{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5CqoPjt7CeWb"
      },
      "source": [
        "Loading necessary libraries and datasets"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "HYNiTM3g35Zb"
      },
      "outputs": [],
      "source": [
        "# our usual library for dataframa manipulation\n",
        "import pandas as pd\n",
        "# plotting library\n",
        "import seaborn as sns\n",
        "# library for missing data handling\n",
        "import missingno as msno\n",
        "# imports for missing data handling (imputation)\n",
        "from sklearn.experimental import enable_iterative_imputer\n",
        "from sklearn.impute import SimpleImputer, KNNImputer, IterativeImputer"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5xsn2QqRCmlp"
      },
      "source": [
        "Reading the Titanic dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "CpuLqNPE4HUs"
      },
      "outputs": [],
      "source": [
        "df = pd.read_csv('titanic.csv')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "2GImut934MV-",
        "outputId": "e84ddc54-36f6-4a97-b26a-17e48b442b4f"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>pclass</th>\n",
              "      <th>survived</th>\n",
              "      <th>name</th>\n",
              "      <th>sex</th>\n",
              "      <th>age</th>\n",
              "      <th>sibsp</th>\n",
              "      <th>parch</th>\n",
              "      <th>ticket</th>\n",
              "      <th>fare</th>\n",
              "      <th>cabin</th>\n",
              "      <th>embarked</th>\n",
              "      <th>boat</th>\n",
              "      <th>body</th>\n",
              "      <th>home.dest</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Allen, Miss. Elisabeth Walton</td>\n",
              "      <td>female</td>\n",
              "      <td>29.0000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>24160</td>\n",
              "      <td>211.3375</td>\n",
              "      <td>B5</td>\n",
              "      <td>S</td>\n",
              "      <td>2</td>\n",
              "      <td>NaN</td>\n",
              "      <td>St Louis, MO</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Allison, Master. Hudson Trevor</td>\n",
              "      <td>male</td>\n",
              "      <td>0.9167</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>113781</td>\n",
              "      <td>151.5500</td>\n",
              "      <td>C22 C26</td>\n",
              "      <td>S</td>\n",
              "      <td>11</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Montreal, PQ / Chesterville, ON</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Allison, Miss. Helen Loraine</td>\n",
              "      <td>female</td>\n",
              "      <td>2.0000</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>113781</td>\n",
              "      <td>151.5500</td>\n",
              "      <td>C22 C26</td>\n",
              "      <td>S</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Montreal, PQ / Chesterville, ON</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Allison, Mr. Hudson Joshua Creighton</td>\n",
              "      <td>male</td>\n",
              "      <td>30.0000</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>113781</td>\n",
              "      <td>151.5500</td>\n",
              "      <td>C22 C26</td>\n",
              "      <td>S</td>\n",
              "      <td>NaN</td>\n",
              "      <td>135.0</td>\n",
              "      <td>Montreal, PQ / Chesterville, ON</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Allison, Mrs. Hudson J C (Bessie Waldo Daniels)</td>\n",
              "      <td>female</td>\n",
              "      <td>25.0000</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>113781</td>\n",
              "      <td>151.5500</td>\n",
              "      <td>C22 C26</td>\n",
              "      <td>S</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Montreal, PQ / Chesterville, ON</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   pclass  survived                                             name     sex  \\\n",
              "0     1.0       1.0                    Allen, Miss. Elisabeth Walton  female   \n",
              "1     1.0       1.0                   Allison, Master. Hudson Trevor    male   \n",
              "2     1.0       0.0                     Allison, Miss. Helen Loraine  female   \n",
              "3     1.0       0.0             Allison, Mr. Hudson Joshua Creighton    male   \n",
              "4     1.0       0.0  Allison, Mrs. Hudson J C (Bessie Waldo Daniels)  female   \n",
              "\n",
              "       age  sibsp  parch  ticket      fare    cabin embarked boat   body  \\\n",
              "0  29.0000    0.0    0.0   24160  211.3375       B5        S    2    NaN   \n",
              "1   0.9167    1.0    2.0  113781  151.5500  C22 C26        S   11    NaN   \n",
              "2   2.0000    1.0    2.0  113781  151.5500  C22 C26        S  NaN    NaN   \n",
              "3  30.0000    1.0    2.0  113781  151.5500  C22 C26        S  NaN  135.0   \n",
              "4  25.0000    1.0    2.0  113781  151.5500  C22 C26        S  NaN    NaN   \n",
              "\n",
              "                         home.dest  \n",
              "0                     St Louis, MO  \n",
              "1  Montreal, PQ / Chesterville, ON  \n",
              "2  Montreal, PQ / Chesterville, ON  \n",
              "3  Montreal, PQ / Chesterville, ON  \n",
              "4  Montreal, PQ / Chesterville, ON  "
            ]
          },
          "execution_count": 3,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zEtv-71VC2Hf"
      },
      "source": [
        "\n",
        "**Examining the Target column**\n",
        "\n",
        "   The *Titanic dataset*  was created to help  build a model that predicts whether or not the passengers survived the sinking of the Titanic. The target column for such a model (i.e labels for a supervised model) is the `survived` column. We examine the distribution of this special column.\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iF_hUBn04Qoe",
        "outputId": "c4cde6e8-8dcd-45c5-9e14-7284dc195aa0"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0.0    809\n",
              "1.0    500\n",
              "Name: survived, dtype: int64"
            ]
          },
          "execution_count": 4,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df['survived'].value_counts()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "RIog9Vqv4byZ",
        "outputId": "6fbe5c3e-0cd9-4aa4-d092-aacdf01c15d7"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "s = sns.countplot(x = 'survived',data = df)\n",
        "sizes=[]\n",
        "for p in s.patches:\n",
