Analysis_of_Covid_19_India.ipynb

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  "metadata": {
    "colab": {
      "name": "Analysis of Covid-19 India.ipynb",
      "provenance": [],
      "collapsed_sections": [
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      "name": "python"
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qsTBIrkRiQQq"
      },
      "source": [
        "# Impact of Covid-19 in India\n",
        "\n",
        "\n",
        "---\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IKsDIV3E2Gu4"
      },
      "source": [
        "<center><img src=\"https://c.ndtvimg.com/2020-08/s4buv5c_coronavirus-india-generic_625x300_10_August_20.jpg\"></center>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6-RLrfzCq_Ox"
      },
      "source": [
        "COVID-19 is an contagious disease caused by Coronovirus, biologically known as (SARS-CoV-2). The first known case was identified in Wuhan, China, in December 2019. The disease has since spread exponentially worldwide, leading to an ongoing pandemic.\n",
        "As of 6 August 2021 there are **200 million** (202,576,760) **confirmed cases**  and more than **4 million** (4,294,317) **deaths**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QsIXvjSwjgMF"
      },
      "source": [
        "######## Image\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6GPS8FXukeYH"
      },
      "source": [
        "## Introduction\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LCGaWo4pk6sd"
      },
      "source": [
        "This dataset contains data of various Indian states.\n",
        "[Link to Dataset](https://www.kaggle.com/sudalairajkumar/covid19-in-india?select=covid_19_india.csv)\n",
        "\n",
        "It contains columns 'Sno', 'Date', 'Time', 'State/UnionTerritory', 'ConfirmedIndianNational', 'ConfirmedForeignNational', 'Cured', 'Deaths', 'Confirmed'.\n",
        "\n",
        "The second dataset contains aprroximate population and area of various state which I collected myself from Statewise Aadhar saturation data in uidai website and Wikipedia\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0rtBttOwkyyC"
      },
      "source": [
        "## Data Preprocessing "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "t3rysCusk9ih"
      },
      "source": [
        "**Description of columns**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6DdSmc8BmkE-"
      },
      "source": [
        "- Sno - Number of reported Case\n",
        "- Date - YYYY-MM-DD\n",
        "- Time - HH:MM AM/PM\n",
        "- State/UnionTerritory - Indian States or Union Territories\n",
        "- ConfirmedIndianNational - Confirmed corona cases of Indian National\n",
        "- ConfirmedForeignNational - Confirmed corona cases of Foreign National\n",
        "- Cured - Recovered from Covid-19\n",
        "- Deaths - Deaths due to Covid-19\n",
        "- Confirmed - Confirmed corona cases"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "p2a1KXzunAjY"
      },
      "source": [
        "**Necessary Imports and Mounting Data**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nr1-pkmDkIMn",
        "outputId": "793e32f9-cde9-4228-f018-cae6895a028f"
      },
      "source": [
        "import os\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import seaborn as sns\n",
        "%matplotlib inline\n",
        "\n",
        "from google.colab import drive\n",
        "drive.mount('/gdrive', force_remount=True)\n"
      ],
      "execution_count": 137,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mounted at /gdrive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "X6IqWrfKnK-x"
      },
      "source": [
        "**Loading Data into Dataframe**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iZhwa6FLnTU5"
      },
      "source": [
        "# changing directory to where data is stored\n",
        "os.chdir('/gdrive/MyDrive/Data')\n",
        "covid19_IN = pd.read_csv(\"covid_19_india.csv\")\n",
        "\n",
        "excel_file = pd.ExcelFile(\"ISPA.xlsx\")\n",
        "indian_states = excel_file.parse('Sheet1')"
      ],
      "execution_count": 138,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "iKWflQnjnbkA",
        "outputId": "7b206a5d-92a5-49c7-8fa6-6f44cd51c6be"
      },
      "source": [
        "covid19_IN.head()"
      ],
      "execution_count": 139,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Sno</th>\n",
              "      <th>Date</th>\n",
              "      <th>Time</th>\n",
              "      <th>State/UnionTerritory</th>\n",
              "      <th>ConfirmedIndianNational</th>\n",
              "      <th>ConfirmedForeignNational</th>\n",
              "      <th>Cured</th>\n",
              "      <th>Deaths</th>\n",
              "      <th>Confirmed</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>2020-01-30</td>\n",
              "      <td>6:00 PM</td>\n",
              "      <td>Kerala</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>2020-01-31</td>\n",
              "      <td>6:00 PM</td>\n",
              "      <td>Kerala</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
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              "      <td>3</td>\n",
              "      <td>2020-02-01</td>\n",
              "      <td>6:00 PM</td>\n",
              "      <td>Kerala</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>2020-02-02</td>\n",
              "      <td>6:00 PM</td>\n",
              "      <td>Kerala</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>2020-02-03</td>\n",
              "      <td>6:00 PM</td>\n",
              "      <td>Kerala</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Sno        Date     Time  ... Cured Deaths Confirmed\n",
              "0    1  2020-01-30  6:00 PM  ...     0      0         1\n",
              "1    2  2020-01-31  6:00 PM  ...     0      0         1\n",
              "2    3  2020-02-01  6:00 PM  ...     0      0         2\n",
              "3    4  2020-02-02  6:00 PM  ...     0      0         3\n",
              "4    5  2020-02-03  6:00 PM  ...     0      0         3\n",
              "\n",
              "[5 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 139
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "w4D08lMAnkxT",
        "outputId": "7735ffdd-d98b-4a15-8218-756a1dfb5f3a"
      },
      "source": [
        "covid19_IN.tail()"
      ],
      "execution_count": 140,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Sno</th>\n",
              "      <th>Date</th>\n",
              "      <th>Time</th>\n",
              "      <th>State/UnionTerritory</th>\n",
              "      <th>ConfirmedIndianNational</th>\n",
              "      <th>ConfirmedForeignNational</th>\n",
              "      <th>Cured</th>\n",
              "      <th>Deaths</th>\n",
              "      <th>Confirmed</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>17781</th>\n",
              "      <td>17782</td>\n",
              "      <td>2021-08-02</td>\n",
              "      <td>8:00 AM</td>\n",
              "      <td>Telangana</td>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
              "      <td>632728</td>\n",
              "      <td>3805</td>\n",
              "      <td>645406</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17782</th>\n",
              "      <td>17783</td>\n",
              "      <td>2021-08-02</td>\n",
              "      <td>8:00 AM</td>\n",
              "      <td>Tripura</td>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
              "      <td>74875</td>\n",
              "      <td>755</td>\n",
              "      <td>78722</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17783</th>\n",
              "      <td>17784</td>\n",
              "      <td>2021-08-02</td>\n",
              "      <td>8:00 AM</td>\n",
              "      <td>Uttarakhand</td>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
              "      <td>334190</td>\n",
              "      <td>7362</td>\n",
              "      <td>342161</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17784</th>\n",
              "      <td>17785</td>\n",
              "      <td>2021-08-02</td>\n",
              "      <td>8:00 AM</td>\n",
              "      <td>Uttar Pradesh</td>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
              "      <td>1685049</td>\n",
              "      <td>22763</td>\n",
              "      <td>1708476</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17785</th>\n",
              "      <td>17786</td>\n",
              "      <td>2021-08-02</td>\n",
              "      <td>8:00 AM</td>\n",
              "      <td>West Bengal</td>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
              "      <td>1499597</td>\n",
              "      <td>18149</td>\n",
              "      <td>1528720</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         Sno        Date     Time  ...    Cured Deaths Confirmed\n",
              "17781  17782  2021-08-02  8:00 AM  ...   632728   3805    645406\n",
              "17782  17783  2021-08-02  8:00 AM  ...    74875    755     78722\n",
              "17783  17784  2021-08-02  8:00 AM  ...   334190   7362    342161\n",
              "17784  17785  2021-08-02  8:00 AM  ...  1685049  22763   1708476\n",
              "17785  17786  2021-08-02  8:00 AM  ...  1499597  18149   1528720\n",
              "\n",
              "[5 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 140
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kRt0Um7FoCKw"
      },
      "source": [
        "We can see the dataset provides day by record of number of cases found in a specific state in India from 31st Jan 2021 till 2 August 2021. It contains 17785 entries and 9 features. The 'Confirmed' column has total number of cases and not the new cases found that day, we have to keep this in mind"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5hJAYh8WndQ7",
        "outputId": "bb7432ed-3ad9-46d8-c831-ffb6188a3e7c"
      },
      "source": [
        "covid19_IN.shape"
      ],
      "execution_count": 141,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(17786, 9)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 141
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "W4ZObGKzneQk",
        "outputId": "d30ee9ae-4478-4d89-eea1-fcb1216a530a"
      },
      "source": [
        "covid19_IN.isna().sum()"
      ],
      "execution_count": 142,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Sno                         0\n",
              "Date                        0\n",
              "Time                        0\n",
              "State/UnionTerritory        0\n",
              "ConfirmedIndianNational     0\n",
              "ConfirmedForeignNational    0\n",
              "Cured                       0\n",
              "Deaths                      0\n",
              "Confirmed                   0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 142
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SndZsr2TpGfO"
      },
      "source": [
        "Here, we see that the dataset has no missing values in this dataset which will make analyzing it easier. Lets load the records of the last day in the dataset to have an idea of the state we're in"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "YGBBy6IgqIUA",
        "outputId": "f70a1cd8-8807-497e-be53-2b9fa61e8561"
      },
      "source": [
        "covid19_IN_recent = covid19_IN[covid19_IN['Date'] == '2021-08-02']\n",
        "covid19_IN_recent.head()"
      ],
      "execution_count": 143,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Sno</th>\n",
              "      <th>Date</th>\n",
              "      <th>Time</th>\n",
              "      <th>State/UnionTerritory</th>\n",
              "      <th>ConfirmedIndianNational</th>\n",
              "      <th>ConfirmedForeignNational</th>\n",
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              "      <th>Confirmed</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>17750</th>\n",
              "      <td>17751</td>\n",
              "      <td>2021-08-02</td>\n",
              "      <td>8:00 AM</td>\n",
              "      <td>Andaman and Nicobar Islands</td>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
              "      <td>7403</td>\n",
              "      <td>129</td>\n",
              "      <td>7539</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17751</th>\n",
              "      <td>17752</td>\n",
              "      <td>2021-08-02</td>\n",
              "      <td>8:00 AM</td>\n",
              "      <td>Andhra Pradesh</td>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
              "      <td>1934048</td>\n",
              "      <td>13395</td>\n",
              "      <td>1968462</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17752</th>\n",
              "      <td>17753</td>\n",
              "      <td>2021-08-02</td>\n",
              "      <td>8:00 AM</td>\n",
              "      <td>Arunachal Pradesh</td>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
              "      <td>44354</td>\n",
              "      <td>231</td>\n",
              "      <td>48260</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17753</th>\n",
              "      <td>17754</td>\n",
              "      <td>2021-08-02</td>\n",
              "      <td>8:00 AM</td>\n",
              "      <td>Assam</td>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
              "      <td>549065</td>\n",
              "      <td>5275</td>\n",
              "      <td>566982</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17754</th>\n",
              "      <td>17755</td>\n",
              "      <td>2021-08-02</td>\n",
              "      <td>8:00 AM</td>\n",
              "      <td>Bihar</td>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
              "      <td>714801</td>\n",
              "      <td>9644</td>\n",
              "      <td>724880</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         Sno        Date     Time  ...    Cured Deaths Confirmed\n",
              "17750  17751  2021-08-02  8:00 AM  ...     7403    129      7539\n",
              "17751  17752  2021-08-02  8:00 AM  ...  1934048  13395   1968462\n",
              "17752  17753  2021-08-02  8:00 AM  ...    44354    231     48260\n",
              "17753  17754  2021-08-02  8:00 AM  ...   549065   5275    566982\n",
              "17754  17755  2021-08-02  8:00 AM  ...   714801   9644    724880\n",
              "\n",
              "[5 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 143
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "f7FDEcW5qbeT",
        "outputId": "4ac74699-278f-45cc-d6e5-f1f3bfe22579"
      },
      "source": [
        "covid19_IN_recent['Confirmed'].sum()"
      ],
      "execution_count": 144,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "31695958"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 144
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Uynyt0kCqjFc"
      },
      "source": [
        "After preprocessing the dataset we see that India has total of **31,695,958** cases as of 2 August 2021"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Zo9xn6cAq4p1"
      },
      "source": [
        "## Statewise Figures"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 716
        },
        "id": "tWoRaO0EvKor",
        "outputId": "848761d6-b730-4184-d883-71714b1588b8"
      },
      "source": [
        "covid19_IN_recent = covid19_IN_recent.sort_values(by=['Confirmed'], ascending= False)\n",
        "plt.figure(figsize =(14,8), dpi=100)\n",
        "plt.barh(covid19_IN_recent['State/UnionTerritory'][:10], covid19_IN_recent['Confirmed'][:10],\n",
        "         align='center', color='#D32F2F')\n",
        "plt.xticks([0,1000000,2000000,3000000,4000000,5000000,6000000],['0','1M','2M','3M','4M','5M','6M'])\n",
        "plt.xlabel('Number of Confirmed Cases', size = 14)\n",
        "plt.title('Top 10 States with maximum confirmed cases', size = 18)\n",
        "plt.show()"
      ],
      "execution_count": 145,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1400x800 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lvR6yW5-xJJH"
      },
      "source": [
        "On inspecting the visualization, we can see that Maharashtra has more than 6 million confirmed cases, highest in India. Wheras Kerala and Karnataka has around 3 million reported cases, Tamil Nadu has approximately 2.5 million reported cases while the rest have less than 2 million reported cases."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 716
        },
        "id": "-tk1N9-pyCY-",
        "outputId": "29d3ee60-7013-4c1d-ae45-3bcd06302a94"
      },
      "source": [
        "covid19_IN_recent = covid19_IN_recent.sort_values(by=['Deaths'], ascending= False)\n",
        "plt.figure(figsize =(14,8), dpi=100)\n",
        "plt.barh(covid19_IN_recent['State/UnionTerritory'][:10], covid19_IN_recent['Deaths'][:10],\n",
        "         align='center', color='#D32F2F')\n",
        "plt.xticks([0,20000,40000,60000,80000,100000,120000],['0','20K','40K','60K','80K','10K','12K'])\n",
        "plt.xlabel('Number of Deaths', size = 14)\n",
        "plt.title('Top 10 States with maximum deaths', size = 18)\n",
        "plt.show()"
      ],
      "execution_count": 146,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1400x800 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "geI9q09m1Klh",
        "outputId": "7d214c60-e489-4be2-90b7-5de8f1093ad0"
      },
      "source": [
        "covid19_IN_recent['Deaths'].sum()"
      ],
      "execution_count": 147,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "424773"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 147
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aOQIFVAtyc-6"
      },
      "source": [
        "India has total of 424,773 deaths due to Covid-19 in which Maharashtra accounts for more than 1.2 million deaths, Karnataka and Tamil Nadu has around 400,000 deaths, Delhi has less 300,000 deaths and rest have comparatively less deaths."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 716
        },
        "id": "6KIp5gtC1w1z",
        "outputId": "a0fb530a-bfca-4ba3-fb13-d82bb28e3745"
      },
      "source": [
        "covid19_IN_recent = covid19_IN_recent.sort_values(by=['Cured'], ascending= False)\n",
        "plt.figure(figsize =(14,8), dpi=100)\n",
        "plt.barh(covid19_IN_recent['State/UnionTerritory'][:10], covid19_IN_recent['Cured'][:10],\n",
        "         align='center', color='#388E3C')\n",
        "plt.xticks([0,1000000,2000000,3000000,4000000,5000000,6000000],['0','1M','2M','3M','4M','5M','6M'])\n",
        "plt.xlabel('Number of Recoveries', size = 14)\n",
        "plt.title('Top 10 States with maximum recoveries', size = 18)\n",
        "plt.show()"
      ],
      "execution_count": 148,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1400x800 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "djvytu1A2Aph"
      },
      "source": [
        "The states which contributes most to the number of cases in India also have most number of recoveries which means recovering from Covid-19 has very high chances if proper treatment is followed.\n",
        "\n",
        "But this doesn't gives us idea about the state of healthcare in different states, so to get an idea we should check Death rate(Death per confirmed case) of states."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bjQfSy8QEEl9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "e3e307fb-12aa-43e5-d077-ec6a921b08ff"
      },
      "source": [
        "covid19_IN_recent['Death Rate'] = (covid19_IN_recent['Deaths'] / covid19_IN_recent['Confirmed'])\n",
        "covid19_IN_recent.isna().sum()\n",
        "covid19_IN_recent"
      ],
      "execution_count": 149,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Sno</th>\n",
              "      <th>Date</th>\n",
              "      <th>Time</th>\n",
              "      <th>State/UnionTerritory</th>\n",
              "      <th>ConfirmedIndianNational</th>\n",
              "      <th>ConfirmedForeignNational</th>\n",
              "      <th>Cured</th>\n",
              "      <th>Deaths</th>\n",
              "      <th>Confirmed</th>\n",
              "      <th>Death Rate</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>17770</th>\n",
              "      <td>17771</td>\n",
              "      <td>2021-08-02</td>\n",
              "      <td>8:00 AM</td>\n",
              "      <td>Maharashtra</td>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
              "      <td>6094896</td>\n",
              "      <td>132948</td>\n",
              "      <td>6310194</td>\n",
              "      <td>0.021069</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17766</th>\n",
              "      <td>17767</td>\n",
              "      <td>2021-08-02</td>\n",
              "      <td>8:00 AM</td>\n",
              "      <td>Kerala</td>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
              "      <td>3226761</td>\n",
              "      <td>16837</td>\n",
              "      <td>3411489</td>\n",
              "      <td>0.004935</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17765</th>\n",
              "      <td>17766</td>\n",
              "      <td>2021-08-02</td>\n",
              "      <td>8:00 AM</td>\n",
              "      <td>Karnataka</td>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
              "      <td>2846244</td>\n",
              "      <td>36587</td>\n",
              "      <td>2906999</td>\n",
              "      <td>0.012586</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17780</th>\n",
              "      <td>17781</td>\n",
              "      <td>2021-08-02</td>\n",
              "      <td>8:00 AM</td>\n",
              "      <td>Tamil Nadu</td>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
              "      <td>2506961</td>\n",
              "      <td>34102</td>\n",
              "      <td>2561587</td>\n",
              "      <td>0.013313</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17751</th>\n",
              "      <td>17752</td>\n",
              "      <td>2021-08-02</td>\n",
              "      <td>8:00 AM</td>\n",
              "      <td>Andhra Pradesh</td>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
              "      <td>1934048</td>\n",
              "      <td>13395</td>\n",
              "      <td>1968462</td>\n",
              "      <td>0.006805</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17784</th>\n",
              "      <td>17785</td>\n",
              "      <td>2021-08-02</td>\n",
              "      <td>8:00 AM</td>\n",
              "      <td>Uttar Pradesh</td>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
              "      <td>1685049</td>\n",
              "      <td>22763</td>\n",
              "      <td>1708476</td>\n",
              "      <td>0.013324</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17785</th>\n",
              "      <td>17786</td>\n",
              "      <td>2021-08-02</td>\n",
              "      <td>8:00 AM</td>\n",
              "      <td>West Bengal</td>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
              "      <td>1499597</td>\n",
              "      <td>18149</td>\n",
              "      <td>1528720</td>\n",
              "      <td>0.011872</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17758</th>\n",
              "      <td>17759</td>\n",
              "      <td>2021-08-02</td>\n",
              "      <td>8:00 AM</td>\n",
              "      <td>Delhi</td>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
              "      <td>1410714</td>\n",
              "      <td>25054</td>\n",
              "      <td>1436350</td>\n",
              "      <td>0.017443</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17756</th>\n",
              "      <td>17757</td>\n",
              "      <td>2021-08-02</td>\n",
              "      <td>8:00 AM</td>\n",
              "      <td>Chhattisgarh</td>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
              "      <td>986778</td>\n",
              "      <td>13525</td>\n",
              "      <td>1002222</td>\n",
              "      <td>0.013495</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17775</th>\n",
              "      <td>17776</td>\n",
              "      <td>2021-08-02</td>\n",
              "      <td>8:00 AM</td>\n",
              "      <td>Odisha</td>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
              "      <td>958601</td>\n",
              "      <td>5966</td>\n",
              "      <td>978705</td>\n",
              "      <td>0.006096</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17778</th>\n",
              "      <td>17779</td>\n",
              "      <td>2021-08-02</td>\n",
              "      <td>8:00 AM</td>\n",
              "      <td>Rajasthan</td>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
              "      <td>944484</td>\n",
              "      <td>8954</td>\n",
              "      <td>953688</td>\n",
              "      <td>0.009389</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17760</th>\n",
              "      <td>17761</td>\n",
              "      <td>2021-08-02</td>\n",
              "      <td>8:00 AM</td>\n",
              "      <td>Gujarat</td>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
              "      <td>814570</td>\n",
              "      <td>10076</td>\n",
              "      <td>824900</td>\n",
              "      <td>0.012215</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17769</th>\n",
              "      <td>17770</td>\n",
              "      <td>2021-08-02</td>\n",
              "      <td>8:00 AM</td>\n",
              "      <td>Madhya Pradesh</td>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
              "      <td>781207</td>\n",
              "      <td>10513</td>\n",
              "      <td>791845</td>\n",
              "      <td>0.013277</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17761</th>\n",
              "      <td>17762</td>\n",
              "      <td>2021-08-02</td>\n",
              "      <td>8:00 AM</td>\n",
              "      <td>Haryana</td>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
              "      <td>759589</td>\n",
              "      <td>9638</td>\n",
              "      <td>769942</td>\n",
              "      <td>0.012518</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17754</th>\n",
              "      <td>17755</td>\n",
              "      <td>2021-08-02</td>\n",
              "      <td>8:00 AM</td>\n",
              "      <td>Bihar</td>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
              "      <td>714801</td>\n",
              "      <td>9644</td>\n",
              "      <td>724880</td>\n",
              "      <td>0.013304</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17781</th>\n",
              "      <td>17782</td>\n",
              "      <td>2021-08-02</td>\n",
              "      <td>8:00 AM</td>\n",
              "      <td>Telangana</td>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
              "      <td>632728</td>\n",
              "      <td>3805</td>\n",
              "      <td>645406</td>\n",
              "      <td>0.005896</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17777</th>\n",
              "      <td>17778</td>\n",
              "      <td>2021-08-02</td>\n",
              "      <td>8:00 AM</td>\n",
              "      <td>Punjab</td>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
              "      <td>582332</td>\n",
              "      <td>16294</td>\n",
              "      <td>599130</td>\n",
              "      <td>0.027196</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17753</th>\n",
              "      <td>17754</td>\n",
              "      <td>2021-08-02</td>\n",
              "      <td>8:00 AM</td>\n",
              "      <td>Assam</td>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
              "      <td>549065</td>\n",
              "      <td>5275</td>\n",
              "      <td>566982</td>\n",
              "      <td>0.009304</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17764</th>\n",
              "      <td>17765</td>\n",
              "      <td>2021-08-02</td>\n",
              "      <td>8:00 AM</td>\n",
              "      <td>Jharkhand</td>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
              "      <td>341817</td>\n",
              "      <td>5129</td>\n",
              "      <td>347200</td>\n",
              "      <td>0.014772</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17783</th>\n",
              "      <td>17784</td>\n",
              "      <td>2021-08-02</td>\n",
              "      <td>8:00 AM</td>\n",
              "      <td>Uttarakhand</td>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
              "      <td>334190</td>\n",
              "      <td>7362</td>\n",
              "      <td>342161</td>\n",
              "      <td>0.021516</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17763</th>\n",
              "      <td>17764</td>\n",
              "      <td>2021-08-02</td>\n",
              "      <td>8:00 AM</td>\n",
              "      <td>Jammu and Kashmir</td>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
              "      <td>316017</td>\n",
              "      <td>4379</td>\n",
              "      <td>321607</td>\n",
              "      <td>0.013616</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17762</th>\n",
              "      <td>17763</td>\n",
              "      <td>2021-08-02</td>\n",
              "      <td>8:00 AM</td>\n",
              "      <td>Himachal Pradesh</td>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
              "      <td>201411</td>\n",
              "      <td>3521</td>\n",
              "      <td>206161</td>\n",
              "      <td>0.017079</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17759</th>\n",
              "      <td>17760</td>\n",
              "      <td>2021-08-02</td>\n",
              "      <td>8:00 AM</td>\n",
              "      <td>Goa</td>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
              "      <td>167046</td>\n",
              "      <td>3148</td>\n",
              "      <td>171205</td>\n",
              "      <td>0.018387</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17776</th>\n",
              "      <td>17777</td>\n",
              "      <td>2021-08-02</td>\n",
              "      <td>8:00 AM</td>\n",
              "      <td>Puducherry</td>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
              "      <td>118228</td>\n",
              "      <td>1795</td>\n",
              "      <td>121005</td>\n",
              "      <td>0.014834</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17771</th>\n",
              "      <td>17772</td>\n",
              "      <td>2021-08-02</td>\n",
              "      <td>8:00 AM</td>\n",
              "      <td>Manipur</td>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
              "      <td>87360</td>\n",
              "      <td>1566</td>\n",
              "      <td>99331</td>\n",
              "      <td>0.015765</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17782</th>\n",
              "      <td>17783</td>\n",
              "      <td>2021-08-02</td>\n",
              "      <td>8:00 AM</td>\n",
              "      <td>Tripura</td>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
              "      <td>74875</td>\n",
              "      <td>755</td>\n",
              "      <td>78722</td>\n",
              "      <td>0.009591</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17755</th>\n",
              "      <td>17756</td>\n",
              "      <td>2021-08-02</td>\n",
              "      <td>8:00 AM</td>\n",
              "      <td>Chandigarh</td>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
              "      <td>61113</td>\n",
              "      <td>811</td>\n",
              "      <td>61954</td>\n",
              "      <td>0.013090</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17772</th>\n",
              "      <td>17773</td>\n",
              "      <td>2021-08-02</td>\n",
              "      <td>8:00 AM</td>\n",
              "      <td>Meghalaya</td>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
              "      <td>58450</td>\n",
              "      <td>1096</td>\n",
              "      <td>65589</td>\n",
              "      <td>0.016710</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17752</th>\n",
              "      <td>17753</td>\n",
              "      <td>2021-08-02</td>\n",
              "      <td>8:00 AM</td>\n",
              "      <td>Arunachal Pradesh</td>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
              "      <td>44354</td>\n",
              "      <td>231</td>\n",
              "      <td>48260</td>\n",
              "      <td>0.004787</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17773</th>\n",
              "      <td>17774</td>\n",
              "      <td>2021-08-02</td>\n",
              "      <td>8:00 AM</td>\n",
              "      <td>Mizoram</td>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
              "      <td>27024</td>\n",
              "      <td>150</td>\n",
              "      <td>39363</td>\n",
              "      <td>0.003811</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17774</th>\n",
              "      <td>17775</td>\n",
              "      <td>2021-08-02</td>\n",
              "      <td>8:00 AM</td>\n",
              "      <td>Nagaland</td>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
              "      <td>26031</td>\n",
              "      <td>570</td>\n",
              "      <td>27945</td>\n",
              "      <td>0.020397</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17779</th>\n",
              "      <td>17780</td>\n",
              "      <td>2021-08-02</td>\n",
              "      <td>8:00 AM</td>\n",
              "      <td>Sikkim</td>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
              "      <td>22955</td>\n",
              "      <td>345</td>\n",
              "      <td>26754</td>\n",
              "      <td>0.012895</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17767</th>\n",
              "      <td>17768</td>\n",
              "      <td>2021-08-02</td>\n",
              "      <td>8:00 AM</td>\n",
              "      <td>Ladakh</td>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
              "      <td>20076</td>\n",
              "      <td>207</td>\n",
              "      <td>20340</td>\n",
              "      <td>0.010177</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17757</th>\n",
              "      <td>17758</td>\n",
              "      <td>2021-08-02</td>\n",
              "      <td>8:00 AM</td>\n",
              "      <td>Dadra and Nagar Haveli and Daman and Diu</td>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
              "      <td>10622</td>\n",
              "      <td>4</td>\n",
              "      <td>10650</td>\n",
              "      <td>0.000376</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17768</th>\n",
              "      <td>17769</td>\n",
              "      <td>2021-08-02</td>\n",
              "      <td>8:00 AM</td>\n",
              "      <td>Lakshadweep</td>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
              "      <td>10070</td>\n",
              "      <td>50</td>\n",
              "      <td>10195</td>\n",
              "      <td>0.004904</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17750</th>\n",
              "      <td>17751</td>\n",
              "      <td>2021-08-02</td>\n",
              "      <td>8:00 AM</td>\n",
              "      <td>Andaman and Nicobar Islands</td>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
              "      <td>7403</td>\n",
              "      <td>129</td>\n",
              "      <td>7539</td>\n",
              "      <td>0.017111</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         Sno        Date     Time  ...  Deaths Confirmed Death Rate\n",
              "17770  17771  2021-08-02  8:00 AM  ...  132948   6310194   0.021069\n",
              "17766  17767  2021-08-02  8:00 AM  ...   16837   3411489   0.004935\n",
              "17765  17766  2021-08-02  8:00 AM  ...   36587   2906999   0.012586\n",
              "17780  17781  2021-08-02  8:00 AM  ...   34102   2561587   0.013313\n",
              "17751  17752  2021-08-02  8:00 AM  ...   13395   1968462   0.006805\n",
              "17784  17785  2021-08-02  8:00 AM  ...   22763   1708476   0.013324\n",
              "17785  17786  2021-08-02  8:00 AM  ...   18149   1528720   0.011872\n",
              "17758  17759  2021-08-02  8:00 AM  ...   25054   1436350   0.017443\n",
              "17756  17757  2021-08-02  8:00 AM  ...   13525   1002222   0.013495\n",
              "17775  17776  2021-08-02  8:00 AM  ...    5966    978705   0.006096\n",
              "17778  17779  2021-08-02  8:00 AM  ...    8954    953688   0.009389\n",
              "17760  17761  2021-08-02  8:00 AM  ...   10076    824900   0.012215\n",
              "17769  17770  2021-08-02  8:00 AM  ...   10513    791845   0.013277\n",
              "17761  17762  2021-08-02  8:00 AM  ...    9638    769942   0.012518\n",
              "17754  17755  2021-08-02  8:00 AM  ...    9644    724880   0.013304\n",
              "17781  17782  2021-08-02  8:00 AM  ...    3805    645406   0.005896\n",
              "17777  17778  2021-08-02  8:00 AM  ...   16294    599130   0.027196\n",
              "17753  17754  2021-08-02  8:00 AM  ...    5275    566982   0.009304\n",
              "17764  17765  2021-08-02  8:00 AM  ...    5129    347200   0.014772\n",
              "17783  17784  2021-08-02  8:00 AM  ...    7362    342161   0.021516\n",
              "17763  17764  2021-08-02  8:00 AM  ...    4379    321607   0.013616\n",
              "17762  17763  2021-08-02  8:00 AM  ...    3521    206161   0.017079\n",
              "17759  17760  2021-08-02  8:00 AM  ...    3148    171205   0.018387\n",
              "17776  17777  2021-08-02  8:00 AM  ...    1795    121005   0.014834\n",
              "17771  17772  2021-08-02  8:00 AM  ...    1566     99331   0.015765\n",
              "17782  17783  2021-08-02  8:00 AM  ...     755     78722   0.009591\n",
              "17755  17756  2021-08-02  8:00 AM  ...     811     61954   0.013090\n",
              "17772  17773  2021-08-02  8:00 AM  ...    1096     65589   0.016710\n",
              "17752  17753  2021-08-02  8:00 AM  ...     231     48260   0.004787\n",
              "17773  17774  2021-08-02  8:00 AM  ...     150     39363   0.003811\n",
              "17774  17775  2021-08-02  8:00 AM  ...     570     27945   0.020397\n",
              "17779  17780  2021-08-02  8:00 AM  ...     345     26754   0.012895\n",
              "17767  17768  2021-08-02  8:00 AM  ...     207     20340   0.010177\n",
              "17757  17758  2021-08-02  8:00 AM  ...       4     10650   0.000376\n",
