Dibimbing - Funnel and Cohort Analysis - Part_02.ipynb

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      "source": [
        "<a href=\"https://colab.research.google.com/github/dartwinshu/dibimbing-funnel-and-cohort-analysis/blob/main/Dibimbing%20-%20Funnel%20and%20Cohort%20Analysis%20-%20Part_02.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lA--0v0Tr9NB"
      },
      "source": [
        "# Cohort Analysis (Retention over User & Product Lifetime)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "whpQY4JKr9NC"
      },
      "source": [
        "![](http://blog.appsee.com/wp-content/uploads/2018/06/action-cohort-analysis-4.png)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "v5uQ5w5qr9NC"
      },
      "source": [
        "![](http://d35fo82fjcw0y8.cloudfront.net/2016/03/03210554/table1a2.png)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
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      "source": [
        "**A cohort** is a group of subjects who share a defining characteristic. We can observe how a cohort behaves across time and compare it to other cohorts. Cohorts are used in medicine, psychology, econometrics, ecology and many other areas to perform a cross-section (compare difference across subjects) at intervals through time. \n",
        "\n",
        "**Types of cohorts: \n",
        "**\n",
        "- Time Cohorts are customers who signed up for a product or service during a particular time frame. Analyzing these cohorts shows the customers’ behavior depending on the time they started using the company’s products or services. The time may be monthly or quarterly even daily.\n",
        "- Behaovior cohorts are customers who purchased a product or subscribed to a service in the past. It groups customers by the type of product or service they signed up. Customers who signed up for basic level services might have different needs than those who signed up for advanced services. Understaning the needs of the various cohorts can help a company design custom-made services or products for particular segments.\n",
        "- Size cohorts refer to the various sizes of customers who purchase company’s products or services. This categorization can be based on the amount of spending in some periodic time after acquisition or the product type that the customer spent most of their order amount in some period of time.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Z6od0O0sr9ND"
      },
      "source": [
        "**Import Libraries and DataSet **"
      ]
    },
    {
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      "source": [
        "# import library\n",
        "import numpy as np # linear algebra\n",
        "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
        "import datetime as dt\n",
        "\n",
        "#For Data  Visualization\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "#For Machine Learning Algorithm\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.cluster import KMeans\n",
        "\n",
        "# Input data files are available in the \"../input/\" directory.\n",
        "# For example, running this (by clicking run or pressing Shift+Enter) will list the files in the input directory\n",
        "\n",
        "#import os\n",
        "#print(os.listdir(\"..input\\\\\"))\n",
        "\n",
        "df = pd.read_excel('Online Retail.xlsx')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Mh6ga7UXr9NF"
      },
      "source": [
        "**Explore + Clean the data **"
      ]
    },
    {
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      "execution_count": 8,
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      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  InvoiceNo StockCode                          Description  Quantity  \\\n",
              "0    536365    85123A   WHITE HANGING HEART T-LIGHT HOLDER         6   \n",
              "1    536365     71053                  WHITE METAL LANTERN         6   \n",
              "2    536365    84406B       CREAM CUPID HEARTS COAT HANGER         8   \n",
              "3    536365    84029G  KNITTED UNION FLAG HOT WATER BOTTLE         6   \n",
              "4    536365    84029E       RED WOOLLY HOTTIE WHITE HEART.         6   \n",
              "\n",
              "          InvoiceDate  UnitPrice  CustomerID         Country  \n",
              "0 2010-12-01 08:26:00       2.55     17850.0  United Kingdom  \n",
              "1 2010-12-01 08:26:00       3.39     17850.0  United Kingdom  \n",
              "2 2010-12-01 08:26:00       2.75     17850.0  United Kingdom  \n",
              "3 2010-12-01 08:26:00       3.39     17850.0  United Kingdom  \n",
              "4 2010-12-01 08:26:00       3.39     17850.0  United Kingdom  "
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              "      <th>4</th>\n",
              "      <td>536365</td>\n",
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              "      <td>6</td>\n",
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              "      <td>3.39</td>\n",
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      "source": [
        "df.head(5)"
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      "execution_count": 9,
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          "base_uri": "https://localhost:8080/"
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      },
      "outputs": [
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          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 541909 entries, 0 to 541908\n",
            "Data columns (total 8 columns):\n",
            " #   Column       Non-Null Count   Dtype         \n",
            "---  ------       --------------   -----         \n",
            " 0   InvoiceNo    541909 non-null  object        \n",
            " 1   StockCode    541909 non-null  object        \n",
            " 2   Description  540455 non-null  object        \n",
            " 3   Quantity     541909 non-null  int64         \n",
            " 4   InvoiceDate  541909 non-null  datetime64[ns]\n",
            " 5   UnitPrice    541909 non-null  float64       \n",
            " 6   CustomerID   406829 non-null  float64       \n",
            " 7   Country      541909 non-null  object        \n",
            "dtypes: datetime64[ns](1), float64(2), int64(1), object(4)\n",
            "memory usage: 33.1+ MB\n"
          ]
        }
      ],
      "source": [
        "df.info()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Alotpl2Er9NG"
      },
      "source": [
        "**Nots that: There are Missing Data in Description and The Customer ID Columns , let's check that**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QfiRkqZXr9NH"
      },
      "source": [
        "Check and Clean Missing Data "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "EucNPc33r9NH",
        "outputId": "44f7c8f6-6e39-43db-ab93-066325878e8f",
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          "base_uri": "https://localhost:8080/"
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      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "InvoiceNo           0\n",
              "StockCode           0\n",
              "Description      1454\n",
              "Quantity            0\n",
              "InvoiceDate         0\n",
              "UnitPrice           0\n",
              "CustomerID     135080\n",
              "Country             0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
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      "source": [
        "df.isnull().sum()"
      ]
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      "execution_count": 11,
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        "id": "Cnu3Ch_Ir9NH"
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      "source": [
        "df= df.dropna(subset=['CustomerID'])"
      ]
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          "metadata": {},
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      "source": [
        "df.isnull().sum().sum()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Rl3AqsyBr9NI"
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      "source": [
        "Check & Clean Duplicates Data"
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    {
      "cell_type": "code",
      "execution_count": 13,
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      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "5225"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ],
      "source": [
        "df.duplicated().sum()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "id": "H-mu4f-Nr9NI"
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      "outputs": [],
      "source": [
        "df = df.drop_duplicates()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "id": "aZX0JKtxr9NI",
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            "text/plain": [
              "0"
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          "metadata": {},
          "execution_count": 15
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      "source": [
        "df.duplicated().sum()\n"
      ]
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      "metadata": {
        "id": "wDP6eGWlr9NI"
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      "source": []
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      "execution_count": 16,
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          "height": 300
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      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            Quantity      UnitPrice     CustomerID\n",
              "count  401604.000000  401604.000000  401604.000000\n",
              "mean       12.183273       3.474064   15281.160818\n",
              "std       250.283037      69.764035    1714.006089\n",
              "min    -80995.000000       0.000000   12346.000000\n",
              "25%         2.000000       1.250000   13939.000000\n",
              "50%         5.000000       1.950000   15145.000000\n",
              "75%        12.000000       3.750000   16784.000000\n",
              "max     80995.000000   38970.000000   18287.000000"
            ],
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              "      <td>401604.000000</td>\n",
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              "      <th>mean</th>\n",
              "      <td>12.183273</td>\n",
              "      <td>3.474064</td>\n",
              "      <td>15281.160818</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>250.283037</td>\n",
              "      <td>69.764035</td>\n",
              "      <td>1714.006089</td>\n",
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              "      <th>min</th>\n",
              "      <td>-80995.000000</td>\n",
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              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.250000</td>\n",
              "      <td>13939.000000</td>\n",
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              "      <th>50%</th>\n",
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              "      <th>75%</th>\n",
              "      <td>12.000000</td>\n",
              "      <td>3.750000</td>\n",
              "      <td>16784.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>80995.000000</td>\n",
              "      <td>38970.000000</td>\n",
              "      <td>18287.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-bb734ec6-e6ca-4572-b6ed-b5eee643f913')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "  </svg>\n",
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              "      \n",
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              "    .colab-df-container {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "\n",
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              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-bb734ec6-e6ca-4572-b6ed-b5eee643f913');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ],
      "source": [
        "df.describe()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LlbhN2JQr9NJ"
      },
      "source": [
        "Note That : The min for unit price = 0 and the min for Quantity with negative value "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "id": "u0PcQv3Lr9NJ",
        "outputId": "1d4edfb5-975a-4ab8-d77b-65244ee2159d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            Quantity      UnitPrice     CustomerID\n",
              "count  392692.000000  392692.000000  392692.000000\n",
              "mean       13.119702       3.125914   15287.843865\n",
              "std       180.492832      22.241836    1713.539549\n",
              "min         1.000000       0.001000   12346.000000\n",
              "25%         2.000000       1.250000   13955.000000\n",
              "50%         6.000000       1.950000   15150.000000\n",
              "75%        12.000000       3.750000   16791.000000\n",
              "max     80995.000000    8142.750000   18287.000000"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-7ba6b5b1-4ecb-4965-92f5-35ec8f1acf32\">\n",
              "    <div class=\"colab-df-container\">\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Quantity</th>\n",
              "      <th>UnitPrice</th>\n",
              "      <th>CustomerID</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>392692.000000</td>\n",
              "      <td>392692.000000</td>\n",
              "      <td>392692.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>13.119702</td>\n",
              "      <td>3.125914</td>\n",
              "      <td>15287.843865</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>180.492832</td>\n",
              "      <td>22.241836</td>\n",
              "      <td>1713.539549</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.001000</td>\n",
              "      <td>12346.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.250000</td>\n",
              "      <td>13955.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>6.000000</td>\n",
              "      <td>1.950000</td>\n",
              "      <td>15150.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>12.000000</td>\n",
              "      <td>3.750000</td>\n",
              "      <td>16791.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>80995.000000</td>\n",
              "      <td>8142.750000</td>\n",
              "      <td>18287.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-7ba6b5b1-4ecb-4965-92f5-35ec8f1acf32')\"\n",
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              "              style=\"display:none;\">\n",
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              "\n",
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              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-7ba6b5b1-4ecb-4965-92f5-35ec8f1acf32 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-7ba6b5b1-4ecb-4965-92f5-35ec8f1acf32');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ],
      "source": [
        "df=df[(df['Quantity']>0) & (df['UnitPrice']>0)]\n",
        "df.describe() "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "id": "0UB3g73Wr9NJ",
        "outputId": "7fb2706f-ca5e-470e-dd68-972a2c3660cd",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(392692, 8)"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ],
      "source": [
        "df.shape"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2vB_NNBGr9NJ"
      },
      "source": [
        "# Let's Make Cohort Analysis"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pjJlcl71r9NK"
      },
      "source": [
        "**For cohort analysis, there are a few labels that we have to create:**\n",
        "- Invoice period: A string representation of the year and month of a single transaction/invoice.\n",
        "- Cohort group: A string representation of the the year and month of a customer’s first purchase. This label is common across all invoices for a particular customer.\n",
        "- Cohort period / Cohort Index: A integer representation a customer’s stage in its “lifetime”. The number represents the number of months passed since the first purchase.\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "id": "8eFXt7Far9NK",
        "outputId": "37f60be5-d1e6-4641-cc64-d9e165bb4d64",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 337
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       InvoiceNo StockCode                      Description  Quantity  \\\n",
              "541904    581587     22613      PACK OF 20 SPACEBOY NAPKINS        12   \n",
              "541905    581587     22899     CHILDREN'S APRON DOLLY GIRL          6   \n",
              "541906    581587     23254    CHILDRENS CUTLERY DOLLY GIRL          4   \n",
              "541907    581587     23255  CHILDRENS CUTLERY CIRCUS PARADE         4   \n",
              "541908    581587     22138    BAKING SET 9 PIECE RETROSPOT          3   \n",
              "\n",
              "               InvoiceDate  UnitPrice  CustomerID Country InvoiceMonth  \\\n",
              "541904 2011-12-09 12:50:00       0.85     12680.0  France   2011-12-01   \n",
              "541905 2011-12-09 12:50:00       2.10     12680.0  France   2011-12-01   \n",
              "541906 2011-12-09 12:50:00       4.15     12680.0  France   2011-12-01   \n",
              "541907 2011-12-09 12:50:00       4.15     12680.0  France   2011-12-01   \n",
              "541908 2011-12-09 12:50:00       4.95     12680.0  France   2011-12-01   \n",
              "\n",
              "       CohortMonth  \n",
              "541904  2011-08-01  \n",
              "541905  2011-08-01  \n",
              "541906  2011-08-01  \n",
              "541907  2011-08-01  \n",
              "541908  2011-08-01  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-47de2d08-6a71-4ed9-8f81-8dbb55d101ab\">\n",
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              "      <th></th>\n",
              "      <th>InvoiceNo</th>\n",
              "      <th>StockCode</th>\n",
              "      <th>Description</th>\n",
              "      <th>Quantity</th>\n",
              "      <th>InvoiceDate</th>\n",
              "      <th>UnitPrice</th>\n",
              "      <th>CustomerID</th>\n",
              "      <th>Country</th>\n",
              "      <th>InvoiceMonth</th>\n",
              "      <th>CohortMonth</th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>541904</th>\n",
              "      <td>581587</td>\n",
              "      <td>22613</td>\n",
              "      <td>PACK OF 20 SPACEBOY NAPKINS</td>\n",
              "      <td>12</td>\n",
              "      <td>2011-12-09 12:50:00</td>\n",
              "      <td>0.85</td>\n",
              "      <td>12680.0</td>\n",
              "      <td>France</td>\n",
              "      <td>2011-12-01</td>\n",
              "      <td>2011-08-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>541905</th>\n",
              "      <td>581587</td>\n",
              "      <td>22899</td>\n",
              "      <td>CHILDREN'S APRON DOLLY GIRL</td>\n",
              "      <td>6</td>\n",
              "      <td>2011-12-09 12:50:00</td>\n",
              "      <td>2.10</td>\n",
              "      <td>12680.0</td>\n",
              "      <td>France</td>\n",
              "      <td>2011-12-01</td>\n",
              "      <td>2011-08-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>541906</th>\n",
              "      <td>581587</td>\n",
              "      <td>23254</td>\n",
              "      <td>CHILDRENS CUTLERY DOLLY GIRL</td>\n",
              "      <td>4</td>\n",
              "      <td>2011-12-09 12:50:00</td>\n",
              "      <td>4.15</td>\n",
              "      <td>12680.0</td>\n",
              "      <td>France</td>\n",
              "      <td>2011-12-01</td>\n",
              "      <td>2011-08-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>541907</th>\n",
              "      <td>581587</td>\n",
              "      <td>23255</td>\n",
              "      <td>CHILDRENS CUTLERY CIRCUS PARADE</td>\n",
              "      <td>4</td>\n",
              "      <td>2011-12-09 12:50:00</td>\n",
              "      <td>4.15</td>\n",
              "      <td>12680.0</td>\n",
              "      <td>France</td>\n",
              "      <td>2011-12-01</td>\n",
              "      <td>2011-08-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>541908</th>\n",
              "      <td>581587</td>\n",
              "      <td>22138</td>\n",
              "      <td>BAKING SET 9 PIECE RETROSPOT</td>\n",
              "      <td>3</td>\n",
              "      <td>2011-12-09 12:50:00</td>\n",
              "      <td>4.95</td>\n",
              "      <td>12680.0</td>\n",