        "    height = p.get_height()\n",
        "    sizes.append(height)\n",
        "    s.text(p.get_x()+p.get_width()/2.,\n",
        "            height + 3,\n",
        "            '{:1.2f}%'.format(height/len(df)*100),\n",
        "            ha=\"center\", fontsize=14)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ti91KkfhFiyN"
      },
      "source": [
        "\n",
        "## Detecting Missing values\n",
        "\n",
        "**Detecting missing values numerically**\n",
        "\n",
        "We first try and compute the count/percentage of missing values in every column of the dataset. This gives an idea about the distribution of missing values.\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "9YgHOIF247kG"
      },
      "outputs": [],
      "source": [
        "# credit: https://www.kaggle.com/willkoehrsen/start-here-a-gentle-introduction.\n",
        "# One of the best notebooks on getting started with a ML problem.\n",
        "\n",
        "def missing_values_table(df):\n",
        "        # Total missing values\n",
        "        mis_val = df.isnull().sum()\n",
        "\n",
        "        # Percentage of missing values\n",
        "        mis_val_percent = 100 * df.isnull().sum() / len(df)\n",
        "\n",
        "        # Make a table with the results\n",
        "        mis_val_table = pd.concat([mis_val, mis_val_percent], axis=1)\n",
        "\n",
        "        # Rename the columns\n",
        "        mis_val_table_ren_columns = mis_val_table.rename(\n",
        "        columns = {0 : 'Missing Values', 1 : '% of Total Values'})\n",
        "\n",
        "        # Sort the table by percentage of missing descending\n",
        "        mis_val_table_ren_columns = mis_val_table_ren_columns[\n",
        "            mis_val_table_ren_columns.iloc[:,1] != 0].sort_values(\n",
        "        '% of Total Values', ascending=False).round(1)\n",
        "\n",
        "        # Print some summary information\n",
        "        print (\"Your selected dataframe has \" + str(df.shape[1]) + \" columns.\\n\"\n",
        "            \"There are \" + str(mis_val_table_ren_columns.shape[0]) +\n",
        "              \" columns that have missing values.\")\n",
        "\n",
        "        # Return the dataframe with missing information\n",
        "        return mis_val_table_ren_columns"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VvZ2Qi7f5OYj",
        "outputId": "39f2c173-bad7-40c7-f661-326455716a05"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Your selected dataframe has 14 columns.\n",
            "There are 14 columns that have missing values.\n"
          ]
        }
      ],
      "source": [
        "df_missing= missing_values_table(df)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 488
        },
        "id": "kp_YV8XB5Xe3",
        "outputId": "89cd3b7d-39d0-4313-9cfa-d866508845bc"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Missing Values</th>\n",
              "      <th>% of Total Values</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>body</th>\n",
              "      <td>1189</td>\n",
              "      <td>90.8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>cabin</th>\n",
              "      <td>1015</td>\n",
              "      <td>77.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>boat</th>\n",
              "      <td>824</td>\n",
              "      <td>62.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>home.dest</th>\n",
              "      <td>565</td>\n",
              "      <td>43.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>age</th>\n",
              "      <td>264</td>\n",
              "      <td>20.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>embarked</th>\n",
              "      <td>3</td>\n",
              "      <td>0.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>fare</th>\n",
              "      <td>2</td>\n",
              "      <td>0.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>pclass</th>\n",
              "      <td>1</td>\n",
              "      <td>0.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>survived</th>\n",
              "      <td>1</td>\n",
              "      <td>0.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>name</th>\n",
              "      <td>1</td>\n",
              "      <td>0.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>sex</th>\n",
              "      <td>1</td>\n",
              "      <td>0.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>sibsp</th>\n",
              "      <td>1</td>\n",
              "      <td>0.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>parch</th>\n",
              "      <td>1</td>\n",
              "      <td>0.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ticket</th>\n",
              "      <td>1</td>\n",
              "      <td>0.1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "           Missing Values  % of Total Values\n",
              "body                 1189               90.8\n",
              "cabin                1015               77.5\n",
              "boat                  824               62.9\n",
              "home.dest             565               43.1\n",
              "age                   264               20.2\n",
              "embarked                3                0.2\n",
              "fare                    2                0.2\n",
              "pclass                  1                0.1\n",
              "survived                1                0.1\n",
              "name                    1                0.1\n",
              "sex                     1                0.1\n",
              "sibsp                   1                0.1\n",
              "parch                   1                0.1\n",
              "ticket                  1                0.1"
            ]
          },
          "execution_count": 10,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_missing"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kNl5xIaxGJSV"
      },
      "source": [
        "All columns in the dataset have missing values, with some to an extreme degree (e.g `body` with 90.8% of missing values)."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6sYRLHJ2GbRr"
      },
      "source": [
        "\n",
        "**Detecting missing data visually using `Missingno` library**\n",
        "\n",
        "  To graphically/visually analyse the missingness in the data, we use a library called `Missingno`: it is a package for the graphical analysis of missing values. To use this library, we need to import it as follows: `import missingno as msno` (see library import step above).\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "YG5WVWvb5jlM",
