Martin_Lukan_Pandas_Apply_and_Concatenate.ipynb

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      "name": "Martin Lukan Pandas Apply and Concatenate.ipynb",
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      "name": "python3",
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/mlukan/GDA3B2021/blob/main/Martin_Lukan_Pandas_Apply_and_Concatenate.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qXQwzrfmw_Uf"
      },
      "source": [
        "# Data preparation from the previous quest"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3lIRxl5vNZtI"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "link = \"https://raw.githubusercontent.com/murpi/wilddata/master/quests/weather2019.csv\"\n",
        "df_weather = pd.read_csv(link)"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YT8V5rYmxFPp"
      },
      "source": [
        "## You have now a DataFrame with lots of weather data\n",
        "\n",
        "*   List item\n",
        "*   List item\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KoUPLmHxz1us",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 309
        },
        "outputId": "e320bac5-de85-4e7e-8509-248da3123a7a"
      },
      "source": [
        "df_weather.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>DATE</th>\n",
              "      <th>MAX_TEMPERATURE_C</th>\n",
              "      <th>MIN_TEMPERATURE_C</th>\n",
              "      <th>WINDSPEED_MAX_KMH</th>\n",
              "      <th>TEMPERATURE_MORNING_C</th>\n",
              "      <th>TEMPERATURE_NOON_C</th>\n",
              "      <th>TEMPERATURE_EVENING_C</th>\n",
              "      <th>PRECIP_TOTAL_DAY_MM</th>\n",
              "      <th>HUMIDITY_MAX_PERCENT</th>\n",
              "      <th>VISIBILITY_AVG_KM</th>\n",
              "      <th>PRESSURE_MAX_MB</th>\n",
              "      <th>CLOUDCOVER_AVG_PERCENT</th>\n",
              "      <th>HEATINDEX_MAX_C</th>\n",
              "      <th>DEWPOINT_MAX_C</th>\n",
              "      <th>WINDTEMP_MAX_C</th>\n",
              "      <th>WEATHER_CODE_MORNING</th>\n",
              "      <th>WEATHER_CODE_NOON</th>\n",
              "      <th>WEATHER_CODE_EVENING</th>\n",
              "      <th>TOTAL_SNOW_MM</th>\n",
              "      <th>UV_INDEX</th>\n",
              "      <th>SUNHOUR</th>\n",
              "      <th>OPINION</th>\n",
              "      <th>MONTH</th>\n",
              "      <th>DAY</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2019-01-01</td>\n",
              "      <td>9</td>\n",
              "      <td>4</td>\n",
              "      <td>10</td>\n",
              "      <td>4</td>\n",
              "      <td>7</td>\n",
              "      <td>8</td>\n",
              "      <td>0.2</td>\n",
              "      <td>94</td>\n",
              "      <td>9.0</td>\n",
              "      <td>1037</td>\n",
              "      <td>62.250</td>\n",
              "      <td>9</td>\n",
              "      <td>6</td>\n",
              "      <td>3</td>\n",
              "      <td>116</td>\n",
              "      <td>143</td>\n",
              "      <td>176</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>5.1</td>\n",
              "      <td>very bad</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2019-01-02</td>\n",
              "      <td>8</td>\n",
              "      <td>5</td>\n",
              "      <td>18</td>\n",
              "      <td>7</td>\n",
              "      <td>7</td>\n",
              "      <td>5</td>\n",
              "      <td>0.0</td>\n",
              "      <td>90</td>\n",
              "      <td>9.0</td>\n",
              "      <td>1040</td>\n",
              "      <td>58.125</td>\n",
              "      <td>8</td>\n",
              "      <td>6</td>\n",
              "      <td>3</td>\n",
              "      <td>119</td>\n",
              "      <td>116</td>\n",
              "      <td>116</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>8.7</td>\n",
              "      <td>very bad</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2019-01-03</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "      <td>18</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>0.0</td>\n",
              "      <td>88</td>\n",
              "      <td>10.0</td>\n",
              "      <td>1040</td>\n",
              "      <td>37.000</td>\n",
              "      <td>6</td>\n",
              "      <td>1</td>\n",
              "      <td>-4</td>\n",
              "      <td>116</td>\n",
              "      <td>116</td>\n",
              "      <td>116</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>8.7</td>\n",
              "      <td>very bad</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2019-01-04</td>\n",
              "      <td>5</td>\n",
              "      <td>-1</td>\n",
              "      <td>15</td>\n",
              "      <td>-1</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>0.0</td>\n",
              "      <td>91</td>\n",
              "      <td>10.0</td>\n",
              "      <td>1040</td>\n",
              "      <td>42.625</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>-4</td>\n",
              "      <td>116</td>\n",
              "      <td>116</td>\n",
              "      <td>122</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>5.1</td>\n",
              "      <td>very bad</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2019-01-05</td>\n",
              "      <td>6</td>\n",
              "      <td>-1</td>\n",
              "      <td>8</td>\n",
              "      <td>-1</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>0.0</td>\n",
              "      <td>91</td>\n",
              "      <td>8.0</td>\n",
              "      <td>1040</td>\n",
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              "      <td>2</td>\n",
              "      <td>-2</td>\n",
              "      <td>143</td>\n",
              "      <td>116</td>\n",
              "      <td>116</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>8.7</td>\n",
              "      <td>very bad</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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            ],
            "text/plain": [
              "         DATE  MAX_TEMPERATURE_C  MIN_TEMPERATURE_C  ...   OPINION  MONTH  DAY\n",
              "0  2019-01-01                  9                  4  ...  very bad      1    1\n",
              "1  2019-01-02                  8                  5  ...  very bad      1    2\n",
              "2  2019-01-03                  6                  0  ...  very bad      1    3\n",
              "3  2019-01-04                  5                 -1  ...  very bad      1    4\n",
              "4  2019-01-05                  6                 -1  ...  very bad      1    5\n",
              "\n",
              "[5 rows x 24 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LDcCOwCSxQyZ"
      },
      "source": [
        "## Now, complete the following tasks :\n",
        "### Let's go in the deep !"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tqkE-0r_rnGq"
      },
      "source": [
        "*italicized text*## 1 - Merge\n",
        "OK, you know very well the previous dataset. Now, you want more, much more. Another year for example ?\n",
        "\n",
        "But it's too bad. The year 2018 is not in the good  shape. There are 2 files :\n",
        "- The main file with Temperature, etc...\n",
        "- The opinion file, with especially the Opinion\n",
        "\n",
        "The goal is [to merge](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.merge.html) both files in one DataFrame called **df2018**. It seems both files have a date column with the same format (but not the same name). \n",
        "\n",
        "Think about checking if both datasets have the same length. If they don't, what kind of merge do you have to do to keep each date of the main dataset ? Then apply this specific merge. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WbfmDeaZmny4",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2a7990cd-2094-4ff3-f2ba-c345e97a3ef0"
      },
      "source": [
        "link_main = \"https://raw.githubusercontent.com/murpi/wilddata/master/quests/weather_main_2018.csv\"\n",
        "link_opinion = \"https://raw.githubusercontent.com/murpi/wilddata/master/quests/weather_opinion_2018.csv\"\n",
        "df_main = pd.read_csv(link_main)\n",
        "df_opinion = pd.read_csv(link_opinion)\n",
        "print(df_main.head())\n",
        "print(df_opinion.head())"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "         DATE  MAX_TEMPERATURE_C  ...  DEWPOINT_MAX_C  WINDTEMP_MAX_C\n",
            "0  2018-01-01                 12  ...               8               7\n",
            "1  2018-01-02                 13  ...              12               6\n",
            "2  2018-01-03                 15  ...              13               7\n",
            "3  2018-01-04                 14  ...              12              10\n",
            "4  2018-01-05                 12  ...              10               7\n",
            "\n",
            "[5 rows x 15 columns]\n",
            "         date  WEATHER_CODE_EVENING  TOTAL_SNOW_MM  UV_INDEX  SUNHOUR OPINION\n",
            "0  2018-01-01                   113              0         3      5.1     bad\n",
            "1  2018-03-12                   119              0         2      8.8     bad\n",
            "2  2018-03-09                   116              0         3     10.2     bad\n",
            "3  2018-10-07                   122              0         1      5.6     bad\n",
            "4  2018-06-18                   119              0         1     12.9     bad\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4Qy062tcw3c0",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a19661da-fce6-444d-d31e-638f393aad23"
      },
      "source": [
        "# Your code here :\n",
        "print(len(df_main))\n",
        "print(len(df_opinion))\n",
        "df2018 = pd.merge(df_main,df_opinion,how='left',left_on='DATE',right_on='date')"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "365\n",
            "341\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XhmGb7SJz9T7"
      },
      "source": [
        "### Result"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QB62g1r_wzGa"
      },
      "source": [
        "Your DataFrame **df2018** should now have 21 columns and 365 rows. If not, try again.\n",
        "\n",
        "**Be careful:** \n",
        "- if you've renamed your `date` column, then merged both DataFrames with `on` parameter, Pandas kept only one `DATE` column. So you'll have 20 columns in your DataFrame. And you can skip the \"drop\" step just after.\n",
        "- if you merged with `left_on` and `right_on` parameters, Pandas kept both columns `DATE` and `date`, and you'll have 21 columns in your DataFrame. You can do this \"drop\" step:\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1hz3j3Bz5pnz",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 309
        },
        "outputId": "807ac802-11f6-4b0f-e11d-a997e0c104d7"
      },
      "source": [
        "# You should now probably drop the \"date\" column. \n",
        "# This column is incomplete, it were provided from the opinion file. \n",
        "# It was very useful to perform the \"merge\". But we don't need it anymore.\n",
        "df2018.head()\n"
      ],
      "execution_count": 126,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>DATE</th>\n",
