Covid_Ticino.ipynb

{
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  "metadata": {
    "colab": {
      "name": "Covid-Ticino.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true,
      "mount_file_id": "1D7Xl7WQnOdxWHxjAl_IV4FGAdLOosN5v",
      "authorship_tag": "ABX9TyPYEiPuW9rNHmP1Wl4U7Hdr",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/davidnussio/covid-19-ticino/blob/master/Covid_Ticino.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "71nrZ_eh2IeS",
        "colab_type": "code",
        "cellView": "both",
        "outputId": "165e118d-b0d5-404f-9232-5d69c90eb045",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 119
        }
      },
      "source": [
        "#@title\n",
        "!pip install -q lmfit"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\u001b[K     |████████████████████████████████| 266kB 2.6MB/s \n",
            "\u001b[K     |████████████████████████████████| 61kB 6.0MB/s \n",
            "\u001b[K     |████████████████████████████████| 235kB 8.4MB/s \n",
            "\u001b[?25h  Building wheel for lmfit (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Building wheel for asteval (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Building wheel for uncertainties (setup.py) ... \u001b[?25l\u001b[?25hdone\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "loBNjDpqWNWc",
        "colab_type": "code",
        "cellView": "both",
        "colab": {}
      },
      "source": [
        "#@title\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy \n",
        "from scipy.optimize import curve_fit\n",
        "from scipy.optimize import differential_evolution\n",
        "import warnings\n",
        "from lmfit import Model, Parameters"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Z_AH_WAYWRTX",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# https://www.wikiwand.com/en/Generalised_logistic_function\n",
        "\n",
        "# Sigmoid model\n",
        "def sigmoid(x, a, b, c):\n",
        "  return a / (1 + numpy.exp(-b*(x-c)))\n",
        "\n",
        "# Exponational model\n",
        "def exponational(x, a, b):\n",
        "  return a * numpy.exp(b * x)\n",
        "\n",
        "# Fst derivate\n",
        "def sigmoid_dx(x, a, b, c):\n",
        "  return (a * b * numpy.exp(-b*(x-c))) / ((numpy.exp(-b*(x-c)) + 1) ** 2)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3iK7L2TgqSkQ",
        "colab_type": "code",
        "outputId": "836b8432-0e80-4940-d36c-a8d051c6badf",
        "cellView": "both",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        }
      },
      "source": [
        "#@title Configuration\n",
        "\n",
        "num_sample = 120 #@param {type:\"slider\", min:10, max:120, step:1}\n",
        "\n",
        "moving_avarage_win =  5#@param {type:\"integer\"}\n",
        "\n",
        "# Predicted n days\n",
        "predicted_days = 90 #@param {type:\"slider\", min:30, max:90, step:1}\n",
        "\n",
        "csv_url_file = \"https://raw.githubusercontent.com/davidnussio/covid-19-ticino/master/data/covid-19-ticino.csv\" #@param [\"https://raw.githubusercontent.com/davidnussio/covid-19-ticino/master/data/covid-19-ticino.csv\", \"https://raw.githubusercontent.com/openZH/covid_19/master/fallzahlen_kanton_total_csv/COVID19_Fallzahlen_Kanton_VD_total.csv\"]\n",
        "\n",
        "print('num_sample: ',num_sample)\n",
        "print('moving_avarage_win: ', moving_avarage_win)\n",
        "print('predicted_days: ', predicted_days)"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "num_sample:  120\n",
            "moving_avarage_win:  5\n",
            "predicted_days:  90\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "82tDE5tPWUE3",
        "colab_type": "code",
        "cellView": "both",
        "outputId": "74386ca7-acdb-47f9-c202-85e95035c290",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        }
      },
      "source": [
        "# Load data from github repo (github.com/davidnussio/covid-19-ticino)\n",
        "csv_data = pd.read_csv(csv_url_file)\n",
        "data = csv_data.fillna(method='ffill').fillna(0)\n",
        "\n",
        "# Daily infected patients\n",
        "new_infected = csv_data['ncumul_conf'].fillna(method='ffill').diff()\n",
        "new_infected[0] = 1\n",
        "\n",
        "# Number of data days\n",
        "number_of_days = len(data['ncumul_conf'])\n",
        "\n",
        "# Cumulative infected\n",
        "yData = data['ncumul_conf']\n",
        "\n",
        "# Fit number of sample to number of days\n",
        "num_sample = min(num_sample, number_of_days)\n",
        "\n",
        "# Days vector\n",
        "xData = numpy.linspace(0, number_of_days, number_of_days)\n",
        "xDataSample = numpy.linspace(0, num_sample, num_sample)\n",
        "\n",
        "# Moving avarage\n",
        "r = new_infected.rolling(moving_avarage_win, center=True, min_periods=1, win_type='gaussian')\n",
        "new_infected_rolling = r.mean(std=3)\n",
        "\n",
        "#\n",
        "yData_rolling = new_infected_rolling.cumsum().fillna(0)\n",
        "\n",
        "yData = yData_rolling[:num_sample]\n",
        "\n",
        "print('Numer of sample', num_sample)\n",
        "print('Number of days: ', number_of_days)\n",
        "print('Date range: [', data['date'][0], '- ', data['date'][number_of_days - 1], ']')"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Numer of sample 77\n",
            "Number of days:  77\n",
            "Date range: [ 2020-02-25 -  2020-05-12 ]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QH7UjejqIjMF",
        "colab_type": "code",
        "cellView": "both",
        "outputId": "cac875e7-c115-4df4-9028-5ae3acfde912",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 544
        }
      },
      "source": [
        "# Model\n",
        "smodel = Model(sigmoid)\n",
        "fit_params = Parameters()\n",
        "fit_params.add('a', value=500, min=200, max=10000)\n",
        "fit_params.add('b', value=0.05, min=0, max=5)\n",
        "fit_params.add('c', value=30, min=1, max=200)\n",
        "\n",
        "result = smodel.fit(yData, fit_params, x=xDataSample) \n",
        "#, fit_kws={'reduce_fcn':'negentropy'})\n",
        "\n",
        "result"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<h2> Model</h2> Model(sigmoid) <h2>Fit Statistics</h2><table><tr><td>fitting method</td><td>leastsq</td><td></td></tr><tr><td># function evals</td><td>51</td><td></td></tr><tr><td># data points</td><td>77</td><td></td></tr><tr><td># variables</td><td>3</td><td></td></tr><tr><td>chi-square</td><td> 292997.352</td><td></td></tr><tr><td>reduced chi-square</td><td> 3959.42368</td><td></td></tr><tr><td>Akaike info crit.</td><td> 640.796734</td><td></td></tr><tr><td>Bayesian info crit.</td><td> 647.828150</td><td></td></tr></table><h2>Variables</h2><table><tr><th> name </th><th> value </th><th> standard error </th><th> relative error </th><th> initial value </th><th> min </th><th> max </th><th> vary </th></tr><tr><td> a </td><td>  3189.14790 </td><td>  13.4337326 </td><td> (0.42%) </td><td> 500 </td><td>  200.000000 </td><td>  10000.0000 </td><td> True </td></tr><tr><td> b </td><td>  0.15508344 </td><td>  0.00302035 </td><td> (1.95%) </td><td> 0.05 </td><td>  0.00000000 </td><td>  5.00000000 </td><td> True </td></tr><tr><td> c </td><td>  31.8884078 </td><td>  0.14789733 </td><td> (0.46%) </td><td> 30 </td><td>  1.00000000 </td><td>  200.000000 </td><td> True </td></tr></table><h2>Correlations (unreported correlations are < 0.100)</h2><table><tr><td>a</td><td>c</td><td>0.5498</td></tr><tr><td>a</td><td>b</td><td>-0.4975</td></tr><tr><td>b</td><td>c</td><td>-0.2731</td></tr></table>"
            ],
            "text/plain": [
              "<lmfit.model.ModelResult at 0x7f6dd74ecc88>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CCIdzW7ybHit",
        "colab_type": "code",
        "outputId": "2dc6471b-1f2e-4bf7-b525-aa08bfb0d791",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 421
        }
      },
      "source": [
        "result.plot()"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(<Figure size 432x432 with 2 Axes>, GridSpec(2, 1, height_ratios=[1, 4]))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        },
        {
          "output_type": "display_data",
          "data": {
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\n",
            "text/plain": [
              "<Figure size 432x432 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hqiQ_dKp1PLp",
        "colab_type": "code",
        "outputId": "5a466e0a-5a27-4a9e-eaa5-64a59f6f7649",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 481
        }
      },
      "source": [
        "# Model\n",
        "emodel = Model(exponational)\n",
        "exp_fit_params = Parameters()\n",
        "exp_fit_params.add('a', value=1, min=0.01, max=200)\n",
        "exp_fit_params.add('b', value=1, min=0.01, max=5)\n",
        "\n",
        "exp_result = emodel.fit(yData[:25], exp_fit_params, x=xDataSample[:25]) #, fit_kws={'reduce_fcn':'negentropy'})\n",
        "\n",
        "exp_result"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<h2> Model</h2> Model(exponational) <h2>Fit Statistics</h2><table><tr><td>fitting method</td><td>leastsq</td><td></td></tr><tr><td># function evals</td><td>1314</td><td></td></tr><tr><td># data points</td><td>25</td><td></td></tr><tr><td># variables</td><td>2</td><td></td></tr><tr><td>chi-square</td><td> 7025.99654</td><td></td></tr><tr><td>reduced chi-square</td><td> 305.478111</td><td></td></tr><tr><td>Akaike info crit.</td><td> 144.962413</td><td></td></tr><tr><td>Bayesian info crit.</td><td> 147.400165</td><td></td></tr></table><h2>Variables</h2><table><tr><th> name </th><th> value </th><th> standard error </th><th> relative error </th><th> initial value </th><th> min </th><th> max </th><th> vary </th></tr><tr><td> a </td><td>  7.05172472 </td><td>  0.77565969 </td><td> (11.00%) </td><td> 1 </td><td>  0.01000000 </td><td>  200.000000 </td><td> True </td></tr><tr><td> b </td><td>  0.19432559 </td><td>  0.00491875 </td><td> (2.53%) </td><td> 1 </td><td>  0.01000000 </td><td>  5.00000000 </td><td> True </td></tr></table><h2>Correlations (unreported correlations are < 0.100)</h2><table><tr><td>a</td><td>b</td><td>-0.9935</td></tr></table>"
            ],
            "text/plain": [
              "<lmfit.model.ModelResult at 0x7f6dd7459198>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WSP9Cp9E4onb",
        "colab_type": "code",
        "outputId": "32f33a7e-3294-4ecf-f2e8-1dea6c7a9244",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 421
        }
      },
      "source": [