              "      <td>France</td>\n",
              "      <td>2011-12-01</td>\n",
              "      <td>2011-08-01</td>\n",
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              "                                                     [key], {});\n",
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ],
      "source": [
        "def get_month(x) : return dt.datetime(x.year,x.month,1)\n",
        "df['InvoiceMonth'] = df['InvoiceDate'].apply(get_month)\n",
        "grouping = df.groupby('CustomerID')['InvoiceMonth']\n",
        "df['CohortMonth'] = grouping.transform('min')\n",
        "df.tail()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "id": "Mp0xDFwCr9NK"
      },
      "outputs": [],
      "source": [
        "def get_month_int (dframe,column):\n",
        "    year = dframe[column].dt.year\n",
        "    month = dframe[column].dt.month\n",
        "    day = dframe[column].dt.day\n",
        "    return year, month , day \n",
        "\n",
        "invoice_year,invoice_month,_ = get_month_int(df,'InvoiceMonth')\n",
        "cohort_year,cohort_month,_ = get_month_int(df,'CohortMonth')\n",
        "\n",
        "year_diff = invoice_year - cohort_year \n",
        "month_diff = invoice_month - cohort_month \n",
        "\n",
        "df['CohortIndex'] = year_diff * 12 + month_diff + 1 "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "id": "_XdqomTir9NK",
        "outputId": "6c8dc385-24b4-4c21-8d3e-31de4c75fea3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 488
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "CohortIndex     1      2      3      4      5      6      7      8      9   \\\n",
              "CohortMonth                                                                  \n",
              "2010-12-01   885.0  324.0  286.0  340.0  321.0  352.0  321.0  309.0  313.0   \n",
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              "2011-02-01   380.0   71.0   71.0  108.0  103.0   94.0   96.0  106.0   94.0   \n",
              "2011-03-01   452.0   68.0  114.0   90.0  101.0   76.0  121.0  104.0  126.0   \n",
              "2011-04-01   300.0   64.0   61.0   63.0   59.0   68.0   65.0   78.0   22.0   \n",
              "2011-05-01   284.0   54.0   49.0   49.0   59.0   66.0   75.0   27.0    NaN   \n",
              "2011-06-01   242.0   42.0   38.0   64.0   56.0   81.0   23.0    NaN    NaN   \n",
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              "2011-12-01    41.0    NaN    NaN    NaN    NaN    NaN    NaN    NaN    NaN   \n",
              "\n",
              "CohortIndex     10     11     12     13  \n",
              "CohortMonth                              \n",
              "2010-12-01   350.0  331.0  445.0  235.0  \n",
              "2011-01-01   136.0  152.0   49.0    NaN  \n",
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              "2011-10-01     NaN    NaN    NaN    NaN  \n",
              "2011-11-01     NaN    NaN    NaN    NaN  \n",
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              "      <td>340.0</td>\n",
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              "      <td>96.0</td>\n",
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              "      <td>64.0</td>\n",
              "      <td>61.0</td>\n",
              "      <td>63.0</td>\n",
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              "      <td>68.0</td>\n",
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              "      <td>42.0</td>\n",
              "      <td>38.0</td>\n",
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              "    [theme=dark] .colab-df-convert {\n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-d1b752e1-7b61-45f1-ba54-7a5a601dd4cc button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-d1b752e1-7b61-45f1-ba54-7a5a601dd4cc');\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ],
      "source": [
        "#Count monthly active customers from each cohort\n",
        "grouping = df.groupby(['CohortMonth', 'CohortIndex'])\n",
        "cohort_data = grouping['CustomerID'].apply(pd.Series.nunique)\n",
        "# Return number of unique elements in the object.\n",
        "cohort_data = cohort_data.reset_index()\n",
        "cohort_counts = cohort_data.pivot(index='CohortMonth',columns='CohortIndex',values='CustomerID')\n",
        "cohort_counts\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "54TPf-vrr9NL"
      },
      "source": [
        "**Retention Rate Table **"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "id": "3H-kAsybr9NL",
        "outputId": "6ba8e47a-76ff-4f55-9a44-26377bf3d113",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 488
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "CohortIndex     1     2     3     4     5     6     7     8     9     10  \\\n",
              "CohortMonth                                                                \n",
              "2010-12-01   100.0  36.6  32.3  38.4  36.3  39.8  36.3  34.9  35.4  39.5   \n",
              "2011-01-01   100.0  22.1  26.6  23.0  32.1  28.8  24.7  24.2  30.0  32.6   \n",
              "2011-02-01   100.0  18.7  18.7  28.4  27.1  24.7  25.3  27.9  24.7  30.5   \n",
              "2011-03-01   100.0  15.0  25.2  19.9  22.3  16.8  26.8  23.0  27.9   8.6   \n",
              "2011-04-01   100.0  21.3  20.3  21.0  19.7  22.7  21.7  26.0   7.3   NaN   \n",
              "2011-05-01   100.0  19.0  17.3  17.3  20.8  23.2  26.4   9.5   NaN   NaN   \n",
              "2011-06-01   100.0  17.4  15.7  26.4  23.1  33.5   9.5   NaN   NaN   NaN   \n",
              "2011-07-01   100.0  18.1  20.7  22.3  27.1  11.2   NaN   NaN   NaN   NaN   \n",
              "2011-08-01   100.0  20.7  24.9  24.3  12.4   NaN   NaN   NaN   NaN   NaN   \n",
              "2011-09-01   100.0  23.4  30.1  11.4   NaN   NaN   NaN   NaN   NaN   NaN   \n",
              "2011-10-01   100.0  24.0  11.5   NaN   NaN   NaN   NaN   NaN   NaN   NaN   \n",
              "2011-11-01   100.0  11.1   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   \n",
              "2011-12-01   100.0   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   \n",
              "\n",
              "CohortIndex    11    12    13  \n",
              "CohortMonth                    \n",
              "2010-12-01   37.4  50.3  26.6  \n",
              "2011-01-01   36.5  11.8   NaN  \n",
              "2011-02-01    6.8   NaN   NaN  \n",
              "2011-03-01    NaN   NaN   NaN  \n",
              "2011-04-01    NaN   NaN   NaN  \n",
              "2011-05-01    NaN   NaN   NaN  \n",
              "2011-06-01    NaN   NaN   NaN  \n",
              "2011-07-01    NaN   NaN   NaN  \n",
              "2011-08-01    NaN   NaN   NaN  \n",
              "2011-09-01    NaN   NaN   NaN  \n",
              "2011-10-01    NaN   NaN   NaN  \n",
              "2011-11-01    NaN   NaN   NaN  \n",
              "2011-12-01    NaN   NaN   NaN  "
            ],
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              "    <tr style=\"text-align: right;\">\n",
              "      <th>CohortIndex</th>\n",
              "      <th>1</th>\n",
              "      <th>2</th>\n",
              "      <th>3</th>\n",
              "      <th>4</th>\n",
              "      <th>5</th>\n",
              "      <th>6</th>\n",
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              "      <th>8</th>\n",
              "      <th>9</th>\n",
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              "      <th>11</th>\n",
              "      <th>12</th>\n",
              "      <th>13</th>\n",
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              "    <tr>\n",
              "      <th>CohortMonth</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2010-12-01</th>\n",
              "      <td>100.0</td>\n",
              "      <td>36.6</td>\n",
              "      <td>32.3</td>\n",
              "      <td>38.4</td>\n",
              "      <td>36.3</td>\n",
              "      <td>39.8</td>\n",
              "      <td>36.3</td>\n",
              "      <td>34.9</td>\n",
              "      <td>35.4</td>\n",
              "      <td>39.5</td>\n",
              "      <td>37.4</td>\n",
              "      <td>50.3</td>\n",
              "      <td>26.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2011-01-01</th>\n",
              "      <td>100.0</td>\n",
              "      <td>22.1</td>\n",
              "      <td>26.6</td>\n",
              "      <td>23.0</td>\n",
              "      <td>32.1</td>\n",
              "      <td>28.8</td>\n",
              "      <td>24.7</td>\n",
              "      <td>24.2</td>\n",
              "      <td>30.0</td>\n",
              "      <td>32.6</td>\n",
              "      <td>36.5</td>\n",
              "      <td>11.8</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2011-02-01</th>\n",
              "      <td>100.0</td>\n",
              "      <td>18.7</td>\n",
              "      <td>18.7</td>\n",
              "      <td>28.4</td>\n",
              "      <td>27.1</td>\n",
              "      <td>24.7</td>\n",
              "      <td>25.3</td>\n",
              "      <td>27.9</td>\n",
              "      <td>24.7</td>\n",
              "      <td>30.5</td>\n",
              "      <td>6.8</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2011-03-01</th>\n",
              "      <td>100.0</td>\n",
              "      <td>15.0</td>\n",
              "      <td>25.2</td>\n",
              "      <td>19.9</td>\n",
              "      <td>22.3</td>\n",
              "      <td>16.8</td>\n",
              "      <td>26.8</td>\n",
              "      <td>23.0</td>\n",
              "      <td>27.9</td>\n",
              "      <td>8.6</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2011-04-01</th>\n",
              "      <td>100.0</td>\n",
              "      <td>21.3</td>\n",
              "      <td>20.3</td>\n",
              "      <td>21.0</td>\n",
              "      <td>19.7</td>\n",
              "      <td>22.7</td>\n",
              "      <td>21.7</td>\n",
              "      <td>26.0</td>\n",
              "      <td>7.3</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2011-05-01</th>\n",
              "      <td>100.0</td>\n",
              "      <td>19.0</td>\n",
              "      <td>17.3</td>\n",
              "      <td>17.3</td>\n",
              "      <td>20.8</td>\n",
              "      <td>23.2</td>\n",
              "      <td>26.4</td>\n",
              "      <td>9.5</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>2011-06-01</th>\n",
              "      <td>100.0</td>\n",
              "      <td>17.4</td>\n",
              "      <td>15.7</td>\n",
              "      <td>26.4</td>\n",
              "      <td>23.1</td>\n",
              "      <td>33.5</td>\n",
              "      <td>9.5</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>2011-07-01</th>\n",
              "      <td>100.0</td>\n",
              "      <td>18.1</td>\n",
              "      <td>20.7</td>\n",
              "      <td>22.3</td>\n",
              "      <td>27.1</td>\n",
              "      <td>11.2</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>2011-08-01</th>\n",
              "      <td>100.0</td>\n",
              "      <td>20.7</td>\n",
              "      <td>24.9</td>\n",
              "      <td>24.3</td>\n",
              "      <td>12.4</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>2011-09-01</th>\n",
              "      <td>100.0</td>\n",
              "      <td>23.4</td>\n",
              "      <td>30.1</td>\n",
              "      <td>11.4</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>2011-10-01</th>\n",
              "      <td>100.0</td>\n",
              "      <td>24.0</td>\n",
              "      <td>11.5</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>2011-11-01</th>\n",
              "      <td>100.0</td>\n",
              "      <td>11.1</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>2011-12-01</th>\n",
              "      <td>100.0</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",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f7053285-5a11-4f47-925a-4f381457ba66')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-f7053285-5a11-4f47-925a-4f381457ba66 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-f7053285-5a11-4f47-925a-4f381457ba66');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ],
      "source": [
        "# Retention table\n",
        "cohort_size = cohort_counts.iloc[:,0]\n",
        "retention = cohort_counts.divide(cohort_size,axis=0) #axis=0 to ensure the divide along the row axis \n",
        "retention.round(3) * 100 #to show the number as percentage "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "id": "9DbAaoJ5r9NL",
        "outputId": "99de7783-ea2e-4ecb-9097-90bde5996bfe",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 513
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x576 with 2 Axes>"
            ],
            "image/png": 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ueXjtg6/xyOeJ3WZj7DsvU71OfYZ//G3KOhPHvkfN+sZ359q/5vPW2Ins3raBHRtX06pDDxb+PoWufQa4qggZmjztFwoUKJDyd2a/df6YMZ3fZ89h9apVLFuylL79+jHhf+MZMtT1F/McGjbuPE9EVALuZhN9Olfl7MVr/LX+ZMo6TUOKk2y1A1CnWiAL/z6Or3degisVZmPoeerXKMrOA5ddVQSRRfdtjq/W+tL1HjmtdSxwBAgCugLXeyl/wdnQ1VqHa613YvTKplYfOKG1Pqm1tmD0yHbNZJurgdgMli/TThg9vsVvXsfZo9gKmJtBbKe11vsBxx0Wvy2wSmsd5WyArQLaKaWKAr5a623OWH7lpoa+U1fgV2fI24D8zrQZ5ptJ+nT7+Bb5pt4Pt4rxbvO9l/2QE9vINnVD6uHrlz/NsrVr1tClm3GIdunWlbWrV6cs79y1K0opatSsRWzsNSIiwjGbzSQlJZGYlIjZbObatWusX7eWzl0zPMxzjFIKDw+j4W2323DYbaBUyvuJCfH8c2hPSsNXKYXNasViScbNzcyJI/vxze9PQNF01S3HKaXI4yyLw27DYTe+1CoE10MphVKKoNIVuXb1Ssr6NpsVqyUZk5sbZ44fxNu3AAUDg1xWhtSUUnjkM3qn7HYbdrsNUIAiMcHoUUhMiCe/fyEAtNbY7TYsycm4mc1sXbeS4LoN8PbxdVEJblBKkTfVcWa321AoNq5aRPtHn8JkMr6mfPyMH1zK5DzOklMdZ34PxnEGEHklnL07t9CybWfA2PeH9u+ifpMWADR7pAOhWzcYK2ucn0sSbm5mNq1ZQc2Qhg/E5wK577O5uTwOZ3kyX99knAeSk3Fzc+PkPwfw8fOncBHXlyfDz0bdpixWK1bnsXby6AF88heg8APy2cCNutMiVd05nKruNH24A7u2pa07yc7ybF67gpp1H4y6k+78bEt7nCUmxPPPwd3Uqt/Eub4Ja0q9ceP44f34FvAnsNiD89lkJLPfOspkwmqxkJSUhNlsZndoKIUKFaJU6dIujNaQkGglIsr4jrTaHETFJOLtmSfNOhXK+HPsZBQADofGbDZhNptwaI2fT158vPJw4XK6pkaupu/TyxVy5OZWSqnSQG2MnrdArfUl51uXMYZC30oQcC7V3+edy+4lDnfgSWB5Bm8XBKK11rasbofMYw5y/v/m5SilnldKPX8H6TPcF0qpyUqpEOfyzPbxnezLTGO8h3zvej/k0Dbuq6jISAoXDgCgUKHCREVGAhAeHkZgkSIp6wUGFiE8LJzeffoyZdKPvPvmmwwc/ByTfviBgYOfS/lR6UoOh50xwwcxcmAPKgeHUKZClZT39u/cTKXqdcjnHFLXpntf/vfRMA6EbiGkSSv+mvcb7Xs+6arQ03E47Pz44RC+HNaXslVrU7xs5ZT37DYb+7etoVz1ugA0bv8Y0795m2P7t1O9Xgs2LvmDZp36uCr0DDnsdt5/9Vlefaor1WqFUK5SVZ4ZMoJxH43gjWcfZcvaFXR49AkAHu7Yg0+Gv0BkRBjlKwezafUyWnXo4eIS3OBw2Pls5CDeHGwcZ6UrVOFK2CV2b13L2Lee5/tPRxF+yajObbr2ZcInwzi4ewt1G7dixfzfaPfog3Oc/fbjePo8+yLKZPzQjbsWg5eXN25uxgAr/0KFuRoZAUDrzo/y/muDiYwIo2LVYDasWkrrTo+6LPaM5KbPBozyfP32c4x+qRcVqtehZHnjnLZ8zs989dZgFk3/AZvVAkCrzr2Z9NlIDu/ZRq2HWvH3guk80u0JV4afhsNh58s3n+O953tSMbgupZxlWTZ7Kl+MHMSC375PKcvDXfswccwIDu3eRu1GLVn553Rad+93q+xz3PRJ4+nzzIspDfi4azF43qLufPD6YCLDb9SdRx6guuOw2/nojQEMe7YbVWqGUKZi1ZT39u7YROXgG9+d7Xo8wbjRb7A/dAv1mzzM0rm/0rHngzG6KIVSPD9wAL17Psrc2bOBzH/rDBg0iMEDnmX9urW079iRHyf+wODnX3BZ6Jnx8c5DgL8nl6/EpSwrFuhNQqKV6NhkAHYeuESbpmUICS7CviPhPFQniK27L7gqZJEN7vvNrZRS3sA84FWt9bXUVySdczpzstH/PbBBa70xB7d5R7TWE7OYfmAmy+/LPs6Jz84Fx0e2U0ql6SXNSNFixZjyy68AnD1zhrCwy5QtW5a3Ro7AarXy0tChlC5dJifCTcdkcuOtL34iIT6OSV++x8WzpyhW0ogldPMaGrXqkLJulRohVKlhXHvZvn4l1Wo3IPzief5ePBtPL296PTOEPHk9XFIOMMry3HsTSEqIY9b3HxN+4TQBQaUBWPb7d5SqWJ1SFaoDUK5qHcpVrQPAvq2rKR9cj8iwC2xdOQ8PT2/aPf4c7i4sC4DJzY3R46aSEBfLhE/f4fyZk6xcNJtX3x1LuUpV+Wv+H8ycMoFnXh5Jo5ZtadSyLQCLZk7jkU49ObBrG1vWrsC/UACPP/uSSy+0mExujPrcOM4mf/UeF8+dwma1YHbPw4gxE9m7YwMzJn7Ba6PHU7lGCJWvH2cbVlK1dgPCL51nzeLZ5PP2pufTrjvOdm/fjF/+ApSpUJnD+9PdPiGdpg+3o+nDxqCd+b9PpW2XXuwL3crG1cspWDiAJwa+7PILYLnls0ldntc/+ZHE+Dh+Gf8Bl8+dosPjA/Dx88duszJ36jjWLplF6+5PUjG4LhWDjYthoZtWUaVmAyIun2f9srnk8/Kma78XXX5OG/apUZap37zPpXOn6Pj4AHzyG2WZPfkbVi+eRdseT1IpuC6VnGXZuWElVWrVJ+LSeeYsnUM+L2+6P/WSS8uyZ8dmfP3uvO40adWOJq2MuvPn71Np46w7m9Ysp2ChAPq6uO6Y3Nx496spJMTH8sPn73Lh7EmCSpYFYOem1TR5uGPKulVrhlC1plFvtq5bQfU6DQi7dI5VE2fh6e3D48++7PJ6M236DAIDA4mMjOT5gQMoUzbtb5LUv3UeatSYhxoZI8EWL1xA02bNOHPmNL988DO+vr6MePMtl07jAnA3m+jYohzrd5zDYr0xmLNSGX/+ORWV8veVqERmLz0KOBvFCVZQ0L55WRwOzcad50hIsqXLP9fJRZN87+tZwdnDOg+YobWe71wcdn14rfPf8NtkcwEokerv4sAFpVQDpdRe56vLHcTyPlAYeD3VshXO9JOBSIzhs9cvBhR3bvteZBiz81U8g+V3kz6j5TfLbB/fSfpbxXi3+d7LfsiJbaShlBqslApVSoVO+WlSRqvcFf+CBYmIMMKOiAjH398fgICAQMIu35gXEhZ2mYDAgDRpvx0/jiFDX+H36dPp0bMnr70xjB+/+z7LMWWVp5c3FavV4vDeHYBxJf7MiaNUr9Mw3bqW5CS2rVtO87bdWDpnGk+9NJJylYPZufHvnA47Qx6e3pSuXIMTh3YBsH7xDBJiY2jTa1C6da3JSezbsop6LTqxbtF0uj7zBiXLV+PAjnU5HHXmPL19qBxcmwO7tnPu9L+Uq2T0LNRv2ooTRw+mWfdq5BVOHj9CnYZNWbFwFi8M/wBPb2+O7N/litDT8fTypkK1WhzZu4P8BQtTs35TAGrWa8rFsyfTrGtJTmL7+uU0a9ONZXOm0e/FkZSrFMzOTa47zo4d3s+ubZt4pf+jTPj8fQ7v38WvP44jPj7OORwdoq5EUKBg4TTprkZG8O8/Rwhp1Ixl82cydNSHeHr5cGhvqCuKkaH/+mdzs3xe3pSrUpOj+0PxzV8QpRRm9zzUa9aWcyf/SbOuJTmJ0A0rafRIF1bO/5Xeg4dTpmJ19mxZ46Lo08rn5U35qrU4um8nvgVulKV+87ac+/domnUtyUns3LCSJq27smLuL/R5fgRlKwWza/NqF0VvOHZ4P7u3b+LVZx7lO2fd+W3SOBLupO4cO0LIQ83468+ZvDzyQzy9fTi078GoO55ePlSqXptDe65/d0Zz+vhRgutm/N25de1yWrbrzuKZ0+j/8puUrxzM9g2urzeBgcbgu4IFC9Lq4Uc4uP9Apr91rktMTGThggU83qcv30+YwEdjPqV2nbosW7I4x+NPzaQUHVuW45+TUfx7NjpluVJQvlQBjqdq+KZWv0ZRtu+7SIOaxdgUep6DxyKoWeV2g1ZzBxnqfAecc2anAEe01l+nemsR8LTz/08DC2+T1U6gglKqjFIqD9AbWKS13q61ruV8LbpNLAMx5oL20VqnXNrRWrd1ph/onAe6Fuh5F7FlZgXQRilVQClVAGgDrHAO4b2mlGro3D9PZbKNRcBTytAQiHGmzTDfTNJntI8zyzfFbWK823zvZT/kxDbS0FpP0lqHaK1DBgwanNEqd6VFy1YsWmBsatGChbRs1cpY3qolixcuRGvN/n178fbxSRkmBBC6cwcBAQGUKl2apKRETMqEyWQiMSkxyzHdi9hr0STEG0OALJZkju7fRWBQSQD2bFtP9ToNcc+TJ126VYtm0aJ9D9zMZiyWZGMOrUlhsSTnaPypxcfGkJRglMVqSebk4T0UKlKc3RuX8++h3fQYNBKVQe/AlpXzqN+qC25mMzZrsjGLVimslqQcLkFa12KiSYgz5hhZkpM5tC+UoiVKkRgfz+ULxsj/Q3t3UqxEqTTp/vx9Mt37GjcaslqSQSmUMmFJdl15MjzOipWkRkhjjh/aC8CJw/vSzRP9e/EsmrczjjPr9eNMKazJrjvOej/zAhN+W8D4afMYMnI0VWvU5aURH1C1Rh12bFoHwIa/l1G3YdM06eb8NpmeTxqDdizOz8WkFMkuLAvkrs8GjEZHYvyN88Dxg7sJKFaCa9HGEE2tNQd3baZI8dJp0q1bOocmbbo5y2Nx1huFxYXngdRlsViSOXZgFwHFSnLt6o2yHAjdkq4sa5fMpmnb7kZZrJYH5rN5vP8LfPvrAsb9PI+XnHXnxeEfUDX4Rt3ZuHoZdRqkrTtzf5vMo/3S1h2lFJYk15UnNiaahPgb5+cj+0Mp4vzu3LV1PcEhD+GeJ2+6dCsWzqRVh5u+O118fgZISEggPj4+5f9bt2ymfIUKmf7Wue6XqVPp+0Q/3N3dSU5KSvktkJTk2vI80rgUUTFJ7DkclmZ5yWK+RMUkEZeQ7qEvVClXkNMXYki22DGbTWit0droORb/LfdzqHNjjPm0B5RSe53L3gI+A2YrpQYAZ4DHAJRSRYBQwBdwKKVeBao6h0cPwWjguAFTtdaHMtqgUmojUBnwVkqdBwZorVcAE53b2uocaj1fa/1hBlmMBGYqpT4G9mA03FFK1QP+BAoAnZVSo7XW1TIruNY6Sin1EUajHeBDrfX1S0gvcuMRO385X1yf3+sc8rwM47E9JzAe3fPM7fJ19lpP1FqHZraPM8vXmX6v1rrWrWK823zvZT/k0DayzchhbxC6YwfR0dG0btmCF4YM4dlBAxn+2ussmDeXosWKpdy+v2mz5mzasIFO7dri4eHBh5+MSclHa81PEycy9ivjGtGjvR7jzRHDsdvtvP2ea+68e+1qJL9+9zkOhwOtHdR5qAXBdR8CYNeWtbTuln7Oa3TUFc6cOErHXsa1ixbtuvP5my/g6eXN4OEf5Wj8qcXFRLHw56+cZdFUDWlKxRoN+Oj5TuT3D2DqZ28AULlOI5p36gtAbHQkF04do3lnY05f/ZZdmDzmVfJ6evH4i659NEvM1UimjBuDw2FHa029xi2pVa8RT780nO8+fwelTHh5+/DMyzfuNn7m5DEASpWrBECDZo/w3tD++BcKoH0P181fvnY1kuk/OI8zh4PaD7Wget2HKFs5mF8mfMLaZXPJ65GPPs+l3FSfmKgrnPn3KB16GsdZ83bd+eLtF8jn6c2gYa47zjLT55kX+Pbz95nz6yRKlatIi7YpT9vj9L/G51KmvPG5NGrRmlEvPknBwoF06uXa+aS57bO5Fh3FrEljneXR1GzQjKq1GzJxzHDiY6PRGoqVKsejz9y4+2zM1SucO3mUNj2MucqNW3flf+8PwcPTi/6vuu4Owteio/jj+mejNTUbNqdanYZ8//Ew4mKjwVmWXgNeTUkTc/UKZ/89SttHjfmjTdp045t3XiKfl9fs9ssAACAASURBVDfPvu76uyFnpPczLzBh7PvM+W0Spcvepu40b82bLz2Jf6FAOvV0Xd2JuRrJtAmf4rAb3511G7WkRkgjwJgi1LZ733RpoqOucPr4UTo/1h+AVh16MGbE83h6efPCyI9zMvx0oiIjeW2o8dg7m81Gh46daNy0KdWCq2f4WwcgPDycgwf28/xLLwHQ54l+9H2sF76+vnzz7QSXlAOgWIA3VcoX4kpUAn27GKOjtuy6wOkLMVQs48+xDHp7zW4mqpQvyIKVxuOL9hwKo2vrCtjtmuUbTqZbPzfKRSOdUTo3lUaILEqyO3JNhdh08NLtV/qPCIt27RXi7FY60NvVIWSb+MTcNb/J3zd9T8x/VdQ11/biZbck5yNGcgM3t1vf++G/JsDPtXM2s1N8Uvoev/+yhrloOO6Pv91+/vd/ySv9Q/4TJ4LoROt9+W2cP597jpf/vt/cSgghhBBCCCHEf0+u6REihx5nJIQQQgghhBBCuIr0+AohhBBCCCGESC8XTYuVhq8QQgghhBBCiHRyT7NXhjoLIYQQQgghhMjlpMdXCCGEEEIIIUQ6uWiks/T4CiGEEEIIIYTI3aTHVwghhBBCCCFEOjoXzfKVhq8QQgghhBBCiPRyT7tXhjoLIYQQQgghhMjdpMdXCCGEEEIIIUQ6uajDV3p8hRBCCCGEEELkbtLjK0QutW/hUVeHkG3cS/m5OoRsdXnbOVeHkG0CG5RwdQjZ6uSpKFeHkG3iL8W6OoTsZc89/Q6+5fxdHUK2iom3uDqEbFPY18PVIWSrcc0nuzqEbJVvYB1Xh/D/Tm56nJE0fIUQQgghhBAPvFf6h7g6hP+Hck/LV4Y6CyGEEEIIIYTI1aTHVwghhBBCCCFEOrlpqLP0+AohhBBCCCGEyNWkx1cIIYQQQgghRDq5qMNXGr5CCCGEEEIIIdKToc5CCCGEEEIIIcR/hPT4CiGEEEIIIYTIQO7p8pUeXyGEEEIIIYQQuZr0+AohhBBCCCGESEfm+AohhBBCCCGEEP8R963HVylVAvgVCMQYHD5Jaz1eKeUPzAJKA6eBx7TWV5VSlYGfgTrA21rrL1PlNRXoBIRrravfYpvtgPGAGzBZa/2Zc/kQ4FWgHFBYa30lk/RlgJlAQWAX8KTW2qKUagaMA2oAvbXWc5VSwcBvzqQlgRjn64rW+hGl1NPAO873P9Za/+LcRl1gGpAPWAa8onXaaylKKeUsRwcgAeivtd7tfC/DfG9Kn9k+zjTfm9JnGOO95Hu3+yEntnFzebPivbffZsP6dfj7+zN/0WIAYqKjGfHG61y8cIFiQUF88fU3+Pr5obXm8zFj2LRhAx75PPhozBiqVK3G6VOnGDV8GDabjXc/+ICatWpjs9l4cfAgxn/3Pfny5cvOkO+Yj29e2nWrhpdXHrTW7N99kT07ztHskfKUq1gIu91B9NVEViw8QnKyjWIl/HikQyXsds3S+QeJjkokb14znXpWZ96MvS4pw3Xenu60aVIGz3zuaODgsQj2HQmnXbOyFPDzACBvHjeSLXb+WHyYooW9admwJHaHZvmGk8TEJpPH3Y0OLcqyYNVxl5YFwMcnL+06VjY+G2D/3ovs2XWBRk1LU758IbSGhAQLy5cdJT7OQoWKhWjUtAxJiVYWzj9IUpINv/weNG1WliWLDru0LDarhV++HIHNZsVht1OlThNadOnHn1PGcvHMcdzczBQrXZGO/V7Gzc3Mkd2bWLdoOvm8fHjshXfx9PYlKuISa/+cxqOD33RpWQBio6+wfM63JMTFABBcvzV1Gnck4tJpVv85CYslCd8ChWn/+Cvk9fDkwumjrFk4CZObmQ69X6NAoaIkJcaz9Pev6PHMOyiT665P+3jnod3DFfDyNOrN/kNh7Nl/iYfqlSC4agCJSTYANm07w6kz0RQr4sMjzctid2iWrjxGdEwSefO40altJeYtdu1xBs7ytKlolEfD/oOX2bPvUsr7dWsXo0XTMnw/aTuJSTYqlCtIo4YlSUqysnDpUaPe+HnQ9KFSLFn+jwtLYtSbmRPexm6z4nDYqVizEY3b9SE6Mowlv31JUnwsgSXK0aHvq7iZ3dm9cQn7tq7EN38huj37Jm5md86fPMzx/Vtp2W2AS8tyvTw/jx1hlMdup0rdJrTs2o+F08Zx6cxxtNYUDAyi2zOvk8cjH9tXL2LXhr/w8y9M75fexc3sztnjhzi8ezPtHh/s0rJYLRbGffAKNqsVu8NO7QbN6fhYf7TWLJ41lT3b1mNSJpq26UKL9j3Ys30DS2f/jJe3L4OGfYi3jx8Rly+weOYUnn31PZeWBaDDm80p16gkCVcTmfLUXABavtiA8o1LYbfaib54jaVj1pMcZyEoOJC2bzTBbnOw6IPVXD1/jbzeeej24SPMemNZbpoq+v9KbvrYVDa3A25krFRRoKjWerdSygejIdkN6A9Eaa0/U0qNAgporUcqpQKAUs51rt7U8G0GxAG/ZtbwVUq5AceA1sB5YCfQR2t9WClVG7gKrANCbtHwnQ3M11rPVEpNBPZprX9QSpUGfIFhwCKt9dyb0k0Dllxf7my8hQIhGMfLLqCuswG3AxgKbMdojP1Pa/3XTfl1AF7GaOQ1AMZrrRvcKt+b0o/NZB9nmG8G+yHDGO8233vZDzmxjXQffCpJdsddVYhdoTvx9PTk7VGjUhq+33z5Bb5++RkwaBBTfvqJa9dieO2NYWxcv54/Zszgux9/5MD+fXw+5lNmzJrFF59/xsOPtCYoKIjPPx3D1+P/x+/Tp+Pl5UXX7t3vJpw0vvtk7T2nBfDyzoOXd17CL8finseNfoPqs3DWfnx883L21FW01jR9uBwAG1f/S5dewaxZfgy//B6Ur1yY9atO0Lx1ef49doXzZ6KzFIt7Kb8spffM545XPnciohJwN5vo3akqS9eeIComKWWdJiHFsVjs7Nh/iQ4tyrFhx1l8vPNSrmR+NoWep0lIcU6di+FCWGyWYgGwXo7LUnovrzx4eechPCzO+GyersvC+QeJi03GYrEDULtuEAULevH3ymM81qcW8+fsp0LFwnh4mNmz+wIdO1dh86bTRF9NzFIsgQ1KZCm91hprchJ5PPJht9uYNnYYbR9/nsT4WMpXDwHgzyljKVmhOiHNO/LrVyPp8/JojuzeQlJCHPVbdWH+5M9p3rkfBQODshQLQFxUQtbSX7tKfOxVAoPKYklOZMa3I+jy5AhWzJlAsw5PUbxsNQ6GruZaVDiN2vRh8fSxtOg8gGtXwzlxaAfNOz7NhmW/UKZyXUqUzfQ67x2Jv5S1Y9XL0x0vzzyEX4nH3d1Ev8dqsnDZUSqVL4TVaid078U063dpV4k1G0/h55uX8mULsn7zaZo3KsW/p69y/uK1LMUCgD1rv1e8PN3x8spDeEQ87u5u9Otdk4VLjxAVlYiPdx7aPFwe/wL5mD5zH4lJNh7rUZ35iw5ToVxBPPKa2bP/Eh3bVmTztrNEpzp33Avfcv5ZSq+1xmpJIk9eo9788e2btOo2kND1C6lY4yEq127Kqjk/ULhYaWo1bs+McSPoO/Qztq2eS0Cx0pStWo95k0bTsd8b5PPyyVIsAF4+ebJenuvnAZuNn8cOo93jz1O4WEny5vMEYMWsSXj55qdJ+8eYPOY1Boz6io3LZhFYoiwVa9Rnxrh3eXTwyCyXp7CvR5bLYklOIq+zLF+/P5SeTw/h8oUzHD+0l34vjsRkMhEbcxUfvwKMG/0aL476lL07NpIQF0uL9j34efxHdHzsGQKKFs9SLAC7hizNUvoSNYtgSbTS6Z2WKQ3f0vWCOLP7ItquafFCfQDW/bCD7p+05u9xW/Ar6k3FZmVYM2EbLV9qwL9bznJ2z6VbbeaOjdrk2gsb2Uy5OoA7ceFqwn1pLAYV8Mzx8t+3S8la60vXe+S01rHAESAI6Apc76X8BaOhi9Y6XGu9E7BmkNcGIOo2m6wPnNBan9RaWzB6brs60+/RWp++VWJnj2Ir4HqjNnVsp7XW+wHHbWK4ri2wSmsd5WyUrgLaOS8G+Gqttzl7Hn+9vo2bdMVo5Gut9TYgvzNthvlmkj7dPr5Fvqn3w61ivNt872U/5MQ2sk3dkHr4+uVPs2ztmjV06dYVgC7durJ29eqU5Z27dkUpRY2atYiNvUZERDhms5mkpCQSkxIxm81cu3aN9evW0rlr1+wO967Ex1kIv2z8cLZa7ERdicfHNy9nTkZx/YLZpfPX8HH+SHA4NO7uJszubjgcGr8C+fDx9chyozc7JCRaiXA2aKw2B1djEvHyTPtDrUJpf/45ZZxmHA6N2WzC3WwyyuKTF2/PPNnS6M0O8fEWwsOMxrPVYicqMgEfn7wpjV4Ad3c3tPM6rdYaNzcTZncTdocmqLgf8fGWLDd6s4NSijwexqgGh92Gw25HKagQXA+lFEopipWuyLWrV1LWt1mt2CzJuLm5cfb4Qbx9C2RLozc7ePsWIDCoLAB58ubDPyCIuGtRXL1yiaAyVQEoVb4mxw9tB8BkMmOzJGOzJGNycyM68jKx0ZFZbvRmh/gEK+FX4gGwWh1EXU3ExyvzBo7DoXE3mzCbTTjsDvx88+LjnTd7Gr3ZID7BSnjE9fLYibqakFKeFs3KsGHz6TQ9G0a9UZjNznpTzJf4BGuWG73ZQSlFnrzX643dWW8U504coGKNRgBUq9eSEweN40yjsTvsxnFmMnN41zrKVK6TLY3e7HDzecBut4MipdGrtcZqtXCjnaCx2+1YneeB/dvWUD445IEoj1KKvM6y2O027DYbSik2rVpE+55PYXKO4vDxKwCAyXlOsyQn42Y2c+LIfnzz+2dLozc7nNt3maRryWmWnd55Ae28EHXxUDg+hb0AcNgcmD3MmPOasdsc5C/mg2+Ad7Y1eoXIqhy5uZWzx7Q2Rs9boNb6eg24jDEUOjsEAedS/X0eo2fwThUEorXWtlTp7/WXVEaxBDlf5zNYjlLqeQCt9cTbpM9oOUqpycBErXUome/jzNKnPiNlGuM95HvX+yGHtnFfRUVGUrhwAACFChUmKjISgPDwMAKLFElZLzCwCOFh4fTu05d33hyFxWLh3Q9GM+mHHxg4+LmUL8cHga+fBwFFfLh0PibN8uq1i/LPoXAAtm86Tbtu1bDZHPz15yGat67AprX/uiLcW/LxykNhf0/CrtzodS0W6E1CopWYWOPLPfTAJdo0KYPN5mDlplM0CSnB1j0XXBXyLfn6ehAQ6M0lZ+OicdMyVKseSHKyndl/GEPMd2w7S6/eNYmLS2bZ4iN07laNpS4e4pyaw2Fn8ievEBVxkZDmnQgqUznlPbvdxoFta2j7+HMANG73GNPHvY2Pnz/dBgxn3o9j6DFolKtCv6WYq+FEXDxNkRIVKBhYnH8P76R8tfocO7CV2GijIV+vRXeWz/kWs3se2j02lA3LfqFRmz4ujjw9X5+8BBTy4lJYHMWK+lIruAhVKxUmLCKOdZtPk5xsZ/vu87R7pIJxDvj7OM0bl2bT9rOuDj1Dvj55CSjszaWwOMqV9ScuzkLElbS9/TtCz9Ore3Xi4i0sW3GMzh0qsfSvYy6KOD2Hw85vX79B9JXL1GrcHr+CRcjr4YXJzQ0Ab7+CxMYYF/NqN+nA7+NHUDCwJEFlqvDn1DH0fO59V4afjsNhZ9JHxnmgXotOFC9rnAcW/vw1xw+EUrhYSdr2GghAvZadmfLpaxQuVooS5asyc8KH9Hv1Y1eGn4bDYefzUc8TcfkCzdp2o3SFKkSEXWLXlrXs27kJH9/89Ow/hICixWndrS/ffjwMvwIFefrlt5jyzWieeeVdVxfhjtXoWIkjq43v+q2/7aXTOy2wJdtZ8tFaWr7UkA0/7XRxhCKrctNQ5/ve8FVKeQPzgFe11teMjlWDc05nbtqf98zZ4M1K+oGZLL8v+zgnPrvccHwopUDdeiRH0WLFmPLLrwCcPXOGsLDLlC1blrdGjsBqtfLS0KGULl0mJ8LNkLu7G116BbN2xbE0PYoNmpTG4dAcOXAZgIiwOP6YGgpAUMn8xMclo4BOj1bHbnewftUJEuItrihCCneziY4ty7Fh5zks1hsDOCqW8efYqRuDSq5cTWT2sqOA0SiOT7SiFLRrVhaHQ7Mx9FzK/EZXcnd3o0v3aqxdfSLls9m88RSbN56ifsOS1K4bxJZNpzlz+ipnTu8CoGq1QE6djKKAvych9UuQlGRl7d8nsNnudEBL9jOZ3Bj87gSSEuKY/cPHhF84TUBQaQD++v07SlaoTskKRg9o2ap1KFu1DgD7tq6mfPV6RIZdYOuqeeTz9Kbt48/hnidrQxWzgyU5kSXTv6R5p/7k9fCkzaMvsXbxFLavmUvZKiG4uRlfvwHFytDnxU8BOH/qMF4+BUBrlv7+NSY3N5p1eBovn/y32tR95+5uoku7SqzddAqL1c6+g5fZFnoOraFxg5K0aFyaFWv+JeJKAn/MOwBAUFFf4uMtxjmgTUXsDs36zadJSEw3qCvHubub6NKxMms3nMTh0DQIKc7cBYfSrXfmXAxnZu4DoGrlwpw6fZUCBTwIqRNEUpKNtRtOubzePD1sHEmJcSyc+hlR4eczXbdaSEuqhbQEYMuKWdRp2pFTR3ZzKHQtvvkL0aLLMy6dTw5GeZ5/3zgPzPr+xnmg6zOv43DY+euPiRwM3UDtxm2o+dDD1HzoYQDWL/6dBg934cTBUPZtXY2vf2Ha9hro0vKYTG68OfYnEuLj+OnL97h49hQ2qwV39zyM/HQie7dvYMbEL3ht9Hiq1AihSg1jWsf29SupVqsB4RfPs3rJbDy9vOnZfwh58rr+nJaRh56qjcPu4NDKEwCEn4jkt+cWAsYQ6fjIBFCKrqMfxm5zsGbCNhIegJFG4v+v+3pWUEq5YzR6Z2it5zsXh10fXuv8N/we8y6hlNrrfD0PXABSTzYr7lx2qzxWONNPBiIxhs9evxhw2/S3kFksF5z/v12Mt0p/J2XMbB/fSfpbxXi3+d7LfsiJbaShlBqslApVSoVO+WlSRqvcFf+CBYmIMMKOiAjH39+YyxUQEEjY5csp64WFXSYgMCBN2m/Hj2PI0Ff4ffp0evTsyWtvDOPH777Pckz3ymRSdHksmCMHL3PiaETK8mo1i1K2YiGWzU//YxGgYdPSbNtwioeal2X93yc4sOciteu7dtiWSSk6tCjHPyej+PfsjeHXSkH5kgU4fjrj2RT1axRlx76L1K9ZjM27znPoeAS1qmTXQJV7ZzIpunSvxpHDYZw4lv62BUcOhVGhYuE0y8xmE9WCi7B39wUaNSnNX0uPcOF8DFWqub48AB6e3pSuVIN/DxmN9PWLZxAfG0ObXoPSrWu1JLF/6ypCWnZi/eLpdO3/BiXKV+PA9nU5HHV6druNJTO+pHKtplSo3hAA/4AgHh3wHk+8PJbKNZvgV7BImjRaa7avmUvDVj3Ztno2Tds/SXC9R9i7ZZkripDCZFJ0aVeJI8ciOHHSqCMJidaUx1scOBxGkYD0w0sbhhRnW+h5HqpfgvVbz3DgcBi1axRNt15OM5kUXTpU5sg/EZz4N4r8fh74+eblqb61GNi/Lj7eeenXpxaenu4pacxmE9WqBLB3/2UaNSjJXyuPc+HiNapUKnyLLeUcj3zelCgfzMXT/5CcFI/DblwEi4uJxMcv7VziuJgoLp87ToXghoSuW0jnp4aRN58XZ47vd0XoGbp+HjhxcFfKMpPJjer1mnFk1+Y068ZGR3Lh1D9Urt2Iravm0/O5UXjk8+LkUdfeUPE6Ty9vKlarxeF9OyhQsDA1GzQFoGb9plw4czLNupbkJLavX06ztt1YOmcaT744krKVg9m56W9XhH5bwe0rUr5RSRaNXpPh+42ersPmabtp8kwd1n6/nX2LjxLSy/VTOMTd0/r+vFzhvjV8nXNmpwBHtNZfp3prEfC08/9PAwvvJX+t9TmtdS3nayLGzawqKKXKKKXyAL2d27pVHm2d6Qc654GuBXpmNTZgBdBGKVVAKVUAaAOscA7hvaaUaujcP09lso1FwFPK0BCIcabNMN9M0me0jzPLN8VtYrzbfO9lP+TENtLQWk/SWodorUMGDMr6TRNatGzFogXGphYtWEjLVq2M5a1asnjhQuMOyfv24u3jkzIkGiB05w4CAgIoVbo0SUmJmJQJk8lEYpLrro626VyFyIh4dm27MZq8dDl/6jUqxYKZ+zLs7ahaowinTkSSlGTD7G4CrdHa6J10pYcblyIqJok9h8PSLC9Z1JerMUnEJaTviapcriCnz8eQbLHjbjahnWUxm10/DL1N+0pERiawa+eNXp78BW7cAbx8hUJE3XSjpnoNSrBn14WUOcw4v3xcWZ742BiSEq7PV07m5JE9FCxSnD2blnPy8G56DByZYc/NlhXzqNeqC25uxhxZY3CFwmpx7fxLrTWr5n2Pf+Hi1G3aOWX59bs8a4eD7WvnUqNB6zTpDu9eT5lKdfDw9MFqtaSMFrFa086ty2ltWpYj8moiu1Ld/dgrVaOwfFl/rtx0nFWtVJhTZ66SlGxzHmcarY35v67W5uHyREYlsmuPcWOuK5EJ/DB5J5On7WLytF3ExiUz/Y+9JKQ6H9SrE8SefZdu1BuMoX+urDcJcTEkJd6oN2eO7aVgYHFKlA/m2P4tABzauZZy1eunSbfprxk0bmcMpbdZLSiUc968a4+zdOeBw8Z5ICrc+Jy01vyzbzuFiqa9md6aBb/RsuuTznTO8pgUVovryhN7LZqEeKMsFksyRw/sIrBYSWrUa8zxQ0aD/Pjhfenm8P69aBbN2/fAzWzGaklGKYVJKSzJrv1sMlKmQXEa9K3J3FErsCXb071fvV0F/t16lqTYZNw9zMZ3p0PjnjdHZliKbKbv08sV7ucR2Bh4EjiglLp+6e0t4DNgtlJqAHAGeAxAKVUE4+68voBDKfUqUNU5PPoPoAVQSCl1Hnhfaz0l9ca01jZlPLZoBcbjjKZqrQ858x4KjACKAPuVUssyGRo8EpiplPoY2IPRcEcpVQ/4EygAdFZKjdZaV8us4FrrKKXURxiNcYAPtdbXu5Ne5MYjdv5yvm6e47sM4+7FJzAe3fPM7fK9aY5vhvs4s3yd6fdqrWvdKsa7zfde9kMObSPbjBz2BqE7dhAdHU3rli14YcgQnh00kOGvvc6CeXMpWqwYX3z9DQBNmzVn04YNdGrXFg8PDz78ZExKPlprfpo4kbFfGdeIHu31GG+OGI7dbuft91wzDyuohB/VahYlIiyWJwcbP542rfmXlu0qYnYz0bNfbQAunY/h72XGoz3MZhPVahVj3vQ9AOzado7ufWrhsDtY+mfGvcM5oWiAN1XKFeJKVAJ9Ohs3GNqy+wJnLsRQocyNm1qlZnYzUbVcwZTHF+05FEaXRyrgcD7iyJWCgvyoVr0IEeFxPNnfGCK3acNJqtcoir+/J1prrl1L4u8VN+YjennnoUhRX7ZuPgPAnl0XeOLpuiQn2Vg4/6BLygFGD9TCaV+hHQ601lSt25SKNRrw8QudyO8fwM+fvwFA5dqNaNapL2D08lw8fYzmnZ8AoF6rLkwe8yoenl489oJrH/9x8cxRjuzZQKEiJZn+v2EANG7Tl6uRl9i3dTkA5as3oFrdVilprJZkDu9eS49njXl9dZp04s9pY3BzM9O+9ys5XwinoKI+VKscQMSVeJ58vCZgPLqocoVCFC7kBRquxSazat2Nufxms4lqlQNSHl+0a+9FuneqgsOuWbrKtfNjg4r6UK2Kszx9nOXZcpZTZ65mmsbLKw9FAr3ZusO4+Ldn3yWeeLwmyck2Fi49kiNxZyT+2lX++mM8Dme9qVSzMeWq1aNgkRIs+fUrNi2bQUDxsgSnusASdt44bwUWN+7GX6VOU6Z98Qo++QtRr1UPl5TjuriYKBZM/SqlPNVCmlIxuB4/jx1OclICWkOR4mXo2G9ISppLZ43jrmip8gAEN2jBDx+8iK9/YRq37eWScgBcuxrJb99/bpTF4aDOQy0IrvsQ5SoH88u3n7Bm6VzyeuSj73PDUtJER13hzL9H6dDLuO7fvF13xr71Ap6e3gwa/pGrigJAlw9aUbJWMfLl9+DF+X3ZNGUXDz1ZCzd3N3p/0wEwbnC14stNAJjzuhHcoRKzXjPuJr1j5gF6fdEeh83Oog8y7h0WIqfct8cZCfFfdLePM3qQZfVxRg+SrD7O6EGT1ccZPUiy+jijB01WH2f0IMnq44weOFl8nNGDJKuPM3rQZPVxRg+SrD7O6EGT1ccZPWjkcUY572xk/H05+ZYs6JV7HmckhBBCCCGEEEI8CGSwvRBCCCGEEEKIdHLPWBtp+AohhBBCCCGEyEBumhUrQ52FEEIIIYQQQuRq/8fencdXVZ0LH/+tk3MyDyRkDkhmIGEI8zyqGBTBWbTV2qvSyde2aq3Wt9Zar7fe9mp9WyecZ6yCAgqiDCEECRAIhCEMgRBICJlD5umc9f6xNyEhCZMhB3Ofr598TPZZa+1n7b3P5qy9hiM9vkIIIYQQQgghOuhNCyFLj68QQgghhBBCiF5NenyFEEIIIYQQQnTQe/p7pcdXCCGEEEIIIUQvJz2+QgghhBBCCCE66E1zfKXhK4QQQgghhBCig17U7pWhzkIIIYQQQgghejfp8RVCCCGEEEII0YEMdRZCXP6Kap0dQbdp7kV1AfCcEensELpNRf5JZ4fQrZpONjg7hG6jrL1rUJdbX3dnh9Btqo5UODuEbmWJ7evsELpN+s2vOzuEbmcbGO7sEIS4LEjDVwghhBBCiF7qsbQFzg5B/ID1og5fafgKIYQQQgghhOhI96Jv8u1d46CEEEIIIYQQQogzSI+vEEIIIYQQQogOetNQZ+nxFUIIIYQQQgjRq0mPrxBCCCGEEEKIDnpTj680fIUQQgghhBBCdODoRS1fGeosgdJ+lwAAIABJREFUhBBCCCGEEKJXkx5fIYQQQgghhBAd9KIOX+nxFUIIIYQQQgjRu0mPrxBCCCGEEEKIDjS9p8tXenyFEEIIIYQQQvRql6zHVynVH3gPCAE0sFBr/aJSKgD4BIgEjgC3aa0rlFKDgLeBkcATWuu/tynrLWAOUKy1HnKWfSYDLwIuwBta67+a2z8ERgPNwBbgZ1rr5k7yRwGLgL7ANuAurXWTUmoq8A9gGDBfa/2ZUmoo8L6Z9QrgpPlTqrW+Sin1E+D/mq8/o7V+19zHKOAdwANYAfxa6/aj55VSyqzHtUAdcI/Werv5WqflnpG/q2PcZbln5O80xosp90KPQ0/s48z6fh9PPvEEqetTCAgIYMmy5QCcrKzk0Ycf4nhBAeEREfzt+Rfw9fNDa81zzz5LWmoq7h7u/OXZZxmckMiR3Fwe+90jtLS08MennmJ40ghaWlr45YL7efGll/Hw8OjOkM/bNT9OInpICHXVjbz7nykABEX4ctX8YdjcrFSV17Hine00NbQQHh3AVfOHYW9x8NXb26gsqcXNw8qce0ez+KV0LoeHhb2pPl4eNq4c0x8Pdyto2Jtbxq6cMvr6uTNtZAQuLhYcDs2GzAKKK+qJjvBlTEIojc12Vn53hMYmO75erowbEsq3m486tS7enjaunhiJp7sVDew5WMrO/SUkT46ij68bAG6uLjQ22Vm0Yh9hQV5MH3sFdoeDVWlHOFndiKvNhdlToli6NsepdQHw8XYl+co4vDxtaCBrTxGZWYVMGNOfoQnB1De0AJCWnkduXiXhoT5cNS0au0Pz1TcHqDzZgJurC3OuGcji5XudWhdvL1dmz4jB09OG1rAru5jM3SeYMKofQwcHU1dv/DO6ccsxco9VEh7izZVTorA7NCtW51BZZdTluqviWLJin1PrAsa1duW4K/B0twGaPYfKyDpQSmAfD6aN7ofVxYJDa9Zn5FNcXkd0Pz/GDQ2locnOig25xvvG25Xxw8L45rs8p9alN11nAC3NTXz04uPYW5pxOOwMTJrE5GvvZHvql2SkLKOy9AQPPPsBnt6+AOzf8R1pKz7Ew9OHG+//Ax5evlSUFJL65fvM++mjTq7NaX1j+nLTG7e2/u0/wJ+U59bhE+xNzJVxFO0+wdIHPgdg6C3D8OjryZbX0p0VrhCdkjm+56cFeFhrnQCMB36llEoAHgPWaK3jgDXm3wDlwIPA3zsp6x0g+Ww7U0q5AC8Bs4EE4A5zfwAfAoOAoRiNoPu6KOY54AWtdSxQAdxrbj8K3AN8dCqh1nqX1jpJa50ELAN+Z/59ldl4+xMwDhgL/Ekp5W9mfQW4H4gzfzqr1+w2ry8w83COctvq6hh3Wm4nuorxgsq9yOPQE/voNvNuvIFXFi5st+2tN15n7PgJLP96FWPHT+DNN14HIC01laN5eSz/+mue/POfeebPTwPw6b8/4dHH/8BLr77Gu2+/DcC/Fy3iuuvnOq3RC7A7/ajRyGtj1o+Gs2FpNu89m0LOzkJGXxUDwOgrY1jycjopi3czfEokAOOT49m86qDTG4mn9Kb6aK35LquQT745wJJ1OQyJCcTfx40Jw8LIyC7m09UH2bq3iPHDwgAYEhvI4rUH2XO4jLj+fQAYmxjKlj0nnFkNwPiahLTt+Xz4ZTaffr2foQOD8Pdz5+u0XBat2MeiFfs4dLSSQ8cqARgxOIRl63LYkJHP0LhAAMYMDSXjMqgLgMOhWb/xCO98vIOPPssiaWgoAf7G+3j7zkLe/2Qn73+yk9w8oz6jk8JZ8mU2KWm5DB8SCsD40f3YvC3faXU4RWvN+vQ83v13Fh9/sZukxBAC+hh12ZZVyAeLd/HB4l3kmudm1PBwPl+5n5Tv8hiWEAzAuJERbMk87rQ6tOVwaDbuOM7HK/fx2bcHGRobiL+vGxOSwti65wSfrNrPll2FTEwKB2BYfBCffnOAPTllxA8w/lkZNzSMzVmFzqwG0LuuMwAXq435/+cZfvrY/+Oe379IbvZ2jufuIyJqMLf/6i/4BgS3S7899UvufuR5hk+6hr0ZqQBs+OoDplz3Y2eE36WyQ2W8PuNVXp/xKm9c+RrN9c3s/yqb0GFhLJz+CvZmO8GDg7G6Wxl+RxIZb25xdshCdKC1viQ/znDJGr5a68JTPXJa62ogG4gA5gGneinfBW4w0xRrrbdi9MqeWVYqRsP4bMYCOVrrw1rrJoye23lm/hXahNHj2+/MzGaP4kzgs05iO6K1zgIc51n9a4BvtdblWusK4FsgWSkVBvhqrdPNWN47tY8zzAPeM0NOB/qYeTstt4v8HY7xWcptexzOFuOFlnsxx6En9tFtRo0eg69fn3bb1q1dy9wb5gEw94Z5rFuzpnX79fPmoZRi2PAkqqurKCkpxmq10tDQQH1DPVarlaqqKtanrOP6efO6O9wLUpBTTkNtU7tt/sHe5OeUAZCXXUK8+QHRYXdgc3XBanPBYXfgF+iJj78H+QfLejzurvSm+tQ1tFBaWQ9Ac4uDiuoGvDyMXjmb1bitu9pcWnvk0GCxWFp7tMICPalrbOZkTVNXu+gxdfUtlJS3qcvJBrw9bO3SxA7w58CRCsD4wG9zsWC1Gr3avt6u+Hi6UlBU0+Oxd6a2rpni0loAmpsdlFfU4+Pl2mV6h0Njs5r1sTvw83XDx9uN/ONVPRVyl4y61AFGXcoq6/E+R12sVgs289y01qXQ+XUB831T0eZaq2rEy8MGGlytLoDxvqk13zdaa1wsp6+1sCAv6houj/dNb7rOAJRSuLoZDXeH3Y7d3gJKEdI/Br++IZ2mt7c009zUiMXFhWOH9uDl609AcHhPh37eoqZGU3GknPqKeiw243qzediwNzuY8MuJbH1jC46W8/2YKYS4GD2yuJVSKhIYAWwGQrTWpx6XnsAYCt0dIoBjbf7Ox+gFbBuHDbgL+HUn+fsClVrrljb5I7oxlgjzJ7+T7Silfg6gtX71HPk7245S6g3gVa11Bl0f467yt3183WWMF1HuBR+HHtrHJVVeVkZQkPF0OjAwiPIyo7FUXFxESGhoa7qQkFCKi4qZf8ed/N/HH6OpqYk/PvVnFr7yCvct+BkWy+U3Bb+ssJrYYaHkZJ0gfmQ4PmYPw+ZvDpJ89whamh2sfHc7025MJG2584c2nktvqI+Pp43APh4Uldexcedx5kyJYuKwMFCKz9cZQ3+37ytm7tQoautbWLPlKLMmDODbdOcOce6Mj5crQQGenCirbd0WHuxtNDaqGwHI2HOCqydG0mJ38M13R5g8MoJNOy+PHsUz+fq4ERzoRWFRDeFhviQNDSVhYBBFJTWkbDxCY6OdzdvzSb4qjpYWBytXH2TapEjSnDz8vDO+3m4E9/XiRHENEaE+JA0JJSE+kKKSWtZvyqOxyc6WzAJmz4gx6rLuEFPHX8HGLcfOXbgT+Hi5EujvQVFZHWmZBVw/LYaJI8JRwJLVBwHYtreIuTNiqK1vZnV6HtdMinT6EOfO9JbrzOGw897fHqKipJARU64lPHJgl2nHX30Ln/zrj3j7BTDn7odY+tZzXH/P73ow2guXeOMQdi/ZTVNtEzmrD3L/up+Tm3qYxuoGwkf1Y8Pzqc4OUYhO9aahzpe84auU8gYWA7/RWlcZHasGc05nTx7Ol4FUrfWGHtzneTEbvN8nf6fDty/VMe6Jc+eE66PbKaWgzTXfmbDwcN589z0AjublUVR0gujoaP7w+0dpbm7mVw8+SGRkVE+Ee06rPtjBjFuHMH52PId2ncBuPp0uya/i47+nARARG0BtVQNKwZz/GIXdrlm/ZA91ZsPlcvJDr4/VxcI1EwawccdxmlscJEb35budxzlcUEVMPz9mjOrH8g255BfX8NkaoxEcf0UfjhZW08fHjeHxgTQ22dm48zgtdue+1WxWC9dOjWZDRj7Nzad7PeIj/Tlo9vYClFbU8+mq/YDRKK6tb0YByZOjsGtN2rb81vmNzmSzWZibPJB1abk0NdvZufsE6RnH0BomjbuC6ZMiWbX2ECWldXy8eBcAEWG+1NY2oYA5s+Kxm8NZW3vunVUXq4XrZ8WRsumIUZe9RaRvzzfqMqY/0yYM4Jv1hykpq+PjL/aYdfGhtq4ZFFx3VRwOh4P1m446vS5g1Cd5UiRpmQU0tzgYEhtIWmYBh/NPEtu/DzPGXsGylEPkF9Xw6TcHABgY6U/eceN9kzQomMYmO2nb853/vulF15nF4sI9v3+RhroaPn/jvyg5nkdQ+IBO00YOGkHkoBEA7N6yluiEUVQUF7Bq7Re4e3pz5c33Y3N168nwz8picyH+moGsfWY1AJv+tZFN/9oIwJwX5rL+uXUk/XgkMdNjKNpbRJo0goUAul6/qZN0N2OM2h1jdgJ26pJ2KZk9rIuBD7XWS8zNRaeG15r/L77IsvsrpXaYPz8HCoD+bZL0M7edSv8nIAh4qM22VWb+N4AyjOGz1s7yX6CuYimg/TDrrvZxtvxd1rGNro7x+eQ/W4wXWu7FHIee2Ec7SqkFSqkMpVTGm68v7CzJBQno25eSEiPskpJiAgICAAgODqHoxOl5iEVFJwgOaT9v6Z8v/oMHHvw1H33wATfdcgu/ffgRXnvp5e8dU3cpL6ph8b/S+eC5VPZlFFBZWtshzfjkeNJXHmDCtQNZ/8Vedm3MY8T0y6PhfqYfcn0sCq6ZMIADRyvJNYcrDoz053CB8fuh/JMEB3i2y2N1UQyKDGD3oVJGJ4SwdusxTpTVEXdFZ0sF9ByLgtlTo9l/pLx1Li8Yz4xi+vfhQF5Fp/nGDAll664TjB0WxsbMAvYcLGX4wOBO0/Yki0UxN3kg2QdKyDlszNKpq29ufWq+a28RocE+HfKNH92P9Ix8Joztz/pNeezaW8SIYWEd0vUki0Vx/ax4sg+WkpNrnId2dckuJjTYu0O+cSMi2Lw9nwmj+pGanseu7GJGDAntkK6nWRQkT4rkQF4Fh/NPAjAwMqD195xjlYT07eR9ExXA7oMljB0Sypr0oxSW1LTO+3WW3nSdteXu6c0VcUPJze6w9mYHzU2N7N68hhFTryNt5cdc++Pf0C96MHszUi59oBcg9spYCrMKqS1p/29M6NBQUFCWU0rC3AQW3/cp/pH+BEQHOClSITrSl+i/cznH+k1t0/lgjObdfK4yL1nD15wz+yaQrbV+vs1Ly4CfmL//BFh6MeVrrY+dWlzK7C3dCsQppaKUUq7AfHNfKKXuw5gLeofW2tGmjGvM/PeZ80DXAbd839iAVcAspZS/udDSLGCVOYS3Sik13jw+d3exj2XA3cowHjhp5u203C7yd3aMuyq31TlivNByL+Y49MQ+2tFaL9Raj9Zaj773/gWdJbkg02fMZNkXxq6WfbGUGTNnGttnzmD50qVorcnauQNvH5/WIdEAGVu3EBwczIDISBoa6rEoCxaLhfqG+u8dU3fx8DbnkCkYlxxPVtqRdq8njOtP7p5iGuqasdpcwGEsYGBzden5YM/DD7k+00f3p7K6gayDpa3b6uqbCQ/yAiAi2JuTNe17pZPig8jKKcWhjQ/zYMxjPPW7s1w5YQAVJxvYkd3+OWj/UF8qqhqMnsMzDIoO4MjxkzQ22bG6WIyFMrTGanVuXQBmzYihrKKebTtP3169PE/PW46NDqC0vK5dnoSBQeTmVdDQ2ILVagFz8Y9Tc7adZda0aMor69m+6/RDu3Z1ifLvWJf4QHKPVdLQaMdmtWCeGqNeTjZj7BVUVDWyc39J67ba+mbCzcZ7vxBvKs8YzTFiUDBZB069byyc+lZLZ9enN11nddUnaagz5uk3NzWSt38HASEdlmPpYMuaJYyadj0uLlZamhtRSqGUheYm54/IaWvITUPZ8/muDtunPTaTlP9ah8XqgjKnN2mHxnbGOgdCONOpe3h3/5yHLtdvOsNfMBYobjhXgZdyqPMkjPm0u5RSO8xtfwD+CvxbKXUvkAfcBqCUCgUyAF/AoZT6DZBgDo/+GJgOBCql8oE/aa3fbLszrXWLUuoBjIaQC/CW1nqP+fKr5r42mUOtl2itn+4k5t8Di5RSzwCZGA13lFJjgM8Bf+B6pdSftdaJXVVca12ulPoLRmMc4Gmt9anFuX7J6a/YWWn+nDnHdwXG1/bkYHx1z0/PVe4Zc3w7PcZdlWvm36GNFaq7jPFCy72Y49BD++g2v3/kYTK2bKGyspKrZ0znFw88wH/cfx+/++1DfLH4M8LCw/nb8y8AMGXqNNJSU5mTfA3u7u48/Z/Ptpajteb1V1/lv//HeEZ086238fijv8Nut/PEk3/q7rDPy3U/HUm/uEA8vF1Z8MzVfPfVfmxuLiRNNXo7c3YWsnvT6fl7VpsLieP7s/ifmwDYtvYQN/5yPA678ZVAztab6hPa15OBA/wpq6zn1qviANi8+wQp2/KZnBRuLPzi0KRsOz3IwdPdSnCAJxlm43JXThk3z4yjsdnO198dcUY1AAgL8mJQdF9KK+qZf+0gADbtOE7e8SriI08vatWW1UUxOLovS9cYczF3ZBdx/YxYHA7Nqo25PRr/mSLCfEgcFExJaS133T4cML5SZlBcIEGBXqChqrqRb1MOteaxWi0kDgpu/VqZbTuOc+OcwTjsmq++PeCUegCEh/qQEB9ESVktP755KGB8ddHA2L4E9/VCo6mqbmT1htPH3Gq1kBgfxGLz64u2ZRVy0+xB2B0OVqxx7tdNhQV6MSgqgNLKem6/xpg/mp51nJStx5g8MgKLUtgdDlK2nr4PeLpbCe7rydY9RQBkHSzh1lnxNDbZWZHmvGutN11nADVV5az44B9o7UBrzcCkycQOGcO29cvZvHoJtdUVvP3XB4lOGMXsO/8PANUnyyjMO8ik2XcAMHLqHN77+0O4e3hx431POLM67dg8bURNi+arh5e32z5w9iAKdxynpqgagKLdJ/jZ+l9QtLeIIvN6E+J/ufNZv2kk0F9r/ZVS6pwT/ZXuTTOWhfieGuyOXvOGeOnBL50dguiC54xIZ4fQbVoug/m03anp5DkfGP9gqMugh7U7ufm5OzuEbtNQVnfuRD8gfWL7OjuEbnP8xx87O4Ru98eSp5wdguic84conYdth8suyWfj0TGBP8P4itJTFmqtW+ccKqVuAZJPrWOklLoLGKe1fsD82wKsBe7RWh9RSqUAj5xtjm+PrOoshBBCCCGEEEKAMdUQONviOudam8gHGAKkmCN6Q4FlSqm5XTV+peErhBBCCCGEEKIDJ44Obl2/CaPBOx+4s01cJ4HAU39Lj68QQgghhBBCiIvirHZvV+s3KaWeBjK01ssutExp+AohhBBCCCGEuKxorVdgLHDbdtuTXaSdfq7ypOErhBBCCCGEEKKD8/nO3R+K3rXkoxBCCCGEEEIIcQbp8RVCCCGEEEII0UFv+uZb6fEVQgghhBBCCNGrSY+vEEIIIYQQQogOnPh1Rt1OGr5CCCGEEEIIITroRe1eGeoshBBCCCGEEKJ3kx5fIYQQQgghhBAd9KIOX2n4CtFbOU7UODuEbuMyK9rZIXSruk35zg6h+wS4OzuCbqUPlDs7hG5juzLK2SF0q4a8SmeH0G3cB/Rxdgjd6sQvlzo7hG5j8fXEUVXn7DCEEJeANHyFEEIIIYQw/bHkKWeHIMRlQxa3EkIIIYQQQgjRq/Widq8sbiWEEEIIIYQQoneTHl8hhBBCCCGEEB30pqHO0uMrhBBCCCGEEKJXkx5fIYQQQgghhBAd9KIOX2n4CiGEEEIIIYToSPeib/KVoc5CCCGEEEIIIXo16fEVQgghhBBCCNFBbxrqLD2+QgghhBBCCCF6NenxFUIIIYQQQgjRQW/q8b1kDV+lVH/gPSAE0MBCrfWLSqkA4BMgEjgC3Ka1rlBKDQLeBkYCT2it/96mrLeAOUCx1nrIWfaZDLwIuABvaK3/am5/ExgNKOAAcI/WuqaT/KOAdwAPYAXwa621VkrdCjwFDAbGaq0zlFLXAM+ZWWOBAqAeyNJa362Uehy4F7ADD2qtV50txjPicDOP3SigDLhda33EfK3Tcs/IHwUsAvoC24C7tNZNZyv3PI/jBZd7ocehJ/bRnZ584glS16cQEBDAkmXLAThZWcmjDz/E8YICwiMi+NvzL+Dr54fWmueefZa01FTcPdz5y7PPMjghkSO5uTz2u0doaWnhj089xfCkEbS0tPDLBffz4ksv4+Hh0d1hn5fkX44jenQEdScbeOe3KwCYeNtQhl0VQ31VIwCpH+0kd/txIgYGcvXPxmBvdrD8H99RWViNm6eNuQ9P5tNn1uHsdRG8vVyZPSMGT08bWsOu7GIyd59gwqh+DB0cTF19MwAbtxwj91gl4SHeXDklCrtDs2J1DpVVDbi5unDdVXEsWbHPuZUBfPq4k3zHcLy8XdFAVvoxMjccwd3Dxpy7R+Dr70FVRT3L39tOY30LcUNDmZgcR0NdM0vf3kZDXTN+fT2Zcm08X76/w7l18XEjeW4CXl6uaDRZmcfJ3JrP1JkxxMQFYrdrKivrWbU8m8bGFsL7+XFV8kDsDgdffb6Hyop63NyszLlpCIs/dm5dAK65dxQxSWHUVTXyzhPfAjDnl+MICPUBwM3TRmNdM+89uZrwuL5c/ZMR2FscfPnKFiqLanDztHH9r8bz2d83OP9942lj1uQoPN2taGD3gVJ27ismeWoU/r7uRn1cXWhssvPxl9mEBXkxY/wV2O2arzfkcrK6EVebC9dOi+aL1QedWxnMa+26Qea1Blk7jpO5rYCJUyKJjQ1Ea6ira+LrFfuorWkiLj6QiVOiaKhvZumS3TQ0tODXx50pU6P5ctlep9bF29PGleOuwNPdBmj2HCoj60ApgX08mDa6H1YXCw6tWZ+RT3F5HdH9/Bg3NJSGJjsrNuTS2GTH19uV8cPC+Oa7PKfWpTNj7hlF0u3DUCgyP9nJ1ne2MePRacRMi6Iou5jljxj/Jg2Zl4CHvwdb39nm5IiFED8El7LHtwV4WGu9XSnlA2xTSn0L3AOs0Vr/VSn1GPAY8HugHHgQuKGTst4B/oXR8OmUUsoFeAm4GsgHtiqllmmt9wK/1VpXmemeBx4AOmsEvQLcD2zGaPgmAyuB3cBNwGunEpqNq1MNrBTgEa11hvl3AjAfSATCgdVKqXgza1cxtnUvUKG1jlVKzcdoYN/eVblaa/sZ+Z8DXtBaL1JKvWqW90pX5V7Acbygci/yOPTEPrrNvBtv4I4f3ckTjz3Wuu2tN15n7PgJ3Hv//bz5+uu8+cbr/PbhR0hLTeVoXh7Lv/6aXVk7eebPT/PhJ5/w6b8/4dHH/0BERATP/dezPP/iCP69aBHXXT/XaY1egN0ph9m+8gDXPjih3fZtX+5j67L2jb/Rcwfz2TMp+AV7kzQrlpR3M5lwyxDSl+xx+od3ML58fX16HsWlddhsFn5801Dy8k8CsC2rkG1Zhe3Sjxoezucr9+Pr48awhGBS048ybmQEWzKPOyP8Dhx2zfpl2RQXVGFzc+HHv51M3oFShoyJ4OjBUrasPczYmdGMnRnDhq/2M2LyAD78x0bihoYyeGQ4mWl5TJ4dT9rKA86uivHhfM1Bik/UYHN14cf/MYa83HLycivYsO4wWmumzIhh7MQBbFh3iNHj+rPkk534+bkzfGQE69fkMH5yJJs3HnF2VQDYk5ZH5upDXLtgTOu2L1/e3Pr79PnDaDQftIxOjmfx/2zEL9CTpBnRpCzKYvzcwWxevu+yeN84tGZDxjFKyuuxWS3MnzOYY4VVfJ2a25pm8qh+NDUb/wSNSAhh2ZocfLxdGRofRNq2fMYOC2XrrsKudtGjHA7N+nWHKC4yr7WfjCLvSAUZm4/x3YYjAIwYFcGEiZGs/uYAI0b148N3txEXH8TghBAytxcweUoUaRtyz76jHuBwaDbuOE5phXFubpsVz7ET1UxICmPrnhMcLaxmQJgPE5PC+WJtDsPig/j0mwNE9+tD/AB/dh0sZdzQMDZnXR7npq2g+ECSbh/G2ze+j73Zzh1v30pe+lFCE0N447p3uO7ZZILiA6nIq2TYLUNZ9NNPnR2yEL2a7kVdvpdsjq/WulBrvd38vRrIBiKAecC7ZrJ3MRu6WutirfVWoLmTslIxGsZnMxbI0Vof1lo3YfQazjPzn2r0Koze3A5nUCkVBvhqrdO1cYbfaxNbttZ6/wVUfx6wSGvdqLXOBXLM+LqMsZP8p47RZ8CVZuxdldu2HgqYaeaDNsf4LOW21WmMF1nuBR2HnthHhyP9PY0aPQZfvz7ttq1bu5a5Nxi7mnvDPNatWdO6/fp581BKMWx4EtXVVZSUFGO1WmloaKC+oR6r1UpVVRXrU9Zx/bxuD/eC5O8toaGm6bzSOuwObG5WbG4uOOwO+oR44xPoybE9xZc4yvNTW9dMcWkdAM3NDsoq6/H2cu0yvcOhsVot2KwWHA6Nn68bPt5u5BdW9VTIZ1Vb3UhxgRFLc6Od8qIafPzciUkMYc/WAgD2bC0gdkgIYPyj5WK1YHV1wW7XRET5U1vVSKV5TJyptqaJ4hPGAJzmJjvlZbX4+LiRl1ve+o9t4fGT+Pi6Aca5sdksWG0uxrnp44GPrxv5RyudVoe28veX0lDb9fsmfmw/stOPAeb7xtUFq5sVu92BX7AXPgEeHNtX0lPhnlVdfQsl5fUANLc4qDjZgJenrV2auEh/9uca/zw7tMbqYsFm9jb6ebvi7eVKQVGHAVZOUVvbRHFR22utDh8fN5qaTj87ttlcWr+6Q2uNi4sFq82C3aGJ6OdHbW0TlRX1Tom/rbqGFkor2pybqka8PGygwdXqAoCrzYVa8yGL1hoXiwWreU8LC/KirqGZk+d5j+9JfWP6cnxHIS0NLWi75uiWYwy8Og4Xq/GR1ephxdHiYPz9Y8h4bxuOFoeTIxaid9OX6McZemRxK6VUJDACoydjR+vrAAAgAElEQVQ1RGt96hHjCYyh0N0hAjjW5u98c9upGN429zcI+GcX+fO7yt9NsXQZo1LqaaXU3DPza61bgJMYQ3/Pln+FUircTFdp5juzHl2Vez6xX0y5F3ocemIfl1x5WRlBQcEABAYGUV5WBkBxcREhoaGt6UJCQikuKmb+HXfy5sLX+OPjj3Pfgp+x8JVXuG/Bz7BYLs+150bMjuee52eT/MtxuHkZH4I3L9nDtQ9OYNxNiWxfcYDJdw5nw8dZTo60c77ebgT39eJEsfEBOGlIKHfdMpRZ06JxczU+MG7JLGD2jBjGJoWzY08Rk8b0Z+OWY2cr1ml8/T0IjvClMK8STx83aquNYei11Y14+hiNxS1rD3Prz8YRkxDMvszjjL86lvRvc5wZdqd8/dwJDvGhsKD9A4Yhw8PJPWS8jzZ/l0fy3ATGThxAZkY+k6dHk5Zy2BnhXrB+AwOpq2qg0mx8bf5yH7MXjGHcnIFkrj7ElJuHkLZ4j5Oj7JyPlytBAZ4Ulda2bgsP9qauvpmT5jWXsesEsyZHMnpoGFn7ipkwIoJNl8koiTP5+roTHOJN4XHjWps0JYoFvxjP4ISQ1t7fLelHuXX+cGJi+7JvbxHjJw4g/TIcFuzj5UqgvwdFZXWkZRYwMSmcu+cmMDEpnPSdxvHftreIuTNiiAz35eDRCkYnhpCxp8jJkXeu5EAJ/cf0w6OPO1Z3KzHTovEI8CBn/WHuW/4TaopraaxuJHx4OAcuw/uYEOLydckXt1JKeQOLgd9oravadjCa82d7pNGvtf6pOYz3nxjDe9/uif2eL631k98z/7UASqnA7olIdAelFHToVG8vLDycN981RvEfzcujqOgE0dHR/OH3j9