              "      <th>MAX_TEMPERATURE_C</th>\n",
              "      <th>MIN_TEMPERATURE_C</th>\n",
              "      <th>WINDSPEED_MAX_KMH</th>\n",
              "      <th>TEMPERATURE_MORNING_C</th>\n",
              "      <th>TEMPERATURE_NOON_C</th>\n",
              "      <th>TEMPERATURE_EVENING_C</th>\n",
              "      <th>PRECIP_TOTAL_DAY_MM</th>\n",
              "      <th>HUMIDITY_MAX_PERCENT</th>\n",
              "      <th>VISIBILITY_AVG_KM</th>\n",
              "      <th>PRESSURE_MAX_MB</th>\n",
              "      <th>CLOUDCOVER_AVG_PERCENT</th>\n",
              "      <th>HEATINDEX_MAX_C</th>\n",
              "      <th>DEWPOINT_MAX_C</th>\n",
              "      <th>WINDTEMP_MAX_C</th>\n",
              "      <th>WEATHER_CODE_EVENING</th>\n",
              "      <th>TOTAL_SNOW_MM</th>\n",
              "      <th>UV_INDEX</th>\n",
              "      <th>SUNHOUR</th>\n",
              "      <th>OPINION</th>\n",
              "      <th>flag</th>\n",
              "      <th>DATETIME</th>\n",
              "      <th>MONTH</th>\n",
              "      <th>DAY</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2018-01-01</td>\n",
              "      <td>12</td>\n",
              "      <td>8</td>\n",
              "      <td>61</td>\n",
              "      <td>9</td>\n",
              "      <td>11</td>\n",
              "      <td>8</td>\n",
              "      <td>8.9</td>\n",
              "      <td>79</td>\n",
              "      <td>9.500</td>\n",
              "      <td>1018</td>\n",
              "      <td>41.750</td>\n",
              "      <td>12</td>\n",
              "      <td>8</td>\n",
              "      <td>7</td>\n",
              "      <td>113.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.1</td>\n",
              "      <td>zlé</td>\n",
              "      <td>False</td>\n",
              "      <td>2018-01-01</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2018-01-02</td>\n",
              "      <td>13</td>\n",
              "      <td>6</td>\n",
              "      <td>26</td>\n",
              "      <td>8</td>\n",
              "      <td>12</td>\n",
              "      <td>13</td>\n",
              "      <td>0.6</td>\n",
              "      <td>96</td>\n",
              "      <td>9.000</td>\n",
              "      <td>1020</td>\n",
              "      <td>87.875</td>\n",
              "      <td>13</td>\n",
              "      <td>12</td>\n",
              "      <td>6</td>\n",
              "      <td>122.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.3</td>\n",
              "      <td>zlé</td>\n",
              "      <td>False</td>\n",
              "      <td>2018-01-02</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2018-01-03</td>\n",
              "      <td>15</td>\n",
              "      <td>10</td>\n",
              "      <td>40</td>\n",
              "      <td>11</td>\n",
              "      <td>12</td>\n",
              "      <td>10</td>\n",
              "      <td>5.5</td>\n",
              "      <td>82</td>\n",
              "      <td>8.500</td>\n",
              "      <td>1017</td>\n",
              "      <td>91.500</td>\n",
              "      <td>15</td>\n",
              "      <td>13</td>\n",
              "      <td>7</td>\n",
              "      <td>122.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.3</td>\n",
              "      <td>zlé</td>\n",
              "      <td>False</td>\n",
              "      <td>2018-01-03</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2018-01-04</td>\n",
              "      <td>14</td>\n",
              "      <td>11</td>\n",
              "      <td>45</td>\n",
              "      <td>14</td>\n",
              "      <td>14</td>\n",
              "      <td>11</td>\n",
              "      <td>0.0</td>\n",
              "      <td>89</td>\n",
              "      <td>10.000</td>\n",
              "      <td>1011</td>\n",
              "      <td>90.125</td>\n",
              "      <td>14</td>\n",
              "      <td>12</td>\n",
              "      <td>10</td>\n",
              "      <td>116.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.3</td>\n",
              "      <td>zlé</td>\n",
              "      <td>False</td>\n",
              "      <td>2018-01-04</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2018-01-05</td>\n",
              "      <td>12</td>\n",
              "      <td>7</td>\n",
              "      <td>21</td>\n",
              "      <td>10</td>\n",
              "      <td>11</td>\n",
              "      <td>8</td>\n",
              "      <td>1.5</td>\n",
              "      <td>85</td>\n",
              "      <td>9.875</td>\n",
              "      <td>1005</td>\n",
              "      <td>62.375</td>\n",
              "      <td>12</td>\n",
              "      <td>10</td>\n",
              "      <td>7</td>\n",
              "      <td>116.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>6.9</td>\n",
              "      <td>zlé</td>\n",
              "      <td>False</td>\n",
              "      <td>2018-01-05</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         DATE  MAX_TEMPERATURE_C  MIN_TEMPERATURE_C  ...   DATETIME  MONTH  DAY\n",
              "0  2018-01-01                 12                  8  ... 2018-01-01      1    1\n",
              "1  2018-01-02                 13                  6  ... 2018-01-02      1    2\n",
              "2  2018-01-03                 15                 10  ... 2018-01-03      1    3\n",
              "3  2018-01-04                 14                 11  ... 2018-01-04      1    4\n",
              "4  2018-01-05                 12                  7  ... 2018-01-05      1    5\n",
              "\n",
              "[5 rows x 24 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 126
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JkJlhOExk4YY"
      },
      "source": [
        "# Read and execute the code below :\n",
        "df2018.drop('date',             # Select column to drop\n",
        "            axis = 1,           # Precise if you want to drop a column (1) or a row (0)\n",
        "            inplace = True)     # If we don't use this argument, result is not stored"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oArS7K5Xf1Yl"
      },
      "source": [
        "Now, everybody should have a 20 columns and 365 rows.\n",
        "\n",
        "**Ohhhh, there are some missing values, we have to fill them. That will be the goal of this quest: to learn how to fill in the missing values, column by column, with different techniques.**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oQSMcwAGvCyL",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "252b2bc1-7477-49d2-a889-700a9387e8d5"
      },
      "source": [
        "# Just execute the code below, to display a \"non-null\" column :\n",
        "print(\"My DataFrame shape is : \", df2018.shape, end = '\\n\\n\\n\\n')\n",
        "print('Which columns have missing values ? (Non-null column)')\n",
        "print(df2018.info())\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "My DataFrame shape is :  (365, 20)\n",
            "\n",
            "\n",
            "\n",
            "Which columns have missing values ? (Non-null column)\n",
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 365 entries, 0 to 364\n",
            "Data columns (total 20 columns):\n",
            " #   Column                  Non-Null Count  Dtype  \n",
            "---  ------                  --------------  -----  \n",
            " 0   DATE                    365 non-null    object \n",
            " 1   MAX_TEMPERATURE_C       365 non-null    int64  \n",
            " 2   MIN_TEMPERATURE_C       365 non-null    int64  \n",
            " 3   WINDSPEED_MAX_KMH       365 non-null    int64  \n",
            " 4   TEMPERATURE_MORNING_C   365 non-null    int64  \n",
            " 5   TEMPERATURE_NOON_C      365 non-null    int64  \n",
            " 6   TEMPERATURE_EVENING_C   365 non-null    int64  \n",
            " 7   PRECIP_TOTAL_DAY_MM     365 non-null    float64\n",
            " 8   HUMIDITY_MAX_PERCENT    365 non-null    int64  \n",
            " 9   VISIBILITY_AVG_KM       365 non-null    float64\n",
            " 10  PRESSURE_MAX_MB         365 non-null    int64  \n",
            " 11  CLOUDCOVER_AVG_PERCENT  365 non-null    float64\n",
            " 12  HEATINDEX_MAX_C         365 non-null    int64  \n",
            " 13  DEWPOINT_MAX_C          365 non-null    int64  \n",
            " 14  WINDTEMP_MAX_C          365 non-null    int64  \n",
            " 15  WEATHER_CODE_EVENING    341 non-null    float64\n",
            " 16  TOTAL_SNOW_MM           341 non-null    float64\n",
            " 17  UV_INDEX                341 non-null    float64\n",
            " 18  SUNHOUR                 341 non-null    float64\n",
            " 19  OPINION                 341 non-null    object \n",
            "dtypes: float64(7), int64(11), object(2)\n",
            "memory usage: 59.9+ KB\n",
            "None\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "G8JuzY6wiXGP"
      },
      "source": [
        "### Find and display the Dates of missing values.\n",
        "You can help you with the method `.isna()`\n",
        "\n",
        "You have to find 24 different dates."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "k3aBVUumirL-",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "87a1f172-ae89-44a3-c9f1-9dec284b04f5"
      },
      "source": [
        "# Your code here :\n",
        "df2018.isna().sum()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DATE                       0\n",
              "MAX_TEMPERATURE_C          0\n",
              "MIN_TEMPERATURE_C          0\n",
              "WINDSPEED_MAX_KMH          0\n",
              "TEMPERATURE_MORNING_C      0\n",
              "TEMPERATURE_NOON_C         0\n",
              "TEMPERATURE_EVENING_C      0\n",
              "PRECIP_TOTAL_DAY_MM        0\n",
              "HUMIDITY_MAX_PERCENT       0\n",
              "VISIBILITY_AVG_KM          0\n",
              "PRESSURE_MAX_MB            0\n",
              "CLOUDCOVER_AVG_PERCENT     0\n",
              "HEATINDEX_MAX_C            0\n",
              "DEWPOINT_MAX_C             0\n",
              "WINDTEMP_MAX_C             0\n",
              "WEATHER_CODE_EVENING      24\n",
              "TOTAL_SNOW_MM             24\n",
              "UV_INDEX                  24\n",
              "SUNHOUR                   24\n",
              "OPINION                   24\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4uyBORXD7SGL"
      },
      "source": [
        "## 2 - Before filling missing values, you have to create a new axis \"flag\", to document and to remember which lines you modify."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zAPjwqRb673g",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8a08d6be-4278-42c9-f5ee-a876a98625db"
      },
      "source": [
        "# Just execute the code below :\n",
        "df2018['flag'] = df2018['OPINION'].isna()\n",
        "df2018['flag'].value_counts()"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "False    341\n",
              "True      24\n",
              "Name: flag, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xV589xMUwi_y"
      },
      "source": [
        "## 3 - Visualize and Fill the NaN"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "loj1tBax0WEO"
      },
      "source": [
