Copy_of_asthana_ajeeta_dwd_assignment_08.ipynb

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      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
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      "source": [
        "<a href=\"https://colab.research.google.com/github/ajita-asthana/JupyterNotes/blob/master/Copy_of_asthana_ajeeta_dwd_assignment_08.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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    {
      "cell_type": "markdown",
      "source": [
        "##Using NYC Open Data called \"Class Size Distribution by District\""
      ],
      "metadata": {
        "id": "wbSMAVoFJMs4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "%matplotlib inline\n",
        "import requests\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import seaborn as sns"
      ],
      "metadata": {
        "id": "3VuwzLVzJVER"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Data Set: New York Class Size distribution by District\n",
        "url = 'https://data.cityofnewyork.us/resource/ign9-rzrp.json'\n",
        "results = requests.get(url).json()\n",
        "print(len(results))\n"
      ],
      "metadata": {
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          "base_uri": "https://localhost:8080/"
        },
        "id": "5N6nK3uqJjLW",
        "outputId": "503aa9b8-65c2-4c03-b6d3-951dd0c9171e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1000\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "url=  'https://data.cityofnewyork.us/resource/ign9-rzrp.json?$limit=4958'\n",
        "results = requests.get(url).json()\n",
        "print(len(results))\n",
        "print(type(results))\n",
        "print(results[4956])"
      ],
      "metadata": {
        "colab": {
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        "id": "yzeBD45JJ7I6",
        "outputId": "f59d7f35-ef34-4fed-f110-25e4a3f4d343"
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      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "4957\n",
            "<class 'list'>\n",
            "{'district': '32', 'grade_level': 'K-8 SC', 'program_type': 'SC 8:1:1', 'class_size': '12', 'number_of_students': '12', 'number_of_classes': '1', 'of_students_within_grade': '21.1'}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#data dictionary \n",
        "results[0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "27fMnqBWLBUu",
        "outputId": "a5937dd5-9287-4e3a-b798-cd5e48aea93e"
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      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'district': '1',\n",
              " 'grade_level': 'K',\n",
              " 'program_type': 'Gen Ed/ICT/G&T',\n",
              " 'class_size': '<15',\n",
              " 'number_of_students': '122',\n",
              " 'number_of_classes': '10',\n",
              " 'of_students_within_grade': '20.1'}"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Creating a DataFrame from JSON data"
      ],
      "metadata": {
        "id": "W6pvuH2QLJwA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.DataFrame(results)\n",
        "df"
      ],
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          "height": 424
        },
        "id": "qD7iPF1ZLLAe",
        "outputId": "778c1f50-b83c-44fa-acc7-efc6cb7cc052"
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      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     district grade_level    program_type class_size number_of_students  \\\n",
              "0           1           K  Gen Ed/ICT/G&T        <15                122   \n",
              "1           1           K  Gen Ed/ICT/G&T         15                 30   \n",
              "2           1           K  Gen Ed/ICT/G&T         16                 32   \n",
              "3           1           K  Gen Ed/ICT/G&T         17                 51   \n",
              "4           1           K  Gen Ed/ICT/G&T         18                 18   \n",
              "...       ...         ...             ...        ...                ...   \n",
              "1267       10           5  Gen Ed/ICT/G&T         17                 68   \n",
              "1268       10           5  Gen Ed/ICT/G&T         18                 90   \n",
              "1269       10           5  Gen Ed/ICT/G&T         19                171   \n",
              "1270       10           5  Gen Ed/ICT/G&T         20                220   \n",
              "1271       10           5  Gen Ed/ICT/G&T         21                189   \n",
              "\n",
              "     number_of_classes of_students_within_grade  \n",
              "0                   10                     20.1  \n",
              "1                    2                      4.9  \n",
              "2                    2                      5.3  \n",
              "3                    3                      8.4  \n",
              "4                    1                      3.0  \n",
              "...                ...                      ...  \n",
              "1267                 4                      2.2  \n",
              "1268                 5                      2.9  \n",
              "1269                 9                      5.6  \n",
              "1270                11                      7.2  \n",
              "1271                 9                      6.2  \n",
              "\n",
              "[1272 rows x 7 columns]"
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          "metadata": {},
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    {
      "cell_type": "code",
      "source": [
        "df.columns"
      ],
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          "base_uri": "https://localhost:8080/"
        },
        "id": "GZMRSZgFLQ1o",
        "outputId": "3122f4d1-e1cc-44ee-9556-f2668147c6f4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['district', 'grade_level', 'program_type', 'class_size',\n",
              "       'number_of_students', 'number_of_classes', 'of_students_within_grade'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df[\"class_size\"].unique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Vz26bRjjdUgp",
        "outputId": "a94efc48-2f36-4b07-cbd7-d56648331619"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['<15', '15', '16', '17', '18', '19', '20', '21', '22', '24', '25',\n",
              "       '26', '28', '23', '27', '29', '30', '>34', '32', '31', '33'],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.dtypes"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GM7p-4CrLWIq",
        "outputId": "1418ff54-6209-4b9a-e779-786040ec888e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "district                    object\n",
              "grade_level                 object\n",
              "program_type                object\n",
              "class_size                  object\n",
              "number_of_students          object\n",
              "number_of_classes           object\n",
              "of_students_within_grade    object\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#number of null values in each column\n",
        "df.isna().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jgMP3zkwLhfA",
        "outputId": "e45a7b2a-3405-48d6-bac8-ec1ed214a8db"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "district                    0\n",
              "grade_level                 0\n",
              "program_type                0\n",
              "class_size                  0\n",
              "number_of_students          0\n",
              "number_of_classes           0\n",
              "of_students_within_grade    0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df[\"district\"] = pd.to_numeric(df[\"district\"])\n",
        "\n",
        "df[\"grade_level\"] = pd.to_numeric(df[\"grade_level\"]) # Gives errror for kindergarden level as some rows have string value 'K'"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "u-FtK0I0MNCZ",
        "outputId": "563fa929-8946-44e5-891f-f10d58e9f868"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "ValueError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/_libs/lib.pyx\u001b[0m in \u001b[0;36mpandas._libs.lib.maybe_convert_numeric\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;31mValueError\u001b[0m: Unable to parse string \"K\"",
            "\nDuring handling of the above exception, another exception occurred:\n",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-11-4c98c0c5da9f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"district\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_numeric\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"district\"\u001b[0m\u001b[0;34m]\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;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"grade_level\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_numeric\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"grade_level\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# Gives errror for kindergarden level as some rows have string value 'K'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/core/tools/numeric.py\u001b[0m in \u001b[0;36mto_numeric\u001b[0;34m(arg, errors, downcast)\u001b[0m\n\u001b[1;32m    182\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    183\u001b[0m             values, _ = lib.maybe_convert_numeric(\n\u001b[0;32m--> 184\u001b[0;31m                 \u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcoerce_numeric\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcoerce_numeric\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    185\u001b[0m             )\n\u001b[1;32m    186\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mValueError\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/_libs/lib.pyx\u001b[0m in \u001b[0;36mpandas._libs.lib.maybe_convert_numeric\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;31mValueError\u001b[0m: Unable to parse string \"K\" at position 0"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df[\"grade_level\"].iloc[0:11]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7q0oOkITMgIx",
        "outputId": "05f01c90-86d4-4b6d-e689-d6441b564e95"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0     K\n",
              "1     K\n",
              "2     K\n",
              "3     K\n",
              "4     K\n",
              "5     K\n",
              "6     K\n",
              "7     K\n",
              "8     K\n",
              "9     K\n",
              "10    K\n",
              "Name: grade_level, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Replacing grade_level text value 'K' as numeric value 0\n",
        "df[\"grade_level\"] = df[\"grade_level\"].replace(['K'],'0') "
      ],
      "metadata": {
        "id": "--UvbZl5NGXU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df[\"grade_level\"].iloc[0:11]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0FGEkFyxOC-Q",
        "outputId": "9ffa110a-7fdc-4979-f141-6dbf92313a7a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0     0\n",
              "1     0\n",
              "2     0\n",
              "3     0\n",
              "4     0\n",
              "5     0\n",
              "6     0\n",
              "7     0\n",
              "8     0\n",
              "9     0\n",
              "10    0\n",
              "Name: grade_level, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df[\"grade_level\"] = pd.to_numeric(df[\"grade_level\"])"
      ],
      "metadata": {
        "id": "G6-nDDjEOITX"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# selecting rows where \"class_size\" is \"<15\"\n",
        "rslt_df = df.loc[df['class_size'] == \"<15\"]\n",
        "rslt_df\n",
        "rslt_df['class_size'].count()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9rdAMw8GOTWp",
        "outputId": "d0094954-7741-49b6-c6b0-39fb6e1723b4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "84"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.head(3)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "XRlbx_jzQCvS",
        "outputId": "5c1855d1-c593-494a-859b-1f4a6a7e786c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   district  grade_level    program_type class_size number_of_students  \\\n",
              "0         1            0  Gen Ed/ICT/G&T        <15                122   \n",
              "1         1            0  Gen Ed/ICT/G&T         15                 30   \n",
              "2         1            0  Gen Ed/ICT/G&T         16                 32   \n",
              "\n",
              "  number_of_classes of_students_within_grade  \n",
              "0                10                     20.1  \n",
              "1                 2                      4.9  \n",
              "2                 2                      5.3  "
            ],
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          },