        "exp_result.plot()"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(<Figure size 432x432 with 2 Axes>, GridSpec(2, 1, height_ratios=[1, 4]))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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bNjVFiNIMpdMDglLt/2yseO6nSNUn+jW05lBiZn8FLgSeN7PWceyb8pLxnFh3p6qq4RN83XvvvbUSg8jeSpcHBKXTs51TdVqPhl7gLyCYymKEu5cCnYFxCYsqyRLV5rd06VL69evHZZddxmGHHcavf/1rjjzySAYOHMiNN94IwObNmznjjDMYNGgQhx12GI8//jgAJ5xwAtUPMLr//vs5+OCDOeqoo3jrrbd2fP7o0aN56qmndrxv164dAJs2bWL48OEcfvjhDBgwgGnTptWKbcWKFRx//PEMHjyYww47jDfeSNhsKJJiRg3J561rT+Kz8Wfw1rUnNbvEAKnbTt8YqZqwG3oT3BYzWwUcRzDxXkX4My0kss1v0aJFTJ48mQ0bNvDUU0/x3nvv4e6cffbZvP7666xevZqePXvy3HPPAbB+/fpd9l+xYgU33ngjRUVFdOzYkRNPPJEhQ3Y/FiAnJ4enn36aDh06sGbNGoYOHcrZZ5+9SyfzI488wogRI7j++uuprKxky5Yte32uIsmSqu30jZWK03o0dLTSjcA1wHVhUTbw90QFlWyJ7KTr06cPQ4cO5aWXXuKll15iyJAhHH744XzyyScsWrSIAQMG8PLLL3PNNdfwxhtv0LFjx132f/fddznhhBPo1q0brVq14sILL9zjMd2dX/7ylwwcOJBvfOMblJSU8OWXX+6yzZFHHsn999/PTTfdxLx582jfvv1en6tIsqhjPfEa2qz0TeBsYDOAuy8H0uZqksg2v7Zt2wLBBfu6665j7ty5zJ07l8WLFzNmzBgOPvhgPvjgAwYMGMANN9zAzTff3ODPbtmy5Y5+jKqqKrZv3w7Aww8/zOrVqykqKmLu3Ll079691p3ixx9/PK+//jr5+fmMHj2aBx98cK/PVSRZUrWdPp00NDlsd3cnfPCPmbVNXEjJl4w2vxEjRnDfffftGClUUlLCqlWrWL58OW3atOHSSy9l3LhxfPDBrjOSHH300bz22musXbuW8vJynnzyyR3rCgoKKCoqAmD69OmUl5cDQdPUPvvsQ3Z2Nq+++irLli2rFc+yZcvo3r07V155JVdccUWt44qkslRtp08ne+xzsKCh+tlwtFKemV0JfAf4W6KDS6ZEt/mdcsopLFiwgGOOCZ441a5dO/7+97+zePFixo0bR4sWLcjOzuaee+7ZZb8ePXpw0003ccwxx5CXl8fgwYN3rLvyyisZOXIkgwYN4tRTT91RS7nkkks466yzGDBgAIWFhRxyyCG14pk1axYTJkwgOzubdu3aqeYg9UrWkNF4j5OK7fTpxIIKwR42MptHMM/SKQRTds9w95cTHFuDFRYWevXInmoLFiygf//+EUWUHvRvKDWndoCg+aapv6Un6ziyKzMrcvfCutY1tFnpA6DU3ce5+89TKTGISOIka8hoOg1NTRcNvUP6aOASM1tG2CkN4O4DExKViKSEZA0ZTbehqemgoclhRFMe1MwmAGcB24ElwLfdvdTMCoAFQPXXhXfc/arGHsfdNYFcIzWkuVHSX7KmdkjVKSQyWYOaldx9WV2vvTjuy8BhYc3jP+y8fwJgibsPDl+NTgw5OTmsXbtWF7lGqH6eQ05OTtShSMQaO2R06pwSho2fSd9rn2PY+Jl7nNZCQ1NTT0NrDk3K3V+KefsOcF5TH6NXr14UFxfrmQSNVP0kOMls1Z3B8YwiaszzCRpzHEmsBo1WSmgAZs8Aj7v738NmpY8JahMbgBvcvc5Jf8xsLMFT6ujdu/cRdY3lF5HkGzZ+Zp1NRPl5ubx1bco+PDIj7W60UsJqDmb2T2DfOlZd7+7Twm2uJ5in6eFw3Qqgt7uvNbMjgKlm9jV331DzQ9x9IjARgqGsiTgHEYmfOpfTQ8KSg7t/Y3frzWw0cCYwPLz7GnffBmwLl4vMbAlwMDC7vs8RkdSizuX0EMkzGczsVOAXwNnuviWmvJuZZYXL+wMHAZ9GEaOINI46l9NDJH0OZrYYaA2sDYvecferzOxc4GagHKgCbnT3ZxrweauBvel06Aqs2Yv9m6tMPW/I3HNPynm3yO3QOatd53zLatnKKyu2V276qqSqbMNXiT7uHuh3Xlsfd+9W14rIO6RTgZnNrq9TJp1l6nlD5p57pp43ZO65N/a80+ZRnyIi0nSUHEREpBYlh8DEqAOISKaeN2TuuWfqeUPmnnujzlt9DiIiUotqDiIiUouSg4iI1KLkICIitSg5iIhILUoOIiJSi5KDiIjUouQgIiK1KDmIiEgtSg4iIlKLkoOIiNSi5CAiIrUoOYiISC1KDiIiUouSg4iI1KLkICIitSg5iIhILUoOIiJSi5KDiIjUouQgIiK1KDmIiEgtSg4iIlKLkoOIiNSi5CAiIrUoOYiISC0tow6gKXTt2tULCgqiDkNEpFkpKipa4+7d6lqXFsmhoKCA2bNnRx2GiEizYmbL6lunZiUREalFyUFERGpRchARkVrSos+hLuXl5RQXF7N169aoQ0k5OTk59OrVi+zs7KhDEZEUlZTkYGY/Aa4AHJgHfBvoATwGdAGKgP929+1m1hp4EDgCWAtc6O5L4z1mcXEx7du3p6CgADNrmhNJA+7O2rVrKS4upm/fvlGHIyIpKuHNSmaWD1wNFLr7YUAWcBHwW+AOdz8QWAeMCXcZA6wLy+8It4vb1q1b6dKlixJDDWZGly5dVKMSkd1KVp9DSyDXzFoCbYAVwEnAU+H6ycCocHlk+J5w/XBr5BVeiaFu+ncRkT1JeHJw9xLgduBzgqSwnqAZqdTdK8LNioH8cDkf+CLctyLcvkvNzzWzsWY228xmr169OrEn0URuuukmbr/99nrXT506lfnz5ycxIhFp1j6eCiVFCfnoZDQrdSKoDfQFegJtgVP39nPdfaK7F7p7Ybdudd7gF5epc0oYNn4mfa99jmHjZzJ1Tslef2bcMSg5iEhDVVbAcz+Dt+9KyMcno1npG8Bn7r7a3cuBKcAwIC9sZgLoBVRfjUuA/QDC9R0JOqYTZuqcEq6bMo+S0jIcKCkt47op85okQdx6660cfPDBHHfccSxcuBCAv/3tbxx55JEMGjSIc889ly1btvD2228zffp0xo0bx+DBg1myZEmd24mIAPD527BlDRw6MiEfn4zk8Dkw1MzahH0Hw4H5wKvAeeE2lwPTwuXp4XvC9TPd3RMZ4IQZCykrr9ylrKy8kgkzFu7V5xYVFfHYY48xd+5cnn/+ed5//30AzjnnHN5//33+/e9/079/fyZNmsSxxx7L2WefzYQJE5g7dy4HHHBAnduJiAAwfxq0zIWDTk7Ixyd8KKu7v2tmTwEfABXAHGAi8BzwmJndEpZVX/kmAQ+Z2WLgK4KRTQm1vLQsrvKGeuONN/jmN79JmzZtADj77LMB+Oijj7jhhhsoLS1l06ZNjBgxos79G7qdiGSYqkpY8AwcfAq0apuQQyTlPgd3vxG4sUbxp8BRdWy7FTg/GXFV65mXS0kdiaBnXm5Cjjd69GimTp3KoEGDeOCBB5g1a9ZebSciGebzd2DTl/xq0YE8dO1z9MzLZdyIfowakr/nfRtI02cA40b0Izc7a5ey3Owsxo3ot1efe/zxxzN16lTKysrYuHEjzzzzDAAbN26kR48elJeX8/DDD+/Yvn379mzcuHHH+/q2E5HMtuS1h9nq2fxj46FN3k9aTckBGDUkn9vOGUB+Xi4G5Oflcts5A/Y6Cx9++OFceOGFDBo0iNNOO40jjzwSgF//+tccffTRDBs2jEMOOWTH9hdddBETJkxgyJAhLFmypN7tRCSDVVXR4bMXeK1qEJvZ2brRFP2ksSzBfb1JUVhY6DWf57BgwQL69+8fUUSpT/8+Is3U5+/Cfadw9fbvM71q2C6rDPhs/BkN/igzK3L3wrrWqeYgItKczJ/Gdloys2pIrVVN2U+q5CAi0lxUVcH8aazd97+ozG6/y6qm6CeNpeQgItJcLP8ANhTTY+iFCeknjZW2z3MQEUk786dCi2zodxqjcvOaNBnUpJqDiEhz4B7cFX3AiZCbl/DDKTmIiDQHK+ZC6ecJm0upJiWHBFq6dCmHHXbYXn3GrFmzePvtt+td/+STT9K/f39OPPFEZs+ezdVXX92g/USkmfl4KrRoCf1OT8rh1OeQ4mbNmkW7du049thj61w/adIk/va3v3HccccBUFhY2KD9RKQZqW5S6ns8tOmclEOq5pBgFRUVXHLJJfTv35/zzjuPLVu2UFRUxNe//nWOOOIIRowYwYoVKwC48847OfTQQxk4cCAXXXQRS5cu5S9/+Qt33HEHgwcP5o033tjls2+++WbefPNNxowZw7hx45g1axZnnnnmHvcTkWZm5TxY9xkcOmrP2zaRzKg5vHBt8I/blPYdAKeN3+NmCxcuZNKkSQwbNozvfOc73H333Tz99NNMmzaNbt268fjjj3P99ddz3333MX78eD777DNat25NaWkpeXl5XHXVVbRr146f//zntT77V7/6FTNnzuT222+nsLBwx8R8BQUFu91PRJqZ+dPAsuCQM5N2yMxIDhHab7/9GDYsuMX90ksv5Te/+Q0fffQRJ58czMFeWVlJjx49ABg4cCCXXHIJo0aNYtSo5H1DEJEU5h4MYS04DtrWemJywmRGcmjAN/xECZ5vtFP79u352te+xr/+9a9a2z733HO8/vrrPPPMM9x6663Mm7drbaeyspIjjjgCCJ4NcfPNNycucBFJDavmw9rFMPR/knpY9Tkk2Oeff74jETzyyCMMHTqU1atX7ygrLy/n448/pqqqii+++IITTzyR3/72t6xfv55NmzbtMo13VlYWc+fOZe7cuXtMDDWn/xaRZmr+NLAW0P+spB5WySHB+vXrx913303//v1Zt24dP/zhD3nqqae45pprGDRoEIMHD+btt9+msrKSSy+9lAEDBjBkyBCuvvpq8vLyOOuss3j66afj7lhu7H4ikmLmT4M+w6DdPkk9rKbszlD69xFpBlZ9Av/vaDj9djjqyib/+Ein7DazfmY2N+a1wcx+bGadzexlM1sU/uwUbm9mdqeZLTazD83s8ETHKCKSkuZPAyypo5SqJTw5uPtCdx/s7oOBI4AtwNPAtcAr7n4Q8Er4HuA04KDwNRa4J9ExioikpPnToPdQ6NAj6YdOdp/DcGCJuy8DRgKTw/LJQPXYzZHAgx54B8gzs+T/y4iIRGnNIlj1cVJvfIuV7ORwEfBouNzd3VeEyyuB7uFyPvBFzD7FYdkuzGysmc02s9mrV6+u82Dp0J+SCPp3EWkG5k8LfiZ5lFK1pCUHM2sFnA08WXOdB1eruK5Y7j7R3QvdvbBbt2611ufk5LB27VpdCGtwd9auXUtOTk7UoYjI7syfBr2Ogo6Je2bD7iTzJrjTgA/c/cvw/Zdm1sPdV4TNRqvC8hJgv5j9eoVlcenVqxfFxcXUV6vIZDk5OfTq1SvqMESkPl99Cis/hFNujSyEZCaHi9nZpAQwHbgcGB/+nBZT/gMzeww4Glgf0/zUYNnZ2fTt23fvIhYRiUJ1k9KhZ0cWQlKSg5m1BU4GvhtTPB54wszGAMuAC8Ly54HTgcUEI5u+nYwYRURSxvxpkH8E5PWOLISkJAd33wx0qVG2lmD0Us1tHfh+MuISEUk565bB8jlwcrRzp2n6DBGRVLJjlFJ0TUqQKbOyioiksKlzSpgwYyHLS8t4Nvch8jseSl7naPtMVXMQEYnQ1DklXDdlHiWlZfRgDV/z/zBp3SCmzol7kGaTUnIQEYnQhBkLKSuvBODUrPcBeKa8kAkzFkYZlpqVRESitLy0bMfyaVnvMr+qD0u9BxZTHgXVHEREItQzLxeA7nzFkS3+w/OVR+1SHhUlBxGRCI0b0Y/c7CxOy3oPgBeqjiI3O4txI/pFGpealUREIjRqSD6487VnruGjygK2djyQ20b0C8ojpOQgIhKxUfusBF8KZ9/BW4UnRR0OoGYlEZHoFd0P2W3hsPOijmQHJQcRkShtXQ8fTYEB50FOh6ij2UHJQUQkSh8+AeVb4IjRUUeyCyUHEZGouEPRA9BjEOQfHnU0u1ByEBGJSvFs+PKjlKs1gJKDiEh0ih4IOqIHnB91JLUoOYiIRClWdCkAACAASURBVKGsFD76R9AR3bp91NHUouQgIhKFeU9CRRkUpubDLpUcRESSzR1m3x90RPccEnU0dUpKcjCzPDN7ysw+MbMFZnaMmXU2s5fNbFH4s1O4rZnZnWa22Mw+NLPU6sIXEdlbxbNh1cdwRGrWGiB5NYc/AS+6+yHAIGABcC3wirsfBLwSvgc4DTgofI0F7klSjCIiyVF0P7RqF/Q3pKiEJwcz6wgcD0wCcPft7l4KjAQmh5tNBkaFyyOBBz3wDpBnZj0SHaeISFKUle68IzoFO6KrJaPm0BdYDdxvZnPM7F4zawt0d/cV4TYrge7hcj7wRcz+xWHZLsxsrJnNNrPZq1evTmD4IiJN6MMngo7oFG5SguQkh5bA4cA97j4E2MzOJiQA3N0Bj+dD3X2iuxe6e2G3bt2aLFgRkYTZcUf0YOg5OOpodisZyaEYKHb3d8P3TxEkiy+rm4vCn6vC9SXAfjH79wrLRESat+L3g47oFB2+GivhycHdVwJfmFn1Y42GA/OB6cDlYdnlwLRweTpwWThqaSiwPqb5SUSk+Sp6IOiIPuzcqCPZo2Q97OeHwMNm1gr4FPg2QWJ6wszGAMuAC8JtnwdOBxYDW8JtRUSat+qO6EEXpXRHdLWkJAd3nwsU1rFqeB3bOvD9hAclIpJMHz4edkSPjjqSBtEd0iIiiVbdEd1zSMp3RFdTchARSbQv3oNV85tNrQGUHEREEm9HR3Tq3hFdk5KDiEgila2Dj6cEz2xo3S7qaBpMyUFEJJE+fAIqtjaLextiKTmIiCRK9dTcPYcE03M3I8m6z0FEJGNMnVPChBkL2Xf9v/lH6wXMGXwzqfnUhvqp5iAi0oSmzinhuinzKCkt41stX2Gj53JFUR+mzmleswApOYiINKEJMxZSVl5JBzZxRot3mFZ5LGvLs5kwY2HUocVFyUFEpAktLy0D4JysN8mxch6tHL5LeXOh5CAi0oR65uUCzsVZM5lbtT8fe0FMefOh5CAi0oTGjejH17M/oV+L4h21htzsLMaN6LeHPVOLRiuJiDShUUPyGfbWS6xdk8e0ymHk5+UybkQ/Rg2p9UDLlKbkICLSlD5/l25r3oVTbuGTY78ZdTSNpmYlEZGm9PoEaNMFCr8TdSR7RclBRKSplHwAi1+GY74PrdpGHc1eUXIQEWkqr98OOR3hyCujjmSvKTmIiDSFlR/Bwufg6O9BToeoo9lrSUkOZrbUzOaZ2Vwzmx2WdTazl81sUfizU1huZnanmS02sw/N7PBkxCgisldenwCt2sPQq6KOpEkks+ZworsPdvfqZ0lfC7zi7gcBr4TvAU4DDgpfY4F7khijiEj8Vi+E+dPgqCsht1PU0TSJKJuVRgKTw+XJwKiY8gc98A6QZ2Y9oghQRKRB3vg9ZOcGHdFpIlnJwYGXzKzIzMaGZd3dfUW4vBLoHi7nA1/E7Fsclu3CzMaa2Wwzm7169epExS0isntrl8C8J4Ohq227Rh1Nk0nWTXDHuXuJme0DvGxmn8SudHc3M4/nA919IjARoLCwMK59RUSazJt/gBbZcOzVUUfSpJJSc3D3kvDnKuBp4Cjgy+rmovDnqnDzEmC/mN17hWUiIqll3TL492NwxOXQvvuet29GEp4czKytmbWvXgZOAT4CpgOXh5tdDkwLl6cDl4WjloYC62Oan0REUsdbfwQMhv0o6kiaXDKalboDT5tZ9fEecfcXzex94AkzGwMsAy4It38eOB1YDGwBmtdTuUUkM2xYDnP+DkMugY69oo6mySU8Obj7p0CtJ2u7+1pgeB3lDqRPl7+IpKe37oSqSjjuJ1FHkhC6Q1pEJF6bVkHR/TDoIuhUEHU0CaHkICISr7f/DJXb4bifRh1Jwig5iIjEY/NaeH8SfO0c6Hpg1NEkjJKDiEg83vl/UL4Zjv951JEklJKDiEhDlZXCexOh/9mwT/+oo0koJQcRkYZ6byJs2wDHj4s6koRTchARaYhtG4MmpYNPgx4Do44m4ZQcREQa4v17oWxdRtQaQMlBRGTPtm+Gt++CA06CXkdEHU1SKDmIiOzG1Dkl3Pm762HLGr73xXCmzsmMeUCTNWW3iEizM3VOCbdMeZcXWkzhX1WH8sKGvsyaMg+AUUNqPWYmrajmICJSjwkzFjLWn6ILG7it4mIAysormTBjYcSRJZ6Sg4hIPdqsX8S3s17k8coT+NAP2FG+vLQswqiSQ8lBRKQu7vwm9+9soTUTKi7cZVXPvNyIgkoeJQcRkbrMn8aRVR9yp1/EV3TYUZybncW4Ef0iDCw5lBxERGravhlmXA/dBzBw5I/Jz8vFgPy8XG47Z0Dad0aDRiuJiNT2xh9gQzGcey8j+/Rh5BF9oo4o6VRzEBGJtXYJvH0nDLwQ+hwTdTSRSVpyMLMsM5tjZs+G7/ua2btmttjMHjezVmF56/D94nB9QbJiFBHhxesgqxWcfHPUkUQqmTWHHwELYt7/FrjD3Q8E1gFjwvIxwLqw/I5wOxGRxFv4IiyaASdcC+33jTqaSCUlOZhZL+AM4N7wvQEnAU+Fm0wGRoXLI8P3hOuHh9uLiCRO+VZ48Rro2g+OvirqaCIXV3Iwsx+aWadGHOePwC+AqvB9F6DU3SvC98VAdfd/PvAFQLh+fbi9iEjivP1nWLcUTvstZGVHHU3k4q05dAfeN7MnzOzUhnyjN7MzgVXuXtSoCOv/3LFmNtvMZq9evbopP1pEMk3p5/DG7+HQkXDAiVFHkxLiSg7ufgNwEDAJGA0sMrPfmNkBu9ltGHC2mS0FHiNoTvoTkGdm1UNpewHVUx2WAPsBhOs7AmvriGWiuxe6e2G3bt3iOQ0RkV3NuD74ecqt0caRQuLuc3B3B1aGrwqgE/CUmf2unu2vc/de7l4AXATMdPdLgFeB88LNLgemhcvTw/eE62eGxxQRaXpLZsKC6XD8zyBvv6ijSRnx9jn8yMyKgN8BbwED3P17wBHAuXEe+xrgp2a2mKBPYVJYPgnoEpb/FLg2zs8VEWmYiu3wwjXQqS8c88Ooo0kp8d4h3Rk4x92XxRa6e1XYt7Bb7j4LmBUufwocVcc2W4Hz44xLRCR+7/4F1vwHvvUEZOdEHU1KiSs5uPuNu1m3oL51IiIpZ8MKeO23cPCpcPCIqKNJOZo+Q0Qy08u/gsrtcOptUUeSkjTxnohkjKlzSpgwYyG91n/A462fYOHBV9Gv8/5Rh5WSVHMQkYwwdU4J102Zx8rSTdyUPZli78pFC45l6pySPe+cgZQcRCQjTJixkLLySi7Peon+LT7nlvJLWVfeMiOeB90YSg4ikhGWl5bRzz7nmpaPMbNyMC9WHbmjXGpTchCRjFDQsQV3Zt/FBtrwi/LvAsHsP5nwPOjGUHIQkYwwqcdU+rUo5mflV7GGjkDmPA+6MZQcRCT9LXiW/Zc+xqIDRrOkw9CMex50Y2goq4ikt/UlMP0H0GMQB108gbdatoo6omZBNQcRSV9VlfD0d4M5lM69D5QYGkw1BxFJX2/eAUvfgJF3Q9cDo46mWVHNQUTS0xfvw6u/ga+dA4MviTqaZkfJQUTSz9b18I8x0CEfzrwD9Bj6uKlZSUTSizs89zNYXwzffgFy86KOqFlSzUFE0su/H4N5T8IJ10Lvo6OOptlSzUFEmqXqGVaXl5bRMy+XcSP6Mar31qDW0GcY/NfPog6xWVNyEJFmp3qG1bLySgBKSsv41ZQ5fL3zb+iUlQ3nTIQWWRFH2bwlvFnJzHLM7D0z+7eZfWxm/xeW9zWzd81ssZk9bmatwvLW4fvF4fqCRMcoIs1L9Qyrsf7HH6XT+o9h5F3QsVdEkaWPZPQ5bANOcvdBwGDgVDMbCvwWuMPdDwTWAWPC7ccA68LyO8LtRER2qDmT6nEt5nFVy2d5uGI49D8roqjSS8KTgwc2hW+zw5cDJwFPheWTgVHh8sjwPeH64WYahyYiO8XOpNqZDfwh+x7+U5XPpLZXRhhVeknKaCUzyzKzucAq4GVgCVDq7hXhJsVA9exX+cAXAOH69UCXZMQpIs3DuBH9yM3OApwJ2X+lI5sZ5z/i6lMHRh1a2khKh7S7VwKDzSwPeBo4ZG8/08zGAmMBevfuvbcfJyLNSPVMql89exPDK+dwR8sr+PYZZ2qG1SaU1NFK7l5qZq8CxwB5ZtYyrB30Aqof5FoC7AcUm1lLoCOwto7PmghMBCgsLPRkxC8iqWOUvwKVT8DgS/jJyNt1F3QTS8ZopW5hjQEzywVOBhYArwLnhZtdDkwLl6eH7wnXz3R3XfxFZKdF/4RnfgwHnARn/UmJIQGSUXPoAUw2syyCZPSEuz9rZvOBx8zsFmAOMCncfhLwkJktBr4CLkpCjCLSXCyfC09cBt0PhfMnQ1Z21BGlpYQnB3f/EBhSR/mnwFF1lG8Fzk90XCLSDK1bBo9cAG06w7eehJwOUUeUtnSHtIg0D1u+gofPh/KtcNk06NAj6ojSmpKDiKSEOudKqh59VL4VHrsE1n0Gl06BffpHG2wGUHIQkcjVNVfSdVPmATBqUA+YehV8/jacOwn6/leUoWYMTdktIpGra66ksvJKJsxYCP/8FXz8NJx8Mww4r55PkKammoOIRK7mXEnVTt44Fd6eDEdeCcdeneSoMptqDiISudi5kqqNaPE+v8p+EPqdAaf9VvcyJJmSg4hEbudcSYHD7T/8KfsuSjsNgHPv1bMZIqBmJRGJXPWopAkzFtJ6/afc1/r3VLTtQecrnoZWbSKOLjMpOYhIShg1JJ9RB7WCST+Bba3gO1Ohbdeow8pYSg4ikhq2fAWPnA8bv4TRz0KXA6KOKKMpOYhI9DauhIe+CWsXwwUPQa/CqCPKeEoOIhKtrz6Dh0bBptVwyVOw/9ejjkhQchCRBNjtVBixVi2AB0dB5Ta4/BnodUTyg5U6KTmISJPa7VQYsQmiuAgePheyWsPo54MpuCVl6D4HEWlSu50Ko9qnr8GDZ0NOR/jOi0oMKUjJQUSaVH1TYewoX/AsPHwe5PWG78yAzn2TGJ00lJKDiDSpuqbC2FE+99HgKW77DoTRz0H7fZMcnTSUkoOINKmaU2EA5GZncc+B7wVTbxccFzysp03niCKUhkh4cjCz/czsVTObb2Yfm9mPwvLOZvaymS0Kf3YKy83M7jSzxWb2oZkdnugYRaTpjBqSz23nDCA/LxcD8jvmMOXQ1xn40W1wyJlwyZPQul3UYcoeJGO0UgXwM3f/wMzaA0Vm9jIwGnjF3ceb2bXAtcA1wGnAQeHraOCe8KeINBOjhuQHI5OqqmDGdfDuX2DwJXDWnZClQZLNQcJrDu6+wt0/CJc3AguAfGAkMDncbDIwKlweCTzogXeAPDPTw2JFmpvKCpj2/SAxDP0fOPsuJYZmJKm/KTMrAIYA7wLd3X1FuGol0D1czge+iNmtOCxbgYg0D1u+gn9cAUtegROvh+PH6XkMzUzSkoOZtQP+AfzY3TdYzB+Ku7uZeZyfNxYYC9C7d++mDFVE9kbxbHjicti8Cs76ExwxOuqIpBGSkhzMLJsgMTzs7lPC4i/NrIe7rwibjVaF5SXAfjG79wrLduHuE4GJAIWFhXElFhFpuAZPheEO7/0NZvwSOvSAMS9BzyHJD1iaRDJGKxkwCVjg7n+IWTUduDxcvhyYFlN+WThqaSiwPqb5SUSSqHoqjJLSMpydU2FMnVPj+9q2jfDUd+CFcXDgcBj7mhJDM5eMmsMw4L+BeWY2Nyz7JTAeeMLMxgDLgAvCdc8DpwOLgS3At5MQo4jUYXdTYeyoPaxaAI//N3y1BIbfCMN+DC10C1Vzl/Dk4O5vAvX1RA2vY3sHvp/QoESkQfY4Fca/H4dnfwyt2sFl06HvfyUxOkkkjSsTkXr1zMulpI4EUdAxC575MRTdD32GwXn3aSqMNKO6n4jUq66pMA7MXsuUnJuDxDDsR0GNQYkh7ajmICL1qu5XqB6tdH77j7iFu2i11eCiR+CQMyKOUBJFyUFEdmvUkHxGDdwHXr0V3rwjmFH1ggc11XaaU3IQkd0rLoJnroYvP4LDL4fTfgfZOVFHJQmm5CAiddu6AWb+OrixrX0PuPDv0P+sqKOSJFFyEJHaFjwLz4+DjSvgqCvhpP+FnA5RRyVJpOQgkmF2Ox3G+hJ44RfwybPQ/TC48CHoVRhtwBIJJQeRDFI9HUb1Xc/V02HglYwqfwFe+TVUVcA3/g+O+T5kZUccsURFyUEkg9Q1HUZBxacc9MwN4IvggOFw5h+gU0E0AUrKUHIQySCx02HkspUftZzCFVnPU1rVDs6bBIedq+cuCKA7pEUySs+8XMA5ocUcXmp1DVe1fJYnK7/OpTl3wYDzlBhkB9UcRDLIbYWbyHnjNo6y+Syu6skF2/6XeS0P47ZTB0QdmqQYJQeRTFDyAbx6K8cv/idbc7ryh8or+eum4+ia14Hb6nt4j2Q0JQeRdPblx/Dqb4Khqbmd4eSbyTnySn7aqg0/jTo2SWlKDiLpaM1imHUbfPQPaN0eTrwejr5KN7JJgyk5iKSTdcvg9d/B3EehZWs47idw7A+hTeeoI5NmRslBJB1sWAFv3A5Fk8FaBLWE434C7bpFHZk0U0oOIs3YP19/k9LX7ubMipdpaVV8UXAefb95I3RUB7PsnaTc52Bm95nZKjP7KKass5m9bGaLwp+dwnIzszvNbLGZfWhmhycjRpFmo6oSFr7AqrtP4xszz+Csipd4pvIYTtx2O6cvOYepn0YdoKSDZN0E9wBwao2ya4FX3P0g4JXwPcBpwEHhayxwT5JiFEltZevg7T/DnUPg0Yuw1Z9we/n5DNv2Z8ZVXMUX3p2y8komzFgYdaSSBpLSrOTur5tZQY3ikcAJ4fJkYBZwTVj+oLs78I6Z5ZlZD3dfkYxYRVLOyo/gvYnw4RNQUQZ9hsHJ/8exD7WgvI7/wrFTZIg0VpR9Dt1jLvgrge7hcj7wRcx2xWHZLsnBzMYS1Czo3bt3YiMVSbbKiuDehPcmwrK3oGUuDDwfjhoL+wZ3M++TN5OSOhJBMEWGyN5JiQ5pd3cz8zj3mQhMBCgsLIxrX5GUtb4E/v0IzL4fNpRAXm84+dcw5NJaw1HHjei3y/TbALnZWYwb0S/ZUUsaijI5fFndXGRmPYBVYXkJsF/Mdr3CMpH0tHktzJ8a3LC27G3AYf8T4fTb4eAR0CKrzt2qp7yo98E9InshyuQwHbgcGB/+nBZT/gMzeww4Gliv/gZJO9s2wifPwbyn4NNXoaqCDe325/GWF/Hw5iMpX96XcWX9GFVPYqg2aki+koEkRFKSg5k9StD53NXMioEbCZLCE2Y2BlgGXBBu/jxwOrAY2AJ8OxkxiiRc+VZY/HKQEP7zIlRshY77wTHfZ2b21/n+K9spK68Ktq1+Qhvo4i+RSNZopYvrWTW8jm0d+H5iIxJJksoKWPo6zPsHLHgGtq2HNl1hyH8Hz0/odRS0aMH/jp+5MzGEqoelKjlIFFKiQ1okrWz5CpbMhEUvweJ/wpa10Ko99D8LBpwLfU+ArF3/69U3/FTDUiUqSg4ie6uqClbMDRLBopegpAi8Kpgi+8DhQVI46BTIrn+Iac+8XA1LlZSi5CDSGNW1g8X/DF6bVwMGPYfA8eOCZNBzSL0jjWrSsFRJNUoOIg1RsR1WfghLXg06lYvfD2sHneCA4XDQycHPmFlQp84pafAwUw1LlVSj5CBSl60bggTw+b/g83egeHYwdQXsrB0ceDLkH15n7WDqnJJdagIlDRh9pGGpkkqUHEQgeB5CdSL4/F/w5UdBzcBawL4D4YjR0HtoMK9RA56RMGHGwl2aiECjj6R5UXKQzFOxHVZ/Ass/2JkM1i0N1mW3gV6FcPwvgmTQqzB4zGacNPpImjslB0lv2zfDlx/Din8Hr5UfwqoFULk9WN+mK/Q5JpjQrvfQoJaQlV3rY+LpPwCNPpLmT8lB0kfZOljx4c4ksOJDWLsoaB6CYGhpj0Ew9HvBzx6DofP+YLbbj21M/4FGH0lzp+Qgzc/mNbDmP7B6YfCzenl9zEzvHfKDWsDXvgk9BgbJoEP+HhNBXRrTf6DRR9LcKTlIaqqqgvWfw+rw4r9m4c7lsq92btcyF7oeCPsdDUeOCRJCj0HQtmu9Hx1vE1Fj+w80+kiaMyUHic72zbBuGZQuCzqEY5e/+jSYmK5amy7QtR8cejZ0PThY7npQMHFdi4Y/7bYxTUTqP5BMpOQgibN9C2xcAeuLd00A65YG7zev3nX77LbQqSB4HXBSmATCV9suTRJSY5qI1H8gmUjJQeLnHnT+blgevDYuj1lesXN5a+mu+1kW5O0HeX2g32lBEsjrA536Qqc+Qe2gEX0C8TQTNaaJSP0HkomUHCRQfcHfvAY2rwq+1W9aHfzcvCoo37QqWN64ctcmHwAM2u0DHXoGF/s+w6BDD2jfMywrCDqEs5r2Ty7eZqLGNhGp/0AyjZJDOqqqhK3rg8nhytYFHbhl6+p+v2VtmABWQ1VF7c+yFsE3+rb7BJ28vY6E9vvuvOhXv9p1r/P+gESLt5lITUQiDaPkkGrcobws6KzdtiFomtm6IVyu+XN98Kou27o+uOhvXQ943Z9vLSAnL5gwrk3n4MLeYxC07RZ882/bbddXm84Nnlm0KSR6JJGaiEQaRsmhsSoroHxLcCEv3xz+DC/q5WUx67YEr+2bw9cm2LZp53Jd771qz8dv1R5yOkDrDpDTMbiwdzkwuJjndt558c/tFL7PC9637hjX6J5Y8V64G7N9MkYSqYlIZM9SNjmY2anAn4As4F53H9/Ux5jx1nt8NfPPVGwrI691FYP3zWG/9gYV24I29epX+Vao2MbWrZup2FZGK99GK6vc8wFq2EIOLVq3I6dtR2jVNpizp223oD2+Vbvg1bodH6+p5MX/bKR4SzbZbfM46+hD+K/DDtiZDFq33+XbfLwX4cbsE++FuzEXeo0kEkkdKZkczCwLuBs4GSgG3jez6e4+v6mOMXVOCQ+9OJsHW8xgW1Y2WytaUV7civUdO9CxXTtombPz4t0yh883VPH2us1srmrJNrLZ4q2pyMrh1CH7M3j/nsFTvrLbhK9g+aVFG7jpxaWUlmdRRiucFuRWZnHbqQPqvdhNnVPCda/GXOw2wjOvZXFbl05NdhFOxoW7MRd6jSQSSR0pmRyAo4DF7v4pgJk9BowEmiw5TJixkJLyPnyN+3cpz9+ey1tjT6q1/cXjZ1KyvcZFqhKeWZjLW9+svT3A/903k+XlbXcp29MFMhkX4WRcuBtzoddIIpHU0bjG58TLB2ImyqE4LNvBzMaa2Wwzm716dY2bqRogGRe7ZOyTrLjqu0A3VTkETUS52bt2fquJSCQaqZoc9sjdJ7p7obsXduu254ev1JSMi10y9klWXPFeuBtzoR81JJ/bzhlAfl4uBuTn5XLbOfU3wYlI4qRqcigB9ot53yssazLJuNglY59kxRXvhbuxF/pRQ/J569qT+Gz8Gbx17UlKDCIRMfd6xsNHyMxaAv8BhhMkhfeBb7n7x3VtX1hY6LNnz477OIkempmsfZIVl4ikFzMrcvfCOtelYnIAMLPTgT8SDGW9z91vrW/bxiYHEZFMtrvkkKqjlXD354Hno45DRCQTpWqfg4iIREjJQUREalFyEBGRWpQcRESkFiUHERGpJWWHssbDzFYDy/biI7oCa5oonOYkU88bMvfcM/W8IXPPfXfn3cfd65xiIi2Sw94ys9n1jfVNZ5l63pC5556p5w2Ze+6NPW81K4mISC1KDiIiUouSQ2Bi1AFEJFPPGzL33DP1vCFzz71R560+BxERqUU1BxERqSWjk4OZnWpmC81ssZldG3U8yWRmS81snpnNNbO0ntLWzO4zs1Vm9lFMWWcze9nMFoU/O0UZYyLUc943mVlJ+HufG85+nFbMbD8ze9XM5pvZx2b2o7A8E37n9Z173L/3jG1WMrMsgmdGnEzwGNL3gYvdvcmeU53KzGwpUOjuaT/u28yOBzYBD7r7YWHZ74Cv3H18+MWgk7tfE2WcTa2e874J2OTut0cZWyKZWQ+gh7t/YGbtgSJgFDCa9P+d13fuFxDn7z2Taw5HAYvd/VN33w48BoyMOCZJAHd/HfiqRvFIYHK4PJngP1Baqee80567r3D3D8LljcACgmfQZ8LvvL5zj1smJ4d84IuY98U08h+xmXLgJTMrMrOxUQcTge7uviJcXgl0jzKYJPuBmX0YNjulXdNKLDMrAIYA75Jhv/Ma5w5x/t4zOTlkuuPc/XDgNOD7YRNERvKgbTVT2lfvAQ4ABgMrgN9HG07imFk74B/Aj919Q+y6dP+d13Hucf/eMzk5lAD7xbzvFZZlBHcvCX+uAp4maGbLJF+G7bPV7bSrIo4nKdz9S3evdPcq4G+k6e/dzLIJLo4Pu/uUsDgjfud1nXtjfu+ZnBzeBw4ys75m1gq4CJgecUxJYWZtw84qzKwtcArw0e73SjvTgcvD5cuBaRHGkjTVF8fQN0nD37uZGTAJWODuf4hZlfa/8/rOvTG/94wdrQQQDuf6I5AF3Ofut0YcUlKY2f4EtQUIniP+SDqfu5k9CpxAMDvll8CNwFTgCaA3wYy+F7h7WnXe1nPeJxA0LTiwFPhuTDt8WjCz44A3gHlAVVj8S4K293T/ndd37hcT5+89o5ODiIjULZOblUREpB5KDiIiUouSg4iI1KLkICIitSg5iIhILUoOIiJSi5KDiIjUouQgkgBmdmQ4yVlOeEf6x2Z2WNRxiTSUboITSRAzuwXIAXKBYne/LeKQRBpMyUEkQcI5u94HtgLHuntlxCGJNJialUQSpwvQDmhPUIMQaTZUcxBJEDObTvCEwb4Ej278QcQhiTRYy6gDEElHZnYZUO7uj4TPK3/bzE5y95lRxybSEKo5iIhILepzEBGRWpQcRESkFiUHERGpko9VrAAAACNJREFURclBRERqUXIQEZFalBxERKQWJQcREalFyUFERGr5//0ozugv7akcAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x432 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Cu3M_hUzbiZw",
        "colab_type": "code",
        "cellView": "both",
        "outputId": "4d14f380-960a-45f5-be05-b5ea811a4315",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        }
      },
      "source": [
        "#@title\n",
        "print('Number of day', number_of_days)\n",
        "\n",
        "# Infected guess\n",
        "infected_lower = int(round(result.params['a'].value - result.params['a'].stderr))\n",
        "infected_upper = int(round(result.params['a'].value + result.params['a'].stderr))\n",
        "print('Infected: ', infected_lower, ' - ', infected_upper)\n",
        "\n",
        "# Peak day\n",
        "peak_day_lower = int(round(result.params['c'].value - result.params['c'].stderr))\n",
        "peak_day_upper = int(round(result.params['c'].value + result.params['c'].stderr))\n",
        "print('Peak day: ', peak_day_lower, ' - ', peak_day_upper)\n",
        "\n",
        "# Date\n",
        "print('Peak date: ', data['date'][peak_day_lower], ' - ', data['date'][peak_day_upper])"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Number of day 77\n",
            "Infected:  3176  -  3203\n",
            "Peak day:  32  -  32\n",
            "Peak date:  2020-03-28  -  2020-03-28\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "W0xuPYLk73Il",
        "colab_type": "text"
      },
      "source": [
        "- Number of day 31\n",
        "- Infected:  1779  -  2064\n",
        "- Peak day:  26  -  27\n",
        "- Peak date:  2020-03-22  -  2020-03-23\n",
        "\n",
        "---\n",
        "\n",
        "- Number of day 32\n",
        "- Infected:  2040  -  2345\n",
        "- Peak day:  27  -  28\n",
        "- Peak date:  2020-03-23  -  2020-03-24\n",
        "\n",
        "---\n",
        "\n",
        "- Number of day 34\n",
        "- Infected:  2209  -  2421\n",
        "- Peak day:  28  -  29\n",
        "- Peak date:  2020-03-24  -  2020-03-25\n",
        "\n",
        "---\n",
        "\n",
        "- Number of day 35\n",
        "- Infected:  2304  -  2492\n",
        "- Peak day:  28  -  29\n",
        "- Peak date:  2020-03-24  -  2020-03-25\n",
        "\n",
        "---\n",
        "\n",
        "- Number of day 36\n",
        "- Infected:  2411  -  2587\n",
        "- Peak day:  28  -  29\n",
        "- Peak date:  2020-03-24  -  2020-03-25\n",
        "\n",
        "---\n",
        "\n",
        "- Number of day 38\n",
        "- Infected:  2560  -  2701\n",
        "- Peak day:  29  -  30\n",
        "- Peak date:  2020-03-25  -  2020-03-26\n",
        "\n",
        "---\n",
        "\n",
        "- Number of day 39\n",
        "- Infected:  2626  -  2755\n",
        "- Peak day:  29  -  30\n",
        "- Peak date:  2020-03-25  -  2020-03-26\n",
        "\n",
        "---\n",
        "\n",
        "- Number of day 42\n",
        "- Infected:  2743  -  2802\n",
        "- Peak day:  30  -  30\n",
        "- Peak date:  2020-03-26  -  2020-03-26\n",
        "\n",
        "---\n",
        "\n",
        "- Number of day 51\n",
        "- Infected:  2934  -  2971\n",
        "- Peak day:  31  -  31\n",
        "- Peak date:  2020-03-27  -  2020-03-27\n",
        "\n",
        "---\n",
        "\n",
        "- Number of day 53\n",
        "- Infected:  2959  -  2993\n",
        "- Peak day:  31  -  31\n",
        "- Peak date:  2020-03-27  -  2020-03-27"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "T6-E_SnSWyVI",
        "colab_type": "code",
        "outputId": "d913d279-f92e-4be6-b86e-af48af9a9bc3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 428
        }
      },
      "source": [
        "\n",
        "x = numpy.linspace(0, predicted_days + 1, predicted_days)\n",
        "\n",
        "ySigmoid_dx = [sigmoid_dx(i, \n",
        "                          result.params['a'].value,\n",
        "                          result.params['b'].value,\n",
        "                          result.params['c'].value,\n",
        "                          ) for i in x]\n",
        "\n",
        "ySigmoid = smodel.eval(result.params, x=x)\n",
        "yExponential = emodel.eval(exp_result.params, x=x)\n",
        "\n",
        "ySigmoid2 = [sigmoid(i, \n",
        "                    result.params['a'].value - result.params['a'].stderr,\n",
        "                    result.params['b'].value,\n",
        "                    result.params['c'].value + result.params['c'].stderr,\n",
        "                    ) for i in x]\n",
        "\n",
        "ySigmoid3 = [sigmoid(i, \n",
        "                    result.params['a'].value + result.params['a'].stderr,\n",
        "                    result.params['b'].value,\n",
        "                    result.params['c'].value - result.params['c'].stderr,\n",
        "                    ) for i in x]\n",
        "\n",
        "fig, ax = plt.subplots()\n",
        "fig.set_size_inches(12.5, 7, forward=True)\n",
        "\n",
        "ax.scatter(xData, data['ncumul_conf'])\n",
        "\n",
        "# \n",
        "dely = result.eval_uncertainty(sigma=5)\n",
        "exp_dely = exp_result.eval_uncertainty(sigma=5)\n",
        "\n",
        "plt.fill_between(xDataSample[:num_sample], result.best_fit-dely, result.best_fit+dely, color=\"#bbebfb\", alpha=0.3)\n",
        "\n",
        "ax.plot(x, smodel.eval(params=result.params, x=x), label='sigmoid', zorder=100)\n",
        "\n",
        "ax.plot(x[:30], emodel.eval(params=exp_result.params, x=x[:30]), label='exp', zorder=100)\n",
        "\n",
        "\n",
        "# plt.fill_between(xDataSample, \n",
