ctnBd6e3K5nbljGxFR9HSncs1du9OdvTffk85GvZ7L1fJ7ofvfvpfKFc89Od1ZG9yZzpaN3Pm+6Mo/1hobNqVyff97V7qzuTOfLbrH7/uyHNnnOuXISJTFU/bb9h3pznj0iVyultzzERFlM5lr4TVDK1q5rUnlSP78yc+WpbVl6ezK5opDuJLDq5XbybPTZNp/ABHeOQgAAAAAlWU4CAAAAAAV1dJw8Morr4yiKOLSSy8du6wsy1i+fHksWLAgent74/zzz4977mnhx9UAAAAAgMMiPRxctWpV/OVf/mWcccYZ4y6/6qqr4uqrr45rr702Vq1aFfPmzYulS5fGjh0t/E4gAAAAAOCQSw0Hd+7cGb/8y78cn/70p2PGjBljl5dlGddcc01cfvnl8cY3vjFOO+20uO6662L37t1x/fXXH7JFAwAAAACtSw0H3/3ud8cv/MIvxKtf/epxl69evTrWrVsXF1xwwdhl3d3dcd5558Wtt9663681NDQU27dvH/cPAAAAADj8OiYa+OIXvxh33nlnrFq1ap/PrVu3LiIi5s6dO+7yuXPnxiOPPLLfr3fllVfGhz70oYkuAwAAAABo0YTeObh27dp4z3veE1/4wheip6fngNcrimLcx2VZ7nPZkz7wgQ/E4ODg2L+1a9dOZEkAAAAAQNKE3jl4xx13xIYNG+LMM88cu6zRaMQtt9wS1157bdx///0R8ZN3EM6fP3/sOhs2bNjn3YRP6u7uju7u7szaAQAAAIAWTOidg6961avi7rvvjrvuumvs31lnnRW//Mu/HHfddVccf/zxMW/evFi5cuVYZnh4OG6++eZ48YtffMgXDwAAAADkTeidg319fXHaaaeNu2zq1Klx1FFHjV1+6aWXxhVXXBFLliyJJUuWxBVXXBFTpkyJiy666NCtGgAAAABo2YT/IMkzef/73x979uyJSy65JLZu3Rpnn3123HjjjdHX13eoqwAAAACAFrQ8HLzpppvGfVwURSxfvjyWL1/e6pcGAAAAAA6jQ/7OwUPl6Ot/FB21rokHj5qZKzxqRi4XEcX2nalcc+eOdGdt79RU7tHzO9OdJ945kM42tmxL5cpdw+nOji2Dqdxj7znzma90AMf+3Zp0tvG9H6Vy5ZQp6c7RU45N5Tof35LuLOv1dDaS92nRlTiX/F9ls0zlag8/ke88wF93f8bOqfm90IpyOPc4LQe350truWMUD+Ufo810MqJ89LFUrjbQn+5s7tmTyjUGR9KdrTzWmrt3j/+4HNrPdfZEs3jKPbFrV7ozko+1KHPnhUmpTO78YkK/1npSKzpz+74cyb/GyVrZXJHOLq0ta0tvFVTp+GRvq/0HcHhV55UbAAAAADCO4SAAAAAAVJThIAAAAABUlOEgAAAAAFSU4SAAAAAAVJThIAAAAABUlOEgAAAAAFSU4SAAAAAAVJThIAAAAABUlOEgAAAAAFSU4SAAAAAAVJThIAAAAABUlOEgAAAAAFSU4SAAAAAAVFRHuxdwQDMGIurdE441HnwkVVcv8nPScmQ4lxsdzXdu2ZrKHX3zrHRnc3BHOlvU67nc1Cnpzubs6ancMZ+6O9/ZbKaz9flzc52bc3shIqLjoSdSubJZpjtb2ffF8Egq19i9O91Z6574eSgiopjWn+4s9w7lgiO54xMRUZs+kM42G41Urlx8dLqz9tiGVG7v849Ld3ZtS94vEbFz8dRUbvqq3GM0IqJ52uJUrmP73nTnnqP70tkptz887uN6c0/EpvHXqR81M+q13nGX7X3BonRn7w+T58ApPenO5uq1uc7kaw0Ov7bcN0WRii2tv+kQL+Qge2vL2tJ7pK1srjjina0c2+x6J9v9WZVj1Mr+a8d623G/AAfmnYMAAAAAUFGGgwAAAABQUYaDAAAAAFBRhoMAAAAAUFGGgwAAAABQUYaDAAAAAFBRhoMAAAAAUFGGgwAAAABQUYaDAAAAAFBRhoMAAAAAUFGGgwAAAABQUYaDAAAAAFBRhoMAAAAAUFEd7V7AgZTrNkRZdE04V+vqzPXt2JHKRUSUo6OpXNHRwuFfMDcV6xocznfWiny0uzuVK/r70p3NB9fmgrX8zLw2c0Y6W3bUU7nG6cenOzvXbErlho+bne98IL+PynlHpXLFQ4+mO4fPPDGVG5qeOxdFRPR9ayiVG3zFCenO6aueSGe3LE3uwfxWiBmducfLSF/+vNvK+XO0J3deKaf0pDuLZpnLPb4x3RlH58/ZUTaf/uMnL3vK5T2P5Z+/ozO3H4rde9OVtd7cfbqfo3Hw9ncsD1JLr1WSmkPJ9Za5PV8pjtFhtbS2rN1LmJC2rLdo4ck/q4V9P5nu08m01oiIlc0V6Wz2trajEyYL7xwEAAAAgIoyHAQAAACAijIcBAAAAICKMhwEAAAAgIoyHAQAAACAijIcBAAAAICKMhwEAAAAgIoyHAQAAACAijIcBAAAAICKMhwEAAAAgIoyHAQAAACAijIcBAAAAICKMhwEAAAAgIoyHAQAAACAiupo9wIOpLl7TzSL0Qnniq6uVN/6d56ZykVEzPnzb6dy5dBQurPWkZvrzvj4o+nObeeX6WxWc2ZfOvvgnx6VyvX9+9R0585j09E48a8eT+V+7XNfTXd+6LO/nMod99kH053bX7w4nd18aj2VW3TNmnTn9mO7U7npP96T7tzy8yekcjO/szHdufvkOels5+5mKrdrbu7+jIgo7l2dyvVvnpXuvO/DM9PZJdfsSOV2LxpId3bunPhzaEREFPnvG/as253Oxoyn3NbRzojNT7nOQH9Ex5RxFxU7852NWf254A9+nO6MRiMVK0eT92eL0r21/ON70kne1vrM6enKxqanPjh+Nq1srkhnl9aWHcKVHF7tup2t9Ga15X4pinR0ZeNLh3AhB2cy7d3Jph2PF/cnk4V3DgIAAABARRkOAgAAAEBFGQ4CAAAAQEUZDgIAAABARRkOAgAAAEBFGQ4CAAAAQEUZDgIAAABARRkOAgAAAEBFGQ4CAAAAQEUZDgIAAABARRkOAgAAAEBFGQ4CAAAAQEUZDgIAAABARRVlWZbtXsRP2759ewwMDMTg4GD09/e3ezmH1QXdv5zO1k5clMqt/YXZ6c4ZD4yms9Nuvj8XnJtfb7O/N5Ur9oykO2ubtqazj7/xhFRu/ooH0p0xcyAVyx7biIj4/o/S0WLJ4lSutm1HurMcye2HPc8/Nt3Z880f5oJLcueFiIhitJnOxmPrUrHd556UrpzyH7lzyt5z8p0938k/1kZOPz6V69y0M90Zm7alYsW0KfnO4fz5sxwaGvfxQHNP3LD58+Mu+6Wj3h6DtfHnn2La1HRnc+PmVK5s4XZmlSPD+XBR5KMdnalc2WikO6PZQnYyacP90oqW9iBUzMrminR2aW3ZpOnkmblfaKfRciRuiq8d1HzNOwcBAAAAoKIMBwEAAACgogwHAQAAAKCiDAcBAAAAoKIMBwEAAACgogwHAQAAAKCiDAcBAAAAoKIMBwEAAACgogwHAQAAAKCiDAcBAAAAoKIMBwEAAACgogwHAQAAAKCiDAcBAAAAoKIMBwEAAACgojravYADeeOZH4yOeveEc+UTG1J9tYH+VC4iouyd+DojIh7/nbPSncd87YlUbuHXN6c7y6JIZ//5hzencr/wktenO+//jemp3Iw7pqU75/+vHensvFu2pHKPv3lJuvP8d3wnlbvjI2emO/sezj/Wtp4xPZXbcuqMdOfxK7alco9c1Ex3nnJ7Tyr36Kvyt3PvnDKdPfGPc+fdRm/++1OPfv7oVO6Y39uW7tzxylPS2Z6Nw6nc6Myp6c7Bs2alcrP+I/f8EhExsmh2OlvfOTTu43J0V8RTnrLKeUdF2TH+mAzNyh+j7im5x1o8tCbd2dy7N51NK/OP73Ikt3ejVk93Rvb1RtHC97zL/Dk7rZX1JqXvz4iW7tOilrtPy9HRdGfWyuaKdHZpbdkR72xFO9Y7mTpb6W2lk2fmfoFDzzsHAQAAAKCiDAcBAAAAoKIMBwEAAACgogwHAQAAAKCiDAcBAAAAoKIMBwEAAACgogwHAQAAAKCiDAcBAAAAoKIMBwEAAACgogwHAQAAAKCiDAcBAAAAoKIMBwEAAACgogwHAQAAAKCiDAcBAAAAoKI62r2AAyqKn/yboNr0gVTdnufMS+UiInq+tyaVm3X3cLoztg6mYs3jj05XNrvq6exr/tNbU7liw+p05yn/dUsq9+hvnJ7u3PjqY9PZWf9wTyo340fT0p13fPTMVK7/zsfTndE3NR99eE8qN/D396U7m887KZU74a/KdGc0c9npD42mK/u+9Fg62zjxmFRutGfi5/gnHb08l20M9KY7uwbzx7dja27vNvq7050z7t2RzmYNzcyvd+qPHh33cdHc95gV6zZHUds97rLGMX3pzmJX7n6JafnzWL0vt97Gpk3pzihbOB8lXou1rGjD965bOUbpzkY+WjYP4UIOv3I0f/480pbWllWisxWO0TNb2VxxxDsn2zECnl28cxAAAAAAKspwEAAAAAAqynAQAAAAACrKcBAAAAAAKspwEAAAAAAqynAQAAAAACrKcBAAAAAAKspwEAAAAAAqynAQAAAAACrKcBAAAAAAKspwEAAAAAAqynAQAAAAACrKcBAAAAAAKqqj3Qs4oPUbI4quCceeuOi5qbqp65upXEREx/HzU7kdR3emO3tWT0/lHn7dtHRnY+J3x5glV9yTyjVHR9Odz7ttKJXb8ZFGurNrRz67/s25vdu7Jb93Nz+3nsqte9Ex6c4lf/qjdHbNO3KPteMHj0t3bjo195jZM7tId86L41K53Ufl7s+IiG2/tCidLZPfZjr2y4+lOx/4LwtSufm35h+ju+blj+/mi3L74ZT/sTPd+cQrZ6ZynTv60p2zv705nX3gfSeN+3jmzp0RHx5/nYcuOSG2TBv/mFzy+Xzn6NzpqdyeBblzUUTElP/3rlywaOX7ufnniWxvrac739nMrbc5PJLvLPLn7HYco3J4OJdrlunOKFvYR7Xk+bOZP2dnrWyuSGeX1pYdwpU8e7XlGLXyGC1b2PdJVdkLk00rexd+1nnnIAAAAABUlOEgAAAAAFSU4SAAAAAAVJThIAAAAABUlOEgAAAAAFSU4SAAAAAAVJThIAAAAABUlOEgAAAAAFSU4SAAAAAAVJThIAAAAABUlOEgAAAAAFSU4SAAAAAAVJThIAAAAABUlOEgAAAAAFRUR7sXcEA9PRG1rgnH5n1zS65v3cZcLiKKgf5Urr+3hcNflqnYCX/xSLpy7ynz09nm0FAqV3RNfA886fb3npbK9X37B+nO5p696eyM856XynWt3Zru7P/X3OOlccqidGe5c1c6e/z/uC+dzZp9S27vlrt2pzuLqVNSubkPDac7ozN/Pio76qncyPzp6c4ln3golRt6ztHpzjlfXZ3O9q09Lhds4Vt4C274cSpXzp+V7hw5amo6e/Q3R8d9PDA0us915t02Gr3d4y8fnd6b7tx5bC47/fb16c4y+VgrR0fSnS0pm6lYc2/u3NlKZ9tkj9Hu/PNEFEUuVs+dryMiytHca8+fhBv57BG2tLYsnV3ZXHHEO1sxqdab/H+fiPztbEUrndn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          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "Next we add the correct tick labels for the x and y axes."