          "metadata": {},
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        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df[\"number_of_students\"] = pd.to_numeric(df[\"number_of_students\"],  errors='coerce')\n",
        "df[\"number_of_classes\"] = pd.to_numeric(df[\"number_of_classes\"],  errors='coerce')"
      ],
      "metadata": {
        "id": "HBJiyy4LRMUo"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df[\"grade_level\"].value_counts().sort_index()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dNz6vNTpTNJq",
        "outputId": "07a7210b-dc7f-4923-a54b-fdfabd538637"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    117\n",
              "1    138\n",
              "2    145\n",
              "3    150\n",
              "4    153\n",
              "5    146\n",
              "6    140\n",
              "7    141\n",
              "8    142\n",
              "Name: grade_level, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df[\"grade_level\"].unique(), df[\"district\"].unique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CA76_7HKSZO7",
        "outputId": "37a61b93-0ad1-4122-e9f6-3ff900068b51"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([0, 1, 2, 3, 4, 5, 6, 7, 8]),\n",
              " array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10]))"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#we notice that for class_size <15, we can calculate the exact class size by doing number_of_students/number_of_classes\n",
        "df[\"class_size\"].where(df[\"class_size\"] != \"<15\", df[\"number_of_students\"]//df[\"number_of_classes\"], axis = 0, inplace=True)"
      ],
      "metadata": {
        "id": "O9-2ebflZE42"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.loc[df['class_size'] == \"<15\"].count()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8lv7LXuAVDfy",
        "outputId": "a3454ba8-72ae-44e8-bb3e-f33abe3fa4fe"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "district                    0\n",
              "grade_level                 0\n",
              "program_type                0\n",
              "class_size                  0\n",
              "number_of_students          0\n",
              "number_of_classes           0\n",
              "of_students_within_grade    0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Calculating class size where number of students is frater than 34\n",
        "df.loc[df[\"class_size\"]==\">34\", \"class_size\"].count()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qUVMabSgWLFl",
        "outputId": "eb726b0c-c17c-4c15-b2d0-77d4a0dc07c3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#replacing it by the exact count of students\n",
        "df[\"class_size\"].where(df[\"class_size\"] != \">34\", df[\"number_of_students\"]//df[\"number_of_classes\"], axis = 0, inplace=True)"
      ],
      "metadata": {
        "id": "_dTVohzwW2om"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df[\"class_size\"].unique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IW2Vo97GdAIe",
        "outputId": "2e086052-5847-417a-fe82-83adce9fb5ca"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([12, '15', '16', '17', '18', '19', '20', '21', '22', '24', '25', 10,\n",
              "       '26', '28', '23', 11, '27', '29', '30', 90, 13, '32', '31', '33',\n",
              "       9, 14, 36, 8], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.loc[df[\"class_size\"]==\">34\", \"class_size\"].count()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7YAWNeeRW5t_",
        "outputId": "96af5a4d-fd5a-42d2-e97f-b73be17607f6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#checking for incosistent string values\n",
        "df[\"class_size\"].str.contains(\"<=\").count()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_P9iW2u7cMKr",
        "outputId": "09251782-8b33-44cb-d715-e1715feb8a3a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1186"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Check <= values inclass_size column:\\n\")\n",
        "df.loc[df['class_size']==\"<=\"]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        },
        "id": "ZGzajJoRl1jC",
        "outputId": "ea36ea27-80eb-4aa5-94f9-dcb729efbf10"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Check <= values inclass_size column:\n",
            "\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Empty DataFrame\n",
              "Columns: [district, grade_level, program_type, class_size, number_of_students, number_of_classes, of_students_within_grade]\n",
              "Index: []"
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              "                                                     [key], {});\n",
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              "\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df[\"class_size\"].str.contains(\">\").count()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "S93Xa5ySlYY6",
        "outputId": "10a86d87-5afa-4237-832b-12ee1692ab8b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1186"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#grouping the data by district\n",
        "df[\"district\"].value_counts().sort_index()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "21XPm1_Nckpn",
        "outputId": "98270f64-c45d-4a5c-8c39-27dd428a708d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1     113\n",
              "2     154\n",
              "3     140\n",
              "4     122\n",
              "5     112\n",
              "6     140\n",
              "7     125\n",
              "8     136\n",
              "9     145\n",
              "10     85\n",
              "Name: district, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#consists of only onw value hence this column can be deleted\n",
        "df[\"program_type\"].value_counts().sort_index()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_TGnhv2UyLoc",
        "outputId": "e047a966-2712-498b-a301-6d49bcb93cd6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Gen Ed/ICT/G&T    1272\n",
              "Name: program_type, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.drop(['program_type'], axis=1, inplace=True)\n",
        "df.head()"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
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        "id": "kehk-kBT0gJY",
        "outputId": "116f8de1-9673-4d53-d0d1-5897ece2e797"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
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            "text/plain": [
              "   district  grade_level class_size  number_of_students  number_of_classes  \\\n",
              "0         1            0         12                 122                 10   \n",
              "1         1            0         15                  30                  2   \n",
              "2         1            0         16                  32                  2   \n",
              "3         1            0         17                  51                  3   \n",
              "4         1            0         18                  18                  1   \n",
              "\n",
              "  of_students_within_grade  \n",
              "0                     20.1  \n",
              "1                      4.9  \n",
              "2                      5.3  \n",
              "3                      8.4  \n",
              "4                      3.0  "
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              "                                                     [key], {});\n",
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              "\n",
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    {
      "cell_type": "code",
      "source": [
        "df.groupby([\"district\"]).sum() #total number of students at each district level"
      ],
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          "height": 394
        },
        "id": "_sSBEeKpc_Go",
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      "execution_count": null,
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        {
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              "          grade_level  number_of_students  number_of_classes\n",
              "district                                                    \n",
              "1                 457                5406                274\n",
              "2                 648               20948                919\n",
              "3                 613                9546                458\n",
              "4                 516                6300                332\n",
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              "  "
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          "metadata": {},
          "execution_count": 33
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    },
    {
      "cell_type": "code",
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        "df_temp = df[[\"district\" , \"grade_level\"]]\n",
        "df_temp\n"
      ],
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        "colab": {
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          "height": 424
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      "execution_count": null,
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              "      district  grade_level\n",
              "0            1            0\n",
              "1            1            0\n",
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              "...        ...          ...\n",
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              "1271        10            5\n",
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              "[1272 rows x 2 columns]"
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              "<p>1272 rows × 2 columns</p>\n",
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              "                                                     [key], {});\n",
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              "\n",
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      "execution_count": null,
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        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "district  grade_level\n",
              "1         0              11\n",
              "          1              13\n",
              "          2              12\n",
              "          3              11\n",
              "          4              14\n",
              "                         ..\n",
              "10        1              15\n",
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              "          3              17\n",
              "          4              16\n",
              "          5               8\n",
              "Length: 87, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 35
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      "source": [
        "res_df = df.groupby([\"district\", \"grade_level\"]).agg({'number_of_students': 'sum'})\n",
        "res_df.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
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        "id": "kfdF21YnRooQ",
        "outputId": "6ca9e491-8caf-486d-c63e-4fd8b81f84ab"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['number_of_students'], dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "res_df.rename(columns={'number_of_students' : 'agg_number_of_students'}, inplace=True)"
      ],
      "metadata": {
        "id": "InM1wAraTz6U"
      },
      "execution_count": null,
      "outputs": []
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    {
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        "id": "6jr-bmr2SGL8",
        "outputId": "873f58a5-44d2-45e4-d4f1-75c237b69c71"
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      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                      agg_number_of_students\n",
              "district grade_level                        \n",
              "1        0                               608\n",
              "         1                               565\n",
              "         2                               543\n",
              "         3                               598\n",
              "         4                               583\n",
              "...                                      ...\n",
              "10       1                              2638\n",
              "         2                              2828\n",
              "         3                              2856\n",
              "         4                              3101\n",
              "         5                               964\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "res_df[\"number_of_students\"].sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HON7tdkTR6DW",