        "outputId": "0b94d084-1c5f-401c-e87f-85c2a9f3752d"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "execution_count": 12,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
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",
            "text/plain": [
              "<Figure size 2500x1000 with 3 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "msno.bar(df)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PGw2K24NG__Y"
      },
      "source": [
        "The bar chart above gives a quick graphical overview of the completeness of the dataset. We can see that the `age`, `cabin` and `embarked` columns have missing values. Next, it would make sense to try and find out the locations of the missing data."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dVWntd5ZHU6y"
      },
      "source": [
        "\n",
        "**Visualizing the locations of the missing data**\n",
        "\n",
        "  The `msno.matrix` nullity matrix is a data-dense display which allows the user to quickly visually pick out patterns in data completeness.\n",
        "\n",
        "  The plot appears blank/white where there are missing values. For example, in the `embarked` column, there are only three instances of missing data and therefore three white lines.\n",
        "\n",
        "  The sparkline on the right gives an idea of the general shape of the completeness of the data and points out the row with the minimum nullities and the total number of columns in a given dataset, at the bottom."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 847
        },
        "id": "6yfYzqgj58zy",
        "outputId": "c33be4ec-7c4b-498d-c7f8-b5a61fde8d27"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "execution_count": 13,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 2500x1000 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "msno.matrix(df)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ogkHmQT3IX2k"
      },
      "source": [
        "It is also possible to sample the dataset to pinpoint the exact location of the missing values. Let's check the first 100 rows for example."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 850
        },
        "id": "tduSEtQf6QqZ",
        "outputId": "6787fd92-a45b-4a0a-d89c-309b34f5b435"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "execution_count": 14,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 2500x1000 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "msno.matrix(df.sample(100))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "erSGyxA_IpVf"
      },
      "source": [
        "This shows that the `embarked` column has no nullity in the first 100 cases."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "i_r3ODvrMs7W"
      },
      "source": [
        "**Reasons for Missing Values**\n",
        "\n",
        "Before we start handling the missing values, it is important to understand the various reasons behind the missingness in the data. Broadly speaking, there can be three possible reasons (also called (data) missingness mechanisms):\n",
        "\n",
        "  1. Missing Completely at Random (MCAR)\n",
        "\n",
        "  The missing values on a given random variable (Y) are not associated/correlated with other variables in a given data set or with the variable (Y) itself. In other words, there is no particular reason for the missing values.\n",
        "\n",
        "  2. Missing at Random (MAR)\n",
        "\n",
        "  The MAR mechanism occurs when the probability of missing values for a given random variable Y is related to some other measured variable (or variables) in the analysis model but not to the the values of Y itself.\n",
        "\n",
        "  3. Missing Not at Random (MNAR)\n",
        "\n",
        "  Missingness depends on variables unobserved in the data available or on the value of the random variable Y (which is missing values) itself.\n",
        "\n",
        "  For more details, see Enders, Craig K. *Applied missing data analysis*. Guilford Publications, 2022. or Scheffer, Judi. *Dealing with missing data.* (2002).\n",
        "\n",
        "  Now let us look at nullity matrix again to see if can find what type of missingness is present in the dataset."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 847
        },
        "id": "Rfmywsn1MurW",
        "outputId": "de16a8a3-d630-4dfb-e80d-d6571a504bc5"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "execution_count": 12,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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3F19f39T+KkRpCgMHiIiSQdasWcXDw0Pu3bsncXFx4ubmpgUMWLYyEBGpVauW6PV6yZ49uwwZMkS+//57AWDPohMREREREREROTzL+MmsWbPk1q1bUrlyZe29Tz/9VGrVqiWnTp2Ss2fPSp8+fWT8+PFaCmsRkXnz5sn06dOlYMGCMnjwYGYcICKyE7PZLG+99ZasXbtW8ufPL6tWrZJevXppwQOWLL69e/eWfPnyybfffiunTp2SAQMGyJgxY2yu7QsXLpR58+ZJQECA9OzZU8tOQESvh4EDRETJIF++fPLGG2/I5cuX5euvv9ZS51keapVSIiJSqlQpyZo1q7z33nuSJ08eqV+/vvYeERERERERERGJHDhwIFHqasv4iSUNtWXiyGw2i8FgkMDAQClTpowkJCTIn3/+KVeuXJHbt2/LjRs3pG/fvvLFF1+IyWSSn3/+WfLly5e6X4iIiDSWhXblypWTNWvWaMED3bt3l7CwMNHr9SIiUqRIEWnfvr1ky5ZNXF1dxWAwSEJCgpjNZomOjpYhQ4bIwIEDxWQyyY8//shMMkTJgIEDREQv6XmZAcxms3h6esqAAQPEy8tLfvzxR/npp5/EZDKJUkpiY2O1zs7SpUvF29tb5s+fL3/99ZcUL148Nb8CEREREREREZFDGzlypFSvXl2WLFkiERER2uuWbI6PHj0SFxcXMZlMYjKZRKd7OsRdrVo1GTdunFSpUkUOHTokRYsWlVKlSkmhQoVkzpw5EhAQIHv37uVYDBGRnRmNRjEYDBIfHy9ly5aV0aNHS+7cuWXt2rXSu3dvLfNAxowZpU+fPtKpUyfJmDGjfP311/LGG29IhQoVpGDBgjJlyhTJly+fBAUFSdGiRe38rYjSBoO9C0BE5AxMJpPo9XoxmUxy7949+fvvv8VgMEiRIkW0fZOqVasm77//vvzyyy8yadIkuX//vnzxxRfi7u4uIiILFiyQzZs3S7ly5SQmJka8vb3t+ZWIiIiIiIiIiByOu7u7eHh4yPDhw0UpJR999JH4+PhoAQJ3796VzJkzS4YMGWyyOLq7u0ujRo2katWqMm3aNDl//rw8ePBA3njjDWnUqJHUrVtXcuTIYa+vRURE8nSc3WAwSFRUlPTp00eCgoLEw8ND/vnnHxER+eWXX0Sv18vs2bMlU6ZMkjNnThk6dKjUqVNHFi1aJH/++afcuXNHSpcuLZ9//rl06NBBcufObedvRZR2KHBzbSKiF7JEQEZHR0ufPn1kz549cvPmTRERKVu2rNSvX18mTZokIiLHjh2TadOmyebNmyU6OlrefPNNKVKkiAQHB8vu3bvFz89P9u3bxwhIIiIiIiIiIiIrALRAgJkzZ8qIESMkPj5evv76a+nWrZu2AKNq1aoSFhYmf/31V6LPsCz8EHmaITI2NlY8PT1T70sQEdFzWa7z0dHRUrNmTbly5Yo0adJEBg4cKH/99ZfcvHlTRo0aJWazWdq0aSNz586VTJky2XxGcHCwxMfHS44cOWzuG0SUPBg4QET0AmazWXQ6nURFRUmNGjXk5MmTUrFiRalWrZr8/fffcvDgQXnw4IHUqVNHfv/9dxERuXz5smzfvl1mz54tV69eFRGRLFmySNGiRWXx4sUMGiAiIiIiIiIiSoJlHEZEZPr06TJq1CgteKBTp06SOXNmKVKkiGTMmFGOHj360hNGnFwiInIMJpNJevXqJUuWLJHBgwfLqFGjbAK8Dh06JB988IE8fvxY2rVrJ3PmzJGMGTNqgWEABIDodDpe24lSAAMHiIj+RXx8vLRs2VK2bNkiQ4cOlWHDhmlR7seOHZOqVauKTqeTWbNmSc+ePbXzQkND5cyZM/L48WMpUaKE+Pn5SZYsWez1NYiIiIiIiIiIHJol66OFdfDAxIkTpWXLllKuXDkpXbq0jBgxQtzc3MRkMklUVJQYjUaJi4uT+Ph4+fvvvyVv3rzSvn17O34bIiJ6VkxMjFSpUkUbO8+QIYN27bcEjx0+fFhq1KghRqNRWrduLfPnz5eMGTPau+hE6QIDB4iI/sVvv/0mTZo0kffff19WrFgh7u7uIiKSkJAgdevWlSNHjsjAgQPliy++EF9fXzuXloiIiIiIiIjI+VhWk0ZGRsrs2bNl6NChImIbPDB69GiZNm2aBAcHv/Cz9Hq9nDt3jlkfiYgczN9//y2FChWSgIAAOXbsmCilbALGLMEDs2bNks8++0xERFq1aiXz589PtG0BESU/w78fQkSUvh06dEiMRqMMGTJECxowm81Ss2ZNOXz4sAwfPlwGDx4sPj4+EhUVJZcvX5ayZcvaudRERERERERERM5Dr9dLdHS01K5dW/7880/JmjWrdO/eXfr37y8iIqNGjZKRI0eKr6+vtGvXTvLkySM6nU50Op1kyJBB3NzcxGw2i6enp9SqVYtBA0REDsjV1VW8vLzk7NmzcuLECalcubLN+5btagoUKCBKKcmVK5esWrVKvLy8ZNGiRdyagCiFMXCA6F88u08O981JP0wmkyil5NatWyIiEhERob1evXr1REEDIiK3bt2SESNGyKBBg6RWrVr2KjoRERERERERkVOw7FetlJIJEybI7du3JTAwUNq0aaMd079/fzGZTPLNN99IcHCwVKpUSXr27Kkt8CAiIueQK1cuadasmSxfvlyWL18uefPmlVy5cmnvW7LPVKpUSYoUKSJdu3aVlStXSr9+/TgvQ5QKdPYuAJEjM5vN2s0oJCRERESUUsIdPtImo9EoIk/rXeRppLtOp5PcuXPbHPf2228nGTQgIhIYGCgHDx4UPz+/1Cs4EREREREREZETMhqN2libUkr27dsnJUqUkJEjR4q3t7eYTCZtnGbgwIEyePBgcXV1lUGDBsncuXMlPDxc+yyO1xEROTbL9fy9996TnDlzyqpVq+Snn36S+/fvi8jTe4JerxeRp9vU/PPPP9K3b185ceKElC5d2m7lJkpPGDhA9Bwmk0lLi7Nq1Srp27evjB07VkSEkW1plMFgkMjISGnbtq2cOHFCe92S2q5Lly5SoUIFOXr0qAwdOlQGDRpkEzQwffp02blzpzRv3lzeeOON1C4+EREREREREZFTMRgMEhERIf7+/hIYGCju7u7y2WefiZubmyQkJGiLOiyTTQMGDJCvvvpKXF1dZdiwYbJ8+XIteIDjdUREjs0y3/Luu+9Ku3btJCwsTCZPnixTpkyRkydPisHwNEn67NmzZcWKFVKpUiWJj4/n9Z0oFSkwFJMoEUs6HBGRsWPHyuTJkyU+Pl4mT54snTt3lixZsti5hJQSAMiAAQNk5syZsn79evnggw+09xo3bizbt28XV1dX6dmzp3z77bc2586dO1fGjx8vmTNnli1btjBwgIiIiIiIiIjoJaxdu1Zatmyp/XvYsGEyfvz4RMeZzWZt0mn69Ony1VdfSWhoqMydO1d69erFiSUiIidguZZHR0fLyJEj5YcffpDHjx+Li4uLVK9eXUJDQ+XEiRPi5+cnQUFBUrx4cXsXmShdYcYBomcA0IIGhgwZIl9++aVUrlxZdu3aJQMGDEgUNMDYm7RDKSWFCxcWkacBI9bp7qZNmyaVK1eW+Ph42bp1qxw/flxOnz4tf/31l/Tu3Vv69+8vCQkJsmrVKgYNEBERERElE8t2YkRElHY1b95c5s6dq/375s2bEhsbKyL/S2stIjaZB/r37y8DBw6U3LlzS82aNRk0QETkJCzXck9PTxk/frxMmzZNWrVqJQkJCbJ79265e/eu1KtXT/bv38+gASI7YMYBoueYM2eO9O3bVxo3biwTJ06UUqVK2bxvNBq11DmUdsTHx0uNGjXk6NGjsm7dOmnatKmYzWZRSsnZs2dlwIABsnv3bhER0ev1YjKZRCklb731lixZskTb1oCIiIiIiF6NJfOb5e+EhARxcXGRiIgImT9/vnTs2FFy5Mhh72ISEVEyss4iMHfuXOnTp4+IiEydOlX69++f6Jhn/x0cHCy+vr6pXGoiIvqvnr22X7hwQeLi4iRHjhzi5eVls0UwEaUeBg4QJSE0NFTeeecduX79umzfvl3Kli2rvXfx4kU5efKkbNiwQby8vKRHjx5SunRp8fT0tGOJKTlYBii/++47+fjjj6V9+/byww8/JDpm1qxZcvHiRbl7967kzJlT3nnnHalSpYpkz57dTiUnIiIiSh8AJLmi8Hmvk/M5ePCgTJs2TWbOnCm5c+eWiIgIqVChgly5ckV27twpdevWtXcRiYjoNVnfr60njKwX5yxYsEB69+4tIk8X9Xz88ceJjk/q30REZB//9XrMBZpEjoWBA0RJuHTpkhQvXlyaNWsma9as0W5+K1askBkzZsiJEye0YwsWLChz586V+vXrc8DSSTzbmXm23i5duiRVq1aVkJAQ2bBhg7z//vtJnhcfHy+urq6pV3BKFgkJCXL+/Hm5cOGCxMfHS7ly5SRHjhySNWtWexeNiIiIXsAS5CkiEhYWJiEhIZI7d25RSonBYOAEgpMDIPHx8VK1alU5efKktGzZUiZPnizvvfeeXL9+XYYOHSqDBg0Sd3d3exeViJIJx1DSF8t93LLVwI0bN6RAgQJiNptFr9fbjLG8bPAAERHZl2XS3zLeev78eYmPj5fy5cvbjLcmFTj25MkT8fX1FaUUr+9EDoQtkSgJbm5ukiNHDjlz5oxcunRJ9u3bJ3379pVOnTrJuXPnZPTo0bJhwwb54IMP5Nq1azJ79mwRET7wOgGTySQ6nU4SEhLk3r17IvK03iwxVACkaNGiEhgYKCIiBw4c0F5/tvNieaBl/JXziIqKkjZt2ki1atWkXbt20qVLF6lcubJ06dJF9uzZY+/iERERURLMZrM2qSAiMn36dGnYsKEULFhQqlevLp9//rmEh4eLTqcTk8lk59LS61JKiZubmyxatEjKlSsnq1evltKlS8vly5dl1KhRMnDgQHF3d7fZ65qInJPlWv3sGAqfrdMuo9Eoer1eYmJiZMSIEVKnTh0pWbKk1KpVSwIDAyUiIkJcXV0lLi5ORER69uwp8+fPFxGRTz/9VObNmyci/9sXm4iI7M8SNGA93tq+fXvp2rWrVK5cWTp37qyNt1rf83U6nZw9e1Y6dOggs2bNSnLcnYjshxkHKF2zjmSzjnozGo3StWtX+fHHH8XLy0uioqLE3d1dmjRpIp9++qnUrFlTRETu3r0r+fLlk2rVqsnOnTvFxcXFbt+FkrZv3z4JCAiw2e8uKipKqlatKh4eHvLee+9J//79xWAwiKurq9bh2b9/v3zwwQcSExMjx44dk4CAADt+C0oOUVFRUr16dTl16pQ0atRIOnToIJcuXZLz58/L2rVrJUuWLLJixQpp2LChvYtKRESU7v3xxx/y119/Sa9evWxeHzZsmEyePFk8PDwkQ4YMEhsbK2FhYVKpUiXZvn27ZMqUySYzATkXS1/82LFjUr16dTEajVK4cGHZuXOn5MmTh3VLlAZY2nlcXJxs2rRJQkNDBYC0a9dOPD09RafTMRNBGmO5dkdGRkqDBg3k8OHDkj17dvHz85PHjx/L/fv3pVKlSvLrr7+Kr6+vxMXFiZubm4jYZh6YOnWq9O/f355fhYiI/p9lXuV1xltjYmJk4MCBsnDhQvH09JTJkycneu4jIvthGA+lW5aV5yIiW7dulcDAQHnw4IGIiBgMBlm4cKEMGjRI6tevL7Vq1ZI1a9bIt99+qwUNiDydlDabzVKvXj1xcXFhdLyDGTdunNSqVUvmzJkj8fHx2uvbtm2T8PBwOXv2rIwcOVJq1qwpgYGBcvXqVW0/perVq0uzZs0kLi5OVq5cKUajkVHtTiwhIUF69uwpp06dkuHDh8u6deukXbt2MmbMGPn2228la9as4urqKkeOHBGj0Wjv4hIRURJedB/mPTptuXXrltSsWVM++eQTWbhwofb6ypUrZfbs2fLuu+/K/v375fTp07J582YpU6aMHD16VGrWrCkhISGi1+uZecBJGQwGiY+Pl4ULF0p8fLxkyZJFLl26JH379pUHDx4waIDIyZlMJm1lYuPGjaV169bSq1cv6d27t9SrV09+/PFHCQ8Pt8kKSM7NkjEoJiZG3nnnHfnzzz/lk08+kStXrsiZM2dk9erV4ufnJ0ePHpXq1atLSEiIuLm52WQesPQFvvjiCy3QhIgcB/vd6ZMlo+/rjLd6eHhIp06dpFKlSuLi4iJ169a14zchomcxcIDSJeuVKpMmTZLOnTvL5MmT5fvvvxez2Swmk0nc3d1l0qRJsm7dOtm1a5c0btxYcuTIoX3G4cOHZfr06eLn5yc1atQQEW5V4Gjc3NzEw8NDvL29tW0FRERatGghhw8flq1bt0qtWrXkn3/+kWnTpknlypVl6NChsmnTJhER+eSTTyRv3ryyefNmiYuL01Y+kPO5cOGCbNu2TerUqSPDhg3T9sYFIM2aNZPg4GDp0qWL9OvXTwwGAx96iIgcjHXA57Fjx2Tr1q2yaNEi2bZtm0RHRzOtYRqTIUMGGTlypLi4uMjQoUO19MQnT54UX19f+eqrr6RcuXKSLVs2qVatmuzevVsqVqwoZ8+elVq1ajF4wMnp9Xpp1aqVrF+/XrZv3y4BAQGyceNG6d69uzx+/NjmWAYNETkXywRygwYNZM+ePVK3bl0ZO3asVKpUSU6ePCnDhw+XxYsXS2hoKIMH0gidTidGo1GGDRsmx48fl88++0wmT54s3t7ecuHCBVm6dKk8evRIcuTIIRcuXNCCAK2DB7p16yZLly6V06dPS6ZMmTj2RuRgLGPso0ePlp07d4oIt55JL/7LeGvlypVlxowZcuLECSlUqJC9vgIRJQVE6YzJZNL+e9CgQVBKoUKFCti9e7fNcUajEWazWftvazt37kStWrWglMJ3332X8oWm13bmzBkAQGRkJBYvXoxHjx7ZvP/48WMcOXIEXbp0QebMmaGUglIKPXr0wHfffYfKlStDKYVRo0bZo/iUTBYvXgylFH766SftNZPJhLfeegtKKYwYMQLh4eEAgNjYWBw8eBB37tyxV3GJiMiKdd9t3LhxyJo1KwwGg3bPLleuHFavXo2HDx/asZSU3EJDQzFhwgTodDpkyJABs2bNQsOGDTF27FgASNRPj4qKQqVKlaCUwptvvong4GCb98lxWerSmtFoRHx8PADg7NmzKFmyJJRSaNKkidaft67b48eP48GDB6lTYCJ6Zdb38qVLlyJz5swYPXo0YmJiAAD37t3DiBEj4Ofnhxw5cmDq1KkIDQ0FkPQ1gpzLiRMnkCVLFjRo0ACRkZEAgCtXrqBTp05QSqFfv34wm80oU6YMlFIoWbIknjx5AgDab4SIHNsvv/wCpRSaNWuGuLg4exeHUgnHW4nSJi7NoXTHsiJt6tSp8s0330jTpk1l0aJFUrt2bZvj9Hq9FsWs1+sFgJw7d06mTJkirVu3lqNHj8rMmTOlZ8+eIsJISkdjWX1UqlQpMRqN0rhxY+nevbssWbJEQkNDteOyZMkilSpVkqVLl8qvv/4qU6ZMkSxZssiiRYtk5MiRcvv2bRF5GkEZExNjj69CySA4OFhERGJjY0VEJD4+XqpVqyZHjhyR4cOHy+DBg8XHx0dERB4/fix9+vSRoKAgexWXiIisWPpuo0aNkpEjR4qbm5t88skn0rlzZylUqJCcPHlS+vXrJ3PnzpW7d+/aubSUXDJmzCiffPKJjB8/XiIjI2Xq1Kly/Phxbfspy2oVvV4vRqNRPD09bTIP1K1bV8s8wH664zKZTNqq4tjYWLl586bcvXtX9Hq9uLi4iIhIQECArF69WkqUKCFbt26Vrl27yqNHj7TVbcuWLZOGDRvKzJkzmWWCyAFZsgbFx8dLWFiYBAUFSb58+WT48OHi7u4uCQkJkiNHDunfv78MGjRIzGazTJkyRRYvXixhYWHMPJAGREVFSb58+eSrr74SLy8vuXfvnsyePVt++OEH6datm8ycOVOUUtK3b1/x8fGR8+fPS506dSQ4OFhbvUqOC4CWgpxtNf2qXbu2FCxYUC5evCiPHj0SEWaGSg843kqUNjFwgNKlM2fOyOzZsyVv3rwyatQoKV26tPbe3bt3JSgoSAYOHCirVq2SS5cuicjTh92pU6fKkCFDpFixYrJ8+XLp27eviDztCDFVmmOxrg+DwSDvv/++ZM2aVQIDA2X+/Pk2wQOWB5y33npLvvjiCwkKCpL58+dLzpw55d69e+Lu7i5ffvmleHh4pPbXoP/I8tDq7+8vIiJ//fWXiDx9oDl8+HCiTqzI04mpU6dOSc6cOVO/wEREpLEeaDp+/LjMnz9fPvzwQ9m2bZvMnDlTli5dKocPH5bevXtLVFSUzJo1S37++Wdt0IKcX8aMGaV3794yduxYuXXrlgQHB8v169dF5Gn/zvIbMRgMYjQaxcvLS3bv3i1VqlSRU6dOSZkyZbRAA3I8RqNR9Hq9REdHy8CBA6VGjRpSqlQpCQgIkJ49e8qaNWu0Y4sXLy6rV6+W4sWLy9atW6VLly6yb98+mThxoowePVqioqKkdevWWjABETkOSzsvU6aM9O3bV8LCwqRVq1bi4uIicXFxWpCQr6+vdO/eXb744gsGD6QxpUuXlqVLl0qpUqVE5Gm/bvHixdKyZUtZuHChdlzx4sUlKipKcubMKWfOnJEPPvhAzGYz697BWPpfERERIvJ0/M1gMEhkZKT069dPjh07Zs/ikR2YzWbx9PSUcuXKaVuQiAi3k0vDON5KlMbZKdMBkV1t374der0ew4cPt3l97dq1aNSoEdzd3aGUgoeHBxo0aKClu4+KisKPP/6IS5cuaedYp9wjx5CQkAAAiIuLw927d7XX58+fj8yZM0On02HChAkICQl54efExsZi0aJFuHHjRgqWllLCs+3y0qVL8PDwgFIKBQoUgFIKo0eP1tIYW8yaNQuZM2dGq1attNSYRESU+qzTEt+5cwe//fYbvLy8cPjwYe11y/0+IiICgwcPhl6vR4ECBXD16tVEn0HO4Xn96idPnuDrr7+GTqeDUgrTpk1L8hzLbyIyMhJFixZFYGBgyhaYXptlm4HIyEhUrFgRSikUKlQIDRo0QOHCheHi4gIvLy+MGTPG5rzz58+jQoUK2lYlSinkzZsX586ds8fXIKJnzJw5EydOnEj0+t69e+Ht7a2123bt2j33M4KDgzF58mRky5YNefPmxVdffYWwsLCULDYlI0v/69l7unW/rEaNGnBzc8PRo0cBQEtrfvv2bZQsWRLbt29Hw4YN8eeff6ZSqelVHT9+HA0bNsSGDRsAPB0/K1KkCDJkyIDVq1fbuXRkL7t374ZSClWrVsWTJ0/4PJaGPO/azvFWorSJgQOULq1YsQJKKXTs2BHXr1/HrVu3MGTIEO0htmfPnujTpw8qVqwINzc3fPfdd0l+DjtAjssyCLlw4UKb162DByZOnPjc4AHuh+ucLG3SMnFg3UZnzZqlTTi8++67iTq7M2bMQI4cOVCoUCFcu3Yt9QpNRETPNXjwYBQtWhRNmzZFkSJFEr1vuZZHRESgUaNGUEqhe/fuANhPczbWfa8jR45g48aNNvdjy0SSTqeDr68v5s+fr72XVPCA5W+AvwVHFRsbi3r16kGv12Pw4MHaIGJERASmT58OpRT8/f2xc+dOAP+r5+vXr2PkyJFo3749Pv/8c1y/ft1u34GI/mf58uVaEJBlf3prGzduRMmSJaHX61G+fHkcP378uZ8VHByMb775Bnq9HkWLFk3y88ixPDuG8uy913INj4yMRMGCBVGoUCHExsbaHDNkyBB4eXlxUsnBRUdHo1OnTlBK4Z133sH69esREBAAd3d3jBkzJlG9UtphacdJ9a3NZjNCQ0NRv359KKXw+++/p3bxKAVYru0cbyVKXwz2znhAZA/VqlWTt99+W1atWiVHjx6VsLAwefjwodSpU0cGDhwojRo1EqPRKDNnzpTjx4/LgQMHpGfPnok+h9sTOK7Vq1fL8ePHBYC8//77ki1bNhER6dWrl4iIDBs2TIYPHy4A5OOPP5ZMmTLZnM80p84nJiZGJk+eLPfu3ZPQ0FDp1q2bVKpUSavbDz74QG7cuCEzZsyQHTt2yJgxY6RmzZoSFRUlK1eulFWrVomfn59s3LhRChQoYN8vQ0RE8vDhQ7l06ZJcvnxZnjx5Ij4+PhISEiKZM2cWs9ksOp1OdDqdmEwm8fb2liFDhkhQUJDcu3dPRNhPcyYmk0nre02ZMkVmzJgh9+/fl5UrV0q2bNnE29tbMmfOLD179hSTySQjRoyQYcOGicjTvp1Op9N+EwaDQQCIwfD0URcAfwsO6ueff5Zdu3ZJ586dZfTo0eLp6SkiIl5eXvLLL7+Il5eXtG3bVipUqCAi/0t3mz9/fhk1apQYDAZJSEjQ0pwTkX29//77UrduXalRo4b4+vpq11/LNf79998Xs9ksQ4YMkRMnTsjChQslV65cSaYszpw5s3Tp0kVcXFykUaNG4uvra4dvRC/LaDSKwWCQ2NhY+fnnn+Xy5cty9+5dqVq1qhQrVkxq1Kih9dkSEhLEaDTKrVu3ZMGCBdoWoPPnz5cVK1ZI9erVed92cB4eHtKrVy9xcXGRpUuXyv79+yUuLk7Gjh0rgwYNEr1er/XLyPlZruUAtDq9ffu25MuXTzvGcp3PmDGj1K1bV37//XeZN2+eVK5cWby9ve1VdPqPLNd2jrcSpUN2DFogSlEvWllkNpuxdetWfPjhh8iaNSvefPNNLFu2TIt6s5w7ffp0uLq6YuXKlalSZko+RqMRNWvWRIYMGbQoV0v6O+DlMw+Qc4iMjESlSpVs0tbqdDp88sknOHnypHbc9evXMXHiRC0S1vLH3d0dDRo0wOXLl+33JYiIKJGzZ8+ie/fu2vV6xowZ2nuW/prl73PnzkGv16N48eKIjY3lKnMnYV1PgwcPhlIKFSpUwM8//5xkBqiQkBBMmDABOp0OmTNnfm7mAXJ83bt3h8FgwM2bN7XXjEYjqlSpAqUURowYoaUnj4yMxK1bt7Tjnm3/RGRflhWI8fHxAIDw8HA0b94cp06dAmC7Gn3jxo0oWLAglFL49NNPbbYXtHheSmRyDFevXtXq3HrrmerVq9s8Z1u2AB01apTN+T/88IP2TN6sWTPUqVMHSinkzJkTFy9eTPXvQy/P+r67d+9eZM2aFTqdDgUKFMDmzZuTPI6c08GDB3Hw4EEAtvXZt29fKKUwbNgwbNy4MdF5wcHBCAgIQP78+XH79u1E55NzsM4Sw/FWovSHgQOUJlk/XJrNZjx48ABxcXGJHjrj4+Nx7do1REZGJvqM/fv3o2DBgsifP7/2sEvOwWQywWw24+uvv4ZSCq1atdLesx6wsA4emDx5cqL9l8g5xMfHo2XLlvDy8sJHH32EgwcPYvTo0ShWrBj0ej1atWqFI0eO2Jxz6tQpzJgxA4GBgRg/fjyOHDnC+icisiPrPtqzk8VnzpxBr169tL3M161bp71nnY5+/fr12pZTAAeonM2sWbOglELTpk3/te8dGhqqBQ9ky5YNc+bMSaVS0uuybo+WVLYVK1ZEhgwZ8PfffwN4eh2wDhoIDw/Xzvnjjz/w2Wef2QQZkGP7t63feI1Oe6zrtE+fPlBKoXTp0jh37hwA29/Epk2b/jV4gBxT//794efnh127dmn9sJiYGNSqVQsuLi5o27YtfvvtN3zzzTdaOnulFPr166d9RmhoKL799lvo9XoopeDj44Pq1aszaMBJWNry+++/D6UUypQpA51Oh7p162Lbtm3acbzOO68LFy5AKYUMGTLg0KFDAJ7W5507d9C8eXO4uLho7bdhw4ZYtGiR1p+Lj4/X2v7AgQPt+TXoP+J4K1H6xcABSnOsH0aXLVuGDz/8EJ6enggICEDz5s3xzz//JHmedYc2KCgIderUgV6vx9KlS1O6yJRCbt68iWzZssHFxQW//vqr9rr15MT8+fORLVs2KKUwbdo0Ptg4obt378Lf3x+fffYZoqOjtdc3b96MOnXqQKfToWXLljh69KgdS0lERM9j3XfbtWsXJk2aZLOyGHiaeeCjjz6CUgolS5bEihUrbN4/cOCANuG4fv361Cg2JaNbt26hVKlSyJYtm03QwIv6ZSEhIZg8ebI2IXHmzJnUKCq9BsvEUlxcnM0+5R988AE8PDwQEREBAKhcuXKSQQMAUKNGDfj7+z/3WY4ci6XOY2NjsXnzZnz55ZcYPHgwZs2ahStXriTaL5fSntDQULz33nvafZvBA2lDdHS0Vq8BAQH4/fffYTKZsGPHDmTKlAmBgYGIiYnRjo+Pj8dPP/2k3asDAwNtPu/MmTPYsGEDTpw4wYklJxQcHIzFixfjwIED6NChA3Q6HWrVqoUdO3Zox5jNZmYOcUIRERFo0aKFFrj9bOaBo0ePYuHChShatCjc3NyglIKfnx9GjRqFY8eO4dy5c8iQIQNq1qypZY8i58PxVqL0i4EDlKZYd0aHDRumpcQpXbo0ihYtCqUUihQpgt9++01LoWctLCwMc+fOhb+/P1xcXDBt2jTtPQ5qOB7rVYYWz6Y0tKxGGzFihM1x1r+V6dOnI3/+/Dh//nwKlpZSyt69e+Hq6qoNNFhvSbFr1y7UrVtX68weO3ZMe8/698P2TeRYrNskB5rSNusJhMmTJ8PPz08L5ktISLD5LZw7dw5dunTRBp+7dOmCcePGYeTIkShQoABcXFwwffp0O3wL+q+CgoKglMIXX3wB4OXb/ZMnTzBq1CibLSzIMYWHh6NkyZLaSkSTyaSlum3RogXeeustbVLp2aCBCRMmwN3dHYMGDUJsbKw9ik+vwDpteaNGjbQViZY/JUqUwLhx47S6ZD887bE8Z4WFhaFx48YvHTzQuXNn3L9/3y5lpn9naauPHz9G586doZRC8eLFsX//fowaNQqZM2fWJpaeHW/7+eeftWvA1q1bbT6PnNOzdXz06FG0adMmyeABiz179tikNifHFhkZqWUOyJs3Lw4cOJDomAcPHiAoKAidO3fWFmUppVCnTh1kzZoVSin8/PPPdig9vY5nn8E43kqUfjFwgNIM6xvRmDFjtI7K/v37AUBLh6mUQoECBbBlyxabju65c+e0PXSLFi1qs5KNkxaOx1LfERER2Lx5s01dWtdXUFAQXFxcYDAYbDoxzx4XEhKSsgWmZBUfH49bt27h3LlzOH36NMqXL48HDx5ovwPr68Hvv/9u05k9fvy4vYpN/xEfONIPs9mM4OBgmEwmREVFaa9R2mJ9H7bsbV+uXDmbFKeA7QTD2bNn0aNHD21QKlu2bAgICECnTp2wcuXKJD+bHMuz6ctNJhPmzZsHpRSGDBny3PMs14D79+9rqVAB2wEs1rvj6tevH5RSNn3umzdv4o033oBSCm5ubhg6dGii8+bPn4+cOXOiXLlyiTKRkOOxtNOoqChUqFABOp0Obdq0wR9//IH169djzJgxyJo1Kzw9PTF8+PB/3c6AHN+z/TPLvy3X5pcJHtiyZQsyZ84MDw8P3Lt3L5VKTq/Dcp998uQJOnTooAUDtWrVCu+88w6AxBPKFp9++imUUpg0aRIA9u2dyYuu1dYThH/++Sfatm2rBQ9s375de2/ZsmUoUKAA2rRpY5OVghyTpa2Hh4ejW7duWvDAH3/8ob3/7O/i9OnTWLBgAUqVKoXMmTNrz2stWrRAdHQ0++kOztKWOd5KRAADB8jJJfWgsW7dOvj6+qJevXpaJGtkZCTmzp0Ld3d3rfNSsGBBbN26VXugNRqNWLJkCWbMmIE///xT+zx2bByPpXMaHx+PPHnyQCmFxo0bY+LEidoEkzXL/ooTJ060OR9g/TqjyMhItGrVCgUKFIBer0f27Nmh1+u1ICGLpDqzbm5uaNiwIU6cOJHaxabXFBERgSFDhiAmJobtNZ34/vvv0a5dO+TLlw9ly5bFBx98gD179ti7WJSCJk2apO1tf/r06X89/tSpU+jZsyeUUqhVqxbWrl1r8z6vFY7r2e2iLGmp165dC6UUunXrBiDxALX1Pb1///4YMmQIIiMjU6HE9Cr++usvm0AOax07doSPj4+2GtVkMsFsNmPZsmXIkSMHlFJo164dEhIS8OTJEzx+/BgDBw6Et7c3smXLhgsXLqTmV6H/wGg04pNPPoFSCkOHDrXJEvHkyRMUKFAA2bJlw+jRozl55OQs12qTyYSEhATcv3/fZjsSi5cJHti2bRuuXLmSOgWn/ySp4AGlFLy9vXH9+vXnnrdw4UIopfDOO+8gPj6egQNOwjKZGBMTg4ULF6J79+5o1aoVBgwYkOT2In/++SfatGkDvV6Pt956C/Pnz8e4ceOQI0cOeHp6au2fHJf1tTk2NhaXLl1CnTp1oJRCvnz5tOABs9mc5FYUd+7cwZEjR9CiRQvkyJEDWbNmfeG1gezPOlsUx1uJCGDgADkp64FC65tVcHAwmjRpAl9fX+zduxfA08nlqVOnwsvLC4ULF8aNGzfQunVrbduCrVu3JtqDLanPJsdgeWiJjo7GiRMnsHjxYlSoUAHe3t5QSqF06dIYO3Yszp49q52zd+9euLm5oUSJEtoequScoqKitMwhRYoUQf78+bVgoBo1auDSpUs2x1u34d27d6NChQrw9fXFnTt3Urvo9Bri4uJQrVo15MuXz95FoVQyZMgQKKXg6uqKokWLokCBAtpg5JQpUxAaGmrvIlIy+/PPP5EzZ04ULVo00R71T548wZEjRzB16lQEBQXZrEI8c+YMOnbsCKUUKlasqKW9Bdh/cwbDhw+HUgoffvgh4uPjcejQIbi4uEAphaCgIABJD0bu2LEDOp0OnTp1YuCAgxk4cCDc3NywYcMGm+cpS9+9YcOGyJo1KyIiImxWJz5+/BgLFixA3rx5tSwiBQoUQKZMmbS+PbcTcy537txBkSJFULZsWZtnL5PJhCpVqkCv1yMwMFDLPvG81cnkmCyTC5Z2HBUVhY8//hiVK1eGj48PcubMiS+++MJmpTHwcsED5Dws9+bHjx+jXbt2yJAhA9zd3TF9+vRE92fLb+XcuXNQSqFjx46pXl56PdaTifXr14dSCnq9Hq6urlq2iTVr1iTaw/7kyZP46KOP4Obmpj3L5c+fn0EDTsC63z1t2jTUrl0bWbJkgb+/v1aXefPmxcGDBwG8+LnLaDSid+/eUEph8ODB/3o82RfHW4nIGgMHyOns2bMH7du3TzL9TUREBCpUqIBBgwYBeNpJWbp0KXx9fVGgQAFtwPnmzZvIly8flFIoVKgQNm3a9NzVMeQ4LB3YyMhIlCpVCpkyZUJoaChu3ryJXbt2oVq1alqnxtvbG6NGjcKuXbsAAI0aNYJSCuPGjeNKRCc2duxYZMmSBSNGjEBMTAzCw8Px3XffoXz58lBKoVOnTrh69arNOdad2b179+LmzZupXWx6Tffu3UPevHnh5+eHJ0+e8CEzjZswYQKUUmjYsCGOHDmiDVR99913UErB3d0dq1ev5jU8jVmzZo1NViCLjRs34oMPPoDBYNCCSfr164cbN25ox5w7d07bY7dy5cqJJinIcVhfvzdu3AhfX1+0aNECR48e1V63ZIjKkydPknuoHjx4EFWqVEGmTJnw66+/pkq56eUkJCRogTxvvPEGNm7caJPO1GQyoVy5cihRooT2mrXY2FicO3cOrVu3RpUqVVC4cGG8++67+Pbbbzn46ASerc8tW7ZAKYUvvvhCe81kMuGtt96CUgojRoxAeHg4gKdBA3v27MFff/2VqmWmVzdjxgxt0sDSviMjI1GhQgUopZArVy4ULlxYm1gqWLAgxo0bZ/P7sA4eKFOmzEtlGSLHY6lTS0DA48eP0b59e218bePGjVp2GevAIEuA8IQJE7TgQHJclmeuqKgoVKpUCUoptG7