        "### 3.1 - First column : WEATHER_CODE_EVENING\n",
        "\n",
        "You have to visualize how are the well-filled data. Of course you know Matplotlib, and maybe, you know Seaborn and Plotly. But, did you know that Pandas integrate directly some parts of Matplotlib ? It's very useful, and don't need to import other libraries..."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0OfJmw2cFTqL"
      },
      "source": [
        "#### Scatterplot directly with Pandas"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SjLKF6l6yzxs",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 180
        },
        "outputId": "5931e256-efa7-41cb-f8e7-4fc6642fc625"
      },
      "source": [
        "# Draw a scatterplot with Date on X-axis and WEATHER_CODE_EVENING on Y-axis\n",
        "df2018.plot.scatter(figsize=(30,10),x='DATE',y='WEATHER_CODE_EVENING' ) #Code to complete\n"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-2-d394802721ee>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# Draw a scatterplot with Date on X-axis and WEATHER_CODE_EVENING on Y-axis\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mdf2018\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscatter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfigsize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m30\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'DATE'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'WEATHER_CODE_EVENING'\u001b[0m \u001b[0;34m)\u001b[0m \u001b[0;31m#Code to complete\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m: name 'df2018' is not defined"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0gmFthwQFV43"
      },
      "source": [
        "#### Boxplot directly with Pandas\n",
        "\n",
        "Here you can find [a little help](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.boxplot.html)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HNXtBQVC1OHM",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 197
        },
        "outputId": "bed1380e-b77a-41d0-d429-b0b465c7d3a4"
      },
      "source": [
        "# Find a way to draw directly with pandas a boxplot on the column WEATHER_CODE_EVENING\n",
        "#df2018['WEATHER_CODE_EVENING'].plot.box()\n",
        "df2018['WEATHER_CODE_EVENING'].plot.box(showfliers=False)"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-3-cf87b8a98257>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# Find a way to draw directly with pandas a boxplot on the column WEATHER_CODE_EVENING\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;31m#df2018['WEATHER_CODE_EVENING'].plot.box()\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mdf2018\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'WEATHER_CODE_EVENING'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbox\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mshowfliers\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m: name 'df2018' is not defined"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WS1Qn73uFd2G"
      },
      "source": [
        "#### Deciles\n",
        "\n",
        "Here you can find [a little help](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.quantile.html)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZSwhF8404BK6"
      },
      "source": [
        "# Find a way to print the 9 deciles + min + max of the column WEATHER_CODE_EVENING with the quantile method of pandas and a loop\n",
        "print(f\"Min: {df2018['WEATHER_CODE_EVENING'].min()}\")\n",
        "for qu in range(10,100,10):\n",
        "  percentile=df2018['WEATHER_CODE_EVENING'].quantile(q=qu/100)\n",
        "  print(f\"{qu} percentile: {percentile}\")\n",
        "print(f\"Max: {df2018['WEATHER_CODE_EVENING'].max()}\")\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QZKwDI4c5LFf"
      },
      "source": [
        "#### Fill values\n",
        "OK it seems that the most of datas are between 113 and 122. Maybe, we could fill the missing values with Mean or Median, it's up to you to choose. So you need :\n",
        "- Calculate the mean or the median\n",
        "- Fill the missing values only for **WEATHER_CODE_EVENING** with the `fillna()` method \n",
        "\n",
        "When you're OK, remember tu use the `inplace=True` argument, otherwise, your modification will not be stored."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mUsfFo3z5yPA",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3edb9d3c-0117-4d41-ad98-2bfce170b92b"
      },
      "source": [
        "# Hop ! Your code here :\n",
        "\n",
        "median_weather_column = df2018['WEATHER_CODE_EVENING'].median()      #You have to complete this\n",
        "df2018['WEATHER_CODE_EVENING'].fillna(median_weather_column, inplace = True) # When you are OK, you can replace this argument by True\n",
        "print(df2018['WEATHER_CODE_EVENING'].value_counts())\n"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "113.0    154\n",
            "116.0    102\n",
            "122.0     59\n",
            "119.0     34\n",
            "143.0     12\n",
            "200.0      2\n",
            "353.0      1\n",
            "248.0      1\n",
            "Name: WEATHER_CODE_EVENING, dtype: int64\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qlg_bKw4mICl"
      },
      "source": [
        "# You can use this code to check how many missing values you have :\n",
        "column_name = 'WEATHER_CODE_EVENING'\n",
        "print(\"I have \" ,df2018[column_name].isna().sum(), \"missing values in the column\", column_name)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "le_hQxxU8ZWL"
      },
      "source": [
        "### 3.2 - Next columns : TOTAL_SNOW_MM and UV_INDEX\n",
        "\n",
        "We trust you to choose right values to fill for both columns"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GIxOv06L8wSj"
      },
      "source": [
        "# Your code here\n",
        "#df2018['TOTAL_SNOW_MM'].value_counts()\n",
        "#df2018['TOTAL_SNOW_MM'].plot.box(showfliers=False)\n",
        "df2018['TOTAL_SNOW_MM'].fillna(0.0, inplace = True) \n",
        "\n",
        "median_UV_index=df2018['UV_INDEX'].median()\n",
        "#df2018['UV_INDEX'].value_counts()\n",
        "#df2018['UV_INDEX'].plot.box(showfliers=False)\n",
        "df2018['UV_INDEX'].fillna(median_UV_index, inplace = True) \n"
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7vesplgF8v5g"
      },
      "source": [
        "### 3.3 - Next column : \n",
        "SUNHOUR"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ysILwt6X84bv",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "outputId": "d9cf3bea-ebe2-412d-c560-9988254949e2"
      },
      "source": [
        "# Draw a scatterplot with Date on X-axis and SUNHOUR on Y-axis\n",
        "df2018.plot.scatter(x='DATE',y='SUNHOUR')\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fc7faed6e10>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 51
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BWhXVf-o9O0N"
      },
      "source": [
        "#### Propagation\n",
        "**It seems this is not a good idea to apply the same value all year long...**\n",
        "\n",
        "You could use the the `method = 'ffill'` or `'bfill'` argument in `fill_na()` to propagate non-null values forward or backward."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ox3IsoPc-mAE"
      },
      "source": [
        "# Your code here\n",
        "#df2018['SUNHOUR'].describe()\n",
        "df2018['SUNHOUR'].bfill(axis ='rows',inplace=True)"
      ],
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "11f1by-w-4u3"
      },
      "source": [
        "### 3.4 - Column : OPINION\n",
        "For Opinion, we have not yet idea. So, we can write a new value 'unknown' in this column."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wpFiSPfjpBnO"
      },
      "source": [
        "# Your code here\n",
        "df2018['OPINION'].fillna('unknown', inplace=True)\n",
        "\n",
        "\n"
      ],
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iEokSY6O-u-H"
      },
      "source": [
        "## Now, each axis should have 365 non-null values."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1BioxU79956l",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "eb465c58-8644-4876-8ced-ad92234e303d"
      },
      "source": [
        "df2018.info()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 365 entries, 0 to 364\n",
            "Data columns (total 21 columns):\n",
            " #   Column                  Non-Null Count  Dtype  \n",
            "---  ------                  --------------  -----  \n",
            " 0   DATE                    365 non-null    object \n",
            " 1   MAX_TEMPERATURE_C       365 non-null    int64  \n",
            " 2   MIN_TEMPERATURE_C       365 non-null    int64  \n",
            " 3   WINDSPEED_MAX_KMH       365 non-null    int64  \n",
            " 4   TEMPERATURE_MORNING_C   365 non-null    int64  \n",
            " 5   TEMPERATURE_NOON_C      365 non-null    int64  \n",
            " 6   TEMPERATURE_EVENING_C   365 non-null    int64  \n",
            " 7   PRECIP_TOTAL_DAY_MM     365 non-null    float64\n",
            " 8   HUMIDITY_MAX_PERCENT    365 non-null    int64  \n",
            " 9   VISIBILITY_AVG_KM       365 non-null    float64\n",
            " 10  PRESSURE_MAX_MB         365 non-null    int64  \n",
            " 11  CLOUDCOVER_AVG_PERCENT  365 non-null    float64\n",
            " 12  HEATINDEX_MAX_C         365 non-null    int64  \n",
            " 13  DEWPOINT_MAX_C          365 non-null    int64  \n",
            " 14  WINDTEMP_MAX_C          365 non-null    int64  \n",
            " 15  WEATHER_CODE_EVENING    365 non-null    float64\n",
            " 16  TOTAL_SNOW_MM           365 non-null    float64\n",
            " 17  UV_INDEX                365 non-null    float64\n",
            " 18  SUNHOUR                 365 non-null    float64\n",
            " 19  OPINION                 365 non-null    object \n",
            " 20  flag                    365 non-null    bool   \n",
            "dtypes: bool(1), float64(7), int64(11), object(2)\n",
            "memory usage: 60.2+ KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BiXcI3AdJCJW"
      },
      "source": [
        "## 4 - Visualize your modifications\n",
        "Draw 4 scatterplots with Date on X-axis, and each of the 4 numeric modified columns on Y-axis :\n",
        "- The original datas will be in blue\n",
        "- The new data (your datas) in red\n",
        "\n",
        "It's the moment to use your \"flag\" column created at the beginning. It'll be easier in seaborn with `hue` argument."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0B2-rD6IGG50",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 336
        },
        "outputId": "f4bf756e-4b20-46e9-b0cd-fefe8ca74716"
      },
      "source": [
        "# Your code here\n",
        "import  matplotlib.pyplot as plt\n",
        "fig, ax = plt.subplots(figsize=(25,5))\n",
        "\n",
        "ax1 = plt.subplot(141) \n",
        "ax2 = plt.subplot(142)\n",
        "ax3 = plt.subplot(143)\n",
        "ax4 = plt.subplot(144)\n",
        "flagdf=df2018[df2018['flag']==False]\n",
        "\n",
        "\n",
        "ax1.scatter(x = df2018[\"DATE\"], y = df2018['WEATHER_CODE_EVENING'],c='r')\n",
        "ax1.scatter(x = flagdf[\"DATE\"], y = flagdf['WEATHER_CODE_EVENING'],c='b',marker='+')\n",
        "\n",
        "ax2.scatter(x = df2018[\"DATE\"], y = df2018['TOTAL_SNOW_MM'],c='r')\n",
        "ax2.scatter(x = flagdf[\"DATE\"], y = flagdf['TOTAL_SNOW_MM'],c='b',marker='+')\n",
        "\n",
        "ax3.scatter(x = df2018[\"DATE\"], y = df2018['UV_INDEX'],c='r')\n",
        "ax3.scatter(x = flagdf[\"DATE\"], y = flagdf['UV_INDEX'],c='b',marker='+')\n",
        "\n",
        "ax4.scatter(x = df2018[\"DATE\"], y = df2018['SUNHOUR'],c='r')\n",
        "ax4.scatter(x = flagdf[\"DATE\"], y = flagdf['SUNHOUR'],c='b',marker='+')\n",
        "\n",
        "\n",
        "\n",
        "ax1.set_title(\"WEATHER CODE EVENING\")\n",
        "ax2.set_title(\"TOTAL SNOW MM\")\n",
        "ax3.set_title(\"UV_INDEX\")\n",
        "ax4.set_title(\"SUNHOUR\")\n",
        "plt.show()\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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ERERESkqlP0RERERERERERESkpGb1gnkiIiIiIiJSHqKLFN6Xb5m7L5jl6YiIiMghTqU/RERERERERERERKSkVPpDREREREREREREREqqLEp/1NfXe2NjY6mnISIyLdu2bdvl7otKPY/ZpP21iFQi7a9FRCqD9tciIpVhpvbXZRGobmxsZOvWraWehojItJhZb6nnMNu0vxaRSqT9tYhIZdD+WkSkMszU/lqlP0RERERERERERESkpBSoFhEREREREREREZGSUqBaREREREREREREREpKgWoRERERERERERERKSkFqkVERERERERERESkpBSoFhEREREREREREZGSUqBaREREREREREREREpKgWqRgyGZhMZGqKoK98lkqWckIiIiFcLMdpjZD83sETPbmme5mdnnzOwJM9tuZieXYp4yQ5JJqK8Hs3Crry98LJl7zNnWpmPQctXWFj6XzOeafTv8cH1mFc7MNpjZb8zs0Zz295rZT8zsR2Z2fanmJ3NY7ndGvv2L9itSwWpKPQGRipdMQiJBz8BSUqwk3psilkiEZc3NpZ2biIiIVIrl7r6rwLKzgeOj22nA+uheKl0yCa2t9AyeQhdrAWjp7yK2alVYnn0sme+Yc/16ejhdx6Dlpq0N1q+nk0u4hYs5jOcAeI7DuJhbeOWeR0kR12dW2b4E3Ah0ZRrMbDlwDvAqd99nZn9eornJXJXznfEsR41b/CJ+TcueLmIXXRQatF+RCmTuXuo5sGzZMt+6dULyiEhlaGykp3cxTXSTpo460nTTRGzJM7BjR6lnJzPIzLa5+7JSz2M2aX8tIpWo3PfXZrYDWFYoUG1mXwBS7v7v0fPHgbi7P1NondpfV4joODLOZtLMA2Ae+9jM8onHknmOOT/LGt7PDToGLTc1NXQOt3IpnXkX1zLICFX6zPIo9/11NjNrBL7q7q+Inv8H0Onu35zOerS/llHJJKxZA/39Y20LF8INN4Sgc57vjFyj3yFsGWusq4OhIRgZgepqSCRg3boZ3hiZ62Zqfz1l6Q8zO8zMHjSzH0Snr3wiav+Smf0yOkXxETM7MWrXqYlyaOnrI0WcNHUMU0OaWlLEoa+v1DMTERGRyuDA181sm5kl8iw/Gngy6/lTUds4ZpYws61mtnXnzp0zNFU5qKLjyEFqAQOs8LFknmPOTazQMWg5Gh5mEyuiJ5Zzg0Fq9JnNTS8FzjCzB8zs22b26kIdtb+WCTLZ0v3Hs5p1nMcmVrOOnv7jYdWqsDzPd0bubR91nMNd/E82j60jfTKdI6s4i/voHG6F9evDmR8iZaiY0h/7gNe7+x4zqwW+a2b3Rcs+4u535vTXqYlyaGloIN6boo40aZw6BomTgoaGUs9MREREKsNr3f3p6DTxb5jZT9z9/umuxN07IaRwLlu2rPSnTcrUouPIWgZJRzlEBY8l8xxzrmAT3+F1OgYtN9XVrBjexNc5i/A71Hi1DDGC6TObe2qAI4HTgVcD/2Fm/8PznMau/bVM0N5Oz+ApE7KlN9LK5vRyYu3teb8zJjJ2chQ7s8qCfJGLGaYWINovQWL9+hCwhvFZ2yIlNmWgOtqp7ome1ka3yXak5wBd0bgtZnaEmS2e7NREkYrW0UEskaB7oCnUmiNFbP526Mh/qp+IiIhINnd/Orr/jZndBZwKZAeqnwaOzXp+TNQmla6jg1hrK6nB5XTRAkALXcTqHoKODRP75h5zsoVX8qiOQctNIhGCQJC/RrU+s7nqKeArUSzkQTMbAeoBpUzL1Pr6SLEyK1s6yJx5Eeu7Dm67bdx3RnaN6p/yUn7MCeQrnDA8GvozwPkYa7mPs4GornWhayOIlEBRF1M0s2pgG3Ac8Hl3f8DMVgMdZnY10A1c4e77KHxq4jM560wACYAG/YIslSzakcfa28OXR0NDONjUDl5ERESmYGbPB6rc/Y/R4zcCn8zpdg/wHjP7MuFMxd8rCWSOyBxHrllDrD86DXvhQrhhw8RjyXzHnG9eTezee3UMWm6i2q+Jm24i4TdPXL5gAbG9D+gzm3vuBpYDm83spUAdUOgiuSLjFciWHnfmRb7vjEgPp/M6UgxRN2HV1QxFGdUh53QnR3E3540uH5e1rf2RlFhRgWp3HwZONLMjgLvM7BXAlcCzhJ1vJ/BRJh5UT7ZOneoic0dzs3boIiIisj+OIhxfQzg2/zd3/28zuwzA3W8C7gXeDDwBDACtJZqrzITpHEfqmLNyrFuni5XNYWb270AcqDezp4CPAxuADWb2KJAGLsxX9kMkr5wzbJ7lqJDtnHuWTb7vgWSSWEsL94/EuZ6P8DgvZRG7OJLdo+u4no9wN+eSL+N6XNa2SIkVFajOcPffmdlm4E3u/r+j5n1mthH4cPRcpyaKiIiIiBTB3X8BvCpP+01Zjx34h9mcl4iIFObuKwssumBWJyJzxyTZ0hy+sLixl17KXXtX5O1yOZ/mXt48rv51hurlSzkpVH19lJktijKpMbPnAW8AfmJmi6M2A84FHo2G3AO0WHA6OjVRRERERERERESksOZm2LULbr8d5s+nh9O5livo6T8eEglIJicfu2cPuE8cz+nE2EKK5VzGTZzLXaO3y7iJzSyPsrY7Zm9bRQooJqN6MXBrVKe6CvgPd/+qmX3LzBYRqrE/AlwW9depiSIiIiIiIiIiItPV3k7PwFKa6CZNHXWk6R5oKr6GdL7xNBGzB4j5lon9C10bQaQEpgxUu/t24KQ87a8v0F+nJoqIiIiIiIiIiExXXx8pVpKmjmFqSOPTqyFdaDwPhIxrkTI2ZekPERERERERERERmQUNDcRJUUeaaganX0P6QMeLlJAC1SIiIiIiIiIiIuWgo4PY/O1008Rarg5lO+ZvL76G9IGOFymhYmpUi4iIiIiIiIiIyEyLakXH2ttDuY+GBujoLL6G9IGOFykhBapFRERERERERETKRXPzgQWWD3S8SImo9IeIiIiIiIiIiIiIlJQC1SIiIiIiIiIiIiJSUgpUi4iIiIiIiIiIiEhJKVAtIiIiIiIiIiIiIiWlQLWIiIiIiIiIiIiIlJQC1SIiIiIiIiIiIiJSUgpUi4iIiIiIiIiIiEhJKVAtIiIiIiIiIiIiIiWlQLWISIUzszeZ2eNm9oSZXZFn+TwzuyNa/oCZNeYsbzCzPWb24dmas4iIiIiIiIhINgWqRUQqmJlVA58HzgZOAFaa2Qk53S4GfuvuxwGfAa7LWf4vwH0zPVcRERERERERkUIUqBYRqWynAk+4+y/cPQ18GTgnp885wK3R4zuBJjMzADM7F/gl8KNZmq+IiIiIiIgUq60NamrALNy3tRU3LpmE+vowziw8TiZndq4iB6im1BMQEZEDcjTwZNbzp4DTCvVx9yEz+z2w0MyeAz4KvAFQ2Q8REREREZFy0tYG69fTw+mkiBMfThFbvz4sW7eu8LhkElpbYXBwbGx/itiqVWF5c/PMz11kPyhQLSJy6LoG+Iy774kSrPMyswSQAGhoaJidmYmIiIiIiBzqOjvp4XSa6CZNHXWk6aaJWGfn5IHq9vbRIPW4sekmYu3tClRL2VLpDxGRyvY0cGzW82Oitrx9zKwGeCHQT8i8vt7MdgDvB/7RzN6T+wLu3unuy9x92aJFiw7+FoiIiIiIiMhEw8OkiJOmjmFqSFNLijgMD08+rq8PIP/YaJlIOVKgWkSksn0fON7MXmJmdcD5wD05fe4BLowevx34lgdnuHujuzcCnwX+l7vfOFsTFxGRMWZWbWYPm9lX8yy7yMx2mtkj0e2SUsxRRERkzksmobERqqpCTecFC8ZqPGffqqrG6j5n6kBn6khnluXeDj88LGtsLL5WdHU1cVLUkaaaQeoYJE4KqqsnHxedCZt3bOYs2ba2wnPNd6uvhzPPnHz72trG3r/pbKdIRKU/REQqWFRz+j3A14BqYIO7/8jMPglsdfd7gFuA28zsCWA3IZgtIiLlZQ3wGPCCAsvvcPcJZ72IiIjIQZJMQiJBz8BSUqwk3p8CoIsWnuUodnMkO6nnZfyUs/0++qkf7ZPi71k4vIt+6lnou3iYk0fHPMdhXMwtvHLPo6FWdG+KWCIRXnOqEhyJBLH16+mmKYwlRYwtkFg9+biODmhtJTa4ZfzYuoegY8O42tddtABwEg/xMCePPu6nnoXsGm1r6e8i1t1NJ5dwCxdzGM8BjL4nl+/5NLHRetorp7edIhFz91LPgWXLlvnWrVtLPQ0RkWkxs23uvqzU85hN2l+LSCUq9/21mR0D3Ap0AB9097fkLL8IWDadQLX21yJSicp9fz0TtL8uI42N9PQuHq3pXM0QjjFIXd7uVQxTwxAGDFLDCNUYwzj5s51rGWSEqrE600uegR07pp5XWxt0doZyH9XVkEhMXp86I5mENWugvz88X7gQbrghBI1raugZfjVxNpNmXt7hudsyj32s4TNczxUFt+9G/oH3c8P4etrFbqdUlJnaX6v0h4iIiIhIaX0WuBwYmaTPCjPbbmZ3mtmxk/QTERGR/dHXN66m8yC1DFILWJ4bjER90tQyEhUs8NHCBRP7D1Kzf7Wi162DoSFwD/fFBKkhBKR37Qrj3MPjTGZzVPt6/PYx7nHutqSp5SusKLB9xiA1bGKFamLLAVGgWkRERESkRMzsLcBv3H3bJN3+C2h096XANwjZ1/nWlTCzrWa2defOnTMwWxERkTmsoWFcTefa6Aae5wZVDFEb1X6uYggAi+7z9a9lKH+t6FKIal+P3z7GPc7dljoG+Vs25fQbu9UyxAo2Fa6JLVIE1agWERERESmd1wBvM7M3A4cBLzCz2939gkwHd+/P6n8zcH2+Fbl7J9AJ4VTymZuyiIjIHNTRQSyRoHtgrKYzFKhRTVSjOuqTIs5Cdo2r6zyhRjWPjtWKnr8dOjpLt61R7esUy4uvUU0XMbbwF/wif41qPk2MLeO30x6AN19Wmm2UiqRAtYiIiIhIibj7lcCVAGYWBz6cHaSO2he7+zPR07cRLrooIiIiB1NUFiPW3k6s7zo48kh47jlie9sm9jUL5TQWLgxj+reE+tHDw9GymyeOWbCA2N4HQoZxR2dpLzAYlQ+J3XQTMd9S3JiFC+HEJhLfuoVEge3jtCZi3/oWeAje4xC79VZ4zWt0QUUpigLVIiIiIiJlxsw+CWx193uA95nZ24AhYDdwUSnnJiIiMmc1Nx86AdV164qvd12sxkZ6/LTRC1LWkaZ7oIlYe/uh877KAVGNahERERGRMuDuKXd/S/T46ihIjbtf6e4vd/dXuftyd/9JaWcqInJoM7MNZvYbM3s0z7IPmZmbWX0p5iZSUjkXpNQFFWW6FKgWERERERERESnel4A35Taa2bHAGwFF5eTQlHNBSl1QUaZLgWoRERERERERkSK5+/2EUky5PgNcDuiCtnJo6uggNn873TSxlqvppim6cGRHqWcmFUI1qkVEREREREREDoCZnQM87e4/MLNST0ekNHIvSFkOF46UiqJAtYiIiIiIiIjIfjKz+cA/Esp+FNM/ASQAGlQSQeaaQ+mClHLQqfSHiIiIiIiIiMj++wvgJcAPzGwHcAzwkJm9KF9nd+9092XuvmzRokWzOE2peMkkNDZCVVW4TyZLPSORg0oZ1SIiIiIiIiIi+8ndfwj8eeZ5FKxe5u67SjYpmXuSSUgk6BlYSoqVxHtTxBKJsEwZzDJHKKNaRERERERERKRIZvbvQA/wMjN7yswuLvWc5BDQ3k7PwFKa6OYq1tJENz0DS6G9vdQzEzlolFEtIiIiIiIiIlIkd185xfLGWZqKHEr6+kixkjR1DFNDGidFPFy0UGSOUEa1iIiIiIiIiIhIOWtoIE6KOtJUM0gdg8RJgS7IKXOIAtUiIiIiIiIiIiLlrKOD2PztdNPEWq6mmyZi87dDR0epZyZy0ChQLSIiIiIiIiIiMpVkEhobwQxqasJ9VVW4N4Pq6nDf2Bj65o6trx/rW18/sc9kmpuhs5PYkme40q4jtuQZ6Oyc+QsptrWNbWtNTXguMkNUo1pERERERERERGQyySQkEvQMLCXFShYO76Kfehb6Lh7mZABOGnmIfuqJ96aIJRJhXHNzGNvaSs/gKXSxFoCW/i5iq79J1qoAACAASURBVFaN9SlGc/PMB6aztbXB+vV0cgmbWMGK4U0k1q8Py9atm715yCFDgWoRERERERERETm0JZPQ3g59faHuc0fH+KBwezs9A0tpopt91DFCNcYwTvW41VQxzDzSdA80EWtvD+tob6dn8BTibCbNPAA20srm9PKxPuWos5NOLuFSOgH4OmcBkOjsHAtUJ5Nw6aWwd+/YuAUL4Kabyne7pGyp9IeIiIiIiIiIiBy6MtnSvYu51j9KT+9iSCTGl+bo6yNFnDR1jER5nz6a/2nRDUaoIU0tKeIh6J01dpDa0b4T+pSj4WE2sSJ6ErZvEytgeDg0JZPQ0kLP3leymnWcxyZWs46ePa+Aiy6aXmkTEZRRLSIiIiIiIiIih7KsbOk0ddTlZkQDNDQQ701RR5p9EGVUD0UZ1T66qiqGqGOQOKmQmZ01tpZB0lHO6IQ+5ai6mhXDm6JM6rCNK9gUanFDeN9GTh2XKQ5RtvhQmWeLS1maMlBtZocB9wPzov53uvvHzewlwJeBhcA24N3unjazeUAXcArQD/ydu++YofmLiIiIiIiIiIjsv74+UqwkTR3D1JDGSREn1nfdWJ+ODmKJBN0DTaSIs5CoRjVZNaqJalSTIjZ/O3R0jo1tbSU1uJwuWgBooYtY3UPQsWG2t7Z4icRoTepNrGAFm0hwMyRWh+XR+zaWKR5kssXHvX8iRSgmo3of8Hp332NmtcB3zew+4IPAZ9z9y2Z2E3AxsD66/627H2dm5wPXAX83Q/MXERERERERERHZf1nZ0mk8f7ZzlBkca28n1vtPIat4eBjMwG8OfaqqYGQEliwJQepMNnFm7Jo1xPrbQtvChXDDhvLOOI7qUCc6O0kM3xy2ObF6rD51nkxxqJBscSlLUwaq3d2BPdHT2ujmwOuBd0XttwLXEALV50SPAe4EbjQzi9YjIiIiIiIiIiJSPnKypSdkRGc0N+9/YPlAxpbSunVjgelcHR3EWlpIjYRM8Wc5ihfx65AtXrMVOr40mzOVOaCoGtVmVk0o73Ec8Hng58Dv3H0o6vIUcHT0+GjgSQB3HzKz3xPKg+w6iPMWERERERERERE5cNnZ0n3XhUzg7IxoyS/zvl16KbG9bWPtCxbATV/S+yfTVlSg2t2HgRPN7AjgLuAvD/SFzSwBJAAadCqAiIiIiIiIiIiUSqVmPJea3jc5iKqm7jLG3X8HbAZiwBFmlgl0HwM8HT1+GjgWIFr+QsJFFXPX1enuy9x92aJFi/Zz+iIiIiIiIiIiIiJS6aYMVJvZoiiTGjN7HvAG4DFCwPrtUbcLgf+MHt8TPSda/i3VpxYRERERERERERGRQorJqF4MbDaz7cD3gW+4+1eBjwIfNLMnCDWob4n63wIsjNo/CFxx8KctIiIiIjJ3mFm1mT1sZl/Ns2yemd1hZk+Y2QNm1jj7MxQREZFZlUxCfT2YTbzV14flInPMlIFqd9/u7ie5+1J3f4W7fzJq/4W7n+rux7n7O9x9X9T+XPT8uGj5L2Z6I0REREREKtwawlmL+VwM/NbdjwM+A1w3a7OqVMkkNDZCVVW4L8c/5pPJcLGpTNChuhpe/nKoqckflJjOrboa2tqmnoPMvskCT1VV4b5c/82KyOxJJqG1Ffr76eF0ruUKOrmE1axjNevo6T8eVq3SvkLmnKIupigiIiIiIjPDzI4B/gboIJyRmOsc4Jro8Z3AjWZmKq9XQDIJiQQ9A0tJsZJ4b4pYIhGWlcvFnpJJaGmhZ+RUrucj/IoXc/HILSR+fDOdXMImVrCCTQDcwsW8mF9xNvfxMCcD8AJ+xyOcxIk8zB84gmc5CoDdHMlzHBbWtX59eK1160qyiZJHFHjqGTyFLtbyLEexmyPZST0v46ec7ffRT315/psVkdnV3g6Dg/RwOk10s486RqgeXbyRVjanlxNrb9d+QuYUBapFRERERErrs8DlwOEFlh8NPAng7kNm9ntC6b1dszO9CtPeTs/AUproJk0ddaTpHmgqrz/m29vpGTmV15FiiDoAHuQ07ucMkrwbgK9z1rghd3PehNXk9sl4kNMASHR2KlBdTtrb6Rk8hTibSTNv3KLHeDl3cx5VDDOvHP/Nisjs6usDIEWcNHWMUAM4YACkqSVFnFifTrKSuaWYGtUiIiIiIjIDzOwtwG/cfdtBWFfCzLaa2dadO3cehNlVqL6+0T/sh6kZ/WM+80d/WYjmOEQtIegQAg/3cXbUwbI6W9bz3MfZbTZu+SZWwPDwDG2A7Jfocx8c97mP/9xGyvXfrIjMroYGAOKkqCNNFUPRAgecOgaJkxrtJzJXKFAtIiIiIlI6rwHeZmY7gC8Drzez23P6PA0cC2BmNcALgf7cFbl7p7svc/dlixYtmtlZl7OGhtE/7KsZLM8/5qM51jBIJugAcDb3RR2yq7p41vPcx9ltPm75CjaFWtVSPqLPvXbc5z7+c6tiqDz/zYrI7OrogNpaYmyhmyY+xVV8gQSXcROXcRObWU6s7qHQT2QOUekPEREREZEScfcrgSsBzCwOfNjdL8jpdg9wIdADvB34lupTT6Kjg1giQfdAEynixEkRm78dOjpLPbMxHR3EWlq4fyQ+VqOaW0hwM6/jOwdeozpaF4nVJdtEyaOjg1hrK6nB5XTRMrFGNVGN6nL8NysisytT9mfNGmL9W4ixZfzyhQvhhg0qDyRzjgLVIiIiIiJlxsw+CWx193uAW4DbzOwJYDdwfkknV+6iP9pj7e2hdmdDQwj4ldMf85k5Xnopd+1dEdqqquAvTyDx+EYSwzePdk1wc741TK6qCi5drfrU5Sbzua9ZQ6y/beJyM3CHJUvK79+siEwtmQwXQezri757Osb+H0+2rJDm5tLsB/ZnriIHiQLVIiIiIiJlwN1TQCp6fHVW+3PAO0ozqwpVqj/up6MS5igHnz53kbkpmYREgp6BpaRYSbw3RSyRGFteaFm57Q8m245ym6vMSapRLSIiIiIiIiIiUoxkEhobw9krjY2jGcg9A0tpopurWEsT3fQMLA2ZyZMtKzcHY6753h+RIilQLSIiIiIiIiIiMpVMxnHvYq71j9LTuxgSCejtJUWcNHUMU0OaWlLEQ/mMvr7Cy8rNgc610PujYLUUSaU/REREREREREREppKVcZymjjrSdA80Eav+PvHhFHWkSePUMUicVKjxDMR7Cy8rKw0NBzbXQu9Pe7tKh0hRFKgWERERERERERGZSl8fKVZmZRw7KeLEhrcQm7+d7oEmUsSJkyI2f3u4MCoQSyQKLisrHR0HNtdC70/fdTM7b5kzVPpDRKTCmdmbzOxxM3vCzK7Is3yemd0RLX/AzBqj9jeY2TYz+2F0//rZnruIiIiIiEjJFVtXuaGBOCHjuJrBsYzjJUugs5PYkme40q4jtuQZ6Owcu4BqoWXl5kDnWuj9KcfscSlLyqgWEalgZlYNfB54A/AU8H0zu8fdf5zV7WLgt+5+nJmdD1wH/B2wC3iru//KzF4BfA04ena3QEREREREpIQydZUHlpJiJfHeFLFEIizLDdBOlnGcCUrnM9mycnMgcz3QjGw55ClQLSJS2U4FnnD3XwCY2ZeBc4DsQPU5wDXR4zuBG83M3P3hrD4/Ap5nZvPcfd/MT1tERERERKQMTKeucvQ81t4eylk0NIwFqUXvjxwwlf4QEalsRwNPZj1/iolZ0aN93H0I+D2wMKfPCuChfEFqM0uY2VYz27pz586DNnERERERkUpkZhvM7Ddm9mhW26fN7Cdmtt3M7jKzI0o5R5mGvj5SxLPqKteSIg59ffn7NzfDjh0wMhLuFYQdT++PHAAFqkVEDnFm9nJCOZBL8y139053X+buyxYtWjS7kxMRERERKT9fAt6U0/YN4BXuvhT4KXDlbE9K9pPqKouUDQWqRUQq29PAsVnPj4na8vYxsxrghUB/9PwY4C6gxd1/PuOzFRERERGpcO5+P7A7p+3r0dmLAFsIx+VSCTo6iM3fTjdNrOVqummK6ip3lHpmIoccBapFRCrb94HjzewlZlYHnA/ck9PnHuDC6PHbgW+5u0enI/5f4Ap3/96szVhEREREZG5bBdxX6klIkZqbobOT2JJnuNKuI7bkGegscV3lZBIaG6GqKtwnk6Wbi8gs0sUURUQqmLsPmdl7gK8B1cAGd/+RmX0S2Oru9wC3ALeZ2ROEzI/zo+HvAY4Drjazq6O2N7r7b2Z3K0RERERE5gYzaweGgIKRRTNLAAmABpWXKA/NzeVTSzmZhESCnoGlpFhJvDdFLJEIy8pljiIzRIFqEZEK5+73AvfmtF2d9fg54B15xn0K+NSMT1BERERE5BBgZhcBbwGa3N0L9XP3TqATYNmyZQX7ySGqvZ2egaU00U2aOupI0z3QRKy9XYFqmfNU+kNERERERERE5ACY2ZuAy4G3uftAqecjFayvjxRx0tQxTA1pakkRh76+Us9MZMYpUC0iIiIiIiIiUiQz+3egB3iZmT1lZhcDNwKHA98ws0fM7KaSTlIqV0MDcVLUkaaaQeoYJE4KVCZGDgEq/SEiIiIiIiIiUiR3X5mn+ZZZn4jMTR0dxBIJugeaSBEnTorY/O3Q0VnqmYnMOGVUi4iIiIiIiIjI7EgmobERzKCmJtw3Nob2qcZUVY31zdc21etVVYX7Qrf6+snncTAkk+F18r3+4YfDu98Nz3sesYU/40q7jtiSZ6Czs7LqU0+2jbm3BQvG+k71+Uz12dXXj/17OPPMsX9fNTXQ1lbqd0WKoIxqERERERERERGZeckkJBL0DCwlxUoWDu+in3rivSliiUTokxuQzRkT700Ra20FM3rSJ4+15Ruf+3q+i4c5mWc5it0cyU7qWcQujmQ3L+LXtPR3EVu1Kv88Dtb2t7bSM3gKXawF4AX8jkc4iRVs4pV7Hg1Z1P1RFvVtt1VWgBpGt5HBQXo4nS5aeJajAHgRv+YkHqKfehYSffZ7U7AXUvz96OfzY/5q9LM5gcc4iYfGtb+Mn/JSHucRTuJEHuYI/kC8PwUQ3r/eFLHebjq5hE2sYMXwJhLr14f5rVtXmvdFimKTXIh21ixbtsy3bt1a6mmIiEyLmW1z92Wlnsds0v5aRCqR9tciIpVB++tDQGMjPb2LaaKbfdQxQjVVDDOPNN00hezhHTsKjklTR13UF5jQNmF8ntebzDz2sZnl+eexP5JJWLMG+vvD86oqekZOJc5m0syb0L2WQUaoKrw9MyWZhPZ26O2F6moYHoYlS6CjY/qB8sZG6O2lh9MLbqcxjEeffQ1DGDBIzZSfTyHGCLUMYsAQNdSR5r3cwPVcMdrnCyRIcDPcfnvlBf/L0Eztr1X6Q0REREREREREZl5fHynipKljJDrJf4Qa0tSSIg59fZOOGc7qm69twvg8rwdW8DbpPKYrk1nc308Pp3MtV9Azciop4gxSm/W6jN4PUjP59syETNZ572Ku5Qo6h1vDXHsXQyIx/VIo0ZwnbufY9nrWZz9ILWlqp/h88rWTtb7q0fVk3r+vsGJcn7VcRQ+nw6pVM1/eRfabAtUiIiIiIiIiIjLzGhqIk6KONFUMAVDFEHUMEicFDQ2TjqlmcLRvvrYJ4/O8HnjB26TzyDVVjez29tHyF010cxVraaKbheyilsGs12X0vpahybdnJrS30zOwlCa6+RhruZROPhbNtWdgadiO6YjmHCeVs51j22tZn31ttL2Tfz752hm9r2J4dD2Z9+9v2TSuz1Mcy3I205M+GS69dHrbJLNGNapFRERERERERGTmdXQQSyToHmgiRXysTjFRTeaOzinHxEkRq90GZnSns9ryjc/zepPWqKaLWN1D0LFh8u3IVzc7t0Z2VmbxWOa30089KZbTRQuQU6OaRyffnpnQ10eKlVlZ5x5luTsp4sT6rpve+jo6oLWV2OCW0e2ctEY1KYBxn89+1ajOWk+cFDG28FNext2cSyZPdx91XM9HuGvvinBxRdWrLjuqUS0isp9UQ09EpDKU8/7azA4D7gfmEZJI7nT3j+f0uQj4NPB01HSju9882Xq1vxaRSlTO++uZckjur/enHnJmTF9fyNjt6AjtuW35xme/nhlMFgdbuBBuuGHqGsYF6maPqymdVas59KuljsHQzx7IP48FC2Dv3sm352Dbn7rhU8mtzT2Z5z8fDjss9J3q85nMwoXhfvfu8P79+tf0PHdi3jrZo/WqM+OK+cxlnJnaXyujWkRERESkdPYBr3f3PWZWC3zXzO5z9y05/e5w9/eUYH4iIiIHV3Pz9IOChcYUs579eb2pZGUhDxfKPs7KLO4mK/O77iHYcFv5BEb3J8t9KjPxnk9XMkmstZXU4HLeyR08xbGEetXOLVzMw5wMQEt/F7FVq8KYUs9ZFKgWERERESkVD6c37ome1ka30p/yKCIiIoU1NBDvDbWv04VqW2eCnmvWEOvfQowtUfbuhvIKiEZzibW3E+v9p5ws987ymut0ZLbrggu4Kqq9nTnE2sYpPMhpAGyklc3p5cQuvHDcOCkNBapFRERERErIzKqBbcBxwOfd/YE83VaY2euAnwIfcPcnZ3OOIiIikiVf3ex82cflkFlcjEqZ53Q1N8P3vkdi/XoANrGC+Qzwn5xDyK6GfdRyDR/nmuFPTKwzLrOuqtQTEBERERE5lLn7sLufCBwDnGpmr8jp8l9Ao7svBb4B3JpvPWaWMLOtZrZ1586dMztpERGR6UgmQ83mqqpwn0yWekYHprkZOjuJLXmGK+26UMe5s4Kzj+eydetg9WoS1Rv5GmdzOZ+mlkFCdrUDVXyTM2mim56BpaG2tpSMAtUiIiIiImXA3X8HbAbelNPe7+77oqc3A6cUGN/p7svcfdmiRYtmdrIiIiLFSiYhkaCndzHX+kfp6V0MicTcCFbv2AEjI+FeQerytW4dDA3B7bcTq91GiuVcxk2cygNUMcwINTzHPLpoCRd1rPR/mxVMgWoRERERkRIxs0VmdkT0+HnAG4Cf5PRZnPX0bcBjszdDERGR/ZBMQn09mMEFF9AzsJQmurmKtWOZq+3tpZ6lHGqam2HjRmILf8Z62vgsH6CGIcBxqthIKz2cDhdcEP79KmA96xSoFhEREREpncXAZjPbDnwf+Ia7f9XMPmlmb4v6vM/MfmRmPwDeB1xUormKiIhMLZmE1lZ6+o9nNetYzTq6aCFNHcPUkKaWFHHo6yv1TOVQ1NwMu3aF7Gq2sIqNGCOAMUgN7+czrGYdPf3Hw6pVClbPMl1MUURERESkRNx9O3BSnvarsx5fCVw5m/MSERHZb5ddRs/gKcTZTJp5ANSSHs1crWOQOCloaCjpNOUQ19wMa9bQ0t/FrVzIPuoYoZoHOY0HOY2NtLI5vZzYhReO9ZcZp4xqERERERERERE5cG1tsGcP1/MR0tQBBhhD1NDKRtZyNd00EZu/HTo6Sj1bOdTdcAOx+dvppokz+eZoZjUY+6jlGj5Oz/Cr50ZN9QqhQLWIiIiIiIiIiByYZBLWr6eTS7ib8wgBPyeTRd1CF1fyT8SWPAOdncpQldJrbobOTmLV3+caPkEtg2T+zUIV3+TMsZrqF1wQaq6rdvWMUqBaRERERERERET2X6YuNaezlquiRgPgGJ5kM8uJ1T0Et98OO3YoSC3lo7kZbr2VWO02UiznMm7iVB6gimFGqOFPzONivsh5bBqrXX3BBXD44QpYzwDVqBYRERERERERkf3X3j6hLnXISoWrWEuMLbDhdgWopTxF/y5ja9YQ62+jh9Ojf8vVQBWP8XIe4+UAdPL3vI17uHzPp4mtWjVuvBw4ZVSLiIiIiIiIiMj+6+sjRZxBasnU+AXnXO4iwc2werWCeVLempth1y64/XZibGEVGwk/tti42wjV3M15LGczPemTYc2akk57rpkyUG1mx5rZZjP7sZn9yMzWRO3XmNnTZvZIdHtz1pgrzewJM3vczM6ayQ0QEREREREREZESamggTmpcjd95pLmcT8Pznw/r1pV6hiLFaW6GhQtpoYs60ozVrM7cMhdbrKOLFujvVwmQg6iYjOoh4EPufgJwOvAPZnZCtOwz7n5idLsXIFp2PvBy4E3AOjOrnoG5i4iIiIiIiIhIqXV0jKvxexk3jdWl/sIXSj07KZVkEhoboaoq3FdKQPeGG8b9ez6Xu3gd36aKYbID1hsJddlpby/xhOeOKQPV7v6Muz8UPf4j8Bhw9CRDzgG+7O773P2XwBPAqQdjsjOmUv/jiIiIiIiIiIiUWnMzbNxIbOHPWE8b62kjtvBnsGGDSn4cqpJJSCTo6V3Mtf5RenoXQyJRGTG3nH/Pd7GCb7Oc73IGp/IAFgWqh6gmRRz6+ko94zljWjWqzawROAl4IGp6j5ltN7MNZvZnUdvRwJNZw54iT2DbzBJmttXMtu7cuXPaEz9oKvk/joiIiIiIiIhIqSWTIat0925YsgRuvz3U+y2XIHVbG9TUgFm4b2sr9YwqSzIJ9fXh/TMLj6eKm7W30zOwlCa6uYq1NNFNz8BSuOCCMH7BgrH1Zd+KWfdsyNSsdg+3228nVvUgn+UDHMZzVDNIHYPESUFDQ6lnO2fUFNvRzBYAm4D3u/sfzGw9sJaQ774W+GdgVbHrc/dOoBNg2bJlPp1JH1RZ/3HS1FFHmu6BJmLt7eWzQxURERERERERKUeZBMCBpaRYSbw3RSyRCMvKIa7S1gbr19PJJWxiBSuGN5FYvz4sU+3sqSWT0NpKz+ApdLEWgJb+LmKrohBgoc+4r48UK0lTxzA1pHG6aCFFnHh/CoAuWniWo0aHvIhfF7fuUojmEksk6B5oCttBitj87dDRWeLJzR1FBarNrJYQpE66+1cA3P3XWcu/CHw1evo0cGzW8GOitvKU5z9OijixvutKPTMRERERERERkfI2WwmAmaztvr6QwdrRUdz6Ozvp5BIuDbmSfJ2zAEh0dipQXYz2dnoGTyHOZtLMA2AjrWxOL5/8M25oIN6boo40aZxqhtlIK0PUUM0QjjFI3YRh49b9ve9BZycMD0N1daiAkPnMsv89HHlkaNu9e3r/NqYrE6xubw9xw4aGEKQup4B6hZuy9IeZGXAL8Ji7/0tW++KsbucBj0aP7wHON7N5ZvYS4HjgwYM35YMsujJtHWml7YuIiIiIiIiITEdfHyniWQmAtQe/bu+BlG0dHmYTK6InBhCeDw8fvPnNZdHnO0gt4f2z4j7jjg5i87fTTRNruZpVbGSIGoapYZDacevLvo2uu7c3ZMIPt3IW99E53Arr14cM+dx/D/3H09N//OyU9G1uhh07YGQk3CtIfVAVk1H9GuDdwA/N7JGo7R+B/8/e/ce5dd91vn99pRk5dQzbWuObOE1nhmVDL6EN+TFOfTb7KDITaJvtEnaH2we+09iME5TYpTvtBdoE0bTgzsahCxtDsVPhOHiwoHAZLvSyLoXOtaCbykkcp6RpuyVd8Axp3TQzoZRkGksjfe4f50gjaaQZzU9Jnvfz8TiP0Tn6nqOPzq/EX33O57vbOXc9fumP88DdAGb2JefcHwFfBmaB95hZ6179IyNK2xcRERERERERWY6qzNmGEgAPHJjLlHUOLr8cXnnFX+e22+DUKb+jMhwuZdNm8juWl7UdDjOQHwsyqf3KswOM+duuttys7UtZcHw7yZEN8l0bOsbl2ccTh8iwkxPsLWVX18uoLm0b6mfCnzpVkcUfZhYHzNIxd268+91+PexoFA4f1nFsE4t2VJvZ/6D4k1OlUwusMwKMrCCu9aO0fRERERERERGR5VlqAmBQMzrDTr+9peFl/NcTabzSe7uJ5qeYpotYPk2aGBeJUKCDi/jtvYlDi8cXj5dqUo8xwABjxDkG8f2V7Vq91nazjIzgDQ2Rzu1ilD0A7GEUL3IORo4vvO7goD+lUvPOEahTo7q47SxVmfDGGAPE88fmlfEtBN2WRrhU0hco1cNuyZrXUlPDgyle0ooXjoiIiIiIiIiI1LZAxnHdBMDiOmUZ0hl21s2GfYhh3sfhoFM6TIg8m8jyXg5TIAwYBcJEmaqdFV0tqGkcTyb9Ts5w2O+krq5PvV61tttN8fgOD+NNH/CXRaNw+Hjj+6X6HNm6FV59Fe+VA/PbFre9d2/9TPirr55X/9o/hwpEyBFlqvI4ZoPjCPPPX4DhYZieLvv8w7XbbuTzYJ2oo1pEREREREREpEHOuePAO4FvmdmbgmVbgT8EevHLo77LzP6pWTGuicUyjmt14lWtU8yQnqS7bjbsGANkg8xpv1O6gyzGF7iBEHkKdBBilmm6Gq8zfeTI4gMnVmXpFjNzvckHG99Hl6rVSPBc6jYee6x+Jvwtt9TM0C6+rqyZbnPZ99Xn79AQFApk8jsY5SAAe6ZH8fbu9cvNZG9Udv06U0e1iIiIiIiIiEjjfhf4ODBatuxeYNzMDjnn7g3mP9iE2FZfWUZ0eSZ0RS3gvXv9AewWyFIuz5DuYJYOZqFGNuwAY3yOt3IRgvazFcsr6mD39Kze91xOrW1ZOw1kwldkaAPe9JnSe/OOYzg8P2M+1w9AjNNk2QTAowxxOr8L8ii7vgnUUS0iIiIiIiIi0iAz+xvnXG/V4tshKIwLJ4A0l0JHdVVGdHkmdBbj1/hFZtjMQH6slP1a0VldlqVcniGdx/hZjtHN5LxsWI8zvJlnSRMjSlCjumr5onWwl2OptbZl7S2UCV8rQzuVgqEhvNwZxik7jpFzkM3XzLQGyNFJcXi+LJ2l5cquX3/qqBYRERERERERWZkrzOxC8PqbUDZCXDuqk0UdLsuEdhT4U/4jQFBH2M9+rehYLMtSrs6Q3sMoHmfAObj8crxXHvezl2/bj3fqlF+qIahpTU/P3PJadbBXw2K1tqX1FY/V8DDe9Bn//CrWvE4kamfMA53kyBICqFiu7Pr1p45qEREREREREZFVYmbmnLN67zvn4kAcB9/xVgAAIABJREFUoLsVO75qZFFfDDKiDYjzO3QzyZ9yO0/wFvxMVL+2dDx/rHJbVVnKFRnSm5+B5MnW6ghejVrM0lwLHMN5GfOdT0GhQDq/i1H2APg/oISfhHCY8ayy69ebOqpFRERERERERFbmBefcdjO74JzbDnyrXkMzSwJJgL6+vrod2usulYLhYZiersiiduQpEMYv2xHmBs4R5xhRpoKOav8rDDDmZ0CXK89Srs6QVrayrKeaGfOP+suGh/GmD/jtolE4fKJGW52v60Ed1SIiIiIiIiIiK/MpYC9wKPj7Z80NZ4mC2r7kcmTYyUf4cCmL2uFwGEaIELNM0wVAHD97eowBBhjz5+P7529bWcrSKuqdi/XOT5236y7U7ABERGRlnHNvd8591Tn3tWCE8er3Nznn/jB4//HygV+cc/cFy7/qnHvbesYtIiLgnLvMOfeEc+5vnXNfcs79So02de/jIiKy/pxzfwBkgDc65553zt2J30H9Y86554Bbg/n2kUiUOqn7Geez3FqqJ91JjghZwuTYRNav1dvTA/v3Ew8/ymd4B/Hwo7B/f/2B76S+VAq6uvxa3c75r1OpZkcl0hTqqBYRaWPOuTDw28A7gGuB3c65a6ua3Qn8k5n9G+C/AQ8G614L/DTwQ8DbgSPB9lZPKgVbtsz9T5cmTZo0rcZ0af0D7iLwo2b2w8D1wNudczur2tS8j6+qevfrri44cAB6eyEU8ufL27XKsThwwI+vHc6f6li/53v8eFIpfz87V/n+li2tuc9laRbqiKp37HWsW5aZ7Taz7WbWaWZXm9kjZjZtZv1mdo2Z3WpmLzU7ziWZnAQgTYxskEkdIs+tfJY0uzjNLg5yP+P0B7V6R/xO6dlZMPP/qpN66YJM9sz0NeznCPs5Qmb6Gti3T9e/bEgq/SEi0t5uBr5mZn8P4Jz7JHA78OWyNrcDHwle/zHwceecC5Z/0swuAv/gnPtasL3MqkSWSsGePWQKN/Nr/CJf5QfYxhQAL9I17/VWXuIltlbMF5Uvr15nsTaNft5i67yRv+MH+CppdnEZr67JZyznO5W3X85+qH79KpcR4zR/xxtLx6z4PWq9VyuWK3mB7+XbNfdVve+30Peo3u4XuIHreXpejCvZbwvtw8W+90qP/3qex0t9/Ub+jnfwaT7NO/gGV5X2Q/nrpeyT8u3+AF8tHcvv8Fq+zA829H2v5AX2TI/i7dvn77Q2fyTTzAx4OZjtDKbqeqU17+PBuitX5359LV/x9/XRo2TYSZrdxKbTAHMDDrXCsThwAI4eJcldPMKdXMU3SufXAGMAPMKdXMarc9+pWTHXiPUDL38M7447oLOTTPZG0uwmalM8zY0A7HllFGixfS5LU+yIyt3EKAeBsuP42GNw4kRp0LqKY69jLWstlfIzqScn/R9J8nlipImQJYsRIcdH+BU89zhs3Yr30uOq1bsaqvZ7Jr+DGKfJsgmARxnidHYXXiKh/Swbjlut/79dib6+Pjt79myzwxARWRLn3FNm1tfkGH4KeLuZ3RXM3wG8xcx+rqzNs0Gb54P5/wW8Bb/T44yZnQyWPwJ82sz+uN7nLel+3dtLZmI7byXNLJHlfD0RkZo2cZHT7MLruQDnzy/avhXu1wsJnmZ5Cvg3wG+b2Qer3q95HzezqXrbXK379SYu8pu8l/dxmCwRwsxiOHJBu6UeizXR0UEyP8Td/rhki2pqzDVi7STHX/MjAPQzHtSEDZe9nwVorX0uSxNcY+UdUaXjGH6STH5HzWO/EY91q9+v10LT+kNSKYjHgx9JYsRI43EGIPhxMlgWOQfHj6vDdLXU2O9pYiT4KBZc/448I/wy97kHoVBocsAita3V/VqlP0REZEHOubhz7qxz7uyLL77Y+IqTk6SJMUsn4C6RiRaIodnfs9F9sFb7ijXe/kY/9ot993qv1/dYZukkTaz0mHK7M7O8mV0PXA3c7Jx703K2sxb36yydjDFAlgh5OsjRSa6sXUsci3yeMQaCmVrnV/nyJsdcI9YcHaSJVTxuX/l+C+5zWZrgGqt5HPP5usdex1rWVCJBZuY6+hnnQxykn3Ey7ATn8DjDfRzCiz6nTurVVmO/R5mikxz+A1V+JnuMtJ+9LrLBqKNaRKS9fR14Q9n81cGymm2ccx3AvwKmG1wXM0uaWZ+Z9W3btq3xyLq7iZGmo+x/utp/ogViaPb3bHQfrNW+Yo23v9GP/WLfvd7r9T2Wl+o/4Mzs28Bp/HEDytW7j1evv+r36wg5BhgrDaLVGUwtdSzC4VKJj9rnV/nyJsdcI9ZOZoNuav9x+xCzVe+34D6XpQmusZrHMRyue+x1rGVNBT+gFH+ILP0wAmDmT1NT6qRebTX2+zRdpNnFPTzMPTzsP0kROefXARfZYFSjWkSkvT0JXOOc+z78joyfBv7PqjafAvbi157+KeD/MzNzzn0K+H3n3G8AVwHXAE+sWmQjI3h79vA3hZhqVKtGtWpUq0b16taoZjT4B9zxusexXTjntgE5M/u2c+41wI8xf7DEmvfxVQuizv36Wr7i72vO8GaeLT2iDGX1klvhWMTjxI8eBWisRnUzY64R6wf4mF//tbOT8Ww/aWJEKatTTFWN6lbY57I0IyN4Q0Okc7vmH8c743gnTjA+U/vY61jLmunuJjZRWY9aP4ysgzr73XOP45lfeoVoFA4rk102KDNr+nTTTTeZiEi7Ac5aC9xDgduAvwP+F5AIlv0q8BPB68uA/xv4Gn5H9L8uWzcRrPdV4B2LfdaS79cnT5pdfnkxJ0OTJk2aVmeKRv37S5vdr2tNwHXA08AzwLPA/cHyhu7j9aZVu19Ho2b795v19Jg558+Xt1visVgz+/f78a3B+bPmsW7Z4sdz8qS/n6Hy/csvb819Lktz8qR/7Godx3rHfgMe61a+X6/V1LT+kJMnzTZvts+z0/4L99rn2Wm2efOGO+fWnfa7XCLW6n6twRRFRJZJg72IiLQH3a9FRNqD7tfrLJWCRMKvg97d7Zea2IhZvOu9Hzbqft+o3/sStVb3a5X+EBERERERERHZaAYH1VGYSkE8TmbmOtLsJjaRxovH/ffWat9sxP3ejP0sbUmDKYqIiIiIiIiIyMaTSJCZuY5+xvkQB+lnnMzMdXDHHeDc3NTV5Xe2yvLU28+JRLMjkxajjmoREREREREREdl4JidJEyNLhDwdZOkkTQzMyLCT/RxhP0fITF8D+/aps3q56u3nyclmRyYtRh3VIiIiIiIiIiLS3lIpP/O5mAW9ZYs/HwpBb2/tTububmKkiZAlTI4IOWKkybCTGKd5mHt4mHvYxWky2RuVAbxcdfYz3d3NjkxajDqqRURERERERESkfaVSMDREZvqauSzoV95MZvoaHrAPkpnYDvH4/M7qkRG8zc8wTj8HuZ9x+vE4Q5oYOToBBzhlAK9Urf28+Rl/QEWRMhpMUURERERERERE2kMq5Wc2T076Gbm33QbJJJn8DmKcJssmAB5hHyGMWTqIkGV8ph8vkagcvC947SUSeJMP+tnXeYiRppMc2SC/UxnAK1S9n7u7YSS5OgMpHjgAySTk8xAO+z9IHDmy8u1KUyijWkREREREREREWl8qBfE4mYntc5nSR4+Sye/gI3y4Igs6RydZOheviTw4COfPQ6EAJ05AZ2eQVb0rKPzxMKfZhRc5pwzglSjfz+fPr14n9dGjJPNDvI1Pk8wPwdGj/nJpS8qoFhERERERERGR1pdIkJm5jn7GyRIhQpaHGOZ9HOYiEYwQYAB0kgsyqguNZ0QXO0+Hh/Gmz+Bxxp+PRuHw8dXpXJXVk0yS5C7uJgnAX/I2AOLJpLKq25QyqkVEREREREREpDlSKX+wQ+f80hvFwRDDYf9vR4f/t7cXJiZIEyNLpJQpPcYAWSIU6CBEnpt5nHt4mL8mxml2Lb0m8uAgTE2B2dw0NaVO6laUzzPGQDDjAPz5fL55McmKKKNaRERERERERETWX7GUx8x1pNlN1KZ4mhsBuKFwjmm6iOanmKaL2EQaz00SszQRsmQxIuQYYIzP8dbS/EO838+EvvxyuOwyvJceX92ayNI6wmEG8mNBJrWfST/AmP8jh7QldVSLiIiIiIiIiLS7Awfg4Yf9DGCALVvgjjvg1Km5gQdHRuY6a6sHJSx/b72UlfK4SIQClR2MjjxGmBB5NpFl3Prx3OOMWz9pYsRI43GGN/Ps3PzmZyB5Up3SG0E8TvzoUcDPpB5gjDjHIL5/fT6//BravBleeWXuvXB46ZndoZBfw7unZy77v941mkrB3XdXfmZRNAqHD1deA0tt3yTqqBYRERERERERaWfFQeW4i0e4k6v4Bh94+WN4R4+SYSdpdvsZyfH43DplmcwV761nZ9XkJGl2l0p3+FmxLnjTsGBZgQ6yGGlieHYGr+cC3uSDfufdbfvxTp2am1fm9MYR1KGOJ5PE88f8zuH4/vWpT131NEDslTRf5E1z11/+Y6X5y3iVa/kKN+A/JfBtvpc0u7iKb/AOPj3vKYLYRBpvaAicI5O9sfb1u2cPmcLN/Bq/yDe4ijt5ZO4Hm+k03r59frvBQT/WsvZf5QfYxhRbeYkreYE906OV7ZvIWfGXtibq6+uzs2fPNjsMEZElcc49ZWZ9zY5jPel+LSLtSPdrEZH2oPv1CnR0kMwPlQaVA38wwY/zHt7H4dLAg+P04/VcACAzsb1iUMLSe+fPrzyeeqqzuF9+mcz0NY1nVK9HjHLpW42nCXp7K64hR55ZIqW3Q+Tnnc++PNRcPrde8VwHal+j+NfvW0lXfGYnOQqE5l/PQazV7Ys2cZHT7FrStbVW92tlVIuIiIiIiIiItLMag8rl6CgNNJgvz0iefBCglMlc6701UZ2BOpHG63wKL3KO8axfyiNKWY3qIPs0SlCjuljWYyS5yAeJLKDWebicpwnKngbI0wGEgjf8669QNe8zqPHkQPnr8qcHgAWv31k6K9bNBXHMu56DWCvbz8nSufbXf4PUUS0iIiIiIiIi0s5qDCrXyey8gQZjpP0MUiA2UTkoYfl7a6KsHnUpQzTXjxd9Dm/LBbyJQ+Ac2DG/fbFeb7HWb0+PynrIytU6D2f68RKJ0vvzMq1rZWB3d1dcQ45CkK3sX38hCkFGdXUli1mYt3zudYjZuesR6l6jsYk0HeSqMqpnKeDmX89BrNXti9bl+m+QOqpFRERERERERNpZ2aBypRq5fKz2QINBRrIXjzM+01/zvTVRlYFayvp86XGYmlq7zxUpV+88nDhUO9P6scfgxIn5y/fuxTtxouIaqqhRzTJqVAfvx/CfNsC50tMG867fPXv4m0Ksdo1q0niRczBy3P/OIyMV7efVqGa0sn0TqaNaRERERERERKSdFQeVe/hh4sWM5C1b