Python_Learning_Day_15_Linier_algebra,_Vector_&_Matriks.ipynb

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  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyNqf86xUTAqxC8I9FUP0ehb",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/4zmyyy/Belajar-Python-/blob/main/Python_Learning_Day_15_Linier_algebra%2C_Vector_%26_Matriks.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Import library yang diperlukan\n",
        "import pandas as pd\n",
        "import geopandas as gpd\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Data jumlah penduduk dan luas wilayah di setiap kecamatan Tangsel\n",
        "data = {\n",
        "    'kecamatan': ['Ciputat', 'Ciputat Timur', 'Pamulang', 'Serpong', 'Serpong Utara', 'Setu'],\n",
        "    'penduduk': [150000, 200000, 250000, 100000, 100000, 50000],\n",
        "    'luas': [15, 20, 25, 10, 10, 5]\n",
        "}\n",
        "\n",
        "# Membuat DataFrame dari data\n",
        "df = pd.DataFrame(data)\n",
        "\n",
        "# Menghitung kepadatan penduduk\n",
        "df['kepadatan'] = df['penduduk'] / df['luas']\n",
        "\n",
        "# Membaca file GeoJSON untuk peta Tangsel\n",
        "# Pastikan Anda memiliki file GeoJSON yang sesuai dengan batas administratif Tangsel\n",
        "gdf = gpd.read_file('tangsel.geojson')\n",
        "\n",
        "# Menggabungkan data dengan GeoDataFrame\n",
        "merged = gdf.set_index('kecamatan').join(df.set_index('kecamatan'))\n",
        "\n",
        "# Membuat peta dengan menggunakan GeoPandas\n",
        "fig, ax = plt.subplots(1, figsize=(10, 6))\n",
        "merged.plot(column='kepadatan', cmap='Blues', linewidth=0.8, ax=ax, edgecolor='0.8', legend=True)\n",
        "ax.set_title('Peta Kepadatan Penduduk Kota Tangerang Selatan')\n",
        "ax.set_axis_off()\n",
        "\n",
        "# Menampilkan peta\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 443
        },
        "id": "NYUsnFyRJB3F",
        "outputId": "790eb5d4-42b0-497a-e918-017a0228bcb3"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "error",
          "ename": "DataSourceError",
          "evalue": "tangsel.geojson: No such file or directory",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mDataSourceError\u001b[0m                           Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-4-58fac587ac68>\u001b[0m in \u001b[0;36m<cell line: 0>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     19\u001b[0m \u001b[0;31m# Membaca file GeoJSON untuk peta Tangsel\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     20\u001b[0m \u001b[0;31m# Pastikan Anda memiliki file GeoJSON yang sesuai dengan batas administratif Tangsel\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 21\u001b[0;31m \u001b[0mgdf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_file\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'tangsel.geojson'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     22\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     23\u001b[0m \u001b[0;31m# Menggabungkan data dengan GeoDataFrame\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.11/dist-packages/geopandas/io/file.py\u001b[0m in \u001b[0;36m_read_file\u001b[0;34m(filename, bbox, mask, columns, rows, engine, **kwargs)\u001b[0m\n\u001b[1;32m    292\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    293\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mengine\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"pyogrio\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 294\u001b[0;31m         return _read_file_pyogrio(\n\u001b[0m\u001b[1;32m    295\u001b[0m             \u001b[0mfilename\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbbox\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mbbox\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmask\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmask\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolumns\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrows\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mrows\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    296\u001b[0m         )\n",
            "\u001b[0;32m/usr/local/lib/python3.11/dist-packages/geopandas/io/file.py\u001b[0m in \u001b[0;36m_read_file_pyogrio\u001b[0;34m(path_or_bytes, bbox, mask, rows, **kwargs)\u001b[0m\n\u001b[1;32m    545\u001b[0m         \u001b[0mkwargs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"columns\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"include_fields\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    546\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 547\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mpyogrio\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_dataframe\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpath_or_bytes\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbbox\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mbbox\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    548\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    549\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.11/dist-packages/pyogrio/geopandas.py\u001b[0m in \u001b[0;36mread_dataframe\u001b[0;34m(path_or_buffer, layer, encoding, columns, read_geometry, force_2d, skip_features, max_features, where, bbox, mask, fids, sql, sql_dialect, fid_as_index, use_arrow, on_invalid, arrow_to_pandas_kwargs, **kwargs)\u001b[0m\n\u001b[1;32m    263\u001b[0m         \u001b[0;31m# as numpy does not directly support timezones.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    264\u001b[0m         \u001b[0mkwargs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"datetime_as_string\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 265\u001b[0;31m     result = read_func(\n\u001b[0m\u001b[1;32m    266\u001b[0m         \u001b[0mpath_or_buffer\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    267\u001b[0m         \u001b[0mlayer\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlayer\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/python3.11/dist-packages/pyogrio/raw.py\u001b[0m in \u001b[0;36mread\u001b[0;34m(path_or_buffer, layer, encoding, columns, read_geometry, force_2d, skip_features, max_features, where, bbox, mask, fids, sql, sql_dialect, return_fids, datetime_as_string, **kwargs)\u001b[0m\n\u001b[1;32m    196\u001b[0m     \u001b[0mdataset_kwargs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_preprocess_options_key_value\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mkwargs\u001b[0m \u001b[0;32melse\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    197\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 198\u001b[0;31m     return ogr_read(\n\u001b[0m\u001b[1;32m    199\u001b[0m         \u001b[0mget_vsi_path_or_buffer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpath_or_buffer\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    200\u001b[0m         \u001b[0mlayer\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlayer\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32mpyogrio/_io.pyx\u001b[0m in \u001b[0;36mpyogrio._io.ogr_read\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;32mpyogrio/_io.pyx\u001b[0m in \u001b[0;36mpyogrio._io.ogr_open\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;31mDataSourceError\u001b[0m: tangsel.geojson: No such file or directory"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Import library yang diperlukan\n",
        "import pandas as pd\n",
        "import geopandas as gpd\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "from shapely.geometry import Point, Polygon\n",
        "\n",
        "# Data jumlah penduduk dan luas wilayah di setiap kecamatan Tangsel (data random)\n",
        "data = {\n",
        "    'kecamatan': ['Ciputat', 'Ciputat Timur', 'Pamulang', 'Serpong', 'Serpong Utara', 'Setu'],\n",
        "    'penduduk': np.random.randint(50000, 300000, 6),\n",
        "    'luas': np.random.randint(5, 30, 6)\n",
        "}\n",
        "\n",
        "# Membuat DataFrame dari data\n",
        "df = pd.DataFrame(data)\n",
        "\n",
        "# Menghitung kepadatan penduduk\n",
        "df['kepadatan'] = df['penduduk'] / df['luas']\n",
        "\n",
        "# Membuat geometri random untuk setiap kecamatan\n",
        "# Koordinat random di sekitar Jakarta/Tangsel\n",
        "np.random.seed(42)\n",
        "x = np.random.uniform(106.6, 106.8, 6)\n",
        "y = np.random.uniform(-6.4, -6.2, 6)\n",
        "geometry = [Point(xy) for xy in zip(x, y)]\n",
        "\n",
        "# Membuat GeoDataFrame\n",
        "gdf = gpd.GeoDataFrame(df, geometry=geometry)\n",
        "\n",
        "# Buat peta dengan menggunakan GeoPandas\n",
        "fig, ax = plt.subplots(1, figsize=(10, 6))\n",
        "\n",
        "# Plot peta Indonesia sebagai latar belakang\n",
        "world = gpd.read_file(gpd.datasets.get_path('naturalearth_lowres'))\n",
        "indonesia = world[world.name == 'Indonesia']\n",
        "indonesia.plot(ax=ax, color='lightgrey')\n",
        "\n",
        "# Plot kecamatan Tangsel dengan warna berdasarkan kepadatan penduduk\n",
        "gdf.plot(column='kepadatan', cmap='Blues', markersize=100, linewidth=0.8, ax=ax, legend=True, legend_kwds={'shrink': 0.5})\n",
        "\n",
        "ax.set_title('Peta Kepadatan Penduduk Kota Tangerang Selatan (Data Random)')\n",
        "ax.set_axis_off()\n",
        "\n",
        "# Menampilkan peta\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 860
        },
        "id": "h2BFHbLuJf5L",
        "outputId": "619ee694-c1d8-402c-8a6c-2c9054a353f3"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "error",
          "ename": "AttributeError",
          "evalue": "The geopandas.dataset has been deprecated and was removed in GeoPandas 1.0. You can get the original 'naturalearth_lowres' data from https://www.naturalearthdata.com/downloads/110m-cultural-vectors/.",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-5-7da9901e89f1>\u001b[0m in \u001b[0;36m<cell line: 0>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     33\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     34\u001b[0m \u001b[0;31m# Plot peta Indonesia sebagai latar belakang\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 35\u001b[0;31m \u001b[0mworld\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_file\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdatasets\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_path\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'naturalearth_lowres'\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[0m\u001b[1;32m     36\u001b[0m \u001b[0mindonesia\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mworld\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mworld\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'Indonesia'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     37\u001b[0m \u001b[0mindonesia\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolor\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'lightgrey'\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/python3.11/dist-packages/geopandas/datasets/__init__.py\u001b[0m in \u001b[0;36mget_path\u001b[0;34m(dataset)\u001b[0m\n\u001b[1;32m     16\u001b[0m     )\n\u001b[1;32m     17\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mdataset\u001b[0m \u001b[0;32min\u001b[0m \u001b[0m_prev_available\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 18\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mAttributeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0merror_msg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     19\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     20\u001b[0m         error_msg = (\n",
            "\u001b[0;31mAttributeError\u001b[0m: The geopandas.dataset has been deprecated and was removed in GeoPandas 1.0. You can get the original 'naturalearth_lowres' data from https://www.naturalearthdata.com/downloads/110m-cultural-vectors/."