        "outputId": "b3eede0b-1f45-4a42-d884-1df32ab04d7f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "109603"
            ]
          },
          "metadata": {},
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df[\"number_of_students\"].sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_k7jXTb1SDQL",
        "outputId": "a071f226-354a-43a6-8704-016d85d362ec"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "109603"
            ]
          },
          "metadata": {},
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df[\"class_size\"] = pd.to_numeric(df[\"class_size\"],  errors='coerce')"
      ],
      "metadata": {
        "id": "qpowGW8cwCGZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df[\"of_students_within_grade\"] = pd.to_numeric(df[\"of_students_within_grade\"],  errors='coerce')"
      ],
      "metadata": {
        "id": "nhx_zkRChsJk"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#after cleaning the data we rest the index\n",
        "df.reset_index()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "3Tzd5kysj6yA",
        "outputId": "b0d5b667-bfdd-43ce-81ea-db87072af297"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      index  district  grade_level  class_size  number_of_students  \\\n",
              "0         0         1            0          12                 122   \n",
              "1         1         1            0          15                  30   \n",
              "2         2         1            0          16                  32   \n",
              "3         3         1            0          17                  51   \n",
              "4         4         1            0          18                  18   \n",
              "...     ...       ...          ...         ...                 ...   \n",
              "1267   1267        10            5          17                  68   \n",
              "1268   1268        10            5          18                  90   \n",
              "1269   1269        10            5          19                 171   \n",
              "1270   1270        10            5          20                 220   \n",
              "1271   1271        10            5          21                 189   \n",
              "\n",
              "      number_of_classes  of_students_within_grade  \n",
              "0                    10                      20.1  \n",
              "1                     2                       4.9  \n",
              "2                     2                       5.3  \n",
              "3                     3                       8.4  \n",
              "4                     1                       3.0  \n",
              "...                 ...                       ...  \n",
              "1267                  4                       2.2  \n",
              "1268                  5                       2.9  \n",
              "1269                  9                       5.6  \n",
              "1270                 11                       7.2  \n",
              "1271                  9                       6.2  \n",
              "\n",
              "[1272 rows x 7 columns]"
            ],
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>index</th>\n",
              "      <th>district</th>\n",
              "      <th>grade_level</th>\n",
              "      <th>class_size</th>\n",
              "      <th>number_of_students</th>\n",
              "      <th>number_of_classes</th>\n",
              "      <th>of_students_within_grade</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>12</td>\n",
              "      <td>122</td>\n",
              "      <td>10</td>\n",
              "      <td>20.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>15</td>\n",
              "      <td>30</td>\n",
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              "      <td>4.9</td>\n",
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              "      <th>2</th>\n",
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              "      <td>16</td>\n",
              "      <td>32</td>\n",
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              "      <td>5.3</td>\n",
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              "      <th>3</th>\n",
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              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>17</td>\n",
              "      <td>51</td>\n",
              "      <td>3</td>\n",
              "      <td>8.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4</td>\n",
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              "      <td>0</td>\n",
              "      <td>18</td>\n",
              "      <td>18</td>\n",
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              "      <td>3.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1267</th>\n",
              "      <td>1267</td>\n",
              "      <td>10</td>\n",
              "      <td>5</td>\n",
              "      <td>17</td>\n",
              "      <td>68</td>\n",
              "      <td>4</td>\n",
              "      <td>2.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1268</th>\n",
              "      <td>1268</td>\n",
              "      <td>10</td>\n",
              "      <td>5</td>\n",
              "      <td>18</td>\n",
              "      <td>90</td>\n",
              "      <td>5</td>\n",
              "      <td>2.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1269</th>\n",
              "      <td>1269</td>\n",
              "      <td>10</td>\n",
              "      <td>5</td>\n",
              "      <td>19</td>\n",
              "      <td>171</td>\n",
              "      <td>9</td>\n",
              "      <td>5.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1270</th>\n",
              "      <td>1270</td>\n",
              "      <td>10</td>\n",
              "      <td>5</td>\n",
              "      <td>20</td>\n",
              "      <td>220</td>\n",
              "      <td>11</td>\n",
              "      <td>7.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1271</th>\n",
              "      <td>1271</td>\n",
              "      <td>10</td>\n",
              "      <td>5</td>\n",
              "      <td>21</td>\n",
              "      <td>189</td>\n",
              "      <td>9</td>\n",
              "      <td>6.2</td>\n",
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              "  </tbody>\n",
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              "<p>1272 rows × 7 columns</p>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#displaying the cleaned data\n",
        "#Number of students per district\n",
        "import matplotlib.pyplot as plt\n",
        "df.groupby([\"number_of_students\"]).median()\n",
        "plt.bar(df.index, df['district'])\n",
        "plt.xlabel('number_of_students')\n",
        "plt.ylabel('district')\n",
        "plt.title('Number of students per district', y=1.1)\n",
        "plt.grid()\n",
        "plt.show()\n",
        "\n",
        "# df.plot(x=\"number_of_students\", y=\"district\", kind=\"bar\", title=\"Number of students per district\")\n",
        "# plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 318
        },
        "id": "64Q1uX5Iff0I",
        "outputId": "73bcd196-c4db-4f55-83ee-eea7d58f607d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "df[\"class_size\"].value_counts().sort_index()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mAjbPxG1kBLp",
        "outputId": "e67d9b12-da68-47d8-a73f-1462acbcd27f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "8      1\n",
              "9      4\n",
              "10     6\n",
              "11    22\n",
              "12    34\n",
              "13    14\n",
              "14     3\n",
              "15    68\n",
              "16    75\n",
              "17    77\n",
              "18    77\n",
              "19    85\n",
              "20    83\n",
              "21    83\n",
              "22    82\n",
              "23    79\n",
              "24    79\n",
              "25    78\n",
              "26    67\n",
              "27    62\n",
              "28    58\n",
              "29    49\n",
              "30    39\n",
              "31    18\n",
              "32    21\n",
              "33     6\n",
              "36     1\n",
              "90     1\n",
              "Name: class_size, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Number of students per grade level\n",
        "df.groupby([\"grade_level\"]).agg({'number_of_students': 'mean'}).plot(legend = True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 302
        },
        "id": "S4ANImvthIzK",
        "outputId": "92748fe5-df86-4209-c14f-a90d1a34da78"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f4803676d50>"
            ]
          },
          "metadata": {},
          "execution_count": 57
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_num_classes = df.groupby([\"grade_level\"]).agg({'number_of_classes': 'sum'})\n",
        "data_row = df_num_classes[\"number_of_classes\"]\n",
        "df_num_classes"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "_82Pays8ZdsL",
        "outputId": "9996c448-a653-461b-e131-7299ba044afa"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             number_of_classes\n",
              "grade_level                   \n",
              "0                          670\n",
              "1                          634\n",
              "2                          638\n",
              "3                          637\n",
              "4                          636\n",
              "5                          556\n",
              "6                          478\n",
              "7                          473\n",
              "8                          485"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-8f17ed0b-54f3-4072-bf32-676d607f63b0\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>number_of_classes</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>grade_level</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>670</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>634</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>638</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>637</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>636</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>556</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>478</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>473</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>485</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-8f17ed0b-54f3-4072-bf32-676d607f63b0')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-8f17ed0b-54f3-4072-bf32-676d607f63b0 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-8f17ed0b-54f3-4072-bf32-676d607f63b0');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# declaring data\n",
        "keys = df[\"grade_level\"].unique()\n",
        "# define Seaborn color palette to use\n",
        "palette_color = sns.color_palette('pastel')\n",
        "# plotting data on chart\n",
        "plt.pie(data_row, labels=keys, colors=palette_color, autopct='%.0f%%')\n",
        "  \n",
        "# displaying chart\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 248
        },
        "id": "IKGSKxfU8Or_",
        "outputId": "f6369cf5-9c76-4251-a973-8d811299a8dc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(type(res_df))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6zMic74GVedY",
        "outputId": "db1f9ee4-6876-4add-8c62-8e5c3dcc54ab"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pivot = pd.pivot_table(res_df, \n",
        "                       values='agg_number_of_students', \n",
        "                       index=['district'], # rows\n",
        "                       columns=['grade_level']).copy() # aggregation function\n",
        "pivot"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 394
        },
        "id": "QDtRHYMsVpm6",
        "outputId": "eacdd993-1b35-48b6-ef1a-8061ba4f12e8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "grade_level       0       1       2       3       4       5       6       7  \\\n",
              "district                                                                      \n",
              "1             608.0   565.0   543.0   598.0   583.0   607.0   686.0   655.0   \n",
              "2            2502.0  2411.0  2240.0  2213.0  2285.0  2208.0  2292.0  2346.0   \n",
              "3            1040.0  1056.0  1064.0  1015.0   953.0   986.0  1110.0  1132.0   \n",
              "4             604.0   594.0   599.0   661.0   619.0   691.0   832.0   833.0   \n",
              "5             430.0   423.0   493.0   449.0   464.0   512.0   505.0   541.0   \n",