4I6FBxr0Eon1G4SwKgO1lbI4ZQOpdx6Gw7UzrZNJMvkd85efOgXJ5Nw1tHkz3itniBNcf+EwXr5svhHFpwh6emDkUWDha9S7+27+n1cGKjbhcQaiUTh8fK5t8LdWe2B++yZSR7WIiIiIiIiISLs7cqTUYd2QwcH17ZgaGVn/LG6RavXOw5k8aWLzM63zZ2ovn3xwfa6hettf6mev9/W+TKHFm4iIiIiIiIiIyIaQSkFvr5/d2dvrzxeXd3X5daSd818X32vE4KCfgdpzgfvcg3g9FyCpmtOyzuqdhz09xPAzrcPkKjKtay7XkwBrQhnVIiIiIiIiIiLidzzXq9N77BiZ3E2MchCAPdOjePv2+es12tncJlmdcomrcx7WzLTeG59Xi1pPAqwddVSLiIiIiIiIiAgkEgvW6Y1xmiybAHiUIU5nd+ElEup8lvZXrONcqyb0Lbesbz33DUylP0REREREREREBCYnq+rxdpImBnm/fm+OTsABbu69ycnmxiyyWgYH4fx5f0DD8+crByOstVxWnTqqRURERESaxDn3Bufcaefcl51zX3LODddoE3PO/bNz7gvBdH8zYhURkQ2gu3vBOr2d5AADTLV6RWTVqfSHiIiIiEjzzAI/b2bnnHPfAzzlnPsrM/tyVbvPmdk7mxCfiIgsgXPu/cBd+L25XwSGzOzV5ka1BCMj9ev0HjtGOreLUfYAsIdRvMg5GDne5KBF5FKhjmoRERERkSYxswvAheD1vzjnvgK8HqjuqBYRkRbnnHs98J+Ba83su865PwJ+Gvjdpga2FIvV6R0exps+4LeNRuHwcZVBEJFVs2jpj3qPIzrntjrn/so591zw93XBcuec+03n3Necc884525c6y8hIiIiItLunHO9wA3A4zXe9pxzf+uc+7Rz7ofWNTAREVmKDuA1zrkOYDPwjSbHs3QL1emdmgIzf5qaUie1iKyqRmpUFx9HvBbYCbzHOXctcC8wbmbXAOPBPMA7gGuCKQ4cXfWopb2kUtDbC85BR4f/t7fXXy4iIiIiOOe2AGPA+8zsO1VvnwN6zOyHgd8C/rTONuLOubPOubMvvvji2gYsIiLzmNnXgf8KTOI/LfPPZvaXzY1qA0uloKvL74Nwzn+tfgiRlrZoR7WZXTCzc8HrfwGKjyPeDpwImp0AfjJ4fTswar4zwGudc9tXPXJpD6kUxONkJrbzAPeSzA/xAPeSmdgO8bj+IyEiIiIbnnOuE7+TOmVmf1L9vpl9x8xeDl6fAjqdc1012iXNrM/M+rZt27bmcYuISKXgSfPbge8DrgIud869u0Y7/bC41lIpGBqC6Wky7PT7IaavgX371A8h0sKWVKO66nHEK4KaegDfBK4IXr8e+Mey1Z4Pll1ANp5EgszMdfQzzkUiFAgTIs8msozP9OMlEnpUSERERDYs55wDHgG+Yma/UafNlcALZmbOuZvxk02m1zFMERFpzK3AP5jZiwDOuT8B/i1wsryRmSWBJEBfX5+td5CXnFQKhodhOvhPYzTq/83lyLCTfsbJEiFClvGs+iFEWlkjpT+AhR9HNDPDH9G2YfoFcYOYnCRNjCwRCsHvIgU6yNJJmhhMTjY3PhEREZHmugW4A/hR59wXguk259w9zrl7gjY/BTzrnPtb4DeBnw7+/1tERFrLJLDTObc5+CGyH/+pdFkrQeZ0Zvoa9nOE/RzxM6eDTutif0Re/RAibaGhjOo6jyO+4JzbbmYXgtIe3wqWfx14Q9nqVwfLKugXxA2iu5vYRJoIWS5CkFE9S4QcMdL+CMIiIiIiG5SZ/Q/ALdLm48DH1yciERFZLjN73Dn3x/hjC8wCTxP0e8gqS6UgkYCJCTLsJMZpsmwC4FGGOM0uPM4Qw++PyGLqhxBpA4t2VC/wOOKngL3AoeDvn5Ut/znn3CeBt+APHqCyHxvVyAhePM74TD9pYkSZYpouYqTxNj8DI/pvtoiIiIiIiFwazOzDwIebHcclrTgW1sx1pNnNE+wgS4Ti777FzGmPM3icYRy/PyJGGi9yDkaONzd+EamrkYzq4uOIX3TOfSFY9kv4HdR/5Jy7E5gA3hW8dwq4DfgaMAMMrWrE0l6Cuk9eIoE3cQjCYcjnoafH76RWXSgRERERERERaVSNsbB8/sP6pczpoFa1N+13WBONwuHj6oe4lBUz7Scn/cz5kREd7zazaEf1Io8j9tdob8B7VhiXXEoGB3VjEBEREREREZGVm5wkze6ysbAMv9sqz808yUO838+cVqf0xlKVaR+bSOPF4/57Og/aRsODKYqIiIiIiIiIiKybVAp6eyEU8v+mUv5YWEHt6RCzAISY5TVc9DupQ0/AcXVSbzhlmfYf4iD9jJOZuc7PsJa20dBgiiIiIiIiIiIiIuumXobs3r14J07UHwsrOapO6o2oLNM+TwdZzK9VPvlgsyOTJVBHtYiIiIiIiIiINE+t2sJlGbJZIkTIMj7Tj3fqFCSTGgtLKnV3E5vwM+2z2Fyt8u7uZkcmS6DSHyIiIiIiIiIi0hzFzOmJ7TxgHyQzsR3icZiYIE2sLEO2kzQxvzN7cBDOnwczmJ31/54/r07qjWxkBG/zM4zTz0HuZ5x+P8N+ZKTZkckSKKNaRERERERERETWXyoFe/eSye+YnzkdfpJYXhmy0qDgRwovkfDLfXR3K8O+DamjWkRERERERERE1lcxkzq/g4/wYS4SoVBeWzh/xs+QDWpRl2pQjySbHbm0qsFBdUy3OZX+EBERERERERGR9VVWg/qz3EqBMCFm5zKne3r8WtQ9F7jPPYjXcwGSypCVZUqloLcXQiH/byrV7IikBnVUi4i0KefcVufcXznnngv+vq5Ou71Bm+ecc3uDZZudc//dOfc/nXNfcs4dWt/oRURERERkQ5ucLNWgLtBBiDy38tnK2sLFWtSFgmpQy/LVq4OuzuqWo45qEZH2dS8wbmbXAOPBfAXn3Fbgw8BbgJuBD5d1aP9XM/vfgRuAW5xz71ifsEVEREREZMOpzmjdupUYfg3qMDk2keUj/Ape+EllTsvqKsve/xAH6WeczMx1kEgsb3vV5/KBA8rWXiXqqBYRaV+3AyeC1yeAn6zR5m3AX5nZS2b2T8BfAW83sxkzOw1gZlngHHD1OsQsIiIiIiIbRbFDzzm4447KjNbvfAcvco5x+jnI/XOZ1CdOqJNaVldZ9n6eDrJ0kiYGk5Pz2y5WIqRWdvbRo8rWXiUaTFFEpH1dYWYXgtffBK6o0eb1wD+WzT8fLCtxzr0W+A/A4Vof4pyLA3GAbo2wLSIiIiIijSh26M1cR5rdRG2K93GYLBEiZBnP9eNFn8PbcgFv8kHo7vYHSlQntay27m5iE372fhabq4Ne/e/bqnM2NpHGi8f994rnZVl2dvFcfojhynN7ph8vkdC5vAzKqBYRaWHOuc86556tMd1e3s7MDLBlbL8D+APgN83s72u1MbOkmfWZWd+2bduW9T1ERERERGSVrWRwuPUYWK6q3MJ7+G0uVme0vvSSalDL2hsZwdv8zPzs/ZGRynaNlAipkZ09xkBj2dqNWOjabCTbu6vLf4LBOQiH/b9dXf7UBqVJlFEtItLCzOzWeu85515wzm03swvOue3At2o0+zoQK5u/GkiXzSeB58zsoVUIV0RERERE1kMjmZ9rse5STE6SZnepAy/ELGEKOHL1M1pF1kJwXnuJxMLZ+1XnbBYjTcxfp6hGdvYAY3yOty6crd2Iha5NWPi6TaVgaAhyOTLsJE2MaGGKabqITacBSBNbu+t9lTg/Ca+5+vr67OzZs80OQ0RkSZxzT5lZXxM//2PAtJkdcs7dC2w1sw9UtdkKPAXcGCw6B9xkZi855z4K/CDwf5hZoZHP1P1aRNpRs+/XzaD7tYi0I92vl6C3l8zE9oryA+P04/Vc8DOT12rdZcfYSYQcDzHsd5yR9jNaNWiitJIa5+y8a6OiMznmn8ucKXUOr+jcXujahIWv295emJggw076GeciEQqECZGng1kcMEvHql3va3W/VukPEZH2dQj4Mefcc8CtwTzOuT7n3DEAM3sJOAg8GUy/GnRSXw0kgGuBc865Lzjn7mrGlxARERERkSVayuBwq7nuUtQotxB3j3Afh/xOMnVSS6tppETI4CAkk3g9F7jPPeify/v3V84v99xe6Npc7LoN/hbbFIIiGgU6yNFJls61vd5XiUp/iIi0KTObBvprLD8L3FU2fxw4XtXmecCtdYwiIiIiIrIGGh0cbrXXXYqa5RZ+T53T0roaLREyOLg25/Ei1+aC1213N0xMEMNvcxGCjOrZsozqQsuX3VFHtYiIiIiIiIhIOxkZwYvHGZ/pryw3MJJc23WXaq069ETWSjPP2UWuzQWv25ERGBrCy51hHL9NlKlSqR1g7a/3VaCOahERERERERGRdtJo5mej6962FxIJuOOOYFtBqYPhYZie9l9Ho3D4cGt3PKdSlTFDe8QtAg1d13XfK/4dHsabPoPHGQiFoFDwrwHAe+nxpd0rmkCDKYqILJMGexERaQ+6X4uItAfdr5uk1uBwnU9BoUAmv4NR9gCwh1G8yDk4frw1O7lSKRgaIpO7iVH28E2u4EpeaP24RdrQWt2vlVEtIiIiItIkzrk3AKPAFYABSTM7XNXGAYeB24AZ4GfM7Nx6xyoiIpeoRILMzHX0M06WCBGyjOf8oXBinCbLJgAeZYjT2V14iURrdvgmEmRyN1XEDG0Qt4iUhJodgIiIiIjIBjYL/LyZXQvsBN7jnLu2qs07gGuCKQ4cXfOoDhzwHxd1rv4UDvt/e3v9LLZmSaX8GEKhyliqlx84ULudSKsonrPOVV5/XV06X2VtTUyQJsZFIuTp4CIR0sRIEyNHJ/4Y7I4snaSJweRkkwOuY3JyXsxtEbeIlCijWkRERESkSczsAnAheP0vzrmvAK8HvlzW7HZg1PyafWecc691zm0P1l19Bw7A0aMkuYtHuJPLeJWtvMSVvMANnONpbgTghsI5f4CeiTRePO6vu96ZahWPq++ei+Wxx+DEicrlR4+SYWdlu2bELFJL1bkctanStbZnehRv3z6/nc5XWQvhMNH8FAXCgFEgTJQp3syzdJIjG+Q4Rsj5g7J1dzcv1lTKr6U9MeH/YJrPQ0+PX1O7u5vYRLoi5paJW1ZP8RyYnJyrp6574yVDHdUiIiIiIi3AOdcL3AA8XvXW64F/LJt/Pli2Nh3VySRJ7uJuFh8NPkSeTWQZn+lvziPVtR5Xn+nHSybJ5HdULH+IYd7H4cp2egxcWkXZuXyRSNBh6FPZAllz+TzTdBEiT4EOQswyTRceZ0iza36N6pHjzYmz+ged/FTlD6Z79+IdO0Y6t6t2jepmxS2rp94P1KD74yVCHdUiIiIiIk3mnNsCjAHvM7PvLHMbcfzSIHSvJGssn2eMgeJWq960smVGgQ6yGGli/gj0621ykjS7yQaPq5diyZ8hTaxi+RgD89s1I2aRWsrO5QIdlF9rxbIFOl9lzfT0EJtIs4ksWWwuAzkaxeM5vOkXFL44AAAgAElEQVQDfrtoFA43cUDCGj/oVPxgeuoUPPoo3vDwXMytELesnno/UOuHvEuGalSLiIiIiDSRc64Tv5M6ZWZ/UqPJ14E3lM1fHSyrYGZJM+szs75t27YtP6BwmAHGilutmqh4HWK2uY9Ud3cTI02ELGFyc7GEw/OWDzA2v50eA5dWUXYuh5gNFvrXms5XWXMjI3ibn2Gcfg5yP+P0421+Bg4fhqkpMPOnqanmdgYGNajnftAh+MG0rAb14GBlzK0Qt6yesnMgX33s5ZKgjGoRERERkSZxzjngEeArZvYbdZp9Cvg559wngbcA/7xm9akB4nHiR/3xGhesUU1Qo5q036ExsnipkFU3MoIXjzM+00+a2Fwse+N4J05ULucMb+bZynbNiFmklqpzOUpZjWqVLZC1FnTieomEn7nf3e3fH1utczeoQR0hy0UIMqqb/IOprK+ycyBb64c81a9ue84fk6W5+vr67OzZs80OQ0RkSZxzT5lZX7PjWE+6X4tIO2rl+7Vz7t8BnwO+CBSCxb8EdAOY2cNBZ/bHgbcDM8CQmS14M17x/frAAXj4YT8TrZ5QCAqFuUGsmvUPwXr/KK1eftttcOqU/vEqrat8kDjn5q6/aNTPbN0A52sr36/Xiv7/egkq6hP7P+hU/GCabMHOdVldVedAxbGH+u/pvFh1a3W/Vke1iMgy6X+kRUTag+7XIiLtQfdrqan8h8etW/1l09MQDkM+3/wfTGV91fuBureXzMT2yvrV9OP1XIDz55sd9SVnre7XqlEtIiIiIiIiIrIKnHOvdc79sXPufzrnvuKc85odU1srZtBObOcB+yCZ6Wvgu9+FkydhdtZ/8uD8eXVSbySDg/4xLxQqj73qV18SVKNaRERERERERGR1HAb+wsx+yjkXATY3O6C2U54xGwqRye+ozJKd6cdLJNQ5LZUWq18tbUEZ1SIiIiIiIiIiK+Sc+1fAW/EHycXMsmb27eZG1WaqM6jzO5QlK40ZGcHb/Azj9HOQ+/2yH5uf8UuDSNtQRrWIiIiIiIiIyMp9H/Ai8Khz7oeBp4BhM3uluWG1kUSCzMx1FRnUDzGsLFlZXJBh7yUSeJMPBvWrNZBiu1FHtYiIiIiIiIjIynUANwLvNbPHnXOHgXuBD5U3cs7FgThAtzpcK01OkmZ3WQa1MU0X4/STJkaMdJAlm2x2pNKKBgfVMd3mVPpDRERERERERGTlngeeN7PHg/k/xu+4rmBmSTPrM7O+bdu2rWuALa+7mxh+neEwuVIGtRd+kvvcg3g9FyCpLFmRS5U6qkVEREREREREVsjMvgn8o3PujcGifuDLTQyp/dSrM3ziBBQKcP68OqlFLmEq/SEiIiIiIiIisjreC6SccxHg74GhJsfTXlRnWGRDU0e1iIiIiIiIiMgqMLMvAH3NjqOtqc6wyIal0h8iIiIiIiIiIiKycaRS0NsLoZD/N5VqdkSCOqpFRERERERERERko0ilIB4nM7GdB+yDZCa2QzyuzuoWoI5qERERERERERFZfcpalVaUSJCZuY5+xvkQB+lnnMzMdZBINDuyDU8d1SIiIiIiIiIisrqUtSqtanKSNDGyRMjTQZZO0sRgcrLZkW146qgWEREREREREZHVpaxVaVXd3cRIEyFLmBwRcsRIQ3d349soPi3gHHR0+H/11MCKdTQ7ABERERERERERucRMTpJmd1nWqpEmhjf5YLMjk41uZAQvHmd8pp80MWKk8TY/AyPJxtYvPi0wcx1pdhPNTzFNF7GJNF487rcZHFy7+C9hi3ZUO+eOA+8EvmVmbwqWfQT4WeDFoNkvmdmp4L37gDuBPPCfzewzaxC3iIiIiIiIiIi0qu5uYhN+1moWW17WqshaCDqRvUTC/+Gku9vvpG60c7nsaYGLRCgQJkSeTWQZn+nHSyTUUb1MjZT++F3g7TWW/zczuz6Yip3U1wI/DfxQsM4R51x4tYIVEREREREREZFlSqWgq8svU+Cc/3qlpQrqbXNkBG/zM4zTz0HuZ5z+IGt1ZHW+i8hKDA7C+fNQKPh/l9KxXFbjuhDkABcWqnV94MBceZCODn8eNNhoDYt2VJvZ3wAvNbi924FPmtlFM/sH4GvAzSuIT0REREREREREViqVgqEhMtPXsJ8j7OcImelrYN++5XeQLbRNgGQSr+cC97kH8XouQHIJWasiraqsxnWIWQBCzNZ+auDAATh6lGR+iLfxaZL5ITh6FG69VYON1rCSGtU/55zbA5wFft7M/gl4PXCmrM3zwbJ5nHNxIA7Qrcc+REREREREREQWlkr5gxFOTgblCoLs5OFhmJ72X0ej8K53walTle0SCTK5m4hxmiybAHiUIU5ndy2tVEEqVfF5GXbW3+ZSM1VF2kFVjesoQY3qWrWuk0mS3MXd+Mv+krcBEB8/Road9DNOlgiR8rIhUP+anpiAcBjyeejpgdtum3+tP/aY/6NQPu+3jcfhyJH13EPLttyO6qPAQcCCv78O7FvKBswsCf5R6uvrs2XGISIiIiIiIiJy6asawC02kcYbGoJCgUx+B6McBGDP9Cje0aNk2DnXLh6HmRnS7CZHJ+AASqUKGh7gsJhBnbup9Hnf5AqyRJa/TZF2U17jeuJQZcdxda3rfJ4xBoIZBxhjDBDnWKl8SMVgoxOH5l1j1dd0xeCN1df6nj1QKJDkLsYYYCA/RvzoUf/j26Czelkd1Wb2QvG1c+53gD8PZr8OvKGs6dXBMhERERERERERWa6yAdxKGZi5foB5Gc2/yXt5H4crMzXDTxLLp+kkRzaoBNvwAIfFTO6JiXkZ1HNsadsUaWeDg409LRAOM5AfCzKp/WtkgDGAUvmQisFGw+GaTz4Ur+nqwRsfYrjyWi/080XeND+DO5m8dDuqnXPbzexCMPsfgWeD158Cft859xvAVcA1wBMrjlJEREREREREZCObnCTN7nkZmMC8LOkxBuZnaubP4HU+RTq3i1H2ALCHUbzIORg5Xv9zqzK5J+mu+Dy/880BeW7mSR7i/YtvU2SjiMdLGc1jDDDAGHGOQX8/XiZTKh9SKhsykydNrO417Q/eaMHgjVbzWk/zI8GHl2Vw548149sv2aId1c65PwBiQJdz7nngw0DMOXc9/t3oPHA3gJl9yTn3R8CXgVngPWaWX5vQRURERETam3PuOPBO4Ftm9qYa78eAPwP+IVj0J2b2q+sXoYiItIzubmITNTIwYV6W9ABjfI63Vrbr6fFr6w4P400f8LcZjcLh45WZodV1sF9+uSKTO8wsHcySCz4P/IHk/OzO9+OFnoDjo6pNLQKlLOZ4Mul3FofDEN/vL0+l/PIhkw8G9aWTkEgQm5j/5EPxmr4IQUb1bN1rPcrU/AzucLhJO2BpQos1MLPdZrbdzDrN7Goze8TM7jCzN5vZdWb2E2XZ1ZjZiJl9v5m90cw+vbbhN1kqBb29EApBV5c/hUL+svUepbM8lt5ef/TQUAic86fv+Z6VjeJbvu0NPgKpLFMj59FibZZ7LtZbT+e2iIg03+8Cb1+kzefM7PpgUie1iMhGNTKCt/kZxunnIPczTj9e51N44SdJs4t7eJh7eJjT7CLOscp2m5/xB1kbHISpKTDzp6mp+Z3U8TiZie08YB8kM7EdpqcraunmCXMnx0uf9wnifJQPzX3OqDqpRSocOQKzs/41Nzs7V4JjcNAfcLRQmBt4dGTEf/KhzjX9UT5Ucc3Nu9ZDTxDnGJ8gzo/zGT5B3M/gjsebuQcaZ2ZNn2666SZrOydPmm3ebJ9np/0X7rXPs7PitW3e7LdpUiwG9gnuspvJ2E8y5i/r6Fh6TLW2vZ7fTS4NjZxHi7VZ7rlYb739+1d8bgNnrQXuoes5teX9WkQ2vFa/XwO9wLN13osBf77Ubep+LSLtqNXv12sxLfl+ffKkWU+PmXP+35Mn/SkaNSt2P0ej/r93qtvV2175uqGQfZ6d9hpesTA5ew2vlPo7/GXZ0rLydQwW/hwRadxC1zSYhcNz11yta33//rk24bA/v8rW6n7t/G03V19fn509e7bZYSxNby+Zie0Vj744YJYOv3g5/Xg9F/xfRNY5lghZ3sthfo17S006yfHX/MjSY6qx7XX9bnJpaOQ8WqzNcs/FeuuFnyST37Gic9s595SZ9a1w77SVtrxfi8iG1+r3a+dcL35ndL3SH2PA88A3gF8wsy8ttk3dr0WkHbX6/XotNPV+nUrB0BCZ3E0VNavTxPgQB8nTQZgcB7mf+zhEhp2VtXSTSWVOi2xQa3W/XrT0h9QxOVnx6EuOTrJ0BsXLO/0BDSYnmxJLlk7+hIHgTQc4cnQsL6Ya217X7yaXhkbOo8XaLPdcrLdePq9zW0RE2sE5oMfMfhj4LeBP6zV0zsWdc2edc2dffPHFdQtQRETaVCJBJncTMU4HBQbuYReniTJFhCxhcnP1raNRvJ4L3Oce9JN71EktImtAHdXL1d1NjHTp5t0Z3MArbuTd3U2JJUKO/8RY8KYBRiezy4upxrbX9bvJpaGR82ixNss9F+utFw7r3BYRkZZnZt8xs5eD16eATudcV522STPrM7O+bdu2rWucIiLShoKknhydFJPcsnQyTdf8+taHD8+vpSsisso6mh1A2xoZwYvHGZ/pLz36AlQ+BjOSbFosHmf4fv6eR7iTq/gGH+BjeB1nYeR3V77t9fxucmlo5DxarM1yz8V66+2N45040dbntnNuK/CH+LVNzwPvMrN/qtFuL/DLwexHzexE1fufAv51rUfORUSkuZxzVwIvmJk5527GTzSZbnJYIiJyKejuJjaRppMc2SCPsZjA44WfxCs87ifyjCh7WkTWyVoUvl7q1LaDvZQPYhCN+tNiAxWsRyw9PWb9/f7rYuH1LVuWH1OtwRpElqqR82ixNss9F+utt8JzmyYP9gL8GnBv8Ppe4MEabbYCfx/8fV3w+nVl7/8n4PepM4hX9dS292sR2dCafb9eaAL+ALgA5PDrUN8J3APcE7z/c8CXgL8FzgD/tpHt6n4tIu2ole/XazU19X598qRZZ6d9np12D0fsHo74gyRGIvp3v4gsaK3u1xpMUURkmZo92Itz7qtAzMwuOOe2A2kze2NVm91Bm7uD+U8E7f7AObcF+AsgDvyRNZBRrfu1iLSjZt+vm0H3axFpR7pfN0EqBcPDMB08rBON+mU+lEEtIgtYq/u1Sn+IiLSvK8zsQvD6m8AVNdq8HvjHsvnng2UAB4FfB2bWLEIREREREWldg4PqlBaRlqGOahGRFuac+yxwZY23EuUzZmbOuYYfkXHOXQ98v5m93znXu0jbOH7WNd0abFJERERERERE1oA6qkVEWpiZ3VrvPefcC8657WWlP75Vo9nXgVjZ/NVAGvCAPufcefz/Fvxvzrm0mcWq1sfMkkAS/EcTl/dNRERERERERETqCzU7ABERWbZPAXuD13uBP6vR5jPAjzvnXuecex3w48BnzOyomV1lZr3AvwP+rlYntYiIiIiIiIjIelBHtYhI+zoE/Jhz7jng1mAe51yfc+4YgJm9hF+L+slg+tVgmYiIiIiIiIhIy1DpDxGRNmVm00B/jeVngbvK5o8DxxfYznngTWsQooiIiIiIiIhIQ5RRLSIiIiIiIiIiIiJNpY5qEREREREREREREWkqdVSLiIiIiIiIiKwS51zYOfe0c+7Pmx2LiEg7UUe1iIiIiIiIiMjqGQa+0uwgRETajTqqRURERERERERWgXPuauDfA8eaHYuISLtRR7WIiIiIiIiIyOp4CPgAUGh2ICIi7UYd1SIiIiIiIiIiK+SceyfwLTN7apF2cefcWefc2RdffHGdohMRaX3t21F94AB0dIBzEArBZZf5r8uncNj/29sLqZQ/dXXNb9fR4W9vMamUv61QyN9OcVuh0Ny2urr8do2sGwrNxVberjzG4ncoX2cpn129vXrx1Yq1uG/K92Ejyr/rQutVtztwoPZ8eRzLiWe1NPq9ltp2ta1GnOXXV3Gqta1653VXF2zZsrTzrnqb1ef3QlP59utd50uNQ0REREREZGluAX7COXce+CTwo865