Education_at_df_Marriage_age.ipynb

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    "colab": {
      "name": "Education_at_df_Marriage_age",
      "provenance": [],
      "authorship_tag": "ABX9TyPLTbaWNe8Jl52eXpISYz1V"
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    {
      "cell_type": "code",
      "metadata": {
        "id": "O1NrVKNY0xbN",
        "colab_type": "code",
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      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BvPcaQE41-Q0",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import ssl\n",
        "ssl._create_default_https_context = ssl._create_unverified_context"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Mhisg78X2M-P",
        "colab_type": "text"
      },
      "source": [
        "### Data Source: CensusIndia.gov.in"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ka8REDlk01BB",
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        "outputId": "c1db3715-7066-4d0b-dfd0-da5c8eef16b7"
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      "source": [
        "#Data on Education:https://censusindia.gov.in/2011census/population_enumeration.html\n",
        "df = pd.read_excel('https://censusindia.gov.in/2011census/C-series/C-06/DDW-0000C-06.XLSX')\n",
        "df.head(10)"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
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              "      <th>Unnamed: 5</th>\n",
              "      <th>C-6 EVER MARRIED AND CURRENTLY MARRIED POPULATION BY AGE AT MARRIAGE, DURATION OF MARRIAGE AND EDUCATIONAL LEVEL</th>\n",
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              "  Unnamed: 0 Unnamed: 1 Unnamed: 2  ... Unnamed: 22 Unnamed: 23 Unnamed: 24\n",
              "0      Table      State     Distt.  ...         NaN         NaN         NaN\n",
              "1       Name       Code       Code  ...         NaN    Duration         NaN\n",
              "2        NaN        NaN        NaN  ...         NaN   not known         NaN\n",
              "3        NaN        NaN        NaN  ...     Females       Males     Females\n",
              "4        NaN        NaN        NaN  ...          17          18          19\n",
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              "6      C1706         00        000  ...    29913924    16167256    15183266\n",
              "7      C1706         00        000  ...     1885059       14281       28956\n",
              "8      C1706         00        000  ...      907532        4646       14250\n",
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        "df.head(7)"
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              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>5</td>\n",
              "      <td>6</td>\n",
              "      <td>7</td>\n",
              "      <td>8</td>\n",
              "      <td>9</td>\n",
              "      <td>10</td>\n",
              "      <td>11</td>\n",
              "      <td>12</td>\n",
              "      <td>13</td>\n",
              "      <td>14</td>\n",
              "      <td>15</td>\n",
              "      <td>16</td>\n",
              "      <td>17</td>\n",
              "      <td>18</td>\n",
              "      <td>19</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Total</td>\n",
              "      <td>All ages</td>\n",
              "      <td>300399731</td>\n",
              "      <td>339621277</td>\n",
              "      <td>286507311</td>\n",
              "      <td>293077472</td>\n",
              "      <td>35274698</td>\n",
              "      <td>37626730</td>\n",
              "      <td>38198243</td>\n",
              "      <td>39768834</td>\n",
              "      <td>72875228</td>\n",
              "      <td>75983591</td>\n",
              "      <td>58626709</td>\n",
              "      <td>59744823</td>\n",
              "      <td>34791780</td>\n",
              "      <td>34856304</td>\n",
              "      <td>30573397</td>\n",
              "      <td>29913924</td>\n",
              "      <td>16167256</td>\n",
              "      <td>15183266</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          Unnamed: 5  ... Unnamed: 24\n",
              "0  Educational level  ...         NaN\n",
              "1                NaN  ...         NaN\n",
              "2                NaN  ...         NaN\n",
              "3                NaN  ...     Females\n",
              "4                NaN  ...          19\n",
              "5                NaN  ...         NaN\n",
              "6              Total  ...    15183266\n",
              "\n",
              "[7 rows x 20 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LDGOsZwc7w4h",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 159
        },
        "outputId": "196e38be-34ee-463f-ac77-5000db8b322a"
      },
      "source": [
        "df.columns"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Unnamed: 5',\n",
              "       'C-6 EVER MARRIED AND CURRENTLY MARRIED POPULATION BY AGE AT MARRIAGE, DURATION OF MARRIAGE AND EDUCATIONAL LEVEL',\n",
              "       'Unnamed: 7', 'Unnamed: 8', 'Unnamed: 9', 'Unnamed: 10', 'Unnamed: 11',\n",
              "       'Unnamed: 12', 'Unnamed: 13', 'Unnamed: 14', 'Unnamed: 15',\n",
              "       'Unnamed: 16', 'Unnamed: 17', 'Unnamed: 18', 'Unnamed: 19',\n",
              "       'Unnamed: 20', 'Unnamed: 21', 'Unnamed: 22', 'Unnamed: 23',\n",
              "       'Unnamed: 24'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wanAW8926PHW",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 200
        },
        "outputId": "646d5e1d-7ef7-449e-d4d5-18d8815bfaef"
      },
      "source": [
        "df1 = df[df.columns[0:4]]\n",
        "df1.head()"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
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              "    }\n",
              "\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Unnamed: 5</th>\n",
              "      <th>C-6 EVER MARRIED AND CURRENTLY MARRIED POPULATION BY AGE AT MARRIAGE, DURATION OF MARRIAGE AND EDUCATIONAL LEVEL</th>\n",
              "      <th>Unnamed: 7</th>\n",
              "      <th>Unnamed: 8</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Educational level</td>\n",
              "      <td>Age at</td>\n",
              "      <td>Number</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>NaN</td>\n",
              "      <td>marriage</td>\n",
              "      <td>of ever</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>married persons</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Males</td>\n",
              "      <td>Females</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          Unnamed: 5  ... Unnamed: 8\n",
              "0  Educational level  ...        NaN\n",
              "1                NaN  ...        NaN\n",
              "2                NaN  ...        NaN\n",
              "3                NaN  ...    Females\n",
              "4                NaN  ...          3\n",
              "\n",
              "[5 rows x 4 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UVDNtgs776DQ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 200
        },
        "outputId": "c3ad0ea1-b5fe-43be-9275-7c38f45cbb32"
      },
      "source": [
        "df1.columns = ['Educational_level','Age_at_marriage','N_males','N_females']\n",
        "df1.head()"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
              "    .dataframe thead th {\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Educational_level</th>\n",
              "      <th>Age_at_marriage</th>\n",
              "      <th>N_males</th>\n",
              "      <th>N_females</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Educational level</td>\n",
              "      <td>Age at</td>\n",
              "      <td>Number</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>NaN</td>\n",
              "      <td>marriage</td>\n",
              "      <td>of ever</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>married persons</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Males</td>\n",
              "      <td>Females</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Educational_level Age_at_marriage          N_males N_females\n",
              "0  Educational level          Age at           Number       NaN\n",
              "1                NaN        marriage          of ever       NaN\n",
              "2                NaN             NaN  married persons       NaN\n",
              "3                NaN             NaN            Males   Females\n",
              "4                NaN               1                2         3"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "T1DShtXR8c0v",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 289
        },
        "outputId": "07137192-0522-4fbb-fdf4-7250737b9a84"
      },
      "source": [
        "df1.drop([0,1,2,3,4,5],axis=0,inplace=True)\n",
        "df1.reset_index(inplace=True)\n",
        "del df1['index']\n",
        "df1.head()"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/pandas/core/frame.py:3997: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  errors=errors,\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Educational_level</th>\n",
              "      <th>Age_at_marriage</th>\n",
              "      <th>N_males</th>\n",
              "      <th>N_females</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Total</td>\n",
              "      <td>All ages</td>\n",
              "      <td>300399731</td>\n",
              "      <td>339621277</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Total</td>\n",
              "      <td>Less than 10</td>\n",
              "      <td>4276588</td>\n",
              "      <td>7849859</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Total</td>\n",
              "      <td>10-11</td>\n",
              "      <td>979213</td>\n",
              "      <td>3434492</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Total</td>\n",
              "      <td>12-13</td>\n",
              "      <td>1144889</td>\n",
              "      <td>7717216</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Total</td>\n",
              "      <td>14-15</td>\n",
              "      <td>3900762</td>\n",
              "      <td>28124694</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  Educational_level Age_at_marriage    N_males  N_females\n",
              "0             Total        All ages  300399731  339621277\n",
              "1             Total    Less than 10    4276588    7849859\n",
              "2             Total           10-11     979213    3434492\n",
              "3             Total           12-13    1144889    7717216\n",