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Percobaan visualisasi data penduduk kota Tangsel, wilayah dan kepadatan perkecamatan\n",
        "\n",
        "# Import library yang diperlukan\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Data jumlah penduduk dan luas wilayah di setiap kecamatan Tangsel (data random)\n",
        "data = {\n",
        "    'kecamatan': ['Ciputat', 'Ciputat Timur', 'Pamulang', 'Serpong', 'Serpong Utara', 'Setu'],\n",
        "    'penduduk': [150000, 200000, 250000, 100000, 100000, 50000],\n",
        "    'luas': [15, 20, 25, 10, 10, 5]\n",
        "}\n",
        "\n",
        "# Membuat DataFrame dari data\n",
        "df = pd.DataFrame(data)\n",
        "\n",
        "# Menghitung kepadatan penduduk\n",
        "df['kepadatan'] = df['penduduk'] / df['luas']\n",
        "\n",
        "# Mengatur style untuk plot\n",
        "sns.set(style=\"whitegrid\")\n",
        "\n",
        "# Membuat plot bar untuk jumlah penduduk\n",
        "plt.figure(figsize=(10, 6))\n",
        "bar_plot = sns.barplot(x='kecamatan', y='penduduk', data=df, palette=\"Blues_d\")\n",
        "\n",
        "# Menambahkan judul dan label\n",
        "plt.title('Jumlah Penduduk di Setiap Kecamatan Tangsel', fontsize=14)\n",
        "plt.xlabel('Kecamatan', fontsize=12)\n",
        "plt.ylabel('Jumlah Penduduk', fontsize=12)\n",
        "\n",
        "# Menampilkan nilai di atas batang\n",
        "for p in bar_plot.patches:\n",
        "    bar_plot.annotate(f'{int(p.get_height()):,}',\n",
        "                      (p.get_x() + p.get_width() / 2., p.get_height()),\n",
        "                      ha='center', va='center',\n",
        "                      xytext=(0, 5),\n",
        "                      textcoords='offset points')\n",
        "\n",
        "# Menampilkan plot\n",
        "plt.tight_layout()\n",
        "plt.show()\n",
        "\n",
        "# Membuat plot bar untuk kepadatan penduduk\n",
        "plt.figure(figsize=(10, 6))\n",
        "density_plot = sns.barplot(x='kecamatan', y='kepadatan', data=df, palette=\"Greens_d\")\n",
        "\n",
        "# Menambahkan judul dan label\n",
        "plt.title('Kepadatan Penduduk di Setiap Kecamatan Tangsel', fontsize=14)\n",
        "plt.xlabel('Kecamatan', fontsize=12)\n",
        "plt.ylabel('Kepadatan Penduduk (jiwa/km²)', fontsize=12)\n",
        "\n",
        "# Menampilkan nilai di atas batang\n",
        "for p in density_plot.patches:\n",
        "    density_plot.annotate(f'{int(p.get_height()):,}',\n",
        "                          (p.get_x() + p.get_width() / 2., p.get_height()),\n",
        "                          ha='center', va='center',\n",
        "                          xytext=(0, 5),\n",
        "                          textcoords='offset points')\n",
        "\n",
        "# Menampilkan plot\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "dwDA8Wz8J7nL",
        "outputId": "ebae637e-b2ed-4ff5-fba4-8fabb1aca495"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-6-90be8c699d1e>:24: FutureWarning: \n",
            "\n",
            "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
            "\n",
            "  bar_plot = sns.barplot(x='kecamatan', y='penduduk', data=df, palette=\"Blues_d\")\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-6-90be8c699d1e>:45: FutureWarning: \n",
            "\n",
            "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
            "\n",
            "  density_plot = sns.barplot(x='kecamatan', y='kepadatan', data=df, palette=\"Greens_d\")\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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vXj31799fBw4csGl/9epVrV69WjVq1FDLli1t5uXOnVtBQUG6evXqA8dpcuXLly/JsxQSzmbZvn17kst1797d5mioq6ur3n33XRmGYf0sk+PMmTNau3atChUqpEmTJj3wJmzz5s1T1qxZNWTIEJsj6s7OztajrCtXrrRO/+6775QpUyYNHTpUWbJksekrS5Ys1jMKpLvX0t+/37RYLGrbtq2kB38G9x7llKSmTZtKunuqcu/evW32swnz/v77b5s+nqZ92sO4urrafOYJatSooVKlSj2wtvbt26tEiRLW11myZFHTpk0VHx9vPR1fkn766SdJd39+7x1DefPmfejP7/1jQ5JNnU/q5xDAXZwiDiBdKFWqlCIiIrRnzx5NnDjR5vTQBCdOnFB4eLjy5s2riRMnJpqfcL1vUo/z8vPzSzTN19dXjo6Oia6tPnz4sKZNm6Y///xT//33X6LrF69du2b9Q+6ff/5R1qxZk7zu1M/PL8nrKq9cuaKQkBBt2bJF586dS3QdXkpvErVu3TotXLhQBw8eVEREhO7cufPIvpKqN+GLhfvvpvsw169ft26LTJkyKVeuXHr++efVuXNn62e+d+9eSdJvv/2W5B/5jo6OSf4BnFSNCV+SREREWKcl/LFfpUqVRO0TtnFq2bOd7dWrVy9t2LBB77zzTopPbf3nn3+sNd0vqWn2ePnll9WwYUNt3bpVf/75pw4ePKg9e/Zo6dKl+vHHHzV48GC1adNG0t0vQu7cuaOYmJgkT1FOuJ43LCxMAQEBdtdk/P/rlJctW6ajR48qMjLS5trbB/0cPOxzSsnjxvLmzascOXLo6NGjGjZsmIYPH24TTCUpOjpaR44cUd68eRUaGpqoj7i4OEn/t/+6ceOGjh8/rmLFiql48eKPrCEmJkbz58/XypUrFRYWpps3b9pcg5vUZ5AjR45EN7nz8PCQJBUvXjzRFwUJ8+7v62napz3Kjh07NHv2bO3bt0/Xrl2zbjdJSV6G8KDaUrovq1y5cqJpAQEBGjdunD799FNt375dderUUbVq1RJ94fGkfg4B3EXABpAu5M+fX99++63at2+vCRMmKD4+XsHBwTZtrl+/Lkk6evSojh49+sC+knrkUO7cuRNNy5Qpk3LmzGnzx9fu3bvVoUMHSVLt2rVVvHhxZc2aVRaLRevXr9fff/+tmJgYa/vIyEibI+OPWuf169f1+uuv69y5c6pcubJq1aolV1dXZcqUyfqIq3v7f5QZM2boiy++kLu7u2rXrq38+fNbj2bMnj07yZsbSXeP+N8vIYje+8fso3h6emrNmjUPbZNwo7EpU6Yku18p6RoTjtDdG54Stt/DtnFqpXQ7p8auXbuUOXNm1atXL8XLRkZGysHBIdGZApK5dWbOnNl6kyzp7iPapk+frm+++Uaff/65XnjhBXl4eFi3/e7dux/6HOjkPibsQYYPH6558+apQIECatCggTw8PKxHtCdOnPjAn6k8efI8cFpUVFSy158tWzbNmTNHHTp00OLFixUfH6/PP/9cDg7/d6JgRESEDMPQxYsXk/yCMMHNmzdt1p/UGTVJCQ4O1qZNm1S8eHEFBgYqd+7ccnR0VEREhObMmZPkZ/Cw/cDDfv7uDZVP2z7tYVavXq0+ffooa9as8vf3V6FCheTi4iKLxWK9d0Bya0tqXxYVFZWin9/ChQtr4cKFmjhxojZv3qzVq1dLkkqUKKHg4GDrUxye1M8hgLsI2ADSjWLFimnevHlq3769Jk2apDt37tgcwUv4I6VRo0Y2NyZLjitXrticoifd/aPr+vXrNn+4TJkyxXok6P6jWwl3UL6Xq6urrl279sB13m/x4sU6d+6c3nvvPXXv3t1mXkhISJJ3Qn+QuLg4ffvtt/Lw8NDy5ctt3odhGJo2bVqy+3qcErbbn3/+meQfmqmVcIpvUp93wja+P6QkHF2Mi4tLdIQ7qaNdKd3OCcHq3iDysP7vNWvWLHXq1EldunTRtGnTkjxy9SCurq6Kj4/XtWvXEt19P6k6zZI5c2Z1795d27Zt086dO7V79241atTIur07d+5sc/MuM125ckXz58+Xt7e3Fi5caHPU9fLlyw8Ns//991+iI7j//fefpKRD0cO4u7tr9uzZ6tSpk5YuXar4+HiNHDnSOhYSTnsuV66cli5d+sj+EtZ/8eLFR7bdt2+fNm3aJH9/f4WEhNicKv7XX39pzpw5KXovKfEs7dMmTpyozJkza+nSpYnOKrj31H57Zc+ePcU/v15eXho/frxiY2N18OBBbdmyRXPnzlWfPn2UN29eValS5Yn8HAL4P1yDDSBdKVKkiObMmaNChQppypQpNndYLVmypLJnz64DBw488CjGgyT1GJI9e/YoLi5OZcuWtU47deqUcubMmShcR0dHJ3nKqLe3t27evGlzHd3D1pnwKJeER9U8qn3CH8pJHYG5du2aIiMj5evrm+joxv79+5N8BExaSLjresKp4mYrXbq0pLsB/n4J2/h+OXLkkJQ4vMTHxye6vlRK+XZOuHt0UuHoUacely1bVrNnz5aTk5O6dOmS5Pt6kITHbSVV05N4FM+9131LUoUKFWSxWFJ0B/OEQJrco46nT5+WYRiqVatWolOaH/WeH/Y53btfSK6EkF22bFn9+OOP+vjjj63vI3v27CpZsqTCwsJsTgt+kGzZsqlUqVI6c+ZMko/Gutfp06clSfXr1090Hfbj3u7P0j7t1KlTKlmyZKJwfenSpQc+PjAlEvZlSR1lftTPkJOTkypVqqTg4GB98sknMgxDv/76qyT7fg4B2I+ADSDdKVKkiObOnavChQsrJCREo0ePlnT3dL82bdro7Nmz+uKLL5IM2UeOHEnym/45c+bowoUL1tcxMTH6+uuvJUnNmze3Ti9UqJDCw8NtTkG/c+eOvvjiC5tnOidIuMHVV199ZfMH4z///KPly5cnap/wCJf7Q9PPP/+szZs3J2rv5uYmi8ViU3uC3LlzK0uWLDp48KDNqX3h4eEaPnx4ovZp5c0335Sjo6M+++wzm+f2JoiIiEjR9a73e/7555U9e3YtWbLE5lru2NhYffPNN0kuU6FCBUm2N7iTpJkzZyb5h3JKt3P27Nnl6emp3bt3699//7VOj4qK0rhx4x75nkqXLq3Zs2fL2dlZXbp0SXZISqhz0qRJ1lONpbtB34yjmCtXrtT27duTfE70X3/9pR07dsjR0VGVKlWSdPea3SZNmmjPnj2aNm1aksvt3bvXZvwmfPlx/vz5ZNWUcAR6z549NqfbXrhw4ZGf9bfffmtzRkFkZKQmT54si8WiV199NVnrv1/OnDk1a9YslStXTj///LM++ugj65hp166doqOjNXDgQJvtk+D06dM24+/NN9/UnTt3NGzYsETh8vbt29bLZhI+g/v3K0ePHlVISIhd7yO5nqV9WsGCBfXvv/9az3KQ7m6HoUOHpvhL36S8/PLLku7+/N67vS9fvpzkz++BAweSvJQh4Xdgws0R7fk5BGA/ThEHkC4VKlTIerr49OnTFR8fr379+ik4OFiHDh3S3LlztXnzZvn5+Sl37ty6ePGijhw5or///lsLFy5MdPSjYsWKatasmZo0aSIXFxdt2rRJJ06cUMOGDdWoUSNru7feeku///673nzzTTVp0kTOzs76448/dPHiRVWrVk1//PGHTb/NmzfXihUr9Ntvv+nVV19V3bp1FR4erpUrV8rf31+bNm2yad+sWTOFhoZq+PDh2rFjhwoWLKh//vlH27dvV8OGDbVu3Tqb9tmyZVOFChW0c+dOffTRRypWrJgcHBzUrFkzFSpUSG+++aZmzJihZs2aKSAgQFFRUdqyZYsKFSpkc0fdtOTl5aUhQ4Zo6NChaty4serVq6ciRYroxo0bOnPmjP744w81b95cn376qV39u7q6auDAgerXr59ef/1167OCf/31V2XJksV6Y6Z7vfbaa5o2bZomTJigw4cPq2jRojpw4ICOHDliynaW7p6OOWjQILVq1UqNGzdWfHy8fvvtN2u4f5SEkN2xY0e9/fbbCg0NfeSNymrUqKHXXntNS5cu1csvv6wXX3xRMTExWrVqlSpVqpRknSmRcLpxvnz5VLVqVRUoUECxsbE6fvy4tm7dqvj4eH3wwQc2p+QPGTJEJ06c0Jdffqnly5fL19dXrq6uunDhgg4cOKCTJ0/q999/tx59rlGjhtauXavg4GDVqVNHmTNnVunSpa3Xe98vb968atSokdauXasWLVqoRo0aunLlin799VfVqFHDeoQ1KQnPor73OdgXLlxQp06dkr2dkpIjRw7NmjVLQUFBWrlypeLj4zVmzBi1bt1ae/fu1bJly7R7927VqlVLefPm1ZUrVxQWFqa9e/dq7NixKly4sKS7AXvnzp1avXq1GjZsqAYNGih79uw6f/68fv/9d+v17j4+PvLx8dHq1at1+fJlVaxYUefPn9fGjRtVr149rV271u738ijP0j6tXbt2+uyzz/Tqq6+qcePGiouL07Zt22QYhkqXLp3k2S8pUatWLTVt2lQrVqzQyy+/rBdeeEExMTFavXq1fHx8tGnTJpub5y1fvlwLFy5U1apVVaRIEWXPnl3Hjh3Tli1blDNnTr322mvWtin9OQRgPwI2gHSrQIEC1pA9c+ZM6+OLQkNDtXjxYv34449at26dYmJilCdPHpUsWVKtW7eWl5dXor4++eQTrV692nq9YN68edWrVy917drVpl1AQIDGjx+vqVOn6qefflKWLFlUo0YNTZo0SZMmTUrUr4ODg7799ltNnDhRP//8s+bMmaOiRYuqf//+KlasWKJAkz9/fs2bN09ffvmltm/frri4OJUrV04zZszQ+fPnE/0xKkmjR4/WyJEj9euvvyoyMlKGYahKlSoqVKiQ3n//feXIkUPLli3Td999pzx58qhp06bq2bOn9WhIevDGG2+odOnSmjVrlnbu3KlNmzYpe/bsKliwoDp27Gj30cIEzZs3l6urq7799lstW7ZMrq6uatCggT766CObMxQS5MmTR3PmzNGoUaO0detW/e9//1P16tW1aNEiTZ48OVH7lG7nhPccGxurOXPmaPHixfLw8FDz5s317rvvJjvweHt724TskJAQVa1a9aHLDB8+XJ6enlq0aJHmzZun/Pnzq1OnTmrSpEmqA3bnzp1VrFgx/f7779q/f782btyo2NhYeXh4qGHDhmrdurVq1qxps0zOnDn1/fffa968eVq1apV+/vlnxcfHK0+ePCpdurTeffddm5s6vfHGGzp79qxWrVqladOmKS4uTs2bN39gwJakkSNHqlChQlq7dq3mzZtnHVdvv/32Qz/rb775RuPHj9fKlSv133//qXDhwho4cKD18V6p4ebmppkzZyooKEirV69WfHy8xo4dq1GjRqlu3br64Ycf9Ouvv+rmzZtyd3dXsWLF1LdvX5vPz2Kx6KuvvlLt2rW1ePFiLV++XIZhKF++fGrcuLH17tSZMmXS1KlTNWbMGP3222/av3+/ihUrpo8//lh169Z9rAH7WdqntW3bVo6Ojpo3b54WLVokNzc31atXTx988IHee+89U9bxxRdfqGTJklqyZInmzp2r/Pnzq0OHDqpZs6Z1v5mgadOmun37tvbs2aN9+/YpJiZG+fPnV5s2bRQUFGRzf4GU/hwCsJ/FSOo8EQB4SvTr10/Lli3Thg0brEeFADzb2rVrpz/++MP6WDMgvfvhhx80cOBADRkyRG+++WZalwPgIbgGGwAAAEgHLl++nOga6YsXL2ry5MnKlCkTz6kGMgBOEQcAAADSgZCQEG3evFlVqlRR7ty5df78eW3atEk3btxQr169VKBAgbQuEcAjELABAACAdKBOnTo6fvy4Nm/erIiICDk7O8vb21tvvvlmml+DDiB5uAYbAAAAAAATcA02AAAAAAAm4BTxNLJnzx4ZhiEnJ6e0LgUAAAAA8ACxsbGyWCzy9fV9ZFsCdhoxDCPRXSIBAAAAAOlLSnIbATuNJBy5rlChQhpXAgAAAAB4kP379ye7LddgAwAAAABgAgI2AAAAAAAmIGADAAAAAGACAjYAAAAAACYgYAMAAAAAYAICNgAAAAAAJiBgAwAAAABgAgI2AAAAAAAmIGAjxW7cuKHx48crKChI1apVk7e3t5YuXZpk2+PHjysoKEi+vr6qVq2aPvroI129ejXZ69q9e7fatGmjihUrqnbt2ho+fLhu3LiRqF1MTIy+/PJL+fv7y8fHRy1bttTWrVtT1SceP8YSzMA4glkYSzAD4whmYSxlTARspNi1a9c0adIkhYWFydvb+4HtLly4oLZt2+rUqVPq06ePOnfurM2bN6tTp06KiYl55HoOHz6sjh076tatW+rXr59ef/11LVy4UO+9916itv369dOsWbP08ssv65NPPlGmTJnUtWtX7dq1y+4+8fgxlmAGxhHMwliCGRhHMAtjKYMykCb27dtn7Nu3L63LsMvt27eNS5cuGYZx9314eXkZS5YsSdRuyJAhho+Pj3H27FnrtK1btxpeXl7G999//8j1dOnSxahdu7YRGRlpnbZo0SLDy8vL+O2336zT9u7da3h5eRnTpk2zTrt165bxwgsvGK1atbKrTzwZjCWYgXEEszCWYAbGEczCWEo/UpLdOIKNFHN2dpaHh8cj261bt07169dXwYIFrdNq1aql4sWLa/Xq1Q9dNioqStu2bdMrr7yi7NmzW6c3a9ZMWbNmtVl+zZo1ypQpk1q1amWdljlzZr3++uvas2ePzp8/n+I+8WQwlmAGxhHMwliCGRhHMAtjKWMiYOOxuHjxoq5cuaLy5csnmufj46PDhw8/dPl//vlHcXFxiZZ3dnZWmTJlbJY/fPiwihcvbvMDnLCehPkp7RPpB2MJZmAcwSyMJZiBcQSzMJbSHwI2HotLly5JUpLfunl4eOj69esPvSbk8uXLkqS8efMmuXxC/wltH7See2tJSZ9IPxhLMAPjCGZhLMEMjCOYhbGU/hCw8Vjcvn1b0t1vqu6XOXNmSdKtW7ceuHzCvActf++yt27dStZ6UtIn0g/GEszAOIJZGEswA+MIZmEspT8EbDwWCT9oSX1jlrAjyJIlywOXT5j3oOXvXTZLlizJWk9K+kT6wViCGRhHMAtjCWZgHMEsjKX0h4CNxyLhlJCEU0TudfnyZeXMmTPJb7US3H+qyf3L33vKiYeHxwPXc28tKekT6QdjCWZgHMEsjCWYgXEEszCW0p90FbD//fdfDR48WM2aNVPZsmXVtGnTJNv98MMPatSokSpUqKBXXnlFmzZtStQmMjJSAwYMULVq1eTr66vg4OAkN/Lu3bvVqlUr+fj4KCAgQCEhITIMw6aNYRgKCQlR/fr15ePjo1atWumvv/4y5T0/rfLlyyd3d3cdOHAg0bx9+/apdOnSD13ey8tLjo6OiZaPiYnR4cOHbZYvXbq0Tp48qaioKJu2e/fulSSVKVMmxX0i/WAswQyMI5iFsQQzMI5gFsZS+pOuAvbRo0e1efNmFStWTCVLlkyyzcqVKzVo0CA1adJEoaGhqlSpknr27Jko8Pbu3Vtbt27V0KFDNWbMGJ04cUJvv/224uLirG3+/fdfBQUFycPDQ1OnTlWHDh00fvx4zZgxw6av0NBQjR8/Xh07dtTUqVPl4eGhzp076/Tp06Z/Bk+Thg0b6tdff7Xesl+Stm/frpMnT6px48YPXdbV1VU1a9bUTz/9ZPNDvHz5ct28edNm+caNG+vOnTtauHChdVpMTIyWLl2qihUrqkCBAinuE+kLYwlmYBzBLIwlmIFxBLMwltIXi3H/4do0FB8fLweHu5m/X79+OnDggFasWGHTplGjRipfvrzGjh1rnda6dWu5uroqNDRUkrRnzx61bt1a06dPl7+/vyQpLCxMgYGBGjdunAIDAyVJgwcP1u+//641a9ZYT50YN26cFixYoK1bt8rZ2Vm3b99WrVq11LZtW73//vuS7g6kxo0bq27duho6dKhd73X//v2SpAoVKti1fFqbN2+eIiIidOnSJS1YsEANGza0fmvVrl07ubq66vz583r11Vfl5uam9u3b6+bNm5o+fbry5cunJUuW2Jyu0qBBA0nSxo0brdMOHjyo1q1bq1SpUnrjjTd04cIFzZw5U1WrVtX06dNt6nnvvfe0fv16dejQQcWKFdOyZcu0f/9+zZo1S1WrVrWrTzwZjCWYgXEEszCWYAbGEczCWEofUpTdjHSqb9++xksvvWQz7dSpU4aXl5fxyy+/2EyfPXu2Ua5cOeP27duGYRjG119/bfj5+Rnx8fE27V599VWjb9++1tf16tUzhg8fbtPm8OHDhpeXl/G///3PMAzD2LZtm+Hl5WUcOnTIpt2IESOMgIAAu9/fvn37jH379tm9fFoLCAgwvLy8kvx3+vRpa7sjR44YnTt3NipWrGj4+fkZH3zwgXH58uVE/VWvXt144403Ek3fuXOn0apVK6NChQpGjRo1jGHDhhmRkZGJ2t26dcsYNWqUUbt2baN8+fJGixYtjC1btiRZe3L7xJPBWIIZGEcwC2MJZmAcwSyMpfQhJdktXR3BvldSR7A3b96srl27atWqVTankG/dulWdO3e2Tn/vvfd0/vx5LVq0yKbPDz74QKdPn9aiRYt08+ZN+fr6aujQoWrTpo21TUxMjHx8fDRkyBC1adNG8+fP16effqp9+/ZZ79InSYsWLdLgwYP1119/2XUnvP3798swDJUqVSrFy0qSxWKxa7n06Pjx42rRooXGjx+vunXrpnU5aepJ/zg+TeNIYiwlSIvd+tM0lhhH/4d9Uuowlu5in5Q6jKP/wz4pdRhLd9kzjo4dOyaLxZKsI9iO9hSVVsLDwyVJbm5uNtMTXifMj4iIkKura6Llc+TIYb3YPjIyMsm+nJ2d5eLiYtOXs7OzTbhOWM4wDIWHh9t9q/nY2FgdPnw4xcs5OTmpXLlyypQpk13rTW/27t0rX19fNWrUKK1LSVN37tzRwYMHFRsb+0TW5+TkpLLlysoxU4baDTwUY0mKuxOnQwcPPbFxJD19Y4lxdNeTHktP2ziSGEtSGu6TypaVo+PTMZYYR3fFxcXp0KEnvE96isaRxFiSUjeOHnY39ns9PSMmA3JycrLrCLbFYlGmTJn0y5ENuhp97TFU9mQ5ls+s5uVf18K9i9O