k9WNzCxpZn1m1rdt27b1jlFEVqLR/j1Zlo5mB7AsBw7A0aMkuYsxBhiwMd588VlG2cM3uQKAK3mBGwrnmKaL2EQab2gICgUy+R2MchCA7+XbfIEbGMiPET961N/2kSO1PzOVgniczMx1pNlNbDoNQJqfJWpTPM2NAOyZHsXbt89fZ3BwkXVjfmzx+NznDA2Ryd1UirH0HcrXafSzU6l526sZ3wLfM5qfmtuHxThrrVdvP9Vbr1a7o0fJsLPmfDGOJcezWhr9Xktt24pxPvoojI+Xrq/reZrX8p3521rovA5ej7IHaOC8qxNT+fl9A+f4NO/gG1xFjNN8h9fyTa7gSl6Y2/5jj8GxY6Vzvng/ACrbLRaHiIiIiIjIEpnZfcB9AM65GPALZvbupgYlIqun0f499Tcsn5k1fbrppptsScJh+wR3GRRKU5hsxXxxCpGz1/CKfZ6d9nl2WoTv1mz3Ce4yC4frf2ZPj32enfYaXrEwOYvwXdvEdy1ErmI7m/iufZ6dZj09i64bLovNenpK7apjDFWt0/Bn19hezfjqfM/i9kPVcS6k6rvWXa9Gu09wV835YhxuOfGslka/11LbtmKcMO/6cszO39YC53WE71onrzZ+3tWIqfr8XmgqbT8cXvA6bziOBgFnrQXuoes5Lfl+LSLSAnS/FhFpD5fS/RqIAX++WDvdr0XaSCP9e9GoP4E/RaNmJ082O/JVt1b36/Ys/ZHPM8ZAMOP8RYSD1+UTFOggSydpYqSJkaOz4v3i3zEGIJ+vLA9QXoJgYoI0MbJEyNNBjk6ydFIoJaX72yx+FhMTcyn/ddbNl8XGxESpXXWMhap1GvpsgMnJedsrtZmcrL1vg3WyRErbL9+HFevVKhtRtn6+3npVn1NsN8ZAzfliHFYrnuJ3XWtVx3DB/VhvH0xM1C5xspxHQ+qV7Fjos8tLY/T21v9OMO/6MsLzv3PVZ1WfozXPu/I4aqlx/s2/Xqtfl20/n69xDdVoNzGhx3NERERERGTNmFnazN7Z7DhEZBUt0A9S6m+YniYzfQ37OcJ+jpCZvgb27VO/Q4Pas/RHOMxAfoy/5G2A+YvIky91YM0JMUuYPE+wA4AOZslV9M/761/P0+zHL/uxZ3oUb+9eCIfJZG/0SxAwxSTddDCL323nf1oOo0C4tJ0QBSbpJsNOP+X/scfAOaI2RYgCxiwdzGKEmAU6yBMjTYadjLKHL/ODOAwLtudv0z/959aZBSAXvFv+2VGm/I71VAq6u4lNpOkkRzb4zhFyxEhDd3ftfRusEyHLRaBAmBCz89erVzZi61Zi0/76Waz+55V9TjbYn69yGQTfMkKOAcb4HG8txeGYxQhTHDw5ytRcLGv5WEUqNe8YLrgfq75bhBxRpniAe2uXOFnqoyELlfeosV/nnY8nTsyV1mBq/rECBqi8vkLk53/nqnOlyJXOcUeOCOCfsxVx1Pu+Nc4/yq6Feq/LY4/iH6d8jd/hyuNgWo/niIiIiIiIiEiDavS5OGCWQqlfIsNOYpwmyyYAHmWI09ldeImE+hwa4Pxs7ebq6+uzs2fPNr5CdY1qxngzNWpUc46nuZFH2FfqMOsky7/nv3MlL5RqVF/P0zzE+0sn0SYucppdAPQzzkUiQYdtng5m2cej7GEU8Du6vs338uv8QtAxFiJEnk1kGacfL/wkmfyO0nZCFPi/+HUe4v3k6KSTHL/Fe3kvv1X6fAAXnOwGhKvWCTOLwzFLmA5m8cjwGLdguLnP7bkAIyNlNaqDWsGM4kXOwfHjDdSojhElqAlNGm/zM5BM+uv19pKZ2E4/42SJECl+bvQ5+O53S+vPW6/G54yyp+IYhZnlCAeIcyzo0PXjeJob+R3uJE9nxXHyei7A+fONnz9LVfZdi8fwt3kPcfcI/N7vLVL72Y/9fRwu7aeHGK6YLx2vRr9DvX1fPOZl+/VRhpilY65N2flYHk/FMfa82jWqq49lKgV33EHS7uQAR8gHv3sVj1/5NflpbquMo973rbHvGqpRzSgeZ8iws3ScHMYtPMZWXiptvhiHfw3RWEwLcM49ZWZ9S1qpzS35fi0i0gJ0vxYRaQ+6X4tIS6vqsygmzJX6vzjDA9xLgo8GiZZ+/94Iv8x97kEoFJoY/Opaq/t1e2ZUBwMexh9+mLgdKy32ODOv6QPcyywdFMsFzNLBzTzJfRyqaDNXKoCKEghzJQiMAh3kMbqZ9D8rGsWb9k9Cf91wqV0WI00ML3+mopSBI8cXuIE8HcFvLwXGGKj4fPDzisEwwti8dYptwhQocBmvAq7ycycOwfAw3HUX3ugo3isH5nZKFnj3u/0pGoXDhys7W1/zGryZM/P352ui/t9UKigbsbusbETwudP+Ol7oCbyCv4941c19XhUPf/+UH6MCIabpKr1fjOMB7g0ybCuPkzdxyI9peBimp/0NR6PwrnfB6Ci88srcB27aBFu2zLUrFw775V9qKH7X4jGcpsuvNlQtlYJEAmZmSrE/wL0V+6myxEnZ8erqgldfnYs3EoFsdm7bW7bAww8Hj5rU2PcThyo+u7hf83XOx+Lyabr86yEcBi8GX/saAHGOEWfu+qKnB0aqfnAwf/0CoXnHr/z7/7/8xPxYQyHYutXfzksvzb0u23fVKuIpd/nl8AoV11qYHG/nM6VrvTyOQkVJk7KYenv9zn79yikiIiIiIiIi5YK+Ai+RwJt8sNSP4b30uP/0+ctRYtNLrGwgFdqzoxrgllvg2LGKbOEbOFfKDP0ib2KMAbbxrYpSGsXyGOWZusWSHsWSIA7jCXbwA3w1KCEwC0FGdekE6+nxsy97exculREOE8tXloEoL2nhMIqlLMpLGXSSI4QxS4EweTYzE5RTmOOqSmQUHzt4gh3s54hfwiSZLLWvzk6GoMzJvn1zGy39MvSzpV+DiuvFptN4Q0PgHBl2VpRCKZa3KJVPKYzihZ+Eb3+bTH5HRXZ28fOL2bA3cK7iIg5R4C94G3/K7dzJI6XOyRjpee1K5T/27oV8vjLWoMRGMav3Sl5gz8VRuAhpfrYyW5wzpU7q8kzi7/Da0rlVXuLiCXb4ZSyK+66YYRyPw8xMxT4v7icLjvc2vlVRQqRUFiQYLbaU/Z4d5Yu8iUe4k6v4Bh94+WN4P/Mz9curOEdmYrtfDoR0qQSGLXI+lj4/n8YbH58rS1IvG/7/b+/+Y+Q46zuOv5/dvXOwUzdwFwgBfIeqJkqVUPI7JwVqy3+EhJYmsvgjchTk2j3s0CrqrzSUSv0jcgjQqrSiSWzZUaHnIkW6NigtDkgm11JiN4EEUkMVg0rsQmgSm6aGOPbtj6d/PPPMPDM7e7e73r2dXX9e0ur2x+w8zzzzzNfjZ2e+DyS/Igbb54+fMJ2N3+956UWwNv2DQfDc7zff9nF/zbsjYN8+d+dATp/M1qNMjQaGEnXcjzsWg+U11iapWZQGREREREREhl32Yq48eReuicjSNm9ufczs28fMli0sVDc0ZzbY+UjrdeYdr6WSuwJ7aurcuqCuHzM0dvroapbbaKbNcd6w0IgfJaq2wunUe+HDUItn5SxRjb8zzhv2/TwZv5c8/OuarXDG7mKbtcYkM3bOzVm7erV9ihvs/dxrd7HN3s+9bqbP1aut3bEj9flT3GAt2F1ss2OcCdbvHu/lW3Y7D9qnuME+xQ12Ow/GM4hWOJ2qX5lFVx+Ilx0Ltn0Vb8Tl+VlJs9sXLzM11TR76Zt43e5iW+q1r1c4w+l2HrS72JbaF3692XJNU/u6Ze/hfltm0UKt6XO/jX47bmXellm0pXBW1WAbw7pn+8cYp5v2fbiOXWzL7TfpOua0XdQn41ldM/WpcNqWWYy331C1Fc7Ye7g/1Zbh/itzJlP3M8kMstk+ZUzuzLOlqJy4DTP9Mbt/8/Z3vH05x1+4fSWq8fGVt96w/y/1yDuuW7Z5Tl18n8zrq+6Y830sObab+kLeNudghGYlb/ehWclFZBgpXouIDAfF6x6Zm7N2bCweJ/D/Z9/Og/ZW5u2tzMf/Z7Lj48n4hoicvbk5N27jxzkmJpY+xqLjNRzb88dsanyxYMdpv+J182xjwyBOPbE+SJnh0w5UqEU5jJNUGsnnlnI8K2cjuqDcpfQocx6nsfGy/rv+ovMyFpOkfLjjDjdpYfR3ZuL7fJwHmGUPH+cBd4XuqVPw0ENxKoP4fQjSJVSCOsJbeYWHuCtOfbCOY3H6BpeyoBTXL5siwy/rPw9TmIQpEcI2iZc5ejRu0yQtxFgmTYVbNlymTpl1HOMEk6l9kV3Wl2sz5ftlv82VJOlT0vtsnk3xrp/hENfxDEmqk+ZtDOue7h8md9+H60jKSvcdX8ew/VNtZ4z7G0i3k9t3NmgHi+HbXJmaLTasbz3TVlUq8QyyTX3K2tyZZxtROb6fZPvjCSaX3d/x9oWPTF+pR+W4a/rLTevx6UXy0nlk5R3XuW0+Pd2iLuU4PU+2TWqUgz6WHNtNfeHYsWXrKSIi0gvGmEeMMa8YYw63+NwYY/7aGPMDY8zzxpirVrqOgDv/np52V/dMT7vXd93lXmfPEwb1mJx0dcrWM9yGycnB1zOsb1i/s90fRdxGPbrfz6OyL8+mn0u+pfpGpeLi4N13c7B6Net5kofZzsNs56Ps5mG28xi38Ri38TDb2cCTHFy8yqWQFJHe2LwZjh9PLsc7fnzpq6E/8QmoVuNJGMNj9k+5j40c4OCp9yTjkP7cq1Jxf6enXVzIOzfYt8+lkfUxolx2MQJan0sM2PCl/lgi5QBAiRol6tQYJ0mlYVOfV6KJ1KpYGpQx1DBY3stz0cBWOH5fx0+Q6NNq3MZ8/OlFvMydr3+BmeqzsHFjPAndXrZGuaPhVSa5lE8ZKGQAABC3SURBVCPczP445QVAhRoNiBKsuzpuYj5JXxGkb2hQoxQtU48G7yrUOMY6drMtTqkRtkeYGmM9Lt3D6Ux5YHmUD/N3bOZCjnOStdgoNUJeypEnuClexkTZsp/gpqCdkxQr3+UynuWqKOVFDRu1dbp8t+x7eS5Oh9LIfH4hr7i0DFGKjnBbGhge5cM8zbX8lLcANirD8DyXp+rkNIBSvF/dpJWW73IZN7Gf5jQs7q/BcprzMv3Nsptt8faf5rzUJH9+H/mUH743heVeyCtRGY2o7jb6scTXNWyLRm55R7iUF7iEVSwGPcRnMHepY57gpngfh5MLhm0G8EPeHe/bMCXGa6xlgQ2pPu3LM9QpU6McpKo5zXm4TOu11HGTncT0CJfGkyOGz8MUL+F+yEtzMsFxnubaqC6NOPUIJP0+nI13Mcr9HvYBSymdske5o0REZOX8LfA5iGbqbnYz8MvR43rgoejvyklNHHS7S5V1553QaKTSrP2Ut/Aqk/H5Rva1F74P9OT5r/CfLqXdQw8l9T561KVl8+JJxu9rqm87ZfRim/z52wWcdKnqwjRyHe6PON2c385vfCNOjbjAb+eev2Xr7c/Nllsuu33Z89BW6w3P+fKWa6fdssu3KrvVZ+3s23aeh/Vot+5xv+x0P/vUdh3010s5wiW8wAIbuJiXcp+fx+mmfrzctvv15u3L5drqIl7ubvultZy+ER5vm+rzzEZx8AvcxyLjEP//0gbPnXjep2OfWtHNEJFAdKFc+qI9COfA+zR/xEtczMW8xM12vxsDrEfpbI8GaXoXr0rO1bZsgVqNg/b6+PtbG3tdjDhyBA4eTJ/bFSQNqnFXaw9WR7PcTk9z8Ojb2cgBFhmnTI1b+HKc6zibozqbZ9h/Dq4TvMZa/pI/oE6JVSzyWe5mPzfzOL8RDWuVcUOnbgCznjO2v4ozPMkGZjjEbrbxUXY3LZNnjEW28kjyjwrzXMHh1La5gbVKNMjbYIwqt/BlAPZzC1UqUV7sejQo6q68dQNwDc7jDAfYGNftd/ibaKC1vFTVAOL1GepRCyz/nYQfEHZK1PlDPsNJLmAvv0WVCiYYkvVt7wfc97GZf+N9Uau7HwpWsRhvyx9zP5/m3rZqYqjzqzzPd3hPPOuq4/+hTtc1eW2jHM/E3xtjkRkO8nVuzKwrX5kqbvjU5CyfLbcVP3je2Q0QYyzyQf6Zx/l16vFdBp3w7VNnuf4yRpXP8TGe46po/45HNXYD3nnHzVLu4YHUcZvKUR3lTd/IAc4w3tQvx1jkX1gfX70d/vDzH1yeOQbcwPbv8xfuP4xL5eXOoVnJRUSGQ9HjtTFmGvgna+3lOZ/tAhastV+MXr8ArLfW/mSpdfY0XmfOv8ejczKA9TzJIqt6U85ZCs/JU6amADh49O2FqW/qPH3qJ27um3ZNTzfdyQdAuczB+rVs5ACnGW/rXFX6L+6Xnezn6JgrSn89G51uf9HjdT90Mx6yVN/YxSxXcLit/tNV/xSR3or+XfdXVIfHbYl6cDFuwl1wV06NlQG552rvZyH1/V3MMsueeFwlXL6TWNCveD18V1QfO8YCt8e384PlOp7h4zyQJBpfs4aZN55mtrGn9XpKJWYah/gk99KgFP9KcYJJruMZHudDEK2faJg6SQuSFv8KyaGc1BGh9C+YNSqs45ire+ST3BtvWyNIV0J0FWidRpT6Ah7nQ1EKC/crSzVed7J8WLd0upHmX1OzdXVpEkqYeEg5u3yrdViSK4Hd5w1KXMBJLuBkNLDormj1g8C+7X1bnGCSb3BjXFe/f/y2uFQh7bWzpcRbeQUbp+3wy5Ry6hq+LmHjK5zd92pUohQxJdopux4NyNtMe+S1Uat1JM+XLy9Uo8IpVkfHyVL7Om89Yfss319qlDnBZCb9DFF/y9Y9bOu8X/ct3+ZKvsLNzQVNTMCJbCqb5uPK9xMmJpjh+24G3ig9SvoYKGNpcAEnXd+bmoKd7Q1Si4iIrJB3AP8dvP5R9F7TQLUxZhaYBVjXy7uDMuff/pwMyFz5M1jheW9KfKXS7QWpb+Y8vdMrGVulKKvX43Mk29b5flKf7tpk+f9PNJ/z9cpS6+xHed3raj9Hx1zn/bX1+fXZtUn3+1JX7PZYy76R7KN5NnGCyeD/gXWu4xm2sjd1h/dFvNzeJG8i0l87d8KWLcxUD7FAMgmjv9j2MX6Tp7meMKbbnLEyIPdcrZa5SnueTcyyJ5M+1xYmVg/fQPW6daw/mtzOH9+qPzXV+S+A09P56wLGWYzTUPh0Ie5q5fGm1YTf28Q8X+Um0ukmQsn7TXXP1MenKvApSlKpCVrUseXy5TLr6wup77SuozNGjQaGMvWW2956HTXCtBVj1FL1DrevRiPdFhC3Q7h93bbzGDU2Mc/X2JD6FcmnfMjWNUwFkd3v41Rz19W67ColbLxPws/y0qC0WkcDs8RV0flt0F5dW6+nVfu0KsvvmzBtxxhVgJy+05yWJ3y+iXmXO6lWay4sOE7y+nLLmJDzvVS/6iaGiIiIFIi1dje4W/uuueaa3t022er8m/S/+4MW1islGrRff3ShMPUtUe8+5di6dS2vqPbn+83p/pbTbXdZ7nt553y9stQ6B3/XsNfVfo6Oue76a/759dm3SXf7Uqn1emzJvpGkE72Cw6mY/Vl+L3++oIkJ+KtHdKGOyCD54+/uu5k5cSg5VqOLcV260+vJG0/KGyfMnqtVqKbGhDZF6YzDVKlFitXDN1C9cyczs7McOLUxvp1/ZvXz7irIXqxr7FtgDAcW3XsTHE+lC/E5+LzUr5Dv28jsAXcV93I5qnN/vcypD9BUj+Xq2LT86ufhI7PMfP7z8bonOB7XJS/n3Fb2cgWHU/UI8w/m5WP27eF/9fF58S7mJe7hM8yYf4exsbjeYX2z+zFsh6ZtmWlu5zAP2pU8y35u5gUu4VKOuLI5xBUcjvPy+NyAft2vsTbOuxa+H247JKkn/LrC/GzZNvH7ONwnPoWFb6NwP4T1z/YToGV5eTn/4v6VU9dsfr2wzO9xWSp3Ytg+rXIXxmWV3ZX+C/UNqfYK+047Oaq3spdZ9sDsjraO27ANl7wqIOd7qX7VTQwRERHpvx8D7wpevzN6b+XknTOXnoZGI77ypxA5qqNzn5TVq92VSsDMli0sVPPr204ZPc9RzUJ3VzLu3JnOUe238yMfYWbPHg5UN8YpDpWjugA5qru5YnXnzo77a2FzVOuK3d7K6RupHNXMu/9LVSocqGXGTHbPaUBapKg2b84/Pu+6K847v5etLkc1++PxpFbjhPF7tRr/atcnOar9eMvGjcwcPNibsdVes9YO/HH11VfbjszNWTs1Za0x7u/cXGffX25d/j2wtlx2fycmrF2zxs/ZmX5MTCR12LHDrStvuaW+l1efiQn3COuxXB1bLR+uG9qr4/nnJ/Vote15j1Ipf11hvcPty9uPedsXLtNJO59/vls+bxv8ulu9n932iYnW68p7rFmT7JOwvr6N2tmGNWusHR9vv/27retS7dBuX56bS7bX173T8kslV+92jtu8Nsw7rtrtVx0Cvmnt4GPoSj46jtciIgVQ9HgNTAOHW3z2QWA/7p7NG4Cn21lnz+N13jlzJ+djK/Hw5z6t/p+QPU8Z9GOpc5Zu9kcRt1GP7vfzqOzLDre/6PG6H4+uxkNa9Y1y2cXBXo6ZiMhgZc+3/PNW44TZ8c1wTCYcbznLONGveD18kymKiBSEJnsRERkORY7XxpgvAuuBSeBl4M/A5fuy1j5sjDHA54APAKeALdbaZQOx4rWIDKMix+t+UbwWkWGkyRRFREREREaMtfb2ZT63wMdWqDoiIiIiIgMz+JlEREREREREREREROScpoFqERERERERERERERkoDVSLiIiIiIiIiIiIyEBpoFpEREREREREREREBkoD1SIiIiIiIiIiIiIyUBqoFhEREREREREREZGB0kC1iIiIiIiIiIiIiAyUsdYOug4YY14Fjnbx1UngF4CfBe+Fr/vxXGUUq4xR3CaVMZjyAI7TmSlr7YUdfmeo9TBej0q/URnDV8YobtO5VgYoXi9rBeJ1UfpNUfupyliZMkZxm0apDFC8XtZZxOt3M3r9ZlSPBZVRzPJURvL8h3SuL/G60usVdqPbDTPGfBO4kHSDhq/78VxlFKuMUdwmlTGY8rDWXoMsqYfxelT6jcoYvjJGcZvOtTIUr9uwAvG6KP2mqP1UZaxMGaO4TaNUhuJ1G84iXr/O6PWbUT0WVEYxy1MZ0fMixWql/hARERERERERERGRgdJAtYiIiIiIiIiIiIgMVCFSf5yF3cD7gK8H74Wv+/FcZRSrjFHcJpUxmPKkv7LxelT6jcoYvjJGcZvOtTKkv9qN10XpN0XtpypjZcoYxW0apTKkv/6B0es3o3osqIxilqcykueFUYjJFEVERERERERERETk3KXUHyIiIiIiIiIiIiIyUD1N/WGMeRfwj8CVaBBcRCTrGPBr1toXB10RxWsRkSUVKV5fB3wVWAuYAVdHRKRoFK9FRIZDW/G614MTNeDPgd8FbgX+J3rvDaAeLVMPllfeEREZNlXScawBnIn+got5Pra9CHwH+Gn0PQN8akVquTzFaxEZVTb4+/Pg/WGN12eAPwE+AOzA1dvXE9LbISIyjBq4WAYunp0kidXhuXcD+AHwPeA4wxevq6T/jRIRGTZ9j9d9zVFtjPkSLin3GmAMtxH16DnRa/3SKCLDoA6UcXErjF0N4Ge4YD0ZvTbR41XcD4KPAncAp3Dxb9IWbIIAxWsRGSFVktj1c2A1Ln6NSrz+CW7g4zJcbK7h6qoYLSLDJIytBhev15DE5LW4O8Ab0V8fx08wGvFa59YiMixWNF73baDaGDMNHML9R6BEEoQVkEXkXBAOgJwBngeuBl4CrrTWHh9g3VIUr0XkHDdM8fpGYAF3ZcrborcVq0VkVP0f8IvB6zrJueop4LsoXouIFEHP4nVf8pIaY84HvgS8GTeCHt7eUqhfOUVEOuRjWIN0PPO39IW3lb8Y/S3hckG/SMEoXovIiPN3h4xCvH4b8BVcrH4jeluxWkRGgQ0ejeD9tSQxzt9SfjR6/QqK1yIiK63v8brnA9XGmDHcBF2/hBtR/1+SXw3r6BdEERluPoZlrzx+NXq+CliMPn8n7ha/r+Fuj3kz7hb0EytV2aUoXovIOcDg0jYNe7x+E+5KlNdxsfrC6KM6mk9ARIafCR5+jGKRJKepT5sxDrwjev33uKuVhyle69xaRIZd3+N1TweqjTEG2AtcG1X0OHAR7oqVWlRe+EuiflUUkWHjr8w7k3l/TfT3BPDj6LnP33R99PmrwIEi5M9TvBaREeYnULS4+DYK8fowLi77WL0InCZ9G/nA6yoisoxsnApf/yjzegw30PES7mo8fzejz316OfAWhidel0jf4aNzaxEpsoHF657mqI7yMH29ZysUERlePjCHgwh14L+stZcMrFYRxWsRkVjR4/UO4MFB10NEpADCgQ9/0Z3itYhI8XQdr/s2maKIiIiIiIiIiIiISDv6MpmiiIiIiIiIiIiIiEi7NFAtIiIiIiIiIiIiIgOlgWoRERERERERERERGSgNVIuIiIiIiIiIiIjIQGmgWkREREREREREREQGSgPVIiIiIiIiIiIiIjJQGqgWERERERERERERkYHSQLWIiIiIiIiIiIiIDNT/A1gKjcWQLGLgAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 1800x360 with 4 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_1MAn6ov8aTT",
        "outputId": "6c598555-a49b-45fa-e207-70c3460fbd97"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 365 entries, 0 to 364\n",
            "Data columns (total 21 columns):\n",
            " #   Column                  Non-Null Count  Dtype  \n",
            "---  ------                  --------------  -----  \n",
            " 0   DATE                    365 non-null    object \n",
            " 1   MAX_TEMPERATURE_C       365 non-null    int64  \n",
            " 2   MIN_TEMPERATURE_C       365 non-null    int64  \n",
            " 3   WINDSPEED_MAX_KMH       365 non-null    int64  \n",
            " 4   TEMPERATURE_MORNING_C   365 non-null    int64  \n",
            " 5   TEMPERATURE_NOON_C      365 non-null    int64  \n",
            " 6   TEMPERATURE_EVENING_C   365 non-null    int64  \n",
            " 7   PRECIP_TOTAL_DAY_MM     365 non-null    float64\n",