              "4             Total           14-15    3900762   28124694"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eGESuakS8yl1",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 230
        },
        "outputId": "6d9b534a-3845-46e0-e074-68c2f725012d"
      },
      "source": [
        "df1.tail(6)"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Educational_level</th>\n",
              "      <th>Age_at_marriage</th>\n",
              "      <th>N_males</th>\n",
              "      <th>N_females</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>13821</th>\n",
              "      <td>Graduate and above</td>\n",
              "      <td>32-33</td>\n",
              "      <td>470</td>\n",
              "      <td>91</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13822</th>\n",
              "      <td>Graduate and above</td>\n",
              "      <td>34+</td>\n",
              "      <td>412</td>\n",
              "      <td>72</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13823</th>\n",
              "      <td>Graduate and above</td>\n",
              "      <td>Age Not stated</td>\n",
              "      <td>205</td>\n",
              "      <td>178</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13824</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13825</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13826</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        Educational_level Age_at_marriage N_males N_females\n",
              "13821  Graduate and above           32-33     470        91\n",
              "13822  Graduate and above             34+     412        72\n",
              "13823  Graduate and above  Age Not stated     205       178\n",
              "13824                 NaN             NaN     NaN       NaN\n",
              "13825                 NaN             NaN     NaN       NaN\n",
              "13826                 NaN             NaN     NaN       NaN"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VcdjBD9O9Abc",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "d8603719-a83a-428a-a794-3d8cd75b2c04"
      },
      "source": [
        "df1['Educational_level'].isnull().sum()"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YCb9FLNb9JYQ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 106
        },
        "outputId": "dbab2b21-7a06-4065-ff34-08cdf7788437"
      },
      "source": [
        "df1.drop(df1.tail(3).index,inplace=True)"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/pandas/core/frame.py:3997: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  errors=errors,\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "o_6zUVlW9ia8",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 200
        },
        "outputId": "635d7798-39b5-4b0b-c896-9d9c89e25627"
      },
      "source": [
        "df1.tail()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\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>Educational_level</th>\n",
              "      <th>Age_at_marriage</th>\n",
              "      <th>N_males</th>\n",
              "      <th>N_females</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>13819</th>\n",
              "      <td>Graduate and above</td>\n",
              "      <td>28-29</td>\n",
              "      <td>1162</td>\n",
              "      <td>411</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13820</th>\n",
              "      <td>Graduate and above</td>\n",
              "      <td>30-31</td>\n",
              "      <td>808</td>\n",
              "      <td>175</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13821</th>\n",
              "      <td>Graduate and above</td>\n",
              "      <td>32-33</td>\n",
              "      <td>470</td>\n",
              "      <td>91</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13822</th>\n",
              "      <td>Graduate and above</td>\n",
              "      <td>34+</td>\n",
              "      <td>412</td>\n",
              "      <td>72</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13823</th>\n",
              "      <td>Graduate and above</td>\n",
              "      <td>Age Not stated</td>\n",
              "      <td>205</td>\n",
              "      <td>178</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        Educational_level Age_at_marriage N_males N_females\n",
              "13819  Graduate and above           28-29    1162       411\n",
              "13820  Graduate and above           30-31     808       175\n",
              "13821  Graduate and above           32-33     470        91\n",
              "13822  Graduate and above             34+     412        72\n",
              "13823  Graduate and above  Age Not stated     205       178"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bsbixw69IsZK",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df1['N_females'] = df1['N_females'].astype('int')\n",
        "df1['N_males'] = df1['N_males'].astype('int')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qXDTkNTHJ9M8",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Nm5S2hkTKGnV",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 704
        },
        "outputId": "09e695ea-d7d7-4129-9339-2a9cdac5157e"
      },
      "source": [
        "scatter = plt.scatter(y = df1['Educational_level'], x = df1['Age_at_marriage'],\n",
        "                      s = 0.00001*df1['N_males'])\n",
        "plt.xticks(rotation = 45)\n",
        "fig = plt.gcf() #Get the current figure\n",
        "fig.set_size_inches(11.5, 10.6)\n",
        "fig.savefig('xyz.png', dpi=100)\n",
        "plt.xlabel('Age at Marriage')\n",
        "plt.ylabel('Educational Level')\n",
        "plt.title('Education level at marriage age of Males')\n",
        "# plt.legend(*scatter.legend_elements(\"sizes\", num=16),bbox_to_anchor=(1.1, 1.05))\n",
        "fig.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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jd3THACXu/kjFBHef5+73A5jZZWb2tpl9DnxmZk3M7DMzG2dmk83stIr1zOwmM5tpZkOBbknTB5lZn/B1azObG77OM7Mvw22NM7NDw1XuAA43swlm9mszSzezu8xstJlNMrOrUx2Imb1pZmPNbKqZXZU0fZ2Z3W5mE81sRFiziJl1NrPh4XHcVlUBVbXdcN494fTPzKxNOK1nuJ9JZjbQzFqa2T5mNippvTwzmxy+7m1mg8N9fGRmDVpl9cGUpdUOPFIbCWDIzO/qJqBacnf+8tZkTrp3MF8vXtOg+5b6s6GsPJZ9JBOJ4I/0yNkNe57XZPX6Eg669UPOeXQ4h//zC54dPjfqkAC4+a2pmxK2UGnC+cWL40kktrFav47c+Pok1oYJG4ADa4vLufH1SVGGxfuTl7Aov2iL6e7wweQlzFu5PoKoAt8uX8ubEzYlbBVKy53fvDIhkpgqLCko4uFBs7aIbe7KQp76am4UIW3mgsdGsLigGCc41wqKyjj7keG4R/s9gCBhO/SOz/jNKxO57oXxHPfvQZRH/P2skF9YQt/bPuGG1yZx+VOjueCxkVGHtNE/P5zOre99zeRFa/ho2jKOueuL2AzKpqRtx7Y/MK6GZXoBZ7v7kUAxcIa79wKOBu62QG/gPKAn8AOgby32vRw4PtzWucB94fQbgS/dvae73wP8BChw977hdq80s84ptneFu/cG+gC/NLNW4fTGwAh3PxAYAlwZTv8P8LC79wCWVBNnddsd4+77A4OBv4TTnwF+7+4HAJOBv7j7dCArKe5zgZfNLBO4n6B8ewNPALdXW2p1bHVhyXZvo6QsQUHR9m9na3wxYzlPD5/P9KXrOPm+Lxt037WRd+N75N34XtRhpBTX2DaUldPtTx+y100fRB3KFm4aOIm/vjuNc/87koLCePxxBnh2+DyKk9o//PWdqdEFk2TIzBUppxcUlTD7u3UNHM0mpeUJRs9dnXLeyDmrIr1gfX/SEqrafcJhyDfR3TD47OvlVJVjfL1kbaQ3Wj6fvrzKcntx9PyGDaaSdRvK+HbFlsn2mqJSZq2I7ntQ4eOpy1i1ftPv2dyVRUxemB9hRJs8PWwuxWWbPtjhs1eyYu2GCCPa5Onhczd7v740wbBvV0YSS2VK2nYiZvZgWCM1OmnyJ+6+qmIR4O9mNgn4FOgAtAMOBwa6e6G7rwHersXuMoHHwhqnV4H9qljuBOASM5sAjARaAXunWO6XZjYRGAHsnrRMCfBu+HosQfNLgMOAF8PXz1YTZ1XbTQAvh6+fAwaYWXOghbsPDqc/DRwRvn6FIFkj/P9lghrJ7sAn4fH9CehYOQAzu8rMxpjZmBUrUl8Mbau0OmhuYECaNexPRZOk58NlN+iepb7UxblYX1o12XSWpafHJ86crM2/d3Fp2VRVk2t3yEiLLkgL/6WSZlXPawiZGdWXS+Z2NmPfHpk1nPNRfnczqimXzAjPNYB0syrPqcwYfFmzM7aMLg5xAeSmaNZa03nYUNJTXO/EJbZ4fHpSX6YS1KQB4O4/B44F2iQtk3yb6MJwXm937wksA2pqyFvGpvMoedlfh+sfSFCLlVXF+gb8Iqx56+nund39480WMDsKOA7oH9aojU/aV6lvaodQzub9NKu9rVrDdiur6Rbty8A5ZtYVcHf/Jjy2qUnH1sPdT9hiw+7/dfc+7t6nTZs2lWdvl1ZNqir22svOSGOXxg3bif/gzq2446wenHFQe77847ENuu/amHvHKcy945Sow0gprrFlpqfFNrYbTtyHgT87lOF/OGazGwZRu7hfHrs22/QdfviiPhFGs8kPeuyacnqHlo3o1Cq6fncZ6Wkcs2/blPOO3bcdaREmRmcc1JGqrvvSDI7ZJ3XcDeHE7rtRVdEc0nmXSMvthP12rTK2nwzIa9BYKsvNSqdPXsstpndokROLAVyO229X9m67aTy1Q7u0Yr/20fZPrHDJoXm0bbrpt+3cvh1p0Wj7r1fqwo0n77PZ+7ZNszgs4r66FeLz10nqw+cENWfXuvvD4bTq/qI2B5a7e6mZHQ10CqcPAZ4ys38QnDM/BB4N580FegOjgLMrbWuhuyfM7FKg4rbKWiC5h+5HwLVm9nm4367AIndfX2lbq9290Mz2IRgJsyZfETTpfI4gGa3qeKvablp4PC8RDJwy1N0LzGy1mR3u7l8CFxM0ncTdZ5lZOfBnNtXQzQDamFl/dx8eNpfs6u4N1sbpjIM6MmF+Puu3o1+bA8fs267ugqql8/ruwXl992jw/crO6aA9trz4ilpuVjrDbjyOZWuLaZGbRW5WPEbLu/nU/Rk3L58FqwqpaDjXJCudRy/uHflgAv848wCmP/QVS/KLKHdIN2jfMpe/n9kj0riO2actPTo0Z+LCgs3uAKYbXHnEnrSNcKCDDi1y+eWxe3HfZ99u1hSxcVY6d59zYGRxAbRsnMUdZx7AjW9M2hibESST58Tg78OTlx/MFU+O2tgst2u7pjz704MjjiqQnmZ8dP0RTFuyhsz0NLq2i8/IljmZ6Qy78Vi+XrKWZrkZsUhyK1zYrxMdW+byzsTFdGiZy1VHdIl8wJsKFofOklJ/woEv7gEOAVYQ1Kw94u4vm9llQB93vy5ctjXwDtAEGEOQxJzs7nPN7CbgUoK+avOBce7+rzDZeYWglus94CJ3zzOzvYHXCa75PwR+7u5NwsTlI4JmkE8R9D27jSARtDDG0929IOkYsglGwcwjSIRaALe4+6DkofnN7GzgVHe/LOxf9kJ4LG8B11cewr+m7QL/JWi+uRw4191XmFlP4BGC5Hc2cLm7rw63dwNwF9DZ3eeG03oS9OdrTpDw3uvuj1X1efXp08fHjBlT1eytVlxaTq+/fbJdg5Ectlcrnv9pbfJkEdlZlJYn+HTaMqYuXkPHlrmcemD72NRSlpUn+GLGCmatWMdebZpwVLc2sRg1r7i0nAe/+Ianh81jbXEZu7XI4TfHd+WsXh1jcTE9dt5qHhk8i6X5RRy3XzsuPTQvNrUfi/KLeHX0fPKLyji5+64c3HmXWJRZhaKSchLuNI7Jd0C+v8xsrLunbFahpE0kRuo6aYNg8ILnRsyjdBse1tYoK51HLurNEV3rttmmiIiIiGyuuqQt+ltPIlKvfnXs3rRukr3VnfBzMtM4Yu82HL53PNpyi4iIiOyslLSJ7OBaNMrilav707ppdrUjcSXLzUznoN1b8p/ze8aqCYqIiIjIzkhJm8hOYPddGvHBrw6nT15LsjPSqhy+NjczneyMNM7p05Fnf3Iw2Rnx6HwrIiIisjNTj0mRnUTrJtm8dFV/5n63nqeGzeWVMQvYUJogI90oLU/QtmkOVx7embP77E7z3IYd4l9EREREqqaBSERipD4GIqmKu1NcmqCotJwm2Rlk1fDwVxERERGpP9UNRKKaNpGdlJmRm5Uem2c/iYiIiEhqurUuIiIiIiISY0raREREREREYkxJm4iIiIiISIwpaRMREREREYkxJW0iIiIiIiIxpqRNREREREQkxpS0iYiIiIiIxJiSNhERERERkRhT0iYiIiIiIhJjStpERERERERiTEmbiIiIiIhIjClpExERERERiTElbSIiIiIiIjGmpE1ERERERCTGlLSJiIiIiIjEmJI2ERERERGRGFPSJiIiIiIiEmNK2kRERERERGJMSZuIiIiIiEiMKWkTERERERGJMSVtIiIiIiIiMaakTUREREREJMaUtImIiIiIiMSYkjYREREREZEYU9ImIiIiIiISY0raREREREREYkxJm4iIiIiISIwpaRMREREREYkxJW0iIiIiIiIxpqRNREREREQkxpS0iYiIiIiIxJiSNhERERERkRhT0iYiIiIiIhJjStpERERERERiTEmbiIiIiIhIjClpExERERERiTElbSIiIiIiIjGmpE1ERERERCTGlLSJiIiIiIjEmJI2ERERERGRGFPSJiIiIiIiEmNK2kRERERERGJMSZuIiIiIiEiMKWkTERERERGJMSVtIiIiIiIiMaakTUREREREJMaUtImIiIiIiMSYkjYREREREZEYU9ImIiIiIiISY0raREREREREYiwj6gBEpGHlF5YwcWEBkxbmM3vFOkrKnMbZ6ey3WzN6dGzO/u2bk5OZHnWYIiIiIhJS0iayE3B3hs9eyX8Hz2bYrJVkZ6RRVFpOWcI3LpOTkUZGehrlCees3h244rDO7NmmSYRRi4iIiAgoaRPZ4S1bU8yvXhrPpIUFFJaUA1BSnthiueKyBJQF018atYDXxi7k4n6duOHEbmRnqOZNREREJCrq0yayA/ti+nKO/tcgxsxdvTFhq42yhFNcmuDZEfM49u7BLFhVWI9RioiIiEh1lLSJ7KA+nrqUa58fS2HJ5s0gt0ZxaYLF+UX88IGhzF+pxE1EREQkCkraRHZAUxYV8KuXxlNcumUzyK2VcFhTVMqPHx1GYUlZHUQnIiIiIltDSZvIDqakLMHPnh9HUR0kbBUSDvmFpfzt3a/rbJsiIiIiUjsaiERkB/PokFmsWFtc59vdUJZg4PiFnNd3dw7cvUWdb19Evj+mLi7grQmLWb6mmGa5mRy3bzsG7NWatDSLOjQRkR2SkjaRHUhpeYLHv5xTp7VsyTaUJXh40Lc8cnGfetm+iMTbzGVr+cUL45m3cn0w4mzo1dELaJqbyR1n9eCYfdpFFt+i/CKeGTaX18ctZN2GMppkZ3B2745c0j+P9i1yI4srOba3Ji6mqKScXRpncUm/TpzVpyPNcjIjjU1E4s/ct22AAhGpe3369PExY8Zs8/ofTlnKb1+dwPoNtR8pcmtlZ6Qx7MZjaNUku972ISKBb5evZcqiNTRvlMlhXVqTlRFdr4Zpi9dw9iNfUVhS9U2h7AzjXz8+kB8e2KEBIwt8NGUJ1788gdLyBEn5JBlpkJmezn3nH8Tx+0WTUA4ct5A/DJxMaVmC8qTLrux0Iycrg+d/egjdOzSPJDaA8oTz+fTlvDx6PvmFpfTcowWX9s9j910aRRZTskTCmbwoeGxN9w7NaKokV+qZu2PW8C0HzGysu6e8M15vSZuZOfC8u18Uvs8AlgAj3f3UatbrCbR39/ermN8HuMTdf1kPYceGmQ0CbnD3bb+Cr4PtmlkL4AJ3fyjFvDzgXXfvvhX7fypc57XartOQzOwyoI+7X7cN61ZZVrW1vUnbr1+ewMDxi7Z5/dpokp3OnWcdyCkH7Fav+xHZmeUXlnDp/0YxeXEB7mAGWRlpPHhBL47dt+ETj0TC6X/HZyxbs6HGZbMzjC//7xjaNstpgMgCExbkc+6jw9hQVvU1TXaG8eo1h3JAx4Zt3j3s2++44unR1Q4M1TQng09/cyTtGrDMKhSVlHPB4yP4enEBxWH5GZCVYdxx5gGc0atjg8eU7MtvVvDrlyewpqgUAxy4YkBn/u/EfSJvjptIOG9PXMwLI+dRWp7grN4dOafPHpHeXJHtM/Sb77jtvalMX7qOxlnpnHfwHtxwQjdysxrmebXVJW31eVatB7qbWUV7hOOB2lxN9gR+kGqGmWW4+5i6TtjM7Hv/5OB6PIYWwM/qadsNIrxh0BAiL6vx81fX+z4KS8qZsKD+9zNv5XpGzF5JcWn91RqKAHy9OBht9fb3plG0Fc8zrE8XPjaCiYsKSHhwkZrw4BEcVz07hmmL1zR4PIO/WUFBYWmtli0rd54bOa+eI9rc3R/PqDZhA9hQ5vz745kNFNEm//jg6xpH8i3cUMYTQ+c0UESbu+WdqUxakL8xYYPgnNtQ5tz4xiRmr1gXSVwQ1O7+5KlRfLeuhJJyZ0O5U1LuPD5kNvd82vCfZWXXvzSe37wygVFzVzN+QQE3vzmV8/87nPJtfMxOXRs8Yzmn/GcIpz84lHENcH1QW+7Ow4O+5ZDbP+G4uwcxdl48Yhs8cwWXPzWS6UuDc359STlPDp3Dxf8bSRxaJtb3rYD3gVPC1+cDL1bMMLODzWy4mY03s2Fm1s3MsoBbgXPNbIKZnWtmt5jZs2b2FfCsmR1lZu+G22hiZk+a2WQzm2RmZ1UOwMyODfcx2cyeMLPscPpcM7vTzMYBP660zo/NbIqZTTSzIeG0dDO7y8xGh/u6Omn531MvcpcAACAASURBVIfbn2hmd4TTeprZiHDZgWbWMpw+KNzvKDObaWaHh9NzzewlM/vazAYCuUnbf9jMxpjZVDP7a9L05GO4Mfy/Yt7eye8ruTgs3ylmdnC4/C1mdkPS+lPCmrQ7gC7h8nel2FaGmT0fxv2amTUK1+9tZoPNbKyZfWRmW1TLpPpszKyvmb0Rzj/NzIrMLMvMcsxsdoptdAnLebKZ3WZm68LpR5nZl2b2NjAtnPZmGM9UM7sqaRuXh5/FKOCwpOlPmdnZSe8rtt3EzD4zs3Hhfk8LF9mirMzsd0nnzMbPrj64OwtWF9XnLoDg4nH8/Px63cfb4xdx5F2DOO+/Izjsjs9il7iNn7+Kl0bNjTqMlAbPWMa7k+q3tnVbzFiylr3/8B49/vweK9fVXFvTUGYuW8vJ9w3lrQmLeezLORzzr88j/+M8ZVEB05euTTmvPAH3RnCx+uzweZv1YatOucMLI+fXc0SbfLduAyPnrKrVssNmr2TV+pJ6jmiTud+t55tlNSc95Q7Pj5zf4Ofeug1lDBy3cLMmm8lKypwnvoommQS4//NvSHUfpczhsSGzI73JMmVRAe9NXkJyfpYgqPX9fPryyOKqMHbeai59cjRTl6xlwoICznpoWKQJeLInhs7hzg9nsGxtCd+uWM85jwxj3sr1UYfFrW9PofLlRgKYsDC/1r8x9am+k7aXgPPMLAc4ABiZNG86cLi7HwTcDPzd3UvC1y+7e093fzlcdj/gOHc/v9L2/wwUuHsPdz8A+Dx5Zrjfp4Bz3b0HwcAr1yYtstLde7n7S5W2ezNworsfCPwonPaTcF99gb7AlWbW2cxOBk4DDgmX/2e4/DPA78O4JgN/Sdp+hrsfDFyfNP1aoNDd9w2n9U5a/qawqvQA4EgzOyDFMdwOFFjQvBTgcuBJUmvk7j0JaoWeqGKZCjcCs8LP43cp5ncDHgrjXgP8zMwygfuBs929d7iP25NXquazGU9Q2wpwODCFoLwPYfPzp8J/gP+E21hYaV4v4Ffu3jV8f0UYTx/gl2bWKkwm/0qQrA0gONdqUgyc4e69gKOBu83MqFRWZnYCsDdwcHhMvc3siFpsf5uUJZxEA/3BX7ehfp/X9rf3pm18vXJ9KaPnRv9jmeyMh4Zz4xtTGTHru6hD2UxxaTmXPjmG616YwJL8+k/gt8Y1z46m1GFtKfzt3alRh7PRcyM2rxFasqaEJQV1P/rq1hi/IL/Ki2iAUXNWNlwwoUVbeUNodS1r5erC4vwistNrdzmTlZ7GkoKG+24sWF1IZi2byhWVltfJszW3xtzv1pORVnV8DpHWgtT02z/7u+iSkBGzV6b8npZ7UMMVtbcmbH5J5AQ1SXHw8ugFm70vd/jq24b/XUtWUpZg9neFKeeVlTujd/Skzd0nAXkEtWyV+6g1B141synAPcD+1WzqbXdP9St7HPBg0v4q/7J0A+a4e8VtyaeB5Ivml0ntK+ApM7sSqGh2eAJwiZlNIEgeWhFckB8HPOnuhWEMq8ysOdDC3QdXsd83wv/HEpQP4fznwm1MAiYlLX9OWGs2nqCckhOL5GN4HLjcgqaS5wIvVHF8L4b7GQI0s6Av1rZa4O5fha+fI0h8ugHdgU/C8voTULlRfMrPxt3LgFlmti9BsvNvgrI5HPgyxf77A6+Grysf7yh3T75F+EszmwiMAHYn+PwOAQa5+4rwpkFV50QyA/5uZpOAT4EOQKpOJieE/8YD44B9wn1uvjGzq8Ka1DErVmz7D2q6GQ11kzYjvX77EVTu/L5rBP08amOvdk2jDmEzWUkXri0bx6uj/kF7bPqZ6d1plwgj2VzHlluOKNgsN9qya5qdQXXfsCbZDT/w89b20clowL5G2Rnptb5hlXAnuwH7G2VnpAdXy7Xg7mTW829rZblZNZddFOdbbfadcKdpdnTf1RaNsqjqNI/DQF27Ntvyt22XxlkRRLKlNs22LJ+WjaL93c1IsyqvbYzo/y5Awzxc+23gXyQ1jQz9DfgiHMTih0B1V2X1VWeacrvufg1BorE7MNbMWhF8Zr8Ia1F6untnd/94G/db0TaonBoeu2BmnYEbgGPDWrv32Lysko/hdeBk4FRgrLtXddui8i+0A2Vsfj7U9io51bYMmJpUVj3c/YRabg9gCMFxlBIkRQPCf6mStupsLBszO4ogwe4f1oiOp+Zj3FgmZpYGVPzaXQi0AXqHNZbLqtiWAf9IKoe93P1/lRdy9/+6ex9379OmTZutOsBkaWlG0wb647pb8/odOvvxS/vSo0NzmudmcOtp+7F3zJKjuXecwtw7TqF1DP4wJ0tLs42x5WTG64kud597EPee25MnL+vDxf3zog5noysO60yPDs02vr/51P0ivUgFOHbftlRXcXTZoXkNFkuFAXu1rvICNZUeDTgSYpc2jcmoZU1bdkYanVs3qeeINunRoTlltezf1L1D81ofR13Zs3Vj2jSt+ncsK904r+8eDRjR5i48pBNV5bGdWjVij1bRjW55UvddyUoRXGYanN072sFbAC49NI8OSTelurdvxik94jGA2O2n9yAnY1PZde/QLLKRXSukpRmn9Uw96m1mGrEYfK0hfh2eAP7q7pMrTW/OpoFJLkuavhao7RXaJ8DPK95U9BtLMgPIM7O9wvcXA4OpgZl1cfeR7n4zsIIgefsIuDZs+oeZdTWzxmEMlyf15drF3QuA1RX91Wq53yHABeE2uhM0hQRoRpB8FJhZO4JkJiV3Lw7jfJiqm0ZCUAuHmQ0gaPJZAMwlaE6ImfUCOofL1vR57GFm/cPXFwBDCcq9TcV0M8s0s8o1qdV9Nl8SNB0d7u4rCGo1uxE0laxsBFDRl/G8auJsDqx290Iz2wfoF04fSdDktFX42Sb3b5zLpmaqPwIyk7a13N1LzexooFM4vXJZfQRcYWZNAMysg5m1rSbG7bbPbvWf3GRlpHFI5/qtKdmlcRbv/GIAE/9yIpf071zzChJ7ZsbpB3Xg6Aif4ZVKRnoa7/zicCbfcgIzbzuZKwZEf741zcnklh/tn/Jidc82jbkogqT34v6dSK9l1paTkcY1R3ap54g2yUhP49JDO6W8gE6WlW5cdmjnWh9HXcjNSufs3h3JqGGXjbLSubYBy6yCmXHrad3JThGgAR13aRTpxerF/Tux327NtvguNMpK455zD4omqFCT7Ayev7IfzXIyyDBIt+Dcf/DC3rF4VELj7Ay++O1RvHZNfwb+7FDeum5Ag98UqEpe68YM/8NxPHJRb579ycG89fN4xPbnU/eja9smVFTGG0HC9u9zD4rFTdp6v53o7guB+1LM+ifwtJn9iaD2qMIXBINqTAD+UcPmbwMeDJtYlhP0Tapoeoi7F5vZ5QTNMDOA0cAjtQj7LjPbm+Dz+gyYSNBcMQ8YF/ZfWgGc7u4fhv3IxphZCUEz0D8ClwKPhMncbII+ZtV5GHjSzL4GviZoOom7TzSz8QR9ABcQNN2szvPAGUB1tYDF4TYzgSvCaa8TNP+cSpDIzAz3v9LMvgrL+IMU/dpmAD83sycIBvx42N1LwgE87gubimYA9wIbO7LU8NmMJGhuOCR8PwnY1VP30L4eeM7MbgI+BAqqOOYPgWvC8p1BkOzh7kvM7BZgOJAPTEha5zHgrbBJ5Ydsqrl7HnjHzCYDYwg+m5RlFTbzHB6cMqwDLgLqrbF7/y6tmLAgn9LqOsRsp8x0o+fuDTtktkh9i9tzny7ql8eebZrwzw+nM2PpWhplpXPZYXn8dEAXcjIbfsDj9i1yObNXR94Yt7Da35c0g85tGnP0PvV6f2oL1x65Fx9NXcbsFetSxpeZbnRp24Srj9yzQeMC+N1J3RjyzQoWripM2QcqJyONI7u24aTuuzZ4bABH79OWhy7szZ/enMLqwhLSzCgtT3B0t7b88+wDIjnfKuRkpvPqtYfy+tiFvDBqPoUl5RzZtQ1XHrEnHSJ+WDoETb3H/fl4xs3Pp6w8Qe+8lkGT2JjIykijT158mqMna9k4K7JzvirNczP54PojGDRjOaPmrKJVkyxO79mhQR9fUh09XHsHY8EIkM3d/c9Rx9IQwqS4yN3dzM4Dznf302paL6629zlt81au54R7hrChlqO8bYs2TbMZ+YdjI38+jog0rLLyBNe9MJ5BM5anHEkyKz2NTq0a8fLV/SPpO7N+Qxm/f30Sn0xbBkBpWWLjICAn7N+OO848gMYRNX0tKCzlxjcm8fn05aSnGWWJoP+aO1zaP48bTuzWoDWAqbg705asYV1xGXu2aVJts0kRqR8WxcO1peFZ8KiALsAx7h6vYe3qSdgE9QGCWtF8ghEiv402qm23vUkbwNmPDGPM3PoZ7SsnM43fHN+Vq45o+GY8IhI9d+fjact4ZNAsJi/KJyM9jfKEs1vzXK4+ck/O6tUx0poZCB4B8PHUZeQXldAiN4sT9m8Xi6ZNEMT2xfTlrNtQRtumORy7b9vIy0tE4kNJm8j3RF0kbRMW5HPeo8Nr/UylrbFL4yyG/N/RkQ/UICLRKygsJb+ohNysdNo0ySZsBi4iItuouqQt+l5/IlKneu7eggv7dSKnjoe1zslM477zDlLCJiIANG+USadWjWnbNEcJm4hIPVPSJrID+t2J3ejStslmz+3aHrmZ6VzUrxMD9m5dJ9sTERERkdpT0iayA8rJTOfFq/qxV9sm2/0g2dzMdM7q3YGbfrBvHUUnIiIiIltDSZvIDqpZTiavX3soZ/bqSE7m1n/V09OM3Mx0/nTqvvzttO5q/iQiIiISEXVOEdmB5Wal848ze3Baz/bcNHAyi/OL2VBWTqKa8Yey0tPAoP+erfj7mT1i8SwcERERkZ2ZkjaRnUC/PVvx2W+PYuKCfJ4ZPpdRc1axpKCYrIw0zCCRgPKE07lNY47dpy0X9etEeyVrIiIiIrGgpE1kJ3Lg7i24e/eeABSXlrNsTTGl5U5OZhq7Nc+N/OGuIiIiIrIlJW0iO6mczHQ6tWocdRgiIiIiUgMNRCIiIiIiIhJjStpERERERERiTEmbiIiIiIhIjClpExERERERiTElbSIiIiIiIjGmpE1ERERERCTGlLSJiIiIiIjEmJI2ERERERGRGFPSJiIiIiIiEmNK2kRERERERGJMSZuIiIiIiEiMKWkTERERERGJMSVtIiIiIiIiMaakTUREREREJMaUtImIiIiIiMSYkjYREREREZEYU9ImIiIiIiISY0raREREREREYkxJm4iIiIiISIwpaRMREREREYkxJW0iIiIiIiIxpqRNREREREQkxpS0iYiIiIiIxJiSNhERERERkRhT0iYiIiIiIhJjStpERERERERiTEmbiIiIiIhIjClpExERERERiTElbSIiIiIiIjGmpE1ERERERCTGlLSJiIiIiIjEmJI2ERERERGRGFPSJiIiIiIiEmNK2kRERERERGJMSZuIiIiIiEiMKWkTERERERGJMSVtIiIiIiIiMaakTUREREREJMaUtImIiIiIiMSYkjYREREREZEYU9ImIiIiIiISY0raREREREREYkxJm4iIiIiISIwpaRMREREREYkxJW0iIiIiIiIxpqRNREREREQkxpS0iYiIiIiIxJiSNhERERERkRhT0iYiIiIiIhJjGVEHICINa8GqQgaOW8iw2Sv5dvk6ysqdRlnp7N+hOUd1a8MPD2xPs5zMqMMUERERkZCSNpGdxKL8In7/2iRGz11Fwp3Sct84L7+olMUFxQz99jtufWcaFxyyB78/aR9yMtMjjFhEREREQM0jRXYK705azHH/HsywWd+xoSyxWcKWrKiknA1lCV4cNZ+j7hrEN8vWNnCkIiIiIlKZkjaRHdwb4xZyw6sTKSopJ5E6V9tCcWmCpWuKOeOhYUrcRERERCKmpE1kBzZz2Vr+OHAyxaWJbVp/3YYyLn1iFMWl5XUcmYiIiIjUlvq0ieyg3J2fPz9umxO2CqsKS/jXxzP40yn71VFkIvJ9tnxNMU8Pm8uzI+axbkMZWRlpnNJjN64+sgtd2zWNOryNEgknLc2iDkNEpE4oaRPZQQ2fvZJF+UXbvZ3i0gTPjZjHr4/rSuNs/WSINKRV60uYvmQNLRplse9uTTGLNgkZO281Fz4+YrObQcWlCV4ft4i3Jizi9jN6cG7fPSKLr6CwlIcGfctzI+axvqScxlnpXNI/j2uO6kLz3OhHxf12+VpeGrWARflF7N+hGef22YM2TbOjDktEvgdi0TzSzNzMnkt6n2FmK8zs3fD9j8zsxirWXVfF9KfM7Ozw9SAz67MV8Wzt8i3M7GdVzMszsym13Va4zsbY48DMrjezRtXMf9zMduhqGDM7quJ8/L54YugcCkvqplljmhnvTVpSJ9sSkZqVlSe47vlx9LntEy54fCSn3PclfW77lBlL10QW0/I1xVskbMnKEvDHgZMZMXtlA0cWWL2+hBPvHcKjQ2azPvztW19SzsODZ3HSvUPILyyJJK4K93wyk5Pv/ZLHh87hgylLufujmQy48zO+mL480rgqrF5fwuNfzua2d6cxeOYK3GvZCbqBrFpfwsLVhZTXtnO2yDZanF/Ep9OWMWbuKhIxOt9ikbQB64HuZpYbvj8eWFQx093fdvc7IomsdloAKZO2HcT1QMqkzczS3f2n7j6trnZmZt/76pw4HMOYuavrbFuFJeV8+e2KOttebbi7/jhLg/h46lJOvncIFz0+kpXrNkQdDgB/eGMy705esnHwIAdWri/hR/cPZd2GskhiemrYnBqbW5cn4K4PpzdQRJv7+/vTWLqmOOW8JQXF3PFBNHEBfPnNCh764ltKk37THNhQ5lzz3NjIz7vZK9Zx2B2fcdt7X/P40Dlc9sQofvHiuEhjquDu3PDqBHr97ROOuPMLDrvzM5ZV8TlHYdi333H4nZ/T7++f8s7ERTWv0MBWrttAQWFp1GFsIZFwxs5bxewVKeteIvPB5CUMuONzfvrMGM5+ZDinPzSUkrLt62ZSV+KStAG8D5wSvj4feLFihpldZmYPhK87m9lwM5tsZrclLWNm9oCZzTCzT4G2qXZiZieE648zs1fNrEkV8VxsZhPMbIqZHRyue4uZ3ZC0rSlmlgfcAXQJl78rxbYyzOx5M/vazF6rqLUys95mNtjMxprZR2a2W4p4jzWz8eHxPmFm2WbW18zeCOefZmZFZpZlZjlmNjvFNp4ys4fNbISZzQ5rjZ4I43kqabmHzWyMmU01s7+G034JtAe+MLMvwmnrzOxuM5sI9E+umTSzk8KynWhmn6WIJcfMngyPZ7yZHR1Ov8zM3jazz4HPKq3T2MzeC7c5xczOra78zGwvM/s0XH6cmXUJz4+7wvUnJ23jqDD+18xsevg5WdKxTDezccCZSfEcHJ5D481smJl1S3UMZvaMmZ2etN7zZnZaivOjzhUUlrK+pG4v7CYvLKjT7VVn9foNdP7D+3T54/vc/OakBttvbf3no6mc8/CXsbsTDfCzZ0ZwwaNDog5jCx9NWULeje+Rd+N7TFvccOdSTcbPX81Vz47l66VrGfrtdxz5z88j/1yLSsp5Y9zClPM2lDsvj5rfwBEFnhtRu/1OWFDQ4ElIcWk5b45fXO0yb4xbyIayaAZVenjQ5glbspKyBK+MWdDAEW3ulrenUpiUkDvw/qSlTFqYH11QoTfGLeK1sUEylACWFmzgqqdHRxtUaP7KQi54fCQLVhexdM0GfvHiBMbOq7sbptvroUHf0Pu2Tznw1o95Z0K8EsozHvyKsx4ezjF3D+bl0fOiDgcIBl+77oVxJKdokxau4aFB30YWU7I4JW0vAeeZWQ5wADCyiuX+Azzs7j2A5PZaZwDdgP2AS4BDK69oZq2BPwHHuXsvYAzwmyr208jdexLUoD1RQ+w3ArPcvae7/y7F/G7AQ+6+L7AG+JmZZQL3A2e7e+9wH7dXijcHeAo4NzzeDOBaYDzQM1zscGAK0Bc4hKrLrSXQH/g18DZwD7A/0MPMKrZ1k7v3ISj/I83sAHe/D1gMHO3uR4fLNQZGuvuB7j40Kd42wGPAWe5+IPDjFHH8HPDweM4Hng6PE6BXWB5HVlrnJGBxuL/uwIc1lN/zwINhDIcSnCdnhmV2IHAccFdSknwQQW3ifsCewGFhTI8BPwR6A7smxTMdONzdDwJuBv6eNC/5GP4HXBaWTfMwlvdSlEmdW1dSRkZa3X6919dRU8vaeH3cpj8uz4yI9mImlXu+mMuoeWsYHlEzsKpsKCvn/WkrGTZnLfNXro86nM3c8tamVuL3ffZNhJFs7t1KzX7XlSRYUxxNTVaFxQVF1T6eI6rzrrY1fGkW1Ao2pFW12F/CIT+iGodvllVdm+DA1MXRNXsFGL9gy+QsAYyfH33SNnLOqi2mTV8aj0fRTEyR1I6Zu2W8Ubn/05kbX98ZUQ14KmXlCSYu2nTz7v7P45EULVhVmPK3d8SsePytj03S5u6TgDyCC/n3q1n0MDbVwj2bNP0I4EV3L3f3xcDnKdbtR3Bh/pWZTQAuBTpVsZ8Xw7iGAM3MrEUtDyWVBe7+Vfj6OWAAQSLXHfgkjOVPQMdK63UD5rh7xbfuaeAIdy8DZpnZvsDBwL8Jjv9w4MsqYnjHg9vHk4Fl7j7Z3RPAVIJyBzgnrFUaT5DQVdVPrRx4PcX0fsAQd58D4O6pfrkGhGWAu08H5gFdw3mfVLHOZOB4M7vTzA539wKqKD8zawp0cPeB4T6K3b0w3G/F+bEMGEyQ6AKMcveFYXlMCMtjH4Ky/yYst+c2hUNz4FUL+ipWJL8VNh6Duw8G9g6T2fOB18PPbjNmdlVYwzlmxYq6aYKYnZFGeR3XFmSlN9zPxdHdNlWUd98tPqPRVdZ7j5ZRh7CZ7Iz0ja87tqyyG2okzumz+8bXp/XsEGEkm+vTafPP0ICmEQ+407pxdhBIFfZqW1UDkfqVlVG734CEO00auAyb5WaSoPrfPMdpmhPNZ9u2hsFGOrTIrXZ+fdu95Zb7TwM6tYr+d6RL6y1jiMvgLZ1bN67VtKgcmfS39NQD2kcYyeYy0tNonPRVPK5bu+iCSVLV9zSq39zKIu93U8nbwL+Ao4BW1Sy3rVejRnBRfX4tlq28DwfK2DzRzaF2Um3LgKnu3r+W26hsCHAyUAp8SlAjlw6kqukDqGirkkh6XfE+w8w6AzcAfd19ddhssqrjK3b3+qh2SVk14O4zzawX8APgtrDZ5UBSlF+YtG2t5PIop+bvxd+AL9z9DAuaxw5Kmlf5GJ4BLgLOAy5PtTF3/y/wX4A+ffrUSabVqnEW6XU8ylzXdg33o9WlbRNG33Qsi1YXc0DH5g2239qae8cpNS8UkbjG9usT9+GsvruTlZHGrs2ivUBNdnKP3fjJgDyeGTaXxtkZPHZp38iHiW/eKJO+eS0ZNWfLZlZpBpcf1jmCqODUHrvx2riam1h1aJlL+wZOQppkZ3DYXq0ZMvO7Kpc5omsbGmVFc9lzxYA9+d1rE1Pexc9MM847OLoRNwFu/uH+XPjYCMqT4tuzbWMO37tNdEGFLj2sM6+NW8A3ywsByEqDhy+q9Vhx9ap7h+bceFI37vxwBg5c1G8Pjt8vHgkIBOU0ZOYKsjPS6NelddThbGbYH0/g2RFz2a15Dmf2qlxnEY1WTbL5+VFdeGDQrI3TmmWn89sTukUY1SZxS9qeAPLdfbKZHVXFMl8RXAA/B1yYNH0IcLWZPU3Qn+1o4IVK644AHjSzvdz9WzNrTFArM5MtnUvQj2sAUODuBWY2FzgVIEwiKv5yrgWqSxb2MLP+7j4cuAAYCswA2lRMD5v7dXX3qUnrzQDyKuIFLiaoIYKgRu0Z4Bl3X2FmrYB2BE0lt0UzgoSjwMzaESSEgyodX9V/DQMjgIfMrLO7zzGzXVLUnH1J8Ll9bmZdgT3C4+xV1UbNrD2wyt2fM7N84KcE/QhTlp+ZLTSz0939TTPLJkhmv2TT+bELQc3k7whq1FKZTlD2Xdx9FkFNWYXmbBoo57IayuQpYBSwtC4Ha6mJmbF/+2aMqaO29dkZaRzapbr7KHWvTdMc2jSt7X0R+T7YY5f43IFO9udT9+fPp+5f84IN6LGL+3LK/UNYuHrTgAtpBvedfxDtmkXzvbj6yC68OWER1fXJTzf4zfHRXODcfOp+nHr/0JSDpeRkpkf6rMnTD+rAWxMWMWzWSsqSMrfMNOP647tGXjvTb89WvP6zw/jPpzNZuLqIH/TYlauO6EJ6DJ5zl5OZzvu/OpJhs1ZSuKGM3nktaRujvw3XHLUXVx/ZBXciv+FTmZltVtsWJ80bZXLdMXtHHcYWbjhpHw7esxXDZn1H6ybZ/LjP7rF4XAjELGlz94XAfTUs9ivgBTP7PfBW0vSBwDHANGA+MDzF9leY2WXAi+HFPATN6lIlbcVmNh7IBK4Ip70OXGJmUwn6js0Mt7vSzL4Km8t9kKJf2wzg52b2RBjfw+5eYsGw/veF/Z0ygHsJmitWxFtsZpcTNMXLAEYDj4SzRxIkaRUjDkwCdvVt7EHv7hPD450OLCBIjiv8l6Af2eKkfm2ptrHC7P/Zu+8wqeqzjePfZ2Y7S2/SlKrS24qiqFhj1EjsvUSNRhNfU0xijEZNYmI0lljRqFETY+9RwYJ0BJbee+9tG1tn5vf+MbOw7M4us8s04P5c115MOefMM8OUc59fOXYL8IGZeYCtBGcCreo54Hkzm0ew5fIG51yZ1d0q1JfgGLQAwZbF2/bz+l0LvGBmfwwtfynB98dQYA7Bls7fOOc2m1nY0BZ67W8BPjOzYoKhrzKYP0JwLN697GeMmnNui5ktAj6qa7lYuP7EzizaXMDusgNvFHUkV5c2kUNd06xUJvzmdCav2MHkFTs4okk6IwZ2oElG4nYeerRtzEMX9uOeD+fiDxPcvAYXD+7IiAGJ6YbVvU1jPv7pMH7z3hwWbCzADJwLFHizvgAAIABJREFUtoY8ckk/urVOXBcnr8f414+G8PHsDbwycRXbiso4uk02t53WnROTpAVkQKdm/OtHQxJdRlipXg+nHp34Vr/amBkJPoWiRNEpR7fmlCR8v1miZ8kSOZRZcKbQecCg0Fi8OuXk5Ljc3NyoPHa5L8AJf/mGnQd4bqJUr3HGsW0YeW1ydEcRkcSaunIHj45ezKx1+XgIjmHr0DyTX551DCMGtE/4CcABNuWXsKWgjLZN0mnXNHm644qI1MXMZoQmBax5n0KbSGyY2ZkEZ5B8wjn3ZCTrRDO0AYxbuo1b/52733Mr1aVRupexd52WNAO/RSQ57CgqY8fucrLTU+I+hk1E5FBUV2hLqu6RIocS59zX1D47aVycenRrrhpyJG9OW0dJRf27SWakenjmykEKbCJSQ8vsdFpm67tBRCQekmbKfxGJjfvO78VVxx9JRmrkH3cPkJnq5akrBnLasck5iFlERETkcKHQJnKIMzPuO78XL113HC0bpdEozVvrsh4LhrW+HZsy+uencHbvI2pdVkRERETiQ90jRQ4Tw3q04rt7zuCbRVt4fcoa5m3Ip6Tcj9dj+AKOVtlpHN+lJTcO60L/jk2TYjIBEREREVFoEzmspHo9nNOnHef0aQdAQWkFZRUBstNTyKyjBU5EREREEkehTeQw1iQjFZLnHKUiIiIiEobGtImIiIiIiCQxhTYREREREZEkptAmIiIiIiKSxBTaREREREREkphCm4iIiIiISBJTaBMREREREUliCm0iIiIiIiJJTKFNREREREQkiSm0iYiIiIiIJDGFNhERERERkSSm0CYiIiIiIpLEFNpERERERESSmEKbiIiIiIhIElNoExERERERSWIKbSIiIiIiIklMoU1ERERERCSJKbSJiIiIiIgkMYU2ERERERGRJKbQJiIiIiIiksQU2kRERERERJKYQpuIiIiIiEgSU2gTERERERFJYgptIiIiIiIiSUyhTUREREREJIkptImIiIiIiCQxhTYREREREZEkptAmIiIiIiKSxBTaREREREREkphCm4iIiIiISBJTaBMREREREUliCm0iIiIiIiJJTKFNREREREQkiSm0iYiIiIiIJDGFNhERERERkSSm0CYiIiIiIpLEFNpERERERESSmEKbiIiIiIhIElNoExERERERSWIKbSIiIiIiIklMoU1ERERERCSJKbSJiIiIiIgkMYU2ERERERGRJKbQJiIiIiIiksQU2kRERERERJKYQpuIiIiIiEgSU2gTERERERFJYgptIiIiIiIiSUyhTUREREREJIkptImIiIiIiCQxhTYREREREZEkptAmIiIiIiKSxFISXYCIxI9zjqmrdvK/ORuZvnoX63cV4ws4MlK9dG+TzdCuLbl4cEe6tGqU6FJFREREJEShTeQwMWbxFu77aAG7isspLvfvc1+ZL8CMNbuYsy6Pf05YSb+OTfnbxf3o2jo7QdWKiIiISCV1jxQ5xJVW+PnZf2fy0zdmsSGvpEZgq8oXcJT5AuSu3sW5T03g5Ymr4lipiIiIiISjljaRQ1hphZ8rXpzCok2FlPkCEa/ngNKKAH8fvYRthaXc/f2esStSREREROqkljaRQ9idb82ud2CrqqTCz2uT1/DO9HVRrkxEREREIqWWNpFD1Kj5mxm/dFuDA1ulkgo/D3y6gJOPbkW7pplRqk5EDlb+gGPM4q28OW0NmwtKaZqZyoj+HbhgQHuy0rRbISISC7V+u5rZRXWt6Jz7IPrliEg0BAKOez+aR0lF7ePX6qPM5+dvXyzmySsGRmV7InJwmr0ujxv+NY2i0gqqHg+aunIn93+ygIcv6suFgzomrkBg2ZZC3sldx8a8Ujo0y+Sy4zrSvU3jhNZU1ab8EnbuLqdDs0yaZaUluhwROUjUdUjsB3Xc5wCFNpEkNW7ZNkrqmHCkvvwB+GL+Zh4sqaBpZmrUtisitVuwMZ9HRi1h3vo8GmekcsupXbl0cCfSUhIzsmH+hnwuGzmFcn/N1vuAC85Ce9d7c3AOLhoc/+BW5vPzszdmMWbxFvxu7+0vT1zJmT3b8tRVA0lP8ca9rkqz1u7i3o/ms2RzIR4znHOcekxrHrqwL22bZCSsLhE5ONT6ze+c+1EdfzfGs8hDkZn5zWy2mc03s3fNLKuW5SbHu7Yqj32DmT3TgHXa13LfWDPLqce2hpvZ/+rz+NFgZj8xs+vC3N7ZzObXss6e52Zmq82sVazrrMsHM9ezO4qhDSDFa4xdsjWq2xRJFqUVfvwBt/8F4+StaWs4/+mJjFu6jZ3FFazZWczvP5zPuU+NpzRKLej19dM3ZoQNbFX5A/DbD+ZSVOaLU1V7/fzN2Xy9aN/ABuB38OXCLfzyndlxr6nSjDW7uPyF71iwsQBfwFHuD1ARcHy9aCvnPDmebYVlCaut0sy1u7jmpe84/bGxPDJqEYWlFYkuaY9AwDFjzU7GL91GfnHy1FUpEHBJ9f0hDeec45vFW/jVO7P5x9dL2VGU+M9mpf0erjOztmb2spl9Ebrey8xuin1ph7wS59wA51wfoBz4SdU7zSwFwDl3YrQfuHLbMXIDEDa0HSyccyOdc68nuo4DMWttXtS3ubvMz8w1u6K+3dos2VzAx3M2UOZLzA6qRJdzjo9nb+DbxckV/Esr/Jz52FiOvW8U3e75nMe/XJLokti1u5x7PpyPC7MPuHzrbp4ZszzuNc1Zl8f6XSURLesPOD6cuT7GFe1rzY7dfLlwM7XtNjtg9PwtrNtZHM+y9rj7/Tm1Bt684gqe/Tb+/6dVTVy2nUufn8zE5TtYuW03z41dyflPTcS3n5AeD+W+ABc8M4FLnp/Cda9MY8hDX7F0S2Giy9rjH18vpes9n9Ptns+5691ZuHAf3ARauqWQ1dt3J7qMGvJLKnju22V8MHN9Ur1md707h5tezeX9mRt44utlnPTXb9iQF9l3X6xF0sfiVWA0e3fElwI/j1VBh6kJQPdQy9IEM/sEWAhgZkWhf4eb2Tgz+9jMVprZw2Z2tZlNM7N5ZtYttNwPzGyqmc0ys6/NrG3o9gfM7N9mNgn4t5mNN7MBlQWY2UQz6x+mtk6hVqRlZnZ/aNl9WpzM7K7Q9i8BcoA3Qq2I4WatuLZKC+OQ0PqNzOyV0HOZZWYjqq9kZi3M7CMzm2tm35lZv9Dt88ysmQXtqGwhM7PXzeysatuI9DV8wMzuCl0ebGZzzGwO8NMq28o0s7fMbJGZfQiEnaHDzK4JbX+2mb1gZnHpm7MxRl8w8zcWxGS71Y1bupXvPTmBO9+czYkPfRWXx6yPznd/Rue7P6MoiY5EQ/Bob2VtJeXxb+moy5UjJ3HnW7P50avT+dOnCxJdzh6/fX8uy7ft3aF5aszyhO8QvjtjHXUdtH91UvzPn/jVws01WrBqE3Dw3owNsS2omo9nb9xvfb5A8MBBvK3cVsSqHbWHRQe8PX1t/AoK496P5td4/dbsLOabJDjI8sbUNczfWLgnkJf5HT9+bXpCa6qUu3onT3y9bM/192Zs5OM5GxNY0b7++tlCzn5iPMP/Ppb/frcm0eXs4Zzj5Ie/4pHRS/nlO3P4w0dhOzHF3Ya8Ej6Yue93RKnf8fDnixJU0b4iCW2tnHPvAAEA55wP0KHvKAm1en0fmBe6aRBwp3Pu6DCL9yfYItcTuBY42jk3BHgJuCO0zETgBOfcQOAt4DdV1u8FnOmcuxJ4mWCrGGZ2NJDhnJsT5jGHABcD/YBL6+re6Jx7D8gFrg61IoZLDlnOuQHA7cArodt+D4wJPZfTgEfNrFG19R4EZjnn+gH3AJUtYZOAk4DewErg5NDtQ4FwXUsjeQ2r+hdwh3OueqC9DSh2zvUE7gcGV1/RzHoClwMnhZ6zH7g6zHK3mFmumeVu27YtTAn1Ewi4Onf4DkS8umV9OmfTnss7SvxJdRSuqs/mbdr/QnG0fFvRnsuTlu9IYCU1TVuTv+fy5/OSZ6dmQZgDEfM35IdZMn5Wbav7qHi0uz5HIq+eXdIKSstjVEl4WwtLI1puWwK6Om0vKsdjVucyJRWBhHav25QXPlSuqPKdkiiLNtX8jG7Oj+z/O9bCvT4L1if2+6Oq96u0eL8xNbEHBqoq8wUoKNv7fv9q0ZYEVrPXiq1FYVvr5yX4N6FSJKFtt5m1JHgwCDM7AUiO6g9umWY2m2DIWUswRAFMc87Vdhh1unNuk3OuDFgBfBm6fR7QOXS5IzDazOYBvyYYZip9UiVIvQucb2apwI0EW1TD+co5tyO03gfAsHo8x3DeBHDOjQeamFkz4Gzg7tDrMRbIAI6stt4w4N+hdccALc2sCcFWylNCf88Dfc2sA7DLORduzyeS1xCAUG3NQrVS+fghpwD/CdUzF5gb5rHOIBjmpoee2xlA1+oLOededM7lOOdyWrduHWYz9ePxGCmeuncQGqpRWnwG8V839Kg9l3u3y8b2s8MTb8OOzCQDuGhgh0SXso8ebbJpQnCGqdOOOfD3UjRdkrP3tbrp5Bofg4QZ1r3m8NPjurRIQCV79enQtM77mydgxsHWjdPrtXzL7Potf6A6Nc9if98SFlou3jo0zySwn0DWJCMFb4y+tyPRo23N2TUN6NehWfyLqSanc83PY7h6E6FvmNdnaPeWCagkvDvP7LHn8i/O7lHHkvGVkerlyGZ7J9+5vspvfiL1at8k7PdIuN+JRIhkbNOvgE+AbqGuda2BS2Ja1eGhJNT6skdox7SuQ6xVDxEGqlwPsPf/8mngcefcJ2Y2HHigyjp7tu2cKzazr4ARwGWEaSmqXDTMdR/7Bv76THsVbnsGXOyc22cwSWXXzv0YT7Db4pEEW+wuJPj+nFDL8pG8htFiwGvOud9Febv71alFFqui3IfdgP6d4vMD3q9jM+bcfzbbCsvo1rp6o2vi/ef20xNdQlhmxtyHz0t0GWE9cskAbj2lO+mpHjomYMe5Nved34u1O3czbsk2Ur0e/npxv4Ts2Ff1w4EduP+T+dR2isWfnd4tvgUB5/Vrz1PfLIuoi6TX4Orjqx93i60LB3bgkdFL6myt8nqMEQPif6ClQ7NM+nRowux14Y93m8H1J3aOb1HV/PWiflz43MR93nODj2rOid0SH0AuHdyRcUu38fncYM+GVtlp/PP6iOc0i6le7Zvw5OX9ue/j+fgDcOcZPTj92Eh2XeLj2qFdGDGwI14zGqUn1zkUv77rNCYu20arxun065j4gwMArbLT+flZPXjiq2VVbkvjV2cfk8Cq9trv/6BzboaZnQocQ3C/bYlzLrkGckhVTYHKDrnX72fZl4BPgQnOudpmmDjLzFoAJcAPCbbKbQHahFpgi4DzgVGh5QuBug6BXQ58a2bDgHznXL6ZjQbuMLM7nHPOzAY652ZVW28Cwa6FfwqF0e3OuQKgIDRTY5pzbqWZTQTuAn62n+e+X865PDPLM7NhzrmJ7Nu1cTxwFTDGzPoQ7D5a3TfAx2b2hHNua+h1bOyci3nH8uM6N496aGuUnsLgo5pHdZt1aZqZqtMLHGK6tclOdAk1eD3GKzcMSXQZ+2iUnsK/bhjCDa9Oo+o8EAacfHQrbjixS9xr6t4mm57tmkQ0rjU9xcu5fdvFoaq92jTJ4LoTjuT1KWvCBkuvwQ0ndq53i2G0PHbZAC54emKNrq0GdG6Rxa2nxj+IV9W3Y1O+ves0Xp+ymjU7ivl+3yM4v197PAls/atkZjx71SC2nl/K7nI/nZpnkuJNzGkvwvnhwI78cGBiz01YlyYZyfk7mpbi4fSeyRNwK915xtGc26cd45duo12zTM7o2Sahpwqpar+hzczmEhwb9bZzbkXsS5ID9ADwrpntAsYAtf66hwJ5AcFxW7WZBrxPsNvlf5xzuQBm9sfQfRuAxVWWfxUYaWYlwNAw49pKzWwWUNktE+BPwJPAXDPzAKsIBsHqz+uV0PuxmH0D6VSg8hM1AfgrwbF90fCj0OM69nalhGBXzH+Z2SJgETCj+orOuYVmdi/wZeh5VRBsFYx5aLsspxP/m7uJ4iiOfQk4x6lHt4na9kSkdicf3ZopvzuDf45fyeQVO2iVncatp3RjaLeWCesu/Pw1g/n+PybUOZ1/igdevC4nITs5953fm1Svh1cmrSbggmN7PQYeM24a1oXfnnNs3Guq1K11Nl/ceQqPjF7EF/M3A5Du9XLNCUfxf2f2IDsJWkE6tcji9+f1SnQZtWqjc9lJnPRo2zhpuuBWZfsb4G9mRxFsHbmcYBeyt4F3nHPJM6JRGsSC51MbCxzrnEv8vL5CTk6Oy83NPeDtOOcY/uhY1kRpeuu0FA9XHteJB0f0icr2ROTgtHZHMbe8Pp1l24r2aQVM8UCzzDSeuXoQJ3RNbJe6vOJyPp+3me1FZbTKTue8vu1ompU8rQ0+f4CSCj+N0lKSoiVLRJKHmc1wzoXt/7vf0FZtQz2A+wjODpgcbYXSIKGp8R8CfumcezfR9UhQtEIbwLRVO7nulamUVhx4Hm+amcr4X5+WVDs+IpI4izcX8NGsDWwpKKVJRipn9z6CExPYCigiciioK7RF1B5frbXNz77TyMtBKHTy6IP6BNJStyFdWnBZTifezV1PyQFM1Z+R6uHJywcosInIHsce0YS7v98k0WWIiBw2IhnTNpXg+KN3gUudcytjXpWIRMX9P+jN1oIyxi3d1qDglpHq4b7zenHasRrLJiIiIpIokbS0XVd9KnYROTh4PcazVw/iia+W8tKElZTWNod4NaleIyPVy+OXDeCsXsk3u5OIiIjI4SSSOVPzzOxlM/sCwMx6mdlNMa5LRKLE6zHu+t4xfHD7SZzQtQXpKR7SU8J/9LPSvKSnePjhwA6M//VpCmwiIiIiSSCSlrZXCU4J//vQ9aUEZ5B8OUY1iUgM9GrfhLduGcq6ncV8tXALU1ftYNmWIir8ARqlp9C/UzOO69yC7/VuS+MkPa+LiIiIyOEoktDWyjn3jpn9DsA55zOz6J38SUTiqlOLLG4c1oUbh8X/BL0iIiIiUn+RdI/cbWYtAQdgZicA+TGtSkRERERERIDIWtp+CXwCdDOzSUBr4JKYViUiIiIiIiJABKHNOTfTzE4FjgEMWAJcAMyNcW0iIiIiIiKHvUi6R+Kc8znnFjjn5jvnKoAnYlyXiIiIiIiIEGFoC8OiWoWIiIiIiIiE1dDQ5qJahYiIiIiIiIRV65g2M5tH+HBmgM64KyIiIiIiEgd1TURyftyqEBERERERkbBqDW3OuTXxLERERERERERqauiYNhEREREREYkDhTYREREREZEkptAmIiIiIiKSxBo6e6RzzvWLWVUiIiIiIiICaPZIERERERGRpKbZI0VERERERJLYfse0mdkJZjbdzIrMrNzM/GZWEI/iREREREREDneRTETyDHAlsAzIBG4Gno1lUSIiIiIiIhIU0eyRzrnlgNc553fO/Qs4J7ZliYiIiIiICNQ9EUmlYjNLA2ab2SPAJnSqABERERERkbiIJHxdG1ruZ8BuoBNwcSyLEhERERERkaD9trRVmUWyFHgwtuWIiIiIiIhIVfsNbWZ2EvAAcFTV5Z1zXWNXloiIiIiIiEBkY9peBn4BzAD8sS1HREREREREqooktOU7576IeSUiIiIiIiJSQySh7VszexT4ACirvNE5NzNmVYmIiIiIiAgQWWg7PvRvTpXbHHB69MsRERERERGRqiKZPfK0eBQiIiIiIiIiNe33PG1m1tTMHjez3NDfY2bWNB7FiYiIiIiIHO4iObn2K0AhcFnorwD4VyyLEhERERERkaBIxrR1c85dXOX6g2Y2O1YFiYiIiIiIyF6RtLSVmNmwyiuhk22XxK4kERERERERqRRJS9ttwGuhcWwG7ARuiGVRIiIiIiIiEhTJ7JGzgf5m1iR0vSDmVYmIiIiIiAhQR2gzs2ucc/8xs19Wux0A59zjMa5NRERERETksFdXS1uj0L+Nw9znYlCLiIiIiIiIVFNraHPOvRC6+LVzblLV+0KTkYiIiIiIiEiMRTJ75NMR3iYiIiIiIiJRVteYtqHAiUDrauPamgDeWBcmIiIiIiIidY9pSwOyQ8tUHddWAFwSy6JEREREREQkqK4xbeOAcWb2qnNuTRxrEhERERERkZBITq5dbGaPAr2BjMobnXOnx6wqERERERERASKbiOQNYDHQBXgQWA1Mj2FNIiIiIiIiEhJJaGvpnHsZqHDOjXPO3QiolU1ERERERCQOIukeWRH6d5OZnQdsBFrEriQRERERERGpFElo+7OZNQV+RfD8bE2AX8S0KhEREREREQEiCG3Ouf+FLuYDp8W2HBEREREREalqv2PazOw1M2tW5XpzM3sltmWJiIiIiIgIRDYRST/nXF7lFefcLmBg7EoSERERERGRSpGENo+ZNa+8YmYtiGwsnIiIiIiIiBygSMLXY8AUM3sXMOAS4KGYViUiIiIiIiJAZBORvG5muew9N9tFzrmFsS1LREREREREIILQZmZHAkXAJ1Vvc86tjWVhIiIiIiIiEln3yM8AF7qcCXQBlgC9Y1WUiIiIiIiIBEXSPbJv1etmNgi4PWYViYiIiIiIyB71ngXSOTfTzI6PRTEiEntlPj9fzNvMi+NXsmp7ERX+ABmpXoZ0bsktp3bl+C4tMLNElykiIiIiIZGMaftllaseYBCwMWYViUjM/HfqGh76bBEV/gDlfrfn9qIyP2OWbGXSiu20apTGc9cMpn+nZgmsVEREREQqRXKetsZV/tIJjnEbEcuiRCT6Hv9yCQ98spDd5f59AltVZb4AG/JLufyFKUxavj3OFYqIiIhIOJGMaXswHoWISOy8N2M9I8etqDWsVVfqC3Dza9P59I6T6d4mO8bVicjBKL+4gg15JbTMTqNtk4xElyMickirNbSZ2afsnTWyBufcBTGpSESiyh9wPPTZwogDW6WSigD/+HopT181KEaVicjBaEdRGbe/MYNpq3ft2Uvo3CqL564eRM92TRNbHFBc7uPDmRuYvzGfPu2bctGgjmSmeRNd1h4b8krYmFfC0W0a0zQrNdHliMhBoq6Wtr+H/r0IOAL4T+j6lcCWWBZ1KDCzIudcdrXbfgIUh05YfgPwpXMuKuMDzWw4UO6cm1yPdW4AcpxzP6vnOmHrNrOxwF3OudwItzU8tPz5kT5+tJjZS8Djh8OJ4scu2Upxma9B645esJm84nKaZaVFuSoR2Z/84grufHsWuat30iwzlQdG9OHMnm0TWlNhaQWnPzaW/JJ9v1NWbS/mB09P5Is7T6FH28YJqg4Wbcrnh89MomzPQap1/PF/C/n4pydxbLsmCasLYHeZj2temsrsdXkAmMHlx3XiLxf2TZrJn/JLKti1u5yOzTNJ8UYygkZE4qXWT6RzbpxzbhxwknPucufcp6G/q4CT41fiocM5N9I593ro6g1A+/qsb2Z1hezhwIkNq6xebqCedScbM/M6526OdmDbz/9Pwrw8cVWVHZj68TvHx7MTM+9QhT/AjqKyhDy2xMa2wlLySyoSXUYNuat3cu4/JnDjq9MpKW/YAY5oCwQcw//+LWOXbKOozM/6vFJufi2XbxYl9pjpyxNX1ghslXwB+O37c+Nc0V7OOS5/4bsa33dlvgBXvDgF5xr2PRgtP/nPDGaty8MRbKAMOHhz2jpenbwqoXVVemHccvo/+CXD/z6WQX/6km2FpYkuaY9FmwrI+dOXHHvv54wcuzzR5exjc34JFz47ke8/OZ4lmwsSXU4NRWU+Ssr9iS6jBn/A8V7uOqat2pHoUmr44/8WcOy9nzPsb9+wYVdJosvZI5LDKI3MrGvlFTPrAjSKXUmHLjN7wMzuMrNLgBzgDTObbWaZZjbYzMaZ2QwzG21m7ULrjDWzJ80sF7jTzH5gZlPNbJaZfW1mbc2sM/AT4Beh7Z1sZq3N7H0zmx76O6mWsjqFHmOZmd0feszOZja/St13hWqvUXeY7V0bum++mQ0Jrd/IzF4xs2mhumtMZGNmLczsIzOba2bfmVm/0O3zzKyZBe0ws+tCt79uZmdV28ZwMxtvZp+Z2RIzG2lmntB9RWb2mJnNAYaGnnNOlfseNbMFodd0SOj+lWZ2QZXXZIKZzQz9nVjlMSeY2SfAQjP7o5n9vEpND5nZnXW8LWJuzY7dDV7XH4AV24qiWE1kyn0Bevz+Cwb/+Wvu/zBxO4G16Xz3Z3S++7OE7wSGU1lbsvl49gaOe+gb+j/4JfPW5ye6nD18/gCXjJzCwk0FjFm8letenpbokgAYs3gru4prBtwHP12QgGr2+s+UtXXeP2ttHhX+QJyq2dfsdXkUloYPlPklPuZtSNz7rrC0gonLwk/u9Oy3K+JcTU35JRX89Ysle64XlPr5+duzE1jRvi55fhLbd1dQ6nM8PGoJy7YUJrqkPc57ajyz1uWzaHMh5z45Ial+FyYu20af+0fT8w+jWL41eV4zgIufHc9d783lshe+44t5mxJdzh7jlmzllYmrKfU51u8q5fIXI+7AFnORhLZfAGNDO7HjgG+Bn+9nHamDc+49IBe42jk3APABTwOXOOcGA68AD1VZJc05l+OcewyYCJzgnBsIvAX8xjm3GhgJPOGcG+CcmwD8I3T9OOBi4KVayhkSur8fcGllkImkbudcuMMPWaHndHvoeQD8HhjjnBsCnAY8ambVg/+DwCznXD/gHqCyRXIScBLQG1jJ3lbeoUC4T9IQ4A6gF9CNYPdeCB5omOqc6++cm1htnUah+noDhcCfgbOAC4E/hpbZCpzlnBsEXA48VWX9QcCdzrmjQ8+5Mlh6gCvY27U4LDO7xcxyzSx327ZtdS3aIPUdy1bd7gZ2rTwQu4rL91z+cnHy9sbeXZZ8Ry8r+QPJs+MAMH7p3vd2Mh1ZLa7Y9/9w1faGH+SIprU7w9eRFybIxVNJRd3veUewZSsR8oqPdzYDAAAgAElEQVQrah2I74Cdu8truTf2Ckt9UEsPyKJagmY85Yd5X23KT56Wtt3l+76n1ucVJ6iSmnbt3vv/5ye5vnurHiiYtTYvgZXUtHjT3gPC01fvTGAl+1q2dd8D1dsLk6fHz35Dm3NuFNADuBP4P+AY59zoWBd2mDkG6AN8ZWazgXuBjlXuf7vK5Y7AaDObB/yaYJgJ50zgmdD2PgGamFm4aQC/cs7tCAWwD4BhB/ZUeBPAOTc+9JjNgLOBu0O1jAUygCOrrTcM+Hdo3TFASzNrAkwATgn9PQ/0NbMOwC7nXLi9mmnOuZXOOX+olsrn4wfer6XmcmBU6PI8YJxzriJ0uXPo9lTgn6HX/V2CobDqY64K1b4a2GFmA0PPe5Zzrs49VOfci6FQntO6deu6Fm2Q7PQD67XZunF6lCqJXNsmGVye0542jVN4/cYT4v74+zPj3jMZdefJZGckX4/YL+4cxke3n4jXkxxjZCrde34v2mRBj5apXDf0qESXs0eTjFT6tN871umXZx+TwGr2Orv3EWFvP7FbqzhXsq/ubeoer5aZ6qFRgib96NOhaW25CI9B3w6JmyTliCYZZKaG3+Xq1zHx58Ts0DyTppn7fp9dOaT6z3TinN2zzZ7LGV4Y0rllAqvZ17VD975Op/ZomVRjAe88swfdW6bRv10WFw3quP8V4uiJKwcC4AXuOL1HYoupYsSA9vt8j1x1QvL8XtU1e+RvnHOPhK5e4Jx7t8p9f3HO3RPz6g4fBixwzg2t5f6q4eRpghNofBKayOOBWtbxEGyR29+hsuqHhBzBlr+q3zr1mcs53PYMuNg5t6TqHWYWyYj68cBPCYa83xNs/bqEYJiL9PEBSkNBLpwKt7c/QwAoA3DOBaqMU/sFwQl4+hN8baq+rtXD40sEx/4dwd7WxoQ5u1dbXhy/svapYOuQnuLhlKOjHyQj8bdLBibkcSPRMjudltnxD7ORSIbZ+8JpnpXGtD+cl+gywvr0jmEs21JEi+w0WiXJ/2vH5lncPrwbz43d23WudeN0Hr20XwKrgru/fwxX/XNqrd8nPz65a8Im1WjdOJ1z+7bjszBdrc7r2y6hn1mPx3j4on7c+fZsqvaeS/UYf724b8LqquT1GN/edRq/eGs26/NKuHJIJ24+uev+V4yTF67L4Yv5m9lSUMpFgzrS6AAPRkbTgyP6ct2JXSj3BTj2iMRNwhNOZloKX//6rP0vmADf79ue1Q8n3xQJrRtnMO33Z/DRrI0c3TabU49ps/+V4qSuwwFXVLn8u2r3nRODWg43hQRPWA6wBGhtZkMBzCzVzGprQWsKbAhdvr6W7QF8SbCbIKFtDqhle2eFxpNlAj8k2B1xC9DGzFqaWTpQdXbH6o9T3eWhxxsG5Dvn8oHRwB0W+iUPtUJVNwG4OnT/cGC7c67AObcOaAX0cM6tJNg99C6CYS6cIWbWJdQ18fLQ8tHQFNjknAsA1xI8OFSbDwl+Ro4j+NwT6tqhR5HibdhOVPOsVIZ2TZ4jmiKxYGYcfUTjpAlslX5zzrFMvvt0/vzDPvznpiFMu+cMGmckdor4od1a8ZPh3Wq0aBlwfJcW3Hnm0Ykoa4+nrhzITcM6k+o1DEj1GjcP68yTVyT+INAFAzrw7q1DyTmqGW0bp3Ne33aMuWs43Vonx7kwWzRK47WbhvDNr05NqsAGwc/ouX3b8aOTutA0M/lOk9CtdTY92zVJmllA5cC0bpzBj0/pmlSBDeqe8t9quRzuutSUZWbrq1x/vNr9rwIjzayE4PisS4CnzKwpwf+XJ4FwI84fAN41s13AGKBL6PZPgfdCk3zcQbAr67NmNje0vfEEJyupbhrBboMdgf9UTtdvZn8M3bcBWFxb3WHGtZWa2SyC3QlvDN32p9DzmRsKU6vYNwhWPq9XQvUWs28gncrekDQB+Cu1h7HpwDNAd4LjLz+sZbn6eg54PzQRyihqtq7t4ZwrN7Nvgbw6WvfipmPzLIZ0acGk5fUbR5TqMX5yajf9CIkkUPtmmVyTRN1zAH57zrFc0L89T3y1lKVbCmnTOIM7zujOsO6tEv594fUY953fm3vO7cXuch/ZaSl4kqircE7nFrx3W23zgomI1M5qm+XGzGaGJl3Y53K46yLJwBJ43rdqdXiAmcClzrll9Vk3JyfH5eZGdJq7etlSUMo5T4xnV4TTrXsNju/aktdvHJJU/fNFREREDlVmNsM5F3ZSwLr2xvqbWYGZFQL9Qpcrrye+A7ZIEjKzXsBy4Jv6BrZYatskgw9/ehKts9PYX0/JVK8xtFsrXr7+OAU2ERERkSRQa0ubiMRfrFraKuUXV/Dq5NW8MmklpRV+yn0OR/DoTYrXw5EtMrlteHd+OLBD0s0+KCIiInIoq6ulTaFNJInEOrRV8gcc45ZuZfnWIkrKAzTOSOG4zi3o2zE5Zx4UEREROdTVFdqSZ85UEYkbr8c4/di2nH5sJGddEBEREZFE0oAVERERERGRJKbQJiIiIiIiksQU2kRERERERJKYQpuIiIiIiEgSU2gTERERERFJYgptIiIiIiIiSUyhTUREREREJIkptImIiIiIiCQxhTYREREREZEkptAmIiIiIiKSxBTaREREREREkphCm4iIiIiISBJTaBMREREREUliCm0iIiIiIiJJTKFNREREREQkiSm0iYiIiIiIJDGFNhERERERkSSm0CYiIiIiIpLEFNpERERERESSmEKbiIiIiIhIElNoExERERERSWIKbSIiIiIiIklMoU1ERERERCSJKbSJiIiIiIgkMYU2ERERERGRJKbQJiIiIiIiksQU2kRERERERJKYQpuIiIiIiEgSU2gTERERERFJYgptIiIiIiIiSUyhTUREREREJIkptImIiIiIiCQxhTYREREREZEkptAmIiIiIiKSxBTaREREREREkphCm4iIiIiISBJTaBMREREREUliCm0iIiIiIiJJTKFNREREREQkiSm0iYiIiIiIJDGFNhERERERkSSWkugCRCT2KvwBlm4pZP6GfHJX72LGml1sKyqjwh8gEIAUr5Hm9XBkyyyO79KCAZ2a07dDUzq1yMTMEl2+iIiIyGFNoU3kELZgYz4vTVjFZ/M2keo1nIPicn+N5cr9UIyfvPX5zN+QT1baOvwBR3qKh+tOPIprjj+KNk0yEvAMRERERMScc4muQURCcnJyXG5u7gFtw+cP8OncjTw/dgVrdxZT4XP4D+Bznp7iwQEndWvJbcO7M6RLiwOqT0RERERqMrMZzrmccPeppU3kELJkcyG3vzGDTfmlYVvUGqLMFwBg7JJtfLdyJ6ce05qHL+pLs6y0qGxfREREROqmiUhEDgE+f4Anv1rKiGcnsnLb7qgFtqocUFLhZ8yirZzyyLd8vXBL1B9DRERERGpSS5vIQW5TfgnXvjSVDXkllFYEYv545f4A5f4Ad7w5kzN7tuWxywaQlqLjPyIiIiKxoj0tkYPYqu27Of+piazaUUxJHAJbVSUVAb5atIVrX55KSQxa9kREREQkSKFN5CC1dkcxP3x2EruKy/EHEjOhUGlFgNnr8rjqn99RWqHgJiIiIhILCm0iB6GtBaVc/PxkCksrSFBe26PMF2DRpgJufi03YeFRRERE5FCm0CZykHHOcdsbM9lZXJbwwFap1BdgxpqdjBy3ItGliIiIiBxyFNpEDjJvTVvHwo0F+OM7hG2/SioCPP3NMpZvLUx0KSIiIiKHFIU2kYPIhrwS/vjZQkqSdPxYmT/A7W/MVDdJERERkSjSlP8iB5GfvzWLcl+SNbFV4Rys21nCi+NXcNvw7okuR0SizB9wzF63i7nr85mzLo+iMh/pKV56tW9Cv45NOa5zCzJSvYkuU0TkkKPQJnKQmLMuj/kbCpK+Faukws+z367gxmFdSE/RzpvIoWB3mY9/TV7FKxNXU+bz4/M7yqocQBq9YDMZqV78AcelOR25bXg32jXNTGDFIiKHFoU2kYPESxNWUuZLzm6R1TnnGDV/MyMGdEh0KSJygKas2MEdb86kqNRHaS0t/b6Ao6jMB8CbU9fy3oz1/OH8Xlx+XCfMLC51lpT7+XTORl6dvIrNBWWUVfjJSPVyRNMMrj+xMxf0b5/QVsCthaX8b84mNuSVUFhaQYtGaXRrnc15/dqRlabdMRGpmzmX3EftRQ4nOTk5Ljc3t8btecXlHP+Xb/Y5sp3sjj2iMaN+fkqiyxCRA/DvKat56PNFlFbU/7snM9XLuX2P4NFL+uPxxC64bS8q4x9fL+O9Gesxg+Lymge3stKCYe2SwR2584wetMxOj1k9VTnnmLZqJy+MX8nE5dsx2Od7PCvNi3Nw0aAO3DisC91aZ8elLhFJTmY2wzmXE+4+HdoROQi8k7uOOB2sjprVO3azcGMBvdo3SXQpIgcd5xwz1+7i64Vb2VZURlaal97tm3B+v/Y0So/PT/f7M9Y3OLBBsKv05/M2k5nq5c8X9o1ydUErthVx+QtTyC+uoKKOruOVQe7NqWsZNX8zb91yAl1jHJAq/AF+895cRi/YTEm5n3DVVdb19vR1vD9jPfec25PrTuwc07pqk19cwcrtRZT5AjTPSqNHm+yYhm0RqR+FNpGDwLu56xu845QoFb4An83bqNAmUk8fzlzP418tZWth2T6tMike4/6PF3DhoI78+nvH0KJRWsxqWLezmHs/mn/A3zslFX7en7meM3q25bRj20SpuqB1O4u56LnJFJRWEGmnoYqAY1tRGRc+N5nP/m8YHZtnRbWmSv6A46bXpjN91U5KIngNfQGHL+D46xeLKSit4Gen94hJXeHMXpfHi+NX8M2iraSlBCcV9wcc2ekp/Pjkrlx2XCeaZqbGrZ7qnHPkrtnF/A35+PyOI5pmcFavtprwRmJmW2EZ2wrLSEvx0LllFine5JhsX90jRZJIuO6RPn+Ann8YRYX/4Pus5hzVnPduOzHRZYgcFJxz3P/JAt6ato7yOk7EaEDrxml8cPtJMQsdlzw/mZlrdhGtQ0XNslKZcvcZZKZFZ0e7wh9g+KNj2ZRfQkPmZvKa0b55Bt/+anhMdsju+2g+781Y36DTs2Skenjs0v6c16991OuqyjnHk18v44XxKyj3BcK+jpmpHrLSUnj71hPo3qZxTOsJ56NZ63l09FJ2FZfj8zsCzpGe6sE5uCynI789p2fU3lMNUTmOcsrKHQSco3/HZlw8qCNNsxIXcqXhxizewrPfrmDehnzSvB4CzpHiMa4b2pkbh3WJ6YGySnV1j0yO6CgitVqxbTdpSXKUp74Wby5EB4Yk2VX4A6zevpstBaUJrePpMct5c+raOgMbgAO2FpZz2chgK1O0Ld5cwPyN+VELbADlvgCfzt0Yte19tXALecXlDQpsAH7n2Lm7nG8Wb41aTZW2FJTydu66Bp9Ps7QiwB//tzDm353PjFnOi+NXUloRPrABlFQE2Lm7nIufn8L6XcUxrae6R0ct5ncfzGdDXgnF5X7K/QF8AcfuMj/F5X7+O3UtP3x20p4JcOLtrWlrGfSnr7j/k/l8OGsDH8/eyCOjFjPkL1/z5NdL9du3H84FJy9KhgnWnHP84eP5/PSNmcxYs4tyX4CiMh/F5X4KSn28MG45Zz8xjtXbdye0zoNzT1DkMDJvQ37YsRAHgwp/gC0FZQ1ad2NeCfPW5yfFF7ocmpxzPP7VEvr8YRSnPzaWE/76DSc9PIbpq3bEvZZdu8t5ZsyyOsdlVbe5oIx/T1kT9Vr+NWk1ZVHujl1c7ueFcSuitr2RY1ewO8yEI/Wxu8zPyLHRq6nSf75bw4GOBCss9TF5Rezeh+t3FfPMt8sjCpYOKCr18YePFsSsnuo+n7eJVyatrrO+cr9j1fYi7vjvzLjVVem/U9dy/yfzKanw79P9tdQXoMwXYOTYFTwyanHc66pq1+5yHvtyCac/NpaznxjHyLHLExZwq/t0zkZOeeRb+j0wmp73jeKal6aybEthwup5ccJK3pm+rtauzBUB2F5UzmUvTKG4PHGvoUKbHNLMrCjMbT8xs+tCl28ws6j1QTGz4WYW1f6As9buCjsb2sEgLcXD/A359VqnsLSCK16cwrC/jeHCZyfS74HRfDonekfoJTH+PXk1p/19LGc9Po6vF25JdDkA/Pb9uTz1zXLK/I6AC54cfkNeCZe/+B0z1uyMay1vT19b73MwBhy8PGFl1M/dOGHptpgcKFqzo5jdUdhpXL61kKVR2sFbuKmAldtq/Ew0WIU/wGtTVh/wTL/RDrnVvTZ5NYF6tAT5nWPiiu1sjVNr9GNfLokoUJb7HZNX7GDtjvi1AhaV+fjj/xZQ5qv99Sv1BXhl0mrW7Yxv62SlrYWlnP3EOJ4Zs5yV23azdEsRj4xawg+enkBhDFrn6+O1yav51TuzWLcr2LU54GDi8u2MeHYiy7dG77MYqTKfn6e/WV7r6UyqKiyt4KNZG+JQVXgKbXLYcc6NdM69Hrp6A1Cv0GZmdU3gMxyIamiL149kLPgDjryS+v1A/Pa9uXy3cicBBz4HZT7Hz9+axYoo7lgdCJ8/wJ1vzuSi5ybFdUchEtsKSzn/qXGc/fi3rNmR2G4cVT3z7TLu+2QBq7bvZtnWIm5+PZfR8zcntKatBaW8N2N92PsCDu5+f25c63l18moaMmx1d7mPqSuj1yJTUu5na2HDWsf3JyPVy8JNBQe8nQnLtkc1VE5cvj1q21q8qTBqIXrS8h0x6WLnnOO/09bWe5y0Ae/OWBf1eqqbvyGfjXmR/+75AwFe/2517Aqq5sOZ64nkDegPBAN8Ijw6agnbisr3KTMArN5ezCsTVyWkJgh+vzz8xSLCHYcuLg/wtwS0To5esIVAILKDLCUVAV4YtzLGFdVOoU0OO2b2gJndZWaXADnAG2Y228wyzWywmY0zsxlmNtrM2oXWGWtmT5pZLnCnmf3AzKaa2Swz+9rM2ppZZ+AnwC9C2zvZzFqb2ftmNj30d1J96y0+yGaNrCrgHKX1GNdR7gswakHNnXm/C04/ngz+8PF8Pp6ziZlr8zjr8W8TXc4+LntuIvM3FrF0azEXPDM+0eXs8ey3NVsM/vL5ogRUsteYxVvrHA+1fOvuuHaD2V7UsKDk9zvW55VErY7KGdNiweHYnH/gB6Hyisujds7Kcl+A/OLotTzsKi7HE6Xzs5jR4HFxddld7m9Q99cyX4BlW2J/8Gzx5sJ6neLGF4DZa/NiV1A1E5Ztj6hVxheASVE8IFAfn80L3zvFAe/mxj541+a7VTvq/N4ds2hL3McCzl2XV6/9rLW7iglEuXdDpDTlvxy2nHPvmdnPgLucc7lmlgo8DYxwzm0zs8uBh4AbQ6ukVc7oY2bNgROcc87MbgZ+45z7lZmNBIqcc38PLfdf4Ann3EQzOxIYDfSsWoeZ3QLcAnDkkUeGqzP6Tz5eXP3qDzhX6xd6snQR3VZUvudyDPanDkhhla5nJWXJE/bD/cBV7GeyjViL5PHjOWNrQ/cBHES1e6SL8Qja+nTJq02U8hoQfP18ER5lj4Q/it/XZsFTAUSbP+AafN7P+oy5bKiG7BBH83Xfn/r8n0S763KkfHV8d+1voqNYqvAF6twnSMQk2Q35jAWcw3PAI1frTy1tInsdA/QBvjKz2cC9QMcq979d5XJHYLSZzQN+DfSuZZtnAs+EtvcJ0MTM9jmjq3PuRedcjnMup3Xr1jU2cDCfi8bjMdLrUX9Gqpf+HZvW3A5wXr92Uays4f52UT9aZaWQ6oF/Xh92Vt6EeeH6IXgJvl7PX5s8tZ3Zs22N264Y0ikBlex1QteWde64tsxOo0lG/I5rNvSxUrweWmWnR62OZllpMQvUHiwqU2Y3z0ol1RudHaY0r4emmdGbxrtJRmpEXeci4Qs4stOi/x5s3MCTs3s90LFZZpSrqalj88x6hUoDurZqFLN6quvboWlE7z8DerWr+XsWDyd2a1XrfWf1OiKOlexr8FHN67x/QKdmWJRaqiPVrU02GfXoXdA8Ky1h521TaBPZy4AFzrkBob++zrmzq9xfdZDQ08Azzrm+wK1ARi3b9BBskavcZgfnXL36lzQ7iM/34jEju547CE9dOZBmWSl4Qt/bHuBHJ3XmuM4tol9gA7TITiP3D99j2V/O44wwYSSRBh/VghUPn8fKh5OrtievGMA5vY8gxWOkeY0bT+rMHXE8eXA4Pdo2pn/HZmHvM+AP5/eO687DhYM6Nui4rcfg5B6176DVV9PM1GDwiIFSn58+7Q98J7Zfx2akRmmnKcVr9O8UvR3rY49oTEWUWu6OPaIxHk/034Mej/H9Pu3w1vP9nerxcMngjvtf8AAd37UlGSn1O9h33dDOsSuomquOPzKi74aMVC83n9wlDhXV9NvvH0tamGCZne7l9tO6J6CioJbZ6Vya04lwGSnNC78555i413RB//YRH2dJ8xrXDz0qpvXURaFNDneFQOUZQ5cArc1sKICZpZpZbS1oTYHKKYSur2V7AF8Cd1ReMbMB9S1wYKdmZKQenB/VgHP0bNekXusc1bIR0+45i+euHsxDF/bh61+dyn0/qO2/QQ4GqV4PI68dzPK/nMvSh87lDz+IbyCqzRs3H8/xXVrsOUBggNfg9+f25IIBsT2xcXU/OrELKfVsPTLgspxOUW+NH7Sfo+EN1TwrjeZRaGk7rnNzWmRFp3WsVXY6g46M3vNtlJ7CiAEdONBM2Sjdy23DY7dzfcspXes9drFH22x6tI39Cba9HuPWU7uSGcH72oBOLTLp3yn8AZhYaNskg8tzOpGRUvvnNc1rnNC1BX06JKalrWe7Jnxw+0mc0KUFRvDgzhnHtuHTO06mQxxaS+vy4AW9ueb4zqR5jYwUDxkpRvOsVJ66clCdLYSx0jQzlQsHdiA9gu/fFK+HK4+vOYwlXjSmTQ51WWZWdQaLx6vd/yow0sxKgKHAJcBTZtaU4OfjSSDcyWkeAN41s13AGKDycNqnwHtmNoJgWPs/4Fkzmxva3niCk5VErE+HpqR4PBDVU93Ghz/gOKpFVr3XS0vxcE6fxHXhkMNDo/QU3r51KOt2FjNrXR6ZqV6GdW9FZlr8uyQf2TKLs3sfwah5myIe15GZ5uHHp3SNei0/OrEzk5Zvj+o40owUD9edGJ0j1GbBnfq/frH4gGrMSvNy66ldo34A4aZhXfh41gb8B9DiZhjn9I7dd2CfDk0Zfkxrvl28NaJJNTJSPTw4ok/M6qnupmFdmbBsO7mrd9Z67iwDmmSm8vL1x8WtrkoPXNCb0go/n87dSLlv35OTZ6V5yencnOevGRz3uqrq06Epb906lEBoDGMyHCiDYPB5YERvfnH20SzYkE9Gmpf+HZvhjUGrcqQeHNGbpVsKWbAxv9ZTOWSmevnndTm0aVxbx6rYs4N6kgORQ0xOTo7Lzc3d57bSCj997h8dkwHpsda7fRM++7+TE12GyEGhzOfnihemMHd9/n6DW0aqh3/fdHxMug075zjlkW9Ztyt6s1JmpHqY9NvTaRml8XdFZT5O+Ms3B3Sy4MYZKXz3uzNo1MAxXnW5+p/fMX3NLsobMGtKZqqX24d3444zYtuFuNwX4ObXpzN91a5aZ6k0g4wUL89cNTDuXa4r/AEe/GQB785YjxmUhsKbB0hL9dC9TTbPXz2YTg04MBgtizcX8PKEVUxbvZNAwNG7Q1N+fHJXBh0Z/7FZcmDKfQGe+mYZr01ZjXMOn9/h9Ri+gGPgkc2497xecWk5NbMZlZPe1bhPoU0keYQLbQDD/jaG9VHcgYoHA64