Lc3MyvHnyQyMiongj3nHasOsimz3ajtWbyHcOY8ZORfP3yZoqPVPLh498A0C8hiNqKehRw/UOTcLQ4WPduJnUnG5wbPGCzWrh+Vhwpm47Q1Gxn594i0rfnozVMGtOfaRMG8M36w5SU1fHxF0YDJCLMh9q6ZlBw3VVxOBwO1m862jov2Jlsri7M/clI1i3dS1NjS8cE5t0170ApeQdKAUgYFUFudgn+QV6Mnm7MX1z3xV5amp3ba2KzuTD35iGs+/Zgux64cZMG4HBosncbH9JLimr42JzPF9G/D7U1jSgFc25MxG43hk3X1Tr/3HRm0Pj+7Es//QCl5OhJPvrLOsBoFNecbEBhzAl22B2kfJxFnTmf3plsVgvXTY8mdesxmtpcJ/FRARw4cnowVmlFPf9eaQyOCg/2pra+GQUkT43C4dBsyMinvqGT67SH2WwuzL0xkXVrclqvtY0bctm4IZex469gxKgIvks7Qt6RCvKOGNdaQmIIuYfL8Q/wZPTY/jQ0NLNudQ4tTu5ttFktJE+KJC2zgOYWB0NiA0nLLOBw/kli+/dhxtgrWJZyiPyiGj79xpjaMDDSn7zj1fTxcSNpUDCNTXbStufTYr88hjOWHSpn02ubuePd22iua6Youxht16Qv3EL6wi0AXPdsMqn/SCPptmFETY6keH8JG1/a5OTIheidZKjzeVJK2TAavR9qrZeYm4vMYcWnhhdf1FhIpVR/pdQO8+fnGItL9W+TpJ+5rZU5F3YRcLNSyqVN/qfNtP3Olv8CdBXLOWM8M79Sygr4YSzodD75y4A+Zr4z03RV7vnEfjHlXuhx6Il9dKCUWqCUylBKZbz5+sLOklyQgL59KSkxLuuSkmICAgIACA4OoejEidZ0RUUnCA4Jbpf3ny/+gwce/DUfffABN91yC799+BFee+nl7x1Td6k72YB2aNCQ9e0hQuPOHDAAE24ewqbPdjPxtqGsfz+TnasPMfK6+E5K61kWi+L6WfFkHywlJ7cCgLr65tbVCndlFxMa7N0h37gREWzens+EUf1ITc9jV3YxI4aEdkjX0ywWxdx7RpK9/Tg5u4xGYV11I15mL6+Xjxt1Ne0bTVabhcQxEezYmMfE5DhWLsqiILeCwSN7ZDBElywWxdybh5C9u4ic/aeH+SYOCyU6NpAVX3TeCzp+ciTpaUeYMCWK9WsOsWvHcUaM6d9pWmdTFkXcqAj2bc7v9PXxcweTvnQvE25IIPWTXWSl5DLy6tgejrIji4Jrp0ez/3A5h9oMJ1cKYq/ow8EjFZ3mGzssjC1ZhYwdHsbGbQXsOVhK0qDgTtP2JItFMffGRLL3FpFjPgxqK3tPEXHxQe22Wa0WEoeGsmN7ARMnR7Lyq2wK8k8yOLG7BqxdHIuC5EmRHMir4LC5ZsHAyIDW33OOVRLS17NdHquLYlBUALsPljB2SChr0o9SWFJD/AD/Ho//bHZ+uou35r3H+3d8TMPJBspzTz9gCUkIBgVlh8sZNHsgnz+4DP8r+uAfeXnVQQhx+blkDV9z/uWbQLbW+vk2Ly0DfmL+/hNg6cWUr7U+prVOMn9eBbYCcUqpKKWUK8aCR8uUIbZNTHOBfVpre5v8T5rDr6uUUuPNdHdfbGxmHecrpdzMVYrjgC1dxdhF/lPH6BZgrTnvuKty2x4XDawz80H7Y9xVuW11GuNFlntBx6En9tHhSBvHbKHWerTWevS99y/oLMkFmT5jJsu+MMJe9sVSZsycaWyfOYPlS5eitSZr5w68fXxah0QDZGzdQnBwMAMiI2loqMeiLFgsFuobnD+f7BSvPu6tv8eN60fp0ZPtXk+cHsXh7cdpqGnC5uaCdhhPCm2uzv/mtFnToimvrGf7rtMPH9rOV4yN8qe0vP2c14T4QHKPVdLQaMdmtaC1say/1er8Yeizbh9KWVEN29osNHRoTzGJY4xGbOKYCA6dMZRxzIxoMtPyzDnMLqA1Wmus5hBvZ5l13SDKyurY1mY4eWR0AGPGD+CLT7M67VVLGBpKbk4pDQ0tWG2n62K7DM5NZwYkBlNeWE1NJ/NDEycN4PDOQhpqm433jdbGdebm/PfNlRMjKa9sIDO7/TPqK8J8qTjZQE1dx971QdEBHCk4SWPTqfeNvnzeN7MHGtfa1tMPIPr4n15EMDYukPIz7gNjxvUnc1tB69x/LpP7wIyxV1BR1cjONg+LauubCTcf4PUL8aayuv3DrxGDgsk6UIpDg9XFAuaMZmfX5UyeZoPdN8yHgdfEs3tZdutr0347hfUvpGGxWrC4GCOqtENjc3f++0WI3ujUZ5/u/nGGS3mXmATcBexSSp36jok/YKym/G+l1L1AHnAbgFIqFMgAfAGHUuo3QII5PPpjYDoQqJTKB/6ktX6z7c601i1KqQcwVlp2Ad7SWu9RSlmAd5VSvhhfZ7QT+EUXMf+S019ntNL8QSl1I8YQ6SDgK6XUDq31NV1V3Nzvv4G9GKtb/+rUysudxWhufxrI0Fovw3hg8L5SKgdjUa/551HuCuA+rfVxjFWyFymlngEyzfLoqlxzbvAbWutruzqOZv4LKvdijkMP7aPb/P6Rh8nYsoXKykqunjGdXzzwAP9x/3387rcP8cXizwgLD+dvz78AwJSp00hLTWVO8jW4u7vz9H8+21qO1prXX32V//4f4xnRzbfexuOP/g673c4TT/6pu8M+L3N+O5H+iSF4+Ljx84U3sPGTLPonhhAc6Q9oThbX8s2rp5+7WF1dGDIjmk+fXgvA1uX7uPmJ6cZXtfxjo1PqcEp4qA8J8UGUlNXy45uHAsZXFw2M7UtwXy80mqrqRla3Wa3VarWQGB/EYvPri7ZlFXLT7EHYHQ5WrHHuvLKIKH8SR/ej5HgVdz00GYC0FfvZsvYQc+4ewZCx/amqqOfL9zJb83j5uhHavw+bvjFiz0w7wo9+M4nG+maWvr3dKfUAiOjnR+KwMEqKarjrPmMl5LR1h5kxKw6r1cItdyYBUFhQxWpzGK3VaiFxWFjr1xdt23yUG+cPx2F38NUXzv2amet+MZb+g4Lw8HbjZy9cy8bP97I79QiDxrUf5nyK1dWFxCkD+OxvGwDI+PogNz00GbvdwVevbO6QvieFBXsxOKYvpRV13DFnMADfZRaQV1BlLGp1pOOak1YXRUJsIF98awyrzdxbzNwrjSkCXzt5NeSICD8Sh4RSUlzDXfeMBiAt9TBDhoUREOCJ1pqqqgZWrzq92rmXtyuhYb5s2mjM7c3cVsCPfjKKxoYWli7Z7ZR6AIQFejEoKoDSynpuv2YgAOlZx0nZeozJIyOwKIXd4SBl6+lrztPdSnBfT7aaD8SyDpZw66x4GpvsrEhz/krVbd380jw8+njgaHGw6qlvaTQb8PFXx1K46wQ15hoNRdnF3L/ipxTvK6H4MlkUTojepheNdEb1pnHbQnxfDXZHr3lD/PO2Rc4Oodu4zIp2dgjdSh841yL1PyAB7udO8wPSm86N65WXx9oA3aW5sNrZIXQb9wF9zp3oB6TyiW+cHUK3euLQo84OQfzvcPZFYC4T32QWXJLPxrNGRPR4/WVciBBCCCGEEEKIDhyXwxfLd5PLa1KHEEIIIYQQQgjRzaTHVwghhBBCCCFEB71pVqw0fIUQQgghhBBCdNCbGr4y1FkIIYQQQgghRK8mPb5CCCGEEEIIITrQsriVEEIIIYQQQgjxwyA9vkIIIYQQQgghOpA5vkIIIYQQQgghxA+E9PgKIYQQQgghhOhA96IuX2n4CiGEEEIIIYTooBe1e2WosxBCCCGEEEKI3k16fIXopSyJQc4Oodvow5XODqF7ubs4O4LuU1bv7Ai6lRrU19khdJvmQ+XODqFbuQzwc3YI3ab8N8ucHUK3s3i5OzsEIcQl0Is6fKXhK4QQQgghvp8nDj3q7BCEEOKspOErhBBCCCGEEKIDWdxKCCGEEEIIIUSv1ovavbK4lRBCCCGEEEKI3k16fIUQQgghhBBCdNCLOnylx1cIIYQQQgghRO8mPb5CCCGEEEIIITqQxa2EEEIIIYQQQvRqvajdK0OdhRBCCCGEEEL0btLjK4QQQgghhBCig9401Fl6fIUQQgghhBBC9GrS4yuEEEIIIYQQooPe0997CRu+Sqn+wHtACMYxW6i1flEpFQB8AkQCR4DbtNYVSqlBwNvASOAJrfXf25T1FjAHKNZaDznLPpOBFwEX4A2t9V/PeP3/Af+htfbuIv8o4B3AA1gB/FprrZVStwJPAYOBsVrrDKXUNcBzZtZYoACoB7K01ncrpR4H7gXswINa61XnE6OZxs08dqOAMuB2rfUR87VOyz0jfxSwCOgLbAPu0lo3na3c8zmOF1PuhR6HnthHd3ryiSdIXZ9CQEAAS5YtB+BkZSWPPvwQxwsKCI+I4G/Pv4Cvnx9aa5579lnSUlNx93DnL88+y+CERI7k5vLY7x6hpaWFPz71FMOTRtDS0sIvF9zPiy+9jIeHR3eHfV58fN1IvnkIXl6uaCArI5/M9GPEJwYzYUYMfQO9+HDhFoqOVwEQfoUfV80ZjN2u+erTXVSW1+HmbmXObcNY/P52p945ffzcSb59KF7ebmg0WZvzydyYh7uHjTk/Go6vvwdVFfUs/3AHjfUtxA0JYeKsWBrqmln6XiYNdc34BXgwJTmeLz/a6byKtK3PLUPx8nZFa8jaeozMTUeJHxLChJmx9A3y4sNX0ykqOHVu+nDVvATsdgdffZJFZZl5buYPZ/G725x7bvq4k3xnklEXIGvTUTI3HMHd08acu0bgG+BJVXkdy9/bbpybYaFMTI43zs1bGca56evJlGsH8uX7mc6ryKn6+LiRfP1g432jIWvHcTIz8pk4NYrYuEC01tTVNfP1l9nU1jQRNzCIiVOiaGhoZuniXTTUt+DXx50p06L5cule59bF143km9rcA7YZ94Cps+KIiQ/CbndQWVHPqi/20NjQQnj/NveAz9rcA24dxuIPnHsPAPD2cuWaqdF4utsAza79JezYWwTA8MEhDB8cjNaa3GMnScs4RliwNzMnRuJwaFam5FBZ1YibqwvXzojl81X7nVuZNvrG9OWm125u/dt/gD8p/52CT4g3MTNjKdpzgqX/ZykAQ28eikeAJ1te3+yscIUQwmku5VDnFuBhrXUCMB74lVIqAXgMWKO1jgPWmH8DlAMPAn/vpKx3gOSz7Uwp5QK8BMwGEoA7zP2den004H+OmF8B7gfizJ9T+9wN3ASknkqotV6ltU7SWicBGcCPzL/vNvc7H0g0y3hZKeVyrhjbuBeo0FrHAi9gNrC7KreT/M8BL5j5K8zyuiy3rXPEeEHlXuRx6Il9dJt5N97AKwsXttv21huvM3b8BJZ/vYqx4yfw5huvA5CWmsrRvDyWf/01T/75zzzz56cB+PTfn/Do43/gpVdf49233wbg34sWcd31c53W6AVwODTrvz7AO//axEcLt5A0tj8BQV6UFtWy7OOd5OdVtEs/euIAlnyQScrK/Qwf0w+A8dOi2Jya6/QPvA6HZv2X+3nn+TQ++lc6SROuICDYi7HToziaU8Zbf9vA0Zwyxk6PBmDExAF8+M9NZG0+xuCkMAAmXxNP2qqDzqxGK4fDwfqV+3jnxY189Go6SeOvMM9NDcs+yiT/yBnnZnIkS97dRspX+xg+tj8A46dHs3n9YeefG7tm/dK9vPPfqXz04kaSJg0gIMSbsTNjOHqwjLf+K4WjB8sYe2UsACMmR/LhC2lkbTrK4JERAEyeHU/aysujIeJwaNavyeGd17fw0XvbSBoVQUBfTzLSj/Lem1t5/60MDueUMmFSJAAjRkXw4TsZZGUeZ3BCCACTp0WTlprrxFoYHA7N+lUHeOelTXz0+haSxhj3gLxDZbzz8ibeeyWdirJaxk6JBMx7wIeZpHzd5h4wNYrNG5x/DwCjPqlbjvL+57tYtHwvwweHENDHnX6hPsQM6MOHX+zm/c93s213IQCjhoSy9Jv9rN+cx9BBwQCMHR7O1p3HnVmNDsoOlfH6VQt5/aqFvDHrdZrrm9m/ch+hQ8NYOPM17E12ggcFY3W3Mnx+Ehlvb3V2yEKIHxCtL82PM1yyhq/WulBrvd38vRrIBiKAecC7ZrJ3gRvMNMVa661AcydlpWI0jM9mLJCjtT6stW7C6DWcB62Nub8Bj3aVWSkVBvhqrdO1MYv7vTaxZWutL+RT1Txgkda6UWudC+SY8XUZYyf5Tx2jz4ArlVLqLOW2rYcCZpr5oM0xPku5bXUa40WWe0HHoSf20eFIf0+jRo/B169Pu23r1q5l7g3GrubeMI91a9a0br9+3jyUUgwbnkR1dRUlJcVYrVYaGhqob6jHarVSVVXF+pR1XD+v28O9ILU1TRQXVgPQ3GSnvKQWH183yktrqSir65DeYdfYbC5YbRYcDgd+/h74+Lp3aIQ5Q211I8Vmz3Rzk53y4hp8/NyJSQxhzzbjQ+yebceJTTQaHlprXKwWrDYX7HZNRKQ/tdWNVHZSb2eorW6i+PiZ58ad8pJaKko7OzcO89y44LA78AvwwMfPnfzcy+TcmD3TzY1tzs2QEPZszQdgz9Z8YoeceW4s2O0OIqLMc9NJvZ2htraJ4qIawDw3pbX4+LjR1GRvTWOzubS2A7XmdH0cmoh+ftTWNFFZUe+E6NvrcA8w65J3qBztMGpQeOwkPr7ugNGwbL0H2M17gN/lcQ8AqKtvpsR8Dze3OCivrMfb05Vhg4PZmlWI3axTfUMLYNTHanXB6mLB4dD4+bjh4+VK/olqp9XhXKKmRFFxpIL6inosNuMjns3Dhr3FzoRfTGDrm1twtDicHKUQ4odEa31JfpyhRxa3UkpFAiOAzUCI1rrQfOkExlDo7hABHGvzd765DeABYFmb/XaVP7+L/N0VS5cxKqWeVkrNPTO/1roFOIkx9Pds+VcopcLNdJVmvjPr0VW55xP7xZR7ocehJ/ZxyZWXlREUZPQOBAYGUV5WBkBxcREhoaGt6UJCQikuKmb+HXfy5sLX+OPjj3Pfgp+x8JVXuG/Bz7BYLp+153z7uBMc5kNh/sku02zekEvyTYmMnRpF5uZjTL4qlrQ1h3owyvPj6+9BcIQvhUcr8fR2pba6ETAaYJ7ergBsSTnMrfeNIWZwMPt2FjL+yhjSL8O6QNtzU9llms2puSTfMpSx06LJTD/K5KvjSFud04NRnh/j3PhRmFeJp49b+3Pj4wbAljWHuPXn44lJDGFf5nHGz4oj/dvLry4Avn7uBIf4UGg+dJk0NYoFv5rA4MQQvjN7dLdsyuPW+UnExAayb28R4ydHkr7xiBOj7pxvH3eCQ30oLGh/DxgyMoLcg6WAeQ+4MZGxU6LI3HKMyVdenvcAAF9vV4L6enKipAZ/X3ciQnyYf30Ct8weREigFwBbswq5Zmo0Y4aHs3NvERNH9eO77fnnKNm5Em9IZPcXu2mqbSJnTQ73r15AdXENjVWNhI+MYP/Xl8fICCGEcIZLvriVUsobWAz8Rmtd1baD0Zw/e0mb/GZj8FZg+qXcz/eltX7ye+a/FkApFdg9Ef3voZRaACwA+Ncrr3Dv/Qu6s2zo0KneXlh4OG+++x4AR/PyKCo6QXR0NH/4/aM0NzfzqwcfJDIyqttiulA2Vxfmzh/OupUHaGq0d5mu5EQNH79uDKGLGNCH2upGlII5tw7Fbg6brqtt6qmwO2VzdWHuj5NYt2xf53Ux70Z5B8vIO7gJgISR4eTuL8E/yJPRU6NoqG9m3bJsWpqd32tic3Vh7p1JrPuqi/qYSgqr+fg1Y07fqd5rBcy5fZhxblbsvzzOzT2jWPfFXpoaWzomOHVuDpSSdyANgITREeRmF+Mf5MXo6dHGufl8z+VxbmwuzL1xCOtWH2zt7d2YmsvG1FzGTriCEaMj+G7DEfKOVJD3TgYACUNCyD1Uhn+AJ6PH9aehoYV13x6kxck9dDZXF+bePpx1X7e/B4ybGoXDocnOOgGY94A32twDatrcA+zGsGlnX2cANquF62bGsX7zUZqaHSiLwt3NyqLlewkJ9OLaGbG8/elOSsrr+ORLY551RIgPtXXNgOLa6THYHZoNW45S19DJteokFpuF+FkDWfufawHY9NJ3bHrpOwDm/M8c1v93Ckl3jiBmejRFe4tJ+8cGZ4YrhPiBuAxmqnSbS9qlpJSyYTR6P9RaLzE3F5nDik8NLy6+yLL7K6V2mD8/x1hcqn+bJP3MbSMwFp/KUUodATyVUjnmPNBT+Z820/brJP/F6CqWrrZ3mV8pZQX8MBZ0Op/8ZUAfM9+Zaboq93xiv5hyL/Q49MQ+OtBaL9Raj9Zaj+6ORm9A376UlBiXdUlJMQEBAQAEB4dQdOJEa7qiohMEhwS3y/vPF//BAw/+mo8++ICbbrmF3z78CK+99PL3juliWSyKufOHkZ1VSE72+b9Vx0+LJn39YSZMj2b9NwfZta2AEeP7nzvjJWSxKObeNYLsHYXk7DEWtKmracLL7En08nHr8KHcarOQOCqCHd8dZeLVcaz8ZBcFuRUMHhHe4/GfyWJRzL0zieydheTsvYBzMz2a9HWHmTAzhvWrDrBraz4jJl5xCSM9N4tFMfeeUWRvLyBnl/EeqatubH9uahrb5bHaLCSO6ceOtDwmXhPPyo93UnC4nMGjemRgx1lZLIq5Nw0he08ROQdKO7yevaeIuIFB7bZZrRYSh4axY1sBE6dEsfLLbAr+P3v3HR9VlT5+/HOSSYOEEiAJCSVUIaE3aSKgAiIQUOxr79/F3nXX9lV/7nd3Xd1VYa2AgoqiFEEsSO8JvfcOSUgIIWWSzMzz++PehATSgCQT4/N+veZF5sw95zznzmTCuafcw6doH1tRk6IujI+PYdSN534HxHZpTMu2DZk7fVOx+XoPqH7fAQA+xjBicBu270lhj71PQUZmLrv3W6upEk9kIiIEBRYdF+jVJZJV64/Qu2skS9YcYvPOZLrERpxTvje1HtyaY5uOkXkis0h6RIcIMIaUPSnEjIxh+v3TqR9dn9AWoV6KVCmlvKPSOr72+stPgG0i8nahl2YBd9g/3wHMvJDyReRQ/uZSIjIBWAO0Mca0MMb4Y214NEtE5ohIhIhEi0g0kCUirUXEXSj/S/Y06HRjTG879tsvNDa7jTcZYwLsXYrbAKtLirGE/PnnaCzwm73uuKRyC58XARbY+aDoOS6p3MJKOo8XUu55nYeqqOOcM10JBg4azKwZVtizZsxk0ODBVvrgQcyeORMRYeOG9QSHhBRMiQaIX7OasLAwmkdH43Rm42N88PHxIdvpvbV+Q0bHkJKcScLyg+XOE9OlMft2ncCZ7cLh5wv2Wg4/v+L2Yas6Q8Z2ICUpg4Ql+wvS9mxNIra71YmN7R7JHrtDnK/n5S1Yt+yAvdbPBxBEsNrlZUOujSUlKZOEZQfKnSemayT7dp7AmZ2Hw98XhOrx3tzYyXpvFp3Z0GnPlkRi7Q2SYns2Yc/ms96bQa1Yt2S/9d74+difs2ry3gxvR0pKJglrzqy2qFf/zEZ1rds0JPWs9eI9ezdjXfzhM581+73xdnuGxNnfASvOfAdEt25Az37RzJi6vtjR9ZjO1fM7AODKy1qQeiqbdVvOXITcc+AkTRrXAaBenUB8fUzBOl+A9q0bsv9QGjm5bhwOHwT7vfGtPstRADqM6cCWGZvPSb/82YEs/NsCfBw+GF9rBpJ4BL8gv6oOUSn1O1ST1vhW5lTnfsBtwCZjzHo77QXgLWCaMeYe4ABwA4AxJgJrd+Q6gMcY8xgQY0+P/hJrqnJDY8xh4GUR+aRwZSLiMsaMA37Cun3NpyKy5Txj/h/O3M7oR/uBMWYM8B+gETDHGLNeRIaWVIiIbDHGTAO2Yu1u/WcRcdtlFRujPeocLyKzsC4YfG6M2Y21qddN5Sh3LnCviBwFngW+Msa8Dqyzy6Okcu3p4B+LyPAyzuN5lXsh56GK6qgwzz71JPGrV5OWlsZVgwby0Lhx3H3fvTz9+BPMmP4tjSMj+fvb/wLgsgGXs3TxYkYMG0pgYCCvvfFmQTkiwkcTJvB//7SuESmNTv8AACAASURBVF13/Q08/8zTuN1uXnzp5YoOu1yimtUjtkskycdPc9tDvQFY+utufB2GwcPbEVTbnzF/6kLy8dNMn2zdRsbh50Ns10imT1oLQMKKA4y5rSse+xZH3hIVXY/Y7lEkHzvNbY/2tdoybyerF+5lxK1d6NCzCekns/lhyplbFdUOCSCiSV1W/GqtUVy3/AC3PtyHnGwXMyev9Uo78kU1r0ds1yjrvRnXB4ClP+/C1+HD4BHtrffm9m4kHzvN9IkJgP3edIti+mfWtNqEpfsZc3s3PG4Pc6Zt9F5bWtQntmcTko+mc9uT/a22zN3B6vl7GHF7Nzpc2tR6bwqd89p1AohoVo8VP1u7bK9bup9bH+9PTnYeMz9N8Eo78kU1qUtsxwiSkzK47e4eACxdtJcOnRoT2qAWIpB+ysmvhdZa1g72J6JxHVYs3Q/AuoTD3HpnD3JyXMz81ou/N4W/Ax60vwPm72bQ1ZfgcPgw9vbuABw7fIpff9gGFPoOsN+vhOUHGPMn+zvAi20BiAwPJqZ1Q5JTs7g1LhaAZQmH2bLrBFf1b8GfxnTA4xZ+WrK3II/D14eYNg353n6/1m4+zuir2uL2CPMWVp/1y361/GgxoCVznp5TJP2SYZdwbMMxMuwN1xK3JPLAggdI3JpE4tbE4opSSqkivLUDc2Uw3upxK1UdOfO39awB3n91vrdDqDilrF/9XXKUvu77dyW7+qxxrBCNank7gopTjdafVgTf5nW9HUKFOf3CPG+HUOH+evyitipR6o/od/GfgSmL9lTK/41vvbxVlbe/0je3UkoppZRSSin1+1OTxkir1wIVpZRSSimllFKqgumIr1JKKaWUUkqpc0gNuqGRdnyVUkoppZRSSp1DpzorpZRSSimllFK/Ezriq5RSSimllFLqHDriq5RSSimllFJK/U7oiK9SSimllFJKqXNIDRry1Y6vUkoppZRSSqlz1Jxur051VkoppZRSSilVw+mIr1JKKaWUUkqpc4in5oz56oivUkoppZRSSqkaTUd8laqp0pzejqDCSEaut0NQJTARwd4OoWL51pzrwY4W9b0dQoXK/Ntib4dQYXzrB+M+meHtMJRSqmw1Z8BXO75KKaWUUlXtr8df8nYISin1h6IdX6WUUkoppZRS56hJtzOqOXO6lFJKKaWUUkpVHJHKeZSDMWaYMWaHMWa3Mea5Yl5/0BizyRiz3hiz1BgTU1p52vFVSimllFJKKVVtGGN8gfeBq4EY4OZiOrZTRaSjiHQB/g94u7QydaqzUkoppZRSSqlzeHGmcy9gt4jsBTDGfAXEAVvzDxCR9ELH16aMrbi046uUUkoppZRSqjqJAg4Ven4YuPTsg4wxfwaeAPyBwaUVqFOdlVJKKaWUUkqdQzxSKQ9jzP3GmPhCj/svKD6R90WkFfAs8JfSjtURX6WUUkoppZRS56qkuc4i8iHwYSmHHAGaFnrexE4ryVfA+NLq1BFfpZRSSimllFLVyRqgjTGmhTHGH7gJmFX4AGNMm0JPrwF2lVagjvgqpZRSSimllDqHtza3EhGXMWYc8BPgC3wqIluMMa8B8SIyCxhnjLkSyANOAneUVqZ2fJVSSimllFJKVSsiMheYe1baS4V+fvR8yqu0jq8xpikwGQjH2lr6QxF51xgTCnwNRAP7gRtE5KQxph3wGdANeFFE/lGorE+BEUCSiHQopc5hwLtYVwU+FpG37PSJwOXAKfvQO0VkfTH5W2DND28AJAC3iUiuMWYA8A7QCbhJRL41xnQEPrezNrPLPgWcEJErjTF3cGaB9esiMsmuozswEQjCeiMfFSl6LcUYY+x2DAey7HjX2q8VW+5Z+Us6xyWWe1b+YmO8kHLP9zxURR1nt/divPTiiyxetJDQ0FC+mzUbgFNpaTzz5BMcPXKEyKgo/v72v6hTty4iwt/efJOlixcTGBTI/775Ju1jYtm/bx/PPf0ULpeLv77yCp27dMXlcvE/99/Hu+9/QFBQUEWGXG5Db+lMy9hwsk7nMOmtRQA0iqzDlTd2xC/AQXpqFnMnryPX6SKyRX2uvKETbreHOZPWkpacSUCQgxF39WD6+JVlbC5fNYbe1Z1WnSPISs9h4ku/AjDiwV6ERoQAEFDLj5ysPCa/Mp/I1g246rauuN0efpiwmrSkDAKC/Bj50KV8+6+lXm9PTWpLSN1Ahl3fkdrB/ojAxjWHWLf8IG07hNPnitY0aFSbKeNXknjEumNBZLN6XBkXY33Wvt5IWkoWAYEORtzcmekTE7zfnpAAhl3Tjtq1/BBg44ZjrEs4Qt/+0bRu3QARyMrKZd6PO8jMyKVN24b07R+NM9vFzO8343S6qFsvkMsGtOCHWdu82pbgWn4MuawFtYL8QGDzzmTWb0sCoHO7MDq1b4R4YN/hUyxLOEzjsGAG926G2yPMW7SXtNM5+Pv7Mvzylsz4pdTZZ1XimteH0HpgS7JSs/ho1GQAAusGMubta6gbVYdTR9L5/vEfcKbncMlVbRjwSB+caU6+fXgW2WlO6jWty8DH+zPjiTlebolSSlUhL97PqKJV5hpfF/CkiMQAvYE/2zcdfg6YLyJtgPn2c4BU4BHgH8WUNREYVlpl5bjJ8dMi0sV+nNPptf0N+JeItMYaLr/HTj8I3AlMzT9QRDbll4c13zy//CvtztvLWFtu9wJeNsbUt7OOB+4D2tiP4tp1daHX77fzUEa5hZV0josttxglxXhe5V7geaiKOipM3JjRjP+w6Lr8Tz/+iF69+zB73k/06t2HTz7+CIClixdz8MABZs+bx0uvvsrrr74GwDfTvuaZ51/g/Qn/ZdJnnwEw7auvuGbkKK91egE2rzrE9PGriqQNubkzS2ZvZ/Jbi9i98Tg9BrcCoMfgVnz331Us/G4Lnfs1B6D3kLas+nmX1zsi+bYsO8C3by8rkvbDhNVMfmU+k1+Zz66EI+xKsPZM6DG0DdPfWcaCLzfQZVALAHqPbMeqOdurRXtqUls8Hg+L5m5n4jvLmDp+JV16NyM0rDYnEjOYNWUdh/efLHJ8j8ui+W5SAgvnbKdzL2vPi96DWrJq4d5q0h5h0YI9TPw0nqlfrKNL10hCG9QifvUhJk9M4PNJCezdk0qfvtbvSdduUUyZvJaNG47SPiYMgP6XtWDpkv1ebIXFI7BkzWG+mLGFr+dso1O7MELrBtIkIoSWzeoxdeZWvpi5hbVbjgPQLTacmb/uYvHqQ3S8pBEAvTo1Zs2m495sRoGNM7bw1f3fFUnrc19P9q84yIRhn7F/xUH63NcLgB5/6sJn109l7bRNxF7TDoDLH+3HoneXnVOuUkrVZCJSKQ9vqLSOr4gcyx+RE5HTwDas+zHFAfmjlJOA0fYxSSKyBmuO9tllLcbqGJem4CbHIpKLNXIbV9547RHFwcC3xcS2X0Q2Ap5yFjcU+EVEUkXkJPALMMwY0xioIyIr7ZHHyfl1nCUOmCyWlUA9O2+x5ZaQ/5xzXEq5hc9DaTGeb7kXch6qoo4K071HT+rUrVckbcFvvzFqtPXRGzU6jgXz5xekj4yLwxhDp85dOH06neTkJBwOB06nk2xnNg6Hg/T0dBYtXMDIuHJ/fCvFkT2pOLNyi6TVD6vN4d0pABzYnkzbLtbHx+P24Ofni8PfF4/bQ92GtQipH1hwbHVweOcJnJm5Jb7etmcTtq06DNjt8bfa43YLdRvVJiQ0iEM7TlRVuKWqSW3JPJ1L0tHTAOTluklNyiSkTiCpyZmcPJF1zvEFnzU/XzweD3VDgwipG8jhfSfPOdYbMjNzSUrMAOz2pGQREhxAbq674Bg/P5+CC+gigq+vDw4/X9weIapJXTIzc0k7me2N8IvIys4jOdV6D/JcHlJPZRNcy5+OlzQiftMx3B6rEdlOF2B1+h0OHxwOHzwi1A0JIKS2P0eOn/ZaGwo7FH8EZ5qzSFrbwa3YOHMrABtnbqXtFdbFPPEIDn9f/AIduF0emnaPIvNEJicPpFV53EoppSpGlazxNcZEA12BVUC4iByzXzqONRW6IpR1k+M3jDEvYY8iikjOWfkbAGki4iqUP6oCY4myH4eLSccY8yCAiEwoI39x6RhjPgYmiEg8JZ/jkvIfK5RWYowXUO55n4cqqqNSpaak0KiRNXLTsGEjUlOszl9SUiLhEREFx4WHR5CUmMRNN9/CX55/jtzcXP76yqt8OH48997/AD4+1W/T9ZTjp2ndMYLdm47TtmskIfWsEelVv+xm2G1dceW5+fHzdVweF8PSOTu8HG35NWnbkKz0HNKSrA7Lqjk7uPreHrhy3cz9OJ6BN3Rk6XdbvRxl+fye21KnXiBhkSEcO1Ry52LVon0Mu74jLpeHH6dt5PLhl7D0l91VGGX51akTQFh4MMeOWdO0+10WTWxsODk5bqZ9tQGA1SsPcf2NncjIyGXuD9sYGRfDnNneneJcnJBgf8JCa3H8RAb9ezYhKjyEvt2icLmFpWsOkZiSxZpNxxhyWQtcLg8/L9lH/55NWbG2tDtPeF/tBrXITM4EIDM5k9oNagGw/KPV3PzpWDKSMpj1zI+MeWcEM56cW1pRSilVM5V32O93oNI7vsaYYGA68JiIpFsDqxZ7TWdVjHU/j9WJ8se6X9SzwGtVUG+52R3ei8l/bwnplXKOq+K9q8LPR6UxxkChz3xxGkdG8skka73ZwQMHSEw8TsuWLXnh2WfIy8vjz488QnR0i6oIt0w/TdnAoLEd6D2sDXs2JeJ2W9+GyUfS+fLtpQBEtQolMz0HA4y4sxtut7BoxhayTpc8Qult7S5tyvZVZ66fJB86xdQ3FgJWRzLjlBNjrHW0Hrew8OuNZKWffe2sevi9tsXP35dRt3ZhwZzt5Oa4Szwu+dhpvpxgTcGPiq5/5rN2Uyfrs/bjDrIyvP9Z8/PzYdToWBbM31Mw2rtsyX6WLdlPr0ub0rVbJMuXHeDAgZMcmGyNVsfEhrNvbyr16wfRo1dTnE4XC+bvxuXy7v86/Bw+XDOwFYtWHyI3z4MxhoAAX76es53whrW5emArJk7fxInUbKbN2Q5AZHgwWVl5YODqy1vi8QhL1hwiy+kqozbvyh+J37/8IJ8tnwJAh7j27Fm8j9Do+vS+qzvZ6Tn88uYCXNW8LUoppYqq1CElY4wfVqd3iojkL6xJzJ9ea/+bdIFlNzXGrLcfD1LKTY7taddij/J+hjUtGmPMT3b+j4EUrOmzjrPzX4CSYjli/3xOjOeRvzw3ci7pHJcnf2kxnm+5F3IeqqKOIowx9xtj4o0x8Z98VNp9tMsntEEDkpOtsJOTkwgNDQUgLCycxONn1rolJh4nLDysSN7/vPsO4x55lKlffMG1Y8fy+JNP8d/3P7jomCpKalIG0z9YyRd/X8L2hCOkncg855jeQ9uy8qed9Lm6LYtmbmPTioN0HdDSC9GWj/ExtOkWyfbVh4t9vfeIdqycvY0+o9qz+JvNbFy0j25Xtq7iKMvn99oWHx/DqFu6sG39MXZvKf+fhN6DWrJywV76XNGKRfN2sin+MF37NKvESMvHx8cwanQs27YmsXvXuVPKt21Nok3bRkXSHA4fYjuEs37dUfr2j+bHuds5cvhUwbpfb/ExhmsGtWLH3lT2HLRG4jOyctljT/lNPJGJiBAUUPQ6eq9OjVm14SiXdo5kafxhNu9MpnP7iprgVXEyU7Ko3ag2ALUb1SYrtej0ekegg06jY0mYuoEB4/ow+/l5HE44QoeR7b0RrlJKVTld41sO9prZT4BtIvJ2oZdmceYeS3cAMy+kfBE5JGc2q5pAKTc5LtSRMljrPDfbZQy1899rrwNdAIy92Niw7jc1xBhT395oaQjwkz2FN90Y09uO5fYS6pgF3G4svYFTdt5iyy0hf3HnuKRyC5QR4/mWeyHnoSrqKEJEPhSRHiLS45777i/ukPMycNBgZs2wqpo1YyaDBg+20gcPYvbMmYgIGzesJzgkpGBKNED8mtWEhYXRPDoapzMbH+ODj48P2U7vr/XLFxTsb/1g4NKhbdi47ECR12N6NWHf1kScWXk4/HxBBPEIfv6+Xoi2fJrHhJF6/DQZxaypjO3bjL2bjuPMzMMvwBfxWF/Wjmrant9rW4ZcG0tKciYJZ32eShPTNZJ9O07gzM7/rFl/nP38qkF7hrUlJSWLhPgzFyDq1T+zUV3rNg1IPauD1bNXU9YlHClYJ5vfHoeX23Nlv+aknnKybmtiQdreg2k0sXcQr1cnAF9fH7Jzzox+tm/VgP1HTpGT68bh8LH/k2ONHFc3u37bS6c4ax/MTnEx7PxtT5HXe9/dg/gv1uFxeXAEOpD8z1mg3g1SKaV+b0xl9biNMf2BJcAmzswOfwFrne80rFsAHcC6XU2qMSYCiAfq2MdnADH29OgvgYFAQyAReFlEPimmzuFYtx3Kv8nxG3b6b0AjwADrgQdFJKOY/C2xNsUKBdYBfxKRHGNMT+B7oD7gBI6LSGyhfBOBH0Tk20Jpd9vtBXhDRD6z03tw5hY7PwIP21N6C9b42h2197A2rsoC7rLX7pZWbsEaX2NMgxLOcWnlrhdrh+rSYryQcs/3PFR6HZTCmb9bSzk9+9STxK9eTVpaGqENGvDQuHEMvuIKnn78CY4fO0rjyEj+/va/qFuvHiLC/3v9f1m2dCmBgYG89sabxHaw7s4lIjx47z383z/fpm69euzds4fnn3kat9vNiy+9TNdu3c4nLADef/zibrlxzR3daNK6AUHB/mSdzmH53B34BTjoclk0ALs3HGPJ7O0Fxzv8fBnzYC+mv78Sj0eIahnKFTd0xGPf4uhk0rmjw+UlFTB19ZoHetH0koYEBQeQle5k2cxtbF6yn2F3d+fY3lQ2LNxX5HiHvy/XPtqXb99eisctRLVpwJW3dcXt8jDnw9WcPH7OV0iVqU5tMRHBF9WWqOb1uOmBS0k+drrgCvDSn3fh6/Bh8Mj2BNX2J8eZR/LR09btigCHnw9j7ujO9E/jrc9adD2uGBVjfda+3ljspljlVi/w4toTVYebbu1KclJGwbTZpUv20aFjBKGhtRAR0tNz+PXnnWTYn+vawf4MGdqW76dvBqDtJQ3p0y+aHKeLmd9vITv7nD0fy8URVvui2hIZFsz1w9txIjWrYMPs5QlHOHgsnav6RdMwtBYej4claw5z2N7AyuHrw6grWzPj5114RIgMC2ZQn2a43cK8xXtJu4hp9Zl/W3xR7Yn7x3Ca92pCUL0gMlOyWPLeCnbO382Yt0dQJzKEU0fT+f7xOThPWRtgBTeqzfD/vYppD84AoN3QNlw2rg/O9Bymj5tF1kVuQPbCticuKr9S6nev9LVw1cT46ZsqpbP40HUdq7z9ldbxVer36Hw7vtXZxXZ8q5OK6PiqynGxHd9q5yI7vtXJxXZ8q5uL7fhWN9rxVeoP7/fR8f12Y+V0fMd2qvL2l2uujjGmEdb9UKML5xGRuysnLKWUUkoppZRSqmKUd5HKTKxpy78CJW+3qZRSSimllFKqRqhJs4PL2/GtJSLPVmokSimllFJKKaVUJSjvFos/2BtHKaWUUkoppZT6I/BU0sMLSh3xNcacBgRr8fULxpgcIM9+LiJSp/JDVEoppZRSSilV1f4wU51FJKSqAlFKKaWUUkoppSpDuaY6G2PmlydNKaWUUkoppVQNIVI5Dy8oa6pzIFAbaGiMqc+Z+03VAaIqOTallFJKKaWUUuqilbWr8wPAY0AkkMCZjm868F4lxqWUUkoppZRSyotq0BLfMtf4vgu8a4x5WET+U0UxKaWUUkoppZTysj/M5lb5ROQ/xpi+QHThPCIyuZLiUkoppZRSSimlKkS5Or7GmM+BVsB6wG0nC6AdX6WUUkoppZSqiWrOgG/5Or5ADyBGatJYt1I1nGTkejuECiOZed4OoUL5X93a2yFUGFdSprdDqFDN+jXzdggVZte933s7BKWUUqraKG/HdzMQARyrxFiUUkoppUr0wrYnvB2CUkr9oYin5ox7lrfj2xDYaoxZDeTkJ4rIqEqJSimllFJKKaWUqiDl7fi+UplBKKWUUkoppZSqZmrQStfy7uq8yBgTDvS0k1aLSFLlhaWUUkoppZRSyptqUL8Xn/IcZIy5AVgNXA/cAKwyxoytzMCUUkoppZRSSqmKUN6pzi8CPfNHeY0xjYBfgW8rKzCllFJKKaWUUl5Ug4Z8yzXiC/icNbU55TzyKqWUUkoppZRSXlPeEd95xpifgC/t5zcCcysnJKWUUkoppZRS3iY1aMS3vJtbPW2MuQ7oZyd9KCLfV15YSimllFJKKaW8yuPtACpOeUd8EZHpwPRKjEUppZRSSimllKpwpXZ8jTGngeLGtw0gIlKnUqJSSimllFJKKeVVf6SpzvOBCOA74GsROVD5ISmllFJKKaWUUhWn1I6viIw2xtQFrgU+NMYEAl8DX4lIaml5jTFNgclAONao8Yci8q4xJtQuIxrYD9wgIieNMe2Az4BuwIsi8o9CZX0KjACSRKRDKXUOA94FfIGPReQtO90Ar2Pdh9gNjBeRfxeTvwXwFdAASABuE5FcY8wA4B2gE3CTiHxrjOkIfG5nbQacsh8nRORKY8wdwF/s118XkUl2Hd2BiUAQ1gZhj8pZl1LseN8FhgNZwJ0istZ+rdhyz8pf0jkusdyz8hcb44WUe77noSrqOLu9F+OlF19k8aKFhIaG8t2s2QCcSkvjmSef4OiRI0RGRfH3t/9Fnbp1ERH+9uabLF28mMCgQP73zTdpHxPL/n37eO7pp3C5XPz1lVfo3KUrLpeL/7n/Pt59/wOCgoIqMuRyG3pXd1p1jiArPYeJL/0KwIgHexEaEQJAQC0/crLymPzKfCJbN+Cq27ridnv4YcJq0pIyCAjyY+RDl/Ltv5YWP2+kig17sBctu0WSle5k4lPzCtK7DmtD1yFtEI+wd91RFk3ZQNQlDbnqnh64XR5m/3s5acczCKjlx6jH+vHN/1vo9fYE1/JjyGUtqBXkBwKbdyazfpu18X7ndmF0at8I8cC+w6dYlnCYxmHBDO7dDLdHmLdoL2mnc/D392X45S2Z8csur7YlJCSAYde0o3YtPwTYuOEY6xKO0Ld/NK1bN0AEsrJymffjDjIzcmnTtiF9+0fjzHYx8/vNOJ0u6tYL5LIBLfhh1javtgUgLy+Xj996CndeHh6Pm9gel3HF6Nv47tO3Obp/F4LQMLwJ197zJAGBQaz4dSZrFs2lXmgYtzz8Eg6HH/t3bmZrwjKG3/yAt5vDNa8PofXAlmSlZvHRqMkABNYNZMzb11A3qg6njqTz/eM/4EzP4ZKr2jDgkT4405x8+/AsstOc1Gtal4GP92fGE3O83BKllFIXogYN+JZ9SyIROSUinwFXA/8FXgPuLEfZLuBJEYkBegN/NsbEAM8B80WkDdaI8nP28anAI8A/iilrIjCstMqMMb7A+3acMcDNdn3Y8TYF2olIe6zObXH+BvxLRFoDJ4F77PSDdhlT8w8UkU0i0kVEugCzgKft51fanbeXgUuBXsDLxpj6dtbxwH1AG/tRXLuuLvT6/XYeyii3sJLOcbHlFqOkGM+r3As8D1VRR4WJGzOa8R9+WCTt048/olfvPsye9xO9evfhk48/AmDp4sUcPHCA2fPm8dKrr/L6q68B8M20r3nm+Rd4f8J/mfTZZwBM++orrhk5ymudXoAtyw7w7dvLiqT9MGE1k1+Zz+RX5rMr4Qi7Eo4A0GNoG6a/s4wFX26gy6AWAPQe2Y5Vc7Z7vZOYb/OifXz7/xYVSWsaG0abHlFMemYenz31I2tmbwegx4hL+PatRfw2aS1drmoNQJ9rY1k5Y2u1aI9HYMmaw3wxYwtfz9lGp3ZhhNYNpElECC2b1WPqzK18MXMLa7ccB6BbbDgzf93F4tWH6HhJIwB6dWrMmk3HvdkMADweYdGCPUz8NJ6pX6yjS9dIQhvUIn71ISZPTODzSQns3ZNKn77NAejaLYopk9eyccNR2seEAdD/shYsXbLfi604w+Hw4+6n/8a418bz51c+YNemeA7t2cbwmx9g3Gvjefi1CdRt0IiV82cBsGHlAsa9Op6mrduze3MCIsLC2VMZOPIWL7fEsnHGFr66/7siaX3u68n+FQeZMOwz9q84SJ/7egHQ409d+Oz6qaydtonYa9oBcPmj/Vj07rJzylVKKfU7IVI5Dy8os+NrjOlrjPkPsBboC4wRkbfLyicix/JH5ETkNLANiALigPxRyknAaPuYJBFZA+QVU9ZirI5xaXoBu0Vkr4jkYnVu4+zXHgJeExFPfl3FtNMAg4Fvi4ltv4hspPz7mg0FfhGRVBE5CfwCDDPGNAbqiMhKe+Rxcn4dZ4kDJotlJVDPzltsuSXkP+ccl1Ju4fNQWoznW+6FnIeqqKPCdO/Rkzp16xVJW/Dbb4wabX30Ro2OY8H8+QXpI+PiMMbQqXMXTp9OJzk5CYfDgdPpJNuZjcPhID09nUULFzAyLu6c+qrS4Z0ncGbmlvh6255N2LbqMAAetwc/f18c/r643ULdRrUJCQ3i0I4TVRVumQ5vS8aZUbQ9Xa5qzaqZ23C7rF/trPQcADwuwS/AgV+AA4/LQ73wYEIa1OLQ1nO+OrwiKzuP5NQsAPJcHlJPZRNcy5+OlzQiftMx3B7rD0q20wVYnUuHwweHwwePCHVDAgip7c+R46e91oZ8mZm5JCVmAJCX6yY1JYuQ4AByc90Fx/j5+RT8jRQRfH19cPj54vYIUU3qkpmZS9rJbG+Efw5jDAGB1gUrt9uF2+0CDIFBtQErflduLtafHEAEj9tFXm4OPr4O1q+YT9uOPakVHOKlFhR1KP4IzjRnkbS2g1uxceZWADbO3ErbK1oBIB7B4e+LX6ADt8tD0+5RZJ7I5OSBtCqPWymllDpbWZtb7QfSsDqR92ON4mKM6QZQ3DTZvYAdPgAAIABJREFUEsqJBroCq4BwETlmv3Qcayp0RYgCDhV6fhhrFBCgFXCjMWYMkAw8IiJnz+9rAKSJiKtQ/qgKjCXKfhwuJh1jzIMAIjKhjPzFpWOM+RiYICLxlHyOS8p/rFBaiTFeQLnnfR6qqI5KlZqSQqNG1khUw4aNSE1JASApKZHwiIiC48LDI0hKTOKmm2/hL88/R25uLn995VU+HD+ee+9/AB+fMq9LeU2Ttg3JSs8hLcnqsKyas4Or7+2BK9fN3I/jGXhDR5Z+t9XLUZYttHEITdo1ov+NnXDnuVn4xXqO70ll1YytDP/zpbhy3cx5byUDb+vCkq83ejvcYoUE+xMWWovjJzLo37MJUeEh9O0WhcstLF1ziMSULNZsOsaQy1rgcnn4eck++vdsyoq1R7wd+jnq1AkgLDyYY8fSAeh3WTSxseHk5LiZ9tUGAFavPMT1N3YiIyOXuT9sY2RcDHNme3+Kc2Eej5sPXn2Y1KSjXDp4JE1bWaOf0z/5Jzs3rSEsshnDbrwPgEuvGMl/33icsMhmNG8dwxf/eZU7n3jDm+GXqXaDWmQmZwKQmZxJ7Qa1AFj+0Wpu/nQsGUkZzHrmR8a8M4IZT871ZqhKKaUu0h9pc6v9WBP7hgJDsHZzzidYI6SlMsYEY90G6TERSS+4yo21LbQxpirOZgDgFJEexphrgU+By6qg3nKzO7wXk//eEtIr5RxXxXtXVZ8PY8z9WBd2eG/8eO657/6KLBuMKfWYxpGRfDLJWjt38MABEhOP07JlS1549hny8vL48yOPEB3dosJiqgjtLm3K9lVnrjckHzrF1DcWAlanOOOUE2OsNcEet7Dw640Fo6nVifE1BAb7M+UvvxDRKpSRj/Xlo4d/IOlAGlP+Yq1rbtK+EZknnRhjGPloXzxuDws+X0fWKe+3x8/hwzUDW7Fo9SFy8zzWaGOAL1/P2U54w9pcPbAVE6dv4kRqNtPmWNO4I8ODycrKAwNXX94Sj0dYsuYQWU5XGbVVclv8fBg1OpYF8/cUjPYuW7KfZUv20+vSpnTtFsnyZQc4cOAkByafBCAmNpx9e1OpXz+IHr2a4nS6WDB/Ny6Xd2866OPjy7hXPyA7K4Op771G4uH9hDeJ5rp7nsTjcfPDlPFsWr2Y7pcNoWvfK+na90oAfps1hT5XxrFz0xrWL59P3dBGDLvxvmp9EQzOzFjbv/wgny2fAkCHuPbsWbyP0Oj69L6rO9npOfzy5gJcXv6cKaWU+uMq9a+piAwUkUHA1SIyWEQG5T+wNhkqlTHGD6vTO0VE8hcJJeZPr7X/vaC5g8aYpsaY9fbjQeAI1jrefE3sNLBG+/Lr/x5rkyqMMT/Z+T8GUrCmzzqKyX++SorliP1zcTGWN39JbSyspHNcnvylxXi+5V7IeaiKOooQkQ9FpIeI9KiITm9ogwYkJ1thJycnERoaCkBYWDiJx8+sqUxMPE5YeFiRvP959x3GPfIoU7/4gmvHjuXxJ5/iv+9/cNExVSTjY2jTLZLtqw8X+3rvEe1YOXsbfUa1Z/E3m9m4aB/drmxdxVGWT0ZKNjvtdhzfkwoeCAoJKHJMnzGxrJi+hb5jY1k0ZT0b5u+h27C23gi3CB9juGZQK3bsTWXPQWsqaUZWLnvsaaWJJzIREYICil7f7NWpMas2HOXSzpEsjT/M5p3JdG5fURNvLoyPj2HU6Fi2bU1i965zp8dv25pEm7aNiqQ5HD7Edghn/bqj9O0fzY9zt3Pk8KmCdb/VQVCtYFq068yuzfEFaT4+vnTsdTlbE5YWOTb9ZApH9u4gpltflv30HTc+9DyBtWqzd9v6qg67TJkpWdRuZE3drt2oNln2tPt8jkAHnUbHkjB1AwPG9WH28/M4nHCEDiPbeyNcpZRSF8NTSQ8vKO9l5OXlTCtgr5n9BNh21prgWcAd9s93ADPLGUMRInIof3Mpe7R0DdDGGNPCGOMP3GTXBTADGGT/fDmw0y5jqJ3/Xnsd6AJg7MXGBvwEDDHG1Lc3WhoC/GRP4U03xvS2z8/tJdQxC7jdWHoDp+y8xZZbQv7iznFJ5RYoI8bzLfdCzkNV1FGpBg4azKwZVlWzZsxk0GBrYsTAwYOYPXMmIsLGDesJDgkpmBINEL9mNWFhYTSPjsbpzMbH+ODj40O2s3qsXczXPCaM1OOnyShmTWVs32bs3XQcZ2YefgG+iEcQsdb9VUe71hymmd1Rqt84BB+HD9mnz4zkxg6IZu/6ozgzc/HzdxTsx+AXUNZkmcp3Zb/mpJ5ysm5rYkHa3oNpNLF33a5XJwBfXx+yc86MsLVv1YD9R06Rk+vG4fBBRKz2OLw7ojhkWFtSUrJIiD9zMaVe/TMbu7Vu04DUszpXPXs1ZV3CkYL1y4g1Hcvh593PWmZ6GtlZ+WuWc9izZS0NI5qQkngUsGLcvn4lDRs3LZLv1+8nccWY26x8eTmAwRhDXq73Zxacbddve+kUZ+0d2Skuhp2/7Snyeu+7exD/xTo8Lg+OwPzfG8Ev0Pu/N0oppf64ylrjG4G1LjLIGNOVM1Od6wC1yii7H3AbsMkYk3/J+gXgLWCaMeYe4ABwQ6G64u2yPcaYx4AYe3r0l8BAoKEx5jDwsoh8UrgyEXEZY8ZhdYR8gU9FZIv98lvAFGPM40AGUOy0YOBZ4CtjzOvAOqyOO8aYnlgjxfWBkcaYV0UktqSGi0iqMeZ/sTrjYG2slb851/9w5hY7P9qPs9f4zsUaUd+Ndeueu8oq96w1vsWe45LKtfOvF2uH6hJjPN9yL+Q8VFEdFebZp54kfvVq0tLSuGrQQB4aN46777uXpx9/ghnTv6VxZCR/f/tfAFw24HKWLl7MiGFDCQwM5LU33iwoR0T4aMIE/u+f1jWi666/geefeRq3282LL71c0WGXyzUP9KLpJQ0JCg7ggX9czbKZ29i8ZD/tejUpMs05n8Pfl9h+zfn2bWskK/6nXVz7eD/cLg9zPlxd1eGfY8QjfWgaE0ZQSAAPfjCKZd9sZtOCfVz9UC/u/McwPC4PP36wsuB4h78vHQa24Bt7CveaOTu47rkBuF0efvj3Ci+1whIZFkz71g05kZrFLaOsDsjyhCNs2XWCq/pFc2tcLB6PtZ43n8PXh/atGzDjZ2t7g3VbEom7qg1utzBv8V6vtAMgKqoOsR0iSE7K4LY7ugOwdMk+OnSMIDS0FiJCenoOv/68syBP7WB/IhqHsGK5dWv5dWuPcOvt3chxupj5/ZZi66kqp0+lMv2Tf+LxuBEROvQcQNtOvfj4rafIyc5CECKatmTUbeMK8hw9sBuAyOZtAOh86SDee+lB6oY24rKrr/dKO/LF/WM4zXs1IaheEOMW3MeS91aw4uPVjHl7BJ3HduDU0XS+f/zMrYqCG9UmslMES+3fpfgv1nHXN7fgTM9h+rhZJVWjlFKquqpBa3xNaQuWjXV/1DuBHlgdi/yObzowqdD0ZaVqBGf+drg1wHv3fe/tECqMZJ6z2fvvmv/V1XPq94VwJWV6O4QK1axfM2+HUGF23VtzvgPyvbDtCW+HoJRSFaX0TWCqiX++t6xS/m/85Lh+Vd7+Ukd8RWSSMeZz4GYRmVJFMSmllFJKKaWUUhWmzIVd9r1vH6+CWJRSSimllFJKVRd/wM2tfjXGPGXvpBya/6jUyJRSSimllFJKqQpQ3i0Wb7T//XOhNAFaVmw4SimllFJKKaWqhRq0uVW5Or4i0qKyA1FKKaWUUkopVY380Tq+xhg/4CFggJ20EPiviNSsrVaVUkoppZRSStU45Z3qPB7wAz6wn99mp5V0P1yllFJKKaWUUr9nXtqIqjKUt+PbU0Q6F3r+mzFmQ2UEpJRSSimllFJKVaTydnzdxphWIrIHwBjTEnBXXlhKKaWUUkoppbzqj7bGF3gaWGCM2QsYoDlwV6VFpZRSSimllFLKu/5oHV8RmW+MaQNcYiftEJGcygtLKaWUUkoppZSqGOUd8QXoDkTbeboYYxCRyZUSlVJKKaWUUkop7/qjbW5ljPkcaAWs58zaXgG046tUNWX8fL0dQoUxrYK9HUKFch1J93YIFca/eT1vh1Ch9jwxz9shVBifOkF40rO9HYZSSilVLZR3xLcHECNSgyZ5K6WUUjXcC9ue8HYISimlfs9qUPfPp5zHbQYiKjMQpZRSSimllFKqMpQ64muMmY01pTkE2GqMWQ0UbGolIqMqNzyllFJKKaWUUl7hqTkjvmVNdf5HlUShlFJKKaWUUqp6qTn93tI7viKyKP9nY0w40NN+ulpEkiozMKWUUkoppZRSqiKUa42vMeYGYDVwPXADsMoYM7YyA1NKKaWUUkop5UUilfPwgvLu6vwi0DN/lNcY0wj4Ffi2sgJTSimllFJKKaUqQnk7vj5nTW1Oofw7QiullFJKKaWU+r35A21ulW+eMeYn4Ev7+Y3A3MoJSSmllFJKKaWU19Wcfm+ZtzNqDYSLyNPGmGuB/vZLK4AplR2cUkoppZRSSil1scoa8X0HeB5ARL4DvgMwxnS0XxtZqdEppZRSSimllPIOL21EVRnKWqcbLiKbzk6006JLy2iMaWqMWWCM2WqM2WKMedRODzXG/GKM2WX/W99Ob2eMWWGMyTHGPHVWWZ8aY5KMMZvLqHOYMWaHMWa3Mea5QulLjDHr7cdRY8yMEvK3MMassvN/bYzxt9MHGGPWGmNc+btZG2M6Fioz1Rizz/75V/v1O+w27jLG3FGoju7GmE12Hf82xphi4jD2a7uNMRuNMd0KvVZsuWflL+kcl1juWfmLjfFCyj3f81AVdVSkl158kYH9+3HtqDPXgE6lpfHAPXczcthQHrjnbtJPnQJARHjrjTcYMXQoY0fHsW3rFgD279vHTWOvY+zoODasXweAy+Xi/rvvIjs7u6JDLreht3flob9fzR0vDS5Ia9SkLjc/O4Db/jKIW1+4nIjoegC06RrJHS8P5san+hNY2w+Aug1rMeK+Hl6J/WwhdQO5/t6e3PlYP+54tB9d+zYDoG2HcO54tB9PvD6E8Kg6BcdHNqvH7Q/35db/6U29BrUACAh0cN1d3aHCP0XnLyQkgOtv7cqd91/KHff3omvPJgAMGNyKux64lNvv7cWosR0JCLCubUY2qcvt9/bi1rt7UK9+EAABAQ6uu7mL19qQL7iWH2OubMOtI9pzy4j2dL6kEQDD+rfgpuHtuGl4O+4YHctNw9sB0LhRbW6+pj03XH0JdUMCAPD38yVucGuvtaGw4X8ZyMM/3sk9U28sSLtkcEvu+fJGnl3xIBHtGhWkR3WK4O4vbuCOiddRv2ldAAKC/bnx3yOqxedMKaWUqknK6vjWK+W1oDLyuoAnRSQG6A382RgTAzwHzBeRNsB8+zlAKvAI8I9iypoIDCutMmOML/A+cDUQA9xs14eIXCYiXUSkC9Y07e9KKOZvwL9EpDVwErjHTj8I3AlMzT9QRDYVKnMW8LT9/EpjTCjwMnAp0At4Ob8DB4wH7gPa2I/i2nV1odfvt/NQRrmFlXSOiy23GCXFeF7lXuB5qIo6KkzcmNGM//DDImmffvwRvXr3Yfa8n+jVuw+ffPwRAEsXL+bggQPMnjePl159lddffQ2Ab6Z9zTPPv8D7E/7LpM8+A2DaV19xzchRBAWV9WtWeTavOMj0fy8vkjbgulhW/LCdz19fwPJZ2xlwbQcAug5qyZQ3F7FxyX7a92oKQP+4GJbO3FblcRfH4/GwaO52Jr6zjKnjV9KldzNCw2pzIjGDWVPWcXj/ySLH97gsmu8mJbBwznY62+3pPaglqxburRZrXTwiLJq/i4kfrmLqxAS6dG9CaMNaHNh3kokfrmbyx6s5mZJFr77NAehxaVO++3oDC3/eReduUQD07h/NqmX7vdgKi0eEpWsPM+WHbXwzbwcdL2lE/bqBzFu6j6/mbuerudvZczCNPYfSAOjaPpxZC3azJP4wHds0BKBnxwjitxz3ZjMKbPphB9Me+6FI2om9qXz/7E8cWne0SHqvWzrzzRNzmP+vZXQdEwNA37u7s2Li2mrxOVNKKaXwSOU8vKCsjm+8Mea+sxONMfcCCaVlFJFjIrLW/vk0sA2IAuKASfZhk4DR9jFJIrIGyCumrMVYHePS9AJ2i8heEckFvrLrKhx3HWAwcM6Irz0aOJgzt2gqHNt+EdkIeMqIId9Q4BcRSRWRk8AvwDBjTGOgjoisFBEBJufXcZY4YLJYVgL17LzFlltC/nPOcSnlFj4PpcV4vuVeyHmoijoqTPcePalTt+j1oQW//cao0dZHb9ToOBbMn1+QPjIuDmMMnTp34fTpdJKTk3A4HDidTrKd2TgcDtLT01m0cAEj4+LOqa8qHdmVgjPrrF9HEQKCrBHdgCAHGaey7WTB188Hh78vbreHqNYNyEx3kpaUWdVhFyvzdC5JR08DkJfrJjUpk5A6gaQmZ3LyRNY5x3vcHvz8fHH4+eLxeKgbGkRI3UAO7zt5zrHekJmRS9LxDMBuT0omISEBHNiXithTko4dPUVIHWtE1OMR/Px87PYIdesFEVIngMMH07zWhnxZ2S6SU63PUZ7Lw8lTToLtz1i+1s3rs9O+OOHxCH6+PjgcPng8Qp1gf0Jq+XMkMaPKYy/OofXHcKbnFElL2Z9GajHn2uPy4Ahw4Ah04HZ5qBdVhzphwRxce/ScY5VSSimvkEp6eEFZa3wfA743xtzKmY5uD8AfGFPeSowx0UBXYBXW9Olj9kvHgfDziLc0UcChQs8PY40CFjYaazQxvZj8DYA0EXEVyh9VgbFE2Y/DxaRjjHkQQEQmlJG/uHSMMR8DE0QknpLPcUn5jxVKKzHGCyj3vM9DFdVRqVJTUmjUKAyAhg0bkZqSAkBSUiLhEREFx4WHR5CUmMRNN9/CX55/jtzcXP76yqt8OH48997/AD4+1e+OYQumbeK6R/ty+XWxYAxf/t9iAFbP28n1j/Uj45STuZ/EM/KBXsz5aI2Xoy1enXqBhEWGcOxQyZ2+VYv2Mez6jrhcHn6ctpHLh1/C0l92V2GU5VenbiBh4SEcO1L0a61D50h2bE0EYNXyAwwbFYMrz8OPs7Zy+RWtWbpwrzfCLVVIbX8ahdbieMqZCyaRYcFkOfM4ddrqTMZvOc5VfaNxuT38vHw//btFsWLD77OjuGLSWka8cgWuHBc/vDKfQY/0YfF/V3s7LKWUUqpGKrXjKyKJQF9jzCCgg508R0R+K28FxphgYDrwmIikF15mKSJijKnKPv/NwMdVWF+52R3ei8l/bwnplXKOq+K988Lno8IZY6CMpcWNIyP5ZNJkAA4eOEBi4nFatmzJC88+Q15eHn9+5BGio1tURbhl6nx5CxZO28yudUdp2z2Sobd35dt3lnNgWzIHti0EIKZ3U/ZtTqR+eDA9rmqNMyuPBV9vwpXn9m7wgJ+/L6Nu7cKCOdvJzSk5nuRjp/lywioAoqLrk5megwFG3NQJt1tY9OMOsjJyqyjqkvn5+TLqug4s+GUXubln2nNpv+Z4PMK2zVbHNzkxgy8nWtcuo5rWIzMjB2NgxJhYqz3zd5GVec5kmyrl5/Bh+ICWLIk/TF7emck1baPrs6vQVPQTJ7P55qcdgNUpzszOw2CtCXaLsDThMNlO19nFV0tJu1L4/B5r5U3TLo3JtGcfxL1+FW6Xh9/+vZysVO+t81dKKaX+SJtbASAiC0TkP/bjfDq9flid3in2rtAAifnTa+1/k843aDtvU3Nmc6kHgSNA00KHNLHT8o9viDUdek6htJ/s/B8DKVjTZx3F5T9PJcVyxP652BjLmb/ENhZS0jkuT/7SYjzfci/kPFRFHUUYY+43xsQbY+I/+ejD4g45L6ENGpCcbIWdnJxEaGgoAGFh4SQeP7MOMTHxOGHhYUXy/ufddxj3yKNM/eILrh07lseffIr/vv/BRcdUUWL7NGOXvU5xZ8JRIqKLLjF3+PkS26cZ6xfspe/Idvw4cS1HdqfQ/tImxRVXpXx8DKNu6cK29cfYvaX8Xzu9B7Vk5YK99LmiFYvm7WRT/GG69mlWiZGWj4+PYdR1Hdi2OZHdO5IL0mM7RdCydUPmzthSbL7e/aNZuXQ/fS5rwaL5e9i0/ihdezYt9tiq4mPg6gEt2bE/tWAtL1jXjFo1rcfOA8VPMe/ZIYI1m47Tq1Njlq07wpZdJ+h8SVixx1Z3fe/uzrJPE+h/bw8WvLeCDTO30uOGjt4OSymllKoxKm0upb1m9hNgm4i8XeilWUD+zrt3ADMvpHwROZS/uZQ9WroGaGOsnZn9gZvsuvKNBX4QEWehMoba+e+114EusI+7qNiAn4Ahxpj69kZLQ4Cf7Cm86caY3vb5ub2EOmYBt9s7GfcGTtl5iy23hPzFneOSyi1QRoznW+6FnIeqqKMIEflQRHqISI977ru/uEPOy8BBg5k1w6pq1oyZDBps7Yo8cPAgZs+ciYiwccN6gkNCCqZEA8SvWU1YWBjNo6NxOrPxMT74+PiQ7aw+Iz4ZaU6atLU2FGrWruE5a3h7Dm3Nut/24vEIDn9fEOtCocPf1xvhFjHk2lhSkjNJWHag3Hliukayb8cJnNl5OPzy2yP4+VWD9lzTjpSULBJWn5npH90ylJ69mzPjm424XOduSRDTMYJ9u0/gdLrs9ojVHod3p9Vf0ac5J085Wb+t6AWJphF1OJnuJPPsteZAu5ah7D96ipxcNw5fH2ttswgOx+9vO+QOwy9hz/KDONNz8At0IB5BBPwCy1qNpJRSSlWyGrS5lZFKGr42xvQHlgCbOLMp1AtY63ynAc2AA8ANIpJqjIkA4oE69vEZQIw9PfpLYCDQEEgEXhaRT4qpczjW/YV9gU9F5I1Cry0E3hKReaXE3BJrU6xQYB3wJxHJMcb0BL4H6gNO4LiIxBbKNxGrU/1tobS77fYCvCEin9npPbB2qQ4CfgQetqf0FqzxtTtq72FtXJUF3GWv3S2t3II1vsaYBiWc49LK/f/s3Xl4lNX5//H3HQYIsgoSloCilaqgyCaC2hbQCqIkqKCoVRQQbaXuC2qLhWp/XdW2XwsioLgidQPrQhVRiiKLsrjgguwCSUgIYUmAZO7fH/OQZpmsJBlIP69eczFz5pz73M8TsDlzllnhkROqS8uxMnEreh+qvQ9KkZNXsX+J99x5B8uWLCEzM5PmLVrw87Fj6X/uudx12+1s27qFNm3b8qeHH6Fps2a4O//vwd/y4cKFxMfHM/Gh39H51MgOAnfnxtGj+ONfHqZps2as/e477r37LvLy8rh//AN06x71m6dK9dgv5pRdqRQXjupJu5OOoUGjeuzN2sdHr3/Fjm276Hd5FyzOyMvN493nV5K6MfJ1TQ2bxnP+1V159f8+BuCH3dvSZ/DJ7Nt7gNmTFpN9KEuDWxza6daJxzVj+A1nkrZ1V/7hTwv//S11QnH0H3wKDRrWY1/OAdK27OLlYElwqG4cF4/owcvTlxEOO4kdmnFuUifCeWHeeHFV1EOxyq1h3bLrlHY97ZoyfEQP0lJ248EJEQvnr6Xf+R0JheLIzo4MFLd+n8W7b0WWBYdCcVx8+em8/MKKyPW0b8q5A0+KXM9rX7Ijo3LXU++40g7/L1ublg0ZOuAktu/Izv/ZLFqxhQ1bsjivz3Fs276Hz7/dXqhNqI4xuN+JzJ73LWGHti0b8pNexxIOO3M/XEdmkcOlKmLP3xcf0vUk/fY8ju3elgbN4tmTkc3CKUvJydrHeXeew1HNGrBv9z5SvtnOrFsii49C9UMMe2QQL/7yX4TzwrTr2obz7/oR4dwwc379btRDsSpi3OKfH1J7ERGpNkfEJ7V/uX9utQwW73hoQI1ff7UNfEWORBUd+B7ODnXge1g5xIHvYecQB76Hk0Md+B5uDnXge7jRwFdE5LB1ZAx876umge/van7gq3VUIiIiIiIiUlwtmiQ9/L4vRURERERERKQKaeArIiIiIiIixYWr6VEOZjbQzL42szVmNi7K+7eb2ZdmtsrM5pnZcaXF08BXREREREREDhtmVgd4DLgA6ARcYWadilRbDvR09y7AS8AfS4upga+IiIiIiIgUF3xdYJU/ytYLWOPua919P5Fv3kkunJrPd/eDX0vxMdCutIA63EpERERERESKi93hVonApgKvNwNnllJ/FJGvLy2RBr4iIiIiIiJSY8xsDDCmQNEUd59SyVg/A3oCPymtnga+IiIiIiIiUlw5D6KqqGCQW9pA93ugfYHX7YKyQszsPOB+4Cfuvq+0PrXHV0RERERERA4nS4GOZna8mdUDhgNzClYws27A40CSu6eWFVAzviIiIiIiIlJcjPb4unuumY0F5gJ1gOnu/oWZTQSWufsc4E9AI+CfZgaw0d2TSoqpga+IiIiIiIgUF7vDrXD3N4E3i5SNL/D8vIrE01JnERERERERqdU04ytSW7VoEOsMqk67JrHOoGrlVtNJETGwd9ryWKdQpeyoevje/bFOQ0RE5PBQe35l0cBXRESkoHGLfx7rFERERKSKaeArIiIiIiIixcVwj29V0x5fERERERERqdU04ysiIiIiIiLF1aIZXw18RUREREREpLhadLiVljqLiIiIiIhIraYZXxERERERESmuFi111oyviIiIiIiI1Gqa8RUREREREZHiatGMrwa+IiIiIiIiUpwOtxIRERERERE5MmjGV0RERERERIrxWrTUudpmfM2svZnNN7MvzewLM7slKG9uZu+Y2bfBn0cH5Seb2SIz22dmdxaJNd3MUs3s8zL6HGhmX5vZGjMbV6D8XDP71MxWmNlCMzuxhPY9zOyzoP3fzMyC8mHBNYTNrGdQNiCIt8LMdgf9rjCzp4P37w3ifG1mA8rKsUge9c3sxaDOYjPrUOC9qHGLtD8+aLcmiFOvrLjlvI8VjlvR+1ATfVSl8fffT99zzuaSpMH5ZTszM7nsC3uCAAAgAElEQVRh1EgGDxzADaNGkrVzJxD5D8fvH3qIiwYMYOiQZFZ/+QUA69etY/jQSxk6JJmVK5YDkJuby5iR15GdnV0daZdL46bxDBt9BtfeejYjbjmbbmcdC8APT23FiFvO5vYHz6dVYpP8+m2PbcY1vzyLq37Rm2YtjgKgfnyIS6/rARaTS8jXuFE9hiV34trhpzNi+Ol069IagD5ntGPMNT24+rIuXH1ZF44/thkAbVs35prLu3DV0NNo1jQegPr16nDp4FNidg0FNW5Uj2GXnMq1P+vGiKu60e30NoXe79GtLXfcfDYN4iOfbXb8QQtGXNWNyy89lfigrGnTeC4aeFKN5x7NBXf/iLGvXMnI6Zfkl/W94QxGz7iU66ZezMUTz6V+w3oAJJ6awHVTL+aayUkcHfz9q9+wHpf9cWDM/56JiIjI4as6lzrnAne4eyegN3CTmXUCxgHz3L0jMC94DZAB3Az8OUqsp4CBpXVmZnWAx4ALgE7AFUF/AJOAq9y9K/A88KsSwkwCrgc6Bo+DfX4OXAIsOFjR3ee6e9cg5rKD8d39mqDf4UDnIMY/zKxOGTkWNArY4e4nAo8AfwiuMWrcKO3/ADwStN8RxCsxbgXuY4XiVvI+1EQfVSb54iFMmjKlUNn0qU/Qq3cfXn97Lr1692Ha1CcAWLhgARs3bOD1t99m/IQJPDhhIgD/nPUid997H49NfpwZTz4JwKyZM7lwcBINGjSo6pTLLRwO88GbX/HUox/y/KSP6dr7WJonNGR7ym7mPLeczet3FKrf80cdeGXGJ7z/xlec3qs9AL37ncDi99dCjD8sDIedDz7cwFMzV/L8y5/R9dTWND86cm8/XbWFZ2at4plZq1i3MROAnl3b8Mq/vuL9hes5vXMrAHr3bMfiT76P2TUUFA47H/xnHU89u5znZ62ia5c2NG8euZ7GjerR4dhmZGXl5NfvdnobnntxJas+T+GUH7YE4Jzex7Jw0YaY5F/UZ29/yz/vmVuobP0nW5h23Ss8OfpVMjZn0fuq0wE4Y9hpvDRuLvP+bzFdk04G4Kyru7LouRUx/3smIiJS67hXzyMGqm3g6+5b3f3T4PkuYDWQCCQDM4JqM4AhQZ1Ud18KHIgSawGRgXFpegFr3H2tu+8HZgZ9QeTXoYNTU02BLUUbm1kboIm7f+yROf2nC+S22t2/LteFRyQDM919n7uvA9YE+ZWWY9H2B+/RS8C5wexzSXELXocB/YN2UOAelxK3oKg5VjJuhe5DTfRR7E4foh49z6BJ02aFyua/9x5JQyJdJQ1JZv68efnlg5OTMTO6nN6VXbuySEtLJRQKkZOTQ3ZONqFQiKysLD54fz6Dk6s83QrZs2s/qVt2AXBgfx4ZqXto3CSejLQ97Ni+t1j9cF6YunXrEKpbh3A4TNPmDWjcNJ7N63YUq1vT9uw9QOr2PQAcOBAmY0c2jYMZxGjCYaduKI5Q3TjCYadpk/o0blSPzVuyairlUu3Ze4DUtIPXk0fGjr3519P3x8ez4MP1hcaA7k6dOkYoFEde2Els24Q9ew+QuTMnSvSat3nVNrKz9hUqW7/sezwcuYotX6bSuGVkFUE4L0woPkTd+BDh3DDN2jamcUJDNq3cVuN5i4iI1HrhanrEQI3s8Q2WpXYDFgOt3H1r8NY2oFUVdZMIbCrwejNwZvB8NPCmmWUDWURmoKO131ykfeIh5PJxCbGi5mhmE4Fl7j6HAtfi7rlmthNoUVpcM3uTyHXuBzLdPTdK3yXF3V4k92g5tqhE3Ireh5roo9plpKfTsmUCAMcc05KM9HQAUlNTaNW6dX69Vq1ak5qSyvArruRX945j//79/Po3E5gyaRKjx9xAXNzhc/Zck2bxJLRtzNZNmSXWWfzBOgYOO43c3DBvzVrFTwadxMJ31tRgluXTpHF9Eo5pyNaU3bRt05iup7am00ktSUndw/sfrWffvjwWf/I9A889kdy8MG+9u4afnHUcCxdvKjt4DDRpXJ+Elo3YmrKbH5zQnN2795NW5IOJJcs2M+ziU9m9Zz9vzv2GwYNO4o23volRxhXX5YIfsnr+WgA+fm4lF937Ew7sy+WN331Av5+fyX+mfRLjDEVERORwV+0DXzNrBLwM3OruWQUnGN3dzawm5rpvAwa5+2Izuwt4mMgg8bDh7uMPsf0gADM7pmoy+t9hZmOAMQD/N2kSo64fU5WxodikemFt2rZl2oynAdi4YQMpKds44YQTuO+euzlw4AA33XwzHTocX2U5VVTdenVIuqor89/4iv378kqsl7Z1Fy9MXgxAYoej2ZO1DwMuGt6FvDzng7e+Zu/u/TWUdXR1Q3EkDfgh8z9cz/4Deaz8PIWPl23GHc4+sz19z+rA3PnfkZa+lxdeiRwpkNimMXv27o9cy/kdyQuWTe/NLrY4pcbVrRtH0oUnM3/BWsJh58ye7XjptS+K1duwaScbZq4EoNPJLVm3fgdHHx1Pz+6J5OTkMn/BOnJzD8/vK+hz1emE88J8+e53AKR+l8EzN70OQLsurdmdvhcMksb3I5wb5r1Ji9m74/CYyRYRETni6XCr8jGzukQGvc+5+ytBcUqwrPjg8uLUSsZub/89XOpG4HugfYEq7YDvzawlcLq7Lw7KXwTOCvaBHmw/MWjfrmj7yuRWUi6llJfY3sxCRJZnp5ezfTrQLGhXtE5JccuTe2XiVvQ+1EQfxbj7FHfv6e49q2LQ27xFC9LSIn+t09JSad68OQAJCa1I2fbf5ZgpKdtIaJVQqO3f//ooY2++heeffZZLhg7ltjvu5PHH/nHIOVVWXJyRdGVXVq/Yypovyv9PtXe/E/h4/lr6nPsDPnj7Gz5btplufY6txkzLFhdnJA08idXfbmfN2sjOib3ZB/L/e/7Zl6m0TmhUrF3vnu34eNn39DmjHR98tIHPvkzNPxwrluLijKRBJ7P66zTWfJdBs6bxNG1Sn2uu7Mroa3vQuFF9fnZFV446qm5+m1Aojs6nJLBi1TbOOvNY3vr3t3y/JYtTTmoZwysp2akDOvKDPsfy+kPvR33/rJ915aNnlnP2iG68//hSVr7xNT0u6VyzSYqIiMgRoTpPdTZgGrDa3R8u8NYcYETwfAQwuzLx3X3TwcOl3H0ysBToGJwKXI/IgUdziByQ1NTMfhg0/WmQU16B9uOD5ddZZtY7yP2ayuYW9Ds8OIn4eCIHZS0pJcdo7Q/eo6HAe8G+45LiFrwvDswP2kHhe1xS3IKi5ljJuBW6DzXRR7E7XQ369uvPnNciac95bTb9+vePlPfvx+uzZ+PurFq5gkaNG+cviQZYtnQJCQkJHNehAzk52cRZHHFxcWTnxO5k5/Mv6Ux62h4++bD8hyB16taWdV9vJyf7AKG6dcAje0zr1o12DlvNOb/fD0jfkc0nK7fmlzUsMCg88fjmbM8ovES400ktWbdhBzn7cgmFIvm7R/b/xtr5555IekY2nyyPHFmwPX0vk6YuZepTnzD1qU/YtXsfz76wgr17/zszfUb3RJav3Eo47ISCa3DIf344Of6MRM4cfhov3/8OuVFWGpw64ETWLt5Ezq791K0fwsOOh5269fUtfSIiIlXGq+kRA9X5G8LZwNXAZ2a2Iii7D/g9MMvMRgEbgMsAzKw1kdORmwBhM7sV6BQsj34B6AscY2abgQfcfVrBzoJ9n2OBuUAdYLq7fxHEvh542czCRAbCI0vI+RdETpBuALwVPDCzi4G/Ay2BN8xshbtH/SqhIJcvzGwW8CWR061vcve8IFZJORbc4zsNeMbM1hA51Gt4OeK+CYx29y3APcBMM3sQWB7Eo6S4ZtYWmOrug0q7jxWNW5n7UEN9VJl77ryDZUuWkJmZyU/79eXnY8cy8vrR3HXb7bz28ku0aduWPz38CAA/+vFPWLhgARcNHEB8fDwTH/pdfhx354nJk/njXyKfEV067DLuvfsu8vLyuH/8A1WddrkkHteMzt0TSdu6i6vH9gFg4b+/pU4ojv6DT6FBw3pcPKI7aVt28fJTkT2WobpxdO6RyMvTlwHwyYfruXhEd8J5Yd54cVVMrgMgsXVjOp/UkrT0PVx9WZfItXy8kZM7HkPLYxoCTlbWPt75YG1+m1Aojs4nt+Tl11cD8MnKLVx84SmRa3n321hcRr7ENo3pfEoCadv3cPUVkdOOF360kXUbSj5IrGHDerRu1YhFSyJ7lZev3MpVl5/Ovn25zH5jdY3kXZLBv+rLsV3b0KBpPL+YNZyFT31K7ytPp07dOC7/c+Rw/S1fpvLvRz4CIFS/DqcO6Misu94GYOk/P2fY788nLzfM6w++H6vLEBERkcOY1aYvJRY5VDl54VrzD+KxX78T6xSqTrsmZdc5khym+2krI/fVr2KdQpW7Z/6osiuJiIgcmiPi2+f/POKlavnd+M4ZQ2v8+rUmTERERERERIqrRZOkh9/GLhEREREREZEqpBlfERERERERKa72TPhqxldERERERERqN834ioiIiIiISDFee8591cBXREREREREotDhViIiIiIiIiJHBs34ioiIiIiISHG1Z8JXM74iIiIiIiJSu2nGV0RERERERIrT4VYiIiIiIiJSq9Weca8GviK1VvP4WGdQdfbup2nnVrHOospsn/h+rFOoUhZfN9YpiIiIiJRKA18ROezVpkFvbXTP/FGxTkFERESqg77OSEREREREROTIoBlfERERERERKU6HW4mIiIiIiEitVnvGvVrqLCIiIiIiIrWbZnxFRERERESkGNfhViIiIiIiIiJHBs34ioiIiIiISHGa8RURERERERE5MmjGV0RERERERIoLxzqBqqOBr4iIiIiIiBSnpc4iIiIiIiIiR4ZqG/iaWXszm29mX5rZF2Z2S1De3MzeMbNvgz+PDspPNrNFZrbPzO4sEmu6maWa2edl9DnQzL42szVmNq5AeX8z+9TMPjezGWYWdabbzI43s8VB+xfNrF5Q/uOgfa6ZDQ3KTjOzFcEjw8zWBc/fDd4fEVzjt2Y2okAfPczss6CPv5mZRcnDgvfWmNkqM+te4L2ocYu0L+kelxi3SPuoOVYmbkXvQ030UZXG338/fc85m0uSBueX7czM5IZRIxk8cAA3jBpJ1s6dQOQ4+N8/9BAXDRjA0CHJrP7yCwDWr1vH8KGXMnRIMitXLAcgNzeXMSOvIzs7u6pTLrfGjesz7LLTufa6noy4tifduicCcNbZHbhmRA+uvqYHlw49jYYN6wHQseMxjLi2J5cP70p8fOSfWNOm8Vx00Skxu4aDcg/s55mH7+SpP97C9N+PZeFbzwOQmZ7Csw/fyRMP3sCcp/5IXu4BAD5d8C+e/P0veenxifllm9d+yXuvTo3ZNRQ06P6+/PKNEYx69rL8spP6n8Co5y7jng9voPXJLfPLE7u0ZuQzwxgx/RKObtcUgPqN6nH5oxdClf+LEBERkVrFq+kRA9U545sL3OHunYDewE1m1gkYB8xz947AvOA1QAZwM/DnKLGeAgaW1pmZ1QEeAy4AOgFXmFknM4sDZgDD3f1UYAMQdcAI/AF4xN1PBHYAo4LyjcC1wPMHK7r7Z+7e1d27AnOAu4LX55lZc+AB4EygF/DAwQEcMAm4HugYPKJd1wUF3h8TtKGMuAWVdI+jxo2ipBwrFLeS96Em+qgyyRcPYdKUKYXKpk99gl69+/D623Pp1bsP06Y+AcDCBQvYuGEDr7/9NuMnTODBCRMB+OesF7n73vt4bPLjzHjySQBmzZzJhYOTaNCgQVWnXG7hsPPB+9/x1JPLeP655XTt2pbmLY5i2dJNPD3jE555+hPWfpdBnz7HAdCteyLPPfspq1Zu4ZRTEgA455zjWbhwfcyu4aA6obpcftNvufbuvzLirkdZv/pTtqz/mgWvz6BH3ySu/9XjxB/ViFUfvwvAl598wLV3/5XE409m3VfLcXcWzX2RPudfHuMrifjsja+Zddsbhcq2f5fBq/fOZdOKrYXKe13RhX/e/ibzHv2Ibhd3AuCs67qzaMbymP0fj4iIiEhNq7aBr7tvdfdPg+e7gNVAIpBMZCBK8OeQoE6quy8FDkSJtYDIwLg0vYA17r7W3fcDM4O+WgD73f2boN47wKVFGwezgf2Bl6Lktt7dV1H+7d0DgHfcPcPddwR9DjSzNkATd//YI98G/fTBPopIBp72iI+BZkHbqHFLaF/sHpcSt+B9KC3HisatzH2oiT6qTI+eZ9CkabNCZfPfe4+kIckAJA1JZv68efnlg5OTMTO6nN6VXbuySEtLJRQKkZOTQ3ZONqFQiKysLD54fz6Dk5OrOt0K2bNnP6mpuwE4cCCPjIy9NG5Un/378/Lr1K0blz92cnfq1IkjVLcOeWEnMbEpe/bsJzMzdrPWB5kZ9epHPkQI5+WRF45cw8ZvV3HS6WcD0PmM/qz57ONIA3fywnkc2L+POnVCfLnsfY4/pQcNGjaOSf5FbVqxlZysfYXK0jdkkrFxZ7G64dwwofgQofgQeblhmiU2oUlCIzYu31JT6YqIiMiRKuzV84iBGjncysw6AN2AxUArdz84JbENaFVF3SQCmwq83kxkFnA7EDKznu6+DBgKtI/SvgWQ6e65BdonVmEuicFjc5RyzOxGAHefXEb7aOWY2VRgcnCNJd3jktoXnCIqMcdKxK3wfaihPqpVRno6LVtGZjyPOaYlGenpAKSmptCqdev8eq1atSY1JZXhV1zJr+4dx/79+/n1byYwZdIkRo+5gbi4w2cLfpMm9UlIaMTWrVkAnH1OBzp3asW+/XnMenElAEsWb2LYsC7s3rOfN99YzeCkTrzxr9WxTLuQcDiPp/98B5nbt9LtnEE0O6YN9Rs0JK5OHQAaN2vB7p2Rz9e6/ehCnnvkLlq0PpbE40/m1Wm/Y+iNv4lh9pW36OnlXDS+P7n7cvnXhPfo98s+LHh8aazTEhERkSNBLTrcqtoHvmbWCHgZuNXdswpus3R3N7NqvZtBH8OBR8ysPvBvIK+MZjUuGPAeSvvRJZRXyz2uwZ/dEf2vzcygjK3Fbdq2ZdqMpwHYuGEDKSnbOOGEE7jvnrs5cOAAN918Mx06HF8T6UZVt24cSUmdmT//u/zZ3g8XrufDhevp1as93bq15aOPNrBhww42bNgBQKdOrVi3NoOjj25AzzPak5OTy/z31pCbG7sz8ePi6nDt3Y+Ss3c3r03/f2Skbi6xbucz+tH5jH4AfPT2TLr/6CLWrf6EL5bOp3GzY+iXPBI7jD6YKE3qt+k8c/2rALTv2oY96XvBIPm355GXG+a9vy1i747Yz8qLiIiIVKdq/c3NzOoSGfQ+5+6vBMUpB5fXBn+mVjJ2e/vv4VI3At9TeCa3XVCGuy9y9x+5ey9gAfBNEGNu0H4qkE5k+WyoaPtKKCmX74PnxXKsQPuo11hESfe4PO1Ly7GicStzH2qij0LMbIyZLTOzZdOemBKtSoU0b9GCtLRI2mlpqTRv3hyAhIRWpGzbll8vJWUbCa0SCrX9+18fZezNt/D8s89yydCh3HbHnTz+2D8OOafKioszkpI6s3p1Kmu+3V7s/dWrU+n4w5aFykKhODqf2ooVK7Zw1lkdeOutr/j++535+35jLf6oRhx74mlsWf8V+7L3EM6LDOZ3ZabTqGnzQnV370xn68Zv6dilN0vnz2bwiLuo36ARG75dFYvUD9lZ13bnwyc/4ZxRPZn/2MesnLOanpedFuu0RERE5HClw63KFuyZnQasdveHC7w1h/8eLjUCmF2Z+O6+6eDhUsFs6VKgo0VOZq4HDA/6wswSgj/rA/cAk4MYA4L2o4N9oPOJLIU+pNyAucD5ZnZ0cNDS+cDcYAlvlpn1Du7PNSX0MQe4JjjJuDewM2gbNW4J7aPd45Li5isjx4rGrcx9qIk+CnH3Ke7e0917jrp+TLQqFdK3X3/mvBbpas5rs+nXv3+kvH8/Xp89G3dn1coVNGrcOH9JNMCypUtISEjguA4dyMnJJs7iiIuLIzsndrNx5w/4IekZe/nkk//OjjZr9t8Dt048sQUZGXsLtTnjjPYs//R7wmEnFIoDj+z/DdWtU2N5F7V3905y9gb7lffvY/03K2nRqj3tTzyNr1d+CMAXS9/jxNPOLNRu4ZvPc84FVwKRk6ENw8w4sL/w/tojwamDfsh3izaSk7WPuvEhPOx42Kkbr69zFxERkdqvOn/jORu4GvjMzFYEZfcBvwdmmdkoIicsXwZgZq2BZUATIGxmtwKdguXRLwB9gWPMbDPwgLtPK9iZu+ea2VgiA6E6wHR3/yJ4+y4zu4jIQH+Su79XQs73ADPN7EFgOZGBO2Z2BvAqcDQw2MwmuHvnki7c3TPM7LdEBuMAE9394OFcvyBySnUD4K3gUXSP75vAIGANsBe4rqy4Rfb4Rr3HJcUN2q8ITqguMceKxq3MfaihPqrMPXfewbIlS8jMzOSn/fry87FjGXn9aO667XZee/kl2rRty58efgSAH/34JyxcsICLBg4gPj6eiQ/9Lj+Ou/PE5Mn88S+Rz4guHXYZ9959F3l5edw//oGqTrtcEhOb0Llza9LSdnP1NT0AWPifdZx6WmuaNz8Kdycrax/vvvNNfpuGDevRuk1jFi3aAMDy5d9z1c+6s29fLrNf+yJqPzVhd9YO3nruUcLhMLhzUtez+UHnM2jRqj2vP/1nFr75HAmJJ3Ba75/mt0nZvBaAVu1/AMAp3X/Mk3+8mcbNjqHXuZfE5DoOSppwLsd2b0uDZvH8YvbPWDh1GTlZOZx3+zkc1awBw/5yASnfpOef/ByqH+K0QSfx4i2R10teWMmwhwcRPhBmzgPvxvJSRERE5DDmMTqIqjqY16INyyKHKiev9vzrfuyR/8Q6hSrTtHNVnYF3eNg+8f1Yp1Clxi26MdYpiIiIHGlKPwTmMPGHnz5ZLb8b3/POdTV+/UfG6SwiIiIiIiIilaTNXSIiIiIiIlJcrVkLqRlfERERERERqeU04ysiIiIiIiLF1Z7jbzTjKyIiIiIiIrWbZnxFRERERESkuFr0DUAa+IqIiIiIiEhxWuosIiIiIiIicmTQjK+IiIiIiIgU47VoqbNmfEVERERERKRW04yviIiIiIiIFFeL9vhq4CsiIiIiIiLF1aKlzhr4itRWodqzk2H7xPdjnYKIiIiIHME08BURiYFxi26MdQoiIiIipatFS51rz5SQiIiIiIiISBSa8RUREREREZHiatGMrwa+IiIiIiIiUoy+x1dERERERETkCKEZXxERERERESmuFi111oyviIiIiIiI1Gqa8RUREREREZHitMdXRERERERE5Migga+IiIiIiIgUF/bqeZSDmQ00s6/NbI2ZjYvy/o/N7FMzyzWzoWXF01JnERERERERKSZWX2dkZnWAx4CfApuBpWY2x92/LFBtI3AtcGd5YlbbjK+ZtTez+Wb2pZl9YWa3BOXNzewdM/s2+PPooPxkM1tkZvvM7M4isaabWaqZfV5Gn1HrldRnlPbHm9ni4FOFF82sXlBe7NMEMzvNzFYEjwwzWxc8fzd4f0TQ37dmNqJAHz3M7LOgj7+ZmUXJw4L31pjZKjPrXuC9qHHLc72lxS3SPmqOlYlb0ftQE31UpfH330/fc87mkqTB+WU7MzO5YdRIBg8cwA2jRpK1cycQ+Q/H7x96iIsGDGDokGRWf/kFAOvXrWP40EsZOiSZlSuWA5Cbm8uYkdeRnZ1d1SmXW+NG9Rh26Wlce3UPRlzdnW5d2xZ6v0f3RO649Uc0iI98ftbxxBaMuLo7lw/rQnxQ1rRpPBcNOrnGc49m0P19+eUbIxj17GX5ZSf1P4FRz13GPR/eQOuTW+aXJ3ZpzchnhjFi+iUc3a4pAPUb1ePyRy+EKv9bJCIiIiJF9ALWuPtad98PzASSC1Zw9/XuvgoIlydgdS51zgXucPdOQG/gJjPrBIwD5rl7R2Be8BogA7gZ+HOUWE8BA8vRZ0n1SuqzqD8Aj7j7icAOYFRQfvDThOcPVnT3z9y9q7t3BeYAdwWvzzOz5sADwJlEfmgPFBhsTwKuBzoGj2j5XlDg/TFBG8qIW57rjRo3ipJyrFDcSt6HmuijyiRfPIRJU6YUKps+9Ql