              "6            1100.0  1135.0  1125.0  1179.0  1196.0  1250.0  1167.0  1220.0   \n",
              "7             748.0   727.0   722.0   798.0   843.0   865.0   812.0   842.0   \n",
              "8            1421.0  1504.0  1535.0  1519.0  1545.0  1645.0  1653.0  1756.0   \n",
              "9            1603.0  1561.0  1687.0  1755.0  1739.0  1821.0  1781.0  1846.0   \n",
              "10           2636.0  2638.0  2828.0  2856.0  3101.0   964.0     NaN     NaN   \n",
              "\n",
              "grade_level       8  \n",
              "district             \n",
              "1             561.0  \n",
              "2            2451.0  \n",
              "3            1190.0  \n",
              "4             867.0  \n",
              "5             539.0  \n",
              "6            1282.0  \n",
              "7             866.0  \n",
              "8            1713.0  \n",
              "9            2063.0  \n",
              "10              NaN  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-98bdf5ef-f8a4-4592-b730-b772468cccf9\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th>grade_level</th>\n",
              "      <th>0</th>\n",
              "      <th>1</th>\n",
              "      <th>2</th>\n",
              "      <th>3</th>\n",
              "      <th>4</th>\n",
              "      <th>5</th>\n",
              "      <th>6</th>\n",
              "      <th>7</th>\n",
              "      <th>8</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>district</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>608.0</td>\n",
              "      <td>565.0</td>\n",
              "      <td>543.0</td>\n",
              "      <td>598.0</td>\n",
              "      <td>583.0</td>\n",
              "      <td>607.0</td>\n",
              "      <td>686.0</td>\n",
              "      <td>655.0</td>\n",
              "      <td>561.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2502.0</td>\n",
              "      <td>2411.0</td>\n",
              "      <td>2240.0</td>\n",
              "      <td>2213.0</td>\n",
              "      <td>2285.0</td>\n",
              "      <td>2208.0</td>\n",
              "      <td>2292.0</td>\n",
              "      <td>2346.0</td>\n",
              "      <td>2451.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1040.0</td>\n",
              "      <td>1056.0</td>\n",
              "      <td>1064.0</td>\n",
              "      <td>1015.0</td>\n",
              "      <td>953.0</td>\n",
              "      <td>986.0</td>\n",
              "      <td>1110.0</td>\n",
              "      <td>1132.0</td>\n",
              "      <td>1190.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>604.0</td>\n",
              "      <td>594.0</td>\n",
              "      <td>599.0</td>\n",
              "      <td>661.0</td>\n",
              "      <td>619.0</td>\n",
              "      <td>691.0</td>\n",
              "      <td>832.0</td>\n",
              "      <td>833.0</td>\n",
              "      <td>867.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>430.0</td>\n",
              "      <td>423.0</td>\n",
              "      <td>493.0</td>\n",
              "      <td>449.0</td>\n",
              "      <td>464.0</td>\n",
              "      <td>512.0</td>\n",
              "      <td>505.0</td>\n",
              "      <td>541.0</td>\n",
              "      <td>539.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>1100.0</td>\n",
              "      <td>1135.0</td>\n",
              "      <td>1125.0</td>\n",
              "      <td>1179.0</td>\n",
              "      <td>1196.0</td>\n",
              "      <td>1250.0</td>\n",
              "      <td>1167.0</td>\n",
              "      <td>1220.0</td>\n",
              "      <td>1282.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>748.0</td>\n",
              "      <td>727.0</td>\n",
              "      <td>722.0</td>\n",
              "      <td>798.0</td>\n",
              "      <td>843.0</td>\n",
              "      <td>865.0</td>\n",
              "      <td>812.0</td>\n",
              "      <td>842.0</td>\n",
              "      <td>866.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>1421.0</td>\n",
              "      <td>1504.0</td>\n",
              "      <td>1535.0</td>\n",
              "      <td>1519.0</td>\n",
              "      <td>1545.0</td>\n",
              "      <td>1645.0</td>\n",
              "      <td>1653.0</td>\n",
              "      <td>1756.0</td>\n",
              "      <td>1713.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>1603.0</td>\n",
              "      <td>1561.0</td>\n",
              "      <td>1687.0</td>\n",
              "      <td>1755.0</td>\n",
              "      <td>1739.0</td>\n",
              "      <td>1821.0</td>\n",
              "      <td>1781.0</td>\n",
              "      <td>1846.0</td>\n",
              "      <td>2063.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>2636.0</td>\n",
              "      <td>2638.0</td>\n",
              "      <td>2828.0</td>\n",
              "      <td>2856.0</td>\n",
              "      <td>3101.0</td>\n",
              "      <td>964.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-98bdf5ef-f8a4-4592-b730-b772468cccf9')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-98bdf5ef-f8a4-4592-b730-b772468cccf9 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-98bdf5ef-f8a4-4592-b730-b772468cccf9');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 61
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pivot = pivot.fillna(0)"
      ],
      "metadata": {
        "id": "Ms-4kxenW63n"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "fig, ax = plt.subplots(figsize=(10,10)) \n",
        "sns.heatmap(pivot, annot=True, fmt=\"g\", cmap='viridis', linewidths=0.6, ax=ax)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 624
        },
        "id": "GGfX8joEWW7J",
        "outputId": "fde4036c-b371-49d0-fafe-345d6692ec57"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f48031df710>"
            ]
          },
          "metadata": {},
          "execution_count": 66
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x720 with 2 Axes>"
            ],
            "image/png": 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newmIqIptnsmHHy7k2Wevx+JhJikpmwkT5mIyKf7z/I0MGNCSlJRcXnr5ZwC++no1Tz91LZ9MH45SimnTl7r8hA8QHOzLi6/cDBjl6MV/7GT9ukNYLCaeePo6ps+4D5vNzoTXfgGgZava3D28GzabA6017737W7W4JRAU4suLrxnfm2U2m1iycCcb/jx01vmv6tGYPv1bYLc5KCkp45UX/s9ZoZ5VcLAv418aBBg5LFq0i/XrDzFoUHsGDmwHwIqVe5lfPpD055838tTT1/LpZyNQKOYv2MahQ2kui/+E4GBfxr9cKY8/jDyK3i7lwYf7YDabKC218+47xlcydOvWiL5XN8dmc1BaYuPll352ZfgVzuf4BujatSEbNsafMiC8Onh/6iKee+o6PCwmjifn8MbEeaxau5+HR/U22qLMxtsfLgDgcGIm6zbG89nkYTgcmrkLthF/ON3FGcAHk//guaeuq2iLN96dxyP396FBvQg0muSUHN75wMihVfPaDPvXVdhtdhxa8+6HC8jLd/056nzPtWA8iu/ja8XDYqZL14Y8/cR3HKkG7SHOn6qKJ0OUUinA1cBfb2QrYLXW+vT6++l0r95vXPTYnGnxomcA6NP9NRdHcmH+WPYsfbue11c2VDsLV/4bgN69XndxJBdm0eJx9O55ieewZBwAl8vx3f2aCS6O5MIsm/cUPfq/6eowLsjS+U8Dl8e5FuNz0mn2H63ptCHUcdHHnZrb+aiqAdS/An5a6y1/fUEptbSKtimEEEIIcd6qpDOktb73HK/dURXbFEIIIcT5ccij9cBl/KWLQgghhBD/RLX6niEhhBBCOI/duUOUqi2pDAkhhBDCrUllSAghhHBTUhkySGVICCGEEG5NKkNCCCGEm3JoqQyBVIaEEEII4eakMiSEEEK4KRkzZJDKkBBCCCHcmlSGhBBCCDdll5oIIJUhIYQQQrg5qQwJIYQQbkqeJjNIZUgIIYQQbk06Q0IIIYRwa3KbTAghhHBT8mi9QSpDQgghhHBrUhkSQggh3JRdS00EpDIkhBBCCDcnlSEhhBDCTTmkJgJIZUgIIYQQbk5prV0dw9lU28CEEEKIKuLUx7sWJzRy2mdtr5i91fbRtWp9m+zFHQNdHcIFebH5bAAe3TzExZFcmPfbzGTgyodcHcYFmd31IwCuXf6IiyO5MHO7fcDta0a7OowL8n2nqcDl0RYAQ9aOdHEkF2Zmx2kMWz/M1WFckBkdZgDw1NZbXRzJhZnQ6kdXh+C2qnVnSAghhBBVR54mM8i7IIQQQgi3JpUhIYQQwk055BuoAakMCSGEEMLNSWVICCGEcFN2qYkAUhkSQgghhJuTypAQQgjhpuRpMoO8C0IIIYRwa1IZEkIIIdyU/G0yg7wLQgghhHBr0hkSQgghhFuT22RCCCGEm7Jr+dJFkMqQEEIIIdycVIaEEEIINyVfumiQd0EIIYQQbk0qQ0IIIYSbcsiXLgJSGRJCCCGEm5PKkBBCCOGmZMyQQd4FIYQQQrg1qQwJIYQQbkq+Z8gglSEhhBBCuDWpDAkhhBBuSv5Qq+Gy6gwVpJex9oMkinNsADToG0Sj60LY/n0aB//IwRpgBqDVHWHUbOcHwM7/y+DQomyUSdFueDhRbfzOuh5nKUovZfvkBEpybCgF0b1qEHNNeMXr8b+msPfrY/Sa1hLPAAv5x4rZPvUwufGFNLy9JrHXR1TMu33qYdI25eAZYKHr202dlkNZZjFJn+zAllsKCoK6RRPStw6pP+wjf2saymLCI8ybqOHNMPt4nFwuo4hDz6+hxg31CO0fA0D+9nRSZ+5Fa03QVbUIvSbWiXkUkfrZdmy5pSggoFttgvrUJf3HvRRsS0OZFR5hPoQPa47ZxwN7finJU7dQnJBLQOeahN1x8j0//t4GbDklYNd4xQUTNrQpylT1JerSjGKOTN9NWXkOoT1qEtavNse+O0DulgyURWEN96b2vY2x+HqgbQ6OzNhL0eE8tF0T0iWSiOvqArBz7BrM3mZQCmVWNHqxfZXHf8L5tgVA5rxD5K08CiZFjcFN8G1eA4DshQnkrjgKSuFZy4/wYc0xeZidkkdpRjEJ0/Zgyy0DoEbPKML7RXP0u4PkbMlAmU1Yw72oO6IxFl8LDpuDIzP2UZiQb5wPhjbAv0kQjhI7hybtoiS1CKUUgW1CqXVbPafkUJJRwv6p+ynLKQMFET0jqNm/Jkd+PELmpkxQ4BHgQdyoODyDPSuWyzuYx/bx22n4UENqXGG0RUl6CQc+OUBpZikATZ5sgleYl1PyKEovZdOkRIqzjXNt3T6h1L+mRsXrB35JY+dXSfT/pCnWAAvpO/P5c0ICPuFGTjWvDKTRLcb5dvPkRJI35WINtNDrnUZOiV9cPJdVZ8hkVrS5J5yQel6UFdlZ8GQCka18AWh0XTBNBoaeMn9OYglHVuZyzXuxFGXaWDI+kWs/9D3regJrW52ShzIrGt0VTWCsD7YiO6vH7aFGS3/8or0pSi8lfVsuXjVOnmA8/Mw0vSealPXZp62rVvcQ6lwdxvZJCU6J/QRlUoTf3hCvugHYi2wkvPwnvs1C8G0aStjNDVBmE6k/7idjbgLht8ZVLJf6/T78mp9sJ+3QpHyzh9pj2+IR7EXCy3/i1zoMa00/J+VhIvTWxnjVDcBRbCPx5TX4NA3Fp2kooYPiUGYT6T/tJWveIWrc0gjlYSJkYBylx/IpPZ53yroiR7XG5G1Ba03y1C3kb0jG/4qoqs/BrKg5uAE+Mf7Yi2zse3ED/s1C8G8eQs1b66HMJo7/cJDUuUeoeVt9stenocscNH7lChwldnY/u46gK8OxhnkD0ODp1lj8Pf9mq1WQx3m2RenxfPLXJ1FnfFds2cUcm7gBn1euwp5TQvaiI9R5qQsmT7PRFuuSCehSyzl5mBXRQ+pXtMeeFzbh3yyYgGbB1Lq1HsqsOPb9IVJ+PUKt2+uRsTQJgKavtqcst5QDb2+n8YttAYgYEI1/k2AcNgf739xKztYMAluFnmvzFycHkyLmjhj8Yv2wF9nZ+vxWgloEUfPamtS5tQ4ASQuSSJyVSP3h9QHjWD78/WGCWgSdsq79U/cTPTCaoBZB2Ivt4MQhLMqsaHZXFEH1fCgrsrPsmf2EtfQjINqLovRSUrfl4V3D45RlQpv40vGZ0y/IavcIJrZ/KJsmJTor/IvCLt8zBFxmY4a8gy2E1DOuKDy8zQREWynMtJ11/qPr86nTNQCzhwm/CE/8Ij3JPFB83uu52LyCPQiM9QHA4m3Gr5YXxZnGVeSeL4/SaOipJ21roAeB9X1R5tPPIiFN/PHwdc4Vb2WWICtedQMAMHtbsEb5Yssqwbd5KMps7Hbe9QOxZRVXLJO3KRWPGt541jrZ0Sk+lINnuA+eYT4oi4mAKyLJ35zmkjxMXhY8o3yxZRfj06xGRR5e9YKwZZUY81gteMcFozxOP7RM3uXXHnaNtmmnnfQ9gqz4xPgD5W1R05eyrBICmodU5OBTP4CyTCMHFDhK7Gi7A0eZA5NFYfZ2/XXT+bZF/pZU/DpEoTxMeIT54BHmQ3F8jrEyh0aXledY6sAS5JwLHTi9Pbxq+hjt0SKk4hj2rR9AaXkeRccL8W8abCwb4InF10JhfB4mqxn/JsZ0k8WET11/yrJKnZKDZ7AnfrF+5TmY8a7pTWlmKRafk/uJveTUjk3S70mEdgjFI+Bk56LwWCHaoSs6SGYvM2ar885XXsEeBNUzzrUe3mb8K51rt3+RRLOhUf/4OK3R1A9PP9cfJ+J/U2WdIaVUY6VUb6WU31+m96+qbVaWn1pKVnwxNeKMTs3+37KYNyaetZOSKM23A1CUUYZP6Mmd1yfUQmH5gXC29ThbYWoJuQmFBDXwJWVDNl4hHgTU9XFJLP+r0vQiio/k4VUv8JTp2SuP4dvCKEk7im1k/JZAjRtOLfOXZZdgCTn5QWUJtlKWXVL1QZ9BWXoRJYl5eMWeemWbu+pkHn/n2MQNxI9dgsnLjF+7yKoI85xK0oooOpyHT/2AU6ZnLk/Cv6VxKziofRgmq5kdj61m1+OrCRtQB4uf8QGmFBx8eyt7X1hP+tLjTo//hH/SFvbsYjxCTh63lmAv7NnFWIK9COoXQ8LTy4l/Yikmbws+zf5Z+11sJWnFFB7Ox/cv7ZG+IomAFkZ7+NT2JWdzOtquKUkrojAhj9LMU48BW4GNnC0Z+Dc99f1whuK0YgoOF+BX3zjVH/7hMBse2UDa6jTq3GxUiUoyS8jckElk71P3+aKkIiw+Fva8t4et/95KwrcJaId2eg4Ahaml5MQXEdzAh6T1OXiHWAiM8T5tvsx9hSx5ch9rXosnN7H4DGu6tDhQTvupzqqkM6SUegSYDTwM7FBKDaz08mvnWG6kUmqDUmrDtGnT/uftlxU5WPnWMdoOi8DDx0yDq4O5blJ9BrwTg3eQhU1fpP5P63E2W7GdLRMP0fjuaJRZcWhWMg1uq+n0OC6Eo9jGsclbiRjc8JTqQvqvh1AmRUBH4+SYPvsQIf3qYPKqnldWjmIbyVO2UOP2xicrPEDm3IMok8Lvyn92u6vWmPbEvN0DbXNQtCejqsI9I3uxjYSPdlDrjrhT2iJ5TgLKrAjuZIx9KIjPRZkUzSd2psnbnUibf4SS1CIAGvy7LY3Gd6De2FakLzpK/t7Tb81WtQttC3tBGQVbUol5vRuxb/VAl9rJW+v8jp292M6hD3cSPbT+Ke2RNOcwyqQI6WyMEwztFoVnsJU9L27k6DcH8W0QeMpYM23XJEzZRXjfWljDT//wruoc9r6/l9g7YyuqQnVvq0v7D9oT1jmMpIXGLb6ErxOoO7juaWPktEOTuzeXmDtiaPlSS4rTikld/s/OzxeTrdjOuncO0/yemiizYt+sVBrffvrFSmCsN/0mN6bnWw2p1z+UdW8lOD1WUTWq6pPnPqCd1jpfKRUD/KSUitFav885io5a62nAiV6QfnHH3PPesMOmWfnWMWKuCqR2R6MU7R10Ms36fQNZ/tpRY3qoB4UZJ29/FWbY8AnxOOt6nMlh02x+9xBRXUOIvCKYvCNFFKWVsuqp3QCUZJayetxuOr3aGGuQx9+szTW0zcGxydsIvDIK/3YnB3VnrzxO/tZ06jzRDqWM3aEoPofcjSmk/rgfR6ENFJg8THjVDcBW6SrYllWChxNvaZzII2nKFvyujMKv7ck8clcdo2BbGrUe71CRxz9h8jDj2yqcgi2p+DR1TkVC2xwkfLSD4E4RBLUPq5iesSKJ3K0ZNHiqdUUO2WtS8W8RYgxyD/DENy6QwoQ8rOHeeAYb771HgCeBbcMoPJSLXyPnVSPOpy3MQV6UZZ68crdlFWMO8qJodwaWGt6Yy8c9+bYJp+hgNv4dnXehoW0ODn24k5DO4QSf0h7J5G7JIO7pVhV5KLMiemiDinn2vrwZa+TJTs+RGfuwRvoQfnW00+IHcNgc7H1/L2GdwwjtcPo4pbDOYex6exd1bq5Dfnw++z7aB0BZXhlZW7NQJoU1xIpvXV+8wo0KXki7EPIP5Ds5D826dw4TfVUQNa8MJPdIEYWppSx50oi3OKOMZU/vp9vrDfCqdK6NaBvA1k+PUZJrwxpQPS/ixD9XVS1o0lrnA2itE5RSPTA6RHWpwpESWmv+nJxEQLQnjW84+fRXUZYN72Aj1aN/5hNYxzihR7f3Y/V7x2l8fTBFmTbykkoJaeB11vU4i9aaHR8fxq+WF7HXGid8/zre9JrWsmKepQ/toPNrjfGspgeh1pqkz3fhGeVLyNV1K6bnb08nc34CdZ5uj6nS2IC6z3So+D1t9kFMVjPBveug7Q5KUwopTSvCI9hK7rpkao5s4dQ8Ur/YiWeUL8H9YiqmF+xII2tBPNFPXnFKHmfjKLbhKLZjCbKi7Q4Kt6fjFeecToTWmiOf7cEa5Ut4/zoV03O3ZZD62xHinmlzSg4eoVbyd2cR0iUSe4mdgoO5hPWrbYwBcWjM3hbsJXbydmYSeUOMU3I4kcf5tIVvq3BSPtlKcN8YbNnFlKUW4hUbSImCkkPZOErsKE8TRXsysdYNOMMWqy6Pw5/uw6umDxH9a1dMz9mWScq8ROLGtTolD0eJHQ2YrS24dfMAACAASURBVGZyd2SiTArvWsaDIcd/isdeZKPO8IZOi/9EDgc/OYh3TW9qXnOyE1mUXIR3eUctc1Mm3lHG7+0mtquYZ//H+wluE0xo+1C0Q2MrtFGWW4ZHgAc5O3Pwq+echyNO5LF5aiL+tbxocJ3RKQ2o482AT5pVzPP7g7vp/noc1gALxdllWAMtKKXIOlAIDvD0d/5dg4tJBlAbquqTNEUp1VprvQWgvEJ0HfAZUGWfZOl7ikhYlktgHSu/jY0HjMfoD6/MJSvBqC74hXvQYbRR/gysY6VOZ3/mPRqPMiva3xeByaxI2114xvWceBy/qmXvLeD4ikz86nix6mmjEtRwcE3C2gSecf6S7DJWP7sHW5EdpRQJv6Vy1dtNsfiY2fJBPFm78ijNs7Hkge3E3RJFdK+qr0YUHcgmd00S1mg/4l9cA0DYoAakzNyLLnOQ+M5GALzrBRL5r7M/8q/MJiKGNiJx4iZwaAK71sRay3kny+ID2eStPY5nLT+OjF8NQOigONJn7kbbNMfe3QCAV71Awu8yTqAJzyzDUWRD2zX5m1OpNaY9Jj8Pkj7ahLY5QIN3oxACu9c+63YvpoL9OWStTsEr2pc9z68HoOYt9Tj6zX60zcGBt7YCxqDd2vc0okbvWhz5ZA97nv0TDYR2jcK7th8lqUXEf7jdWKldE9QxgoCWVf/k0gnn2xbWWn74tY/k8AsrUSZF2B1NUCaFV70gfNtFkvjKGqM6UcefwG7OaQuAgv25ZJa3x+7njZhr3hLL0a8P4LBpDry1DTDao849DSnLLePA29uMrwEI9qTuqMYAlGaWkPzLEaxRPux5wTiewnrXokaPqn9CMW9fHmkr0/Cp7cOWZ7cAxu2xlGUpFCUZj/pba1ipN+zcj/orkyJmSAw7X98JGnxjfYnoGXHOZS6mzL2FHF2eTUAdr4pKUNMhkUS0PXPn+PjaHBJ+z0CZFWZPE+0fq1NRwdvw3mHSdxVQmmdjwejdNL4tgrq9nH8xLf43SuuLP1hNKRUN2LTWyWd4rYvWetU/WI1+ccfAv5+rGnux+WwAHt08xMWRXJj328xk4MqHXB3GBZnd9SMArl3+iIsjuTBzu33A7WtGuzqMC/J9p6nA5dEWAEPWjnRxJBdmZsdpDFs/zNVhXJAZHWYA8NTWW10cyYWZ0OpHcOqXC8DkvT2dNmL9gUZLqu0o6iqpDGmtj57jtX/SERJCCCGEcIrqOeBECCGEEFXOIX+oFbjMvnRRCCGEEOJ8SWVICCGEcFN2qYkAUhkSQgghhJuTypAQQgjhphzyPUOAVIaEEEII4eakMiSEEEK4KXs1/wOqziKVISGEEEK4NakMCSGEEG5KxgwZ5F0QQgghhFuTypAQQgjhpmTMkEEqQ0IIIYRwa9IZEkIIIYRbk9tkQgghhJuSAdQGeReEEEII4dakMySEEEK4Kbs2Oe3nXJRStZVSS5RSu5RSO5VSj5ZPf1EpdUwptaX855pKy4xTSh1QSu1VSl1daXr/8mkHlFLP/JP3QW6TCSGEEMLVbMBYrfUmpZQ/sFEptbD8tYla67crz6yUagoMBpoBNYE/lFINy1+eBPQFjgLrlVJztNa7zrVx6QwJIYQQbspRTR6t11onAUnlv+cppXYDtc6xyEDgO611CRCvlDoAXFH+2gGt9SEApdR35fOeszMkt8mEEEIIUeWUUiOVUhsq/Yw8y3wxQBvgz/JJDymltimlPlNKBZdPqwUkVlrsaPm0s00/J6kMCSGEEG7q78byXExa62nAtHPNo5TyA/4LPKa1zlVKTQFeBnT5v+8Awy92bEprfbHXebFU28CEEEKIKuLU+1bPb7/JaZ+1L7eYdc7clFIewK/AAq31u2d4PQb4VWvdXCk1DkBr/Xr5awuAF8tnfVFrfXX59FPmOxu5TSaEEEK4KYdWTvs5F6WUAj4FdlfuCCmloirNdhOwo/z3OcBgpZRVKRULxAHrgPVAnFIqVinliTHIes7fvQ/V+jZZ2/snujqEC7JpyhgA2jx4aeexedKYyyIHgLajLu08Nn08hg7DT7tguqSs/+xxANrdd2m3xcbpxj7V6tFLO4+t74+h5ZhLO4dtE422aP7kpZ3HjrfGuDoEV+oC3AVsV0ptKZ/2LDBEKdUa425RAjAKQGu9Uyn1A8bAaBvwoNbaDqCUeghYAJiBz7TWO/9u49W6MySEEEKIqmOvJjeItNYrOfMtwnnnWOZV4NUzTJ93ruXOpHq8C0IIIYQQLiKVISGEEMJN/d1YHnchlSEhhBBCuDWpDAkhhBBuyiE1EUAqQ0IIIYRwc9IZEkIIIYRbk9tkQgghhJuyywBqQCpDQgghhHBzUhkSQggh3JQ8Wm+QypAQQggh3JpUhoQQQgg35dBSEwGpDAkhhBDCzUllSAghhHBT9jP+bVT3I5UhIYQQQrg1qQwJIYQQbkqeJjNIZUgIIYQQbk0qQ0IIIYSbkqfJDPIuCCGEEMKtSWVICCGEcFMOeZoMkMqQEEIIIdzcZVcZeuGuvlzVoh6ZeYXc9vJXAAT4WHljxLXUDA3geEYuT38yl7zCkoplmtaN4PMnBzPu03ks2rwfgOs6NmXEgCsA+OS3dfy6dpfzcrizL92aGznc+urJHN4cfjKHpz6dS15RCe3iopk46gaOZ+QAsHjLAab99icAft5WXhjal/pRoWg0479eyLb4pGqZB0C7uGievKU7FrOZ7PwiRrz3Y8W6TErxzdN3kJqdz6NTZzsvh39V2p9eqrQ/3Vdpf5pu7E8xEcG8eE8/GtcOZ9Ls1Xy1cGPFen59dTgFJWU4HA7sDs2dr33rtBzOZHCfNtzYrQVKwc/LtzNz4WbuG9iJG7u1IDuvEIBJ/13F6u3xNI2N5N939zEWVIrps9ewdNMBF0ZvGNK7DTde1RylFLOWb2fmos00rB3Gs3f2xtPDjN2ueeObRexMSAGgXcNoxg4u37/yihj59o9/s4WqMX5IX7o1q0dmfiE3v2HsU31bx3F//07ERoQw9N2Z7Eo0Yg708eKd4dfRrE4Ec/7cxev/XVKxnibR4bw89GqsHhZW7ornzf9b6tw8Bvele1Mjj0ETyvNoZeRRLzyEO947mUfzOhH85zZjH1IopixYw+LtB4kI8uPVO/oT6u+DBv67ZjvfLN/stBxevrUv3cpzuOkdI4d+LeN4oK+Rw5APZ7LzqJGDxWzihZv70Cw6Aq01b8xeyvpDRwFoWiucV26/Gi8PCyv2xPP67KVOy+FCyV+tN1x2naFf1uzi+6VbeemeqyumDbv6CtbtSeTz39dzT78ODOvXgQ9+XgkYH7KP3tSVtbsPV8wf4GNl5LUdufP1b9DAN+OGsmzbwVM6UFWaw9pdfL9sKy//q1IO/a5g3d5EZixcz7C+5TnMNnLYfODYGTsIT93Sg9W7Enjyk1+xmE14eXo4Jf4TzicPP28rz97eiwcnzSI5K49gP+9T1nVHzzbEJ2fi6+Xp3BzW7OL7JVt5aVilHPqX708L1nPP1R0Y1r8DH/zfSnIKi5nw3VJ6tq5/xnWNeudHsguKnRX6WdWvFcqN3Vpw9yvfYrPZ+eDxQazYGg/AzN838vWCjafMf/BYOv966RvsDk1ooC/fjr+LFVsOYndoV4QPQP2aodx4VXPufm0mZTY7Hz46iBXbDvHozVcx7Ze1rN6RQJfmMTxyy1WMevsn/LytPDO0Fw+/P4vkzDyC/b3/fiNVZPa6XcxcsZVX7zy5Tx1IymDMZ7/w/G29T5m31GZj0rzVNIiqQYPI0FNee+623oz/biHbDyczadSNdGkSw6rdCc5IAYA563bx3cqtvHrHqXk8foY8DiRlMOTdb7E7NDUCfPnpiTtZtvMQdofmnTnL2X00FR+rB989PpQ1ew9zKCXTKTn8vGEX367eymuDK+WQnMFjX/7CCzefmsMtV7YAYNC7XxHi682UETcx+INv0RqeH9SbF39ayLYjyUy590a6Noph5d4Ep+QgLo7L7jbZpgPHyPnLB073VvUqKju/rt1Fj0ofVoN7tmbR5gNkll8NA3RqGsOfuw+TW1hCXmEJf+4+TOemMU6JH86cQ4+W9fjlTyOHX/7cRc9WZ/7APcHPy5O2DWoxa/UOAGx2B/lFzunMnXA+eQxo34hFWw6QnJUHQFZ+UcUy4UF+dG0eW5GLM23af4ycwjPsT2vK96c1u+hRnkNWXhG7DqdgszucHuf5iIkKYUd8MiWlNuwOzaa9R+nZtsFZ5z8xH4DVw4zWrusEnRBbnkPxiRz2HaVX2zg0uqLD7OdjJT27AIABVzZi8eYDJGeW7195RWddd1XbdPAYuX/Zp+JTMjmcmnXavEWlNjYfOk5Jme2U6TUCfPH18mT74WQAflm/m14tzn1OuNg2Hjr9+I5PzSQh7fQ8issq7UMWMxrj9/TcAnYfTQWgsKSM+JRMwgP9qjjykzbGn358HzpLDvUjQlh3IBGAzIIi8opKaBYdQQ1/oy22HTHaYs7G3fRq7ty2EBeuyipDSqkrAK21Xq+Uagr0B/ZoredV1TbPJtTfh/Rc46SYnltAqL8PAGGBvvRs1YCR7/3IC3f1q5g/PMiv4kMZICUrn/Ag5x2gZ3K2HABaxkbx/bg7ScvJ591ZKziUlEHNGoFk5Rcx/q5+NKwVxu4jKUz4aSnFpbazbcIpzpZH3fBgLGYT0x+9BR8vT2Yu2cyv63YD8OQtPXh/1gp8nFwVOpvQgL/kEODzN0uABiY9Ngg0/HfFdv5vxfYqjvLsDh7L4P5BXQn09aK4zEbnFrHsTkghp6CYW3u35prOTdmdkMJ73y+rqIY2qxfJf4b1IzI0gBc+me/SqhDAgWMZPHBTFwJ9vSgps9GlRQy7Dqfw9nfLmPTYTTx2azdMSjHsje8AqBNh7F8fP3ELvl6ezFy0mblrdrs0hwsRHuhHSnZ+xf9Tsl1/jvo7LepEMn5IP2oG+/PsN6fvQzWDA2gcHVbRwatu9h5Pp0fTeszbsofIQH+aRocTGeSP1pCSU6ktcvKJCKjebVGZPFpvqJLOkFLqBWAAYFFKLQSuBJYAzyil2mitXz3LciOBkQAff/xxVYQGwIlD8Ilbe/DBzyuoBhe65+1EyHsSU7nmP59SVFJG12YxTBx5PQPHf47FZKJx7XDe/HEJOxKSefKWHgzv14HJv65xadx/dSIPs9lEkzoRjPrgJ7w8LHzxxGC2JSRRNzyYzLxCdiem0i4u2qWxns0/2X+Gv/U9adkFBPt7M+XRm0lIzmTT/mNVH9wZJCRl8uVv6/lw7M0UlZSxLzENh9b8d8lWPp2zFo1m9E1deOz27rw843cAdh5K5vbnvyQmKoQX7+3P6m3xlNrsLokfICE5ky/mr2fSmEEnc3Bobu3Rknd+WMbiTQfo274h/7m7Hw9M/C9mk4kmdSMY/c5PeHlamPHMYLYfSuJISrbLcnA3248kM+jNL4kND+GVO65m5e6Ein3I29ODd4ddx4RZyygoKXVxpGc2a/0O6oWH8P2jd3A8K48tCUk4XHxRIC6eqqoM3QK0BqxAMhCttc5VSr0N/AmcsTOktZ4GTDvx36n3T7wowWTkFVIjwJf03AJqBPhW3BJrWjeC1++9BoAgX2+6No/F7nCQmp1P+4YnP3gjgv3YsO/oRYnlf3W2HAqKT544Vu5MYNztJoJ8vUjJziM1O48dCcZV1h+b9zOsX3uXxF7Z2fJIzconp6CY4lIbxaU2Nh04RsNaYTSpHU73FvXo2iwGTw8Lvl6evHJ3f577Yr7rcsg9cw7nklZ+uyYrr4glWw7QLCbSZZ0hgDkrdjBnhXHb8YFBXUjNyicz92QePy/bzsRHbzxtuYSkTApLSqkfXYPd5QOTXWX2yp3MXrkTgAdv6kJqVh4P3dSVt75bCsDCDft47l/GoN3T9q/9x2gYHXbJdoZSc/KJqFQJigjyI7VSpag6i0/NpKi0lAZRNdiVmILFZOLdYdcxd+MeFm13/cD8s7E7NBN+WVbx/68fvJ2EtCxyi0qIqHRrLyLQj5TcS6MtQP4cxwlVVR+zaa3tWutC4KDWOhdAa10EOH1AxfJth7iuY1PAeEps2dZDAFz//Gdc95zx88fm/bw+czFLtx5kza4EOjapi7+PFX8fKx2b1GXNrgRnh32KZdsPcf2VRg7XX9mUpduMHCrfomlWNwKlFNkFxWTkFpKclU/d8GAArmhUm0PJzhmUeC5ny2PptoO0rl8Ts0nh5WGheUwk8cmZfDhnFf2f+4Rr//MZz3w2j/V7E13aEYLy/alT+f7U6eT+dDZenhZ8rB4Vv3dsWpeDx9OrPM5zOTGAOCLEn57t4pi/dg+hgb4Vr/do24CDx4wYa9YIwGwyTpiRof7ERIVwPD3H+UH/xYkcIkP86dWmAb/9uZe0nHzalV/IdGhcm8RUo7OzdMtBWjco3788LTSPjSQ+yfXHw/8qPbeAguJSWtSNBOD6Dk1YsuOgi6M6u1ohJ/ehqGB/YsJDOJ5p7EPjB/clPiWTr5ZtcmWIf8vLw4K3h1E/6BRXB5vDwaHUTNLzjLZoWcdoixvaNWHJzurbFuLMqqoyVKqU8invDLU7MVEpFUgVd4ZeGz6Adg1rE+TnxW+vjWDqr2uYsWA9b464lhu7NCMpM4+np/96znXkFpbwybw/+frpOwCYPm8tuU56kgzg9WEDaBdn5DD/lRFMnbuGGb+v5817r+XGzkYOT31q5NCnTRy3XtUKu91BcZmNcZ+dHJL15o9LeO2eAVgsJo6l5/DCV787LYfzzSM+JZPVuxL44dm7cGjNrNU7OJiU4dR4z+S1ewfQrlH5/vTGCKb+soYZ89fz5shK+9M0I4fQAB++fvYOfL080VpzR+823PLilwT5efPO6OsB43bg/HV7WL3z8Lk2W+XefPB6Av28sdkdTPh6EflFJTw5tCcN64SjtSYpPZfXvvwDgFZxtbjnmg7Y7A4cWvPmV4vIyXf9U3Fv3X89gb5e2OwO3vh2MflFJbzy5R88MbgHZpOJ0jIbr5TnkJCcyeodCXz3grF//bxiBwePu2b/euNfA2jfwNinfh8/gim/rSGnsJhnbu5JsJ83H40ayN6jadw/dRYA8/4zHD8vKx4WEz1b1mf05P/jUEomr/64mJeH9sPqYWHVrgRWOvmC7c27yvPw9WLhCyOYPN/IY9wgI49J9w1kz7E07v94Fm3q1WJ47w7Y7Ha01rz602KyC4ppE1uT6zs0Zd/xNH54YigAH8xdxUonPRU34Y4BdKhv5PDHv0cw+fc15BQVM25gT0L8vJk8fCB7jqcx6pNZhPj58PGIm9Bak5JbwLiZJy/KXpm1mFdu71f+aH0CK/Y4J/6LQb500aCq4skQpZRVa31a70EpVQOI0lr/k9Gjuu1Fuk3mKpumjAGgzYOXdh6bJ425LHIAaDvq0s5j08dj6DD8XVeHcUHWf/Y4AO3uu7TbYuN0Y59q9eilncfW98fQcsylncO2iUZbNH/y0s5jx1tjAOf2Tob+eZ/TBj59c+X0atvzqpLK0Jk6QuXT0wHX3h8QQgghBCBjhk6QZ+qEEEII4dYuu2+gFkIIIcQ/I98zZJB3QQghhBBuTSpDQgghhJuSMUMGqQwJIYQQwq1JZUgIIYRwU/I9QwapDAkhhBDCrUllSAghhHBTMmbIIJUhIYQQQrg16QwJIYQQwq3JbTIhhBDCTcltMoNUhoQQQgjh1qQyJIQQQrgpqQwZpDIkhBBCCLcmlSEhhBDCTUllyCCVISGEEEK4NakMCSGEEG5K/hyHQSpDQgghhHBrUhkSQggh3JSMGTJIZUgIIYQQbk0qQ0IIIYSbksqQQWmtXR3D2VTbwIQQQogq4tTeSd+lY5z2Wbuwx8Rq2/Oq1pWh3r1ed3UIF2TR4nHA5ZHH5ZADQI/+b7o4kguzdP7T9Or7hqvDuCCLFz4DQPdrJ7g4kguzbO5TAHS+/R0XR3JhVn8/9rLIAaDj0Es7j7XfjHX6NqUyZJAxQ0IIIYRwa9W6MiSEEEKIqiOVIYNUhoQQQgjh1qQzJIQQQgi3JrfJhBBCCDel5TYZIJUhIYQQQrg5qQwJIYQQbkr+UKtBKkNCCCGEcGtSGRJCCCHclDxab5DKkBBCCCHcmlSGhBBCCDclT5MZpDIkhBBCCLcmlSEhhBDCTcmYIYNUhoQQQgjh1qQyJIQQQrgpGTNkkMqQEEIIIdyaVIaEEEIINyVjhgxSGRJCCCGEW5PKkBBCCOGmtHZ1BNWDVIaEEEII4dakMySEEEIIt3ZZ3ybz9bXyxBPXEBMbhtaat9+aR2JiBs8/fyMRkYGkJOfw0ks/k59fXLFMo0ZRfPjRv3jl5Z9ZvnyvC6M/6Ztv76ewsBSHQ2O3O3jg/s+pVy+cMWP64+XtQUpKDq+9OofCwtKKZcLDA/hsxn188cUKfvxhnQujN5xPDhaLiTGPD6Bhw0i01kz66A+2bj3i6hQA8PO18uRjA4iNqYHW8ObEeezafZybbmjLTde3xe7QrF13kI8/XUqAvxfjn7uRxg2jmL9wO+9P/sPV4Vfw9bXyxOMDiI0JQ6N56+15hIX5c/ddXalTpwYPPPwF+/YlA2CxmHj8sf5Gezjgo8l/sHWb69vDz9fKk4/0J7ZuDQDefO83ikvKGPtgP7y9PUlOyeHlt36lsKiUAH8vXnr2RhrFRTL/jx28P7X6tMXt17Tl+l4tADh4JJ1Xp8xn7PDeNK4fgUKRmJTFK5PnU1RSxuBr23F9rxbY7Q6ycwt5beoCktPzXJzB+eVwY5+W3Hx1a+wOTVFxGW9O+52EY5kuzsAwuH9bbujZAq3hYGI6r0ybT2mZndG3dqHXlQ1xODT/t2grPyzYzNBr23N1lyYAmE0mYmqFMGD0FHILiv9mK9WLAxlADZd5Z+ihh/qyfv0hxo+fhcViwmr1YOjQzmzanMB3M9cyeEhHhgzpyPTpSwEwmRT3jezBhg3xrg38DMY+/i25uUUn///ENXw8dRHbtiXSv39Lbru9I5/PWF7x+v3392bduoOuCPWs/mkO117bGoD7RnxKUJAPr79xGw/c/3m1uLf90OjerNt4iBde/RmLxYSX1YPWLevQtVMc9z4wg7IyO0GBPgCUltr57MsVxNYNIzamhosjP9VDD/Rh/YZDjH/554pjI7+ghBfGz2LMY/1Pmffaa4z2GDHyM4KCfHjj1du4/yHXt8fDI3uzbmM8L7w+u6It3nnlNiZ/upStOxK5pm8LBt98BZ99vZLSUjufflXeFnWrT1vUCPbj1gFtuePxzykts/HyY9fRp3Nj3v9yKYVFxsXNI3d155b+bfhq9jr2JaQyfNzXlJTauKlvKx4Y2p3/vP/rJZXD76v28PMf2wDo2q4+j/yrB4+//n+uTAGAsGA/bru6LUOe+pySMhuvPHwdfTs1BiA81J/bn5yB1hAc4A3AN3M38M3cDQB0bVOPwQPaXXIdIXHSZXubzNfXSouWtZk3bysANpuDgoISOneJ4/cF2wH4fcF2unRtWLHMjTe1Z8XyvWRnFbgk5vMRHR3Mtm2JAGzcGE+3qxpVvNalSxxJydkkJKS7Krx/5Gw51K1bg82bDwOQnV1Ifn4JDRtFuSzOE3x9PGnVojZz5xsncpvNQX5BCQOva8O3P6ylrMwOQHZOIQDFJWVs33mM0jKby2I+E18fKy1b1GbebyfzKCgo4ciRDBKPnn6FXrduKJu3VGqPgmIaNXRte/j6eNKqeTRzfz+1LaJrhbB1h7FPrd+cQPcuxvFdXFLG9l3Vry3AqCpYPS2YTQovTwvpWfkVnQgAT08LurznuWlnIiWlRg479ycRHurnkpj/6nxyqDzd2+rh8k51ZWZzpTysFtKy8hnUpxWfzVpbEWdWpQu6E/p2bszCNXucHO3FobVy2k91dtl2hiIjA8nJKeSpp65l6sfDGDt2AF5eHgQH+5KZaXR2MjMLCA72BaBGDT+6dm3InDmbXBn2GWkNE94azJSp91RUTQ4fTqdLlzgAundvTFi4PwBeXh4MHtyJL79Y6bJ4z+R8cjh4MJXOnRtgMikiIwNp2DCS8LAAl8V+QlRkENk5hTwz9hqmf3QPTz7WHy+rB7VrBdOiWW0mv3cX700YQqOGka4O9Zwio8qPjSev5eMpwxj7uHFsnM3Bg6l07hR3sj3iIglzcXsYbVHEM2MG8MkHd/PkI0ZbJBxJp2vHBgD07NqI8Bqu32/OJT0rn5m/rmfW5PuY8/Fo8otKWbfN6Hj++/6r+fXj0dStFcKP8zeftux1PZuzdovrq9j/Sw6D+rXmx/fv5YGh3Zj4+WJXhX6KtKx8vpm7np8/uI9fJ42moLCUddsPEx0eRJ+OjZjx8lAmPjWI2hFBpyxn9bTQsWUMS9btd1Hk4mJwWmdIKfWls7YFRg8/Li6SOXM2M3rUDIqLyxg8pNNp8524WnngwT5Mn7akWl2lnPDYo18xetQMxj3zAwNvbEuLlrV5a8JcbhjYjilT78Hbx4qtzAHA3fdcxU8/raO4uMzFUZ/qfHL47betpKXlMWXqMB54sA87dx7D4XC4OANjn2rYIJLZv27mvoc+5//Zu+/wqIougMO/SSEhAVLovfcOoYfeld6bIkVEwYKABRVROqIgKggiVcBKU3qRXqT3HggBQiophECS3fn+uEtIqEG+7C7ueZ9nn2Qn9+6ek7mbnZyZezf+diI9utbE2dmJLJndeeOdhXw/ewujRrS1daiPlfza+PMgr71uvDa6d635yO3XrD1KWFgs309/hUGvN+HESdv3h7OTE8WL5WTF6sP0f2s+t28n0KNzDSZOXUO7Fysz6+uXyZgxA4lJJpvG+SSZPd2o61eMToNn02bgTDK6udLc31iHMnbGOtoMnEng1Uia1C6Zar/m/qUpVTQni1but0XYqfybHJauP0znt39k+uJtvNLh0ceeNWX2cKNe1WJ0eGc2VF2qjgAAIABJREFUrQbPxN3NlRZ1SuPq6kxCYhJ9PlnEis1H+WhA81T71a1SlGNnrz23U2Rmrax2exylVH6l1N9KqZNKqRNKqbct7b5KqQ1KqXOWrz6WdqWUmqaUOq+UOqqUqpLisXpbtj+nlOqdlt9DuqwZUkqtvL8JaKiU8gbQWrd5xH4DgAEAM2fOfKYYwsJiCQuL4fTpawBs23aabt1rceNGHL6+RnXI19eTqChjSqNEidx8/InxJubl5UH1GkUxmczs3Gn70X54+E3AmKLYseMspUrl5rdf/+H9934GIF8+X2rWLApA6VJ5qFevJANea0imTO6YzZqEBBMrlh+wWfzwdDmYzZoZ0zcl7zvtm5e48pDpG2sLC48lLDyWU2eCAdi6/Qw9utYkLDyWbTvPAnD6bDBms8bLKyPR0Q+W0+2B8dqI5fRpI49t207Tvduj35DMZs307+/1xzdTe9m8P8Ii7uuLnWfp0bkGc37awbBPfgMgXx4falUrasswn8ivfEGuhUYTFWscK1v+OUf5knlYt+MUAGat2bjrND1bV2PVlhOWfQrQu0MNBo36xS4Ge/8mh7s27jrN8P5NrB7zw1QrV5BrYSny2HeO8sXzEBp5k7/3Ge8DW/af5+PXUq+pa1KzJOuf0ykyO5MEDNVaH1RKZQYOKKU2AK8Am7TWE5RSHwAfAO8DLYHillsNYAZQQynlC3wK+AHa8jgrtdY3Hvfk6bWAOh9wEphtCUZZAvvycTtprWcBs+7e/eXn8f86gBs34ggLjSVffl+uBEVSuUohAgPDCQwMp1nz8vy8ZA/Nmpdnl2Ww06vnjOR933vvRfbsOW8XAyF3d1eUUsTHJ+Du7oqfX2EWLtiJt7cHUVG3UAp69qrNnyuNEvQ77/yUvO/Lvf2Jj0+w+UDoaXNwc3NBKcXt24lUrVoIk8lMYGCETXMAiLwRR2hYDPnz+RJ0JZKqlQsSeDmca9eiqFyxAIePXiZfXh9cXZ3tdiAExmsjZR5VKhd67O83VX9UKYTJpAm8bNv+iLwRR1hYDPnz+hJ0NZIqFQty6XIE3l4eREUbx9TL3Wqxcs1hm8b5JCHhMZQtnhu3DC7cSUjCr1wBTgeEkDenN1dDogDwr1qMwGvG3/EShXLwfv+mDBn/x0PXrtjC0+aQL5c3V64b7bUrFyEo+LHvUVYTEhFDuWIp8ihbgNMXQ4iLT6BqmQL8tfU4VUrn43KKeD0zZqBy6XyMmrHahpE/G3uZDdFaBwPBlu9jlVKngLxAW6CBZbP5wBaMwVBbYIE2pnf2KKW8lVK5Ldtu0FpHAlgGVC2AJY97/vQaDPkBbwMfAcO11oeVUvFa663p9HwP9c036xkxog2uLs4EB0cxadIqnJwUn4xsR8uWFQkJiWb058utGdJT8/Hx5LPPOwDG9MamTSfZty+ADh38aNu2KgDbd5xhrWVRrz162hy8vT2ZOKkrZrMmPDyW8eP/tFns95s2fSMfv9cKF1fjmJrw1Wpu307k/XdfYO73fUlMMjF+8qrk7X+ePxAPjwy4ujjjX6sEwz76xeYDCYBvvtvAiA9b43L3tTF5Ff51SvDmoCZ4eXkwbkxnLlwI4f0Pf8Xb25NJ47tg1prw8JuMn2gf/fH1zE18PLwVri5OXLsezYSpq2neqBztW1UGYNuus6zecCx5+5/nvIanRwZcXJzxr1WcYR//SmCQbfvi5Pnr/L33HPMmvITJbObsxVBWbDzKNyM745kxA0opzgWG8cVs41IAg3rVI6O7K2OGtAYgJDyW97+w7d+wp82hU/PK+JUvQJLJTGzcbcZMX2vT+O86ceE6m/85x/yxL2EymTkbGMryzUdxy+DCZ2+8QLeWVYi/nci42euT92lQrTj/HAvk9h37W5hvj1LO/ljMshRB7t+uEFAZ2AvktAyUAK4DOS3f5wWCUux2xdL2qPbHx6bTcViolMoHTAFCgDZa6wJPsbtu3OjfV4bswabNHwLwX8jjv5ADQIMWE20cybPZsvZ9GjWdYOswnsnmDR8AUP/FSTaO5NlsXfUeALW7Prbgbfd2/TL0P5EDQM2ez3ceexYNBax74Z8Kf460Wm3oaOvPn5ibUioTsBUYq7VeqpSK0lp7p/j5Da21j1LqL2CC1nqHpX0TRsWoAeCutR5jaf8EiNdaT37c86brAmqt9RWtdWdgDfDTk7YXQgghhGNSSrkCfwCLtNZ3Lz4VYpn+wvI11NJ+FcifYvd8lrZHtT+WVc4m01qv0lqPsMZzCSGEECJt7OU6Q0opBfwInNJaf5XiRyuBu2eE9QZWpGh/2XJWWU0g2jKdtg5oppTysZx51szS9lj/6StQCyGEEOK5UAd4CTimlLp75sMIYALwq1KqHxAIdLH8bDXwAnAeuAX0AdBaRyqlRgP7LNt9fncx9ePIYEgIIYRwUE+6/o+1WNb+PCqYxg/ZXgODHvFYc4A