6lDTj7pJLL3o9r+eee+6JfTtrsVjkmMlR3/25RJei/nsi63zsCks13g3Q15unpnUlaSJv9jx6s0qLJzqOpP8bS9/8OkNnw88/eoH0LrtUtE0Ffbz887SuJM0UylFA79XvnCb7pBHLx+vUf2efyDqfhJwvFtM70/umdRlpomieQhrQLDht9kmOjvps1pf698LTcUPa7JXyqsuo4LQuI80Uy19Egzp+9OT3SY6OGjLuM508/e8TWeeTkLlAdnXo0yWty0gTxYsU07D3B9k1jo4dO5bsthkqYOfIkUPS3aPPCc9Xk+4eZb53vpubmy5cuJBo+fDwcGubhCPcCUeyE8TExCg6Otqmr5iYGN2+fdvmKHZERIQsFou1nT0sFouyZs1q9/JXo6/pvxtPSTCCJMnFxeWJr/NS1H9PRyiCVVqMI0k6G35eJ648HX/M4q60GEun/juroxdPPPH14vFJq33SvxdO68jp42mybjweaTGWTp7+V0fCjj7x9eLxsWccpeRygXT1mK5HKVGihKS7dwS/V1hYmJycnFSkSBFruxMnTiT6ZuLEiRPWPrJmzaoCBQok6ithuYR2Cf89ccL2l31YWJgKFixo9+nhAAAAAICnS4YK2EWKFFHx4sW1Zs0am+mrVq1SzZo1refF161bV+Hh4dq+fbu1zYkTJ3To0CGbC/rr1q2rDRs22JyDv2rVKrm5ucnX11eSVLlyZWXPnl2rV6+2tomNjdW6deue6ZsDAAAAAABspatTxKOjo7V582ZJ0tmzZxUVFWUN09WqVZO7u7t69eqlDz/8UEWLFlX16tW1atUq7du3T/PmzbP24+vrK39/fw0YMEB9+/ZV5syZ9dVXX8nb21sNGza0tgsKCtLPP/+sDz74QG3atNGRI0c0ffp09enTxxrWM2fOrG7dumnChAlyd3eXl5eXFixYoOvXrysoKOgJfjoAAAAAgPQsXQXsK1eu6L333rOZlvB6zpw5ql69upo2baro6GiFhoYqJCREnp6emjhxovWIc4Kvv/5aI0eO1ODBgxUXFyd/f38NHDjQ5k6AxYoV0/Tp0zVq1Ch17dpV7u7uCg4OVufOnW36evvtt2UYhmbMmKGrV6+qTJkymj59uvWUdAAAAAAA0lXALly4sP75559HtmvZsqVatmz50Daurq4aMWKERowY8dB2lStXTvS87PtZLBZ169ZN3bp1e2RtAAAAAIBnU4a6BhsAAAAAgPSKgA0AAAAAgAkI2AAAAAAAmICADQAAAACACQjYAAAAAACYgIANAAAAAIAJCNgAAAAAAJiAgA0AAAAAgAkI2AAAAAAAmICADQAAAACACQjYAAAAAACYgIANAAAAAIAJCNgAAAAAAJiAgA0AAAAAgAkI2AAAAAAAmICADQAAAACACQjYAAAAAACYgIANAAAAAIAJCNgAAAAAAJiAgA0AAAAAgAkI2AAAAAAAmICADQAAAACACQjYAAAAAACYgIANAAAAAIAJCNgAAAAAAJiAgA0AAAAAgAkI2AAAAAAAmICADQAAAACACQjYAAAAAACYgIANAAAAAIAJCNgAAAAAAJiAgA0AAAAAgAkI2AAAAAAAmICADQAAAACACQjYAAAAAACYgIANAAAAAIAJCNgAAAAAAJiAgA0AAAAAgAkI2AAAAAAAmICADQAAAACACQjYAAAAAACYgIANAAAAAIAJCNgAAAAAAJiAgA0AAAAAgAkI2AAAAAAAmICADQAAAACACQjYAAAAAACYgIANAAAAAIAJCNgAAAAAAJiAgA0AAAAAgAkI2AAAAAAAmICADQAAAACACQjYAAAAAACYgIANAAAAAIAJCNgAAAAAAJiAgA0AAAAAgAkI2AAAAAAAmICADQAAAACACQjYAAAAAACYgIANAAAAAIAJCNgAAAAAAJiAgA0AAAAAgAkI2AAAAAAAmICADQAAAACACQjYAAAAAACYgIANAAAAAIAJCNgAAAAAAJiAgA0AAAAAgAkI2AAAAAAAmICADQAAAACACQjYAAAAAACYgIANAAAAAIAJCNgAAAAAAJiAgA0AAAAAgAkI2AAAAAAAmICADQAAAACACQjYAAAAAACYgIANAAAAAIAJCNgAAAAAAJiAgA0AAAAAgAkyZMDesGGDWrZsKV9fX/n7++u9997T6dOnE7X74Ycf1KhRI1WoUEGvvPKKNm3alKhNZGSkBgwYoGrVqsnX11fBwcG6dOlSona7d+9Wq1at5OPjo4CAAIWEhMgwjMfy/gAAAAAAGU+GC9g7duxQz549VapUKU2aNEkDBgzQ33//rc6dO+vWrVvWditXrtSgQYPUpEkThYaGqlKlSurZs6f++usvm/569+6trVu3aujQoRozZoxOnDiht99+W3FxcdY2//77r4KCguTh4aGpU6eqQ4cOGj9+vGbMmPGk3jYAAAAAIJ1zTOsCUmrlypUqWLCgRowYIYvFIklyd3dXhw4ddODAAfn5+UmSxo8fr5deekm9e/eWJNWoUUNHjhzRpEmTFBoaKknas2ePfv/9d02fPl3+/v6SJE9PTwUGBmrdunUKDAyUJE2fPl25cuXSuHHj5OzsrJo1a+rq1auaMmWK2rVrJ2dn5yf8KQAAAAAA0psUB+wzZ85ow4YN2r17t44fP65r167JYrEoV65cKlGihCpXrqwGDRqoSJEij6NexcXFKVu2bNZwLUmurq6SZD1l+/Tp0zp58qQ++ugjm2UDAwM1evRoxcTEyNnZWVu2bJGbm5tq165tbVOiRAmVKVNGW7ZssQbsLVu26MUXX7QJ0oGBgZo6dar27Nmj6tWrP5b3CgAAAADIOJIdsDdt2qQZM2bozz//lGEYKlq0qAoXLiwvLy8ZhqGIiAj9/fffWrdunUaNGqUqVaooKChIAQEBphb82muvafny5Zo/f75eeeUVXb9+XePGjVPZsmVVuXJlSVJYWJiku0ej71WyZEnFxsbq9OnTKlmypMLCwuTp6WkT1qW7ITuhj5s3b+r8+fMqUaJEojYWi0VhYWF2B2zDMHTz5s0UL2exWOTi4mLXOpG+RUdHP7Fr+xlHT68nOY4kxtLTjH0SzMA+CWZhnwQz2DOODMNIlBkfJFkB+4033tDff/+t559/Xl9//bVq1aql7NmzJ9k2KipKW7du1dq1a9W7d2+VLl1aCxcuTH71j+Dn56eJEyfqgw8+0KeffipJKlOmjKZNm6ZMmTJJksLDwyVJbm5uNssmvE6YHxERYT36fa8cOXLowIEDku7eBC2pvpydneXi4mLtyx6xsbE6fPhwipdzcXFR2bJl7V4v0q8TJ04oOjr6iayLcfT0epLjSGIsPc3YJ8EM7JNgFvZJMIO94yi5lwUnK2BXr15d3377rfLkyfPIttmzZ1ejRo3UqFEjXb58WXPmzElWIcm1e/duffzxx3rjjTdUv359Xb9+Xd9++626du2q7777TlmyZDF1fY+Tk5OTSpUqleLlkvvtCTIeT0/PJ/rNLJ5OT3IcSYylpxn7JJiBfRLMwj4JZrBnHB07dizZbZMVsD/44IMUFZDAw8PD7mUfZPjw4apRo4b69etnnVapUiXVr19fy5cvV6tWrZQjRw5Jd48+e3h4WNtFRERIknW+m5ubLly4kGgd4eHh1jYJR7gTjmQniImJUXR0tLWdPSwWi7JmzWr38nj6cCoSzMA4glkYSzAD4whmYSzBDPaMo5R84ZLhHtN1/PhxlS5d2mZa/vz5lStXLp06dUqSrNdLJ1xHnSAsLExOTk7WG7CVKFFCJ06cSPQNxokTJ6x9ZM2aVQUKFEjUV8Jy91+bDQAAAAB4NiU7YP/3338aMGCABg4cqPPnz1unDxky5LEU9iAFCxbUoUOHbKadPXtW165dU6FChSRJRYoUUfHixbVmzRqbdqtWrVLNmjWt58/XrVtX4eHh2r59u7XNiRMndOjQIdWtW9c6rW7dutqwYYNiY2Nt+nJzc5Ovr6/p7xEAAAAAkPEkO2APHDhQERERunr1ql599VXt3r1bUuKjxI9b69attX79eg0fPlzbtm3TqlWr9M477yh37txq0qSJtV2vXr20YsUKjR8/Xjt27NCQIUO0b98+de/e3drG19dX/v7+GjBggFavXq2NGzcqODhY3t7eatiwobVdUFCQrl69qg8++EDbt2/X7NmzNX36dL3zzjs8AxsAAAAAICkFj+m6ePGili1bJklatmyZ3n33XYWEhDzxGwC0b99ezs7OWrBggZYsWaJs2bKpUqVK+vrrr5UrVy5ru6ZNmyo6OlqhoaEKCQmRp6enJk6cmOiI89dff62RI0dq8ODBiouLk7+/vwYOHChHx//7aIoVK6bp06dr1KhR6tq1q9zd3RUcHKzOnTs/sfcNAAAAAEjfkh2w730sV/PmzZUrVy716NHjid9swGKxqE2bNmrTps0j27Zs2VItW7Z8aBtXV1eNGDFCI0aMeGi7ypUra9GiRSmqFQAAAADw7Ej2KeLOzs7Wm4hJUv369TV8+PAk78INAAAAAMCzJtlHsKdNm5bodPD69evrjz/+ML0oAAAAAAAymmQfwX7QtdY8jw4AAAAAgBQcwb7f2bNntWzZMp05c0bh4eGJniVtsVg0efLkVBcIAAAAAEBGYFfAXrFihfr166e4uDi5ubnZ3AAtwZO+uzgAAAAAAGnJroA9btw4eXp6avz48fL09DS7JgAAAAAAMpxkX4N9r2vXrql169aEawAAAAAA/j+7AraPj4/Onz9vdi0AAAAAAGRYdgXsAQMG6KefftKaNWvMrgcAAAAAgAzJrmuwvb291adPH73//vv65JNPlD9/fjk42GZ1i8Win376yZQiAQAAAABI7+wK2PPnz9fw4cOVOXNmFS1aNMm7iAMAAAAA8CyxK2BPnTpVvr6+mjp1qlxdXc2uCQAAAACADMeua7AjIyP18ssvE64BAAAAAPj/7ArY1apV05EjR8yuBQAAAACADMuugD106FDt3LlToaGhunbtmtk1AQAAAACQ4dh1DXZgYKAMw9C4ceM0btw4Zc6cOcm7iP/555+mFAkAAAAAQHpnV8Bu1KiRLBaL2bUAAAAAAJBh2RWwR40aZXYdAAAAAABkaHZdg33y5MlHttm4caM9XQMAAAAAkCHZFbA7duyoM2fOPHD+Tz/9pODgYLuLAgAAAAAgo7ErYOfJk0cdOnTQhQsXEs37/vvv1bdvXzVq1CjVxQEAAAAAkFHYFbBnzJghV1dXdejQQZcvX7ZODw0N1dChQ9WyZUuNGTPGtCIBAAAAAEjv7ArYbm5umjFjhpycnNShQwdduXJF48aN09ixY9W5c2d9+umn3GUcAAAAAPBMsesu4pLk7u6umTNn6q233lJgYKAiIiIUHBys7t27m1kfAAAAAAAZQrIC9sGDBx8476OPPtLHH3+sV199VfXq1bNpW65cudRXCAAAAABABpCsgN2iRYuHnvJtGIaWLVumH3/80fraYrHo8OHDphQJAAAAAEB6l6yAPXLkyMddBwAAAAAAGVqyAnbz5s11584dZcqU6XHXAwAAAABAhpTsm5xVr15dderUUb169VS3bl25u7s/zroAAAAAAMhQkh2w33vvPW3evFlDhgxRbGysypUrp3r16ql+/foqX77846wRAAAAAIB0L9kBu127dmrXrp1u3bqlbdu2acuWLVq6dKkmTpyoPHnyqE6dOgoICFCtWrWUPXv2x1kzAAAAAADpToqfg50lSxY1aNBADRo0kCQdOXJEv/76q3777Te9//77kqQqVaqoXr16qlevnkqWLGluxQAAAAAApEMOqe3Ay8tLXbt21dy5c7V9+3aNHj1aBQoU0PTp09W0aVOFhISYUScAAAAAAOlaio9gP4yrq6sCAwMVGBgoSdq3b5+Z3QMAAAAAkG6ZGrDv5+Pj8zi7BwAAAAAg3bA7YG/evFmzZs3SoUOHFBkZKcMwErU5fPhwqooDAAAAACCjsOsa7LVr1+qdd97Rf//9p8DAQMXHx+ull15SYGCgsmTJIm9vb/Xo0cPsWgEAAAAASLfsOoI9depU+fj46LvvvlN4eLgWLFigFi1aqGbNmjpz5oxatWqlwoULm10rAAAAAADpll1HsI8fP67AwEBlypRJjo53M3pcXJwkqXDhwmrTpo1CQ0PNqxIAAAAAgHTOroCdJUsWOTk5SZLc3Nzk7Oysy5cvW+fnyZNHZ86cMadCAAAAAAAyALsCtqenp44fP259XaZMGS1fvlxxcXG6ffu2VqxYoQIFCphWJAAAAAAA6Z1dAfvFF1/Uhg0bFBMTI0l655139Mcff6hq1aqqUaOGdu3apa5du5paKAAAAAAA6ZldNzkLCgpSUFCQ9XVAQIDmzp2rdevWKVOmTKpXr55q1KhhWpEAAAAAAKR3dj8H+35+fn7y8/MzqzsAAAAAADIUu04RHzt2rLZs2aKoqCiz6wEAAAAAIEOy6wj2vHnzNG3aNDk4OOi5556Tn5+fqlSpoqpVqypPnjxm1wgAAAAAQLpnV8D+888/dejQIe3atUt//vmnVq1apXnz5slisaho0aLWsN28eXOz6wUAAAAAIF2yK2A7ODiofPnyKl++vDp27ChJCgsL0/bt2zV37lwtXbpUy5YtI2ADAAAAAJ4ZqbrJWXR0tP766y/t2rVLu3bt0t69e3Xr1i15enpywzMAAAAAwDPFroD9xRdfaNeuXTp8+LDi4+NVunRpValSRW3btpWfn5/c3d3NrhMAAAAAgHTNroA9c+ZMZcqUSQ0bNlTXrl1VpkwZs+sCAAAAACBDsStgf/zxx/rzzz+1fft2rVmzRh4eHtY7ifv5+cnb29vsOgEAAAAASNfsCtidO3dW586dJUnHjh2z3k18+vTpGj58uFxdXVW5cmVNmTLF1GIBAAAAAEivUnWTM0kqVaqUihYtKk9PTxUrVkwrV67UiRMntHnzZjPqAwAAAAAgQ7ArYEdFRenPP//Url27tHPnTh08eFBxcXFydHRUhQoV9OKLL6pKlSpm1woAAAAAQLplV8CuVq2aDMNQtmzZ5Ovrq549e6pKlSry8fGRs7Oz2TUCAAAAAJDu2RWw+/fvLz8/P5UuXVoWi8XsmgAAAAAAyHDsCtjt2rUzuw4AAAAAADK0ZAXsnTt3SpKqVq1q8/pREtoDAAAAAPC0S1bAbteunSwWi/bu3StnZ2fr6wcxDEMWi0WHDx82rVAAAAAAANKzZAXsOXPmSJL1BmYJrwEAAAAAwF3JCtjVqlV76GsAAAAAAJ51DmldAAAAAAAAT4NkHcHu37+/LBaLPvvsM2XKlEn9+/dPXueOjsqVK5dq1qypmjVrpqpQAAAAAADSs2QF7B07dshisSg+Pl6ZMmXSjh07ktV5fHy8rl+/rtDQUPXp00ddu3ZNVbEAAAAAAKRXyQrYGzdufOjrh4mNjdXgwYO1YMECAjYAAAAA4KmVrICdGk5OTnrppZd09uzZx70qAAAAAADSTLICdnR0tFxcXOxaQXR0tPz9/eXv72/X8gAAAAAAZATJuot4/fr1NXHiRF26dCnZHV+8eFHffPON6tevb29tAAAAAABkGMk6gj1kyBBNnDhR3377rSpXrqyaNWuqXLlyKly4sNzc3GQYhiIiInTmzBkdOHBA27Zt0969e1WsWDENGTLkcb8HAAAAAADSXLICdmBgoBo3bqyNGzdq6dKlmjJlimJjY2WxWGzaGYYhJycn1a5dW+PHj1eDBg3k4MCjtgEAAAAAT79k3+TMwcFBL7zwgl544QXFxMTowIEDCgsL0/Xr1yVJOXPmVIkSJVS+fHk5Ozs/rnoBAAAAAEiX7LqLuLOzsypXrqzKlSubXQ8AAAAAABkS528DAAAAAGACAjYAAAAAACYgYAMAAAAAYAICNgAAAAAAJiBgAwAAAABgArsC9smTJx/ZZuPGjfZ0DQAAAABAhmRXwO7YsaPOnDnzwPk//fSTgoOD7S4KAAAAAICMxq6AnSdPHnXo0EEXLlxINO/7779X37591ahRo1QX9zDLli3Tq6++qgoVKqh69erq0qWLbt26ZZ2/ceNGvfLKK6pQoYIaNWqkJUuWJOojJiZGX3zxhWrXrq1KlSqpU6dOCgsLS9Tu+PHj6tSpkypVqqTatWtr9OjRiomJeazvDwAAAACQsdgVsGfMmCFXV1d16NBBly9ftk4PDQ3V0KFD1bJlS40ZM8a0Iu83efJkffbZZwoMDNT06dP16aefqnDhwrpz544kadeuXerZs6cqVaqk0NBQNWnSRJ988onWrFlj08/w4cP1ww8/qE+fPpowYYJiYmLUsWNHRUZGWtuEh4erQ4cOio2N1YQJE9SnTx8tWrRIo0aNemzvDwAAAACQ8Tjas5Cbm5tmzJih9u3bq0OHDpo7d65mz56tkJAQde7cWR9//LHZdVqFhYVp4sSJ+vbbb1WvXj3r9HuPmE+ePFk+Pj769NNPJUk1atTQ6dOnNX78eDVu3FiSdOHCBS1evFhDhgzR66+/LkmqUKGCAgIC9P333+vtt9+WdPeI/I0bNzRx4kTlzJlTknTnzh0NGzZM3bp1U758+R7bewUAAAAAZBx230Xc3d1dM2fO1J07dxQYGKjQ0FAFBwc/1nAtSUuXLlXhwoVtwvW9YmJitGPHDmuQThAYGKjjx49brx3//fffFR8fb9MuZ86cql27trZs2WKdtmXLFtWsWdMariWpSZMmio+P19atW018ZwAAAACAjCxZR7APHjz4wHkfffSRPv74Y7366quqV6+eTdty5cqlvsL77N27V15eXvr22281d+5cRUZGqnz58urfv78qVqyoU6dOKTY2ViVKlLBZrmTJkpLuHgEvXLiwwsLClDt3buXIkSNRu8WLF1tfh4WFqUWLFjZt3Nzc5OHhkeT12ilhGIZu3ryZ4uUsFotcXFxStW6kT9HR0TIM44msi3H09HqS40hiLD3N2CfBDOyTYBb2STCDPePIMAxZLJZktU1WwG7RosVDOzQMQ8uWLdOPP/5oU8Dhw4eTVURKXL58WQcOHNCRI0c0ZMgQubi4aMqUKercubPWrVun8PBwSXdD8L0SXifMj4iIkKura6L+3dzcrG0S2t3flyTlyJHDpp09YmNj7fqMXFxcVLZs2VStG+nTiRMnFB0d/UTWxTh6ej3JcSQxlp5m7JNgBvZJMAv7JJjB3nHk7OycrHbJCtgjR45McQGPS8JR32+++UalS5eWJFWsWFENGjTQvHnz5O/vn8YVJp+Tk5NKlSqV4uWS++0JMh5PT88n+s0snk5PchxJjKWnGfskmIF9EszCPglmsGccHTt2LNltkxWwmzdvnqICHic3NzflzJnTGq6lu9dOly1bVseOHdNLL70kSTZ3ApfuHomWZD0l3M3NTVFRUYn6j4iIsDlt3M3NLVFf0t0j4fefXp5SFotFWbNmTVUfeLpwKhLMwDiCWRhLMAPjCGZhLMEM9oyjlHzhYvdNztLKw4743r59W0WLFpWTk1Oi66MTXidcm12iRAn9999/iU7zDgsLs7l+u0SJEon6ioyM1OXLlxNd5w0AAAAAeHbZ9Ziu/v37P7KNxWLRiBEj7On+oQICArR06VIdPnxYZcqUkSRdu3ZNBw8eVMeOHeXs7Kzq1atr7dq16tChg3W5VatWqWTJkipcuLAkyd/fXw4ODlq3bp1atmwp6e5R6d9//13du3e3Lle3bl1NmTLF5lrsNWvWyMHBQbVr1zb9/QEAAAAAMia7AvaOHTsSTYuPj9fly5d1584dubu7P7ZTOF544QVVqFBBwcHB6tOnjzJnzqyQkBA5OzvrzTfflCS9++67at++vYYOHaomTZpox44dWrFihb766itrP/nz59frr7+u0aNHy8HBQfny5dPUqVPl6uqq1q1bW9u1bt1ac+fOVY8ePdStWzddvHhRo0ePVuvWrXkGNgAAAADAyq6AvXHjxiSnx8bGauHChZo9e7ZmzJiRqsIexMHBQSEhIRo5cqQGDx6s2NhY+fn5af78+fLw8JAk+fn5acKECfr666+1ePFiFSxYUMOHD1eTJk1s+ho4cKCyZcumsWPH6saNG6pcubJmzpxpc3fxHDlyaPbs2frss8/Uo0cPZcuWTa+//rr69OnzWN4fAAAAACBjsitgP4iTk5PeeustHTt2TJ999plCQkLM7N7K3d1dX3755UPbPP/883r++ecf2sbZ2Vl9+/ZV3759H9quZMmSmjVrVkrLBAAAAAA8Qx7LTc5Kly6tnTt3Po6uAQAAAABIlx5LwN62bRu30QcAAAAAPFPsOkV84sSJSU6PjIzUzp07dejQIXXt2jVVhQEAAAAAkJGYGrBz5MihIkWKaNiwYXrjjTdSVRgAAAAAABmJXQH777//NrsOAAAAAAAytMdyDTYAAAAAAM+aZB3BPnfunF2dFyxY0K7lAAAAAADIaJIVsBs0aCCLxZLizg8fPpziZQAAAAAAyIiSFbBHjBhhE7Dj4+M1Z84cnTt3Ti+//LI8PT0lSWFhYVqxYoUKFSqkdu3aPZ6KAQAAAABIh5IVsF977TWb15MnT9bt27e1bt065cqVy2Zer1691KZNG/3333/mVQkAAAAAQDpn103Ovv/+e7Vq1SpRuJYkd3d3vfHGG1qwYEGqiwMAAAAAIKOwK2Bfv35d0dHRD5wfHR2t69ev21sTAAAAAAAZjl0Bu2LFipo9e7YOHDiQaN7+/fs1d+5c+fj4pLo4AAAAAAAyimRdg32/wYMHq127dmrZsqUqVqyo4sWLS5JOnjypvXv3KkeOHBo0aJCZdQIAAAAAkK7ZFbBLlSqln3/+WSEhIdqyZYtWrVol6e5zr9u3b68uXbrIw8PD1EIBAAAAAEjP7ArYkpQnTx4NGDBAAwYMMLMeAAAAAAAyJLuuwQYAAAAAALbsOoLdv3//R7axWCwaMWKEPd0DAAAAAJDh2BWwd+zYkWhafHy8Ll++rDt37sjd3V0uLi6pLg4AAAAAgIzCroC9cePGJKfHxsZq4cKFmj17tmbMmJGqwgAAAAAAyEhMvQbbyclJb731lmrXrq3PPvvMzK4BAAAAAEjXHstNzkqXLq2dO3c+jq4BAAAAAEiXHkvA3rZtG9dgAwAAAACeKXZdgz1x4sQkp0dGRmrnzp06dOiQunbtmqrCAAAAAADISEwN2Dly5FCRIkU0bNgwvfHGG6kqDAAAAACAjMSugP3333+bXQcAAAAAABnaY7kGGwAAAACAZ02yjmCfO3fOrs4LFixo13IAAAAAAGQ0yQrYDRo0kMViSXHnhw8fTvEyAAAAAABkRMkK2CNGjLAJ2PHx8ZozZ47OnTunl19+WZ6enpKksLAwrVixQoUKFVK7du0eT8UAAAAAAKRDyQrYr732ms3ryZMn6/bt21q3bp1y5cplM69Xr15q06aN/vvvP/OqBAAAAAAgnbPrJmfff/+9WrVqlShcS5K7u7veeOMNLViwINXFAQAAAACQUdgVsK9fv67o6OgHzo+Ojtb169ftrQkAAAAAgAzHroBdsWJFzZ49WwcOHEg0b//+/Zo7d658fHxSXRwAAAAAABlFsq7Bvt/gwYPVrl07tWzZUhUrVlTx4sUlSSdPntTevXuVI0cODRo0yMw6AQAAAABI1+wK2KVKldLPP/+skJAQbdmyRatWrZJ097nX7du3V5cuXeTh4WFqoQAAAAAApGd2BWxJypMnjwYMGKABAwaYWQ8AAAAAABmSXddgAwAAAAAAW3YfwT5+/LiWLFmiM2fOKDw8XIZh2My3WCyaPXt2qgsEAAAAACAjsCtg//jjjxowYIAcHR3l6ekpNze3RG3uD9wAAAAAADzN7ArYEydOVJkyZRQaGip3d3ezawIAAAAAIMOx6xrsS5cuqUWLFoRrAAAAAAD+P7sCtre3ty5dumR2LQAAAAAAZFh2Bex+/fpp8eLF2r17t9n1AAAAAACQIdl1DXZoaKhcXV3Vtm1blSpVSgUKFJCDg21Wt1gsmjx5silFAgAAAACQ3tkVsI8cOSJJKlCggG7cuKFjx44lamOxWFJXGQAAAAAAGYhdAXvjxo1m1wEAAAAAQIZm1zXYAAAAAADAll1HsCXpzp07WrNmjXbs2KErV64oODhY3t7eioyM1Pbt21W5cmXlyZPHzFoBAAAAAEi37ArYERER6tKli/bt26esWbMqOjpab731liQpa9asGj58uF599VW9//77phYLAAAAAEB6Zdcp4mPGjNHRo0c1ffp0rV+/XoZhWOdlypRJjRo10ubNm00rEgAAAACA9M6ugL1hwwa1a9dOtWvXTvJu4cWLF9fZs2dTXRwAAAAAABmFXQE7MjJShQsXfuD8uLg43blzx+6iAAAAAADIaOwK2EWLFtXBgwcfOH/r1q0qWbKk3UUBAAAAAJDR2BWwX3/9dS1ZskSrVq2yXn9tsVgUExOjr776Sr/99ptatWplaqEAAAAAAKRndt1FvEOHDjp27Jjef/99ubm5SZI+/PBDXb9+XXFxcWrVqpVatmxpaqEAAAAAAKRndgVsi8VifRTXmjVrdOrUKcXHx6to0aJq0qSJqlatanadAAAAAACkaykO2Hv37tWZM2eUM2dO+fn5yc/P73HUBQAAAABAhpLsgB0VFaW3335bf/31l3Vanjx5FBISojJlyjyO2gAAAAAAyDCSfZOzadOmac+ePXrxxRc1cOBAtW/fXuHh4erbt+/jrA8AAAAAgAwh2Uewf/nlFzVs2FDjx4+3TitRooSGDh2q06dPq0iRIo+lQAAAAAAAMoJkH8E+e/asateubTPN399fhmHo4sWLphcGAAAAAEBGkuyAfevWLWXNmtVmWsLr2NhYc6sCAAAAACCDSdFdxKOjo3X9+nXr6/DwcEnSjRs3bKYnyJkzZ2pqAwAAAAAgw0hRwB4yZIiGDBmSaHqvXr2SbH/48GH7qgIAAAAAIINJdsDu2bPn46wDAAAAAIAMjYANAAAAAIAJkn2TMwAAAAAA8GAEbAAAAAAATEDABgAAAADABARsAAAAAABMQMAGAAAAAMAEBGwAAAAAAEyQ7Md0JSUqKkrnzp1TRESEDMNINL9q1aqp6R4AAAAAgAzDroB97do1ffbZZ1q3bp3u3LmTaL5hGLJYLDp8+HCqCwQAAAAAICOwK2APGjRImzZtUrt27eTn5yc3Nzez6wIAAAAAIEOxK2Bv3bpVHTp00Mcff2x2PQAAAAAAZEh23eQsS5YsKlSokNm1AAAAAACQYdkVsF955RWtX7/e7FoAAAAAAMiw7DpFvFGjRtq5c6eCgoLUqlUr5c+fX5kyZUrUrly5cqkuEAAAAACAjMCugP3mm29a/3/btm2J5j/Ju4jfuHFDTZo00cWLF7V48WJVqFDBOu+HH37QtGnTdO7cOXl6eqpPnz4KCAiwWT4yMlIjR47U+vXrFRsbqzp16mjgwIHKmzevTbvdu3friy++0OHDh5U7d261adNGb7/9tiwWy2N/jwAAAACA9M+ugD1y5Eiz67Dbt99+m+SjwlauXKlBgwbpnXfeUY0aNbRq1Sr17NlT8+fPV6VKlaztevfurWPHjmno0KHKnDmzvv76a7399ttasmSJHB3vfjz//vuvgoKCVLt2bfXu3Vv//POPxowZo0yZMikoKOhJvVUAAAAAQDpmV8Bu3ry52XXY5fjx4/ruu+/Ut29fDRkyxGbe+PHj9dJLL6l3796SpBo1aujIkSOaNGmSQkNDJUl79uzR77//runTp8vf31+S5OnpqcDAQK1bt06BgYGSpOnTpytXrlwaN26cnJ2dVbNmTV29elVTpkxRu3bt5Ozs/OTeNAAAAAAgXbLrJmfpxfDhw9W6dWt5enraTD99+rROnjypJk2a2EwPDAzU9u3bFRMTI0nasmWL3NzcVLt2bWubEiVKqEyZMtqyZYt12pYtW/T888/bBOnAwEBFRERoz549j+OtAQAAAAAyGLuOYEvS7du3tXbtWh06dEiRkZGKj4+3mW+xWDRixIhUF/gga9as0ZEjRzRhwgQdPHjQZl5YWJgkJQreJUuWVGxsrE6fPq2SJUsqLCxMnp6eia6jLlGihLWPmzdv6vz58ypRokSiNhaLRWFhYapevbpd78EwDN28eTPFy1ksFrm4uNi1TqRv0dHRMgzjiayLcfT0epLjSGIsPc3YJ8EM7JNgFvZJMIM94yjhHmPJYVfAPnv2rNq3b6+zZ8/Kzc1NkZGRypEjhyIjI3Xnzh3lypVLWbNmtafrZImOjtaoUaPUp08fZc+ePdH88PBwSZKbm5vN9ITXCfMjIiLk6uqaaPkcOXLowIEDku7eBC2pvpydneXi4mLtyx6xsbF23QjOxcVFZcuWtXu9SL9OnDih6OjoJ7IuxtHT60mOI4mx9DRjnwQzsE+CWdgnwQz2jqPkXhZsV8AePXq0oqKitGjRIhUuXFi1atXSV199pSpVqmjOnDmaP3++pk+fbk/XyTJ58mTlzp1bLVq0eGzreBKcnJxUqlSpFC/HncufXp6enk/0m1k8nZ7kOJIYS08z9kkwA/skmIV9Esxgzzg6duxYstvaFbD/97//qU2bNvLx8dH169et052dndWlSxcdP35cI0aMUEhIiD3dP9TZs2c1Y8YMTZo0yXp0OeE065s3b+rGjRvKkSOHpLtHnz08PKzLRkRESJJ1vpubmy5cuJBoHeHh4dY2CUe4E9aVICYmRtHR0dZ29rBYLI/1SD8yHk5FghkYRzALYwlmYBzBLIwlmMGecZSSL1zsCti3bt1SoUKFJEnZs2eXxWKxCaC+vr764osv7On6kc6cOaPY2Fh17do10bz27durYsWKGjt2rKS712Lfe+10WFiYnJycVKRIEUl3r6Pevn17onPqT5w4IS8vL0lS1qxZVaBAAes12fe2MQwj0bXZAAAAAIBnk113ES9QoIAuXrwoSXJ0dFS+fPn0119/WecfO3ZMmTNnNqXA+5UpU0Zz5syx+de/f39J0rBhwzRkyBAVKVJExYsX15o1a2yWXbVqlWrWrGk9f75u3boKDw/X9u3brW1OnDihQ4cOqW7dutZpdevW1YYNGxQbG2vTl5ubm3x9fR/L+wQAAAAAZCx2HcGuUaOGNmzYoJ49e0q6+1zskJAQRUREKD4+Xj/99JOaNWtmaqEJ3NzcHnjX7nLlyqlcuXKSpF69eunDDz9U0aJFVb16da1atUr79u3TvHnzrO19fX3l7++vAQMGqG/fvsqcObO++uoreXt7q2HDhtZ2QUFB+vnnn/XBBx+oTZs2OnLkiKZPn64+ffrwDGwAAAAAgCQ7A3bXrl21f/9+xcTEyNnZWe+8844uXbqktWvXysHBQU2bNlW/fv3MrjVFmjZtqujoaIWGhiokJESenp6aOHFioiPOX3/9tUaOHKnBgwcrLi5O/v7+GjhwoBwd/++jKVasmKZPn65Ro0apa9eucnd3V3BwsDp37vyk3xYAAAAAIJ2yK2AXLFhQBQsWtL7OnDmzPv/8c33++eemFZYS1atX1z///JNoesuWLdWyZcuHLuvq6qoRI0Y88pndlStX1qJFi1JVJwAAAADg6WXXNdj9+/fX3r17Hzh/37591uuiAQAAAAB4FtgVsJctW6ZTp049cP6ZM2f0448/2lsTAAAAAAAZjl0B+1EuXbqkLFmyPI6uAQAAAABIl5J9Dfb69eu1YcMG6+tFixZp27ZtidpFRkZq27ZtKl++vDkVAgAAAACQASQ7YB8/ftz6XGmLxaK9e/fqwIEDNm0sFouyZs2qqlWrpvldxAEAAAAAeJKSHbC7deumbt26SZJKly6tzz//XC+//PJjKwwAAAAAgIzErsd0/f3332bXAQAAAABAhvZYbnIGAAAAAMCzxq4j2JK0efNmzZo1S4cOHVJkZKQMw0jU5vDhw6kqDgAAAACAjMKuI9hr167VO++8o//++0+BgYGKj4/XSy+9pMDAQGXJkkXe3t7q0aOH2bUCAAAAAJBu2XUEe+rUqfLx8dF3332n8PBwLViwQC1atFDNmjV15swZtWrVSoULFza7VgAAAAAA0i27jmAfP35cgYGBypQpkxwd72b0uLg4SVLhwoXVpk0bhYaGmlclAAAAAADpnF0BO0uWLHJycpIkubm5ydnZWZcvX7bOz5Mnj86cOWNOhQAAAAAAZAB2BWxPT08dP37c+rpMmTJavny54uLidPv2ba1YsUIFChQwrUgAAAAAANI7uwL2iy++qA0bNigmJkaS9M477+iPP/5Q1apVVaNGDe3atUtdu3Y1tVAAAAAAANIzu25yFhQUpKCgIOvrgIAAzZ07V+vWrVOmTJlUr1491ahRw7QiAQAAAABI7+x+Dvb9/Pz85OfnZ1Z3AAAAAABkKHadIg4AAAAAAGwl6wh2gwYNZLFYUtSxxWLR+vXr7SoKAAAAAICMJlkBu1q1aokC9oEDB3T06FGVKlVKnp6ekqQTJ07o2LFjeu6551S+fHnzqwUAAAAAIJ1KVsAeNWqUzev169dr/fr1mjlzpmrWrGkzb+vWrerdu7fee+8986oEAAAAACCds+sa7G+++UZvvfVWonAtSbVr11bbtm31zTffpLo4AAAAAAAyCrsC9r///qucOXM+cH7OnDl16tQpe2sCAAAAACDDsStgFy1aVEuXLtWNGzcSzYuKitKSJUtUpEiRVBcHAAAAAEBGYddzsHv37q3g4GA1adJEzZs3V7FixSTdPbK9bNkyXblyhVPEAQAAAADPFLsC9gsvvKCQkBCNGTNGU6dOtZlXpkwZff7556pTp44pBQIAAAAAkBHYFbAlyd/fX/7+/rp8+bLOnTsnSSpYsKA8PDxMKw4AAAAAgIzC7oCdwMPDg1ANAAAAAHjmpSpgX7hwQYcOHVJkZKQMw0g0/9VXX01N9wAAAAAAZBh2Bezbt2+rb9++WrduneLj42WxWKwB22KxWNsRsAEAAAAAzwq7HtM1btw4/fLLL+rdu7fmzp0rwzA0atQozZgxQ3Xr1lXp0qW1fPlys2sFAAAAACDdsitgr127Vq+99pq6du2qUqVKSZLy5cunWrVqaerUqXJ1ddX8+fNNLRQAAAAAgPTMroB95coV+fj4SJKyZMkiSYqOjrbOb9SokX755RcTygMAAAAAIGOwK2DnyZNH165dkyS5uLgoR44cOnHihHV+VFSUbt++bU6FAAAAAABkAHbd5MzHx0e7d++2vg4ICND06dPl4eGh+Ph4zZo1S5UqVTKrRgAAAAAA0j27Ana7du20Zs0axcTEyNnZWe+995727Nmjjz/+WJJUtGhRffLJJ6YWCgAAAABAemZXwPbz85Ofn5/1dYECBbR69WodOXJEDg4OKlGihBwdU/WIbQAAAAAAMhTTUrCDg4NKly5tVncAAAAAAGQodgfsmJgYLVq0SJs3b9bZs2clSYUKFVK9evXUsmVLZc6c2bQiAQAAAABI7+wK2BcuXFCnTp104sQJeXh4qFixYpKkv//+W7/99pvmzZunWbNmKX/+/KYWCwAAAABAemVXwB42bJjOnTunr7/+Wo0bN7aZt3r1avXr10/Dhg3T5MmTTSkSAAAAAID0zq6A/b///U8dO3ZMFK4lqUmTJjp06JDmzZuX6uIAAAAAAMgoHOxZKFu2bHJ3d3/g/Dx58ihbtmx2FwUAAAAAQEZjV8B+7bXXtGzZMkVHRyead+PGDS1dulQtWrRIdXEAAAAAAGQUdp0iXrp0af36669q0qSJXn31VetNzk6ePKnly5crR44c8vb21rp162yWa9iwYeorBgAAAAAgHbIrYL///vvW/58yZUqi+RcuXNAHH3wgwzCs0ywWiw4fPmzP6gAAAAAASPfsCthz5swxuw4AAAAAADI0uwJ2tWrVzK4DAAAAAIAMza6AnSAmJkYHDx7UlStXVLly5YfeWRwAAAAAgKeZXXcRl+6eJu7v768333xTvXr10j///CNJunr1qqpXr67FixebViQAAAAAAOmdXQF7yZIlGjFihOrUqaPPP//c5mZm7u7uqlGjhlatWmVakQAAAAAApHd2BeyZM2fq+eef19ixYxUQEJBofrly5XT06NFUFwcAAAAAQEZhV8D+999/Vbdu3QfOz5kzp65fv25vTQAAAAAAZDh2BWw3Nzddu3btgfOPHTsmDw8Pu4sCAAAAACCjsStg161bV4sWLVJERESieUePHtUPP/ygBg0apLo4AAAAAAAyCrse09W7d2+98cYbatq0qQICAmSxWPTjjz9qyZIlWrdunTw8PNS9e3ezawUAAAAAIN2y6wh2vnz5tHTpUtWpU0erV6+WYRhavny5Nm3apJdeekmLFi1Szpw5TS4VAAAAAID0K9lHsH///Xf5+/tbX+fOnVuff/65Pv/8c129elXx8fFyd3eXg4ODYmJi1KNHD02ePPmxFA0AAAAAQHqT7CPYPXr00O+//57kPHd3d+XJk0cODg66ceOGgoKC9Ouvv5pVIwAAAAAA6V6yA/Zzzz2nHj16aMuWLQ9sc+3aNbVv3147d+7Uxx9/bEqBAAAAAABkBMkO2LNmzZKXl5d69uypzZs3J5p/8eJFtW3bVn///bdGjBihTp06mVooAAAAAADpWbIDdvbs2TVz5kyVKVNGvXr1sjkF/OTJk2rTpo3OnDmjb775Rq+99trjqBUAAAAAgHQrRXcRz549u2bMmKGyZcuqV69e2rRpkw4fPqw333xT169f19SpU/XCCy88rloBAAAAAEi3Uvwc7GzZsmnGjBnq0qWLgoODlTlzZjk6Omr27NmqUKHC46gRAAAAAIB0L9kB++DBgzav+/Tpo759++rq1asaNGiQHBwcErUpV66cOVUCAAAAAJDOJTtgt2jRQhaLxWaaYRiSpH79+iWabrFYdPjwYRNKBAAAAAAg/Ut2wB45cuTjrAMAAAAAgAwt2QG7efPmj7MOAAAAAAAytBTdRRwAAAAAACSNgA0AAAAAgAkI2AAAAAAAmICADQAAAACACQjYAAAAAACYgIANAAAAAIAJCNgAAAAAAJiAgA0AAAAAgAkI2AAAAAAAmICADQAAAACACQjYAAAAAACYgIANAAAAAIAJMlzAXr16td59913VrVtXlSpVUrNmzbR48WIZhmHT7ocfflCjRo1UoUIFvfLKK9q0aVOiviIjIzVgwABVq1ZNvr6+Cg4O1qVLlxK12717t1q1aiUfHx8FBAQoJCQk0foAAAAAAM+2DBewZ82aJRcXF/Xr10+TJ09W3bp1NWjQIE2aNMnaZuXKlRo0aJCaNGmi0NBQVapUST179tRff/1l01fv3r21detWDR06VGPGjNGJEyf09ttvKy4uztrm33//