              "17768  17769  2021-08-02  8:00 AM  ...      50     10195   0.004904\n",
              "17750  17751  2021-08-02  8:00 AM  ...     129      7539   0.017111\n",
              "\n",
              "[36 rows x 10 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 149
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "lfLnEECfncA7",
        "outputId": "24275fdf-bb25-41c1-ccda-178a3e81ccf6"
      },
      "source": [
        "covid19_IN_recent['Recovery Rate'] = (covid19_IN_recent['Cured'] / covid19_IN_recent['Confirmed'])\n",
        "#covid19_IN_recent.isna().sum()\n",
        "covid19_IN_recent"
      ],
      "execution_count": 150,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Sno</th>\n",
              "      <th>Date</th>\n",
              "      <th>Time</th>\n",
              "      <th>State/UnionTerritory</th>\n",
              "      <th>ConfirmedIndianNational</th>\n",
              "      <th>ConfirmedForeignNational</th>\n",
              "      <th>Cured</th>\n",
              "      <th>Deaths</th>\n",
              "      <th>Confirmed</th>\n",
              "      <th>Death Rate</th>\n",
              "      <th>Recovery Rate</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>17770</th>\n",
              "      <td>17771</td>\n",
              "      <td>2021-08-02</td>\n",
              "      <td>8:00 AM</td>\n",
              "      <td>Maharashtra</td>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
              "      <td>6094896</td>\n",
              "      <td>132948</td>\n",
              "      <td>6310194</td>\n",
              "      <td>0.021069</td>\n",
              "      <td>0.965881</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17766</th>\n",
              "      <td>17767</td>\n",
              "      <td>2021-08-02</td>\n",
              "      <td>8:00 AM</td>\n",
              "      <td>Kerala</td>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
              "      <td>3226761</td>\n",
              "      <td>16837</td>\n",
              "      <td>3411489</td>\n",
              "      <td>0.004935</td>\n",
              "      <td>0.945851</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17765</th>\n",
              "      <td>17766</td>\n",
              "      <td>2021-08-02</td>\n",
              "      <td>8:00 AM</td>\n",
              "      <td>Karnataka</td>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
              "      <td>2846244</td>\n",
              "      <td>36587</td>\n",
              "      <td>2906999</td>\n",
              "      <td>0.012586</td>\n",
              "      <td>0.979100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17780</th>\n",
              "      <td>17781</td>\n",
              "      <td>2021-08-02</td>\n",
              "      <td>8:00 AM</td>\n",
              "      <td>Tamil Nadu</td>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
              "      <td>2506961</td>\n",
              "      <td>34102</td>\n",
              "      <td>2561587</td>\n",
              "      <td>0.013313</td>\n",
              "      <td>0.978675</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17751</th>\n",
              "      <td>17752</td>\n",
              "      <td>2021-08-02</td>\n",
              "      <td>8:00 AM</td>\n",
              "      <td>Andhra Pradesh</td>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
              "      <td>1934048</td>\n",
              "      <td>13395</td>\n",
              "      <td>1968462</td>\n",
              "      <td>0.006805</td>\n",
              "      <td>0.982517</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17784</th>\n",
              "      <td>17785</td>\n",
              "      <td>2021-08-02</td>\n",
              "      <td>8:00 AM</td>\n",
              "      <td>Uttar Pradesh</td>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
              "      <td>1685049</td>\n",
              "      <td>22763</td>\n",
              "      <td>1708476</td>\n",
              "      <td>0.013324</td>\n",
              "      <td>0.986288</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17785</th>\n",
              "      <td>17786</td>\n",
              "      <td>2021-08-02</td>\n",
              "      <td>8:00 AM</td>\n",
              "      <td>West Bengal</td>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
              "      <td>1499597</td>\n",
              "      <td>18149</td>\n",
              "      <td>1528720</td>\n",
              "      <td>0.011872</td>\n",
              "      <td>0.980949</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17758</th>\n",
              "      <td>17759</td>\n",
              "      <td>2021-08-02</td>\n",
              "      <td>8:00 AM</td>\n",
              "      <td>Delhi</td>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
              "      <td>1410714</td>\n",
              "      <td>25054</td>\n",
              "      <td>1436350</td>\n",
              "      <td>0.017443</td>\n",
              "      <td>0.982152</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17756</th>\n",
              "      <td>17757</td>\n",
              "      <td>2021-08-02</td>\n",
              "      <td>8:00 AM</td>\n",
              "      <td>Chhattisgarh</td>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
              "      <td>986778</td>\n",
              "      <td>13525</td>\n",
              "      <td>1002222</td>\n",
              "      <td>0.013495</td>\n",
              "      <td>0.984590</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17775</th>\n",
              "      <td>17776</td>\n",
              "      <td>2021-08-02</td>\n",
              "      <td>8:00 AM</td>\n",
              "      <td>Odisha</td>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
              "      <td>958601</td>\n",
              "      <td>5966</td>\n",
              "      <td>978705</td>\n",
              "      <td>0.006096</td>\n",
              "      <td>0.979459</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17778</th>\n",
              "      <td>17779</td>\n",
              "      <td>2021-08-02</td>\n",
              "      <td>8:00 AM</td>\n",
              "      <td>Rajasthan</td>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
              "      <td>944484</td>\n",
              "      <td>8954</td>\n",
              "      <td>953688</td>\n",
              "      <td>0.009389</td>\n",
              "      <td>0.990349</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17760</th>\n",
              "      <td>17761</td>\n",
              "      <td>2021-08-02</td>\n",
              "      <td>8:00 AM</td>\n",
              "      <td>Gujarat</td>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
              "      <td>814570</td>\n",
              "      <td>10076</td>\n",
              "      <td>824900</td>\n",
              "      <td>0.012215</td>\n",
              "      <td>0.987477</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17769</th>\n",
              "      <td>17770</td>\n",
              "      <td>2021-08-02</td>\n",
              "      <td>8:00 AM</td>\n",
              "      <td>Madhya Pradesh</td>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
              "      <td>781207</td>\n",
              "      <td>10513</td>\n",
              "      <td>791845</td>\n",
              "      <td>0.013277</td>\n",
              "      <td>0.986566</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17761</th>\n",
              "      <td>17762</td>\n",
              "      <td>2021-08-02</td>\n",
              "      <td>8:00 AM</td>\n",
              "      <td>Haryana</td>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
              "      <td>759589</td>\n",
              "      <td>9638</td>\n",
              "      <td>769942</td>\n",
              "      <td>0.012518</td>\n",
              "      <td>0.986554</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17754</th>\n",
              "      <td>17755</td>\n",
              "      <td>2021-08-02</td>\n",
              "      <td>8:00 AM</td>\n",
              "      <td>Bihar</td>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
              "      <td>714801</td>\n",
              "      <td>9644</td>\n",
              "      <td>724880</td>\n",
              "      <td>0.013304</td>\n",
              "      <td>0.986096</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17781</th>\n",
              "      <td>17782</td>\n",
              "      <td>2021-08-02</td>\n",
              "      <td>8:00 AM</td>\n",
              "      <td>Telangana</td>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
              "      <td>632728</td>\n",
              "      <td>3805</td>\n",
              "      <td>645406</td>\n",
              "      <td>0.005896</td>\n",
              "      <td>0.980357</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17777</th>\n",
              "      <td>17778</td>\n",
              "      <td>2021-08-02</td>\n",
              "      <td>8:00 AM</td>\n",
              "      <td>Punjab</td>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
              "      <td>582332</td>\n",
              "      <td>16294</td>\n",
              "      <td>599130</td>\n",
              "      <td>0.027196</td>\n",
              "      <td>0.971963</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17753</th>\n",
              "      <td>17754</td>\n",
              "      <td>2021-08-02</td>\n",
              "      <td>8:00 AM</td>\n",
              "      <td>Assam</td>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
              "      <td>549065</td>\n",
              "      <td>5275</td>\n",
              "      <td>566982</td>\n",
              "      <td>0.009304</td>\n",
              "      <td>0.968399</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17764</th>\n",
              "      <td>17765</td>\n",
              "      <td>2021-08-02</td>\n",
              "      <td>8:00 AM</td>\n",
              "      <td>Jharkhand</td>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
              "      <td>341817</td>\n",
              "      <td>5129</td>\n",
              "      <td>347200</td>\n",
              "      <td>0.014772</td>\n",
              "      <td>0.984496</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17783</th>\n",
              "      <td>17784</td>\n",
              "      <td>2021-08-02</td>\n",
              "      <td>8:00 AM</td>\n",
              "      <td>Uttarakhand</td>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
              "      <td>334190</td>\n",
              "      <td>7362</td>\n",
              "      <td>342161</td>\n",
              "      <td>0.021516</td>\n",
              "      <td>0.976704</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17763</th>\n",
              "      <td>17764</td>\n",
              "      <td>2021-08-02</td>\n",
              "      <td>8:00 AM</td>\n",
              "      <td>Jammu and Kashmir</td>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
              "      <td>316017</td>\n",
              "      <td>4379</td>\n",
              "      <td>321607</td>\n",
              "      <td>0.013616</td>\n",
              "      <td>0.982619</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17762</th>\n",
              "      <td>17763</td>\n",
              "      <td>2021-08-02</td>\n",
              "      <td>8:00 AM</td>\n",
              "      <td>Himachal Pradesh</td>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
              "      <td>201411</td>\n",
              "      <td>3521</td>\n",
              "      <td>206161</td>\n",
              "      <td>0.017079</td>\n",
              "      <td>0.976960</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17759</th>\n",
              "      <td>17760</td>\n",
              "      <td>2021-08-02</td>\n",
              "      <td>8:00 AM</td>\n",
              "      <td>Goa</td>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
              "      <td>167046</td>\n",
              "      <td>3148</td>\n",
              "      <td>171205</td>\n",
              "      <td>0.018387</td>\n",
              "      <td>0.975707</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17776</th>\n",
              "      <td>17777</td>\n",
              "      <td>2021-08-02</td>\n",
              "      <td>8:00 AM</td>\n",
              "      <td>Puducherry</td>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
              "      <td>118228</td>\n",
              "      <td>1795</td>\n",
              "      <td>121005</td>\n",
              "      <td>0.014834</td>\n",
              "      <td>0.977051</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17771</th>\n",
              "      <td>17772</td>\n",
              "      <td>2021-08-02</td>\n",
              "      <td>8:00 AM</td>\n",
              "      <td>Manipur</td>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
              "      <td>87360</td>\n",
              "      <td>1566</td>\n",
              "      <td>99331</td>\n",
              "      <td>0.015765</td>\n",
              "      <td>0.879484</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17782</th>\n",
              "      <td>17783</td>\n",
              "      <td>2021-08-02</td>\n",
              "      <td>8:00 AM</td>\n",
              "      <td>Tripura</td>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
              "      <td>74875</td>\n",
              "      <td>755</td>\n",
              "      <td>78722</td>\n",
              "      <td>0.009591</td>\n",
              "      <td>0.951132</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17755</th>\n",
              "      <td>17756</td>\n",
              "      <td>2021-08-02</td>\n",
              "      <td>8:00 AM</td>\n",
              "      <td>Chandigarh</td>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
              "      <td>61113</td>\n",
              "      <td>811</td>\n",
              "      <td>61954</td>\n",
              "      <td>0.013090</td>\n",
              "      <td>0.986425</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17772</th>\n",
              "      <td>17773</td>\n",
              "      <td>2021-08-02</td>\n",
              "      <td>8:00 AM</td>\n",
              "      <td>Meghalaya</td>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
              "      <td>58450</td>\n",
              "      <td>1096</td>\n",
              "      <td>65589</td>\n",
              "      <td>0.016710</td>\n",
              "      <td>0.891156</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17752</th>\n",
              "      <td>17753</td>\n",
              "      <td>2021-08-02</td>\n",
              "      <td>8:00 AM</td>\n",
              "      <td>Arunachal Pradesh</td>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
              "      <td>44354</td>\n",
              "      <td>231</td>\n",
              "      <td>48260</td>\n",
              "      <td>0.004787</td>\n",
              "      <td>0.919063</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17773</th>\n",
              "      <td>17774</td>\n",
              "      <td>2021-08-02</td>\n",
              "      <td>8:00 AM</td>\n",
              "      <td>Mizoram</td>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
              "      <td>27024</td>\n",
              "      <td>150</td>\n",
              "      <td>39363</td>\n",
              "      <td>0.003811</td>\n",
              "      <td>0.686533</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17774</th>\n",
              "      <td>17775</td>\n",
              "      <td>2021-08-02</td>\n",
              "      <td>8:00 AM</td>\n",
              "      <td>Nagaland</td>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
              "      <td>26031</td>\n",
              "      <td>570</td>\n",
              "      <td>27945</td>\n",
              "      <td>0.020397</td>\n",
              "      <td>0.931508</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17779</th>\n",
              "      <td>17780</td>\n",
              "      <td>2021-08-02</td>\n",
              "      <td>8:00 AM</td>\n",
              "      <td>Sikkim</td>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
              "      <td>22955</td>\n",
              "      <td>345</td>\n",
              "      <td>26754</td>\n",
              "      <td>0.012895</td>\n",
              "      <td>0.858003</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17767</th>\n",
              "      <td>17768</td>\n",
              "      <td>2021-08-02</td>\n",
              "      <td>8:00 AM</td>\n",
              "      <td>Ladakh</td>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
              "      <td>20076</td>\n",
              "      <td>207</td>\n",
              "      <td>20340</td>\n",
              "      <td>0.010177</td>\n",
              "      <td>0.987021</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17757</th>\n",
              "      <td>17758</td>\n",
              "      <td>2021-08-02</td>\n",
              "      <td>8:00 AM</td>\n",
              "      <td>Dadra and Nagar Haveli and Daman and Diu</td>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
              "      <td>10622</td>\n",
              "      <td>4</td>\n",
              "      <td>10650</td>\n",
              "      <td>0.000376</td>\n",
              "      <td>0.997371</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17768</th>\n",
              "      <td>17769</td>\n",
              "      <td>2021-08-02</td>\n",
              "      <td>8:00 AM</td>\n",
              "      <td>Lakshadweep</td>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
              "      <td>10070</td>\n",
              "      <td>50</td>\n",
              "      <td>10195</td>\n",
              "      <td>0.004904</td>\n",
              "      <td>0.987739</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17750</th>\n",
              "      <td>17751</td>\n",
              "      <td>2021-08-02</td>\n",
              "      <td>8:00 AM</td>\n",
              "      <td>Andaman and Nicobar Islands</td>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
              "      <td>7403</td>\n",
              "      <td>129</td>\n",
              "      <td>7539</td>\n",
              "      <td>0.017111</td>\n",
              "      <td>0.981960</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         Sno        Date     Time  ... Confirmed Death Rate Recovery Rate\n",
              "17770  17771  2021-08-02  8:00 AM  ...   6310194   0.021069      0.965881\n",
              "17766  17767  2021-08-02  8:00 AM  ...   3411489   0.004935      0.945851\n",
              "17765  17766  2021-08-02  8:00 AM  ...   2906999   0.012586      0.979100\n",
              "17780  17781  2021-08-02  8:00 AM  ...   2561587   0.013313      0.978675\n",
              "17751  17752  2021-08-02  8:00 AM  ...   1968462   0.006805      0.982517\n",
              "17784  17785  2021-08-02  8:00 AM  ...   1708476   0.013324      0.986288\n",
              "17785  17786  2021-08-02  8:00 AM  ...   1528720   0.011872      0.980949\n",
              "17758  17759  2021-08-02  8:00 AM  ...   1436350   0.017443      0.982152\n",
              "17756  17757  2021-08-02  8:00 AM  ...   1002222   0.013495      0.984590\n",
              "17775  17776  2021-08-02  8:00 AM  ...    978705   0.006096      0.979459\n",
              "17778  17779  2021-08-02  8:00 AM  ...    953688   0.009389      0.990349\n",
              "17760  17761  2021-08-02  8:00 AM  ...    824900   0.012215      0.987477\n",
              "17769  17770  2021-08-02  8:00 AM  ...    791845   0.013277      0.986566\n",
              "17761  17762  2021-08-02  8:00 AM  ...    769942   0.012518      0.986554\n",
              "17754  17755  2021-08-02  8:00 AM  ...    724880   0.013304      0.986096\n",
              "17781  17782  2021-08-02  8:00 AM  ...    645406   0.005896      0.980357\n",
              "17777  17778  2021-08-02  8:00 AM  ...    599130   0.027196      0.971963\n",
              "17753  17754  2021-08-02  8:00 AM  ...    566982   0.009304      0.968399\n",
              "17764  17765  2021-08-02  8:00 AM  ...    347200   0.014772      0.984496\n",
              "17783  17784  2021-08-02  8:00 AM  ...    342161   0.021516      0.976704\n",
              "17763  17764  2021-08-02  8:00 AM  ...    321607   0.013616      0.982619\n",
              "17762  17763  2021-08-02  8:00 AM  ...    206161   0.017079      0.976960\n",
              "17759  17760  2021-08-02  8:00 AM  ...    171205   0.018387      0.975707\n",
              "17776  17777  2021-08-02  8:00 AM  ...    121005   0.014834      0.977051\n",
              "17771  17772  2021-08-02  8:00 AM  ...     99331   0.015765      0.879484\n",
              "17782  17783  2021-08-02  8:00 AM  ...     78722   0.009591      0.951132\n",
              "17755  17756  2021-08-02  8:00 AM  ...     61954   0.013090      0.986425\n",
              "17772  17773  2021-08-02  8:00 AM  ...     65589   0.016710      0.891156\n",
              "17752  17753  2021-08-02  8:00 AM  ...     48260   0.004787      0.919063\n",
              "17773  17774  2021-08-02  8:00 AM  ...     39363   0.003811      0.686533\n",
              "17774  17775  2021-08-02  8:00 AM  ...     27945   0.020397      0.931508\n",
              "17779  17780  2021-08-02  8:00 AM  ...     26754   0.012895      0.858003\n",
              "17767  17768  2021-08-02  8:00 AM  ...     20340   0.010177      0.987021\n",
              "17757  17758  2021-08-02  8:00 AM  ...     10650   0.000376      0.997371\n",
              "17768  17769  2021-08-02  8:00 AM  ...     10195   0.004904      0.987739\n",
              "17750  17751  2021-08-02  8:00 AM  ...      7539   0.017111      0.981960\n",
              "\n",
              "[36 rows x 11 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 150
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 716
        },
        "id": "RngOeOChEEom",
        "outputId": "3062f95d-0670-47c2-f1f9-b289fec27f5a"
      },
      "source": [
        "covid19_IN_recent = covid19_IN_recent.sort_values(by = ['Death Rate'], ascending=False)\n",
        "plt.figure(figsize =(14,8), dpi=100)\n",
        "plt.barh(covid19_IN_recent['State/UnionTerritory'][:10], covid19_IN_recent['Death Rate'][:10],\n",
        "         align='center', color='#D32F2F')\n",
        "plt.xlabel('Death Rate', size = 14)\n",
        "plt.title('Top 10 States with maximum Death Rate', size = 18)\n",
        "plt.show()"
      ],
      "execution_count": 151,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1400x800 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 575
        },
        "id": "1wwvlWGbnAIx",
        "outputId": "ec756fe8-8446-4136-bc48-f9625501f68e"
      },
      "source": [
        "covid19_IN_recent = covid19_IN_recent.sort_values(by = ['Recovery Rate'], ascending = False)\n",
        "plt.figure(figsize=(12,8), dpi=80)\n",
        "plt.barh(covid19_IN_recent['State/UnionTerritory'][:10], covid19_IN_recent['Recovery Rate'][:10], \n",
        "        align='center',color='#388E3C')\n",
        "plt.xlabel('Recovery Rate', size = 14)\n",
        "plt.xlim(0.985,1)\n",
        "plt.title('Top 10 States with maximum Recovery Rate', size = 18)\n",
        "plt.show()"
      ],
      "execution_count": 152,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 960x640 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "L-hu4EWgFDZQ"
      },
      "source": [
        "After plotting this graph we get to know about the state of healthcare in different states of India. From this we can infer that Punjab's healthcare is in very poor state, with death rate more than 0.025%. Dadra and Nagar Haveli and Daman and Diu has the best recovery rate all over India\n",
        "\n",
        "However these statistics do not portray a clear picture since India is a country of varying demographics and there's a huge difference between two states. We can try to analyze the spread of virus among a specific sample size in various states to get a clear picture of the spread of Covid-19."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-6LDkUR5G7OT"
      },
      "source": [
        "## Cases per 10 Million"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "lrko13V1IWWl",
        "outputId": "6f58ebb3-98d2-43bf-9865-467741e28dba"
      },
      "source": [
        "indian_states.head()"
      ],
      "execution_count": 153,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>State</th>\n",
              "      <th>Aadhaar assigned as of June 21</th>\n",
              "      <th>Area (per sq km)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Andaman and Nicobar Islands</td>\n",
              "      <td>386484</td>\n",
              "      <td>8249</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Andhra Pradesh</td>\n",
              "      <td>50987456</td>\n",
              "      <td>160205</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Arunachal Pradesh</td>\n",
              "      <td>1232971</td>\n",
              "      <td>83743</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Assam</td>\n",
              "      <td>22329699</td>\n",
              "      <td>78438</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Bihar</td>\n",
              "      <td>104581560</td>\n",
              "      <td>94163</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                         State  ...  Area (per sq km)\n",
              "0  Andaman and Nicobar Islands  ...              8249\n",
              "1               Andhra Pradesh  ...            160205\n",
              "2            Arunachal Pradesh  ...             83743\n",
              "3                        Assam  ...             78438\n",
              "4                        Bihar  ...             94163\n",
              "\n",
              "[5 rows x 3 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 153
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "4C7EMwUGJlwx",
        "outputId": "a60eeef2-1adf-4151-8ffa-bbc423ed97fc"
      },
      "source": [
        "indian_states.columns = ['State/UnionTerritory', 'Population', 'Area']\n",
        "indian_states.head()"
      ],
      "execution_count": 154,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>State/UnionTerritory</th>\n",
              "      <th>Population</th>\n",
              "      <th>Area</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Andaman and Nicobar Islands</td>\n",
              "      <td>386484</td>\n",
              "      <td>8249</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Andhra Pradesh</td>\n",
              "      <td>50987456</td>\n",
              "      <td>160205</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Arunachal Pradesh</td>\n",
              "      <td>1232971</td>\n",
              "      <td>83743</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Assam</td>\n",
              "      <td>22329699</td>\n",
              "      <td>78438</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Bihar</td>\n",
              "      <td>104581560</td>\n",
              "      <td>94163</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          State/UnionTerritory  Population    Area\n",
              "0  Andaman and Nicobar Islands      386484    8249\n",
              "1               Andhra Pradesh    50987456  160205\n",
              "2            Arunachal Pradesh     1232971   83743\n",
              "3                        Assam    22329699   78438\n",
              "4                        Bihar   104581560   94163"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 154
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KB_u4U7NIb_k"
      },
      "source": [
        "This dataset is collected by me from the UIDAI website(for number of Aadhar cards assigned) and wikipedia(for area of a state). The number of Aadhar cards assigned is a good estimate of the population of the state rather than using the population census which was last done in 2011 and the population has changed quite a lot after a that "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "l2EFflUGI9Im",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "0262f568-712c-4978-9078-240a8ca25083"
      },
      "source": [
        "covid19_IN_recent = covid19_IN_recent.drop(['Sno', 'Date', 'Time', 'ConfirmedIndianNational', 'ConfirmedForeignNational'], axis =1)\n",
        "covid19_IN_recent = covid19_IN_recent.sort_values(by=['State/UnionTerritory'])\n",
        "covid19_IN_recent"
      ],
      "execution_count": 155,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
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              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>State/UnionTerritory</th>\n",
              "      <th>Cured</th>\n",
              "      <th>Deaths</th>\n",
              "      <th>Confirmed</th>\n",
              "      <th>Death Rate</th>\n",
              "      <th>Recovery Rate</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>17750</th>\n",
              "      <td>Andaman and Nicobar Islands</td>\n",
              "      <td>7403</td>\n",
              "      <td>129</td>\n",
              "      <td>7539</td>\n",
              "      <td>0.017111</td>\n",
              "      <td>0.981960</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17751</th>\n",
              "      <td>Andhra Pradesh</td>\n",
              "      <td>1934048</td>\n",
              "      <td>13395</td>\n",
              "      <td>1968462</td>\n",
              "      <td>0.006805</td>\n",
              "      <td>0.982517</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17752</th>\n",
              "      <td>Arunachal Pradesh</td>\n",
              "      <td>44354</td>\n",
              "      <td>231</td>\n",
              "      <td>48260</td>\n",
              "      <td>0.004787</td>\n",
              "      <td>0.919063</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17753</th>\n",
              "      <td>Assam</td>\n",
              "      <td>549065</td>\n",
              "      <td>5275</td>\n",
              "      <td>566982</td>\n",
              "      <td>0.009304</td>\n",
              "      <td>0.968399</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17754</th>\n",
              "      <td>Bihar</td>\n",
              "      <td>714801</td>\n",
              "      <td>9644</td>\n",
              "      <td>724880</td>\n",
              "      <td>0.013304</td>\n",
              "      <td>0.986096</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17755</th>\n",
              "      <td>Chandigarh</td>\n",
              "      <td>61113</td>\n",
              "      <td>811</td>\n",
              "      <td>61954</td>\n",
              "      <td>0.013090</td>\n",
              "      <td>0.986425</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17756</th>\n",
              "      <td>Chhattisgarh</td>\n",
              "      <td>986778</td>\n",
              "      <td>13525</td>\n",
              "      <td>1002222</td>\n",
              "      <td>0.013495</td>\n",
              "      <td>0.984590</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17757</th>\n",
              "      <td>Dadra and Nagar Haveli and Daman and Diu</td>\n",
              "      <td>10622</td>\n",
              "      <td>4</td>\n",
              "      <td>10650</td>\n",
              "      <td>0.000376</td>\n",
              "      <td>0.997371</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17758</th>\n",
              "      <td>Delhi</td>\n",
              "      <td>1410714</td>\n",
              "      <td>25054</td>\n",
              "      <td>1436350</td>\n",
              "      <td>0.017443</td>\n",
              "      <td>0.982152</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17759</th>\n",
              "      <td>Goa</td>\n",
              "      <td>167046</td>\n",
              "      <td>3148</td>\n",
              "      <td>171205</td>\n",
              "      <td>0.018387</td>\n",
              "      <td>0.975707</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17760</th>\n",
              "      <td>Gujarat</td>\n",
              "      <td>814570</td>\n",
              "      <td>10076</td>\n",
              "      <td>824900</td>\n",
              "      <td>0.012215</td>\n",
              "      <td>0.987477</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17761</th>\n",
              "      <td>Haryana</td>\n",
              "      <td>759589</td>\n",
              "      <td>9638</td>\n",
              "      <td>769942</td>\n",
              "      <td>0.012518</td>\n",
              "      <td>0.986554</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17762</th>\n",
              "      <td>Himachal Pradesh</td>\n",
              "      <td>201411</td>\n",
              "      <td>3521</td>\n",
              "      <td>206161</td>\n",
              "      <td>0.017079</td>\n",
              "      <td>0.976960</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17763</th>\n",
              "      <td>Jammu and Kashmir</td>\n",
              "      <td>316017</td>\n",
              "      <td>4379</td>\n",
              "      <td>321607</td>\n",
              "      <td>0.013616</td>\n",
              "      <td>0.982619</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17764</th>\n",
              "      <td>Jharkhand</td>\n",
              "      <td>341817</td>\n",
              "      <td>5129</td>\n",
              "      <td>347200</td>\n",
              "      <td>0.014772</td>\n",
              "      <td>0.984496</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17765</th>\n",
              "      <td>Karnataka</td>\n",
              "      <td>2846244</td>\n",
              "      <td>36587</td>\n",
              "      <td>2906999</td>\n",
              "      <td>0.012586</td>\n",
              "      <td>0.979100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17766</th>\n",
              "      <td>Kerala</td>\n",
              "      <td>3226761</td>\n",
              "      <td>16837</td>\n",
              "      <td>3411489</td>\n",
              "      <td>0.004935</td>\n",
              "      <td>0.945851</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17767</th>\n",
              "      <td>Ladakh</td>\n",
              "      <td>20076</td>\n",
              "      <td>207</td>\n",
              "      <td>20340</td>\n",
              "      <td>0.010177</td>\n",
              "      <td>0.987021</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17768</th>\n",
              "      <td>Lakshadweep</td>\n",
              "      <td>10070</td>\n",
              "      <td>50</td>\n",
              "      <td>10195</td>\n",
              "      <td>0.004904</td>\n",
              "      <td>0.987739</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17769</th>\n",
              "      <td>Madhya Pradesh</td>\n",
              "      <td>781207</td>\n",
              "      <td>10513</td>\n",
              "      <td>791845</td>\n",
              "      <td>0.013277</td>\n",
              "      <td>0.986566</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17770</th>\n",
              "      <td>Maharashtra</td>\n",
              "      <td>6094896</td>\n",
              "      <td>132948</td>\n",
              "      <td>6310194</td>\n",
              "      <td>0.021069</td>\n",
              "      <td>0.965881</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17771</th>\n",
              "      <td>Manipur</td>\n",
              "      <td>87360</td>\n",
              "      <td>1566</td>\n",
              "      <td>99331</td>\n",
              "      <td>0.015765</td>\n",
              "      <td>0.879484</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17772</th>\n",
              "      <td>Meghalaya</td>\n",
              "      <td>58450</td>\n",
              "      <td>1096</td>\n",
              "      <td>65589</td>\n",
              "      <td>0.016710</td>\n",
              "      <td>0.891156</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17773</th>\n",
              "      <td>Mizoram</td>\n",
              "      <td>27024</td>\n",
              "      <td>150</td>\n",
              "      <td>39363</td>\n",
              "      <td>0.003811</td>\n",
              "      <td>0.686533</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17774</th>\n",
              "      <td>Nagaland</td>\n",
              "      <td>26031</td>\n",
              "      <td>570</td>\n",
              "      <td>27945</td>\n",
              "      <td>0.020397</td>\n",
              "      <td>0.931508</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17775</th>\n",
              "      <td>Odisha</td>\n",
              "      <td>958601</td>\n",
              "      <td>5966</td>\n",
              "      <td>978705</td>\n",
              "      <td>0.006096</td>\n",
              "      <td>0.979459</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17776</th>\n",
              "      <td>Puducherry</td>\n",
              "      <td>118228</td>\n",
              "      <td>1795</td>\n",
              "      <td>121005</td>\n",
              "      <td>0.014834</td>\n",
              "      <td>0.977051</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17777</th>\n",
              "      <td>Punjab</td>\n",
              "      <td>582332</td>\n",
              "      <td>16294</td>\n",
              "      <td>599130</td>\n",
              "      <td>0.027196</td>\n",
              "      <td>0.971963</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17778</th>\n",
              "      <td>Rajasthan</td>\n",
              "      <td>944484</td>\n",
              "      <td>8954</td>\n",
              "      <td>953688</td>\n",
              "      <td>0.009389</td>\n",
              "      <td>0.990349</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17779</th>\n",
              "      <td>Sikkim</td>\n",
              "      <td>22955</td>\n",
              "      <td>345</td>\n",
              "      <td>26754</td>\n",
              "      <td>0.012895</td>\n",
              "      <td>0.858003</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17780</th>\n",
              "      <td>Tamil Nadu</td>\n",
              "      <td>2506961</td>\n",
              "      <td>34102</td>\n",
              "      <td>2561587</td>\n",
              "      <td>0.013313</td>\n",
              "      <td>0.978675</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17781</th>\n",
              "      <td>Telangana</td>\n",
              "      <td>632728</td>\n",
              "      <td>3805</td>\n",
              "      <td>645406</td>\n",