        "#                  exp_result.best_fit-exp_dely, exp_result.best_fit+exp_dely, \n",
        "#                  color=\"#44cc44\", alpha=0.1)\n",
        "\n",
        "plt.savefig('drive/My Drive/Colab Notebooks/Covid-Ticino/data-vs-model.png', dpi=100)"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 900x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WCQHJNAAnFpF",
        "colab_type": "text"
      },
      "source": [
        "# Probability density function\n",
        "\n",
        "**rolling average** (or moving average)\n",
        "\n",
        "[jhsph study → 5.1 days](https://www.jhsph.edu/news/news-releases/2020/new-study-on-COVID-19-estimates-5-days-for-incubation-period.html)\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "l_8mWVlNc0ch",
        "outputId": "9f531ec4-b829-41b4-9d0a-44d89d13d678",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 394
        }
      },
      "source": [
        "fig, ax = plt.subplots(1, 2, sharex='all', sharey='all')\n",
        "\n",
        "fig.set_size_inches(15, 6, forward=True)\n",
        "\n",
        "xInf = numpy.linspace(0, len(new_infected), len(new_infected))\n",
        "\n",
        "ax[0].plot(x, ySigmoid_dx)\n",
        "ax[0].bar(xInf, new_infected, alpha=0.8)\n",
        "\n",
        "ax[1].plot(x, ySigmoid_dx)\n",
        "ax[1].bar(xInf, new_infected_rolling, alpha=0.8)\n"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<BarContainer object of 77 artists>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x432 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "LSxvWsCAijMv",
        "outputId": "e41810ab-a336-4797-83ad-26fe5bad88b7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 735
        }
      },
      "source": [
        "fig, ax = plt.subplots()\n",
        "\n",
        "fig.set_size_inches(20, 12, forward=True)\n",
        "\n",
        "#ax.axhline(result.params['a'].value + result.params['a'].stderr, linestyle='dashed', color='#ff5555')\n",
        "#ax.axhline(result.params['a'].value - result.params['a'].stderr, linestyle='dashed', color='#55ff55')\n",
        "\n",
        "ax.axhline(ySigmoid3[-1], linestyle='dashed', color='#ff5555')\n",
        "ax.axhline(ySigmoid2[-1], linestyle='dashed', color='#55ff55')\n",
        "\n",
        "ax.legend((\"max: \" + str(infected_upper), \"min: \" + str(infected_lower)))\n",
        "\n",
        "next_data = len(result.best_fit) - 1\n",
        "\n",
        "ax.fill_between(x[next_data:], ySigmoid2[next_data:], ySigmoid3[next_data:], color=\"#bbebfb\", alpha=0.2)\n",
        "\n",
        "ax.plot(x, ySigmoid)\n",
        "\n",
        "plt.fill_between(xDataSample, result.best_fit-dely, result.best_fit+dely, color=\"#bbebfb\", alpha=0.6)\n",
        "\n",
        "ax.scatter(xData, data['ncumul_conf'])\n",
        "\n",
        "print('Tomorrow prediction:', data['date'][-1:], '+1', '+2', '+3', '+4', '+5')\n",
        "print(' ↓ ', int(round(ySigmoid3[number_of_days + 1])),\n",
        "      int(round(ySigmoid3[number_of_days + 2])),\n",
        "      int(round(ySigmoid3[number_of_days + 3])),\n",
        "      int(round(ySigmoid3[number_of_days + 4])),\n",
        "      int(round(ySigmoid3[number_of_days + 5])))\n",
        "print(' → ', int(round(ySigmoid[number_of_days + 1])),\n",
        "      int(round(ySigmoid[number_of_days + 2])),\n",
        "      int(round(ySigmoid[number_of_days + 3])),\n",
        "      int(round(ySigmoid[number_of_days + 4])),\n",
        "      int(round(ySigmoid[number_of_days + 5])))\n",
        "print(' ↑ ', int(round(ySigmoid2[number_of_days + 1])),\n",
        "      int(round(ySigmoid2[number_of_days + 2])),\n",
        "      int(round(ySigmoid2[number_of_days + 3])),\n",
        "      int(round(ySigmoid2[number_of_days + 4])),\n",
        "      int(round(ySigmoid2[number_of_days + 5])))\n",
        "\n",
        "#print('sample + 1', ySigmoid)\n",
        "\n"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Tomorrow prediction: 76    2020-05-12\n",
            "Name: date, dtype: object +1 +2 +3 +4 +5\n",
            " ↓  3201 3201 3201 3201 3202\n",
            " →  3187 3188 3188 3188 3188\n",
            " ↑  3174 3174 3174 3175 3175\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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2S+CKiuqsgpFcgh/IbeRPLmFOLn1B4Y8IwJAhQ6iqqqKmpuZ030qP5Pf7GTJkSE7HKCgSEREREemBEiOAwvGviGMIORCIP4YNhJ2WuMfCSgl/7ET4c9zgp2v/5X9FRXVK6HKwPsDi5yoA0sKSrpzylUuYk2vwkzhG4Y+czTweDyNGjDjdtyGtKCgSEREREemGnPjIn1A88AnFRwEF00Kg2BigxLPWAZDXPrXhTy66anoYdGzkT7ZhjoIfEenuzuygqLoavve91H0zZ8K8eRAMwo9/nH7MnDmxr4YG+OUv09svvxwuuggOHYL77ktvv/JKmDEjdu3f/ja9/eqrYfJk2LkTlixJb7/2WhgzBrZuhUceSW9ftAiGD4cNG+DJJ9Pbb7oJyspgzRp49tn09ltugZISeOMNePHF9PbbboPCQli1KvbV1p13gs8Hy5fDm2+mtye+38uWwdq1qW1eL3zrW7Htxx+HiorU9oIC+MY3YtsPPwyVlantffvCrbfGtpcsiX0PWysthS9/Obb94IOwb19q+/Dhse8fwOLFUFub2l5eDp/9bGz75z+HxsbU9okTYeHC2Pbdd0MolNo+fTrMnx/bbvu+A7339N6Lbeu9l96u915sW++99Ha99/TeA733TuK9Z/7+d8yLLyULOCdWAqv6ytdo7lVI4eur6PvGq1iAJ/5VAHzw1W9i+X0MXPUShWv+kXb63f/+XQD6vvAsBe+mvveMx8ue2+4AoGTZk+RvTn3vRQt6s/fmrwPQ//FHyNuxLaU90qcv1TfdAsDAh5fi27MrpT00sJSHZ1zB0pWVfH37CspNI4OK8ynu5QUgOPQcDnz2BgC+te0lBkWOpRy/1t+fezgPgMG/vhdX41EAnjoWourwMV73D+K/+kwB4KEDL3NuyE/xT19NHt84ZTq1H72SSyaWseDP97O/rolwxMHjthlUnI/rIByhDCsYZOh//jTte1c/azb1/2c2rqMNDL7/P9Paj8y9jKMXzMRde5iy3/06rb123hU0Tj0P775qBj30+7T2Q1deRdOESfh272TgIw+ltdd86hqaR5WTt72S/k88mtZ+4NrrCQ4bTv6mjZQ8+1Ra+/7rv0iotIyC9e/Qd/lzae3VX7qZSN9+9F79Jn1WvpTWvvcrXyPau5Ci11+h6O+vpLXv+dq/Y3w++qx4kd5vn5r3njHgtoGSfvrc6wGfe/o79zS89zI4s4MiEREREZEezBD7RTcZBAH7mhyOHY2S3+xQEjXx0UAxFrHC0bZlcMeLPyf2J3hsC2NZnIk1YauPNCVH8hggHHGoOhwLgxJhUYLPY0Mk/RyZRgklju0VdWPF+5zj9KbYd/xvQnEvb9o1j+b2cqQbSPzZMo4hHImNIPM4BqvNSlBOvN0APkNae7TV8ZnaI63a/abl/CZDe16GVajCifaIQ36m9ohDOOxgRZyMx4datWc6PhA1RMIOdvgE7SEHV9ghz0lvbw47RNtpj4Qc3BGDP0N7U8jBCTl4IgZfhvZj4Vi7N+JkbG8MO5gTtB8NOWA5+CIG7/HaAX/U4GnTbhxD44naDcn2vKjBawyuM/EDthNZHVkq7VSZMWOGWbNmzem+DRERERGRkxI1sSlhISf2C1mzYwg4EIya+HLvLfWBXPEAKFYYumf9MnLDf63KOEVsQJGfpf86J2Vf2xpFEAuPbr1ioqZ2SVLIMfTxWAzLc2VsDzqGurCJrabXCdfL9RwmPhLQJP6EJ7ZNoj2+k5bQ2JjWfVuWN28Jlk0ygUrZlzxfy7VNcjt+iEkPQdoe2+qWUq6d2se0PVXqtTOds02ntudM6dfq2m33k+E6ifNYbfuS9pJTjkk/eabjW/Y4Bs7pl095SS+6O8uy3jHGzMjUphFFIiIiIiKdpHUg1Bx1OBaF5mhs1bDWYVCiQHRqjaDuGwpluzpZLkWnO1IY+mwTCyEMUccQiRqijkPUMRm/HOcE7SbWnjiXkzim1fPkdrJvbBRO62OTj4bkc5PYbvU8mqEtsW1MohB7y3bb9rb9bMBr0XJd0/p7E7teIiRx2hwfG3HUpg2g1XYiKHBaHWMS54CUc7U8noY3hJwSX72snPJLR5/u2+hSCopERERERHLkmMQKYrERQseihmYnNn2k9b9pJ0YH+SziU8G6RxiUbfCT6Jvt6mS5Fp0+nYWhjTGEo4ZwJEo46sSm9iQeI4nH1LZINNaWmEYUSeyLOkSihnB8OxyJBTbhqBMPeWLtkahDxIlvO632RWOBSDTqEHFij1HHEMkwxeZ0si2wLQvbtrAsKxaIJrdjz23bwsLCtuN949MkbbvVttVyfNt9VnxkkMtlYVs2BvDaFvnulmu0PtYBHMDV+jyQPFf6cyBxHlqu1/raLdutjmnbN7ar1fnjZeWTfVqOJ9GW3J+pf7y9VR/S9rWcIyF5LynnTv0cynRs64+rtLZW+9sOeky5dqZzZrh++nGp959pPxmuk+lamc7f9phMB6QfH9vjGMM5ffPp6c7oqWdlM8rMTWtuStk3k5nMYx5BgvyY9CJfc+L/a6CBX5Je5OtyLuciLuIQh7iP9CJfV3IlM5hBNdX8lvQiX1dzNZOZzE52soQlae3Xci1jGMNWtvII6UW+FrGI4QxnAxt4kvQiXzdxE2WUsYY1PEt6ka9buIUSSniDN3iR9CJft3EbhRSyKv6/tu7kTnz4WM5y3iS9yNf3+B4Ay1jGWlKLfHnx8i1iRb4e53EqSC3yVUAB3yBW5OthHqaS1CJffenLrcSKfC1hCTvZmdJeSilfJlbk60EeZB+pRb6GM5xFLAJgMYupJbXIVznlfJZYka+f83MaSS3yNZGJLCRW5Otu7iZEapGv6UxnPvNTvg+t6b2n9x7ovaf3nt57rem9p/cenB3vPWNiv2gmRirMDV7DgPBo9rgqecX/WPK4xC8WHwtdT6kznB2ujbzqTS8o/InAFykxZWx1vcMb3vSCwlcHbqbI9KPC/SZve9ILCn+6+Wv0opB17ldY73klrf265n/Hi4/V7hfZ5EkvKPz55lhB4b97nqXSnfrecxsvg9dcz+LnKuj34XUUjoq9N2zLYnC/XpTl9+OaQKyg8EveR6hybWNLVR2haCwkCtX34v3HPgzA2E+9w+Tp0ZTzBw8V8cJ94wiGHYZf9Qb+/g3Jc/fp5WVQ9Bw+FooVs37Cdx8Ndup7b0h0NB8JXkMw4vCI9Wsaw81EQjbRsE0kZFMSGEF54EICoSiv8QKhiEMk5CIatomGbHqF+lMcLCMUcXjf2UY0YsXawjZOxMYK+zFhD6FIlFAkdcW1k2G7HGyXwety43d5sVwOQffR5H7bNthuQx+rH73t3kRcAQ579mC7DJZtkn2GMopiuw9Nrjr2eDantFu2YYKZQR+rH7XufWz3rI3vB9uOtV8Y+QjFdgn73NvZ7P1HSptlwUcin6bQKuZ9z7ts8r6BZcXb4u0LQjfR2+5NhecNNnpfT+5PPH4ucHLvvf8biBWzXuV5kg/cqZ97eaZ32nsPYqN83BaUufpl/Nxz4iOYSinjeif2u+VD9m/Zb6V+7g0z53CNswiA39n/xREr9b13rhnNp5zY59799i9otFI/98Y5E/mE+RQA/2n/mJCV+rk3xZnOPPMJAH7m+n7a9+Z850PMNbHPvV+5fpLWPsuZzSwzh6M08IDr3rT2Oc5lXGAuopZD/N6VXkj9cudKpprz2E81D7l+l9Z+pXMV481kdrOTR11L09qvjl7DKMawna086UovpH5N9AaGMZz3rA08a6d/7l0f/RKDKGO99Q4v2ul/534xejN9KWE1f2eVnf6598/Rr9ObQv5ureINO/1z79boHfjwsdJ+kTVW+t+5t0fvAmC5/QwbrPS/c78avROAZ+0n2Gy1/Tu3N/8SvQ2Ax+1H2GlXptQo6q5/537f+r6mnomIiIiItMcYCDiGw1GH6miUY560ahZEjMFrx0Yx2G3/2fkMduRYiANHmvj4Ay/Qv8jPJZ+thyHp/TItS+8Yw4EjTZTl90vrnwiJ2moKtoRExoFIyKbQ1ZvPfngUT7+1k+YDxTjHetHLnc/B3T72Bl1UNhewtbmC5lCEDyKlBEIDiIRcREI2kaCLaMjDf4aWx6YKkXkpe3g3/tjy4lxuB5fHweexKHQ34HW7CPtcuNwOvvwILk+svcT2M9hVittj2Or/B7bbweU2uNwOttthpDWG0dYEou4Ab+Y/nWxPPE51ZjGR82hy1/O3Xr+LBTwukxzNcFHoCsZEz+OQVc0z/vRVzy4OXcW50Unss3fygm9lWvulwQ8xzClnt13J//qq0tovC/al1DmHHa4GHO+htPbpgSJKTH+2unZT600v3T08kE+R6c0xt4vdnvSRX0XNHnrhxe+28Hg6L0jraoZYWBSKxv40Ry2TVqcm6pBsd6z0OjbJdgscO7VuDYnzx0d4ObZJa4+0ajd2+mCNqInVWwpjMrYnjj9uu2MIO4bQCdpDjiFsHac9CiFz/PawY7Jqj1hgrBO02yZjeyh+7xE7vUYQxL4/ERL/bU7QbsBk+FyOxA/K1O7k0H62OKNHFKmYtYiIiIh0FROvJ9TsQGPE4WgkVvg2UUvIsmIjFXpCUelcCkN//IcvZPxFDODR2y6hoSlEQ3OYxkCYY4EIDyx/j8ZA+vJkbtuib4GPxmCEpmCG5csyyPO6yPe5yfO6yfO68Htc5Hnd+L0u8uOPecnHWLvf68bniW3HHu3kduLL7ub//eT42itm7RhDxGQ36TOnd0k7U5VOxql4t57KPxHd/fOzp1IxaxERERE560WcWB2hpqjhaCRWVyhR4sWyYvWEfHb3qiWUrUyjhIJhhz+8vIVBRXkcORaivinE0eYQfq+L5lA043mu+eWKrK5nWTB8QG+GDyigl99Dgc9NfquvXq2fe2OPeT63Ah3pdLZl4dXbSiQnCopEREREpMcxxhAy0BSFo/HRQqFWhaZdidFC3WnuWAZti05fd/EoJg/ry6GjQWobAxxpDFJ3LJSxgDRAbWOIf1v6Vso+jyv9e2LbcOHoAUwZ3o/CPA+FeR4K/B4K8jz08nl4e0cN/++VbVqdTESkB9DUMxERERHp9loHQw1hh4ZIrKYH0COmkBljqG8KUdMQ4PDRIIePBnhnxyHe2naQ9ha+io3SMRn79c7z8M0FkynO91KU76Uw34vP48pp1TOR06m9qWcikpmmnomIiIhIj5JNMBRbkr57BENRx+FQQ5CD9c2xr4ZAy3Z9gJr6ZoJZrsLVO8/Nvy+YSt/ePvoW+Oid52HVpn0ZaxT987xxzDi3f9o5Tuey9CIicnopKBIRERGRbiHiGI5FoT7iUB/ufsFQUzBC9ZEm9h1pYl9tE9VHmqiuPcaBumYOHQ3itBnpX9zLy4CiPIYPKOCC0f0ZUOinf1EeJb399Ovt43O/WpXxOo3NEc47tyRlXyL00SghERFpj4IiERERETkjGRMrPn00YqgLG5qiqcGQ/wysL9QUjLDn8DH21R6LB0GxYKj6SBN1x0Ipffv08lLaJ5+Jw/oyoMjPgKI8quuaWLFhL7WNIbxum09ecM5xw5wBRf6MtYf6F/kz9tcoIRERyYaCIhERERE5Y4Tjo4bqwg4NEUPUtBSfjq1IdmaEQ0ebw+w51MjuxFfNMXYfaqSmITW4Kentp6xvPh8aPYDSPvmU9s2nrE8+pX3yyfel/ii+oqKaZ97elZwedrA+wOLnKgAyBjw3zC3POJ3shrnlnf1yRUTkLKKgSEREREROG2MMAQcaIobasCHQatSQxwKPDadzqfrmUIT3Dxxl58GjKYFQbWMw2cfrthlaUsDEYX0YVlLA0JIChvTLZ1BxPj5P9gV2j7eE/dKVlRmDIk0nExGRrqCgSEREREROKWMMTQ7Uhx1qw7FRRBCvM3QaRw0daQyy40AD7+8/yo4DDezY30B1bROJykF5XhfDSgo4b2QJw/oXMKykF0NLChhQlIfrBNPgsl1BrOY4S9gfbz9oOpmIiHQ+BUUiIiIi0uUcEy9EHQ+HYgOHDB4b/K5TGwwZY9hf18z2/bEw6P0DR9mxvyFllNCg4jxGDizkkkllnDuokBEDetO/0J9ziLWiojpletiJppP1z7HmkJyZTJui5OY42/uel3oAACAASURBVMnn5vjtxz2mzZP2juvInzCr1Tms+BlMq3O13m57oDGpbSc6zmBS2jriDCxXJtKtKSgSERERkS4RNYbGSKzeUF3E4MTrDXls8NgWp2pKWSAUobK6ns1769iyt54te+uShaVty2JY/15MG9GPcwcVcu7A3owcVEiB39Mp185lOplqDmUvEcYkgoWUR3Oc/cQCDNrsy/wutFr2t05MWh15onAm8e62rMR27Gwtz1ttW62OsVruJ/0cbfqm7Wu550Rw0vZ8ZNqOnyBTv+Rzi+P2sdp0znj8ic7dpiHTf48TnRPgFGfNIj2egiIRERER6TSJkUO1IYcjEYMxFpZl8Fhgn4J6Q8YYqo80sbmqji1769hcVcfOg43JpeeH9OvFjHNLGDu4mPKyIs7pX4DXnX0doYSumE7Wk2oOGWNSQhrTJrxJ7Es8Tw0TrOS+40cysXbbivW3rZbnia/kc2KBiZ18bqX0iYUtVkr4QuK4+LZF7Dm0CW5atbeOPs+UousiIh2hoEhERERETkpiGfu6sMOhUGylslMVDkUdw/sHGnh3Zy0bd9eypaqOhuYwEKspNHZwMZ+ZNZKxQ4oZW1ZEYb73pK/ZldPJTnfNoUTA0zrMSWzTarslFGn5b2vaBDouKzZiywZcdiyYccX3u+JBjdtqHfJYKaFOMtxps691uCMiIp1PQZGIiIiIdEjQMdSFDYdChlC8IHVsWhl0VThkjGFXTSPv7jzMu7tq2birlsZABIDBffO5sHwAYwcXM25IMcNKCk5YZLqjusN0ssTULIeWsCex3Vpi9E7rUTu2BS7LwmPFAh438cd4sJMSAFkto3bsVtsKckREui8FRSIiIiKStYhjqI8YDocMx+JL2bvtrlutLDGV7N0PYsHQhl21yfpCg4rzmDV2EFOG92XyOX3p1/vUFH0+XdPJEqN9EoFP6+AnMbqndeiTCHy88ZDHY4PHsvDYsf2JoKf1CB8XCnhERM52CopERERE5ISMMTRFoSbkUBeOhRUuq+vCoWPBCOveP8TqbTWs++Awh47GAph+vX1MH1HClOF9mTK8LwOL8zvtmtnWHILOn07WerqXY9qO/LHitXrioVw86HHb4LXBGw+CXJaF22ob/CjwERGR3CkoEhEREZGMIvGpZQdDhqADlmXwdlE4VF3bxOrtB1m9rYaNu2qJOIYCv4dpI/rFg6F+DO6bn9O1sw1/cqk5BLlPJzPG4NASAjmm9UpYsQjItsBrW+TFR2f5bAuPbcWne8WCH7el0T4iItL1FBSJiIiISFKiMPWhoENtfPSQuwtGD0Udh/f21LF6ew1vbTvInkPHABha0osFFw7nglH9GT+0GJdtt3OmzHIJf3KpOdT6+NYh1PVzRnPxhFJCjsFpNRooMQ3Ma1vku1pCIG88BPLYiSBIAZCIiJwZFBSJiIiICFETHz0UNAQcg0UsxOjM6UtNwQirtx3krW01rNlRQ2Mggtu2mHROX66YPpTzRw2grG/nTCfLJfzJtuZQYmRQ1MBF40qZNa6UWF0gg23F6v/4XeC3LXwuC2+yLpBGAomISPehoEhERETkLBaIGg6FHA6HYyNhOnv0UDAc5e3tNazatI+3t9cQijgU5XuZWT6QC0b3Z9rIEnr5Ov9H0lwKTh+v5lBJoZ9ANLYYvBWfIuazLXq5IN9l4XfF6gN57cTS7QqDRESk+1NQJCIiInKWMcZwLAoHgg4NkcTUKLA7aSn5cNRh3fuHeGXTft6sPEBzKEpxLy/zpg7h4vGljB9a3OWFlrMpOJ0oIv252aP59d82EYyk1hz68qXlDPHb+FwWPqvzR1iJiIiciRQUiYiIiJwljDE0RGB/0KEpCrZlOm30UNQxbNhVy6ub9vH6lgM0BsIU+D3MHl/KxRNKmXxOnw7XG2ot2wLVGQtOu20+N3s0zVGDHV9K3m1ZXDFlMEVui9/8byX76wOUFfu5fd5YFkwbfNL3KyIi0t0oKBIRERHp4aLGUBcy7AsZQg64OykgMsZQua+B/92wl9c37+fIsRB5Xhczywcye8Igpo0sweM6+XAoIZcC1XMmlBJ1DH9atY1DDQFKCmOh0pVTyujlsshzWfjslpXEzr1gKDdcMLTT7lVERKS7UlAkIiIi0kNFHMPhkOFAyCFqwG1Dniu2MPvJOBYIs7JiH8+v28P7B47iddtcMLo/s8eXcv6o/vg8rs55AW0ct0D1ikpmTyglYsDEVxxzWXDllMFce96QeD0hrSwmIiKSDQVFIiIiIj1M0DHUBB0OhWI1eDw2eE6y/pAxhi1763h+bRWvvrePYMTh3EGF3PKx8cyZWNYlBanbOm6B6oYANhb9PNDbbZPnAq9WGhMREekQBUUiIiIiPUTIMRyIB0QQL1B9kmHJ0eYQKzZW8/y6KnbVNJLndXHJpDI+Nn0oo0uLOuO222WMIWpiq5DVNKSHRaXFfsb37ppRTCIiImebdoMiy7L8wKuAL97/cWPMXZZljQAeBfoB7wD/1xgTsizLBzwEnAccBj5jjNkZP9edwBeAKHCrMWZ5578kERERkbNL2DEcDDkcDMYCopOtP2SMYdOeIzy/torXNu8nHHUYU1bEV6+YwOwJpeR5u/7fGqPGpEwl89sWN3+knJ88U0Gg1fSzPI+Lb84b2+X3IyIicrbI5m/5IHCJMabRsiwP8LplWc8DtwH3GmMetSzrN8QCoAfij0eMMaMsy7oG+CnwGcuyxgPXABOAMuBly7LKjTHRLnhdIiIiIj1exDHUhGKjiAwnP4IoFImyYmM1T761kz2HjpHvczNv6hA+Om0I5w4q7Lwbz8AYQ9iAEw+G3JZFXw8Uum16uWJT58ZdMJRij809y7dSXddMWXEet88bo9XJREREOlG7QZExxgCN8aee+JcBLgE+G9+/FPgesaDok/FtgMeB+6zYP2l9EnjUGBMEPrAsaztwAfBmZ7wQERERkbNF1BgOhQz7gw6OOfmA6GhzmOfe2c2yt3dx5FiIkQN78/UrJ3Lx+EH4u3D0kBMPh1ZV7ON/VlVyqCHAwCI//3b5GBZOH5xxVNSCaYMVDImIiHShrP7mtyzLRWx62Sjg18AOoM4YE4l3qQISf2MPBvYAGGMilmXVE5ueNhj4R6vTtj5GRERERNrhmNgqZvuCsVXMPDZ4T6JI9YG6Zp5avZPl66oIhKOcN7KET80cwdThfbusEHTUGGIzxyxsC9Zs3sdvnm+ZTra/PsB3n67AY1sKhERERE6DrIKi+PSwqZZlFQNPAV02EdyyrJuAmwCGDRvWVZcRERER6TYcYzgSNlQHDGFj8J7kKmbb9zfwxJsf8Op7+7EsmDOhlE99aAQjBvbuxLtu0TocclkW/b1Q5IlNKfvKym0pNYcAmsNR7lm+VUGRiIjIaZDTWGJjTJ1lWSuBmUCxZVnu+KiiIcDeeLe9wFCgyrIsN1BErKh1Yn9C62NaX+O3wG8BZsyYYXJ7OSIiIiI9S2PEsKfZodmJBUR5HQyIjDGsff8Qj7+5k/U7D5PndbHgwnNYcP459C/K69ybpnU4BB7LYqAPitw2+a7UQtvVdc0Zjz/efhEREela2ax61h8Ix0OiPOAyYgWqVwILia18dgPw1/ghy+LP34y3rzDGGMuylgEPW5b1S2LFrEcDqzv59YiIiIj0CCHHUB1wqA0b3Bb4O7iSmTGGv285wMOv7eCDg0fpW+Dj85eU8/HpQynwezr1no0xhAwYY+G2LAb5LIo91gnvvaw4j70ZQqGy4s4Pr0RERKR92YwoKgWWxusU2cCfjTHPWpb1HvCoZVk/BNYBf4j3/wPwp3ix6lpiK51hjNlkWdafgfeACHCzVjwTERERSeWY2Epm+wKxlcxOJiBa+/4hlq7axrZ9DQzt14uvXzmRORPL8LrtTrtfE1/GPmrAAoo9Fv28NgWu7O779nljuPPJjTSHW34szPO4uH3emE67RxEREcmeFVvU7Mw0Y8YMs2bNmtN9GyIiIiJdzhjD0QjsDjiEHIPvJFYy27TnCEtWVlKx+wgDivx87uLRXDKpDNdJ1DVqq/XUsjyXRX+vRZHbwt2Bazy9bq+WvBcRETmFLMt6xxgzI1Nb1613KiIiIiJZCUQNVQGHhgh4LEOeq2OBzo79DTy0ahurt9fQp5eXr3x0HPOmDu20EUTpU8ugj8fG3+Z+cw1+tOS9iIjImUNBkYiIiMhpEjWGA0GHA0GDRcenme2tPcafVm3jlff2U+B38/m55cw/fxh+b+f8qOcYQyg+eqjYY1Hija1YlmnE09Pr9qZMJdtb18ydT24EUBgkIiLSDSgoEhERETnFjDHUhw17AoZIfLn7jkwzq6lv5uHXd/Di+r143DbXzBrJp2aO6LQi1RFjiDhgWzDQF6s95Gtnatk9y7em1BsCLXcvIiLSnSgoEhERETmFIk5smlltGDy2wd+Bmj5NwQgPv7adZW/vxmD4xIxhfGbWSPoU+E76/lqKU1t4bIuhfos+XgtXlkGWlrsXERHp3hQUiYiIiJwCiVFEuwMOjunYNDNjDK9s2sfvXt7KkcYgH5k8mOsuHsXATlhK3sSnlxmgl8tioM+m0J37PWq5exERke5NQZGIiIhIF0sdRUS707cy2VVzlPtf2MyGXbWMLi3ku/80jTGDi0/63lrXH+rjsRjgs8nLEGJlW6Bay92LiIh0bwqKRERERLpIZ4wiagpG+J/XtvPX1bvI97n514+NZ960oSe91H0iILItGOSz6ee18B7nnLkUqE4813L3IiIi3ZOCIhEREZEucLKjiNpOM5s3bQiL5pZTlO89qftKBEQWsQLVA7w27k4uUK3l7kVERLovBUUiIiIinagzRhF1xTSz1lPMBsQDouferc5q5I8KVIuIiJw9FBSJiIiIdJKTHUXUFdPMjDEE4wFRf2+sBpHXtnKaTqYC1SIiImcPBUUiIiIineBYxPB+k0PUmA6NIlq97SCL/7aJ2qMdn2a2oqKapSsrqakP0L/Qz3VzRnPxhDL6eSwG+u2U4CqX6WQqUC0iInL2UFAkIiIichKMMdSEDHsDDi4LfK7cAqJjwQi/e2kLy9dXMWJAb76zsGPTzFZUVLP4uQqC4djwoYMNAe5/fhODfTbTzhuS1j+X6WQqUC0iInL2UFAkIiIi0kERJ1aLqC4MPhvsHEcRbdh5mF88s5FDDQE+c9FIPnvxKLxuu0P3snRlZTIkSgiGHe59qZKFGYKiXKeTqUC1iIjI2aFjP4mIiIiInOWORQ1bGh0awga/bXIKiYLhKA++uJl//39v47Ztfn7DhSy6pLzDIZFjDDX1gYxtxxs5dPu8MeR5XCn7NJ1MRERENKJIREREJAfGGA6FYkWrOzLVbGt1Pb/46wb2HD7GJ2YM48ZLyvF7O/YjmYmvZGaAgUV+9mcIi040Qgg0nUxERERSKSgSERERyVLEGPY0OxzpwFSzSNTh4dd28Njf36dvgY+7PzuDaSNLOnwvYccQMVDssRjst7njo2NzLjit6WQiIiLSloIiERERkSw0RQ0fNDmEnNxXNdtVc5Sf/3Uj2/c3cOmkMv553jgK/J6sjk1ZyazIz/VzRjNrfBle22JEvk2BK3YvGiEkIiIinUFBkYiIiMgJtJ1q5s9hqlnUMTz11k4eWrWNfJ+b7/zTNC4aMzDr49NWMqsP8F/PbaLQZfG584ekjWjSCCERERE5WQqKRERERI7DMYbdzQ61YfDa4MphFFHdsSA/fWoD63ce5qIxA/nXj4+nuJcvp+tnXMks4vDgim1cf8HQnM4lIiIikg0FRSIiIiIZhB3D+00Ox6K5TzXbXFXHj55Yx9HmMF+7ciKXTxmc0/EJua5kJiIiInKyFBSJiIiItBGIGnZ0oB6RMYZn1uzmdy9toaTQzy8XfYhzBxV26B4ixlBS6KemIfuVzEREREROln26b0BERETkTNIYMWw9FiViDH6XlXVIFAhF+NnTG3hg+Wamn1vC4i9c1KGQyBhDIGowBr5+WTl5ntQf19pbyUxERETkZGhEkYiIiEjc4ZDD7mYHtwVuO/upYlWHj/HDx9ex51AjN8wZzadnjUwrNJ2NqDGEHItiDwz120w+fyj5blsrmYmIiMgpo6BIREREznrGGPYFHfYHTc5Fq/++ZT+/WLYRj9vmh9fOYNrIkg5dP+SAZcHwPIs+npaRTFrJTERERE4lBUUiIiJyVmtZ2czgs8l6JFDUcViychuPv/kBY8qK+NanpjKgKPfaQYlRRIVuGJZn481hJJOIiIhIZ1NQJCIiImetjq5sVtsY5CdPrmfj7iNccd5QbrpsHF539qUfV1RUs3RFJTUNAUoK/dz6kXI+d/6QDq2MJiIiItKZFBSJiIjIWSmxslk4x5XNNu05wt1PrOdYIMw35k/i0sm5TQtbUVHN4mcrCEYcAGoaAtz9zCZ6e2xNMRMREZHTTqueiYiIyFnnaKuVzXw5rGz2vxv2csefVuP3uLj38zNzDokAlqyoTIZECc3hKPcs35rzuUREREQ6m0YUiYiIyFmlLuTwQY4rmxljePTv7/PQqm1MOacv//FP0yjwe3K+dsgxHGoIZGyrrmvO+XwiIiIinU1BkYiIiJw1akMOu5odPDmsbBaJOvz6+fd4YX0Vl0ws42ufmIjHldugbGMMQQf8tkVpsZ/quvSwqKw490LYIiIiIp1NU89ERETkrHCoAyFRUzDC9/+8lhfWV3HNrJF845OTcg6JHGMIOLEl78sLbL45byx5HldKnzyPi9vnjcnpvCIiIiJdQSOKREREpMerCTrsCTh4cwiJao8G+O5ja/ngwFH+9eMT+Pj0oTlfN+wYogaG+i1KvLFaSImC1fcs30p1XTNlxXncPm+MClmLiIjIGUFBkYiIiPRYxhhqQoaqgIPPBjvLkGhXTSPffWQNDc1h7vrMdC4Y1f+E/VdUVLN0ZSU19QH6F/m5Yc5oZo0vw2VZlPey6eVOve6CaYMVDImIiMgZSUGRiIiI9EjGGA4EDdVBk1NItGHnYX7wl3V43TY/u/4CRpcWnbD/iopqFj9XQTAcW8nsYH2Axc9twm1Z3HjBEDxZFswWEREROROoRpGIiIj0OMYY9gWdeEhksg6JVlZU8+1H1tC3wMe9n/9QuyERwNKVlcmQKCEYcVi6qlIhkYiIiHQ7GlEkIiIiPYoxhuqgw4EgWYdExhj+8sYH/PfKSiYO68N3/2k6vfM8WV2vpj7zcvf7MqxsJiIiInKmU1AkIiIiPYYxsXpENSHw2wYri5Ao6jjc/8Jm/rZ2D7PHD+K2+ZPxurMfdN2/yM/BDGGRlrsXERGR7khTz0RERKRHMMawp9mhJmSyDokiUYefPrWBv63dwz9dNIJvXjUlp5DIGMNnZ4/G1+YYLXcvIiIi3ZVGFImIiEi358RDosNhg98mq5AoHHX46VPv8vctB/jCpWNYOHNETtc0xhBw4OOTBzPMZ/OLlyq13L2IiIh0ewqKREREpFszHQyJfvzEet6sPMhNl43lqguH53zNgAN9PRbD8mzKzxvC1ecN6eArEBERETlzKCgSERGRbssYw95gbiFRKOLwoyfWsXpbDf8ybxzzzz8np2s6xhB0LAb6oMxnZ3VNERERke5CQZGIiIh0WwdDhoNBcgiJovx/f1nHmh2HuPlj47nyvGE5XS9qDCEHhvotSryWQiIRERHpcRQUiYiISLd0KOiwN+DgyzIkCoaj/OAva1n7/mFu/fgEPjZ9aE7XCzsGx8C5+TZFHq0HIiIiIj2TgiIRERHpdupCDrvjIZGdRUgUCEf5/mNreXfnYb525UTmTc2tnlDIMVjA6AIXvVwaRSQiIiI9l4IiERER6VaORgwfNDt4sw2JQhHuemwtG3fVctv8SXxkcnarka2oqGbpikpqGgL0L/TzzXljmKyC1SIiItLDKSgSERGRbqMpathxLIrbAlcWIVFTMMJdj77De1VH+MYnJ3PJpLKsrrOioprFz1YQjDgAHGwI8J2nK3Dblpa9FxERkR5NE+xFRESkWwhEDduPRbEtcNvth0THghG+88ga3quq45sLpmQdEgEsXVGZDIkSmsNR7lm+Nef7FhEREelONKJIREREznghx7D9mIMBvNmERIEw//HIGrbta+COq6fw4XGDsr6WMYaahkDGtuq65qzPIyIiItIdaUSRiIiInNEi8ZAogskqJAqEIsmQ6M6rp+YcEgUc6F/kz9heVpyX9blEREREuiMFRSIiInLGihrDjiaHoGPwZRESRaIOP3piPZXV9dx59RRmjR2Y9bWMMQQdKHRb3DFvDHkeV0p7nsfF7fPG5PwaRERERLoTTT0TERGRM5JjDB80OTRFDb4s/mnLMYZ7n6lgzY5DfPWKCcwam/tIokK3xYh8m1HTh2BbFvcs30p1XTNlxXncPm+MClmLiIhIj6egSERERM44xhh2NzscjcRCIqudFc6MMfzh5a2sqKjm+jmj+ei0oTldK+hAUTwksuPXWjBtsIIhEREROeto6pmIiIiccQ4EDbVhsgqJAJ74x06efGsn888fxjWzRmZ9ncRIoiJPakgkIiIicrbSiCIRERE5o9SFHKqDTtYh0Uvv7uUP/7uVi8cP4suXj8vqGGgJiYo9FsPzFBKJiIiIgIIiEREROYM0Rw07mx28NlkFN29tO8h/PlvBtBH9+Lf5k9s9ZkVFNUtXVlJTH6Ck0M9Nl5bzhQuGKCQSERERidPUMxERETkjhB3DjmMOtgWuLIKb9/Yc4cdPrOfcQb35j4XT8LpP/GPNiopqFj9XwcH6AAaoaQjwy2crWLa+upNegYiIiEj3p6BIRERETjvHGHY2OYSNwWO3HxLtqjnKXY+tpaTQzw+umUG+r/1B0ktXVhIMOyn7msMO9yzf2uH7FhEREelpFBSJiIjIaWWMYW/A4Wg0tsJZew7WN/Pth9fgddv86LMzKO7lzeo6NfWBjPur65pzuV0RERGRHk1BkYiIiJxWh0KGmhD4syheXd8U4tsPryEQivLDa2cwsDg/6+uUFPoz7i8rzsvpfkVERER6MgVFIiIictocjRiqAg4+27QbEgVCEe567B0O1DVz16enM2Jg76yvE4waFs0tx+9J/dEnz+Pi9nljOnTvIiIiIj2RVj0TERGR0yIYNXzQFMVttb/CWSTq8KMn1rOtup7/WDiNSef0zfo6Icfgtiy+dOEQBvtt7lm+leq6ZsqK87h93hgWTBt8si9FREREpMdQUCQiIiKnXNQY3m9yMJBV8eoHX9zCmh2H+OoVE5g5ZmDW1wk7BgsY1cvGa1ssmDZYwZCIiIjICWjqmYiIiJxSJr7CWcAxeLMIiZ57ZzfPvrObhTNH8NFpQ7O+TsQxOAZG9XLhd7V/HRERERFRUCQiIiKn2L6gQ30kuxXONuyq5YHlm5lxbgmL5pZnfY2oMUTiIVG+QiIRERGRrCkoEhERkVOmNuSwP2iyWuFs/5EmfvT4Okr75HPHVVNwZTH6CMAxhpADI/JsCtwKiURERERyoRpFIiIicko0Rw27mx28WYREzaEI3//LWhxjuOvT0+nl9xy374qKapaurKSmPkBJoZ/r5ozmc+cNodirfw8TERERyZV+ghIREZEuFzWGD5ocLAtc7YREjjH8/K8b2V3TyB1XTWVIv17H7buioprFz1VwsD6AAWoaAvzm+U28/t6+Tn4FIiIiImcHBUUiIiLSpYwx7Gl2CGZZvPp/Xt3OG1sP8MWPjOW8c0tO2HfpykqCYSdlXyDscM/yrSd1zyIiIiJnKwVFIiIi0qWOhA21YbIqXv3ae/t5+LUdXDZlMAsuOKfd/jX1gYz7q+uac71NEREREUFBkYiIiHShQLIukWm3LtH2/Q38YtkGxg8p5paPTWi3P0D/In/G/WXFeR26XxEREZGznYIiERER6RK51CU60hjkB39eS+88L/+xcBped3Y/onxu9mh8bfrmeVzcPm9Mh+9bRERE5GymVc9ERESk0xlj2NvsEHAMfteJQ6Jw1OGHj6+joSnEz2+4kD4FvqyuETWGiyeUMchn86uXKqmua6asOI/b541hwbTBnfEyRERERM46CopERESk09WFDYfC4G9nYJAxhl8//x7vVdVx59VTGFValNX5HWMIOzAy32bqeUP49HlDOuGuRURERERBkYiIiHSqYLIuEe3WGVq2ZjfL11dx7f85l4vHl2Z1fmMMQcdiiN+iyKNZ9CIiIiKdSUGRiIiIdBonXpfIZFGXaN0Hh/nti1uYWT6Az80eldy/oqKapSsrqakP0L/Izw1zy7lkYhmQCImgnwf6e9svdi0iIiIiuVFQJCIiIp2mOuDQ7Bh87Qz0OXw0wE+fepch/XrxjU9Oxo6HSisqqln8XAXBsAPAwfoAi5+rAOCSiWWEHOjlshiaZ2e1KpqIiIiI5EbjtUVERKRT1IUcDoZiIdGJQpyoY/jZ0xsIhKN8e+FU8n0t/261dGVlMiRKCIYdlq6sJOwY3JbFiHw7GSyJiIiISOdSUCQiIiInLegYdgWyq0v08Gvb2bCrlps/Op5hJQUpbTX1gYzH1NQHcAyc28vGYyskEhEREekqCopERETkpDjGsLPJwZBdXaJHXtvBRyaXcdmU9CXs+xf5Mx5XUuhnRL5NnkshkYiIiEhXUlAkIiIiJ2V/0OFY1NBebekjjUHuefpdhpT04v9n706j6zrrPN//nn1GeZInybYkT7EsJ44yYjIQ4sRKIIFABeibAuIQM3W41dBQq/tyV1FvqNvV3KpbNFVN7qrKbWgCTooOlSIDgQRMKnYSCJmcAaIMthzbsSXZljxJtqUzPv/7QkfGw95HtiNZ0tH3s5ZX7H3Ofjh6w9nrp9/zf7584/LQ96xZ1aTUCSeZpeKBvnJ9EyecAQAAnAUMswYAAGfsUMG0O2tKn8Jcom//7A/qyxb0f69+r9LJ8EeQwdPNBk89mz0t0knYYwAAIABJREFUrS9d16Tb39swIp8fAAAAx3NmNtqfIdKKFSts48aNo/0xAABAiII3vXnYSzLFh5gb9K0HXtFv39wjSao94cj7KNmiqSrmtHQyw6sBAACGk3PuJTNbEfbakB1u59x859wG59wbzrnXnXNfK13/K+dch3Pu1dKfDx9zzzecc1ucc5ucczccc/3G0rUtzrm/GI4fDgAAjI6OjFfBhg6J1j65+WhIJP3xyPv1rZ2R9+S9KeaczuGEMwAAgLPqVLaeFST9ZzN72Tk3VdJLzrnHS6/9g5n9t2Pf7JxbLulTks6XVCfp35xzTaWX/1HSByS1S3rROfeImb0xHD8IAAA4e3ryXvvyA1vOyjl4JKf7n9l60vXBI+/DWkXeTEWTlnLCGQAAwFk3ZFBkZrsk7Sr9/ZBz7k1JJx9T8kc3S/qJmWUlbXPObZF0Wem1LWa2VZKccz8pvZegCACAcaTgTTv6TQlXfi6RN9N/+9kf5CN2uXf3ZE66ZmbKeqeGtNOUOCERAADA2XZax4c45xZJukTS86VLX3HO/cE5d7dzbkbpWr2kncfc1l66FnUdAACME2am9lPccvbTZ7fppa17NTUd/nupmur0SdeyXpqekGqGOkINAAAAI+KUgyLn3BRJD0j6czPrlXSXpCWSLtZA4+g7w/GBnHN3OOc2Ouc2dnd3D8eSAABgmPTkTfvzUmqIJ4jXdx7Q2g1tWrl8rr50w3knH3mfCLRmVdNx1/LelHBOC9JB2aYSAAAARs6pzCiScy6hgZDox2b2oCSZ2Z5jXv++pF+U/tkhaf4xtzeUrqnM9aPM7HuSvicNnHp2Sj8FAAAYcXlv2pHxSgTlt5z19uX0tw/+XnOmV+mrNzVrciou59zRI+9rQk49K5bmEjVOCYZsKgEAAGDkDBkUuYEnwR9IetPM/v6Y6/NK84sk6eOSWkt/f0TS/3LO/b0GhlkvlfSCJCdpqXNusQYCok9JunW4fhAAADByzEw7M15Fk9Jlghwz03d+/poO9mX1D5+9QpNTA48aLc11oYOrB+/Jeaf5aadJMUIiAACA0XQqjaKrJH1G0mvOuVdL1/5S0qedcxdLMknbJX1Jkszsdefc/RoYUl2Q9GUzK0qSc+4rktZJikm628xeH8afBQAAjJADedPBvIY85ezB57frhbZu/dkN56lxXvUprZ310syENJu5RAAAAKPuVE49+60G2kAneqzMPd+S9K2Q64+Vuw8AAIw9OT/QJkoOseVsy+5e/XD9Zl117hx9dMWCU147FTjNr2IuEQAAwFhwWqeeAQCAicXMtLPfy0yKlQlycgWv7/zsD6qelNTXbjr/lEKfopnMpMWTgrJrAwAA4OwhKAIAAJH25009BSk5xBPDPz/dpu3dh/W1jzRralVyyHUH5xItqApUxVwiAACAMeOUTj0DAAATz1Bbzta3dmrths3q6slIki5cOFOXNdac0toZL81OSjMShEQAAABjCY0iAABwEjPTjn4vRWw5W9/aqTsfbT0aEknSWx0HtL61c8i1s940KXBqSDOXCAAAYKwhKAIAACfZlzP1ltlytnbDZmXz/rhruYJp7YbNZdctmslJWjw5UEBIBAAAMOYQFAEAgONkvak945UMLLLx031Mk+hUrkuDc4mkhelAqYCQCAAAYCwiKAIAAEcNnnImlT/lbNa0VOj1mup05D1ZL9UknaYPNRkbAAAAo4YnNQAAcNSBfPktZ4Nqq6tOupZKBFqzqin0/XlvSgVOdWkePQAAAMYyntYAAIAkqXAKW84k6Zm39uiNnQd15bJa1Van5STVVqf11Zua1dJcd9L7vZmKJi2aFJRtKQEAAGD0xUf7AwAAgLGhM+tVNClRZn7QwSNZ/b+Pva7GudP0l5+4WPFY+d85mZmyXqpPB5oUIyQCAAAY62gUAQAAHSmY9uVMqTJPBmamOx97XX3Zgv6Pmy8YMiSSpJyXpsScapOERAAAAOMBQREAABOcN9OOfq+YU9ktZ0+81qlnN3Xp9muXamHN1CHXLZpJTlo4KSi7LgAAAMYOgiIAACa4vTlTv7eyW866e/p117o31Tx/hj5++aIh1zQz5bzTgnSgVJl1AQAAMLYQFAEAMIFlvakz48tuOfNm+odftMp703/6kwsUO4XgJ+ulGQlpRoKQCAAAYDxhmDUAABOUmamj38skBc5pfWun1m7YrO6ejGqq01qzqkktzXV69KUdemXbPv3HDy3XvBmThly34E1x5zQ/zZYzAACA8YagCACACaonbzpYkNKBtL61U3c+2qps3kuSunoyuvPRVh04nNW9T23RiiWz9aFL5w+5ppmpYFLj5EBxtpwBAACMO2w9AwBgAiqaaWfGlHAm55zWbth8NCQalM17rd3Qpnjg9LWbmk+pHZT1Um3KaWqckAgAAGA8IigCAGAC2pXxKpgdbf1092RC35cven3+umWaPS095Jo5b0oHTvPKDTwCAADAmMaTHAAAE0xf0dSds+MGWNdUhwdBiZjTjZc0DLmmN5M3adGkQAFziQAAAMYtgiIAACYQb6Yd/V6B03FbydasalIqcfJjwWeuXTpk8GNmynqpPh2oKkZIBAAAMJ4xzBoAgAlkX87UVzSlT8iEWprrJElrN2xWV2kb2pXLanXLlecMuWbOS9PiTjVJQiIAAIDxjkYRAAATRM6bOrJeyUChg6lbmuv0P/73qzV3epUaZk3WX3z8oiHXLJrJOWlBVXBKw64BAAAwthEUAQAwAZiZ2jNeZlKsTKDz46e3aPfBfv3HD5+vZDw25Jo57zQ/HSgZEBIBAABUAoIiAAAmgENF6WD++AHWJ3p7d68efG67bry4QRcunDnkmjkvTY9LMxKERAAAAJWCoAgAgArnzbSjzyvhwrecSVLRm777aKuqJyX0+euWDbnm4JazBracAQAAVBSCIgAAKlx3zpQ3U7zM9rCfb3xHbbt6dccHz9PUqkTZ9Qa3nDWw5QwAAKDiEBQBAFDBct60KzMwwDpKV0+/1m5o04ols3XN8rmnsKY0NS7NZMsZAABAxSEoAgCggnVmvExSELE9zMz0j796QybpKx9aPuQ2sqKZJE45AwAAqFQERQAAVKgjBdOBIQZY//atPXqhrVu3X9OoOdMnlV1vYMuZVJ8OlGLLGQAAQEUiKAIAoAKZmXZmvIIyA6wPZ/K6a92bapw7TTdftnDINXMmTY45zU4SEgEAAFQqgiIAACrQgbypryiVGyP0w/Wb1XMkq6/ddL5iQflHAm8mM2khW84AAAAqGkERAAAVpmimjowpEVhkqNO644Aee3mnPnbZIjXOqy67npkp66W6dKB0jJAIAACgkhEUAQBQYfZkvQpmikeERLmC152Ptaq2Oq3brmkccr28SZMCpxq2nAEAAFS8+Gh/AAAAMHyy3tSVNSVLvwpa39qptRs2q7sno5rqtNasatLuA33aufeI/q9PvUdVyfKPAt5M3qQFk4PIk9MAAABQOQiKAACoEGamjoyXSQqc0/rWTt35aKuyeS9J6urJ6Lu/eE1FL61cPleXNdYMuWbOS3NTTpPYcgYAADAhEBQBAFAhDhelg3lTutQmWrth89GQaFCuYJKkL15/7pDr5b0pFTjNSbFTHQAAYKLgyQ8AgArgzbSz3yvudHSAdXdPJvL9NdPSZdczMxVLp5yx5QwAAGDiICgCAKAC7M+Zst6UCP4Y6tRUh4dBNdNSQ66X9VJNymlynJAIAABgIiEoAgBgnCt4U0fWK3HCt/qaVU1KnXAxHnP6bMuysuvlS4HTPLacAQAATDjMKAIAYJzbnfXyJiWD49s/Lc11kqS7n9ikfYeySsYDfe0jzUevhxnccrZ4UqAYW84AAAAmHH5VCADAONZfNHXnTFHln5bmOl20cJbiMae77riqbEgkDWw5m5FwmsqWMwAAgAmJoAgAgHHKzNSR8XLHDLA+0es7D2h9a6f+3RWLVTdzctn1imYKnFSf5vEAAABgouJJEACAcaq3MPAnGVH+KXrTP/3qDc2emtanrjqn7FpmppyX6lLBcQOxAQAAMLEQFAEAMA55M+3MeCWcRbaJfvXKTm3dc0hfvH6Z0snyYwnzJk2KOc2OSp0AAAAwIRAUAQAwDu3LmfLeFI9o//T25bT2yTZduHCmVi6fW3YtM5M3aUFVEBk6AQAAYGIgKAIAYJwpeFNn1itR5lv8nqfadCRT0J/dcN6Q4U/WSzVJp0kxQiIAAICJjqAIAIBxpivn5U2Rx9e/vbtXv3x5pz66YoEW1U4tu1bBTDEnzWWANQAAACSVH1gAAADGlJw33f9yh378ZJv29mZUU53WmlVNR4+9NxsYYD21Kqnbrmksu5aZKe+dFlU5xdlyBgAAANEoAgBgXLn3xXbd9djr6u7NyCR19WR056OtWt/aKUna0LpLb7Qf1OdWNWlKOlF2rZxJU+PSjAQhEQAAAAYQFAEAME70F03/3xOblS34465n815rN2xWX7agHzyxSU111frAxfVl1/JmMpPmpxlgDQAAgD8iKAIAYBwwM3VkvPb2ZkJf7+7J6L7fvq39h7P6sxvOUzBE+JPz0txUoDQDrAEAAHAMgiIAAMaBI0Wpt2CqmZYOfX3GlKQefn67PnBRvc6tn152rYI3JZxTbYqQCAAAAMcjKAIAYIwzM7VnvOJOWtPSpFTi+K/vZNxp2qSkkomYPreqaci18ua0YFIQeWoaAAAAJi6CIgAAxriDeVNfUYo7qaW5Tl+9qVm11Wk5SbXVaX1kxUJt7zqs21Y2asaUVNm1cl6anpCmxs7OZwcAAMD4Eh/tDwAAAKJ5M3VkTQlnR4dOtzTXqaW5TpJUKHr9h+89o/qZk/TRFQuGXEuSGhhgDQAAgAg0igAAGMP25Uw5b4oH4cHOr15p1859R/SF65YpHiv/tZ71Ul06UDJiLQAAAICgCACAMapopl1Zr2TEt/WRTF73Pt2mCxbM0BVNtWXXyntTVeA0O0lIBAAAgGgERQAAjFFdWVPRFDl0+v7fbVVvX17//gPnlt1KZjawzoKqQAFbzgAAAFAGQREAAGNQzpv2lGkT7TnYr4eef0ctzXVaOq+67FpZL81IOE2OExIBAACgPIIiAADGoN1ZL5MiG0Brn9ws56TPrlpadh1vJqeB2UQAAADAUHhqBABgjMkUTftyplTEt/Smzh5taN2lj1++SDXVVWXXynlpHgOsAQAAcIoIigAAGGM6M17OKXTukJnpfz7+lqZPTuqW951Tdp2CNyWcUw0DrAEAAHCKCIoAABhDDhdMPQVTVLbz7OYute48oNtWNmpyKh65jpkpb04NaccAawAAAJwygiIAAMYIM1NHxiuIaBPli153P7FJ82dP1o2XNJRdK2fS1LhUnSAkAgAAwKkjKAIAYIzoLUhHilJUtvPYSzvUsb9PX7xumWJB9Fe4mclMakgHoYETAAAAEIWgCACAMcCbqT3jlXAWGu4c6s/rx795WxcvmqX3NtaUXSvrpZqkU1WMkAgAAACnJ3q4AQAAOGv+18Z2fffxzdrbm1FNdVprVjWppbnu6Ov/8szbOtyf1xevX1a2JVQ0U+CkuVFHpgEAAABlEBQBADDKHnq5XX/9s1ZlC16S1NWT0Z2PtkqSWprrtPtAn3724ju6/sJ6LZk7rexaeT+w5Swe0CYCAADA6ePXjQAAjLL/Z92moyHRoGzea+2GzZKkH27YrJhzuv3apWXXyXtTKnCaFXVkGgAAADAEgiIAAEZR0Ux7ejKhr3X3ZPRm+wE9/cZufeKKxZo9LR25jpmpYNL8qkABA6wBAABwhgiKAAAYRftyFhkAzZ6W0vf/bZNmTEnplvctLrtO1kvTE05TYiPxKQEAADBREBQBADBKimbanfW6fdVSpRLHfyWnEoGuXDZHb7Yf1O3XNKoqGT1W0JtJkurTQdlB1wAAAMBQCIoAABgle3OmoknXX1Cvr97UrNrqtJyk2uq0/sONy/VCW7cW1UzRBy5qKLtOzktzUk4pBlgDAADgXeLUMwAARkHBD7SJkqVf2bQ016mlue7o6w8+t027D/brv376PYqVCYCKZoo7pzkpfvcDAACAd4+nSgAARkFXzsubQgdPH8nk9ZNntuqSxbP0niU1kWuYmXLeqT7tFGPLGQAAAIYBQREAAGdZ3pu6sna0TXSiB57brkP9eX2upan8OiZNjkkzEoREAAAAGB4ERQAAnGV7sl6m8DbRgcNZPfj8dq1cPldL51VHrmFm8iY1MMAaAAAAw4igCACAsyjnTd256DbRfb99W/mC12euWTrEOgNNoslxQiIAAAAMH4IiAADOot1ZLym8TbTrQJ9++fJO3XhJgxpmTY5cw5vJJNWl+RoHAADA8OIJEwCAsyRbNO3LmaIOKLv3qTYFgdOtVy8pu07OS3NSTskyp6EBAAAAZ4KgCACAs2RX1stJoTOFtu7p1ZOtu3TzZQs1a2o6co2imQIn1UalTQAAAMC7wFMmAABnQX/RdCAfPZto7YY2TU7HdcuV55RdJ+elulSgOAOsAQAAMAKGDIqcc/Odcxucc2845153zn2tdH2mc+5x51xb6b8zStedc+5O59wW59wfnHOXHrPWmtL725xza0buxwIAYGzZlfVyLrxN1Lpjv17Y0q1b3neOplYlItcoeFMqcJqVJCQCAADAyDiVRlFB0n82s+WSrpD0Zefcckl/IekJM1sq6YnSvyXpQ5KWlv7cIekuaSBYkvRNSZdLukzSNwfDJQAAKllf0dSTN4XlO2amH67frJlTUvqT9y6MXMPMVDCnhnQQOggbAAAAGA5DBkVmtsvMXi79/ZCkNyXVS7pZ0trS29ZK+ljp7zdLuscGPCdpunNunqQbJD1uZvvN7ICkxyXdOKw/DQAAY4yZqSMT3SZ6YUu33mg/qNVXL1E6EYtcJ2/S5Jg0LT6SnxYAAAAT3WnNKHLOLZJ0iaTnJc0xs12ll3ZLmlP6e72kncfc1l66FnX9xP+NO5xzG51zG7u7u0/n4wEAMOYcKUqHCuFtoqI3/Wj9ZtXNmKQPXtwQuYaZyZtUnw5CwyYAAABguJxyUOScmyLpAUl/bma9x75mZibJhuMDmdn3zGyFma2oqakZjiUBABgVZqbOjFcsok30ZGuntncf1u3XLlU8Fv2VnPPS9ITT5DghEQAAAEbWKQVFzrmEBkKiH5vZg6XLe0pbylT6b1fpeoek+cfc3lC6FnUdAICKdLg48CcRku/kCl73PrVFjXOn6erlcyPX8GYySXVpDioFAADAyDuVU8+cpB9IetPM/v6Ylx6RNHhy2RpJPzvm+u2l08+ukNRT2qK2TtIHnXMzSkOsP1i6BgBAxfljm8hC20S/fGWn9vT067OrmsoOp855qSbllApoEwEAAGDkncpIzKskfUbSa865V0vX/lLS30q63zn3BUnvSPrT0muPSfqwpC2S+iR9TpLMbL9z7q8lvVh6338xs/3D8lMAADDG/MtLHfrOrzdpb29GNdVprVnVpJbmOklSX7ag+37zti5aOFOXnjMrco2imQInzU3SJgIAAMDZMWRQZGa/lRT1a8zrQt5vkr4csdbdku4+nQ8IAMB489DL7frmz15TNu8lSV09Gd35aKskqaW5Tg+/sF09fTl9tqWp7HDqnJca0oHitIkAAABwlvArSgAAhtnfrdt0NCQalM17rd2wWQeP5PTTZ7fpqnPn6Nz66ZFrFLwp6Zxmhx2XBgAAAIwQgiIAAIaRmWl3Tyb0te6ejO7/3dvK5ou6/dqlZdfIm1N92pWdXwQAAAAMN4IiAACG0aGiNHtaOvS1mVNT+vnGHbr+wnotmD0lco2CSZNi0vSw49IAAACAEURQBADAMBk86ewz1y5VKnH8V2wqEWjejElyclq9srHsGgUbmE1Ubn4RAAAAMBIIigAAGCaHilJfUbr+gjp99aZm1Van5STVVqe1emWj3th5QB9ZMV+11VWRa+T8QJNocuzsfW4AAABg0JCnngEAgKENtonizuScU0tznVqa646+/jcPvqpkPKY/fd+SsmuYpLoUbSIAAACMDhpFAAAMg0MFqb8oxUPynW17DunpN3br5ssWavrkZOQaOS/NTjqlY4REAAAAGB0ERQAAvEtmpo6MV6zUJjrRvU+3aXIqrn93xeLINbyZJGluiq9mAAAAjB6eRgEAeJd6C1K/D28Tbe7s0bObuvSJKxZpalUico2cl+akAiUC2kQAAAAYPQRFAAC8CyfOJjrRvU+1aWpVQjdftihyjaKZAifVJAmJAAAAMLoIigAAeBfKtYne2HlAG9/eq1uuXKzJqejzI/JempcKFKdNBAAAgFFGUAQAwBkaqk10z1Ntmj45qY+uWBC5RtFMcec0izYRAAAAxgCCIgAAzlC5NtGr2/bp99v365NXnaN0snybqC7tFAsJmgAAAICzjaAIAIAzMHjSWVibyMx071NtmjU1pQ9fOj9yjYI3JQOnGQlCIgAAAIwNBEUAAJyBnrwpE9EmemnrXr3RflCffv8SJeOx0PvNTHlzakgHCmgTAQAAYIwgKAIA4DSZmTqzFtkmuufJNs2prtIHL26IXKNg0qSYNC16VxoAAABw1hEUAQBwmg6W2kSJkFPKntvcpbZdvbr16iVKxMK/Zs1MBXOqTwehQ7ABAACA0UJQBADAaTi2TXQib6Z7nmpT/cxJuu7Cusg18iZNjUtTwnelAQAAAKOGoAgAgNNwMG/KRrSJfvvmbm3vOqzVKxsVC6LbRN5EmwgAAABjEkERAACnyMy0K6JNVPRe9z61RQtrpmjl8nmRa+S8VJ1wmhQjJAIAAMDYQ1AEAMAp6i0ocjbRhtZdat93RLetbFQs5HVpIGgySXUpvn4BAAAwNnHWCgAAp8DM9M8b2/WjDZu1tzejmuq01qxqUktznQpFrx8/vUVL5k7T+86dE7lG1kuzkk5p2kQAAAAYowiKAAA4Bfe91KE7H21VtuAlSV09Gd35aKskKZsravfBfv3VJ89TEDF3yNvAdrW5tIkAAAAwhhEUAQAwBDPTf//1pqMh0aBs3utH6zdJclpWX63LGmsi18h5aU7KKRmxLQ0AAAAYCwiKAAAYQm9B6u7NhL7W3ZuVJP35R5ojTzHzZgqcVJukTQQAAICxjSdWAADKMDN1ZrxmT0uHvh44qXnBDF2yeFbkGlk/sOUsTpsIAAAAYxxBEQAAZRwqnXT22VVLlUoc/7UZC5y8SbdfszSyTVQ0U9xJs5OERAAAABj72HoGAEAEM1Nn1ivmTC0X1EvOae2GzeruyWj2tJSOZItqmletCxbOjFwj56X56UCxiCAJAAAAGEsIigAAiHCoKPUVpXSpSNTSXKeW5jpJ0k+f3aYfPLFJq1c2Rt5fMFPSOc2iTQQAAIBxgq1nAACEGJxNFHd20rayTK6gnz67TZcsnqXmBTMi18h7qS7tFNAmAgAAwDhBUAQAQIjBNlE8JOP5xUs71dOX023l2kTelAqcpicIiQAAADB+EBQBAHACM9OuiDZRf66gf312qy49Z5aWzy/TJjKnuhRtIgAAAIwvBEUAAJzgcLk20cYd6u3Ll20T5b0pHYg2EQAAAMYdgiIAAI4xOJsoiGgT/fS5bXrPObN1XkN4m8jMVDCn+nRw0v0AAADAWEdQBADAMQ4XpSNFKawM9PNSm6j8SWfSpJg0jXNFAQAAMA4RFAEAUDI4mygW0ibqyxb0wLPbtGLJbJ3XMD3y/qI51dEmAgAAwDhFUAQAQMmRsm2id9TbX75NlC+1iabGRvBDAgAAACOIoAgAAJWfTdSXLeiB57ZrxZLZOrc+uk3kmU0EAACAcY6gCAAADTSJDhcttE30yIvv6FD/ECedmTQlLk2mTQQAAIBxjKAIADDhmZk6s14xp5PaQEeyBT34/Ha9t7FGy8q2iaS6FG0iAAAAjG8ERQCACa+vKB0ulG8TlZtNlDNpWtxpcpyQCAAAAOMbh/cCACa0h15u19+s26TunoxqqtNas6pJLc11kqQjmbwefG67Lltao2V11aH3m5nMpHlpfvcCAACA8Y+gCAAwYT38Soe+8dBryuS9JKmrJ6M7H22VJLU01+mRF3focCav1VeXaRN5qTrhNClGmwgAAADjH7/+BABMWN9et+loSDQom/dau2HzQJvo+W26fGmNmsq1iSTNS/F1CgAAgMrAky0AYMLqPNgfer27J6OHX3hHhzOFsiedZb00I+FURZsIAAAAFYKgCAAwYdVUp0Ovz5qW0sMvbNcVTbVqnBfdJpKkubSJAAAAUEF4ugUATEh9RdOt1yxVKn78V2EqEaipbroOZwplTzrLemlWwilNmwgAAAAVhKAIADAh7cp4Xdtcp69+pFm11Wk5SbXVad3xgfP0+237dGVTrRrnTgu915faRHM46QwAAAAVhlPPAAAV5eFXOvTtdZvUebBfddOr9PUbluljl9Qf956+oqm3YEoFA6ebtTTXHX3tn59q05Fs+TZRzks1SadUQJsIAAAAlYWgCABQMR5+pUPfePA19eeLkqSOg/36xoOvSdJxYdHurJdzknPHBz2HM3k9/MI7unJZrZYM0SaqZTYRAAAAKhBPuQCAivHtdZuOhkSD+vNFfXvdpj/+u2jqyZuSIWWgn73wzkCb6OrybaLalFOSNhEAAAAqEEERAKBiRB13f+z1cm2ih57friubyreJnKTaJF+fAAAAqEw86QIAKkbd9Kqy1/uLpoNDtIluHWI2UW3KKUGbCAAAABWKoAgAUDG+fsMyVSVix12rSsT09RuWSZL2ZL2cTm4THcnk9fAL23XFECedOUk1tIkAAABQwRhmDQCoGIMDq8NOPcsUTQfyAyedneiRF3focKagW69eErl2zktzaBMBAACgwhEUAQAqyscuqT/uhLNBu7NeUkibKFvQg89v1+VLa7R0XnXomrSJAAAAMFHwxAsAqHjl20THhWUGAAAgAElEQVTv6HAmr9VDzCaiTQQAAICJgKAIAFDxyrWJHnp+uy5rHKJN5GgTAQAAYGLgqRcAUNGyZdpEP3/xHR3qzw950tmcZKA4bSIAAABMAARFAICKFnXSWV9pNtF7G2u0rC66TRQ4qSZJSAQAAICJgaAIAFCxst60L28K2zX2i407dKg/r9VDnHRWS5sIAAAAEwhBEQCgYu2JmE3Unyvogee2acWS2VpWPz30XtpEAAAAmIgIigAAFSnrTftyEbOJNu5Qb39et15dfjbRXNpEAAAAmGAIigAAFalriDbRe86ZrfMawttExVKbaHaKkAgAAAATC0ERAKDi5Lxpb0Sb6NGXdqq3L6/VK6NnE+W9NDcVKOYIigAAADCxEBQBACpO1GyiTK6gnz67TZeeM0vnNcwIvfdom4jZRAAAAJiACIoAABWlXJvoFy/tVE9fTqvLzCaiTQQAAICJjKAIAFBRyrWJHnhumy5ZPEvL50e3iWK0iQAAADCBERQBACpG2dlEL+/UwSO5sied0SYCAADAREdQBACoGJFtonxRP312my5eNEvNC8q3iWbRJgIAAMAERlAEAKgIOW/alzMlQ77ZfllqE3HSGQAAAFAeQREAoCJ0Zb1MUnBC0JPNF/Wvv9uqixbOVPOCmaH30iYCAAAABhAUAQDGvcHZRGFtosde3qkDR3K6dWX0bKIcbSIAAABAEkERAKAClG0TPbtNFy6cqQsXRreJ4rSJAAAAAEkERQCAcS7nTd1Rs4le2akDh7NafXX0bKKcl+bRJgIAAAAkERQBAMa5rtJJZ2Ftovt/V2oTLZoVeu9gm2gmbSIAAABAEkERAGAcK9cm+hVtIgAAAOC0ERQBAMatodpEFyyYQZsIAAAAOA0ERQCAcWmoNtH+w1ndNsRJZ7SJAAAAgOMRFAEAxqWoNlGuMNAmaqZNBAAAAJw2giIAwLhT/qSzdtpEAAAAwBkiKAIAjDvl2kT/+rutA22ihTND76VNBAAAAEQbMihyzt3tnOtyzrUec+2vnHMdzrlXS38+fMxr33DObXHObXLO3XDM9RtL17Y45/5i+H8UAMBEUH42Ubv2Hcpq9dWNchFtIdpEAAAAQLRTaRT9SNKNIdf/wcwuLv15TJKcc8slfUrS+aV7/sk5F3POxST9o6QPSVou6dOl9wIAcFrKzybaqub5M3TRItpEAAAAwJkYMigys6cl7T/F9W6W9BMzy5rZNklbJF1W+rPFzLaaWU7ST0rvBQDglJVrE617tdQmWkmbCAAAADhT72ZG0Vecc38obU2bUbpWL2nnMe9pL12Lun4S59wdzrmNzrmN3d3d7+LjAQAqTXSbyOtfntmq82kTAQAAAO/KmQZFd0laIuliSbskfWe4PpCZfc/MVpjZipqamuFaFgAwzpVrE/2aNhEAAAAwLOJncpOZ7Rn8u3Pu+5J+Ufpnh6T5x7y1oXRNZa4DADCkPWXaRD955m2dP3+GLqZNBAAAALwrZ9Qocs7NO+afH5c0eCLaI5I+5ZxLOecWS1oq6QVJL0pa6pxb7JxLamDg9SNn/rEBABNJ1pv25kypcm2iq5fQJgIAAADepSEbRc65+yRdK2m2c65d0jclXeucu1iSSdou6UuSZGavO+ful/SGpIKkL5tZsbTOVyStkxSTdLeZvT7sPw0AoCLtyQy0iU4MggZnEy1vmK6LF88KvZc2EQAAAHDqhgyKzOzTIZd/UOb935L0rZDrj0l67LQ+HQBgwssWTfvyEW2i37dr76GM/tNHm8u2ieanaRMBAAAAp+LdnHoGAMCI25X1cgpvE91PmwgAAAAYVgRFAIAxK1M0HciHn3T2+O/b1d2b4aQzAAAAYBgRFAEAxqzd2fKzic5rmK5LaBMBAAAAw4agCAAwJvWX2kRhs4n+bbBNdDVtIgAAAGA4ERQBAMakcrOJflJqE116TnSbKOEcbSIAAADgNBEUAQDGnL6iqSdiNtGvXx1oE902xGyiuSlHmwgAAAA4TQRFAIAxZ1fGy7mwNlFRP3nmbZ0/f0bZ2US0iQAAAIAzQ1AEABhTjhRNvQVTWM7zy1fate9QljYRAAAAMEIIigAAY4aZRbaJsvmi7n9mqy5YMEMXLZoZev9gm2gWbSIAAADgjBAUAQDGjL6idCikTbS+tVO3fXeD9h/Oaue+I9rw+q7Q+/OlNlFAmwgAAAA4I/HR/gAAAEgDbaLOrFdwQptofWunvvuL15QrmCTp4JGc7ny0VZLU0lx39H1FM8VpEwEAAADvCo0iAMCYcKTUJkqckPOs3bD5aEg0KJv3Wrth83HXaBMBAAAA7x5BEQBg1JmZOjNesZDZRF09mdB7uo+5TpsIAAAAGB4ERQCAUXe4OPDnxDaRJE1Oh++SrqlOH/07bSIAAABgeBAUAQBG1WCbKO7spDZRX7Yg700n5j+pRKA1q5ok0SYCAAAAhhNBEQBgVB0qDswniofkPD/f+I76c0WtXtmo2uq0nKTa6rS+elPz0UHWeS/No00EAAAADAtOPQMAjBozU0d/eJvoSLagB57brssaa7T66katvrrxpPsH20QzaRMBAAAAw4JGEQBg1PQWpH4f3iZ65IV3dKg/r9uuOTkgGkSbCAAAABheNIoAAKPioZfb9Te/2qTu3oxqqtNas6rp6HayI5m8Hnx+m65oqtXSedWh99MmAgAAAIYfQREA4Kx7+JUOfeOh15TJe0lSV09Gdz7aKklqaa7Twy+8o8OZglavLN8mmp+mTQQAAAAMJ7aeAQDOur9b99bRkGhQNu+1dsNmHerP66Hnt+t9y+aoce600PtpEwEAAAAjg6AIAHDW7TqYCb3e3ZPRQ89v15FsQbcN0SZiNhEAAAAw/AiKAABnVdFMs6elQ1+bNTWlh1/YrvefN0eL50yNvJ82EQAAADAyCIoAAGfV3pxp9bVLlUoc/xWUSgRqnFetTK6o1VfTJgIAAABGA0ERAOCsKXjT7qzXBy6o01dvalZtdVpOUm11Wl+8/lz9fvs+Xb18rhbV0iYCAAAARgOnngEAzpqunJc3KQicWprr1NJcd/S1u9dvGmgTcdIZAAAAMGpoFAEAzoqcN+3JmpIh3zwHj2T18xd36Jrz52nB7Cmh99MmAgAAAEYeQREA4KzYnfWSFNoG+pdntipX8EO2ieqYTQQAAACMKIIiAMCIyxRN+3KmVMi3TndvRo++tFPXX1inhlmTQ+8vlNpEM2gTAQAAACOKoAgAMOIG20QupA1032+2yGS6tcxJZwUv1TGbCAAAABhxBEUAgBHVVzQdyIe3iTr3H9G6Vzv04Uvna870qtD7C2ZKOKcZCUIiAAAAYKQRFAEARoyZqTPj5Vx4m+ifn96iRMzpk1ctiVyDNhEAAABw9hAUAQBGzJGi1FswhY0W2t51SE+27tKfXLZQM6ekQu8veFMicJpOmwgAAAA4KwiKAAAjYrBNFItoE9371BZVpeK65crFkWsUzHHSGQAAAHAWERQBAEbEoaJ0uCiFlYE2d/bod5v26BOXL9LUqmTo/QVvSgaiTQQAAACcRQRFAIBhZ2bq6PeKOwttE93zZJumVSX0scsXRa6Rp00EAAAAnHUERQCAYXcwb+r3Ujwk42ndsV8vbd2rW953jian4qH3F/zAKWm0iQAAAICzi6AIADCsvJk6sqZESJvIzLR2Q5tmTknpoysWRK6RN6f6lAttIwEAAAAYOQRFAIBhtT9nynlTPDg55Hl561617jygT79/iVKJWOj9eW9KB1I1bSIAAADgrCMoAgAMm6KZOrNeyZBvFzPT2ifbNKe6Sjdc0hB6v5mpYE716YA2EQAAADAKCIoAAMOmK2sqmhQLCXl+t6lLbbt6tXrlEiVi4V8/BZOqAmla+OgiAAAAACOMoAgAMCxy3rQnok1U9KZ7nmxTw6zJarmgLvR+M1PRnOpoEwEAAACjhqAIADAsdme8TAo9zv6p13dpx97D+sw1jYoFZdpEMdpEAAAAwGgiKAIAvGv9RdO+/MCR9icqFL3ufapN58yZqvefNzf0/sHZRLSJAAAAgNHF720BAO+Kmakj4/X065368VNt6u7JqKY6rTWrmtTSXKdf/75Duw/2668+eWlo20gaaBNNjklTww9CAwAAAHCWEBQBAN6Vw0XpF7/v0F2Pva5swUuSunoyuvPRVhWKXvf95m2dW1+tyxprQu9nNhEAAAAwdrD1DABwxsxM7f1eP36y7WhINCib9/r+429p76GMPruqKTIEyps0KSZNoU0EAAAAjDqCIgDAGTuQN/V7aW9vJvT1w5mCLl40SxctmhX6Om0iAAAAYGwhKAIAnJGimToypkRgqqlOR77v9muXRr6Wt4EmEW0iAAAAYGwgKAIAnJG9OVPBTHHntGZVk1KJk79SGudN03kN00PvH2gTiTYRAAAAMIYwzBoAcNry3rQ76zWYDbU010mS1m7YrO6ejNLJmDK5ov7Pmy+MXCNn0rS402TaRAAAAMCYQVAEADhte7Je3qRk8McmUEtznVqa67TrQJ/uuOs3uuGSBs2fPSX0fjOT0SYCAAAAxhyCIgBApIdf6dC3121S58F+1U2v0tdvWKYbL6xTd86Uiti8fM+TbYoFTretbIxcN+el6oTTpBghEQAAADCWEBQBAEI9/EqHvvHga+rPFyVJHQf79Y0HX1NXzuuyc+eFNoG27OrRk6/v0ievOkezpoYPuDYzmaS6qKQJAAAAwKjhKR0AEOrb6zYdDYkG9eeL+v4Tm5WM+Pa4e/1mTatK6JYrF0eum/PSjIRTmjYRAAAAMOYQFAEAQnUe7A+9vrc3E9omennrXr2ybZ8+9f4lmpxOhN472Caal+brBwAAABiLeFIHAISqm14Ver1m2slbyryZ7l6/WXOqq3TTexZErpn10uykUyqgTQQAAACMRQRFAIBQX79hmaoSx59dn4oHWtPSdNJ7n3p9l97e3avbr12qZDz8q8WbSZLmMJsIAAAAGLMYZg0ACPWxS+ol6eipZ7OnpfXZlia1NNcd975cwWvthjadM2eqrm2eF7lezktzUk5J2kQAAADAmEVQBACI9LFL6vWRi+r0+uGiYk6KhcwmeuzlHdrT06//+uEVCkJelwbaRE5STdQUbAAAAABjAk/sAICydme9vIWHREcyed33m7d18aJZuvScWZFrDLSJAiVoEwEAAABjGkERACBSf9HUnTNFjRV64Lnt6u3P6/PXNYWehCZJRTMFTqpJERIBAAAAYx1BEQAglJmpPePlnEJDoP2HMnrw+e26ZvlcLZ1XHblO3kvzUoHiEUESAAAAgLGDoAgAEKonbzpUkJIR+c6Pf/O2ikWv2689+RS0QUUzxZw0K2oRAAAAAGMKQREA4CTeTO0ZU8JZaJuofd9h/eqVdn34PfNVN3NS5Dp5L9WlgtD5RgAAAADGHoIiAMBJunOmvJniEcOnf7ShTalEoE+/f0nkGgUzJZzTTNpEAAAAwLhBUAQAOE7Om3ZlvKJOsn+z/YCeeWuP/rcrF2v65FTkOnkv1aWdAtpEAAAAwLhBUAQAOE5nxsuk0IDHzHT3E5s1Y3JSH798UeQaBW9KBU7TE4REAAAAwHhCUAQAOOpIwXQgb0pFfDs839at1p0HtHplo6qS8dD3mJny5lSXok0EAAAAjDcERQAASQMBz86MV+AUOsC66L1+uH6z6mdO0g0XN0SuUzCpKhBtIgAAAGAcIigCAEiS9udNfUUpKt/55cvt2rH3sD5/3TLFY+FfH2amojnVVwWhYRMAAACAsS183wAAYEIpmKmjNMA6LOA51J/XvU+16aKFM3VlU23kOnmTJsekqbGR/LQAAAAARgqNIgCAurJeRZNiES2g+367RYf68/r3Hzg3silkZvIm2kQAAADAOEZQBAATXKZo2pM1JSO+Edr3HdEjL+7QDRc3aMncaZHrZP3AXKLJMUIiAAAAYLwiKAKACczM1J7xck6RJ5T94IlNSsYD3X7t0rLrSFJd1HFpAAAAAMYFnugBYAI7VJR6C1IyogT0yrZ9em5zlz511RLNmJKKXCfrpZqkU4o2EQAAADCuERQBwATlzbSz3yvuLHSmUNGbvvf4m5pTXaWPXb6w7DpO0hzaRAAAAMC4x1M9AExQe3OmnDclgvAW0LpX27W967C+cP0yJePRx5hlvTQ3FUSuAwAAAGD8ICgCgAko7027sl6JiG+BI5m87nmyTc3zZ+j9586JXKdopriTalKERAAAAEAlICgCgAmoI+PlTYpFDLD+yTNb1duX0x0fPLfsUfd5PzDAOmodAAAAAOMLQREATDCHCqb9eVPUSKFdB/r08Avbdd2FdVo6rzpynYI3JQOnmVGTsAEAAACMOwRFADCBeDPt6PeKO0U2hX7wxCbFgkCfXdUUuY6ZKW9ODelAAW0iAAAAoGIMGRQ55+52znU551qPuTbTOfe4c66t9N8ZpevOOXenc26Lc+4PzrlLj7lnTen9bc65NSPz4wAAyunKlh9g/Yd39uuZt/bok+87R7OmpiPXyZs0OSZNi4/UJwUAAAAwGk6lUfQjSTeecO0vJD1hZkslPVH6tyR9SNLS0p87JN0lDQRLkr4p6XJJl0n65mC4BAA4O7JF0+6sVzLi//mL3vS9x99SzbS0PnHFosh1zEzepPp0UHZ+EQAAAIDxZ8igyMyelrT/hMs3S1pb+vtaSR875vo9NuA5SdOdc/Mk3SDpcTPbb2YHJD2uk8MnAMAIMTPtzHjJKXKr2BN/6NDbu3v1+ZYmpRKxyLVyJlXHnSZHvwUAAADAOHWmM4rmmNmu0t93Sxo8O7le0s5j3tdeuhZ1/STOuTuccxudcxu7u7vP8OMBAI51MG/qLUhRc6f7cwX96Mk2nVtfrWvOnxe5jpnJTJpHmwgAAACoSO96mLWZmSQbhs8yuN73zGyFma2oqakZrmUBYMIqlNpEicAiw537n9mqA4ez+tIHzysbAGW9NCvhVBUjJAIAAAAq0ZkGRXtKW8pU+m9X6XqHpPnHvK+hdC3qOgBghO3OeBVNikcEQHsO9uuB57ZrVfM8nVs/PXIdbyYnaW6aAzMBAACASnWmT/uPSBo8uWyNpJ8dc/320ulnV0jqKW1RWyfpg865GaUh1h8sXQMAjKC+oqk7Z0qV+X/7H67fpMBJn1vVVHatnJfmpJySESemAQAAABj/hjzY2Dl3n6RrJc12zrVr4PSyv5V0v3PuC5LekfSnpbc/JunDkrZI6pP0OUkys/3Oub+W9GLpff/FzE4ckA0AGEbeTDv6vAKnyO1krTsO6Kk3duvWq5eoproqcq2imQIn1UQdmQYAAACgIgwZFJnZpyNeui7kvSbpyxHr3C3p7tP6dACAM7YvZ+rzpqidYkXv9Y+/el2109K65crFZdfKe6k+HShOmwgAAACoaPxqGAAqUM6bOrJeySC6TfTIizu0veuwvnTDeUono39vUPCmROA0O+rINAAAAAAVg6AIACqMmakj42UmxSJCon2HMrr3qTa9t7FGVzbVll0rb07z04GCMqehAQAAAKgMBEUAUGEOFaUD+egB1utbO/WFf3pa/bmi3t7dow2v74pcK2fS1Lg0bciNygAAAAAqAY/+AFBBvJl29HvFIwZYr2/t1H//+WvKF02StP9wTnc+2ipJammuO+69ZiYzqSEdRG5fAwAAAFBZaBQBQAXZkzXlSzOFwvxo/aajIdGgbN5r7YbNJ70366VZSaeqGCERAAAAMFEQFAFAhcgUTbtLA6yjdPdmw6/3ZI77d9FMgZPmRe1fAwAAAFCR2HoGABXgoZfb9TfrNqm7J6Oa6rTWrGo6aStZV09/5P011enj/p33Un06iGwmAQAAAKhMBEUAMM49/EqHvvHQa8rkvSSpqycTOnfoe4+/pXjMKXBSrvDH7WepRKA1q5qO/rvgTcnAaXaSkAgAAACYaNhTAADj3N+te+toSDToxLlDG9/u1jNv7dFtKxv1tY9coNrqtJyk2ur/v707D6/rru88/vmdu2u3LXmRvO9JnMWJEyAEspCQkAAJTBqWAJkOS+eBGWg7dQuUp9AONIV0KKS0aVk64ykwhIYkhCyYkJgdQhIc4y2W9022JS/a736+88c9kuVYlu6Vtev9eh4/0j06Oud3/7j3Rp98v99fXB+9bVVvoGRmyprT3LgnjwHWAAAAwJRDRREATGBmpiOtqX5/1jN3KJPL659/uF0N08v0tlctUjTsndWW1iNjUmVYquLTAQAAAJiSqCgCgAnsRMZUWxXv92c9c4ce+vVeHTnVrY/ccqGi4XO/7ZuZzKS5cU+OaiIAAABgSiIoAoAJKuObDqV8ve/6ZYpFznw775k7dPRUtx785R69/sLZWr24dsDrpX2pNuqUCBESAQAAAFMVzQUAMAGZmQ4kC3OJbry4QZ5zWreh8axdzz794IsKeU4fvHHlgNfLm8lz0uwY//8AAAAAmMoIigBgAjqZNbXnpHiQ69ywqv6suUO/aWzWb3e26AM3rjhne1qPjF9oOYt4VBMBAAAAUxlBEQBMMD0tZ1FP55wllMrm9cD6bVpQV6Hbr1ww4PWyvinmOdVGCYkAAACAqY4eAwCYQMxMh5K+zKTQAAOnH/zFbjW3pfThWy5UODTwAOucOc2Le/IYYA0AAABMeQRFADCBnMqaWnNSdIB370MnuvTQb/bqhlX1umTB9AGvlzGpKixVUl8KAAAAQLSeAcCEkfVNBwdpOTMzfeWprYqGQ3r/jSsGvJ6ZyUxqiHvnvB4AAACAqYWKIgCYAMwKIZE/SMvZ+pcOadO+k3r/G1ZoekVswGumfaku6pQIERIBAAAAKCAoAoAJoDVras1KA+1ef7w9pa/9eIcuXTBdb1o9d8Dr5c3kOWn2QBcEAAAAMOXwFwIAjHOnW85s0JazfN7XR29bNWArmZkp40sNMU9hj2oiAAAAAKcxowgAxqFHNx7Wfet3qKk1qZnVcb3r2mW6+ZKGc57/021H9dzOFn3wxhWqn1424LWzJpWFnGZECYkAAAAAnImgCADGmUc3HtYnHt6sZDYvSTrWltIDT25VxHO6YVX9Wee3dWf0wPptWlFfrduvWjjgtc1MvknzEwywBgAAAHA2Ws8AYJy5b/2O3pCoRzrna92Gxn7P/5f129WdyumP37xKoUFayXoGWJcxwBoAAABAPwiKAGCcaWpN9nu8pS111rHndjbrJ1uP6J3XLNHCmZUDXjdnprBzmh3nrR8AAABA//hrAQDGmfqaRL/H66rjZzzuSuf0j09u1cK6Ct312sUDXtPMlPWd5sadwrScAQAAADgHgiIAGGc+dtNyxcJnvj3HIp7uuX75Gcf+7ZkdOtWZ1h+/eZUioYHfzjMmVYWlmgghEQAAAIBzIygCgHHEN9PFy+foI7depJnVcTlJM6vj+uhtq84YZP37fSf05O8O6o5XLdSKhppBr2kmzY0zwBoAAADAwNj1DADGkSNpXynfdNMlDbrpkoZ+z0ll8/rSE1s1Z1qZ3nvtskGvmfGl2TFPcQZYAwAAABgEFUUAME505EzNaVNskHfmb/50p46c6tbHbrtI8UhowHOzviniOc2MERIBAAAAGBxBEQCMAznftK/bV9hpwPawHU1teuS5fXrT6rm6dOGMAa9pZsqZND/hKUTLGQAAAIAiEBQBwBgzMx1I+YXt671zBzrZvK8vPb5Z0ypiev8bVgx63YxfGF5dOXDREQAAAAD0IigCgDF2MmtqzWrQlrPv/nKP9jV36r/fepHK45EBz82bSWKANQAAAIDSEBQBwBhK5U0Hk76ing0Y6Oxr7tB3frFb1100R69aNnPQ62Z8qT7uKTpAhRIAAAAAvBJBEQCMEd9M+5K+5DTgDKG87+tLj29RWTysP3rjBYNeN+ubEp5TbZSQCAAAAEBpCIoAYIwcS/tK5k2D5Tnf+cUe7Whq04dvvlA15dEBzzUz5YMB1h4tZwAAAABKRFAEAGOgM2c6mjZFvYF3Odt+qFXf/vlu3bCqXtdeNGfQ66Z9aUbEqTxMSAQAAACgdARFADDKcmba1+0r5DRg1U8yk9N93/+9aqti+vAtg7ec5c3kucJsIgAAAAAYCv6aAIBRZGY6lPSVNVNkkEHT//qjl3X0VLfW3n7JoLucmZkyvtO8uKcwA6wBAAAADBFBEQCMopNZ08msFBvk3feXLx/T+pcO6a6rF2vV/OmDXjfjS9VhaVqEkAgAAADA0IXHegEAMFV0500Hkv6gc4lOdKT05Se2aNmcKt197dJBr5s3k3PSvIQ34HUBAAAAYDBUFAHAKMj5pr3BXKLQAGGOb6Yv/mCzMtm8/vyOSxQJDfw23dNyNjfuKUrLGQAAAIDzRFAEACPMzHQg5SvjDz6X6AfP79fv9pzQB29aqbkzKga9dsaXqsLSdFrOAAAAAAwDgiIAGGHNGVNr1gadS7SvuUPfeKZRVy2r062Xzxv0unkzyUnzaTkDAAAAMEwIigBgBHXmTE0pX7FB5hJlcr6+8OjvVR4P60/evGrQ4KfvLme0nAEAAAAYLgRFADBCMr5pT3deYSd5gwQ/637SqL3NHfqTN69STXmsiGvTcgYAAABg+BEUAcAI8M20r9uXb1J4kIqfjXtP6OHf7NNtV8zTVctmDnrtvJkkWs4AAAAADD+CIgAYAUfSvrrypugg77IdyYz+12O/17wZ5frAjSsHvW5vy1mCljMAAAAAw4+gCACGWWvG17G0DTqXyMz0j09uU2tXRn9+xyWKR0KDXpuWMwAAAAAjiaAIAIZROm/an/IVHSQkkqRnNjfp59uP6n3XLtPSOdWDXrun5WweLWcAAAAARghBEQAMk7yZ9nT7kqTQIEHOkVPd+ucfbtOq+dP0n16zaNBr993lLEbLGQAAAIARQlAEAMPAzHQo6Svl26CzgzK5vP72ey/Jc05/9tZLFCoi+OltOYsSEgEAAOiRs0kAACAASURBVAAYOQRFADAMTmZNJ7JSrIh31QfWb9euo+36s9sv0ayaxKDn03IGAAAAYLQQFAHAeerOmw4kfUU9GzTIeXrTIf1w4yG94+rFevXymYNeu6flrIGWMwAAAACjgKAIAM5Dxjft7vIVcoPPJdpzrF1feWqbLl04Xe+9bmmR15cqw1ItLWcAAAAARgFBEQAMUc/w6ryZIoNU+3SmsvrsQy+pMhHRx992qULe4G+/eTM5SfNpOQMAAAAwSgiKAGAIzArtZsm8KTrIO6mZ6YuPbVZzW1KffPtlqimPFXX9jO80P0HLGQAAAIDREx7rBQDARHQ07etU1hT3NGi1z0O/3qtfNzbrhovr9flHN6mlLaW66rjuuX65blhV3+/vpH1pRkSqiRASAQAAABg9BEUAUKKTGV9H0qZYESHR7/ed0P/Z0KgVDdX6xfYjyuQKO5g1t6V0/xNbJOmssCjrm6Ke01xazgAAAACMMlrPAKAEXbmeHc4kb5AQ50RHSvc+skkN08t1oiPVGxL1SGd9rdvQeMYx30x5kxaVeYMOxwYAAACA4UZQBABFSvum3d15eUXscJbL+7r34U1KZfL6yztX60R7ut/zWtpSvd+bmdK+U0PcU1mIkAgAAADA6CMoAoAi5M20p8uXLw26w5kk/e9nG7X14Cl97LaLtKCuQnXV8X7P63s87UvVYakuSkgEAAAAYGwQFAHAIMxM+5O+Ur4VtQPZz7cf1cPP7dNb1szXdcH8oXuuX65Y5My33FjE0z3XL5ck5XxT2BV2OWMuEQAAAICxwjBrABiAmakp7as12OFsMIdOdOoffrBZKxqq9cGbVvYe7xlYvW5D41m7nvlmypm0tNwrqloJAAAAAEYKQREADOBE1nQsLcWL2OEslcnpsw+9pEjY01++/TJFQmcmSzesqj9rhzMzU8aXZsWcKsOERAAAAADGFq1nAHAOnTnTwaSvqGeDhkRmpi8/sVUHWjr1F3dcqrrqRFH3yJhUFnKaHePtGAAAAMDY4y8TAOhHOm/a051XuIgdziTp2z/frZ9sPaJ7rl+myxfXFnWPvJmcpIVlnjzmEgEAAAAYBwiKAOAVsr5pV7cvkxQuYmbQhi1N+ubPdunGS+p119WLi7pHoeXMaX7cK2pANgAAAACMBoIiAOgjZ6bdXb6yvilaRICz9eApffEHm3Xx/Gn66G2rit6xLO1LtRGpJkJIBAAAAGD8ICgCgEDeTHu7fCV9Uyw0eIBz5FS3/uY/fqdZ1Ql96s7VZw2vPpeMb4p5Tg0Jr+hgCQAAAABGA0ERAEjyzbQ/6aszbypmrnRnKqtPf+dFmUl//c4rVFUWLeo+eTP5VphLVMzsIwAAAAAYTQRFAKY8s8LuZq3ZQkg0WJVPLu/rcw+9pCOnuvWpO1erYXp50ffJ+E7zE57KiqhYAgAAAIDRRlAEYEozMzWlfZ3ImuJFhERmpn96apte2ndCH71tlS5ZML3o+6R8qS4qTWcuEQAAAIBxiqAIwJTWnDEdS6uokEiSvvebffrhS4f0ztcu1k2XNhR9n4wvVYacGuLMJQIAAAAwfoXHegEAMFaOZ3wdTvlFtZtJ0i9fPqZ/e2aHXn/hbL33umVF3yfrm8LOaWGZJ4+QCAAAAMA4RkURgCmpNePrQLIQEhUT3jQ2tem+RzdpRUO1/vQtFxcd+PQMr15c7iniERIBAAAAGN8IigBMOR05096kr2iRIVFLW1Kf+e7vVFMe01/9weWKRUJF3acwvFoMrwYAAAAwYdB6BmBK6c6bdnflFXYqanv67nROn37wd0