VVBQkDw8PDR16lR16NBB48eP14wZM57UWwYAAAAAZACOaV1ASk2ePFnu7u7W1zVr1tT169c1c+ZMde/eXQ4ODho/frxeeukl9e7dW5JUo0YNHTlyRJMmTVJoaKgkac+ePfr99981ffp0+fv7S5I8PT0VGBiodevWKTAwUJI0ffp05cqVS+PGjZOzs7Nq1qypq1evasqUKWrXrp2cnZ2f7AcAAAAAAEiXMtwR7HvDdYIyZcooKipKN2/e1OnTp3Xy5Ek1adLEpk1gYKC2b9+umJgYSdKWLVvk5uam2rVrW9uUKFFCZcqU0ZYtW6zTtmzZoueff94mSAcGBioiIkJ79uwx++0BAAAAADKoDHcEOyl//vmn8uXLp+zZs+vPP/+UdPdo9L1Kliyp2NhYnT59WiVLllRYWJg8PT1lsVhs2pUoUUJhYWGSpJs3b+r8+fMqUaJEojYWi0VhYWGqXr263XUbhqGbN2+meDmLxSIXFxe714v0Kzo6+old3884eno9yXEkMZaeZuyTYAb2STAL+ySYwZ5xZBhGotz4IBk+YO/atUurVq1S3759JUnh4eGSJDc3N5t2Ca8T5kdERMjV1TVRfzly5NCBAwck3b0JWlJ9OTs7y8XFxdqXvWJjY3X48OEUL+fi4qKyZcumat1In06cOKHo6Ognsi7G0dPrSY4jibH0NGOfBDOwT4JZ2CfBDPaOo+ReGpyhA/aFCxfUp08fVa9eXe3bt0/rclLMyclJpUqVSvFyyf32BBmPp6fnE/1mFk+nJzmOJMbS04x9EszAPglmYZ8EM9gzjo4dO5bsthk2YEdEROjtt99Wzpw5NWHCBDk43L2cPEeOHJLuHn328PCwaX/vfDc3N124cCFRv+Hh4dY2CUe4E45kJ4iJiVF0dLS1nb0sFouyZs2aqj7wdOFUJJiBcQSzMJZgBsYRzMJYghnsGUcp+cIlw93kTJJu3bqlbt26KTIyUtOmTbM51TvheumE66gThIWFycnJSUWKFLG2O3HiRKJvL06cOGHtI2vWrCpQoECivhKWu//abAAAAADAsyvDBey4uDj17t1bYWFhmjZtmvLly2czv0iRIipevLjWrFljM33VqlWqWbOm9dz5unXrKjw8XNu3b7e2OXHihA4dOqS6detap9WtW1cbNmxQbGysTV9ubm7y9fV9HG8RAAAAAJABZbhTxIcNG6ZNmzapX79+ioqK0l9//WWdV7ZsWTk7O6tXr1768MMPVbRoUVWvXl2rVq3Svn37NG/ePGtbX19f+fv7a8CAAerbt68yZ86sr776St7e3mrYsKG1XVBQkH7++Wd98MEHatOmjY4cOaLp06erT58+PAMbAAAAAGCV4QL21q1bJUmjRo1KNG/Dhg0qXLiwmjZtqujoaIWGhiokJESenp6aOHFioiPOX3/9tUaOHKnBgwcrLi5O/v7+GjhwoBwd/+9jKVasmKZPn65Ro0apa9eucnd3V3BwsDp37vx43ygAAAAAIEPJcAF748aNyWrXsmVLtWzZ8qFtXF1dNWLECI0YMeKh7SpXrqxFixYlu0YAAAAAwLMnw12DDQAAAABAekTABgAAAADABARsAAAAAABMQMAGAAAAAMAEBGwAAAAAAExAwAYAAAAAwAQEbAAAAAAATEDABgAAAADABARsAAAAAABMQMAGAAAAAMAEBGwAAAAAAExAwAYAAAAAwAQEbAAAAAAATEDABgAAAADABARsAAAAAABMQMAGAAAAAMAEBGwAAAAAAExAwAYAAAAAwAQEbAAAAAAATEDABgAAAADABARsAAAAAABMQMAGAAAAAMAEBGwAAAAAAExAwAYAAAAAwAQEbAAAAAAATEDABgAAAADABARsAAAAAABMQMAGAAAAAMAEBGwAAAAAAExAwAYAAAAAwAQEbAAAAAAATEDABgAAAADABARsAAAAAABMQMAGAAAAAMAEBGwAAAAAAExAwAYAAAAAwAQEbAAAAAAATEDABgAAAADABARsAAAAAABMQMAGAAAAAMAEBGwAAAAAAExAwAYAAAAAwAQEbAAAAAAATEDABgAAAADABARsAAAAAABMQMAGAAAAAMAEBGwAAAAAAExAwAYAAAAAwAQEbAAAAAAATEDABgAAAADABARsAAAAAABMQMAGAAAAAMAEBGwAAAAAAExAwAYAAAAAwAQEbAAAAAAATEDABgAAAADABARsAAAAAABMQMAGAAAAAMAEBGwAAAAAAExAwAYAAAAAwAQEbAAAAAAATEDABgAAAADABARsAAAAAABMQMAGAAAAAMAEBGwAAAAAAExAwAYAAAAAwAQEbAAAAAAATEDABgAAAADABARsAAAAAABMQMAGAAAAAMAEBGwAAAAAAExAwAYAAAAAwAQEbAAAAAAATEDABgAAAADABARsAAAAAABMQMAGAAAAAMAEBGwAAAAAAExAwAYAAAAAwAQEbAAAAAAATEDABgAAAADABATsZDp+/Lg6deqkSpUqqXbt2ho9erRiYmLSuiwAAAAAQDrhmNYFZATh4eHq0KGDihcvrgkTJujixYsaNWqUbt26pcGDB6d1eQAAAACAdICAnQzff/+9bty4oYkTJypnzpySpDt37mjYsGHq1q2b8uXLl7YFAgAAAADSHKeIJ8OWLVtUs2ZNa7iWpCZNmig+Pl5bt25Nu8IAAAAAAOmGxTAMI62LSO9q1qypFi1a6MMPP7SZXqdOHTVr1izR9OTYvXu3DMOQk5OTXTVZLBZFx0Yr3oi3a3mkLw4WB7k4uehJ/zhaLBZFxdzQnXjG0dMgk4ODsjtne+LjSLo7liJuRSou/s4TXzfM5+iQSW5ZXNNkn3T9ZoTi7sQ90fXi8XDM5KicWd3SbJ90PSpccXGMpaeBo6OjcmbPkSb7pGvh1xlHTwlHR0flypHTrnEUGxsri8WiypUrP3o99hT3rImIiJCbm1ui6Tly5FB4eLhdfVosFpv/2sPFycXuZZE+pWY82Cu7c7Ynvk48XmkxjiTJLYtrmqwXj09ajKWcWRP/vkXGllb7pJzZc6TJevH4pMVYypUj5xNfJx4ve8aRxWJJ9nIE7DTi6+ub1iUAAAAAAEzENdjJ4ObmpsjIyETTw8PDlSMH344CAAAAAAjYyVKiRAmFhYXZTIuMjNTly5dVokSJNKoKAAAAAJCeELCToW7dutq2bZsiIiKs09asWSMHBwfVrl07DSsDAAAAAKQX3EU8GcLDw/XSSy/J09NT3bp108WLFzVq1Ci9/PLLGjx4cFqXBwAAAABIBwjYyXT8+HF99tln2rNnj7Jly6ZmzZqpT58+cnZ2TuvSAAAAAADpAAEbAAAAAAATcA02AAAAAAAmIGADAAAAAGACAjYAAAAAACYgYAMAAAAAYAICNgAAAAAAJiBgAwAAAABgAgI2UmXDhg3q3LmzqlWrpvLly6tBgwYaPHiwTpw4IUny9vbW9OnTH8u6Dx8+rAkTJig6Otqu5SdMmKDdu3ebXNXT51HbWMr423nChAny9vZ+6L8GDRpox44d8vb21v79++2qBSl3/7apUaOG2rdvr127dqV1aVZnzpyRt7e31qxZk9alwA4//fSTXn/9dVWpUkWVK1dWkyZN9Mknn+jKlStpXRpMxra21a9fPzVt2jTJeZ9//rkaNGhgfZ3a38V4/Mwa32zr1HNM6wKQcY0ZM0ahoaFq1KiRPvvsM7m7u+vUqVNasmSJ+vTpox9//FELFy5UwYIFH8v6Dx8+rIkTJ6pt27ZycXFJ8fITJ05U1qxZVbly5cdQ3dMhOdtYUobfzi1btlSdOnWsr3/44QetWLFCs2fPtk5zdnZW0aJFtXDhQpUsWTLFdcB+WbJksW6LCxcu6Ntvv1XHjh21dOlSeXl5pXF1yMhCQ0M1duxYdezYUcHBwTIMQ0ePHtXPP/+sS5cuKXfu3GldIkzCtk6d1P4uxuNl5vhmW6ceARt22bx5s0JDQ9W9e3e999571ulVq1ZVixYttGnTJklSpUqV0qhCpFZyt7GU8bdz/vz5lT9/fuvr3377TQ4ODkm+r/TwXm/duqUsWbKkdRlPzP3bwsfHRw0aNND333+vwYMHp11hyPDmzp2r5s2bq1+/ftZp9erVU5cuXRQfH5/q/p+1n9X0jG2dvvB5metxj2+kDKeIwy4zZsxQnjx51L179yTnBwQESEp86nC7du3UrVs3/fjjj3rhhRfk4+Ojdu3aKSwszNrmQadb3nu60tKlS9W/f39JUs2aNa2n8ErSpUuX1L9/fz3//PPy8fFRw4YNNW7cOMXExFj78vb2liSNHj3aeurpjh07UvuxPFWSu42lZ2c7J3WKuLe3t0JCQvTVV1+pZs2a8vPz0+jRo2UYhrZv365mzZrJ19dXHTp00Pnz5x/alyR1795d7dq1s76eMGGCfH19tW/fPrVq1UoVKlTQ/Pnz7X4PT4OCBQvK3d1dZ86c0YwZM9SiRQtVqVJFNWvWVLdu3WwuX5D+7zTIbdu26eWXX5aPj4/eeustnTlzRtevX9d7772nypUr64UXXtCqVatslm3QoIE+/fRTm2nr16+Xt7e3zpw588Aaf/zxR7Vp00bVqlVT1apV1a5dO+3bt8+mTcK2/eeff9SmTRtVrFhRTZs21W+//WbTLiYmRsOHD1e1atXk5+enwYMH6+eff35kDXi0iIgI5c2bN8l5Dg62fyItXbpUL7/8sipUqKA6deroq6++0p07d2zme3t7a8+ePerUqZMqVaqk0aNHS/q//cTo0aNVo0YN+fr6ql+/foqKirJZx9mzZxUcHKwqVaqoUqVKCgoK0j///GPTJmFMzp8/XwEBAapSpYq6d++uq1ev2rQ7evSo2rZtqwoVKqhhw4b66aefEu1fniVsa/ul9nfxvZ/Ll19+qdq1a6tmzZqSpD179uidd96Rv7+/KlWqpGbNmlnPjkPymTW+H7atE35n3c/Pz08TJkww8+1keBzBRorFxcVp9+7datiwoZycnFK8/MGDB3Xq1Cl98MEHkqSvv/5aXbp00Zo1a+Ts7JysPurXr693331XkydP1rRp0+Tq6mpd9tq1a8qZM6f69+8vNzc3nTx5UhMmTNDly5c1cuRISXdPaW7VqpXatWtnvf6oVKlSKX4vT6vUbmPp2drO8+fPV7Vq1TR69Gjt3btXEyZMUHx8vLZu3ap3331XTk5OGj58uD755BPNmDEjxf3Hxsbqgw8+UMeOHdWnTx/lzJnT9PeQkURFRen69evKmzevLly4oLfeeksFCxZUVFSUvv/+e7Vu3Vpr1661+ZwuX76sUaNG6d1335Wjo6OGDx+uDz/8UC4uLvLz89Mbb7yhRYsW6aOPPlLFihVVqFChVNV45swZvfrqqypatKhiYmK0cuVKtW3bVj/99JM8PT2t7WJjY/Xhhx+qffv26t69u0JDQxUcHKyNGzcqV65ckqSxY8fq+++/V3BwsMqUKaO1a9dq7NixqaoPd5UrV07ff/+9ChcurPr168vDwyPJdjNnztSXX36pDh06qF+/fjp+/Lj1j9IPP/zQpu0HH3ygVq1aqVu3bjanV86dO1flypXTF198oTNnzmjMmDG6ffu2vvrqK0l3x3W7du3k4OCgYcOGKXPmzJo8ebLeeust/fTTTypQoIC1r40bN+rff//V4MGDde3aNY0cOVKfffaZta9bt26pc+fOcnNz05dffilJmjRpkiIiIlS0aFFTP8OMgm1tv9T+Lk4wZ84cVaxYUZ9//rni4uIkSefOnVPlypXVpk0bOTs7a/fu3Ro4cKAMw1Dz5s1Nqf9ZYNb4fti2RvIRsJFi169fV0xMjN3X3F65ckXz5s1T8eLFJUlly5ZV48aNtXTpUrVu3TpZfbi7u1t/cZQrV07u7u7Wed7e3urbt6/1deXKleXi4qJ+/fpp8ODBcnFxsZ5uWqBAgXRxym96k9ptLD1b2zlv3rzWP2zq1KmjjRs3atasWVq5cqX1eu2LFy/qs88+U0REhNzc3FLUf2xsrPr06aPAwEDTa88oEv4Yu3Dhgr744gvduXNHjRo1srl2/s6dO9YjI2vXrlWrVq2s88LDwzVv3jw999xzku4edfnss8/09ttvq0ePHpKkChUq6JdfftH69evVoUOHVNXbs2dP6//Hx8erdu3a2rdvn5YtW6b333/fOi8hYNerV0+S5Onpqeeff15btmxRs2bNdP36dS1YsEDvvvuuunbtKunuGOvYsaPNGRGwz5AhQ9SzZ08NHDhQklS4cGEFBASoY8eOKly4sKS7YWj8+PHq0qWLddvVrl1bTk5OGjVqlIKCgqxfhkhS69atrdvqXs7Ozpo0aZIyZcokScqcObMGDhyonj17qmTJklq6dKnOnTtns9+oWrWqAgICNHv2bJtTPw3D0OTJk61/+J49e1ZTp05VfHy8HBwctGTJEl25ckULFiywvo/y5curYcOGz2zAZlvbL7W/ixPkyJFDEydOlMVisU576aWXbN5r1apVdfHiRS1cuJCAnQJmje+HbWskH6eIw2737iBT4rnnnrOGLkkqVqyYSpcurb1795pSl2EYmjVrlgIDA+Xj46Ny5crpww8/VFxcnE6fPm3KOp4V9m5j6dnazrVq1bJ57enpqbx589rcDC3hs7hw4YJd60gIYM+imzdvqly5cipXrpyef/557dixQ4MHD1adOnX0119/qVOnTqpevbrKli2rihUr6ubNmzp58qRNH3nz5rWGa+n/tse9287NzU3u7u52b6N7HT9+XD169FCtWrVUpkwZlStXTidOnEhUl4ODg/VUSenuH0VZsmTRxYsXJUlHjhzR7du39fzzz9ssd/9r2MfLy0srVqxQSEiI2rdvL1dXV82dO1evvPKKDh8+LOnuKaw3b95U48aNFRcXZ/1Xq1Yt3bp1S0ePHrXps379+kmuKyAgwBq4JKlx48YyDMN6mciuXbv03HPP2ew3cubMqVq1aunPP/+06atq1ao2R5VKliyp2NhY692CDxw4IC8vL+sf1tLdsVW6dGk7PqWnA9v68UjJ7+K6desm+rsiPDxcw4cPV0BAgHU/v3DhwkSX+uDhHsf4hv04go0Uy5kzpzJnzqxz587ZtXxSdzLMnTu3Ll++nNrSJEmzZ8/WF198oS5duqh69epyc3PT/v379emnn+r27dumrONpl9ptLD1b2/n+I9JOTk5JTpNkV20uLi7Kli2b/QVmcFmyZNG8efNksViUK1cuFShQQA4ODjp37pw6d+6s8uXLa9iwYcqbN6+cnJzUrVu3RJ/zg7aHq6urzXRnZ+dUj5+oqCh17txZ7u7u6tevnwoWLGg9gnV/31myZEl0+p2Tk5O1XcLPy71HzaSkf75gH2dnZ9WrV8/6JdZvv/2mbt26adKkSZo4caKuXbsmSQ88mnb/mQR58uRJst392yx79uzKnDmzLl26JOnuNZRJLZs7d+5Ef/jeP54TxlDCuLl06VKSR57c3d2f6d+DbGtbmTJlsrm2/F7x8fFydHx0TEjJ7+Kk9lv9+vXTnj171KNHD5UqVUrZs2fXggULtHr16keuG7bMHt+wHwEbKebo6KjKlSvrf//7n+Li4pK1A75XUs/ju3LlivXb1syZM0u6e+rkvSIiIpLV/5o1a9SgQQPrtb/S3aNJSL7UbmOJ7fwoD3v/93/Dn5ozCZ4GDg4OqlChQqLpv/32m27evKmJEyda/wiNi4tTeHi4aet2dnZOtI0e1f9ff/2lCxcuaOrUqTZHkSIjI23uVp8cCdfRXbt2Tfny5bNOf1af2/sk1KlTR6VLl7buT3LkyCHp7iP/ktp+9x45fJj7t1lUVJRu375tvTFRjhw5kjxqd+XKFWsNyZU3b17rUat7Xb169Zn+su5+z/q2dnd313///ZfkvAcF9/ul5Hfx/b/Lbt++rV9//VX9+vWzuSHbd99998j14tHMHt+ZM2dO9PswNjZWN2/eNKnipweniMMunTp10uXLlzVlypQk52/evPmByx49elT//vuv9fW///6rv//+WxUrVpR09xtOJycnmx10TEyMdu7cadNPwhGo++9UeevWrUQ35vr5558T1XHvUSIklpptLLGdHyXhl9u97//q1as6ePBgWpWU4dy6dUsWi8XmC6DVq1dbr9c2Q/78+RP9sbh169ZH1iXJZnzu3r1bZ8+eTfH6n3vuOWXOnFnr16+3mX7/a9gnqXBx69YtnT9/3nqE0dfXVy4uLrpw4YIqVKiQ6N/9Zxc8yKZNm2yOFq5Zs0YWi8X65VGVKlV05MgRm6cthIeHa9u2bapSpUqK3lf58uX1zz//2Jyie+bMGf39998p6udpwrZOrGrVqoqIiEj0ezcqKko7duxQ1apVrdNS+7s4KTExMYqPj7dZPioqShs3bkzW8vg/Zo7vB23rfPnyKTY2VqdOnbJO+9///vfAsyCeZRzBhl0Snq03YcIEHTt2TC+99JJy5cqlM2fOaMmSJYqMjHzgNaO5c+fWO++8o+DgYEnSN998o3z58um1116TdPdo1Ysvvqj58+erWLFiypUrl+bNmyfDMGy+/Uy4dmn+/Pl64YUXlCVLFnl7e6tWrVqaM2eO9QZbP/30k03QS1CiRAlt2LBBfn5+cnFxkaenp7Jnz272R5VhpWYbS2znR8mfP78qVqyoSZMmydXVVY6OjgoNDU10yjIerEaNGpKk/v37q3Xr1jp69KhmzpyZ4pvIPUyjRo00dOhQTZw4Ub6+vtq8ebP++uuvhy5TqVIlZc2aVcOGDVPXrl118eJFTZgwweYIdHLlypVLbdq00ZQpU5Q5c2aVKVNGa9assV7Lff/jV5AyL7/8sgICAuTv76+8efPq4sWLmjdvnq5du2a90Z2bm5uCg4P15Zdf6sKFC6pWrZoyZcqk06dPa8OGDZowYYLNjZweJCYmRj169FCbNm2sd5Zu1KiRdR/32muvadasWerWrZt69+5tvbO0o6Njim+616JFC02ZMkXvvPOOevXqJenuUas8efI8s2fEsK0T8/f3l5+fn3r27KkePXroueee06VLlzRt2jQ5ODjYHFVO7e/ipLi6uqpChQoKDQ2Vu7u7HB0dFRISouzZsyd6FBkezszx/aBtXbduXWXNmlUDBw7U22+/rQsXLmjOnDnWM/LwfwjYsNtHH30kX19fzZ8/XwMGDFB0dLTy5s0rf39/BQUFPXC5cuXKqWHDhvryyy91+fJlVaxYUcOGDbO5DnHQoEEaNGiQhg8frmzZsikoKEienp7asGGDtU3ZsmXVq1cv/fDDD5o2bZoKFCigjRs3qkePHrp27ZrGjx8v6e4fyAMHDtQ777xjU8fgwYM1YsQIvf3227p165bmzJmj6tWrm/wpZWz2bmOJ7ZwcY8aM0cCBA9W/f3/lyZNHvXv31sqVKxUZGZnWpWUI3t7eGjlypCZOnKhu3bqpTJky+uabb9S7d2/T1tGyZUudOnVKCxYssN7I5/3337c5HfJ+efLk0TfffKPRo0ere/fuKl68uIYNG6Zp06bZVcMHH3yguLg4hYSEKD4+Xi+++KK6du2qTz/9lC9kUqlnz57atGmTRo0apatXrypXrlzy9vbWrFmzrF/gSFLnzp2VL18+zZw5U/+vvfuPqar+4zj+gitqeFvgauV0d4vcJccPl6wVyAKd6BaUlvkLu4Z2+2E1Wxs5oqVLTcXNtoCiKylLEHVJc/6YguWvjbGctqkRk78KtYXZrSsoiuN+vn/05czbpcQ88qOej+1uh/f5fM5533vudvfm8znnU1VVpSFDhsjlcikzM7PXSxl6PB75/X4tXbpUnZ2dysrK0rJly6z9TqdTlZWVWrt2rd577z0Fg0FNmDBBVVVVIcs29cbw4cO1adMmLV++XPn5+br//vv12muvaefOnf/Z7wzXOlxkZKR8Pp+Ki4tVUVGhCxcuyOl06vHHH1dJSUnIusq3+1v8V9avX69ly5apoKBAMTEx8ng8unLlyj9a0vK/zM7v919d69jYWBUXF6uoqEivv/66xo0bp3Xr1tm23vq/SYQxxvR3Evjv8Hg8io6Ols/n6+9UcAdxnYE76+2339aJEyeYSjlIxMfHa+nSpTf9x+Sd9Pvvv2vKlCnKy8sLWUYO9uJaA2AEGwCAAezYsWP69ttvlZCQoGAwqMOHD2v37t0ha+UCf7Zhwwbde++9Gj16tH755Rdt2rRJXV1dmjlzZn+nBptxrYGBhQIbAIABLDo6WocPH1Z5ebmuXbum0aNHq6CgQHl5ef2dGgawyMhIlZWVqbW1VQ6HQ+PHj9fnn39+y1OQMfBxrYGBhSniAAAAAADYgMePAgAAAABgAwpsAAAAAABsQIENAAAAAIANKLABAAAAALABBTYAAAAAADagwAYAAAAAwAYU2AAADGBffvml4uPjdfr06ZB4W1ubnnvuOSUlJeno0aP9lF3fOHLkiEpKSvo7DQAAbooCGwCAQaa9vV2LFi3SmTNnVFpaqieeeKK/U7qjjhw5otLS0v5OAwCAmxrS3wkAAIDea29v14svvqimpiaVlpYqIyOjv1MCAAD/xwg2AACDxOXLl+X1etXY2KiSkhJlZmZa+1pbW/XOO+8oLS1NiYmJys7O1o4dO8KOce3aNZWUlGjatGlKSkpSenq63njjDbW0tFhtNm7cqLlz5+qxxx5TcnKynn32We3fvz/sWPHx8VqxYoX27dunJ598UsnJyZozZ47OnDkjSdq2bZuysrKUlJQkj8ejc+fOhfQ/fvy4lixZoszMTCUmJiojI0OrV6/W1atXrTYFBQXasmWLdb7u1z/N9auvvlJOTo71Gf3bp9cDAPoWI9gAAAwCHR0deumll/Tdd9/po48+0qRJk6x9Fy9e1OzZsxUREaH58+dr5MiROnr0qN599121t7crLy9PktTV1aVXXnlFDQ0Nys7O1oIFC3T58mXV19erublZLpdLkrR582ZNnjxZTz31lK5fv669e/fqzTfflM/nCynqpT+K5IMHDyo3N1eStGHDBr366qvyer2qrq5Wbm6uAoGAPvvsMxUWFmrz5s1W3/379+vq1auaN2+eYmJidOrUKVVVVennn39WcXGxJGnOnDm6cOGC6uvrtW7durDP5VZyPXHihOrq6pSbm6sRI0aosrJSS5Ys0aFDhxQbG3u7lwgAAMkAAIABq6amxrjdbjNp0iSTkJBgDhw4ENamsLDQTJw40fj9/pD4W2+9ZVJSUkxHR4cxxpgdO3YYt9ttKioqwo4RDAat7e723To7O01OTo5ZsGBBSNztdpvExERz9uxZK7Zt2zbjdrvNxIkTTVtbmxVfv369cbvdIW3/fB5jjPH5fCY+Pt6cP3/eir3//vvG7XaHtb3VXBMSEsyPP/5oxZqamozb7TaVlZU9HhsAgFvFFHEAAAaBixcvaujQoRo1alRI3Bijuro6TZ48WcYY+f1+65Wenq62tjY1NjZKkurq6hQbG6vnn38+7PgRERHW9vDhw63tQCCgtrY2paSk6Pvvvw/rl5qaqjFjxlh/jx8/XpI0depUOZ1OK56cnCxJOnv2bI/nuXLlivx+vx555BEZY3o8V09uJde0tDRrlF6SHn74YTmdzpCcAAC4HUwRBwBgEFixYoXWrFkjr9erLVu2KC4uTpLk9/t16dIlbd++Xdu3b++xr9/vlyS1tLTowQcf1JAhf//zf+jQIZWVlampqUmdnZ1W/MYivNufC/7uovqBBx4Iid99992SpEuXLlmxn376ScXFxTp48KACgUBI+/b29r/N0Y5cJemee+4JyQkAgNtBgQ0AwCDw0EMPqby8XC+88IIWLVqkrVu3atSoUQoGg5Kkp59+Ws8880yPfW98KNjNHD9+XIsXL9ajjz6q5cuX67777lNUVJRqamq0Z8+esPYOh6PH4/xV3Bgj6Y/7wRcuXKhAICCv16u4uDhFR0ertbVVBQUF1vvqi1y7cwIA4HZRYAMAMEgkJyfrk08+0csvv6yFCxequrpaI0eO1IgRIxQMBpWWlva3/V0ul06ePKnr168rKiqqxza1tbUaNmyYNm7cqKFDh1rxmpoaW99Lc3OzfvjhBxUVFWnGjBlWvL6+PqxtT6PRfZkrAAC9xT3YAAAMIqmpqfrwww/V0tIir9erjo4OTZs2TbW1tWpubg5r3z09XPrjvujffvvNWvbqRt2juA6HQxEREerq6rL2nTt3Tl9//bWt7yMyMjLkvN3bNz5lvNtdd90lSWFTufsqVwAAeosRbAAABpmsrCytXLlShYWFWrx4sdauXatvvvlGs2fP1qxZszR27FgFAgE1NjaqoaFBx44dkyTNmDFDO3fu1Jo1a3Tq1CmlpKSoo6NDDQ0NmjdvnqZMmaKMjAxVVFTI6/UqJydHv/76q6qrq+Vyuaz1re0QFxcnl8uloqIitba2yul0qra2tsf7oRMSEiRJq1atUnp6uhwOh7Kzs/ssVwAAeosCGwCAQWjmzJkKBAIqKirSypUr9cUXX+jjjz/WgQMHtHXrVsXExGjs2LHKz8+3+jgcDpWXl6usrEx79uxRXV2dYmJiNGHCBOs+7dTUVH3wwQcqLy/X6tWrNWbMGOXn5+v8+fO2Fq1RUVH69NNPtWrVKvl8Pg0bNkxZWVmaP3++pk+fHtJ26tSp8ng82rt3r3bt2iVjjLKzs/ssVwAAeivC8GQPAAAAAABuG/dgAwAAAABgAwpsAAAAAABsQIENAAAAAIANKLABAAAAALABBTYAAAAAADagwAYAAAAAwAYU2AAAAAAA2IACGwAAAAAAG1BgAwAAAABgAwpsAAAAAABsQIENAAAAAIANKLABAAAAALDB/wCHjsYC4zu6owAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}