              "      <td>0.005896</td>\n",
              "      <td>0.980357</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17782</th>\n",
              "      <td>Tripura</td>\n",
              "      <td>74875</td>\n",
              "      <td>755</td>\n",
              "      <td>78722</td>\n",
              "      <td>0.009591</td>\n",
              "      <td>0.951132</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17784</th>\n",
              "      <td>Uttar Pradesh</td>\n",
              "      <td>1685049</td>\n",
              "      <td>22763</td>\n",
              "      <td>1708476</td>\n",
              "      <td>0.013324</td>\n",
              "      <td>0.986288</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17783</th>\n",
              "      <td>Uttarakhand</td>\n",
              "      <td>334190</td>\n",
              "      <td>7362</td>\n",
              "      <td>342161</td>\n",
              "      <td>0.021516</td>\n",
              "      <td>0.976704</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17785</th>\n",
              "      <td>West Bengal</td>\n",
              "      <td>1499597</td>\n",
              "      <td>18149</td>\n",
              "      <td>1528720</td>\n",
              "      <td>0.011872</td>\n",
              "      <td>0.980949</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                           State/UnionTerritory  ...  Recovery Rate\n",
              "17750               Andaman and Nicobar Islands  ...       0.981960\n",
              "17751                            Andhra Pradesh  ...       0.982517\n",
              "17752                         Arunachal Pradesh  ...       0.919063\n",
              "17753                                     Assam  ...       0.968399\n",
              "17754                                     Bihar  ...       0.986096\n",
              "17755                                Chandigarh  ...       0.986425\n",
              "17756                              Chhattisgarh  ...       0.984590\n",
              "17757  Dadra and Nagar Haveli and Daman and Diu  ...       0.997371\n",
              "17758                                     Delhi  ...       0.982152\n",
              "17759                                       Goa  ...       0.975707\n",
              "17760                                   Gujarat  ...       0.987477\n",
              "17761                                   Haryana  ...       0.986554\n",
              "17762                          Himachal Pradesh  ...       0.976960\n",
              "17763                         Jammu and Kashmir  ...       0.982619\n",
              "17764                                 Jharkhand  ...       0.984496\n",
              "17765                                 Karnataka  ...       0.979100\n",
              "17766                                    Kerala  ...       0.945851\n",
              "17767                                    Ladakh  ...       0.987021\n",
              "17768                               Lakshadweep  ...       0.987739\n",
              "17769                            Madhya Pradesh  ...       0.986566\n",
              "17770                               Maharashtra  ...       0.965881\n",
              "17771                                   Manipur  ...       0.879484\n",
              "17772                                 Meghalaya  ...       0.891156\n",
              "17773                                   Mizoram  ...       0.686533\n",
              "17774                                  Nagaland  ...       0.931508\n",
              "17775                                    Odisha  ...       0.979459\n",
              "17776                                Puducherry  ...       0.977051\n",
              "17777                                    Punjab  ...       0.971963\n",
              "17778                                 Rajasthan  ...       0.990349\n",
              "17779                                    Sikkim  ...       0.858003\n",
              "17780                                Tamil Nadu  ...       0.978675\n",
              "17781                                 Telangana  ...       0.980357\n",
              "17782                                   Tripura  ...       0.951132\n",
              "17784                             Uttar Pradesh  ...       0.986288\n",
              "17783                               Uttarakhand  ...       0.976704\n",
              "17785                               West Bengal  ...       0.980949\n",
              "\n",
              "[36 rows x 6 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 155
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "lz5hpCXlJ5Dw",
        "outputId": "32d0dbd6-4ed0-48a9-8a99-a58b4948157c"
      },
      "source": [
        "covid19_merged = pd.merge(covid19_IN_recent, indian_states, on= 'State/UnionTerritory')\n",
        "covid19_merged.head()"
      ],
      "execution_count": 156,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "    .dataframe thead th {\n",
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>State/UnionTerritory</th>\n",
              "      <th>Cured</th>\n",
              "      <th>Deaths</th>\n",
              "      <th>Confirmed</th>\n",
              "      <th>Death Rate</th>\n",
              "      <th>Recovery Rate</th>\n",
              "      <th>Population</th>\n",
              "      <th>Area</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Andaman and Nicobar Islands</td>\n",
              "      <td>7403</td>\n",
              "      <td>129</td>\n",
              "      <td>7539</td>\n",
              "      <td>0.017111</td>\n",
              "      <td>0.981960</td>\n",
              "      <td>386484</td>\n",
              "      <td>8249</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Andhra Pradesh</td>\n",
              "      <td>1934048</td>\n",
              "      <td>13395</td>\n",
              "      <td>1968462</td>\n",
              "      <td>0.006805</td>\n",
              "      <td>0.982517</td>\n",
              "      <td>50987456</td>\n",
              "      <td>160205</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Arunachal Pradesh</td>\n",
              "      <td>44354</td>\n",
              "      <td>231</td>\n",
              "      <td>48260</td>\n",
              "      <td>0.004787</td>\n",
              "      <td>0.919063</td>\n",
              "      <td>1232971</td>\n",
              "      <td>83743</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Assam</td>\n",
              "      <td>549065</td>\n",
              "      <td>5275</td>\n",
              "      <td>566982</td>\n",
              "      <td>0.009304</td>\n",
              "      <td>0.968399</td>\n",
              "      <td>22329699</td>\n",
              "      <td>78438</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Bihar</td>\n",
              "      <td>714801</td>\n",
              "      <td>9644</td>\n",
              "      <td>724880</td>\n",
              "      <td>0.013304</td>\n",
              "      <td>0.986096</td>\n",
              "      <td>104581560</td>\n",
              "      <td>94163</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          State/UnionTerritory    Cured  ...  Population    Area\n",
              "0  Andaman and Nicobar Islands     7403  ...      386484    8249\n",
              "1               Andhra Pradesh  1934048  ...    50987456  160205\n",
              "2            Arunachal Pradesh    44354  ...     1232971   83743\n",
              "3                        Assam   549065  ...    22329699   78438\n",
              "4                        Bihar   714801  ...   104581560   94163\n",
              "\n",
              "[5 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 156
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "HXGM8N0aKHuB",
        "outputId": "6fdeb034-6db1-4b4d-8c5a-c4d72b5e0126"
      },
      "source": [
        "covid19_merged['Cases/10million'] = (covid19_merged['Confirmed'] / covid19_merged['Population']) * 10000000\n",
        "covid19_merged.head()"
      ],
      "execution_count": 157,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>State/UnionTerritory</th>\n",
              "      <th>Cured</th>\n",
              "      <th>Deaths</th>\n",
              "      <th>Confirmed</th>\n",
              "      <th>Death Rate</th>\n",
              "      <th>Recovery Rate</th>\n",
              "      <th>Population</th>\n",
              "      <th>Area</th>\n",
              "      <th>Cases/10million</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Andaman and Nicobar Islands</td>\n",
              "      <td>7403</td>\n",
              "      <td>129</td>\n",
              "      <td>7539</td>\n",
              "      <td>0.017111</td>\n",
              "      <td>0.981960</td>\n",
              "      <td>386484</td>\n",
              "      <td>8249</td>\n",
              "      <td>195066.289937</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Andhra Pradesh</td>\n",
              "      <td>1934048</td>\n",
              "      <td>13395</td>\n",
              "      <td>1968462</td>\n",
              "      <td>0.006805</td>\n",
              "      <td>0.982517</td>\n",
              "      <td>50987456</td>\n",
              "      <td>160205</td>\n",
              "      <td>386067.898740</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Arunachal Pradesh</td>\n",
              "      <td>44354</td>\n",
              "      <td>231</td>\n",
              "      <td>48260</td>\n",
              "      <td>0.004787</td>\n",
              "      <td>0.919063</td>\n",
              "      <td>1232971</td>\n",
              "      <td>83743</td>\n",
              "      <td>391412.287880</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Assam</td>\n",
              "      <td>549065</td>\n",
              "      <td>5275</td>\n",
              "      <td>566982</td>\n",
              "      <td>0.009304</td>\n",
              "      <td>0.968399</td>\n",
              "      <td>22329699</td>\n",
              "      <td>78438</td>\n",
              "      <td>253913.857056</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Bihar</td>\n",
              "      <td>714801</td>\n",
              "      <td>9644</td>\n",
              "      <td>724880</td>\n",
              "      <td>0.013304</td>\n",
              "      <td>0.986096</td>\n",
              "      <td>104581560</td>\n",
              "      <td>94163</td>\n",
              "      <td>69312.410333</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          State/UnionTerritory    Cured  ...    Area  Cases/10million\n",
              "0  Andaman and Nicobar Islands     7403  ...    8249    195066.289937\n",
              "1               Andhra Pradesh  1934048  ...  160205    386067.898740\n",
              "2            Arunachal Pradesh    44354  ...   83743    391412.287880\n",
              "3                        Assam   549065  ...   78438    253913.857056\n",
              "4                        Bihar   714801  ...   94163     69312.410333\n",
              "\n",
              "[5 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 157
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 716
        },
        "id": "2pA_oaL0KeEG",
        "outputId": "5ed502a0-b140-4b15-9dee-48a5604b576e"
      },
      "source": [
        "covid19_merged = covid19_merged.sort_values(by = ['Cases/10million'], ascending = False)\n",
        "plt.figure(figsize=(14,8), dpi=100)\n",
        "plt.barh(covid19_merged['State/UnionTerritory'][:10], covid19_merged['Cases/10million'][:10], \n",
        "        align='center',color='#D32F2F')         \n",
        "plt.xticks([0,2000000,4000000,6000000,8000000],['0','2M','4M','6M','8M'])\n",
        "plt.xlabel('Confirmed Cases / 10 million people', size = 14)\n",
        "plt.title('Top 10 States with maximum cases per 10 million population', size = 18)\n",
        "plt.show()"
      ],
      "execution_count": 158,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1400x800 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VgIN0C12MDR8"
      },
      "source": [
        "Here we can see that spread of the virus in Maharashtra is not that bad even when it accounts for most of the confirmed cases and deaths in country, it can be attributed to its large population since it's the most densely packed state in the state. Kerala is in very bad state, the spread of virus is very high as compared to other states "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 834
        },
        "id": "E859d4rgLmIc",
        "outputId": "28e4831a-4fef-42b7-863b-cd83542ce3e1"
      },
      "source": [
        "df = covid19_merged[(covid19_merged['Confirmed']>=1000000) | (covid19_merged['Cases/10million']>=1000000)]\n",
        "plt.figure(figsize = (14,8), dpi= 100)\n",
        "plt.scatter(covid19_merged['Confirmed'], covid19_merged['Cases/10million'], alpha = 0.5)\n",
        "plt.scatter(df['Confirmed'], df['Cases/10million'], color=\"#D32F2F\")\n",
        "for i in range(df.shape[0]):\n",
        "  plt.annotate(df['State/UnionTerritory'].tolist()[i], xy=(df['Confirmed'].tolist()[i], df['Cases/10million'].tolist()[i]),\n",
        "    xytext = (df['Confirmed'].tolist()[i]+1.0, df['Cases/10million'].tolist()[i]+12.0), size=11)\n",
        "plt.xticks([0,1000000,2000000,3000000,4000000,5000000,6000000],['0','1M','2M','3M','4M','5M','6M'])\n",
        "plt.xlabel('Number of confirmed Cases', size=14)\n",
        "plt.yticks([0,2000000,4000000,6000000,8000000],['0','2M','4M','6M','8M'])\n",
        "plt.ylabel('Number of cases per 10 million people', size=14)\n",
        "plt.tight_layout()\n",
        "plt.title('Variation in spread of Covid-19 in different Indian states', size=18)\n",
        "plt.show()"
      ],
      "execution_count": 159,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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gylprl6YbHT1J7lcj/fNV1Zp0rQ2S5F1tcQOdDPqKvH2/nrksaITxxWpdH43/2b88dBmKeED/eEa6UeZn88z+8WrpWu7M5tTW2qdGZ7bWNqcLIiXJ2H4yt8F7Wmvfm6XsM9O1lp2k7HP6x2tNWKdTW2ufGJ3ZWrsgXcvsJLlHVa2fI/9b2tyjyw8GOHpba+1nsyXo8w6Ox90XWOeBwTm/1flWVXsNresl/baMlntyZvb5olTXj+pN+5fPa7P0T9pa+1CSL/Uv/3KScsa46tDzs7ZhPYNr6guttY+OLmzdAIPP7l/evKoOGlr283QBp2TmnnWZqrpWuuBx0rWWXqgH9o9znZe/S/LiRaxvOfx7/3i9dK2/t9Lf1/92aNa6ZarH8+aYP+jn9oZVtdscaS5nua+bJfLnSfZK8rW+LpfTn7fv6F/OdU+5NN0PoJdTXb/Od07XCv+lY+ry5v7xLmPec1/Qv++PGhyjXdO1sFxJL5vtWKf798tF/fODRpYNrtN3t9a+O5qxtfa/Sf5l6aq41bq/kuTX6Vp9Xq7P6yGTbNd8tvX9dmKL2O5xBu/JL2+tXTxHmvena8l9tXQtzwdWbNsBVitBWYCV8YZ0rQ12z0xQZOCemfnA+7rRjP2gFc/uB1n4bVVtGRos4zt9st2SzDXIywVJ/mtbKl9Vd6yqN1bV96obWKUN1eGpfbL9tqWMOdy2f/zUHF9y0385/PlI+lFfHFPGL/rHvRdfvQVZjrI/3D8eXVXvqKo/rTkGuZnDJxewbId0f2uezalj8h/SPz6yH0Rk1indX0qTrq+7rVTV9avqpf0gKudUNzDd4HwbBMdGz7dbZ+ZzzkK2b7EG59aWJOMG2PrYSPrtzaBeHx+T5lPpWsQOpx8YBKXuW1W7jyx7ULrW87+YZ/1z1Wk5jttSeUNmBtZ5bT8Az1WrauequkOSj6a7j2/p08x6v9pGZ7XWfjjHsl8MPV/MgF/Lfd0shcE95abz3FOe1ae73D2l98PW2q/nKWOHJN8ZU8bgR8jds/UPJcPmuucPH6Pler9ZqFnr2Ae3z+xfXlbH6gZmHAQzl+U86Qeiekx1g7D9oh9Ma/izxjX6pOM+ayxquxZo8H77pqp6WVUdvpgfPuazRNs917r3zcz18Lox5/Uv03WZkGx9/WzrZw0ARqxd6QoAXBm11n7Uj4Z7p3QDqgwHXx/RP36vtfb54XxVdft0Qai9hmafl5m/061J17Ih6b4k/maW4n87V0BzIarqnzITeE26YM3ZmWl1skdf9miAZikMvoz8fGyqriXtvkPpR507Ju8giDLryM5LYMnLbq29var+MN3fYR/YT6mqH6YLDr2+tfbVMasYtz+Hl821P+cKbCRdP3NJ11JwIa0Ft/pyW1X3Tdfibeeh2ZsyM2DLTumCTqPn23Bdx23fXC185zNY/2/6FtbzrX+ufTep3w4935aAzrzXVGvtwqr6TZJ9cvnteF+S16S77v8sW7eIHbSefdsi7zkLuc5nPW59QPR9c+R5Qmvt/y6iHnNqrZ1fVfdKdz/eP8k/99NlSdLdJ5+a7p589lKUO2Ih95JkcfeT5b5ulsLgnrJLP81nroDZQu5bO6Q77xdi1nJaa7Mep9balqoavFyu95uFWuz70t6Z+R655OdJVV0j3Q85w61YL8zMwKJJ1+XPDhn/WWM53uufmuSG6T6//V0/XVJVX0/3z41/7f9FsGhLuN1zufbQ84UGUy87r5fgswYAI7SUBVg5g0DsHarqwCSpqqunGxwmSV4/nLiq1qYLTu2VbvTbP0o3YNKerbV9WmvXTDfoxGVZ5ij3cn+zXqiqumtmArKvSffFYefW2t6ttWv2dTh+nvJZBq21J6YbMOzp6f6aeU66L45/k+QrVXXCMhY/7pwa/KX3sa21WsB01CBjVV01XVcfO6drcXVEuoHd1g+d83+xLFu0/fvO0PNbrVQlWmvnZyYI+tDB/L6bg1v0LxfTdcG22ildEG22adelLKi19p1098C/T9c/44+TnJ7kXekGz3lFZn5A+8FSln0lN7in/N8F3lMOmGM9C7lv/WqBZVRrbcNSbSA5Pt219dt0P1Rfq7W2a2vt6kOfNQYtjaf6WaO1dk5r7c7p+kt/cbp/imxJ9zf/ZyU5vaom7a5mubd7uIuNmy7wvH7j8ApW+LMGwKojKAuwct6bmf65Bq1jH5Ku1caWzPwteOD26VpkXZLkXq21/5ilBc41l6muA4M+5E5urT2utfbtWfrSXM46DFo2zfe3vcHycS2hVp3W2g9bay9srf1Rur/S3j5d33BJ8oSquvccWfcds9rhZZPsz//tH+f6C/E4f5Sude3ZSf6ktfbpWfoHnOt8G67rQrdvMQbrv1pV7Twm3bKci621X2YmMHufGmpyt0jzXlNVtUtm/po923YMgq537v8em8y0kv16a+1bE9Zp0cettXbKQoMLS6G1dm5r7aWttTu11q7fWjuwtfaA1tpn0wWlB60JPz9mNduT5b5ulsK23FMWW8bVZumWg64f68F7/5KeJ1W1Y7pW90lydGvtDX3/tMNphv8VtCJaa59rrR3TWjs03Y8v90nyrXQ//ry+qhbawjrJ1LZ7eH0TXz/b8FkDgBGCsgArpLV2YZK39y8f2n/Yfnj/+sOttV+NZLlO/3jmmL/G3WWO+UtlUIevzbawDwzdeUz+wV+YJw0gfaV/vFNVzfoeVlU3ycwXwS9PWM4VXmvt0tbaaUnul+Sn/ey5Bj6705hVDZZdmjmO+zwG/c3ea2yq2Q3Ot++3bnCn2cx1zv9XZs63cds37nwdZ3Aurk3XKnIug/otx7n46v7xRul+0FmQkWtnsB1Hzpa2d0Rmgouzbccn0/1NeYckD+rXPxj8avTHpYW47Dofk2bS4zZNg2PyxdbaFaWl7HJfN0thcE+5TXWDyS1nGWvS9fM+bdv6XrmsWmsXJflm/3Kpz5OrZ6Zbirnecw7NwrqumIrW2oWttQ9mJqi6S7YefHK4+5a5jum2bve8ZfStuQefH/9kjvUsyiI/awAwQlAWYGUNujC4VpJnZqYfsdfPknZj/7jPbC0wqmq/bD3a93IY1OEWcyx/TJLrj8m/qX/ca0yacd7ZP+6b5FFzpHlO//ibLG5woSuscS01+5bMg/5+5+rX89CqOmKW9e6S5Mn9y5Nba+eMplmAf+0fb15Vjx2XsKp27weQGRicbwf2dRlNf8vMBP+20tf1o/3Lp8yR/y5J7jBP/WfVWvtmZlqqPqNmGXm9qv4oye36l+8YXb4EXpeuZVaSvLqqDhuXuKrWVNWzkvzx0OzBNXX7qrrbLHnWZmbApG+31r49mqZ1/cW+rX/5f9IFYvZN15Lu7aPpF2DQ7+tc5+Wu6boM2G5V1cFJBuf781ayLoux3NfNEnl3un+Z7Jjk5eNaiVfVDlW16Peb1trp6bqkSJLnV9X6cemraqkH6trW98ppGFynf1FVNx5d2PeP+pgJ1rspXZ/MySyfNfp70vMnWO82q6q1c/0g3Bv+J8fw++2moedzHdNt3e6FlJEkJ/WPj6yqsV3fjJ7XS/BZA4ARgrIAK6i19l/p+odNuqBs0o16+++zJP9ckvPTtYB411A/tGuq6u7pvkC2WfItpcFI0/esqmcO/tZZVXtV1dOTnJitByAaNQjo/NHQ35wXrLX2pXTdPiTJiVV19GDU46q6ZlWdlJk+Rp/Zt0a+MvhiVb2yqo4Y/qttVV27qk5M199bMvt5lXTBz/dW1f36L36DFscfSXKTdMG1Z82Rd6zW2qfTjVSfdIHD46vqssB9daPVH1xVL07yk2w90NBH03252zvJ2wbnTHWjU9+/Xz5uIJdn9nW/SZKPDAIH/Rfr+6fr+3OSQPPAMf3jHZO8p6qu169/x6p6cGYCsZ/PzF87l0zrBhi7b7p7xp5JPl5Vr66qPxgOElfVAVX1N0m+l+TZ2bpfwfdmZoTyd1XVg/q/0abfnvem+2tqsvUAf6MGXRgclOSF/fOPztLifyHem67FZtKdl38+2J6qumm6fgyvPsF6t9Ifp6sNpiTDgberDC8b7JOR/E/o99c+Q/OuUVVPTvKxdEHDf22tfXg073Zuua+bbdIHjp/Yv3xgujrebhAs6wOxN+2Pw39nslb6STeY0XlJDkxyWlXdZzhIXVX7VtX/qapPJPmnSbdnDoP3ynX9Pt8e/XO6FvI7J/nPqjpyECCvqtul+1F00d81W2vnZaal8sur6s5Dx/bm6d7Hbpvu89C07Zeuz9hnVNWtBu+Xfd1+P8lb+5fnJ/n0YFl/zg5aqD58ON9Qmm3a7oWU0XtZuh/zdknyqf5z1KB7msHnuXtW1ZuTfHYk77Z+1gBgVGvNZDKZTCs4JTk6XTB1ML1wTNrHjKQ9N13LjJbkzHR/RxssO2Ak71H9/A0LqNOGPu1RI/N3TPKZoTIuzUzfci3Jh5M8t39+yizrvdFQfS9J17/Zhn7ab77y+2XrMxOAbkku7utw6dC8l8yxXYN8x43Z9uPmqv8C9tuc6x+3TUNp3tineeMiy92QrY/J2emCCcPnysvH5HtSuoBdSzfS8zkj6/urOcodpDlinvrtlK51zui5O3zuDKZ9R/K+aGT5Oela47QkP0rXUrYlaXOU/eiRc+Ocfhtbku/2276g62KO9T9pZP1nJ9k89PqbSa69mOtsgjpcO10AZHg/XZLuB5LNI/NPS3L9kfz7pgsCDdJs7rdjeF1/u4B6fHWkrAfOk37O8yddi/ufDqUZPi83J7n3Qs+/MeUfMVLfcdNsdXz/0PIL0v24MXzdvDLJDttat1mWHTXfOZvkgKG6HDBB+RNfNxlzD11IvcZt+0i6x4yc34NR6i/K1sfuwQut3yxlHJLuR4/Burb0ZfxupIyTJtyGcefX8DW9KTPvlU9cxHE8KnPs74WeIxn/fnzbbH2vOD/dvX1Q5/uPKX/cOX6bbP0edmFmWpJenK5F/qz1WortWuB1NTgfRu+zm5Pcb5a8zxjZnp/2dXjnUmz3Qsvo0107yReG0g4+Nwzfw1qS0+fYZ8N55v2sYTKZTKa5Jy1lAVbe29J9eB6YreuCJElr7V/S/fX4lHQfhNemaxlxYrq/uy12QJ1Faa1dnORu6Vrb/SDdl4RK8qV0f9W9d8aMaN26v4TeKckH0wWRr5pusIn9M9Nn5Xx12Jiu/8tHptsP5ybZI12A971J7tRa267/2rwMHpjk2CSfSDcC/E7pAug/SfcX0yNba383Jv/ZSf4wXQD0p+laPp2V5ENJDmmtnTQm77xaaxe11v4q3V+e35jkf9K11twj3cBCp6TrduL320h/ya21pyV5aLpz7IJ+u36Y5AVJbpWZkajnKvtf0wVWPtRv087p9ssL+20+exu37fh0gYm3JvlZkt36ep6WLnD1B621sXXcVq21X7TW7pLksHT9zH4zXRBtXV+XbyT5l3SBn4Nbaz8ayf/zfhv+rq/3Bf12/CxdC9jbtNZeuYCqDPcfuynJB7Zhm36U5JZJXp7unK5098n3JLlD6/pvXGmvTbd/vp8uELhTuh8K3pCujn/buq4drnCW+7pZCv374Y2TvDTdOb453d+2z0vXL/GJ6fq2nLjrkNbaqelayj4l3Q+S5/RlXJIuOP3WJA/OTMvdpXS/JMene6/dMTPvldtNlwatta8k+f0k/5bus8jadIG9NyW5dbr79iTr/Wq68+xd6YLgO6R7r39XumvrLWOyL6efp/ucc3y6e+Uv072PbUnXnc2rk9y8tfaeWfK+IMkT0p2bF6drdbt/hgarXILtnreMvpxfpOuf9i/TfR77Zbp7/k7pAq8fSndOj3aJs62fNQAYUa21la4DALACqmpDui9sD2/LMDI9AAAAs9NSFgAAAABgigRlAQAAAACmSFAWAAAAAGCKBGUBAAAAAKbIQF8AAAAAAFOkpSwAAAAAwBStXekKrLSqqiTXTnLuStcFAAAAAFiQPZP8ol1BuwG40gdl0wVkz1jpSgAAAAAAi7Jfkp+vdCUmISjbt5D92c9+lnXr1q10XQAAAACAMTZt2pTrXOc6yRX4n++Csr1169YJygIAAAAAy85AXwAAAAAAUyQoCwAAAAAwRYKyAAAAAABTJCgLAFBhBzsAACAASURBVAAAADBFgrIAAAAAAFMkKAsAAAAAMEWCsgAAAAAAUyQoCwAAAAAwRYKyAAAAAABTJCgLAAAAADBFgrIAAAAAAFMkKAsAAAAAMEWCsgAAAAAAUyQoCwAAAAAwRYKyAAAAAABTJCgLAAAAADBFgrIAAAAAAFMkKAsAAAAAMEWCsgAAsJ067rjjsscee1xu/pOf/OTssMMOed3rXrfsddiwYUOqKu95z3uWvSwAgCsLQVkAALgCOeaYY3L88cfnX/7lX/LIRz5ypasDAMAEBGUBAOAK4hnPeEZe/OIX59WvfnUe/ehHT7yezZs359JLL13CmgEAsBiCsgAAcAVw3HHH5fnPf35OPPHEPPaxj71s/hlnnJGHPOQhudrVrpZdd901hx12WL761a9ulfeAAw7I0UcfnRe/+MXZf//9s+uuu+ass87K9773vTzwgQ/Mda5zney222652c1ulpe97GXzBmzf/OY359BDD83ee++dq1zlKjniiCPypS99aVm2GwBgNVq70hUAAADGe/7zn59nP/vZOf7443P00UdfNv/ss8/OoYcemj322CMnnnhi1q9fnxNPPDF3vvOdc/rpp+ca17jGZWnf+9735kY3ulFe8YpXZM2aNdl9993zjW98Ize+8Y3z4Ac/OHvuuWe+/vWv59hjj815552XY489ds76bNiwIQ996ENzgxvcIBdddFHe8Y535LDDDss3v/nNHHjggcu6LwAAVgNBWQAA2I6df/75ecYznpFHPepReeITn7jVshNOOCHnnHNOvvSlL10WgD3yyCNz4IEH5qUvfWle/OIXX5b24osvzn/8x39k9913v2zekUcemSOPPDJJ0lrLoYcemt/97nd51ateNTYo+6xnPeuy55deemnuete75ktf+lLe+MY35gUveMGSbDcAwGqm+wIAANiODbokePvb355TTz11q2Uf/ehHc6c73Sl77713tmzZki1btmTNmjU5/PDD8+Uvf3mrtEccccRWAdkkufDCC3Psscfmhje8YXbeeefsuOOO+cd//Mf88pe/zHnnnTdnnb773e/mvve9b/bZZ5+sWbMmO+64Y77//e/nBz/4wdJtOADAKqalLAAAbCcuuXhLfvrZ03LBr36dXfe5Ri695NLssMMO+eAHP5gjjjgi97rXvfKZz3wmBx10UJLkN7/5TU477bTsuOOOl1vXDW5wg61e77PPPpdLc8wxx+Skk07Ksccem9vc5jbZa6+98oEPfCDPe97zcuGFF2aPPfa4XJ5zzz03d7vb3XL1q189L3/5y7P//vtnl112yaMe9ahceOGFS7QnAABWN0FZAADYDnz/fR/Oua84Prts/G2S5Pwk/3v2xrRLLsn69etz8skn55BDDsnd7373nHrqqbne9a6XvffeO/e4xz3y3Oc+93Lr23nnnbd6XVWXS/Pud787f/3Xf51jjjnmsnkf+chHxtbzC1/4Qs4444x8+MMfzi1ucYvL5m/cuDH77bffYjYZAOBKS1AWAABW2Pff9+Fc9JxnZOeR+Ws2X5B20UX5/vs+nBv/2b3ysY99LIccckjuete75nOf+1zucpe75K1vfWtuetObXq5rgoW44IILstNOO132+pJLLsk73/nOefMk2Srf5z//+WzYsCG/93u/t+g6AABcGQnKAgDACrrk4i059xXHZ+cko21ZB6/PfeXxueRP7pEDDjggJ598cg477LDc4x73yAc+8IG87W1vy+GHH54nPOEJue51r5szzzwzX/ziF3Pta187T3rSk8aWfde73jUnnXRSbnazm+VqV7taXvOa12Tz5s1j8xx88MHZY4898rjHPS5Pe9rT8vOf/zzHHnts9t1334n3AQDAlY2BvgAAYAX99LOnZZeNv71cQHbYLuf8Nj/97GlJkpvf/Ob5yEc+ktNPPz0PechD8oUvfCG3vOUtc8wxx+Rud7tbnvSkJ2XDhg253e1uN2/ZJ554Yg4//PA8/vGPzyMf+cgcdNBBefrTnz42zz777JN3v/vd+fWvf5373Oc+OeGEE/La1742N7zhDRez2QAAV2rVWlvpOqyoqlqXZOPGjRuzbt26la4OAABXMt95x/ty6T89Z950OxzzrNzsL/9sCjUCANi+bdq0KevXr0+S9a21TStdn0loKQsAACto132usaTpAADY/gnKAgDACrruHQ/Oheuvmrn+v9aSXLjXVXPdOx48zWoBALCMBGUBAGAFrdlxbfZ8Qjcg12hgdvB6z799UtbsaIxeAIDVQlAWAABW2I3/7F7Z6VnPy+b1V91q/ua9rpqdnvW83PjP7rVCNQMAYDkY6MtAXwAAbCcuuXhLfvrZ03LBr36dXfe5Rq57x4O1kAUAGLEaBvryCQ8AALYTa3Zcm+vd+dCVrgYAAMtM9wUAAAAAAFMkKAsAAAAAMEWCsgAAAAAAUyQoCwAAAAAwRYKyAAAAAABTJCgLAAAAADBFgrIAAAAAAFMkKAsAAAAAMEWCsgAAAAAAUyQoCwAAAAAwRYKyAAAAAABTJCgLAAAAADBFgrIAAAAAAFMkKAsAAAAAMEWCsgAAAAAAUyQoCwAAAAAwRYKyAAAAAABTJCgLAAAAADBFgrIAAAAAAFMkKAsAAAAAMEWCsgAAAAAAUyQoCwAAAAAwRYKyAAAAAABTJCgLAAAAADBFgrIAAAAAAFMkKAsAAAAAMEWCsgAAAAAAUyQoCwAAAAAwRYKyAAAAAABTJCgLAAAAADBFgrIAAAAAAFMkKAsAAAAAMEWCsgAAAAAAUyQoCwAAAAAwRYKyAAAAAABTJCgLAAAAADBFgrIAAAAAAFMkKAsAAAAAMEWCsgAAAAAAUyQoCwAAAAAwRYKyAAAAAABTJCgLAAAAADBFgrIAAAAAAFMkKAsAAAAAMEWCsgAAAAAAUyQoCwAAAAAwRYKyAAAAAABTJCgLAAAAADBFgrIAAAAAAFMkKAsAAAAAMEWCsgAAAAAAUyQoCwAAAAAwRYKyAAAAAABTJCgLAAAAADBFgrIAAAAAAFMkKAsAAAAAMEWCsgAAAAAAUyQoCwAAAAAwRYKyAAAAAABTJCgLAAAAADBFgrIAAAAAAFMkKAsAAAAAMEWCsgAAAAAAUyQoCwAAAAAwRYKyAAAAAABTJCgLAAAAADBFgrIAAAAAAFMkKAsAAAAAMEWCsgAAAAAAUyQoCwAAAAAwRYKyAAAAAABTJCgLAAAAADBFgrIAAAAAAFMkKAsAAAAAMEWCsgAAAAAAUyQoCwAAAAAwRYKyAAAAAABTJCgLAAAAADBFgrIAAAAAAFMkKAsAAAAAMEWCsgAAAAAAUyQoCwAAAAAwRYKyAAAAAABTJCgLAAAAADBF211QtqrWVNVzq+rHVXVBVf1PVT2zqmoozSlV1arqabPk/0i/7LipVhwAAAAAYAG2u6BskmOSPDbJ0Ulu2r9+apLHj6T7WZKjhmdU1b5Jjkzyy2WvJQAAAADABLbHoOwdknygtfaR1tqG1tp7knw0yR+OpPtwkqtV1SFD8x7Wp/31dKoKAAAAALA422NQ9vNJjqyqA5Okqm6R5NAk/zGS7qIkb0vy8KF5RyV5/biVV9XOVbVuMCXZc6kqDgAAAAAwn+0xKPuiJO9M8r2qujjJ15Kc0Fp72yxpX5/k/lW1e1UdlmR9uha04/xDko1D0xlLVnMAAAAAgHlsj0HZ+yd5cJIHJbl1ui4JnlJVDxtN2Fr7RpLTk9wvySOSvKW1tmWe9b8wXfB2MO23dFUHAAAAABhv7UpXYBYvSfKi1to7+9ffqqr907VwfdMs6V+f5HFJbpbL9zt7Oa21zUk2D15X1TZXGAAAAABgobbHlrK7Jbl0ZN4lmbuub09yUJJvt9a+s5wVAwAAAADYVttjS9kPJfnHqvppkv9Ocqskf5c5BvBqrZ1dVddKcvH0qggAAAAAMJntMSj7+CTPTfKaJNdI8oskr03ynLkytNbOmU7VAAAAAAC2TbXWVroOK6qq1iXZuHHjxqxbt26lqwMAAAAAjLFp06asX78+Sda31jatdH0msT32KQsAAAAAsGoJygIAAAAATJGgLAAAAADAFAnKAgAAAABMkaAsAAAAAMAUCcoCAAAAAEyRoCwAAAAAwBQJygIAAAAATJGgLAAAAADAFAnKAgAAAABMkaAsAAAAAMAUCcoCAAAAAEyRoCwAAAAAwBQJygIAAAAATJGgLAAAAADAFAnKAgAAAABMkaAsAAAAAMAUCcoCAAAAAEyRoCwAAAAAwBQJygIAAAAATJGgLAAAAADAFAnKAgAAAABMkaAsAAAAAMAUCcoCAAAAAEyRoCwAAAAAwBQJygIAAAAATJGgLAAAAADAFAnKAgAAAABMkaAsAAAAAMAUCcoCAAAAAEyRoCwAAAAAwBQJygIAAAAATJGgLAAAAADAFAnKAgAAAABMkaAsAAAAAMAUCcoCAAAAAEyRoCwAAAAAwBQJygIAAAAATJGgLAAAAADAFE0clK2qtVX1pKr6UlVtqqotQ8tuWVWvqaoDl6aaAAAAAACrw9pJMlXVrkk+muQOSX6TZFOS3YeS/DjJw5OcleQZ21hHAAAAAIBVY9KWsk9PckiSf0hyzST/NrywtbYxyaeT3H2bagcAAAAAsMpMGpR9QJJPtdZe3FprSdosaX6U5LoT1wwAAAAAYBWaNCh73SRfmSfNuUnWT7h+AAAAAIBVadKg7LlJrjFPmhskOXPC9QMAAAAArEqTBmVPS/InVbXXbAur6jpJ/ijJZyatGAAAAADAajRpUPYlSa6S5BNVdUiStUlSVbtV1ZFJTu7nvXxJagkAAAAAsEqsnSRTa+0zVXV0kldk69aw5/aPlyT5m9baV7exfgAAAAAAq8pEQdkkaa39c1WdkuQxSW6XZO8km5J8MclrWmv/vSQ1BAAAAABYRSYOyiZJa+27SZ6wRHUBAAAAAFj1Ju1TFgAAAACACSyopWxVXXfSAlprP500LwAAAADAarPQ7gs2JGkTrL8togwAAAAAgFVvoQHTN2eyoCwAAAAAAEMWFJRtrR21zPUAAAAAALhSMNAXAAAAAMAUbXN/r1V1qyS3SrI+ycYkX2utfW1b1wsAAAAAsBpNHJStqj9IclKSgwaz0vc7W1XfSvKo1tpXtrmGAAAAAACryERB2aq6dZJPJdktySeSfDbJr5Lsk+SwJHdO8qmqumNr7etLVFcAAAAAgCu8SVvKvjDJjknu3lr72OjCqrp7kg8leVGSe0xePQAAAACA1WXSgb7ukOTdswVkk6S1dnKS9/TpAAAAAADoTRqU3ZLkJ/Ok+XGSSyZcPwAAAADAqjRpUPbzSW43T5qDk3xuwvUDAAAAAKxKkwZl/z7JbarqeVW12/CCqtqtql6Q5FZJnrqtFQQAAAAAWE2qtbb4TFWvT3KDJIcmOSfJ15L8Ksk+6YKxeyX5bJIfjWRtrbVHbkuFl1pVrUuycePGjVm3bt1KVwcAAAAAGGPTpk1Zv359kqxvrW1a6fpMYtKg7KUTltdaa2smzLssBGUBAAAA4IpjNQRl106Y73pLWgsAAAAAgCuJiYKyrbWfLHVFAAAAAACuDCYd6AsAAAAAgAlsU1C2qh5cVR+rqjOranP/+NGqetBSVRAAAAAAYDWZqPuCqlqT5F1J/jRJJbkwyS+S7JPkLkmOrKo/T/IXrbVJBwUDAAAAAFh1Jm0p+7dJ7pvk1CSHtNZ2a61dr7W2W5I7JPlcuoDt45emmgAAAAAAq0O11hafqerrSXZJclBr7eJZlu+Y5JtJNrfWbrnNtVxGVbUuycaNGzdm3bp1K10dAAAAAGCMTZs2Zf369UmyvrW2aaXrM4lJW8oemOSDswVkk6Sf/6E+HQAAAAAAvUmDshcl2X2eNLv36QAAAAAA6E0alP1akvtX1bVnW1hV10py/yT/NWnFAAAAAABWo0mDsi9PctUkX6mqJ1fVbavqOv3jU5J8NcnefToAAAAAAHprJ8nUWvtQH3x9UZIXjyyuJFuSPKW19uFtrB8AAAAAwKoyUVA2SVprL6+q9yd5cJJbJlmXZFO6rg3e3lr70dJUEQAAAABg9Zg4KJskfeD1uUtUFwAAAACAVW/SPmW3UlV7V9V1lmJdAAAAAACr2cRB2apaX1WvqKpfJTkzyY+Hlt2uqv69qm6zFJUEAAAAAFgtJgrKVtXeSb6Y5PFJfpbku+kG+Br4ZpJD0vU3CwAAAABAb9KWssclOTDJA1trt03y7uGFrbULknw6yZ23qXYAAAAAAKvMpEHZeyf5cGvtXWPSbEiy34TrBwAAAABYlSYNyl4ryXfmSbM5ye4Trh8AAAAAYFWaNCj72yTXmSfNTZL8csL1AwAAAACsSpMGZT+T5D5VNWv3BFV1syT3SPLxSSsGAAAAALAaTRqUfX6SNUlOraoHJ7laklTVTavqkUk+ma77gpcsSS0BAAAAAFaJtZNkaq19q6oekOQtSd7cz64k3+4fz01y/9ba6UtSSwAAAACAVWKioGyStNY+WFXXS/KwJLdLsneSTUm+mOQNrbXfLE0VAQAAAABWj4mDsknSWjsryfFLVBcAAAAAgFVv0j5lt1JVa6vqKlW1TUFeAAAAAIDVbuKgbFWtqaonVdU3klyY5DdJLqyqb1TVEwVoAQAAAAAub6LAaVXtkeTkJAcnuTTJT5P8Ksk+SW6W5GVJ7ldVd2+tnb9EdQUAAAAAuMKbtKXsc5LcPsk7ktygtXb91trtW2vXT3KDJO9Mcoc+HQAAAAAAvWqtLT5T1RlJftFa+8Mxab6c5Fqttf22oX7LrqrWJdm4cePGrFu3bqWrAwAAAACMsWnTpqxfvz5J1rfWNq10fSYxaUvZqyb5+DxpPp5k7wnXDwAAAACwKk0alD09yTXmSXP1JD+ccP0AAAAAAKvSpEHZVyR5QFX93mwLq+qgJA9McsKkFQMAAAAAWI3WTpjv9CSfTPKVqnpTks8l+VWSfZLcMclDk5yc5IdVddhwxtbaZyavLgAAAADAFdukA31dmqQlqX7W8Epmm3eZ1tqaRRe4jAz0BQAAAABXHKthoK9JW8o+J3MEXQEAAAAAmNtEQdnW2nFLXA8AAAAAgCuFSQf6AgAAAABgAoKyAAAAAABTJCgLAAAAADBFgrIAAAAAAFMkKAsAAAAAMEWCsgAAAAAAUyQoCwAAAAAwRYKyAAAAAABTtHbSjFV1syRHJ/mDJHslWTNLstZau8GkZQAAAAAArDYTBWWr6vAk/5lk5yRbkvyqf7xc0smrBgAAAACw+kzaUvZFfd5HJXlTa+2SpasSAAAAAMDqNWlQ9hZJ3tlae/1SVgYAAAAAYLWbdKCv85P8eikrAgAAAABwZTBpUPbfk9xxKSsCAAAAAHBlMGlQ9u+T7FVVr6yq3ZayQgAAAAAAq9mkfcq+M8l5SR6X5Kiq+kGSTbOka621IyetHAAAAADAajNpUPaIoed7JLn1HOnahOsHAAAAAFiVJgrKttYm7fYAAAAAAOBKTXAVAAAAAGCKliQoW1W7V9W1qmr3pVgfAAAAAMBqNXFQtqp2qqp/rKrT0w3ydUaSTVV1elU9vap2WrJaAgAAAACsEhP1KVtVuyb5RJLbJbkkyelJfpnkmklukOS5Se5VVUe21i5YoroCAAAAAFzhTdpS9pgkByd5V5IbtNZu0lq7U2vtpkmun+T/9sufujTVBAAAAABYHaq1tvhMVd9Ncn5r7bZj0nw5yR59oHa7VVXrkmzcuHFj1q1bt9LVAQAAAADG2LRpU9avX58k61trm1a6PpOYtKXsAUk+Ok+aj/fpAAAAAADoTRqU/V2Sq8+T5up9OgAAAAAAepMGZU9L8sCq+r3ZFlbVzZI8IMkXJq0YAAAAAMBqNGlQ9gVJdkny5ao6saruV1V37B9fleTLSXZO8sJJVl5V+1bVW6vqt1V1QVV9q6puO7T8lKpqVfW0WfJ+pF923GSbBgAAAACwfNZOkqm1dmpVPSjJSUkel+RvhhZXko1JHtZaO3Wx666qqyQ5NcmnktwzyZlJbpTk7JGkP0tyVJIXDeXdN8mRSX652HIBAAAAAKZhoqBskrTW3l1V/5nkPklulWRdkk1JvpbkA621cydc9TFJftZae/jQvB/Pku7DSe5fVYcMBX8flm4AsutOWDYAAAAAwLKaOCibJH3g9a39tFTuneTkqnp3ksOT/DzJa1prJ42kuyjJ25I8PF3L2qRrOfvUJMfNtfKq2jld1woDey5JrQEAAAAAFmDSPmWX0/WTPDbJ6UnunuSfk7yyqh42S9rXp2stu3tVHZZkfboWtOP8Q7ruFQbTGUtVcQAAAACA+SyopWxVPbR/+v9aa+cOvZ5Xa+3Ni6zTDkm+0lp7ev/6a1V18ySPSfKmkXV/o6pOT3K/JHdK8pb/z96dRtlV1fnD/26SEMYqCGMUZFBBEehGERk0QQaJiCAtg+AE0v3XRwHBqQG1iUgLSMvQojb++8EBkSY0oIyNA5gADRgnWlFBZtE0yFSFGDCV7OdFVeqpChmqTt2qkJvPZ627bp19fmef3w28+q699q619pRSljb/aUnOGnC9dgSzAAAAAMAYGer2BV9PUpPcluTpAddLU/pqhhvKzkny60XGfpPk7UuovyC9h41tk2SnZU1ea30uyXP9TS49wAUAAAAAaKmhhrLvS2/AOqfv+sil1I7ULUm2XmRsqyQPLqH+20n+JckdtdZFw1wAAAAAgBeUIYWytdavL3L9jSWUtsLZSf67lHJSkhnpXf36f/o+i+vtyVLK5CTzRrEnAAAAAICWeMEd9FVrnZ3kwCSHJflVkk8nOa7WetFSnnmq1vrMGLUIAAAAANBYqXVZW8O2t1JKR5Kurq6udHR0LO92AAAAAICl6O7uTmdnZ5J01lq7l3c/TQxp+4JSyn0N56+11pc2fBYAAAAAoO0M9aCvVdJ70NdwlQbPAAAAAAC0raEe9LX5KPcBAAAAALBSeMEd9AUAAAAA0M6EsgAAAAAAY2ioB329p+kLaq3fbPosAAAAAEC7GepBX1/P8A/6Kn3PCGUBAAAAAPoMNZQ9clS7AAAAAABYSQwplK21fmO0GwEAAAAAWBk46AsAAAAAYAwJZQEAAAAAxtCQti8opdyX3kO79qq13t93PRS11vrSxt0BAAAAALSZoR70tUp6Q9klXS9JGXZHAAAAAABtbKgHfW2+tGsAAAAAAIbGnrIAAAAAAGNIKAsAAAAAMIaGuqfs85RSSpIDkvxNkhclmbCYslprParpOwAAAAAA2k2jULaU8rIkVyd5eZZ+mFdNIpQFAAAAAOjTdKXsl5JsleQrSS5OMidJT6uaAgAAAABoV01D2TckubLW+qFWNgMAAAAA0O6aHvT1dJJ7WtkIAAAAAMDKoGko+/0ku7ayEQAAAACAlUHTUPbjSV5USjmzlLJaKxsCAAAAAGhnjfaUrbXOKaXsk+TWJP+nlPK7JN2LL617jqRBAAAAAIB20iiULaXskN4tDNbpG3r1Ekprk/kBAAAAANpV0+0LzklvIPuPSV6SZEKtdZXFfMa1rFMAAAAAgDbQaKVsktckuaTWemYrmwEAAAAAaHdNV8p2J3mklY0AAAAAAKwMmoay302yRyml6fMAAAAAACulpqHqPyZ5LslFpZQXt7AfAAAAAIC21nRP2V8kWTXJjkkOKaU8md4tDRZVa60vbdocAAAAAEC7aRrKrpJkXpKHBoyVxdQtbgwAAAAAYKXVKJSttW7e4j4AAAAAAFYKDuoCAAAAABhDQlkAAAAAgDEklAUAAAAAGENCWQAAAACAMSSUBQAAAAAYQ0JZAAAAAIAxJJQFAAAAABhDQlkAAAAAgDE0frgPlFLWTHJgkqlJXp6ks+9WV5LfJflRku/UWp9pUY8AAAAAAG1jWKFsKeXvknwlyfpJymJKpiR5X5I/lVI+WGu9fOQtAgAAAAC0jyFvX1BK2SPJpekNYz+TZJf0hrOr9n3W7xs7Jcm4JDNKKW9sdcMAAAAAACuyUmsdWmEpNyTZJsmra61/XEbtJkl+muRXtdY9R9zlKCqldCTp6urqSkdHx/JuBwAAAABYiu7u7nR2diZJZ621e3n308RwDvp6TZJLlhXIJkmt9eEklyTZsWljAAAAAADtaDih7OL2kB2NZwAAAAAA2tZwQtmfJzm0lDJ5WYWllBcnOTTJz5o2BgAAAADQjoYTyn4uyYZJflFK+WQpZadSyrqllFX6Puv2jX0qvWHs+n3PAAAAAADQZ8gHfSVJKeVdSf41yTpJlvRgSdKV5Oha60Uj7nCUOegLAAAAAFYc7XDQ1/jhFNdav1VKuTrJIUmmJnl5ks6+211JfpdkZpIZtdanWtkoAAAAAEA7GFYomyR9YetX+z4AAAAAAAzDcPaUBQAAAABghEYtlC2lTCqlvGS05gcAAAAAWBGN5krZLyS5bxTnBwAAAABY4Yz29gVllOcHAAAAAFih2FMWAAAAAGAMjR9qYSlluFsRrD/MegAAAACAtjfkUDbJ5kkWJJk3xPoJw+4GAAAAAKDNDWf7gj8mubPWuvpQPkm+NUo9AwAAAACssIYTyv40yStLKROHWF8b9AMAAAAA0NaGE8r+PL3bHfzNEOtL3wcAAAAAgD7DCWW/meTIJI8Osf6jSbYYdkcAAAAAAG1syAd91VrvS3LfMOofT/J4k6YAAAAAANrVcFbKAgAAAAAwQkJZAAAAAIAxJJQFAAAAABhDQlkAAAAAgDEklAUAAAAAGENCWQAAAACAMdQolC2lvKSUsnGrmwEAAAAAaHdNV8ren+RzrWwEAAAAAGBl0DSUfTLJ461sBAAAAABgZdA0lL0pyeta2QgAAAAAwMqgaSh7YpLtSyn/VEoZ38qGAAAAAADaWdNA9RNJfpnk5CTvL6XckeSRJHWRulprPWoE/QEAAAAAtJWmoewRA/6e3PdZnJpE5gn2+AAAIABJREFUKAsAAAAA0KdpKLtFS7sAAAAAAFhJNApla60PtroRAAAAAICVQdODvgYppUwqpWzairkAAAAAANpZ41C2lNJZSjm3lPJIkj8luX/AvdeVUq4tpbymFU0CAAAAALSLRqFsKWVSktuTHJPk90l+k6QMKPmfJLsleedIGwQAAAAAaCdNV8pOT7JVknfUWndMcunAm7XWuUlmJtljRN0BAAAAALSZpqHs/kmurrXOWErNA0k2aTg/AAAAAEBbahrKTk7y62XUPJdkzYbzAwAAAAC0paah7ONJNl1GzSuSzGk4PwAAAABAW2oays5KckApZbHbE5RStkkyLckPmjYGAAAAANCOmoay/5xkXJJbSinvTLJ+kpRSXllKOSrJDendvuDMlnQJAAAAANAmxjd5qNb6y1LKoUkuTPLNvuGS5Fd9308nOaTW+ruWdAkAAAAA0CYahbJJUmu9spSyRZL3JnldkklJupPcnuRrtdbHWtMiAAAAAED7aBzKJkmt9YkkZ7eoFwAAAACAttd0T9lBSinjSynrllJGFPICAAAAALS7xqFsKWVcKeX4UsodSZ5N8liSZ0spd5RSjhPQAgAAAAA8X6PgtJSyVpLrk+ycZEGSh5I8kmSjJNsk+UKSg0op+9Ran2lRrwAAAAAAK7ymK2VPSbJLkouTvLTWumWtdZda65ZJXprkP5Ls2lcHAAAAAECfUmsd/kOlPJzkj7XWnZZSMzvJ5FrrJiPob9SVUjqSdHV1daWjo2N5twMAAAAALEV3d3c6OzuTpLPW2r28+2mi6UrZ9ZL8YBk1P0gyqeH8AAAAAABtqWko+7skGy6jZoMk9zScHwAAAACgLTUNZc9Ncmgp5VWLu1lK2S7JO5Kc07QxAAAAAIB2NL7hc79LckOSn5RSvpHk5iSPJNkoyRuSvCfJ9UnuKaVMGfhgrXVW83YBAAAAAFZsTQ/6WpCkJil9QwMnWdxYv1rruGG/cBQ56AsAAAAAVhztcNBX05Wyp2QJoSsAAAAAAEvWKJSttU5vcR8AAAAAACuFpgd9AQAAAADQgFAWAAAAAGAMCWUBAAAAAMaQUBYAAAAAYAwJZQEAAAAAxpBQFgAAAABgDAllAQAAAADG0PimD5ZSVqm1LlhkbJck+yV5NsnXaq0Pj7A/AAAAAIC20milbCnl7CR/KaWsM2DsoCQ3JTkxyWeS/KyUsklLugQAAAAAaBNNty94Y5Ibaq1PDRg7JUlXkvck+USSdZN8bGTtAQAAAAC0l6bbF2yaZObCi1LKFklekeQztdZv9Y29Icm0EXcIAAAAANBGmq6UXTPJMwOupyapSa4bMPbrJLYvAAAAAAAYoGko+8ckWw+4npbkz0l+OmCsI8lzDecHAAAAAGhLTbcvmJnksFLK0UmeTfJ3Sb5Ta50/oOalSR4eYX8AAAAAAG2l6UrZf04yN8m5Sb6a3hWx0xfeLKWsnWRKkltG2B8AAAAAQFtptFK21npPKWWbJG/vG7qq1vrggJKXJzk/ybdH2B8AAAAAQFtpun1Baq1zkpy3hHs/S/KzpnMDAAAAALSrxqHsQn0rZl+RZM1a64UjbwkAAAAAoH013VM2pZTXllJ+keSXSS5N8vUB96aUUv5SStl/5C0CAAAAALSPRqFsKeVVSW5IskWSs5Nct0jJTUkeS3LwiLoDAAAAAGgzTVfKfqbv+zW11o8lmT3wZq21Jrk1yWtH0BsAAAAAQNtpGspOTXJZrfWepdQ8lGRyw/kBAAAAANpS01B27SSPLqNm9STjGs4PAAAAANCWmoayv0+y3TJqXp3k3obzAwAAAAC0paah7NVJ3lRK2WtxN0sphyTZOcl3mjYGAAAAANCOxjd87nNJDkpybSnlG0k2TpJSygeT7JLksCQPJDmrBT0CAAAAALSNRqFsrfVPpZSpSS5MctSAW+f1fd+e5LBaa9cI+wMAAAAAaCtNty9IrfW+Wutu6d079oNJPpXk2CSvq7XuUmt9YKTNlVJOKKXUUso5A8Ye6Bt7x2Lq7+y7d8RI3w0AAAAAMBqabl/Qr9b6iyS/aEEvg5RSXpvk/Un+ZzG3f5/kyCT/MaB+5/Ruo/BMq3sBAAAAAGiVxitlF6eUsnkp5R9KKe8upaw1gnnWSnJRkn9I8uRiSi5KMrWUsumAsff1jfc0fS8AAAAAwGhrFMqWUk4qpdxfSll3wNjuSX6V5N+SfD3Jz0opkxr29aUk19Raf7CE+48kuT7Je/vevUaSQ5NcMITeJ5ZSOhZ+kqzdsEcAAAAAgGFrulL2bUkeqLUOXMV6Rt98Jyf5SpKXJTluuBP37RX76iQnLqP0giRHlFJKkoOS3Nu3lcKynJika8Dn4eH2CAAAAADQVNNQdvMkv154UUp5UZLXJvlSrfXUWuvRSX6Y5MDhTNq3HcG5Sd5Za312GeXXJFkryZT0bl2wzFWyfU5L0jngs8lwegQAAAAAGImmB311JHlqwPWUJDXJVQPGfpbkA8Oc9zVJNkzv1gcLx8YlmVJKOTrJxIWDtdaeUsqFST6T5HUZYgBca30uyXMLrwe8BwAAAABg1DUNZR9JstmA673TG3TePmBstfQGtcPxwyTbLTL2tSS/TXJGrXX+IiHqBUk+luSSRbZSAAAAAAB4QWoays5OckApZb8kz6b3kK0b+1ahLrRFkj8OZ9Ja69PpPSysXynlmSSP11p/tZj635RS1k/yl2H2DwAAAACwXDTdU/Zz6Q10v5vk+vSuiv3cwpullInp3dLg9sU+3UK11sdrrXNH+z0AAAAAAK3QaKVsrfVnpZSdk7y7b2hGrfXHA0p2SHJjkm+PsL/UWndf5HrzZdSvM9J3AgAAAACMlqbbF6TWekeSO5Zw77YM8eAtAAAAAICVSdPtCwAAAAAAaKDxStkkKaVskuSNSV6UZOJiSmqt9bMjeQcAAAAAQDtpHMqWUs5M8uEk4wYOJ6mL/C2UBQAAAADo02j7glLKPyT5aHoP8zoovQHsN5IcluTfkvQkuTTJHq1pEwAAAACgPTRdKft/kjyQ5M211gWllCR5oNZ6SZJLSikzknw/vcEsAAAAAAB9mh709Yok/1VrXTBgrD/grbXOTHJNko+NoDcAAAAAgLbTNJRNkqcG/P1MkvUWuX9XkleNYH4AAAAAgLbTNJT9Q5JNBlzfm+R1i9Rsm96wFgAAAACAPk1D2VuS7Dzg+rtJdiilnF9KeUsp5bQkb04ya6QNAgAAAAC0k6YHfV2Y5EWllM1qrQ8mOTPJfkn+IcnfJynpPQjs461oEgAAAACgXTQKZWutP0ryowHXfy6l7JzkgCQvTfJgkqtqrbYvAAAAAAAYoOlK2eeptc5L8p+tmg8AAAAAoB012lO2lNJZStm+lLLGEu6v2Xe/Y2TtAQAAAAC0l6YHff1Teg/7GreE++P67n+y4fwAAAAAAG2paSg7Lcn3a61PL+5mrbU7yfVJ9m3aGAAAAABAO2oayr4kye+WUXNvXx0AAAAAAH2ahrI1ycRl1EzMkrc3AAAAAABYKTUNZX+bZFoppSzuZilllSRvTnJX08YAAAAAANpR01D24iRbJbmglNI58Ebf9QVJXpbkWyNrDwAAAACgvZRa6/AfKmVCkhuT7JrkqSSzk/whyYuTvDbJOklmJdm71jqvZd2OglJKR5Kurq6udHR0LO92AAAAAICl6O7uTmdnZ5J01lq7l3c/TTRaKdsXtO6V5Kz07hu7d5Ij+r5XSXJmkn1e6IEsAAAAAMBYa7RSdtAEpYxL8ookneldNXtXrXV+C3obE1bKAgAAAMCKox1Wyo4f6QR9AeydLegFAAAAAKDtNT3oCwAAAACABoSyAAAAAABjSCgLAAAAADCGhLIAAAAAAGNIKAsAAAAAMIaGFMqWUi4vpRwy4HpKKeUlo9cWAAAAAEB7GupK2bclecWA6xuTHNHybgAAAAAA2txQQ9mnknQMuC6j0AsAAAAAQNsbP8S6Xyc5rJQyO8mcvrHNSylTlvVgrXVW0+YAAAAAANpNqbUuu6iUNyX5TpKJC4eSLPvBJLXWcY27GwOllI4kXV1dXeno6FhmPQAAAACw/HR3d6ezszNJOmut3cu7nyaGtFK21vq9Usork+yV5MVJpieZ2fcBAAAAAGCIhrp9QWqtDyb5f5OklDI9yY9qraeMUl8AAAAAAG1pyKHsIrZI7+FfAAAAAAAMQ6NQtm/VbJKklDI+ydZJOpJ0J7mr1trTmvYAAAAAANrLKk0fLKVMKqX83yRdSf4nyc1930+VUr5aSlmvRT0CAAAAALSNRitlSymTktyW5GVJnkhyU5I5STZOsmOSv08ytZSyS631iRb1CgAAAACwwmu6UvbT6Q1kz0yyWa11Wq31yFrrm5NsluSMJC9P8snWtAkAAAAA0B5KrXX4D5VyX5IHaq17LKXmhiSb11q3HEF/o66U0pGkq6urKx0dHcu7HQAAAABgKbq7u9PZ2ZkknbXW7uXdTxNNV8q+KMmty6i5ta8OAAAAAIA+TUPZrvRuU7A0m/XVAQAAAADQp2koOzPJwaWUvRZ3s5SyZ5KDk/yo4fwAAAAAAG1pfMPnPpPkLUmuL6Vcm96Q9pEkGyXZPcmbk/wlySkt6BEAAAAAoG00CmVrrXeWUvZJ8vX0hrNvSVKTlL6Se5McUWu9sxVNAgAAAAC0i6YrZVNrvbmU8vIkuyXZIUlHku4kP09yS621tqZFAAAAAID20TiUTZK+4PXmvg8AAAAAAMvQ9KAvAAAAAAAaEMoCAAAAAIwhoSwAAAAAwBgSygIAAAAAjCGhLAAAAADAGBLKAgAAAACMoUahbCllfinlolY3AwAAAADQ7pqulO1O8vtWNgIAAAAAsDJoGsr+OMnftLIRAAAAAICVQdNQdnqSPUop72lhLwAAAAAAbW98w+f2TvKjJF8rpRyTZHaSR5LURepqrfWzzdsDAAAAAGgvpdZFc9QhPFTKgiGW1lrruGG/YAyVUjqSdHV1daWjo2N5twMAAAAALEV3d3c6OzuTpLPW2r28+2mi6UrZN7a0CwAAAACAlUSjULbWOrPVjQAAAAAArAyaHvQFAAAAAEADjUPZUsr4UsrxpZQfl1K6Syk9A+79bSnly6WUrVrTJgAAAABAe2i0fUEpZfUk30uya5LHknQnWXNAyf1JjkzyRJJPjbBHAAAAAIC20XSl7ElJdktyYpKNk/z7wJu11q4kM5PsM6LuAAAAAADaTNNQ9tAkN9ZaP19rrUnqYmruS/KSxp0BAAAAALShpqHsS5L8ZBk1TyfpbDg/AAAAAEBbahrKPp1kw2XUvDTJnxrODwAAAADQlpqGsrcleWspZZ3F3SylbJpk3ySzmjYGAAAAANCOmoayZyZZN8kPSym7JRmfJKWUNUopeya5vm/srJZ0CQAAAADQJsY3eajWOquUcnSSczN4NezTfd/zk3yw1vrTEfYHAAAAANBWGoWySVJr/Uop5UdJPpDkdUkmJelOcnuSL9da72xJhwAAAAAAbaRxKJsktdbfJPlwi3oBAAAAAGh7TfeUBQAAAACggRGFsqWUA0sp3y2lPFRK6er7/m4p5W2tahAAAAAAoJ002r6glDI+ybeTvD1JSdKT5PEkGyd5a5L9SimXJTm81trTol4BAAAAAFZ4TVfKnpjkoCQ3JXlDktVqrZOTrJZkSpKb0xvYntCKJgEAAAAA2kWptQ7/oVLuS/Jsku0XtxK2lDIhyf8kmVhr3XLEXY6iUkpHkq6urq50dHQs73YAAAAAgKXo7u5OZ2dnknTWWruXdz9NNF0pOznJVUvamqDWOi/JVX11AAAAAAD0aRrK/j7JWsuoWTPJQw3nBwAAAABoS01D2X9PckgpZbErYUspL05yaF8dAAAAAAB9xg+lqJTykkWGZiTZLcnPSynnpPdgr0eSbJTeg78+3Dd2aetaBQAAAABY8Q3poK9SyoIkiyssSxlPkgW11iEFv8uLg74AAAAAYMXRDgd9DTUw/WYWH74CAAAAADAMQwpla61HjHIfAAAAAAArhaYHfQEAAAAA0IBQFgAAAABgDDUOZUspry+lfKeUcn8p5blSyvzFfHpa2SwAAAAAwIpuqAd9DVJKeXeSrycpSe5L8uMkAlgAAAAAgGVoFMom+XSSJ5PsW2v9cQv7AQAAAABoa023L9g0yX8IZAEAAAAAhqdpKPtgklVb2QgAAAAAwMqgaSj7f5PsV0qZ1MpmAAAAAADaXaM9ZWutXyilbJnkllLKqUnuSNK9hNqHRtAfAAAAAEBbaXrQV5L8LMnhSb65lJo6wncAAAAAALSVRoFpKeWYJOckmZfkxiRzkvS0sC8AAAAAgLbUdBXr8Un+kGTXWuvDLewHAAAAAKCtNT3oa+MklwlkAQAAAACGp2koe0+SdVrZCAAAAADAyqBpKHt2kgNKKZu1shkAAAAAgHbXdE/Ze5PMTPKTUso5Se5I0r24wlrrrIbvAAAAAABoO01D2R8lqUlKks/2/b0k4xq+AwAAAACg7TQNZU/J0oNYAAAAAAAWo1EoW2ud3uI+AAAAAABWCk0P+gIAAAAAoAGhLAAAAADAGGq0fUEpZUGGtqdsrbU23bcWAAAAAKDtNA1MZ2XxoWxnkpcnWTPJHUmeajg/AAAAAEBbanrQ1+5LuldKWSPJ6UmmJdm7WVsAAAAAAO2p5XvK1lr/Ums9NklXkjNbPT8AAAAAwIpsNA/6uinJW0ZxfgAAAACAFc5ohrIbJFlrFOcHAAAAAFjhND3oa4lKKaskeWeSQ5P8pNXzAwAAAACsyBqFsqWU+5Yy34ZJJiSZl+TEhn0BAAAAALSlpitlV0lSFzM+L8mvksxOcl6t9c6mjQEAAAAAtKNGoWytdfMW9wEAAAAAsFIYzYO+AAAAAABYhFAWAAAAAGAMDXn7glLKBQ3mr7XWoxo8BwAAAADQloazp+wRw6itSUrft1AWAAAAAKDPcELZXYZY97Ik05O8dNjdAAAAAAC0uSGHsrXW25d2v5SyfpKTk/xDklWT3JzkH0fUHQAAAABAmxnOStnFKqWskeRjST6aZO0kdyY5qdZ61UjnBgAAAABoN41D2VLKuCTvT/LpJBsleTjJcUm+UWtd0Jr2AAAAAADaS6NQtpRycJJT07t/bFeSE5L8a6312Rb2BgAAAADQdoYVypZSdk9yRpIdk/w1yReSfK7W+lTrWwMAAAAAaD9DDmVLKdcleVOSBUm+keSfaq0Pj1ZjAAAAAADtaDgrZfdJUpM8lGTjJF8tpSzrmVprfUvD3gAAAAAA2s5w95QtSbbo+wxFHeb8KaWcmOTvkrwiydwk/53kH2utdw2oeSDJZkkOq7X+xyLP35lkmyRH1lq/Ptz3AwAAAACMpuGEskMNYkdqapIvJZmd3v4+l+R7pZRtaq3PDKj7fZIjk/SHsqWUndO7indgHQAAAADAC8aQQ9la64Oj2ciA90wbeF1KOSLJo0lek2TWgFsXJTm+lLJprfX3fWPv6xt/zxi0CgAAAAAwbKss7waGoLPv+4lFxh9Jcn2S9yZJKWWNJIcmuWBpk5VSJpZSOhZ+kqzd4n4BAAAAAJboBR3KllJWSXJOkltqrb9aTMkFSY4ovSeOHZTk3lrrL5Yx7YlJugZ8Hm5hywAAAAAAS/WCDmXTu7fstknesYT71yRZK8mU9G5dsNRVsn1OS+/q24WfTUbeJgAAAADA0AznoK8xVUo5L8l+SabUWhe7mrXW2lNKuTDJZ5K8LsmBy5q31vpckucGvKc1DQMAAAAADMELbqVs6XVeegPWPWqt9y/jkQuSTE3y3Vrrk6PeIAAAAADACLwQV8p+KcnhSQ5I8nQpZeO+8a5a69xFi2utvymlrJ/kL2PYIwAAAABAIy+4lbJJ/p/07vX6oyRzBnwOXdIDtdbHFxfYAgAAAAC80LzgVsrWWpe5yWutdfNl3F+nZQ0BAAAAALTQC3GlLAAAAABA2xLKAgAAAACMIaEsAAAAAMAYEsoCAAAAAIwhoSwAAAAAwBgSygIAAAAAjCGhLAAAAADAGBLKAgAAAACMIaEsAAAAAMAYEsoCAAAAAIwhoSwAAAAAwBgSygIAAAAAjCGhLAAAAADAGBLKAgAAAACMIaEsAAAAAMAYEsoCAAAAAIwhoSwAAAAAwBgSygIAAAAAjCGhLAAAAADAGBLKAgAAAACMIaEsAAAAAMAYEsoCAAAAAIwhoSwAAAAAwBgSyq6Epk+fnrXWWmtEc+y+++7Zb7/9RjTH5ptvnqOPPnpEcyzNd77znZRS8sADD4zaOwAAAABguISyAAAAAABjSCgLAAAAADCGhLI8zwknnJDtttsua621Vl784hfnsMMOy5w5c5b6zNy5c/OWt7wlW265Ze67774kyZVXXpkdd9wxa621VtZZZ53suOOOufbaa5/37Je+9KVsttlm6ezszNve9rb86U9/6r/3zDPP5Oijj87WW2+dNdZYI5tvvnk+8IEPpKura9Ac8+bNy3HHHZdJkyals7MzRx11VP785z8Pqtlzzz3z7ne/u//6pz/9aUopOfjgg/vH7rrrrpRSMnv27P6xW2+9NXvssUfWXHPNdHZ25vDDD8+jjz46aO7nnnsuJ510UjbbbLNMnDgxr3zlK/Ptb397UM0RRxyRbbfdNtddd1223XbbrLbaannNa16T2267ban/tgAAAAC0F6Esz/Poo4/mpJNOyjXXXJNzzz03DzzwQKZOnZqenp7F1v/5z3/Ovvvum3vvvTc33XRTttxyy9x777056KCD8qpXvSpXXHFFLrnkkhxyyCF58sknBz175ZVX5sorr8yXvvSlnHvuuZk5c2aOOeaY/vt/+ctfMn/+/PzzP/9zrrvuupx66qmZOXNm3va2tw2a58QTT8yXv/zlfPzjH8+MGTMyf/78nHDCCYNqpkyZklmzZvVfz5w5M6uttlpuuummQWNrrbVWXv3qVyfpDWR33333dHZ25pJLLslXv/rVzJ49OwcccMCguQ855JCcf/75+ehHP5qrr74606ZNy7ve9a5cd911g+rmzJmTD37wg/19Tpw4Mfvss8/zQl4AAAAA2tf45d0ALzwXXHBB/9/z58/PLrvskk022SQ33HBD3vSmNw2qffLJJ/PmN785zz77bGbNmpUNN9wwSfLzn/888+bNy3nnnZe11147SbLPPvs871211lx55ZWZOHFikuSBBx7I5z73uSxYsCCrrLJKNthgg3zlK1/pr+/p6ckWW2yR17/+9bn77ruz1VZb5YknnsiXv/zlnHDCCTnxxBP73zV16tT84Q9/6H92ypQpmT59eh588MFsttlmmTVrVt73vvfl3/7t3/Lb3/42r3jFKzJr1qzsuuuuGTduXJLeVcM77rhjLr/88pRSkiTbbbddtt1221x77bXZd999c+ONN+bKK6/M9ddf3//vs/fee2fOnDk5+eST8+Y3v7m/hyeeeCKXXnpp9thjjyTJ1KlTs+mmm+bss8/Oaaed1uQ/FwAAAAArGCtlVwLz5/Xk/htuzq8vvjz333BzFsxfsNT66667Lrvuums6Ozszfvz4bLLJJkmSu+++e1DdY489lje+8Y1JkhtvvLE/kE2S7bffPuPGjcvhhx+eq6666nnbDSw0derU/kA2SbbZZpvMmzdv0MrRCy+8MDvssEPWWmutTJgwIa9//esH9fPLX/4yc+fOzYEHHjho7re//e2DrnfeeeesuuqqmTVrVmqtufnmm3PAAQdk++23z8yZM5MkN910U6ZMmZKkd5XuLbfckoMPPjjz589PT09Penp6stVWW2XTTTft3+Lge9/7XiZNmpQ99tijv6anpyd77713fv7zn2f+/Pn9PXR2dvYHsguv99prr9x+++2L/48BAAAAQNsRyra5uy6/Oj/fa58885Gjs+CMU/LMR47O/37zwtQBQeFAs2fPzv77758XvehFufDCC3Prrbf273n67LPPDqq9++67c8cdd+Swww7LuuuuO+jeVlttlauvvjpdXV058MADs8EGG2T//ffPQw89NKhunXXWGXS96qqrDnrXFVdckfe85z3ZaaedMmPGjNx222254oorBtUs3O92YCicJBtttNGg69VXXz2vfe1rM2vWrNx5553p6urKrrvu2r+twQMPPJCHHnqoP5R98sknM3/+/Bx//PGZMGHCoM9DDz2U3//+90l6w+knnnjieTV///d/n56enkH78W6wwQbP+zffaKONlrlnLwAAAADtw/YFbeyuy6/OX0/5VCYuMj7uubmpf/1r7rr86mz9d/sNunfFFVeks7MzM2bMyCqr9Gb2Dz744GLn33XXXbPXXnvlIx/5SNZbb728613vGnR/2rRpmTZtWrq7u/Nf//VfOf7443PkkUfmhz/84ZB/w6WXXpq//du/zfnnn98/tnBV60KTJ09O0rsX7otf/OL+8UceeeR5802ZMiWXXXZZdthhh/7Vt1OmTMmHP/zhzJo1KxMnTsxOO+2UpDcwLqXkpJNOet4etkmy/vrrJ0kmTZqUDTbYYLGHmCWDw+KBh5gN7HPhbwAAAACg/Qll29T8eT15+tyzMzFJWeTewuun//XszH/rtIyb8P//bzB37txMmDChf//UJLnooouW+J7jjjsuc+fOzRFHHJHVVlstBx100PNqOjo6csghh+T222/PxRdfPKzfMXfu3P7Vs0vqZ7vttsvqq6+eK664IjvssEP/+GWXXfa8+aZMmZLTTjstM2bMyNSpU/vH/vCHP+SCCy7ITjvt1L+dwpprrplddtklv/nNb3Lqqacusce99torn//857Pqqqtm++23X+rv6erqyg033NC/hUFXV1d+8IMf5EMf+tBSnwMAAACgfQhl29RDN92W1boeX+L9BbVm1gP35vZTT8tG272yf3yHHXbIOeeck2OOOSYHHnhgbr311lx44YVLfdeJJ54CqMQKAAAgAElEQVSYuXPn5vDDD89qq62W/fbbL+eff35uvfXWTJs2LZMnT87999+fb33rW887KGxZ9t5773zoQx/KZz/72eyyyy659tprn7fSdtKkSfnABz6Q008/Pauvvnpe/epX5+KLL8699977vPl22223jBs3LjNnzsxHP/rRJL1bCrzyla/MzJkz88lPfnJQ/Zlnnpk99tgjhx56aN7xjndk3XXXzcMPP5zvf//7OfLII7P77rtn7733zlvf+tZMmzYtn/jEJ7L99tvnmWeeyZ133pl77rkn//7v/z6o16OOOiqf+cxnss466+T0009PrTXHHXfcsP5dAAAAAFhxCWXb1NxHHl3q/edqzUcffDA55Z8GjV944YU544wz8sUvfjFf+9rXsttuu+Xqq6/OVltttdT5TjnllMydOzcHHXRQrr766my//fa56qqr8pGPfCSPP/54Nt544xx22GH57Gc/O6zf8f73vz/33XdfvvjFL+bMM8/MPvvsk29/+9vZeeedB9Wdfvrp6enpyec///ksWLAgBx54YE4//fS8+93vHlS39tprZ4cddsjPfvaz/gPDkt4Dx37zm9/07ye70K677pqbb745J598co488sj89a9/zSabbJI999wzL3vZy/rr/vM//zOnn356vvzlL+fBBx9MZ2dntt122xx55JGD5ps8eXLOOOOMfPzjH8+9996bV73qVbn++uuft/8tAAAAAO2r1FqXdw/LVSmlI0lXV1dXOjo6lnc7LXP/DTfnmY8cvcy6Nc86L1vs8fpl1jFyRxxxRH7yk5/kV7/61fJuBQAAAGCF1d3dnc7OziTprLV2L+9+mlhleTfA6HjJG3bOs53rZUmRe03y7Drr5SVv2HkJFQAAAADAaBDKtqlxE8Zn7Q8fnyTPC2YXXq997PGDDvkCAAAAAEaf7QvadPuChe66/Oo8fe7Zgw79enad9bL2scdn67/bbzl2BgAAAADD1w7bFwhl2zyUTZL583ry0E23Ze4jj2b1jTbMS96wsxWyAAAAAKyQ2iGUlcytBMZNGO8wLwAAAAB4gbCnLAAAAADAGBLKAgAAAACMIaEsAAAAAMAYEsoCAAAAAIwhoSwAAAAAwBgSygIAAAAAjCGhLGPuuuuuy7777psNNtggEyZMyEYbbZS3vOUtufjii7NgwYLl3R4AAAAAjCqhLGPqpJNOyr777pvVVlst5513Xn74wx/mvPPOyzrrrJN3vetd+f73v7+8WwQAAACAUVVqrcu7h+WqlNKRpKurqysdHR3Lu522ds0112S//fbLySefnOnTpz/v/o9//ONMmDAhO+yww9g3BwAAAMAKobu7O52dnUnSWWvtXt79NGGlLGPmrLPOyuTJk/OpT31qsfd32mmnQYHs+eefn6233joTJ07M5ptvnlNPPXXQ9gZz5szJ+973vmy55ZZZffXV8/KXvzwnnXRSnnvuuVH/LQAAAADQ1Pjl3QArh56entxyyy056KCDMn78sv+3++IXv5hjjz02xxxzTPbbb7/893//d6ZPn56nnnoq//Iv/5IkeeyxxzJp0qScddZZWXfddXP33Xdn+vTpmTNnTr72ta+N9k8CAAAAgEZsX2D7gjHxyCOPZOONN84JJ5yQ0047rX+81pr58+f3X6+yyiqptWbjjTfOXnvtlYsvvrj/3kknnZQvfOEL+eMf/5j11lvvee/o6enJjBkz8t73vjddXV1ZY401RvdHAQAAADDmbF8Aw1RKGXR92WWXZcKECf2fY489Nr/97W/z2GOP5eCDDx5Ue+ihh+avf/1rfvzjHyfpDXTPOeecbLPNNll99dUzYcKEvPOd70xPT0/uu+++MftNAAAAADActi9g1Myf15OHbrotcx95NBPWn5SJEyfm4YcfHlSz5557Zvbs2UmS/fffP0ny5JNPJkk22mijQbULr5944okkyTnnnJOPfexj+cQnPpE3vvGNWXfddTN79ux86EMfyrPPPjuqvw0AAAAAmhLKMiruuvzqPH3u2Vmt6/EkyXNJtlt9jVx/zTWZP39+xo0blyRZd911s+OOOyZJVl111STJpEmTkiSPPvrooDkfeeSRQfcvvfTS7L///oO2Q/j1r389ej8KAAAAAFrA9gW03F2XX52/nvKpTOwLZBc6Yt118ugTT+Qj73rvUp/feuuts8EGG+TSSy8dND5jxoysuuqq2WmnnZIkc+fO7Q9yF7rooota8AsAAAAAYPRYKUtLzZ/Xk6fPPTsTk5RF7k3t6MhRG26Yf/2Pi/L7Z/+Sdxz2jkyePDldXV256aab8r//+79Ze+21M27cuHz605/Osccemw033DD77rtvbrvttpxxxhk57rjj+g/52nvvvXPuuefmvPPOy1ZbbZVvfetbueeee8b8NwMAAADAcJRa6/LuYbkqpXQk6erq6kpHR8fybmeFd/8NN+eZjxy91JpZ3d2ZscFG+dW9v0tXV1cmTZr0/7F33+FRVOsDx7+zm012U0gPhBBaAgiCgIAizVACwdAvoCIXgqAgojQVlBZRkcv10kEF/RHwCggISBGJCioGEFSQpjRDDwQI6W3L+f0RsrJsqAbIJe/nefLInDlz5szuJibvnHlfGjRoQO/evXnqqafQ6QoWcH/wwQdMnTqVY8eOERwczHPPPccbb7xh35+ZmclLL73EF198AUD37t3p1KkTHTt2ZOfOnfa0CEIIIYQQQgghhBDi/pGeno63tzeAt1Iq/V7P53ZIUFaCssXqwJKV2P418Yb9dKPGU+vpbndhRkIIIYQQQgghhBDifnI/BGUlp6woVqayQcXaTwghhBBCCCGEEEKI+40EZUWxqti8Mbne/lxr/bUCcn38qdi88d2clhBCCCGEEEIIIYQQJYYEZUWx0htc8Bo6HMApMFu47fXycPQGqTEnhBBCCCGEEEIIIUoniYyJ22KzKU6n5pCVb8HD1YUQHxM6nQZAjW4dOAhkzJiGMe2i/Zg8H3+8Xh5OjW4d7tGshRBCCCGEEEIIIYS496TQlxT6umVHkjPYuO8cR89nkmuxYnTRExboSbvaZQkP8rL3s5otnNiynZxzyZjKBlGxeWNZISuEEEIIIYQQQggh/pb7odCXRMjELTmSnMGChGOkZOUT7G3E3dVEdr6FfWfSOJOWQ7+mle2BWb3BhSqtmt3jGQshhBBCCCGEEEIIUbJITllx02w2xcZ950jJyqdakCdeRgN6nYaX0UC1IE9SsvKJ338Om610r74WQgghhBBCCCGEEOJ6JChbithsipMp2fxxNp2TKdm3HDw9nZrD0fOZBHsb0TTNYZ+maQR7GzmSnMnp1JzinLYQQgghhBBCCCGEEPcVSV9QStxsHtjrycq3kGux4u5qKnK/yVXPufRcsvItxTl1IYQQQgghhBBCCCHuKxKULQVuJQ/s9Xi4umB00ZOdb8HLaHDan5Nvxc1Fj4erfKyEEEIIIYQQQgghhLgWSV9wnyvOPLAhPibCAj1JSstFKcf+SimS0nIJD/IkxKfolbRCCCGEEEIIIYQQQggJyt73ijMPrE6n0a52Wfw8XDmcnElGrhmLzUZGrpnDyZn4ebjS9sGy6HTaDccSQgghhBBCCCGEEKK0kqDsfe6vPLBFpxQwuerJs1hvOg9seJAX/ZpWpnZ5b1KzzRy7kEVqtpk6Id43nQZBCCGEEEIIIYQQQojSTJJ/3ufuRB7Y8CAvqkZ4cjo1h6x8Cx6uLoT4mGSFrBBCCCGEEEIIIYQQN0GCsve5wjyw+86k4enm4pDCoDAPbJ0Q71vOA6vTaYT6uRf3dIUQQgghhBBCCCGEuO9J+oL7nOSBFUIIIYQQQgghhBCiZJGgbCkgeWCFEEIIIYQQQgghhCg5JChbSoQHefFCRBjDI6vzUutqDI+szqDHwyQgK0QpFxsbi6ZpaJqGTqfD29ubOnXqMGTIEH7//fdbHi8uLg5N07hw4QIAx44dQ9M0VqxYccN5eHp63tY1CCGEEEIIIYQQ/2skp2wpInlghRBFMZlMbNq0CYCMjAz27t3LvHnzmD9/Ph9//DG9e/e+43MYMGAA0dHRd/w8QgghhBBCCCFESSBBWSGEKOV0Oh2NGze2b0dGRjJ48GCio6Pp378/TZo0oWrVqnd0DhUqVKBChQp39BxCCCGEEEIIIURJIekLhBBCODEajcyaNYv8/Hw++ugje3tcXBwPPfQQRqORkJAQxowZg9VqveF4ubm5DBkyBF9fX4KDg3nllVewWCz2/ZK+QAghhBBCCCFEaSJBWSGEEEWqVasWISEhbNu2DYCpU6cyYMAA2rVrx9q1axk1ahQzZ85kzJgxNxxrzJgx6HQ6li1bxqBBg/jPf/7jEOwVQgghhBBCCCFKE0lfIIQQ4ppCQ0M5e/YsGRkZTJgwgddee41JkyYBBWkOXF1dGTFiBK+++ir+/v7XHOfRRx9l5syZ9uM2b97MihUrGDRo0F25DiGEEEIIIYQQoiSRlbJCCCGuSSmFpmls3bqVzMxMevTogcVisX+1adOGnJwc9u3bd91x2rZt67Bdq1YtTp06dSenLoQQQgghhBBClFgSlBVCiFLGaraQuOlHDixZyaXEE9fte+rUKcqVK8eFCxcAePjhhzEYDPavatWqAXDy5MnrjuPj4+Ow7erqSm5u7t+4CiGEEEIIIYQQ1xMbG4umaYSEhGCz2Zz2N23aFE3TiImJuekxjx07hqZprFixohhnemdomhajaZrSNC3gJvr1ulvzKiTpC4QQohQ5uHIdGTOmYUy7CID57FlsObkcXLmOGt06OPTdv38/p0+fJiYmBj8/PwBWrlxJaGio07hVqlS585MXQgghhBBCCHFLDAYDFy5c4IcffiAiIsLefvz4cbZt2yYFlwvEAJnA4rt5UgnKCiFEKXFw5TryJ47FzWmPIn/iWA6CPTCbm5vLSy+9hJubGwMGDMDHxwd3d3dOnTpF165d7/LMhRBCCCGEEELcDldXV9q0acOSJUscgrJLly7lwQcfRK/X37vJFSEnJweTyXSvp1EkTdP0gE4pZS6O8SR9gRBClAJWs4WMGdMA0K7ap5Tit6wstr4dy8avNjJt2jTq169PQkICCxYsoHLlyvj4+DBx4kRee+01Ro0axYYNG4iPj+eDDz6gffv2ZGdn3/VrEkIIIYQQQghxY08//TQrVqzAbP4rlrh48WJ69XJ8Yv+PP/7gqaeeIjQ0FHd3d2rVqsV//vOfIlMf5ObmMmTIEHx9fQkODuaVV17BYrHc0liFqRDi4uJ47rnn8Pf355FHHgFg/fr1REZGEhQURJkyZXj00Uf56quvirq8mZqmndY0LVfTtJOapi0tok+opmkbNE3L0jTtsKZpfQp3aJr2HfA4EH051YHSNC22cJ+maes0TeuradpBIA+oq2lasKZp/6dp2p+apuVcHnOSpmnOa6CuQ1bKCiFEKXBiy3Z7yoKr5SrFP48cAcCjW1eqhIXRunVrVq1axQMPPGDvN3LkSEJCQpg6dSqzZs3CYDAQFhZGhw4dcHV1vSvXIYQQQgghhBDi1nTs2JH+/fsTHx9PdHQ0Bw4cYM+ePaxevZrPPvvM3u/06dPUqFGDZ555Bi8vL3bv3s2ECRPIzMxkwoQJDmOOGTOGzp07s2zZMrZu3UpsbCzh4eEMGjTolsd6/fXXiY6OZsmSJfagbWJiIh07duSVV15Bp9OxYcMGnnjiCTZt2uSw4heIAkYBx4BgoH0RL8GnwHxgKvAcEKdp2k6l1O/AYOC/QDbwyuX+V1albghUBsYDl4CTQBCQAoy43FYdiL18/n7XficcaUqpm+17X9I0rQyQlpaWRpkyZe71dIQQ4o44sGQltn9NvGE/3ajx1Hq6212YkRBCCCGEEEKIOyk2Npb33nuPzMxMnnnmGXQ6HZ988gnjxo3j22+/ZevWrdSrV4969eoRFxfncKxSCqvVypQpU5g9ezZnzpwBCla3VqlShR49erBs2TJ7/4iICFxcXPjmm2+c5nGjsaKiotiwYcM1r8Nms2Gz2YiOjsbf35/FixeTnp6Ot7c3wGyl1EtFHadpWgywAHhRKTX3cpsHkAy8q5R6+3Lbd0CmUqrDVcd/BzQBwpRS16xurWmaC9ATWAh4K6Vu6lFSSV8ghBClgKlsULH2E0KUTrGxsUUWgxg5ciQ6nY6PP/74HsyqaLt37yY2Nva206tomsZ7771XzLMSQgghhLg3nn76ab744gtycnJYunQpTz/9tFOf3NxcJkyYQHh4OG5ubhgMBsaMGUNSUhKZmZkOfdu2beuwXatWLU6dOnVbY0VHRzvN5dSpU/Tt25eQkBBcXFwwGAzEx8dz6NChq7s+o2naK5qm1b7O5ccX/kMplQUcBypcp/+V9lwdkNUKDNM07YCmaTmAmYLVuC5A1ZscV4Ky4i82m+JkSjZ/nE3nZEo2NlvpXkUtxP2kYvPG5Hr7c63vagXk+vhTsXnjuzktIcR9YNSoUUybNo0PPviA/v373+vp2O3evZs333xTcl4LIYQQotSwmi0kbvqRA0tWkrjpR2zWv/K3tmvXDoPBwPjx40lMTKRnz55Ox48aNYp///vfPPfcc3z55Zfs3LmTsWPHAgVB1iv5+Pg4bLu6ujr0uZWxypYt67Bts9no1KkTP/74IxMnTmTz5s3s3LmT9u3bOx0LLAVGAns1TTuhadoLRbw0qVdt5wPGIvoV5VwRbcOA/wBfAJ2BR4AXL++72XElp6wocCQ5g437znH0fCa5FitGFz1hgZ60q12W8CCvez09IcTfpDe44DV0OPkTx6JwLPZVGKj1enk4eoP8b0EIcfPGjh3LlClTmDt3Ls8///zfGqskV9oVQgghhCjpDq5cR8aMafZaIlnA2UtpKKsVAIPBwD/+8Q+mTp1K69atnQKhAMuXL2fgwIGMGjXK3rZ+/frbms+tjKVpjuWojxw5wq5du1i9ejWdO3e2t+fk5BR1+Gil1GBN0+oAQ4G5mqbtU0ptua2JOytqbVMPYI1S6vXCBk3Tat3qwLJSVnAkOYMFCcfYdyYNH3cDVQM88XE3sO9MGgsSjnEkOeNeT1EIUQxqdOuA6/i3yfP2d2jP8/HHdfzb1OjW4RpHCiGEs9jYWN555x1mzZrFCy/8tSBh0aJFNGvWDD8/P3x9fYmIiGDHjh1Ox3p6erJjxw4ee+wxjEYjc+bMIS4uDk3T2LVrF+3bt8fDw4Nq1aqxaNEih+NvVI03Li6Ofv0KaiwEBgaiaRqVK1cGICkpiWeffZaqVatiMpmoVq0ab7zxBnl5ede93sTERMLCwmjfvj05OTm3PY4QQgghRHE7uHId+RPH4nZVcWd9Xg4qP5+DK9cBMGDAADp27MjQoUOLHCcnJ8ehiLPVamXp0qW3Nae/M1Zh8PXK448fP05CQsI1j1FK7QWGX96seYvTvZWVswCmy8dc6ZlbPKeslC3tbDbFxn3nSMnKp1qQp/3uhJfRgKebC4eTM4nff46qAZ7odNoNRhNClHQ1unXA2jGKE1u2k3MuGVPZIGo2bywrZIUQt+Sdd97hzTffZNq0aQwZMsRh37Fjx+jTpw9hYWHk5+ezZMkSWrRowZ49e6hevbq9X35+Pr169WL48OFMmjQJf39/fv31VwCeeeYZnnvuOUaMGMH8+fOJiYmhUaNG1KxZ8Pv1jarxRkdHM3bsWN5++22++uorvL29cXNzA+DChQv4+fkxdepUfH19OXToELGxsSQlJbFgwYIir/fgwYO0bt2aRx99lCVLluDq6sqRI0dueRwhhBBCiOJmNVvImDENNxyfiOSK7YyZ07B2jOKRRx5h9erV1xwrMjKS+fPnU6tWLQICApg7d+5t33D+O2M98MADVKhQgdGjR2O1WsnMzGTChAmEhIQU1f0lTdN+AaxAHwqCpbe6SvZ3oK+maR2BJOCMUurMdfp/DQzVNG0IcAjoDYTf4jklKFvanU7N4ej5TIK9jU7LxTVNI9jbyJHkTE6n5hDq536PZimEKE56gwtVWjW719MQQvyPysrKYuzYsQwYMIBhw4Y57R8/frz93zabjcjISHbs2EFcXByTJk2y7zObzbzzzjs8+eST9rbCoOyQIUMYPHgwAE2aNGH9+vV8/vnn9jxkVwaCbTYbLVu2ZP/+/cybN4+IiAgCAwMJCwsDoEGDBgQEBNj716lTx6GAV9OmTfHw8KBv377MmTMHd3fH33d+++032rZtS7t27ViwYAF6vf62xhFCCCGEuBNObNluT1lwLcbUi5zYsv2GfwfOmjWLQYMG8dJLL+Hu7k5MTAxdu3blueeeu+V5/Z2x3NzcWLlyJS+++CI9evQgNDSUsWPHsmnTJn7++eeruz8NjAZswF6go1Lq91uc7hQKgqqLAB/gTSD2Ov0nAoGX/wuwAngZWHsrJ5X0BaVcVr6FXIsVd9ei4/MmVz15FitZ+Za7PDMhhCh96tati6ZpbNlSXOmPCqSmpqJpGnFxcfa2ypUrO61wvBtiY2PRNM3+FRgYSKtWrYr9mq+2evVqNE3j2LFjxTpuTEwMtWtfr9Dr/cdkMtGiRQsWL15c5CNkv//+O127dqVs2bLo9XoMBgMHDx4sqlJukZV2wbGar4eHB5UqVXKo5nsL1XidKKWYPn06tWrVwmQyYTAYeOaZZ7BYLPz5558OfXfu3ElERATdunVj4cKF9oDsrY4jhBBCCHGn5JxLvua+weXKsaNOnev22717t/3vhLJly7Jq1SrS09M5e/YskydPZsCAASil7De5K1eujFKK7t27O4wzffp0h9+1/85YAI0aNWLHjh3k5ORw6NAh+vTpQ1xcHPv27bu6axOllJdSylsp1UwpFV+4QykVp5TSlFIXrjxAKVVPKRVzxfZppVS0Usr3cv/Yy+0RSimnPH9KqUylVD+llN/lr+eVUusuH+sUNb4WCcqWMjab4mRKNn+cTedkSjYmgx6ji57sawRdc/KtuLno8bhG0FaIkmbNmjW0bdsWPz8/XF1dqVKlCgMHDrT/oa5pmsPKpqIU5jS8cOHCdfvdjN27dxMbG+tU/fta7cV57uJ0rwJ4pcn+/fvZs2cPAIsXL77Hs7mzTCYT27ZtY9u2bbz//vtcvHiR1q1bF/ULligBrqzieynxBDqdjjVr1lC9enU6dOjA3r177X0zMjJo27Ytx48fZ+rUqWzZsoWdO3dSt25dp0q57u7ueHp6FnnO61XzvcVqvE6mT5/OyJEj6dy5M1988QU7duxgzpw5gHMl4G+++YasrCz69+/v9ETRrYwjhBBCCHGnmMoGFWs/cfdIULYUOZKcwfvfHWXa14eY+e1hpn19iLW/ncHHZCApLRelHAvKKaVISsslPMiTEB+phixKvtGjR9O5c2e8vb2ZP38+33zzDePHj+fAgQMOj8feTbt37+bNN98sMihbVHt0dDTbtm1zCkiI+9+nn36KTqejZcuWLF++HLPZfK+nVKRrVDy9JTqdjsaNG9O4cWO6d+/O2rVrsVgsfPDBB0X2V0qVmOJJV67y1TSNhQsXsn//foe2K1ckF7fCAlmFvvvuOzRNK+oxriLnvWnTJof2olZRX+ngynXsatOOrBFDsP1rIuaNX2LLyeXst1vYuHEjAQEBtGvXjsTERADq1avHqVOnWLBgAc888wzNmjWjYcOGpKWlFTmn21FYjXfq1Kn079+fxx9/nIYNG970Z3P58uV06tSJd999l7Zt29KoUSM8PDyK7Pvaa6/RqVMn2rVr5xB8vtVxhBBCCCHulIrNG5Pr7Y+6xn4F5Pr4U7F547s5LXETJChbShxJzmBBwjH2nUnDx91A1QBPfNwN7D+TTnJmHnqdxuHkTDJyzVhsNjJyzRxOzsTPw5W2D5aVIl+ixPvyyy/517/+xbhx41i+fDn/+Mc/aNGiBf369WPLli289dZb93qKNyUwMJDGjRvj4lIyVqcXRwBO3JhSiiVLltCqVStGjBjBxYsXHSrJw1/Bt6+//ppevXrh5eVFpUqVmDJlitN48+fPp3Llyri7u9O6dWuOHDlyzXPPmTOHSpUq4e3tTZcuXTh//rzTOdevX0/37t0pU6YMPXr0AGDRokU0a9YMPz8/fH19iYiIYMeOHbd1/RUrViQwMNAe2CtMCfDll19St25d3NzcWLt2LVlZWQwZMoQaNWrg7u5O5cqVGTRokFPAz2w2M2zYMPz8/PD29qZ///5kZmY6nTcvL4833niDSpUq4ebmRs2aNZ1WKe/fv58nnngCf39/3N3dCQ0NZfDgwfaVvgB+fn7MmTOHatWq4ebmxowZM/jll19u67W4kQEDBrB58+bbPn7ixIk37nTZtar4giJ/4lgu/biDr7/+Gk3TiIyM5OzZs9hsNsCxUu7WrVuLNW3EzVbjLdx/9arVqysBQ8FNkaLo9XqWLFlCkyZNaNOmDQcPHrytcYQQQggh7hS9wQWvocMBnAKzhdteLw+X4s4lkARlSwGbTbFx3zlSsvKpFuSJl9GAXqfhZTRQLcgTq00R5OXGg8FlSM02c+xCFqnZZuqEeNOvaWXCg7zu9SUIcUP/+c9/KFu2LOPGjStyf4cOf6WBsdlsxMbGUrZsWQICAujXrx9ZWVlOx5w8eZL27dvj4eFBtWrVWLRokcP+9evXExkZSVBQEGXKlOHRRx91CKTFxcXRr18/oCDYqmkalStXvmZ74TFXpy+YPHky4eHhGI1GAgMDadOmjT14BQW5FTt06GAPGE2bNo1hw4bZxwRISkri2WefpWrVqphMJqpVq8Ybb7zhtPpQ0zQmT57MqFGjKFeuHEFBjo+4XC+AJ25fYdCqV69etGvXDn9//2umMBg0aBDVq1dn1apVdOzYkVGjRjl87tatW8fzzz9Py5YtWbVqFa1bt7YHUq+2Zs0a1qxZw5w5c5gxYwbff/89L730klO/559/nrCwMFatWsUrr+8+tD0AACAASURBVLwCwLFjx+jTpw/Lly9n8eLFVKxYkRYtWtxUTs+rpaenc/HiRcqXL29vO3PmDC+//DLDhw/nq6++ol69emRnZ2O1WnnnnXfYsGEDb7/9Nt9//z1dunRxGO/1119n7ty5vPrqqyxbtgyr1cro0aOdztuzZ08+/PBDRo4cybp164iKiqJ3795s2LDB3qdjx45cunSJjz/+mPXr1zNu3DgCAgLsK32hIOj3wQcfEBsby+eff47VaqVr1653ZLVzhQoVaNSo0W0d27JlS77//vubyt9bWMUXnKv4FsqYOY3QkAps3LiRlJQUoqKiMJlMuLi48OKLLxIfH8+CBQt46qmnrlUp97ZcWY133bp1LF26lLZt2zqdo2bNmkDBz62ffvrJvtI1MjKS1atXM3v2bOLj4+nTp891b1wYDAZWrFhB3bp1ad26tT1f7K2OI4QQQghxp9To1gHX8W+T5+3v0J7n44/r+Lep0c0pLaooCZRSpfoLKAOotLQ0db86cTFLDV+6S725Zp+aGn/Q6evNNfvU8KW7VOL5DPXTnxfUl3vPqJ/+vKDMZuu9nroQN8VsNis3NzfVq1evG/YFVGhoqOrVq5fasGGDmjFjhnJ1dVWjRo2y91mwYIECVM2aNdXUqVNVfHy86tGjh9I0TR04cMDeb9asWWrGjBnqq6++UvHx8Wr48OFK0zS1efNmpZRSycnJauzYsQpQX331ldq2bZv69ddfr9l+5bnPnz+vlFJq4cKFysXFRU2aNElt3rxZrV69Wo0cOVLt3r1bKaWUzWZTDz/8sAoJCVGLFi1SX3zxhWrWrJkKDQ1VlSpVss91z549auTIkWrVqlXqu+++U/PmzVPly5dXMTExTq9PuXLlVJcuXdS6devU6tWrlVJKVapUSYWGhqq2bduqtWvXqgULFigfHx/15JNP3vobJpwMHjxYGY1GlZqaqpRSauDAgcrd3V1lZGTY+2zevFkB6tVXX7W32Ww2VblyZdW/f39726OPPqqaN2/uMP64ceMUoBYsWGBvq1SpkqpQoYLKzc21t02YMEEZDAZltVodzjlo0KDrzt9qtSqz2axq1KihXn/99ev2nTBhgvLw8FBms1mZzWaVmJiounXrZv9+UEqpvn37KkBt3779umOZzWb1448/KkAdPHhQKaXUxYsXlclkUuPGjXPo26JFCwWoxMREpZRSmzZtUoDauHGjQ78nn3xSNWrUSCml1Pnz5xWg1qxZc805ULAAQe3bt08ppdS6detUgwYNFKDc3d3VI488ojZs2OBwTOH3+c6dO1VkZKQymUyqevXq6uuvv1ZWq1WNGTNGBQUFqaCgIDV69Gj7+3Hl61eo8D3auXPndV8rQP373/9WDRo0UG3atLG3X7p0yemzsW7dOtX04YbKz8VFeeh0qo67u3q/ShW1t25d9ULZssqk06m9deuqT8LDVe1q1ZWbm5uqUqWKcnNzUx4eHqpNmzbqwQcfVEajUXl4eKhGjRqpxx9/XEVHRyullNq1a5cClNFodJrn1T8DC9WtW1f17dvXvr1jxw7VqFEjZTQaVbVq1dTChQtV37591YMPPuhwXGxsrKpQoYLS6XT2n4kZGRkqJiZG+fr6Kl9fX/Xcc8+ptWvXOr2Oha9ZoaysLNWiRQtVuXJldeLEiZseRwghhBDibrHkm9Wf325R+xd/rv78douy5Jvv9ZTumLS0tMLfxcuoEhBfvJ0vWbtcCmTlW8i1WHF3dc4Lq5TCbLVx/GIWszYdwWZT5FltGF307Ey8RLvaZQkP8sJmU5xOzSEr34KHqwshPiZJaSBKjIsXL5KXl0fFihVvqn9wcLD9EdOoqCh+/fVXVqxYweTJkx36DRkyhMGDBwPQpEkT1q9fz+eff87YsWPt+wvZbDZatmzJ/v37mTdvHhEREQQGBhIWFgZAgwYN7BUmgWu2X23Hjh089NBDvP766/a2zp072/+9YcMGfv31V3744QeaN28OQKtWrahQoYJDXto6deo4FDhr2rQpHh4e9O3blzlz5uDu7m7f5+fnx8qVK53yPSqlWLNmDW5ubkDBSslJkyZhs9nQ6eTBi9tlsVhYvnw5TzzxBN7e3gD06tWLDz/8kFWrVvHPf/7Tof+VVek1TaNmzZr2qvRWq5VffvnFKaVB9+7di0zh8fjjj9vfT4BatWphNptJTk6mXLly9vbo6GinY3///XfeeOMNtm7dSnLyX5Vcb2albFZWFgaDwb7t6+vL7Nmzadeunb3N39+fRx991OnYTz75hKlTp3L48GGHFe6HDh2ievXq7N27l5ycHLp27epw3D/+8Q9++OEH+3Z8fDx+fn60atUKi+WvYpeRkZEMGjQIq9WKv78/lSpV4vXXXyclJYXWrVtToUIFpzmVKVOGBx98EIDExES6du3KL7/8wtChQ8nNzeWJJ55g06ZNREREOBzXp08fBg0axMiRI5k8eTLdunWjb9++pKens2jRIn766ScmTJhAnTp16NWr1w1f15sxbtw4unTpwvbt2+0rfa+WmJhIi9oP8c9LF9E0jR/T0xmcmMjHYWEMLleOweXKccFsZuCff1I9rBrLli3j0qVLTJgwgaysLEJCQvj6668BiIiIwNPTk3Xr1jmd58oVyYViYmKIiYlxat+9e7fDdmE13iv16dPH6bgJEyYwYcIEhzZPT08WLFjg1Fcp59z6V3J3d+f77793aLuZcYQQQggh7ha9wYUqrZrd62mImyRB2VLAw9UFo4ue7HwLXsa//ghOycrnSHImpy5lczEzj5OXsgn1dadGuTIYDTr2nUnjTFoOrR4I4o+kDI6ezyTXYsXooics0NMesBWipLjZojGRkZEO27Vq1WLp0qVO/a4Mfnl4eFCpUiV78AsK0gaMGTOGb775hqSkJPsf4g0aNLid6Rfp4YcfZu7cuYwYMYJu3brx6KOPOgSzdu7ciY+Pjz0gCwUBh9atWzvktFRKMWPGDObNm0diYqJDjsU///yT2rVr27fbt29f5Gt5swE8cWNWs4UTW7aTcy6Z7ceOcv78eTp27EhqaipQEEQPDg5m8eLFTkHZoqrSFx53/vx5LBaLU9qJsmXLFjmPosYC5xycVx+fkZFB27ZtCQwMZOrUqVSqVAmj0ciAAQNuquq8yWTihx9+QNM0AgICCA0NdQrsFzXnVatW0adPH55//nneeecd/P39SUpKomvXrvbzJiUlAdzwNbhw4QIpKSkO309XSkpKokKFCsTHxzNmzBhefPFFsrKyaNCgAVOnTqVFixYO11NoyJAhpKamMnbsWMLDw+nTp4/DzZorvfTSS7zwwgsAhISEUKdOHX7++Wd7rtp27dqxZs0ali9fXmxB2U6dOvHQQw8xceJEvvzyyyL7DBkyhMRNP5L12y/YlOIRT0+O5uay/OJFGl0uMvbJ+fNoQNx706nbKQqA0NBQWrduXSzzFEIIIYQQ4n4nQdlSIMTHRFigJ/vOpOHp5oKmaaRk5bP7ZCrZeRYycs24uego720iLcfM3tNp1Av1oVqQJ7tOpDLz28MEe5so72PE3dVEdr7FHrCVnLPiXrkyqGUI8MNoNHLixImbOraoQFRRld2L6lcY+LHZbHTq1Im0tDQmTpxIeHg4Hh4ejB8//qbncTNiYmLIyMhg3rx5TJs2DW9vb/r27cvkyZMxmUwkJSURGBjodNzVAanp06fzyiuv8Nprr9GyZUt8fX3ZuXMnL7744g0DcIVuNoAnru/gynVkzJiG8XLxpJXHjwPQr18/e67hQufPnyc5Odnp/byWwMBAXFxcHFauApw7d+5vzfnqIP22bds4deoU69ato27duvb2tLS0IleSXk2n09GwYcNbOicUVLuvV68eH374ob3t6pWLwcHBACQnJzvkGL36NfDz8yMwMPCagcnC17x69eosX74cs9nM1q1beeONN+jYsSOnT5/G83KA8kqnTp3i1VdfBQqKcvXv3x8o+mbNlTeIqlevDuAU1Kxevfpt5em9Fk3TGDt2LD179uTnn38mPDy8yGuYsOBDvvr9dy7k59sLRNS6Ivi8NzubBr5+1G7fxt7WqlUr/Pz8im2uQgghhBBC3M8kKFsK6HQa7WqX5UxaDoeTMylXxo1D59JJy87HYrVitSkCvdxwddHh5+JKSlY+R89n0qCiD9n5Fs5n5FE/1Me+ytbLaMDTzYXDyZnE7z9H1QBPSWUg7qqrg1p5QB2jifj1X2KxWHBxufM/2o4cOcKuXbtYvXq1QzqBwqrgxUWn0zF06FCGDh3K6dOnWbp0KaNHjyYgIIBx48YRHBxcZLGtq4Nyy5cvp1OnTrz77rv2tgMHDhR5zptdcSxunb2a/eXtHJuNzenptCpThmcCA3Hp05+KLR4D4OzZszz99NN89tlnRRbfKoper+fhhx9m1apVDB8+3N6+YsWKYr2Ows/5lZXnC4uVFT7GfyfcTLX7OnXqYDKZWLVqFfXr17e3f/755w792rRpw5QpU3B1deWhhx664bl1aFS06un1WAuGbN3KyeMnqPlgLYc+hTdrLl26BMBrr71G9+7dr3mz5sobHYXXdb2bQcXlH//4B7Vq1eKtt95i4cKFRV5DWloaQ/sPpHz8l5h0OuacPcvZy4XLFHDeYqFR/Yedqvje7A0EIYQQQgghSjtJAlhKhAd50a9pZWqX9+ZMai6JF7LIyLOQY1ZYbIq0HDNnUnPINdvwNLqQkpVPUlouGXkWXF00zDbH/GiaphHsbeRIcianU4s3CCXE9diDWpcDsoVifH04n3qJ4b37FnnctVbD3a6iglLHjx8nISHBod+1VpPezirTkJAQRo4cyUMPPcTvv/8OFORVTE1NdciVmZmZybfffus03xsFs8SdVVQ1+81paWTbbDwTGEgjT09qbfqG5k2bERERwVNPPUX9+vVZvHjxLZ1nzJgxbNmyhX79+rFx40YmTZrEJ598UqzX0rhxYzw9PXnxxReJj49nwYIFPPXUUw4rU++EyMhIduzYwVtvvcU333zDiBEjnD7rfn5+DBo0iMmTJ/Puu++yceNGYmJiOHr0qNNYHTt2JCoqiunTp7Np0ybWrl3L5MmTGTBgAAB79uwhMjKStwe/xPyGj7Cm/z/58P2ZhLi6kv7CEA6udMyTWniz5p133gHggQceoGHDhsV+s+bv0ul0jBkzhrVr1/Lbb7857Cu8hqlTpzJm7kyaTJlOeHAoeTabvU+ejz9B4dXJczc6jX31DSGj0Uh+fr5DW2HQWgghhBBCiNJMgrKlSHiQFy9EhPF4jUB0Og0vowvlvI2YDHo0TSMr38rZ9FwsVoXFZiPbbCXfYsPNRY+r3vmjYnLVk2exkpVvKeJsQhS/ooJahVqUKUO/wEBmf7aYnj16smrVKrZs2cKiRYuIiIhgzJgxxTqXBx54gAoVKjB69GjWrVvH0qVLadu2rVNQqmbNmgDMmTOHn376ib179163/WoDBw5k9OjRrF69mu+//55Jkybx22+/2R9xbt++PQ8//DC9evXik08+Ye3atbRv3x4vLy+HHJ2RkZGsXr2a2bNnEx8fT58+fThy5Eixvibi+k5s2Y4x7aLDZ/fL1FSCDQYaeXigAcbUi5zYst2+v2/fvmzfvt0poHg9nTp14oMPPuDbb7+lS5cuxMfH89lnnxXfhVCQ4mL58uUkJyfTuXNnpk+fzocffljko/DFaeDAgYwcOZJZs2bRrVs3Tp48WWTQevLkyQwaNIgpU6bQs2dPe9vVVqxYwaBBg5g7dy7t27enf//+xMfH8/jjjwNQrlw53PMtfPjxPIbu3c3EU6coZzDwYdWquKenkD9xrMN4hcHXK/PUFnWzpiR48sknCQ8PZ+LEiQ7tV99wqtGtA34fz2N3Xh4EBOIxdTb1v95IRHR7Nm/eTFpamv3YTZs2kZKS4jBehQoVOHjwoEPxq/j4+Dt1WUIIIYQQQvzPkPQFpdCxC1no0PB1N+Dmoicj14WsfAtGFx25FhsXsvLwNhowuejJt9jw93TFy+j8UcnJt+LmosfDteR9jGw2xenUHLLyLXi4uhDiY5IUC/eBwqDWtYwoX556Hh4s+zORZ5991l4FvF27drzyyivFOhc3NzdWrlzJiy++SI8ePQgNDWXs2LFs2rSJn3/+2d6vfv36xMbG8tFHHzFlyhRCQ0M5duzYNduv1qRJE+bPn8/8+fPJzs6matWqTJs2zZ6nUtM0vvjiCwYOHMjzzz+Pr68vL7/8MgcPHnSoVj5+/HjOnz/P+PHjAejevTszZ86kY8eOxfq6iGvLOZfs1Da7SpXr9itMXQEQFhZWZFX31atXO7UNHDiQgQMHOrRdfWxRn7cuXbo49IuIiLhmJfmoqCiioqIc2tq3b19k3yvFxsYSGxt73T5xcXFFtuv1et577z3ee+89h/ar5+jq6srMmTOZOXOmQ3vv3r2d+o0fP97+fXE1f18/xqHDrWZNpxtBgD3X6ojhI4C/bta8/fbbrF27lszMzCJv1pQEer2eN954wymP8ZU3nKxWK5mZmUyYMIGQChVw8/CwV/MdNmwYc+bMoX379owePZpLly4xYcIE/P39Hcbr3r07H3/8MS+99BJdunRh69atxZ5OQwghhBBCiP9FJS+aJu6o06k5nM/II9jbSFqOGTcXPX4eBvKtVnItNvQaZOZYCPB0JTPPTKCXG+5FBF2VUiSl5VInxJsQH1MRZ7p3jiRnsHHfOY6ezyTXYsXooics0JN2tctKUbL/cUUFta7WytubNq+8Tq2nuxW5v6gA07Bhwxg2bJh9OyYmhpiYGKd+VwY5oSB1wI4dOxza+vTp43TchAkTmDBhwk21X33uvn370rdv0SkZClWoUIH169fbt/Pz86lVqxbNmze3t3l6erJgwQKnY69+Pa4VgLuZAJ64PlPZILJusp8oGW50I6gwUJuaWFCs7WZv1pQUvXv3ZuLEiSQmJtrbbvYagoOD2bBhAy+//DI9evQgLCyMOXPmOD2VEBUVxZQpU5g1axZxcXE88cQTfPDBB7Rp0wYhhBBCCCFKM620/0GtaVoZIC0tLY0yZcrc6+nccX+cTWfmt4fxMRnYezqNnHwrnkYXLFbFxcw80nPN2BTUreBDi+qB1CjnxaY/kknJyifY24jJVU9OvpWktFz8PFzp17RyiQp0HknOYEHCMft83V1dyM63lNj5iluTuOlHskYMuWE/j6mz7au5SoN58+Zhs9moUaMGly5d4v3332fz5s1s3779hhXuxd1jNVvY1aYdblelMCikKMjVWf/rjU7Fk8S9cWDJSmz/mnjDfrpR4695I0gIIYQQQghR/NLT0/H29gbwVkql3+v53A7JKVvKeLi6YHTRYzToqRfqQ5CXkVyzjTyLFW+TgSoBntQN9WFE2+oMejyM1jXL2guEpWabOXYhi9RsM3VCvG87wGmzKU6mZPPH2XROpmRjsxXPjQGbTbFx3zlSsvKpFuSJl9GAXqfhZTRQLciTlKx84vefK7bz3Y/u1HtTXCo2b0yutz/XmpUCcn38qdi88d2c1j1nNBqZOXMm0dHR9O7dm8zMTNavX39XArL/93//h6ZpHD582KF91qxZaJrmtBI4JSUFnU7HlClTim0O06dPv+lCbpUrV0bTNPuXv78/rVq1YsuWLcU2n2vRG1zwGjocwOkzXLjt9fLw6wZkv/vuOzRNK5GrLu9HN7tqWVY3CyGEEEIIIW6VLMUpZUJ8TIQFerLvTBrVgjxpWNmVjFwL+VYbBp3G2fQ8HqrgTcNKfvYcrOFBXlSN8CyWHK13MrXA6dQcjp7PJNjbiKY5zk3TNIK9jRxJzuR0ag6hfu5/61zFoaTlvf1fSPtQGNTKnzgWhWOxr5sNat2P+vTpU2TahLuhadOmAGzdupVq1arZ2xMSEnB3d2fr1q0O/bdu3YpSimbNim8l8/Tp0+nQoQNPPPHETfXv3r07I0eOBAoqxU+fPp2oqCj27NlDWFhYsc2rKDW6deAgkDFjmsNj8Xk+/ni9PJwa3Trc0fOLW1OxeWN2efvfcHVzzVJ2I0gIIYQQQgjx95WuyIVAp9NoV7ssZ9JyOJxcEMB0d9Oj5UNSWi7+nq60fbCsU3BQp9P+diDTObWAiex8C/vOpHEmLedvpxbIyreQa7Hi7lp0jluTq55z6blk5Vtu+xzFpaQFQO/0e1OcJKhVstSoUYPAwEASEhIcct8mJCQQExPDokWLsFqt6PV6e7vRaLynaRXKli1L48Z/BdGaN2+Ov78/GzduZPDgwXf8/DW6dcDaMYoTW7aTcy4ZU9kgajZvXOpuJvwvkBtBQgghhBBCiDtF0heUMjabws1Fz+PVAwn1dedSVn6xpCS4mfPe6dQChakZsq8RdM3Jt+LmosejiMJld1NhAHTfmTR83A1UDfDEx93AvjNpLEg4xpHkjLs6n/+FtA9Xp1Wo1iWa+t9sxGPqbHSjxuMxdTb1v94oAdl7pGnTpiQkJNi3T5w4walTpxg6dCi5ubns2bPHvi8hIYGGDRvi6uoKwKlTp+jduzcBAQGYTCZatGjBL7/84jD+mjVraNiwIZ6envj4+NCwYUN7uoLKlStz/Phx5syZY09JEBcXd0vz9/DwQK/XYzabHdp///13OnfujLe3Nx4eHkRHR3P06FGHPpqmMWXKFGJjYylbtiwBAQH069ePrCzHkl4//vgj9evXx2g08tBDD7Hpu810HTGEKRvXUKVVM/QGF7Zt20anTp0oX748Hh4e1KtXj08++eSWrkUUvxrdOuA6/m3yvP0d2vN8/HEd/7b83BFCCCGEEELcFlnaUYpcvTrTTa8j0MuNhlX8qFmujMPj88X9aP3dSC1wZWoGTzcXh/MopUhKy6VOiDchPkWvpL0brg6AFs7Ry2jA082Fw8mZxO8/R9UAz7uWyqCkp3247qriUlTMqyRr2rQpX3zxBZcuXcLX15eEhARCQ0OpXr06devWJSEhgfr162M2m9m5cyfDhg0D4NKlSzRr1gxPT09mzZqFt7c3s2bNolWrVhw+fJigoCCOHj1K9+7defrpp3n33Xex2Wz89ttvXLp0CYBVq1bxxBNP0KxZM3tKghulIFBKYbEU3Lw5f/48b7/9Ni4uLkRHR9v7/PnnnzRp0oTatWsTFxeHTqfjnXfeoXXr1hw8eBA3Nzd739mzZ9O8eXMWLlzIoUOHePXVVylbtiyTJ08GICkpiaioKB5++GGWLVtGWloaL7zwAmlpadSrV88+zvHjx2natCmDBg3CaDSSkJBA//79sdlsDquQxd0nq5uFEEIIIYQQxU3+migFbDbF1qMXWLzjBFl5FqoGeFDereDx9JOXcsjKP0/VAA97EPBOPFp/N1ILFJWaweSqJyffSlJaLn4eRadmuJtuJgB6+FwGPx9PoYzJcFdyzZbktA//S2kVSrNmzZqhlGLbtm088cQTbN26lSZNmgDQpEkTtm7dypAhQ/j111/Jzc2155OdPn06qamp7Nixg6CggkJJrVu3pnr16rz33ntMmTKFXbt2YTabmT17Nl5eBe91u3bt7OeuX78+bm5uTikJrmfu3LnMnTvXvm0ymVi0aBHh4eH2tjfffBM/Pz++/vprjEaj/VqqVq3Kxx9/7JDmIDg4mE8//RSAqKgofv31V1asWGEPyk6bNg0XFxfWr19vv4YqVarQvHlzh3k99dRT9n8rpWjRogWnTp3iww8/lKBsCaA3uFBFbgQJIYQQQgghiomkL7jPHUnOYM7mw8Su2c/2Py9yMiWb35MySMsxF/l4+p16tP5upRYID/KiX9PK1C7vTWq2+a6kZrgVfwVAi77OHLOVA0npfPj9UWZ+e5hpXx/i/e+O3tGUBiU17cP/QloFUaBBgwaYTCZ7CoOEhAR7UPaxxx5zaNc0zb4vPj6eli1b4ufnh8ViwWKxoNfrefzxx9m5cycADz30EHq9nl69erF27VrS0tKKnMO3335L7dq1i9w3bNgwKleubN+OjIzkueeeY8uWLWzcuJGePXvyz3/+kw8++IDY2Fiys7OJj4+nU6dOuLi42Ofm6+tL/fr17XO7crwr1apVi1OnTtm3d+7cScuWLe0BWYCPPvoIgIULFxbckAkOJioqil69elGpUiUMBgMGg4F58+Zx6NCh678Bt2D69OlON4SKQ0REBB06yGP8QgghhBBCCHGzZKXsfexIcgbTvznMvtNpXMjMQ6dpXMo2k5pt5mJWPo2r+uPn4Wp/PP3kpew79mj93UwtEB7kRdUIz2JNv1BcrgyAehkNDvtSsvL59fglMnIt+Hu4EVTGeFdWhZbUtA8lPa1CaWc1Wxwe5W7YsCEJCQlkZmayZ88eh5WyhTlmExISqFWrFr6+vgBcuHCB7du3YzAYnMYvTEFQvXp11q1bx6RJk+jatSs6nY6oqChmz55NxYoVb2/uVivz589n0qRJBAQEEBkZya5du5g8eTLHjx9nyJAhXLhwgenTpzN9+nSn4wvz4Rby8fFx2p+Xl2ffTkpKolq1ak7jGAwGIiMjGTt2LEeOHGHQoEHk5OTw1ltv8dhjj1GmTBnef/99Pvvss9u6TiGEEEIIIYQQJZcEZe9TNpti8fYT/HYyFQC9puHuqkcBeRYb59Jz2Xs6lRbVAu2PpydeyLpjQbC7nVpAp9NKZKDuWgFQpQpWKafmmKka4EG5y+/B3cg1W1LTPpTktAql3cGV68iYMQ1j2kUAsoDwlFSWJp/lxx9/xM3NzZ4rtVKlSgQHB5OQkMDWrVvp3LmzfRw/Pz+ioqJ46623nM5xZc7WqKgooqKiSE9P56uvvmL48OH069ePb7/9tliuR9M0HnjgAVauXOkwt+joaIc0BYWuXPF6PTk5OZhMJoKDgzl//rzTfpvNRmBgII899hj169enb9++KKVwc3OjVatW9j6FrFarPReuEEIIIYQQQoj/bZK+4D51DwepAgAAIABJREFU8lI22xNT0Gkafu4GXPQ6bIBep2Ey6NA0OHUph/Qcs/3xdOC6j9abXPXkWay3HQQr6akF7obCAKifhyuHkzPJyDVjsdlISsvh+MVsfN0NhF+xShmcA+J3Qkl7b2w2RXqOmTyzjXPpOSjlnKLgXqVVKO0OrlxH/sSxuF0OyBZq6KIjJy+PyWPH0ahRI1xc/npfmjRpwieffMLZs2dp2rSpvb1NmzYcOHCAmjVr0rBhQ4evOnXqOJ27TJky9OzZk6eeeorff//d3u7q6orVar2p+WdmZrJp0yYAAgMD0TSNypUrk5CQYA94BgYGkpyczKeffkr9+vUJCQlh7ty59OzZk+bNm9OhQwfeeOMNh9WwUPC9OnnyZNauXQtgz5PbqFEjNm3aREbGX2lIzp075zDnvLw8++c8MTERgObNm/Ppp59iNpupUaMGbm5uHD16FICJEydStWpVTCYT1apVK3I+6enp9OnTBy8vLwIDA3nttdeKDOqmpqYyePBggoODcXNzo0GDBsTHxzv0SUhIoEWLFnh7e+Pl5UWdOnVYuHCh01grVqygRo0aeHp60qpVK/t8hRBCCCGEEEI4kmjGfSrxQhapOfkEerrh5qLDZNCTlW9Bb9CjaRpGl4LtlOx8zFZFnRBvqgZ4XPPReiieIFhJTi1wtxQGQAuLqZ1LzyXXbMPL6MLDFX3x83BzOuZurAq9lffGZlN37D0sLDR3JDnjcg5kC5X83QkP8sLPo+Cx8XuZVqE0s5otZMyYhhtw9btdz8MDHfDDLz/z2mujHPY99thjvPrqqwD2Il8AI0aM4NNPP+Xxxx9n6NChVKxYkfPnz/PTTz9Rvnx5hg8fzocffsi2bduIiooiODiYxMRE/vvf/9K2bVv7ODVr1mTTpk34+/vz888/U6VKFfz9/Yu8BpPJxAMPPMAff/zBtGnTUErxzTff8OWXXxIREcF3333HV199RXp6Os8++yzt2rUjKiqKrKwsunXrxpEjRwgMDGThwoUkJSU5jT9jxgwCAwMB+O9//wvA8OHDmTt3LtHR0bz66qukpqaSkJCAXq9Hpyu4N+rt7U3t2rXZt28fKSkprF69mt9++w2bzYbNZmPixIn4+vqSmZkJFASop06diq+vL4cOHSI2NpakpCQWLFhgn8uzzz7Lxo0bmTx5MlWqVGHu3LksXrzYYb75+flERkZy7tw53nnnHUJCQvjvf/9LdHQ0v/76K3Xq1CE9PZ3o6GiaNWvGkiVLcHNz48CBA6SmpjqMtXv3bv79738zefJkrFYrI0aMoHfv3mzbtq3I90IIIYQQQgghSjMJyt7HNAUKhaZp+HkYyLdayTFbcXXRYVMKpQrydj5QrgxtHyxLBV/3u5JbtKSmFribrg6ApueYWfLTCYwGfZH979aq0KvfG5tNcTIl2yH4+ueFTHtAOddixeiiJyzQk3a1y/7tFbWFheZSsvIJ9jbSsLIvv564xJ8XskjJyufhSr6YDPp7mlahNDuxZbs9ZcHVyuj1hBmNHM7NJbyMr8O+Jk2aoJSifPnyVKlSxd7u7+/P9u3bGTt2LKNGjeLixYsEBQXRuHFjunbtChQU+lq7di0jRozg4sWLlCtXjqefftoh5cGkSZNo1aoVJ0+epFGjRixYsICYmJgi56nX6/njjz+AgmCpl5cX4eHhfPzxx2iaxnfffUeDBg0ICAigXr16jB07lsmTJ5OZmUlwcDAtWrRgyJAhtGzZkr59+zqN7+fnR79+/RgxYoQ9VUNwcDAbNmzg5Zdfpnv37oSFhfHII4/www8/4OXlhdls5ujRoxiNBWlLVq9eTUJCAoGBgRw7dgyj0ciTTz4JwHfffQcUFC9r2LAhAE2bNsXDw4O+ffsyZ84c3N3dOXDgACtXruSjjz7i2WefBaBdu3ZOuW0//fRTdu/ezW+//UatWrXs/Q4fPsxbb73FsmXLOHToEGlpabz77rv2FcytW7d2uvbU1FR27dplD0pnZmbSr18/Tp06RYUKFYp8P4QQQgghhBCitJKg7H2qaoAH3u4G0rPNGMvoMbm6UK6MkZQsMzlmC9n5Vgx6HQ0q+tKzUag9mFYSc4verju5mrM4XBkAtdkUOxMvlahiW4UrVq8Mvvq4G0jOyMNqUwR7G3F3NRVbMTKbTTkVmvMyGmhU2Y8j5zI5npLNr8cvUSu4DHVCvGn74N8PAotbk3Mu+br7V9aoAYCuqmPg77HHHisyBQVAuXLl+Oijj6455mOPPca6deuue94HH3yQ9u3b8/PPP7Nv377r9j127BhxcXH069eP8+fPExAQYN8XFxfn0LdatWp89tlnKKWYMWMG8+bNY9myZSxatMjeZ+/evdSuXdu+3b59e4YPH87w4cMdxmrevDm7du2yb3fr1g2z2czMmTOZOXMmAOXLl+ezzz6jR48eAERERODr68vPP/9sPy4iIgKbzcaMGTPo06cPiYmJ5Obm2vf/+eef1K5dm507d6KUsge3oSAg3aVLF6ZNm2Zvi4+Pp06dOlSvXt0htUFkZKR9pW9YWBhlypThhRde4OWXX6Zly5b2wOuV6tWr59BeGOSVoKwQQgghhBBCOJOg7H2qgq87jav68/WBc//f3r3Hy1WWhx7/Pdm5kISQGyYkSA5oAKGFiuUSxcQoVy1YohxpbZXgqaUCclEEEU6InHpoOV4IN2sVBBUvUBErl3IRsEAV0QKKWOSWcgsJSciFkGTn8pw/1tph9mR2svfO3jOZye/7+azPMO9615pn1rzAzLPf9bwsenU1I4YOYsigNnbcPli8IhnUNoCpe7yBs454CwMHvl5auNat9UMGtjVdEqxWQrGvZnP2h61tsa3qGavDBg9lxeq13PfEQlauWce79njDhhIXfbUY2QtLVtZcaG7M8CEcsNtg3jhmGItXrOYvD5rE/v9jzFaVYN9WDB0/jhXd7FcP69as5dl7f8HK+QtYuWAh69bWriu7bt06Bg3auCRLd1188cWceeaZnHXWWbz73e9m9OjRPPjgg5x88smdEqIA48ePr3mOc845h3333ZeJEyfy9NNPc+edd9LW1sZdd93F8OHDmTBhAhMmTNhokcVa5+tOPPPmzWPQoEGMHj16k+dbuHAhDz30UM3r09ZWzNwfPXo0d9xxB+effz4f+chHWLt2LVOnTuXSSy/tVPt31KhRnY4fPLgoN1J9jSRJkiRJJmVb1oABwYcPmsSC5av5w0vLWb5qLZBAsN2ggey7ywhOnPamTgnZDs1e97VWQrGvZnP2p3okxLsze7jWjNUOETAggqcXrmDM8MEb9lUvRtab8hQr2teWC81tPBs4Ihi3wxBea1/LDkMHNc1YbDWTpk7hoZFjGbJ00UY1ZaH4L8zqUWPZa+qUfo/l8RtuYvmcr2wopzBi3jxeWLyYx2+4iT0/cFSnvi+++OKGRbd64/rrr+f9738/F1544Ya2xx57rGbf6qRqh/b2ds4++2zmz5/P0KFDGTVqFDvttBPTpk3b5GvXOl934pkwYQJr1qzhlVde6ZSYnT9/fqd+Y8aMYd999+XKK6/cZBwHHnggt956KytXruTuu+/mzDPP5JhjjnEhL0mSJEnqJZOyLWzyuBGcfuju/NujL/HbF5byWvs6hg1uY9+dR212xmiz1n3tKqHYV7M5+1t/JsS7O3u4qxmr7evWsy6TkcMGsXhFO8tXFQnSDlu6GNnwwQP7faE5bZm2QQMZcdoZtF9wXvknntd1FCcYceoZtA3q38/o8Rtuov2C86hcEm//4cO5csEC/uOs0wE2JGaXLVvG3XffzYknnrihb1czOLtqX7ly5YZ9Ha699toexfylL32JL33pSxuez5w5s1NZgp7oTjwHHHAAAD/60Y821JRdt24dN954Y6d+hx56KLfccgsTJ05k4sSJm33toUOH8r73vY+nnnqK0047jVWrVrHddtv16n1IkiRJ0rbM7EaLmzxuBCc18azXnuoqoQh9M5uzHvojId6T2cNdzVgd3DaAgQMGAMna9etpX7e+0/4tTZruPGpoXRaa05bZ8wNH8Th0mqUKxQzZEaeesdEs1b62bs1als/5CkPonBR+x4gRvG34cD41dy7/66xPccT2Q5i/YD4XXXQRbW1tnHrqqRv67rXXXgBcfvnlHHPMMQwbNox99tmny/bDDjuMOXPmcNlll7HHHnvwne98hyeffLJf3+emdCeevffemxkzZnD66aezatUqdt11V6644gra29s79fvoRz/K1772NaZPn86ZZ57JHnvssWHBrvb2di688EJuvvlmrrzySmbMmMGkSZN46aWXuPTSSzn44INNyEqSJElSL5mU3QY066zX3tjULfCw5bM5m1FPZw93NWN1xHYDGTNsMC8seY2hg9oY3PZ66Yu+SJpubXV11bU9P3AU644+ckM916Hjx7HX1Cn9PkMW4Nl7f9EpGdxhQARX7LYbl730Et99di5z3vdeRo4axXve8x5++MMfMmHChA1999tvP2bPns03vvENLrroInbZZRfmzp3bZfusWbN4+eWXmTVrFgDHHnssl1xyCUcffXS/v99auhvPVVddxSmnnMJZZ53Fdtttx/HHH8/06dP5zGc+s6HPkCFDuOuuu5g9ezZf+MIXmDdvHjvuuCP77bcfJ510EgCTJ09mwIABnHvuuSxYsICxY8dy+OGHdyqfIEmSJEnqmehqRextRUTsACxdunQpO+ywQ6PD0RZ6bvFrfOWOPzBq2KCat8AvX7WGJa+t4YzD9thmEtU9vSbr1ydfvecpHn1x6UY1ZRe9uoqf/WEhQwe3cfCbxzJsyMBOSdO+qNdbWWZh9dpi9u3kcds31UJz6j+Pfe8G1v/jBZvtN+DsWez9lx+oQ0SSJEmSpHpbtmwZI0eOBBiZmcsaHU9vOFNWLcVb4DfW09nDm5qxumjFGv5kl1GM234IS1auYcHy1X2+GFmzLzSn/jV0/DhWdLOfJEmSJElbq5ZJykbEycBngJ2AR4BPZuYvGxuV6s1b4DfWmwW0Jo8bwQkH77phxur8Zas6JV/ftGP/Jk23pZIb6plJU6fw0MixDFm6iFojLinq2+41dUq9Q5MkSZIkqdtaIikbEccBXwb+DngAOB24LSL2zMwFDQ1Odbe5hOK2dgt8b2cPb27GqklTNULboIGMOO0M2i84j6TzYl8dxXhGnHpGXerbSpIkSZLUWy1RUzYiHgAezMxTyucDgOeASzPzHzZzrDVlW9T69ekt8KUnFyznm/fPZfGK9pqzh/uiFqxUT4/fcBPL53yl06Jfq0aNZcSpZ7DnB45qYGSSJEmSpP7WCjVlmz4pGxGDgdeAYzPzxor2a4BRmfnnVf2HAEMqmkYAz5uUVatzAS21mnVr1vLsvb9g5fwFDB0/jklTpzhDVpIkSZK2Aa2QlG2FX687Am3A/Kr2+cBbavQ/Bzi/v4OStjYuoKVW0zZoILu9552NDkOSJEmSpB5rhaRsT11IUX+2wwjg+QbFItWVC2hJkiRJkiQ1XiskZRcC64DxVe3jgZeqO2fmamB1x/PKRY8kSZIkSZIkqb8NaHQAWyoz24FfA4d0tJULfR0C/LxRcUmSJEmSJElSLa0wUxaKcgTXRMSvgF8CpwPDgW82NCpJkiRJkiRJqtISSdnM/EFEvAG4ANgJeBg4MjOrF/+SJEmSJEmSpIZqiaQsQGZeBlzW6DgkSZIkSZIkaVOavqasJEmSJEmSJDUTk7KSJEmSJEmSVEcmZSVJkiRJkiSpjkzKSpIkSZIkSVIdmZSVJEmSJEmSpDoyKStJkiRJkiRJdWRSVpIkSZIkSZLqyKSsJEmSJEmSJNWRSVlJkiRJkiRJqiOTspIkSZIkSZJURyZlJUmSJEmSJKmOTMpKkiRJkiRJUh2ZlJUkSZIkSZKkOjIpK0mSJEmSJEl1ZFJWkiRJkiRJkurIpKwkSZIkSZIk1ZFJWUmSJEmSJEmqI5OykiRJkiRJklRHJmUlSZIkSZIkqY5MykqSJEmSJElSHQ1sdABbi2XLljU6BEmSJEmSJEmb0Qp5vMjMRsfQUBGxM/B8o+OQJEmSJEmS1CNvzMwXGh1Eb5iUjQhgIrC80bH0kxEUSec30rrvUdsmx7ZalWNbrcqxrVbl2FarcmyrFTmuW8sI4MVs0uTmNl++oPzgmjKj3h1FzhmA5ZnZ/HO7pZJjW63Ksa1W5dhWq3Jsq1U5ttWKHNctp6k/Qxf6kiRJkiRJkqQ6MikrSZIkSZIkSXVkUrb1rQY+Xz5KrcSxrVbl2FarcmyrVTm21aoc22pFjmttNbb5hb4kSZIkSZIkqZ6cKStJkiRJkiRJdWRSVpIkSZIkSZLqyKSsJEmSJEmSJNWRSVlJkiRJkiRJqiOTsi0uIk6OiLkRsSoiHoiIAxsdk9QdETEtIn4SES9GREbEMVX77ynbP1vj2JvLfbPrFrDUTRFxTkQ8GBHLI2JBRNwYEXtW9ZlbjuG/qHH878p9M+sWtNQNEfGJiPhNRCwrt59HxHsr9juu1fQi4rPlWL24os2xraYTEbPLsVm5/VfFfse1mlZE7BwR34mIRRGxMiJ+GxH7V+z3t6S2CiZlW1hEHAd8Gfg88DbgEeC2iBjX0MCk7hlOMWZP3kSf54CZlQ0RsTNwCDCv3yKTtsy7gMuBKcBhwCDg9ogYXtXvOeCEyoaImALsBKyoQ5xSTz0PfBb4U2B/4C7gxxHxRxV9HNdqWhFxAHAi8Jsaux3baka/AyZUbO+s2u+4VtOJiNHA/cAa4L3A3sCngVequvpbUg1nUra1fQr4emZ+MzMfA/4OeA34WGPDkjYvM2/NzPMy80eb6HYTsGNEHFzRdjxwO7CgXwOUeikzj8zMqzPzd5n5CMWXwUkUiaxK1wLviohdKto+VravrUuwUg9k5k8y85bMfCIz/5CZ5wKvUvwBooPjWk0pIranGKcfZ+Mf9uDYVnNam5kvVWwLq/Y7rtWMzgaey8wTMvOXmflMZt6emU9V9fO3pBrOpGyLiojBFD/w7+xoy8z15fO3NyouqY+1U3wprPwL/kzgqoZEI/XOyPJxcVX7fOA2ii+HRMQw4Dgc32oCEdFW3vI6HPh5xS7HtZrV5cDNmXlnF/sd22pGu5elwp6OiGsjYlLVfse1mtH7gV9FxPVlqbCHIuLjNfr5W1INZ1K2de0ItFH8j7TSfIrbTaRWcRXwoYgYHhHTKBJcNzU4JqlbImIAcDFwf2Y+WqPLVcDMiAjgWOCpzHy4njFKPRER+0TEq8Bq4J+AGeXdOpUc12oq5R8Y3gacs5mujm01kwcoElBHAp8AdgPujYgRVf0c12o2b6IY008ARwBfBS6JiONr9PW3pBrKpKykplbe/v0ExZfEjwHfzkxvp1KzuBz4Y2CjRTRKNwPbA9Moxrd/udfW7nHgrcBBFD+CromIvav6OK7VNMrbtucAf5WZqzbT3bGtplGWCrs+M3+TmbcB7wNGAR+q6uq4VrMZAPxnZn4uMx/KzH8Gvk5RzrETf0uq0QY2OgD1m4XAOmB8Vft44KX6hyP1q6soFgTbGziwwbFI3RIRlwFHAdMy8/lafTJzbUR8m2LBxoOAGXUMUeqxzGwHniyf/rpcGOk0isWROvo4rtVM/hQYB/xnMVEQKO5GmxYRpwBDOhod22pmmbkkIv4ATK5qd1yr2cwDqu/S+T3wwS76+1tSDeNM2RZV/ij6NcXKgcCG22QPoXNtN6kVfBfYB3i0xm2y0lYlCpdR/Kh5T2Y+s5lDrgLeBfw4M2stLiNtzQZQkbSq4LhWs/gpxXeMt1Zsv6KoQ/jWzFxX1d+xraZULmb3ZmqvOu+4VjO5H9izqm0P4L+76O9vSTWMM2Vb25cpbhv8FfBL4HSKBTe+2dCopG4ovxhW/qV+t4h4K7A4M5+t7JuZr0TEBGBNPWOUeuly4MPAnwPLI6KjzvfSzFxZ3Tkzfx8ROwKv1TFGqcci4kLgVuBZYATFOJ9OUc+tE8e1mkVmLgc61fyOiBXAolq1wB3bahYR8UXgJxSJqokUM2HXAd+r7uu4VpP5CvAfEfE54DqK2a9/W24b8bekGsmkbAvLzB9ExBuACygW93oYODIzqxf/krZG+wN3Vzz/cvl4DcWiBJ1k5pI6xCT1hU+Uj/dUtZ8AXF3rgMxc1I/xSH1lHPAtYAKwFPgNcERm3lGrs+NarcqxrSbxRooE7FjgZeA+YEpmvlyrs+NazSIzH4yIGcCFwCzgGeD0zLx2E8f4W1INEZnZ6BgkSZIkSZIkaZthTVlJkiRJkiRJqiOTspIkSZIkSZJURyZlJUmSJEmSJKmOTMpKkiRJkiRJUh2ZlJUkSZIkSZKkOjIpK0mSJEmSJEl1ZFJWkiRJkiRJkurIpKwkSVKDRMTVEZERsWujY+kLEXF4RNwfEa+U7+vGRsfUExGxe0T8KCLmRcT6iFhSts8u38/0Boe4xSJiZvleZjY6FkmSpG3ZwEYHIEmStKXKpOYz5dPbM/OIGn2mAD8HrsnMmXULbhtRfgY/BpYA3wSWAf/VwJB6JCLagBuBycC3geeBVQ0NaisSEaOBk4E/A/YAdqD4rB+h+Ny/mZmvNi5CSZKk5mJSVpIktZrDI+I9mXlXowPZxhwKbAd8OjO/2+hgemE3YG/g65n5t1X7LgO+Dzxb96i2AhFxCHAdMAb4PXA9sAgYC0wDLgFOB97cqBglSZKajUlZSZLUSuYCk4B/jIgDMzMbHM+2ZGL5+GJDo+i9LuPPzIXAwvqGs3WIiD8BflI+/evMvLZGn+nAhfWMS5IkqdlZU1aSJLWSxyluPd8f+FB3DoiIuRExt4t990REVrVtqC8aESdExG8jYmVEPBMRp5Z9IiI+HRGPR8SqiHgiIj66iTAGRMRZZb9V5blmRcSgLuKaFhE/iYiFEbG6PO7vI2JYVb/pZayzI+IdEXF7RCypfk+buDZ/HBHXRcSC8nWeiYiLI2JsRZ9dy/N9vmy6u3zNbtVgjYjBEXFGRDwYEcsj4tWIeCwivlzeMt+jeCr6zi237SNiTkS8WB7zm4g4trov8LPy6fkV8c8u929UU7bjfZd1gfcqa9Eu6qgRXFm7NSKOjogHIuK1iHghIv5PRAwoz3N8RDxSjqFnI+IzXVyniIiPRVGzd1l5rl9FxMe66D8mIv4pIuaXfR+MiBmb+zxquAQYCnyyVkIWIDPvASqvzeCI+GRE3BYRz5XXfUFE3BAR+9WIdUBE/E1E/DIiFpfX4vlyjE+v0b9b47/s+8GI+Fn5+qvKcXBnRHywF9dCkiSpzzhTVpIktZpZwF8Afx8RN2Tmmn56ndMpElE/Bu4CPgjMiYjXgP3K5zcBPy3juSYi5mbmv9c418XAwRS3iL8KHE2R5NwXqE4gfgK4nKKe50+ABRRJ6HOBd0fEuzOzver87wA+B9wN/DPFbOJNioh3ArcBg4F/oZiF/HbgNOCoiJhSziBdUsY6HXgXcE3Zl4rHrl5jKHBH+d6foKhFuxrYHTgR+BbwSg/jqTQIuB0YDfwQGEbxWVwXEUdm5u1lv4uBtwLHUyRn7ynb72HzJgO/AH4LXE1xS3/l9Z8BHE5Rr/Z+ipqs5xVvKZaW//zj8rU+CFwUEfMz81sV1ymAa4G/LK/Td8vXOAy4MiL2zswzK/oPK8+3D0Ud5Z8BuwA/KK9Ht0TEZIryBM9RfDZdyszVFU/HUFzTe4FbKD7DNwHvB94bEdMy88GK/hcCZwFPle9tObAz8E6Kshj3VMTU7fFf9r0CmAf8iKLkwk7AgRSfyw+7ey0kSZL6XGa6ubm5ubm5uTX1BuwKJPBv5fP/Vz4/paLPlLLt6qpj5wJzuzjvPcXXpU5ts8vzLALeVNG+C0VCcQnFjN03VOw7qDzmX6vOdXXZvgB4Y0X7YIpEWgIfrGjfG1gDPAyMrTrXZ8v+n65om162JXBCD67nAODJ8rgjqvZdVLZf2cV1md6D1/liecy3gLaqfSOB7bcgnrll+43A4Ir2QyrHSo1rNbtGnBu9t4oxl8Dnaxwzs9zXDhxQ0T4CmA+soEgW1hpDv6k618fLc10FDKoaJ/9a7vvTGvH+c9V5jqiIeWY3Pp/jy77f7uG/j0OAnWu0/xFFwvWOqvZFwAvAsBrHjNmC8f/r8nqOq3Hesd19P25ubm5ubm5u/bFZvkCSJLWi/0uRHP3fEbF9P73GnMx8uuNJZj4H3EeRTPxCZr5cse8B4GngTzZxrucr+rdTzPyDIrnX4USKO50+mZmLqs5xEfAyxWzKav+ZmZuc6VjlYIpFm27NzNuq9l0ALAY+HBGDe3DOTiJiIPC3wFLgtMxcV7k/M5dm5qt9EM8ZWTFzODN/Cvw3cEBvY6/yEvCFTez/TlbMCs3M5RQzqIcBX+1iDO1dXp8Op1AkcU/OipnfVeOk8nP/KEUyeFZlIOW1+2n33xo7lY/Pb7JXlcxcnZkv1Gj/HcVs7WmxcWmOdmBdjWMWVzztzfhfU27V560+XpIkqa4sXyBJklpOZr4SEf8A/ANwJsXMwb72cI22eZvZd1AX57q3RtvPgbUUpRA6TCkfj4iIQ2ocswZ4S432B2u0bUrHa95TvSMzX42IX1Hckr8nxW37vfEWilmjd2bmK/0Uz5LMfKbG+Z6nKH3QFx7JjctFVOrNOGkDxgMvlKUI9qFYgOzsopJBJx3JzbcARMQOwG7AY5n5Uo3z30sxW7hfRcRbKUoSvJMiuVudhN2R16/D94GTgEcj4vsUidufZ+bKqmN6Ov6/T5GsfTQivlue977MXNa7dyVJktR3TMpKkqRWdQnFDMNPR8QV/XD+WomdtZvZ19V3r/nVDZm5LiIWUcy87TCmfDy3uv9mbHT+zdhhM8fNq+rXGx3va6MZlX0Yz9Iu+q+l7xa83dy17c04gdeTmKOBoKixev4mXmd4+dhxDRZ00a8nY6EjqbtzD44hIt5OfeWPAAAFM0lEQVRBUWcZihq2T1DUSk7gGIoZ40MqDjkNeAY4gaLG7nnAqoi4jqIcQUet4J6O/y9SlEb4BPBpij/QrI2ImylmUNdK2EuSJNWFSVlJktSSMnNlRJwPXEmRzPp2F13XU9TmrGVkF+19bTxFHdoNIqKNYtGoyiRaRxJvh/I2+O7KHsbT8Trju9i/U1W/3lhSPnYn4VePeHqrp9e2pzre068zc/8e9B/Xxf6urmEt95eP0yNiQGau7+Zx51IkXadm5n2VOyJiClVlPDJzLUUC9YsRMZFiwbgTKMow7ERRCxd6OP4zs6MO71URMRaYSlHe4EPA7hGxb3XZDEmSpHqxpqwkSWpl1wC/o1goaXIXfV4BxlXV8CQihgO79294G0yt0fZ2ij+gP1TR9kD5OGXj7n2q4zWnV+8or8v+wEqqEsk99DhFku2AiBi9FcSzVSqTj78H9oqIUd3ov4xi1unkiNipRpdaY62rcz0J/DvFAmTHb6pvRFTOfH0zsLhGQnYY8LbNvOaLmfk94EiKxd0OjYih5e5ej//MXJSZN2bmcRSzePem6/8mSJIk9TuTspIkqWWVs+A+R3Er+Owuuj1Y7v+rjoYoCndeyOu3hPe30yLijRWvP5jXF4+6uqLfFRS3t18aEZOqTxIRoyJiv+r2XrgfeAp4b0QcWrXvPIoZvN/bTC3VTSpnR36NYjbynHJm8AYRMbJikbZ+j2crdwnFwmBfL5PQnUTEbhGxa0XTtylmf19Q1e9wel5P9jSKhPdlEXFcrQ4RMZXXyxVAsZDa6Ij4o4o+bRSzYd9QdeyQstxBteHA9hR1Yjtm6PZo/EfE9KgqwlsuMNZRBmFVrfcjSZJUD5YvkCRJLS0z/zUi7qNYcKiWyyhulf5GRBxGsYL7VGAU8AhVt1r3k18Aj0TED4AVwNEUi1bdkJk/7OiUmY9GxEnAV4HHI+IWimTlCOBNFLd9Xw383ZYEk5nrI2ImcBtwS0RcT5FoezvFbNWngM9uyWuUZlHMevwIMCUibgVWU7yXIyk+s4frGM/W6msU1+l44OCIuJNi4a/xFAtbHQR8GJhb9r8I+ADw8TIx2jHb9UPAzcCfdfeFM/PhiDgauA74fkTMKs+3mCK5eTDFQmRPVhx2KcXCa/eVdWFXUXxOO1Ms1ja9ou9Q4P6I+APwa+BZimTsURSlC76YmavLWHo6/m8ElkXELyjGyyDgMIpZsv+Smf/d3esgSZLU10zKSpKkbcHZvF4fs5My0XMkxczYYykWJLqFYlGg6+oU3+nA/wT+BphEsXDV7DKm6ni/HhEPA58CplEkcJdSJLO+QlGyYYtl5n1l/c9ZFAm2kRSJwDnA31csvrQlr7GqTISfAvw1RZmJdRTv5Z94PclYl3i2VmVt1JllEvLjFAnL7SkW83qCYqzeWdF/RUS8i2L8zKAoGfA74DiK69btpGx5vp9GxO7ASeWxx1EkQpcCvwVOpajd2tH/pog4lmKW+l8Dr1HMpJ1B8flVWkHx7+chFH8MGUdRUuRx4Bzg+1Wx9GT8n0OR3D+w7LeCIon7CYpa05IkSQ0TxXc8SZIkSZIkSVI9WFNWkiRJkiRJkurIpKwkSZIkSZIk1ZFJWUmSJEmSJEmqI5OykiRJkiRJklRHJmUlSZIkSZIkqY5MykqSJEmSJElSHZmUlSRJkiRJkqQ6MikrSZIkSZIkSXVkUlaSJEmSJEmS6sikrCRJkiRJkiTVkUlZSZIkSZIkSaojk7KSJEmSJEmSVEcmZSVJkiRJkiSpjv4/Kh5TjVHi3RMAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 1400x800 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FHTn0UDTMB6k"
      },
      "source": [
        "Here we can see that Maharashtra, Karnataka, Kerala and Tamil Nadu are badly hit indeed but the spread of virus is controlled as compared to Kerala"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "e6ymFTYOiZzY"
      },
      "source": [
        "## Cases per 100km squared"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1YEfOr0wilA7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "b642e6ce-fd22-442f-c504-84ece5dfaed9"
      },
      "source": [
        "covid19_merged['Cases/100km2'] = (covid19_merged['Confirmed'] / covid19_merged['Area'] * 100)\n",
        "covid19_merged"
      ],
      "execution_count": 160,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>State/UnionTerritory</th>\n",
              "      <th>Cured</th>\n",
              "      <th>Deaths</th>\n",
              "      <th>Confirmed</th>\n",
              "      <th>Death Rate</th>\n",
              "      <th>Recovery Rate</th>\n",
              "      <th>Population</th>\n",
              "      <th>Area</th>\n",
              "      <th>Cases/10million</th>\n",
              "      <th>Cases/100km2</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>Kerala</td>\n",
              "      <td>3226761</td>\n",
              "      <td>16837</td>\n",
              "      <td>3411489</td>\n",
              "      <td>0.004935</td>\n",
              "      <td>0.945851</td>\n",
              "      <td>3647164</td>\n",
              "      <td>38863</td>\n",
              "      <td>9.353813e+06</td>\n",
              "      <td>8778.244088</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>Lakshadweep</td>\n",
              "      <td>10070</td>\n",
              "      <td>50</td>\n",
              "      <td>10195</td>\n",
              "      <td>0.004904</td>\n",
              "      <td>0.987739</td>\n",
              "      <td>71769</td>\n",
              "      <td>32</td>\n",
              "      <td>1.420530e+06</td>\n",
              "      <td>31859.375000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>Goa</td>\n",
              "      <td>167046</td>\n",
              "      <td>3148</td>\n",
              "      <td>171205</td>\n",
              "      <td>0.018387</td>\n",
              "      <td>0.975707</td>\n",
              "      <td>1585610</td>\n",
              "      <td>3702</td>\n",
              "      <td>1.079742e+06</td>\n",
              "      <td>4624.662345</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>Puducherry</td>\n",
              "      <td>118228</td>\n",
              "      <td>1795</td>\n",
              "      <td>121005</td>\n",
              "      <td>0.014834</td>\n",
              "      <td>0.977051</td>\n",
              "      <td>1280316</td>\n",
              "      <td>479</td>\n",
              "      <td>9.451182e+05</td>\n",
              "      <td>25262.004175</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>Ladakh</td>\n",
              "      <td>20076</td>\n",
              "      <td>207</td>\n",
              "      <td>20340</td>\n",
              "      <td>0.010177</td>\n",
              "      <td>0.987021</td>\n",
              "      <td>228569</td>\n",
              "      <td>59146</td>\n",
              "      <td>8.898845e+05</td>\n",
              "      <td>34.389477</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Delhi</td>\n",
              "      <td>1410714</td>\n",
              "      <td>25054</td>\n",
              "      <td>1436350</td>\n",
              "      <td>0.017443</td>\n",
              "      <td>0.982152</td>\n",
              "      <td>21960417</td>\n",
              "      <td>1483</td>\n",
              "      <td>6.540632e+05</td>\n",
              "      <td>96854.349292</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>Maharashtra</td>\n",
              "      <td>6094896</td>\n",
              "      <td>132948</td>\n",
              "      <td>6310194</td>\n",
              "      <td>0.021069</td>\n",
              "      <td>0.965881</td>\n",
              "      <td>114872435</td>\n",
              "      <td>307713</td>\n",
              "      <td>5.493219e+05</td>\n",
              "      <td>2050.675142</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Chandigarh</td>\n",
              "      <td>61113</td>\n",
              "      <td>811</td>\n",
              "      <td>61954</td>\n",
              "      <td>0.013090</td>\n",
              "      <td>0.986425</td>\n",
              "      <td>1133219</td>\n",
              "      <td>114</td>\n",
              "      <td>5.467081e+05</td>\n",
              "      <td>54345.614035</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>Sikkim</td>\n",
              "      <td>22955</td>\n",
              "      <td>345</td>\n",
              "      <td>26754</td>\n",
              "      <td>0.012895</td>\n",
              "      <td>0.858003</td>\n",
              "      <td>574280</td>\n",
              "      <td>7096</td>\n",
              "      <td>4.658703e+05</td>\n",
              "      <td>377.029312</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>Karnataka</td>\n",
              "      <td>2846244</td>\n",
              "      <td>36587</td>\n",
              "      <td>2906999</td>\n",
              "      <td>0.012586</td>\n",
              "      <td>0.979100</td>\n",
              "      <td>62780331</td>\n",
              "      <td>191791</td>\n",
              "      <td>4.630430e+05</td>\n",
              "      <td>1515.711895</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>Meghalaya</td>\n",
              "      <td>58450</td>\n",
              "      <td>1096</td>\n",
              "      <td>65589</td>\n",
              "      <td>0.016710</td>\n",
              "      <td>0.891156</td>\n",
              "      <td>1597484</td>\n",
              "      <td>22429</td>\n",
              "      <td>4.105769e+05</td>\n",
              "      <td>292.429444</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>Manipur</td>\n",
              "      <td>87360</td>\n",
              "      <td>1566</td>\n",
              "      <td>99331</td>\n",
              "      <td>0.015765</td>\n",
              "      <td>0.879484</td>\n",
              "      <td>2536695</td>\n",
              "      <td>22327</td>\n",
              "      <td>3.915764e+05</td>\n",
              "      <td>444.891835</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Arunachal Pradesh</td>\n",
              "      <td>44354</td>\n",
              "      <td>231</td>\n",
              "      <td>48260</td>\n",
              "      <td>0.004787</td>\n",
              "      <td>0.919063</td>\n",
              "      <td>1232971</td>\n",
              "      <td>83743</td>\n",
              "      <td>3.914123e+05</td>\n",
              "      <td>57.628697</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Andhra Pradesh</td>\n",
              "      <td>1934048</td>\n",
              "      <td>13395</td>\n",
              "      <td>1968462</td>\n",
              "      <td>0.006805</td>\n",
              "      <td>0.982517</td>\n",
              "      <td>50987456</td>\n",
              "      <td>160205</td>\n",
              "      <td>3.860679e+05</td>\n",
              "      <td>1228.714460</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Chhattisgarh</td>\n",
              "      <td>986778</td>\n",
              "      <td>13525</td>\n",
              "      <td>1002222</td>\n",
              "      <td>0.013495</td>\n",
              "      <td>0.984590</td>\n",
              "      <td>27490276</td>\n",
              "      <td>135191</td>\n",
              "      <td>3.645733e+05</td>\n",
              "      <td>741.337811</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>Tamil Nadu</td>\n",
              "      <td>2506961</td>\n",
              "      <td>34102</td>\n",
              "      <td>2561587</td>\n",
              "      <td>0.013313</td>\n",
              "      <td>0.978675</td>\n",
              "      <td>72437625</td>\n",
              "      <td>130058</td>\n",
              "      <td>3.536266e+05</td>\n",
              "      <td>1969.572806</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>Mizoram</td>\n",
              "      <td>27024</td>\n",
              "      <td>150</td>\n",
              "      <td>39363</td>\n",
              "      <td>0.003811</td>\n",
              "      <td>0.686533</td>\n",
              "      <td>1131993</td>\n",
              "      <td>21081</td>\n",
              "      <td>3.477318e+05</td>\n",
              "      <td>186.722641</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32</th>\n",
              "      <td>Uttarakhand</td>\n",
              "      <td>334190</td>\n",
              "      <td>7362</td>\n",
              "      <td>342161</td>\n",
              "      <td>0.021516</td>\n",
              "      <td>0.976704</td>\n",
              "      <td>11152865</td>\n",
              "      <td>53483</td>\n",
              "      <td>3.067920e+05</td>\n",
              "      <td>639.756558</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>Himachal Pradesh</td>\n",
              "      <td>201411</td>\n",
              "      <td>3521</td>\n",
              "      <td>206161</td>\n",
              "      <td>0.017079</td>\n",
              "      <td>0.976960</td>\n",
              "      <td>7592344</td>\n",
              "      <td>55673</td>\n",
              "      <td>2.715380e+05</td>\n",
              "      <td>370.306971</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>Haryana</td>\n",
              "      <td>759589</td>\n",
              "      <td>9638</td>\n",
              "      <td>769942</td>\n",
              "      <td>0.012518</td>\n",
              "      <td>0.986554</td>\n",
              "      <td>29370278</td>\n",
              "      <td>44212</td>\n",
              "      <td>2.621501e+05</td>\n",
              "      <td>1741.477427</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Assam</td>\n",
              "      <td>549065</td>\n",
              "      <td>5275</td>\n",
              "      <td>566982</td>\n",
              "      <td>0.009304</td>\n",
              "      <td>0.968399</td>\n",
              "      <td>22329699</td>\n",
              "      <td>78438</td>\n",
              "      <td>2.539139e+05</td>\n",
              "      <td>722.840970</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>Odisha</td>\n",
              "      <td>958601</td>\n",
              "      <td>5966</td>\n",
              "      <td>978705</td>\n",
              "      <td>0.006096</td>\n",
              "      <td>0.979459</td>\n",
              "      <td>42754533</td>\n",
              "      <td>155707</td>\n",
              "      <td>2.289126e+05</td>\n",
              "      <td>628.555556</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>Nagaland</td>\n",
              "      <td>26031</td>\n",
              "      <td>570</td>\n",
              "      <td>27945</td>\n",
              "      <td>0.020397</td>\n",
              "      <td>0.931508</td>\n",
              "      <td>1289154</td>\n",
              "      <td>16579</td>\n",
              "      <td>2.167701e+05</td>\n",
              "      <td>168.556608</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>Tripura</td>\n",
              "      <td>74875</td>\n",
              "      <td>755</td>\n",
              "      <td>78722</td>\n",
              "      <td>0.009591</td>\n",
              "      <td>0.951132</td>\n",
              "      <td>3695979</td>\n",
              "      <td>10486</td>\n",
              "      <td>2.129936e+05</td>\n",
              "      <td>750.734312</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>Punjab</td>\n",
              "      <td>582332</td>\n",
              "      <td>16294</td>\n",
              "      <td>599130</td>\n",
              "      <td>0.027196</td>\n",
              "      <td>0.971963</td>\n",
              "      <td>30445967</td>\n",
              "      <td>50362</td>\n",
              "      <td>1.967847e+05</td>\n",
              "      <td>1189.646956</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Andaman and Nicobar Islands</td>\n",
              "      <td>7403</td>\n",
              "      <td>129</td>\n",
              "      <td>7539</td>\n",
              "      <td>0.017111</td>\n",
              "      <td>0.981960</td>\n",
              "      <td>386484</td>\n",
              "      <td>8249</td>\n",
              "      <td>1.950663e+05</td>\n",
              "      <td>91.392896</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Dadra and Nagar Haveli and Daman and Diu</td>\n",
              "      <td>10622</td>\n",
              "      <td>4</td>\n",
              "      <td>10650</td>\n",
              "      <td>0.000376</td>\n",
              "      <td>0.997371</td>\n",
              "      <td>592659</td>\n",
              "      <td>603</td>\n",
              "      <td>1.796986e+05</td>\n",
              "      <td>1766.169154</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33</th>\n",
              "      <td>West Bengal</td>\n",
              "      <td>1499597</td>\n",
              "      <td>18149</td>\n",
              "      <td>1528720</td>\n",
              "      <td>0.011872</td>\n",
              "      <td>0.980949</td>\n",
              "      <td>91949146</td>\n",
              "      <td>88752</td>\n",
              "      <td>1.662571e+05</td>\n",
              "      <td>1722.462592</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>Rajasthan</td>\n",
              "      <td>944484</td>\n",
              "      <td>8954</td>\n",
              "      <td>953688</td>\n",
              "      <td>0.009389</td>\n",
              "      <td>0.990349</td>\n",
              "      <td>71679490</td>\n",
              "      <td>342239</td>\n",
              "      <td>1.330489e+05</td>\n",
              "      <td>278.661403</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>Gujarat</td>\n",
              "      <td>814570</td>\n",
              "      <td>10076</td>\n",
              "      <td>824900</td>\n",
              "      <td>0.012215</td>\n",
              "      <td>0.987477</td>\n",
              "      <td>63151058</td>\n",
              "      <td>196024</td>\n",
              "      <td>1.306233e+05</td>\n",
              "      <td>420.815818</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>Madhya Pradesh</td>\n",
              "      <td>781207</td>\n",
              "      <td>10513</td>\n",
              "      <td>791845</td>\n",
              "      <td>0.013277</td>\n",
              "      <td>0.986566</td>\n",
              "      <td>75204641</td>\n",
              "      <td>308252</td>\n",
              "      <td>1.052920e+05</td>\n",
              "      <td>256.882356</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>Jharkhand</td>\n",
              "      <td>341817</td>\n",
              "      <td>5129</td>\n",
              "      <td>347200</td>\n",
              "      <td>0.014772</td>\n",
              "      <td>0.984496</td>\n",
              "      <td>34731235</td>\n",
              "      <td>79714</td>\n",
              "      <td>9.996765e+04</td>\n",
              "      <td>435.557117</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31</th>\n",
              "      <td>Uttar Pradesh</td>\n",
              "      <td>1685049</td>\n",
              "      <td>22763</td>\n",
              "      <td>1708476</td>\n",
              "      <td>0.013324</td>\n",
              "      <td>0.986288</td>\n",
              "      <td>206199237</td>\n",
              "      <td>240928</td>\n",
              "      <td>8.285559e+04</td>\n",
              "      <td>709.123058</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Bihar</td>\n",
              "      <td>714801</td>\n",
              "      <td>9644</td>\n",
              "      <td>724880</td>\n",
              "      <td>0.013304</td>\n",
              "      <td>0.986096</td>\n",
              "      <td>104581560</td>\n",
              "      <td>94163</td>\n",
              "      <td>6.931241e+04</td>\n",
              "      <td>769.814046</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                        State/UnionTerritory  ...  Cases/100km2\n",
              "15                                    Kerala  ...   8778.244088\n",
              "17                               Lakshadweep  ...  31859.375000\n",
              "9                                        Goa  ...   4624.662345\n",
              "25                                Puducherry  ...  25262.004175\n",
              "16                                    Ladakh  ...     34.389477\n",
              "8                                      Delhi  ...  96854.349292\n",
              "19                               Maharashtra  ...   2050.675142\n",
              "5                                 Chandigarh  ...  54345.614035\n",
              "28                                    Sikkim  ...    377.029312\n",
              "14                                 Karnataka  ...   1515.711895\n",
              "21                                 Meghalaya  ...    292.429444\n",
              "20                                   Manipur  ...    444.891835\n",
              "2                          Arunachal Pradesh  ...     57.628697\n",
              "1                             Andhra Pradesh  ...   1228.714460\n",
              "6                               Chhattisgarh  ...    741.337811\n",
              "29                                Tamil Nadu  ...   1969.572806\n",
              "22                                   Mizoram  ...    186.722641\n",
              "32                               Uttarakhand  ...    639.756558\n",
              "12                          Himachal Pradesh  ...    370.306971\n",
              "11                                   Haryana  ...   1741.477427\n",
              "3                                      Assam  ...    722.840970\n",
              "24                                    Odisha  ...    628.555556\n",
              "23                                  Nagaland  ...    168.556608\n",
              "30                                   Tripura  ...    750.734312\n",
              "26                                    Punjab  ...   1189.646956\n",
              "0                Andaman and Nicobar Islands  ...     91.392896\n",
              "7   Dadra and Nagar Haveli and Daman and Diu  ...   1766.169154\n",
              "33                               West Bengal  ...   1722.462592\n",
              "27                                 Rajasthan  ...    278.661403\n",
              "10                                   Gujarat  ...    420.815818\n",
              "18                            Madhya Pradesh  ...    256.882356\n",
              "13                                 Jharkhand  ...    435.557117\n",
              "31                             Uttar Pradesh  ...    709.123058\n",
              "4                                      Bihar  ...    769.814046\n",
              "\n",
              "[34 rows x 10 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 160
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KtajcSPninBf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "7fa12ea4-a6f0-4214-e3ec-f595c5c13b39"
      },
      "source": [
        "covid19_merged = covid19_merged.sort_values(by=['Cases/100km2'], ascending = False)\n",
        "plt.figure(figsize=(14,8), dpi=100)\n",
        "plt.barh(covid19_merged['State/UnionTerritory'][:10], covid19_merged['Cases/100km2'][:10], \n",
        "        align='center',color='#D32F2F')         \n",
        "plt.xticks([0,20000,40000,60000,80000,100000],['0','20K','40K','60K','80K','100K'])\n",
        "plt.xlabel('Confirmed Cases / 100 km squared', size = 14)\n",
        "plt.title('Top 10 States with maximum cases per 100 km squared', size = 18)\n",
        "plt.show()"
      ],
      "execution_count": 161,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1400x800 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AEJ713c8iozW",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "8b615a3f-adc4-413b-bbfa-428513b27493"
      },
      "source": [
        "df = covid19_merged[(covid19_merged['Confirmed']>=1000000) | (covid19_merged['Cases/100km2']>=15000)]\n",
        "plt.figure(figsize = (14,8), dpi= 100)\n",
        "plt.scatter(covid19_merged['Confirmed'], covid19_merged['Cases/100km2'], alpha = 0.5)\n",
        "plt.scatter(df['Confirmed'], df['Cases/100km2'], color=\"#D32F2F\")\n",
        "for i in range(df.shape[0]):\n",
        "  plt.annotate(df['State/UnionTerritory'].tolist()[i], xy=(df['Confirmed'].tolist()[i], df['Cases/100km2'].tolist()[i]),\n",
        "    xytext = (df['Confirmed'].tolist()[i]+1.0, df['Cases/100km2'].tolist()[i]+12.0), size=11)\n",
        "plt.xticks([0,1000000,2000000,3000000,4000000,5000000,6000000],['0','1M','2M','3M','4M','5M','6M'])\n",
        "plt.xlabel('Number of confirmed Cases', size=14)\n",
        "plt.yticks([0,20000,40000,60000,80000,100000],['0','20K','40K','60K','80K','100K'])\n",
        "plt.ylabel('Number of cases per 100 km squared', size=14)\n",
        "plt.tight_layout()\n",
        "plt.title('Variation in spread of Covid-19 in different Indian states', size=18)\n",
        "plt.show()"
      ],
      "execution_count": 162,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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T1ad3b3Xq1EkDBw7MtU5CQoLddV5aWlqhF5q9lrbyc82ZnTHmd0mDL2/elWfF3BVkBK0keV7eJ6snCnhMSUxlgFwciLqoOZsOKzo+WaV9PeTl5qlLyanadTJGJ2MS1KdxJVULKnb1hnDbutW/I6tXr9bbb7+tN954wy50aNasmfr06aOVK1fewN7lX8mSJe1CvBst+6Prtju22erVcXfT/D27tejtd+Xu7q57771XklSxYkWVLl1amzZt0ubNm9WlSxfbPgEBAQoPD891Bc+sj62Hh4crPDxcsbGx+uabbzR48GD16dNH33//fYHPJzk5Wdu3b9elS5fk5eUlKeNOrp+fn/bv328rDwgIUIcOHeymLMiUdeRrfpQuXdpuJKokValSRWfPnlXjxo2VnJysH374QXv37tXo0aN14sQJffrpp7rzzjuVnp5ut1/Tpk31+eefq379+nrhhRe0atUqLVmyROHh4ZKksWPHqmnTprZHqtu2bavExMRc32NjjFasWGF7rw8fPqw333xT6enpcnL6/3u8nTt31ttvv22378iRI22/BwQEqHz58jp48KDmzp2rN99886rvSWpqqqSMoO3ll19WWlqaHnroIdvr58+f15o1a1S/fn27/f7973/btVG5cmU1adJE+/btU/Xq1RUdHa2ZM2dq+PDhevXVV23vQWhoqN2cvuvXr9eKFSu0du1aWxjYunVrRUZGauTIkfq3V3FdSk3VieRkDS9TRs19fSVJ//DxkZE06/RppadlfDYxMTEKDg7W2rVr1axZM3Xt2lVjxoxRqVKlrvo+OJqzs7Nee+012zysme68806VK1dOw4cPV1pamuLi4hQxMkLBgSWUFBOr84eOysnJSd99951CQkLUoUMHTZo0Sb6+voqIiLBN+1GjRg1NmjRJkZGRGjp0qAIDA3X8+HGNHj1aS5YskSR5eHjYPeKW1ZUenSvgo285TJkyRUOGDNErr7yiFi1ayN/fX9u3b1e/fv1yTBvx3XffKT4+Xn379s0x0qMg7QAAAFyLoxu32qYvyI0lyePCOQUmpuvLL7/Ms17r1q01e/ZshYSEqESJEpo5c2ah58e/lrZyveaMiFDZsmVzq/6iZVn/k5QmqacyAtONBezuHkm9LMvqJClS0kljzMkr1P9W0kDLsvpL2ieph6RqV6ifJ0bMwk56utHaXacVHZ+sO4J8VMzDVc5Olop5uOqOIB9Fxydr3R+nb7lH1lF0bofvyLvvvqvg4GC98cYbub7esWNH2+/p6ekaNWqUgoODVaJECfXp00fx8TmfUDh27Nh1WYjmSgvU5DaVwfjx41WtWjV5eHioZMmSatWqle2xbykj3OrYsaO8vLxUvnx5TZ48OcdI0fwuWGNZlsaPH69hw4apVKlStrlfJcmkp+ujYS+r7e7davT77xpw6JCiLwds93t7KzE9XZ/MnJbjcZZGjRrp008/1alTp9S4cWNbeatWrbR7927dddddqlu3rt1PrVq1cnwexYsX1yOPPKLHHntMe/bssZU7OTnlCDALwhijkyft///cqlUr7dq1S/fdd1+OvtWoUaNA7derV08//PCDLl78/5HnxhjFxsaqRIkS+u2339SwYUNJ0o4dO9S9e3ddunRJCxYs0IoVKyRl3InObCuTZVm66667bCMK09LS9L///U8PPPCA3fGzBp5ZhYaG2gXgISEhSklJsS28lim30Y179uyxjY786aeftHr1arvRkVcSHx9vG4FcuXJlrV+/XtOnT7ebOzgwMDBHKCtJn376qe677z75+PjI1dXVNl9x5nF///13JSQk5HgPHnzwQbvtdevWKSAgQC1btrRNVZGamqrWrVtr544dcr1wVv7Ozirj6qqpp07pq+honUrOuHmeGdNdOHREklSmTBl9/fXXcnd318MPP6z58+dLkt28qjeTHj16qHLlynZl7u7uWrZsme0chr/0sro7Oev+pETp7BmlrF2t9IRE6Y/9Wr58uZydnTVw4ECNGTNGM2bMkJ+fny5duqQ5c+boiSee0JAhQzRhwgRduHBB//3vf7Vr1y69//7719TvAj76lsMXX3yhzp0766233lKbNm1Ur149eXt751r3lVdeUefOndW2bVv9/vvvhW4HAADgWiScjrp6pXzUmzZtmkJDQ/Xiiy+qb9++qlWrll577bVC9ela2sp+zTly5Ei9/vrrCg0Nza3645K+kLREUmVJnYwxe3KreAUTJG2SNE/SdknPXKX+aEkLLv+5SFKipAEFPKYkRswimxMXEnTwTJxK+3rkGPlhWZZK+3roQFScTlxIUPkArxvUS2SXnm504kKC4pNT5e3morJ+nnJyyusBhmtzq39HUlNTtWnTJj344INydXW9av3p06eradOm+uSTT7Rv3z4NHTpUwcHBtsWaMl2vhWjKlSuX5wI12c2bN882Crhhw4aKiYnRxo0bFRsbKykj4OvSpYtOnz6tWbNmydfXV++8846OHDliN+qxIAvfTJ06VQ0aNNBHH31kG9koScuXLlXlS5f0erlyOp+aqgknT+qtEyf0TsWKqu3tLSdJm6LP6bmyFezaa9iwoYYOHSpJtiBNkl566SV99tlnCg0N1cCBA1WhQgWdOXNGP//8s8qUKaPBgwdr1qxZ2rJli8LDw1W6dGkdOnRI8+fPt3vk2c/PT6dOndK3334rf39/Va5cWYGBgTney7lz5+rcuYw7zpmjkoOCglS1alXbaMrM8rJlyyo+Pl7NmzeXi4uL9u7dq7Nnz8rLy0uhoaFavHix3WdmWZbeeustnT9/3m5UpyQNHjxYM2fOVIcOHTR06FAdOHBAx48dk3+x4vpr9x6dOXNGY8eO1ebNm+3mtu3Xr58t8Mo838yAtkuXLoqOjpazs7MCAgKUlJSk8+fPKzU1VUFBQYqNjVX//v21fPly26NCmftmfd8uXLig1157TcuXL7fdDFi3bp169uxpq3fq1Ck1a9ZMv/76q9LT01WhQgVFRkaqUqVKmjRpkiZOnCgfHx8dO3ZM33zzjXx8fPSPf/xDs2fPts33mZWnp6c2bNggy7JUokQJlS9f3u67KuVcKECSli9frp49e+qZZ57RuHHjFBgYqMjISD3wwAO2kYqRkZG2z/VK7Z09e1bR0dF5/vfiTEqKSrm5aVbVqnovMlLjTpxQQnq6Qjw9NbRMGf1eu7acQsM0Ye0K+fn5qV69erY5di9cuCB/f3/16dNHvXv3trXZu3dvu+1MxuS84ZU5L2umUaNGadSoUbbt5s2b57pfftp2cXHJ9ZH7zHPIHBUvSQ9WyPi7PPPUKWXOY1Zj5Fjt3bvXdpOlTp06mjRpkrp27Wr3WFrjxo2VlpamDh06aPHixZKkiIiIq87xnZcrPfqWNQTPfD376NXsj91Jsi3al52zs7MWLlyobt26qVWrVtqwYYPthkxB2gEAALgWnsFBuU5w+kKpUnohy9NZnsFBOers3LnT9ntwcLCWL1+eo87TTz9t+71SpUq5XjtOmTJFU6ZMKZK2JNldN2fK+m+PLBoZY2Jze8EYM1fS3FzK7822fUJSjlEmxpjmebQbJ6lPLi8VOIghmIWd+ORUJaamycst95W0Pd2cdTo2UfHJqbm+Dsc7EHVRa3ed1sEzcUpMTZOHi7OqlvRR25rB12U6gVv9O3Lu3DklJSWpQoUKV6+sjEfLM/8hHR4erl9++UVLlizJEcz279/f9ih7o0aNtGrVKi1dulQjRoywvZ4pPT1dLVq00B9//KEPPvhAzZs3z7EQTYkSJWz18yrPbtu2bbrnnntsj2VLspsOYM2aNfrll1+0YcMGNW2aMRd6y5YtVa5cObvHgjMXrMnUuHFjeXt7q1evXpoxY4btsX4p49H0ZcuW5QjpTWqaplWuLLfLIdrJ5GTNjopSujEq7uysqh4e2p+YqJply9vt16hRIxljVKZMGbuReoGBgdq6datGjBihYcOG6dy5cwoKClKDBg1sIx7vueceff3113rppZd07tw5lSpVSo8//rjdo/n333+/vv32Wz344IO6ePGi5syZk2sA1qFDB/n6+totFhUXF6eLFy+qY8eOWrlypV1Q7uPjo/79+2vDhg1KTU1ViRIlVKlSJW3dulXPPfdcnoH2o48+qg8//NBWXrp0aa1Zs0YDBgzQg926yd0YlXByklNSok7syhiN9+yzz0rKGE2a9TFzY4zc3Nw0ZswYvfLKK7ZFrAYPHqx69epp4MCB2rNnj5577jl9+OGHcnFxUVRUlJ566imtXbvW9p3u37+/3eJmUkZQ27p1a50+fVrjxo3TkSNHNHr0aPXt29e2YFLmsUJDQ7Vw4ULbXe6ZM2dq/fr1ql27tmbPnq0DBw7ozJkzqlWrloYPH66XXnpJPXr00JYtW3J8Dk5OTqpbt26O8qyyf/ekjJGK9957r2bNmmUr++mnn+zqlC5dWlLGYlRZH4k6fdp+0dWAgACVLFlSq1evznGcE9t3KvDf70mSKrm7a1KlSkoxRjvj4/VeZKRePHRI34eEqGQuF8C3usx5zNx15XnMqn271jZ1Q3h4uL766iv5+PioX79+Gj58uE6cOHGlx9IKJb+PvmXeOJsxY4a6du0qLy8v1apVS61bt9bUqVM1ffp0Va9eXfPnz7/iwniurq5asmSJOnXqpLCwMG3YsEFVqlQpcDsAAACFVaFpA+3wDZR7zLlcr82MpCS/QN3VtIGju4arYCoD2PF2c5GHi7Mu5RGqJSSnyd3FWd5uZPo3g8y5XnedjJGfl6uqlPCRn5erdp2M0ZxNh6/LQly3y3cktzAnN61bt7bbDgkJsVtgJtP1XIgmv+6//37t2LFDL730kv7zn/8oJSXF7vXt27fLz8/PJClDxQAAIABJREFUFspKko+Pj8LCwuzqFWTBmnbt2uX6Xta/735bKCtJVTw8lGqMbTqDZTVq6PfatdWuXXu7/Ro2bChjjN0cn5lKlSqlDz/8UCdPnlRSUpKOHTumL774wrZ4WMOGDbVy5Urb60eOHNGUKVPs5nn18/NT5cqVFRsbK2NMrqGslDEaNvNu75kzZ2SMUXx8vH7//Xfbo+5ZV3K/4447tHbtWiUkJCglJUWRkZHauHGjJk+ebFuB3RhjWxArM9CePXt2jjvETZs21aI3xuh/d9+tlsWLy9WydCI5WSeSklTf21t3eHjI2dlZH3/8sdavX28LlwYMGKDExETbFAadO3e2HTM0NFQVKlRQlSpVNH/+fCUlJen+++/X/PnztWzZMk2dOlX9+vWzhZLZV0vdt2+fdu7cqW+++UZPPfWULYytWbOmXfAdFxent956S+3bt1dYWJjt70XWUYPR0dFKTk5WqVKl9NBDD2n06NHaunVrrn+vCis/IxVr1aolT0/PHHfxly5darfdqlUrnTlzRm5ubjmmquj4dG+l+Ze0W6bV1bJUz8dHfYOCFJeermOePqpwG14AZ85jltd/STPnMTu6catq1qypVatWaf/+/erRo4cWL16sqKgodenSRVOmTNGsWbNUrVqhpuTKVX4ffbvvvvs0atQozZ8/X40aNVKnTp0kZcyL3L17d40cOVKPPfaYPDw89N577131mF9++aWqVq2qsLAwHTt2rFDtAAAAFIazq4uKDcxY9yr7+NPM7WIDBsvZ9eb+d/rfEZ8I7JT181TVkj7adTJGPu4udoGLMUaRMYmqVdZXZf1yjpZ05OP0f1dZ32MvV2d98/sp21yvmZ9VMQ9X+bi7aH9UnNb9cVpVSvgU+nPI7TO9lu/IjZKWkqqjG7cq4XSUXEsEyMPDI18LwEi5LzCT24Tl13Mhmvzq3bu3Ll68qA8++ECTJ0+Wr6+vevXqpfHjx8vT01ORkZG5LhaW/VHugixYk9tj5JJUtno1Jaak2e7Yul7+niRdDiEdecc26+cfF3k655VKETHGaOrUqfrggw906NAhu/fqr7/+Us2aNW3beQXakjTslWEq/vVKlUlJ0tGkJB29PF9piqSf4+MV5OKisTXuUs8eT8rZ1UVBQUE6fPiw1qxZY7fQVmbAnld/Xn/9dduI6uLFi+vNN9/Up59+KikjsFq3bp2t/rFjx1SrVi1Vr15dqamptqkOmjRpYrdYnre3t55//nkNGDBALVq0UIMGDexGR546dUppaWl2IxdDQkIkZdy8KFeuXMHe9Dy0bt1a/fr105gxY9SwYUOtXr06xwJwAQEBeu6552x/P+6//34tXLhQBw8ezNFWp06dFB4erldeeUX33HOP4uPj9ccff+jAgQMaOnCwdr36siaePKlwPz+Vd3fXxbQ0fRgVpTJubrpr6PDb8gI4r/nJsj8ul1mvYcOGdvNzt2vXzm6/7NvZp2TIlNc0D1kfwZPy/+hbRESEIiIi7Mp8fHxyjHKXck73kH3by8srx8js/LQDAABQFGp066i9ki5OnWy3EFiSX6CKDRisGt065r0zbpjb718KuCZOTpba1gzWyZgE7Y/KmEfU081ZCclpioxJVIC3m9rcHZwj6HP04/TXw80eLGd/j9PSjY5FJ+jOUj7XZa7XK32mhfmOOFLWzzLmu+/kPHuG7X9MSZJqeXhq3arVSk1NzTEy8HrIXIjmyy+/tJtaIL8L0eSXk5OTBg4cqIEDB+rEiRNatGiRhg8frhIlSuiNN95Q6dKldebMmRz7ZV/AKeuCNZl2796d6zHzChctJycVGzhYyaNH3NA7tnuXrbS7MDHHjik+IVF7l63McWGSlpaWr3mH81IUgbYknTt0RPMO7te51FQZY+RpWars7q6o1FQtuuMOBbm6yrIsHd24VZVbZszDW7lyZW3dutUuVFy4cKGmTp1q68+4ceN08uRJHThwQImJiercubO6du2qL7/8Uk8++aTq16+vzz//XPXr11fx4sXt+pSQkKD9+/fneH+mT58uZ2dn2/aMGTO0aNEiPfnkk0pNTVXTpk01YcIEzZgxQ126dJGzs7Nq165tNx1GXvN8Xotnn31Wf/31l6ZNm6Z33nlHbdu21YIFC9Sggf2NgPHjxys1NVUTJkxQenq6HnjgAY0fP15PPvmkXb3M6UtmzpypI0eOyNfXVzVr1lSfPn1Uo1tHnYu5IP/hQ/RhVJSiUlLk4+yse/38NTFirEIe7qLbUV7zmOVWDwAAAI5Ro1tHpXUKtw1M8QwO0l1NG9yWAwVuF3wyyKFaUDH1aVzJFsqdjk2Uu4uzapX1VZu7cwatmY/TR8cnq7Svh7zcPHUpOVW/n7igfVEX1aFWad1VuvhNF3RmlRlCHoi6qPMJyXK2nFS1pI8eqltW1YOLX70BB/Qv+3t84vwlRccnae9pydvdVQHe9o/tXstcr3l9prtOxuhkTIL6NK5UoO+II0PvrIGy/84t6rpmVo46vf391O/QIQ3u0UvTFuVciGX16tVq3759jvLCutaFaAoTXJUtW1Yvv/yyFixYoD17MhakrFevni5cuKANGzaoWbNmkjIePf/+++/tRvwW1YI1We/YKstcrY66Y5u5MFHWpdL8XVx0LjlJyaNHaO/lPmY6efJkjtHDBVEUgbYkvdTtEQ3a/6ck6fWjR7U7IUGfX15MKKusIxYrV66sP/74Q1LGnMTGGDVq1MiuP23atNEnn3xiN9owM5g9efKk/P39JWWM5nvppZdsdQ4fPqxHH31Uf/75pz766KNc+1y3bl3bKMBevXopISFB69ev15AhQzRx4kRbYNy8eXP5+PjYjbLNS14jJrPKvvBVJmdnZ02cODHH4lHZRyq6ubnpvffey/F4eY8ePXLUGzlypN2I5Kwa9emhr3s8ZncBXCHbBXBuffXz87tlR08yjxkAAMDNydnVxTaAAzc/glnkqlpQMVVp7mML0zxdnWVJupSSpmPRl2zBWnq60Te/n9Lx8xllxkhOlpSSZhSTkKIjx2P0Z2SsQkoXV7WgYtc0gvZ6hXuZIeTRc5d0KTlVcUmpSkpN157IWP186JwGhN2hsLvyHt12vaWnG63ddTrHlAV+Xm7y9XRVXGJqRgjp5W8X9hR2rte8jpd9ioTnQqvq+SzfEW83F5Uu7qHI2ET9eSrW9hn9dTbuuo+mzvxu7ImM1arfI5WUkqbSxdwVtuFzSTkXpmlWvLj6lCyp6Z8v0Om0FD3e/XGVKFFChw4d0scff6yYmJgiDWavdSGavMqze/bZZ+Xv768GDRrI399fmzZt0q+//mpblKxdu3a6//771b17d7311lvy8/PThAkTVKxYMbuV7otywZrMO7b/e2uCFPG6vEb8S/c9/MB1v2Ob18JEdb299VFUlP4bF6da701WWqdwObu6KDY2VuvXr7ctrCUVPCgvqkC7qEYi5qc/mfPRLl++XE899ZSkjJHDX375pV29Vq1aafXq1SpTpozKlClz9b55eqp9+/Y6ePCgBg4cqMTERHl4ePxfe3ceJ1dZJnr89/SWTmfpLJAEYpAdjSCLiAgCGVGCF3EGRZntss24ssi4jBsXwasXB/24IMgoLqDjhhsMogOyOcIgiwoKaAxIJCwhkKS7k967671/nFOhulKddHc61dvv+/mcT1nnvOect855IlVPv+d5t7nfRDbVvgAX65gVR8WX/juzjpkkSZI0NH5b1qBqaoIl85p4ZO1Grr//KX7/ZCvtPb3UUMMeO83gtS9ZSH9K/PTBNXT39fPkhk7qamtorK+hvbuflBJzmurpLyTqa2sGjLYcbkJuR5VKKCYhH1/XwYaOHrp6+5nZWM/s6UFvX4Gn27q49JaVLJnbxL6LxqYkw5MtnTz6bFYyoDTxOquxjvkzpvFkSwfrN3WzsauP2dOzx4y3p9brYOeDyiUSimUSHlm7kS/9958H3KM5TfWs3dhNfyENOvJ2e5OzpaOdH3qqjU3dfbxwfhN7PLWS2e0tg+73nl135aAZM7jmz49x5pln0t7ezuLFi1m+fPnmCZpGS3EimrPOOos3v/nNLFmyhPPPP59bb72V++67b3O74kQ0X/nKV7jkkktYsmQJq1atGnR9uSOOOIIrr7ySK6+8ko6ODvbcc08++9nP8k//9E9Adv+uu+463v72t/O2t72NuXPncu6557JixYoB9RkvuOACnn322c2jA08++WQuvfTSzRPjDFdtfR0LX5rVEV1yxMurkqgpTkxU7ohZszhkxgzes2oV7+jsZMWll5MWzueSSy6htraWc889d3Pb4SbKRyuhXToSsZKhjkQcSn+WLl3KSSedxHnnnUdXVxe77747X/ziF+nJ69oWnXrqqXzpS19i2bJlvO9972PfffelpaWF3/72t/T09HDxxRdzww038NWvfpWTTjqJ3XbbjTVr1vCFL3yBI488ctInZacq65hJkiRJ2ycm6iN040FEzAZaW1tbt6jHNxkUCon/efQ5vvSLR3lsXTu9fQW6egv0FQokYHp9LdPqauns6WPJvCam1dfS21fg8Q0d9PUnXjivicaGWlo6ejhsj/nMn9HAyrWbOGBxM+84Zq8hj3bd8rH6Ojp6+jbXM92e5N7q9R185qYVPL6+g9bOXubNaBiQjOzq7ePZjT28/sBd+ODxLx6TUgx/XNPGpbesZM+dZlJbdv717d385vENrNvUw+F7zmfx3OkDar2O5Nps7XwAfYUCq55r55xj9+FFi7K4r3SP2rv7uPOR5+js7eeYfXdm/sznHyhPKY0oFsqVnnfWtDoeeKKF2pqgp6/AIat+w6m//MY2j1HzgQtY+ndvHNH5J4Oenh6WLl3KUUcdVXGSmonq4e/8iMK/fazitvb+fi5bs4ZbWlt5tlCgec4cXv3qV/OJT3yCffbZZ0Dbiy66iK985Ss89dRTAxLildZv2rSJc845h+uuuw7IEtpveMMbOPHEE7n33ns59NBDgSxB/qlPfWqrfwAolmE4Py9l8OO8lEHxv9gNF3x8c9JrsPIAQ+1PS0sLZ599Ntdeey2NjY2cdtpp7LLLLrz//e8f8Jh9W1sbF154IT/84Q95+umn2WmnnTj44IN517vexQknnMCKFSv4yEc+wj333MPatWuZP38+xx13HBdffDGL8smgKvX1/vvv5+CDD+a2225j2bJlg14TjV+lE+xVKuMgSZIkjba2tjaam5sBmlNKbWPdn5Ga8onZiDgLeD+wCHgAOCeldM/W99q876RNzD6ydiP/9eAavnfPap5u6ySlLCFQE1BbW0OhkOjrT0RAbUBzUwO7Nk+nJuDx9R30J5g1rY75M+vp7ku8cs/5zJ5ez8auXlo6evmX1+47pAmpCoXEFbc/yoNPtQ54rB5GJ7n3xzVtfPJnf+Dpli6mN9Qxra5mwPZCSjy7sYu9dp7JhW/Yf0STaG2v1es7+OzP/8ScpnpmNW45MdHq9e38cc1Glsxtoq42mFZXy94LZlas9Toa5yu/h4Pdo7bOXv7n0efo6i2weO50Dn3hwFILw42FcuXnXdfew92PrWNuUwMBzF75EOfdcvk2jzPjM5dNqcePv/zlL1MoFNhvv/3YsGEDV1xxBbfddhu/+tWvNifqJoPHbr2D9vecvc124/n+l09cBtDlSERJkiRJmjSJ2Sk9nCEiTgE+A7wDuBs4D7gxIvZLKa3d6s6TWHEU4oo1bTyzqRtSVjuukKAAFPoKQJaojQQR0NrZSwBzmupJQGNdDZ29fbR0wOK5TcxqzEJtuBNSDfex+uGa0VBHbdTQ3Vdg9vQtE7u9/QUa6mopJEY0idZoWDxnOnvtPJMHn2pl5rS6LZLTnb0FTjhgV15/4C509vZvd/3dbZ2vvETCYPeop79Af0o0N9Wzvr1nQKkF2L7JySqdt6G2hrqaGnr7C0yrq+W5F+7H+unNzO1sdWKaEo2NjXzyk5/cPPLzwAMP5IYbbphUSVmYHBMTOaOqJEmSJE1uNdtuMqm9B7gypfT1lNLDZAnaDuDMse3W2CnWXF23qZtN3X309xeoqw3IR8amlCVoIUvWJmBafS1BYlN3H62dvUCir5CVPWioq2GvnWdsTtgNd0Kq9p4+uvr6aRqk/fSGWrr7+kec3CsmIXv6CvTmCeeilBKbuvqY1VjHnOn1w55Ea7TU1ATL91/IvLwUxMauXvoKBTZ29bJy7SbmzWhg+f4LeeH8Gbxo0WyWzGvarpILQznfcS9ZuPkcg92jYqK0GA89/QOv70gnJysqP++sxjrmNTWwqauPlBL1DXX84NCTgOcf/y6ayhPTnHrqqTz88MN0dHTQ0dHBXXfdxfLly8e6W6OuODERTOz7X5xQaunfvZE9Xv2qcd9fSZIkSdLQTdnEbEQ0AC8Dbi6uSykV8vevHGSfaRExu7gAYzMb1A5UHIU4q7GOTd39ZLm3IJVMuRw8n5QFqK+tobG+jhkNdXT19JMSdPb2M72hlv13bWbejKy2aHG05d4LZg55QqoZDXU01tXSMUjidXuTezU1wcmHLmbnWdN4uq2Lrt4+CinR3dfP+vYeGutraGqoY5+Fs4Y9idZo2nvBLM44cnf237WZlo5eVj3XTktHLwcsbh6VCbS253yD3aNiorS1o5e6fERr0UhioVz5eSOCvRbMYHpDLevbe2jv7uOPex7Mz058J61Ncwbs2z1n/oAanZqc9nvj62m44ON0N88fsN77L0mSJEkaD6by0JudgFrgmbL1zwAvGmSfDwEf3ZGdGmvFUYizG+upDaitCQopqyVbzMSWjz6riaC+NthlTiMbu/pYMm86jz7TzozGOuY01dNXKAyYkKp0tOW2DPex+pHYd+Fszj12Hy69ZSXPbuymoS5oqKuluamepoY6dpvXNKw+7yh7L5jFnstm8mRLJ+09fdtdsmC0zjfYPYoI9ty5idUbOigWv9ieWChX6bzzZkzjoCVzeOSZTfxlfQezGut4+iWH8cAxyzhk02qaO9t8HHyKsRyAJEmSJGm88pfp8FxMVpO2aBbwxBj1ZYcojkLsLxSYXl/LzMZ62jp7s8m/SjKyhbzubH1t0NHbR/P0eoIskVsTwSG7z2XBzGm0dPaydmM30+pqOWBx87AnpCo+Vv9Uaycr12b1RKc31I5acq/o2BcvZMncJn7w69U8+uwmCgnmTK9nn4WzRjyJ1o5QUxNVnYBsKOfb2j1a197LgUvmjEosDPW89bU1NDc18PLmRk546S68eNHsPKG834jPpYmtWA5AkiRJkqTxJFIqH/84NeSlDDqAk1NK15asvxqYk1L66yEcYzbQ2trayuzZs3dcZ6uoONP9759sobWzlydbOtnY2UtHTz89/WnzaNkAGuqCuU31pBRMb6ihq7fAvBnTOOGARSzffxF77jR6ozsfWbuRGx98hkef3UR3X1a+YO8FM0c9aVoopKqNSJ1stnaPRjMWhnPe8ZJQlyRJkiRJo6etrY3m5maA5pRS21j3Z6SmbGIWICLuBu5JKZ2Tv68BHgcuSyl9cgj7T7rELGSJrq/fuYrH13WwdmMXHT39dPX209bVS1+enK2JYKeZ9ey+00x2md3I+o4eZk6r428P240j99pphyQzTZqOf2N1j4wNSZIkSZKmDhOzk0BEnAJcDbwduAc4D3gL8KKUUnnt2Ur7T8rELDw/CvG3qzewen0HHT391NUGC2ZNY/7MadTV1NDd109tBI31jlCUJEmSJElSdUyWxOyUrjGbUvpeROwMfAxYBNwPHD+UpOxkVzrx08buXjZ19TFzWh2zGus3T7TlCEVJkiRJkiRpZKb0iNntNZlHzEqSJEmSJEnj0WQZMVsz1h2QJEmSJEmSpKnGxKwkSZIkSZIkVZmJWUmSJEmSJEmqMhOzkiRJkiRJklRlJmYlSZIkSZIkqcpMzEqSJEmSJElSlZmYlSRJkiRJkqQqMzErSZIkSZIkSVVmYlaSJEmSJEmSqszErCRJkiRJkiRVmYlZSZIkSZIkSaoyE7OSJEmSJEmSVGUmZiVJkiRJkiSpykzMSpIkSZIkSVKVmZiVJEmSJEmSpCozMStJkiRJkiRJVWZiVpIkSZIkSZKqzMSsJEmSJEmSJFWZiVlJkiRJkiRJqjITs5IkSZIkSZJUZSZmJUmSJEmSJKnKTMxKkiRJkiRJUpWZmJUkSZIkSZKkKjMxK0mSJEmSJElVZmJWkiRJkiRJkqqsbqw7MBm0tbWNdRckSZIkSZKkKWGy5OIipTTWfZiwImIx8MRY90OSJEmSJEmagl6QUnpyrDsxUiZmt0NEBLArsHGs+7IDzCJLOr+Ayfn5NPUY05psjGlNJsazJhtjWpONMa3JxpieHGYBT6UJnNy0lMF2yG/8hM3Kb02WcwZgY0ppcowP15RmTGuyMaY1mRjPmmyMaU02xrQmG2N60pjw987JvyRJkiRJkiSpykzMSpIkSZIkSVKVmZjVYLqBi/JXaTIwpjXZGNOaTIxnTTbGtCYbY1qTjTGtccHJvyRJkiRJkiSpyhwxK0mSJEmSJElVZmJWkiRJkiRJkqrMxKwkSZIkSZIkVZmJWUmSJEmSJEmqMhOzqigizoqIVRHRFRF3R8RhY90naWsi4uiIuD4inoqIFBF/U7b99nz9Byvse0O+7cKqdVjaioj4UETcGxEbI2JtRFwbEfuVtVmVx+3fVtj/oXzb6VXrtLQVEfHOiPhdRLTly10R8bqS7cazJrSI+GAep58rWWdca8KIiAvzmCxd/liy3XjWhBIRiyPiPyJiXUR0RsTvI+LQku3+PtS4YGJWW4iIU4DPABcBhwAPADdGxIIx7Zi0dTPIYvWsrbRZDZxeuiIiFgPHAk/vsJ5Jw3cMcDlwOPBaoB64KSJmlLVbDZxRuiIiDgcWAe1V6Kc0VE8AHwReBhwK3ApcFxEvKWljPGtCioiXA28Hfldhs3GtieQhYJeS5VVl241nTQgRMRe4E+gFXgcsBd4LbChr6u9DjTkTs6rkPcCVKaWvp5QeBt4BdABnjm23pMGllH6WUjo/pfTjrTT7CbBTRBxZsu404CZg7Q7toDQMKaXjU0pXpZQeSik9QPaFcTeypFapbwHHRMSSknVn5uv7qtJZaQhSStenlH6aUlqZUvpTSukjwCayPz4UGc+acCJiJlmMvpUtf/CDca2JpS+ltKZkea5su/GsieIDwOqU0hkppXtSSo+llG5KKT1a1s7fhxpzJmY1QEQ0kP3wv7m4LqVUyN+/cqz6JY2SHrIvjqV/6T8d+NqY9EYauub8dX3Z+meAG8m+QBIRTcApGNMaxyKiNn8UdgZwV8km41kT0eXADSmlmwfZblxrItknLwv254j4VkTsVrbdeNZE8Qbgvoj4fl4W7LcR8dYK7fx9qDFnYlbldgJqyf6jW+oZskdUpInua8BbImJGRBxNlvD6yRj3SRpURNQAnwPuTCk9WKHJ14DTIyKAk4FHU0r3V7OP0lBExAERsQnoBv4dOCl/MqeU8awJI/8DwyHAh7bR1LjWRHA3WULqeOCdwB7ALyNiVlk741kTwZ5kcbwSWA5cAVwaEadVaOvvQ40pE7OSppT8sfCVZF8kzwS+mVLy0SuNZ5cD+wNbTLaRuwGYCRxNFtP+hV/j1QrgIOAVZD+Qro6IpWVtjGdNCPmj3J8H/iGl1LWN5sa1xr28LNj3U0q/SyndCPwvYA7wlrKmxrMmghrgNymlD6eUfptS+jJwJVmZxgH8faixVjfWHdC48xzQDywsW78QWFP97kg7xNfIJglbChw2xn2RBhURlwGvB45OKT1RqU1KqS8ivkk2YeMrgJOq2EVpyFJKPcAj+dtf5xMmvZts0qRiG+NZE8XLgAXAb7KBg0D21NnREXE2MK240rjWRJRSaomIPwF7l603njURPA2UP5XzB+BNg7T396HGjCNmNUD+o+nXZLMQApsfoz2WgXXgpIns28ABwIMVHqOVxlxkLiP7sfPqlNJj29jla8AxwHUppUqTz0jjUQ0lyasSxrMmglvIvkscVLLcR1ar8KCUUn9Ze+NaE0o+sd1eVJ6Z3njWeHcnsF/Zun2BvwzS3t+HGjOOmFUlnyF7vPA+4B7gPLIJOr4+pr2StiL/8lj6F/09IuIgYH1K6fHStimlDRGxC9BbzT5Kw3A58PfAXwMbI6JY47s1pdRZ3jil9IeI2AnoqGIfpSGLiIuBnwGPA7PI4nsZWd23AYxnTQQppY3AgLrfEdEOrKtUD9y41ngXEZ8GridLXO1KNiK2H/hOeVvjWRPAZ4H/iYgPA9eQjYJ9W75swd+HGksmZrWFlNL3ImJn4GNkE37dDxyfUiqfEEwaTw4Fbit5/5n89WqyiQwGSCm1VKFP0ki9M3+9vWz9GcBVlXZIKa3bgf2RttcC4BvALkAr8DtgeUrp55UaG8+ajIxrjXMvIEvCzgeeBe4ADk8pPVupsfGs8SyldG9EnARcDFwAPAacl1L61lb28fehxkSklMa6D5IkSZIkSZI0pVhjVpIkSZIkSZKqzMSsJEmSJEmSJFWZiVlJkiRJkiRJqjITs5IkSZIkSZJUZSZmJUmSJEmSJKnKTMxKkiRJkiRJUpWZmJUkSZIkSZKkKjMxK0mSNA5FxFURkSJi97Huy2iIiOMi4s6I2JB/rmvHuk/DERH7RMSPI+LpiChEREu+/sL88ywb4y5ut4g4Pf8sp491XyRJkqaCurHugCRJ0o6UJzYfy9/elFJaXqHN4cBdwNUppdOr1rkpIr8H1wEtwNeBNuCPY9ilYYmIWuBaYG/gm8ATQNeYdmociYi5wFnACcC+wGyye/0A2X3/ekpp09j1UJIkaXwyMStJkqaS4yLi1SmlW8e6I1PMa4BG4L0ppW+PdWdGYA9gKXBlSultZdsuA74LPF71Xo0DEXEscA0wD/gD8H1gHTAfOBq4FDgP2Gus+ihJkjTAXmS3AAALz0lEQVRemZiVJElTxSpgN+DfIuKwlFIa4/5MJbvmr0+NaS9GbtD+p5SeA56rbnfGh4g4ELg+f/uPKaVvVWizDLi4mv2SJEmaKKwxK0mSpooVZI+hHwq8ZSg7RMSqiFg1yLbbIyKVrdtcbzQizoiI30dEZ0Q8FhHn5m0iIt4bESsioisiVkbEqVvpRk1E/Gveris/1gURUT9Iv46OiOsj4rmI6M73+3hENJW1W5b39cKIOCIiboqIlvLPtJVrs39EXBMRa/PzPBYRn4uI+SVtds+Pd1G+6rb8nEOqyRoRDRHxLxFxb0RsjIhNEfFwRHwmf3x+WP0pabsqX2ZGxOcj4ql8n99FxMnlbYFf5G8/WtL/C/PtW9SYLX7uvE7wi/PatOuKNYNLa7lGxIkRcXdEdETEkxHxfyOiJj/OaRHxQB5Dj0fE+we5ThERZ0ZWw7ctP9Z9EXHmIO3nRcS/R8Qzedt7I+Kkbd2PCi4FpgPnVErKAqSUbgdKr01DRJwTETdGxOr8uq+NiB9FxMEV+loTEf8cEfdExPr8WjyRx/iyCu2HFP952zdFxC/y83flcXBzRLxpBNdCkiRp2BwxK0mSppILgL8FPh4RP0op9e6g85xHloy6DrgVeBPw+YjoAA7O3/8EuCXvz9URsSql9N8VjvU54Eiyx8U3ASeSJTpfCpQnEd8JXE5W3/N6YC1ZIvojwF9FxF+llHrKjn8E8GHgNuDLZKOKtyoiXgXcCDQAPyAbjfxK4N3A6yPi8HwkaUve12XAMcDVeVtKXgc7x3Tg5/lnX0lWm7Yb2Ad4O/ANYMMw+1OqHrgJmAv8EGgiuxfXRMTxKaWb8nafAw4CTiNL0N6er7+dbdsb+BXwe+Aqssf7S6//ScBxZPVr7ySr0Xp+9pGiNf/f1+XnehNwSUQ8k1L6Rsl1CuBbwN/l1+nb+TleC3w1IpamlN5X0r4pP94BZHWVfwEsAb6XX48hiYi9yUoVrCa7N4NKKXWXvJ1Hdk1/CfyU7B7uCbwBeF1EHJ1Surek/cXAvwKP5p9tI7AYeBVZiYzbS/o05PjP234ReBr4MVn5hUXAYWT35YdDvRaSJEkjllJycXFxcXFxcZm0C7A7kID/yt9/Kn9/dkmbw/N1V5XtuwpYNchxb8++Sg1Yd2F+nHXAniXrl5AlFVvIRu7uXLLtFfk+/1l2rKvy9WuBF5SsbyBLpiXgTSXrlwK9wP3A/LJjfTBv/96SdcvydQk4YxjXswZ4JN9vedm2S/L1Xx3kuiwbxnk+ne/zDaC2bFszMHM7+rMqX38t0FCy/tjSWKlwrS6s0M8tPltJzCXgogr7nJ5v6wFeXrJ+FvAM0E6WMKwUQ78rO9Zb82N9Dagvi5P/zLe9rEJ/v1x2nOUlfT59CPfntLztN4f573EasLjC+peQJV1/XrZ+HfAk0FRhn3nbEf+/zq/nggrHnT/Uz+Pi4uLi4uLisj2LpQwkSdJU8//IEqT/JyJm7qBzfD6l9Ofim5TSauAOsoTiJ1JKz5Zsuxv4M3DgVo71REn7HrIRgJAl+IreTvY01DkppXVlx7gEeJZsVGW536SUtjriscyRZBM5/SyldGPZto8B64G/j4iGYRxzgIioA94GtALvTin1l25PKbWmlDaNQn/+JZWMIE4p3QL8BXj5SPteZg3wia1s/49UMjo0pbSRbCR1E3DFIDG0NL8+RWeTJXLPSiUjwMvipPS+n0qWEL6gtCP5tbtl6B+NRfnrE1ttVSal1J1SerLC+ofIRm0fHVuW6egB+ivss77k7Ujivzdfyo9bvr8kSdIOYSkDSZI0paSUNkTEJ4FPAu8jG0E42u6vsO7pbWx7xSDH+mWFdXcBfWRlEYoOz1+XR8SxFfbpBV5UYf29FdZtTfGct5dvSCltioj7yB7P34/sEf6ReBHZ6NGbU0obdlB/WlJKj1U43hNkZRBGwwNpy9IRpUYSJ7XAQuDJvCzBAWSTkn0gq2owQDHB+SKAiJgN7AE8nFJaU+H4vyQbNbxDRcRBZOUJXkWW4C1PxO7E89fhu8C7gAcj4rtkydu7UkqdZfsMN/6/S5awfTAivp0f946UUtvIPpUkSdLwmZiVJElT0aVkIw3fGxFf3AHHr5Tc6dvGtsG+lz1TviKl1B8R68hG4BbNy18/Ut5+G7Y4/jbM3sZ+T5e1G4ni59piZOUo9qd1kPZ9jN4Eudu6tiOJE3g+kTkXCLKaqx/dynlm5K/Fa7B2kHbDiYViYnfxMPYhIo4gq7sMWU3blWS1kxPwN2Qjx6eV7PJu4DHgDLKau+cDXRFxDVlpgmLt4OHG/6fJyiS8E3gv2R9p+iLiBrKR1JWS9pIkSaPKxKwkSZpyUkqdEfFR4KtkCa1vDtK0QFars5LmQdaPtoVkdWk3i4hasomkShNpxUTe7PyR+KFKw+xP8TwLB9m+qKzdSLTkr0NJ+lWjPyM13Gs7XMXP9OuU0qHDaL9gkO2DXcNK7sxfl0VETUqpMMT9PkKWeD0qpXRH6YaIOJyykh4ppT6yJOqnI2JXsknkziArybCIrDYuDDP+U0rFurxfi4j5wFFkpQ7eAuwTES8tL6EhSZI02qwxK0mSpqqrgYfIJk/ae5A2G4AFZTU9iYgZwD47tnubHVVh3SvJ/sD+25J1d+evh2/ZfFQVz7msfEN+XQ4FOilLJg/TCrJE28sjYu446M+4lCcg/wC8OCLmDKF9G9no070jYlGFJpVibbBjPQL8N9mkZKdtrW1ElI6A3QtYXyEp2wQcso1zPpVS+g5wPNmEb6+JiOn55hHHf0ppXUrp2pTSKWSjeZcy+P8nSJIkjRoTs5IkaUrKR8N9mOyx8AsHaXZvvv0fiisiK+R5Mc8/Hr6jvTsiXlBy/gaen1DqqpJ2XyR71P0LEbFb+UEiYk5EHFy+fgTuBB4FXhcRrynbdj7ZSN7vbKO26lbloyS/RDYq+fP5COHNIqK5ZOK2Hd6fce5SssnCrswT0QNExB4RsXvJqm+SjQL/WFm74xh+fdl3kyW9L4uIUyo1iIijeL50AWSTq82NiJeUtKklGxW7c9m+0/LSB+VmADPJ6sYWR+oOK/4jYlmUFeXNJx0rlkToqvR5JEmSRpOlDCRJ0pSVUvrPiLiDbBKiSi4je2z6KxHxWrKZ3Y8C5gAPUPbY9Q7yK+CBiPge0A6cSDaR1Y9SSj8sNkopPRgR7wKuAFZExE/JEpazgD3JHgG/CnjH9nQmpVSIiNOBG4GfRsT3yZJtryQbtfoo8MHtOUfuArLRj/8bODwifgZ0k32W48nu2f1V7M949SWy63QacGRE3Ew2GdhCssmuXgH8PbAqb38J8EbgrXlytDjq9S3ADcAJQz1xSun+iDgRuAb4bkRckB9vPVmC80iyyckeKdntC2STsd2R14ntIrtPi8kmcFtW0nY6cGdE/An4NfA4WUL29WRlDD6dUurO+zLc+L8WaIuIX5HFSz3wWrLRsj9IKf1lqNdBkiRppEzMSpKkqe4DPF8vc4A82XM82QjZk8kmKfop2URB11Spf+cBbwb+GdiNbDKrC/M+lff3yoi4H3gPcDRZEreVLKH1WbLyDdstpXRHXg/0ArIkWzNZMvDzwMdLJmTannN05cnws4F/JCs50U/2Wf6d5xONVenPeJXXSj09T0S+lSxpOZNsgq+VZLF6c0n79og4hix+TiIrH/AQcArZdRtyYjY/3i0RsQ/wrnzfU8iSoa3A74FzyWq5Ftv/JCJOJhut/o9AB9mI2pPI7l+pdrJ/n8eS/UFkAVl5kRXAh4DvlvVlOPH/IbIE/2F5u3ayRO47yWpPS5Ik7XCRfZeTJEmSJEmSJFWLNWYlSZIkSZIkqcpMzEqSJEmSJElSlZmYlSRJkiRJkqQqMzErSZIkSZIkSVVmYlaSJEmSJEmSqszErCRJkiRJkiRVmYlZSZIkSZIkSaoyE7OSJEmSJEmSVGUmZiVJkiRJkiSpykzMSpIkSZIkSVKVmZiVJEmSJEmSpCozMStJkiRJkiRJVWZiVpIkSZIkSZKq7P8D6xrwJPvnAX8AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 1400x800 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "er1cu6B4NVeg"