5T/P8/9krUAshhBBCpIVUhoQQQggHZS/XGbI1qQwJIYQQwqHJYEgIIYQQDk2myYQQQggH9aRT3h2FVIaEEEII4dCkMiSEEEI4KKkMGaQyJIQQQgiHJpUhIYQQwkHJmfUGqQwJIYQQwqFJZUgIIYRwULJmyCCVISGEEEI4NKkMCSGEEI5KFg0BUhkSQgghhIOTypAQQgjhoGTNkEEqQ0IIIYRwaFIZEkIIIRyUljVDgFSGhBBCCOHglLbfYaHdBiaEEEKkE6su4in6y1irvdde6PqR3S5QksqQEEIIIRyaXa8Zal75U1uH8EzWHfoMgBaVRto4kmez9vDnNKv5ua3DeCbr9xh90NxvlG0DeUbr9o+iebXPbB3GM1m3z3hdN609xsaRPJsNuz4GoFmt0TaO5Nms3/0JTf3H2jqMZ7Jhx0cA/5k8hPXZ9WBICCGEEOlITq0HZJpMCCGEEA5OKkNCCCGEg7Lfc6isSypDQgghhHBoUhkSQgghHJVUhgCpDAkhhBDCwUllSAghhHBQ8kGtBqkMCSGEEMKhSWVICCGEcFSyZgiQypAQQgghHJxUhoQQQggHJWuGDFIZEkIIIYRDk8qQEEII4ahkzRAglSEhhBBCODipDAkhhBAOS9YMgVSGhBBCCOHgpDIkhBBCOCpZMwRIZUgIIYQQDk4GQ0IIIYRwaDJNJoQQQjgqmSYDpDIkhBBCCAf3n68MOTkpvln0GhGhMYx8ezFDPm1LiTJ5ALh6OYLJI5dzOz4BV1dnho/uQPHSuYmJjmfc+78REhxl4+jvcXJSTFs8kIjQGD59axHvjetIiTJ5SUoyceb4VaaNWYkpyUzNBqXo/UYjzFpjSjIz84s1nDh82dbhA0YO387tT3hYLCOH/UylqoV49a2muLo4c+50MF+OW4nZpKlVtwS9X2uINmtMJjMzpq7jxJEgW4efzMlJ8c3CAUSExjJyyOLk9teHtaR5m8q0qzcOgBy5vHh3ZFu8fDyJjYln0idLCQ+NsVXYqTg5Kb5Z8KqRw7tLAHjl9UbUbVwGs9nMX3/sZ8Uv/yRvX6JMHqb+2I9xH/3Ojs2nbBV2Kgv/GEz8rQTMJjMmk5lB/eZQr2FpXupXjwKFsvFm/zmcPR0MQJVqhen3eiNcXZ1JTDTxw3ebOHzgkm0TsFiw9M1UeQzu+yOZs7jz0eiO5MztRUhwNGM+/oObsbepULkgn03qwvVrxt+mHVtPs2jOdhtnYFj42yAjD8vrdlD/Ock/69StBq8NbkLHF78iJjqe/AWyMmxEK4qVyMXcH7bw+5K9Noz8nofl0Lt/fWr7F0driLoRxxdj/yQi4iaZMrsz9MNW5MnjTUKCiS/H/8Wli2G2TuHpycdxAA4wGGrXoyZBF8Pw8HQDYObktdyKuwPAgKHNadOtOr/O3UHzdlW4GRtPn7bTqN+8HP3ebsq4D36zZeiptOtRK1Uef68+yqQRfwDwwfhOtGhflVW/7ePw3gD2bDkNQOHiORkxqQuvtv/GZnGn1L5rDS5fCsfD0w2lYPjItrw3eCFXgyJ5+dUGNHuhImv/PMyh/RfZvf0sAIWL5eDjMZ3o1226jaO/p133mgRdDE/uC4DipfOQKYt7qu1efacZG1cdYeOqI1T0K0yfwY35YuQya4f7UO261UiVQ7PWlcieMwv9O3+L1uDl45G8rZOTot/gJhzYe8FW4T7SsMELiYmOT75/KSCUz0b8xjvvvZhqu+joW4x87xciwm9SqEh2xk/pTve206wd7iMNH7QgVR5dX6rDof0X+WXhLrq+VJuuL9Xhx+mbADh25DIjh/1iq1Afa9hbP6XKAyB7jsxUrVaYkOvRyW2xMfF8N3U9deqVtHaIT3R/Dr8t3s382VsBaNfJj1596vL15DV0f6k2F86F8NmI38lfICtvvtuc995Z/KiHFXbuPz1Nli1HFqr7l2DNsoPJbXcHQgBubq7J86W1GpRiw5+HAdi+8SSVqhe2aqyPky1HFqrVLcHapQeS2/btOJf8/ZkTV8mWMwsAt+MTktvdM2ZA28l8cLbsmaleuzhrVx4CIIuXB4mJJq4GRQJw8J8A/BuWBuB2fGLyfu7uGdB2NKmdLUcWqtcpzprl944pJyfFq2835cevN6TatmDh7BzZfxGAI/svUqteKavG+ijZcmSmun9x1qy4l0Orjn4smr01+XiJvnEr+Wdtu1Znx9+niLoRZ+1Qn9rlwAiuXI58oP3C2RAiwm8CcCkgjAxurri6Ols7vDSrVbckG1YfBWDD6qPUtsNBQ1oNfLMpP8zYjE7xxygq6hZnTweTlGSyYWRpc+tWir+p7hmS8yhYKHtydTHocgQ5c3vj7eNpixCfidbWu9kzq1SGlFL+QHXguNZ6vTWeE2Dg8BbM/no9Hh5uqdqHjmpHNf/iXA4IY9ZX6wDjDSLsujGFYTaZibt5hyzeHsRE3Xrgca3tteEt+XHqulSViLucXZxo/GJFZkxak9xWu2Fp+rzVBG9fT0a+uciaoT7S60OaM/vbjWT0zABAdNQtnJ2dKF4qN+dOB1O3UWmy58iSvH2d+iXp+3pjvHw8+WToEluF/YCBQ1swe9qGVH3Rpkt1dm87Q2TEzVTbBpwLoU7D0iz/eS91GpbGM5Mbmb0yEnvff87WNvDdFsyethEPjwzJbbnz+lC/aTlqNyhFdFQc0yev5VpQJFmzZ6Z2g1K8N3A+745sa8OoH6Q1TJjaA61h1YqDrF5xKE371W1YivNnrpOYaCdvxFoz/uueoGHV8gOsXnEIH1/P5OMpMuImPr733mTLlMvHjAUDiAyPZdY3Gwm0k6kZrWHCVz3QaFatOMTqlYeo5V+CiPBYAs6H2jq8NHlYDgB9BjSgSfPyxMXdZvhbxt/UgPMh+NcvyfGjQZQsnYecOb3IniPzc/FPg3hQugyGlFL/aK2rW75/FRgELAM+VUpV0VpPeMR+A4ABADNnznymGGrULUFUZBznTwVToWqhVD/7ctRynJwUb7z/AvWblWX9ysPP9FzpqXrdEkTdsOThV+iBnw8e0YpjBwM5cSgwuW3X36fY9fcpylUpyMtvNOLDgfOtGPGDatQpTtSNOM6dCaZClYLJ7eM+WcrAd5rj6urMwX8CMJvv/euwc+sZdm49Q/lKBej9WgM+ePMnW4SeSg1/yzF1+t4x5ZstM3WblGX4a/Me2H7W1PUMeu8FmrauxLGDgYSFxGA22fbfoxr+Rl+cP526L1wzuJCQkMSbvX+gTsNSDP2kDUMHzGPgu8358ZuNdvlf3ZCB84kIj8Xbx4MJU3sSFBjBsSesjytYOBv932jMB3Y0nTFk4Hwiwow8xn/di6DAiAe2uVuNOH8mmF7tp3E7PpFqtYoxamJn+nSxjynkIW8sMPrD24MJU3sQFBhO95dr88EQ+/ln5kkelsOxI0HMnbWFubO20K1Xbdp28GPBnG38/NMu3ni7Gd/P7c/FC6GcP3fd5q/vf+U5DDk9pFdlyDXF9wOAplrrMKXUZGAP8NDBkNZ6FjDr7t0/Znz6rwMoU6kANeuXpJp/cTJkcMHD0433xnRg0sdLATCbNVvWHadL7zqsX3mY8NBYsufKQnhoDE7OTnhmcrOLqlBZSx7V/YvjejePsR2Z9NEf9HytAV4+nkwb/fND9z1+MJBc+XxsXuEqWyE/NeuWpFrte33x/qh2TBy1nKED5wFQtXoR8ub3fWDfY4cvkzuPD1m8Mj6wFsHaylTMT816JalWx5JHJjdm/foGiQkm5i57CwA3d1fmLnuLPu2nERkey+j3jLUd7hkz4N+oDHE3b9syBcpULHCvL9wsx9Pn7QkPjWHH38bC6J1/n2aopQpUonQePhzbCQAvbw+q1y6OyWRm99YzNsvhrojwWACibtxi57YzlCyd57GDoWzZMzNqfGcmfb6C4Ks3rBXmE0WE3ctj19bTlCyThxuRcfhmzURkxE18s2YiyjJtmXLKZt/u8zgPb2kXrw1I0R9RRn9UqFSQXLm9mTmvPwDZs2dhxpx+DH51Ljci7bN6cn8OJcvk4ViKkzc2bTjO2C+6smDONm7dSmDy+L+Sf7bwt0EEX7Of40o8nfQaDDkppXww1iQprXUYgNY6TimVlE7PmcrcbzYy95uNAFSoWohOL9dm0sdLyZPfl2uWdSq16pck6FI4AHu2nqFp60qcOnqFuk3KcGTfRWuE+USp8vArRMeX6zDpoz9o0b4KVWsX44MB81LNxefO70uwJb9ipXLjmsHF5oO6OTM2M2fGZgAqVClIpx61mDhqOd4+HkTduIWrqzNdXqrDknnGWTF58vlw7YrxR6VYyVy4ujrbxR/7ud9tYu53xiLWClUL0alX7VRnkwEs3zaCPu2NhblZvDyIjYlHa023Pv6sX5m2aZz0lCqHKgXp1Ks2k0Yuo+/gxlSsWoj11w5ToUpBrlw2qhO9291bZDz007bs3X7WLgZC7u6uKCdF/K0E3N1dqVq9MD895qwqz0xujJncjR9nbObEsStWjPTx7s+jSo0iLJqznT07ztD0hQr8snAXTV+owO7txu/cx9czeSBRskwenJSyi9eGu7srSini4y39Ua0IP83bTpfWU5O3WfjbIAb1n2MX8T7Mo3LIm8+Hq5a/R7X9SyRX7jwzuXHndiJJSWZatq7EsSOXUw1WnxtyNhmQfoMhL+AAxsfhaqVUbq11sFIqEzb8iFylFMM+b598NlPA2RC+GWeM7NcuP8h7Yzowd8VbxMbEM+6D320VZpq8+VFrQoKjmbLgVQB2bjrF4llb8G9chiatK5GUZCLhdhLj3/vVxpE+WueetanhXxylFH8tPZC8GNG/YWmatKyAKcnMnTtJjP3kD9sG+i9V8CtE30GN0RqOHQrku4mrbB3SI/0ybwfvj+5Ahx41ib+VwNQxf9o6pMfy9vVk1PjOADg7O/H3huPs3xtAnXolGfRuc7y8PRgzuSsXzoXw4ZAltO1UjTz5fOjVpy69+tQF4IMhi5MrLrbi7evJpxO6AJY81h9n/54LnDl5jY/HdqRF60qEXI9m7MfGa6Buo9K0au+HyWQm4U4i40YutWX4ybx9PRk1zqggGv1xgv17Ax65vY+vJ9/N7ouHpxvarOnQuTr9e8206WDiUTmMHNORfAV80WZNSEgMX39hrM8sUDAb733cGq0h8GIYX06w39e3eDKlrbgYQCnlAeTUWqel7KKbV/7302T2YN2hzwBoUWmkjSN5NmsPf06zmp/bOoxnsn6P0QfN/UbZNpBntG7/KJpX+8zWYTyTdfuM13XT2mNsHMmz2bDrYwCa1Rpt40iezfrdn9DUf6ytw3gmG3Z8BPBfycOqBYNCP3xhtUHApVeH220ZyqrXGdJa3wLsY/5JCCGEEAIHuOiiEEIIIR5BziYD/uMXXRRCCCGEeBKpDAkhhBCOSs4mA6QyJIQQQggHJ4MhIYQQQjg0mSYTQgghHJUsoAbSWBlSSr2dljYhhBBCiOdNWqfJej+k7ZX/YxxCCCGEsDZtxZsde+w0mVKqO9ADKKyUWpniR5mByPQMTAghhBDCGp60ZmgXEAxkA75M0R4LHE2voIQQQghhBXZesbGWxw6GtNaBQKBSqidwTWt9G0AplRHIB1xK9wiFEEIIIdJRWtcM/QqYU9w3Ab/9/8MRQgghhNVoZb2bHUvrYMhFa51w947l+wzpE5IQQgghhPWkdTAUppRqc/eOUqotEJ4+IQkhhBDCGpS23s2epfWiiwOBRUqpbwEFBAEvp1tUQgghhBBWkqbBkNb6AlBTKZXJcv9mukYlhBBCiPRn5xUba3nSdYZ6aa1/Ukq9e187AFrrr9IxNiGEEEKIdPekypCn5Wvm9A5ECCGEEMIWnnSdoZlKKWcgRms9xUoxCSGEEEJYzRPPJtNam4DuVohFCCGEEFYkZ5MZlNZPjlApNQVwBX4B4u62a60Ppl9osqxLCCGEw7Hq1QmLTPvSau+1AW8NtdsrL6b11PpKlq+fp2jTQKP/bzhCCCGEENaV1sFQP611QMoGpVSRdIgnlYpvPd/LlI5MGwJAhSHPdx5Hpwyh4jvPdw5Hphp9UX7Y853HsclDKDf8+c7h+BdGX5QZ8XzncXKc5Zga+nzncezLIZR9//nO4cREyzH18fOdx8kxQ6z/pHb+MRnWktYrUP/+kDb5bDIhhBBCPPeedJ2hUkBZwEsp1SHFj7IA7ukZmBBCCCHSmazOBZ48TVYSaAV4A61TtMcCr6ZXUEIIIYQQ1vKk6wytAFYopWpprXdbKSYhhBBCWINUhoC0rxlqr5TKopRyVUptUkqFKaV6pWtkQgghhHAYSqk5SqlQpdTxFG2jlFJXlVKHLbcXUvzsQ6XUeaXUGaVU8xTtLSxt55VSH6TludM6GGqmtY7BmDK7BBQDhqdxXyGEEELYITu76OI8oMVD2qdorStZbqsBlFJlgG4Y65pbANOVUs6WT834DmgJlAG6W7Z9rLQOhlwtX18EftNaR6dxPyGEEEKIJ9JabwMi07h5W+BnrfUdrfVF4DxQ3XI7r7UO0FonAD9btn2stA6G/lRKnQaqApuUUtmB22ncVwghhBD2SFvvppQaoJTan+I2II1RDlZKHbVMo/lY2vICQSm2uWJpe1T7Y6VpMKS1/gCoDfhprRMxPpLjiSMtIYQQQggArfUsrbVfitusNOw2AyiK8UkYwcCX6RHbk64z1EhrvTnlNYaUSnW1yqXpEZQQQgghrMDOzybTWofc/V4p9QPwl+XuVSB/ik3zWdp4TPsjPek6Q/WAzRjXGNIYHyCX8qsMhoQQQgiRLpRSubXWwZa77YG7Z5qtBBYrpb4C8gDFgX8wxifFlVKFMQZB3YAeT3qeJw2GYpVS71qe/O4gCOx+LCmEEEKIJ0njWV5WoZRaAjQAsimlrgCfAg2UUpUwxh2XgNcAtNYnlFK/AieBJGCQ1tpkeZzBwDrAGZijtT7xpOd+0mAok+VrSaAasAJjQNQaYwQmhBBCCPHMtNbdH9L842O2HwuMfUj7amD10zz3k65A/RmAUmobUEVrHWu5PwpY9TRPJIQQQgg7I59aD6T91PqcQEKK+wmWNiGEEEKI59qTpsnuWgD8o5RaZrnfDuNKkUIIIYQQz7U0DYa01mOVUmuAupamPlrrQ+kXlhBCCCHSnR0toLaltFaG0FofBA6mYyxCCCGEEFaX5sGQEEIIIf5b7OnUeltK6wJqIYQQQoj/JKkMCSGEEI5KKkPAf3Aw9FmPptQrW4TI2Ft0nLAQgKaVivN6y1oUzulLzy+XcDIo+aNO6Nu0Gu1rlsNsNjPxjy3sOh0IQO3SBXm/QwOcnJxYtvs4czbus14O3ZpSv0wRIm/eosMkSw4Vi/N6i1oUyeFLj6n3cihXICcjuzQBQKGYsW43m49dAGDNJ325dTsRkzZjMmu6f7XYajkAfNa9KfUseXScmDqPwjl96TnlXh41SxTg7db+uDo7k2gyMWXldv45Z3zw8OzBnciexZPbiUkAvD5jKZE3462Sw+dd7uXQYbKRQ7MKxXm9mdEX3act4eQVI4cXK5filQZVk/ctkTs7XaYu4sy1MJpXLMGAxtVxcnJi26kApqzaYZX47xrd+V4e7b+8l8cbTS15fLOEE5Y8XJyd+LRjE8rmy4nWmgkrtrAv4Eryzz5q14hqRfNh1pppa3ey8dh5q+QwpkNT6pcqQmTcLdp+beQwrEVdGpQuQmKSiaDIaD76Yz2xt++QxzsLfw3pzaWwSACOBF3nsxWbACiTJwfjOjXH3dWFbWcuMu6vLVaJ/67PuzalXumHHFPNLX3x9b1jCqBE7myM7NQYT3c3tNZ0m7oYF2cn5g/qkrxNTu/M/HXgFJNWbLVaHqM7NaW+JY92U4w8hr5g6Q+TiaCIaD7+zeiPWsULMKTFvdf3l6u3s/eC8foukzcHYzvf64/xK7dYLYcx7ZtSv6TlmPrGckw1r0uDUpYcIqP5aKmRg4uTE5+3b0qZ3DlwdlKsPHyKH7btI5dXJsZ3bEG2TB5oDb/uP8ZPu+X8oufNf24wtGLvSZZsO8LYXs2T284HRzDkxz/5pGvjVNsWyeVLiyol6TB+ATmyeDJzcEfajJ4HwIjOjXjtu6WERMWyeFgPthy/QMD1SKvksPKfk/y84whje6TO4d05f/JJl9Q5nA+OoPtXizGZNdmyePL7sF5sPRGAyWwM9/tN/42ouNtWift+K/aeZMn2I4ztmSKP6xEMmftgHlFx8bz1wwrCYuIolisrMwZ2oOmoH5J//uHCtakGsdayYv9Jluw8wtju93I4dz2CIfP/ZGSn1DmsOnSaVYdOA1A8V1a+fqUNZ66F4eXhztBWdek6dTE34uIZ0605NYrlZ+/5IKvlsXz/SRbvOsK4bqn74p0Ff/Jpx9R5dKpRHoAOXy3E1zMjM/q3p9u0xWgNrzWuQeTNW7SaNA+lwCuju9VyWHbwJIv2HGFC53s57DofyJT1OzCZNe829+fV+tX4ap20in2XAAAgAElEQVQx0AyKjKLDt4seeJyRbRszctkGjgZdZ2bvdtQtUYjtZy9ZKw1W7DvJkh0POabmPXhMOTspxvdowYeL13I2OBwvD3eSTGYSkkx0/upebr+804NNVhqU3rX8gHFMje96L4/d5wKZutbSHy39ebVhNb5as4MbcfEMmreCsNg4iuXMyqx+HWg0znh9j2zfmE+XbuDo5et837cd/iULsePMJavksOyQ5ZjqlOKYuhDIlA2WHJr582q9any1fgfNyxUng7Mz7b5diLurC3++9TKrjp4hIcnEpDXbOBUcikcGV35/oye7zwdyIcw67xfPStYMGf5za4YOXrhKzK3Ub/4XQyIJDL3xwLYNyhdl7cEzJCaZuBoZQ1BYFOUK5qJcwVwEhUVxNSKaJJOZtQfP0KB8UWulwIGAq0TfN4C5GBrJpbAHc7idmJQ88HFzcUbbUc3zYEDa++L01TDCYuIA403azdUFV2dnq8T5OAcCrhJ9fw6P6IuUWlYuxZrDZwDIl9WLwPAobsQZ1aw9Zy/TpELx9An4EQ5cfDCPgEfkUTSnL/9YBmqRcfHExt+hbD7jGqvtq5Vl9mbjk3i0hqhb1htoH7j0YA67zl9OPv6PBAWTyyvTw3ZNli2zJ5ncM3A06DoAKw6donEZ67224emOqdolCnI2OJyzweEARN+6jVmnfo0XzOaNb2YPDgQ88YO5/68OXLxKdPx9/XEuRX9cDianpT9OXwsjLNby+g6JwN3y+s6W2RNPtwwcvWz0x8oDp2hc1op/ay89JIdHHFMayJjBFWcnhZuLC4kmM3F37hB+M45TwaEA3EpIJCAskhxZHn8cCvuTLpUhpVQN4JTWOkYplRH4AKiC8YFq47TW0enxvE8rp1cmjl4KTr4fEnWTHN7GQXw9Kja5PTTqJuUL5rJ6fGlVvkAuPuvejDw+mRmxaG3yCxkNMwd2QGv4bfcx/th9zLaBplGTisU5dSWURJMpue3z7s0waTObjpxn1vq9NowubVpULMFb81YCEBQeReHsPuTxyUJIdCyNyhXF1cX2A71HOXMtnAZlirD68GlyeWWmTL4c5PLOTGBYFACDW9SmWpF8BEVEM27530TcvGXjiA0dqpZj7bEzyffz+njxx+Ce3LyTwLQNuzhw6So5s2QiJPpm8jYhMTft+o2rYHYftIbvB7THxzMjaw+fZe7f+1Nt07JySdYePvOIR7CdDn7lWHP0wbialS/OyavG6/v+/rgebV/9kfKYWn/8HI1KFWXr+wNwd3Vl4uqtRMffSbV9Hu8slM6dnaNXrtsi3H/Hfv5/tqn0miabA1S0fP81cAuYCDQG5gIdHraTUmoAMABg5syZ6RTaf8+xy9fpMHEBhXP4MqZHc3acukRCkone3/xCaHQcvpkyMnNgRy6FRFr9v8enVTRXVt5p7c/AGUuT20YsXENodBwebq581ac1raqV5q99p2wY5eOVL5CL24lJnL8eAUBM/B1GL93MFy+9gDZrDgcGkz+rl42jfLRl+45TJIcvv7zdg2s3Yjl8KRizWePspMjlnZnDl4L54s9tvFyvCsNa1ePDn9faOmRea1Adk9nMn4eNacqw2DgaT5xNdPxtyuTJwTe92tDm6wU2jvLpOTs7UblwHrp/vZjbCUnMHtiRk1dC2Hvu3hRri0olGbHE9n2Q0oCG1Ukym/nLMm18V9GcWRnS0p8Bs5c+Yk/78Vp9yzF1xMihfL5cmLWZBhN/IEtGNxb278LuC5e5csP4394jgytfd2/F+NVbibuT8LiHFnYovQZDTlrrJMv3flrrKpbvdyilDj9qJ631LGDW3bvfvTUlncIzhETfJKdP5uT7Ob0zERpl/JeSy/teew7v1P+92KuLoZHEJyRQLHc2TgaFEBptlKUjb8az+dh5yhXIZdeDoRxemZjStzUfL1rHlYh7xcO7edy6k8jqg6cpXyCXXQ+GWlYqyer73gS2ngxg68kAwFiTk1y9s0Mms2bSn/cW4v40qCuXwm4Qdes2txIS2Xj8HADrj5ylQ7VytgozWbsqZahfqjB9f/wjuS3RZCI63qgsnrwWSlBkFIWy+RASczN56gYgZ5ZMhMbY72s7JCqWAwFXk9f9bT91idJ5cyQPhkrkzoazsxMnr4TaMsxU2lUtQ/3Shen3wx+p2nN6ZWLaS60Z8cs6giKN1/f9/ZHLyz76o13lMtQvWZi+c+/l8GKFkmw/F0iS2UxkXDyHLl+jXN6cXLkRjYuTE1O7t+KvI6fZeNK6a7eemf3+KbKq9FozdFwp1cfy/RGllB+AUqoEkJhOz/nUth4LoEWVkri6OJPXNwsFsvtwPPA6Jy5fp0B2H/L6ZsHF2YkWVUqy9ViArcN9qLy+WXB2Mj51OLdPZgrl8OVaZDQZM7jg4eYKQMYMLtQqWZDz18NtGepjZc7oxrcD2vH1Xzs4fPFacruzk8Lb01ik6+LkRL0yRTgfHGGrMJ9IKWhWsQRrD59N1e6bKSMAWTK60bV2BZbutd8pS3dXFzK6Gv8n1SpegCSzmYBQYzHo1pMBVCuSH4AaxQtwIcS2feFfvCD96vkxaOHK5LMNAXw8M+KkjNdFPh8vCmb14UpkFOGxcdy8nUCF/Ma0d9vKpdl88oJNYk+LXWcCKZ47K+6uLjg7KfyK5uNCyL2FuS9UKcWaQ/YzReZfoiB96/sxeH7q/sjs7saMV9oxZc0ODgXee32Hx8YRdyeBCgWM/mhTtTSbT9i2P/yLF6RfXT8G/ZQ6h+DoWGpajv2Mri5UzJ+bAMsi6dHtmxIQFsn8XfIhDc+r9KoM9Qe+Vkp9DIQDu5VSQUCQ5WfpZkLvlvgVy493JnfWf96fGat3E33rNh90aohPpox8+1pbzlwN4/UZy7hwPYL1h86ybMTLmExmxv222VicqGH875uZ8UYHnJwUy/ec4MJ16/3Rn/iSJQdPdzZ82p/pa40cPuxg5PDdq205fTWM12cuo3KRvPRtXI0kkwmtNWN/30xU3G3yZvViap/WgFFqX3PgNDstlw2wlgkvt8SvqKUvRvVnxhpLX3S09MUAS198v4xu/hUpkM2bAc1rMKB5DcA4hT4+IZEZAzvg4uyEs3Jiz9nLVl37NLFnS6oVNfpi48f9+W69kcOIdkYO0/u15fS1MAb+YHyGcdUi+bgeFcuVyNTL4t5v24CSebID8P2GPQSGR1ktB4BJPVLk8VF/pq/fTXT8bT5s2xDfTBmZ3tfI47XZy/DN5MHM/u3RWhMSE8eHKaZgvlq1nfHdW/BBxvpE3ozn41/XWy2HL7q2pHphI4fN7/fn2427GdCgOq7OzvzYx5h5v3sKvV+hvLzZpDZJJhNmrflsxabk9R2jV25mXKdmuLm4sP3sJbZZ8UwygIm9UvTFJ/35bp3lmGpvOab6W46pWcuIib/Dwq0HWfJOD7TWbD99ie2nLiY/VvOKJXhj9rLHPFv6+aJ7S6oVMfLYNKI/323YzasNquPq4szs/pb+uHydz5dtokftiuTP5s3rTWrwehPj9f3q7KVExsUzetlmxnZphpurCzvOXGK7lc4kA/iii+WY8nBn8/D+fLt5NwPqGTmkOqZWbmLJ3iOM7dCMlW++jFKw7OAJzoaEU6VgHtpWLsOZ62EsHdQTgKkbdlr9uPq35Gwyg9I6/X4TSqksQGGMQdcVrfXTnButK6bzNFl6OzJtCAAVhjzfeRydMoSK7zzfORyZavRF+WHPdx7HJg+h3PDnO4fjXxh9UWbE853HyXGWY2ro853HsS+HUPb95zuHExMtx9THz3ceJ8cMAVDWfM6So6dYbTh05pMhVs3taaTrdYa01jHAkfR8DiGEEEKIZ/Gfu86QEEIIIcTTkMGQEEIIIRzaf+7jOIQQQgiRRrKAGpDKkBBCCCEcnFSGhBBCCAclp9YbpDIkhBBCCIcmlSEhhBDCUUllCJDKkBBCCCEcnFSGhBBCCEcllSFAKkNCCCGEcHBSGRJCCCEclJxNZpDKkBBCCCEcmlSGhBBCCEcllSFAKkNCCCGEcHBSGRJCCCEclKwZMkhlSAghhBAOTSpDQgghhKOSyhAglSEhhBBCODgZDAkhhBDCock0mRBCCOGoZJoMkMqQEEIIIRyc0tpuh4V2G5gQQgiRTpQ1n6zsh1Os9l57YvwQq+b2NOx6mqxuuy9sHcIz2b58OAD12j7feWxbMZx6bZ7zHFYafVGn02QbR/Jsdv4+jFrdv7R1GM9k95KhANTs+XznsWeRkUftLs93Hrt+HSrHlJ24e0wJ67PrwZAQQggh0pHMwQCyZkgIIYQQDk4qQ0IIIYSjksoQIJUhIYQQQjg4qQwJIYQQDko+qNUglSEhhBBCODSpDAkhhBCOSipDgFSGhBBCCOHgpDIkhBBCOChZM2SQypAQQgghHJpUhoQQQghHJZUhQCpDQgghhHBwMhgSQgghhEOTaTIhhBDCUck0GSCVISGEEEI4OKkMCSGEEA5K2ToAOyGVISGEEEI4NKkMCSGEEI5K1gwBUhkSQgghhIOTypAQQgjhoOTjOAxSGRJCCCGEQ5PKkBBCCOGopDIE/IcHQ/nz+PDZ8DbJ9/Pk9OLHJTv57c8DAHRt68fgPg1p9dK3RMfG4+mRgU+GvEjObFlwdnbi5+X7WL35uK3CT5Y/rw+jhqXII5cXcxbvJFvWTNSuVpSkJDNXr0cxYdoabsbdoWn90nRrVz15+6KFstP/3QWcvxhqi/ABSw7DH5KDbyZqV7fkEHwvB79KBXnt5Xq4ujiTmGRixrytHDx62Wbx31Ugjw+fD2mdfD9PTi9m/7KTg8eDGD6gKRndXQkOi+Gzr1dxKz4BZ2cnPny9OSUK58DZ2Ym1W0+wcNk/Nszgnm4tq9C6UXm0hgtB4Yz9fi0JiSYAhvRuSKsG5Wjc5xsA2jepQMemlTCZNfG3E5kwez2XrkbaMnwAurWoQpuG93IYM8vIYWDnOjSqUQKzWbN00xF+XXeIKqXzMenddlwLiwZgy75zzFm2x8YZGLq+aPQFd/ti+lpaNypP1xerkC+XDy37TSc6Nh6AZv6l6NW2OkrBrfhEvpi9kfOBYTbO4OmOp24vVKVNw/KYzGaiYm4xduY6rofH2jL8ZE9zTAFUKZ2Pd15qiIuzE1Gx8bwx5lcbZyD+rf/sYCjo2g36DpkPgJOTYumPr7NtzzkAcmTLTPVKhbgeGp28fYcXKnMpKIIPxi7DO0tGFn3Xj/XbTpKUZLZJ/HcFXb1BvxR5/DHHyKNAXl9mLdiGyawZ+HI9enWswfcLtrFh6yk2bD0FQJGC2Rj7YXubDoTAksM7KXKY+zrbdt+XQ+969OpUg+/nbyM6Jp4PxiwlIjKOwgWyMfmzTnTs871NcwC4fO0GrwxfABh5LJ85kK17zzN2WBu+XbCFwyev8GKjcvRsW40fft5Jo1olcHV15uWh83HL4MKiqX3YsOM018NibJpHdp9MdG5RhR7D5nEnMYkxb7eiSa1SrN52glJFcpLZ0z3V9ut2nmbZxqMA+FctytsvNWDIhKW2CD1Zdp9MdGlehe7vWXJ4sxVNa5UCIEfWzHQdPhetwSdLxuR9Dp+5wrDJy20V8kNl88lE55ZV6DFkHgmJSYwe0oomtUtx7Mw1dh4M4LtPu6Ta/lpoDING/UJs3B1qVirE+wOa8upHi20UveFpj6ezl0Lp89FP3ElIon2TigzqUZ9Ppv1lo+jvedpjKpOHG8P7NOGdiX8QEhGb6lh7rkhlCHCQNUNVKxTk2vUoQixvQm/2bcj0+VtTHQNag0fGDABkdM9AzM3bmEy2HQjdL2Ue+w5fwmQ2MjhxNpjs2TI/sH3juqXZtOOUtcN8rEfmcCaY7FmNHM4FhBIRGQfAxcvhuGVwwdXF2WYxP4xf+QJcDYkiJDyG/Ll9OHzyCgD7jgRSv0YJwDim3N1ccXZSuGVwITHJRFx8gi3DTubs7IRbBhecnRTuGVwIv3ETJ6UY3KMe3y3elmrbWylizujmiraTP56pcnBzIezGTTo0qcicZXuSY7wRE2/bINPA2enBvjh7KfShg+bjZ68RG3cHgBPngsmRNZO1w32opzmeDp4M4k5CEgAnzgeTw9c+coCnO6aa1y7Fln3nCImITdUunk/pUhlSSr0FLNNaB6XH4z+txv6l2LjdGBT4Vy9GWMRNLlxKXVr+Y9VBJnzUgeVzXidjxgyMmvyn3fzRv6tR3VJs2vbg4OaFxuXYvOPMg9v7l2LEuGXWCC3NGtV7RA5NHp5D/dolOHshlMQkkzXCS7PGdUqxccdpAC5eCadutWJs33eehrVKkNMyMP17z1nqVi/Gih9ex93NlWnz/ib25m1bhg1A2I2bLP5rH8u+fZU7CUn8czSQf44F0qVFZXYcuEBEVNwD+3RsWoluL1bF1cWZwXYwFRB24yaLVu1j+TRLDseMHEYPepEmNUtS368YUbHxfDV/M0EhUQCUL5aHheNeIjwqjmmLtnLxaoSNs4DwGzdZ8uc+ls2w5HEkkH+OBqZp31aNyrP70KX0DTAN/s3xdFfrBuXYfeSiFaN9tKc9pvLn8sHFxZnpH3XBI2MGfll7kDU7Tto6jacmZ5MZ0qsyNBrYq5TarpR6QymVPS07KaUGKKX2K6X2z5o16/8SiIuLE3WqF+XvnWdwy+DCS51q8OOSHQ9sV6NyYc5fDKVd3xn0HTKfdwY0Tq4U2YOUeaT0UueamMyaDVtTvwhLl8jNnTuJXLwcbs0wH+uxOZg0G7akzqFQ/qwM7F2fydPXWzPMJ3JxccLfryibdxt5jPtuHR1aVOLHib3wyJgheeBWplguzGYzbQd8T6c3fqB7az/y5PCyZegAZPZ0o65fMTq+NZvWb8zE3c2VlnXL0KhGSX6zrIW43x8bDtP5nR+ZvngbfdrXtHLED8rs4Ua9qsXo8M5sWg02cmhRpzSurs4kJCbR55NFrNh8lI8GNAfg9KVQ2r39Ay+NWMiv6w4x6d22Ns7AkNnTjbrVitFp0GzavDaTjO6uNK9b+on7VSmbn9YNyzF90bYnbpve/s3xBNDcvzSliuRk0Z/7rRjtoz3tMeXs7ESpwjl4d/JS3p7wB33b1yR/Lh8bZyH+rfQaDAUA+TAGRVWBk0qptUqp3kqpB+dzLLTWs7TWflprvwEDBvxfAqlZpQhnA0K5EX2LvLm9yZ3Di7lTX+HXWQPInjUzP371Mr7enrzQuBxb95wF4Or1KIJDoimYz/f/EsP/Q80qRTh3wcjjrhaNylLLryijv3xwvr1x3XvVMHtRs6olh6j7cqj2YA7Zs2Zi7Ih2jJ26mmvXo6wd6mPVrFyYsxfv9cXla5EMGf07/d7/iY07TnPVEm/TuqXZc+gSJpOxUPTomauUKprLlqEDUK1cQYJDo4mKjcdkMrN13zn6d6pNvlze/Da1H0un9cc9gyu/Ten7wL4bdp+mnl8xG0SdWrVyBbkWdi+HLfvOUb54HkIjb/L3PmNt4Jb95ylWwPg/7FZ8AvF3EgHYfeQiLs5OeGWy/RoPv/IFuZaiL7bsPUf5Enkeu0/RAtn48LVmvP/FCmLsoNL4b46nauUK8Eq7Grw3ebndVH2f9pgKjbzJnqOB3L6TRPTNeA6dvkLxAmn6v9++aCve7Fh6DYa01tqstV6vte4H5AGmAy0wBkpW0yTF1FJAYDhtXplOlwGz6DJgFmERsfR7dwGRUXGEhMVStUJBAHy8PCiQ15dr16Mf99BW1bhe6sFN9cqF6NGhOh+OXZo8/36XUtCwTkk2bT9t7TAfq3HdUmxMMUVWvYolhzGpc8jk6cbEkR2ZuWAbx09dtUWoj9XUvzQbdtz73Xpn8QCM33vvTjVZvuEIACHhsVQtVwAw1g6VLZ6HwGu2n5q5Hh5D2eK5cctgzJL7lSvAktUHaPX693R4azYd3prN7YREOg+ZA0C+XN7J+9apXISg6zdsEndKIRExlCuWIoeyBbh0LZJt+89TtYzxO69SOh+Xg41Yfb08kvctUyQXSimib9p+jUfI/X1RvsBjz9TLmTUz44e14bNv1xAUbPt+gKc/nkoUysF7/ZsyfPJyu1pn87TH1PYD56lYIk/ymsCyRXNzyQ5e3+LfSa+zyVJ9EK7WOhFYCaxUSnk8fJf/P3c3V/wqFuKLGU+eZpn36y5GvP0C875+BQV8v2Bb8umstnY3j5TTRe+81oQMrs589ZlxtsnJs9f4csYGACqWzU9oeCzBIfYzmHN3c8Wv0kNycHHmq88tOZwxcujwYmXy5vamd9fa9O5aG4Chn/5GVIqqmK24u7lSrUJBJs28l0dT/1J0aFEJgK17z7HKckmGpWsPMWJQC36a8gqgWP33cS4E2n7a8uSF6/y99xzzx71EktnM2UuhrNh09JHbd2pWmWrlC5CUZCY27jajZ6y1YrQPd+LCdTb/c475Y1/CZDJzNjCU5ZuP4pbBhc/eeIFuLasQfzuRcbONfmpUvQQdmlTEZDJzJzGJT75dZeMMDCfPX+fvPeeYN9GSx6VQVmw8SueWlenZphq+3p4s+OJldh+6yISZ6+nTqRZZMmVkWP/GAJhMZvp9uMi2OTzl8TS4Rz083F0Z+7ZxmYqQiFjes4Oz/J72mLp0LZI9Ry/x04TemM2alVuOEXBFBkPPK6XTYZWwUqqE1vrsMz6Mrtvui/9LPLayfflwAOq1fb7z2LZiOPXaPOc5rDT6ok6nyTaO5Nns/H0Ytbp/aeswnsnuJUMBqNnz+c5jzyIjj9pdnu88dv06VI4pO2E5ptSTtvt/qjxoitUmsA59N8SquT2NdJkm+z8MhIQQQgghrOI/e9FFIYQQQjyBnS9sthaHuOiiEEIIIcSjSGVICCGEcFBy0UWDVIaEEEII4dCkMiSEEEI4KqkMAVIZEkIIIYSDk8GQEEII4ajs6OM4lFJzlFKhSqnjKdp8lVIblFLnLF99LO1KKTVNKXVeKXVUKVUlxT69LdufU0r1TsuvQQZDQgghhLAH8zA+tiulD4BNWuviwCbLfYCWQHHLbQAwA4zBE/ApUAOoDnx6dwD1ODIYEkIIIRyU0ta7PYnWehtw/4fztQXmW76fD7RL0b5AG/YA3kqp3EBzYIPWOlJrfQPYwIMDrAfIYEgIIYQQ6U4pNUAptT/FbUAadsuptQ62fH8dyGn5Pi8QlGK7K5a2R7U/lpxNJoQQQjgqK55NprWeBcx6hv21UulzZSSpDAkhhBDCXoVYpr+wfA21tF8F8qfYLp+l7VHtjyWDISGEEMJBKa2tdvuXVgJ3zwjrDaxI0f6y5ayymkC0ZTptHdBMKeVjWTjdzNL2WDJNJoQQQgibU0otARoA2ZRSVzDOCpsA/KqU6gcEAl0sm68GXgDOA7eAPgBa60il1Ghgn2W7z7XW9y/KfoAMhoQQQghhc1rr7o/4UeOHbKuBQY94nDnAnKd5bhkMCSGEEI5KPo4DkDVDQgghhHBwUhkSQgghHFT6nKj+/JHKkBBCCCEcmlSGhBBCCEcllSEAlP735/6nN7sNTAghhEgnyppPVq3PV1Z7r903912r5vY0pDIkhBBCOChZM2Sw68FQ8fFTbB3CMzn34RAAin71lY0jeTYX3n2XIlOf7xwC3nkXgCJTnvM8hrxLka+f8xzeNvqi0PeTbRzJs7k0cBgAhWY+53m8NoxC8yfaOoxncqn3+wAUnDPJxpE8m8C+79k6BIdl14MhIYQQQqQjqQwBcjaZEEIIIRycVIaEEEIIByVrhgxSGRJCCCGEQ5PKkBBCCOGopDIESGVICCGEEA5OKkNCCCGEg5I1QwapDAkhhBDCoclgSAghhBAOTabJhBBCCEdlv59PalVSGRJCCCGEQ5PKkBBCCOGgZAG1QSpDQgghhHBoUhkSQgghHJVUhgCpDAkhhBDCwUllSAghhHBQymzrCOyDVIaEEEII4dCkMiSEEEI4KlkzBEhlSAghhBAOTipDQgghhIOS6wwZpDIkhBBCCIf2n6oMjX+hKQ2LFSHi1i1enL0w1c/6Vq/Ch43rU33qDG7E36ZN2VK8WtMPhSIuIYFP123idGj4Ex/HGiY0a0ajIsbzt1ywAIC3atWia/nyRN66BcCXO3ey5eJFAAZWq0aX8uUxmc18/vffbA8MTH4sJ6VY3rMnITdv8ury5VbNY2LTZjQsbMnjJyOPt2vWomu58kTGG3lM3rmTLZcuUiFnLsY1aQKAQvH1nt2sv3AegG19+xGXkIhJmzGZzbRdsti6Odzti4UpckjRF3dz8HZ357tWramQMyd/nDzJqL83Jz/OiyVKMKh6DZycFH8HXGTiju1WywFgYpMUfbHIkkeN+/pil5HHXXkyZ2Zdr958vXc3sw8eAGBbn/v64mfr9cWkBs1pVLAoEfG3aP7rvOT23uUq83LZSpi0ZvPlACbs2XYvh0yZ2dC1D1P37+KHI/sB2NHzVW4mJGDWmiSzmTZLf7JaDgCT6qfI47cUeZS9L4+928iXKQsbu/YhIOoGAIdCr/HR9o0AzH+hIzk8PHFWTuy7foVPdmzCbMXPmZpUuyWN8hUl4vYtmq+cA8C39dpQxMsXgCwZ3IlJuM0Lf84jn2cWNrbrT0BMpJFH2DU+2rMeAFcnJz6r0ZSaOQug0XxxcBtrL5+1Sg5f+LegUX4jh2bL5ho5NGhDES+f1DmsmI+3mzvfN2pHhWy5+P3ccUbu2fi/9u47PMoqe+D498xMGgkJCSUEAiR0UQRcehNQuoC9V1Ssq666a/vpNlfFAqKuuoglsCAg6gqIFEEEpPdeQ4AACSQhjdSZub8/3iEE6YTMhMz5PM88mXnnLefkTrlz7n1nSvaT0OdmalUJwyE2lqcm8+qSOV5tizK5VOIsZ5WqM/Tdhs2MW7WOdwb1PWF57aphdI1vwP6s7JJl+zKzuGv8N2QXFNK9YRyv97+WmxMmnnE/3vLtpk2MW7uWd/v1O2H5l6tWMWbVqoDeUwEAACAASURBVBOWNY6K4rrmzemXkECt0FDG3nwz1375ZckT8f42bdiVkUFYYKDX4j9myuZNjF27lnf7npjHF6tXlby5HrM9PY0hE8bjMoaaVUL58e57mJu4C5cnjzunTOZIQYHXYj9myuZNjF13mhx+1xaFTicjF/9G0xo1aFq9RsnyasHBvNStO4MnjCcjP593+valc716LN63zys5QKk8+vwujzUnt8Uxr3S7ml/3JJ20/M5vfdQW2zaRsHENI3oNKFnWqU49esc1pv83Yylyu6geXOWEbf6vU0/m7939+11xx7TJHCnIL/eYT2XK9k0kbFrDiJ6nyGPKyXnsyc5iwLdjT9rPE3OmkVtcBMAnvQczsGFTpu3aVv4JeEzZtYGErasZ0XVgybInF0wtuf5K257kFBWW3N6Tk8mAaV+dtJ8nW3YmvSCPXv/7DAGqBYWUZ9gn+GbHRhK2rGFE9+Nt8eT84zn8X/ueZHtyKHS5eHf1QppVq0mzyBon7OeJX6aWtMWnvYYwMK4Z03Zv9UIG6mKpVMNkK/btJ+sUL9KvXNuDt39ZiCnVA16z/yDZBdaDfO2Bg0RXrXrW/XjLiv37yTzH41/bqBHTt26lyOUiOTubPZmZtKpdG4DaYWH0bNiQyRs2lGe4p7Vi/34yC88tjwKns6TjE+SwV5hPK+fTFvlOJysPHKDQ6Txhef2ICJIyM8nIt958f9u7l35Nmlz0WM9kxYFzzwOgd8NG7MvOZkd6ejlGdX6WH0wm63ePp7sub80na5ZR5HYBkF6QV3Jfn7jG7MvJYseRipMDePL4XVvc1aI1n6w9dR6nc+zN12GzEWC3e/2koOWpyWQVnr5DOTCuOVN3bznrfm5p0pKPNywFrBObjpxhnxfb8tRkMs+YQzOmJlo55DuLWZm6n0KX86T1StpCbATYvN8WZSHGe5eKrFwqQyISCNwOHDDG/CwidwKdgS3AaGNMcXkc91SuadKQ1JzckiGwU7nlyitYsOvkT48VzT2tW3NDixZsSE3ljV9/JbuwkOiqVVl78GDJOim5uUSHhQHwfz16MHzBAkJ9UBU6k3tbt+bGy1qw4VAq/1pg5QHQqnZthvfuQ92q4Tw3a2ZJ58gYSLjxJoyBrzesZ+JG33TuSru3lSeH1BNzOJWkzEziIyOpGx5OSk4OfRo1JsBeMT6HnJDHQiuPKgEBPNK2Hfd+/y0PX9X2hPWNgYQbPG2x0fdt0TAikvYxsfy5fTcKXU7+teRX1h9OoYojgEdbt+fu6d8wrHW7E7YxBsYNvBmDYcLm9Xy9Zb2Poj+uJI92njyWWnkA1KsawY833UNuURHvrljEipT9JduNHXATrWrFMH/vbmYkemdo6Vy0j44lLf8oSTlHSpbVC4vgx+vuJ7e4kHfXLGTFoWTCA4IAeK51NzrWrseenEz+umwOaefQGSxv7aNjSSvIIyn7yNlXBsb2uYXWNWOYn5zIjCTvVejUxVFer8hfAgOBp0VkHHALsAxoB4w53UYiMkxEVorIytGjR5c5iGCHg8c6t+f9hYtPu06H+rHc0upy3pm/qMzHK0/j162j5xdfcN24cRw+epSXr776jOv3jI8nPS+PjYcOeSnCczN+/Tp6fPkFA8eP49DRo7zS/Xge61JS6DduLNd/PYHH2rUn0G4H4NbJkxg8YTxD//cd97RqTbu6dX0VPlAqh/+enMOpZBcW8uq8uXw4YCCTbr2N5OwsXG7ff0wav2EdPb4q1RbdrDye7tCJL9asJq/45M8st34zicFfj2foD99xz5WtaVfHt21ht9mICArm+u/H88bSX/l370EAPNO2M59vWEWe8+Qcbv7ha677dhz3//gd917emvYxsd4O+yQlefzPk8e1Vh6H8o7Sefx/GPjtOP65ZD6jrhlIWMDxDzf3zviW9uM+IdBup3Od+r4K/ySD41ucUBU6lH+Uzt9+wsDpX/HPFfMY1X0QYQGB2G026oSGs+rwfq6bnsDqwwd4uW1PH0Z+3OCGl5VUhc7FvbO/od3Ef1ttEVNx2uKsjBcvFVh5zRlqaYy5UkQcwH6gjjHGJSL/BdadbiNjzGjgWC/IvPPmyDIFUT8ygtiICKYNvRuA2uFV+d8Dd3FTwtekHc2jWc0avDGgNw9O/p7MfN8Ni52L9Lzjn5QmbtjAmOuvByA1J4cYTyUIrKGx1NxcrmnUiGsaNaJHfDxBDgdhgYG8178/z/30k9djLy2tdB4bNzBm8PUnrbPrSAZHi4toVr0GGw6lkno0F4D0/Hxm79pJq+jarNi//6TtvOWkHIacnMPvzUtMZF5iIgC3t2xZUvXypdO1RevatenfpAkvdu1GeFAQbgOFThfj1q89uS1q12bFAd+1RUpuDrN27wBg3aEU3MYQFRxC6+gYBjRqyksduxMeGITbGAqdLsZuWnM8h4I8ZiXtpFWt2iw/mOyzHABSjpbK4/DxPDIK8ikqtIbONqalsjc7i/iISDakpZZsW+hyMSdpJ73jGrNo/55T7t+b7CL0rd+UQdMTSpYVuV3H88hIZW9OJvHhUWxITyGvuIiZe6xKyoykrdzW5EqfxF2aXYR+cU257oeEs69cSqHLxey9O+lTvwmLDvi+LdS5K6/OkM0zVBYKVAEigAwgCAgop2OeZPvhdDp+8J+S2788NpQbv5rAkfwCYsKr8u+bBvH8tJkkZWR6K6QLVjM0lMNHjwLQp3FjtqdZw35zExMZOWAAX6xeTa3QUOKqVWNdSgprDh7k3UVWtatDbCwPtW3r844QQM0qoRzOs/Lo26gx29OtPGLDwzmYk4PLGOpUrUqjyCiSs7MIcTiwiXC0uJgQh4Ou9Rvw4bKlvkzhhLYoncOZVA8JIT0/n/CgIO6+shV//HF6eYd5Vie0RePjedw2ZXLJOk936MTR4iLGrV976rZY7tu2mJ20k4516rPkwD7iIyIJsNvIKMjn1h8mlqzzTNvOHC0uYuymNYQ4ArAJnhwC6BbbgA9WLfFhBpbZu0+dR1RwCJmFBbiNoV7VCOIiqrE3J4sqjgBCAwM5nHcUuwi9GjRk+UHfdUpL6xoTR2JWOil5OSXLooJCyCzy5BEWQVx4JHtzrNfducm76Fi7PktS9tIlpgE7Ms/+fCpvXevEsSszg5S83LOuW8URQFhAIIfyPW0R24gVqd47OUJdHOXVGfoc2ArYgVeAb0QkEegITDzThmUxckh/2tevR2RIMAufeIhRC5cwZf2mU677ZJcOVAsO5u99ewHgdBtu/GrCee+nPLw/YAAdYmOJDAlh0cMPM2rJEjrExtKiVi2MMSRnZ/N/P1unde5IT2fGtm3MvO8+XG43f5s3r8Kc0jmqvyeP4BB+e/BhRi315FHzeB6vzLXyaFunLo+2a4fT7cZtDK/9MpcjBQXUC4/g00GDAbDbhKlbt7LgFGc4lWsO9Tw5PORpi3qnzgGsrwEICwoiwGajd6NG3Pfdt+zMyOC1Hj1pXrMmAB8uXcruTO92wEf1K9UWQx9m1LIldKjryYOT8ziVGlVC+fS6Um2xzbtt8cE1A+lYpx6RwSEsufsRRq78jclbN/B2j37MuvV+il0unpt35g5/jZAqjO47BLCGpn7YuYVf9yV5IfrjPrhmIB1jPHnc5cljmyePWzx5/GLl0T4mlmfbdil5XryycA5ZhQXUCKnCmL43EGi3YxNhyYG9jN+81rt5dB9Ex+j6Vh43P87ItYuYvHM9g+IvO2nidPvoejzbphtOt8vKY8kssoqsavxbq+Yzott1vBYQREZhHn/+bYb3cugxiE61rbZYettjjFy9iEk7NjCoYfNTDpEtuuURqgYGEmCz06dBE+6ZZZ2VOObaG4+3xcG9/Herd9uiLCr6xGZvEVNOb5wiUgfAGHNARKoB1wJ7jTHLz3EXpkkZh8l8bcdLfwKg0YgRPo6kbHY9+ywN37+0c0h85lkAGo68xPP407M0HHWJ5/C01RZxn77r40jKJunR5wGI+88lnscjzxOXMNzXYZRJ0n0vANDgi7d9HEnZ7Bn6FwDx5jG73fCu17pDC79/3qu5nY9y+54hY8yBUtczgSnldSyllFJKXYAKMpLgaxXj/F6llFJKKR+pVN9ArZRSSqlzp3OGLFoZUkoppZRf08qQUkop5a+0MgRoZUgppZRSfk4rQ0oppZSf0jlDFq0MKaWUUsqvaWVIKaWU8lcV4EejKwKtDCmllFLKr2llSCmllPJXWhgCtDKklFJKKT+nlSGllFLKT+nZZBatDCmllFLKr2lnSCmllFJ+TYfJlFJKKX9ldJwMtDKklFJKKT+nlSGllFLKT+kEaotWhpRSSinl17QypJRSSvkrrQwBWhlSSimllJ8TU3FnklfYwJRSSqlyIt48WK/eb3ntvXbenBe9mtv50MqQUkoppfxahZ4zFPfv93wdQpkkPfEcAA0/uLTzSHzqORqMecfXYZTJnof+DEBcwnAfR1I2Sfe9QNxXl3gO978AQMMJb/g4krJJvPNlAOLGveXjSMom6Z4XaTTx0m6LXbdbbdF+5ss+jqRslvfzQTu4vX/IikgrQ0oppZTyaxW6MqSUUkqp8iMVd96wV2llSCmllFJ+TStDSimllL/SwhCglSGllFJK+TmtDCmllFL+SucMAVoZUkoppZSf086QUkoppfyaDpMppZRSfkp0lAzQypBSSiml/JxWhpRSSil/pROoAa0MKaWUUsrPaWVIKaWU8lOiP9QKaGVIKaWUUn5OK0NKKaWUv9I5Q4BWhpRSSinl57QzpJRSSvkr48XLWYhIkohsEJG1IrLSsyxKROaIyA7P30jPchGRD0Rkp4isF5GryvJv0M6QUkoppSqKnsaY1saYtp7bLwJzjTFNgLme2wD9gSaeyzDgk7IcVDtDSimllJ8SY7x2uUBDgATP9QTg+lLLxxrLUqCaiMRc6EG0M6SUUkqpciciw0RkZanLsN+tYoDZIrKq1H3RxpiDnuspQLTnel1gX6ltkz3LLoieTaaUUkr5Ky+eTWaMGQ2MPsMqXY0x+0WkFjBHRLb+bnsjUj6/plbpOkNv9+pLrwYNSc/Po+/EhJLl97Vsw70tW+MybuYl7eatJQsAePyq9tza4gpcbsPfF85jwb49BNntTLrhNoLsduw2Gz/t2sHI5Yu9lsPwa/rSM97Kof94K4enO3TitstbkpGfD8C7ixcxf89uAJpXr8HrvXoTFhiIMYYhk8ZT5HLxXKcu3ND8ciKCgmj56Ydei/+Yd7r1o1d9K48+331Vsvz+Fm24p0Ub3MYwb18iby7/FYfYGN6tL1fUiMZhs/Htjk18vG4ZDSMi+ajX4JJt61eNYMSq3/hi0yqv5PB25/70im1EekEefad+AcBH3QfTMCIKgPDAYLKLChgw7StiQ8P5+fqHSMzOAGDN4QO8snQ2ABP73kHNkFAKXU4A7pkzmfSCPK/kAPB2l1J5/ODJ4+pT5DH1K1rViOHNzn0BEIT31y5i1t4dADxw2R+4vWkrBGHijnV8sXml13IY3mEgPes2Jr0gj/4zPgPgsmq1eL19f4LsDlxuN6+unMn69IMMibucRy7rhAjkFhfx6oqZbM08dNr9eNPbnQYcb4tpnwPwUbchNAz/XVv8+KX1vOjUn8ujrOfFd4kb+Xjj0tPux5veaj+QXnU8/8eZx9vjn2097WHcvLZyJuszDpZs0zIqhinX3sfTi//HzGTrfS6mSjhvth9ATEg4BnhwwST2H83ySg61giP4W8tbiAoKA2P4PnkFk/YsJjwghH+1up2YkEgO5h/h5bVfk+MsAOCqqHiebT4Qh9jJLM7j0eWfEWhz8J/2DxNoc2AXG3NTN/LZzrleyaEyMcbs9/w9JCLfA+2BVBGJMcYc9AyDHfKsvh+oV2rzWM+yC1LpOkNTtmwkYf0aRlzbv2RZp7r16B3fiP4Tx1LkdlE9JASAxpFRDGrSjD4TEqgVGsr4IbfQc/wXFLpc3PnDN+QVF+Ow2Zhy4+3M37ObNakHT3fYi57D2PVreLdP/xOWf7FmNWPWnPjmYxdhRN8BPDv7J7amHaZacDBOt/WVonN3JzJ23Vrm3TvUK3H/3jc7NpKweTUjrh5QsqxTTD16N2hC/+8SrLYIrgLAwIbNCLTb6fvdVwTbHfx881Cm7tpCYtYRBnxvdQhtIiy74zFm7dnhtRym7NpAwtbVjOg6sGTZkwumllx/pW1PcooKS27vyclkwLSvTrmvZxZOZ0N6SrnFeiZTdm4gYctqRnQrlcevv8uj2Mpj25HDDJqWgMsYaoaE8tPgB/h5304aRVTn9qatGDJ9LMVuFwm9b2Xuvp3sycn0Tg6J6xm7fSXvdjreOX6xTS8+2LCQXw8m0qNOI15s3Ys7545nX24mt//8X7KLC7g6piFvtO/PjbMTTrsfb5qyawMJ21Yxost1JcueXPhDyfVX/tCr5DE1oEFzAu12+k3/wnpeDH6Yqbu3kHw065T78aZvd69n3I6