/sTMPXKCujSKRKvcFePDTBbybuy54rsJqB2vSvB46t8ziiSsG0DsK48NqM2PNTq5+aeqeHeUDkZXm5a6zj+HGYdEd3zN15Q6u/9e0BtWYkerhPzcdT06MxsoWlFZw/lMT2ZRXUq8ZFzNSPQw/ug3PXzMoLjv9/oDjv1PXMHLcSnYVl+MPOALOker14A84Tj2mNb848+h6d3OPprzict7NXU/ump2U+wIc1bIRVw45kmOOiH1XTTn8lPsCTFq+na2FpaSneBl8VPO4HhhQaBM5SNQW2v782UJem7w6rlOQH6istGBXgpO6x7+PusjBrqC0gvdz1/PxnI3klZSTkeKld/sm/OikLnEbJ/Pn/y3kjalrD+hcYWleo1/HZrxz69CYTKoxZvEWfvrGrHrVmJnq5blrBnHaMW2iXk9VO4rKuOqlqazZvjuiLoiZqV6+17stj17aP2oTrUTKOcf01btYsrmA0ooAzbJSOe3YNlGdmVRE9k+hTeQgUVtoW7ezmDMfHxe1E8rGQ9sm6Xz3uzPURUTkIBUIOH717hxGzd/coOCW5jW6t2nMOz8ZWu9ZZOtj7vo87n5/Hiu3F1Hhd2HPjeU1SE3x0K11Nn+7uF/cgm9phZ9XJ6/m5YmrKC7zsbvaGDxvaEbVo1o24vbTuvODfu30nSlyGFNoEzlI1BbaAK54cQrfrdwZ54oaJjPVy6/OPpqbT47+JAkiEj/OOUaOW8k/vllKWUUg4qmxM1I9/KBfe/44ok/cJnapHF/0yZyNOOfwejz4AwHMjBED2nPTsK4J61IXCDgmLt/OG1PXsGFXCcXl/v9n777DpKruP46/z8xsY5eld6QKCCiKLKAoIPZeYzf2aDRNjaYZY2JiEuMvauy9kBi7JrbYQaoiIEXpHaQtS90+O3N+f9y7MMDusmXm3rvL5/U88+z0+c7ZmTvneyrNMyMc1DGXK0b0YEBn/4YfikhwKGkTaSRqStrGLdjIj/89c6+W2iDKiISYdvvxtMhqvHvMicguS/MLueudeXyxrACgyl7/yt6sXu1y+O2p/Rnh09DoWNyyozRKYVkFzTPSyMmM+LoynYhIbdWUtGnJf5FGYlTfdrTKTqeoPNgLkmREQpwzuIsSNpEmpHe7HF64ehjrt5XywTfr+GL5ZqS/G9UAACAASURBVOat3U5pNEYkbOjdLofhPVtzwoCOvi8QEQ4ZWjZLp2WS9nITEQkC9bSJBEhNPW0As1Zv5aInptZqUrtfWmen8/ltx9A8U0mbiIiISG3V1NPm7fJEItIghx3QkkuP6E5mWjC/uplpIR68aLASNhEREZEkCmbNT0SqddtJ/WiTHbxlmDMiIU47pBNH99ES/yIiIiLJpKRNpJHJTAvzxPeH0MyjFdlqIxIytG+ewR/OOtjvUERERESaHCVtIo3QwV1a8PTleYEYJhk2htbZ6bxxw4iU7sUkIiIisr/yv8YnIvUy4sC2PPH9PLLS/Otxi4QMbZun8+aNI2ifm+lbHCIiIiJNmZI2kUZsdN92/POaYeRkRIh4vA9RVlqIbm2a8e5PRtK1VTNPX1tERERkf6KkTaSRy+vRms9uHc2Rvdt41uuWGQlx1VE9+eBno2jXPHiLooiIiIg0JZqAItIEtG+eydirh/H2rLX85j9zKa+IE40lfw/GrLQQ7Zpn8uilh3NwlxZJf34RERER2ZuSNpEmwhjDWYO7cOSBbfjHJ4t5c+Z3GAPF5bEGP3d2RphmaRGuHdmTq47qSXpEnfQiIiIiXjHWJr81XkTqJy8vz06fPj0pz1VcXsHbs9by+OdL2bijDGuhJFq7BM4YyE6PEI3FGdK9FdeP7s3IA9sS8njenIiIiMj+whgzw1qbV9Vt6mkTaaKapUe4aFg3Lhx6AAs37GD26q1MX7GFGSu3sHpLMeGQIWQMBrBALG4JGcOB7XMY1rM1h3drxZDurejYQqtCioiIiPhJSZtIE2eM4aCOuRzUMZcLh3YDIBqLs7monLJonPJYnIxIiMy0MG1z0jFGvWkiIiIiQaKkTWQ/lBYO0UH7qomIiIg0ClpNQEREREREJMCUtImIiIiIiASYkjYREREREZEAU9ImIiIiIiISYEraREREREREAkxJm4iIiIiISIApaRMREREREQkwJW0iIiIiIiIBpqRNREREREQkwJS0iYiIiIiIBJiSNhERERERkQBT0iYiIiIiIhJgStpEREREREQCTEmbiIiIiIhIgClpExERERERCTAlbSIiIiIiIgGmpE1ERERERCTAlLSJiIiIiIgEmJI2ERERERGRAFPSJiIiIiIiEmBK2kRERERERAJMSZuIiIiIiEiAKWkTEREREREJMCVtIiIiIiIiAaakTUREREREJMCUtImIiIiIiASYkjYREREREZEAU9ImIiIiIiISYEraREREREREAkxJm4iIiIiISIApaRMREREREQkwJW0iIiIiIiIBpqRNREREREQkwJS0iYiIiIiIBFjE7wBExFtFZRXMW7eduWu2sXZrCeWxOM3Sw/Rqm8MhXVvQp30OkbDac0RERESCQkmbyH4gHrdMWJzPE58v46sVm8mIhCiPxYnG7M77ZKaFCIcMFTHLmYd25tqRvejXsbmPUYuIiIgIKGkTafJmr97Kj/89k4LCMoqjcQAqymN73a/UvQ3gjZlreHv2dxzRqy33nj+I9s0zPYtXRERERHanMVAiTVQsbrn7vXlc8MQUVm8p2Zmw1UbcQlmFZfLifMb833jenbM2hZGKiIiISE3U0ybSBEVjca4bO52pSzdRVmH3/YBqVFioKIvx81dnsX5bKdeO7JXEKEVERESkNtTTJtLEWGv56UtfM2XpJkobkLAlKquw3PvhAl75alVSnk9EREREak89bSJNzCtfrWbcgo0N6mGrSlmF5c63vyWvR2t6t8tJ6nOLSONSUh5jycZCdpRFaZYeoUebZrRslu53WCIiTZaSNpEmZN22Eu56dx6lFbWfv1YXZdE4P3pxJu/9dCThkEnJa4hIcC3LL+Tpict5bcZq4hawFmMMcWs5pl87fjSmD4d3a4kxOj6IiCSTkjaRJuQv7y+gNLr3ypDJYoGVBUW8N3cdZx7aOWWvIyLBYq3l/o8X8ci4JcT27MS3zhWfLcjn84X5jOzTjicuH0JGJOx9oDhbnMxfv53tJRXkZkXo3zGXkBqZRKSRU9Im0kRsK47y4bfrndbvFCqJxnls/BIlbSIpZK1l5qotPPjpElZsKiIrPcwFeV25aFg3mqV7/9P9p/fm89zk5fs8vsQsjF+Uz8VPfsGr1x9JJOzd1PnSaIxnJi7j8QnLKC6rwAIGyM6I8MNRvbhmVC/fEknYtV/m6zPWsLmwjB5tc7j0iG4M7NzCt5hEpPEw1qa4hicitZaXl2enT59er8c+M3EZ9364IGmLj9QkI2J4+8cjtfm2SApsK45y7mOTWZZfxJ7f5nAIHrt0CCcO7OhZPJ/M28D1/5y+dw9bDQxw7cie3H7agJTFlaiorIKzHpnEko1F1d6nT/sc/vvjo3xJehdv2MFFT37BluLy3RLfsIGBXXL559VH0KJZmudxiUiwGGNmWGvzqrpNq0dKk2KMKXT/9jDGfOOeP8YY8657/kxjzK/c82cbY5JWozDGHGaMOTVZz1dXH8/f4EnCBs5oqC+WFXjyWiKpZK1l4uJ8fvrSTO787zes31biazzlFXFO+ccEllaRsAHE4nD9v2YweUm+ZzHd88GCOiVs4AylfmHKipQO1070gxem15iwASzeWMj1/5zhSTyJVm8u5oyHJ1FQVL5XT2XMwpw12znr0UmelVV1SqMx3pi5hoc+W8w33231NZbqqKNBUi0Wt3z4zXpufuVr7vtoIRu3l/od0k4aHin7FWvt28Db7sWzgXeBebV9vDEmYq2tqObmw4A84P0GBVlP89ft8Oy1ymOWacsLuGJEj5S9xvrtJazbWsrBXVqQ5uEQK0mNSYvzuf+TxaSHDXeeMZCDOuX6HRLWWi54fCpfrdyy87qxU1dy7/mD+N6QA3yJ6e3Z37F2W82VBGvhlldn8+Vvjk95PIs27GBZfmG9HhuNWd6bs47zhnRNclS7W1lQxBfLa9eINHnJJlZvLuaA1s1SGlOiv7w/n9JozYtDrdxUzDuz13J+nj+fu8UbdnDGQ5N2LmJ130eLOPagdjx9xVDfF5Wx1vLHd+fxwpQVxC307ZDDy9cdSats/1crtdbyfx8u5Fl36PAZh3bmnvMGaaGuWiiNxpi0eBNtstMZ3L2V3+EAUFYR44wHJ7Fo465j3iPjlvDcVUMZ1be9j5E5VBOS/Yox5kpjzMPGmBHAmcC9xphZxpje7ukDY8wMY8xEY8xB7mOeN8Y8boz5EvibMWaYMWaqMeZrY8wUY0w/Y0w6cBdwoft8Fxpjso0xzxpjprn3PStV72tbcZTi8upyydT4Zu32lD33UxOWcsSfP+OcR6cw5I8fUVzm7Xvblz+8/Q2XPvUF20rK/Q5lN0VlFZz7yEROe+BzCgrL/A5np0mL87nsmWnMWLmFqcs2c/I/JrJ8U/0SgWT679drd0vYwOkh+sXrcyir8KfX476PF9Xqfhu3lzEvhd/BSu/PWVfnXrZKFvjnFyuTGk9VajPXrlLcwvOTl6c2oAQ7Sp25xvtigX98ujj1AVXjyuem7bbqsAU+XZDP+3PX+RZTpcfGLeHZySuIWSeuhRsKOeuRSX6HBcBj45fyyPillETjlFXEeX3GGn75xhy/w9pp9uotHHrnBwy96yNWFtTcE+2l4vIKht39MdeOnc45j03hxhe97wGvyrOTlu+WsIHTG379P2cQS/WCAbWgpE32S9baKTg9brdZaw+z1i4FngR+Yq0dAtwKPJrwkK7ACGvtLcACYKS1djDwO+DP1tpy9/wr7vO9AtwOfGatHQaMwUkQs1PxfnaURYmEvP06F6Uwkbr3w4U7z28vjfGsh5WsfXlr5hqem7KSyUsLOP+xKX6Hs5tfvTGHmau38+36Qq55YZrf4exUVSLy5IRlPkSyuxemrqjy+riFqUv9Gf67YR+9bJUssGD9ttQGg7ONSEPk70j90KLZq+s2lG/WGu+G/q0sKIZa9lSt3erP0NwtReWs3Vr1/+n5KSu8DaYKL05btdd1qzaXsK0k6kM0u/vnFyv2uu7d2Wu9D6QaVz7zJdvKYuQXR/nB2PrNl0+FV75azfbSXQ1j789dT0m5v8ODAV6q4rMGUBqNM39d6hvJ9kXDI0UAY0wOMAJ4LWEoSEbCXV6z1lYeUVoALxhj+uDUnaqbPX4icKYx5lb3cibQDZi/x2tfB1wH0K1bt/rGX6/HNUQoha8ZDhkSm/ez0vxb8W1PuVm7/t25mcE6hDbfLbbgLGqQHtm7QaGq67yWVkMMfq4yWFup/A7ufI0GDvPyIsa6Hv/CHjZwOe/f/xb6mtT0Pw7C97S6BslIAIYgVvVZ8rj9tEYZaWFwk6Ps9OAc05pVEUsQyq2mqRhBmKbhfwQiwRACtrq9ZJWn/gm3J44r+CMwzlp7MHAGTjJWFQOcl/B83ay18/e8k7X2SWttnrU2r127dvUKPjczQjSWmg21q5OTkbqE5e8XHLbzfM+22Vx5VM+UvVZdHde/A38/fxA3ju7Fy9eP8Duc3fzxzIFcfkR3zh/SlSe/P9TvcHa684zd1/tJCxt+dmxfn6LZ5UfH9K7y+vSwYVjP1h5H4+jZLqdW9zPAYQekfh5ItwbO/erSMitJkVRvRO+2dbr/kb3apCiSvfVql13rxLVP+9r975OtRVYafTtU/do3jK76O+Klm07os9d1Q7q1JDuFv0G19fMT9z6OXXN0Lx8iqdobNx7FwM65DOnekmevGuZ3ODudd3hXerbddWy5YXTvQDSU/WBk1f+7FlmRar8jXvL/Ey/inx1AcwBr7XZjzHJjzPnW2teM03Q7yFo7u4rHtQC+c89fWdXzuT4EfmKM+Ym11hpjBltrv07+24DmmWm0yEqjoMi7OVaHHdAyZc996iGdWHL3KZREYzQPUI9RpfN8WqRiX8LhEHedfbDfYeylf6cWjPv5aJ6YsIz0SIibjutL6xz/FxEY3a895x/elddmrtl5XTgEz189zLeFBH5xUj+uq8UKhz3bZdOjbUpGW+/mjEM7838fLqzXvLaQgauOTn2Dy+VHdufR8UtqNa8tZOCyI7qnPKZKmWlhzh/SlX9PW13j/UIGbjrev4aMf14znNMemkj+Duc3xADXHt2To/vUryExmc4Z3JXS8hj3fLCA8licow9sx4MXD/Y7LMCJzQB//2gRFXHLVSN68INRwUnaurRqxns/Hel3GHuJhEN89vNjWLW5mJyMCG1yMvb9IA9ckHcAH83bwLgFG3f2j2dEQrx03RG+L8gDStpk//Yy8JQx5qfA94BLgceMMb/FGfL4MlBV0vY3nOGRvwXeS7h+HPArY8ws4C84PXIPAHOMMSFgOXB6qt7MwV1a8Pkib5YBz4gYhqa4JyISDtE8AMMRJDl6tsvhr+cN8juMvdx7waH87IQ+/GfWWtrmpHP2YV3I9HE47vH9OzCoawvmrKl+vlrYwCOXeFNp7dqqGYcd0IoZq7bs+857yEoLc9xBqV9xrX1uJmce1pn/fL3vuUTnDu5Ku+beVhBvPekg/vfNerYUVz8HK697a0/33ttT+9xMpv3meGat3sqmwnIGd2tJ24BUpAEuHt6di4d7l2zXxdmDu3L24NSukNoUGWPo3ib1DU91EQoZnr1yKAvX72DSknw6NM/k+AEdfP1NSKTNtUUCpCGba7/y1Sp+//a3lOxjaelkyIiE+OSW0Z4umy2yvyiriHH92Bl8vih/r9lQzTPCvPiDIxjUNXU93XuatnwzFz85tU69bSEDvzr5IK7zaHhdNBbn6uemMWlJQZUzyEIGRvZpyzNXDCXiQ2PQxh2lXPPcV8xbt323cgwbOG1QZ+49f1AghoeJiL9q2lxbSZtIgDQkaSsur2DIHz+hxIMNWof1bM2r1x+Z8tcR2Z+t31bKc1OWs2DddnIz0zg/7wBG9mnryzCdsVOW8/t35tVqCKIxcPohnXjw4sGexhqPWz78dj33fbSIJfmFhIyzEmif9jnccmJfThzQscELqzTUko2FfPjteraVROncIpPTD+0cqB4tEfGXkjaRRqIhSRvAHf/5hpenrSKawv1EstJCPHrpEMZ4MOxJRILjzRmr+eWbc6mI2WrXQwwbuGJET357Wn9fE6RtJVF2lEZ3zvcVEWkMakraNKdNpAm59cR+vD17bcr2rwkbGNytFcf0839yuoh469whB3D8wI68MX01D49bypbicioXtM+IhLjiyB5ccVQPOrVI/YqR+9IiS8maiDQt6mkTCZCG9rQBfDp/Az96cSalFcmf25adHuazW4+hQ251uxyIyP7AWsv2kgp2lEVplh6hRVaabytuiog0FTX1tGlpNpEm5rj+Hbj0iO5kJnlT1IyI4eFLD1fCJiIYY2jRLI2urZrROjtdCZuISIopaRNpgn57Wn8uGHpA0hK3zLQQD158OGP6aR6biIiIiNeUtIk0QcYY/nDmQH53xkCy0sLUtxE8I2LokJvBi9cewUk+7iEkIiIisj9T0ibSRBljuGR4Nz6+ZRRDe7YmMxIiXMvkLSNiyIiEuHBoN8bfOoYh3VulNlgRERERqZZWjxRp4rq2asYr1x3Jko07eHbSCt6Zs5byijhpIUN5LI4FQsaQFjKUVsRp1zyDK47szoVDu9EqO93v8EVERET2e1o9UiRAkrF6ZG1s2F7K3DXbWL+9lGgsTkYkTM+22QzskktuppbJFhEREfGa9mkTkd10yM2kwwCtAikiIiLSGGhOm4iIiIiISIApaRMREREREQkwJW0iIiIiIiIBpqRNREREREQkwJS0iYiIiIiIBJiSNhERERERkQBT0iYiIiIiIhJgStpEREREREQCTEmbiIiIiIhIgClpExERERERCTAlbSIiIiIiIgGmpE1ERERERCTAlLSJiIiIiIgEmJI2ERERERGRAFPSJiIiIiIiEmBK2kRERERERAJMSZuIiIiIiEiAKWkTEREREREJMCVtIiIiIiIiAaakTUREREREJMCUtImIiIiIiASYkjYREREREZEAU9ImIiIiIiISYEraREREREREAkxJm4iIiIiISIApaRMREREREQkwJW0iIiIiIiIBpqRNREREREQkwJS0iYiIiIiIBJiSNhERERERkQCL+B2AiHivrCJGcVmM0ooYFTFLeiREZiRMTmaEcMj4HZ6IiIiIJFDSJtLElZTHmLduG3PXbOPL5ZuZvXorG3aUEQkZQsZgDMStJR4Hi6VHm2yGdG/FkO6tOKRrCw5sl0MkrE55EREREb8oaRNpgqy1TF1WwJOfL2Py0k1kRsKUx+KUVcR33icWt1U+dvHGQhZvLOTt2WsxQNzCeUO6cPVRPenVLsejdyAiIiIilZS0iTQh20ujvDF9DU9MXMb2kijF5TEAorGKOj9X5WMBXp62mtemr6F/p1x+OLo3x/dvr943EREREY8oaRNpAuJxywtTV/C3DxYCUBKN1fyAOqqIWyrillmrt/Lz12aRnR7hHxcN5sjebZL6OiIiIiKyNyVtIo3cqoJifvzSTJZsLEx6slaVorIYRWUxrnp+Gmcf1oU7Th9AdoYOJSIiIiKpovFNIo1UPG55bvJyTnpgAt9+t3234YxeKI3Geevr7zjm3vFMXVrg6WuLiIiI7E+UtIk0QqXRGJc/O42/fbCQkmiMmK16UZFUK6uIk19YxlXPT+PhcYt9iUFERESkqVPSJtLIFJZVcNGTU/lqxWZPhkPWRmk0ziOfLeVP787D+pRAioiIiDRVmogi0oiUlMe48ImpLN6wg/JYsJKjkmiMF79cRdxafnfGQL/DEREREWky1NMm0kiUV8S54tlpLNlYGLiErVJJNMZL01bz4KcaKikiIiKSLEraRBqJP703jznfbd1tg+wgKonGeGz8Uj5bsMHvUERERESaBCVtIo3A9BWbefWr1ZRGg52wVSqJxrjlldlsK4n6HYqIiIhIo6ekTSTgSspj/OjfMykNeA/bnkqiMW5/a67fYYiIiIg0elqIRCTg/vz+fLYVN74eq7KKOJ/O38hnCzZw7EEd/A5HRJIkGouzeEMhKwqKKKuIEQmF6NwykwGdWpCVHvY7PBGRJklJm0iAzVq9lddmrG50vWyVSqIxbn5lNlN/fSzN0nW4EWmsSqMx3pm9lmcnLWdJfiHpkRAGQ9xaQsbsvE/HFplcMqwbFw3rRuvsdJ+jFhFpOlSLEgmwBz9ZTFkjmcdWnWgszn9nreXiYd38DkVE6igetzwzeTn3f7wIgOJyZ2/IaKzqPSLXbCnhwc8W849PF/O9IV25/bT+arAREUkCHUlFAmrj9lImLd1EMBf3r73i8hiPj1/KRUMPwLgt8iISfCsLirjxxZksyy+iJFp1klaVygWTXp+xho++3cDDlwxmeK82qQpzp7VbS/jw2/V8uWwzCzZspywaJyMtxEEdchneqzUnH9yRTi2yUh5HTbaVRFmWX0h5RZzsjAgHts8hM01DSkVk35S0iQTUv75cSVNJcfILy/h69VYO79bK71BEpBa+XbuNi578gsLSino3HJVVxMkvLOOK56bxf+cfyumDOic1xkqLNuzgrnfmMW3FZoz7uolWbCpm3MKN/OV/CxjeszV3njGAA9s3T0ksVVm7tYR/Tl3J6zPWsKW4nMy0MMaAtc6Q0gNaN+OKI7tz3pCuNM9M8ywuEWlcjLWNvR1fpOnIy8uz06dPpyIWZ8ifPmkyS+YbAycN6Mjj3x/idygijUJ5RZyP521gzpqtFBSV0yw9TNdWWZw+qDOdW6a2t2hpfiFnPzyZHWUVSXvOzLQQj1xyOMf1T96iRPG45eFxS3h0/BLKKuLUpjpjDGREQvz42AO5cfSBhEKpaxorq4hx/8eLeG7yCqyF8lj1Q92bpYcxwB/PPphzBnfRqASR/ZQxZoa1Nq/K25S0iQRHZdL28bwN3PTK1xSV1X5IUtClR0JM+81xtGymxQlEqrN+Wyljp65g7NQVRGNxyip2/UaHgEjYMKxnG64f3YujD2yb9Mp9NBbnxPsnsHxTUVKfFyA7Pcy4W4+hfW5mg58rFrf89OWv+Wz+xjoN3ayUlRbm+AHt+ceFg1OSuOXvKOPCJ6eybmtpneLLSgszum87HrpkMGlhb3dlKo3GyN9RRllFjNzMNNo1z1DyKOKxmpI2DY8UCaBJi/MpbkIJG0BGOMSs1Vs5pl97v0MRCaSpSwu45oVplJbHqapPJg6UxyyTlmziqxWbOX1QJ+45bxCRJFbuH/p0Meu3lSbt+RKVRmPc9MosXrx2eIOTgbve+ZbP5m+gpJ4LNZVEY3wybyN/em8evztjYINi2dO24ijnPDqZ9dtKqYjXrWG8JBpj/KKN3PCvGTz5/byU9gRWWrh+B09PXMY7s9cSChmMgWjM0jE3k+tH9+Lsw7qQneFvdTF/RxnLNxVREYvTPjeD3u1ylFBKSlhrWbKxkIKicjIiIXq3zyE3IMOWlbSJBNBXK7Y0+gVI9lQSjTF79TYlbSJVmLJ0E1c9N223nrWalFXEeXvWWnaUVvD4ZUOSUrnfVFjGExOW7TUnLFli1tnGZMrSAo46sG29n+fLZQW8Mn31zgVP6qskGuPf01Zx6iGdyOvRukHPleiXb8xh4/ayOidslUqjcSYvKeBfX6zk8hE9khbXnipicX795lzembOWaEWc2B7hrtpczN3vzeev7y/g2auGMjSJZVRb05Zv5rHxS5iytID0SMiN29IhN4Mfju7NOYd3ISPi30Iu1lqmLitgwbodxK2lV7tsRvVpl9SGFPFGcXkFr361mqcmLmdzUTmRsHNMLa+Ic/LBHbl+VG8GdM71NUYNjxQJkLy8PDtt2lf0/90HKas4+WlE7zb8+wdH+B2GyE6Vc8fmr9tGRlqYMf3ac3CXFp7GsGZLMSfe9znF9UhC0sMhfji6F7ec2K/BcTz82WIe+mxJyo89o/q0Zew1w+v1WGstR98zju+2liQtngNaZTHhF2OS0nMzbuFGbvzXzHoN2dxTVlqY8bcdQ4ckDCfdUzxu+eG/ZjBx8aZaxZqZFmbs1cMY1tObxM1ay98/WsQzk5ZTGo1V2YiZlRamV7ts/n3tEbRo5n1PyFsz13DPBwvZURolGrNYLOmREGnhED8ecyDXHN1TvYGNxMYdpVz4xBes21pMaRUNZyEgPS3E788YyEUp3r6opuGRagoQCZgVBUWEPRgS44d5a7f7HYLITv/5eg0H3/kBP/73TB4et5S/f7SIMx+exKi/fcbqzcWexfHMpOX1HuZXHovz1MRllJQ3LEmw1vLc5BWeNBZ9uXwzG3fUbwjmlKUFbC0uT2o8BUXlfLl8c1Ke6/6PFyUlYQOIWcsLU1Yk5bn29Nzk5bVO2MAZ2nr181+xvdSbxbGenLjM/V5UnbCB01O6aMMOLnvmS8o9buR86NPF/Oatb1i/vZSi8hjlsTjRmKWoLMbW4ih//2gRv3pjLn53jOTvKOPt2Wt5b846thUHZ2GzpfmF/ObNuRz39/Gc9uBEnp64zLeF14rKKrjg8amsLCiqMmEDZ2h6aTTO79/5lvfnrvU2wARK2kSqYIxpY4yZ5Z7WG2O+S7icvsd9bzLGNKvFc443xlTZepJo7nfbaKqNc0XlFRQUltV4n5LyGHe98y0H3fE/evzqPU68/3OmLNnkUYSSCtuKo9z66iwOvvMDDvvDh9z93jxKk1Sxra+3Z33HLa/Opjxmd6sUxi2s2lzCyQ9MIH9HzZ/VZCiNxnh52qoGDYeuiFvemdOwisTabaUUJXG1yJqkhUPMWLGlXo8dO3UFRQ1MUPdUUh5j7NSVDX6elQVFLFy/IwkROcor4vzri5VJr/jH45ZHxy+tc3IZi1venLEmqbFUZVtJlPs+ql3yG41ZluYX8r9v1qU8rkpfLivYZ/mVRGO8PXst/53lTwU/Frfc8Z9vGPGXT/j5q7O4+ZWvGXr3xzzw8SLfE8n/fv0dpzzwOS9NW8XS/CK+Xbudez5YnGkjlQAAIABJREFUwLF/H8+qAu8ayyq9NG0V67aVUJvRzKXROL958xsqalgJNpWUtIlUwVpbYK09zFp7GPA4cH/lZWvtns28NwH7TNpqa+3WUt8rtKmSEQmzdmv1LezWWi56cirPTl6xc77Kog2FXP7sl0xaHJzEbdbqLXw0bz1Rnw7c1SmviPPUhGU8Mm4xxR5VwPelqKyCUfd+xuszv6OwLMbWkgqemricU/4xgXg95/w0VEUszi/fmFPjj3RReYz7P16Y8ljem7OOWAPLIRqzPDZuSYOeY+6abZ7Nwykur2DW6q31euzMVfV7XE0sMHNlw3vaZqzcQiTJoySiMcvqzckbCgrw+eL8ev3GlERjPDFhWcor/a9PX02oDi2XxeUxHhu/NIUR7e6RcUtqlVCWRGM89NliDyLa2/0fL+TlaauIxp3PULl7enT8El6attqXmMAZCn7b67Moj7FbQ1U0ZikoLOcHY6d7Gk88bnlywrJazyUGKI/F+HTBxhRGVT0lbSK1ZIw5zhjztTFmrjHmWWNMhjHmp0BnYJwxZpx7v8eMMdONMd8aY/5Q19cpq4gRsFwgaYxx3l91pi4rYO532/a6viIOv3v7m1SGVmu/fnM2Zz8yhevGzuCIuz9qcIU7Way1HHPvZ9z9/nzu/XARQ//4YSCSyqcnLGNbyd4J5PJNxXwyf4MPEcHni/JrtYjFa9PXpLxFddqKzZTvuQJEPSwvKG5QErxk4w6Ky71J9OMWZq+pe/JVXF7BlqLkDo2stKmwvMFDTGet3pr0XsBwyPDt2r2PiQ3xv7nr6h3n1uIoq1I8dPjlr1bXuRdw+aYi1m1LbnJblW3FUb5YVvsEf+3WUhZvSF7va22URmM8M2k50SqOB+Uxy/0fL/Stt23s1JXUNJJ1xaZC5tTj2FBf89Ztp7CODZzF5XFe+WpViiKqmZI2kdrJBJ4HLrTWHoKz8uoN1toHgbXAGGvtGPe+t7uTSAcBo40xg+ryQsVJ/tEPmpoqy9NXbKm292NZflEgEqSXpu0aHlRQHGP+uuRWqOpr3bZS1m7bNZyvqAK+qSIB9tr7NQxbereBQ/rqa1l+Ua2GI8asZUuK54FsLkxOEhIJ0aDNsAvLKmo1PChZ6nOc21FasXNFt2QLh0ydK297SsVw2lg8+Z/BhsQZCZmUzz3aXI/EPC0coiBJ36Wa5BeWkhap/WcwEjasS9EWGtVZWVBcY1K2pbicrT7Nb5tRw288OA06Xs59zy8sq9caAhu2p37ofFWUtInUThhYbq1d5F5+ARhVzX0vMMbMBL4GBgIDanpiY8x1bs/c9Pz8/KQPrwmamg6QuZnV70ISCRmCUDR7LlLWrnmGP4HsITdr79XTOuT6H1ubnOpj6NA8+avi1UZmWu1++qyFjFret76S9fzxOGRE6v9ckZC31YH6VJTCIUOqOggsNPjYm6rhpcn+TchIq/8S+RZ2Lr2fKvXdVDzVcQGkh8N1+wxab+JKlJMZqbGB01rISvdnm4ScGn7jAcIhb2PLCPBnrSpK2kSSyBjTE7gVOM5aOwh4D6eXrlrW2iettXnW2rx27drRLD1MAHKT1LA1V5hPG9SZtGoqKOce3iUQyye/deMo2uakkRkx3HXWQDrkZvkdEgA5GRHuOnPXJsE3HdeHzi2TNtWy3m46vm+V1xsDV4/s6XE0jtF929eqAeCA1lkp31S1S8vkfH7CYUNmAyrj7ZpnkOlhRaQ+y9i3apaesqTNAC2qaPioi34dcpKeYEVChu5tkvs9PqhDc9LrWVktj8XplOJj3sFdcuv8GxiLW7q2Sv2xuEurrDqVXXks7vneXl1aZtG7ffNqbx/Zt12DjhUNcX5eVzJq6Km0GE/3cu3ToXmdVx6NhODwbq1SFFHNlLSJ1E4M6GGMOdC9/H3gc/f8DqDyCJkLFAHbjDEdgFPq+kLZGRHfWnFSLW4t2RnVt7S1a57BfRccRuIIqBDQp302d5xeY4elZ/p1as70357Igj+dyuVH9vA7nN1cPqIHK/56Giv+eho3nVB1suS1YT1bc+MxvXarhIUM3HveIDq18Cfh7damGYO61rwXmwF+efJBKY/l7MFdSK/DcKvqnD6oc4Mef3CXFp4tRJIRCTG8Hvt9hUOGnu2yUxARHNg+p8EblA/q2jLpleGSaIyBSd438MJhB1CflkFjYHTfdinfE+26Ub3r1NsSDjnfo2bpNffiJEM4ZLhyRI9a92qfdkinlDf8VOXe7x1KZhXHldzMCH9IaNzz2kkDO9KtdXaVjWYZYcN1o3o1uPGkLto1z+Co3m3r9JhwKMQVPv32p/4TLtI0lAJXAa8ZYyLAVzirSgI8CXxgjFlrrR1jjPkaWACsBibX9YX6ua2gTXFz7fJYnJ5ta650nXFYZ4b3as3bs9eyqbCM4T3bMLpvuwZXqMQ/vzi5P1cf3YtP5q0nPRLmxIEdyakheffCs1cO4+QHJrCxivk9Brhw6AGcekinlMfRv1MuvdvmML8BS8VnhENc28BeywGdcj1btTYtHOKQeiYipwzsyMpNRZQm8fiYEQlxysEdG/w8w3q2Jp7krsCDu7RI+nelU4sshvdszcQ6rsiblRbm+lG9khpLVYb2aEXH3ExWFBTVap5lWjjENUf3SHlcla4Y0YMXp63a59zAnIxItSMNUu2Qri14/2ejeOizJYxbuJGwMZxySEd+NOZA3xrLwPlfvfbDI7nplVlMXpxPOBxy2w8MN4zuzU+OO3Afz5B8N47pzdRlBbVa/CYSgrwereiW5N7v2jJ+79cgIrvk5eXZTydOJe9PHxNNwopyQdOzbTbjbj3G7zBEANhRGuXpSct4esLynavp9Wmfw20n9ePEgQ2vxNfWf2d9x22vz6n3BsF9O+Tw0c2jGxzHJU99wZSlBQ1+nn1pmZXG9N8eX6+evY07Sjn6nnFJ3Uw5PRJiyq+OpW0N8y9r6863v+XfX65MyvG7WXqY+y44jJOTkFDuaeH6HZzz6ORaLwiTGQlxZO82PHvlUE+Gqa8qKOaMhyexozRaY+KWmRbiD2cO5MKh3VIeU6JVBcVc8MQUtpdW7FWGWWlh0iMh/nXNcA7ZR4/+/uy7rSXMXbONjLQQR/Rs49s8O4DHxi/hwU8XU1LDQmmRELTPzeS9n4ykVXZ6tfdrKGPMDHcxu71jSNmriki9tMhKo2VWOvn72IS6MRrS3Z9x4CJVaZ6Zxs3H9+Pm4/tRGo0RCRnPhggmOn1QZ16bvoapSzdR17p+VlqIBy4cnJQ4rh/dm9kpWLY+UUYkxJUjetS7nNs3z+SsQzvz9uy1SRmNkBEJcc7gLklJ2AB+euyBvDFjDdFYw1aiDBmnkeuEAR2SEtee+nVsztNX5HHtC9P3mbhlpoU4uEsLHrtsiGfziru1aca7Pzmaa8dOZ1VBMWUVsd2St+z0MKGQ4S/nHtLgocH1jW/crWN4Z85anvh8KSvcFRs7tcjk2qN7cV5eV1+GRTYmXVpmJW1Ob0PdcMyBtGyWzh/fnQfWUpyQvIWNs8jQ4G4teezSISlN2PZFPW0iAZKXl2enT5/OVc9NY9zCfL/DSaqstDB3njGAi4Z52yIq0hiUlMe45OkvmLt6K7Xd5zUrLcQzVwxlxIF1m5NRnXjccsz/jU/pPlzN0sN8ftuYBq26WlhWwai/javX0vB7apuTzue3jalxrm1d/W/uOm55dVaNrfb7kpUW5n8/G0mPfQwnb6j567bz+7e/ZdbqrcSt3a2HMDs9jDGGK47szk0n9K33qo4NNWfNVp6ZtJxv126nIhanQ24mlx3RnZMGdgzU/G9rbSAWy5L6K43GeH/uOl6atpqCwjIy0kLkdW/NVUf1oFe7HE9iqKmnTUmbSIBUJm1PfL6U//toYZMaIpmdHuaV64/k4CRPqhdpKsor4tzxn7m89fV3xOK22l63jEiIjrkZPHrZEAZ2Tu73ae6abZz/xJRabT5eV1lpYX53xgAuTkLDzezVW7n4qS8atK9ls/Qwr1x3ZEqGsD0ybgkPf1bzcKvqZKaFeeaKPI5KUjJeG6sKinnxy5XMX7edkmiM1tnpnHpIJ04+uCMZEf+GrYnsb5S0iTQSlUnbyoIiTrx/QpNajKRd8wy+/PVxWlBEZB82F5XzylereGriMraXRAmbEHEs1lpOGNCRH4zqxeADWqasVf+e/83n+SkrazUxv7bSwobB3VrxynVHJC3uGSu3cMWz0yiLxojWYWfwNHd7hLFXD2NwCpfufmPGGu747zeUVcSI1eJQnhkJkZuVxuPfH+LbkuIi4i8lbSKNRGXSBvC9x6YwfeUWnyNKjqy0EDef0JfrRvX2OxSRRsNay46yCraXRMlMC5ObmebJcLB43PKjl2YyfkF+UhK3tJChe9ts3rpxBM2TPM8nf0cZt742m2nLN1NaEatxH7eQgYxImCN6tebe8w9N2jy2mqzbVsIf35nHpws2EjJmr/I0QLMMZ8Pmy4Z35+YT+vq6IIOI+EtJm0gjkZi0fTJvAz97+euULgrglYxIiC9/cxwtm/k3gVdEai8Wt/zqjTm8O2ddgxK3rLQwfTvk8M9rh6dsYQZrLTNXbeWpicv4dP4G0sMhjDHErSVkDNadq3V8//ZcO6qXL71YBYVlfDxvA1+t2My8ddspr4iTnRHh8G6tGNqjNcf1b+/bhsciEhxK2kQaicSkLRa35P3pY7YUR32OqmFCxtlg9KFLDvc7FBGpo4++Xc+tr82mtCJep2X2Q0B6WohbTujLNUf3IuzRsOhY3LI0v5DFGwopjcbITAvTp0MOvdvleBaDiEh9acl/kUYoHDJcfXRPHv5sSaOe25YRCfMDDzZkFZHkO3FgRyb0bM3TE5czduoKYtZSUh6rdu+s7PQwMWs589DO3HjMgSlf/XBP4ZChb4fm9O3Q3NPXFRFJNSVtIgF29VE9eWHKCsoKG760tR/Sw4aRfdoyqGtLv0MRkXpq2SydW0/qx8+O78OERfl8tWIzXy7fzMqCYqKxOJGQoX3zTIb2aEVej9YcP6ADOUlcQl9ERJS0iQRadkaEhy4+nKuen5aSJbhTLTMtzD3nDfI7DBFJgrRwiOP6d+C4/qnZ8FlERKoXnF0JRaRKR/Zuw1mHdSEjQJuI1kZWWph7zz+UVtlafERERESkIRpXLVBkP/W70wfQPLPxdIxXDos8aWBHv0MRERERafSUtIk0AtkZER67bAiZacH/yoYM5GalaVikiIiISJIEvwYoIgAM7dGaBy4cHPjErXlmGm/cMELDIkVERESSJNi1PxHZzckHd+Tusw8JZOJmDDTPjPDq9UfSvY23y3yLiIiINGXBq/mJSI3OG9KV+y44NFCJW9gYWjVL5z8/Oop+HbU/koiIiEgyNZ6VDURkp1MP6UzLrHRueHEmJdEY5T5uvp2VFqZH22Y8fcVQurTM8i0OERERkaYqOE31IlInIw5sy4TbxnDigA5kpYU9f/2Qgcy0EDcd34d3fzJSCZuIiIhIiqinTaQRa9EsjYcvOZzPFmzg5ldme9brVtm79vAlh9O7XU7KX09ERERkf6aeNpEm4NiDOjDhF2O4ZNgBNEsP0yw9NT1vzdLDtG+ewa9POYh3fzJSCZuIiIiIB4y11u8YRMSVl5dnp0+f3qDnKI3GeGf2Wh7/fClrt5ZSVhEj3oCveXo4BAaG92zN9aN6M6J3G0Ih06AYRURERGR3xpgZ1tq8Km9T0iYSHMaYfGClxy/bFtjk8WvWlmKrH8VWP4qtfoIaW1DjAsVWX4qtfhRb/fgRW3drbbuqblDSJrKfM8ZMr65Vx2+KrX4UW/0otvoJamxBjQsUW30ptvpRbPUTtNg0p01ERERERCTAlLSJiIiIiIgEmJI2EXnS7wBqoNjqR7HVj2Krn6DGFtS4QLHVl2KrH8VWP4GKTXPaREREREREAkw9bSIiIiIiIgGmpE1EmjRjjDaVExEREd8ZY9Lq+1glbSLS1GUmXlASJyJBpmOUeM0Yo3zAA8aY1sAzxphD6/N4/ZNEZJ8aayXCGHMq8JYx5g/GmJsArLW2sb4f8Z4xJux3DJIaQauoGmM6gnOM8juWPRljhhpjernnA1VuiYJ0bDfGtPA7huoYY44xxnzfGHMVgLU27ndM+4l0YBFwhzHmoLo+OLBfPBEJBmOMcROdY40x1/gdT20ZYwYD/8BZ/elL4HvGmOchtYlbkCoNlYwxzSsrEEGLzxhzsDGmu99xVMUYcwpwq99xVMUYEw5i5dkY07My+QgaY8xJbgPO/0GwKqrGmJOBe40x7fyOZU/GmBNwjqGPQODK7XhjzJXGmOshOAmvMeZcYKox5uigfU/dxsx/AO2BK4wxP0q4LVC/D02NtXY9MBEoAe42xvSty+MD9UESkeBxE5yzgfuANYm3BfwAXwFMtNa+aa19HxgDDDLGPAfJ/XE3xgw3xpyekOAGplyMMacBrwMfGGOuC0qlBnZWVB9njyGsQWCMOR54CvijMeZIv+NJZIw5HXgReMIY8z2/46nkHicmAtcZY3r4G83u3O/BvcBM4FhjzGM+h7STG9sfgaettfl73ObrscSN7S7ge8BWN4ELBGPMccBDQBQ43hjzrjGmp89h4TZC/RRYCfwMGB6UxM3t3bkD+LG19u/Ay0C5MWYg7Py99yXWoJRRKhljzgAeBFYAYeBvlWVfG02+gESkYdwemmuBM4BPjTHDjDG/guC0alaqrOC4B/8Y0NEY0xvAWhsFhgO9jTE/TOJrngBMBS7E6c0LTOLmtqj+GfgDcBvwY2NMS3+jcrix3Q/8zFq7MAjlVcmN7T7gHOCHwBjj8P030xhzBE4F/xHgBZzemd8bY3J9jqslcDnwLk7d4oKg9KAaYzoAtwA/t9b+F/gxUGyM+Z4xxtcGAze2R4DPrbWfG2M6GGNOMMZcCv4O5zbG9AF+BfzaWvsmkA8M8yOWPbllcgHwD2vti9ba84HuwH3GmK4J9/FDFLjTWnsKMB34LU7iFkm8k0/xlQM3WGsnGmPa4CRwJwF/Msa8A973pBpjHjDGnF/5usaY37rHuSbF/f04HfiFtfYO4CacHuw7jTH9avMcvv8AiUjglQPZOJX/54ArgauNMY/7GVQ1WoDzo2OtnYdzQPzA/XGqTNweqrxfQxlnvlNP4BfAZ8BRBCRxM8ZkAIfhVFSnAKtxKhO/Nc5chuY+x3YKsMNaO8MYkwP82RjzsDHmQr/icmNr7cb2E2vtV8B24CygtbU2HoDkshMwyVo70Vo7Ced7eQVwvr9hUYRTwf8RMBknzguNOw+qkk/ltx242lr7sTv88DWgGU6ZPW2MaetDTJUKgd/gNDDdjNPzcRpwkzHmP+Br41gxcLm1drx7eSzwQ2PMKJ/i2cktk+VAxOyaO/Yu0Apn6J/n5WaM6eh+vtcDE9wY7nHP3wEMde93mNfxGWN6GGOygXxr7Sw3gRgF3Gyt/Z619hygvTHmEq9iSjAR+IExZpQx5q/AkcBXPsThhRbAcQDW2hXANKAPTuNb9r6Oj0raRGQ3Cb1VRxpjxgAHAufiVPofs9beiDNUJtMY08y/SHdnjDkTmO7+BcBa+wfgLeALY8wA9+rOOK2eaQ2tQFprY8CrwN+BV4BlOInbBcaYkJ89kdbaMuBha+0n7o/1o8A4nIrN9wHf5ie6sd0HvGOM+R8wBafSPxsnefu+j7FtBu6w1n7uXn4dmI/zo2oC0LtcDOSaXauPtQHeA37jx9A1Y0wntwchbK1dZK2NWWs/Bt7HSdzON878uxFefyeMM7+uBZBlrV3pft9zgWustTdYay/EqUT91KuYEmKrLLeotfbfwDvAz4F3rLU3WWuHAm2NMT/zIbbKciux1i53/38Ra+10nKFdxxqf5lRWlpv7v5wMnIozhPnfQE9r7TE4idxRHsd1KvASTsPmtUBWZc+am7h9jpOI3wv8y+1h9Sq2U3B+px4AfmeMaev2ar1jrX3V7FpsaSLOcdiLmMLu34i19g2cIbj/xJnGcLq1NhaEkQ0NkVCXGup+HnsCtwP9zK7RPtuAGTi92UX7Oj5GarpRRPY/bg/R6TgH0adxhhLdaq39Deyct3IX8FtrbbF/ke7iDi24G3gbZ6gB1tq3Aay1vzDGbMVJBsqAQ4AL3F63+r5es8r3bq3d6l5dbIx5FicZOhJYb4zpD3xnrX2n3m+u7rG1Aba4vY3b3atjwE3W2sXufWI4P95PWGtLPIwtsdyWG2P+iVOxH2et/Zt7n43AZcaYF70cpuOW21Y36djqXhd2E/NHgOuBjsA6r5O3PcrtQ7cS9ms3GY9Ya08xxlyN02L7sYdxnQr8ElgHLDLGPGWtXe3G+bFbZzkK53s5BuiPM8/Hi9hOwTlOLXQv/9laO88Ys8xau9RNIOPAJ3jcgJ1QbuuBhcaZX/cGsMBaOzvhc/cRsMXj2PYst7uttfMT7jIPp2fwkT3n33kQW2K5LQD+Cvwe53vZEafSj3t7uYdxnYzT4/0jnIr5acAL1tqKyvtYa+8xxnyE07t1orV2g0exHYtTTjcCcZzjWNSNqcL9GzPGnA8cgzPHOJXxDLTWfuu+ZiShjE4C1gJlwNE489EDs+BNfbh1qTNxelkrj4GP4yTPDxljjsE5Pt5grf22tk+qk0466bTzhNPy/D7OylKX4bRmdsBp5MnEGVp0pntf43e8bhzZOIkYwHnAXOCsPe5zANAXOKCBr3UeziTigYllAITcvznAJTitlpuBQz0sh3NxFos5BacyX939LgXeBNI9jG23cku4vs0el3+IM1crFJRyc/+nnwB/9SqmWpRbL2BgZbw489x+52Fcx+JU7IcCx+P0Np/s3hZOuN9TOInawR7GNtKNbZj7nb8Dp2K083vqnr8cZ2GS/j6W230J5ZaWcL9L3dj6BaTcEv+nrwFPehVXNeX2AHBSFfe71i237h7FZdxYTnUvHwB87X4fLgf6utcfhNOj4tnvgfu6vwSOd893BpbijLq4Azjcvf5qnOGIKf2O4ozaiQFj97j+e8B/3fNHueV3opfllKLPRRvgA5w61bU4I0pau7c3x5m+sFs9Yp/P6/cb00knnYJzwlnNKA14BmfI0ISEH50z3B+kdPdyUBK2UOJf9/y5JCRubgWkeRJeq7dbJh/iTDAfkFgWCX9vATZV3u5ROXQB/udWqP4DnEjVCciVbuXBy0p0leVWxf0qK9EDPYytxnJL+Hwd6d4v16vPfh3K7QacvX+8rODfCVyfcPl3wDMJl8M484smA4O9ist97euBqxIuXwO8nnA5CydBn+Tl96CW5ZbmxjbFh9j2VW6VDQQDgC4BKLdn3fMGZ/+rQe735TCPY6s87ufiLEr1W+AiN8a7cRo82wFtvYxrj9hycHpzb8PpyfopznDOdCAP6OZBLF2A/+LMRfxvwvXdgcyEy8fjUdKd5PeXnnA+1/37PM4KohOBPu51J9f3/fn+JnXSSadgnHASm8rWwr/gzJ2pTEqOAuYAg/yOsw7v51ychUiecyu9rZLwnJ0Tyuh24Bv2SNzc8z/xoaLaGhjpnv8BTs/QiXv8kByE08rqdWVwn+WGM3TueR9i22e5ubflAi0CWG4tcSq0nn433Ypo14Q4jgWeT7g92/2b6WVc7muGga4Jlw8C3ky4nImzEEk7H2LbV7lVVvA7BrDccr2OqQ7llul3jO7rD0g4fyp79Cr5HFvvhPMH4wwnTfM4hl/iJJDjcLYtGYCbMJLQm9vYTjhDrC/HGRkxBmdocxinJ309bkMCzhDUOdSzd19z2kT2Y5Vzc4wxo3Eqfh2