      },
      "source": [
        "## Progression of Case Count"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aT-37dupGs9S"
      },
      "source": [
        "Taking a look at the rate at which Covid-19 cases increase in different states. To keep it short, selecting the top 10 states with confirmed cases."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "p6QlOYxhOxRE",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "89325782-1d0a-477e-c4a7-bc27af0bda2c"
      },
      "source": [
        "covid19_maharashtra = covid19_IN[covid19_IN['State/UnionTerritory'] == \"Maharashtra\"]\n",
        "covid19_maharashtra.reset_index(inplace = True)\n",
        "covid19_maharashtra = covid19_maharashtra.drop(['index','Sno', 'Time', 'ConfirmedIndianNational', 'ConfirmedForeignNational'], axis = 1)\n",
        "covid19_maharashtra.reset_index(inplace = True)\n",
        "covid19_maharashtra.columns = ['Day Count', 'Date', 'State/UnionTerritory','Cured','Deaths', 'Confirmed']\n",
        "covid19_maharashtra['Day Count'] = covid19_maharashtra['Day Count'] + 1\n",
        "print(covid19_maharashtra.shape)\n",
        "covid19_maharashtra.head()"
      ],
      "execution_count": 163,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(511, 6)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Day Count</th>\n",
              "      <th>Date</th>\n",
              "      <th>State/UnionTerritory</th>\n",
              "      <th>Cured</th>\n",
              "      <th>Deaths</th>\n",
              "      <th>Confirmed</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>2020-03-09</td>\n",
              "      <td>Maharashtra</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>2020-03-10</td>\n",
              "      <td>Maharashtra</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>2020-03-11</td>\n",
              "      <td>Maharashtra</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>2020-03-12</td>\n",
              "      <td>Maharashtra</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>2020-03-13</td>\n",
              "      <td>Maharashtra</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>14</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Day Count        Date State/UnionTerritory  Cured  Deaths  Confirmed\n",
              "0          1  2020-03-09          Maharashtra      0       0          2\n",
              "1          2  2020-03-10          Maharashtra      0       0          5\n",
              "2          3  2020-03-11          Maharashtra      0       0          2\n",
              "3          4  2020-03-12          Maharashtra      0       0         11\n",
              "4          5  2020-03-13          Maharashtra      0       0         14"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 163
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZGsvqNvBOxS4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "1442781a-f738-4424-f031-f9f6b9468307"
      },
      "source": [
        "covid19_kerala = covid19_IN[covid19_IN['State/UnionTerritory'] == \"Kerala\"]\n",
        "covid19_kerala.reset_index(inplace = True)\n",
        "covid19_kerala = covid19_kerala.drop(['index','Sno', 'Time', 'ConfirmedIndianNational', 'ConfirmedForeignNational'], axis = 1)\n",
        "covid19_kerala.reset_index(inplace = True)\n",
        "covid19_kerala.columns = ['Day Count', 'Date', 'State/UnionTerritory','Cured','Deaths', 'Confirmed']\n",
        "covid19_kerala['Day Count'] = covid19_kerala['Day Count'] + 1\n",
        "print(covid19_kerala.shape)\n",
        "covid19_kerala.tail()"
      ],
      "execution_count": 164,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(551, 6)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Day Count</th>\n",
              "      <th>Date</th>\n",
              "      <th>State/UnionTerritory</th>\n",
              "      <th>Cured</th>\n",
              "      <th>Deaths</th>\n",
              "      <th>Confirmed</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>546</th>\n",
              "      <td>547</td>\n",
              "      <td>2021-07-29</td>\n",
              "      <td>Kerala</td>\n",
              "      <td>3160804</td>\n",
              "      <td>16457</td>\n",
              "      <td>3327301</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>547</th>\n",
              "      <td>548</td>\n",
              "      <td>2021-07-30</td>\n",
              "      <td>Kerala</td>\n",
              "      <td>3177453</td>\n",
              "      <td>16585</td>\n",
              "      <td>3349365</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>548</th>\n",
              "      <td>549</td>\n",
              "      <td>2021-07-31</td>\n",
              "      <td>Kerala</td>\n",
              "      <td>3192104</td>\n",
              "      <td>16701</td>\n",
              "      <td>3370137</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>549</th>\n",
              "      <td>550</td>\n",
              "      <td>2021-08-01</td>\n",
              "      <td>Kerala</td>\n",
              "      <td>3208969</td>\n",
              "      <td>16781</td>\n",
              "      <td>3390761</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>550</th>\n",
              "      <td>551</td>\n",
              "      <td>2021-08-02</td>\n",
              "      <td>Kerala</td>\n",
              "      <td>3226761</td>\n",
              "      <td>16837</td>\n",
              "      <td>3411489</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     Day Count        Date State/UnionTerritory    Cured  Deaths  Confirmed\n",
              "546        547  2021-07-29               Kerala  3160804   16457    3327301\n",
              "547        548  2021-07-30               Kerala  3177453   16585    3349365\n",
              "548        549  2021-07-31               Kerala  3192104   16701    3370137\n",
              "549        550  2021-08-01               Kerala  3208969   16781    3390761\n",
              "550        551  2021-08-02               Kerala  3226761   16837    3411489"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 164
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "v33-1yNBGvxn",
        "outputId": "aa5001b5-00da-40fc-ba45-ef58ee969a0a"
      },
      "source": [
        "covid19_karnataka = covid19_IN[covid19_IN['State/UnionTerritory'] == \"Karnataka\"]\n",
        "covid19_karnataka.reset_index(inplace = True)\n",
        "covid19_karnataka = covid19_karnataka.drop(['index','Sno', 'Time', 'ConfirmedIndianNational', 'ConfirmedForeignNational'], axis = 1)\n",
        "covid19_karnataka.reset_index(inplace = True)\n",
        "covid19_karnataka.columns = ['Day Count', 'Date', 'State/UnionTerritory','Cured','Deaths', 'Confirmed']\n",
        "covid19_karnataka['Day Count'] = covid19_karnataka['Day Count'] + 1\n",
        "print(covid19_karnataka.shape)\n",
        "covid19_karnataka.head()"
      ],
      "execution_count": 165,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(511, 6)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Day Count</th>\n",
              "      <th>Date</th>\n",
              "      <th>State/UnionTerritory</th>\n",
              "      <th>Cured</th>\n",
              "      <th>Deaths</th>\n",
              "      <th>Confirmed</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>2020-03-09</td>\n",
              "      <td>Karnataka</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>2020-03-10</td>\n",
              "      <td>Karnataka</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>2020-03-11</td>\n",
              "      <td>Karnataka</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>2020-03-12</td>\n",
              "      <td>Karnataka</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>2020-03-13</td>\n",
              "      <td>Karnataka</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Day Count        Date State/UnionTerritory  Cured  Deaths  Confirmed\n",
              "0          1  2020-03-09            Karnataka      0       0          1\n",
              "1          2  2020-03-10            Karnataka      0       0          4\n",
              "2          3  2020-03-11            Karnataka      0       0          4\n",
              "3          4  2020-03-12            Karnataka      0       0          4\n",
              "4          5  2020-03-13            Karnataka      0       1          6"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 165
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "E-hV_u57Gxhn",
        "outputId": "636532a0-cca5-4a1d-a295-054f0b23f79a"
      },
      "source": [
        "covid19_tamil = covid19_IN[covid19_IN['State/UnionTerritory'] == \"Tamil Nadu\"]\n",
        "covid19_tamil.reset_index(inplace = True)\n",
        "covid19_tamil = covid19_tamil.drop(['index','Sno', 'Time', 'ConfirmedIndianNational', 'ConfirmedForeignNational'], axis = 1)\n",
        "covid19_tamil.reset_index(inplace = True)\n",
        "covid19_tamil.columns = ['Day Count', 'Date', 'State/UnionTerritory','Cured','Deaths', 'Confirmed']\n",
        "covid19_tamil['Day Count'] = covid19_tamil['Day Count'] + 1\n",
        "print(covid19_tamil.shape)\n",
        "covid19_tamil.head()"
      ],
      "execution_count": 166,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(514, 6)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Day Count</th>\n",
              "      <th>Date</th>\n",
              "      <th>State/UnionTerritory</th>\n",
              "      <th>Cured</th>\n",
              "      <th>Deaths</th>\n",
              "      <th>Confirmed</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>2020-03-07</td>\n",
              "      <td>Tamil Nadu</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>2020-03-08</td>\n",
              "      <td>Tamil Nadu</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>2020-03-09</td>\n",
              "      <td>Tamil Nadu</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>2020-03-10</td>\n",
              "      <td>Tamil Nadu</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>2020-03-11</td>\n",
              "      <td>Tamil Nadu</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Day Count        Date State/UnionTerritory  Cured  Deaths  Confirmed\n",
              "0          1  2020-03-07           Tamil Nadu      0       0          1\n",
              "1          2  2020-03-08           Tamil Nadu      0       0          1\n",
              "2          3  2020-03-09           Tamil Nadu      0       0          1\n",
              "3          4  2020-03-10           Tamil Nadu      0       0          1\n",
              "4          5  2020-03-11           Tamil Nadu      0       0          1"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 166
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "icHa6m9wGyio",
        "outputId": "2cd1145c-da68-4100-9dd1-3bef7eba8be9"
      },
      "source": [
        "covid19_andhra = covid19_IN[covid19_IN['State/UnionTerritory'] == \"Andhra Pradesh\"]\n",
        "covid19_andhra.reset_index(inplace = True)\n",
        "covid19_andhra = covid19_andhra.drop(['index','Sno', 'Time', 'ConfirmedIndianNational', 'ConfirmedForeignNational'], axis = 1)\n",
        "covid19_andhra.reset_index(inplace = True)\n",
        "covid19_andhra.columns = ['Day Count', 'Date', 'State/UnionTerritory','Cured','Deaths', 'Confirmed']\n",
        "covid19_andhra['Day Count'] = covid19_andhra['Day Count'] + 1\n",
        "print(covid19_andhra.shape)\n",
        "covid19_andhra.head()"
      ],
      "execution_count": 167,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(509, 6)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Day Count</th>\n",
              "      <th>Date</th>\n",
              "      <th>State/UnionTerritory</th>\n",
              "      <th>Cured</th>\n",
              "      <th>Deaths</th>\n",
              "      <th>Confirmed</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>2020-03-12</td>\n",
              "      <td>Andhra Pradesh</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>2020-03-13</td>\n",
              "      <td>Andhra Pradesh</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>2020-03-14</td>\n",
              "      <td>Andhra Pradesh</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>2020-03-15</td>\n",
              "      <td>Andhra Pradesh</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>2020-03-16</td>\n",
              "      <td>Andhra Pradesh</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Day Count        Date State/UnionTerritory  Cured  Deaths  Confirmed\n",
              "0          1  2020-03-12       Andhra Pradesh      0       0          1\n",
              "1          2  2020-03-13       Andhra Pradesh      0       0          1\n",
              "2          3  2020-03-14       Andhra Pradesh      0       0          1\n",
              "3          4  2020-03-15       Andhra Pradesh      0       0          1\n",
              "4          5  2020-03-16       Andhra Pradesh      0       0          1"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 167
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "jMu7GrMJGzoB",
        "outputId": "8e3879c5-a673-49a3-f7cb-3976fa248d8b"
      },
      "source": [
        "covid19_UP = covid19_IN[covid19_IN['State/UnionTerritory'] == \"Uttar Pradesh\"]\n",
        "covid19_UP.reset_index(inplace = True)\n",
        "covid19_UP = covid19_UP.drop(['index','Sno', 'Time', 'ConfirmedIndianNational', 'ConfirmedForeignNational'], axis = 1)\n",
        "covid19_UP.reset_index(inplace = True)\n",
        "covid19_UP.columns = ['Day Count', 'Date', 'State/UnionTerritory','Cured','Deaths', 'Confirmed']\n",
        "covid19_UP['Day Count'] = covid19_UP['Day Count'] + 1\n",
        "print(covid19_UP.shape)\n",
        "covid19_UP.head()"
      ],
      "execution_count": 168,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(517, 6)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Day Count</th>\n",
              "      <th>Date</th>\n",
              "      <th>State/UnionTerritory</th>\n",
              "      <th>Cured</th>\n",
              "      <th>Deaths</th>\n",
              "      <th>Confirmed</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>2020-03-04</td>\n",
              "      <td>Uttar Pradesh</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>2020-03-05</td>\n",
              "      <td>Uttar Pradesh</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>2020-03-06</td>\n",
              "      <td>Uttar Pradesh</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>2020-03-07</td>\n",
              "      <td>Uttar Pradesh</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>2020-03-08</td>\n",
              "      <td>Uttar Pradesh</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Day Count        Date State/UnionTerritory  Cured  Deaths  Confirmed\n",
              "0          1  2020-03-04        Uttar Pradesh      0       0          6\n",
              "1          2  2020-03-05        Uttar Pradesh      0       0          7\n",
              "2          3  2020-03-06        Uttar Pradesh      0       0          7\n",
              "3          4  2020-03-07        Uttar Pradesh      0       0          7\n",
              "4          5  2020-03-08        Uttar Pradesh      0       0          7"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 168
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "WXS7KC7YG0nr",
        "outputId": "2d95bc55-aab9-4517-f497-52c07a4276d2"
      },
      "source": [
        "covid19_bengal = covid19_IN[covid19_IN['State/UnionTerritory'] == \"West Bengal\"]\n",
        "covid19_bengal.reset_index(inplace = True)\n",
        "covid19_bengal = covid19_bengal.drop(['index','Sno', 'Time', 'ConfirmedIndianNational', 'ConfirmedForeignNational'], axis = 1)\n",
        "covid19_bengal.reset_index(inplace = True)\n",
        "covid19_bengal.columns = ['Day Count', 'Date', 'State/UnionTerritory','Cured','Deaths', 'Confirmed']\n",
        "covid19_bengal['Day Count'] = covid19_bengal['Day Count'] + 1\n",
        "print(covid19_bengal.shape)\n",
        "covid19_bengal.head()"
      ],
      "execution_count": 169,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(503, 6)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Day Count</th>\n",
              "      <th>Date</th>\n",
              "      <th>State/UnionTerritory</th>\n",
              "      <th>Cured</th>\n",
              "      <th>Deaths</th>\n",
              "      <th>Confirmed</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>2020-03-18</td>\n",
              "      <td>West Bengal</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>2020-03-19</td>\n",
              "      <td>West Bengal</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>2020-03-20</td>\n",
              "      <td>West Bengal</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>2020-03-21</td>\n",
              "      <td>West Bengal</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>2020-03-22</td>\n",
              "      <td>West Bengal</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Day Count        Date State/UnionTerritory  Cured  Deaths  Confirmed\n",
              "0          1  2020-03-18          West Bengal      0       0          1\n",
              "1          2  2020-03-19          West Bengal      0       0          1\n",
              "2          3  2020-03-20          West Bengal      0       0          2\n",
              "3          4  2020-03-21          West Bengal      0       0          3\n",
              "4          5  2020-03-22          West Bengal      0       0          4"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 169
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "3vtaRxnAG12v",
        "outputId": "0d35ffce-3976-4467-adf5-07e40b7cf823"
      },
      "source": [
        "covid19_delhi = covid19_IN[covid19_IN['State/UnionTerritory'] == \"Delhi\"]\n",
        "covid19_delhi.reset_index(inplace = True)\n",
        "covid19_delhi = covid19_delhi.drop(['index','Sno', 'Time', 'ConfirmedIndianNational', 'ConfirmedForeignNational'], axis = 1)\n",
        "covid19_delhi.reset_index(inplace = True)\n",
        "covid19_delhi.columns = ['Day Count', 'Date', 'State/UnionTerritory','Cured','Deaths', 'Confirmed']\n",
        "covid19_delhi['Day Count'] = covid19_delhi['Day Count'] + 1\n",
        "print(covid19_delhi.shape)\n",
        "covid19_delhi.head()"
      ],
      "execution_count": 170,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(519, 6)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Day Count</th>\n",
              "      <th>Date</th>\n",
              "      <th>State/UnionTerritory</th>\n",
              "      <th>Cured</th>\n",
              "      <th>Deaths</th>\n",
              "      <th>Confirmed</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>2020-03-02</td>\n",
              "      <td>Delhi</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>2020-03-03</td>\n",
              "      <td>Delhi</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>2020-03-04</td>\n",
              "      <td>Delhi</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>2020-03-05</td>\n",
              "      <td>Delhi</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>2020-03-06</td>\n",
              "      <td>Delhi</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Day Count        Date State/UnionTerritory  Cured  Deaths  Confirmed\n",
              "0          1  2020-03-02                Delhi      0       0          1\n",
              "1          2  2020-03-03                Delhi      0       0          1\n",
              "2          3  2020-03-04                Delhi      0       0          1\n",
              "3          4  2020-03-05                Delhi      0       0          2\n",
              "4          5  2020-03-06                Delhi      0       0          3"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 170
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "P0IlkBwLG26g",
        "outputId": "14cb4fe2-196d-4595-febb-e4b393ba89fc"
      },
      "source": [
        "covid19_chhattisgarh = covid19_IN[covid19_IN['State/UnionTerritory'] == \"Chhattisgarh\"]\n",
        "covid19_chhattisgarh.reset_index(inplace = True)\n",
        "covid19_chhattisgarh = covid19_chhattisgarh.drop(['index','Sno', 'Time', 'ConfirmedIndianNational', 'ConfirmedForeignNational'], axis = 1)\n",
        "covid19_chhattisgarh.reset_index(inplace = True)\n",
        "covid19_chhattisgarh.columns = ['Day Count', 'Date', 'State/UnionTerritory','Cured','Deaths', 'Confirmed']\n",
        "covid19_chhattisgarh['Day Count'] = covid19_chhattisgarh['Day Count'] + 1\n",
        "print(covid19_chhattisgarh.shape)\n",
        "covid19_chhattisgarh.head()"
      ],
      "execution_count": 171,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(502, 6)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Day Count</th>\n",
              "      <th>Date</th>\n",
              "      <th>State/UnionTerritory</th>\n",
              "      <th>Cured</th>\n",
              "      <th>Deaths</th>\n",
              "      <th>Confirmed</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>2020-03-19</td>\n",
              "      <td>Chhattisgarh</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>2020-03-20</td>\n",
              "      <td>Chhattisgarh</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>2020-03-21</td>\n",
              "      <td>Chhattisgarh</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>2020-03-22</td>\n",
              "      <td>Chhattisgarh</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>2020-03-23</td>\n",
              "      <td>Chhattisgarh</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Day Count        Date State/UnionTerritory  Cured  Deaths  Confirmed\n",
              "0          1  2020-03-19         Chhattisgarh      0       0          1\n",
              "1          2  2020-03-20         Chhattisgarh      0       0          1\n",
              "2          3  2020-03-21         Chhattisgarh      0       0          1\n",
              "3          4  2020-03-22         Chhattisgarh      0       0          1\n",
              "4          5  2020-03-23         Chhattisgarh      0       0          1"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 171
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "T9iEIh1kG25d",
        "outputId": "6267c8c5-571a-4d4f-acda-b2f3f1d46e8e"
      },
      "source": [
        "covid19_odisha = covid19_IN[covid19_IN['State/UnionTerritory'] == 'Odisha']\n",
        "covid19_odisha.reset_index(inplace = True)\n",
        "covid19_odisha = covid19_odisha.drop(['index','Sno', 'Time', 'ConfirmedIndianNational', 'ConfirmedForeignNational'], axis = 1)\n",
        "covid19_odisha.reset_index(inplace = True)\n",
        "covid19_odisha.columns = ['Day Count', 'Date', 'State/UnionTerritory','Cured','Deaths', 'Confirmed']\n",
        "covid19_odisha['Day Count'] = covid19_odisha['Day Count'] + 1\n",
        "print(covid19_odisha.shape)\n",
        "covid19_odisha.head()"
      ],
      "execution_count": 172,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(505, 6)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Day Count</th>\n",
              "      <th>Date</th>\n",
              "      <th>State/UnionTerritory</th>\n",
              "      <th>Cured</th>\n",
              "      <th>Deaths</th>\n",
              "      <th>Confirmed</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>2020-03-16</td>\n",
              "      <td>Odisha</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>2020-03-17</td>\n",
              "      <td>Odisha</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>2020-03-18</td>\n",
              "      <td>Odisha</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>2020-03-19</td>\n",
              "      <td>Odisha</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>2020-03-20</td>\n",
              "      <td>Odisha</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Day Count        Date State/UnionTerritory  Cured  Deaths  Confirmed\n",
              "0          1  2020-03-16               Odisha      0       0          1\n",
              "1          2  2020-03-17               Odisha      0       0          1\n",
              "2          3  2020-03-18               Odisha      0       0          1\n",
              "3          4  2020-03-19               Odisha      0       0          1\n",
              "4          5  2020-03-20               Odisha      0       0          2"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 172
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "xyHxTPUHHuDS",
        "outputId": "50dd8278-ea1d-44bb-c518-dea6569f9cab"
      },
      "source": [
        "plt.figure(figsize=(14,8), dpi=100)\n",
        "plt.plot(covid19_maharashtra['Day Count'], covid19_maharashtra['Confirmed'])\n",
        "plt.plot(covid19_kerala['Day Count'], covid19_kerala['Confirmed'])\n",
        "plt.plot(covid19_karnataka['Day Count'], covid19_karnataka['Confirmed'])\n",
        "plt.plot(covid19_tamil['Day Count'], covid19_tamil['Confirmed'])\n",
        "plt.plot(covid19_andhra['Day Count'], covid19_andhra['Confirmed'])\n",
        "plt.plot(covid19_UP['Day Count'], covid19_UP['Confirmed'])\n",
        "plt.plot(covid19_bengal['Day Count'], covid19_bengal['Confirmed'])\n",
        "plt.plot(covid19_delhi['Day Count'], covid19_delhi['Confirmed'])\n",
        "plt.plot(covid19_chhattisgarh['Day Count'], covid19_chhattisgarh['Confirmed'])\n",
        "plt.plot(covid19_odisha['Day Count'], covid19_odisha['Confirmed'])\n",
        "\n",
        "plt.legend(['Maharashtra','Kerala','Karnataka','Tamil Nadu','Andhra Pradesh',\n",
        "            'Uttar Pradesh','West Bengal','Delhi','Chhatisgarh','Odisha'], loc='upper left')\n",
        "plt.xlabel('Day Count', size=14)\n",
        "plt.yticks([0,1000000,2000000,3000000,4000000,5000000,6000000],['0','1M','2M','3M','4M','5M','6M'])\n",
        "plt.ylabel('Confirmed Cases Count', size=14)\n",
        "plt.title('Spread of Covid in different states', size = 18)\n",
        "plt.show()"
      ],
      "execution_count": 173,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1400x800 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "ayY5k35FId8S",
        "outputId": "8402afb6-8999-47c1-a94b-865744162bfb"
      },
      "source": [
        "plt.figure(figsize=(14,8), dpi=100)\n",
        "plt.plot(covid19_maharashtra['Day Count'], covid19_maharashtra['Deaths'])\n",
        "plt.plot(covid19_kerala['Day Count'], covid19_kerala['Deaths'])\n",
        "plt.plot(covid19_karnataka['Day Count'], covid19_karnataka['Deaths'])\n",
        "plt.plot(covid19_tamil['Day Count'], covid19_tamil['Deaths'])\n",
        "plt.plot(covid19_andhra['Day Count'], covid19_andhra['Deaths'])\n",
        "plt.plot(covid19_UP['Day Count'], covid19_UP['Deaths'])\n",
        "plt.plot(covid19_bengal['Day Count'], covid19_bengal['Deaths'])\n",
        "plt.plot(covid19_delhi['Day Count'], covid19_delhi['Deaths'])\n",
        "plt.plot(covid19_chhattisgarh['Day Count'], covid19_chhattisgarh['Deaths'])\n",
        "plt.plot(covid19_odisha['Day Count'], covid19_odisha['Deaths'])\n",
        "\n",
        "plt.legend(['Maharashtra','Kerala','Karnataka','Tamil Nadu','Andhra Pradesh','Uttar Pradesh','West Bengal','Delhi','Chhatisgarh','Rajasthan'], loc='upper left')\n",
        "#plt.annotate('31-02-2020', xy=(0,0), xytext=(50,20000), arrowprops=dict(facecolor='black', shrink=0.05))\n",
        "#plt.annotate('02-08-2021',xy=(551,16837),xytext=(600,20000),arrowprops=dict(facecolor='black', shrink=0.05))\n",
        "\n",
        "plt.xlabel('Day Count', size=14)\n",
        "plt.yticks([0,20000,40000,60000,80000,100000,120000],['0','20K','40K','60K','80K','100K','120K'])\n",
        "plt.ylabel('Deaths Cases Count', size=14)\n",
        "plt.title('Deaths since start of Covid-19', size = 18)\n",
        "plt.show()"
      ],
      "execution_count": 174,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1400x800 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EITODAblXhrW"
      },
      "source": [
        "Almost all curves are similar except Odisha which managed to control the incline of the curve over longer period of time and then slight increas in curve due to the second wave, while Kerala managed to control the curve really well in the start but increased drastically after the first wave. The curve is as expected in Maharashtra since the population is high as compared to other states, and rest of states have similar curve to India as a whole"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UTzfLdaAZqgt"
      },
      "source": [
        "## Cases per Day"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8l2CXig_bUG-"
      },
      "source": [
        "As we saw Maharashtra is the most affected state in India so lets see how many cases were found daily"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Dd8ylJNjZtYo"
      },
      "source": [
        "covid19_maharashtra['NewCases'] = 0\n",
        "covid19_maharashtra['NewCases'][0] = 2\n",
        "covid19_maharashtra['NewDeaths'] = 0\n",
        "x = len(covid19_maharashtra.index)\n",
        "for i in range(1,x):\n",
        "  covid19_maharashtra['NewCases'][i] = covid19_maharashtra['Confirmed'][i] - covid19_maharashtra['Confirmed'][i-1] \n",
        "for i in range(1,x):\n",
        "  covid19_maharashtra['NewDeaths'][i] = covid19_maharashtra['Deaths'][i] - covid19_maharashtra['Deaths'][i-1] "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "HboS_aV0aADl",
        "outputId": "d17a73ee-6f02-4dff-b559-61c5d0483b16"
      },
      "source": [
        "covid19_maharashtra.isna().sum()\n",
        "covid19_maharashtra.head()"
      ],
      "execution_count": 176,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Day Count</th>\n",
              "      <th>Date</th>\n",
              "      <th>State/UnionTerritory</th>\n",
              "      <th>Cured</th>\n",
              "      <th>Deaths</th>\n",
              "      <th>Confirmed</th>\n",
              "      <th>NewCases</th>\n",
              "      <th>NewDeaths</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>2020-03-09</td>\n",
              "      <td>Maharashtra</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>2020-03-10</td>\n",
              "      <td>Maharashtra</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>2020-03-11</td>\n",
              "      <td>Maharashtra</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>-3</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>2020-03-12</td>\n",
              "      <td>Maharashtra</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>11</td>\n",
              "      <td>9</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>2020-03-13</td>\n",
              "      <td>Maharashtra</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>14</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Day Count        Date State/UnionTerritory  ...  Confirmed  NewCases  NewDeaths\n",
              "0          1  2020-03-09          Maharashtra  ...          2         2          0\n",
              "1          2  2020-03-10          Maharashtra  ...          5         3          0\n",
              "2          3  2020-03-11          Maharashtra  ...          2        -3          0\n",
              "3          4  2020-03-12          Maharashtra  ...         11         9          0\n",
              "4          5  2020-03-13          Maharashtra  ...         14         3          0\n",
              "\n",
              "[5 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 176
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 716
        },
        "id": "eGIvQiR8aIFn",
        "outputId": "eda4b594-3640-4d8b-a67e-46ad5ab4b02d"
      },
      "source": [
        "plt.figure(figsize=(14,8), dpi=100 )\n",
        "plt.plot(covid19_maharashtra['Day Count'], covid19_maharashtra['NewCases'], color = '#D32F2F')\n",
        "plt.xlabel('Day Count', size=14)\n",
        "plt.ylabel('Daily Confirmed Cases Count', size=14)\n",
        "plt.title('Daily New Cases in Maharashtra', size = 18)\n",
        "plt.show()"
      ],
      "execution_count": 177,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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XRO3NPkJWRfMEsuq4W8h2YuzEX+ZfL0gpPT+l9LMGQcbB9Q/qkyeQ/d6vAP4ypdSofa4f5y5e45kppbellK5pUNnY8jwppd+mlN6QUvpzshba45idNXZ6RDymD9c5KMN6nyVJ82D4JElasvLQ4xX5j9uZ/WMKsvapFfn3P2ryFA8qremrlNKvyYKYIKt4ejpZ5c+1ZNUHg/AasjaUO1G7TXm9Yr7KKPCIHs/1HbKWn1VkAcx9ydqAflxaU66OKlqGup73lFK6gWyXNYCnRsSdO31saUZS3z8PKaXplNJXgUeSve9B9l4Uivf5oflMrHlLKf0+pfRm4N/zu07ux/MupDwE/BKz7VsfazcDqeT2+deGv9P8939io2N9UJz7JymlSpM1D21yfy/nafYajySrZOpISqmSUrqELNS5Jr+735+jIhzrRyXSsN5nSdI8GD5JkpakfIbQOcy2Yb0hpbSttGQHs38AlatRisePAa8f5DUyO3j8NGbb7z7Yx0HjNVJK1wBn5z++mtIMobp1v2G2Suz1EbGu1fNGxJyBwvlA6u+XzjUGfLPuj8MiaHoMWTtg+b5uvZJsmPlK4LPt2s0iYkNEfIasTQ3m+XmIiOUtTjfBbOtU+fV/kKyi5wDgtW2udzxvIe3kfJC1YNWfb2/2r2SB2r8z+xnuxPb865zfae45zLbl9ltx7j9pNAg+Ih4BnNDH8zR7jU13uWz1Ocr/O1SEfP3+HO3Iv67vw3MN632WJM2D4ZMkacnId3y6W0S8iGwO0FPyQx8B3lxem4cjReXJWyPixIgYyZ/nbsAXyQKR2wZ4yZ8ja706iGzmSl8HjTfxerI/1g4mq0Zq5u/Jwpw7A5dExF+Uq3Mi4rCI+OuI+CrwpibPUQRJ98+/fq3u+PfycxxLFk7dCrQabtxUXkn212R/LB8D/DgiXhYR1YqPiBiNiHtFxJnA75hts+vH5+HqiHhDRDyg/Ad9fv6PkQV9FeCC0jn/F/iX/MeXRsSH83MVjx2LiHtGxKuB35LNkyq8LCK+lP8Obld6zPKIeBLwj/ldX2AJSCldklJ6SX77dRcPLXZkfEREvKpoI42I9RHxCrK2xFuaPnp+inMfA5xdhLQRsToi/j/g0306d3GeV0bE44qW2og4KiI+TjYEf2uTx14aEe+IiBPKLbYRcWhEvJPZiqkv9uE6y36Wf31aPjB9Pob1PkuS5sHwSZK014raLem3krV5XU5WHXEU2S5lz0kpPb3J7m4vJAsTDgO+CuyKiB35czyErDXt5kFdf0ppmtqwaRCDxuvPuYW6IK7Jup+RzcvaDNyFLCjbGRE3R8Qu4DqyGVKtWpbqw6aan/PX/53SXd/O7+tJSulz+fX8lqya6I3AbyJiIiJuIQumfki2y9464BPUhknz+TxsAv6JbJD6rojYEhG7yYaAP5GsqurFKaVf1D3uX/JbIgvPLo+IXRFxM9ncph+RVUXdntnKLMj+N9wpZL+Da/PH3EJW8XRe/vquYHbQ8r7qw8C38+/PBG6NiC1kYcTryYKL/xjEifOWy2Iny+cCt+T/ndpOVvV4BXBGH071SuAGYA1Z++nuiNhGFrA+hWwg+U+bPHY9WdD8dbL3ZmtE7AR+D7wgX3NWSumCJo/vVVH1+XhgW0RcFxFXRcR3Wj2okSG+z5KkeTB8kiTtzTblt4PIKmc2A5eQ/TH5BOCwlFLT4d35FuL3I9uW/may/7t4a/7zA1NKHxno1Wc+Vfq+74PGm3gb8Id2i1JK3yWrfHoJ2dDsbWR/rM6Q/UH3UeBpZKFNIxcxu9X55gbBC9QGUr223NVf813I/uj+GFkQtYfsD/MtZGHX64G7ppSeWh5uPs/Pw8OAN5AFHdeStf+Rn/9DwH1TSnN2DUuZVwN3J9uN7Qqy93cdWbXKRcBb8vN/t/TQ95K1an6CrIpkF9lW8lvza3ghcO/6oen7mvz3+zCyAO/XZAF1kFXdPZes5XMgba654t/HT8n+LYyShZkvJ9vhced8T5BSupqsKu8DzO7GuAc4H3h4SukNLR7+l2Sz4L4KXEk2d24ZcDVZiHlSSqnvAWZK6aNkYet3yD67h5AN3r9dq8e1MPD3WZI0P9H4/xEsSZKGISJeDPwbWWBx1KDmPUmSJEkLxconSZIWSESMklVfALzP4EmSJElLkeGTJEkLIB9m/VrgjmRzht7T+hGSJEnS3mlsoS9AkqR9SUQ8gazNbiPZHCKA16SUblq4q5IkSZIGx/BJkqTh2o9ssO4U8EvgXSmlsxf2kiRJkqTBceC4JEmSJEmSBsaZT5IkSZIkSRoY2+4GKCICOBS4daGvRZIkSZIkqc/WANenNm11hk+DdShw3UJfhCRJkiRJ0oDcDvh9qwWGT4N1K8C1117L2rVrF/paJEmSJEmS+mLHjh3c/va3hw66vQyfhmDt2rWGT5IkSZIkaZ/kwHFJkiRJkiQNjOGTJEmSJEmSBsbwSZIkSZIkSQNj+CRJkiRJkqSBMXySJEmSJEnSwBg+SZIkSZIkaWAMnyRJkiRJkjQwhk+SJEmSJEkaGMMnSZIkSZIkDcyiC58i4qqISA1uZ+fHV0TE2RFxS0TsjIjPRMSmuuc4PCK+EBG7IuLGiHhLRIzVrTkhIn4YERMR8duIOK3BtTw/v549EXFpRNxvoC9ekiRJkiRpiVl04RNwX+CQ0u3k/P5P5V/PAh4NPBH4M+BQ4LPFgyNiFPgCMA48EHgGcBpwZmnNUfmarwP3BN4GvD8iHl5a82TgrcBrgXsDPwEuiIiD+vliJUmSJEmSlrJIKS30NbQUEW8DHgXcCVgL3AQ8NaX06fz4XYArgONSSpdExCOA84FDU0o35GueA7wJODClNBkRbwIemVK6W+k8nwTWp5ROyX++FPh+SukF+c8jwLXAO1NKb+zw2tcC27dv387atWvn/V5IkiRJkiQtBjt27GDdunUA61JKO1qtXYyVT1URMQ78FfDBlKVkxwLLgAuLNSmlXwLXAMfldx0HXF4ET7kLyIKrY0prLqTWBcVz5Oc9tu48lfzn42giIpZHxNriBqzp6gVLkiRJkiQtMYs6fAJOBdYD5+Q/HwxMppS21a27IT9WrLmhwXE6WLM2IlYCBwCjTdYcTHMvB7aXbte1WCtJkiRJkrTkLfbw6W+AL6WUrl/oC+nQG4B1pdvtFvZyJEmSJEmSFtZY+yULIyKOAB4KPK5092ZgPCLW11U/bcqPFWvqd6XbVDpWfN3UYM2OlNLuiJgBZpqs2UwTKaUJYKL0GpotlSRJkiRJ2ics5sqnZwI3ku1KV7gMmAJOKu6IiKOBw4GL87suBv6kble6k4EdwC9Ka06i1snFc6SUJvNzlc8zkv98MZIkSZIkSerIoqx8yoOeZwLnppSmi/tTStsj4gPAWyNiC1mg9E7g4pTSJfmyL5OFTB+JiJeSzWh6HXB2XpkE8B7gBRHxZuCDwInAk4BHli7jrcC5EfED4HvAC4HVwIcG8ZolSZIkSZKWokUZPpG12x1OFgzV+wegAnwGWE62S93zioMppZmIeBTwH2RVSrcB5wKvLq25MiIeCZwFnE42GPxvU0oXlNacFxEHAmeSBVg/Bk6p20VPkiRJkiRJLURKaaGvYcmKiLXA9u3bt7N27dqFvhxJkiRJkqS+2LFjB+vWrQNYl1La0WrtYp75JEmSJEmSpL2c4ZMkSZIkSZIGxvBJkiRJkiRJA2P4JEmSJEnqm6mbb+Z3z3oW2774xYW+FEmLxGLd7U6SJEmStBfaceGF7PrhD4mxMdb/+Z8v9OVIWgSsfJIkSZIk9c3ElVcCkCYnF/hKJC0Whk+SJEmSpL4xfJJUz/BJkiRJktQ31fBpenqBr0TSYmH4JEmSJEnqi5lbb2X6ppsASFNTC3w1khYLwydJkiRJUl8UVU8AFdvuJOUMnyRJkiRJfTHxu99Vv7fySVLB8EmSJEmS1BflyifDJ0kFwydJkiRJUl+0Cp+mt2zh5nPPZXr79mFflqQFZvgkSZIkSeqLVuHTLZ/8JJvPOovfn3HGkK9K0kIzfJIkSZIkzVtlYoLJ3/+++nOanCSlVP15essWAG79+tfZ9dOfzq5LiYotetKSZvgkSZIkSZq3yauvhkqFGBvL7kgJZmaqxyu7dlW/3/z2t5NSIqXE71/1Kq44/nj2/OY3w75kSUMyttAXIEmSJEna+xVVT+OHH17d9S5NTVXDqMru3dW1uy67jC3nnQfAtvPPB2D7V77CijvdaZiXLGlIDJ8kSZIkSfM2kw8SHzvwwJrwiZUrgdnKp/EjjmDy6qv5wxvfCBHVx9/2gx8M+YolDYttd5IkSZKkeZvZsQOAsY0bq/eVh44XlU8Hn346m04/HcbGICVW3v3uAOy+/PKa6ihJS4fhkyRJkiRp3orKp9H164nxcQAqk5PV40WwNLJqFQc+85nc8dxz2XT66Rz57nczdtBBpKkpdl1++fAvXNLAGT5JkiRJkuatqHwaXbuWWLYMaFz5NLJqFQArjzmGA5/5TEb324/V97kPYOudtFQZPkmSJEmS5m162zYAxtatq1Y+1YRP+cynkXwGVFk1fLrssup9277wBW583/sGdr2ShseB45IkSZKkeatWPq1bV93hrhw+pbrKp7LVxx4LwO6f/pTKnj2MrFjBH97yFma2bWPdwx/O8sMPJ01PQwQxOjrolyKpz6x8kiRJkiTNW3Xm07p1c9ruUqUy23bXoPJp/PDDGTvwQNLUFLuvuAKAyp49AEzfcgtpeprfPvGJ/O7pTyelNPDXIqm/rHySJEmSJM1bTfhUtN3lA8fTxER1XaPwKSIYO+AApm+6icptt+VPOJN92bqVqRtuYOLKKwGo3HYbo/vtN7DXIan/rHySJEmSJM1bq4HjxbwnIogVKxo+PkayP09TpZJ9zcOn6W3bmN6yZfY8ecglae9h+CRJkiRJmpfK1FS1YqlR21255S4iGj9JMctpejprrctDqJmtW5neurW6bCYfbC5p72H4JEmSJEmal0pe9UQEo2vWMNKk8qlRy12hGCSeKpVqyx1klU8zpcqnaSufpL2O4ZMkSZIkaV6KQGh0zRpidLR55VODne4K1V3sZmaqrXdg5ZO0FDhwXJIkSZI0L+Vh40B14Hili8qnou0uzczUVj5t3cqoM5+kvZqVT5IkSZKkeamGT2vXAsxWPuW73ZVnPjUTpfAplcKnmW3bmClVPk1b+STtdax8kiRJkiTNSzV8Wr8eoGnbXbSqfMp3u6MufJrets3KJ2kvZ/gkSZIkSZqXmXzgeLvKp9FWM5/Gsj9P69vuZrZuZXrNmtmfrXyS9jq23UmSJEmS5mXOzKcifJqeBmZnPrWqfKoOHK9UaiqfKrt2MXXDDdWfbbuT9j6GT5IkSZKkeWk686lu4Hiryqei7S7NzEBptzugZuaTbXfS3sfwSZIkSZI0L9N5IDRWzHzKd7urb7vrpPIpTU9XK6Yase1O2vsYPkmSJEmS5qVp211d5VOr3e4ot93VVT41OpekvYfhkyRJkiRpXuoHjo802e2uVfhUrXyqGzheGMmHjld27aKSP6+kvYPhkyRJkiRpXtpWPhXhU6vd7krhU2oQPi0/4ojqXChb76S9i+GTJEmSJGleqpVPdeFTpYvKp2rbXZPKp7H9968+v6130t7F8EmSJEmS1LM0NUVl506gFD7VDxwvZj61qnwq7XbXaObT2MaNs+GTlU/SXsXwSZIkSZLUs5lbb61+P5rPZWradtfBzCdmZhrudje6YQNjefg0beWTtFcxfJIkSZIk9axogRtZs6YaIMXYGNDbbnepUoFmlU/r12fntPJJ2qsYPkmSJEmSeja9dSsAY3kwBKW2uyJ82rMH6Hy3u0YDx2va7qx8kvYqhk+SJEmSpJ5N/eEPACw7+ODqfXPa7jqY+dR24PiGDdXKp2krn6S9iuGTJEmSJKmhND1dM9OpkclG4VNp4HhKqauZT+XKp5H99qseH924sTrzaWbbtobVUZIWJ8MnSZIkSVJDV59+Or96+MNbVhpNbd4MwLJDDqneV658ShMT1RlOLXe7Kw8cz4OlsQMOqB4f23//atvd7iuu4FennMJ1r3pVD69K0rCNLfQFSJIkSZIWpwUZe7YAACAASURBVN1XXEFl1y4mr7uuZqZTWbXtrhw+lQaOF1VPACMrVjQ/WanyqWi7G1m1igP/7u+o3HYbyw44oNp2N/Gb3wCw85JLenxlkobJ8EmSJEmS1FCanMy+5rObGinCp/FS+DRSGjhezHuKFStmq5saiNJud0XlU4yMsOl5z6uuKSqfCsUgc0mLm213kiRJkqSGquHT9HTj4yk1rnzK2+4qpcqnVvOesgX5n6fT09U2vfqwamzDhtrzGz5JewXDJ0mSJEnSHCmlavhEk/BpZseOari0bNOm6v3lmU+dhk81A8eL89WFT8uPPJI1f/qnrH3Yw6rP7+BxafGz7U6SJEmSNEc1eKJ5211R9TS6cWPNPKfybnfV8KnFsHEoVTlVKqQmlU8xNsYR73wnld27+cWXv1w9R7SrqpK0oKx8kiRJkiTNkSYmZr9vUvlUnfd08ME199dUPuUzn9q23TUYOF5txasTy5dXvy8PNJe0OBk+SZIkSZLmqHRS+bR5MwDLDj205v6GbXcdVj6lmZnZgeNjjZt1YmSkWl1VKYVkkhYnwydJkiRJ0hydVD5NFsPG+1j5RDl8alL5BLPVT6lF+FSZmGDnxRcbUEkLzPBJkiRJkjRHudqpXdtds/CJSoXKzp1ABwPH86Cppu2ubuZTWTFjqtJix7stn/wkVz33udzy8Y+3PLekwTJ8kiRJkiTNUa4WajdwfLy+7S5viQOY3r4d6KDtLm+xq2m76yB8Si3Cp8nrrsuu88YbW55b0mAZPkmSJEmS5qjZ7a5Z5VMx86lZ5RMwfdNNAIyuW9f6hEWLXaWS3aBl5VPRdteqpW4mb/lr1ZonafAMnyRJkiRJc7Sb+VSZnGT65psBWHbIITXHyoPCp/LwaWz9+pbnqxk4np+v1cynkSJ8alH5VLnttuw5S0GapOEzfJIkSZIkzdFut7upG24AIFasYLQuWIqIavVTp5VP1cBqZqZa+dRst7vivNC67a4InyqGT9KCMnySJEmSJM3RrvKpqHoaO+AAImLO8Tnh04YNrU9YGjhezHyiVeVTMXC8VdudlU/SomD4JEmSJEmao93Mp+ktWwAY27ix4eOLoeMzO3Zk69pVPhXznTodON5J212+057hk7SwDJ8kSZIkSXO0a7ubaRc+lYaOA3Na8+YozXyiqHxqNXC8aLtrUflUyQeOt6qOkjR4hk+SJEmSpDlqQp1GlU+33AK0CJ/q5jW1C5+qA8crFVIx82melU8zReVTg/BM0vAYPkmSJEmS5khtKp+qbXf779/w8UXbHQAjI4yuWdP6hMV8pw53u2s3cDzNzFSPtaqOkjR4hk+SJEmSpDkq7QaOd9F2N7puXcsgCWYrpdL0dHW3O1rsdtdu4Hix0x1Y+SQtNMMnSZIkSdIcbQeOt2u7K4VPY+3mPTFb5ZQqldmB4612u8vb7ppVNc2UwidnPkkLy/BJkiRJkjRHTeVTL213dZVPbZV2u+to4Hgx82n37obHayqf3O1OWlCGT5IkSZKkOcqBU6vKp9FO2u46qXwq7XZXrXxqNXB85Uqgw7Y7wydpQS3K8CkiDouIj0bELRGxOyIuj4j7lI5HRJwZEX/Ij18YEXeqe46NEfGxiNgREdsi4gMRsV/dmrtHxLcjYk9EXBsRL21wLU+MiF/may6PiD8f3CuXJEmSpMUhtZj5VJmcpJLvJNes8mmkNHC8m/CJmZnqzKdW4VPYdiftNRZd+BQRG4DvAlPAI4A/Bl4MbC0teynwf4DnAPcHbgMuiIgVpTUfA44BTgYeBTwYeG/pPGuBLwNXA8cC/wicERF/V1rzQOATwAeAewGfAz4XEXfr3yuWJEmSpMWn0mDm060XXcSt3/52teWOsbGmu9h1O/OpaLFLlcps2NWq8qlou2uy250Dx6XFo/nWAQvnZcC1KaVnlu67svgmIgJ4IfC6lNLn8/ueDtwAnAp8MiLuCpwC3Del9IN8zd8DX4yIl6SUrgeeBowDz0opTQI/j4h7Ai9iNqQ6HfiflNJb8p9fFREnAy8gC74kSZIkaUlKdTOf0vQ017zoRaTpaY54xzsAGNuwoelQ8G5nPlXb7qanSR1UPlV3u+sgfGJmhjQ9Xd1RT9JwLbrKJ+AxwA8i4lMRcWNE/Cginl06fhRwMHBhcUdKaTtwKXBcftdxwLYieMpdCFTIKqWKNd/Kg6fCBcDRefVVseZCal1QOk+NiFgeEWuLG9D4/wUgSZIkSYtEo3lOMHe3u8rEBGnPHpieZudFFwHNd7qDusqnDRuarqsqgqZKZXbgeIvd7iIPnzppuwOrn6SFtBjDpzsAzwV+Azwc+A/gHRHxjPz4wfnXG+oed0Pp2MHAjeWDKaVpYEvdmkbPQQdrDqaxlwPbS7frmqyTJEmSpAW35TOf4RcPehA7v//9OccqdTOfymFUNXxqMu8Jeqh8yoOmjgeOd9F2B859khbSYgyfRoAfppRekVL6UUrpvcD72Dva3N4ArCvdbrewlyNJkiRJzd32ve+R9uxh12WXzTlWs9vd1FRN+DTxu98BnVc+dTRwvGiJ6zR8alP5VB8+NVsnafAWY/j0B+AXdfddARyef785/7qpbs2m0rHNwEHlgxExBmysW9PoOehgzWYaSClNpJR2FDfg1kbrJEmSJGkxmMl3rJvZsWPOsfrd7soDyAstw6fSbncdDRwvVT4Vu921Gjhe7HbXrKLJtjtp8ViM4dN3gaPr7rsz2a50kA0f3wycVBzM5yvdH7g4v+tiYH1EHFt6jhPJXu+lpTUPjohlpTUnA79KKW0trTmJWieXziNJkiRJe62iOqhR+FQTNtW13RU6brvrpPKpNPOpmEPV0cDx3bsbHrftTlo8FmP4dBbwgIh4RUT8UUQ8Ffg74GyAlFIC3ga8MiIeExF/AnwYuB74XL7mCuB/gPdFxP0i4njgXcAn853uAD4OTAIfiIhjIuLJZLvbvbV0LW8HTomIF0fEXSLiDOA++XNJkiRJ0l5tpkX4VF/51Ch8Gu2k7S6C0bVr219MKWiqVil1UPmUJibI/kysNaftzsonacEsuvAppfR94LHAU4CfAa8CXphS+lhp2ZuBdwLvBb4P7AecklIqT5p7GvBL4KvAF4HvkIVYxXm2Aw8j2z3vMuDfgTPzGVPFmouAIvz6CfAE4NSU0s/6+JIlSZIkaUG0qnyq2e2ubuZToZPKp9G1a1tWMFXXl8On/FzRYre7ovKJlBoGS3Pa7qx8khbM2EJfQCMppfOB81scT8Cr81uzNVvIgqNW5/kp8Kdt1nwK+FSrNZIkSZK0N2rZdjffmU9F+NTBTnfQJHwaa/4naxThE5D27IHSjClo0HbX4PolDceiq3ySJEmSJA1eSmm27e7WbK+kHd/6Fte+/OXM3Hpry93uCq3Cp5E8DOpk3lO2cDZ8qnTSdjc2Vh1SXtmzZ87xOW13hk/SgjF8kiRJkqR9UJqYgHywd1H5dNP738/2L32JnRdd1HTm09gBB2R3jo0xtmFD0+cvdrvraKc7alvsOmm7i4jZoeMNWuqKYG1k5crsOW27kxbMomy7kyRJkiQNVrkyKE1MUJmYYPrmmwGY3ratpk2tHD6NH3EEB/7N3zCycmXNjnb19jv+eFbd4x5seOxjO7ugLtvuIB86vmtX1nZXp3h9oxs2UNm924Hj0gIyfJIkSZKkfdDMzp21P+/YwfSWLdn3W7dWq6Iga7srWuFGxsfZ/ylPafv8y29/e+5w7rkdX09EZAHUzMxsUNSi8gmyoeMzzG27S1NT1UqnsY0bmbr++obVUZKGw7Y7SZIkSdoH1c9Emtq8uVpBNJVXQBXS9HQ1zIm6wd79VLTZVSuf2uySV7Td1bfUzezaVf2+aA105pO0cAyfJEmSJGkfNFMXPk1ceWX1++m68ImZmdlAaIDhU9F6V235axM+xfLl2fq68KmSV3XF8uWMrFoFGD5JC8nwSZIkSZL2QZW6truJq66qfj8nfGI2rBpo5VMeNhVtd20rn4rwqa7trpJXPo2sWlW93orhk7RgDJ8kSZIkaR9UX/k02Sh8Ks1cqgY6wwifiqCozcynKNru6sKnYp7V6H77VcMnK5+khWP4JEmSJEn7oFaVT8XMp6JlDWbDp2G03VGpZOdqs9tdMfNpTttdEZStXl0Ny+rnQkkaHsMnSZIkSdoH1Q8cn7zmmtkf8p3uRlaunF0/hPCpvs0u2lU+5W139ZVPRbA2snr1bNtdsYOepKEzfJIkSZKkfVB9213KA6eykRUriGXLgFL4lP88EPVhU4e73dVXPhWvbXT16tmAysonacEYPkmSJEnSPqi6I1we4DQS4+PV1reiUqoY8j0Icyqfeh04XlzrqlXVsMyZT9LCMXySJEmSpH1QUR207OCDm66J5cuhCJ+GOfOpOH+b8KnZwPGamU9FQGX4JC0YwydJkiRJ2gcV1UHjhx7adM3I+Hi1cmhmCG13c8KmNjOfmg4cz8OokZUr3e1OWgQMnyRJkiRpH1SET8sOOaT2QMTst8uXz2m7G+rA8Ta73TWb51TZvRvIZ1YZPkkLzvBJkiRJkpa4NDXFjm9+k5kdO6r3zTQJn5Yddlj1+1i2bDZ8KgKdIbbdta18Klrq8msrVK915crZNQ4clxaM4ZMkSZIkLXHbv/IVrjn9dG5417uq9xUDx8dL4VMsW8Z4KXwaWb58dre7hah8ajdwfOVKoEHbXSl8qg4cn5rq12VK6pLhkyRJkiQtcVM33gjA9M03V+9r1HY3unEjo2vXVn8u73ZXtK0NNHyqr3RqN3C8SdtdMYA8Vq5sukbS8Bg+SZIkSdISVwRH5R3fqm13Bx1UbW8b23//2vCpNPOpMNC2u7pzta18Klrq6ne7K898KiqfnPkkLRjDJ0mSJEla4oqqn6L1LE1NVauDRtasYXTNGgDGNm5kdN266uPKu90Vhln51DZ8yne7S83Cp/LMp8lJprdsYfsFF1CxBU8aKsMnSZIkSVriioqnND0NwMyuXdVjo6tWVaudxvbfvxpEQePKp0GGT3Pa7Nq13eXhU8uZT6Xd7ja/4x1c+7KXsePCC/t0wZI6YfgkSZIkSUtctfKpaL/Lh41H3pZWrXyqa7sbKc18Kgx14Hib3e6atdQVbXgjK1bUhE+TV18N1M6+kjR4Y+2XSJIkSZL2ZtXKp7zdrBg2Prp6NQAjReVT/cDx0m53hUHOfJoTPo21/pO13FJX1qjyqTIxUQ2d3PlOGi4rnyRJkiRpiauf+VQMGx/Jw6d1J5/MskMOYb/jjquZ+RTLlg237a6+0qld5VOpqqks5eFTrFw5Wx01NcX0LbcAc9v0JA2WlU+SJEmStMTVh09F293IfvsBsPHxj2fj4x8PwO5SVdDI8uVzd6AbZOVTl7vdNQqfUkqzbXcrV87ev2cPqfjene+koTJ8kiRJkqQlrtKk8qlouyurGTg+5N3u5lQ6tdvtLm+7S6VKpjQxASmLmUZWrIBKZc7jDJ+k4bLtTpIkSZKWuFQ/86mofGoUPpXb7hrsdje0mU8jI0RE6/VF5dP0NCkPmYp5T1A786nM8EkaLsMnSZIkSVri6iufKnUzn8pGVq+uVhwNfbe70rna7XQHWThWqO7kV8x7Gh8nRkcbXm/9gHJJg2X4JEmSJElLXLXyKf/aqu0uIqqtd412uxta212bne4ARkrXVh8+FfOeYmRkToCWHDguDZXhkyRJkiQtcXPa7lpUPsHs3Keor3waG+uoIqlX5ba7js4zNlYNrKrVXfmw8VixYva5ShVSMPs+SBoOwydJkiRJWuKqwcz0NCmlahg1UhfKFNY94hGMH3EEK485piZ8GuS8J6B2wHibYeOQVWnV73hXX/kEc6u1KlY+SUNl+CRJkiRJS1x5wHaamqrOPGrWQrfpec/jzp//PGPr1tW03Q205Y66yqcOwieYDcS6CZ8cOC4Nl+GTJEmSJC1x5RlHaWqq+nMnYVLNEPC6+U/9Vm616zR8KlrqimqmRuFTfcWW4ZM0XIZPkiRJkrTEVerDpzaVT2U1lU9N2vT6pjxfqsPZUnPa7vKZTyPlmU/1bXeGT9JQGT5JkiRJ0hKWUqqtfJqcrIYvncxwGubMp5rKpw52uwPb7qS9geGTJEmSJC1h9Tu71VQ+dVDJNNS2u3KrXZeVT5UW4dOctjsHjktDZfgkSZIkSUtYfdCSpqcXb9tdDwPHi2sqXmcq2u7KlU91110fyEkaLMMnSZIkSVrC6lvMum27Y4Eqn+a7212UZz4V152/loqVT9JQGT5JkiRJ0hJWH7R0PXB8iDOfalrtOq186qTtLq98WrZpE+DMJ2nYDJ8kSZIkaQmbU/k0NVVtUVtsbXc186W63e0uf02tBo6PH3pottbwSRoqwydJkiRJWsLqK58q89jtbqgDxzvd7a6Y+VRUPjWa+ZS/zmVF+DQ1RapU5n29kjpj+CRJkiRJS1jDyqded7sbYttdx5VPeSA2p+2uNPNp1b3uRSxbxpoHPrB6n0PHpeHpLEqWJEmSJO2VWu5210ElU3nNoGc+1QRd3e5212Lm08bHPpb1j3pUzePSxAQMevc+SYDhkyRJkiQtaS0Hji/iyqdOB46P1M18SsVud6XwCWBk2TJSStWfK5OTdHYGSfNl250kSZIkLWFzKp8mJqotZ13PfBp05VMpcOq28qmSv6bqzKdS2111bcScSilJg2f4JEmSJElLWKUuZKns2lX9vuvd7oY5cLyPu93VrM9fg+GTNDyGT5IkSZK0hNVXPs3s3Fn9vtu2u5FBz0gqVz51uNtdNXxqsdtdWf3ueJIGz/BJkiRJkpaw+pClcttt1e87CXiG2nbXw253RZhU6bTyKX8N9bOwJA2O4ZMkSZIkLWH1IUslr3yK5cuJiLaPH2bbHeUwrNOZT6U2ulSpkFrMfIJSpVQ+I0rS4Bk+SZIkSdISVl/5NJNXPnUcJA2x7a6m8qnLgeNpcrKmxbB+t7tCte3OyidpaAyfJEmSJGkJm1P5lIdPnQZJNW13wxw43mH4NFJqoyta7qBF5VP+GuoHsUsaHMMnSZIkSVrC5lQ+FW13Hc5vqmm7G/DMp5qB453udldUMk1NVcOnWLGi6ePDgePS0Bk+SZIkSdISVt9eVtm1C+gifCpXPg267W4+u92VKp+aVT1B7YwoScNh+CRJkiRJS1g/2+5Ghtl21+lud0Xb3eRk253uoLQ7nuGTNDSGT5IkSZK0hBWVTyOrVgGltrsOg6QFa7vrYeB4pdjprkX4VK6UkjQchk+SJEmStIQVFT4jq1dnPxe73fUycHwxtt2V2ug6arsrwicrn6ShMXySJEmSpCWsCFlG16wBSuFTL5VPA267o5e2u6KNbmKCVAwct+1OWlQMnyRJkiRpCau23eWVT4WOZz6NjkJE9pgBt91FL213pUomB45Li1NP4VNEPD0i7t5mzd0i4um9XZYkSZIkqR+KCp/R/farub+b+U1FC9zA2+7K1U49zHyaKYap1wVtzdZLGo5eK5/OAU5ts+YvgA/1+PySJEmSpD6oVj7NI3waP/xwRlauZGz//ft6bXP0UPk0Uqp8mtm2DYCxdeuarq9WPjlwXBqazia49WYUqAzw+SVJkiRJbaQmlU+dtt0B3OFDH6Kye/ec5+i36GHmUzlEm775ZgBG169vur4682lqqocrlNSLQYZP9wK2DPD5JUmSJEltVJrMfOpmePjo2rWMrl3b1+tqfKIedrsrhWhTN96YPU2ryqeiUsrKJ2loOg6fIuJrdXedFhEnNFg6CtwOOBL4z56vTJIkSZI0b0XIMmfm04DnN/WiZuB4p5VPpZCqGj61qHwqDyiXNBzdVD6dUPo+kYVLRzZYVyGrePoU8MIer0uSJEmS1Af9qHwalpq2u04HjkcQy5eTJiaYzsOnVjOfqm13hk/S0HQcPqWUqrFzRFSAM1JKZw7kqiRJkiRJfVFU+NQPHO9m5tPQ9DBwHLJqpjQxwcz27dnTtKp8KgaOGz5JQ9PrzKeHAFf18TokSZIkSQPQtO2ui93uhiV6DJ9GxsdrdrtqOfMpD90Mn6Th6Sl8Sil9s98XIkmSJEnqrzQzQ5qeBhq03S3C8IkedruDua9lrNVud/naigPHpaHpebe7iBgHTgXuC6wnGzReL6WU/qbXc0iSJEmSeleu7hlds6bm2GJsuysPGe90tzuoG54+MjKnxbBmrQPHpaHrKXyKiCOArwB3BKLF0gQYPkmSJEnSAigP1d4rBo6XA6cuKp9GSpVPo+vWtdwpz/BJGr7O/zXXOgv4I+CjZPOf7gQc1eB2h26fOCLOiIhUd/tl6fiKiDg7Im6JiJ0R8ZmI2FT3HIdHxBciYldE3BgRb4mIsbo1J0TEDyNiIiJ+GxGnNbiW50fEVRGxJyIujYj7dft6JEmSJGmhFPOeGBtjZMWKmmOxCCufyoFTtwPHC63mPUGp7c7wSRqaXtvuTgS+mlJ6Rj8vpuTnwENLP0+Xvj8LeCTwRGA78C7gs8DxABExCnwB2Aw8EDgE+DAwBbwiX3NUvuY9wNOAk4D3R8QfUkoX5GueDLwVeA5wKfBC4IKIODqldGP/X7IkSZIk9Vd1p7vx8TmVTiOLcOZTTeDUTfhUCtLG2oRPDhyXhq/XyqcR4Ef9vJA60ymlzaXbzQARsY6sje9FKaWvpZQuA54JPDAiHpA/9mHAHwN/lVL6cUrpS8CrgOfnc6ogC5SuTCm9OKV0RUrpXcCngX8oXcOLgPellD6UUvpF/phdwLMG+LolSZIkqW+KodqxfPmcodyLfeB4t7vdVZ+ixbBxmG03NHyShqfX8OlS4K79vJA6d4qI6yPidxHxsYg4PL//WGAZcGGxMKX0S+Aa4Lj8ruOAy1NKN5Se7wJgLXBMac2F1LqgeI48pDq27jyV/OfjaCIilkfE2uIGrGm2VpIkSZIGrWi7iwaVT4sxfIoew6du2u6sfJKGr9fw6Z+AEyPiCf28mNylwGnAKcBzyWZHfTsi1gAHA5MppW11j7khP0b+9YYGx+lgzdqIWAkcQLZ7X6M1B9Pcy8laAYvbdS3WSpIkSdJAFZVPe03bXXlQeK/hU5vKJ2c+ScPX68ynRwJfB86LiG8CPwR2NFiXUkr/0s0T521yhZ9GxKXA1cCTgN09Xu+wvIFsTlRhDQZQkiRJkhZIUd0TK1bU7iTHIh04Xq586ma3u25mPhVB1fQ0aWamqworSb3pNXw6o/T9CfmtkQR0FT7NeYKUtkXEr8l21/sKMB4R6+uqnzaRDRgn/1q/K92m0rHi66YGa3aklHZHxAww02TNZppIKU0AE8XPEdHqpUmSJEnSQKVy5VMEsWwZaWoKYE4l1GIQIyMQASnNCctaPq6btrvS2jQ5Saxc2f2FSupKr+HTQ/p6FS1ExH7AHYGPAJeR7Vp3EvCZ/PjRwOHAxflDLgb+OSIOKu1KdzJZZdYvSmv+vO5UJxfPkVKajIjL8vN8Lj/PSP7zu/r8EiVJkiRpIIrWsqLKqSZ8WoyVT2SzntL0NHRR+dRL2x1k78+I4ZM0cD2FTymlb/b7QgoR8W/Af5O12h0KvJasCukTKaXtEfEB4K0RsYUsUHoncHFK6ZL8Kb5MFjJ9JCJeSjaj6XXA2XllEsB7gBdExJuBDwInkrX1PbJ0KW8Fzo2IHwDfA14IrAY+NJhXLkmSJEn9VdmdTS4pwpkYH4ddu4DFOfMJgLExmJ7ufbe7NpVPjI1lwVal4tBxaUh6rXwapNsBnwD2B24CvgM8IKV0U378H4AKWeXTcrJd6p5XPDilNBMRjwL+g6yS6TbgXODVpTVXRsQjgbOA08nmMv1tSumC0przIuJA4EyyAOvHwCl1u+hJkiRJ0qI1ee21AIwfeihQ22q3GHe7g6z1LkF3A8fLM5/aVD5FBDE+Ttqzx/BJGpKewqeIeHCna1NK3+rmuVNKf9nm+B7g+fmt2ZqrmdtWV7/mG8C92qx5F7bZSZIkSdpLTVx5JQDLjzoKoGaO0mINn4rQqZvKp25mPkFWKTVj+CQNTa+VT98gGybeCbcOkCRJkqQFMCd8KoU0i7Xtrho6dbPbXRczn2D2fahMTLRZKakfeg2fzqRx+LQOuDfwYOALwA96fH5JkiRJ0jykmRkmr74aKIVPRdvdyEg2+2gRKsKnrna7y9vuRlau7ChUK8InK5+k4eh14PgZrY5HxBOAc4DX9PL8kiRJkqT5mbz+etLUFLF8OcsOPhiYDZ9ifJyIWMjLay6veIoedrvrpOqpvN7wSRqOzv81dyGl9Gng68AbBvH8kiRJkqTWqi13RxwxW02Uh0+LteUOYGTFCgAi/9qJavjUwbwnmH39FcMnaSgGWWd5BfCcAT6/JEmSJKmJybp5T1Bb+bRYHfziF7P75z9nxZ3v3PFjxg87DIDld7hDR+uLNj0rn6ThGGT4dC+gMsDnlyRJkiQ1sScPn8aPPLJ6394QPq094QTWnnBCV49Zdc97csfzzmP89rfvaH217c6B49JQ9BQ+RcThLZ7vMOA04ETgc71dliRJkiRpPorKpxWlaqCi3Wwxh0+9iAhWHn105+vzEK4yNTWoS5JU0mvl01U03u2uEMD/Av/Q4/NLkiRJknqUUqrOfGpU+bSYZz4NQ3Unvenphb0QaR/Ra/j0YRqHTxVgK/B94PMppT29XpgkSZIkqTczW7cys2MHRLD8iCOq9+8NbXfDULwPyconaSh6Cp9SSqf1+TokSZIkSX1SVD0tO/TQ6u5xUAqf8oHb+6qi8ilZ+SQNxchCX4AkSZIkqb+mt2wBYNlBB9Xcb9tdxsonabjmtdtdRKwGTgXuCawFdgA/Bj6XUrpt/pcnSZIkSepW5bbsz7GRVatq7rftLlMNn6x8koai5/ApIh4PvBdYTzZgvJCAbRHx7JTSZ+d5fZIkSZKkLlV27QJgZPXqmvtjie52161q252VT9JQYifnrwAAIABJREFU9BQ+RcQDgU8CM8D7ga8DfwAOBh4CPAP4ZET8WUrp4j5dqyRJkiSpAzN55dNoffhk5RNg5ZM0bL1WPr0CmACOTyn9pO7YeRHxbuCifN2j53F9kiRJkqQuNW27yyt+9vmZT1Y+SUPV68Dx44DzGgRPAKSUfgr8J/DAXi9MkiRJktSbavhUV/m05vjjWXa727HmhBMW4KoWDyufpOHqtfJpFXBDmzU35OskSZIkSUM0k898qm+7W3WPe3D0+ecvxCUtKlY+ScPVa+XTVcDJbdaclK+TJEmSJA1Rs8onZZpVPqWUSDMzC3FJ0pLWa/j0n8CxEXFuRBxaPhARh0TEOcCxwHnzvD5JkiRJUpequ92tshmlkWaVT9e86EX8+jGPobJ790JclrRk9dp29ybgFOCvgSdHxG/J2uw2AX8EjAPfy9dJkiRJkobIyqfWmlU+7bz4YtKePUxccw0rjz56IS5NWpJ6qnxKKe0CHgycAVwH/DHwkPzrdcBrgD9LKRkXS5IkSdKQWfnUWjV8KlU+pZRIe/YAUNm5c0GuS1qqeq18IqU0AZwJnBkRa4C1wI6U0q39ujhJkiRJUvdm8sqn+oHjyjRquyuCJ4AZwyepr3oOn8rywMnQSZIkSZIWgWrlk+FTQ9XwqdR2V57zZOWT1F8dt91F5n0R8cGIWNZi3Xi+5j39uURJkiRJUqdSSrbdtdGo7a5i5ZM0MN3MfHos8Czg2ymlqWaLUkqTwDeBZ0fEqfO8PkmSJElSF9KePVCpAFY+NWPlkzRc3YRPTwE2A+d2sPYjwPVku+FJkiRJkoakmPdEBCMrVy7sxSxSDSufSuGTlU9Sf3UTPt0fuDClVGm3MF/zVeC+vV6YJEmSJKl7lTx8Glm1iohY4KtZnIrKJ0qVT+WB41Y+Sf3VTfh0EHBtF+t/nz9GkiRJkjQk5fBJTbRpu7PySeqvbsKnCaCb/3qtzB8jSZIkSRqSou3OeU/NVdvuyuGTlU/SwHQTPl0L3LuL9fcCrunuciRJkiRJ81HsdDdq+NTUSLuZT8XcLEl90U349HXgQRFxj3YL8zUPBr7W64VJkiRJkrpXsfKpPXe7k4aqm/DpHUACPhMRRzdbFBF3Bj4DzADvmt/lSZIkSZK6UVQ+OfOpuUa73f3/7N17mKR3Wef/913VxzlPZpKZkBMJEgIhnMJBiCIaIijERVEQxOUQ/YkbVhfJrrAsCIjLCgoq4YI1KkhYFSGCCwiJkeNCJCGQhADhkMn5fJjJzPS5q76/P+p5qp+uru6uerr6MN3v13X1NV1Pfavq2zOTyvSn7/v+FgeO1w4dWvE9SetZX6cLU0o/jIjXA+8CromIj9Gohro9W3IccDbwQmAIuCCl9MMe71eSJEmStAArnxYXDhyXVlTH4RNASulPI+IQ8E7gZcCvtSwJ4CDwuymli3qzRUmSJElSp/J5RVUrn+bVrvJpVtvdyAgpJSJixfcmrUddhU8AKaW/jIiPAr8MnAXsze66G/gq8PGU0kO926IkSZIkqVPNtjsrn+bVtvKp0HZHvU59bMwAT+qRrsMngCxc+uvsQ5IkSZK0Rth2t7jFKp8A6ocOGT5JPdLNwHFJkiRJ0hrnwPHF5ZVP1OukWq3xabHyiZn2RUlLZ/gkSZIkSetIc+aTlU/zyiufYKb1LrVWPjl0XOoZwydJkiRJWkeabXdWPs2rWfnETOtda9udJ95JvWP4JEmSJEnriDOfFteu8qm17a5+6NCK7klazwyfJEmSJGkdqTnzaVFRrUKl8e1wa+VTZXgYcOaT1EuGT5IkSZK0jtSd+dSRvPWuWfmUhU99u3c3btt2J/WM4ZMkSZIkrSPN0+4MnxaUt941K5+ytru+o48GnPkk9VKp8CkizoiIV0XEtsK14Yh4f0TcERE/iohX926bkiRJkqTFpOlp0sQEYPi0mNbKp/y0u/4sfLLySeqdspVP/wP4Q6A4ge1/Ar8FbAVOAN4XEecsbXuSJEmSpE7lVU9g+LSYYuVTSmmm8ilru7PySeqdsuHTU4EvpJQSQET0Aa8ErgSOAU4G7gN+txeblCRJkiQtLh+SHf39VAonummuvPKJvFqs8e2tM5+kZVA2fDoauK1w+ynANuADKaXxlNKdwD8Dj1/i/iRJkiRJHcqHjVv1tLhi5VM+bByc+SQth7Lh0zQwWLj9LCABXyhcewDYXfL5JUmSJEldag4b37RplXey9hVnPuUtd9HfT9+2xmhjK5+k3ikbPt0M/HTh9q8AN6WUbilcO45GACVJkiRJWgG1gwcBqG7duso7WfvaVT5VhoepZL93Vj5JvVM2fLoYeHxEfD0ivkyjve7vWtY8DvjhUjYnSZIkSepcM3zatm2RlSpWPuUn3cXwMNUtWwDDJ6mXyoZPFwIfA54M/ATwWRqn3QEQEafTCKQ+v9QNSpIkSZI6Y/jUhTx8mppqtt1Vhoaa87Ly+VmSlq6vzINSShPAiyNiW+NmOtSy5B7giTTa8yRJkiRJK6B2qPGtmeHT4pptd9PTs9ru8sqnNDFBfWrKUwOlHigVPuVSSgfnuX4/cP9SnluSJEmS1B1nPnUu2lU+DQ83Kp8iICXqhw5ROeqo1dymtC6UbbsDICKeGBHvjIj/GxGXF66fFBEvigj/K5UkSZKkFZKHTxUrnxbVtvJpaIioVqnu2AHA9IMPrtr+pPWkdPgUEe8EvgFcADyf2affBY0B5L++pN1JkiRJkjpWt+2uY7MqnwptdwB9WbXT9P029Ei9UCp8iohX0gidPk3jVLt3FO9PKd0MXAn8whL3J0mSJEnqkAPHO1esfEpZ210MDQHQt3s3ANMPPLA6m5PWmbIzn/4T8D3ghSml6YiYbLPmBuDZpXcmSZIkSeqK4VPnOqp8su1O6omybXePAf41pTS9wJp7gGNKPr8kSZIkqUvN0+4cOL6o+U67A+jbtQuw8knqlbLh0zQwsMiahwGHSz6/JEmSJKlLVj51blb4lJ92l7fd5eGTM5+knigbPn0b+JmIqLa7MyI20Wi5u7rsxiRJkiRJnUvT09RHRgDDp04s2HaXh0+23Uk9UTZ8+hvgVOADETFYvCMitgEfAvYCFy1pd5IkSZKkjuQtd2DbXSfaDRy37U5aHqUGjqeU/iYing2cB7wYOAAQEVcCjwY2Ax9KKX28VxuVJEmSJM0vb7mrbN7crOrR/NpVPkVr253hk9QTZSufSCm9FPgt4CbgOCCAJwO3Ar+dUnpVT3YoSZIkSVqU8566s+DA8fy0u/37SfX66mxQWkeWFIenlC4CLoqIYWAncDCl5JBxSZIkSVphnnTXnVmVT60Dx3fuhAio1agdONAMoySVU7ryqSilNJZSutPgSZIkSZJWh5VP3Vmo8in6+6nu2AHYeif1QqnwKSJOiIifyU61y69VIuL3I+KrEXF5RDyvd9uUJEmSJC3E8Kk7C512B4XWO8MnacnKVj79IfAxYKpw7Y3AO4CnAz8DfDIinrK07UmSJEmSOtEcOG741JG88onpaepZy2Kl0LLYt3t3427DJ2nJyoZPZwGXp5SmACIigNcANwAnAk8FRoD/2otNSpIkSZIWVrfyqSt5+FSfnGw7L8vKJ6l3yoZPxwC3FG4/ATgaeG9K6faU0jeATwJWPkmSJElSD6VajfrExJzrDhzvTt52VztwAFICZgd3fbt2AYZPUi+UDZ8qLY99FpCAzxeu3QHsLfn8AETE6yMiRcSfFa4NRcT7IuKBiDgcEZdExJ6Wx50YEZ+JiNGIuDci3hURfS1rnhUR34yIiYj4UUS8os3rnx8RN0fEeER8PSKeupSvR5IkSZKW6qbzzuMH5547J4By5lN38sqn6QcfbNweHKQyONi83/BJ6p2y4dOtNFrrci8A7kopfb9wbS9woOzGsnlRvwVc13LXe4BzgV8Bfgp4GPBPhcdVgc8AA8AzgJcDrwDeVlhzcrbmCzSqtv4M+KuIeE5hzYuBdwNvBZ4EXAtcGhHHlP2aJEmSJGkpUr3O6LXXMn3vvUzde++s+wyfupNXPuXhU2vFmOGT1Dtlw6dLgLMi4uMR8RHgJ7JrRY8B9pV58ojYAvwf4DeB/YXr24HzgN9LKX0+pXQ18ErgGRHx49myn81e+2UppWtSSp8F3gScHxED2ZpXAzellF6XUvpeSulC4OPAawvb+D3gopTSB1NK380eMwq8qszXJEmSJElLVR8ba7aIpcnJWfcZPnWnOfMpb1ds+X0zfJJ6p2z49CfAVcAvAS8Fvg28Jb8zIk6iURn1xZLP/z7gMymly1uunwn0A83rKaUbaFRiPT279HTg2ymlewqPuxTYBpxeWNP63Jfmz5GFVGe2vE49u/105hERgxGxLf8AbLaWJEmS1DP1kZHm52lqatZ9hk9d6ps1mWX+yqesMkpSeX2LL5krpXQQ+PGIeGx26XsppVrLsl8CvtHtc0fEr9Joc2s3rHwvMJlSam3nu4eZ+VJ7s9ut99PBmm0RMQzsBKrzrDltge2/AfiDBe6XJEmSpNLqo6PNz+dUPs1TwaP2oiV8qrSGT/lpdw8+SKrXiUrZ2g1JpcKnXErp+nmu38Ls0/A6EhEnAH8OnJNSGl/K3lbJO2jMicptBW5fpb1IkiRJWmeKlU/FgeOpVqN++DDgaXedytvucq2hXfN2vU59bIzq5s0rtTVp3Vlr0e2ZwDHANyNiOiKmaQwV/53s83uAgYjY0fK4PcDd2ed3Z7db76eDNQdTSmPA/UBtnjV3M4+U0kRK6WD+ARya/0uVJEmSpO7UipVPhba7vOoJDJ861Vr51Pr7FoODUK0Cs0M/Sd0rHT5FxNaIeENE/FtEfC8i9rX5uLHLp/034AwaJ9DlH9+gMXw8/3wKOLuwj0cBJwJXZJeuAM5oOZXuHOAg8N3CmrOZ7Zz8OVJKk8DVLa9TyW5fgSRJkiStgry6CWa33eXzniqbNs2p6FF7i1U+RQSV4WEgG/QuqbRSbXcRcTTwNeARNEKdbcBDwAAwnC27k0ZQ1LGU0iFgVitfRIwAD+QtfhHx18C7I+LB7LXfC1yRUvr37CGX0QiZLo6I/0ZjvtPbgfellPK61A8Ar4mIdwJ/A/wM8CLgeYWXfjfwtxHxDeBK4L8Am4EPdvM1SZIkSVKvzDfzKWUteDE0tOJ7OlLNqXxqMyursmkT9cOHZ/2+S+pe2cqnt9AInv4jjeHcAO9JKW0GnkYjrLmZmdPleum1wKeBS4Av02iD+6X8zmzw+fNptM1dAXwE+DDw5sKam2gETecA1wKvA34jpXRpYc1HgQuAtwHX0Ki8em7LKXqSJEmStGJq85x2l39u1VPn5lQ+tWlXrGzaBGD4JC1R2YHjPw/8W0rpI9AoR8yllK6KiJ8Dvk3j5LffX8oGU0rPark9Dpyffcz3mFuyPS70vF8EnrjImguBCzvcqiRJkiQtq1kDx4uVT9PTwNxqHs2v08onMHySlqps5dOxwLcKt2vMtNuRUtoPfJZGK5skSZIkqQfmbbszfOpaa+VTpU3lU9XwSeqJsuHTQ0Dxv9T9wPEtaw4y97Q4SZIkSVJJ8w0ct+2ue4uddgczlU81wydpScqGT/uAhxdufws4JyJ2AUTEMHAucOuSdidJkiRJaiqGILNmPln51LXFTrsD2+6kXikbPl0GnB0Rm7Lb/xs4Brg2Ij5G48S6RwAfWvIOJUmSJEnA7JlPtt0tTUczn4Yb02UMn6SlKRs+fQD4TWATQErpn4D/CmwGXgjsBd4NvKsHe5QkSZIkMTsEqdt2tySzfq8qlWaVU1Gz8mlsbKW2Ja1LpWLxlNJdwEdbrv1pRPwZsBu4N6WUerA/SZIkSVJmVuWTbXdLUgyfqlu3EpW5tRm23Um90dN3ppRSDbinl88pSZIkSWqo2XbXM1GtQgSk1LblDgrhU+H3XVL3Om67i4aLIuJvImLeWs6IGMjWfKA3W5QkSZIkwQIzn/K2O8OnruTVT5U2J92BbXdSr3Qz8+kXgVcBX0kpTc23KKU0CXwJ+M2IeMES9ydJkiRJytTnOe2OvPLJmU9dycO66mLhk2130pJ0Ez69BLgb+NsO1l4M3An8eplNSZIkSZJmSynNqnyqt2m7w8qnruRh3aJtd4ZP0pJ0Ez49Dbg8pVRfbGG25t+Ap5TdmCRJkiRpRpqcnAmZsO2uFxatfBoeBgyfpKXqJnw6Briti/V3ZI+RJEmSJC1R69Drtqfd2XbXlcUqn6pZ5VPN8Elakm7CpwlgUxfrh7PHSJIkSZKWqLX6Jk3MfLvlaXcl5ZVP87Xdbd4MWPkkLVU34dNtwJO6WP9E4NbutiNJkiRJaqfWUvlUt+1uySp55ZMDx6Vl1U349AXgJyLi8YstzNY8E/h82Y1JkiRJkmbMqXyy7W7pFqt8ymY+pfFxUq22YtuS1ptuwqe/ABJwSUQ8ar5FEXEqcAlQAy5c2vYkSZIkSQD1w4dn3U5tTruz8qk7/cc0xhQPHH982/vzyieA+vj4iuxJWo86fmdKKf0wIl4PvAu4JiI+RqMa6vZsyXHA2cALgSHggpTSD3u8X0mSJEnakJqVT319MD3taXc9cNxb38rEvn0Mn3562/tjcBAqFajXqY+OUs1mQEnqTlfvTCmlP42IQ8A7gZcBv9ayJICDwO+mlC7qzRYlSZIkaeO660/+hENf+hI7X/ACAPp27GD6/vttu+uB/t276d+9e977I4LKpk3UDx927pO0BF3H4imlv4yIjwK/DJwF7M3uuhv4KvDxlNJDvduiJEmSJG1M9clJHvzYx0gTEzx06aUAVLPwqW7b3YowfJKWrtQ7UxYu/XX2IUmSJElaBmPXXUeamABg/Ac/AKBv504mwLa7FeKJd9LSdTNwXJIkSZK0gg5fddWca9UdO4DZp91h292yMXySls7wSZIkSZLWqJFvfGPOtb48fJqcJKXU+DwLn7Dyqecqw8OA4ZO0FIZPkiRJkrQG1cfGGLvuOmB2O111587GJyk1K55su1s+1azyqWb4JJVm+CRJkiRJa9DotdeSpqbo27OHTWee2byeVz4B1LPQydPulk9l82bAyidpKQyfJEmSJGkNGsnmPW1+8pMZPv305vVm5RMzQ8c97W752HYnLZ3vTJIkSZK0Bo1mLXebn/xk+rZvb16vbt0KlQrU6zPhk213y6Y5cHxsbJV3Ih25fGeSJEmSpDVo+sABAAb27mXg5JOb1yubNxMDA6Tx8WboZNvd8vG0O2npOgqfIuLEsi+QUrq17GMlSZIkaaOqHz4MQGXLFvr37KH/2GOZuusu+o8+mujvb4RPtt0tO8Mnaek6fWe6GUglnj918RqSJEmSpEzt0CGg0WYXEZz053/O5J13MnD88VQGBqgDddvulp0zn6Sl6/Sd6cPMDZ9OAX4SOABcA9wD7AGeAOwAvgLs6802JUmSJGnjSPU69ZERoFH5BDB06qkMnXoqADEw0FjXWvlk213PedqdtHQdhU8ppVcUb0fE6cBXgf8JvCOlNFK4bzPwRuC3sw9JkiRJUhfqIyOQGj//r2bhU9G84ZOVTz1n2520dJWSj3sncGVK6X8UgyeAlNJISum/A98A/nipG5QkSZKkjaaWzXuK/n5icHDO/a3hE4ZPy8a2O2npyoZPZwFXLrLmShpteZIkSZKkLhSHjUfEnPsrWXtdPT/tLp/5ZNtdz1XzyqexsVXeiXTkKhs+VYAfW2TNI4G575KSJEmSpAXllU/tWu5g/rY7rHzqubztrjYysshKSfMpGz59GXhhRPxquzsj4iXAL2XrJEmSJEldqGcn3VW2bm17vzOfVk51xw4AagcOMHHrrau8G+nIVDZ8+m/AKPB/IuJbEfHeiHhT9uu3gI8Ah4Hf79VGJUmSJGmjWLTyKWuva4ZPtt0tm/5jjmHLWWdBvc6973//am9HOiKVCp9SSt+lMffpK8DjgfOBt2a/Pj67fla2TpIkSZLUhY7b7vKZT1Y+Las9//k/A/DQ5z7H+A9+sMq7kY48ZSufSCldn1J6FnAS8AvAr2e/npRSelZK6Tu92aIkSZIkbSzFgePtVLLwqT45SarVoF4HDJ+Wy/Bpp7HtZ38WUuIeq5+kri35nSmldBtwWw/2IkmSJEkCatnMp07a7prDxjF8Wk67X/pSDl52GWPXX7/aW5GOOEt6Z4qIAeDZwGnA5pTSH2bXh4BtwP0ppfqSdylJkiRJG0iz8mmxgeNTU7PDJ2c+LZvq9u0ApImJOfelWo2oVld6S9IRo3TbXUT8AnAr8CngT4C3FO5+HHAX0PY0PEmSJEnS/BatfCqcdmfl08qIQqtj0dgNN/C9n/op7r/44tXYlnREKBU+RcRZwMeBCeB3gb8r3p9SuhL4EfDCpW5QkiRJkjaa5sDxxSqfJiebQ8cbD7D6ZrnE4CDQqHxKKTWvj15zDfXDhzn8ta+t1takNa9sLP4m4ABwZkrp/ojY1WbNN4Cnld6ZJEmSJG1Qiw4cz9rr6oW2u+jvJyJWZoMbUD7knZRI09PNFsf62BgwExhKmqts293TgH9OKd2/wJrbgL0ln1+SJEmSNqxm5VMHbXfk4ZMtd8sqr3yC2XOf8vCpbvgkzats+DQIHFxkzQ7AYeOSJEmS1KXFKp/att0ZPi2r/PccstAvUx8fB6x8khZSNnzaBzxlkTVPB24o+fySJEmStGF1PPOppe1OyyciZoaOFyqfUt52lw2JlzRX2fDpEuCsiHhluzsj4gLgscBHy25MkiRJkjaiVK9THxkB5m+7q7Q57c62u+U3q90xUxsdbVwbH589/F1SU9l3p3fROMnuryLipTTa8IiId9KoeHoGcA1wYS82KUmSJEkbRf3wYchOU5u37S4fdl1ouzN8Wn6VwUHqhw/PmvmUVz4B1EZG6NuxYzW2Jq1ppSqfUkqHgZ8E/gF4FvATQAAX0Aie/hF4dkppYr7nkCRJkiTNlbfcxcAAlcKQ66JoV/lk292ya9d2Vy+ETw4dl9orHY2nlPYDvxYRv0Nj/tNRNIaQX5VSuqdH+5MkSZKkDWWxYeMwT/hk5dOyy0+8K7bXFcMnh45L7S353Sml9ADwuR7sRZIkSZI2vOaw8U7DJ9vuVkxz1tY8lU8OHZfaKztwXJIkSZK0DDoJnyr5zCdPu1tReeVTvTBw3LY7aXGlo/GIOBn4XeDxwMOAdu90KaX0iLKvIUmSJEkbTT2rnqls3TrvGtvuVkcsVvlk+CS1VerdKSKeC3wSGACmgHuB6XZLy29NkiRJkjYe2+7WroqVT1IpZd+d/hioAS8GLkkp1Xu3JUmSJEnauDoaOJ612CXb7lZU28qn8fHm51Y+Se2VDZ9OBT6SUvpYLzcjSZIkSRtdt5VP2Ha3YvLKp5RVPtWnppq//zDTMilptrIDx+8GxhddJUmSJEnqSieVT/mpa/VC2x2GT8suD/3qWeVTKrTcgZVP0nzKhk9/B/xcRAz1cjOSJEmStNHVsuqZ6kIDx227WxXRWvlk+CR1pGz49BbgBuDSiDgrIuaP5CVJkiRJHWuGTx203VGrNatwbLtbfpWWmU+t4ZMDx6X2SoVPKaUp4C+AM4AvAw9FRK3NR7sT8CRJkiRJ86gdOABAdefOedfkFTgA9dHRxjXDp2UXhXZHmPm9z1n5JLVX6t0pIl4M/B8a4dU+4C7AoEmSJEmSlmj6oYcA6Nu+fd41lUL4lFdK2Xa3/FpPu7PySepM2Wj8zcBDwHNTSlf1cD+SJEmStKF1VPlUrVLZsoX64cPUHnywcc3Kp2WXh3711plP1SrUas0gUNJsZWc+nQz8g8GTJEmSJPVOfWqqWT1TXaDyCWYGkk8bPq2Y+Sqf+nbtaty28klqq2z4dBtQ7eVGJEmSJGmjq2Utd1QqC552B4Xwaf9+wLa7lTDfaXf9Rx/dvJ2fPihpRtnw6SLg3Ig4qpebkSRJkqSNrNlyt307UV345/3VbdsAK59WUn7aXb218ikLnwBqIyMrvzFpjSv77vRx4CzgqxHxduBa4GC7hSmlW0u+hiRJkiRtKMXwaTGVrPIpf4zh0/Kbr/KpumULMTREGh9vtN518OcnbSRl3532AQkI4MMLrEtLeA1JkiRJ2lCmsyCpb8eORdfmlU/U64BtdyshHzjeOvOpMjxMdetWpsfHHToutVE2GPowjWBJkiRJklRSfWqKSiE0alY+dRI+tcyEsvJp+eUDx/PT7lIWPsXwMNUtW5i+7z7DJ6mNUu9OKaVX9HgfkiRJkrShHPzSl7jtggt42JvfzM5zzwVmKp86Cp/yyqec4dOyi/kqn4aGqGzZ0rjmiXfSHKUGjkfEvoi4sNebyZ77tyPiuog4mH1cERE/V7h/KCLeFxEPRMThiLgkIva0PMeJEfGZiBiNiHsj4l0R0dey5lkR8c2ImIiIH0XEK9rs5fyIuDkixiPi6xHx1OX4miVJkiRtPCNXX02ammL0m99sXquVabvL2Ha3/CotlU+z2u6y8Klm+CTNUfa0u93ActUS3g68HjgTeDLweeCfI+L07P73AOcCvwL8FPAw4J/yB0dEFfgMMAA8A3g58ArgbYU1J2drvgA8Afgz4K8i4jmFNS8G3g28FXgSjaHql0bEMb3+giVJkiRtPLWHHgJmAgzobuC4bXcrb07l0/g40AifrHyS5lc2fLoOOLWXG8mllD6VUvqXlNIPU0o/SCm9ETgM/HhEbAfOA34vpfT5lNLVwCuBZ0TEj2dP8bPAY4CXpZSuSSl9FngTcH5EDGRrXg3clFJ6XUrpeymlC2mc4PfawlZ+D7gopfTBlNJ3s8eMAq9ajq9bkiRJ0pHj3g98gJvPP580PV36OZrhUxZgQGHg+M6diz7e8Gnl5TOfWk+7s/JJWljZ8OmPgXMj4qd7uZlWEVGNiF8FNgNX0KiG6gcuz9eklG4AbgWenl16OvDtlNI9hae6FNgGnF5YczmzXZr6sqgDAAAgAElEQVQ/RxZSndnyOvXs9tOZR0QMRsS2/APYOt9aSZIkSUeuB/7xHzn81a8ysW9f6eeoHTwIzFP51EHbXcXwacXlp93V25x216x8cuC4NEfZd6edwGXAZRHxSeAq4B7anICXUvpwt08eEWfQCJuGaFQ9/WJK6bsR8QRgMqV0oOUh9wB7s8/3Zrdb76eDNdsiYpjG11edZ81pC2z9DcAfLHC/JEmSpPUgq3hKtVrpp8grn1Kh8im/1lHbnTOfVlyx8imlRH10FJhd+TRx002klIiIVduntNaUDZ8+RCNoCuCF2QfMDp8iu911+AR8n8Yspu3ALwN/GxE/VXKvK+kdNOZE5bbSmGElSZIkaR1J9Xrj11603RUqn6b37wc6bLtrDZ+sfFp2eeUT9TpMT8+qfNrytKdx7wc+wKGvfIX7/vIvOea3fmsVdyqtLWXfnV7Z0120SClNAj/Kbl4dEU8Bfhf4KDAQETtaqp/2AHdnn98NtJ5Kt6dwX/7rnjZrDqaUxiKiBtTmWXM380gpTQAT+W2TbkmSJGl9ykOnsuFTSmnOzKc0NdUcVu3A8bUpr3yCxol3xfBp+PTTOfb1r+eud7yDe9//foZOPZVtP72sk2qkI0apd6eU0t/2eiOLqACDwNXAFHA2cAlARDwKOJFGmx7Zr2+MiGNSSvdm184BDgLfLaz5+ZbXOCd/jpTSZERcnb3OJ7PXqWS3L+z1FydJkiTpCJO325Vsu0vj46SpKWCm8imfAUXEnKqmdipDQ8TAQHP4tW13y68YPqWJieafXQwPA7DrxS9m/IYb2P+JT3D4a18zfJIyay4aj4h3AJ+lMUR8K/BS4FnAc1JKD0XEXwPvjogHaQRK7wWuSCn9e/YUl9EImS6OiP9GY77T24H3ZZVJAB8AXhMR7wT+BvgZ4EXA8wpbeTeNdr9vAFcC/4XG4PMPLssXLkmSJOmIkc96KjvzaTqreoKZyqe85a66fTtRrXb0PNWtW5l+4AHAyqeVEJUK0d/fqFKbnCTllU+bNjXXDD3ykUAhTJS09sIn4Bgac6KOBR4CrqMRPP1rdv9rgTqNyqdBGqfU/af8wSmlWkQ8H3g/jUqmEeBvgTcX1twUEc8D3kOjne924DdSSpcW1nw0Io4G3kYjwLoGeG7LKXqSJEmSNpiUUmPmD+Xb7mqF8CkPMLoZNp6rbttm+LTCYnCwET6NjDT//CtZ5RNAJataKwaM0kbX0btTRNRpBD6PSSn9ILs952S7NlJKqat3wJTSeYvcPw6cn33Mt+YW5rbVta75IvDERdZciG12kiRJkooK1U5lK5+KVTFpepo0NcX0gcZY274dOzp+nkph7pNtdysjb70rBojF8KkvC5/qVj5JTZ0GQ1+mETaNttyWJEmSpA1lVuDUg8onaLTe1fK2uy7Cp1lDx618WhGVwUFqQC0LC6lWZwV/eeXatOGT1NTRu1NK6VkL3ZYkSZKkjaIYPpVuuztwYNbt+vj4TNtdN+FTYTC5bXcro1n5lIVLleHhWSed538mrQGjtJFVOlkUEf8UES8q3H5mRJy4fNuSJEmSpDWqEDj1ou0OGifelWm7q9p2t+Iqg4MAzT+vYssdzFQ+1Q8fLv33Q1pvOgqfgBcApxVufwF4Rc93I0mSJElrXMqGjcMSwqeWqpg0Pt6shrLyaW3LQ778z7AyNDTr/mYgmBK1w4dXdG/SWtVp+HQA2Fa4HfMtlCRJkqR1rRczn9pUPtUOHQJa5jgtwvBp5UVW+VSbp/Ip+vupbNrUWOPcJwnofOD4d4GXRMRVwF3ZtYdHxDMXe2BK6ctlNydJkiRJa01xzlPpmU9tBo7XsyqZypYtHT+Pp92tvHzm03Q2IL41fIJG6119dLTx53zCCSu6P2kt6jR8ehvwSeDvCtdenn0sptrtpiRJkiRprZo1cLxk2910a/hk5dMRI5/5NHnrrQD07dkzZ01161am7rrLyicp0+lpd5dFxKOBZwPHAW8BvpR9SJIkSdLGUZj5xFIHjkdASo3T7rLKp2oXlU+zBo4bPq2IvPJpIgufBo4/fs6afOi44ZPU0PG7U0rpFuCvASLiLcAXU0pvW6Z9SZIkSdKa1IvKp3xeUN/u3Uzfdx/1sbFSbXezKp9su1sReeVTPu9r4Ljj5qxphk8tFW7SRlU2Gj+ZxhBySZIkSdpQejLzKauI6T/mGKbvu480NjZT+dRN211xbdWJJyshr3zKta18yv5crHySGkqFT1kVlCRJkiRtPMXKpxLhU318nDQxAUD/3r2Mfec7jeHV2fN2Ez71H3ssW3/yJ+nbvZsIDyVfCflpdznb7qTFlW4KjoijgVcCTwF20H6weEopnV32NSRJkiRprUlLnPnUbMXq66PvqKMAmLrvvsa1apUYGur4uaJS4aT3vrfrPai8SrHyqVqlv93AcdvupFlKhU8R8Tjg88BOYKF4PZV5fkmSJElaq2a13ZUJn7JqmOq2bVQ2bQJgOgufqlu2WMG0xhUrn/qPPbbtoHfb7qTZKiUf96fAUcAf0Zj/1J9SqrT5sOlYkiRJ0vqyxLa7vBqmb/v2ZpVTHj5Vumi50+ooznxqN2wcbLuTWpVtu3s68MmU0pt7uRlJkiRJWuuWetrddBY+VbdtozI8DMy03VW7OOlOq6NSqHxqN+8JbLuTWpWtfJoEbuzlRiRJkiTpSDArcFpC5VN1+3YqWeVTbf/+xjXDpzVvVuXTfOHTtm2AlU9Srmz49CXgyb3ciCRJkiQdEZZY+TR1zz0A9O3a1ax8ylUMn9a8WZVP87XdGT5Js5QNny4AHhsRF/RyM5IkSZK01qUlznyauLHRRDJ48snNyqdc1ZlPa143lU9pYoL6+PiK7Etay8rOfHojcD3wxxHxauAaoF2km1JK55XdnCRJkiStNUud+TSxbx8Ag494xKwqKrDt7kgQHcx8qmzeDNUq1GrUHnqI+vg4fTt2rNQWpTWnbPj0isLnp2Qf7STA8EmSJEnS+rGEmU/1qSkmbr0VaIRPk7fdNut+T7tb+ypZ5VNl69ZmhVOriKC6bRu1/fu58+1v59BXvsLDL7qILU95ykpuVVozyoZPJ/d0F5IkSZJ0hFhK5dPkLbfA9DSVLVvo37OH6eyUu5yVT2tf/7HHAjB82mkLrsvDp0Nf+QoAY9dfb/ikDatU+JRSuqXXG5EkSZKkI8ISZj415z2dcgoRMXfguJVPa97Qj/0Yp1x88bzDxnPV7dtn3a6PjCzntqQ1rWzlkyRJkiRtSLMCpy4rn8YL4RMwJ3yy8unIsOmMMxZd09qSZ/ikjazsaXcARMSvRcS/RsR9ETGR/XpZRLy0VxuUJEmSpLUk1eszn3cZPuXDxoce8QjA8Gk9aw2faoZP2sBKhU8RUY2IS4APA2cDm4E7s1+fDVwcEZdExJLCLUmSJElac3rRdpeFTzE0NOt+2+7Wj02PfSwAw6efDlj5pI2tbDj0O8AvAl8FzkopbUopnZxS2gQ8A/h/wAuA/9ybbUqSJEnS2lAMnDoJn1K9zoHPfIax73ynedJds/KpJXyqGj6tG0f96q/yqMsvZ9dLG41BVj5pIys78+nlwA+As1NKU8U7Ukr/HhHPBq4DXgn8+dK2KEmSJElrx6xWuw7a7kauvJLb3/jG5u3Kli30HXMMAFGpEIODpIkJwLa79SQqFfp376ayaRNg5ZM2trKVT6cC/7c1eMpl1z+VrZMkSZKk9aPLmU+Tt98+63Z+0l2uOPepYvi07uR/poZP2sjKVj5N0pjvtJDN2TpJkiRJWje6bbubfvBBAIbPOIPK8DBHvehFs+6vDA1RA2JggMrgYE/3qtVX3dz41tm2O21kZcOnbwEviog/Sind2XpnRBwLvAj45lI2J0mSJElrTbHaqZPKp+kHHgBgy9Oexp7XvGbO/Xnlk1VP61MlC5+sfNJGVrbt7t3ALuAbEfG6iHhyRJyQ/XoBcDVwVLZOkiRJktaPYuDUSeVTFj717drV9v78xDvnPa1PxfAppbTKu5FWR6nKp5TSp7KQ6X8B72y5O4Bp4IKU0qeXuD9JkiRJWlNSlzOfFguf8sonw6f1KW+7IyXqY2NUswHk0kZStu2OlNK7I+KTwK8BTwC2AQdptOT9XUppX2+2KEmSJElrR9cznxYLn7LKp8rWrT3YndaaGBqCSgXqdeojI4ZP2pBKh08AWcD0hz3aiyRJ6tI9730vo9dfz0kXXkilv3+1tyNJG0O3M5+ygeOLVj4ZPq1LEUFl0ybqhw835j4dffRqb0lacV3NfIqI4YjY1sG6bRExvNg6SZJUXpqe5v4Pf5iRr3+d8RtuWO3tSNKGMStwWiR8qo+PUz98GIC+o45quyaf+eTA8fUrb6ksDh1/4B//kdte/3rS1NRqbUtaMR2HTxGxG7gT+ExEzPu4iKgCnwFuj4idS9+iJElqZ/L225v/YJ3ev3/R9bXRUW57/et56N/+bbm3JknrW7HyaZG2u7zlLgYG5m2rc+bT+pcPHa9l4dP0gQPc/a538dDnPsfotdeu5takFdFN5dN5NOY6vSalVJ9vUUqpBpwP7AD+v6VtT5IkzWf8xhubn+ff3Czk4Oc/z0Of+xz3/dVfLee2JGndS92ET4WWu4hou2bT4x8P1SqbnvjE3m1Sa0rzxLusCu7Apz898wOkhx5atX1JK6WbmU/PB65OKS0ay6aUrouIq4BzgT8uuzlJkjS/iX0zZ3vUsm9uFjL+ve8BM98ISZLK6abtbvr++4H5W+4Adp57LtvPOac5eFzrT37iXW10lJQS+z/xieZ9NcMnbQDdVD49Bvj3LtZfCTy6u+1IkqROTRQrnzoIlMayuVC1/ftJKS3bviRp3eti4HjzpLvduxdcZ/C0vhUrn8auu27W/8NrBw+u1rakFdNN+LQV6Oa/ioOATcuSJC2T8S7Cp1SvM/797zc+n5ykPjq6rHuTpPUsdRM+5W13C1Q+af1rhk+jo+z/5Cdn3Wf4pI2gm/DpALCni/V7ssdIkqQeS9PTTN58c/P2YuHT5B13NOdMQKP6SZJUTlczn/LKp127lnVPWtuabXeHDzPyrW8BMPy4xzWu2XanDaCb8Ol64OyYb0peQbbmbOA7ZTcmSZLmN3nbbbOOZl4sTBrPWu5ynZyOJ0maR7HaaXp6wVbmTtvutL41K59GRpi6+24ANj32sYDhkzaGbsKnfwZOAn6ng7WvydZ+YrGFkiSpe3nLXWQzQharfBrLho3nrHySpPLmVDvV5z0MfCZ8su1uQ8vDp8nbbyeNjwMwdOqpgG132hi6CZ/+ErgN+JOIeEtEbG1dEBFbIuIPgD8FbgEu6s02JUkSwOTdd/PQZZc1T67bnB3LPb1/P2mBb37yeU85K58kqbzW99uF5j7ZdieYabvLB4337d7drIYzfNJG0NfpwpTSWET8B+Ay4E3ABRFxNXB7tuQ44MnAMHA/8IKU0niP9ytJ0oZ259vexuGvfa15e/OTn8zhK66AWo3awYP07dgx5zEppWbl08CJJzJ5662GT5K0FC1hU5qehoGBtkttuxPMVD7lLXf9xx5Ldft2wLY7bQzdVD6RUroWeBLwERrB1U8CL8k+nplduxh4Ukrput5uVZKkjWPyzju5533vY+r++2ddn7rrrlm3h047jeq2bcD8rXfT991H7cEHoVJh81OfCth2J0lLMafSaZ6h4/XxceojI4BtdxtdHj7l+o89tvn/byuftBF0XPmUSyndAbw8Il4NPAXYm911N3BVSmmsh/uTJGlDeuDii3ng7/+eGBjgmN/8zeb16eyno0e96EUQweanPpXqUUdRO3iwETCdcsqc55q45RYABk44gYFjj208j+GTJJXWOvNpvra7vOopBgaobNmy7PvS2lVtCZ8GCpVP9ZER0tQU0d+/GluTVkTX4VMuC5m+3MO9SJKkzGRWll87dKh5LaXU/Ono0eedR/+ePUDjp+mTN9/c/CanVS2riOrbvZvqzp2Na4ZPklRea9vdfOFT/v67axcdHBqudaw1fOw/9liqW2fGKNcOHbI6TutaV213kiRpZeRBUn4iDkB9dLTZ2pGX6sNMK8d81UzNb3527qQvC5+sfJKk8uYMHJ+n7S7/AUIxZNDGVNm0adbt/mOPJarVZihl653WO8MnSZLWoOls1lN9YqJ5LR9IGgMDxNBQ83ozUJpv5lMWNPUdddTM2gMHer9pSdog5oRN81Q+5T9AqAwPL/eWtMZVWyqf8jZ4h45rozB8kiRpjUkpNcOnVAyfsp+KVrdvn9W+Uc0rn+YJn/IWu+rOnbbdSVIvtDvtrt2y0VFgbtWLNp52lU9g+KSNw/BJkqQ1pn7oEGlysvF5oe2uGT4VWu6gMUsEGrOd6uPjc74Jatd2Vx8ZoZ69hiSpO60znuYLn+qGT8rE4CD0NUYuV7ZsabZiNk+ste1O65zhkyRJa0xxcHhq03aX/5Q0l898mrz9dn70K7/CD1/4wlnfGBXb7ipbtzb/8Wv1kySV1NpmN1/b3VjjIHDb7hQRzRPv8qongL78xDvDJ61zhk+SJK0xU1nLHTCrOqkZPrVWPmXh0/gPfsDkbbcxecsts07Jyyufqjt3EhH07djRuG74JEmlzKl8mid8arbdGT6JmQq4gUL41Kx8su1O61yp8Ckifj8ijun1ZiRJ0sywcZh92l1x5lNRu6OZ81YPmKlwytdVFxlQLklaWKdtd1Y+qSg/2a5/797mtTx88rQ7rXdlK5/eAdwWER+PiOf0ckOSJG10xfCp3Wl3HYVP2Tc8aWqq+bh83lOfQ8claWk6nfmUh0/OfBJQzf4eFNvuHDiujaJs+PQbwDeBXwL+JSJujog3RcTxvduaJEkb07wzn+YZOF7ZunXmG5tK43/t+Tc8zTL+SqX5D9w8fJo+cKD3m5ekDWBOm91ibXeGTwIGTjoJgOHHPKZ5rVn5ZPikda5U+JRS+puU0tOBxwJ/AWwG3grcFBGfiohfiAjnSUmSVMKsyqdi211ewdRS+RQRnPC//hfHveUtDGb/sM3Dp1o+72n7dqJabXyezXyy8kmSyul05pNtdyp62BvewCP+4R/Y/NSnNq81w6fCrEZpPVpSQJRS+m5K6bXAw4CXAF8Efh74BI22vLdHxClL3qUkSRtIceB4sfJpep6B4wBbn/lMdr7gBc1vcJqVT/m8p6zaqfi5A8clqaRuB45b+SQaIeTwaacREc1rtt1po+hJdVJKaSql9NGU0jnATwB3AccC/x34QUT8S0Q8rRevJUnSeldsu6t30HZXFFn4lP+0PR8qXpwL5cBxSVqaPGyKvr7G7flmPnnanRbRDJ8cOK51rifhUzT8fER8gkb108OAW4C3A58DngN8NSL+Yy9eT5Kk9Wy6pfIppQTMP3C8qLXyKW+tqxYqnwaOb4xonNi3r4e7lqSNoxk+DQ42Lsx32l3WOm34pPkUZz6len2VdyMtnyWFTxFxYkS8lUbQ9Cng+cBngecBp6SU3pxSej5wBnAr8OYl7leSpHUtTU1RaxkEnrfeNSufFgqfstaO/Kftzba7QuXT8OmnAzB5yy3+pFWSysjDp4EBwLY7ldesZq7XqY+MrO5mpGVUKnyKiF+OiM8B+4A3AQG8DXh4SukFKaXPpvzHtDRmQwEXAyf1YM+SJK1b0w8+CCk1T62DRutdfXy8+RP0hdru5sx8ytvuijOfduxg4IQTABi9/vrefgGStAHkYVNlaGjW7VZ1wyctojI01Kyg8xRarWdlK5/+EXg2cCnwH4CTUkpvTSndscBjbgD+X8nXkyRpQ8jnPfXt2gX5LJHx8ZkKpWqVypYt8z5+3ra7QuUTwPBjHwvA2He+s+B+bAGQpLnyGU/R3z/r9px1nnanDgw+4hEAHPzXf13lnUjLp2z49Ec02uqel1L6VEpp0X+ZppT+PqX00yVfT5KkDSGf99S3ezeV7Ceh9YmJWcPGi6fktOqk8gkK4dO3vz3vc91/8cV875nPZOyGG8p8KZK0fmXB/EIzn1K93nwvtvJJC9n1kpcA8MDf/z31yclV3o20PEqFTymlN6WUbu31ZiRJ2uimssqn/t27m9/UpImJmWHjC7TcwQIzn1rCp01nnAE02u4KnfKzHPziF6kfPszhK64o86VI0rrVbLtbYOZTPWuVBiuftLDtz30ufUcfzfR99/HQZz+72tuRlkVPTruTJEm9kVc+VXftmql8Gh/v6KQ7mJk/0lr51Np2N/SoRxF9fdQefJCpO+9s+1y17LHz3S9JG1FKaWbg+AIzn/KWOyKa66R2Kv397HrpSwG4/8MftuVd61JH4VNE7Cv5ceNyfwGSJK0nk7c2CosHjjtupvJpcnJW291Cmm134+PUp6aoHzoEzK18qgwOMnTqqQDc/Z73sO+88xj51rdmrcnnT03esdBIR0naYApBUz7zqV3bXfOku+HhBdulJYCjXvhCKps3M3HjjTx02WWrvR2p5zqtfKrQONGu2w8rqyRJ6sLELbcAMHjSSbNnPnVa+VRou8uHjVOptH1cPvfp4OWXM3r11Rz49Keb99ULgVdr+JSmp6kdPtztlyZJ60KxKiV/n16o8smWO3Wium0bu1/+cgDuufBC6lNTq7wjqbc6CodSSg9PKZ1c5qPbDUXEGyLiqog4FBH3RsQnI+JRLWuGIuJ9EfFARByOiEsiYk/LmhMj4jMRMZo9z7sioq9lzbMi4psRMRERP4qIV7TZz/kRcXNEjEfE1yPiqd1+TZIkdSKlxOTNNwMw+PCHz1Q+jY8znYVPfZ1WPo2NUcuObK5u305U5v4vf/tzngMRM4HVyEjzvrxdDxptd8Vvtm5+zWv4/nOe05wnJUkbSrHyKZ/51KbyKZ+957BxdWr3r/86fbt3M3X77ez/2MdWeztST63FyqSfAt4H/DhwDtAPXBYRmwtr3gOcC/xKtv5hwD/ld0ZEFfgMMAA8A3g58ArgbYU1J2drvgA8Afgz4K8i4jmFNS8G3g28FXgScC1waUQc08svWJIkgNr+/Y1qowgGTjxxZn5T8bS7xSqfCuFTM7DasaPt2s1nnsnpV13F3te9rvGY7BslmJk9BZCmppi+777G5ykxes011EdGGP/hD8t8mZJ0RCsGTQtVPhXb7qROVIaHOebVrwbg3osuavv3SjpSrbnwKaX03JTSh1JK30kpXUsjNDoROBMgIrYD5wG/l1L6fErpauCVwDMi4sezp/lZ4DHAy1JK16SUPgu8CTg/IgayNa8GbkopvS6l9L2U0oXAx4HXFrbze8BFKaUPppS+mz1mFHjV8v0OSJI2qoms6ql/714qQ0Mz39SUabsbG+vohLzo66O6ZQsAtXkqn2Cm9a4+OkrKTnDKAylJ2khSm8qndjOf8vdKwyd1Y+cLXkAMDFDbv5+pu+9e7e1IPdO3+BKIiP+YffqJlNKhwu1FpZQ+XGpnM/J/Zef/Cj6TRjXU5YXXuCEibgWeDvx79uu3U0r3FJ7nUuD9wOnAt7I1lzPbpTQqoMhCqjOBdxRepx4Rl2ePnSMiBoHBwqWtHX+VkqQNb6LQcgc02+7q4+PdDxwfHZ0Jn+apfGr3mFw+bDw3eccdbH7Sk2ZVRE3de++CzytJ61KhDTkfOL5g5ZNtd+pC9PUxcPzxTOzbx8QttzBw3HGrvSWpJzoKn4APAYlGsHOocHshka0pHT5FRIVGGPTVlNL12eW9wGRK6UDL8nuy+/I197S5nw7WbIuIYWAnUJ1nzWnzbPkNwB/M+wVJkrSAyZbwKW+7SxMT1LJT66pbF/65xqyZT50GVpsbne3ztd0BTGWVT8WKKCufJG1Ezba7vj6ir/HtVLvwyZlPKmvgxBOZ2LePydtuW+2tSD3Tafj0KhpB0l3Z7Vcuz3bmeB/wWOAnVuj1luodNGZE5bYCt6/SXiRJR5j8pLuB1sqn4synxYKk7JucND7ePO1u0Va9duFTFjJFfz9paqrZdjer8snwSdIGlAdNUa3OhE/t2u487U4lDZx4IgCTt966yjuReqej8Cml9KGW23+7LLspiIgLgecDz0wpFQOcu4GBiNjRUv20J7svX9N6Kt2ewn35r3varDmYUhqLiBpQm2dN2+bblNIEMFH4Gub56iRJmqu17a5SaLurZ+FTpcPKJ5hpi+t4SHlx5lPWdjf06Eczdt11M+FToR1v2rY7SRtRHj5VKlCtNq61CZ8cOK6yBk84AcDKJ60ra27geDRcCPwi8DMppZtallwNTAFnFx7zKBpDya/ILl0BnNFyKt05wEHgu4U1ZzPbOflzpJQms9cqvk4lu30FkiT1ULG6qDnzKW+7Gx+fabtbpPIpr5YCmoNKO267GxsjZbNM8pBp0+MeB9A2fLLySdJG1GyxK1Y+tWu7yyufbLtTl/LKpwkrn7SOrLnwiUar3cuAlwKHImJv9jEMkFJ6CPhr4N0R8dMRcSbwQeCKlNK/Z89xGY2Q6eKIeHxEPAd4O/C+rDoJ4APAKRHxzog4LSL+E/Ai4D2Fvbwb+M2IeHlEPJrGwPLN2etJktQzk3fcAdPTVIaH6Tum8bOTvPJp+sEHITVGLS4aPlUqzdBq6p7G2MLFKp+qWfhEStTzk+zy8OmMMxq3772X+uTk7Mqn++4jpcVGQErS+tJsu+vrI7LKp3bhk213Kmsgq3yauv32tn+3pCNR6fApIk6IiP8dETdGxFhE1Np8zK0/Xdxv0zjh7os0ZkzlHy8urHkt8GngEuDLNNrgfim/M6VUo9GyV6NRpfQRGoPP31xYcxPwPBrVTtcCrwN+I6V0aWHNR4ELgLcB1wBPAJ7bcoqeJElLNnFTo9B34KSTmm3beRVTXmEUg4PNQGoh+U/Zm213iwVWQ0OQvWbeepfPdhp85CMb96fE1F13zQqf0tRU80Q9SdowOpz55Gl3Kqt/715iYIA0Pd2sYpaOdJ0OHJ8lIk4Bvk7jRLjvAPfMxHEAACAASURBVIPALcA4cArQTyPQaT2RblEppUUHJaWUxoHzs4/51twC/Pwiz/NF4ImLrLkQuHCxPUmStBQT+/YBMy13MHPaXX6q3GIn3TUfNzxMDZrfIPUtUvkUEVQ2baI+MkJ9dJT6xAT1w4cB6N+9m4HjjmPixhuZvOOOWeFTvre+HTs62pckrQfNSpRFZj4l2+5UUlQqDBx/fOPEu1tvZeC441Z7S9KSla18+gMa1Ulnp5Qen137YErp0cDDgf9Loz3tl5e8Q0mSNoDRb38bgOHTT29eyyufmuHTIhVMudYWj2oH4VD+zVF9dHTWSXeVrVsZPOkkoBGQNcOnSuOfEFMOHZe00XTYdufAcS2Fc5+03pQNn54N/EtK6UuFawGQUiq2yP3PJexNkqQNIaXEWBY+5TOWACoDA8DM0NqOK59afsreSWjVDJ9GRpotd327dhERDP7YjwEw/sMfNsOnvEJr2qHjkjaYWQPH+/tnXyuoGz5pCfK5T5OGT1onyoZPu4EbCrengea/dLOh3v9KY+6SJElawNSddzZCnb4+hk47rXk9Hxyeq3TRdtdUrVLZsmXxx+Qn3hUqn/p27QJg6JGPBGD0W98iTU42rj3qUY29Gz5J2mBScebTQm132QEOhk8qYzCrfJq87bZV3onUG6VmPgH302irK95+eMuaacAhEJIkLWLs+usBGD711OacJ2DOcPEybXfVbduaA8wXUs0qn2qjo82f1jfDp1NPBWZ++lrZsqX5E9lp2+4kbTD5cPGoVpsznxZsu3Pmk0rI/z9r253Wi7KVTz8EHlG4fSXwnGwQORFxNI15TzcubXuSJK1/o9ddB8BwoeUOZmY+5cqGTx09pth2l7XW5eHTwPHHz6rC6tu1i/6jjwasfJK0AdXrjV8LlU/tTrurGz5pCfLwaerOO0kprfJupKUrGz59FvjpiMgrm/4M2ApcFxFXAT8A9gLvXfoWJUla3/Jh45se97hZ1ystbXelwqdFTrprPqY4cLwlfIpqlcFTTmmu7du9m74sfGo382nshhuY9GhoSevUrLa7vr5Z12aty0+7s+1OJeSHhaTJyWYLp3QkKxs+vR94FjROck4pfRH4VeAW4LHAPcDvpJQuWvoWJUlav+qTk4zf0Bij2Fr5NKftrsTMp44Dq+LMp8LA8dxQNnQ8v95/zDHA3NPupu6/nxtf9jJufvWrO3pdSTriFNru8vCpdeZTqtebh0VY+aQyKps2Qfb3q3bw4CrvRlq6UjOfUkoHga+3XPsY8LFebEqSpI1i/PvfJ01OUt2xo1lin5vTdlfitLuuK59GRmYGjh91VPP+fOg4NMKnZuXTAw+QarVm68nUHXfA9DSTN99MfWzMn/hLWneKp93NN/OpXqhU8X1QZUQE1a1bqe3fT+3gQfr37FntLUlL8v+zd9/xkZVl/8c/95maupvsbrZmG7u0LRRBekcQpPhTxIaCPOjPDoo/1OflIygWiuKDIqBYUFBAlKLgAzyISJMFls6yje3JbpJNL9Pn/P44575zZjKTTLKTNnu9Xy9eJjNnZu4gyZz5nuu67pFWPgkhhBCiCHqefx6A8hUrBgwGL0bbnX+4M58ikf62u+nTzf3ZlU/+2lqwLEinTVgFkOzsNF/HGxoKem0hhJhMcu12lx0+6ZY7lBqwc6kQhdIXkFKe91YhJqsRVT4ppeYC7wcOB/SZaQvwInC/bds7i7M8IYQQonTZtk3Hww8DUP2e9wy4P7vyyRpJ293Uwjae9bltd6kcA8ehf8c7fbvy+bAqKkh3d5uhupB5ghxvaMgIrYQQoiTkmPmU3XZndrorKytox1EhctEXnaTtTpSCYYdPSqnvAFcAQSD7L+kngR8ppX5o2/bVRVifEEIIUbKia9YQ37IFFQ5TffLJA+4fMPNpDAaOp9rbSff0AJnhk3/aNHw1NaTa283tVjjshE/6Cj+Q6ugwX8d37CjotYUQYjLJ2XaXFT7pv6N6np4QIyHhkyglwwqflFLfB74JxIA7gSeBRvfuOcBJwIeAq5RSPtu2ryraSoUQQogSY6qeTjwRX2XlgPsHzHwaSfg0zLa72PbtzmsHg1hZa5r28Y/T/cQTVBx8sPMYt5UkI3zynCAnJHwSQpSgQna7S7a3A+CvqRnbxYmSIm13E0di926n8lsqGUes4JlPSqnFOBVPm4GDbNu+0Lbt39q2/aj7z29t2/4kcBCwDfhPpdSi0Vm2EEIIMbnZySQdjzwCwNQzz8x5jLIsVDBovh/Vyif36nxip9M5n+sEq+6SS9jnj38069Cv490COqPyqbERIYQoOQXMfNKz8HyejRuEGC6pfJoYOh9/nHWnnkrrnXeO91ImteEMHL/QPf4Ttm2vz3eQe98FOFVVn9yz5QkhhBClqffll0m1teGrqaHyqKPyHmeqn3y+grfrVntQ+aQ/VHlb7vK+jq588oZPnhNkabsTQpQib9udmfmUFT6lcuwaKsRwSfg0McQ2bgQg6v6vGJnhhE/HAG/atv3cUAfatv0s8AZw3EgXJoQQQpSy6IYNAFQceigqEMh7nG5t81VVFVzq7fOEVIVWPvmygq1CwicrR/iU9FY+NTRg23ZBry+EEJOFabvz+/POfEpK+CSKQMKnicFOJDL+V4zMcMKnA4AXhnH8C8D+w1uOEEIIsXeIb9sGQHD+/EGP0213vgJ3uoOsyqdhtt1phXxgyhU+eU+Q7WjUXP0XQoiSoaucLKt/5pOET2IU6PfwpMx8Glf691vCpz0znPBpKtA8jOOb3ccIIYQQIkuh4ZPe8W444ZMZXu7z5RxknvN1PIEVgH/69IIfk2/mE0jrnRCi9OgPooXMfJLwSewJv1Q+TQhS+VQcw9ntrgxnl7tCxd3HCCGEECJLzA2fQkNVPrnVRVaBs5sAAnV1TP/Up5yh4e4Ho6EMqHzaw5lP/hkzSLa0EN+xg/KDDip06UIIMfGl00DmbndkVT6lZOC4KAJpu5sYpPKpOIYTPgkhhBCiCNKJhNlVruDKp2GETwCzLr10WMcPqHwazsynSMT530SCdG8vAGXLltH95JOy450QouSYKifvzKfsyqf2dueQmpoxXZsoLbrtLiVtd+PKhE9ZIbMYnuGGTxcopY4s8Nglw12MEEIIsTdI7NgB6TRWefmQ7W1qhOHTcCmfDxUOmxa64YRP+jFpfWVWKcL77eeET9J2J4QoMWbguGVltN01/+pXKKWYfvHFJFtbAWm7E3tGv/ene3qwU6mCq5lFcUnbXXEMN3xawvBCJdniRgghxF7PTqfBts1Jo265C9bXD7mDnXe3u9Hmq6ggOYzwKbvtTu9056uqMu2EEj4JIUpNxswnPXA8GqX5ppsAqD71VOx4HJDwSewZ03Jv26R6evAXuImIKC5puyuO4YRPi0ZtFUIIIUQJSvX20vrHP9J6552Elixh8a9/DRQ+bBw84dMoVz4BWOXloK/WD6ftzg2f9EwK35Qp5meLbdqEnU6jrOHscSKEEBOYO/MJv79/5pNH3yuvAE47c3ZLsxDDYQUCWOXlpPv6SHV1Sfg0TiR8Ko6CwyfbtreO5kKEEEKIUmInk7zzkY8Q374dgL7Vq00IEy9w2DhAzbnnkmxtpfqUU0Z1veCGTzitftkDyHMe736o0jOf9E53vilTCO+/P1Z5Oan2dqLr11O2//6jtGohhBhbpu3O5zMzn7x6V68GZNi4KA5fdbUTPnV2Qn39eC9nryRtd8UhlyGFEEKIUZDYtcsJnjwfTHRIExtG5VPlUUex6LbbCgqq9pQOnPy1tUO2A8LAmU96IKpvyhSsQICKww8HoOff/x6N5QohxPjwtt3lCp/cyidpuRPFIDvejT+pfCoOCZ+EEEKIURBvaACcuU46gEr39Dj3DSN8Gku68mmoIeha9swnb/gETnAGEj4JIUqL2dnOM/PJK+HOupPwSRSDhE/jTyqfikPCJyGEEGIUmPBp7lx8bkVRuq+PdCxGYtcuAEILFozb+nIx4VMB855g4MynpBs++bPCp75XXjFVX0IIMdnZ7swn5fOBN3zKqoKS8EkUg76goy/wiLFnKp/c/xUjI+GTEEIIMQoSnvBJt7OlenqcVjzbxqqsxFdTM55LHECHZCMNn0zl09SpgFPZFZg9GzuRMDNQhBBi0svTdld13HEZh020v/FicpLKpwlA2u6KQsInIYQQYhTEc4RP6d5eki0tAARmzy5ortJYCsyZA0Bo4cKCjtcDx3PNfAJQSknrnRCi5GS03VkWuH/Lp7znPViVleY4qXwSxSDh0/iTtrvikPBJCCGEGAU6fApktd2luruB/pPJiWT6Jz7BgptvpvbDHy7o+KFmPgFUHnkkAL0vvljMpQohxLjJ2O0Ot9rT56PisMMywnsJn0QxmLY7CZ/GjQwcL46BE/KEEEIIscdM5dO8eRltd/rExVdVNW5ry8cKh6k6+uhhHQ/9u/jlCp/KVqwAILppE+lYDCsUKtZyhRBifGSFTwt++lPSfX0EZs4ktHAhkTffBCR8EsVhKp9k5tO48YZPtm1PuMr1yULCJyGEEKLI0pEIqbY2AIJz5vS33fX1mSohn6c1Y7LS4ZOdZ+A4QGDWLHxTp5Lq6CC2cSNly5aN/UKFEKKIvG13AOVuyA4Q9GwkIeGTKAYTPrmV0wDxnTvpef55Yhs2MPWccyjbf//xWt5ewVQ82bYTPufY5VIMTf6tCSGEEEWmq56sqip81dWZlU9ulZA1ASufhsvMfEomsROJnJVPSinC++9P7/PPE1m7VsInIcSkZ9rucnwADS1aZL72SfgkiiC78iny9tu8c8EFpgIvumEDi267bdzWtzfw7nJnJxI5f/fF0GTmkxBCCFFk3pY7IPfMpxIIn/TMJ3BmUegKKG/4BFB2wAEARNeuHbvFCSHEaNGVT9bAj1Jm5pNS+N2dP4XYE3oH2eTu3di2TffTT0MqhX/GDAD63nhDZhGNsuzwSYyMRHZCCCHEHmr57W9pv+8+/HV1VB55pGlHC86dC9DfdtfTU1rhUyDgfPhKp0k0NTk3+nzm59V0O0Dk7bfHeolCCFF02QPHvUKLFzPl9NPx19VJdYQoitCiRahgkFRHB/EtW+h79VUAZlx8MU0330y6u5vohg2UHXjgOK+0dGWET56vxfDIX0QhhBBiD7Xdey+Jxkbi27fTt3o1/pkzAWfeE/RXPqV6e0n39Di3lUL4pBRWOEy6r4/4zp0A+KdOdbYe9wi74VN0wwbsZFI+kAkhJrdBwidlWdRfe+1Yr0iUMCsUonzlSnpfeomeVavoe+01AMoPOYTylSvpefZZ+l5/XcKnUeStdpLKp5GTtjshhBAiS2TNGta99720P/RQQcene3sBqD7tNACSbhVQILvyqbfXVD6Vwswn6G+9S7jhky9Hm0mwvh6rogI7FiO2ZUvO50l2dtL56KOkY7FRW6sQQhSDGTguQboYIxWHHw5A25/+RLq3F6u8nPCSJZSvXAlgAikxOrzVTul4fBxXMrlJ+CSEEEJk6XzkERK7drHrRz8i1dtL5K232P7Nb5pZTl62bZNyw6dZl15KsL7e3KdnPuUKn0phtzvo3/EusWsXkDt8UpZlqp/ytd613Hor27/+ddr+/OdRWqkQQhSHabvLMfNJiNGgw6fYpk0AlK1YgfL7KT/oIEDCp9EmM5+KQ/5iCiGEEFmi7sldqqOD5ltvZeull9L5P/9D+1//OuBYOxYD96TEN3Uqs77yFXOfnvnkyxU+lUjlk97xTlc++Wtqch6n5z5F84RPuiKq75VXirxCIYQoskHa7oQYDWXLl2ds8lFxyCHmdpQi0dhIoqVlvJZX0mzbNud5IOHTnpDwSQghhMiirywCtN5xB8nduwFnR7dsuuUOpbDKyqg66SSmfexjTD33XIILFgBguVVOqd5e0iXWdmdltd3lC5/C++0HOHOfctEnzZE1a4q9RCGEKCppuxNjzQoGTZUTQPnBBwNOFXV46VIA+l5/fVzWVuqyB4xL+DRyEj4JIYQQHulIpD9IqavLvM8NjrxS7gBxq6ICZVkopZh9xRXM+853UEo595WXO8d2dZGORIDSqXzSV2Ljg7TdAYTcIC6+Y0fO+5PNzQAkGhtJtrUVe5lCCFE0g+12J8Ro0a13WBZlK1aY28v03KeXXx6PZZU+CZ+KRsInIYQQwiO2ZQvYNr6aGuZ+61v4amupOOIIoD9o8tKVTzpgykXPd0q1tw+4bbLTlU8pNzDKFz7p+VeJXbtIZ524paPRjKqyfHOhhBBiQnA/jMrMJzGWqk88Efx+Kg4/3LTzA1QdfTQA7X/9q2ntF8WTHTZlV0KJwslfTCGEEMJDt9yFFi2i6vjjOeCJJ6j9wAcAcp7U6WHjg4VJ2cGUVV6OKpF2DT3zSfPnCZ98tbXOsbZNorEx477sORXSeieEmMjsdNr5okT+jovJIbxkCUvvu4/511+fcXvViScSWryYdHc3rXffPU6rK13Sdlc8Ej4JIYQQHtF33gEgtHixuU3PZ0oPVvnkuQqZzcoKprK/n8wszwBUAF+emU9KKQJu9VN2651uudMib71VxBUKIURxSdudGC+h+fPxVVdn3KYsixmXXAJA6513motiojgGVD5J+DRiEj4JIYQQHrHNmwEIe8In0zaXo/KpoPApGEQFAv3PVyLznoCM3Xcg/8Bx6N/9Lzt8Srjhk34uCZ+EEBOaHjgubXdigphy+ukEFywg1dlJ+333jfdySopUPhWP/MUUQgghPEzbnbfyyQ2fclU+6TlQQ81w8oZTpRQ+Dah8ytN2B/1znwZUPrltd5VHHgmWRbKlxQRSQggx0egPo6XSPi0mP+XzUXv++QD0PP/8OK+mtEj4VDwSPgkhhBCudDxOfPt2wJn5pJnKp95ebNvOfEwBlU/Z95dy+JRv5hPkD5900BSaP9+EfjL3SQgxUUn4JCaiioMPBiDyxhsDzlXEyEnbXfFI+CSEEEK44lu3QjqNVVmJv67O3G7ColSKdCSS8RhT+TTIbndAxs40VgmFT962OxUODxhA7mV2vGtoyLhdh0/+ujrKDjgAkB3vhBATl5n5JOGTmEBC++6LCoVIdXU55zOiKKTyqXgkfBJCCCFc3pY7pZS5XYXDZlejdNbcJ1P5JG13g857Ak/l0/btGVdlddtdYMYMwvvvD0B03bpiL1UIIYpDVz7JwHExgViBAGUHHghA3