pn87r37jWaVhEr6j49w6tnxZymR8nkAQAAAEwM/PUCYMpI5U27uvLynBQuog0s7/v6/CObtL+lU598+2VaUFdZ9L3SvlQVdpoT420WAAAAwMTBXzAApoS0b9rZ5cukomYF+Wb6hx9s0W93tejDt1ygK5bUFn2vjG+Kek4LEwyvBgAAADCx0HoGYNJL502NXb58FQKcwZiZ/mX9dj2zuUnvvXapbrti/jnPfXZLk9ZtaFRLW0p11XG997plet2F9Vpc7hVVtQQAAAAA48l5VRQ55/Y55zY7515yzr0QHJvunHvaObcz+DotOO6cc/c753Y5537vnLt8OJ4AAAwkVWJIJEnrNuzUD144oDtfs0jvumbJOc97dkuT7n9ii5rbUjJJzW0pfeWJrdrWeEQJhlcDAAAAmICGo/XsejO7zMzWBI8/LukZM1sm6ZngsSS9SdKy4N+HJD0wDPcGgHNK5QvtZqWERA/+crce/NUe3Xr5PP2XG5bLDdA6tm5Do9JZ/4xj6Zyvf3pm53mtGwAAAADGykjMKLpd0rrg+3WS7uhz/P9awW8k1Tjn5ozA/QFAybypsStfUkj02PP79X827NQNq+r1kTddOGBIJEktbal+jze1JkteLwAAAACMB+cbFJmkHznnXnTOfSg4NsvMjgTfH5U0K/i+QdLBPr97KDgGAMMqmTft7MpLUtEh0dObDumB9dv1mhUz9advXVXUEOq66ni/x+trEsUvFgAAAADGkfMNiq4xs8tVaCv7iHPu9X1/aGamQphUNOfch5xzLzjnXmhpaTnP5QGYarr7hETF7G4mST/fflRfenyLVi+aoY+/7TKFvOLeGu+5frli4TPPTURCWnvzitIWDQAAAADjxHkFRWZ2OPjaLOkRSVdJOtbTUhZ8bQ5OPyxpXp9fnxsce+U1v2pma8xsTV1d3fksD8AU05037SoxJPrtrhZ94ZFNumDuNP3VH6xWNFz82+LrL5yjD996kebUxOUkNdQkdO/bL9YdqymWBAAAADAxhYf6i865ckmemXUE379R0t9IekzSPZL+Lvj6/eBXHpP035xz35H0KkltfVrUAOC89FQSORUfEv1+/0l97qGNWjizUn/9jssVjxb/lpg3U86k/3LVPH3s6vlDXDUAAAAAjC9DDopUmD30SDDsNSzp22b2Q+fc85K+65x7v6T9ku4Kzn9S0q2SdknqlvSH53FvAOjVlTPt6i4tJNpxuFWfefBFza4p02ffvUbl8UjR9/PNlPGlRQlPleHi7gcAAAAAE8GQgyIz2yPp0n6On5D0hn6Om6SPDPV+ANCfrlyh3cy54kOivc0d+tT/e1HVZVH97d1rVF0WLfp+Zqa07zQ37jQtOhIbRwIAAADA2DmfiiIAGFMdOdPurrxCTgoXGRIdPtmlv/zW84pFPP3de67UjMr+dy7rj5kp5UuzY1JdlEoiAAAAAJMPQRGACelkxtf+pK9wCSHRvuYOffLbL8g36fN3X6lZNWVF368nJJoRcZoT8xS03QIAAADApELfBIAJxcx0LO1rX9JXxCs+JNrR1KY///ffynPSF953lebVVpR0z7QvVYed5iUIiQAAAABMXlQUAZgwzEyHU76aM1LMk7wiA5vf7z+pzzxYmEl0791Xava04iuJJCntS2Uhp4VlXtH3BAAAAICJiKAIwITgm2l/0teprCnuqeiqnt/uatHnHtqo2TVl+tu715Q0k0iS0r4p5jktLvMUIiQCAAAAMMkRFAEY93K+aU+3r658aSHRz7Yd0Rce/b0Wz6rU/3xXabubSVLWN4XktKTcK3pHNQAAAACYyAiKAIxrad+0u8sPKnuKD4nWbzyk+5/cogvnTtNn3nG5yuORon7v2S1NWrehUS1tKdVWxbX25hW6+Iq55/MUAAAAAGDCICgCMG515wshUV6meKj4ip5Hntunrz79stYsqdVf3rla8UioqN97dkuT7n9ii9JZX5LU0p7Spx/doqjndMfqhiE9BwAAAACYSNj1DMC41JEzNXbmZSrMCCqGmelbP9ulrz79sl67cpb+6q7Liw6JJGndhsbekKhHMpvXfet3lLR2AAAAAJioqCgCMO6czPjan/QVdlK4hJDo6z/eoYef26ebLmnQx958kUJeaVl4S1uq3+NNrcmSrgMAAAAAExVBEYBxw8x0LG1qSvuKeip6l7G8b/rKk1v1w5cO6fYrF+hDb1xZ8jb2Wd9UWxVXS/vZYVF9TaKkawEAAADAREXrGYBxIW+mvUlfTWlfsRJCokwury88ukk/fOmQ3nXNEv3REEMik/RnN69Q4hWtaolISGtvXlHS9QAAAABgoqKiCMCYS+VNe7oLO5vFS9jZ7FRnWv/zoY3afqhV73/DCt35mkUl3zvrmyRpWXlIl14xVzHP6b71O9TUmlR9TUJrb17BIGsAAAAAUwZBEYAx1Zb1ta/bl5xK2tls77EOfebBF9XWndEn/9Nlet0Fs0u+d09ItLQ8pLLg3nesbiAYAgAAADBlERQBGBNmpqNp05ES5xFJ0m8am/WFRzepLBbWffe8SsvmVJd8/4xvcipUEiVKCKgAAAAAYDIjKAIw6nJmOpD01Zo1xTwVPVPIzPTwc/v0jR/v0NI5VfqrP7hctVXxku9PSAQAAAAA/SMoAjCqUnnT7m5fmRLnEWXzvr7y5Fb9aNNhXXPBLP2Pt16i+CsGTxcj45s8OS0r90pqdQMAAACAqYCgCMCoac342pf05UqcR9TendFnH9qozQdO6V3XLNF7rl1a8s5mEiERAAAAAAyGoAjAiCvMI/J1JG0lzyM6cLxTn3nwdzrentLa2y/RDRfXD+n+GV8Ku0JIFCMkAgAAAIB+ERQBGFFZvzCPqC1X2jwiSXpx93Hd+/BLioQ9ff69V+qCudNKvr+ZKe1LZSGnxWWeIh4hEQAAAACcC0ERgBHTnjPt684rbyppHpEkPfb8fv3rj17W/LpyfeauKzSrJlHy/c1MKV+qiTgtSHglVTIBAAAAwFREUARg2Plmakr5as6YIiXOI0plcvrn9dv19KbDumpZnf7ijktVFiv9rco3U9p3mhmTGmJeSSEVAAAAAExVBEUAhlUyb9rX7StZ4q5mkrS3uUP3PvySDh3v0ruuWaK7X79UoSG0iuXNlPWleXGn2qjT919q0n3rd6ipNan6moTW3rxCd6xuKPm6AAAAADDZERQBGBZmppaM6XDKl+ekRAlVRGampzYe0r/+aLvKY2F97u4rtXrRjCGtI+ubfJMWl3mqjnh6dONhfeLhzUpm85Kkw61JfeLhzZJEWAQAAAAAr0BQBOC8ZX3T/qSv9pxKHljdlc7p/ie26Gfbjmr1ohlae/slmlYRG9I60r4pJKflFZ7KgqDqvvU7ekOiHslsXvet30FQBAAAAACvQFAE4Ly0ZX3tT/ryhzCwurGpTfc+/JKa21L6w+uX686rF5UUMvXo2dks7jktKfcU7dOu1tSa7Pd3znUcAAAAAKYygiIAQ5IPBla3ZEwRT4qVMEvIzPT95/frGz/eoWkVMX3hfVfponnThrSOnp3NqsJOi8rO3tmsviahw/2EQvVD2EUNAAAAACY7giIAJevKFVrN0kMYWN2RzOiLP9ii3zQ269XLZ+pP37JKlYnokNbRs7NZXVRqiHv9ViOtvXnFGTOKJCkRCWntzSuGdE8AAAAAmMwIigAULW+mI0EVUajEbe8laevBU/r8I5t0qjOtP3rjSt1+5YIhb1uf9U15k+bGneqi7pzX6ZlDxK5nAAAAADA4giIAgzIztedMB5KmnJliJVYR5fK+/uNXe/XNn+3SrJqE/td/frWW11cPeS1pXwo7p+XlnsrDg6/jjtUNBEMAAAAAUASCIgADyvimQylfrVkp4qzkKqJdR9v1pce3aPfRdl130Rz9t1svUnlsaG89fhASVYedXnr5iP7r041UCQEAAADAMCIoAtAvM9PJjOlQemg7mmVyeX3757v1H7/aq+ryqD5152q9duWsIa+np9WsIe7pV1ub9KlHt/TOHTrcmtQnHt4sSYRFAAAAAHAeCIoAnCWVNx1I+urMS1FPZ2w3X4xtB0/pS49v0cETXbrp0gZ98MaVqkxEhrSWvq1my8o9VYSd/v5HjWcMp5akZDav+9bvICgCAAAAgPNAUASgl2+mY2nT0bQv50qvIkpmclq3Yacee36/6qrj+uy71uiKJbXntZ60L1WFnRYkPEWCwKqpn+3uBzoOAAAAACgOQREAmZk689LBYMv7qKd+t5ofyMY9x/XlJ7bqWFtSb1kzX//5+uUqK2EW0bNbmrRuQ6Na2lKqq47rvdcu0zUX1as+5mlW7MxdzeprEjrcTyhUX5Moac0AAAAAgDMRFAFTXCpvakr5as2ZwkPY8r4zldXXnn5ZP9p0WA3Ty3Tf+16lVfOnSTo7/Lnn+uW6YVX9Wdd4dkuT7n9ii9JZX5LU3JbSV57cqrqop9Vr5p51/tqbV+gTD28+o/0sEQlp7c0rSlo7AAAAAOBMBEXAFJXzCy1mLRkbUpuZJP16xzF95altau3K6K6rF+ndr1uqWCQkqf/w5/4ntkjSWWHRug2Nvef1SOd8/eOPG/XOfoKinjlE963fwa5nAAAAADCMCIqAKcY30/GM6Uiwm1lsCAHRgeOd+trTL+uF3ce1eFalPvOOy7VsTvUZ5/Qb/mR9rdvQeFZQ1NKW6vc+A80cumN1A8EQAAAAAAwzgiJgijAzteekgylfWd8UGcJuZh3JjL75s116/IWDSkRD+sCNK3T7lQsUDnlnnXuu8OeVx/Nmqq2Kq6X97POZOQQAAAAAo4ugCJgCuvOmQ8F29xFnJc8hyuV9PfG7g/rmT3epO53VLavn6b3XLlNNefScv1NXHVdzP2FRXXVcUiG4yviSnPSxm5brc49tUbJPBRIzhwAAAABg9BEUAZNYxi8Mqj6VNXlDnEP0/K4Wfe3pl3XwRJdWL5qhD960UotmVg76e/dcv/yMGUWSFIt4uuf65cqbKeM7VYeleQlPl105TxVhj5lDAAAAADDGCIqASSjjm5qDQdXS0OYQ7W/p1Nd/XJhD1DC9TJ++63K9alld0dfpmUN0xq5n1y3T1RfMUd6khQmnaZHT294zcwgAAAAAxh5BETCJvDIginqSV2JA1N5dmEP0xIuFOUQfumml3rxmviL9zCEazA2r6nsDo5yZsr5TTUSaG/cUKXE+EgAAAABg5BEUAZPAcAREqUxOj794UA/+co+601ndevl8vefapaouO/ccomKYmdK+FHLS4oRTdZ8qIgAAAADA+EJQBExgGd90LO3r+DAERA/9eq/aujO6YnGtPnDjCi0sYg7RQMxMGZPMpBlRp/qYpzBVRAAAAAAwrhEUARPQcAREyUxOj79wQA/9Zq/au7O6fPEM3f26pbpw3rTzXl/WN+XMqSosNcQ9JUrcZQ0AAAAAMDYIioAJJJU3NWd8nTiPIdXJTE4/eOGAvhcERFcsrtXdr1+iC+aef0CUD7a8j3lOC8s8VYZKXx8AAAAAYOwQFAHjnJmpMy8dS/vqyA09IOpOFyqIvvebvWpPZrVmSa3e/bqlumBuzXmv0Q8CIs9J8+KeZkRdyRVOAAAAAICxR1AEjFN5M7VmTcfShWHQnrNhC4jufv1SrWw4/4DIgoDIJM2MOc2KMocIAAAAACYygiJgnMn4phOZQouZb1LYDa2CqLktqR+8cEA/3HhInamsrlxap7tft0QrhikgypmUM6km4tQQ8xRjDhEAAAAATHgERcA4YGZK+lJz2tepbKG9LOJJ0RKrc8xMWw+e0qO/3a9f7zgmSbp65Szd+epFwxYQ/Xhzk/79Jzt1vD2l2dVx/cUtK7V4dcN5XxsAAAAAMPYIioAx5JupPVcIiLryJqehVQ9lcr5+uvWIvv/8fu0+2q6KeERvf/UivWXNfM2sTpz3Os1MWZM2bDmiB57aqnTWlyQdaUvpEw9vliTdQVgEAAAAABMeQREwynqqh05mfJ3ImnyTQkNsLzvZmdYTLx7Qk787qNaujObVluu/v+lC3XBxveLR83959wREeZMqwk4P/rSxNyTqkczmdd/6HQRFAAAAADAJEBQBoyTnF4ZTt2RMKb/QXhb1JG8Iw58bm9r0/d/u18+2HVHON121tE63X7VAqxfNGJbt6M1MGZPMpMqw05yYp7KQdLQt1e/5Ta3J874nAAAAAGDsERQBI8jM1JWXjmd8tWZNpqEPp27vzmjD1iP68abD2nW0XYloSG+6fJ7eeuUCzZ1RPmzr7dnFrCrsNCfuqazPkOr6moQO9xMK1decf3sbAAAAAGDsERQBIyDdUz2ULrRuOWeKDiEcyvu+Xth9XE9vOqznGpuV801LZlfpv75xpW68pEHl8ciwrNcPAiKpsIvZ7JinRD+7mK29eYU+8fBmJbP53mOJSEhrb14xLOsAAAAAAIwtgiJgmGR8U3vWdDxrSuZP71wW95yk0gKi/S2denrTIT27uUmnujKqKovozWvm66ZLG7R4VtWwrNfMlA+2uPecVBd1qo16ig+wzX3PHKL71u9QU2tS9TUJrb15BfOJAAAAAGCSICgCzkPWN7VlTSezpq4gHBrqYOqOZFY/3XZET286rMamNoU8p6uW1unGSxt05dI6RULesKz59Pwhp5gn1cecaqJOoSLXe8fqBoIhAAAAAJikCIqAEmV9U3vOdCJr6sqdXzjUnc7pt7ta9IvtR/XbnS3K5n0tmlmpD920UtevmqOa8tiwrTtvpmyf9rK6qKfyUOlrBgAAAABMXgRFQBGyvqkjCIc6zzMc6khm9dzOZv3y5WN6cfdxZfO+plXE9KbL5+qmS+dqyazKYQtvera3980p7Jzq407TIk7RV+y09ujGw7STAQAAAAAIioD+mJmSvtSRM53qM3NoqOFQW3dGv95xTL98+Zg27j2hvG+qq4rrtivm6bUrZ+uCuTUKecMXDuUl5YLqoYqw08yop8qw5PWz7kc3Hj5jQPXh1qQ+8fBmSSIsAgAAAIAphqAICOSDrezbsr5as4Uhz5INeTv7kx0p/WpHs37x8lFt3n9Kvplm1yT0tlct1DUrZ2l5ffWwtn31bS2LeoXqoZqIU2yQAOq+9TvO2MVMkpLZvO5bv4OgCAAAAACmGIIiTGmZoKXsVLbwVXJyMoWHsFtZ3ve1/VCbXtzdohf3HNfOI+2SpHkzynXX1Yt0zQWztXgY28qkwrb2WV8yFVrLZsWkmoinRAnBVlNrsqTjAAAAAIDJi6AIU0rOL1QNdeR8teUKQZHUt6VMKiUcamlP6cXdLXph93G9tPeEutI5ec7pwrk1et91y3T1illaUFcxrM+h79whz0nTo07TI0MfTF1fk9DhfkKh+prEcCwXAAAAADCBEBRhUsubqbsnGMpKqSAYcpLCXuktZZmcr60HTxWqhnYf176WTklSbWVcr7tgtq5YUqvLFs1QRTwyrM/DD8Ihs8Jaa8JO0weYO1SKtTevOGNGkSQlIiGtvXnFeV0XAAAAADDxEBRhUvGDYKgzZ2rLmbrzhXYyBe1kQwmGGpvatOXASW05cEpbDp5SOptXOOR08fzpuvHSBq1ZUqv5tRXD2lJ25kBqp5CTpoWdaiKeKsJSaBjv1TOHiF3PAAAAAAAERZiwelqwknmpK++rI6czg6EhtJOlMjltP3w6GHr5cKsywfZhC+sq9MZLG3TFklpdumC64tHhffmYmXImBRusKeY51cacqsJOZSW2lZW63f0dqxsIhgAAAAAABEWYOHwzpX0pmTe150ydOSlr1vvzoQRDnamsth1q1ZYDJ7V5/0ntPNKuvG/ynLRkdpVuu2KeLp4/XRfNm6aqsuiwPh8zk6/C7mo9T6Mi7DQt7FQZcYq6oc0cYrt7AAAAAMBQERRhXOqpFkrlC1VCHXlTV96CGT2FICfkpFgJYUoml9eeYx3a0dSmnU1t2tHUpkMnuiRJYc9peX217nz1Iq1aME0XzJ2m8tjwVwy9MhiKeE4zIlJV2FN5WAoPQ0sZ290DAAAAAIaKoAhjzsyUCUKhZN7UGYRChbnTTha0kUWd5LzCscHkfdOhE53acbhNjUfa1NjUpr3HOpQLhllPq4hpRX21blhVrwvn1WhlQ41ikdCwPzc/aCcLbquwc5oWkarDnspCUmSIVUMDYbt7AAAAAMBQERRhVPlmyvjqbSHryBcGTvunO8gUcoU2Ms/rCVAGDlLS2bz2t3Rqb3OH9jZ3aPfRdu0+2q5kplBVUxYLa9mcKr391Qu1vL5Gy+urVVsZG/aApr+KoZBzqg5LVWGn8vDQ28mk4ucOsd09AAAAAGCoCIowInpbx3wpHVQIdeeldN9ESP2FQgNfs7ktqb3Nndp7rKM3GGo62dUbNMUiIS2cWaEbL2nQivpqLa+vVsOM8vPeQr4/Z1YLFa4fC1rJykNOZSFX8i5r51LK3CG2uwcAAAAADBVBEc5LTyCU9qWMX6gO6g5ayArZzStmChURnOR9U0tbUgdPdOlQ8G9fc4f2tXSqO53rPW92TUKLZlXq2gtna9GsSi2aWanZ08pGLBTyrVAt1CPknCpDUmXYKRFySoRK37a+2CqhUuYOsd09AAAAAGCoCIowKDNT3qRMEAilg3axVBAO9Q2EnJNCkqJnBEL9hyfd6VxvEHToRJcOnujUoRNdOnyiW9m833teRTyi+bXlumFVvRbNrNCiWZVaUFepsmEeNt3zXH0Vtqg/XSlUCLoSntOMsFQe8pQYhvlCpVQJlTp3iO3uAQAAAABDQVAESacrg7K+lLVCAJQMwqCUb2eEJpJ6K4QGCoTMTKc60zrSmtTRU9062prUkT5fT3ame8/1nNOcaQk1zCjXFYtrNXdGuebOqNDcGeWqLosM+zwh6XSVUN4Kz8rJySRFPaeqkFQWVArFveJDoWIrhKTSqoSYOwQAAAAAGA0ERVOEBfN0ciZlgkqgtF8IgtK+lA0qg1ywy5ipEAR5OvdgaTNTW3dGLe0ptbSn1NyW1NFTSR1p7dbRU0kda+1WOne6MshJmlEZ1+xpid4wqGFGuebVlmvOtDJFQt6IPG9f6g2ETq+kUCUU95zKQ1JZyCkezBQqtX2sRykVQlJpVULMHQIAAAAAjAaCokmgpzUsSY6DegAACy1JREFUZwqqgnp2FrNCCGSng6C+VUEuCIK8fiqDzEydqZyOdxRCoJa2pFraUzoehEI93/dtEZOkRDSk2TVlaphRpjVLajV7WkKza8o0Z1pCM6sTioaHfwv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            "text/plain": [
              "<Figure size 1440x864 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9gMtqPnW-WFU",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 527
        },
        "outputId": "685fc315-e164-406c-c8a3-41b49d66d9d7"
      },
      "source": [
        "skip_data = 25\n",
        "dates = data['date'][skip_data:]\n",
        "ncumul_hosp = data['ncumul_hosp'][skip_data:].fillna(0)\n",
        "ncumul_ICU = data['ncumul_ICU'][skip_data:].fillna(0)\n",
        "ncumul_deceased = data['ncumul_deceased'][skip_data:].fillna(0)\n",
        "\n",
        "ncumul_ward = ncumul_hosp - ncumul_ICU\n",
        "\n",
        "width = 0.35       # the width of the bars: can also be len(x) sequence\n",
        "\n",
        "fig, ax = plt.subplots()\n",
        "fig.set_size_inches(14, 8.4, forward=True)\n",
        "\n",
        "#ax.bar(dates, ncumul_ward, width, label='Hospitalized')\n",
        "#ax.plot(dates, ncumul_ward)\n",
        "\n",
        "#ax.bar(dates, ncumul_ICU, width, bottom=ncumul_ward, label='Intensive Care')\n",
        "\n",
        "# ax.bar(dates, ncumul_deceased, width, \n",
        "#        bottom=ncumul_hosp, label='Deceased')\n",
        "\n",
        "x = numpy.arange(len(dates))\n",
        "ax.bar(x - width/2, ncumul_ward, width, label='Hospitalized')\n",
        "ax.bar(x - width/2, ncumul_ICU, width, bottom=ncumul_ward, label='Intensive Care')\n",
        "ax.bar(x + width/2, ncumul_deceased, width, label='Deceased')\n",
        "\n",
        "for tick in ax.get_xticklabels():\n",
        "  tick.set_rotation(55)\n",
        "  tick.set_label(\"ciao\")\n",
        "\n",
        "fig.align_labels()\n",
        "ax.set_ylabel('People')\n",
        "ax.set_title('Hospitalited in ward + intensive care + deceased by day')\n",
        "ax.legend()\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1008x604.8 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}