VvNvh+P/xhda9+HCTpz1iGvFC617cNW88YD1/X2jVk0UpiSfs592Og/h402/8lppEFUcAbi9WKlzGzahtM9iWfYAq9kDGdn6S5Wk7ua7uVaxI38XY3Qu4N7479zW8mo+2zyLMEcxfWgzh6ZVfklqQRWRgKABFbiePr/icfFcRdrHxWYdHWHJ4Oxuz9p0lggqggnwDtYiEAjZjTI7neh/gH8BU4D7gLc/fY0+WqcCTIjIR6ABklRpOO2+Vbs7Q8oP7ySosOGHZXVe04pPVyylyuwBI91RX+sQ3ZtqObRS5XSTnZLMnK5PWtWoDkFdcDIDDZsNhs2HO5bzAi2TFgf1kFhScfUWgW/04tqYdZmvaYQAyCwpKXkzWphzkcN7RcovzbJanJJP5u7a4+7LWfLxu2fG28FRHjDFUCQjALkKww0Gx20VOcdEJ23ap04C9OZnsz832TgLA8tRksgrzT3v/wLjmTN29xWvxXKjlqclkFZ0hj/jmTE208ihwOXF5HkNBdkfJI79xRHXWHj5Ycv+ylH30a9C0vEMvseLwPjKLTnw8GSAsIAiAqgFBHMrPBWB12n6yi61116QdoHaV8DPux5uWH9p30mtUaQMbNGdq0mbPLUOII9B6XtgdFLldJZ3Ws+2nvJ2yPQyEOU5uD4B7m7Rl5r5tpBcer4g2Dq+BQ2z8lpoEQJ6zmAJP9dQb0gtz2JZ9wDq2q4jduYeoGRxO9+jL+PHAGgB+PLCGq6NbANA3phXzUzeRWmBVro4UHX99zXdZr1cOseMQ775nVBLRwCIRWQcsB340xszE6gT1FpEdwLWe2wAzgERgJ/AZ8HhZDl5ulSERaQjciFXGcgHbgQnGGO+9k3k0rBZJ+zqx/LljVwqdTv61+FfWH0olOjTshGrPwdwcosPCINX6FDP91rtpEFGNcRvWsjbVN5/oS7u3VWtuvKwFG1JT+dei+WQXFhIfGYkBvhpyE1EhIUzfvo3Rq1f4OtTTio+Ion3tWP7ctiuFLhf/Wjaf9WkpzNi9nd4NGrPizscJcTj4x9JfTnqhH9ywOVN3VZyOR/voWNLyj5KUc6RkWb2wCH687n5yiwt5d81CVhxKLrnvnS4DcBs3P+3ZzofrvTfsejanyqN1jRje7jKAumHhPLtwOi5j2JaZxvNXdadaUDAFTic9Yxuy3keVrmP+uWoOCT1v56U212AT4ebZCSetc2ujVvx6YJcPojt/7WvVI63geFvM2LON3vWasPzmPxLicPDPlXPJ8mFH7mxeXzOHr6622kMQbvnZao/okDD6xDbjrnn/5crqxytZ8VWjyC4q4OMuNxEbFsHilCTeXv+LV6tDx8SEVKNZeB02Ze4jKjCM9MIcwOowRQWGAVA/tAYOsfNJ+4eoYg9i0p7FzPB0mmwIYzs/QWyV6kzZu5RNWcmnPZY6mTEmEWh1iuXpwDWnWG6AJy7W8culMiQiTwGfAsFAOyAIq1O0VER6nGG7kpnmo0efaY7V+bGLjYigYK6fMoE3Fi/g330HnXUbtzEMmDSOTl+NplWt2jSNqn7R4rkQ49evo0fC5wycMJZDebm80rUHYOXWNqYuf5o1g1unTKRPo8Z0jq3v01jPxCFCtaBgrp86njeWz+fja6y2aF0rBrcxtJ/wCV0nfcbDLdtRr2pEyXYBNhvXNmjEj7u3+Sr0kwyOb3FCVehQ/lE6f/sJA6d/xT9XzGNU90GEBQQC8PTCafSb+gW3/DSBdtGx3Njwcl+FfZLf5wGwNu0gfX74nMHTx/JYy44E2e3sykrn043LGNf7NhJ638rmjEM+edMq7a4mV/H66p/p+sNHvL76Z4Z3HHjC/R1rNeDWRq0YvvYXH0V4fgbHXXZCW7SqEYPLGDpM+Yhu33/KQ5e1p15YxBn24Ft3Nb6K19f8TNepH/GvNT/zVnurPf6vTW/eXjfvpFqJXWy0q1mPN9fO5YbZX1IvrBo3xV/p9bhD7IG81fouRmz9kaOuwpPuPxa3XWw0j6jDn1Yl8NTKLxnaqCf1q1jvDW4Mdy/+iOvmD6dFRD0ahkWftJ+K6BI4td4rymuY7GGgvzHmdayy1uXGmFeAfsDI021kjBltjGlrjGk7bNjvz7i7cCm5OcxKtOaZrDuUgtsYooJDSD2aS52wqiXrxYRVJTU394Rts4sKWbJ/H1fXj79o8VyItPw83MYqvE7cuIEra1vDeSm5OSw/kMyRgnwKnE7mJ+3m8lq1fBrrmRw8msvMpO0ArDucgttAVHAIQxpdxvzk3TiNm/SCPFal7ufKGrVLtusR25CNaYdIy/fepOMzsYvQt35TpicdP9mhyO0qGRbcmJHK3pxM4j2TYlPzrMfVUWcRUxM306rmBX8dxkVlF6Fvg6ZM3731lPfvykonz1lM02o1AZi8Yz2Dpidw28wJZBUVkJiV4c1wT3JTfEtm7rM6yDP2buHK6nVK7mterSZvdhjAIwumkHmGIcKKwnpMNWP6nuOdoSHxLfh1f+Lx58Xh/VxZvWI8dk7lxriWzEr2tMe+4+3RMiqGUZ2v59dBj9Mvtjn/aNuX3nWbkpKfzebMQ+w7monLGObs387lkbXPdIiLzi42hre5k1kH1zI/dRMAGUW5VA+y3huqB1XlSJH1/D1UkMXStB0UuIrJKs5j7ZEkmlQ9sT1ynQWsykikU40mXs1DlU15zhk6NgQXBIQBGGP2AgHleMxTmr17Jx3rWpPO4yMiCbDZySjIZ07SLgY1aUagzU5s1XDiIqqx9lAKUcEhhAda495Bdgdd6zVg1xHfvujXrBJacr1vo8ZsT08DYMHeJJpVr0Gww4FdhA51Y9mZke6rMM9q9p4ddIqxKlfx4ZEE2GxkFOSzPzebznWs5SGOANrUimFXqTfawY0q1hBZ15g4ErPSScnLKVkWFRSCTQSwhsviwiPZm5OJXYTIIGvSvkNs9IptzPYjaT6J+/e61jk5j9iwCOyePOqGhtMoIorkXGuOxLEJ73VCq9KvQVOm7t588k69KDU/lw61rMdN5+g4knKsx0ydKuF83O0mnlsyld05vn3unquuMXEkZp/YFgeOZtO5dgPA87yoUYddWRX3+f379tjj+d/3mP4xV0+zLjOTt/LaylnM2b+d9RkHCQ8IIirIelx1qtWAnVnefW68esWN7M49zISk30qWLTi0hYF12gAwsE4bFqRuKVneOjIOu9gIsgVweUQ9dh89TLWAUMIcwQAE2Rx0qN6YPUcPezWPC2aM9y4VWHnNGRoDrBCRZUA3YDiAiNQEyvWV6YPeA+lYN5bI4BCW3DeMkcsXM3nLRt7u1ZdZt99HsdvFc3N/AmBHRjrTd25nzp3343S7eW3BXNzGUCs0lPeu6Y9NBJsIP+7cxrw9iWc58sUzqu9AOsRaOfw2dBijli6mQ2w9WtSoiQGSs7N5Zd4cALILC/l8zSr+d9tdGGB+0m5+SbJOuX+hS3cGN2tOSEAAvw0dxuRNGxi1bInX8vig53V0iqlHZHAIS+94lJGrfmPy9g28070/s2+8n2K3m+d+tdpi7OY1vNu9P3NuegABvtm+ka0Z1otJiCOAbnXjeHnRbK/FXpJD90F0jK5vPZ5ufpyRaxcxeed6BsVfdtLQUvvoejzbphtOtwu3MbyyZBZZRQWEOAIY2/tWHGLDbrPx24Ekvt6xzvt51PbkcYsnjx2nzqNdrVgea9kRp7HyeHXpHI54JpF/0vN6IoNCcLrdvLp0DtlFJw8plJdRnYfQIboBkUEh/Hb9k4xav5CXl8/g1T/0xiE2Cl1OXllmPZ7+eEVXIoNC+Ee7fgC43G6GzPrytPuZnOi99vig6+Djj6kbH2fkes9jKq7FSZ3LsdtW807ngcwe9CCC8M2u9WzNPHzG/XjL+52G0KGW9X9cNPhJRm1cyMsrZvDaVb2xi41Ct5NXVvx0xn24jeHNtXMZ1/NOBNh4JIVJiWu8kwDQqloDBtS9ih05B/lv5ycB+Hj7bMYm/sobre9kcGxbUvIzeXnd1wAkHT3MksPbGd/lKYwx/JC8gsTcVBqH1eavV95svWdg4+eUDSw6XHGG9NXZiSmn3pqIXA5cBmw0xpy6Bn9mJu7f713kqLwr6YnnAGj4waWdR+JTz9FgzDu+DqNM9jz0ZwDiEob7OJKySbrvBeK+usRzuP8FABpOeMPHkZRN4p0vAxA37q2zrFmxJd3zIo0mXtptset2qy3az3zZx5GUzfJ+bwCIN4/Z9w9/9VrJZtaqv3s1t/NRbmeTGWM2AZvKa/9KKaWUUhdDpfvSRaWUUkqdowrypYu+Vum+dFEppZRS6nxoZUgppZTyUxX9+3+8RStDSimllPJrWhlSSiml/JVWhgCtDCmllFLKz2llSCmllPJXWhkCtDKklFJKKT+nlSGllFLKX2llCNDKkFJKKaX8nHaGlFJKKeXXdJhMKaWU8lf6cxyAVoaUUkop5ee0MqSUUkr5pprmLQAAFilJREFUKf05DotWhpRSSinl17QypJRSSvkrrQwBWhlSSimllJ/TypBSSinlr9xaGQKtDCmllFLKz2llSCmllPJXOmcI0MqQUkoppfycmIrbK6ywgSmllFLlRLx5sP5N/uK199qfdrzt1dzOR4UeJhuzvZuvQyiTh5ouBCpHHov3NPR1GGXSuUEiACv2xvk2kDJqVz8Jd0pTX4dRJrba2wFo++AIH0dSNis/fxaA3rZbfBxJ2cxxf1MpcoDK0RbKNyp0Z0gppZRS5ajijg55lc4ZUkoppZRf08qQUkop5a/0e4YArQwppZRSys9pZ0gppZRSfk2HyZRSSil/Zdy+jqBC0MqQUkoppfyaVoaUUkopf6Wn1gNaGVJKKaWUn9PKkFJKKeWv9NR6QCtDSimllPJzWhlSSiml/JXOGQK0MqSUUkopP6eVIaWUUspfaWUI0MqQUkoppfycVoaUUkopf6WVIUArQ0oppZTyc1oZUkoppfyVW3+bDLQypJRSSik/p5UhpZRSyl/pnCFAK0NKKaWU8nPaGVJKKaWUX6tUw2TZh13MGJlNXqY1IaxVvxD+MLgKAKun5bHmx3zEBg3bBdHjgTAObi9m1kc51sYGOt8ZStNOQQAU5LqZ9WEOaXucINDv6XDqNg+45PJY+b881s/ORwRqxDno/3Q4jkAp9xzSD8GYd+xkHxEQuHqAmz43uNm7CxJG2SkuEux2wz1/dNOwuWHJXGHGZDvGQHAVw71/dFG/kbWvWd/aWDDThgCx8YYHn3cREFjuKZTk8enbAWQdARHoOcBNvxtd7NkpfDHKQXER2O1w/1NOGjU3bF4njHwtgJq1rdJzu65ubrjHBcDRXBgzwkFykiDAw887adLCOyXqwkK45ykoKganC/peDX8cCuO/g7FTYO9+YfEPhshq1vrGwBsfwIJlEBwEb7wElze17nv4z7BuM1zVEj59yyvhn9Lt17bhhu4tAfjfgg18/fMaAG7r1ZpberXG5Xbz2/rdfDBlYck20VFV+eaf9zF66hL+O2uVT+I+V237tubx9x/AZrfx0+dzmTT8f74O6YJUhjwqQw6npcNkQCXrDNns0HNoGNGNAyjKczP2T0do0DqQvEw3O5YVct+HUTgChKOeTkaN+g7uHRmJzS7kZrhIeCqDxu1rYLML8z7LJf6qQIa8FIGr2FBc6L0HzMXK42imm9XT8njg4+oEBAlT38pi64ICrrg2pNxzsNvhtmEu4ppAfh78/QkHl1/lZvJndobc7ebK9oZ1y4XJY2y8+K6LGrXhxXedhFaF9cuFhPftvPqhiyNp8PP/bPxrjJPAIPj4dTvL5gtd+3inPWx2uPMRJ/FNDPl58OrjAbT8g5uvP3Nw4z0uWrV3s3aZja8/c/B/7xUD0Kylm+dfd560r3EfO7iyrZunX3PjLLY6KN4SGAhfjoTQKlDshLufhG4doM0V0KMT3PvMif/PBctgTzLMHG91fP4xAiZ9at039HYoKIBJ07wX/+81qludG7q35N7XJ+B0uvjgTzeycP1uoiOr0r1NI+742ziKnS4iq574WH/2tqtZvDHJN0GfB5vNxh8/epAX+vyTtOQMPlr+JkumrmTvlmRfh3ZeKkMelSEHdXblMkwmIhEi8paIbBWRDBFJF5EtnmXVyuOYAGFRdqIbW9WbwCo2qtezk5vuZu2MfDrcHIojwKqIhFaz0g4IFmx2a5mzCPAUTAqPukneWETLPsEA2AOE4DDvjSherDzAOmvSWWRwu6wOXWiUd/KoVh3imljXQ6pATH1DZppVJcrPs5bnH7XWA2hyuSG0qnW90WWGjLTjSbhcUFR4/G+1KK+kAEBkdYhvYkryqFPfkJFmVYmO5ZF3FCKrn7lzlncUtm2w0aO/1YF1BEBoWLmGfgIRqyME4HRaHSIRaNEU6sacvP68RTCkr7VO68shOxcOpVv3dfrD8X35SlxMFBsTUygscuJyG1ZvS6bXVY25ueeVJMxYQbHTqsYdyckv2ebqNo3Yn5ZN4v50X4V9zpq1b8yBnSmk7D6Es9jJ/Em/0XlIW1+Hdd4qQx6VIYczchvvXSqw8qoMTQbmAT2MMSkAIlIbuM9zX59yOm6JrFQXqbucxDRzMP9LF8mbilg0Lhd7gNBjaBgxTa3OxoFtxcwclU32YTcDng3HZhcyU52ERNj46f0cDic5iW7koNewqgQGl//w0sXMo2p1O+1uqMJ/hqbjCIS4NoHEXxXk9RzSUmDvTqFhc8Odj7l47yUHk0Zb1dlX3j+5grJgpo2W7awnTmQN6HeLm+fvdhAQBFdcZbiirW+eVIdTYM9OG42aO7n7MSdvvxTAhNFg3PDXUUUl6+3cbOPlRwKoVh3uHOYkNs5w+KBQNcIw+h0HexOFuCaGex53Elz+RboSLhfcPAz27oc7rodWLU6/bmoa1K51/HbtmnDoMNSqXv5xnotd+9N5/IauRIQGU1DspMuV8WxJSqV+dCStm9bl8Ru7UFjsYtTkX9mclEpIUAD39W/HE+99yz19K/4bWY26URxOPt5pS0vOoHmHJj6M6MJUhjwqQw7q7MqrTBBnjBl+rCMEYIxJMcYMBxqcbiMRGSYiK0Vk5ejRoy/44EX5bn54M4teD4cRVMWGcRkKcg13vRtJj6FhTBuehfGMk9ZpFsDQj6tzz4hIln1zFGeRwbggdZeT1gNCuG9UFAHBwvIpRy84Hl/lUZDrZueyQoaNqc5jCTUoLjBs+qXAqzkU5MNH/3Bwx2MuQkLhl2k27njUxYgJTu541MWXI+wnrL9lrbBwpo1bH/LMs8mBNYuFt8c6Gfm1k8ICWPyz9zulBfkw6h8B3P2YkyqhMHe6jbsec/LBhCLueszJZ+9ZndK4xob3xxfxxn+K6TPExci/Wp83XC5I2iFcM8jFvz4tJijYMG2S/UyHvOjsdvj+c/jlG9iwBbYnevXwF1XSwQzG/rSCj569iQ//dCPb9x7G5TY47DYiQoO5/19f88E3C3jz0esAGDakExNmrya/sNjHkStVsRjj9tqlIiuvztAeEfmLiEQfWyAi0SLyArDvdBsZY0YbY9oaY9oOGzbsgg7schp+eDOby3oE07SzNcwVVsNO005BiIhVSbFBfvaJ1YXq9RwEhghpe5yE1bBRtYaNOs2sN7hmXYJI3XVyBaM8XYw89qwtIiLaTpUIG3aH0KRzEAe2eO/NwOmEj/5hp1MvN227WnH+NsfGHzzX23U3JG473rHZlwhfjrTz1N+dhIVbyzavEWrWhvBq4HDAH7q62bnZu50hpxNG/T2Azr3ctOtmPaEXzrbTrqt1vUN3N7s8eVQJpaTa07qDG5dLyMmCqJqGqJrQ+DIr9/bd3STt8H6nDiC8KrRvA4uWn36d6BqQcuj47ZTDUKtm+cd2Pn5YtJF7/jmeYcMnk51XwN7UI6Rm5DJv1U4ANu1OwRhDtbAQroivzVO3dGPq8Ae5o3cbHhjYgVt7tfZxBqeXtj+DmrHHy3A1YqNIuwSG936vMuRRGXJQZ1denaHbgOrAr545QxnAfCAKuKWcjokxhpkf5FC9np121x+f1NCkYxB711vDGBn7nbidEBIuZKa4cLusN6esQy7Sk12E17ITFmmnag07GclWB2jPuiKq1/PeXPOLlUfVmnYObC2muMBgjGHvumKq1/NONcIY+HKEnTr1DX1vPv6JoFp12Lbe6gRsWStE17GWpx+yKkgP/8VF7djj+4mqCbu2CoUF1j43r7FRp75XUijJY8x7DurUdzPgZlfJ8sjqhi2ePDatEWrXtf7/mRnHT87YtVUwbggLt+Y5RdU0HNh3bBsbdRt4b7gvIxOyPSccFhTCkpUQf4b/Y88u8MMsK5e1m6BqaMUZIjvm2OTo6Kiq9LqqCTOXbuXXNTtp27weAPWjq+Fw2MnMzefh4ZMZ/MLnDH7hc76es4Yvf1zG5HlrfRn+GW1bsZO6TWKoHVcLR4CDHrd1YcnUlb4O67xVhjwqQw5npHOGgHKaM2SMOQK84LmcQEQeAL4sj+Pu31zM5l8KqBFn56unMgDofm8oLa8N5qcPsvnyiXRsDqH/M+GICPs3F/HdlDxsDkEEej9alSoRVv/wmkfCmP5eNi6noVq0nf7PhJdHyOWaR5UIG027BDH2mQxsdqjV0MGV/bwzSWXHJmHxzzZi4w2vPWr9T28a6uL+P7mY8LEdtxsCAgz3P2N1OH/4r53cbBj3odVZs9sNf/23i0aXGdp2c/O3xx3Y7VC/seHqAd4rt27fJCz62U69eDcvP2LlcetQFw8+62Tcxw7cLggIhAc9eSxfYGPudDt2u7X8iVeKEU8B6L4nnHzypgOnE2rFGIY9771q4+F0eOkNcLmt16R+PaBnZxg3BT6fCGkZMGQodO8Ir/8Fru4IC5ZC3zs9p9a/eHxfdz8JiXshLx963Gyt37W911Ip8fbjg4gIC8HpcjN8/Fxy8wv5YdFGXnugL5P+cS/FThd/+3ym9wO7CNwuNx/98XPenPkKNruNWV/+wp7Nl97ZS5Uhj8qQgzo7MV7+jgER2WuMOZfP9mbM9m7lHk95eqip9f0mlSGPxXsa+jqMMuncwJogs2JvnG8DKaN29ZNwpzT1dRhlYqu9HYC2D47wcSRls/LzZwHobSu3YrdXzHF/UylygMrRFpxwPnD56xf5kNc6ATOPjPHN3IBzUC6VIRFZf7q7gOjT3KeUUkop5XXlNREmGugLHPndcgEWl9MxlVJKKXU+3BX7LC9vKa/O0HQgzBhz0gxFEZlfTsdUSimllDpv5TWB+sEz3HdneRxTKaWUUudJf5sM0F+tV0oppZSfq1Q/1KqUUkqpc2d0zhCglSGllFJK+TntDCmllFLKr+kwmVJKKeWvdAI1oJUhpZRSSvk5rQwppZRS/qqC/4Cqt2hlSCmllFJ+TStDSimllL8yemo9aGVIKaWUUn5OK0NKKaWUnzI6ZwjQypBSSiml/JxWhpRSSil/pXOGAK0MKaWUUsrPaWVIKaWU8lM6Z8iilSGllFJK+ZyI9BORbSKyU0Re9OaxtTKklFJK+asKMmdIROzAv4HeQDKwQkSmGmM2e+X4puL+SFuFDUwppZQqJ+LNg/W23eK199o57m9Om5uIdAL+Zozp67n9EoAx5k1vxFaRK0Pl/oAQkWHGmNHlfZzyVhnyqAw5QOXIozLkAJpHRVIZcoDKk0dpZ+qgXGwiMgwYVmrR6FL/z7rAvlL3JQMdvBWbv88ZGnb2VS4JlSGPypADVI48KkMOoHlUJJUhB6g8efiEMWa0MaZtqUuF6Vj6e2dIKaWUUr63H6hX6nasZ5lXaGdIKaWUUr62AmgiIvEiEgjcDkz11sEr8pwhb6gwJboyqgx5VIYcoHLkURlyAM2jIqkMOUDlyaPCMcY4ReRJYBZgB74wxmzy1vEr8tlkSimllFLlTofJlFJKKeXXtDOklFJKKb/mt50hX37t98UiIl+IyCER2ejrWC6UiNQTkV9EZLOIbBKRp30d0/kSkWARWS4i6zw5/N3XMZWFiNhFZI2ITPd1LBdKRJJEZIOIrBWRlb6O50KISDURmSIiW0Vki+dL6S4pItLM0wbHLtki8oyv4zpfIvInz3N7o4h8LSLBvo5JXVx+OWfI87Xf2yn1td/AHd762u+LRUS6A7nAWGPMFb6O50KISAwQY4xZLSJVgVXA9ZdSW4iIAKHGmFwRCQAWAU8bY5b6OLQLIiLPAm2BcGPMdb6O50KISBLQ1hiT5utYLpSIJAALjTFjPGfXVDHGZPo6rgvled3dD3QwxuzxdTznSkTqYj2nWxhj8kVkMjDDGPOVbyNTF5O/VobaAzuNMYnGmCJgIjDExzGdN2PMAiDD13GUhTHmoDFmted6DrAF65tILxnGkuu5GeC5XJKfMkQkFhgIjPF1LP5MRCKA7sDnAMaYoku5I+RxDbDrUuoIleIAQkTEAVQBDvg4HnWR+Wtn6FRf+31JvQFXRiISB7QBlvk2kvPnGVpaCxwC5hhjLrkcPN4H/gJUjF9vvHAGmC0iqzw/AXCpiQcOA196hizHiEior4Mqo9uBr30dxPkyxuwH3gX2AgeBLGPMbN9GpS42f+0MqQpGRMKAb4FnjDHZvo7nfBljXMaY1ljfmtpeRC65YUsRuQ44ZIxZ5etYLoKuxpirgP7AE54h5UuJA7gK+MQY0wY4ClyScxsBPMN8g4FvfB3L+RKRSKyRg3igDhAqInf7Nip1sflrZ8inX/utTuSZZ/MtMN4Y852v4ykLz1DGL0A/X8dyAboAgz3zbSYCvUTkv74N6cJ4Ps1jjDkEfI81NH4pSQaSS1UYp2B1ji5V/YHVxphUXwdyAa4FdhtjDhtjioHvgM4+jkldZP7aGfLp136r4zyTjz8HthhjRvg6ngshIjVFpJrnegjWxPytvo3q/BljXjLGxBpj4rCeE/OMMZfcJ2ARCfVMxscztNQHuKTOuDTGpAD7RKSZZ9E1wCVzUsEp3MElOETmsRfoKCJVPK9X12DNbVSViF/+HIevv/b7YhGRr4EeQA0RSQb+aoz53LdRnbcuwD3ABs+cG4CXjTEzfBjT+YoBEjxny9iAycaYS/a09EogGvjeet/CAUwwxsz0bUgX5I/AeM8HtkTgAR/Hc0E8HdLewCO+juVCGGOWicgUYDXgBNagP8tR6fjlqfVKKaWUUsf46zCZUkoppRSgnSGllFJK+TntDCmllFLKr2lnSCmllFJ+TTtDSimllPJr2hlSSimllF/TzpBSfkZEkkSkhre28/Y+lVLqfGlnSKlKwPNr2koppS6AdoaUugSIyKsisk1EFonI1yLyvIjMF5H3RWQl8LSIDBKRZZ5fOf9ZRKI921YXkdkisklExgBSar93i8hyEVkrIv/xfIv2ucRz0nYi8qiIvFNqnftF5KOyHEcppbxBO0NKVXAi0g64CWiF9YOXbUvdHWiMaWuMeQ9YBHT0/Mr5ROAvnnX+CiwyxlyO9aOl9T37vQy4DehijGkNuIC7ziGe0233LXBDqVVvAyZe6HGUUspbtLSuVMXXBfjBGFMAFIjItFL3TSp1PRaYJCIxQCCw27O8O3AjgDHmRxE54ll+DfAHYIXnd7xCgEPnEM8ptzPGHBaRRBHpCOwAmgO/AU9c4HGUUsortDOk1KXtaKnrHwIjjDFTRaQH8LezbCtAgjHmpfM85pm2mwjcCmwFvjfGGM8vfV/IcZRSyit0mEypiu83YJCIBItIGHDdadaLAPZ7rt9XavkC4E4AEekPRHqWzwVuFpFanvuiRKTBOcRzpu2+B4YAd2B1jMpyHKWU8grtDClVwRljVgBTgfXAT8AGIOsUq/4N+EZEVgFppZb/HeguIpuwhsv2eva7Gfg/YLaIrAfmADHnEM9ptzPGHAG2AA2MMcvLchyllPIWMcb4Ogal1FmISJgxJldEqmBVeoYZY1b7Oi6llKoMdM6QUpeG0SLSAgjGmn+jHSGllLpItDKklDqBiCwDgn63+B5jzAZfxKOUUuVNO0NKKaWU8ms6gVoppZRSfk07Q0oppZTya9oZUkoppZRf086QUkoppfza/wP9/W96yFo2ZAAAAABJRU5ErkJggg==\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "res_df_dist = df.groupby([\"district\"]).agg({'of_students_within_grade' : 'max'})\n",
        "res_df_dist.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "E6L75y7kd-XC",
        "outputId": "3f7298ef-7794-425b-d0fa-77e15fdc9a9e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['of_students_within_grade'], dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 64
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.lineplot(data=res_df_dist, x=\"district\", y=\"of_students_within_grade\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "1mex4HX-hPaD",
        "outputId": "0a76c39d-c41b-429b-cc70-7d8723d41b43"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f4803a9abd0>"
            ]
          },
          "metadata": {},
          "execution_count": 65
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}