+uvj/NqSodUPhWPhE9CCCGEK+5W5ATr6zNuV0rlHTo+ora7UtrtzlPpNNi8J4DAnDmgFOlIhFR7u7k9o/JJh09r147CaoUQYs/pyiek8klMMOUrVgBO650oDql8Kh4Jn4QQQkxKqa4uelatKupcg8TOnQAEZ88ecF++oeN6S+OhAqVSbbvLqHwaInyygkECM2cCmNlatm33Vz7V1RHebz8AErt2kezoGI0lCyHEHrGl8klMUGUrVwJS+VRMA8KnrO9F4SR8EkIIMSk1/fznbPm//5e2e+7JeX9kzRq6n3pqWM+Z2LULgMCsWQPu061yRal8KqXwyVv5NETbHUBg7lygf+h4qrMTOx4HwD9jBr7KSlN5JtVPQoiJSAaOi4lKVz5FN2wYMKNSjIy03RWPhE9CCCEmpeiGDQC0/uEP2Ol0xn22bbP10kvZ+uUvE9240Tnunnto+/OfB33OuA6fclQ+mba7rMonXQnl20vDJzWMyifob2nU4VPSbbnz1dRgBYMApvopIuGTEGIikvBJTFCBmTPxz5wJ6TSRt94a7+WUBGm7Kx4Jn4QQQkxKulUrvn07Pc89l3Ffqr3d3N/z7LPEtm5l5w9/SOP3vmfCqFx0212u8GmotruhBo77SjR88rbdFVL5FMyqfNLzngIzZphj9I53UvkkhJhobNuWyicxoenqpz6Z+1QcEj4VjYRPQgghJh3btk1oAdB6990Z98c8Wwz3PP883f/6l/m+7d57cz5nqrvbBEuDtt1lVz7p8Km8fNA1WzLzCegP9hJNTc7/uiGh3xM+yY53QogJy1tpKzOfxAQUWrQIgERj4zivpDRI213xSPgkhBBi0kl3d2PHYub7nmefJeYOsAaIe8Kn3pdfpvOxx8z3HQ89RKqvb8Bz6nlPvilT8OUIkvLtdqfDqKEGju8NbXcFzXyqqwP6K550252+HTA73sW2bMn5/5UQQowXbwuOVD6JiUhfzNEXd7LFtmyh68kni7phSymTtrvikfBJCCHEpKODC9+UKZS/611g2/S9/LK531v5ZMdiRN58EwD/9Omke3vp/PvfBz6nbrnLUfUEnrY7T/hkJ5PY0ahz/xAzn/aGtruCKp/ckEm3RerQz+8Jn/zTpjknz7ZNbP36Yi5XCCH2iJ1Kma9ltzsxEWW/z2bbfsUVbLvsMjoffXQslzVpSeVT8Uj4JIQQYtLR4ZN/xgyCbhtXqqPD3K8rn7xXpcP77sv0Cy8EyDl4PD7IvCfI3XaX9lTlDGe3u6HmQ00mGTOfCgifdMiU7u0l1dtrwqdg1r/3suXLAejOmuclhBDjSiqfxASnZyh6xxNoqe5uou5Fnaaf/Yy0u9usyE8qn4pHwichhBCTjrdVSwceyfZ2c7+ufKp+z3vMbVUnnMDUs88GyyK6dq3Z2U5LDLLTHfSHT96B43rYuAoGzU5t+ejwSYXDQx47mVhlZeZrfwFtd77ychO+JZub+0O/OXMyjpty6qkAdP3v/0prgBBiwsj4ICrhk5iA9EWeZGtrRqUeZO4im2hooO2ee8Z0bZPRgPAp63tROAmfhBBCTDreIdUmfHIrn+xUirg7/6n2/PPNY6pOOAH/1KmULVsGQO+qVZnP6YYgwSHa7rwzn8yw8SGqnqC/DD4wc+aQx04mVlkZU88+mynvfS++2tqCHmPmPjU15Q39qk44ARUMEtu8mdggOxQKIcRYMh88fT6UUuO7GCFy8NfWOsPw02mSra0Z90XffhvoP6dpue02cyFN5CZtd8Uj4ZMQQohJR88xCNTVmTlDuu0usWsXdjyO8vspX7mSmZddxvSLLzahU+URRwDQkx0+6RAkT/hkBo572+4KHDYOEJw7l/k//Snzf/Sjwn7ISWTe1VdTf801BX8Q08NQo+vXOzOzlBoQyvkqK6k8+miAjIHxQggxnnQlibTciYlK+Xz4p00DBs59irjh0/QLLiAwezaprq6MmZlioAGVT9KqOGISPgkhhJgU+t54g43nn0/vyy9nzHzSO6yl3LY73XIXnD8f5fMx46KLmPXlL5tgpMINn3pXrcpo58rX/qUN1nZn5dgdL5fq448nvHRpQceWMj2Pou/11wFnEHyuVsQpp58OOOFToa13fW++OaClUgghikV/EJVh42IiC+TZ8U6HT2XLl1Nx+OEA9L322tgubpIxv/PujMu0VD6NmIRPQgghJoW2e+4hun49rX/8Y2blkxs+6ba7+LZtAAQXLMj5POUHHYQKh0m2thJ75x3AKaE2zzmctjs3iCqlAeJjQc+j0Ce8+eZsVR1/PCoUIr51K9F164Z83uimTWz65CfZdtllxVusEEJ4SeWTmAT8OYaOp3p7zYYsZQceSPlBBwESPg1Ft9npGZfSdjdyEj4JIYSYFPTVur5XXzUnUwHPzCfddhfbsgWAUJ7wyQoGqTj0UAB6nn8ecK8MptMov9+UqmczlU+9vabtQlc++QqY+ST6ZW8Dnb3TnearqKDq+OMBaH/wwSGft+/llyGdJrp2LamuriKtVggh+nlnPgkxUWW/zwJE164F28Y/cyb+2loTPkXefFOGaA9C/7uR8GnPSfgkhBBiTNnpNFu/9CW2ffWrBbdSpSMRYps3A5DcvducTPm9M586O7GTSXNVL1/lE/TPfdJDx/W8J/+sWSgr91ujt7pJDxofzsBx0U+fFJvv81SbAdT8n/8DQOfDD5OOxQZ9Xh1QAkTWrNmDFQoh9mY9L7zAhvPOy/l3xLTgSOWTmMByVT7p/57LDjgAgNDixViVlaQjEaKysUdeA8InCepGTMInIYQQYyrR2Ej300/T9cQTBbVSgbs1cDqdeaPPh7+2Fl91NbjznFJdXcTctrvQ/Pl5n6/isMMA6HvrLWdNTU3A4DvRWcEgKhQCoOX229l4/vlmy+JCBo6Lfv7s8ClP5RM4QWFg1ixSXV10/fOfgz6vhE9CiGLY/bvfEdu4kY7/+Z8B90n4JCaD7MqnRFMT3f/6F+C03AEoy6J85UrAqSoXuUnbXfFI+CSEEGJMxRsbzdfdTz9d0GNyBQn+adNQPh/K73cCKJyqKF3FFJw3L+/z6UqbVHs7djJJcvdu5/bp0wddhw6Zdv/2t0TXr6fz4YcBqXwargGVT3mGvIMz1HfqOecA0H7//XmPSycSxDZsMN9L+CSEGAk7kTC7f2XvFObcKOGTmPi8lU9t993HujPPpPellwAoP/hgc5zMfSqArnxyN5eR8GnkJHwSQggxphIjCJ+iencW92od9O/kApgd7yLr10Mq5cxu8tyfzTd1KlgW2DbJ9naSra0Aeec9aab1LqtdUMKn4fFPm+b8+3flm/mk1Zx7LihF76pVpkotW+yddzJOCCV8EkKMRGTNGtKRCDBwpzDAzPyTmU9iItMXeRK7dtH8859DKkX5wQdTf911Zpc7kPCpEDLzqXgkfBJCCDFs6UiErn/+c0TbzXornyJvvEGyvX3Ix+ggofZjHzO3eVu39NyniNtGF5g9O+/sJnCqafy1tQAkW1v7w6cCK5+AjPBEBo4Pj/L5MoK+wWY+AQTnziVYXw9AfMeOjPtSXV2kurr6Z1m4AWWisbGg/7aEEMJLV4dA7sonabsTk4EOn9I9PSRbW/HV1LDottuYctppKHdUAUDZihVgWSQaG2l/6KHxWu6EJm13xSPhkxBCiGFrvesutn3lKzTfdNOwH+utfMK26XnuuUGPT/X1mWHjlUceSWiffYCs0b4VPQAAIABJREFUyqfs8Gnu3CHXkTN8cm/Lx3J3vFPhMHWf+1z/7RI+DZs+MbYqK81OgoPRrZXeXezS0Sjrzz2XDR/4AN1PPQVAxbvfTdCd9+WdASWEEIXoefFF83XO8MmtfFJS+SQmMKuqChUOm++nnH46KhAYcJyvooJpF1wAQMOVV9L5j3+M2RonC6l8Kp4JFz4ppY5XSv1NKdWolLKVUu/Pul8ppb6rlNqplIoopR5XSi3NOqZWKfUHpVSXUqpDKfVrpVRl1jErlVJPK6WiSqntSqkrcqzlQ0qpte4xbyilzhydn1oIISaXqDtbp+Ohh4a964eufNKVLEO13kXXrXO2Bq6rIzB9utmpLrRokTnG77bd6QHmwUFmCJnHuFVOyd27C6580hVWU884g2kf/ag5EbFk4Piw6cq1Qv6/Ak/41N1tbovv2EGqvZ3k7t10P/kk4Ozio6ufdBgphBCFSCcSGYOX05EIKXdXU00qn8RkoJTKuEg39X3vy3vsrMsuc2YrplI0XHkl6Xh8LJY4aUj4VDwTLnwCKoDXgC/kuf8K4MvAZ4EjgF7gUaVU2HPMH4BlwHuAs4DjgV/qO5VS1cBjwFbgXcD/A65SSn3Gc8zRwF3Ar4FDgAeAB5RSy/f8RxRCiMlNb92bbG2l54UXhvdYN3yqPf98ALqffXbQN/LseU91n/sc9T/+MTUf+IA5Rlc+2bEYMPgAa81UPrW1FTzzadrHP87Uc86h7vOfx1dZyazLL6fiiCNMICYKpyufhmq503JVPmVU0bnCBxxA2bJlgMx9EkIUJh2J0HL77TT97GfY0Si+mhpT0Tqg+klfcJHKJzHB6dmXwfnzKVue/yOssizmXnklKhRy2vRyDdrfi5m2Oz1wfJgXXUW/CRc+2bb9P7Ztf8u27QFb2iinQfUy4Hu2bT9o2/brwCeBOcD73WMOAN4LXGLb9irbtp8BvgR8RCmlP418HAgCF9u2/ZZt23cDPwW+6nm5S4FHbNu+3rbtt23b/i/gZeCLo/FzCyHEZJJ0wyeAzhxbUeeTTiRMcDXl9NPxT5tGurt70ADL7F7nVkr5qqqYcsopWKGQOUZXJGnBQtru3CqnRHOzmQ00VPhUvmIF8777XXM1sfa881j0i19kzoISBQkvdYqWw/vvX9DxujXPGz7F3f829MB5X20twfr6/sonCZ+EEAVou/demv77v2n9/e8BqDjssP6BzVkfxE3lU44WJiEmktCCBQBMPeusjDlPuSifL2OHPM1Op2n62c/o+uc/h/XaqZ4eel95BTudHuaqJx6pfCqeCRc+DWERMAt4XN9g23YnsAo4yr3pKKDDtu2XPI97HEjjVErpY56ybdtbU/gosJ9SqsZzzONketTzOgMopUJKqWr9DzD0EAshhJhkbNvO2HGs64knSEejBT022dQE6TQqFMI/YwbVp54KQOdjj+V9TKKAqiQdPmgFtd25zxfbtAncGR7+rOcRo6fm/e9n8e23M+OSSwo6Xs/b8rbdJXbuBJwgc8FNN7Hw5ptRSjmBllIkm5pMVZsQQuTT9/rrAIT33ZfKo45ixqc+ZT6IJ5ubsRMJ4u7fG5n5JCaLus9+ljnf+hbTP/Wpgo7Xgav3AmPfK6/Q8utfs+3yy+ly29sLsfO669j8qU/RcNVV/TtEuux0mvYHH6Tt3nsLfr7xlD1wnFRqwM8kCjPZwiddm5+9z3KT575ZQLP3Ttu2k0Bb1jG5noMCjhmsP+CbQKfnnx2DHCuEEJNSqrMT250H4J85k3RvLx1//3tBjzXznubMQSnFlNNOA9wAK8+VpOTu3c5rDTKPKbvyqaCB4274FF2/HnACLLmSPXaU30/5wQdjBYMFHa/b7tI5wqfg7NlUHXssZW4Vla+iwswEk+onIUQ2O5mk6eab6XvjDaB/c4JZl1/OwltuoezAA/urQFpa2Hnddaw/4wx6V6+WmU9i0gjMnEnteedhFXhuY6r9POGTrj4nnWb7N75B21/+Qt9rrw0ZvkTefBOAjr/+le3f/KYJcOI7drD505+m4corafz+9zMuZk5UpvLJbbvz3iaGZ7KFTxPdD4Epnn/mje9yhBCi+PSJgq+mhmnu3Kad115rrhwP+lg3fNIzmcoPPhj/jBmku7vpff75nI/RlSuBAiufVCg0ZPsc9IdZqQJb7sT4yjnzyQ2fcs2NktY7IUQ+3U89Rcsvf0nDt79NqquLREMD4GxYoAU8m1J0P/MMALGtW02lrFQ+iVLjDVw179d2NErj1Vez6cILafzhD/M+j51KEd+2zfnGsuh67DG2fe1rdD/7LBs/+lH6Vq82x+oLjODsYLvpooto+M53ivUjFUV22x1I691ITbbwyY1emZl1+0zPfbuAOu+dSik/UJt1TK7noIBjdpGHbdsx27a79D9Ad75jhRBiskq64VNg5kymX3QRVccfjx2LsfXSS01lUz76/sDs2YBz8q5b71rvuYdkW9vA19OVT55dW7L5PJVPuqpqKNlhk4RPE9tgM59yDZgPux8iJXwSQmSLuR+MY5s30/XEEwAE5s0zITf0v+dE1683QbedSPRXPEjlkygxudru9DlY7Yc+xPQLL6T8oIMA6Hr88bzznOKNjdjJJCoUYsGNN6JCIbr/9S+2fuELpLu7KVu50pwHJjs7zeMia9bQ9+qrtN9/P5G1awtedzqRIPrOO8P7YYdhQNsdEj6N1GQLnzbjhD+n6Bvc2UpHAP92b/o3MFUp9S7P407G+VlXeY45XinlrUF8D7DOtu12zzGnkOk9ntcRQoiSl2hqYuOHP5zRl6/LsQN1dSifj3nXXEN4//1JtbfTcOWV2Ok0qb4+c3Kf8Xyetjtt6hlnANDzzDOsPe00drsDX8E5oUh1dACDh0PetrtCdrrL9XwSPk1spvLJbbuzEwmzI48+ifWSyichRD46TAJo+c1vAEzbrqY3luh9+WVzmzd8krY7UWpytd3p8Ck4fz6zvvIVFv3qV1gVFaQ6OojmCYjiW7aYx1QddxwLbrrJBDfVp53GottuIzjPaRDS53hAf7UU0HbPPebrVG8vjT/4Ae0PPJAz8Gr5xS/Y+MEP0nr33SP5sYdkfueDQRM6S/g0MhMufFJKVSqlDlZKHezetMj9fr5t2zbw38C3lFLnKKVWAL8HGoEHAGzbfht4BLhNKfVupdQxwE3A3bZt60vyfwTiwK+VUsuUUh/G2d3uBs9SbgTeq5S6XCm1v1LqKuAw97mEEGKv0PnII0TXraP1rrvMbSZ8mukUh/rKy6m//npUOEzviy+y87rr2PD+97PhnHPMPCUtntV2B1C+ciX1115L2bJlkEzS9LOfkXBPdlK6EsrvxzdlSt51WpWV5oNAIcPGwQkzvB8eJHya2LLb7hLNzc7w+kAAf23tgOPL9t8fLItkS0vGibQQQnjDJ/2BVwfWmt/9II5ntktG+CRtd6LE+HPNfHIv8uhKQBUIUHH44QD0/PvfxHfupPH733daUl0xN3zSu+1VHn44+9x9N/NvuIH6a67BCoXMOV3KU/nkvWjZ8fe/m6qo1rvuou1Pf6LhqqvYfMklA97TdVts8y9+Qaqvbw//LeTgCZyVhE97ZMKFTzgBzyvuP+AEQq8A33W/vw74GfBL4EWgEnivbdverZY+DqwF/gH8HXgG+Iy+090h7zSc3fNWAz8Gvmvb9i89xzwHfMx93GvAecD7bdt+s4g/qxBCTGh9b70FOCcS6UgE6J/5ZE7MgVB9PbO+/GUA2u6+25Rs9732Wsbz5ap8Ame3sn3+8AfKDzoIO5EwV7xMy920aSgr/1uWUsrMfSq08klZFj5P4CTh08Tmy9rtTg9BDcyalfO/DausjNDixUD/MGEhhABytoh75z1Bf+WTl1Q+iVKm/5tPtrTg1HzQX2Hs2fSl8ihn8/ee55+n4aqraLv3Xlr/8Adzvw6iQgsXmttCCxZQffLJ5v1aj0tI5ql8smMxOh58EHBa/LS+l1+m+ZZbzPfpaJTohg2AM8PTWzFVLHpDHOX3m41pZOD4yEy48Mm27Sdt21Y5/rnIvd+2bfvbtm3Psm07bNv2qbZtr896jjbbtj9m23aVbdtTbNu+2LbtnqxjXrdt+zj3OebZtn1tjrXca9v2frZth2zbXm7bdmHbOQkhRImIuOET6TTRjRuB/lkAgbqM8XrUfuQj5mqYz61EiW3aZO5Px+P9VVN5AqJpn/gEAG333ks6EjEVUIMNG9f8bvgULGCnO/MYb/g0yG56Yvzpyic7GiUdj5ttz3O13Gmm9U7/dyyE2OvZtt1f+eQJkMJZ4VOu9wQ7kTADx2Xmkyg1urrJjsVMlXGuuZs6fOp96SV6VzlTbbwzO+Nu+BR0K59yvlaOyicdPlWdeCIArX/8I9F33nHa+3w+Zl1+OZBZIRV5++3+30lg9+23E3c3ECgaHTgHAv3hk1Q+jciEC5+EEEJMDMmODrMDEEB03Tqgv/JJt91pyrJYcPPNLL3/fmZ+6UvOYzZvNvfHt22DdBqrsjJvlVH1SScRmDuXVEcHHQ891H/SU0AwNO2CC6g89lgqjz664J/RG2rlat0SE4dVWQnuIPl0d3f/TncFhE/55lIIIfY+6e5u0r29AFS7H3IDc+ZkzA4EsMLhjAHkIJVPorRZoVB/RVJzM6m+PtJuG5v3PCxYX09g7lxwq6MAku3t5mvTduepfMqmX0fPfLJtm/j27QDUffaz+OvqSOzaxTY3cKo47DDC7lw2vQsyQORNpymp6rjjCC5cSKqzk/VnncXWSy8l5f6ej0Sys5NtV1xB91NP9QdN3sonCZ9GRMInIYQQOWUPatY7j2TPfPKyAgFCixYRXrQIcHYS0mLuTiShxYvz7kanfD6mfexjAE745J5gFNISV3POOSy86SZ8lZVDHqt52+4CUvk0oSnLcgIonLlPJnyaNSvvY3QVXMKzlbMQorSkuruJvvOOaRMaim6589XUUHPuuQB5L1pk77Jqx+PYbpWFzHwSpcg7dFy33Fnl5fgqKswxSilT/aTpECnV22seFxqk8il75lNy925nvINlEdpnH2Z+/vNA//DyKaeeas4FM8Int7K57KCDmH/99VS8+91g23T/618Zm+UMV9u999L12GO0/O53/YGzVD7tMQmfhBBC5KTf0C33hCO6bh2p3l7SPU4Xsz+r7c5Lz9pJNjWRco/XQVTIDabyqTrmGOf1163rny+VY/ZGMQRk5tOkYuY+dXWZmU/BQSqfzJVVzxVZIURp2XrZZWz84AfZdMEFdD3xxJDH6+A6OHs2Vccdx5K//IXZbnVFtuyqW6l8EqXO75n7NFj1ee0HP0hg1ixqP/pRoD980i13vtraAZWDXqbCyg2fdMtdYM4crECAqWefTWjJEudgy6L65JPNRcJ0dzfpWAyAPrfyqXz5csJLl7Lol79k9je+ATib5oxUjzvEPNXWlvE7b0n4tEckfBJCCEE6EmHzJZew41vfMtvY6vBp6plnAhDdsMGctFuVlRlXwbL5qqtNmKNDp6g7/ynsBlP5BOfPx6qowI5G6X3xRWD0giFzQmVZ5kRITFxmx7sC2+70HLCkhE9ClKyYO48w8tZbbPvqV502mWSSnTfcwK4bbxywNbv52+HOHgzvs4/ZBj5beOlSoP+Cip1M9s9/kconUYK8lU/ZO915lR1wAPs98ggzLroIcEY12LY9YKe7fMzMJze00nOcQvPnA87v1+zLLwfLouq44/BPm4ZVVWUqj5KtrSTb2kjs2OGsZ9ky89xTTj8d/H6ia9cS27IF27ZzhkXtDz5I56OPDrg92dFB3+uv9/9cuQaOx+OD/nwiN4nshRBC0PXEE/S+9BLgnGTPuPhiEz5NOeMM2v/2NycMco/JHjaeS2jxYpKtrcQ2b6Z8xYqCK5+UZRHef3/6Vq82V8JGaxi4nvPkr6mRDxKTgAmfuroKGjiuA0U7GiUdieT9gCmEmJzS8bhp26k+5RS6/vEPGr73PaqOOYb2++8HnL8bMz71KfOYQv52aDO/+EWqTzmFvtdeo+m//zuj8kkGjotSpIOmRHOzec8cbCyBuXCXSpHu7s65091gj9O/v3reU7C+3hxTedRR7Pu3v5lzNaUU/unTSezcSbK11QRXoUWLTGU0OOd0lUccQc+zz9J6zz1E3nqL+PbtLP3LX8xzxXftouHKK0EpypYvz9ispue558ANrVOe3fhUIGAqHqXyaWSk8kkIIQQdDz1kvm76+c9pvvVWp2ff56PsgAMI77sv0L/d7WAtd1rIM/fJTiZNKXZon32GfOyALa9HKXzSaylkTWL86fApunEjdjQKPt+gM5+sigpzopj07KgjhCgNeoct5fcz73vfI1hfT7K52QRPAE033WSqGCCz7W4oVjhMxSGHYIVCgNt2p2c+SfgkSpC+uJhsbi5o0xcrFMIqL3ce09FhLhoG3QqmfPTMp3RvL+lEIu/jgnPnZlw4MnOfdu82LXfeqidtyhlnANB2111EXn+dVHs73c8+a+6Prl/vfGHbtP3lLxmP7X766f5vbNsMVs+ofNIhtBgWCZ+EEGIvl2hpocfdKrfi8MMhlaL51lsBCO+7L1ZZGWX77QdgKp+CbrvCYHSbQmzzZuINDdjxOCocLuhqc3b4NFqVT+ElS9jnD3+g/tprR+X5RXHpK5t9r74KOOX5VjCY93ilFD639c7sqJNOFzyYWAgxsekPx75p07DKyph71VXmvplf+pLTfpNK0eC5PeEOHC/kvUjTHzjT8bjMfBIlzbTdtbSYzTqGmrvp3bnObEozxO+Xr6rK7GCb6uw04VNoiNBKnw/qynqAsHuO6lV90kkoNzTWet1zXfCET0D7Aw+QdiuZ7FTKqXzKQclud3tMwichhChhtm2z66c/ZdNFFxF3++KzdT7yCKTTlK1cyfwbbqD65JOpPOYYpn3yk8y7+moAZ/cQcHrvTziB6Z4WhnxM5dOmTf0tdwsXoqyh33qyr2LpMunRULZsmZkNJCY2HT7pltBCKta8Q8d7V6/m7WOP5a3DDuPtk06i2x0oKoSYnPSuV3rziIp3vYv5N9zAnCuvZPrFF5vBw7FNm8y268Npu9O8rTay250oZbqyPdHUZMLdwBDhk18PD+/oMBvF5NoR2Uv5fP2t9O3tZubTUBVT+nwwuXv3oFVWvooKZnz605QtX87sr38dgJ4XXzQXn/SsOHCGine7mxX0rFpFqrMTq6qKwLx5mWv27HaXlvBpRCSyF0KIEtZy223s/s1vANj2ta+x+PbbscLhjGN0y93Us87CV1XF/BtuGPA81aeeyqLf/Ibg3LlDnlBoOnyK79hBdN26jNuGooeOp3t7sSorZVaPAMByT1Rtd5eb0BDD68E5KY7hnBRH164l3dcHOCe7zb/4BVXHHjtq6xVCjK5cbUHVJ59svvbX1OCbOtWpyGhoQNXXm90vC6ng1ZRbYWknEv0Dx6XySZSg4Lx5qECAVFsbfa+9BgxdfW4qjNvaSBYYPoHTepfq7CS6YYNppR/q99Jb+WTCJ8+cKK+6Sy6h7pJLSEci7LrhBpJNTcS3bSO0YIGpfCpbvpzIm2+y+847qTrhBHb95CeAs9lOdO1aM9AcpPKpGKTySQghSlTHI4/QfPPNAKhwmOjatTRec03GMYmmJicY8vmYctppeZ9LKUXFoYcWHDyBU6ZtVVZCOm3mbxQSFkD/0HEYvZY7Mflkb9scLqTySZ8Ut7ebK7LTPvYxlN9P5I03iLjBqBBi8tEznwbbEVXvahdvaDBVT1Zl5aDbwGfzznkxs16k8kmUIF9lJTUf+ADgbNYBBYRPbuVTbPNmJ5RRashWPe/jzIY3CxaY37V89O96dP1652KSZRHMqlDKZpWVUbZypfNaL7xAOh43g9Fnf/3rqHCYyBtvsPH884lt2ICvupq6z33OnD/0L9gn4dMekvBJCCFKVMuvfw3AtE9+kgU33giWRccDDxDdsMEcE1mzBnA+xOuy6WJRSlHrnsAkdu0CCg+foH/u02AfKsTexbubDUBoyZIhH5OrHaD8oIOoOukkANqzBo0KISaPQgYi60qKeGOjqWIYTtUTkLG9usx8EqVuxsUXm2o/KLztTl/M8U+bhjVEiOR9nA6fwkuXDv0Y93ddt98HZs0adPajVnnEEYDTVhfbtAlSKXzV1ZQtX878H/0IFQiYSqq6L3wB/9SpGSMZVCCAUkrCpz0k4ZMQQpSgZEcHMTdkmnHRRVQecQQV73oX0P+GDRB5+20AwlkDvotl5le+Qt1nP2u+L+TEQtPtUOU5djEReydvpYLy+4ccTAqZM5/MLIpZs6g97zwAOv7+d9KRyCisVggx2vTMp8EuUugt1BMNDUQ3bQKGdyEEyPjAKbvdiVIXmDmT2g9+EHBaTq2sCz/Z9PusHrEQKGBHZOjf8c4MGy/gHFHPd9Mh8FAzorSKww8HnKBLt9yFli5FKUXVscdSf/31qFCI8oMPNucH3son/fvunf8mhk/+agohRAnqe/llwDnB1sMZw/vuS++LL2bs8BF1w6fs3eWKRSlF3Wc/S9nKlaTa2wsKC7TKI49kv8cek7Y7YXjDp2AB5fmAuXKZbG8n6e7C4585k0BdHcH6euLbt7Pzxz9mzte/XtDzCSEmDlP5VEjbXWMjqZ4eYM/CJ5n5JPYG0//jP+h6+mnKly9HubvS5eP3tLeD8x5bCF9WxX14332HfEz2OWGh55Xly5djVVaS6uig+Re/cF7PE3ZVn3gi+//jH1ihkNlMILvyyfu/Ej6NjFQ+CSFECepdvRrAVDtB/5tsRtvd2rUAlLnzlUZL1dFHM/V97xv24wJ1dQXtjif2Dt62u0J2ugPPLIpNm5wrpZZFYPp0lGUx4zOfAaD9z39m82c/S8odRi6EmBxM5dNgbXdu5VO8ocFpt2EPK5/0zCcJn0QJC0yfzr5/+xv1WbNCc8kOkQKzZhX0GrrySQsX0kqfFTTnGzaeTQUCphI/0dCQ8/V8lZUZF6EyZj7pyicJn/aInNELIUQJ6nUrn8oPPdTcFt5vP8AZ0mjbNomWFpItLaCUuU+Iicxb+VTIsHHonykR27LF+X76dFOxUHP22cy/4Qasigr6Vq+m/c9/Lu6ChRCjqpDKJ2/bXVHCJ912JwPHRYkbquJJy54ZWmjbnfdxVmUlgdmzh3yMVVaGVV5uvg8uWFDQa4Gz2UjGefEQlVb+XG137nypdHd3wa9r2zbNt91G0803Y9t2wY8rRRI+CSHEJGHbNt3PPEPSLWvOJ9XdbfruvZVPocWLwecj1dlJsrmZqFv1FFq0CKusbPQWLkSRjKjySZ88ptPAwCuy1SefbK6G9qxaVYRVCiHGQqqvz8xrG6zySX+gTff1ke7rc+bFFVgtoeWqfJK2OyEcAyqfCm2781Q+hZcsKTzs8oTNwxnnoCyLuVddhVVejlVVNeSmJd5wTP8NKFu+HIDOxx4jHY8X9Lpdjz9O889/Tssvf0nk9dcLXm8pkvBJCCEmia7HHmPrF7/ItssvH/S4vldfhXSaYH19xtUnKxQi5F4hiq5fb1ruRmvYuBDFpgIBc5JbyGwIyHFSnOOKbMW73w04s9LSUkovxIQU37mTlt/+lrS7/buuerLKyvB5KiGyWeFwRjhV6Lw4r5zhk1Q+CQFkVgjBMMInz/vzcDakMb/PlkXArWwsVGj+fJbcey/7/PGPg/7dgNwDx6eccgr+ujqSra10PvLIkK+X6u5m53XXme/b//pXALr+9S/63nhjWGsvBRI+CSHEJNH+t78Bzgfk3ldeyXtcz/PPA1Bx2GED7tMf2KMbNvQPGx/leU9CFNO8H/yAud/5TsFXOwe0A+SYRRFeuhRfTQ3pSITIm28WZZ35RN56i21f+xoxd3cfIURhGr7zHZpuvJH2Bx8ECpv3pOmh4zD8ljvAbOUuA8eFGMjbEg+Fh09+b+VTgReUoL/yKTBnDtYINgoJzp1bUPVjzra7QIBpH/kIAK133mna6GJbt9J6992mLVdr+tnPSLa0mB0DOx99lI6HH2bbpZey5TOfIdnWNuz1T2YSPgkhxCSQ7Ow0oRLA7t/8Judx3U89RevddwNQefTRA+7Xb+6RtWuJrFkDjN5Od0KMhqqjj6bm3HMLPt4KhzPaSnOdFCvLMmFt7wsv5H2utj//md2/+90wVjvQ7j/8ga7HH6fj4Yf36HmE2JskWlrM76ae22TCp0HmPWlBT3XESMKnXDOfZOC4EA4VCJhwBcBf4Mwnb9vdUC1wXjpwHk7L3UhYZWWocBggo1qy5oMfRIXDRNevp/ell7Btm21f+Qo7r7mG7n/9yxyXaGmh7S9/AWD+9dcTmDOHdE8PO779bQDSkcgen1NMNhI+CSHEJND1xBOQTDpVG0rR/fTTRDduzDgmsnYt2664AlIppp59NtWnnjrgeXT41PXYYyR27UKFw4Sl8kmUOG9pf74rspVHHAFAT57wKdXdTeP3v8+un/xkwO/ecMTdweepvexqpxB7ovOxx8zctvj27YBn2HgBlU9BT+VTeE/DJ7c1VyqfhOinq4x9tbWmUnAovpoaVDiMCgaH1XYXWrQIGJuLp7r6yfv77p8yhZpzzgGg5Ve/oveFF0woHtu82RzX/sADkEpRftBBVB55JFPPPtu5I5UyLX2t99yzV1U/SfgkhBCTgO4rrznvPKpPOQWA1rvuyjhm9x13YEejVBx5JHO//e2cgxsz3tx9PuZdfTW+ysrRW7gQE4B3bkO+8EnPfYq89poZYuwV3bgR3PL67meeGdE6bNs27XZDbRwwHmzbxnY/4AsxkXhnq5jwaRiVT965MIVuVuDl/eCZjsWc22TmkxCGfp8ttOUOnHbWhTffzMJbbhnWuWjtBz7Awl/8ghmXXDLsdQ6XL0f4BDD9ootQfj+9q1bR+MMfmtvjO3cCYKdStN93H+CcuwNOYOXzgc/yVNruAAAgAElEQVTHwp//nLJly7CjUVpuv33Uf46JQsInIYQYB+0PPcTOH/94QG94tq4nnmDnDTfQ++KLAEw5/XSmnnUW4A4W94ht2AA4W8nmG6bqr6sjWF8PlkX9D37AlPe8Z09/FCEmPO/cp1wznwBnQP+sWdjJJN1PPTXg/pin2qlnhOFTqq2NdE8PMPHCp1RXF5svuYR1Z5yxVw5BFRNXbPt2Ip7/JuM7d2Ink8OrfNLhk883rK3ZNeWp5NDhtFQ+CdFPv88OJ3wCqDj00IydmQuhAgEqjzhiTHZqNpVPWefVwTlzmPr+9wP9Fc0ACTd86nnuORI7d+Krrjbn2sG5c1l4880suu02yg480Oy02/anP5Hs7BztH2VCkPBJCCHGmJ1Os/Oaa2i94w56nnsu73F9r73Gtq9+ldbf/x7SacpWriRUX0/ZgQcCTmlvqq/Pec5Uipj75jdYS4FSikW//S1L77+fKaefXrwfSogJzLTdWVbeKgmlFFPOPBOAnT/60YATwagb7gL0vvoqqe5uou+8Q3zXroLXEfOcoE6ktrtUdzdbPv95+lavJtnUxOZPf5ruQf42CTFWUj09NP3kJwBUHHGEEwIlkySamvorn2prh3yesgMOwFdTQ9Uxx4xoQLH3g6ft7raHVD4JYej32Vw7yk5mudrutBkXX2xu1zOvEo2NAGbW09Szz8Zy50aB0+JfceihztfHHsu0j36UhbfckjF8vZRJ+CSEEGMssXOnqX7o+fe/8x7X9Y9/AFC2bBmz//M/mf/jHwPOG7t/xgxIp4muWwdAvKEBOx5HhcMEZs8e9PUD06cTGsGVXyEmK31F1j9jxqDVCnWf+QzBhQtJtrRkbI0MZM55SibZ9ZOfsPFDH2LThReSdmfADCW2dWv/U0ygyqcd//VfRN58E9+UKVQcdhh2NMrWL3+Zjr//fbyXJvZisa1b2Xj++c7MQ6WYfsEFpoIpvn078YYGAAIzZgz5XL7qavZ79FHm33jjiNaifD6wnI9NUvkkxEAV73oX+HxUHH74eC+lqHSoluv3PThnDtM+8Qnw+Zh16aWAW5mZTtO7ahVA/5ynHJRSzP7616k45JBRWPnEJOGTEEKMsej69ebrQcMnd8eM6RdeyLTzz884wS5btgxwtm2H/t1/QgsXyhwKIbIUOovCCoeZd/XVYFl0PvwwkbffBpxZSLrySZ9Yt993H6TTJJua6Hn66YLWEfeET6nOTmx3y/bxlGhuNrvzLLz5ZhbccgtT3vteSCbZ8Z//OWC2nBBjpfmWW0g0NhKYM4dFv/kNVccdR3DePMB579Pve/r9cChWMJhzFmKhTPWTO/tNwich+tWcey4HPvNMyY1zyNd2p8388pc58JlnmOoOILejUSJvvUU6EkEFAoRHMGOulEn4JIQQY8zbvhPbvNkMJ/SKbd5MfOtWlN9P5dFHD7jfhE9r1jjH6/BpBLv4CFHqgvX1AAWdBJavWGHmT5gt3ZubSXd3g8/H9E98whyrQiEA2v/614LW4a18wrZJTYAZD13/+AfYNuUHHUTZsmVYgQDzfvADpn30owDsvO46E8IJMVZSfX10PfkkAPXXXmsqA3T41PHQQ2DbBBcuLGjgeDFkf/iU8EmITGMxg2mslR90EFgW4Tw76ymlsMrKsIJBpysB6HYvSIUWLcobWu2tJHwSQogx5q18gtzVT/qku+Ld7865A4ie+6TDp+g77wASPgmRy5RTT2X+jTcy87LLCjpen0AmWlqA/sA4tGABFUcdRWjpUsL77svCW28FnBNNPX9mMHF3pzttLFrvEk1N7Pzxj1l/9tmsO+ssNl14ITF3tzBwt7AHqk87zdymLItZV1zhVEDZNjuvuw7brfYQYix0P/kkdjRKsL6esuXLze06SNbbmY9lu8qAD5FSZSxEyas47DAOePpp6j796SGP1WMvdDVxaMmSUV3bZCThkxBCjDH9Qbb84IMBcg4d129cVSeckPM5ytwrMPEtW0h1d5sKjcGGjQuxt1J+P9UnnFDwQE/d4qp30zLh05IlWIEAS/70J/a5+24qDjnE+WCcSg05H8lOpUz4ZJWXO8/f3k68oYH2Bx4oegtedNMmdnz726x/3/toveMO4tu3k9ixg77XXjPb1ieamuh75RXACei8lFLMvOwyVDhM3yuv0OWGVEKMBf37NOWMMzJa5XT4pJW7g3vHglQ+CbF38lVUFHRccM4cADOPNbx06aitabKS8EkIIcZQOhIxH0CnX3ghAD2rVpkBpgDJ1lb6XnsNyB8++WtrzRWWyJo15ipwSHrLhdhjuvIpqSuf3GHj+kRSKYVyhw/rOQ+7b7+dXjfI8Ypu3Mimiy5i9+9/j51MooJBwvvtBzg73u285hoarrqK7qeeKtr6u596io3nnUfHX/+KnUxScdhhzP/JT6j98IeB/l33Oh9/HIDyQw7JOQ8rOGsWMy66CICmm24q2vqEGEyyrc1UBE8944yM+7LDpwoJn4QQE0TADZ+0sFQ+DSDhkxBCjKHoO++AbeOrraXq+OPxz5hBurubrV/6Eqm+PgA6HnkEbJuyZcsIzpqV97n03KfORx7BjkZRgYDZCUgIMXKBrLa7mFv5lOtEcuqZZxJavJhkayubL7lkwPyn5ltuoe/VV2lyd9kKzp9vZtQk29tNVVUsqyVvpGzbdoKidJqKI45g8R13sOhXv6L6pJPMsHQ9+Lz7n/8EGHRA7LQLLnAes307qa6uoqxRiMF0/u//QipF+IADCC1alHFfYM4ccCuh/DNnDviwN5oGhE/SdieE8Ahm/T0KSeXTABI+CSHEGNIfNMNLl6J8PuZffz1WRQW9L73E1i99CTuZpOPhh4HBt2cFzFDk9vvvByC4cKFciRWiCLyVT3Y6bSqFcs1v8FVWsviOO5z5SKkUze4cKHB2ktPz27TQggVm95xEYyOJXbuc12puLsrae/79b6Lr12OVlVF/3XWUr1jR/9oLFwJO5ZOdTptB4hXvfnfe5/NVVpp/H/kCsr433mD3739Pw/e+R2Tt2qL8HGLv1eEGuFPPPHPAfVYwSMC9KFNx6KF7tHvdcFnBYMb38n4rhPDSHQkAVmXlkDvs7o0kfBJCiDEUc4eNh/fdF3DmPi289Vasykr6Vq+m4eqria5ZA34/U04/fdDnqjnvPKo9c1pk3pMQxeEdOJ5oasKOx1F+P0HPiaWXr6KC2d/4hvOYxkZSPT0AtD/wAKRSlK1cSdVJJwFQtmIFPjd86nv9dfMciSKFT7tvvx2Amg98YMCMq2B9PShFuqeHyOuvk+7tRQWDJpTKJzh/PuBUP2XrfvZZNn3iE+y64Qba//xntn7xi6ZiTIjhim7cSOStt8DvZ+r73pfzGL2xRsVhh43l0gaGTRI+CSE8vJVP4SVLxjQcnywkfBJCiDGSbGuj5/nngcwhhOUrVjD7a18DoOPBBwGoOvZYUx2RjxUIUH/NNUxxrw7rlhohxJ4JTJ8OgB2NEnUreQJz5w5a6eCfOrW/QmjjRuxUivb77gNg2oc/zPwf/YjFd9zB9I9/HH9tLYDzIduVK3wa7g5zkbVr6X3hBfD5TLuclxUKmSuznY8+CrgnyEN8iNZzdnT4lGhuNnPq9M8QWrSI4IIFJHfvZvv/+3+kE4mcz9X36qs03Xxz0Qesi9Kg21arjjvO/J5km/XVrzLzssuoceetjRWZ+SSEGEzAMypDho3nJuGTEEKMgcjbb7PxIx8htnkzVlkZlUcckXH/1HPPzWh9GarlTlN+P/O+/332feQRaj74waKuWYi9lVVWhlVZCUDvyy8DAwcd56IrGqMbN9Lz3HMkdu3CN2UK1aeeivL5KF+xAhUImGDZ9gQ0yaxqoa2XXcb6971vWFVEvatXA1B1zDF5q7R0lZMeNq6Hnw8mpMOnbduIbd3K+rPOYvs3v+nc1tAAwJQzz2TBT3/qVHG++ipbv/AF4jt3Dniuhu9+l5Zf/pLOSb57XnTTJrZ8/vP0vfrqeC+lZNiJBJ1u23nNuefmPS68zz7MuOiiAWHQaJOZT0KIwVhlZfjc0DxXm76Q8EkIIcZE4w9+QLK5meDChSz+/e8H9IErpZjzX/+FVVFBYNYsqo47ruDnVkoRnDVLynuFKCI9dLzPDXRChYRP7slmdP16s3vdlNNOwwqFMo7z5ahq1POlAJKdnXQ/+SSJxkYzqLwQyaYmYPCgLLRggXk9KCx80s8X27aNnmefxY7H6V21Ctu2ie/Y4Rwzdy6hBQuov+YaVDhM7wsvsPFDHyLibjkNThtjbNMmACJvvFHwzzURdTzwAD3PPUfrn/403kspGT3PP0+ytdXZkOOYY8Z7OQMo78wnpSR8EkIMUL5sGShl5rKKTBI+CSHEKEt1d5vWlIW33pq3FDdUX8/SBx5gn7vuGjDYVAgxtnQLnQ5P9NyjwWRUPrkttpVHHz3wuXO0E9nJJKmODuc133zT3N7x0EMFV9fo1r1AXV3eY4JZ850KCp88M5/0nKp0JEKyuZmEW/mkd9qsOvZYltxzD+H99yfd02NaiQF6X3rJfN3naTmcjGLujoG55mCJkdH/fVSfeOKYVzUVwrsmCZ6EELnUX3cdSx94IOfuuELCJyGEGHW9L78M6TTB+fMJevrBcwnMmDHkrCchxOjT4ROpFFBg250bLEfeeMMJJXy+nEOR8/2O6/DIDCK3nNO0xmuvxXbXMRjdoucfZIcdXfkEgFImMBuM/tlT7e30rFplbo9u2EBCV1vNm5fxGrXnnQdk7pDX++KL/Y9dty6j7XCy0TsgSvhUPH1u6Fq+cuU4ryS3jEBM5j0JIXKwysoy32dFBgmfhBBilOkPXDIQXIjJQw8d1woJn4KLFoHfb0KVsmXL8FVVDTjOl7ULXWjRIqA/fNKVTzMuuQSrspLo22/TeuedQ75+cpiVT8H6enwVFUM+r6+iAv+0aYATQGk9//432DYqHDZzLsxz62qpPOGTHYsRfeedIV97IrITCTPrKtXRQaqri55Vq9j0H/9B1G0rFMNjJ5OmQrhsxYpxXk1u3gHjMmxcCCGGT8InIYQYZRI+CTH5mMonAJ+PgGcL5XysQMAM9AaoPPLInMcpv98EUP6ZM01Qk2xuxrZtMw+p+sQTmXX55QA0/fzndD3xBDuvv54Odyizl23bBbXdBerqUOEwUFjLnZYrfOt+9lnnvnnzBsycM+FTYyN2MkmiqcmpErIsypYtA5yQbceVV7Lm2GNZe8op/P/27jw+qur+//jrzCSZyU4CIexhFQFBUKmCFlGxahVrKy5V6lK0bq1VW/1qcf+JWxX3tS5Yly5aRdGqRYWiyO5WVxbZlwAJZF9mMuf3x71znYQQEsiQhLyfjwePSe6cuXNmmDsz+dzP53NWX375TlfJa02q16+HmNX6qtasYevzz1O+ZAlbnnyyBWfWdlWuWIGtrMSXluYFY1ub2J5PCj6JiDSdgk8iInEU3r6dSrdnTH3lNyLSOiXGBJ8Su3bF18geNLF9HtJGjdrpuGjfp0BenhfoCm3eTPWaNdQUF2MCAYIDBpB1yimkjRqFra5mzVVXUfDii6y/5RZsTPADoKaoCFtV5ew7NnBWh/H5CLiBod0NPvncbKlqt/QstuQuKrFzZ0wgAOEw1Rs2UOoG4ZMHDSLVXe2z4KWX2P7660RKSwkXFFAyZw7FbWAVvGjJXVT1mjVUfvMNAMWzZlFTUtICs2o7rLVsmzGD8s8/97ZFA67JQ4ZgfK3zzxP1fBIR2TOt891dRGQfEV36PNC37w5lPCLSesUGcBqz0l1UtIeSLzWVlAMO2Om46Ip3gbw8L1MptHmz1+8pedAgTGKisxLmjTc6mVJ+P/h82OpqqjdurLW/aMmdv0OHHVbXqyvj2GPxpaaSceSRjX5csQ3XM3/yk9rX1ZMVZnw+L2BVvXat10w6deRI73mJrnyXfdppdDrvPAC2Pv881tpGzclGIpR98gk1xcU7HRNxA3LNqdptNh5V/umnhAsKnDlVVVE0c2az3+e+pPiDD1h/ww2s/v3vvRJVr99TA8dMS1PPJxGRPaPgk4jIbqjetInCV15h/S23sOKcc1gxcSIrL7yQbW++WesPJ6/kTllPIm1KbPCpMf2eotJGjwa/n8zjjmtwxa5owCnQt6/3c3jLlh8yQGL63iR17cqA6dPZ/733vLK+uo2uG1NyF9X5wgsZ9OGHO115sz6BmOBTh/Hja11XX+YT/PC8Va9ZU+u9MDkmwOBLS6PzZZfR6bzzMMEgld9+W2tVvIYUvf02K3/9a5aedBJbpk3boYF5/kMP8c0RR1A8a1aj9tdY0cwnX3KyM4/33qt1/fYZM5r1/vYlNhJh8+OPA06/rGhGXEU06NpK+z2BMp9ERPaUwvYiIk0QLixkzdVXU+5mNNVVtmgR219/nR533EFCVhbFH3wANFx+IyKtT2ymYlOCT8n778/+M2fiq6fReKzOF11EUq9edBg/3st2Cm3e7K0eVzcDJLpCXlKvXlR9/73TyHv0aO/6aPApoRHBJ6DJpU2Bvn0BJ2MrZfhw/BkZXsZR4k6CT4FevSgBShcsILR+Pfj9pIwY4TQwz80lnJ9PzqRJJHToAEDW+PEUvvwyBc8/T1ojeuRVLl0KQE1xMfn334+tqqLzRRcBTpCj8LXXsKEQ6268kf7//CdJXbs26THvTJWb+ZR66KGUzJ7tNWFPPfRQyhYtovzTT8l/7DGSBw0i/fDDGwxCtheRUAhbUUHpggVULVvmbS+eOZOUYcOoWrkSgJS2EnxS5pOISJPpnVNE2q3i2bMpW7yYrFNOqdWnpSH5jzziBJ6MIeXAA0k95BCCAwZgkpOp/PZbtjzzDGWLFrHpnnvIOvVUwps340tPJ+3ww+P8aESkOfmSk/GlpREpLa1VctYYCXVWfqtPoE8fci+9FPghW6lq+XJn9bhAYKcLFEQDOrGryEHjVrrbE8EBA+h+661Oc3Gfj6Tevb1slaTu3eu9TfR5K/nwQ8Bd/c/tF9V98mTKPvuMjmef7Y3vOHEihS+/TMmcOYQ2b97lY6nesAGAQL9+VK1YQfHs2V7wqeKrr6gpLAQgUlLCusmT6f344/himkbvTCQUgnDYy2yqK5r5lP7jH1Mye7a3PePIIzEJCZTOncuWJ54AILFLF7JOPZVAr14E99tvh2ba1Rs3UvnNN4QLC0k+4ACS999/l/Nra2woxLJTTiG0fr3TBwyn/LJs0SKKP/iAlIMPBmtJ7NrVW1WxNVLwSURkz+idU0Tapcply1h79dXYUIiCF14gbfRo0seMIfWgg0jKy6u3Z0rVypVsmz4dgD5/+csOpXQZY8aQNno030+cSNF//kPY/cMn89hjG/UHj4i0LmmHHkrpwoVxz8bwgixuyW7WKafsNIAVDehU1Qk+RTOm4hV8Asg6+WTv50Be3g/Bp52sBOgF7WpqgNrlx+ljxpA+Zkyt8YG8PJKHDaPiiy8onj2bjqefTuXy5fgzMup9XCG371X2aaex8c47qfz2W2qKi/FnZFDy3/8CkDJ8OJXLllH+yScsnzCBbpMnk+Y2PK9PaOtWlp92GjXbtnkrEkbKyuhw8sl0u+EGIiUlXlAr/Ygjat02OGgQGePGsX3GDKpWrqR03jxCmzax+ZFHnAF+P/u98YYXrAtv28byCROIlJUBTp+w/WbMaFTwsrWrWr2amuJiUoYOJbR1q5P5htMTy5+RQc+772bZqadSU1jI+ptuAnbsJdbaKPgkIrJn9M4pIu1OJBRi3fXXY0MhErt0IZSfT+nHH1P68cfOAGNIGz2avAcfrNXXYdODD0JNDeljx+60h1PKAQeQdsQRlH70EWULFwLQ4cQT4/6YRKT59bznHmxVFb5gMK7340tPxwQCzmp1fj+dzjlnp2OjAZ2d9XxqbNndnor2nkro1GmnGUKBOhljjSmlyzjqKCq++IKSWbNIGTaMFRMnEuzXj/7/+McOY6OZTynDh5PUs6fT2Pyzz8gYM4aSOXMAyJowgcSOHVl3ww1Ur1nDqksuod/zz5M8ZAih/HwilZUE8vK8fRa8+KJXRldTVORt3/bqqwT69iXlwAOdx52TQ2JuLv6sLGe8MQQHDsSfkkLOpEmA0+x8+4wZlC5cSNnixdQUFlK2aJEXfNr+1ltEysrwZ2VhkpII5+ez+bHH6DZ58i6fp9bMRiKsuugiwlu3st+//+09n/7sbLrfdBOBXr1IyMoi85hjKHz5ZaipIWX4cDr/9rctPPOG+dRwXERkj6jhuIi0O1uffprK777D36EDfV94gQHTp5N7+eWkjhyJLy0NrKV07lxK58/3blPxzTeUzJoFPh+5l1/e4P6jf3iAU3KRMmJE3B6LiMSPMSbugafo/UQzezKPP36nZWwQ08R7/XpsOOxtD2/ZAsQ38ylWcOBAwCl525mEnByM+/yZhARShg/f5X4zjj4agNJFi9h0330QDlO1cuUOK+BFKiu9DKTErl29EwLlixc7pWxLl4LPR/rhh5M2ahQDXnvNKX+ORNj64ovUlJSw4qyzWH7GGYTc566mpMQJhgA97riD/q+8Qv9//Yvc3/8egE333cf6G290HrcbsIoG2AJ9+uBPSak1R18gQPaECfS6+26y3Cbt5W5DeWst2157DYDcSy+l55QpABS++iqV7iqAbVXl0qWENm3ChsNUr19PePt2wOmjlnHkkV7pYeZPfwpAQm4uve69t3ZwpxVSw3ERkT2j4JOI7FXWWmpKS1t0Dttefx2ArldfTWKnTgTy8sj59a/p85e/MOjDD8n+5S8BKHzlFe820T9IMn/yE4Ju492dSR0xwulhgfPluqmNfUWk/ck4+mgSOnWi84UXNjguMTcXk5QE4TChTZu87d5qd7m5cZ1nVNro0XS/7Ta6XX/9TscYn88LliUPHbrTDKlYgbw8p7l5OEzZggUA2OpqbGVlrXHRx+5LTcWfkeEFn8oWL/6h5G7YMK9Ruz893euxVTxzJpseeIBwQQG2stLLkip85RUipaUE+vYl87jjCPbvT7BfPzqdd54TKKmp8RpjZ4wbB/wQDAwOGtTg40oeNgzAW82w4n//o2rFCkwwSObxx5N6yCGkjx0LNTVsmDKFSFWVkzn11ltUu+WFbUU06xecRTq8zCf3/yIqdcQI+k6bRv+XXmrVvZ6iTEz5vMruRESaTn8RiUhc2XCYssWLvTP0+Q89xDc//rHXgHZXqlav3qG3SX0qly8nUuePk+JZs1hx9tneSlLglGmENm4Ev5/0o47aYT/GGLInTADwmt7WlJVR9PbbgNNbpDF6TJlC7u9+t8s/JEVEALpceSUDZ870ytl2xvh8JLmry0XfGyNVVdS42SV7q+zO+HxknXQSgV2sBBh9PDtroF6faPZTrOjKelHRkrvErl0xxngB/4pvv2Xzo486+6nzHp88ZAjJQ4diQyG2xZxcKPnwQyJVVRS8+CIAnc49t9ZJA2MM3W+8kS5/+AM9br+dge+9R8czz3Tu49hjSejYkQ4nnNDgY4quXli5fDmRigov6ylz3Dj87sqIXa68EhMMUr5kCat/9ztWTJzIusmT2XDbbbX2VbZkCcsmTGDT/ffv8Lw0t+oNG1h77bWULV7c6NuUxgSfagoLCbvBp+iqhrFShg9vE4EnqB1wUuaTiEjTKfgkInG15ZlnWHnBBay7/nqqVq1i61//CtayZdq0Xd62dMEClk+YwIozz/TO6ten8LXXWD5hAutizsCXLlrE2muuoeKrr9hw223YSASAsk8+ASB58OAdSiSigv36OaVyNTVse/11it5+m0hFBUm9e5Ny0EGNetxJXbqQM2lSo870i4iAE+RoDK/vkxt8ipbcmUAAf0ZGfCa3mzpffDEdJ06k08SJjb5NbNAomm0S238JIOQGn5K6dnUuu3QhsUcPiESoKS4medgwsk8/fYd9Z59xhvdzNFBXOn8+2159lfDWrSTk5nrlYLF8wSCdfvUrOvz0pyR26vTDXI88kv3ff3+H5uN1JebmOvcXiVA6bx5F77wDQNYvfuGNCeTlkffQQ/iSkylbuJCqZcsAKP/8c+8zDGDzk09StXw5W6dN47sTT2TTgw82+BkZy9bUsObqq1n5m99QMmfODuWMdRX+/e8UvfMOq377W+/zs8H9h0KUx4xrKPOprTHq+SQiskcUfBKRuLGhEIVuk9iid95h1SWXgJsBVb5kibdcdX3KPv2UNVdcgQ2FiJSXs/nJJ+sdF8rPZ9O99wJQ/N57VHz7LZVLl3q3Baf/RPSLfvmSJQCk7iKIlHXqqQBsfeYZ8t2z6NmnntroPw5FROIlUCf4FC3LSuzcudW9RwX79aPrH//YpKBYcPBgulx5Jd1uuMHL8grXCT55mU8xK+1FG5on5eWR98AD9Qb/M91MJYDuN99MQufO2MpKNt1/PwCdJk6MW++hFLf0bsOddxKpqCC433479ARMGzmSvEcfJbFrVzKOPhoTDBIpLaV69WrACeaULVoEOH2mIiUlbH3mGb776U9ZddllbH7iCVZfeSWrr7ii3hL3siVLKJ45k7KFC1l9+eV8f845Xh+q+pR//jkAtrKS1Zdfzva33iKUn8/6W29l6c9+xoYpU6j47jtvfMXXXxMpL/d+DxcWej2f6st8akvU80lEZM8o+CQicVM8ezbhggJwyxei5W7R3hiF//rXDrcpmTOHFRMnsvL8870v5wDbXnuNKvfLd5S11umNEfMFO/+hh1h95ZVEyspIOfhgcn7zG2f7o48SCYW8M7fREo2dyRw3juShQ4lUVFBTWIhJTKTDSSft5jMhItJ8oplPlStWULZkiZf1GW3k3NYZY+h07rlkn3qqF7SqW14Wigbc3MwngJyLLiLnwgvp/cQTXq+nunyBAL2ffJK8Rx8lffRo0seMAXBWNUxPr5WJ1NyS3dK7sJullHPBBfUGC1NHjN9K8j8AACAASURBVGDg22/Ta+pUkt3Py/IvvwSg+IMPIBIhefBg+r/yCr2mTnUCWOEwpXPnsvmxxyiZNYuS2bMpmjlzh30X/ec/AAT69sWXnEzF//7H97/6Fd9PmsTGP/+ZSjfbCiBSXU3F118DENxvPyKlpaybPJnvjjuOba++SvXq1RS+/DIrfvlLSubOBWJK7tzHtU9lPsX2fGrlzdFFRFojBZ9EJG6iDbs7nXceqYceCkD2hAnkXnIJANtnzCBSVeWNr1qzhjV/+AMVX34JPh8Z48bR97nnSP/xj6Gmho133VXr7HfBSy9RMmcOJiGBnnfdBUDp3LmE1q8nsUcP8u67j5zzzyehY0dC69ax6e67nbPHxpC6i1WXfMEgfZ97jj5PPUXHiRPpcdttO/1jRkRkb4oGn8oWLGDlpEmE8/NJ6t2bLn/8YwvPrPn5MzOBesru3OBTUkzmU1KXLuRedhlJXbo0uM9gv36kjx4N4AWfADqecQb+1NRmmXd9UoYO9X4O9O3rNS1vSPKQIQBUfPUV8EPwKOPYYzF+PxlHH03fZ59lwOuv0/nSS8k84QRSDzsMwGvYDmAjEWw4TPH77wPQ9ZprGDBjBh1OPhlwsoILXnyRNVdd5ZXiVX7zDTYUwp+VRd/nnqPzZZd5WWPBwYPpftttzn1FImy67z5sTY3XzzFaoq7MJxERiVLBsojsFmsthS+/TKBnT9JGjdrh+srvv3e++LoNvP0XXEDphx+SfvTRGJ+PhNxcwvn5FL7yCp3OPhuATX/+MzYUIvVHP6LnHXd4X3JzL7+cko8/pvTjj1l64olkn3oqCZ06sWnqVOf63/+ezOOOo+jddyn+4ANMMEjeffd5Z8y7XHUV6yZP9lasC+63X6NKQIzPR+ohh3irKImItAYpQ4eSPGwYVcuXY6urSR87lu433eQ1rt6XeJlPOyu7i8l82h1pI0fiz87GhkJ0POusPdrXriQPHgx+P9TUkDNpUqNWQo1mS1V8+SXhggKv8XfmT35Sa1wgL4/ObqZv2ZIlrJw/n9IFC7CRCKsuvZTqVavoePbZ1Gzbhj8ri9RDDsEkJNDj1lvJmTSJ8s8+Y+Pdd1O9di3ln3xC6sEHeyV3KQceiC85mc4XXkinc86hauVKgvvth/H5yBgzhu9OPJGq5ctZdfHFVHzxBSYhgazx4ylfssTJenKDWf7s7GZ7LltCreCTej6JiDSZ3jlFZLeULV7MxttvxyQm0veFF0geONC7rnThQtZeey0A6Ucc4Z2ZzjzuOG9MzqRJbLz9dvIffJC0ww6jevVqSj78EJOQQLc//anW6jfBAQPo/eijbLr3XiqXLmXrc89512WfcQYd3Ua2uVdeSaS6mo5nnklwwABvTIcTT6Ti66+9lYx21e9JRKQ18yUn0++vf23paewVXuZTTNldJBTymqwnde++R/v3JSfT/29/w1pLQpyDI77kZLr+4Q9Ur1tX6/OwIdHMp8rvvqPw1VedkrshQxp83MnDhmGCQWq2baPgb3+jbP58AK8/YsYxx9QKngTy8gjk5VH+2Wdse+01tk2fvkPwyXsMgQDJ++/v/e7PyCDn/PPJf/BBrxdV12uv/aHEsLDQK8HblzKfUOaTiEiTqexORHZLsZv6b0Mh1l13HZUrVrD1uedYcc45rPrNb6gpLCQwYABd3SBUXdmnnUba6NHYqipWnn8+a666CoCOv/pVvUuNpx16KP3+/nd63nMPmSecQEKnTnQ46SS6XnON1zMj0LMnvR9+uN5Vh7pceSVphx8OQHo9y3iLiEjrU1/ZXWjTJrAWEww2Sx+hxNzcXZbqNZeOZ53lfG41MnMmqUcP/JmZ2FCIzY89BkDWhAkN3saXmEiq29cw/4EHgNqBk7pZU1FZP/sZAEUzZ1JTVkb5Z58BkLKLMvWOv/wlCe4KgNmnnUb2hAleIK9m+3Zq3LK7Nt/zSZlPIiJ7RO+cIkJNcTFVK1fWOrvZEBuJUDxrFuB8Gav6/nuWu6vDRWX9/Od0veaaelcbAqehbPebb2b56ac7X0yNIeOoo8i58MKd3q/x+cgcN47MRvTJ2OG2CQnkPfQQ4S1bSMzNbfLtRURk76uv4bjXbLxLl1a3ul9zM8aQPGQIpR9/DJEIqYcc4gWJGpJ22GGUzp2Lra4GoM8zz7D12Wex1nqBqbqSDzyQpN69qV61ik333ku4oACTkOA1Pd8ZX3IyeQ8+SPnnn3uBMX9mprPYSCTild0luIHEtko9n0RE9oyCTyLC+ptvpviDD+g1dSoZjcgKKv/8c8Jbt+JLS6PHlCle1lLqwQeTccwxZBx9NIk5ObvcT2LnzvR+7DFK588n89hjvSW148X4fAo8iYi0IfVmPrn9nmKbje/Lkg84gNKPP8YEg3S76aZG9YpKcxf5AEg99FBShg6ll9sncWeMMWSdfDL5Dz7ItldfBZzG4r5gcNdzHDzY6WkV3Zffj79DB2oKCwHwpae3+RXilPkkIrJn9M4p0s7VlJRQMmcOANveeKNRwadid/nmjLFjyTjySPZ74w18KSm7tRpc8qBBuzyrKiIi7VN9Dcebq9l4W9Fh/HhKP/6YjmedRaBnz0bdJtC/Pwk5OYS3bGlSI/Xs00+natUqqteuJVJWRqdzz93daZOQne0Fn9p6vyeo0/NJwScRkSbTO6fIPqr8f/8jkJe3y1XdSj/+GBsOOz/PnUtNSUmtFZPC27ezbfp0Qhs3kpCTA5EI2995B8BbJnpPG76KiIjUJ6GehuPRsrv2kvkU6NmTfi+80KTbGJ+PXlOnUrV6NeljxjT6dv60NHrcemtTp1ivhOxsqqL7beP9ngB8SUnez8p8EhFpOr1ziuyDSufNY9Ull5B62GH0efzxBsdGezeB0zy8ePZsssaPB2DLs8+y+YknsJWVO9zOl55O2qhRzTtxERGRGPWV3bW3zKfdlTJ0KClDh7bY/ceuHrg7mdGtjXo+iYjsGQWfRPZBxR98AEDZ/PlULl9OsH//esdFQiFKPvoIgLTDD6d07lyK3n2XrPHjqVy61FslJzhoEGmjRhHeuhV8PpK6dSP9iCPwBQJ75wGJiEi7FM3ejVRUEKmuxpeU1O4yn9qq2IDTvpD5FJvtpMwnEZGm0zunyD6odP587+fCf/6TTueey9Zp07DhMP6sLDqceCLBfv0oX7yYSGkpCR070uWqq1g+dy6l8+cTLigg/9FHAcg49lh63n33Pr+ikIiItD6+tDQwBqylprgY06EDoc2bAWU+tXb+2Mynfaznk4JPIiJNp3dOkX1M9bp1VK9d6/2+/c03KfnwQ+9MMcDWadNIGzWKym+/BSB97FiC/foRHDiQyu++Y8XEic54n4/cSy9V4ElERFqE8fnwZ2RQU1RETVERNhSCcBiTkOD0IZRWK6Fjxx9+3hcyn2J6PqGyOxGRJtv1Wq0i0mpFKitZ+3//x4YpU4i4fZmiWU8pI0YQ6NOHSHk5oY0bScrLo/Nll5E+dixEIpTOnUu4oABfejrZv/gFAD2mTCGxa1cvUNXhxBMJ9OnTIo9NREQEYla8Ky72Pp8Su3bF+PQ1tjWL7fnk3xcyn/x+cF9zynwSEWk6vXOKtDI15eVUr1lD8v7773Ls1hdeoOjddwGoXLaMXg88QOm8eQCkjR6NPyODjXfcQWKPHvR58kkSc3MBKP/yS0rnzSN58GBSR470VnAJ9u9P3+efZ92111K9bh2dL744To9SRESkcfyZmbB2rZP9VFoKqOSuLdjXGo6DU3pnq6oUfBIR2Q165xRpYQUvvUT+Qw/R4/bbSR87ljVXXEHZwoV0v/lmsk45BQBrLdvfeIPqDRvoeOaZJGRlEdqyha1PPw04X4bKP/uM5b/4BZHycgDSDjuM5CFDSOjUidQRI2p9CUw54ABSDjig3vkkdupEn6eewkYiOqssIiItLnbFO6/fk5qNt3q1Mp8UfBIRaff0zimyF1StXUtCx474U1KwNTVUb9hAUpcuVC5bxsapUyEcZsOUKXTevp2yhQsB2HjPPaSNHk1CVhYb7rqLba+8AkDBiy/S4YQTqFq1ikhFBcnDhtH9hhtYfcUVhNavB5wSheTBgzE+H5nHHLNbc1bgSUREWoNaZXcbNgCQpMynVs+/j2Y+Aer5JCKyGxR8Eokjay2bH3uMLU8+iQkGSTv0UCq+/prwli0k9ujhDAqHnYutW9lwyy0AmGCQSGkpa664gkhlJVXffw/GkJSXR/WqVRS+/LJ3H13/+EeCAwaw3/TpbH/nHba/+SaZ48Y5vQlERETauNjMp2o3+KSyu9bPl5xMysEHU7N9u1f239ZFg0/KfBIRaTq9czaCMeYy4GqgC/A58Dtr7cKWnZW0lEhVFdtee42Kr78Gn49AXh7Zp56KPyODSHU1JjERYwzWzWba9tprANjKSkr++19vP6F16wBIyMmh6zXXsPbqq73fe02dysrzz3fuA/ClpDhleWPGUPzBB1R89RWR0lKSDziAlGHDAOcLUdb48WSNH783nw4REZG48jKfioq8huNJKrtr9Ywx9HnqKbB2n8mm9oJPOsEnItJkCj7tgjHmDGAqcDGwALgCeNcYM9Bau7lFJydNEtvDqPyLL9h4zz2kHnIIOb/+Nf60tB/GhcMUzZxJ2cKF+NPT8WdnO30LfD6qV61i24wZhPPza+17y9NPk9StG5XLl5PUqxfdrruOghdfpGTOHPD56PanPxHcf39K588n2L8/qQcdxLbp0ymZM4fOl11G6ogRlP7iF2x79VW6/vGPpAwdSvdbbqF41izSRo8m45hjSHDP/GaOG0fmuHF774kTERFpQdHMp/D27bVWu5PWzxgDxrT0NJqNMp9ERHafsda29BxaNWPMAmCRtfa37u8+YC3wkLX2zl3cNgMoKioqIsM9ayeNF6moIFJejj872/nyglPGFi4oIFJSQlKPHj/U3ruq1693VnszhpShQwn07QvGUPDSS2x+4gmCAwfS8ayzWH/zzURKSgDnS61JSiJcWEhiTg42EiG8ueG4YkJuLlmnnILx+ymaOZOqZcvqHWcCAXreeScZRx21y8drIxHCBQUk5uQ05ukRERFpF7a9+Sbrr7+eQP/+VC1fDn4/QxYsUABA9rplp51G1bJl9LzrLjKPO66lpyMi0uKKi4vJdE4SZVprixsaq0/tBhhjkoCDgTui26y1EWPMe8CoesYHgEDMpvS4TzLOtjz9NNvffhusdf4BWIuN/h6zzbmI2d6I8bWui263FhsOEykrA8CXlkZSjx7YUIhwYSE127YBztmnhJwcbDiMDYWwoRARdwlmj9+PPyPDu035kiWUL1kCQPLgwdSUllK9Zo03PLRpk3OzrCyyxo/HAjWFhYQLC7GhEEm9epE8eDAdTjoJX8D5r8654ALKFiygprycYL9+bHnqKba/+Sb+jAx6PfAAqSNGNOq5Nj6fAk8iIiJ1JLgn8Kq+/x6AxM6dFXiSFuGLnvTU609EpMn0ztmwToAfyK+zPR/Yv57x1wE3xXtSe1NoyxbnLGMLipSWUvnttz9s8PnwBQJEKiq8VW88fj8pBx6ISUyk4ssviZSVUbNtG760NDpffDGl8+ZROncuwYED6f3445jkZMo//RRfSgoJ2dmEN2+mpqSE1JEj8QWDjZqf8flIG/VDLLLHbbeRfeaZJObmKpgkIiKyh6JZzEQigHPySKQlJPXsScXXX5PUvXtLT0VEpM1R2V0DjDHdgPXAaGvtvJjtdwNHWmsPrTO+vsyndW257K5q1SpC+fnOlz639M2r369Tx1/vdmMa3u7+TJ2fjd9PQnY2JhikevVqqjdswBcI4M/IINC7NyYYJLRhA+GtWzFJSU6T78REEjp1wp+aCrhlbFu2ENq4kaQ+fUjIzMRaS+XSpQR69/Yyl0RERKR1q1q1iuoNGzA+HykHHogvObmlpyTtUKSyktDGjQT69GnpqYiItApNKbtT8KkBbtldOTDBWjs9ZvtzQAdr7c92cXv1fBIRERERERGRfU5Tgk/7xrqncWKtrQaWAMdEt7kNx48B5u3sdiIiIiIiIiIi4lDPp12bCjxnjFkMLASuAFKBZ1t0ViIiIiIiIiIibYCCT7tgrf2HMSYHuBXoAnwGHG+trduEXERERERERERE6lDwqRGstQ8DD7f0PERERERERERE2hr1fBIRERERERERkbhR8ElEREREREREROJGwScREREREREREYkbBZ9ERERERERERCRuFHwSEREREREREZG4UfBJRERERERERETiRsEnERERERERERGJGwWfREREREREREQkbhR8EhERERERERGRuFHwSURERERERERE4kbBJxERERERERERiRsFn0REREREREREJG4UfBIRERERERERkbhR8ElEREREREREROJGwScREREREREREYkbBZ9ERERERERERCRuFHwSEREREREREZG4UfBJRERERERERETiRsEnERERERERERGJGwWfREREREREREQkbhJaegLtQXFxcUtPQURERERERESk2TQl1mGstXGcSvtmjOkOrGvpeYiIiIiIiIiIxEkPa+36hgYo+BRHxhgDdANKWnouuykdJ3jWg7b7GET2hI4Bae90DIjoOBDRMSCi46Ah6cAGu4vgksru4sh98huM/rVmTuwMgBJrrWoHpd3RMSDtnY4BER0HIjoGRHQc7EKjng81HBcRERERERERkbhR8ElEREREREREROJGwSdpSBVwi3sp0h7pGJD2TseAiI4DER0DIjoO9pgajouIiIiIiIiISNwo80lEREREREREROJGwScREREREREREYkbBZ9ERERERERERCRuFHwSEREREREREZG4UfBJ6mWMucwYs8oYU2mMWWCM+VFLz0mkORhjxhhjZhhjNhhjrDHmlDrXG2PMrcaYjcaYCmPMe8aYAXXGZBtjXjTGFBtjthtjnjbGpO3dRyKye4wx1xljFhljSowxm40x040xA+uMCRpjHjHGFBhjSo0x/zLG5NYZ08sY85Yxptzdz5+NMQl799GI7B5jzCXGmC/c9/FiY8w8Y8wJMdfrGJB2xRhzrfu96P6YbToOZJ9ljLnZfc3H/vs25nq9/puZgk+yA2PMGcBUnKUkDwI+B941xnRu0YmJNI9UnNf0ZTu5/hrgcuBi4FCgDOf1H4wZ8yIwBDgWOAkYAzwZrwmLNLMjgUeAw3Bew4nAf4wxqTFj7gPGA6e547sBr0avNMb4gbeAJGA0cC5wHnBr/Kcv0izWAdcCBwOHAB8ArxtjhrjX6xiQdsMYMxK4CPiizlU6DmRf9xXQNebfETHX6fXfzIy1tqXnIK2MMWYBsMha+1v3dx+wFnjIWntni05OpBkZYyzwc2vtdPd3A2wA7rXW3uNuywTygfOstX83xgwCvgZGWmsXu2OOB/4N9LDWbmiBhyKy24wxOcBm4Ehr7Rz3Nb8FOMta+4o7Zn/gG2CUtXa+myHyJtDNWpvvjrkYuAvIsdZWt8RjEdkTxphC4GrgFXQMSDvhZm5/AlwKXA98Zq29Qp8Fsq8zxtwMnGKtHV7PdXr9x4Eyn6QWY0wSzlnA96LbrLUR9/dRLTUvkb2kD9CF2q//ImABP7z+RwHbo4En13tABCdTSqStyXQvC93Lg3GyoWKPg2+BNdQ+Dv4X/bLlehfIwMkKFGkzjDF+Y8yZOJmx89AxIO3LI8Bb1tr36mzXcSDtwQC3Fcf3bkuNXu52vf7jQPWIUlcnwI+T6RErH9h/709HZK/q4l7W9/rvEjNmc+yV1tqwe8a8CyJtiJvZej8w11r7pbu5C1Btrd1eZ3jd46C+4wR0HEgbYYwZihNsCgKlOJmwXxtjhqNjQNoBN+h6EDCynqv1WSD7ugU4ZXLf4ZTc3QR8aIw5AL3+40LBJxERkfbrEeAAavc4EGkvvgOG42T/TQCeM8Yc2bJTEtk7jDE9gQeAY621lS09H5G9zVr7dsyvX7itZ1YDpwMVLTOrfZvK7qSurUANkFtney6wae9PR2Svir7GG3r9bwJqNd93V7XIRseItCHGmIdxGuYfZa1dF3PVJiDJGNOhzk3qHgf1HSeg40DaCGtttbV2ubV2ibX2OpzFKH6PjgFpHw7G+T7ziTEmbIwJ4zRVvtz9OR8dB9KOuFlOS4H+6HMgLhR8klrcxmhLgGOi29yyjGNwUtNF9mUrcT4sYl//GTi9nKKv/3lAB2PMwTG3Oxrn/XTBXpqnyG4zjoeBnwNHW2tX1hmyBAhR+zgYCPSi9nEwtM4qqMcCxTgN+UXaIh8QQMeAtA/vA0Nxsv+i/xbjrOgb/VnHgbQbbvP9fsBG9DkQFyq7k/pMxUk9XwwsBK7AacL5bIvOSqQZuB8s/WM29XH7exRaa9cYY+4HrjfGLMMJRv0/nBXwpgNYa78xxrwD/MVd0SIReBj4u1a6kzbiEeAs4GdAiTEm2pegyFpbYa0tMsY8DUx1e5kVAw8B86y1892x/8H5YvW8MeYanN4GtwGPWGur9uaDEdkdxpg7gLdxmsem4xwTY4HjdAxIe2CtLQG+jN1mjCkDCqI9AHUcyL7MGHMPMAOn1K4bcAtOBdDf9DkQHwo+yQ6stf9wl96+Fecg+gw4vk4nf5G26hBgVszvU93L53CaDt6NE2x9EugAfITz+o/th3A2TsDpfZxV7v4FXB7XWYs0n0vcy9l1tp8PTHN/vpIfXtsBnNVbLo0OtNbWGGNOAh7DOfNXhnMM3RivSYs0s87AX3GazBYBX+AEnma61+sYENFxIPu2HsDfgI7AFpzv/IdZa7e41+v138yMtbal5yAiIiIiIiIiIvso9XwSEREREREREZG4UfBJRERERERERETiRsEnERERERERERGJGwWfREREREREREQkbhR8EhERERERERGRuFHwSURERERERERE4kbBJxERERERERERiRsFn0REREREREREJG4UfBIRERERERERkbhR8ElEREQEMMb0NsbYOv/KjTEbjDHvG2NuNcb0a+l51scYk2KM+b0xZpYxZosxJmSMKTTGfGSMudYYk9PSc2yIMWaa+3z3bum5iIiISPMz1tqWnoOIiIhIi3MDHyuBFcAL7uYA0Bn4EXAAUAPcDUy2reRLlDHmQOB1IA9YDbwP5AMZwGHAwUAx0M1aW9ZS82yIMWYacC7Qx1q7qmVnIyIiIs0toaUnICIiItLKLLfW3lx3ozHmCOB54DqcINQNe3leOzDG9AD+A3QC/gA8YK2tqTNmBPAwkLj3ZygiIiKisjsRERGRRrHWfgQcD1QB1xhjekavM8ZkGmP+zxjzX7dMr9q9/GvdUj1jzG1uidnp9d2PMebX7vXXNWJaU3Ays2631k6tG3hy5/0pcCRO9lPs/Yx3y/SKjDEVxpjPjTFXGWMS6owb687n5nrmGi1VnFZn+yr3X5ox5gH3uagyxnxhjJlQdyxO1hPAypiSx9mNePwiIiLSBij4JCIiItJI1trvgH8CScApMVcNAm4FKoDXgPuBxcBZwEJjTF7M2L8AEeCCndzNhUAYeLahuRhjUoAz3fu8ZxfzDltrIzG3vQp4AxgGvAQ8AiQD9wIvG2NMQ/trpEScrKyfAP/CKWXsB/zTGPOTmHH3A5+7Pz8A3OL+m9YMcxAREZFWQGV3IiIiIk0zG/gVMDJm2zdAV2ttYexAY8xRwHvA9ThBJay1q40x7wLHG2N6x/Y4MsYMwenTNN1au2kX8xiJEwT7yFpb1NjJu5lYdwGbgUOstWvd7ZPduZ4CTMQpMdwT3YBFwFhrbbV7Hy+593EVTmAKa+39xpjhwIHA/er5JCIisu9R5pOIiIhI02xwLztFN1hri+oGntzts4CvgHF1rnocMMCkOtuj2VB/acQ8uriX6xoxNtZZOCcg740Gnty5VgH/5/56XhP3uTNXRgNP7n28j9MUfeTObyIiIiL7GgWfRERERJqB2xtpujFmozEmFO1dBAzFyQKK9RawHjjfGON3b5+Ek1G1FngnjlMd4V7Orue6eUAlMLwZ7me7tXZlPdvXAR2aYf8iIiLSRqjsTkRERKRpooGkLdENxpjTgH8ApcC7wCqgHLA4WUSxPZ+w1tYYY54CbgJOAN4Efg50BB6O7c/UgGhZXvcmzj/Dvcyve4W11hpj8ndjn/XZWSlgGJ0AFRERaVcUfBIRERFpmrHu5aKYbTfjZAwdbK1dFjvYGHPmTvbzFD/0gnoTp+QuAjzTyHksAqqBQ4wxGdba4l3dwBUdl4tTAhc7V+Nuj91XNBBW3/fGzEbep4iIiLRjOuskIiIi0kjGmP2A04EqnFXtovoB39QTeOoK9K1vX9badTjldz81xowGjgHetdauacxcrLXlwN9xVqn7wy7mnWCMiX7v+9S9HFvP0EOBIPBZzLZt7mV92VAj6tm2O2rcS38z7U9ERERaEQWfRERERBrBGHM4TkldALjTWrs+5urVQH9jTG7M+CDwGJDYwG6fwMkoehmnAXljGo3HmoxT/jfZGHN5TIApdt7DcPo7RcvtXsIpfbvKGNMtZlwSzip4ANNidvEdUAKcbIzJjhmfi5O51Ryizdp7NtP+REREpBUx1tqWnoOIiIhIizPG9AZWAiuAF9zNSUBn4Ec4jcNrgDuAG23MlyhjzG+Bh4CNwCs4AaVjcQJKpcCB1lpTz336gO9xekJtAnpaa8NNnPdwYLq7j1XA+zj9nDLceY/EKaPrbq0tc29zFXAvUAD8EygDxgMDgdeBn9d5fFOAP+EE2V4H0t3x/wVOBZ6z1p4XM34VgLW2dz3znQ0cGft8GGNOAP4NLAP+5c5ntbX2+aY8FyIiItI6KfgkIiIiQq3gU6wKYDvwLfARTpBlRT23NcBvgN/hlOBtxympuw4nq+nI+oJP7m3/H04G0Z3W2ut2c+4pOL2jfg4MwVlNrhT4Bqef1BPW2oI6tzkZuAo4CCfIvj96IgAAARFJREFUthQn4+nBugEwN0h2AzAJpyfUKuARYAZO8GyPgk/u9qvdx9AbJ1vsv9basU14GkRERKSVUvBJREREpAUZY94EfgrsZ61d3tLzEREREWlu6vkkIiIi0kKMMYNxAk8zFXgSERGRfVV9S+aKiIiISBwZY87C6a90jrvplhacjoiIiEhcKfgkIiIisvf9BvgxTgPvSdbaj1t4PiIiIiJxo55PIiIiIiIiIiISN+r5JCIiIiIiIiIicaPgk4iIiIiIiIiIxI2CTyIiIiIiIiIiEjcKPomIiIiIiIiISNwo+CQiIiIiIiIiInGj4JOIiIiIiIiIiMSNgk8iIiIiIiIiIhI3Cj6JiIiIiIiIiEjc/H+QV5IgPT/oLwAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 1400x800 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 716
        },
        "id": "W6a9KqmZaIEr",
        "outputId": "cb910420-29db-4284-a3d3-14ffac39038b"
      },
      "source": [
        "plt.figure(figsize=(14,8),dpi=100)\n",
        "plt.plot(covid19_maharashtra['Day Count'], covid19_maharashtra['NewDeaths'],color = '#D32F2F')\n",
        "plt.xlabel('Day Count', size=14)\n",
        "plt.ylabel('Daily Death Count', size=14)\n",
        "plt.title('Daily Deaths in Maharashtra', size = 18)\n",
        "plt.show()"
      ],
      "execution_count": 178,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1400x800 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QYXslzXubuEq"
      },
      "source": [
        "Our analysis ties in well with the known fact that the second wave of covid-19 was more deadly than the first wave with regards to daily new cases."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JuiwayzetmSg"
      },
      "source": [
        "## Conclusion"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9_Z_POsg2OrT"
      },
      "source": [
        "<center><img src=\"https://c.ndtvimg.com/2021-03/ve47ako_coronavirus-india-reuters_625x300_18_March_21.jpg\"></center>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OSCAWIdT0QwO"
      },
      "source": [
        "- Maharashtra has the highest number of reported cases as a result it has most deaths and most recoveries.\n",
        "- Punjab's Death rate is quite high(2.5%), every 1 in 40 person who reported positive is likely to die.\n",
        "- Dadra and Nagar Haveli and Daman and Diu has the highest recovery rate(99.7%) which means for every 1000 reported cases 998 are likely to recover. \n",
        "- Kerala has more than 9 million cases per 10 million population which is very high.\n",
        "- Maharashtra is the fastest growing in new cases perday on average. \n",
        "\n",
        "Here are article which validate my analysis\n",
        "\n",
        "[Maharashtra-Covid19](https://economictimes.indiatimes.com/news/india/maha-mortality-rate-less-than-punjab-ukhand-nagaland-goa/articleshow/83351837.cms)\n",
        "\n",
        "[Punjab-Covid19](https://www.thehindu.com/news/national/coronavirus-punjab-excess-deaths-three-times-official-toll/article35641394.ece)\n",
        "\n",
        "[Kerala-Covid19](https://www.indiatoday.in/coronavirus-outbreak/story/kerala-to-witness-two-fold-increase-in-covid-cases-health-minister-veena-george-1837781-2021-08-06)"
      ]
    }
  ]
}