            " 8   HUMIDITY_MAX_PERCENT    365 non-null    int64  \n",
            " 9   VISIBILITY_AVG_KM       365 non-null    float64\n",
            " 10  PRESSURE_MAX_MB         365 non-null    int64  \n",
            " 11  CLOUDCOVER_AVG_PERCENT  365 non-null    float64\n",
            " 12  HEATINDEX_MAX_C         365 non-null    int64  \n",
            " 13  DEWPOINT_MAX_C          365 non-null    int64  \n",
            " 14  WINDTEMP_MAX_C          365 non-null    int64  \n",
            " 15  WEATHER_CODE_EVENING    365 non-null    float64\n",
            " 16  TOTAL_SNOW_MM           365 non-null    float64\n",
            " 17  UV_INDEX                365 non-null    float64\n",
            " 18  SUNHOUR                 365 non-null    float64\n",
            " 19  OPINION                 365 non-null    object \n",
            " 20  flag                    365 non-null    bool   \n",
            "dtypes: bool(1), float64(7), int64(11), object(2)\n",
            "memory usage: 70.2+ KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wv79ZkmS3uBJ",
        "outputId": "e80335df-e953-4256-cbb3-85823d002e3d"
      },
      "source": [
        "for index,row in df2018.iterrows():\n",
        "  if row['flag']==True:\n",
        "    print(list(df2018.loc[index-1,['DATE','SUNHOUR','flag']]),list(row[['DATE','SUNHOUR','flag']]),list(df2018.loc[index+1,['DATE','SUNHOUR','flag']]))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['2018-01-18', 3.4, False] ['2018-01-19', 3.4, True] ['2018-01-20', 3.4, False]\n",
            "['2018-01-31', 6.6, False] ['2018-02-01', 9.8, True] ['2018-02-02', 9.8, False]\n",
            "['2018-03-07', 11.6, False] ['2018-03-08', 10.2, True] ['2018-03-09', 10.2, False]\n",
            "['2018-04-10', 9.1, False] ['2018-04-11', 7.9, True] ['2018-04-12', 7.9, False]\n",
            "['2018-04-28', 14.5, False] ['2018-04-29', 9.2, True] ['2018-04-30', 9.2, False]\n",
            "['2018-05-23', 14.5, False] ['2018-05-24', 11.5, True] ['2018-05-25', 11.5, False]\n",
            "['2018-06-01', 11.6, False] ['2018-06-02', 11.6, True] ['2018-06-03', 11.6, False]\n",
            "['2018-06-12', 15.6, False] ['2018-06-13', 12.9, True] ['2018-06-14', 12.9, False]\n",
            "['2018-06-14', 12.9, False] ['2018-06-15', 14.7, True] ['2018-06-16', 14.7, False]\n",
            "['2018-06-24', 16.0, False] ['2018-06-25', 16.0, True] ['2018-06-26', 16.0, False]\n",
            "['2018-07-03', 15.6, False] ['2018-07-04', 14.7, True] ['2018-07-05', 14.7, False]\n",
            "['2018-07-10', 15.8, False] ['2018-07-11', 15.8, True] ['2018-07-12', 15.8, False]\n",
            "['2018-07-13', 15.8, False] ['2018-07-14', 14.5, True] ['2018-07-15', 14.5, False]\n",
            "['2018-07-16', 14.5, False] ['2018-07-17', 14.5, True] ['2018-07-18', 14.5, False]\n",
            "['2018-07-23', 12.5, False] ['2018-07-24', 14.5, True] ['2018-07-25', 14.5, False]\n",
            "['2018-08-08', 13.4, False] ['2018-08-09', 13.4, True] ['2018-08-10', 13.4, False]\n",
            "['2018-08-13', 14.2, False] ['2018-08-14', 11.6, True] ['2018-08-15', 11.6, False]\n",
            "['2018-08-22', 11.6, False] ['2018-08-23', 11.6, True] ['2018-08-24', 11.6, False]\n",
            "['2018-09-23', 6.2, False] ['2018-09-24', 10.2, True] ['2018-09-25', 10.2, False]\n",
            "['2018-11-13', 8.7, False] ['2018-11-14', 8.7, True] ['2018-11-15', 8.7, False]\n",
            "['2018-11-17', 8.7, False] ['2018-11-18', 8.7, True] ['2018-11-19', 8.7, False]\n",
            "['2018-11-21', 8.7, False] ['2018-11-22', 3.4, True] ['2018-11-23', 3.4, False]\n",
            "['2018-12-12', 5.1, False] ['2018-12-13', 8.7, True] ['2018-12-14', 8.7, False]\n",
            "['2018-12-15', 3.3, False] ['2018-12-16', 6.9, True] ['2018-12-17', 6.9, False]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9Z2OpQvzibK7"
      },
      "source": [
        "---\n",
        "# Apply and concatenate"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iIXTLRQl_fr-"
      },
      "source": [
        "# Check that you have 21 columns and 365 rows, and no missing values:\n",
        "df2018.info()\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iwdRZ8V5itQz"
      },
      "source": [
        "# Transform DATE into datetime format\n",
        "df2018['DATETIME'] = pd.to_datetime(df2018['DATE'])"
      ],
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Q9P1p47ki-ps"
      },
      "source": [
        "df2018.info()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mGifkMSskETu"
      },
      "source": [
        "### Translate function"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "vJPctd1OkHsT",
        "outputId": "5417de7f-8a19-4c01-bc23-51aa9d65068b"
      },
      "source": [
        "opinions = list(df2018['OPINION'].value_counts()._index)\n",
        "#print(opinions)\n",
        "opinions_svk = ['dobré','veľmi zlé','zlé','ani dobré ani zlé','neznáme','veľmi dobré']\n",
        "transdict= dict(zip(opinions,opinions_svk))\n",
        "#print(transdict)\n",
        "\n",
        "def trans(word,transdict):\n",
        "  try:\n",
        "    return transdict[word]\n",
        "  except:\n",
        "    return word\n",
        "trans('good',transdict)"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'dobré'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xFP-iH6EodH-"
      },
      "source": [
        "Apply your function to the OPINION series with apply, check that it works, then store the result in the same OPINION column.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eSnAVRXDofQw"
      },
      "source": [
        "df2018['OPINION']=df2018['OPINION'].apply(lambda x: trans(x,transdict))"
      ],
      "execution_count": 38,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "INljHemMo-fw"
      },
      "source": [
        "Use apply to create a new MONTH column which will contain the number of the month (from 1 to 12). You can define a function, or use a lambda function.\n",
        "\n",
        "Use apply to create a new column DAY which will contain the number of the day (from 1 to 31). You can define a function, or use a lambda function."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "02I30-LUo_b_",
        "outputId": "5ec317bc-b477-4baa-a524-4e4a4a4709b9"
      },
      "source": [
        "df2018['MONTH']=df2018['DATETIME'].dt.month\n",
        "df2018['DAY']=df2018['DATETIME'].dt.day\n"
      ],
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 365 entries, 0 to 364\n",
            "Data columns (total 2 columns):\n",
            " #   Column  Non-Null Count  Dtype\n",
            "---  ------  --------------  -----\n",
            " 0   MONTH   365 non-null    int64\n",
            " 1   DAY     365 non-null    int64\n",
            "dtypes: int64(2)\n",
            "memory usage: 8.6 KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "g2jFniRvqhEp"
      },
      "source": [
        "Concatenate the rows of DataFrame 2018, with those of 2019 (you should have imported it at the top of your notebook).\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "c8mWn6smkwPd"
      },
      "source": [
        "df_weather.info()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sNiJcJeSqidJ"
      },
      "source": [
        "df_weather['DATETIME'] = pd.to_datetime(df_weather['DATE'])\n",
        "df_weather['OPINION']=df_weather['OPINION'].apply(lambda x: trans(x,transdict))"
      ],
      "execution_count": 52,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "P57R4qPorKMY"
      },
      "source": [
        "df_weather.info()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Yik3AJu8rQqH"
      },
      "source": [
        "weather=pd.concat([df2018,df_weather])"
      ],
      "execution_count": 53,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VLF64N7hvYjK"
      },
      "source": [
        "weather['YEAR']=weather['DATETIME'].dt.year"
      ],
      "execution_count": 55,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GWGymOydvwnK"
      },
      "source": [
        "weather.info()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "42Q0Uz3kwDhy"
      },
      "source": [
        "## Graphs"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 337
        },
        "id": "ytMe57wjv5XS",
        "outputId": "737dc054-764a-411b-bc4b-cf02c3ad8a1b"
      },
      "source": [
        "# Histogram of wind speed\n",
        "weather['WINDSPEED_MAX_KMH'].hist(by=weather['YEAR'],figsize=(15,5));"
      ],
      "execution_count": 125,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 1080x360 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 659
        },
        "id": "TMVoNfA9w9hm",
        "outputId": "b118c407-226e-40c2-cd13-e81fd303b63a"
      },
      "source": [
        "# Boxplots for precipitation\n",
        "weather.boxplot(column=['PRECIP_TOTAL_DAY_MM'],by=['YEAR'],figsize=(10,10),patch_artist=True);\n"
      ],
      "execution_count": 73,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 334
        },
        "id": "m3u40a_axk7l",
        "outputId": "8bfa6285-2a1a-45f7-f620-b130d856e50a"
      },
      "source": [
        "# Scatterplots for the SUNHOUR and the date\n",
        "import matplotlib.pyplot as plt\n",
        "fig,ax = plt.subplots(figsize=(15,5))\n",
        "ax1=plt.subplot(121)\n",
        "ax2=plt.subplot(122)\n",
        "ax1.scatter(data=weather[weather['YEAR']==2018],x='DATETIME',y='SUNHOUR')\n",
        "ax2.scatter(data=weather[weather['YEAR']==2019],x='DATETIME',y='SUNHOUR')\n",
        "for axis in [ax1,ax2]:\n",
        "  axis.set_ylabel('Sunhour')\n",
        "  axis.set_xlabel('Date')\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 81,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x360 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "a6OTKP9f-OXX"
      },
      "source": [
        "### Opinions\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 349
        },
        "id": "Hq-ziC6X-VHu",
        "outputId": "b4e86b19-7f86-409a-a9ed-74f76ead447b"
      },
      "source": [
        "weather.groupby(['YEAR','OPINION'])['DATE'].count().unstack().plot.bar(figsize=(15,5));\n"
      ],
      "execution_count": 122,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 270
        },
        "id": "oJ0-SMk8za6j",
        "outputId": "acb9426e-1a5e-4913-9ad2-57494a0db007"
      },
      "source": [
        "# Violinplots for maximum temperature\n",
        "\n",
        "fig, (ax1, ax2) = plt.subplots(nrows=1, ncols=2)\n",
        "ax1.violinplot(weather[weather['YEAR']==2018]['MAX_TEMPERATURE_C'], showmeans=True)\n",
        "ax1.set_title('2018')\n",
        "ax1.set_ylabel('Temperature')\n",
        "ax1.set_xticks(ticks=[])\n",
        "ax1.set_ylim([0,40])\n",
        "\n",
        "ax2.violinplot(weather[weather['YEAR']==2019]['MAX_TEMPERATURE_C'], showmeans=True)\n",
        "ax2.set_title('2019')\n",
        "ax2.set_xticks(ticks=[])\n",
        "ax2.set_ylim([0,40])\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 123,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vJMVV7fr9nKc"
      },
      "source": [
        "## Interpretation\n",
        "Year 2018 seems to have had better weather with less extremly hot days, less precipitation, more  stable temperature and less wind. The comparison of opinions show that year 2019 had subjectively more days with bad and very bad weather and less days with very good weather."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nTGAtpXKE3H0"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}