MMaXAYzgHnOeNMa/jDJO83Vo7x8dw68Ra+6Zx9hg6AzjBWtvguSHW2rXu/DistXe7c3ZeNcZcaK391hhzmLV2lrX2oYa+Vj1i22yM+co9/5Qb2y/cmz8yxgyxzkqNN1tnIRAvY9tXuQ10/95oPZ4nWYtyG26t/dLumh/oZWz7KrdB1to5xpg/Wm/n/xnrzEVJ3LdxO05LOsZZrr6bcTax9vSz5sYW2yO2GNDNvf0KnJ6FW6z3c7L2VW6X4YxmuMfL/2dCbDWV2+XAUGPMz3EWT7Eex7bPcjPG3Jvq72nCb+Zu81or50haZ95k5ZzEXKCFMSbHWluYyrhqGdvShNgG4jQK5ZDCeZPG2YOsLc58zQ04Uy1Os9aOMcbMx2mIOgZYlaoYvGCdlYU/xHk/MZxRPjFjzFj38gPGWQ32OuCXdvd5orWmpE1kP5aQsD2Es3LZ993TK8CvceZm7QButNZO8noBhqokLCCQeF3lD1HidUfjzH84zlo7N1mvb60triyHhIr0c8aYD3D28zrX68pgQmylCbFVJiA3upOhTzTGjLbWrvMptprK7ThjzFnW2k1exZP4Wd5HuR1vjDnWWrvWq9gS7aPcjjXGnOP1562aY0AcKDDOkuG/BM63DVjsp76qia0cWOUu1vIj4DIvY0v4/+2r3H6BU26eJGyJC0HUodw8W+DDjbG635yqyq2iivslW3tgA079OWqcVRCtW2lva63d5FbWf4KzFceVXiRsrjY4w/IrYwvBzoSis3sMs27ifQlwRTIaM6vjLmpzD86qypnGWRX4E5ztLToBGTgL2/wcmLDnb3hj4tZBNhhjXsTZIuZQnJE+c3GGyV6Ak7zdYJ39GOtXl7IB6FbUSSed/Dvh9LD9OeHy7cAXOMNPAjVcgd2HIJ7kHhzbupfDe9w3h4ShPkl47dCeMSTc9gmwEZ+Gj+4jtrdwhmccEpT/nZ/lRhXDzoJYbpWf5yCUG+5cVnYNeYwkfOa6uH/b4Sy8MwVv53J23iO2UEJsPRPiXYkzl9PL2Iawx4I6ASq3MTgNcxlVxOZ3ufXHGTbXsYr/qW/lhrMx8lTgSZzVInsk3HYUznC/bji9SWPxdt7wKe5x4VmcJLZVwm0jcFaV7eV+Vx5LdWw4vWeLgGHu5bdw5tLDj5FNAAAZc0lEQVR1x9lCpYBdC/B8QgMXCPPrxK7hul2BnMrPqPsef+leHoI7n63Br+f3G9ZJJ538PQFn46zWlzjefTLO0rSd/Y4vIabEhO06nNa753D2j6v8Ia+s6DZ45UFgODAaGJpwXeUiLIfgVpiBwTgrb3mZeBzu/gAOS7iu8r3nAUe45/vgLLbg5QqWp+BsyJt4XWWFy+9yq9yv6MAqYvO73I7FmVP3gyCVG87y5d/gVFRfJWGxE5yK6sc4y5yn40y29/L/eTJOJfo54AkSEnKchWNm41T+IzibVnu5wE1HnKGhO+cNJfw/R/hcbqe4x8+9VuhzY/Oz3E7CWSXySfd/2zbhNt8+bziLAi3DSUZG4jRufgn0c2NZh7O/WOX9q129NwWxneCW2SicHp1/ACPc27riJN2neRWP+7r9gTHu+Y44y/m/DdwL/A13RcumcMJpPJ6Ik5jejNOD2BtnW4oH3fd+XFJey+83q5NOOnl3Yler0FC3UjMQZ9z9UzjDX/Lcg+1HOEvV/sXvmKt4D0fjLFjR2b38Z5y5DpWXG9w76FZqFrsVh7fYfXW3McC0hB/FTkAHD9//6ThLIo/FqURfl3DbcJzhGJVLPOeShAVY6hBbBs7qYCXsveXCSJxkw69yG4bTa3BsFbcdhZOY+FVup7ivfyswHrg44bZj/Pi84azId4D7eToGp/fgVpzK6SHufWaze0XVsxVl3e/hIvd4kIczDOsy97aOOBuOn+pVPFXE18o9hs5zv6eVDT6d3O/vaQn39Wo10spVFh9i1wI3LXGG/LV3L7+PxxX8hPj6Ad+yq7L/AM58qBw39jk+ft5aAo8nlKPB6dGa4v6vO3kdU0Jsf8cZ6lh5+RGceZGVl3slxu1DfLfj7OsKzoqk/2LXCBnPtnZJ0XsbiZO8t8FJSJfiDIdMxxkFcGXlsTspr+f3G9ZJJ528PeGsaLUAZ4XDDTh7hfTASX4+wmmVG4jTA3eXHwf5PeLduQ8aTmV6LE7l/9SE+/wJZ7XLTkl4vTBO6/L33cu5OD2Pb7qX7wfOSYzNw7IY7FZcDnUvnw/c756PAH/A3bvIr/8bTm/RyzjLOl/pXpfhViTO8is2nMV07nbPd8bpQbrCvXw7bkXVh/9pNs7qppWv/2PgYnYNK7oPONen2MI4DRddEr6HP8NpJOmIu5omuyqxXlaib6v8jrqXfwE8kXD5gMTYvP68ua99I85wsNdwegOPxklMuvtVbu7rVu4h1tU9lr6AsxDEoezqFfQjrh7AownnN+FsP/M1TgNChtflhvNbONr9v00Dbk24LeQec2/H2arB0wQEZwXIwTgrP3dPuP4c4OGEyzvLzcv4aoj7Pdw94hrbid33UozgNH4Px2lM/RJn1eovcHo7eyT79bUQicj/t3enUZJWVbrH/w/UQDGLDG2riCJKi7RwQaAQaBwoGkUBQaAolBLligItiLQyqLQKIo02aC0EUeReaMHFoEsERPQiLa0CMhQIKopiKzJjgczTcz/sExCmWUVVZWZEZNbzWytXZrwRGbEzMjPi3efss88SRNI6VIKzPfVm9Cj1QjPD9mGSnk+9GU6nErZZbq9OfYq3e7HuZNsPSPoQNZK1qaQ/ubo1HiHpcWpEdkRci8iv7br8APA6SZdLOtb2QcPE1ivTqJOaue3ytcAhkta0/T+ti+CTwzVmGWuSJruaO9xF7Qf078BZkl5F/Z192PYjfWxm80dguqQXU7OB3wO2kbSF7X3az9Cv2G5vj78BNZt1BdV98Sbb7+l1bJJeTs0e/JbaGHaW7WMBbJ8gaVlqW5D9ev2ctdgmUYnGtK6rLqPKhjueaWrT4+dtZeBG249Qo++72n6HpCuoPcTeZvs7fXreVqEGfX5OlRjOovY6+5Kkfal9CDcG/tSH2FakXjs2lvQlatDwWOA4at/Ly4DNgLt6+PvsNNK4Fbgb+CjwRUmP2p7javDxY2owqqdNd1psx1KDY6b2aux4hEp6kbQbsJqkE93jjqTt8Yd2styZZ8slx5XWdGaWpF9RA5H7Uv9DU6jBtYNs/1jSVtRgjUY9iH5nrfnIRz7G9oO/Xgs2jaq13hy4th07mOrE1VkUvAw1m9XXxhVDfob3USUVH6JGF1ehFlN/Eth4lB7jFV1f70md2KzZdWxV4Dx6vI/YMLGt1j4vTe01dT7PbuT5in7G1i6/FDizff1hqgPdiX36u+l+3l5Drak4nGr33jn+U+DAPsd2IDUbcyVwbNfxq6iEqZdxbU+d2F8GzAHeRp20Htp1m7WoNWS9nonpju10utZaUSXfV7Sv96Q2Ne5ZI6Wu2C6luu+uQ5Waf5CaJfottUbrbHq/N1Ynth9SM1czqEGLa/jrMs2vAev2Kbb/osoh39Cer+P56w2XT6OHG3rzt400zm9/Y/+LKoH7F+qkfDbwA2CFXv0/DBPbN+laI0Y1vvgatdZqbj/eF4aJeSpVGnkjfXgPHcWfY0Pgfmpmunu9+39QFRPbUiWz08fi8ZciIiYkSSvAM2391b5+xPYtwMupN22oNRffphI3bD9KlbWNWpv8kZD0fqo98ZeoN6GjqMTtUCpB2FbS1BE+xvbAdZLOArB9BvVG+N+S1mzH7qESkGVH8lijENvdbduDp6gZrEntdu8EjpP0vD7E9vWuw/OAuyXtCuxNJda7tRHfnumK7UwA1+zkhdSJw8skrdxueh71JtyP2Dq/0+Op9uBzePb/EioB6FmLdUmbUzOke9n+J2oEeRNqkOf9ko5osyJbUyeGK8/vvnoQ21+oAaeOecAtkt5BNQM43T2abR4S2+upzniHUGsoD6DK0fe1PZ2aFVmjF3ENE9vWwJPAm6iGFX8GtpK0ZXv9mE4P/xeGxLYVNag4y/YfqNmYD7bbzaJOlnvR0r/jTuB9tq+U9HfU3/vHgF2oNYozqYGLj1CDPn9xO3vvQ2ybAvtL+nL7PT5ElekdAuxh++YexbUgT1MVBW+3/fN+B7OoOudQ1GDumdSg6Spd1x1FDT4cDhxj+ydjEkfv/sYiolckLU+tRbnU9int2DP7m7UShZ2oEfRdqMYH1/axPGxY7Q1pX6r0YC8q5m9RJx3HUKN202zfMYLHWI4q5zuPOjmdantmu+5T1EzDidRM2yxqZPp3i/t4I4xtku0923VLU2sqvk6daG1AdW28aQBiO4Y6cd7D9rmqvQBvs/2bPsU2xfYe7bp9qL/5i6gSwF2pNYq/HIDY9gI+1eLbgCp52q1XJ13tJPoVtk9rl1cDTrP9FkkvA46gBgo2pccDO/OJ7RRgd9c+eysBv6FGwN9l+8Y+x3aq7bdKejPwoO3/6lU8CxHb12xvL+mF1Nq25agZpEMG4Hn7qu23SXoFNTN4KTVLvmuvXtuGifNw6nz505LeC/wj1czlD1Sb957tL/kcsc2mZlE/QyWW7xiPCdKg6ZwXSXoTNQt8FvX/cgbVYOU/Jf0jNZv+tLv21hz1WAbo/CwiRoGkaa61QzOB3YBzbZ/erlvKVYc/mTpZfQkw1/YFfQwZeGa0Sv7bjbNXoBbMn2B7hqSXAOdQXe32c60dGelj/z3wAFUaehLwRFfithM16rsRcHyv3wSHie3RTnLUrv8WtRB9J9u/6nNsj9veQ7Wp68tt39yvgYDn+J1uQSVsm1IzMv1+3h6zPatd9zGqrG5F4KO9/HtrAwHLudaOLk11Ojyfavpze/vfu63dptezk/OLbUabfV4H+Ao1A9GTBHwhYtvG9j2SVgQecR82G19AbNvZvkNt02VJy9l+aEBie0uLaT1qpvl+23f1MrYFUW1sf7jtq/sdy1CSLqYqHHq6LnGik7Qt1Uxrtu3L27EtqaUkZ1DbEO3cuW6spDwyYgJpCc73JG1j+0zqBWW3VjJBS9gm2X7C9n/aPtr2BV1T//30gq6ZwNmSPirpdcBT7WPVdruNqD1pPjIaCRuA7T/ZfrCNmL4PmNIpq6PWDlxo+739GLUcJrZpks6AZxrL3ATs0uvEYz6xTZV0Rvs9LivpH/p14vAcv9MHgGtsHzEgz9synVJJaoPeY+lDGZHtp1yNd6DW68wD7msJ257AYdR6rJ4mbM8R292S3kXNGO3Q64RtAbH9uSVss6hmFlN6HdcCYruvJWx7Ap9oA309TdieI7Y/td/pB4A/9jNhG/re2KpUVqMGL/pqPrGtSs32JGEbBSrLU6XY+9u+XNJ2kg6jyou3pcqhdx3rhA0y0xYx4bTyjQ9QzRZ+KOnt1MjbN4bOuPUzzm6qrpWXAUdSL4THUlsPiGqYcqKk/0Mt7l+FeoG8fgzjWZVaa7E5Vbu+te0/jtXjLYqu2F7XDm1p+84+hvSMrtimU8/b6wfweRv036kYrOftNGotygwGaK0r/E1s7x7L14RFNY6et8S2EFTrpvekmmHtNkhlh4Mc20Qh6SBqTf091KDf/dRenrPVw27NafkfMcHY/oqkR6nWxAfYPq8NyL1L0hTbXx2khA3A9r2tNv8jVLvi7WzfpWpmsaWk99jeq63BeMxjvIagjZBfT216vM2gnEDDsLENRMIG4+55S2wL0EbxJ1Obx04G3mj71/2NqiS2xZPYRqS7kUbPZ+efwyDHNu50l/R3DXB/k9qS4he2r1Gt0z6012XFSdoixjlJr6Q6a/3Y9v9AdT9s64q6E7dJwLslfdd230s7oGbYbN/bLv6Cal7xfapb5PHU2jUDb24vnqf0KK7nUZuQzxiUkd6OxLZ4EtuiaSctj6ua8Vw1SCfQiW3xJLbF51qPeGG/4xjOIMc2nqiaRK1q+/eSXk2tC7wPwPatVOM2JL2RavF/RK/LilMeGTGOtdHJ46g9Y84B7qDWnTxl+3FJ76ZaTn/E9iWS1hikmRlJewNbAD+iujTuSp28fhr4bFfyuQOVlPYsdknLuLY/GDiJbfEktkXXPeo8aBLb4klsEX+rDYAfA1xHlarvN3TwQNXRem/getvf6fXfa5K2iHFO0jbAJ6iW+J+jRoMeAY5rZYczqf1ltuiMGg0SSb8BVgde2pl1k/TP1L4nJ/Vqdi0iIiKWXJKOpvYH/ITt41RdTf+qsYue7dDd8wGGdI+MGOdsX0Itjt3D9o7ANdTs2kWSDqY2eP2nQUvYJE1us2inUhtWfqVzne3vUntC7SlppaFdsiIiIiJGasj5xU+Bo4FZkt7s6nBq1TZJALh1re7HjHBm2iLGsc5Ij6TXUuWF51Atwz9Pbfy5JfAt29f0McyFIuky4GHb20nantrE9/JBLBmLiIiI8W3IOdTawK+Ba6nzqX+jtmS5E5gJ/Lv7sNdityRtEROApNWB04GtgANtn9yOL2v74b4G18yvlEDS5M4LoaSfAE9SGx/vnE5YERERMdo6rfolbUdtnH0hsB7w/6gB8PWoNW6PAwfbvqhvwTZJ2iImiDZS9AWq7e/tg7QX25AWuu+nGqZ8pytZ607c3gzcYPsPfQs4IiIiJhxJq7tt2K7aOPvz1D62P5C0CbA9tan7l1tzkqVt39THkJ+RNW0RE8d1wI3UvmYDk7DBs7XfkvanOi/d0F1mYPuJtiUBti9MwhYRERGjqa1fu1DSqQC2HwSmANu0y1dS51Kz2h5svxqUhA2StEVMGC0JOpkaIRqIhK2zwFfSUpJWA3YEZgO3S9pd0kGSNgOw/WT/Io2IiIiJrA0gbwtsIWlOO3wqMFXSzu3yL4F5VDI3UFIeGRFjYkhJ5Cq275P0BWANYGnqRXFZ4Ge2P9/HUCMiImKCkrQm8DDwpO15klahOkVeaPtASftQZZFLA2sBH7d9Xt8Cno8kbRExpiQdAMyw/VZJmwMvohK130r638AMYGa/uzJFRETExCJpReBy4IVUovYN4AfA3cD1wNdtf1LSSsAmwG22bxrEjd6TtEXEmJG0L1UO+U7bv+5sStmumw0cDOw2SDXjERERMTFImgrsQ3XXvh+YCkwD7gPmAnOAo20f0bcgF1LWtEXEmGiNRV4M7Acs32bcfibpnZLWptrpJmGLiIiIUddmyx4Dvgr8GPg9cAm179r1VB50L3CYpH+QNNB5UWbaImJUDFdK0MofD6BeKL8JGNgFeAfwWJqPRERExFjp2kB7eap79TrAxcAF7fi6VI+Sgd8XNklbRIyq1tb/ZcBywOHA8sBdth+WtDXwSWAn2/f2L8qIiIhYEnRtpL0clbi9FLiGWs/2dNftBm4dW7eBngaMiPGlbZy9I1UjvhnVgenWlrAdTG1iuX8StoiIiBht3VsNdY61hG1p2w9RLf5vo5qOrNH9vYOcsEFm2iJiFEn6OJWwvRt4A7ADMAl4kuoSect4KEGIiIiI8aWrFPKfgd2B3wFzbX9ryPXLAava/n0/411Uk/odQESMb10vggLWBC4CbgF2sP1k6yD5mO1T+hpoRERETFjtXGQb4CiqO/V7gQ0lXWD7iXb9Um3G7aG+BrsYUh4ZEYtM0htbKSRdL4IGPgu8ALi6JWyzgQ8Al/Uv2oiIiFhCrA3sBUymmo4cYPsJSX8P0L2GbbzJTFtELI4HgDmSnrL9ZdtPS5rc9mLbAThN0quAdYFdbN/c33AjIiJiopK0su151B5sZ1Ot/N9m+05J2wLrS5pj+9G+BjoCSdoiYpHZvkrSJsD32yzbScBTLXG7VtI+wG+Ap2zf399oIyIiYqLpWp6xIbCnpK8Bp1Abac9rCdvrgROAD47nhA3SiCQiRkDSxtRGlYfbPrEd2x/YHphp+8/9jC8iIiImLknbAQcC6wFXAcdQ1UCfAZYBVgSOsn1B34IcJUnaImJEuhK39wKPAV+k9mG7rq+BRURExIQlaR3gHGBn4B7gU8BTwCm2b5S0EjDV9l2DvgfbwkjSFhEj1hK3K4FHgc1sX9/nkCIiImICk7Q2VQ65h+07WpJ2PvBn4LCWuC01npuPdEv3yIgYMds/o0oTNkzCFhEREaNJ0isl7S7pxV2H7wNuBLaW9HdtDf1JwPOAvWF8d4scKjNtERERERExkNo+sMcB/0KVQ94BHGr7UUlvAXai9l27hWr3fyjwYWAv27f3J+rRl+6RERERERExkFqHyO8CmwJHAJ8DPivpfuBo4E5gfWAzYB9gKrAS8HB/Ih4bKY+MiIiIiIiBZfsSqtnIHrZ3BK4GDgEuBbYA5tp+H/BCqkRyn4m25VCStoiIiIiIGEitPBLgKGCKpNcA/wrsB3wCeD7Quc2twK4TcX191rRFRERERMRAk7Q6cDq1efaBtk9ux6fZfmQidYocTpK2iIiIiIgYeJJeC3wBeLvt2yd6otYt5ZERERERETEeXEe1+d9ySUrYIN0jIyIiIiJiHLD9hKSTgclLUsIGKY+MiIiIiIgYaCmPjIiIiIiIGGBJ2iIiIiIiIgZYkraIiIiIiIgBlqQtIiIiIiJigCVpi4iIWMJI2lGSJa3bg8faWtLm87ludovjTcPEtssIH3dfSe8ayX1ERAyKJG0RERFLnpnA5e3zWNsaGDZpa24Adu+6PBOYuygPIGnS0Mu2T7L9fxflfiIiBlWStoiIiCWIpOWBLYD30JUsSVpK0omSfinpEkkXdma7JG0k6TJJV0u6WNILhrnft0q6QtK1kr4vaQ1JawH7AgdJuk7SlsOE9CNgE0mTW2wvpzbQ7dzvxyVdJennkr4sSe34DyUdL+lnwAeHuXykpA+32+7T7mOupHMlLduOry3pp5JukPRpSQ92Pe4h7Xuul/RvI3rSIyJGKElbRETEkmUH4Lu2bwbulbRRO/52YC3gVcA7gekAkiYDXwR2sb0RcCpw1DD3ezmwme0NgbOAf7V9K3AS8B+2N7D9o2G+z8D3gW1bbN8ecv0c26+1/WpgGrB913VTbG9s+3PzudxxXruP1wC/oBJWgBOAE2yvD/yxc2NJM4B1gE2ADYCNJG01TOwRET2RpC0iImLJMpNKqmifOyWSWwBn237a9h3Ape34K4FXA5dIug44AnjRMPf7IuBiSTcAhwDrLUJMZ1GzfrsDZw657vVtBu8G4A1D7vcbQ2479HLHqyX9qN3HrK77mA6c3b7+etftZ7SPa4FrgHWpJC4ioi8mPfdNIiIiYiKQtAqV+KwvycDSgCUdsqBvA260Pf057v6LwOdtf1vS1sCRCxuX7SslrQ88bPvmVgGJpGWAE4GNbf9B0pHAMl3f+tCQuxp6ueM0YEfbcyXNptbZLYiAz9g+eWF/hoiIsZSZtoiIiCXHLsDptl9iey3bLwZ+B2wJ/Dewc1vbtgbPJja/AlaT9Ey5pKThZtFWAm5rX+/VdfwvwAoLEdtHgcOGHOskaPe09W6L21FyBeD2Vuo5q+v4T4Gd29fdzVAuBvZuj4mkF0pafTEfOyJixJK0RURELDlmAt8ccuzcdvxcal3XTcAZVFng/bYfp5Klz0qaSzUJGa4b5JHA2ZKuBu7pOn4+sNMCGpEAYPsi25cOOTYPOAX4OZVIXbWQP+dQHwOuoBLTX3YdPxD4kKTrqQYo97fH/R5VLvmTVlJ5DguXeEZEjAnZ7ncMERERMQAkLW/7QUnPB64EXtfWt01IrYvkI7YtaXdgpu0d+h1XRMRQWdMWERERHd+RtDIwBfjURE7Ymo2AOW0bgXnA3n2OJyJiWJlpi4iIiIiIGGBZ0xYRERERETHAkrRFREREREQMsCRtERERERERAyxJW0RERERExABL0hYRERERETHAkrRFREREREQMsP8PzKwefPlDMIQAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 828x763.2 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DK4wsKYiIImV",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 704
        },
        "outputId": "15b48cfe-7d2c-46dc-e02f-b9e1188070b7"
      },
      "source": [
        "scatter = plt.scatter(y = df1['Educational_level'], x = df1['Age_at_marriage'],\n",
        "                      s = 0.00001*df1['N_females'])\n",
        "plt.xticks(rotation = 45)\n",
        "fig = plt.gcf() #Get the current figure\n",
        "fig.set_size_inches(11.5, 10.6)\n",
        "fig.savefig('xyz.png', dpi=100)\n",
        "plt.xlabel('Age at Marriage')\n",
        "plt.ylabel('Educational Level')\n",
        "plt.title('Education level at marriage age of Females')\n",
        "# plt.legend(*scatter.legend_elements(\"sizes\", num=16),bbox_to_anchor=(1.1, 1.05))\n",
        "fig.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 828x763.2 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Yp4BtZgEIV-R",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}