69e7D62/PpVfvPkyb+gQACxcsYOOGDbz+9tuMnzCBBydMBOCfs17k7nvv47HJjzPjyScBmDVzJhcOTqJBgwZVnXK5hcPOBwvW8tQzn/D8zJV0Pb0NzZsfBUQGxR2OO5qsrJz8+t26tuW5F1aw6rOtnHJSAgDnnHUcCz9aH4v0i/nsja+Zddsbhcq2f5fBq/fOZdOKrYXKe13RhX/e/ibzHv2Ibhd3AuCs67qzaMZyqD1nPIiIiIiULnZLnROBTQVebw7KKq3aBr7uvtXdPw2e7wJWE0k2GZgRVJsBDAnqpLr7UuBAlFgLiAyMy+qzpHpR+ywomA3sD7wUJbcKfZoADADecfcMd98BvAMMNLM2QBN3/9gj6waejpZLkO/THvEx0CxoGzVuBa63pLgF70NpOVY0bmXuQ030UWV69DyDJk2bFSqb/957JA2JfCCVNCSZ+fPm5ZcPTk7GzOhyeld27coiLS2VUChETk4O2TnZhEIhsrKy+OD9+QxOTi7WX03as/cAqWl7ADhwII+MjGwaN6oHQN+f/IAF/1lXaAzoDnXqxBEK1SEvHCaxbRP27DlAZmZOlOg1b9OKreRk7StUlr4hk4yNO4vVDeeGCcWHCMWHyMsN0yyxCU0SGrFx+ZaaSldERESk1jKzMWa2rMBjTHX3WSN7fM2sA9ANWAy0cveD0yvbgFY1kEJ5+mwBZLp7bvD6UD5VKOkTisTgedFyzOxGAHefXEb7qJ98mNlUYLK7L6Pk6y2pfcHprhJzrETcCt+HGuqjWmWkp9OyZWTG85hjWpKRng5AamoKrVq3zq/XqlVrUlJc6akAACAASURBVFNSGX7Flfzq3nHs37+fX/9mAlMmTWL0mBuIizt8zp5r0qQ+CS0bsnXbLn5wQnN2795H2vY9heosWbqJYZecyu49+3nz7a8ZfOEpvPHmVzHK+NAseno5F43vT+6+XP414T36/bIPCx5fGuu0RERERGpWOQ+iqih3nwJMKaXK90D7Aq/bBWWVVu0DXzNrBLwM3OruWQW3Wbq7m1mNLhyMRZ/lEQx4D6X96BLKq+V6a+I+Hq4/q4owMyhja3Gbtm2ZNuNpADZu2EBKyjZOOOEE7rvnbg4cOMBNN99Mhw7H10S6UdWtG0fShacw/4O1hMPOmb3a89Irxbfbb9iYyYaNKwDodEoC69ZncPTRDejZox05OQeY/8FacnPLu2gitlK/TeeZ618FoH3XNuxJ3wsGyb89j7zcMO/9bRF7d8Ru/7WIiIhIjYjd9/guBTqa2fFEBrzDgSsPJWC1TimZWV0ig97n3P2VoDjl4PLa4M/USsZub/89XOrGMqpH7dPM5gbtpwLpRJbPHvww4FA+VSjpE4rvg+dFyyvSvjyffJR0j8vTvrQcKxq3MvehJvoopOBSi2lPlPbBU/k0b9GCtLRI2mlpqTRv3hyAhIRWpGzbll8vJWUbCa0SCrX9+18fZezNt/D8s89yydCh3HbHnTz+2D8OOafKioszki7qxOqv0ljzXTrNmsbTtEk81/ysO6NHnkHjRvX52ZXdOOqouvltQqE4OndqxYqVWzmrz3G8Nfdrvt+SxSknJ5TS0+HrrGu78+GTn3DOqJ7Mf+xjVs5ZTc/LTot1WiIiIiK1VrAKdywwl8iW2Vnu/oWZTTSzJAAzO8PMNgPDgMfN7IvSYlbnqc4GTANWu/vDBd6aAxw8eXcEMLsy8d1908HDpcoxWxq1T3cfELQfHewDnQ8MLVqvEuYC55vZ0cFBS+cDc4MlvFlm1ju4P9eU0Mcc4JrgJOPewM6gbdS45b3eUuLmKyPHisatzH2oiT4Kcfcp7t7T3XuOuv7Qtxf07defOa9Fuprz2mz69e8fKe/fj9dnz8bdWbVyBY0aN85fEg2wbOkSEhISOK5DB3JysomzOOLi4sjOid3M4vnndSQ9Yy+fLI98ZrA9fS+Tpixm6vSlTJ2+lF279/Hs88vZu/e/W/PP6NGO5Su2EA47oTqR/8S4RwbER5pTB/2Q7xZtJCdrH3XjQ3jY8bBTN17fBCciIiL/A2L4Pb7u/qa7/9Ddf+DuDwVl4919TvB8qbu3c/eG7t7C3TuXFq86f3s7G7ga+MzMVgRl9wG/B2aZ2ShgA3AZgJm1BpYBTYCwmd0KdAqWR78A9AWOCUb1D7j7tKIdllIvap9R3APMNLMHgeVEBu6Y2RnAq8DRwGAzm1DajXX3DDP7LZEpeoCJ7n7w0K1fEDl9ugHwVvAousf3TWAQsAbYC1xXVtwie3xLut6ocYP2K4ITqkvMsaJxK3MfaqiPKnPPnXewbMkSMjMz+Wm/vvx87FhGXj+au267nddefok2bdvyp4cfAeBHP/4JCxcs4KKBA4iPj2fiQ7/Lj+PuPDF5Mn/8S+QzokuHXca9d99FXl4e949/oKrTLpfEtk3o3KkVaWl7uPqqbgAs/HA969bvKLFNw4b1aN26MYsWbwRg+cotXHVFV/bty2X266trJO+SJE04l2O7t6VBs3h+MftnLJy6jJysHM67/RyOataAYX+5gJRv0vNPfg7VD3HaoJN48ZbI6yUvrGTYw4MIHwgz54F3Y3kpIiIiIlJB5rFbty1y2MnJq6Yd/DHw2N8/jHUKVebAi6WuXDkijVtU1g4NERERqcVKPwTmMPHQyX+plt+N7//qjhq/fq3XExERERERkeJqz5xQ9R5uJSIiIiIiIhJrmvEVERERERGR4mrRtljN+IqIiIiIiEitphlfERERERERKU57fEVERERERESODJrxFRERERERkWK8Fs34auArIiIiIiIixXk41hlUGS11FhERERERkVpNM74iIiIiIiJSXC1a6qwZXxEREREREanVNOMrIiIiIiIixXntmfHVwFekljrwwuexTqFqxVmsMxARERH531KLljpr4CsiR4Rxi26MdQoiIiIicoTSwFdERERERESK8Vq01FmHW4mIiIiIiEitphlfERERERERKU57fEVERERERKRWq0UDXy11FhERERERkVpNM74iIiIiIiJSnA63EhERERERETkyaMZXREREREREinHt8RURERERERE5MmjGV0RERERERIqrRXt8NfAVERERERGR4rTUuWxm1t7M5pvZl2b2hZndEpQ3N7N3zOzb4M+jg/KTzWyRme0zszuLxJpuZqlm9nkZfUatZ2bDghzCZtazlPblzs3MWpjZiuCxzcy+L/C6npkNNLOvzWyNmY0r0MfxZrY4KH/RzOqVkMu9QZ2vzWxAgfKocYu0rR/EXhP01aGsuEXaR82xMnEreh9qoo+qNP7+++l7ztlckjQ4v2xnZiY3jBrJ4IEDuGHUSLJ27gTA3fn9Qw9x0YABDB2SzOovvwBg/bp1DB96KUOHJLNyxXIAcnNzGTPyOrKzs6s65XIb9Ku+/PKtaxn1/OX5ZSf1P4FRL1zOPYtupPXJLfPLE7u0ZuSzlzHiqUs5un1TAOo3qsflf7sIrMZTFxEREREppDr3+OYCd7h7J6A3cJOZdQLGAfPcvSMwL3gNkAHcDPw5SqyngIHl6LOkep8DlwALymhf7tzcPd3du7p7V2Ay8EiB13nAY8AFQCfgiuDaAf4Q1D0R2AGMKppEUHc40Dm4nn+YWR0zq1NK3IJGATuCPh4J+iwxbpT2JeVYobhl5BvLPqpM8sVDmDRlSqGy6VOfoFfvPrz+9lx69e7DtKlPALBwwQI2btjA62+/zfgJE3hwwkQA/jnrRe6+9z4em/w4M558EoBZM2dy4eAkGjRoUNUpl9tn//qaWbf+q1DZ9rUZvHrPXDYt31KovNeVp/PP299g3iMf0u3iyO0/a2QPFj31KdSeDwpFRERE/qd4Nf0vFqpt4OvuW9390+D5LmA1kAgkAzOCajOAIUGdVHdfChyIEmsBkcFnWX1Grefuq93963KkXeHcStALWOPua919PzATSDYzA/oDLxXtI0oeM919n7uvA9YEMaPGLeM6XgLODfouKW6+MnKsaNzK3Iea6KPK9Oh5Bk2aNitUNv+990gaEvmxJA1JZv68efnlg5OTMTO6nN6VXbuySEtLJRQKkZOTQ3ZONqFQiKysLD54fz6Dk6P9aGvOphVbycnaV6gsfX0mGRszi9UN54YJ1Q8Rig+RlxumWWITmiQ0YuOnW4rVFRERERGpaTWyxzdYrtoNWAy0cvetwVvbgFY1kUM5VVVuicCmAq83A2cCLYBMd88tUJ4IYGZJQE93Hx+UfVykfWLwPFpczGwisMzd5xTs391zzWxn0HdpcQ8qMcdKxq3QfaihPqpVRno6LVsmAHDMMS3JSE8HIDU1hVatW+fXa9WqNakpqQy/4kp+de849u/fz69/M4EpkyYxeswNxMUdOYeuL5rxKRf95lxy9+Xyr9/Mo9/NfVjw+JJYpyUiIiIihyDs4VinUGWqfeBrZo2Al4Fb3T0rMhEX4e5uZoflQsiazi0YsM45hPbjqzAdqSJmBlb6Jtc2bdsybcbTAGzcsIGUlG2ccMIJ3HfP3Rw4cICbbr6ZDh2Or4l0Ky3123SeGfUKAO27tmHP9r0AJD/4U/Jyw7z3t4/YmxG7/coiIiIiUnHhWrRnrVqnlMysLpFB73Pu/kpQnGJmbYL32wCplYzdvsBhUjdWMsaTQfs3qzI34HugfYHX7YKydKCZmYWKlJe3fUnlJbYP+moa9F2e9qXlWNG4lbkPNdFHIWY2xsyWmdmyaU9MiValQpq3aEFaWuSvTlpaKs2bNwcgIaEVKdu25ddLSdlGQquEQm3//tdHGXvzLTz/7LNcMnQot91xJ48/9o9DzqkmnTWyBx9O/4RzRvdk/v8tYuXsL+l52WmxTktERERE/odV56nOBkwDVrv7wwXemgOMCJ6PAGZXJr67bzp4mJS7T65kjOuC9oOqMjdgKdAxOFW4HpFDmea4uwPzgaFl9DEHGB6ccHw80BFYUlLcEtofvI6hwHtB3yXFzVdGjhWNW5n7UBN9FOLuU9y9p7v3HHX9mGhVKqRvv/7MeS3S1ZzXZtOvf/9Ief9+vD57Nu7OqpUraNS4cf6SaIBlS5eQkJDAcR06kJOTTZzFERcXR3bOkTNTeuqgk/juo43kZO2jbnwIDzvuUDde35wmIiIicqRxD1fLIxaq87fRs4Grgc/MbEVQdh/we2CWmY0CNgCXAZhZa2AZ0AQIm9mtQKdgefQLQF/gGDPbDDzg7tOKdlhSPTO7GPg70BJ4w8xWuHu0r/KpcG7RLjzYmzoWmAvUAaa7+xfB2/cAM83sQWA5kQ8HCu3xdfcvzGwW8CWR07Fvcve8oF7UuEX2+E4DnjGzNUQO+xoe5FVa3DeB0e6+paQcKxm3QvehhvqoMvfceQfLliwhMzOTn/bry8/HjmXk9aO567bbee3ll2jTti1/evgRAH7045+wcMECLho4gPj4eCY+9Lv8OO7OE5Mn88e/RD4junTYZdx7913k5eVx//gHqjrtckn67Xkc270tDZrF84vXr2bhlKXkZO3jvDvP4ahmDRj2yCBSvtnOrFveACBUP8RpF53Ei7+MnAS95IVVDHvkQsK5Yeb8+t2YXIOIiIiICIBFJsZEBCAnr/Z8S/ejZz0e6xSq1LjFP491CiIiIiJVpfRDYA4T9zW5u1p+N/5d1h9r/Pq1/lBERERERESKqU2nOh8535ciIiIiIiIiUgma8RUREREREZFiHM34ioiIiIiIiBwRNOMrIiIiIiIixYRr0UHImvEVERERERGRWk0zviIiIiIiIlJMbdrjq4GviIiIiIiIFKOlziIiIiIiIiJHCM34ioiIiIiISDHhWrTUWTO+IiIiIiIiUqtpxldERERERESKce3xFZHD3a0f3RDrFERERETkCBaupv/FgmZ8RWqxcYt/HusURERERERiTgNfERERERERKUZfZyQiIiIiIiJyhNCMr4iIiIiIiBTjtejrjDTwFRERERERkWLCaKmziIiIiIiIyBFBM74iIiIiIiJSTNhrz1JnzfiKiIiIiIhIraYZXxERERERESnGtcdXRERERERE5MigGV8REREREREpRnt8y8HM2pvZfDP70sy+MLNbgvLmZvaOmX0b/Hl0UH6ymS0ys31mdmeRWNPNLNXMPi+jz6j1zOxPZvaVma0ys1fNrFkJ7cudm5m1MLMVwWObmX1f4HU9MxtoZl+b2RozG1egj+PNbHFQ/qKZ1Sshl3uDOl+b2YAC5VHjFmlbP4i9JuirQ1lxi7SPmmNl4lb0PtREH1Vp/P330/ecs7kkaXB+2c7MTG4YNZLBAwdww6iRZO3cCYC78/uHHuKiAQMYOiSZ1V9+AcD6desYPvRShg5JZuWK5QDk5uYyZuR1ZGdnV3XKIiIiIiLl4oSr5REL1bnUORe4w907Ab2Bm8ysEzAOmOfuHYF5wWuADOBm4M9RYj0FDCxHnyXVewc41d27AN8A95bQvty5uXu6u3d1967AZOCRAq/zgMeAC4BOwBXBtQP8Iah7IrADGFU0iaDucKBzcD3/MLM6ZlanlLgFjQJ2BH08EvRZYtwo7UvKsUJxy8g3ln1UmeSLhzBpypRCZdOnPkGv3n14/e259Ordh2lTnwBg4YIFbNywgdfffpvxEybw4ISJAPxz1ovcfe99PDb5cWY8+SQAs2bO5MLBSTRo0KCqUxYRERER+Z9TbQNfd9/q7p8Gz3cBq4FEIBmYEVSbAQwJ6qS6+1LgQJRYC4gMPsvqM2o9d/+3u+cGLz8G2pUQosK5laAXsMbd17r7fmAmkGxmBvQHXiraR5Q8Zrr7PndfB6wJYkaNW8Z1vAScG/RdUtx8ZeRY0biVuQ810UeV6dHzDJo0LbyAYP5775E0JPJjSRqSzPx58/LLBycnY2Z0Ob0ru3ZlkZaWSigUIicnh+ycbEKhEFlZWXzw/nwGJ0f70YqIiIiI1Iywe7U8YqFGDrcKlqt2AxYDrdx9a/DWNqBVTeRQwEjgrRLeq6rcEoFNBV5vDspaAJkFBuEHyzGzJDObWEb7ksoxs4lmllS0fdDXzqDvEtsXUGKOlYhb4ftQQ31Uq4z0dFq2TADgmGNakpGeDkBqagqtWrfOr9eqVWtSU1IZfsWVTJvyOL++915Gj7mBKZMmMXrMDcTF6ew5EREREZGqUO2HW5lZI+Bl4FZ3z4pMxEW4u5tZjQ35zex+Ikuwnyurbk3n5u5zgDmH0H58FaYjVcTMoMDf+WjatG3LtBlPA7BxwwZSUrZxwgkncN89d3PgwAFuuvlmOnQ4vibSFRERERHJF47RftzqUK1TSmZWl8ig9zl3fyUoTjGzNsH7bYDUSsZuX+AwqRvLUf9a4CLgKvfI/LqZPRm0f7MqcwO+B9oXeN0uKEsHmplZqEh5eduXVF5i+6CvpkHf5WlfWo4VjVuZ+1ATfRRiZmPMbJmZLZv2xJRoVSqkeYsWpKVF/uqkpaXSvHlzABISWpGybVt+vZSUbSS0SijU9u9/fZSxN9/C888+yyVDh3LbHXfy+GP/OOScREREREQqyt2r5REL1XmqswHTgNXu/nCBt+YAI4LnI4DZlYnv7psOHibl7pPLyGUgcDeQ5O57C8S4Lmg/qCpzA5YCHYNThesROZRpTjDgng8MLaOPOcDw4ITj44GOwJKS4pbQ/uB1DAXeC/ouKW6+MnKsaNzK3Iea6KMQd5/i7j3dveeo68dEq1Ihffv1Z85rka7mvDabfv37R8r79+P12bNxd1atXEGjxo3zl0QDLFu6hISEBI7r0IGcnGziLI64uDiyc3Sys4iIiIjIobDqGnGb2TnAf4DPIH+O/D4i+3xnAccCG4DL3D3DzFoDy4AmQf3dQKdgefQLQF/gGCAFeMDdp0XpM2o9M1sD1CcyCwjwsbsXmyU2sxYVzS1o9xtgt7v/uUCsQcCjQB1gurs/FJSfQOQApubAcuBn7r4v2J/b8+CS5WBZ9kgiS7Nvdfe3yog7EVjm7nPMLB54hsi+6gxguLuvLSPum8Bod99SSo6ViVvR+1DtfRT9uReUkxeu0D+Ie+68g2VLlpCZmUnzFi34+dix9D/3XO667Xa2bd1Cm7Zt+dPDj9C0WTPcnf/34G/5cOFC4uPjmfjQ7+h86qlA5NO0G0eP4o9/eZimzZqx9rvvuPfuu8jLy+P+8Q/QrXv3iqSVL76O9gmLiIiIHIZK3wt3mLiq7vBqGSw+d2BmjV9/tQ18RY5EFR34Hu408BURERE5LGngW8Oq/XArEREREREROfLE6quHqoMGviIiIiIiIlKM61RnERERERERkSODZnxFRERERESkmNq01FkzviIiIiIiIlKracZXREREREREitEeXxEREREREZEjhGZ8RUREREREpJjatMdXA18REREREREpJqylziIiIiIiIiLVw8wGmtnXZrbGzMZFeb++mb0YvL/YzDqUFk8DXxERERERESnG3avlURYzqwM8BlwAdAKuMLNORaqNAna4+4nAI8AfSoupga+IiIiIiIgcTnoBa9x9rbvvB2YCyUXqJAMzgucvAeeamZUUUHt8RUREREREpJgY7vFNBDYVeL0ZOLOkOu6ea2Y7gRbA9mgBNfAVKSC+TlyJnxJVFTMb4+5TqrufmlKbrqc2XQvUruupTdcCup7DWW26Fqhd11ObrgVq1/XUpmuB2nc9h2KO/6tafjc2szHAmAJF/7+9ew+2a7zDOP59mqhLGFolLtGhpS6jhKSG0ox7UUMpxYRx6VAdNNpOtWqG0da0RqfD9A+qpDFElJCRISUZ1+q4JhISQavi1hBD1f2ap3+slemWnMPJ3lves5fnM3PmrL3OOms/v7PP7L1/e73vWhd/0n/zDHWOWPFO+PhNekqT6mlSLdCseppUC6SewaxJtUCz6mlSLdCseppUCzSvnkHH9sW2R7d8Ld30Pgds1HJ7RL2uz20kDQXWBF7q7z7T+EZERERERMRgcj+wmaRNJH0WOByYutQ2U4Gj6+VDgFv9EWfOylDniIiIiIiIGDTqObsnAzcDQ4DxtudJ+iXwgO2pwKXA5ZL+CbxM1Rz3K41vxIrXtDkjTaqnSbVAs+ppUi2QegazJtUCzaqnSbVAs+ppUi3QvHp6ku1pwLSl1p3Zsvw2cOhA96eBXEcpIiIiIiIioldljm9EREREREQ0WhrfiBVE0nhJiyTNLZ2lGyRtJOk2SY9ImidpXOlM7ZK0iqT7JM2pazm7dKZOSRoi6UFJN5TO0ilJCyQ9LGm2pAdK5+mUpLUkTZb0qKT5knYqnakdkjavH5MlX69KOrV0rk5I+lH9HDBX0iRJq5TO1C5J4+o65vXi49LXa6akz0uaIekf9ffPlcw4UP3Ucmj92CyWNLpkvuXVTz3n1c9pD0maImmtkhkHqp9aflXXMVvSdEkblMwY3ZPGN2LFmQDsUzpEF70P/MT2VsCOwEmStiqcqV3vALvb3hYYCewjacfCmTo1DphfOkQX7WZ7pO2eeoPYjwuAm2xvAWxLjz5Oth+rH5ORwCjgTWBK4Vhtk7Qh8ENgtO2tqU6m8pEnShmsJG0NHA/sQPU/tr+kTcumWm4TWPY18+fALbY3A26pb/eCCSxby1zgYODOFZ6mcxNYtp4ZwNa2twEeB05f0aHaNIFlaznP9jb1c9sNwJnL/Fb0pDS+ESuI7TupzjjXCLYX2p5VL79G9eZ9w7Kp2uPK6/XNleqvnj0BgqQRwLeAS0pniQ+TtCYwhupMlNh+1/YrZVN1xR7AE7afKh2kQ0OBVevrQa4G/LtwnnZtCdxr+03b7wN3UDVZPaOf18wDgcvq5cuAb6/QUG3qqxbb820/VihSR/qpZ3r9vwZwD9U1Vwe9fmp5teXmMHr4/UB8WBrfiOiYpI2B7YB7yyZpXz00eDawCJhhu2drAc4HTgMWlw7SJQamS5op6YTSYTq0CfAi8Od6KPolkoaVDtUFhwOTSofohO3ngN8BTwMLgf/anl42VdvmAt+QtLak1YD9gI0KZ+qG4bYX1svPA8NLhol+HQf8tXSITkg6R9IzwFhyxLcx0vhGREckrQ5cC5y61KekPcX2B/WwphHADvVQwZ4jaX9gke2ZpbN00S62twf2pRpSP6Z0oA4MBbYHLrS9HfAGvTNcs0+SPgscAFxTOksn6vmiB1J9OLEBMEzSkWVTtcf2fOBcYDpwEzAb+KBoqC5zdVmSHIkbZCSdQTUVamLpLJ2wfYbtjajqOLl0nuiONL4R0TZJK1E1vRNtX1c6TzfUw05vo3fnY+8MHCBpAXAVsLukK8pG6kx9JA7bi6jmkO5QNlFHngWebRlRMJmqEe5l+wKzbL9QOkiH9gSetP2i7feA64CvF87UNtuX2h5lewzwH6p5l73uBUnrA9TfFxXOEy0kHQPsD4x1c66XOhH4TukQ0R1pfCOiLZJENU9xvu3fl87TCUnrLDkDpaRVgb2AR8umao/t022PsL0x1fDTW2335FErAEnDJK2xZBnYm2oYZ0+y/TzwjKTN61V7AI8UjNQNR9Djw5xrTwM7Slqtfn7bgx498RiApHXr71+kmt97ZdlEXTEVOLpePhq4vmCWaCFpH6opNgfYfrN0nk5I2qzl5oH06PuBWNbQ0gEiPi0kTQJ2Bb4g6VngLNuXlk3VkZ2Bo4CH67mxAL+wPa1gpnatD1wmaQjVB4JX2+75ywA1xHBgStWHMBS40vZNZSN17BRgYj1E+F/AsYXztK3+MGIv4Puls3TK9r2SJgOzqIZqPghcXDZVR66VtDbwHnBSr51Era/XTOC3wNWSvgc8BXy3XMKB66eWl4E/AOsAN0qabfub5VIOXD/1nA6sDMyon6/vsX1isZAD1E8t+9UfTi6m+j8b9HXEwKg5IxEiIiIiIiIilpWhzhEREREREdFoaXwjIiIiIiKi0dL4RkRERERERKOl8Y2IiIiIiIhGS+MbERERERERjZbGNyIiokEkrSfpKklPSJopaZqkr/Sz7a6SOrp0l6RjJG3Qcvt2SaOXcx+vd5IhIiLi46TxjYiIaAhVF9CcAtxu+8u2R1FdX3P4J3R/Q4BjgA0+ZtOIiIii0vhGREQ0x27Ae7YvWrLC9hzgLknnSZor6WFJh7X8zuqSJkt6VNLEunlG0h6SHqy3Hy9p5Xr9AknnSpoFHAGMBiZKmi1p1dYwkl6XdI6kOZLukTS8Xr+JpLvrff96qd/5qaT7JT0k6ex63UGSblFlfUmPS1qv+3++iIhoqjS+ERERzbE1MLOP9QcDI4FtgT2B8yStX/9sO+BUYCvgS8DOklYBJgCH2f4qMBT4Qcv+XrK9ve0rgAeAsbZH2n5rqfsdBtxje1vgTuD4ev0FwIX1vhcu2VjS3sBmwA513lGSxtieUm93EvAn4Czbzy/fnyYiIj7N0vhGREQ03y7AJNsf2H4BuAP4Wv2z+2w/a3sxMBvYGNgceNL24/U2lwFjWvb3lwHe77vAkjnEM+t9A+wMTKqXL2/Zfu/660FgFrAFVSMMcArVsO13bE8iIiJiOQwtHSAiIiK6Zh5wyHL+zjstyx8wsPcGbwxw3+/Zdj/7dh/bC/iN7T/28bMRwGJguKTP1I16RETEgOSIb0RERHPcCqws6YQlKyRtA7wCHCZpiKR1qI7e3vcR+3kM2FjSpvXto6iOEvflNWCN5cz5d+Dwenlsy/qbgeMkrV5n31DSupKGAuOp5hTPB368nPcXERGfcjniGxER0RC2Lekg4HxJPwPeBhZQzeFdHZhDdaT1NNvPS9qin/28LelY4Jq66bwfuKivbanmAl8k6S1gpwFGpN7d9wAAAI9JREFUHQdcWWe8vuV+p0vaEri7PsfW68CRwInA32zfJWkOcL+kG23PH+D9RUTEp5z+PwIpIiIiIiIionky1DkiIiIiIiIaLY1vRERERERENFoa34iIiIiIiGi0NL4RERERERHRaGl8IyIiIiIiotHS+EZERERERESjpfGNiIiIiIiIRkvjGxEREREREY32P6KTwdlLNW2jAAAAAElFTkSuQmCC\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "#Build the heatmap\n",
        "plt.figure(figsize=(15, 8))\n",
        "plt.title('Retention rates')\n",
        "sns.heatmap(data=retention,annot = True,fmt = '.0%',vmin = 0.0,vmax = 0.5,cmap=\"BuPu_r\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KINlFkczr9NL"
      },
      "source": [
        "** Note That: Customer retention is a very useful metric to understand how many of the all customers are still active.Retention gives you the percentage of active customers compared to the total number of customers.**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bKxXV5Sgr9NL"
      },
      "source": [
        "**Average quantity for each cohort **"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "id": "Nx0c5H_Pr9NL",
        "outputId": "3b173fe2-2ff7-4556-abb8-de28b7050db6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 513
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x576 with 2 Axes>"
            ],
            "image/png": 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zsyYzaew4ug8aSM8PBvO3m27wKbqCaXrlFTydS1t+GTuWVwa9R68P3o+qY9OweVPaPvf0YeWb1qxl4eRppJUr60NUhXNhy8t49JUXDyu/vPW1PD+gL88P6Bs1CRJAsyuu5N89e2Qrm5GVxcSxY+k1eBCvf/gBV998k0/RFUxRu6a1bHUV3d98K1vZG6904x9338OAoSO445/teaNHN5+iK5jL/9aKl954M1vZWz260+auu+k7dDj/uOde3urR/QhbH3+uaHUVPd98O1tZ12eepn2n+xky6iOaNLuEQf1D/t5LXxWl6zNAkysu51+vvHxY+RWtr+elgf15aWD/Yp0gFRf5JknOuZXOuSne863AXKAy0AoY4K02ALjKW2eNc24Sgd6eYI2AX51zvzvn9hDo6Wl1hH1+A2zNpfwz5yHQk1Ql5zpmZkBT4GAvUXBsi5xzM4AD+bU7kirXrUXp5MRsZSfUP5WY2FgAyp9ck23rN/oRWoHVbVCfpJTsfxkaM/Ijrr71ZuJKlgQgNSPdj9AKpW6DBiQf1p5RXH3rLYfak5aR4UdoBZZbW74YOZK/R2FbAGqeXpeE5MP/ovq/t/rR8s422JG+g/s4dEqDeiSlJPsdRticekaDw64Dn48YyTVtbo26c62oXdPqn9mQlJTUbGVmxvZt2wDYvm0bZcqW8yO0Aqt3ZkOSc2nLju3bAdi2bRuZZaPnw/gZDRuSkpq9PUsWL6ZBw4YAND7nHL77+is/QiuwonR9BqiTy3VAsnMReBxvCjS7nZlVBxoAE4HyzrmV3qJVBIbj5aUysDTo9TKgcUH2HxRHHIGepvtyWZwJbHLO7QvaT+XC7Mcvc7/5kZPOa+R3GIW2cskS5k6fzpC3ehNXqiRtOrTnL3VO8TusQluxZClzp03n/bfeJq5kSdp0bM9JdY7UCXp8W7FkKXOmTWew15bbOnaI2rYAzP5pIimZmVQ6sYbfoYTFl8NHMe7zL6lZuxY3dfhnVCdSK5YsYc60aQx68y3iSpbkH/d1jNpzrahd0+575FEeuOcuXu/+MgcOON4eOCj/jY5T7R9+lIf/eRdvdH8Zd8Dx+oD3/A7pqNQ88S/88O23NGnWjK/HjGH1qlV+h1RoRe36DDBm+EjGfv4FNWvX5taO7aP6Gi35C3niBjNLAkYAnZxzW4KXeT07xzIJfAMY65wbdwz3eUxkDfsfMbExnHxR9Ay1yWn//v1s27yF5/v25tb299LtiadwUTx4df/+fWzdsoUX+r5Dmw7t6fav6G3P/v372bZlC//t24c2Hdrz8r+ejNq27Nm1m2+HDKf5bdEx/DE/l/69FT2Gvc/zA/qQlpnJ4Fff8Duko7J//362bt7CS/36cnvHDvz38X9F7blW1K5po4Z+SIeHH2XUl9/Q8eFHeP7fhw+TihYfD/uQ9g89wvAxX3PvQw/z4n+ity0AT3XuwogPP+DW669jx44dlIiL8zukQilq12eA5n+/ileHf8CLA/uTXiaTgb1e8zskibCQkiSv52YEMNg5N9IrXm1mFb3lFYE1+VSzHKga9LoKsNzMGpvZNO/xtxBieQYoCzwQVDbG274PsB5IM7ODvWRVvH2HzMzamVmWmWWNH/pxQTY9KnO//ZE/smZw6f3tsGjrmw6SWa4cjS++CDPjpLp1sBhjy6ZNfodVaJnlynF2k6LRnjLlynJ2kyaYGSfXrYvFxERtW9avXMmGVWvocVcnnr/5TjavXU/Pex5g64boGKqaU2pGBjGxscTExNC01RX8Nmeu3yEdlcxy5Tjn4j/PtZgoPteK2jXt8/+NpkmzSwBo2rwFc2bN9Dmiwhvzv9Fc6LXl4uYtmDtrls8RHZ3qNWvyau93GDh0GM0vb0mVqlXz3+g4VNSuzxAYMnzwGt2s1V/5bW50X6OPVnEYbpdvkuTd49MXmOucC74jcjTQxnveBsgvm5gEnGRmNcysJNAaGO2cm+icq+89RucTyx1AC+AG59yh+4qccy287e/werW+A64tQGzZOOd6O+caOucannd9rrdNhd3iKTOZMupzrvxXB+JKlTom+4yURhdewKzJU4DAkJt9e/eRkpbmc1SF1/iiC4tMexpddCEzJ08GYPmSJezbuzdq21KxRnWeGTaAxwe9w+OD3iG1bCb3vdmd5Ci6XyTYxnXrDz2f9MOPVKkZ3UNUzr7ooj/PtcXRfa4VtWtambJlmZo1CYDJv0yk6gnVfI6o8DLLlmVaVhYAU36ZSJUTTvA5oqOzYX3gOnDgwAH69X6bv1//fz5HVDhF7foMsHHdukPPf/l+LFWj/Bot+bP8hgyY2fnAOGAmf0548C8C9yUNBU4AFhOYAnyDmVUAsoAUb/1tQB3n3BYza0lgCu5YoJ9zrusR9jmOwCx2SQR6hto658aY2T5vXwcndRjpnOucy/Y1CUwMkQFMBW52zu02s7OAUUA6sAtY5Zyrm1f7X50zPuzJ7Zhub7F89nx2bdlGfFoKjVu3YvKIz9i/dy+lvRsfy598Ihffc2tY93txxVphrQ+g+1PPMHvKVLZu2kRqRgb/d2dbLrr8Mt549jn+WLiQEiXiaNOxPac1PDPs+45Eb1v3J59mVlB7Wre7g4suv4zXn+3KHwsWUiIujts6tuc078bacDHC35ZuTz7N7ClT2LJpE2mH2nI5r3ltiYsrQZuOHTg9zG0BWLhlddjrHNy1G7/PmMX2zVtITk/j0ltb0+jySw8tf/7mO+n4erewTzFbJTH8H4Zffbozc6dOY+umzaRmpHPNHbczd8o0Fi/8FcwoW7ECbR95kPQymWHfd1KJ+LDX+dKTTzJrsneuZWZww53tuLjl5fTq8ix/LFhAibg4bu/YkXpnhfdc23tgX/4rFZCf17QK8an5r1RAzzz6MFOzJrFp0yYyMjJpe88/OaF6DXq++AL79++jZMlSPPjEk9Suk+evwgLbF4HhiP957BGmZU1i86ZNZGRkcPs991K1enVeffEF9u/fT8mSpbj/X09QK8xtAYiPDf/XSj75yENMnhQ4NpkZmdx5773s3LGDYd40+hc3u4R7O90f9t91369aGNb6wL/rM0D1pPBfJ3s8/W/mTJnqXaMzuP6OfzB76lQWLfgVMyhbsSLtHn2I9DJlwr7vehnlomIo0Za9+8P+Q54SF3tctT3fJKm4i0SS5JdIJEl+iuYhiTlFIknyUySSJL9EIknyUySSJL9EIknyUySSJL9EIknyUySSJL9EIknyUySSJD8pSTp+FGh2OxERERERKd6KQydL0fnTiIiIiIiISBgoSRIREREREQmi4XYiIiIiIhKyoj/YTj1JIiIiIiIi2ShJEhERERERCaIkSUREREREJIjuSRIRERERkZAVgxnAlSSJiIiIiEjoikGOpOF2IiIiIiIiwZQkiYiIiIiIBFGSJCIiIiIiEkT3JOXjmupn+R1C2ExYu9DvEMJq056dfocQNmdkVvU7hLAqSsemelKm3yGE1bZ9RefYFDV95o/1O4SwWbtzs98hhNWGXRv9DiFsEuIS/Q4h7Nqd0tTvEIqd4nBPkpIkEREREYla9TLK+R1CMVT00yQNtxMREREREQminiQREREREQlZcfieJPUkiYiIiIiIBFGSJCIiIiIiEkTD7UREREREJGTFYLSdepJERERERESCqSdJRERERERCpp4kERERERGRYkZJkoiIiIiISBAlSSIiIiIiIkHyvSfJzKoCA4HyBIYg9nbO9TSzDOBDoDqwCLjeObfRzGoD/YEzgCeccy8H1dUPuBJY45w7NY99Xgb0BGKBPs65F7zy9kAn4ESgrHNu3RG2rwF8AGQCk4FbnHN7zOxCoAdwOtDaOTc8v/ZHwn///RQTxo4lLSOD/sNHAfCfRx9i6aJFAGzbupWk5GT6fOhLeAUyvNtrzJuQRVJaKp3e6QnAl+++z9yfJ2FmJKalct3DHUjJzPA50tB81qM3v02aSkJqCm3f+C8AY98bxq8TJ2NmJKSl0LLT3SRnpvscaf7efPYFpvz0Eynp6XQbPACAYX368c3Hn5CSngbADXffSYNzz/EzzJB90uMtfv1lKglpKbR74yUAfnhvKAsmZGEWQ2JaClfefzfJUXCuvfHs80z56SdS09PpNnggAEP79OObj/8XdGzacUaUHJu3u/6XqeN/JiU9jRcHvwvA8D79+W70p6SkpwJw/V130uDcs/0LMkRFqS0AP77xHkunzKR0ajJXd3sKgEnvjWTp5JnElIgluXxZzv/nLZRKTPA50vxN7zOc1dPmUSoliYue6wTAil9msmDU12xbuZbzn/knaTWq+BxlaFZ8k8XqH2eAc5Q/vx6VLml42Dqb5y/hj6Hf4vbvJy4pnlMfutGHSEMzu99HrJu+gJIpiZzT5V4A9m7bwcy3hrFz3Sbiy6Rx2j3XE5cY73OkEi7F4ctkzeXTSjOrCFR0zk0xs2QCScdVwG3ABufcC2b2GJDunHvUzMoB1bx1NuZIki4EtgEDj5QkmVkssAC4FFgGTAJucM7NMbMGwEbge6BhHknSUGCkc+4DM3sLmO6ce9PMqgMpwEPA6FCSpBU79oT9NJg+OYv4hASef+qJQ0lSsDe6vURiUhJt7ronrPudsHZhWOsD+GPGbErGl2bYi70OJUm7tu+gtPcLd/yoT1mzZClX33d32Pe9ac/OsNe5dNZc4kqX5tPubx1Kknbv2EGphEB7skZ/wfoly2nRvm1Y93tGZtWw1gcwZ+o0SifE83rn57IlSaXj4/nrTTeEfX/Bpm9YHvY6l8yaS8nSpRnd/Y1DSVLwsZk0+gvWLVnG5e3vCOt+62eE/0PXn8ema7YkqXR8PH+L8LHZ5/aHvc65U6dTOiGeNzs/ly2xKJ0Qz5U3tg77/iLJz7Z8tWxO2OtcNWchJUqXYtzrAw4lScunz6HiqbWIiY1l0qDA76Czbr46rPtdu3NzWOsDWD/vD0qULsm03sMOJUlbV6zBzJjx7ijqtG4ZsSRpw66NYatr+/K1LOjzP05//BZiYmOZ02sYNW9qTny5P//4tm/HLma+OJg6Ha+jVEYKe7Zsp2RKYlj2nxAXnnqCbZy/iNjSJZndZ9ShJGnh0C+JS4yn+hUXsOjTcezdsZOTrmse9n0DvH5edF1n8mF+BxCK1Tv3hv3zcfn4uOOq7fkOt3POrXTOTfGebwXmApWBVsAAb7UBBJIinHNrnHOTgL251DUW2JDPLhsBvzrnfnfO7SHQI9TK236qc25RXhubmQFNgYMJUHBsi5xzM4AD+cQQUfXObEhKamquy5xzfP/VGJpd1vIYR1U4NU6vS0Jycray0kF/kdy7axeBQxIdqp56CvHJSdnKDn4IB9i7azdESXvqNKhPUkqK32GEzQmnnkLpPI/NLh0bn5zSoB5JKcn5rxgFilJbACrUOYlSSdk/FFeuV4eY2FgAyp1cgx0bNvkRWoFl1q5BXI4er+RK5UiqWNaniApn56r1JNeoSGzJOCw2hpSTq7Jh6oJs66z9ZS6Z9U+mVEbgOhGuBClS0mtVP6yXaO3UeVQ8rz4AFc+rz9op8/wITaTQCjQFuNcT0wCYCJR3zq30Fq0iMBwvHCoDS4NeLwMaF2D7TGCTc25f0PaVwxRbxM2YMpn0jEyqVKvmdyhHZUz/wUz96ntKJyZwx0ud/Q7nqI0dOJRZ346jVEICNzz/hN/hHJUxw0cx9vMx1Kxdm1s63hv1Hwi/H/AhM78dS6nEBG56/im/wzkqY4aPZOznX1Czdm1u7dg+6o/Nl8NHMe7zL6lZuxY3dfhnVLenKLUl2MJvf6LGuWf6HUaxklCpLEs+GsfebTuJKVmCjTN/J6lahWzr7Fq9Abf/ALO6DWH/rj1UbHom5c454l0Kx6U9W7ZTKi3wc1IyNYk9W7b7HJGEkysGk4CHPHGDmSUBI4BOzrktwctcYMxe0X+3joFvv/g8anqR8tLi9pt47P13qN/0Qn4e/bnf4Ry1C2+9nn+++yp1mpzL5E++9DucQrv071fRa/gQ/juwH+llMnmv1+t+h3TUmrT5PzoMeJ1Tm5zH5P+N8TucQmv+96t4dfgHvDiwP+llMhnY6zW/Qzoql/69FT2Gvc/zA/qQlpnJ4Fff8DukQitKbahAXDwAACAASURBVAk2feTnWGwsNS9o5HcoxUpCxUwqt2jMnJ5DmdtzGIlVy2Ex2XvB3YEDbFuyilPaX0Od+65j2Wc/sXN1fgNxjl9mFiWDyET+FFKSZGZxBBKkwc65kV7xau9+pYP3La0pTABmVtXMpnmPu4HlQPANGlW8srzqGONt3wdYD6SZ2cFesny3z6W+dmaWZWZZg/r1KcimR2X/vn2M+/ZrLm7R4pjtM9LqN7uQ2eN+9juMsKnb5DwWjJ/kdxiFlpaRQUxsLDExMTRtdSW/zp3rd0hhU7fJ+cz76Re/wyi04GPTrNVf+S3Kj01qtnPtCn6bE73tKUptOWjh9z+zdPIsLup4e1QNiS4qyp9/OvWeaMOpD99IiYTSlC6ffcKZkunJpNWpQWypksQlJZByUlW2LyvUxyzflExJZPemrQDs3rSVksnH95BBkZzyTZK8e3z6AnOdc92DFo0G2njP2wAfFyYA59xS51x97/EWgYkaTjKzGmZWEmjt7SuvOlp429/h9Wp9B1xb2Nicc72dcw2dcw1v/kd4bwLPy+SJE6havQZly1fIf+Xj2LrlKw49n/PTL5StGjWjHXO1YfmqQ88XTpxMRpWKPkZzdDau+3Ouk0nfj6NqzRo+RnP0Nixfeej5gglZZFap5GM0Ryf42Pzy/dioPzYb160/9HzSDz9SJYrbU5TaArBs2mxmfvwVlzx6NyVKlfQ7nGLp4NCz3Ru2sGHqAso2OiXb8ox6J7H112W4/QfYv2cvW/9YSXyFTD9CLbSyDWqxcvw0AFaOn0bZBrV9jkjCybnwP0JhZv3MbI2ZzQoq+zCow2WRmU07wraLzGymt15WvvsKYXa784FxwEz+nPDgXwTuSxoKnAAsJjAF+AYzqwBkEZhF7gCB2ezqOOe2mNkQoAlQBlgNPOOc65vLPlsSmKo7FujnnOvqlXcEHgEqEOi5+sw5d1gWY2Y1CUz4kAFMBW52zu02s7OAUUA6sAtY5Zyrm1f7IzG7XZfHHmHa5Els3rSJ9IwMbrv7Xq64+u+88PQT1DmtHn+77vpw7xKIzOx2Q57rzh8zZrF981aS0lO55JbWzJ80hXVLl2MxMaSVK8tV991FapnwX9wjMbvd6BdfY8nMuezcspWEtBTOv+lafs+axoZlK7EYI6VsGVrc+w+Sy4R3mulIzG7X8+n/MGfKVLZu2kxqRgbX3XE7c6ZOY9GChZgZZStW4M5HHyK9TJmw7zsSs9t99N9eLPaOTWJaKhfcdC2/ZU1j/fIVmBmp5cpy+b1tw35sIjG7XY+n/53t2Fx/xz+YPXUqixb8ihmUrViRdhE6NpGY3e7Vpzszd+o0rz3pXHPH7cydMo3FC38F71xr+8iDpEfgOhBufrYlErPbfd+jH6vmLGDX1m3Ep6bQ4PormDHqS/bv20vppMBEKGVPqs657cI7vXQkZreb8sYQ1s/7gz3btlMqJYmTr76EuMQEZg8azZ6t2ymREE/qCRVp/PA/wr7vcM5uBzDzpffZt30nFhtD9WubknZKNVb9MBWAChc1AGD5mIms+XkWmFH+vNNznSa8MCIxu93Mt4axcf4i9m7bQcmUJGq2akLZM05h5ptD2bV+M/GZaZx2z3XEJUVmqnnNbnfsrYzA5+OKCSXzbXt+M2WbWTdgs3PusBvizWwRecyOfdj6+SVJxV0kkiS/RCJJ8lMkkiS/RCJJ8lMkkiS/RCJJ8lMkkiQJj0gkSX6JRJLkp3AnSX6KRJLkNyVJx14kPh9XCiFJgkMTyX2SM0nyRr8tAZo65w770FvQJCnkiRtEREREREQiIXhOAO/RroBVXACszi1B8jjgSzObHErdBZoCXEREREREJNycc72B3kdRxQ3AkDyWn++cW25m5YCvzGye9x2uuVKSJCIiIiIiITvevifJm9X678ARv/jNObfc+3+NmY0CGgFHTJI03E5ERERERKLZJcA859yy3BaaWaKZJR98DjQHZuW27kFKkkREREREJGQ+TgE+BPgZqGVmy8ysrbeoNTmG2plZJTP7zHtZHvjRzKYDvwCfOue+yGtfGm4nIiIiIiLHPefcDUcovy2XshVAS+/570C9guxLSZKIiIiIiBTA8XVPUiRouJ2IiIiIiEgQ9SSJiIiIiEjIin4/kpIkEREREREpgONtCvBI0HA7ERERERGRIOpJEhERERGRkLlQ5+yOYkqS8tF73jd+hxA2Z5at4XcIYXVNtfp+hxA27y6c4HcIYRUXE+t3CGEzfvVvfocQVpUSU/0OIWz2Hyhav6S37t3pdwhhc1JaJb9DCKuypWv5HUJYfbt8pt8hiBz3lCSJiIiIFCOvn9fa7xAkyhWtP1HlTkmSiIiIiIgUQNFPkzRxg4iIiIiISBD1JImIiIiISMiKwbwN6kkSEREREREJpp4kEREREREJWXH4MlklSSIiIiIiErLikCRpuJ2IiIiIiEgQ9SSJiIiIiEjIin4/knqSREREREREslFPkoiIiIiIhK4YzAGuniQREREREZEg+fYkmVlVYCBQnsAQxN7OuZ5mlgF8CFQHFgHXO+c2mlltoD9wBvCEc+7loLr6AVcCa5xzp+axz8uAnkAs0Mc594JXPhhoCOwFfgHucs7tzWX7GsAHQCYwGbjFObfHzC4EegCnA62dc8Pza38kTHjrfVZMnUPplCRavvQYAEsmTGPm8C/YsmI1zbvcT+aJJ/gRWoF9+FIv5kzMIiktlYf7vJpt2ffDPuKTt/vznxHvkZia4lOEBdPlqSf5cewPpGdk8MGojwFYMH8eL3TuzM4dO6hYuRKdX3iRpKQknyPN37ev9WNx1gziU5Np3bMLAD8NGMqirOnElChBavmyNO3wD0olJvgcaWi+erUvf2RNIyE1hZt7dQVg3Lsf8MekacSUKEFahXJc2qEtpZISfY40f0WpLQDDXn6Vud514IF3emVbNnbYx3za+12eHj4gKq4DI7q9xryJWSSmpdKpd08AvhrwPnN/noSZkZiWyrUPdSAlM8PnSEOT1fsDVk6dS6mUJJr/92EAlk2czpwRY9iyYg1NO99HRs2qPkcZmi979eF37+fm1lefA2Bs/w/4fdI0YkvEklqhHM073kHpKPm5KWrnmhQvRb8fKbSepH3Ag865OsDZwL1mVgd4DPjGOXcS8I33GmAD0BF4OZe63gUuy2tnZhYLvA5cDtQBbvD2BzAYqA2cBsQDdxyhmv8Crzjn/gJsBNp65UuA24D384oh0mpe1Jgmj92VrSy1agUueOB2ytWu6VNUhdOwRTPufP6Zw8o3rVnLgqyppJUr60NUhXdFq6vo+ebb2cq6PvM07Tvdz5BRH9Gk2SUM6t/Pp+gKpvbF53HlU/dnK6tSrw6te3Sm9Sv/Ia1SeaaM+NSn6AquTtPzuerpB7OVnVDvVG7u1ZWbez5LWqUKTIqS9hSltgCc2bwpbZ97+rDyTWvWsWDytKi6DpzR/GJu6/pUtrILrr2Kjm+9Qoc3u1O7cUO+HTTUp+gKrtoFZ3H+I3dmK0upUoFzOt1GmSj7fVOn2flc/cxD2cqq1a/Lra925ZZeXUmvXIFJIz7xKbqCK2rnmhQvLgL/jjf5JknOuZXOuSne863AXKAy0AoY4K02ALjKW2eNc24Sgd6enHWNJZBE5aUR8Ktz7nfn3B4CPUKtvO0/cx4CPUlVcm5sZgY0BQ72EgXHtsg5NwM4kF+7I6ncKSdSMin7X+9TK1cgpVJ5nyIqvBNPr0tC8uG9Kh+/2Zcr291G4HBEjzMaNiQlNTVb2ZLFi2nQsCEAjc85h+++/sqP0AqsUt1alErO/hfVE+qfSkxsLADlTz6Rbes3+hFaoVSuW+uwvxBXa/BneyrUOpFt6/O7vBwfilJbAGqeXpf45OTDyv/3Vj9a3nkr0XQZqHFaXRJytKV0UG/rnl27iKYGlc3l901K5fIkVyrnU0SFV6Vu7Vx+bk479HNT8eQT2bYueq5pRe1cEylqCjRxg5lVBxoAE4HyzrmV3qJVBIbjhUNlYGnQ62VA4xxxxAG3APflsn0msMk5ty9o+8phik1CMGv8RFLLZFLpxBp+hxIWNU/8Cz98+y1NmjXj6zFjWL1qld8hhcXcb3/kL+ed5XcYYTPn67GcfH7j/FeMAkWhLbN/mkhqZkaRuQ582X8wU7/+nlKJCdzxYme/w5FczPpmHLXOb+R3GEdN55pEA6eJG/5kZknACKCTc25L8DKvZ+dYvltvAGOdc+OO4T4lBHt27eabIcNo0eZGv0MJm6c6d2HEhx9w6/XXsWPHDkrExfkd0lHLGv4JMTExnHzh2X6HEha/DBtNTGwstS46x+9QjlpRaMueXbv5bsgILr3tBr9DCZvmt9/Eo4PfoX7TC5kw+nO/w5EcJg4dTUxMDLUvOtfvUI6azjWR40NISZLXczMCGOycG+kVrzazit7yisCawgRgZlXNbJr3uBtYDgTfRVrFKzu4/jNAWeCBoLIx3vZ9gPVAmpmVyG37EGNqZ2ZZZpY1eaQuUAWxfsVKNqxaQ/e7OtH1pjvZvHYdr9x9P1s2RM8QiJyq16zJq73fYeDQYTS/vCVVqkbHTc5HMu/bH1mcNZ1L7r8z6oZD5mbON+P4I2s6LR64K+rbU1Tasn7lKjasWk3Pu+7nhZvbsXntenre8yBbo/g6cFD9phcy68ef/Q5Dgsz+Zhx/ZE3j8gfvjuqfm5x0ron4K5TZ7QzoC8x1znUPWjQaaAO84P3/cWECcM4tBeoH7a8EcJI3Q91yoDVwo7fsDqAF0Mw5dyCojhY5Yv4OuJbA/UwFjs051xvoDfDvKZ8X/f7EMKpYszr/GT7w0OuuN91Jpze6RcWsVkeyYf16MjIzOXDgAP16v83fr/8/v0MqtCVTZjL1oy+4qsujxJUq5Xc4R23RlBlMHvU513R9LOrbU5TaUrFGNZ4eNuDQ6xdubkeH11+O2uvAuuUrKFO5EgBzfv6FslU1gvt4sWjKDLJGfsZ1zz0e9T83oHNNosfxONFCuFl+YwrN7HxgHDCTPyc8+BeB+5KGAicAiwlMAb7BzCoAWUCKt/42oI5zbouZDQGaAGWA1cAzzrm+ueyzJYGpumOBfs65rl75Pm9fW71VRzrnDhuwa2Y1CSRIGcBU4Gbn3G4zOwsYBaQDu4BVzrm6ebU/EknS+F4DWDP3N3Zv3Ubp1GROu/ZySiYlMPndEezeso24hHjSq1fm4sfvCet+zywb/nsDBnV9md+mz2L75i0kp6fRvM0NNL780kPLI5kkXVj+L2Gv88lHHmLypEls2rSJzIxM7rz3Xnbu2MGwD4YAcHGzS7i30/1h/2vluwsnhLU+gC+7v82KWfPZtXUb8akpnNW6FVNGfsb+vXsp7U22Uf7kmjS5+9aw7zsuJjbsdX7e7U2WzZrHri3bSEhLoXHrq8ga8Sn79+6jtDdBRYVaJ9LsntvCvu9w87MtlRJT81+pgN7v2o3fZ8xm++YtJKWncemtrWl0+SWHlkcqSdp/IPy/pD94vjt/zJjF9s1bSUpP5ZJbWjP/lymsXbacmJgY0sqVpVXHu0gtkxn2fU9Z90fY65z42nusnfsbu7dup3RKMnWubUHJxASmDRjF7q2B3zdp1SpxQY4ZV49W1aQyYa0P4LOX32Bp0M/NOTdczS/DP2H/3n3EpwSuaRVOPpFL/nlb2PddtvThE5McLT/PtWuq5/nRR/wVFd2h8zZvDPsFuHZq+nHV9nyTpOKuKPUkRSJJ8lMkkiS/RCJJ8lMkkiQJj0gkSX6JRJLkp0gkSX6JRJLkp0gkSX5SknRcO64ShSOZG4Ek6ZTjLEkKeeIGERERERGR4qBAU4CLiIiIiEjxVhxGoilJEhERERGRkBWHiRs03E5ERERERCSIkiQREREREZEgSpJERERERESC6J4kEREREREJWXGYuEE9SSIiIiIiIkHUkyQiIiIiIiEr+v1ISpJERERERKQANAW4iIiIiIhIMaOeJBERERERKYCi35OkJCkfK7av9zuEsEmMK+13CGG1a/8+v0MIm3kbl/odQlhdWe0Mv0MIm017dvodQlgdKEIzEsWXiPM7hLAqGVN0fiVPWj3P7xDCrmW1s/wOQUSOoaJzRRYRERGJkGuq1/U7BJHjRhH6e9sRKUkSEREREZGQaeIGERERERGRYkY9SSIiIiIiErKi34+kniQREREREYkCZtbPzNaY2aygsn+b2XIzm+Y9Wh5h28vMbL6Z/Wpmj+W3LyVJIiIiIiISMudc2B8hehe4LJfyV5xz9b3HZzkXmlks8DpwOVAHuMHM6uS1IyVJIiIiIiJSAC4CjxD26txYYEMhAm4E/Oqc+905twf4AGiV1wZKkkREREREJJq1N7MZ3nC89FyWVwaCv5RymVd2REqSREREREQkZJHoRzKzdmaWFfRoF2I4bwInAvWBlUC3cLRRs9uJiIiIiIivnHO9gd6F2G71wedm9g7wSS6rLQeqBr2u4pUdkZIkEREREREJ2YHQJ1qIODOr6Jxb6b28GpiVy2qTgJPMrAaB5Kg1cGNe9eabJJlZVWAgUJ5Ab1hv51xPM8sAPgSqA4uA651zG82sNtAfOAN4wjn3clBd/YArgTXOuVPz2OdlQE8gFujjnHvBK+8LNAQMWADc5pzblsv2ZxKY/SIe+Ay4zznnzOw64N/AKUAj51xWfu2PhPn9R7N+xkLikhM5q/PdAOzdtpM5b49g9/rNlMpMpc7d1xCXGO9HeAUy9vWBLJk8k/jUZK555WkAJg4cwZKsGcSUKEFKhTJceG8bSiUm+BxpaEZ2e535E7NITEulY+8eAHw9YAhzf/4FsxgS01K55qH2pGRm+Bxp/ub2+4h1MxZQMjmRxl3uBWDvth3Mens4u9ZtonSZNE69+7qoOM8APnipJ3MmZpGUlsojfV7Ltuz7YaMY/XZ/Oo8YRFJqik8Rhm70K2+y4JcpJKalcM+bgVEB3w38kPkTsrAYIzE1lVYP3ENyFJxnAMO7vca8CYFj0+mdngB8+e77zP15EmZGYloq1z3cISp+boa81JM5EyaRlJbKo31fz7bsu6GjGP12P7qMHERSaqpPERbMhLfeZ/nU2ZROSeKKlx4HYMmEqcwc/gWbV6ymRZcHyDzxBJ+jzN/ybyaxetw0cFD+gnpUvqRRtuXLxkxg7cTZALgDB9ixcj2Nu98XNdc3EQmNmQ0BmgBlzGwZ8AzQxMzqE8hTFgF3eetWIpBHtHTO7TOz9sAYAvlFP+fc7Lz2Fco9SfuAB51zdYCzgXu9KfMeA75xzp0EfOO9hsCMEx2Bl3Op611yn7bvkHym6LvfOVfPOXc6sARof4Rq3gTuBE7yHgf3OQv4OzA2rxgirfx59TitU/bkdcnn40k/pQaNnruX9FNqsPTz8T5FVzAnXXwOlz3ZIVtZ5dNP4ZpXnuaa7k+RWrE800d+4VN0BdegeRPadH0qW9n517aiw1uv0P7NbtRufCbfDRrmU3QFU+G8+tS//+ZsZYs//5H0U2pwzvMdST+lBos/+9Gn6ArurBbNaPf8vw8r37hmLfOzppFeruyxD6qQ6l1yETd1eTxb2bnX/pW733iJu157kZMancHY90f4FF3BnXnpxdz+XPafmwuvu4r73n6Fjm91p3bjhnwzaKhP0RVMo7zOs8lTo+o8A6h5USMufuzubGWpVStywQP/oFztE32KqmC2L1/L6nHTqPf4bTR4ui0bZvzGzjXZJ7eq0uJsGjzdlgZPt6Xa1U1IPfkEJUgiEXQAF/ZHKJxzNzjnKjrn4pxzVZxzfZ1ztzjnTnPOne6c+9vBXiXn3ArnXMugbT9zzp3snDvROdc1v33lmyQ551Y656Z4z7cCcwnMBtEKGOCtNgC4yltnjXNuErA3l7pCmbbviFP0Oee2AJiZEeglOuwdNbOKQIpzboILTLo+MCi2uc65+fm1OdLSTq522MV7/bT5lD/3dADKn3s666b6HmZIKtY5iVJJ2XuJqtSvQ0xsLADlTq7B9vUb/QitUGqcVpf45KRsZaWDesH27NqN2bGOqnDSa1WnRI7zbN3U+VQ8tz4AFc+tz7qp8/wIrVBOPP1UEnIcG4CP3+zLle1uI2oODFDttDqHnWelEv48z/bu2hVV7alxel0SkpOzlQX/3OzdtQuLkvacePqpJKYkH1b+0Rt9+Gu726PquACUO+UvlMxxjU6tXIGUSuV9iqjgdq5cR3KNSsSWisNiY0g9uSrrpyw44vrrJs2hbKM8v/5ERI6Sj9+TdMwU6J4kM6sONAAmAuWDxv+tIjAcLxxym6KvcVAM/YGWwBzgwSNsvyzH9nlO8Xc82LNlO6XSAr+YS6YmsWfLdp8jCo/53/5EzfMa+h3GUfuq/2Cmfv0DpRMTaPvif/wOp9D2bNmW4zw7bLRqVJk1fgKpZTKpfGINv0MJi28HfMCMb8ZSKjGeW194xu9wjtqY/oOZ+tX3lE5M4I6XOvsdTqHNLGLnWbRJqFyWRR/9wN5tO4iJi2PjrN9IqlYx13X3797Lxlm/U/OG5sc4ShEpakKeAtzMkoARQKeDPToHeT02xyQFdM7dDlQi0KP1f8din8eamUXNX13zMnXEZ8TExvCXCxrlv/Jx7tLbb+KRwb2p1/RCJoz+3O9wwsLMou6v4sH27NrN10OGc1mbPO+7jCpN27Sm08A3OK3J+Uz6X/QMUz2SFrffxGPvv0P9phfyc5T+3OzZtYuv3x/G5bfd5HcoxVZCxTJUuewcZvX4kNm9PiSxanksJvdr14YZC0n+SxUNtROJsAPOhf1xvAkpSTKzOAIJ0mDn3EiveLU3tO3gELc1hQnAzKqa2TTvcTchTNHnnNtPYBjeNWYWG7R9Z2/dKnltH0JMh+Zpnzv628I0q8BKpiSye9NWAHZv2kpccnRMdHAkC777iaWTZ3LxfW2LRMJ3UL2mFzD7xwl+h1FoJVOSsp1nJZMTfY6o8NatWMmGVat5+a776HLTHWxeu47ud3diy4boGd55JKddfAFzx0/0O4ywqd/sQmaP+9nvMApl3YpVbFi1mpfadaTzjW3ZvHYd3YrIeRZNKpxfjwZP3s7pD99MiYTSxJfPfRKQtZPmUvYsDbUTkaMXyux2BvQF5jrnugctGg20AV7w/v+4MAE455YS+PKng/srQS5T9HlxnOic+9V7/jdgnpcw1Q+u08y2mNnZBIYF3gq8WsCYDs3T3m7coGOS2mbWr8Xqn2ZwQsvzWP3TDDLr1zoWu42IpVNnM+PjL7niPw9SolRJv8M5auuWr6BM5UoAzP15EmWrHvejN4+oTP1arPxpGtVbXsDKn6ZRpkH0nmeValan8/D3Dr3uctMd3P9G96iY3S4365evJLNyYAjR/AmTKFMles8zyP5zM+enX6L256ZSzep0GTHo0OvON7blgTe7R83sdkXFni3bKZmSyK71m1k/ZT71Hm9z2Dr7duxiy4Il1Gr7Vx8iFCle3LEZQOYry+9GKTM7HxgHzAQOeMX/IpCADAVOABYTmAJ8g5lVALKAFG/9bUAd59yW4Gn7gNXAM865vrnssyXQgz+n6OtqZjFeHCkEpgCfDtyTc+ift31D/pwC/HOggzcF+NUEEqaywCZgmnOuRV7tj0SSNKf3SDbPX8zebTuIS0mk+t8uokyDWsx5awS7N2wJTAF+1zXEJYV3uMBJaeH/kPLtK31YOXsBu7ZuIz41hTP/769MH/UF+/fuo5TXS1HupBqcf1f4h6pUTw7/LFMfPt+dP2bMZsfmrSSlp9L0lv9jwS9TWLdsBRZjpJUrS6uOd5FSJjOs+/122cyw1gcw6+3hbJq/iL3bdlAyJZEarS6mbIPazHpzGLs2bKZ0ZmpgCvCk8PdaXlntjLDX+V7Xl/h1+iy2b95CcnoaLdrcwNmX/3nfQaSSpE17doa1PoAR/+3J4hlz2LFlK4lpqTS5+ToWTprK+uUrMIshtVwZrmh/Jyllwj9ldnyJuLDXOeS57vwxYxbbvZ+bS25pzfxJU1i3dDkWE0NaubJcdd9dpIb556Z0bPjbMvDZl/h1+sxD59llbW7k7JZ/nmeRTJImrfk97HWO7zWA1XN/ZffWbZROTeb0ay+nZFICWe+OYPeWbZRMiCetehWaPn5PWPe7ZOuqsNY348X32Lt9JxYbS83rmpF2SnVW/jAFgIoXBa43q3+awcZZv1O73VVh3fdBfS+6LSL1iuQQFcNvxq1eHPbPxxeUr3ZctT3fJKm4O1Y9ScdCJJIkP0UiSfJLJJIkP0UiSfJLJJIkP0UiSfJLJJIkP0UiSfJLuJOk44GSJDlGjqtE4UiKQ5JUoNntRERERESkeDseJ1oIt5BntxMRERERESkO1JMkIiIiIiIhKw4TNyhJEhERERGRkB0o+jmShtuJiIiIiIgEU0+SiIiIiIiErDgMt1NPkoiIiIiISBD1JImIiIiISMg0BbiIiIiIiEgxo54kEREREREJWXHoSVKSJCIiIiIiIdPEDSIiIiIiIsWMepLyEVOE8sjl29b5HUJYzd+wxO8QwuaUjGp+hxBWC7es8TuEsPlx2S9+hxBWZRIq+h1C2OzZv9vvEMJq0cb5focQVjUyavsdgohEiIbbiYiIiBRC34tu8zsEEZFCU5IkIiIiIiIhc+pJEhERERER+dMBTdwgIiIiIiJSvKgnSUREREREQlYcJm5QT5KIiIiIiEgQ9SSJiIiIiEjIisOXySpJEhERERGRkGm4nYiIiIiISDGjniQREREREQlZMehIUk+SiIiIiIhIMPUkiYiIiIhIyIrDl8nmmySZWVVgIFAecEBv51xPM8sAPgSqA4uA651zG82sNtAfOAN4wjn3clBd/YArgTXOuVPz2OdlQE8gFujjnHshx/JewD+cc0lH2P5M4F0gHvgMuM8558zsOuDfwClAI+dcVn7tj4S5/T9m/YwFlExOpFHnfwKwd9tOPIx78wAAIABJREFUZr89nF3rN1E6M426d19LXGK8H+EVyIy+I1k7bT4lUxK5oGtHAPZs28H/s3ff8VFV6R/HPycV0kkCSSChd+kqoCCg2NdVXHsFBVF/6qpr17Wvrrv2tio21LWDhXXtWGiK9CYd6YEAgTRII8/vjxnYBBIy4CQ3kO/b17yYOffce57rkOE+ec49M/uF99mxeRsNkxPo+X/nHxTnsm78L2yYOBsMUo/pQbPje1fYXrq9kMWvjaMoOxfbWUazE/uQ2q+7R9FW74fn32D1jHk0jI/lnCfvBeDnN8ewavpcQsPCiEttzMBrhhIZHeVxpIH5/rnXWTl9Lg3jYzn/6QcAmPLGh6yaPoeQsFDiU5pw7HWX1fnzOa1FLwand8UMVudv5vn5X1JStnP39mEdBnFYYgYAkaFhxEdEMfS7570KNyCHymfa+vHT2DhpDmaQ2r87TY8/ssL2tV9NZfMvCwCwsjK2Z26h9+N/rpPnlRGTzN1HXrj7dVp0IqMXfsPY5ZMr9Oue3Jprup5GWEgoOcUF3DhxVG2HKiJSJwVSSSoFbjKzmc65WGCGc+4bYBgw3swecc7dDtwO3AZkA38GhlRyrNHAc/iSrko550KB54ETgLXANOfcODP71b/9CKBRNTG/AFwBTMWXJJ0MfAHMB/4EvFT9adectH49SD+uNwtf/Xh326ovJtGoUytanNqfVZ9PYvUXk2hz9gkeRhmY9P49aTG4L3NfHrO7bcV/J5DUqTVtThvI8s9+ZPl/J9Dx3JM8jLJ6Beuy2DBxNj3uuIyQsFDmP/0eid3a0rBJ4u4+63+YQVRaModdey7FeQXMuPslmvTpQkhYqIeRV63DsUfR5ZRj+f7Z13e3pXfrTO+LziQkNJSpb41l9kdf0OeSszyMMnAdju1Hl1OOY/wzr+5uy+jemb4X/4mQ0FB+enMMM8d+zlGXnu1hlPuWGBnDKc17cePk0RSXlfKX7qfRL7UjP6xfsLvP6MU/7H5+SvOetIptUvuB7qdD4TOtYN0mNk6aQ7c7hhISGsqCZ96nUbe2NGzyv39u0k/qQ/pJfQDInrOU9eOn1ckECWBN/mZGfv8MACE4PjjlTiaV+3sGEB3egOu7n8HtU14ja0cOCRHRXoQqIgchrW4HmFmmmc30P88DFgLNgDOAN/zd3sCfFJlZlplNA0oqOdYEfEnUvvQGlpnZCjMrBt7zj7UrgXoUuLWqnZ1zaUCcmf1sZoYvIdsV20IzW1zdOde0hPYtCNvjH9bNsxeTerSvKpF6dHc2zfI8zIAkdmi110VC1qxFNOvfC4Bm/XuRNXOhF6Htl+2ZW4ht1YzQyHBcaAjx7ZuzeWbF98A5x87CYsyMsqISwqIb4kLq7m19aZ3bExlTsaqS3qMzIaG+pK5J+9YUbNnmRWgHpOlh7YmMrXgRl9HjsN3nk9K+NQVbtnoR2n4JdSFEhIYR4hyRIWFsLcqvsm//1I5M2rCoFqM7MIfCZ9qODVuIadWU0Ij/fQZs2UfMm6YtJPnIzrUY4YHr1aQt6wu2sHFHxZ/3wek9mLR+AVk7cgDYVlzgRXgichAys6A/6pr9usJzzrUEeuKr0KSYWaZ/0wZ80/GCoRmwptzrtf42gGuBceXGrWr/tVXsX2eV5OYTmRALQER8DCW5VV841XVFOfk08J9LZHwMRTl1/1yimzUmd+kaSvK3s7OohOz5yynamluhT9qxh7M9czNTb3mGGfe/TJvzTsCFOI8i/v0WfzeZjF6HeR1G0Cz6bhLNe1U5i7dOyC7KZ9zKabww4ApeHnQV20uLmbNlVaV9kxvE0iQqjvlbVtdylMFxsH2mRTVN9n8G7GBncQlb5y2nODu30r47i0vYtmAFSb061HKUB+bY9O58t3bOXu0ZMcnERDTkif4jeXHQtZyQ0cuD6ERE6qaAF25wzsUAY4EbzCzXuf9dHPrv96nRFNA51xQ4BxhUk+P4xxoJjAQ45ubhdD79uJoecs/xwR28F9/l+c7F6yiqF5WWTPrJfZn/1HuERIYTndFkryrR1gUriM5IoetNF1G4aSvznnyXXu0yCGsY6VHUB27m2M8JCQ2l7TF9vA4lKGaM+YyQkFDaDejrdSj7FB0WyZFN2nLNhFcoKC3ipu5/5Ji0TkzM3Lva2j+tIz9tWHpI3Bx7MHymRaUlk35SXxY8/R6hEeFEZ6RAFZXi7DnLiG3TrM5OtSsvzIVydGonXlnw5V7bQl0I7ROacfOkl4kIDee5gf/Hwq2rWZu/2YNIReRgcij821SdgCpJzrlwfAnS22b2kb95o39q264pblkHEoBzLsM5N9v/uApYB2SU65Lub+sJtAWWOedWAlHOuWXOudBy+z/g75teyf4BM7NRZnaEmR1RWwlSeFwMRdvyACjalkd47ME7NzwyPoZC/7kUbssjMq7S9TXqnNT+Pej518vpfsslhEc1pGFKYoXtGyfPJblXB5xzNGySSIPkBHZs2OJRtAdu8fdTWD1jLsddPxxXxy9cA7Hou8msmj6XwTeOqPPn0y2pBVk7csgt2cFOK2Nq1lI6JDSttG+/1I5MPgim2lXlYPxMS+nfnR53XUbXWy4mLKrBXp8Bu2ye/iuNex8cU+16p3Zg6bZ1lU7r3FSYw7SNSyjcWUJu8Xbmbv6NNnFpHkQpIlL3VJskOd9Vx6vAQjN7otymccBQ//OhwKcHEoCZrTGzHv7Hi8A0oJ1zrpVzLgI4H98Uu/+aWaqZtTSzlsB2M2trZjvL7X+PfypernOurz/2Sw80ttqU3KM9G6b4pkNsmDKH5B4HxzSOyjTp0ZF1k2YCsG7STJr07OhxRIEpzvXNxy/cksPmmYto0rviVLTIpDi2LVzp75vPjo1baJCcUNth/i5rZs1nzqdfc9Jt1xAWGeF1OL/b6pnzmf3Jl5xyx3WER9b9it7mwlzaJ6QREeIr4ndNbM66gr1v02wanUh0eCSLt62v7RCD5mD8TNv1GVCUncOWWYsrTYRKdxSSu2QNid3b1XZ4B+S4KqbaAUzO/JWuSS0JcSFEhobTKTGDVXkH9PtOEaln6sM9Sa66oJxz/YGJwDygzN98J777kj4AmgOr8C0Bnu2cSwWmA3H+/vlAZ/8UvXfxTZdLBjYC95rZq+zBOXcq8BS+JcBfM7OHKumTv48lwI/gf0uAfwFc558SeCbwLNAY2AbMNrN9Lrt21cR3gv6uLRg1lm2LV1KSv52IuGhanj6Ixj07Mv/FMRRl59AgKZ7DrjyH8JjgTuVoEBYe1OMBzH7hfbIX/UZx/nYi4mJoN+Q4Ug7vzOzn32NHdg4Nk+Lp8X/nExET/GWZ84u3B/V4c/75JiUFOwgJDaXVOYNp1KkVmT/6kr20gb0o2pbHktc/ozgnHzAyTj6aJn2Dcw9Mp8QWQTlOeeOffIX1CxZTmJdPVHwch5/3R2Z//CU7S0pp4P+tfpN2rTnmyouCPnZoSPBX/PvmiVGsn+87n4bxcRx5/unM/Ohz//n4PgpS2rdm4FWXBHXcSWt/Cerxzm1zNP1SO7DTyvgtL4sX5n/NWW36sDxnI9M3Lff3OYrwkDDeXjoxqGMDJEcFv1Lg1Wda8c6ioB5v3qP/pqRgBy40hFZnDyahU0syf5wFQNrAngBsnDKXbQt+o8MVZwR1bICVW4O7uEWD0HDePfl2Lv7qnxSU+v5f/bGlb4rtf1ZOBeC8dgM4qfnhGMbnK6fttUT47/HdmY9U30lE9lS3p0T4vbV8VtCvjy9p07NOnXu1SVJ9VxNJkldqIknyUrCTJC/VRJLkpZpIkrwS7CTJazWRJHkl2EmS14KdJHlNSZLIAalTiUJV6kOSFPDCDSIiIiIiIvqeJBERERERkXpGlSQREREREQlYfbhdR0mSiIiIiIgETN+TJCIiIiIiUgc4515zzmU55+aXa3vUObfIOTfXOfexc67S72dxzq10zs3zf7fq9OrGUpIkIiIiIiIB8/B7kkYDJ+/R9g3Qxcy6AUuAO/ax/7H+71Y9orqBlCSJiIiIiEidZ2YTgOw92r42s1L/y5+B9GCMpSRJREREREQCVmbBfwTJ5cAXVWwz4Gvn3Azn3MjqDqSFG0REREREJGA1sXCDP3Epn7yMMrNR+7H/XUAp8HYVXfqb2TrnXBPgG+fcIn9lqlJKkkRERERExFP+hCjgpKg859ww4DRgsFVxg5OZrfP/meWc+xjoDShJEhERERGR38+szOsQdnPOnQzcCgw0s+1V9IkGQswsz//8ROCBfR1X9ySJiIiIiEid55x7F/gJ6OCcW+ucGw48B8Tim0I32zn3or9vU+fc5/5dU4BJzrk5wC/Af83sy32NpUpSNTJik70OIWhW5Kz3OoSguqj9QK9DCJrV+dnVdzqIrMzL8jqEoHGH2O+S4iKjvA4haB44/HyvQwi6U8fd6XUIIiLVKgt8ye6gMrMLKml+tYq+64FT/c9XAN33ZywlSSIiInXEd2c+4nUIIiKCkiQREREREdkPVgOr29U1SpJERERERCRgXk23q02H1mR7ERERERGR30mVJBERERERCZgqSSIiIiIiIvWMKkkiIiIiIhIwLdwgIiIiIiJSTpmVeR1CjdN0OxERERERkXJUSRIRERERkYCZFm4QERERERGpX6qtJDnnMoA3gRTAgFFm9rRzLhF4H2gJrATONbOtzrmOwOtAL+AuM3us3LFeA04Dssysyz7GPBl4GggFXjGzR/zto4GBQI6/6zAzm13J/q2A94AkYAZwiZkVO+cGAE8B3YDzzWxMdedfEya/8DbrZs6nQVwspz9+JwArf5rFnDGfk7NuI6c+dDPJbZp7Edp+WTd+GhsnzgaDlGO60+z43hW2r/3qZzZNXQCAlZWxPXMLfZ64nvDohl6EG5A3//EE836aSmxCAveMfqnCtm/fH8vYF17m0U/eJyYh3qMIA/ffp15i2bRZRMXHccW//gnAj299wNKpM3AuhKiEOE674Spikxp5HGlgfn7xHdbP+pUGcTGc+ujtAKz+eTbzxnxJ7vqNnPjgjSQdBD83f2jRk8HpXTEzVudv5l/zv6akbOfu7ckNYrmm60lEh0US4hxvL5nErM0rvQs4ALNe/pANsxYSGRfDcY/8BYB1U+ey+ONvyFu/iQH3XUuj1um1Fs89d93FhB9/IDExkY/G/Wev7b+tWME9d93Jwl9/5brrb2Do5Zf/7jGLi4u56/bbWLjgV+ITEvjnE0/QrFkzfpoymaefeIKSkhLCw8O58eZb6NO37+8eT0TES2X1YOGGQCpJpcBNZtYZ6Atc45zrDNwOjDezdsB4/2uAbODPwGOVHGs0cPK+BnPOhQLPA6cAnYEL/OPtcouZ9fA/9kqQ/P4BPGlmbYGtwHB/+2pgGPDOvmKoaW0H9mHwHf9XoS0hI41BN40gpVMbj6LaPwXrNrFx4my63zGMnvcMJ3vucnZkZVfok35SX3reM5ye9wynxZmDiG/fvE4nSABHnXwC1/3zb3u1Z2dt4tfpM0hMaeJBVAem6/EDOO/+2yq09T3rNEY89w+GP/t32h7Zk8nvfuRRdPuv9cA+DLr9ygpt8RmpHPOXy2jSsbVHUe2fxMhoTm3ek9t/epubprxFiAuhX2qHCn3Oat2HnzYs4daf3uapOZ8zovNxHkUbuIxjDueoW4dXaItLT+HI6y8lqUOrWo/njDOH8MKoUVVuj4uP57Y772LoZfufHK1bt47hQy/dq/3jsWOIi4vns6++4uKhl/LU475/AhMSGvHMv15g7KfjePDvf+eu22/ba18RkYNNmVnQH3VNtUmSmWWa2Uz/8zxgIdAMOAN4w9/tDWCIv0+WmU0DSio51gR8SdS+9AaWmdkKMyvGVxE6I7DTAeecA44DdlWJyse20szmAp4uyZHSuS2RMVEV2hLSU4lvmuJRRPtvR+ZmYls1JTQyHBcaQnz7DLbMXFJl/83TfqVx785Vbq8r2nXvSnRs7F7tY557iT9dOcKDiA5c8y6daBAbU6EtMup/f+9KCovA1XZUB65JpzZE7PFzE98slbiD6OcGIMSFEBEaRohzRIaEkV2UX2G7YTQMiwAgKiySrYUFXoS5X5I7tiZij1+AxDZLITatsSfxHH7EkcTFJ1S5PSkpiS5duxIWtvdkis/GjePC887l3DPP5IF772Xnzp2VHGFv33/3HacP8f1TdcKJJ/HLzz9jZnTq3JkmTXy/XGnbth1FhUUUFxcfwFmJiEht2q97kpxzLYGewFQgxcwy/Zs24JuOFwzNgDXlXq/1t+3ykHNurnPuSedcZCX7JwHbzKy0iv0lCKKaNSZn6RpK8rezs6iErfOXU7Q1t9K+vu0rSOrVodLtdd2cST+R0DiJ9LYHR7WiOj+++T7PDbuWBT9MZsDF53gdTr2SXVTAf1bO4IUBI3h50Ei2lxYxd8vqCn0+WPYzA9I68eLAEdxx+BBeW/S9R9HWPyuWL+erL7/gjX+/zQcff0xoaAiff7b3dL3KZG3cSGpqGgBhYWHExMaybdu2Cn2+/fprOnXuRERERNBjFxGpTWYW9EddE/Dqds65GGAscIOZ5foKNj5mZs652ji7O/AlZBHAKOA24IFaGFf2EJWWTPrJRzH/qfcJjQwnOiMFF1J5WSJ77lJi26bX+al2lSkuLOTLt9/jz48+7HUoQTPw0vMYeOl5TPngU6Z/9jUDLjrb65DqjeiwSI5s0pprJrxGQWkRf+n+B45J68jEzEW7+/RP68D36xbw2aqZtI9P47quJ/OXyW/Wg9nf3pv6888sXLCAi849F4DCokISE5MAuOG6a1m/dh0lJSVkZmZy7plnAnDhJZcw5E9/qvbYy5Yu5aknHufFl1+puRMQEZGgCShJcs6F40uQ3jazXTcxbHTOpZlZpnMuDcg6kAD8C0Ps+lXdi8AcIKNcl3RgHfim/vnbipxzrwM3+4/xFb5K1nTgCiDBORfmrybt3n8/YhoJjAQ446/Xc+RZpx7AmR36Uvt3J7V/dwBWfvwDkY32nqYGsGnaQhofWfen2lVm0/pMNmdu4G/DrwZg26bNPDzyWm574WnikxI9ju73OWxQPz64759KkmpR16TmZO3IJbdkBwBTs5bRIaFphSTpuPQuPDTD9zG7JCeT8JAwYiMaklu8w5OY6xMz449nDOH6v/xlr21PPfsc4Lsn6Z477+DVN96ssL1JSgobNmSSkppKaWkp+Xl5JCT4pvxt3LCBG/98HX/7+yNkNK/7i4uIiFSnLt5DFGzVTrfz3+PzKrDQzJ4ot2kcMNT/fCjw6YEEYGZryi3E8CIwDWjnnGvlnIsAzvePhT8Z2xXTEGC+/xgn+fcfYb563ffAriu//Y7NzEaZ2RFmdoQSpKoV5/rulSjcksOWmYtp3PuwvfqUbi8kd8lqknq0q+3wgqJZ61Y8+sn7PPT+mzz0/pskNE7mzlHPHbQJUva6zN3Pl06dQVJ6Uw+jqX82F+bRLiGNiBDf76e6JjZnbUHF2zQ378ila6LvQrpZdCLhIaFKkGpJn759+fbrr9iyZQsAOdu2sX5dYL9jG3TssYz7xPdPzTdff0XvPn1xzpGbm8u1V1/F9X/5Cz179aqx2EVEJLhcdXMAnXP9gYnAPP634MGd+O5L+gBoDqzCtwR4tnMuFV9FJ87fPx/o7J+i9y4wCEgGNgL3mtmrlYx5Kr6lukOB18zsIX/7d0BjfLebzwauMrP8SvZvjW/Bh0RgFnCxmRU5544EPgYaAYXABjPb+8q+nIdmfx30VHnC06+z8ddlFObl0zA+ju7nnEpkTBS/vD6Gwtx8IqIb0qhFM06465qgjrsiZ31Qjzf3n29RUrADFxpK63MGk9CpJZk/zgQgbaDvYmDjlLlsnb+CjiOHBHVsgIvaDwz6MV994O8smT2X/Jxc4ho14rTLLqbfH/63IONd513KHS89G/QlwFfnV7eeyf775J/PsnreQnbk5hGdEM8xF53F8umz2bI2ExfiiG+czMnXDCc2OfgJ38q8Ayos79PkZ94ga+FyivLyaRAfS9ezTyEiJooZo8dSlJtPeFRDGrVsxrF3XB3UcRdsWlR9p/1wbpujODq1PTutjJV5m3hh/jec1aY3y3M2Mn3TCtKjE7nysBNoEBYOZry1ZOJe9y39Hq0aBX+1uenPv8PmhSsozi8gMi6Gjn86gfCYKOa9+SnFeQWERzUkrkUaR98a3MVPHjj89Erbb7v5Jqb/8gvbtm0jMSmJq6+9ltIS322q555/Pps3beKCc8+hID+fkJAQGkZF8fF/PiMmJoYvv/ic10a9TJmVERYWxp1330237j12H7uqSlJRURF33XYbixYuJC4hnn8+9jjpGRmMevEFXn35ZVo0b7G77wuvvEJSUlKlsTcI1dcXitRzB8WSSjVxfXxXjxPr1LlXmyTVdzXxl8ArwU6SvFYTSZJXaiJJ8lJNJEleCXaS5LWaSJK8UlWSdDBTkiRS79WpRKEqD876KujXx3f3PKlOnbs+jUVERERERMoJeHU7ERERERERLdwgIiIiIiJSz6iSJCIiIiIiAbN68O19SpJERERERCRgmm4nIiIiIiJSz6iSJCIiIiIiAasPXyGkSpKIiIiIiEg5qiSJiIiIiEjAyrRwg4iIiIiIyP+UWZnXIdQ4TbcTEREREREpR5UkEREREREJWH1YuEFJUjW2FRV4HULQXNH5eK9DCKpPV870OoSg6ZvSzusQgmpBdqHXIQRNo6jGXocQVBsLNnsdQtBcOeE1XhpwuddhiIjIIUhJkoiIHLQahGrWuIhIbasPCzfoXxcREREREZFyVEkSEREREZGA6Z4kERERERGRcsrqQZKk6XYiIiIiIiLlqJIkIiIiIiIBUyVJRERERESknlElSUREREREAmaUeR1CjVOSJCIiIiIiAdN0OxERERERkXpGlSQREREREQlYffieJFWSREREREREyqm2kuScywDeBFIAA0aZ2dPOuUTgfaAlsBI418y2Ouc6Aq8DvYC7zOyxcsd6DTgNyDKzLvsY82TgaSAUeMXMHvG3O+BvwDnATuAFM3umkv1bAe8BScAM4BIzK3bODQCeAroB55vZmOrOvybMevlDNs5eRGRcDMf+/UYA1v8yl8Uff0ve+k0MuPcaElqnexHafnvl4UeZPeVn4hol8PBbrwLw8atv8MN//ktcQgIAZ185nO5H9fEyzIDNGPUBG2b/SmRcDMc/cjMAa6fOYeFH35C3Potj77+ORq0zPI4yMB889iwLp04nJiGem16u+GPy44ef8N9Ro7l3zJtEx8d5FOH+OZR+bha/Po4tc5cSHhvNkQ9cBUBJ/g5+fWksRVtyiEyKp/NVZxEe3dDjSKuXOX4GWZPnghlN+ncjbfARFbbnLF7Nkhc+JjI5HoDEnu1J/8PRXoQqIiJBUoYqSQClwE1m1hnoC1zjnOsM3A6MN7N2wHj/a4Bs4M/AY5UcazRw8r4Gc86FAs8DpwCdgQv84wEMAzKAjmbWCV8iVJl/AE+aWVtgKzDc377af4x39hVDTWt+zOH0veXyCm2xzVI58s+XkNShpTdBHaD+p57EzY//fa/2k849mwdHj+LB0aMOmgQJoMWAIzj6lhEV2uLSU+l7/aUkd2jlUVQH5ogTj2P4w/fs1b4taxNLZ8wmoUljD6I6cIfSz01Kv+50veHCCm2rv5hMo06t6P3wNTTq1Io1X0z2KLrAbV+3iazJc+ly+8V0++swts5bTmHW1r36xbZLp9tfh9Htr8OUIImIHALKzIL+CIRz7jXnXJZzbn65tkTn3DfOuaX+PxtVse9Qf5+lzrmh1Y1VbZJkZplmNtP/PA9YCDQDzgDe8Hd7Axji75NlZtOAkkqONQFfErUvvYFlZrbCzIrxJUJn+LddDTxgZmW7xtpzZ3+16ThgV5WofGwrzWwueLtuYVLH1kTs8Rvi2GZNiEk7uC5aATr26EZ03MFRiQhEcsfWRMREVWiLa5ZCbNMmHkV04Fp3O4yo2Ji92v/z4mucesVQnPMgqN/hUPq5SWjfYq8q0ZbZi0k5uhsAKUd3Y/OsxV6Etl92bMgmpmUaoRHhuNAQ4tplkD1riddhiYjIoWs0exdcqirc7OafAXcv0AdfrnFvVcnULvt1T5JzriXQE5gKpJhZpn/TBnzT8YKhGbCm3Ou1/jaANsB5zrnpzrkvnHPtKtk/CdhmZqWV7C+1ZPxHn3DX0BG88vCjFOTmeR2O+C2YMpW4pCSatjm4qmL1QXFuAZEJsQBExMdQnFvgcUTVi2qaTN6ytZTk72BncQnb5q+gaOveP+/5K9Yz98HRLHx2DNvXb/YgUhERCSYzC/ojwHErK7hUWrjZw0nAN2aWbWZbgW+oZnZbwEmScy4GGAvcYGa5ewRsUCuTEyOBQjM7AngZeK0mBnHOjfQnYtPnfPJ1TQxxSDvuzD/y6Ptv8eDro0hISuTd5170OiQBiguL+O7dMZw47AKvQ5FqOOdwB0Gpr2FaEk1P6s2iZz5k0TNjiMpoggup+M9KdPMUej50Jd3uHkbqoF4seeFjj6IVEZG6rPz1t/8xMsBdAync7KsIU6mAkiTnXDi+BOltM/vI37zROZfm354G7DX1LcBjZzjnZvsfVwHr8N13tEu6vw18J7Rr/I/xLcCAc+4r//6vAFuABOdcWCX7B8TMRpnZEWZ2RPchJx7IadVr8YmJhISGEhISwsDT/8CKhYu8DkmALZmZZG/I4qkrb+DvF19BzqYtPH31X8jL3vseEql9EXHRFG3zVWGKtuURHhtVzR51Q5N+3eh656UcdvMFhEU1oEGTirMXwhpGEtogAoBGXVtTtrOMkvztXoQqIiJBUlYD/5W//vY/Ru1vXMEs3FSbJPnv8XkVWGhmT5TbNA7YddPTUODTAwnAzNaYWQ//40VgGtDOOdfKORcBnO8fC+AT4Fj/84HAEv8xTvLvP8KfWnUvAAAgAElEQVT/P+d74OzfG5scmG2bt+x+PmPCJNJbt/QuGNktrVVL7v3wDe7498vc8e+XiW+cxPUvPEFs4j6n5EotSerRgY1T5gKwccpcknp08DiiwJT4pwUWZeeSPWspyb07VdhenJO/expF/m+ZYEbYQbBqn4iIHDQCKdzsqwhTKVfdHEDnXH9gIjCP/y14cCe++5I+AJoDq/AtAZ7tnEsFpgNx/v75QGczy3XOvQsMApKBjcC9ZvZqJWOeim+p7lDgNTN7yN+eALztHzMfuMrM5lSyf2t8Cz4kArOAi82syDl3JL4KVCOgENhgZoft6/xvmfpx0KcRzvjXu2xeuILi/AIi42Lo8KcTiIhuyLy3xlGcV0BYVEPim6dx1K3Dqz/Yfjir9ZFBPR7Av+79G4tmzyF/Ww5xiY04c/hQFs2aw+qly8FBcmoql91yIwnJSUEf+9OVM4N+zF+ee5tNC5f735tYOp91IuHRDZnz5qcU5+UTHtWQ+BZN6X/bFUEdt29KZbfX/T5vP/Q4K+bOpyAnl9hGCZxw6fn0PuWE3dv/fvEV/Pn5x2tkCfBJmcGvHnr1c5NTHPx7g34d9RE5i1dRkr+d8LhoWp4+kOSeHfj1xbEUZef6lgC/8izCY4KfTBSVFgb1eAsee4fS/EJcaAgtzjmW+I4t2DhhNgApA3qw4fuZbJwwGxcSQkhEGC3OPpbYNsG7TfSNY0dU30lE5OBR9+daA1dMeCvo18cvD7gkoHP3r5Hw2a6vE3LOPQpsMbNHnHO3A4lmduse+yTi+1qgXv6mmcDhZlblgnLVJkn1XU0kSV6piSTJSzWRJHmlJpIkL9VEkuSVmkiSvBTsJMlrSpJE5BBzUCRJI358I+jXx68MHFrtuVdWcME306yyws0R+AoqI/z7Xo6v0APwkJm9vq+xqv0yWREREREREa+ZWVWrTw2upO90YES516+xH4u+KUkSEREREZGAWa0sau2t/fqeJBERERERkUOdKkkiIiIiIhKw+rCmgZIkEREREREJmO1e8PrQpel2IiIiIiIi5aiSJCIiIiIiASurB9PtVEkSEREREREpR5UkEREREREJmNmhf0+SkiQREREREQmYvidJRERERESknlElSUREREREAqbpdsK2onyvQwiaLYUFXocQVD+tnuh1CEEzb+Mcr0MIqvUbl3gdQtDExCZ5HULQtWrcxesQRERE6jQlSSIi9cwbx47wOgQRETmImZYAFxERERERqV9USRIRERERkYAZuidJRERERERktzJNtxMREREREalfVEkSEREREZGA6ctkRURERERE6hlVkkREREREJGD6MlkREREREZFy9D1JIiIiIiIi9YwqSSIiIiIiEjB9TxLgnMsA3gRSAANGmdnTzrlE4H2gJbASONfMtjrnOgKvA72Au8zssXLHeg04Dcgysy77GPNk4GkgFHjFzB7xt08EYv3dmgC/mNmQSvZvBbwHJAEzgEvMrNg5NwB4CugGnG9mY6o7/2Bb9+0vZE6cBWakDuhJ+vF9KmwvKdjBktGfUbhpKyHhYbQfdhrRzZrUdpgBe+/Rp/l16nRiEuK59ZXnKmz74cOPGffS6zww9t/ExMd5FGFgMmIac2+fi3e/TotO5PVfv2LMskl79e3QKJ1/DbqWB355mx/XzavNMAN2Rqs+nNi8J5ixMi+Lp+aMo6Rs5+7tQ/zbd1oZucXbeWrOf9i0I8fDiPctJiKKe/oPp02jdMC4f+IrzM1atnv7KW2OZli3PwCO7SWFPDxlNEuzV3sWb1UyYptwX9+hu183jUnitflf8OHSH3e3ndD8cC7sOBgHbC8t4vEZH7I8Z70H0YqIiNRfgVSSSoGbzGymcy4WmOGc+wYYBow3s0ecc7cDtwO3AdnAn4G9khdgNPAcvqSrUs65UOB54ARgLTDNOTfOzH41s2PK9RsLfFrFYf4BPGlm7znnXgSGAy8Aq/1x3xzAeQddwbosMifOouedlxMSFsq8p98hqVs7GjZJ3N1nzeeTiclI4bBrzmF75maWvfMl3W66eB9H9daRJw2m/5DTeOcfT1Zo35q1icXTZ9OoSWOPIts/a/I3MWK87xxCcIz5w91MXD9/r34hOK7s8gemZS2p7RADltQglj+2OpL/++FFistKua3XWQxoehjj187d3Wd57gZunPgKRWWlnNLicC7rNJh/zvzIw6j37Za+FzNl7Vxu/e5ZwkJCaRAWWWH7urxNjPjvQ+QVb+fo9G78td/lDP3Pfd4Euw9r8rIY/s2jAIQ4x9jT7mfCurkV+mQWbOG6758lv2QHfVI7ccsR53HV+CcrO5yIiIgndE8SYGaZZjbT/zwPWAg0A84A3vB3ewN/UmRmWWY2DSip5FgT8CVR+9IbWGZmK8ysGF9F6IzyHZxzccBxwCd77uycc/5tu6pE5WNbaWZzwZsa4fbMzcS2akpoZDguNIT49i3YPHPRXn0SOrYEICotmcIt2yjOzfcg2sC06daFqNiYvdo/feFVThs5DJyr/aB+p15N2rEufwsbt2/ba9uf2vZjwrp5bCss8CCywIW6ECJCwwhxjsjQMLILK/4dmrdlFUVlpQAs3rqO5AZ1t9IXE96QXqkd+WSJr9pSWraT/OLtFfrMzVpKnr9tXtYyUqIb1Xqc++vwJu1ZX7CZjdu3Vmifv2Ul+SU7AFiwZSWNG8Z7EZ6IiEiVzMqC/qhr9mvhBudcS6AnMBVIMbNM/6YN+KbjBUMzYE2512v9beUNwVfFyq1k/yRgm5mV7mN/T0Q3a0Lu0jWU5G9nZ1EJ2fOWUZRd8RSi05uweZYvccr9bR2FW3Io2prnRbgHbP7kn4lPTqJZm1Zeh3JAjsvozndrZ+3Vntwgjv5Nu/Dpip88iCpwWwrz+HjFz7w++HreOv5GtpcWMWvziir7n9i8BzPKTV2ra5rGNmZrYS73HTOSd4Y8yN39h+9VSSpvSPtBTF47t8rtdcVxzXsxfvXMffY5rXVfpm5YWEsRiYiIyC4BJ0nOuRhgLHDDnsmJ+WputVl3uwB4txbHC4qotGTSTz6KeU++w7yn3yEmIwUXUvEtyDilH6XbC5lx/8us/24aMRmpuIOoGlNcWMS3747h5KEXeh3KAQlzofRLO4wfKrnIvrb76Yya/3md/5bp6PAG9Elpz/DvnuXSb58iMjSCQc26Vtp3ULOutI1PY2wdTvxCQ0LpmNSSMYvGc+End7OjtIjLup1Wad8j0joxpMMAnpn2fi1HuX/CQkLp1/Qwvl8zu8o+PRu35Q+t+vLi3P/UYmQiIiLVK8OC/qhrAlrdzjkXji9BetvMdt24sNE5l2Zmmc65NCDrQALwLwyx6yrgRWAOkFGuSzqwrlz/ZHxT8s4s1/YVvkrWdOAKIME5F+avJlXYP8CYRgIjAfrfdBkdTz92P8+qamnH9CTtmJ4A/PbRd0Q2qjjNKaxhJB0uOx3wzff85Y7naNC47k8d2mXz+kyyN2zksSuvByBn02aeuOoGbnj+ceIS6/559EntyJJt69hatPcUxw6NMrin90UAxEdG0ye1IzutjEnrF9R2mPvUI7kVG7dvI9c//eynzEV0apTOD3ssMtE9uRXnte3P7T+9QWm5RR3qmqyCbLIKspm/aTkA43/7hWHd/7hXv3aNMri7/3Cu++oxcip5/+qSvqmdWLp1baV/zwBax6dx65Hnc8uEl3a/jyIiIlJ7AlndzgGvAgvN7Ilym8YBQ4FH/H9WtYjCPpnZGqBHufHCgHb+FerWAecD5csSZwOfmVlhuWOctEfM3/v7vXcgsZnZKGAUwBUT3gpqalucW0BEXDSFW3LYPGsxPe+4rML20u2FhESEExIWyoaJs4hv15ywhlVPLaprmrZuyQNj3tr9+sGLRnDjv56o86vb7TI4owfj1+w91Q7ggi//vvv57Yefx08bfq1zCRLAph05dGiUTmRIGEVlpXRPbsnSnMwKfVrHpXJt11O595d3yanjF+FbduSwsSCbFvGprMrZQO+mh/Hb1oq/90iNTuKx46/n7h9fYnXuBo8iDdzg5r34toqpdk2iEvjb0Zfz0NR/szZ/Uy1HJiIiUr26eA9RsAVSSeoHXALMc87tmhtyJ77k6APn3HBgFXAugHMuFV9FJw4oc87dAHQ2s1zn3LvAICDZObcWuNfMXi0/mJmVOueuBb7CtwT4a2ZW/kr0fP/Y+3Ib8J5z7m/ALHxJHs65I4GPgUbAH51z95vZYQH8PwiaX18YQ2nBDlxoCG0vPJmwqAas/2EGAE0HHc72zM0sfm0cOIhq2pj2QyufVlRXvPXQoyybM5+CnFzuP/8yThp6AX1POdHrsA5Ig9BwDm/Sjsdnjt3ddnqrvgCM++1nr8Lab0u2rWdy5kKeGnAFZWVlLM/dwJerZ3JR+4Eszcnkl41LuLzTYBqERXB7r7MA2LQjlwen190pav/46U0eGng14aFhrM3bxH0TRnFWx+MAGLvoO67oOYT4yBjuONq3vPbOsp1cPO5eL0OuUoPQCI5I6cBjMz7Y3XZ6m6MBGLd8CsM6n0R8ZDQ39joHgJ22k5HfPlHpsURERKRmuPqwhN/vEexKkpeGtOrtdQhB9egvr3gdQtDERB4clbZArd9Yd5dI318xsUlehxB0E859yusQRESkcgfFjeinf/5w0K+Px516Z50694DuSRIREREREQF9T5KIiIiIiEi9o0qSiIiIiIgErD4s3KBKkoiIiIiISDmqJImIiIiISMCsDn75a7ApSRIRERERkYCVabqdiIiIiIhI/aJKkoiIiIiIBExLgIuIiIiIiNQzqiSJiIiIiEjAjEP/niQlSSIiIiIiEjBNtxMREREREalnVEmqRljIofO/6O6v/+Z1CEG1oyDH6xCCJicqzusQgiomNtHrEILGrAzn9PskERGRXaweLAF+6GQAIiI1ZMK5T3kdgoiISL3mnOsAvF+uqTVwj5k9Va7PIOBT4Dd/00dm9sCBjKckSUREREREAmbU/j1JZrYY6AHgnAsF1gEfV9J1opmd9nvH0xwSERERERE5mAwGlpvZqpoaQJUkEREREREJWJn3q9udD7xbxbajnHNzgPXAzWa24EAGUJIkIiIiIiIBq4mFG5xzI4GR5ZpGmdmoSvpFAKcDd1RymJlACzPLd86dCnwCtDuQeJQkiYiIiIiIp/wJ0V5JUSVOAWaa2cZKjpFb7vnnzrl/OeeSzWzz/sajJElERERERALm8ZfJXkAVU+2cc6nARjMz51xvfOsvbDmQQZQkiYiIiIhIneeciwZOAK4s13YVgJm9CJwNXO2cKwV2AOfbAWZ0SpJERERERCRwHn2ZrJkVAEl7tL1Y7vlzwHPBGEtJkoiIiIiIBKwmFm6oa/Q9SSIiIiIiIuWokiQiIiIiIgHzeOGGWlFtkuScywDeBFIAw7dm+dPOuUTgfaAlsBI418y2Ouc6Aq8DvYC7zOyxcsd6DTgNyDKzLvsY82TgaSAUeMXMHvG3DwYexVcByweGmdmySvY/HBgNNAQ+B673r3JxDnAf0AnobWbTqzv/mrDwtU/YPHcJEbHR9HnwGgBK8rcz/6UxFG7eRoPkBLpcdQ7h0Q29CC9gMRFR3NN/OG0apQPG/RNfYW7W/96OU9oczbBufwAc20sKeXjKaJZmr/Ys3up8O+xlCop3sNPK2Fm2k3Pev6nSfl2atOXdcx/lpi8f5etlU2o5ysDERERx7zEjaNMoHcO4f8LLe7833U/D4dhesoOHJ49mSR19bzJim3D/UcN2v24ak8yr8z/nwyU/7G5rHtuEO3pfRPtGGbw87zPeW/xd7QcqIiIih4xAKkmlwE1mNtM5FwvMcM59AwwDxpvZI86524HbgduAbODPwJBKjjUa381Ub1Y1mHMuFHge38oVa4FpzrlxZvYr8AJwhpktdM79H/BXfxx7egG4ApiKL0k6GfgCmA/8CXgpgPOuMan9epA+uDe/vvLx7rZVX0yiUadWtDz1GFZ+PpFVn0+i7TkneBhl9W7pezFT1s7l1u+eJSwklAZhkRW2r8vbxIj/PkRe8XaOTu/GX/tdztD/3OdNsAEa+tFdbCvMq3J7iAvhpn7DmLJ6Vi1Gtf9u7XsJU9bO5Zbxz1T63qzP28SIz/5GXvF2+qV346/9L+fScfd5E2w11uRlcfnX/wQgxDk++uODTFg7p0Kf3OLtPD1rLMc06+pFiCIiIvWK7kkCzCzTzGb6n+cBC4FmwBnAG/5ub+BPiswsy8ymASWVHGsCviRqX3oDy8xshZkVA+/5xwJfJSvO/zweWL/nzs65NCDOzH72L/n3ZrnYFprZ4urOuaY16tCSsD2qRJtnLSbt6B4ApB3dg82zFnkRWsBiwhvSK7Ujnyz5EYDSsp3kF2+v0Gdu1lLy/G3zspaREt2o1uMMtou7n8Y3y6ewZXuO16FUKSa8Ib3SOvDx4h+Ayt+bOeXem7lZy0iJTqztMA/I4U06sL5gMxu3b63Qvq0on0XZqyktO/Q/tEVERLxmZkF/1DX7dU+Sc64l0BNfhSbFzDL9mzbgm44XDM2ANeVerwX6+J+PAD53zu0AcoG+Vey/do/9mwUpthpTnJtPZEIsABHxMRTn5nsc0b41jW3M1sJc7jtmJO2TMli4eSWP/vxvCkuLKu0/pP0gJq+dW8tR7h8zeHXIAxjG+/O+4sMFX1XY3iQ6kePb9GXo2Lt46Ph2HkVZvaaxjdm6I4/7B4ykfWJzFm5ZyT9/eqvq96ZD3X9vdhncvBffrprhdRgiIiJyiAt4dTvnXAwwFrjBzHLLb/NXbGojBbwRONXM0vHd9/RETQzinBvpnJvunJv+67jxNTFEdeODc7U+7v4IDQmlY1JLxiwaz4Wf3M2O0iIu63ZapX2PSOvEkA4DeGba+7Uc5f65aMxtnPXejYz89H4u7HYqRzQ9rML2OwZcweOT38Bq5a/6gQsLCaVjcks+XDieCz75KztKiri8+x8r7et7bwby9C/v1XKU+y8sJJR+zbrw/ZrZXociIiJSr5mVBf1R1wRUSXLOheNLkN42s4/8zRudc2lmlumf4pZ1IAH4F4b4j//li8AcIKNcl3RgnXOuMdDdzKb6298HvvTfw7TrV8vj8N2PlL7n/vsTk5mNAkYBXD3p3Vq5Io6Ii6FoWx6RCbEUbcsjIja6NoY9YFkF2WQVZDN/03IAxv/2C8MquRBv1yiDu/sP57qvHiOnqG5Xx7IKfDNBs3fk8O2Kn+ma0o7p6xfs3t6lSVseP/lmABIaxDGg5eHsLNvJ+BVTKz2eVzbu8d58+9svXFbZe5OYwT3HjODaLx+t8+8NQN/UzizZupatRVXfMyYiIiISDIGsbueAV4GFZla+cjMOGAo84v/z0wMJwMzWAD3KjRcGtHPOtcKX3JwPXAhsBeKdc+3NbAm+hR0WmtnO8vv7j5HrnOuLb1rgpcCzBxJbbUru0YHMKbNpeeoxZE6ZTXLPDl6HtE9bduSwsSCbFvGprMrZQO+mh/Hb1oq5aGp0Eo8dfz13//gSq3M3eBRpYBqGReJcCNtLdtAwLJJ+zXvwr18qVr5OeOOK3c8fPv56flg5rc4lSOB7bzYUZNMiPo1VOZn0bnYYK7ZV8t4MvoG7f3ixzr83uxzfohfjV2uqnYiIiNfq4j1EwRZIJakfcAkwzzm3a57LnfiSow+cc8OBVcC5AM65VGA6vgUWypxzNwCdzSzXOfcuMAhIds6tBe41s1fLD2Zmpc65a4Gv8C0B/pqZLfAf+wpgrHOuDF/SdHkVMf8f/1sC/Av/A+fcmfgSpsbAf51zs83spAD+HwTV/JfGsG3xSkrytzP55sdpdcaxtDi1P/Nf+JDMibNokBRPl6vOqe2w9ts/fnqThwZeTXhoGGvzNnHfhFGc1fE4AMYu+o4reg4hPjKGO44eCsDOsp1cPO5eL0OuUlJUAs/+4U7AN63rs8U/MmnVTM7rcjIA78//0svw9ts/przBw4OuJiw0jHW5Wdw7YRRn+9+bMYu+Y2SvM0loEMMd/YYBvvfmok/v8TDifWsQGsERKR15dPr/Etcz2vQD4NPlk0lsEMvLJ9xCdHgDyqyMc9oP4pIvHmZ7aaFXIYuIiMhBzNWHTPD3qK3pdrVh6uLPvQ4hqHYU1N0V5vZXg6i46jsdRGJiD47V8gI18bxnvA5BRETqh7p9U7rfUf++KujXxz9d/GKdOvf9Wt1ORERERETqt/pQZAl4dTsREREREZH6QJUkEREREREJWF1csjvYVEkSEREREREpR5UkEREREREJWH24J0lJkoiIiIiIBEzT7UREREREROoZVZJERERERCRg9WG6nSpJIiIiIiIi5aiSJCIiIiIiAasP9yQpSRIRERERkYApSRI6Jzb3OoSg6XzUVYz+eZTXYQRN69ZHeB1CUMVEJngdQtCsz17mdQgiIiIiB0xJUj1yKCVIh5pDKUECaJrYlvdP+LPXYYiIiEgN0MINIiIiIiIi9YwqSSIiIiIiEjDdkyQiIiIiIlJemabbiYiIiIiI1CuqJImIiIiISMDqw3Q7VZJERERERETKUSVJREREREQCpiXARURERERE6hlVkkREREREJGD14Z4kJUkiIiIiIhIwTbcTERERERGpZ6qtJDnnMoA3gRTAgFFm9rRzLhF4H2gJrATONbOtzrmOwOtAL+AuM3us3LFeA04Dssysyz7GPBl4GggFXjGzR/ztxwGPARHADGC4mZVWsn8r4D0gyd/vEjMrds4NAJ4CugHnm9mY6s6/Jox/9jVWTp9Dw/g4LnzmQQAmj/6A36bPJjQsjPjUxgy+bjiR0VFehBewmIgo7u53OW0bNcOA+ye+wrxNy3dvH9i8J1f3OosyK2OnlfH41LeZvXGpdwFXY0irPpzYvCdmsCoviyfnfEpJ2c7d2w9LbM7Iw06iVWwK/5g1lsmZCz2Mdv+dktGdwem+H7vv1i3g89WzPY5IREREDkb1YbpdIJWkUuAmM+sM9AWucc51Bm4HxptZO2C8/zVANvBnfMnMnkYDJ+9rMOdcKPA8cArQGbjAOdfZORcCvIEvuekCrAKGVnGYfwBPmllbYCsw3N++GhgGvFPNOdeojsf144/3/KVCW0aPzlz49INc8NQDJDRNZcbY/3oUXeBu6XMRP62bx1kf3cH5n/yV33IyK2z/Zf2vnP/JX7nw03u4f+Kr3N3vco8irV5Sg1j+2Ko3N0x8hWsmvEiIcwxsWjGP37Qjhydnf8oP6+d5FOWBy4hOZHB6F+6c+j63/vwOvZJbktIw3uuwREREROqkapMkM8s0s5n+53nAQqAZcAa+pAX/n0P8fbLMbBpQUsmxJuBLovalN7DMzFaYWTG+itAZ+KpCxWa2xN/vG+CsPXd2zjngOGBXlah8bCvNbC7gafrb7LAONIiNrtDWvEcXQkJDAUhp35r8LVu9CC1gMeEN6ZnagU+W/AhAadlO8ou3V+izo7Ro9/OGYRHU9dmroS6EiNAwQpwjMjScLYV5FbZn7chhZV7WQTkPt1l0IktzNlBcVkqZGb9uXUefJm28DktEREQOQmYW9Edds18LNzjnWgI9galAipntKh1swDcdLxiaAWvKvV4L9AE2A2HOuSPMbDpwNpBRyf5JwLZy0/DW+o950Fg4fhLt+vX2Oox9ahrbmK2Fedx3zAjaJTZn0eaVPDr13xSWFlfod2yLw7n28LNp1DCO679+wqNoq7elMI+PVvzE6ME3ULyzhJmbVzBr8wqvwwqaNQVbOK/tUcSEN6B4Zyk9k1uyIjfL67BERETkIKTpduU452KAscANZpZbfpv50r8aTQH9Y5wPPOmc+wXIA3bue6+Dz/QP/0NIaAjtB/b1OpR9CnUhdExqwZhF33HRp/ewo7SIy7qdtle/71fN4KyP7uCmb5/h6sP3KvzVGTHhDeib0oHLv3uGS759kgah4RzbrKvXYQXNuoKtjFs5g7t6DeHOXmewMm8TZfXgA05ERETkQASUJDnnwvElSG+b2Uf+5o3OuTT/9jTggH4t7ZzLcM7N9j+uAtZRsUKU7m/DzH4ys2PMrDcwAVjiP8ZX/v1fAbYACc65sD3334+YRjrnpjv3/+3deZRdVZX48e8mCQlmICSBEJJAGMIQaJJARBBExAaZ2qitNDYqijaN0iLd2jLYP9H+2b+GhY1jL2OU0UaGBhG6AQGZZwlJgEAIIiQmkAEImQiEJLV/f7wLPJIq6lXVS92qV9/PWnfVffeed88+VRnern3uuTHtvquua8+w2mX27ffy3LTHOOwfT6Iya7DrWrL6FZa8upRZL1aqLb+f+zC7D92hxfYzFs9h5MCtGdx3QGeF2CYThu3I4tXLWPHGatZnE/cvfIo9thpVdlh1dccLT3LmQ1fwnWnX8Oq6NSxcvazskCRJUjfUE6bbtZokFff4XADMzszq+VLX8/bCCScA7comMnN+Zk4otinAw8DYiNgxIjanUj26vohlm+JrX+B0YEpxjY8U7/9SUXG6g8p0vHbFlplTM3NSZk468NjJ7RlWm82b/jjTr72JY876Kn369u2UPjvi5deWs/jVpewwaFsA9ttuHM8ue+EdbUYN3Oat/d2H7sDmm/Vh2ZpVnRpnrV58bQW7bTWSvptVcuvxw3Zk/qqXSo6qvgb12QKAof0GsN82O3PvojklRyRJktQ1RWuZW0QcBNwDPM7bCx6cReW+pKuA7amsNHdsZi6NiG2BacCgov0qYFxmroiIy4FDgGHAYuDszLygmT6PorJUdy/gwsz8t+L4eVSWEN8M+Flm/rCFmHeisuDDEGAG8JnMXBMR7wWuBbYCXgcWZeae7zb+nzx5X91T25v/YwrPPzGH11esYovBg3jfcZN55JobWb92Lf0GViotw3fdmQ99+XN17ffiB6fW9Xq7Dtme/3PQifTZrDfPr1zCd+75JYfv+D4ArplzBx86Y08AABz9SURBVCf8xVEcvctBrGtax5r1a/nRw1fUdQnwbeu88MDxu36QD2y3J+ubmnh2xSJ+9Nj/cNwuH+CPy1/gocVPM3bL7fiXScdW7utpWscra1bxlbum1KXvAX0H1+U67+Y7k/6agX22YH2u59Kn72HW0gWbtL8rDzt1k15fkqQG1LWnEhV2++HRdf98POe0G7rU2FtNknq6TZEklaXeSVLZ6p0klakzkqTOZpIkSVKbdalEoSW7nn9k3T8fP/1PN3Wpsde8cIMkSZIklSUi5kbE48VaBNOaOR8R8eOIeCYiHouIfdrbV5uWAJckSZLUs5U8E+1DmdnSjeNHAmOL7X3Az4qvbWYlSZIkSVIjmAxcmhUPUlnxekR7LmQlSZIkSVLNSnyYbAK3REQCP8/MDW+4HwnMr3q9oDi2sK0dmSRJkiRJKlVEnAScVHVoajNJ0EGZ+XzxWKBbI+KpzLx7U8RjkiRJkiSpZpvinqQiIXrXpZgz8/ni65KIuBbYD6hOkp4HRle9HlUcazPvSZIkSZJUs8z6b62JiP4RMfDNfeBwYNYGza4HPlescrc/sDwz2zzVDqwkSZIkSer6hgPXRgRUcphfZ+bvIuJkgMycAtwIHAU8A6wGvtDezkySJEmSJNWsjCXAM/NZYHwzx6dU7SdwSj36c7qdJEmSJFWxkiRJkiSpZiU/TLZTmCRJkiRJqlliktTj3ff89LJDqJuBg4aVHUJd9enVt+wQ6mbNutfo23uLssOQJEkSJklSl3HlYaeWHYIkSVKresJ0OxdukCRJkqQqVpIkSZIk1awHFJJMkiRJkiTVrqkHZElOt5MkSZKkKlaSJEmSJNXMhRskSZIkqYexkiRJkiSpZlaSJEmSJKmHsZIkSZIkqWY9oJBkkiRJkiSpdj1hul2rSVJEjAYuBYYDCUzNzB9FxBDgSmAMMBc4NjNfiYjdgYuAfYBvZeb3q651IXAMsCQz93qXPptt11Kfzbx/R+AKYCjwCPDZzHwjIg4GfgjsDRyXmVe3Nv5N7cjR4zl01J4A3P78E9z050dLjqg2owdszdn7f/at1yP6D+WiJ37H1c/c89axA0fsyYl7HkGSrG9q4qePXsfjLz9XRrg1+asxkzhs1HiSZN7KF/nJ4zeytmn9W+cPHbkXJ+z+IZa+vhKAG+ZN5/cLHisrXEmSJG0itVSS1gFfz8zpETEQeCQibgU+D9yWmedExBnAGcDpwFLgVOBjzVzrYuCnVJKud9NSuzNa6HND5wI/yMwrImIK8EXgZ8Cfi7i/0Ur/nWJU/yEcOmpPvvXQVazL9Zw5cTLTX5zL4teWlx1aq+avepEv/f58ADYjuPqYb3PPC7Pe0Wb6kj9y38InANhpyxF8532f43O3nNvpsdZiSN8BHLPDvnz1ngt4o2kd/zxhMh8YsQe3P//OMd27cDa/ePL3JUUpSZJUvp5QSWp14YbMXJiZ04v9lcBsYCQwGbikaHYJRVKUmUsy82FgbTPXuptKEtVany21a7bPahERwKHA1Ru2y8y5mfkY0NRaDJ1hZP8hPLN8EW80raMpk9mvPM9+2+xcdlhtts/wsTy/6mUWr35nUe+19W+8td+v1+YkXfsvVK/YjM179WazCDbv1Zula1aVHZIkSZJK0KZ7kiJiDDAReAgYnpkLi1OLqEzH29Rq6XMosCwz1xWvF1BJ6rqc+a++zHG77M+APv14Y/06JgzbgWdXLCk7rDY7dNREbp8/o9lzB223FyftdTSD+w3gjHt/2cmR1W7pmlX89rk/8ItDvswbTeuY+dJzzHxp7kbtDhi+G3tuNZoXVr/ChbNv46Vi6p0kSVJP0RMqSTUnSRExALgGOC0zV1QKNhWZmRHRqd+tMvqstxdefYXr507nrH0ms2b9WuatfImmbvaHrnf04sDt9uQXs25o9vy9L8zi3hdmsfewnfjinkfw9Xt+3skR1qZ/777sN3wsf3/XFF5du4ZvTpzMB7cbx10vPPlWm4eXPMPdC2ezrmk9h48ez6l7H823/3BFiVFLkiR1vp6QJNX0nKSI6EMlQbosM39THF4cESOK8yOAdpVAImJ0RMwstpNbad5snxFxc/H+XwIvA4Mj4s0EcBTwfBtjOikipkXEtD/dcF/bBtRGd7zwJGc9dCXfnfYbXl23hoWrl23S/urtfdvuztPLFvBKK1PTHnvpWUb0H8qWm/fvpMjaZvywMSxZvZwVb7zG+mzigUVPs/vgdxYgV659nXXFQg6/n/8YOw/atoxQJUmStIm1miQV9/hcAMzOzPOrTl0PnFDsnwBc154AMnN+Zk4otimtNG+2z8z8SPH+L2Ultb0D+GR7Y8vMqZk5KTMn7Xz0gW15a5sN6rMFAEP7DeC92+zMfYvmbNL+6u3D20/ktj83P9VuZP+hb+2PHTySPr16s/yNVzsrtDZ58bUV7Dp4OzbfrJJb7z10Bxa8+vI72mzV9+0E773Dd9novCRJUk+QWf+tq6llut2BwGeBxyNiZnHsLOAc4KqI+CIwDzgWICK2BaYBg4CmiDgNGFdM0bscOAQYFhELgLMz84INO3yXds322YzTgSsi4nvADCpJHhHxXuBaYCvgryLiu5m5Zw3fg03mn8YfxYA+/VifTVz01J2sXvdG62/qIvr12px9t9mV/3jk7ZXUP7rTAQBc/+wDHDxqbw7ffhLrcz1r1q/lXx/8VVmhtuqPyxdy/6I5nH/g51mfTTy3YjE3z3+UT489iGeWL+LhJc9w9A77st82Y1mfTaxa+xo/fqz5KYaSJEnq3qInzCnsiONu/UnDfIMWLZ9bdgh1Nbj/NmWHUFe/PbK51ewlSVIPEq03Kd+Qfzmg7p+Pl37vgS419jatbidJkiSpZ+sJRZaaFm6QJEmSpJ7CSpIkSZKkmnW3R9a0h5UkSZIkSapiJUmSJElSzXpAIclKkiRJkiRVs5IkSZIkqWY9YXU7kyRJkiRJNUsaP0lyup0kSZIkVbGSJEmSJKlmPWG6nZUkSZIkSapiJUmSJElSzXpCJckkqRXLXnup7BDqZvfh+5YdQt0tWjW/7BAkSZJ6lB6QI5kkqXv77ZGnlx2CJEmSGoxJkiRJkqSa9YTpdi7cIEmSJElVrCRJkiRJqllPqCSZJEmSJEmqWU9IkpxuJ0mSJElVrCRJkiRJqlmTlSRJkiRJ6lmsJEmSJEmqWQ8oJFlJkiRJktS1RcToiLgjIp6MiCci4mvNtDkkIpZHxMxi+3Z7+7OSJEmSJKlmJa1utw74emZOj4iBwCMRcWtmPrlBu3sy85iOdmaSJEmSJKlmZSRJmbkQWFjsr4yI2cBIYMMkqS5aTZIiYjRwKTAcSGBqZv4oIoYAVwJjgLnAsZn5SkTsDlwE7AN8KzO/X3WtC4FjgCWZude79Nlsu4j4FPAdYA9gv8yc1sL72xxbZxjVfyhnTvrUW6+3fc9W/GrOHfz22QffOjagTz/+ccLH2K7/Vryxfh3nz7yOeSuXdGaYbTL7out4+bGn2Xxgf/b7168AsHbVazzx86t5/eVl9Bs6mD1P/iR9+m9RcqSSJEnqqiLiJOCkqkNTM3NqC23HABOBh5o5fUBEPAq8AHwjM59oTzy13JP0ZmlrHLA/cEpEjAPOAG7LzLHAbcVrgKXAqUBzCcjFwBE19NlSu1nAJ4C7W3l/e2Lb5Ba8+jKn3DWFU+6awlfv+jlr1q/l/oWz39HmuLEH8+zyRXz5zp9x3oxrOXmvI8sItWYjDpzA+NM+845j8266l6322JH9/99X2WqPHfnzTfeWFJ0kSZLqLuu/ZebUzJxUtbWUIA0ArgFOy8wVG5yeDuyQmeOBnwC/be8QW02SMnNhZk4v9lcCb5a2JgOXFM0uAT5WtFmSmQ8Da5u51t1UEpXW+my2XWbOzsw5rb2/PbF1tglb78TC1a+w5LXl7zi+/cCtmfnSswAsWPUSw98zmMF9+5cRYk0G77oDvTeoEr00cw7bvn88ANu+fzwvzqjlRyZJkiS1LCL6UEmQLsvM32x4PjNXZOaqYv9GoE9EDGtPX21a3W6D0tbwYm4gwCIq0/G6iq4cGwAfHLkXdy54fKPjzy5fxIEjxgGw6+CRDN9iS4b1G9TZ4XXI2hWr6Dt4IACbbzmAtStWlRyRJEmS6iaz/lsrIiKAC4DZmXl+C222LdoREftRyXVebs8Qa164YcPSVtE/AJmZEdElV0zvirH1jl7sP3w3Lpr9+43OXfXMvZy815H85wdPZu6KJfxp+aJu/VTjiICqPyuSJEnq5ppK+Wx6IPBZ4PGImFkcOwvYHiAzpwCfBL4cEeuA14Djsp2rTNSUJLVQ2locESMyc2FEjADatbpAsTDE/xQvpxQDbOs1LqJS4XohM4/qaGzVN46N+8rRjP7Ivm0N6V1NGr4LzyxfyLI1r250bvW6NZw/8+3pk5f85WksWv1KXfvf1PoMGsCaZSvpO3gga5atpM/ArjtdUJIkSV1fZt4LvOtv3jPzp8BP69Ffq9Pt3qW0dT1wQrF/AnBdewLIzPmZOaHY2pwgFdf4QvH+o+oRW/WNY/VOkAAOGfkX3Pn8xlPtAPr37kfv6AXAEdvvy+Mvz2P1ujV1j2FTGjZhVxbd/ygAi+5/lGETdis5IkmSJNXNJli4oauppZLUUmnrHOCqiPgiMA84FipzAYFpwCCgKSJOA8YVU/QuBw4BhkXEAuDszLxgww5bahcRH6eyUsXWwA0RMTMzP9JMzG2OrYbvQ1307dWHfbbemR8/+j9vHTtqh0kA3DhvGtsPHMbXJ34cgHkrl/CDme3KPTvNE1OvYdmcuaxdtZr7//l8xnz0EHY48iBmTbmahffOoN/QLdnz7z/V+oUkSZKkLiJKemJut3HE9Wc3zDdozFaNV9GZ8oG/LTsESZKkeukWN3LHyePq/vk4pzzZpcZe88INkiRJkkRT2QFsem1aAlySJEmSGp2VJEmSJEm16wG361hJkiRJkqQqVpIkSZIk1a7xC0lWkiRJkiSpmpUkSZIkSbXrAfckmSRJkiRJqp1LgEuSJElSz2IlSZIkSVLtesB0OytJkiRJklTFSpIkSZKk2jV+IckkqTX/ftBXyg6hrn7+xG1lhyBJkqTurKnxsySTpB5mygf+tuwQJEmSpC7NJEmSJElS7Rq/kOTCDZIkSZJUzUqSJEmSpNr1gCXATZIkSZIk1a7xcySn20mSJElSNStJkiRJkmrXA5YAt5IkSZIkSVWsJEmSJEmqXQ9YuMFKkiRJkiRVsZIkSZIkqXaNX0hqvZIUEaMj4o6IeDIinoiIrxXHh0TErRHxx+LrVsXx3SPigYhYExHf2OBaF0bEkoiY1UqfzbaLiPMi4qmIeCwiro2IwS28v82xdaYp3zuHk476KN84/oS3jv33Ly/ky3/1CU7/3Imc/rkTmXH/A2WFJ0mSJLWsKeu/dTG1TLdbB3w9M8cB+wOnRMQ44AzgtswcC9xWvAZYCpwKfL+Za10MHFFDny21uxXYKzP3Bp4Gzmzh/e2JrdN88OgjOPMH5210/KjjPsW5l17IuZdeyMT3H1BCZJIkSZJaTZIyc2FmTi/2VwKzgZHAZOCSotklwMeKNksy82FgbTPXuptKotJan822y8xbMnNd8fJBYFQLl2hzbJ1pj4kT6D9oUJkhSJIkSe2Tm2DrYtp0T1JEjAEmAg8BwzNzYXFqETC8rpG17kTgyhbOlR1bu9x89bXcc9PN7LT77nzm1FMYMGhg2SFJkiRJPU7Nq9tFxADgGuC0zFxRfS4zOzUHjIhvUZkGeFlrbTs7tvY67BMf48dXX845l17I4GFD+a8f/2fZIUmSJEkby6z/1sXUlCRFRB8qCdJlmfmb4vDiiBhRnB8BLGlPAMXCEDOL7eQa2n8eOAY4vkiAiIiLivffWI/YIuKkiJgWEdOuueRXbRpPew0eMoTNevVis80249DJx/DM7Nmd0q8kSZLUJk63g4gI4AJgdmaeX3XqeuAE4Jzi63XtCSAz5wMTamkbEUcA3wQ+mJmrq67xhQ2adii2zJwKTAWYsXRxp/zYXnnpJbYaNgyAh++8h9E77dgZ3UqSJEnaQGQr5a2IOAi4B3gcaCoOn0XlvqSrgO2BecCxmbk0IrYFpgGDivargHGZuSIiLgcOAYYBi4GzM/OCZvpstl1EPAP0BV4umj6YmRtVnyJiaFtja2n8myJJ+vG3v8uT02ewctlythwyhE9+6Qs8OWMm857+IxHB1iO25Uunf+OtpKmeJg7pFrdnSZIk9URRdgC1iMlj6v75OK+b26XG3mqS1NN1ViWps5gkSZIkdVldKlFoSU9Iktq0up0kSZKkHq6hSgjNM0mSJEmSVLseMBOt5iXAJUmSJKknsJIkSZIkqXZNrTfp7qwkSZIkSVIVK0mSJEmSauc9SZIkSZLUs1hJkiRJklS7xi8kmSRJkiRJagOn20mSJElS+SLiiIiYExHPRMQZzZzvGxFXFucfiogx7e3LJEmSJElS7Zo2wdaKiOgF/CdwJDAO+HREjNug2ReBVzJzF+AHwLntHaJJkiRJkqSubj/gmcx8NjPfAK4AJm/QZjJwSbF/NfDhiIj2dGaSJEmSJKl2mfXfWjcSmF/1ekFxrNk2mbkOWA4Mbc8QXbihFROHDG9X9tlWEXFSZk7tjL42tUYaCzTWeBppLNBY42mksYDj6coaaSzQWONppLFAY42nkcZSD3nrgrp/Po6Ik4CTqg5NLfN7biWp6zip9SbdRiONBRprPI00Fmis8TTSWMDxdGWNNBZorPE00ligscbTSGPpkjJzamZOqto2TJCeB0ZXvR5VHGu2TUT0BrYEXm5PPCZJkiRJkrq6h4GxEbFjRGwOHAdcv0Gb64ETiv1PArdntm+9cqfbSZIkSerSMnNdRPwDcDPQC7gwM5+IiH8FpmXm9cAFwK8i4hlgKZVEql1MkrqORprn2khjgcYaTyONBRprPI00FnA8XVkjjQUaazyNNBZorPE00li6rcy8Ebhxg2Pfrtp/HfhUPfqKdlagJEmSJKkheU+SJEmSJFUxSSpZRFwYEUsiYlbZsXRURIyOiDsi4smIeCIivlZ2TB0REf0i4g8R8Wgxnu+WHVNHRUSviJgREf9bdiwdFRFzI+LxiJgZEdPKjqcjImJwRFwdEU9FxOyIOKDsmNorInYrfiZvbisi4rSy42qviPjH4u//rIi4PCL6lR1Te0XE14pxPNEdfybN/X8ZEUMi4taI+GPxdasyY2yLFsbzqeLn0xQRk8qMry1aGMt5xb9pj0XEtRExuMwY26KF8fzfYiwzI+KWiNiuzBi16Zkkle9i4Iiyg6iTdcDXM3McsD9wSkSMKzmmjlgDHJqZ44EJwBERsX/JMXXU14DZZQdRRx/KzAmZ2W0+TLTgR8DvMnN3YDzd+GeUmXOKn8kEYF9gNXBtyWG1S0SMBE4FJmXmXlRuFG73TcBlioi9gL+j8sT68cAxEbFLuVG12cVs/P/lGcBtmTkWuK143V1czMbjmQV8Ari706PpmIvZeCy3Antl5t7A08CZnR1UB1zMxuM5LzP3Lv5t+1/g2xu9Sw3FJKlkmXk3ldU3ur3MXJiZ04v9lVQ+6G34JORuIytWFS/7FFu3vYkvIkYBRwO/LDsWvS0itgQOprIiD5n5RmYuKzequvkw8KfMnFd2IB3QG9iieN7Ge4AXSo6nvfYAHsrM1cVT6O+i8mG822jh/8vJwCXF/iXAxzo1qA5objyZOTsz55QUUru1MJZbij9rAA9SeaZNt9DCeFZUvexPN/48oNqYJGmTiIgxwETgoXIj6ZhietpMYAlwa2Z25/H8EPgm0FR2IHWSwC0R8UjxlO7uakfgReCiYirkLyOif9lB1clxwOVlB9Femfk88H3gz8BCYHlm3lJuVO02C/hARAyNiPcAR/HOhzJ2V8Mzc2GxvwgYXmYwatGJwE1lB9FREfFvETEfOB4rSQ3PJEl1FxEDgGuA0zb4zUu3k5nri9L6KGC/YspKtxMRxwBLMvORsmOpo4Mycx/gSCpTOw8uO6B26g3sA/wsMycCr9K9pgw1q3jQ30eB/y47lvYq7m+ZTCWR3Q7oHxGfKTeq9snM2cC5wC3A74CZwPpSg6qz4oGR/na/i4mIb1GZjn9Z2bF0VGZ+KzNHUxnLP5QdjzYtkyTVVUT0oZIgXZaZvyk7nnoppj/dQfe9f+xA4KMRMRe4Ajg0Iv6r3JA6pvgtP5m5hMo9L/uVG1G7LQAWVFUpr6aSNHV3RwLTM3Nx2YF0wF8Cz2Xmi5m5FvgN8P6SY2q3zLwgM/fNzIOBV6jcJ9LdLY6IEQDF1yUlx6MqEfF54Bjg+GysZ85cBvx12UFo0zJJUt1ERFC5r2J2Zp5fdjwdFRFbv7kaT0RsARwGPFVuVO2TmWdm5qjMHENlCtTtmdktfyMOEBH9I2Lgm/vA4VSmE3U7mbkImB8RuxWHPgw8WWJI9fJpuvFUu8Kfgf0j4j3Fv28fphsvqhER2xRft6dyP9Kvy42oLq4HTij2TwCuKzEWVYmII6hM8f5oZq4uO56OioixVS8n000/D6h2vcsOoKeLiMuBQ4BhEbEAODszLyg3qnY7EPgs8HhxHw/AWcXTkbujEcAlEdGLyi8UrsrMbr90doMYDlxb+dxKb+DXmfm7ckPqkK8ClxVT1J4FvlByPB1SJK6HAX9fdiwdkZkPRcTVwHQq04VmAFPLjapDromIocBa4JTutkBIc/9fAucAV0XEF4F5wLHlRdg2LYxnKfATYGvghoiYmZkfKS/K2rQwljOBvsCtxb/VD2bmyaUF2QYtjOeo4pdZTVT+rHWLsaj9orGqn5IkSZLUMU63kyRJkqQqJkmSJEmSVMUkSZIkSZKqmCRJkiRJUhWTJEmSJEmqYpIkSQ0uIraNiCsi4k8R8UhE3BgRu7bQ9pCI6NBS9xHx+YjYrur1nRExqY3XWNWRGCRJ6giTJElqYMVDUK8F7szMnTNzXyrPLxm+ifrrBXwe2K6VppIkdVkmSZLU2D4ErM3MKW8eyMxHgXsj4ryImBURj0fE31S9Z0BEXB0RT0XEZUWiRUR8OCJmFO0vjIi+xfG5EXFuREwHPg1MovJw3JkRsUV1MBGxKiL+LSIejYgHI2J4cXzHiHiguPb3NnjPP0fEwxHxWER8tzj28Yi4LSpGRMTTEbFt/b99kqSeyCRJkhrbXsAjzRz/BDABGA/8JXBeRIwozk0ETgPGATsBB0ZEP+Bi4G8y8y+A3sCXq673cmbuk5n/BUwDjs/MCZn52gb99gcezMzxwN3A3xXHfwT8rLj2wjcbR8ThwFhgvyLefSPi4My8tmh3CvAL4OzMXNS2b40kSc0zSZKknukg4PLMXJ+Zi4G7gPcW5/6QmQsyswmYCYwBdgOey8ynizaXAAdXXe/KGvt9A3jznqdHimsDHAhcXuz/qqr94cU2A5gO7E4laQL4KpWpg2sy83IkSaqT3mUHIEnapJ4APtnG96yp2l9Pbf9XvFrjtddmZrZw7WymfQD/npk/b+bcKKAJGB4RmxVJnSRJHWYlSZIa2+1A34g46c0DEbE3sAz4m4joFRFbU6kK/eFdrjMHGBMRuxSvP0ul+tSclcDANsZ5H3BcsX981fGbgRMjYkAR+8iI2CYiegMXUrkHajbwT23sT5KkFllJkqQGlpkZER8HfhgRpwOvA3Op3HM0AHiUSgXnm5m5KCJ2b+E6r0fEF4D/LhKUh4EpzbWlcu/SlIh4DTigxlC/Bvy6iPG6qn5viYg9gAeK9SNWAZ8BTgbuycx7I+JR4OGIuCEzZ9fYnyRJLYq3Zz1IkiRJkpxuJ0mSJElVTJIkSZIkqYpJkiRJkiRVMUmSJEmSpComSZIkSZJUxSRJkiRJkqqYJEmSJElSFZMkSZIkSary/wEv7Cu88QI4wgAAAABJRU5ErkJggg==\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "#Average quantity for each cohort\n",
        "grouping = df.groupby(['CohortMonth', 'CohortIndex'])\n",
        "cohort_data = grouping['Quantity'].mean()\n",
        "cohort_data = cohort_data.reset_index()\n",
        "average_quantity = cohort_data.pivot(index='CohortMonth',columns='CohortIndex',values='Quantity')\n",
        "average_quantity.round(1)\n",
        "average_quantity.index = average_quantity.index.date\n",
        "\n",
        "#Build the heatmap\n",
        "plt.figure(figsize=(15, 8))\n",
        "plt.title('Average quantity for each cohort')\n",
        "sns.heatmap(data=average_quantity,annot = True,vmin = 0.0,vmax =20,cmap=\"BuGn_r\")\n",
        "plt.show()"
      ]
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3.9.13 ('base')",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.9.13"
    },
    "vscode": {
      "interpreter": {
        "hash": "7ea3a11aaa978d9646058336c84c9a0218aba8c5d679a7a8e354537b3f287648"
      }
    },
    "colab": {
      "provenance": [],
      "include_colab_link": true
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}