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "# Create a figure and axis with a pre defined size\nfig, ax = plt.subplots(1, figsize=(16, 10))\n\n# Generate a histogram of the data\nim = ax.imshow(img)\n\n# Add the vertical line indicating when vaccines became available\nax.axvline(x=vaccine_pos, linewidth=3, color='red')\nax.text(x=vaccine_pos, y=-1, s='Vaccine introduced', fontsize=14)\n\n# Set the size of the axis for the color bar\ncax = fig.add_axes([0.4, 0.05, 0.22, 0.02])\n\n# Add the color bar\nfig.colorbar(im, cax=cax, orientation='horizontal')\n\n# Fix the x-axis ticks and labels\nax.set_xticks(np.arange(2, img.shape[1], 10))\nax.set_xticklabels(np.arange(2, img.shape[1], 10)+1928, fontsize=15)\n\n# Fix the y-axis ticks and labels\nax.set_yticks(np.arange(0,51,2))\nax.set_yticklabels([\n    'Alaska', 'Ark.',  'Calif.', 'Conn.', 'Del.', \n    'Ga.',    'Iowa',  'Ill.',   'Kan.',  'La.', \n    'Md.',    'Mich.', 'Mo.',    'Mont.', 'N.D.',\n    'N.H.',   'N.M.',  'N.Y.',   'Okla.', 'Pa.',\n    'S.C.',   'Tenn.', 'Utah',   'Vt.',   'Wis.',\n    'Wyo.'], fontsize=15);",
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 1600x1000 with 2 Axes>",
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "# Custom colormap"
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "The only tricky part to this figure is creating a custom color scheme for our heatmap. We achieve this by using LinearSegmentColormap"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "cvals  = [0, 500, 1000, 1500, 2000, 2500, 3000, 3500, 4000]\n\ncolors=[\n    \"#ddebf8\",\n    \"#0c9dc5\",\n    \"#44ae57\",\n    \"#fcd43d\",\n    \"#e59e26\",\n    \"#e48925\",\n    \"#e95034\",\n    \"#da4b31\",\n    \"#cf472e\"]\n\nnorm=plt.Normalize(min(cvals),max(cvals))\ntuples = list(zip(map(norm, cvals), colors))\ncmap = matplotlib.colors.LinearSegmentedColormap.from_list(\"\", tuples)",
      "execution_count": 17,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "markdown",
      "source": "Resulting in a colormap object we can easily use in our figure:"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "# Create a figure and axis with a pre defined size\nfig, ax = plt.subplots(1, figsize=(16, 10))\n\n# Generate a histogram of the data\n# With our custom color map\nim = ax.imshow(img, cmap=cmap, vmin=0, vmax=4000)\n\n# Add the vertical line indicating when vaccines became available\n# Use black for the line color\nax.axvline(x=vaccine_pos, linewidth=3, color='black')\nax.text(x=vaccine_pos, y=-1, s='Vaccine introduced', fontsize=14)\n\n# Set the size of the axis for the color bar\ncax = fig.add_axes([0.4, 0.05, 0.22, 0.02])\n\n# Add the color bar\nfig.colorbar(im, cax=cax, orientation='horizontal')\n\n# Fix the x-axis ticks and labels\nax.set_xticks(np.arange(2, img.shape[1], 10))\nax.set_xticklabels(np.arange(2, img.shape[1], 10)+1928, fontsize=15)\n\n# Fix the y-axis ticks and labels\nax.set_yticks(np.arange(0,51,2))\nax.set_yticklabels([\n    'Alaska', 'Ark.',  'Calif.', 'Conn.', 'Del.', \n    'Ga.',    'Iowa',  'Ill.',   'Kan.',  'La.', \n    'Md.',    'Mich.', 'Mo.',    'Mont.', 'N.D.',\n    'N.H.',   'N.M.',  'N.Y.',   'Okla.', 'Pa.',\n    'S.C.',   'Tenn.', 'Utah',   'Vt.',   'Wis.',\n    'Wyo.'], fontsize=15);",
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 1600x1000 with 2 Axes>",
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "Finally, we clean up the color bar to make sure it matches the original one"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "# Create a figure and axis with a pre defined size\nfig, ax = plt.subplots(1, figsize=(16, 10))\n\n# Generate a histogram of the data\n# With our custom color map\nim = ax.imshow(img, cmap=cmap, vmin=0, vmax=4000)\n\n# Add the vertical line indicating when vaccines became available\n# Use black for the line color\nax.axvline(x=vaccine_pos, linewidth=3, color='black')\nax.text(x=vaccine_pos, y=-1, s='Vaccine introduced', fontsize=14)\n\n# Set the size of the axis for the color bar\ncax = fig.add_axes([0.4, 0.05, 0.22, 0.02])\n\n# Add the color bar\nfig.colorbar(im, cax=cax, orientation='horizontal')\n\n# Fix the x-axis ticks and labels\nax.set_xticks(np.arange(2, img.shape[1], 10))\nax.set_xticklabels(np.arange(2, img.shape[1], 10)+1928, fontsize=15)\n\n# Fix the y-axis ticks and labels\nax.set_yticks(np.arange(0,51,2))\nax.set_yticklabels([\n    'Alaska', 'Ark.',  'Calif.', 'Conn.', 'Del.', \n    'Ga.',    'Iowa',  'Ill.',   'Kan.',  'La.', \n    'Md.',    'Mich.', 'Mo.',    'Mont.', 'N.D.',\n    'N.H.',   'N.M.',  'N.Y.',   'Okla.', 'Pa.',\n    'S.C.',   'Tenn.', 'Utah',   'Vt.',   'Wis.',\n    'Wyo.'], fontsize=15);\n\n# Fix the ticks and labels for the color bar\ncax.set_xticks([0, 1000, 2000, 3000, 4000])\ncax.set_xticklabels([0, \"1k\", \"2k\", \"3k\", \"4k\"], fontsize=12)\n\n# Add the vertical divisions of the color bar\ncax.axvline(x=1000, color='darkgray')\ncax.axvline(x=2000, color='darkgray')\ncax.axvline(x=3000, color='darkgray')\ncax.axvline(x=4000, color='darkgray');",
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 1600x1000 with 2 Axes>",
            "image/png": 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      "metadata": {},
      "cell_type": "markdown",
      "source": "<center>\n     <img src=\"https://raw.githubusercontent.com/DataForScience/Networks/master/data/D4Sci_logo_full.png\" alt=\"Data For Science, Inc\" align=\"center\" border=\"0\" width=300px> \n</center>"
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    "_draft": {
      "nbviewer_url": "https://gist.github.com/bmtgoncalves/665358625ce4531cc48432af5462353e"
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    "gist": {
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      "data": {
        "description": "Effect of Vaccination",
        "public": true
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    "kernelspec": {
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      "display_name": "Python 3 (ipykernel)",
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    "language_info": {
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      "pygments_lexer": "ipython3",
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    "varInspector": {
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      "types_to_exclude": [
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}