dGsePH8fVq1e1BVmFChXC4sWLEwUP/P333/jpp5/QqlUrjBkzhivOncywYcOglEL16tUxf/587Nq1C/3790e1atVeKnjA8vs5c+YMMmXKhEaNGqVq+enlWI+tcLyViKwxcICcSnBwMGrWrKndsCxbEQD/u9lZ73t6+/ZtVK5cGblz59YecmNiYhAZGYmKFSsib9680Ol0yJgxo81ANDmu+Ph4tG7dGpkyZcKnn35qszVBWFgYdu/ejV69esHFxQU6nQ5KKXzyySfaKqhGjRpx0skJWQJ7OnfujJo1a9rUe2xsLLZu3ao9zHbs2PGFnVlyDpaJhgYNGkCn0+HixYv2LhKloCNHjiBnzpwoXrx4okHkCRMmQKfToXHjxjaTjJQ2LFmyRAvsi42Nxd27dzFixAgtYKBjx47o0KEDsmfPDg8PD2zatMnmfEvwgIuLC4oUKYKdO3fa6ZvQ8zzb75o5cyayZMmiBQFbTzw0a9YMSil4eHhgyZIlOHz4MG7duoXly5ejVKlSUEphzpw5qf4d6PksfayYmBj07dtXG0y2Dh4AgCJFiqBatWpJfob1CuaYmBjuce5ErPfDtdT3jh07oNfrMXDgQO29pIIGgKf9+Ny5cyMwMDD1C08vzZJivnTp0rh27RqAp23estJ84MCBePz4McLCwrB161a0b98e3t7e8PLywpgxY2w+KywsDO+//z6UUqhSpQoXcDgByzXacr23vrZbZx6wbFtQqFAhzJs3D7dv39aOmzt3LrJmzQp/f39OLDmR+Ph4tG/fHh4eHggMDNSySTx+/FgLHLDc9xcvXpxkdjiOvzmfX375BUop1KxZ0yZLhMlkwuXLl7W6z5s3rxbs++yYm6Xeb9++DT8/PxQvXtzm/k/2Z7m2c7yViJLCwAFyOps3b0adOnWg1+vRvn17m+CBZ29W06dPh1IKU6dOBWD7gNOwYUN06dIFHTt2xJQpU1Kl7PR6rNOZhoSEIHv27Ojdu7cWxW40GhM9jOzevRtTpkyBr68vlFJaijS9Xo+HDx+mavnpv4mKikKJEiXQrFkzlCpVCgsXLgTwv71xgae/kW3btmlptZLqzJLzsG7PltXElu1n+FCSNv3www9QSmHJkiU2r48aNQpKKTRp0sTmfv/48WMA/D2kBX/88Qdy5coFg8GAt99+W8sIVb16dWzZskU7zrLq5dNPPwVgm9r4r7/+QrNmzZAxY0auZnJgAwYMQNu2bVG3bl1069YtyWOuXr1qs1+yXq+Hp6cnlFLw8fHBrFmztGM5EO04LO0xJiYGn376qTaYvGHDBkRFRSE8PByZMmXCO++880qfy2u8Y7PUe0REBBo3bozPP/8ccXFxOHDgAJRSKFasGC5evKitThw+fHiiSYPPPvsMSimsXLnSHl+BXtKdO3dQt25dKKVQtmxZXL9+HSaTCbly5ULXrl21SQZLm7127RpGjhwJb29v5MuXDxs2bADwv/GY0NBQtGrVihkHnID1VpFjxoxBly5d0KlTJ6xZs8ZmnAZ4usWU5R7u7e2NnDlzokOHDihevDgyZcqEggULMruIk1m/fj0yZcqEVq1aaVvPXL58WavnLl26IDAw0CbzALeWc36ff/45lFJYt26d9pr1s9c///yDpk2bav29/fv3A0i63zZv3jwopfDBBx8gNjY25QtPr4TjrUT0PAwcIKdh3QHZtm0batSo8a/BAz169IBSCqtWrbJ5b/v27TAYDFizZo3N/4MDkI4rIiICX3zxBebPn49cuXLhwYMHABLX2bN7JF66dAnffPMNypUrB09PT+6R6oT27t0LV1dXeHt7w9PTE5MmTQKQuK7j4+NtOrNdunRJtAcXObakrsGWycLp06drx3AiIe3p378/lFL4/ffftde+/PJLLWjAemD50aNH6Ny5s829nxzbv7XZ7777Dm+99Ra8vb1RtGhRzJs3D5cvXwbwv+vCpEmToNPpsHnz5iQ/48KFC/jnn3+St+CUbM6fPw93d3e4u7vDz88PzZo1g8lkSjTpYDFz5ky0b98eZcuWRZUqVfDll19i9+7d2vvsszue5wUPrF+/HtevX4ebmxvatm2LK1eu4OLFi7hw4QL+/PNPHD58GPv378fu3buxZMkSm/sAOb7Y2FjUqVMHSikMGTJEa5uWFeVZs2aFTqfD2LFjERISYnPuvHnz4Ofnh3feeQePHj2yQ+npVdy7d0/b47xcuXL45ZdfULhwYe35+tlnsxs3bqB79+5QSuHjjz/WXrdc99mfd3zWW0VanrGt//Tp0ydRIIBl2wIfHx8opdCgQQN07doVy5cvZ6YBJ2M2m7X7+ZMnTwA83fa1V69eUEqhR48eAJ5OPFpSmRcsWBBLly5l8IATS0hI0LZ6PXHiBIDE13ez2YwNGzbAz88PSinkz59fCx6wdurUKRQsWBAeHh7MIOmgON5KRM/DwAFyKi8bPGC5wQ0dOhRKKfTt21d778CBA6hbty5y5cpls38qH1wdl9ls1vbMq1ixIrJkyYILFy7866Dxs3XKCQXnFBcXh82bNyN//vzab8AS7f7sb8DSma1atSqUUujdu7dNphFyXJGRkWjbti0+/vhjTJ48Gdu3b8epU6ewaNEiKKW0BxhKmwYMGAClFBYvXgwAGD16tBY0cOrUKZtjp06dCqUUduzYYY+i0iuyHnR4+PAhzp07h8uXLyfK/nP37l1cvHgxyRSWf/zxBwoUKGAzQWHB/ptziIuLw8aNG1GgQAEopVCkSBFtENr6Xm5dnyaTCREREVqGKevXyXEk1Qatgwf8/f0xf/58eHt7J5p0evaPt7c3VzA5Aevr+m+//YaMGTMiMDDQJrXtzp07ERAQoE0yP7uy/JtvvkG2bNng7++PK1eupFrZ6b+xDh7Inz8/9Hr9cwP6AGDXrl1a9j9OGjunhIQEtG7dGt7e3ujevTt2796Nb775Brlz54ZSCm3btk0UzPv48WO0a9cO7u7uyJEjh824GzmX8PBwfP/99zAajTAajZg3bx4MBgPatWtnc9zs2bOh1+uRNWtWuLm5YcWKFeyjOymTyaRtHTZ+/PjnHhccHIy8efMic+bMWh/u3r17NvV+69YtjBkzBmfPnk2NotNr4HgrET0PAwfI6VjfuKyDB9q1a5fogeXIkSNwdXWFUgqNGjVC8+bNtdT11qlOybGZzWbs3btXi2zU6/XYtWsXgMRRkEl5mWPIsVk6s4UKFYJSCt26ddMGJ5/tzCYkJGDjxo2oW7cuM0w4iYSEBG0Q8tk/Li4u2oNo3759ERgYiB9//BEHDx7UVrg8u4KNnIel/W7btg1ubm7o2LEjBg4c+NyggZ07dyJHjhyoXbs2g8Ec3LPbCH3zzTfaHvWurq7ImjUrRo4ciX379iV5rsW+fftQp04dGAwGLF++PFXKTinj2Xt5ixYttL1ynxc88KLXyP4sbdVoNOLy5cs2bdc6eMDNzQ1+fn5o2LAh2rdvj44dO6Jz5874+OOPMXDgQAwYMABDhw612UeXHJOlrUZHR2Pv3r0IDAxEvnz5tFXklt9AZGQk5syZg6JFi2oD0TNmzMD48eO1Pl/u3LmZttwJ3bt3D7Vr14ZSCgaDAXPmzAGQ+Jnb8lupXLkydDodA0Sc1MOHD5E/f37069cPMTEx2uu//vqrtg1JmzZtkgweeP/99+Hu7o5r166lcqkpOTybFSo8PBzFixdHQECAFgBs2Rt92bJl8PPzQ7du3ZA7d24tcxg5F0t/e+nSpXB1dcU777yT6JnbbDZrx9WuXRtjxoxBq1at8NVXXyX5mZxYdnwcbyWipDBwgJyC9Y3q2ZvWizIPAE9XQVjSJ1lWvVhWNAIciHRESU30m0wmHDp0CG+//TaUUihevLi2aoErz9Iu67q1dGb9/f21lJeWVYhJdWYtkxHkHE6ePIlr165h//79mDVrFkaOHIl3331XCxgyGAzQ6/U2AQVubm7ImjUr6tevrw1akOObPHky1q5da/PaxYsXUbJkSa1+3333Xe1+brlPHzhwALVr10amTJkSnU+OYceOHejZs2ei1y0ZoDJlyoQaNWqgUqVKWiBguXLlbPbPtDCZTFi0aBEKFiwIg8GAadOmae+x7+a4LHXzbOYAi2dXtbzoXk6OzXrf688++wz58uVD+/btbQaUY2Ji0Lt3b3h7eyN79uxYu3YtB5DTgLi4OFSsWBEFCxZE/fr10bBhQwCwmVQEnm41t3btWtStW9cmKDRHjhxo27Yts0s4sbt372oBIL6+vloKautgIouAgAD4+/sz0NdJPHsv3r9/PwwGA+7duwcANs9cu3fvRvXq1Z8bPBAcHMxAXyfwov6X9T198+bNUEqhcePGiI2NtenrtWrVCtWrV0dMTAy3nnECSfXXrf/71KlTWpawPn362Gw9YTlu9+7dcHV1xYoVK2w+m/15x/Rv9RIbG8vxViKywcABcnjWD507duzAkCFDcOPGDZtjtm/fjpo1a0Kn0yWZeeDKlSvYuXMnDhw4YDNAwQ6N47F0QqOiohJNDD0bPPDee+/h9u3b2nvk/EwmE4KDg3Hu3Dk8ePAg0fvx8fHYtGkT8uXLxwmHNMIygfC8+rt9+zZ8fX0REBCAX3/9FbNmzUL//v3RoUMHlC5dGmXKlOFe905k48aNUEqhUKFCiVLbbtq0SZtUaNOmDe7evYvY2FiYTCasXr1aCyyYPXu2dg4nkB1HSEiIFqjZu3dv7fV169YhY8aMaN68Oc6cOaO9vnz5crz77rta2vqtW7dq7506dUp7r1ChQjaZBnitd1zWdZOQkICwsLAkB5Xi4+NtBqZ69+793FUt5Jgsz2cRERGoUqUKDAYDqlWrhu3bt2vpTS3HWGceyJs3L9auXYvY2Fi7lZ3+u5s3b6Jdu3ba9hNFixZNNAnx7P1506ZNWLVqFZYsWYLr169zwNlJWOoxqaww9+7dwzvvvKPdq5Pav3rmzJlaQKj1VhbkmCwBYfHx8bh69SrOnj2Lffv2oVatWnjy5Ik2iWzdvnfv3o0aNWpAKYXWrVsnyhZGjs1yr05ISMDVq1exdu1aBAUF4fDhw4mOPX78OLy9vVGnTh2ba/h3332H7Nmzo3fv3nw2cwLWY+yhoaG4evUqrl27huvXr9sct2HDBu3ZvGvXrti1a5cWIGjJCJctWzYcPXpUO4f175hetp0nFTzAZzSi9IuBA+TQrG9MEyZM0AakBwwYgISEBJtOyb8FDzyLHRrHFR8fj7Jly0IphYULF9q8ZwkesKxWbNq0KW7duqW9R84rOjoaAwYMQNmyZeHp6Yk8efJgyJAhOHTokM1xDB5IW57NMGJ9bTcajbh79y6yZMmCvHnzJtrnOiQkRJugIMcXGxuLAwcOoFmzZnB1dUVAQAA2bdpkc8yqVavg4eGhDUJXqVIF5cuXh1IKPj4+WjpcgO3dEf3222/IlSsXlFLo3r07AGDatGnIkCED/vzzTwC2aU8PHz6MFi1aQCmFDz/8EHfu3NHemzRpEkaNGoVjx45pr7HOHZf1tXzlypXo1KkTSpUqhXLlyuGbb77BiRMnbI5/NnjAemCKW0w5NutsAtWrV4erqyuGDh2a5ESwdfBAnz59oJRCnjx5sGnTJmYKcnKXLl1C3759tb2NJ02apF2jn5dxhJzHs9fhZ4N9LO/fu3cPDRs2hFIKWbJkwffff4+9e/ciLCwMo0aNQt68eZEtWzamLXcC1tuMtGzZEnnz5oWXl5fWxtevX29zfFLBAwaDAe+8845NoCg5LkufPCoqCh999JGWDcryLPb555/jyJEjWl1fvXpVey5r27Ytpk2bhu7du8PT0xO5c+dmFhknYH1tX7hwIerVqwcfHx9kzpwZ2bJlw8iRI3H27Fnt3r169Wrt9+Dn54e3334bTZs2hY+PD5RS+Pbbb+31VeglvUw7P3z4sNbOGTxARBYMHCCHZf0gMmTIECilUKVKFezdu/e5x71q8AA5pvDwcAwdOhQuLi7IkiULvvvuO5v3LcEDlhTmH3zwAYMHnFxERIQWDOLv749atWqhWLFiUEqhUqVKiSYXrYMHdDodOnbsmGhSmRxXTEwMli5din79+qFRo0YYOXKkTYaRZwO73n77bfj6+iaKgifnMXXqVNSrVw/u7u4oUqQIlFLQ6XQoWrQotmzZYnNsUFAQunXrBn9/f2TIkAG5cuVCnz598Ntvv2nH8FrveCx1snv3bi3Qs3v37ujQoQO6du0KIOmVqLt27ULBggXh6emJ3bt323ymddprBnw6Luv2OGzYMG11Up48ebSBxapVqyZq688GD/To0YOrkJ2E2WzGuHHjtBS2lj7Y87YbA/4XPKDT6eDp6WmTZYSch/W1+MKFC+jTpw88PT1RrFgx/Pzzz8/NOEDOwzLJEBMTg7lz56JHjx6oWrUqxo4di99//z3RcdaZB1xdXeHq6opixYrBw8MDNWrU4B7ITiQ6Olp7Ji9WrBgCAgK0yaUGDRrg9OnTNsdbt/OgoCC8+eabyJgxo00gKDkmy705MjIS5cqVg1IKZcqUQZs2bbSsX0op1KpVCxs2bNDqeseOHciTJ4/N9jNvvvkmLly4YM+vQy/Bur8+ePBgKKXg5uaG8uXLo1SpUlp91q9fH+vXr9eO37VrF9q0aYMcOXJoW82VLFnSZgtgPps7ptdt589uE9uhQweOtxKlQwwcIIe3ZMkSKKXQpEmT50YuJxU84ObmhpYtW9qsVCPnERISgvHjx2v7Jv5b8EDz5s3x999/26m09F9ER0ejdu3acHV1Rf/+/REWFgbg6UOpZW/rYsWKJTnhsGXLFvj4+MDHx0fbd5EcW2RkJGrXrq1NHFsPOnzyySc2q5EtWrZsCaUUDh48aIcS039l2d++evXqWLJkCQ4dOoQhQ4agWrVqUEqhcOHCibYtMJvNiIiIwKNHj7RrggUHJhzHiRMncOTIkUSv7969G1mzZoVSCt7e3qhUqRJCQ0Ofu49mr169tBUPAOvYmVjX45gxY6CUQp06dbBr1y4AwMGDB1GpUiXo9XoUL148UVu33Mtz5swJpVSi4BGyv+e1xwYNGiBr1qzavrcvarfWwQNdunRBhgwZuALZCTyvTq37apcuXcInn3wCV1dXlC1bFqtXr2bwgBOzXnVep06dRP31HDlyYMqUKdrx1sEDlswD/v7+mDFjBh4/fownT57Y5XvQy7Nu5+PGjYOvry9GjhyJ8PBwmEwmzJ49G+XKlYPBYED37t3x119/2Zxv3c7379/PMRknEhcXh6ZNm0Kv12PMmDE2mYA2b96MypUrQymFGjVq2CzgOnLkCObMmYPPPvsM33//vbZ9KDmHadOmQSmFZs2a4fjx4wCerkZfvXo1ypQpA6UU3n77bZug/aioKPzzzz/YvXs3Tp8+bRMcxOc2x/Yq7Xz//v3aexxvJSIGDpDDMpvNePz4MerXrw93d/dE6cqTOt5ix44dqFKlCpRS2LZtW0oXlVKIdfDAizIPVK1aFUoptG/fPslJR3JcRqMRAwYMgKenJ/r376+tNLx69So6dOigrXawTC4mlXlg+/btuHLlij2KT68oOjoalStXhl6vR9euXXH16lUcO3YMO3bs0PbJ/fTTT7WHGcvg5RdffAGlFJYsWWLP4tNr+OWXX7Qo9nPnztm8d/LkSXTp0iXJ9v3stZyTD47n/PnzUEohe/bsNtdgS13t2rULWbJkgVIKxYsXxz///APAdkVyfHw8gKep7S37Z5Jz+umnn5AxY0bUrVtX29/YbDbjm2++gZeXl5Z5oFChQkney9euXWuzconsz7K9yLPMZjMuXboEnU6H4sWLIyEhIVEac2txcXE21/DY2Fjcv38/2ctLyct6r/OzZ89i1apVWLt2LR4+fJjo2MuXL+Pjjz9m8ICTs0z+REVFoWLFitDr9ejQoQNu3LiBK1euYOTIkVBKwcXFBWPGjNHOsw4eaNCgAZRSqFmzpj2+Ar0iS58sLi4OcXFx+Oijj1C7dm2bjE8AsH79elSuXBkGgwHdunV7YfAAOY/ffvsN3t7eaNSokXYft+6nHzhwAG+//baWrpyc36VLl1C0aFHkyJEjyYV5f/zxB2rWrAmlFDp16qS17ecFB7DtO77/0s453kqUvjFwgBzauXPn4O3tjYYNG77yuZs3b8bq1atToFSUXF5mkj8kJERLhfq84IH9+/ejbt26iSalyPGdP38eefPmRfXq1bV9s65evYpOnTpBKYWePXsiODgYrVq1glIKJUqUwIYNG+xcanodJpMJAwcOhFIKX3zxRaJ01KVKldJS0lt+CxZr1qyBUgrTp09PxRLTf2EZROjXrx+UUs8NCrh06RJat26tTS5bt28ORDi2K1euoGrVqnj33XcRHh5u815S2xZ8+OGH2vsJCQk29TtjxgwopTBz5szUKTwlq8ePH6Nx48bw8/PDgQMHADwdaJoyZQo8PT1RsGBBnDhxAj169EiyrQPcE93RWAL2Fi5cmOT7d+7cQcaMGVGwYEEt2O/Za7ZlUPLMmTPo0aMHg3udiPV+uG3atNH2OLesOJ85cyZu3rxpc86zwQNr1qzhfdwJxcfHo2PHjnB3d8fw4cO1/vqVK1e05zPLn6+++ko7z/Kb+eeff9CsWTMtgIwcX3R0NMqXL4+2bduiSJEiWl/MaDTatOENGzagUqVKzw0eIOczYcIEKKUwa9YsAP+7b1vX+7Zt27Q2b8kmRc5rz5490Ol06NGjh83rz24jZ1nUwTF158d2TkSvSydEDuzJkycSHR0tHh4eIiJiMpkSHWM2m0VE5MqVK/LgwQPt9SZNmkiLFi1sjiHHAUAMBoNERUXJjBkz5MaNG0kelylTJvn0009lzJgxEhwcLKNGjZL58+dr7+t0Onn77bdly5YtUrJkydQqPiWTJ0+eSL58+WTGjBni6ekpDx48kDlz5sgPP/wgH330kXz33XeSOXNmqVevnog8becjRoyQdevW2bnk9KoSEhJk9+7dUrx4cfnyyy/Fy8tLRESMRqNUqVJFzp07Jx999JGMGzdOPD09JSYmRjs3KipKREQaNmxol7LT60lISJBz586JiEiOHDlE5H/XfovChQtLixYtxMPDQ65evSqBgYGyfft2ERFRSqV+oemlFSpUSNauXSu//PKL+Pj4yOzZs+WPP/4Qkaf3ZrPZLLVr15ZVq1aJn5+fbNiwQdq1ayciIgaDQavfw4cPy7x588TFxYX3cSf15MkTuX//vgwbNkyqVq0qJpNJfvjhB5kwYYLkyJFDjhw5ImXLlpUGDRqIl5eXXL16VUaOHCmbNm3SPsO6vet0fES1NxcXFxERiYiIsHnd8kyVPXt2KVCggFy/fl0WLVokCQkJopQSANpxer1eRES+/vpr+fnnn+Xs2bOp+A3oVVjqTeTp87blGa169eryyy+/SKlSpeTbb7+Vzz77TADIqFGjZMaMGXLlyhXtvMKFC0v//v2lW7ducvnyZRk4cKBNGyfnsHPnTlm/fr28//77MnToUPHy8pLLly/L2LFj5YcffpBevXrJjz/+KCIio0aNkjFjxojI0/u60WiUXLlyyapVq6R06dL2/Br0Cg4cOCDXrl2Tbdu2ye3bt7VnMKWUzXX9gw8+kMDAQClXrpwsX75cZs6cyeu6k7OMr8bGxorI//pf1vXeqFEj6dChg+j1egkNDbVLOSn53L9/P9E9X8S2zuvUqSN9+vQREbG5z5NzYjsnotdmr4gFopexf/9+bWXDrVu3Er1vHSHXt29f5M2bN9FeyOS4jEYjmjZtCqUUhg4dmmQdW1y9ehVNmjSBTqeDv79/oswD5Lz+/PNPbTXLb7/9howZM6JZs2Y2xzx48AC5c+dGpUqVoJRCuXLlEq1YJ8d25syZRKnITSaTtq3MiBEjbFYtb9q0CT/99BMAIDg4mHsnOql27dpBKYWpU6cCeP5K4oCAAGTLlg1KKVSpUuW5KbLJMf3www9QSqF8+fI4cuSI9rqlvvfs2aNlHqhRowZmzZqF/fv3Y+7cuXjrrbeglMK3335rr+JTMti5c6e29+XVq1dRvnx55M6dG1evXtWOMZvNKFq0KHLnzg2lFLJmzYobN27YqcT0b44dOwbg6V7n1hkiLBkGpk6dCldXV1StWhW///67tuLYetuCWbNmwcvLCy1btuQzmoOaPHkydu7caXN/jomJwTvvvAN3d3cMGzZMS1t+/fp11KhRA0opeHt7o0+fPonS116+fBkdO3ZE9uzZcf369VT9LvTfGI1GfPPNN8iVK5e2T/2tW7fQu3dvKKW0FapRUVFaNjiDwWCTeYCcT1RUFNasWaPtbV6sWDHt3pzUliMbNmzQ0lr37dvXZr9sci6Wve5LliypPWtb17Xlvv7ZZ58xM1gasWHDBiilkCtXLm381brOLavR586dC6UUunTpkugYci5s50T0uhg4QA7NZDKhbt262sTykydPADy9yVkPbuzYsQN+fn6oU6cO98x0cNYdlPj4eMyZMwclS5aEt7c3Bg0a9MLgga+++gpKKbi6ukIphWXLlqVGkSmFJJWytmHDhnB1dcUff/wB4H/7X9+/fx8+Pj5YuHAhBg8ezG0pnNDp06ehlNK2nklISHhu0IDJZELZsmXRunXrF+6bTI7L0r5nzZoFpRTq1KmjBftY7t/W9/KqVatiwIAB6NChAwwGAyZNmqQdQ47v/Pnz+PDDD6GUQsWKFZ8bPGAJDtHr9ciWLRvy5cuHypUrY8mSJYmOJ+dgvUemhSUl5ty5cwE8vZdb6rVUqVIIDAzERx99hG+++SZVy0qvLiEhAYULF06y333x4kXUr18fSim8/fbbWLx4sc3v4euvv0b27NmRL18+mwASchwDBgzQtpCKjo4G8PS+O336dHh4eODjjz9GREQEAOCvv/5C586doZRCvXr1UKpUKXh6eqJfv364dOmSzedevXpVCyQix5XU/fbw4cPYsWMHzGYzzGYzvvvuOxgMBrRv397muPHjx9s8l/N67hye168ODw/HqlWrtOCBHj16aG04qeCBVatWoV69ejh//nzKF5qSnaXtP3z4EBUqVICLiwsCAwPx6NEjAE/r2vp+3rRpU/j6+moBheS84uLitADAgQMH2tS59T1h2rRp0Ol0+OGHH+xVVPqP2M6J6L9i4ADZ1b8NDpvNZnz//ffIli0bcubMiUmTJiUKDDhw4ABq1qwJb29vrFu3LiWLS/+RpVNiMpkQGhoK4OmKlmXLlqFo0aLw8vLCoEGDEu2ZaYliX7VqFd566y0MHDgQmTNnxsWLF1P3C1CysG731v8dHh6OgIAAZM6cOdEgxOjRo5E5c2YOPDsRk8lkEwwQFhaGgIAAFCpUCBcvXtRWqjwbNAAAgwYNglIKCxYs4MSxk7t69Sry5s0LpRRatWqV5DF79uyBt7c3VqxYgV27dsHT0xP58uVDSEhI6haWXor1ddsS2GMymXD58mU0b978X4MHsmbNCqUUatWqhYcPH2qTUs9+NjmWl+mzA08nmlu3bg2lFLZv325zzI4dO+Di4oL169fbBA+y3h2LJWjTZDLBaDRi9OjRWsCPdZAPABw/fhx16tSBwWCAUgoBAQGoVasWSpYsCaUU8ubNy2BPBzV58mTt3nzy5Ent9cjISJQtWxYlSpTQnteuX7+Orl27QimFjz/+GAAwffp0KKXg6+uLzz77LFHmAXJslufyuLg4rFu3DgsWLNDes9zb4+LiEBAQgAIFCuDOnTsAoAWYbNq0CZUrV8aECROQOXNmnD17NpW/Ab0qS50nJCTg0aNH2L9/Py5cuICHDx8CeFrva9asQdGiReHq6opPP/30hcED1v03ckzPBnc++1wdHR2NadOmwdfXF9mzZ8fIkSPxzz//2ByzcOFCuLq6ol69etpCLnJc/9anTkhIwIIFC+Dn54ds2bLhyy+/TBTod/DgQRQpUgQ5c+bkJLITeLadW/8GjEYjYmNj2c6J6LUwcIDsxvrmtmfPHixcuBBjx47FzJkzceXKFW0i6f79+/jiiy+QIUMGZM6cGY0aNcKOHTuwY8cOzJ49G4UKFUqUTocTTY7HMkAcExODyZMno1mzZjh69CiAp4MS1sEDgwcP1oIHrAeWmzdvjvLly+PJkyfszDiZ6OhojB8/Hq1bt0bHjh3x9ddfJ3lcw4YNoZTC6NGjtUGM2bNnI2/evKhRo4Y2gEmOzbqdHz9+HMDTiYhOnTpBKYVMmTJBr9dj3LhxidryggULkCNHDtSqVYsZZJzI7t27MW3aNPTu3RtjxozBlStXtAHFffv2wcfHB0opNG3aFEePHsXjx48BPL3/16lTB35+fjh16hQiIyNRsmRJ+Pj4MIW5A7Luu23btg1Dhw61CRB4Nnjg8OHD2nuWQYzdu3fD29sbSins27cvycFociz/1me3tHVLHVsmGK1XMu3fvx916tRB7ty5cebMGe3zWO/2ZalbS6CWJWggLCwMTZs2RVBQEMxmszbJrNPpEgUP/PXXXxg7dqwWLKCUQqlSpdCtWzemqndQd+/eRenSpZE3b16cPn0awNM6nzZtGoCnWwCuWrUKABAaGoqRI0fapCwGnvb1ihcvDqUUMmTIgC5durC+nYTl+ToyMhItW7aEu7s7/Pz8sHfvXpvjjh49CqUUPvjgAwC294I2bdqgcOHCSEhI4ASyE7DUeVRUFLp3746iRYtCKQV3d3e88cYbWLx4sTZ5+CrBA+Q4LPVi6YtZ6jw6Ohpz5sxBjx490LZtWwwYMAB79+5FcHAwgKf3g/79+yNz5szw8PBAhQoVsGTJEqxZswaffPIJMmfODD8/Py7acUDPtsmXHWO/d+8eBgwYgIwZMyJjxoxo2LAhfv/9d5w6dQqrVq1C5cqVoZTCvHnzUv9L0Qu9qJ2/aLz1wYMHGDBgANs5Eb0SBg6QXVhHwI0aNQpubm7aQJNSCkWKFEH37t21/Xf++ecfjBkzRhucsP6TM2dOm/3uuWrJ8VgPTlgmhgsWLIhNmzZpA5TPBg/06dPHptMyd+5cZM6cGYMGDbLLd6DXFxkZqe1hbf2nefPmiTqma9euRb58+eDh4YGSJUtq52XPnh0XLlyw0zegV/Gidv748WMUKVIESinky5cPly9ftjn3q6++QtasWZErV65E75Hj+vLLL+Hl5ZXoPj5hwgRt0nDbtm3IkCEDlFLIkycPypUrh6ZNm2rnzZgxA8DT1Uv58+dHQEAAB6IdjPVglGWFoVIK48eP11YgAsCVK1eeGzxgGezYsWOHTd+NHNfL9Nl79Ohhs3IlKCgI+fPnR6ZMmVC3bl307dtXyzQxZ84ce3wNeoHDhw8jU6ZMWjra8PBwvPnmm4ky/0yaNOm5wQMmkwlRUVE4efIkDhw4gPDwcJvrAjmW69evw83NDUWLFkVYWBiio6NRvHhxZM2aVVtdaAkmuXr1KnLkyIEGDRpoK9EtWw81aNAANWvWRNGiRZE9e3YGfDoByzXdklnCzc0NrVu3xr1797T+usXhw4ehlMIbb7xh88y2YMECZMuWDR9//HGS29WQY7Gu83LlykEphfLly6NHjx5o3LgxPDw84Obmhu7du+PatWtISEjA6tWrUaxYsecGD5DjsQ7KtLTlyMhILcuf9Z/cuXOjXbt22jahd+/exaRJk7StKvR6vfZ3uXLluCWFg7IstAFsF1y97Bj76NGjtSAiFxcX6HQ6KKXg6emJb7/9Vvs8tnvH8bx2/qLxVst9+v79+2znRPRKGDhAdjV27FgopVCyZElMnjwZ3377LWrUqIEcOXJAKYUaNWpoK8/Dw8Nx5coVDB06FF27dkXjxo0xf/58bdU6wKABR/Tsg6qbmxt69OiBsLAw7RhLRzQ2NhZLly5FiRIloNfrUapUKYwbNw5t27aFq6srcufOzVT1TiYuLg6NGjWCq6srunbtis2bN2P58uVa6vJ69erh9OnT2m8gODgYc+fORcWKFbUUt40aNeIkspN4UTu3PLCcOXNG2y+5ZMmS6N69O/r164dq1apBKYX8+fPjr7/+stdXoFcUGBgIpRTKli2LlStX4tSpUxg9ejS8vb2RO3dufPHFF1p2gVOnTuH9999HwYIFoZSCt7c3SpUqZZMed9iwYVBK4ZNPPkk0gE32Y92/Gjx4MJRSqFy5Mn777bckj7906RKaNWv2wswDz/s3OaaX6bNbBiODg4Mxc+ZMbQW6u7s78uTJY9PWOQjpOCyryX18fLBixQqUKlUKbm5umDhxIqKiomyOfV7wgGVbMXIOcXFxaNOmDVxdXdGnTx9tdfGECRO0oAALy33e0n6t780lSpRAr169sHHjRj6jOZG4uDh8+OGH0Ov1GD16tFbnSQUBtGjRAkopvP3225g/fz66du0KHx8f5MmTh9tTOJG4uDi8//770Ol0CAwMtKnr5cuXQymFAgUKYPXq1QCerl61BA94eXmhc+fODAxyYN27d4dSSssUAzytw7p168LV1RWdOnXCjh07sH79erRq1QpvvPEGlFKoVq2aNt4aGRmJmzdvYvTo0ejXrx969uyJn376KVEae3IMv/76K5RS+PHHH21ef5Ux9tDQUJw8eRK9e/dGw4YNUaZMGYwYMQI7d+7UPo/PaY7jee3838ZbT506pV3zo6Oj2c6J6KUxcIBSleVmZTabcfnyZRQqVAhNmjSxmSR68uQJ1q5di9KlS0MphXfffRcPHjz418/mAKTjio2NxXvvvaeloE9qBZJlBUtCQgI2bNigrVi2/ClWrBgjIJ3QsWPHkC1bNgwZMsRmUPnMmTOoXbs2lFKoW7cuTp8+rT2UxMbGIiwsDFu2bMG1a9e0NHrk2F6mnVt+Aw8ePMAHH3yAnDlzam28QIEC6N69O9PTO5F58+bB3d0d7777Lk6dOqW9Pn78eLi7u0MpBS8vLwwcOFC7j4eEhOD27dv49ddfcebMGZuJhlmzZiFDhgzInz8/fwcOasqUKdqWE9YrHpJy9epVfPjhh1BKoUqVKjh06FAqlZKSw+v02Zs0aaK19cjISFy7dg3Tp0/Htm3bcOLECe08DkI6nvHjx2sBAS4uLpg8ebJ2H392MvF5wQOsV+dhNpuxZ88eFCtWDDqdDnq9HgMHDtTq2rrOBw0aBKUUZs2aZfMZM2bMgIeHBzZs2JCqZafXZxkv2bBhA1xdXdGkSRMtaODZ9ms5du/evWjQoIHNc3mpUqX4XO4kLPW4bt06uLq6olmzZokyelWuXBne3t4YOHCglmkEeLqtwZo1a5A9e3Zky5aNE0sO7Pfff9fa588//wwA+O2335ApUyYEBgZqY23A02exzZs3a323Fi1aaEHe5Dz27dun1bllIvm/jrE/G7TPfp1jsW7nv/zyC4DXG28lInpZDBygVGM9sX/9+nXcvn0bSin8+uuv2vuWQYq4uDhs27YNxYsXh6enp5bO1jr9Em96zmPr1q1wc3PDhx9+mKgzevLkSUyZMgUtWrTAzJkzce3aNQBPO63ff/89vv32W6xYsQJ37961R9HpNUVERODAgQPYs2cPfH19tX3sExIStGvBhQsXUKdOHXZm04h/a+dff/01WrRogenTp+P+/fswGo34559/EBQUhH379iEsLAwxMTF2Kj29qkuXLqFs2bIoXry4tsd9bGwsJk+eDA8PDxQsWBAzZ85E3rx5kSVLFgwYMMAmnaI1k8mEfv36IWvWrMiRIwczTjio8+fP44033kD+/PkTBQ3cv38fQUFBGD58OH755RdtJcu1a9fQsmVLLUUmV6s5h//SZ58/f36iz3jeZ5P9WfpdRqMR3t7eMBgMcHFxwcqVK7XXk2IdPLBs2bJUKy/9d5Y2GBcXh2zZssHFxQV6vR59+/a1uUZb6v7777+Hi4sLatWqhU2bNuH+/fsYN24ccubMiYCAANy5c8cu34NenyVz0LZt2wAkPa5ivXf2gwcP8PXXXyMwMBDff/89n8ud0ODBg6HX63Hw4EHtNZPJpKW3HjFihJYpLiQkROuzh4eHY8OGDcwo4gT279+vTSpu27YNmzdvhq+vr7ZtnPVYanx8PLZu3YpChQoha9as2LRpE4DE93z22RybdZ3/+uuvuHnz5iuPsVuP25jNZta5g7Ou8y1btuD3339/pfFWC+t6Zp0T0fMwcIBSXbdu3aCUQteuXVGmTBmEh4cDSPzAGhERoaXOfO+99+xRVEomlnRZlqhI4Gl9DxkyBP7+/jYrGNq0aYPr16/bsbT0X5lMJvj5+Wltt1atWomOse7M1q1bV+vM/tsKVnJcr9LOW7durU0sknOyrFz66aefADx9SJ0zZw58fHxQqFAhbTVTx44dtW1HBg8erD3UWq4Bt2/fRps2bbTU99Z76JJj2b59O5RSGDZsmM3ra9asSbQasW3btlpdXrx4EQ0bNsQ333xjj2LTf8A+e/pgMpkwe/ZsZMiQAVWqVNG2l7CkrH4eS/CAUgorVqxIpdJScjCZTBg+fDi8vb3RvHlzFCtWDAaDAZ9//nmi57CbN2+iUaNGUErBw8MDWbJkgVIK/v7+DPRzUs2bN4der0dQUBCApCcNjEYjjEajzUQDOa+WLVvCy8tLy/6TkJCgXe9HjBih3d8B4OjRoyhcuLC2oIOch/Uq9Lp16+Ktt9567qRgWFgYevToAaUUPvroo1QuKSUX6zr/6KOP2F9PB6zrvEmTJq883spAASJ6WQwcoFRnnV7HYDBg48aNzz32zJkzyJgxI3Lnzv3clYrk+ObPnw+lFEaOHImLFy/it99+Q6VKlaCUQqFChbB8+XJMmTIFBQoUgFIq0W+CHRvns3PnTq2dFy9eXJskTiqy1bozW6FCBZw7d84uZab/5r+2c3Iut2/fxuTJk7X9r/fu3YuCBQvC399f2+ccAP744w9t72y9Xo9PP/3UJl0mAKxevRrLli1jClQHt3XrViil0KdPH4SFheHKlSsYMmSIdq3/5JNPMGDAABQuXNhmP2zg6R6aFrynOw/22dOPkJAQbSXqqFGjoJSCm5sb1q5da3Pcs6sRx4wZA4PBwLTlTuju3bv49ddfYTKZsG7dOi14oH///omCBy5duoTPPvsMJUqUQNmyZdG1a1cGejshy/23efPm2tZiSbFMNpnNZpQsWRJTp05N9BnkHCz11b59eyil8P333wPAc4MGAKBVq1ZwcXFh0IiT2rt3r9Z3y549Ow4fPvzcY3fv3g2DwYBatWolej4j52Fd5+yvpw/Wdc7xViJKKQwcILsICgrSbnLDhw9P9L7lYTU8PBwFChRAjhw5uO+WE7t58yYKFSoEpRQyZMgApRRy5cqFL774wqZeAwMDoZTClClT7FhaSi7W7dx6X9SkOrMXL15E+fLl4erqypXoTortPP2x3kevV69ecHd319LeWt67cOECcuXKheHDh8PX1xdz5szRzrG+FnCbEsf34MEDlChRAkopBAQEIEuWLNDpdKhfvz527NihHTd79mwopdCuXbtEKS854eB82GdPP6yvw0OHDk0UPGAdNLBx40YcOHAARqNRyyRDzse6zleuXInixYs/N3jAaDQiNjYWZrPZJuU1OZ/169fDzc0Nb731ls2kotlstvlNWLY0WLhwIe/fTm7Lli1wcXFB3bp1ERAQoAV7Wwd2AsCUKVPg5eWF3r17a8HB5Hz27Nmj9d0mTpyY6H3L/fzMmTNwcXFBgwYN+Czm5KzrnP319MG6zjneSkQpgYEDZDfWA5HP7o1publt27YNer0eTZs2ZQSsk7t8+bKW6rZXr144cuQIoqOjbY5p3rw5MmfOrKXQI+dnHQlrnfI2qc7spUuX2Il1cmzn6dPt27eRM2dO+Pv7a/veWtr1999/Dzc3Nxw/fhyXL1+2ZzEpGVy8eBHvvPMOcufOjTfffBPLli3TJpcsA1KzZs2CUkrbI52cH/vs6cfzggfWrFmjvb5ixQp4enrirbfe4sRSGmDdJ39R8IB14AgnkZ3bkydPUL9+fSil0KFDBxw9ejTRMQsWLICfnx+qVq3KjFBpQEhICBo1agS9Xg+lFHr06IGwsDCbY+bNm4dcuXIhICAAN27csE9BKdlYj8MsX748yWMsGYaSmmgm58P+evrD8VYiSkkMHCC7sr7JzZkzB3fu3NHe++OPP1CtWjUOPqcxz04iWsybNw8+Pj5o3LgxQkJCUrdQlKKs9+BatWqV9jpXoaZdbOfpy6NHj5AzZ05kypQJ+/fv114/cOAAKlWqhIoVK9oMTnJFi3OLiYnBgwcPEBMTk+i9P/74A8WKFYO/vz9OnTplh9JRSmGfPf1IKnjAw8MD48ePR58+fZA9e3b4+vri5MmT9iskJauXDR6gtOPixYva1kK1atXC119/jUuXLuH06dPo168fMmXKBD8/P1y8eNHeRaVkcuHCBZQsWRJKKbz77rtYv3497ty5g/Pnz6NXr17w8PBA1qxZufVMGmI9DjN16lSbFOVz5syBn58f8uTJw+t8GsL+evrD8VYiSikKAITIjvbv3y81a9YUEZHq1atL9uzZxdvbW3799VeJiIiQiRMnSr9+/UREBIAopexZXPqPLHVoXZczZsyQr7/+WgwGg+zevVsKFSpk51JScrNu57/88ou0bNlSRNim0yq28/Tn888/lzlz5sg777wjDRs2FDc3N5k+fbpcuHBBlixZIl26dLF3ESkFmM1m0el0IiISFBQkY8eOlaCgIFm6dKl07tzZzqWj5MY+e/ph3ba//PJLmT59ukRERIiISJEiRWTdunVSokQJexaRkpl1m/35559l7Nix8vfff0uHDh1k+PDh4u/vb+cSUnK7ePGi9OrVSw4dOiRGo1F8fHwkMjJSAEjp0qXlp59+kuLFi9u7mJSMLly4ID179pRDhw6J2WwWPz8/CQsLk/j4eHnrrbdk6dKlUrRoUXsXk5KRdd+tUKFCkjt3bnn48KHcunVLcuXKJevXr+f9PI1hfz394XgrEaUEBg6QQ9i3b5/UqlVLRESyZs0qrVu3Fnd3d6lZs6Y0adJERGwHsMj5hYeHy927d2XUqFGyadMmyZs3r2zcuJEPLWmYdWd29erV0rx5czuXiFIa23n6cfPmTfnoo4/kjz/+kISEBBER8fT0lMmTJ8unn34qInxwTauioqLk+++/l0mTJsn9+/fl66+/ls8++0xEWOdpEfvs6Yd1PW7dulWuX78uIiLNmjWT3Llz27NolEKsr9m//PKLfP7552I0GuXcuXOSPXt2O5eOUsKjR49k+/btsnDhQlFKiZ+fn9SpU0c+/PBDyZkzp72LRyng0aNHsm3bNlm2bJnodDrJkSOHvPPOO1K/fn3Jli2bvYtHKcC671auXDnp1auXeHh4SO3atXk/T6Os69zPz09at24tbm5uUrt2bWncuLGIsL+e1nC8lYiSGwMHyGEEBQVJnTp1RERkzZo10qxZM+09dmjSnjt37ki9evXk5s2b8t5778mkSZOkQIEC9i4WpTDrzuz69evlgw8+sHOJKCWxnacvd+/elQ0bNsjGjRulRo0aUqlSJalfv76I8D6eVj158kQ6deok27Ztk+LFi0tgYKC0b99eRFjnaRn77OkHg3/SH+s6X7t2rZQpU0YKFixo51JRSuO1O/3h9T19se67/fbbb1KvXj07l4hSmnWd//TTT9KmTRvtPV7z0yaOtxJRcmLgADkU66jIlStXSuvWrcXyE+VDTdpz8eJFuXLlilSvXl0yZcpk7+JQKtm9e7fUq1dPzpw5IwEBAfYuDqUwtvP059mBSA5MpG2nT5+WHTt2SMOGDaV06dIiwjpPD9hnT5840ZQ+sJ7TH+s6Z/2nD6zz9GfXrl1Sv359jsOkI5bggcmTJ8ugQYPsXRxKBRxvJaLkwsABcjhMr5O+8CE1fYqOjhZPT097F4NSCdt5+sL6Tn9MJpPo9XoRYf2nJ+yzExERETkPjsOkP3fv3pVcuXLZuxiUitjOiSg5MHCAHBLT6xAREREROTb22YmIiIiIHBszwhER0avgHYMcUvXq1eX3338XEeGeikREREREDoh9diIiIiIix8agASIiehXMOPD/Hj58KEePHpWjR4/KsWPH5NixY/LkyRMREencubMsW7bMvgVMp5heh4iIiIjIsbHPTkRERERERETk/Az2LoCjyJ49u72LQEngACQRERERkWNjn52IiIiIiIiIyPkxT00S8uXLJw0aNLB3MYiIiIiIiIiIiIiIiIiIiFIcMw78v1GjRknFihWlYsWKkj17dvn7778lf/789i4WERERERERERERERERERFRimLgwP8bM2aMvYtARERERERERERERERERESU6rhVARERERERERERERERERERUTrGwAEiIiIiIiIiIiIiIiIiIqJ0jIEDRERERERERERERERERERE6RgDB4iIiIiIiIiIiIiIiIiIiNIxg70LkF7UqlXL3kWgVOLu7i7bt28XEZFGjRpJbGysnUtEKY11nv6wztMX1nf6wzpPf1jn6Q/rPH1hfac/rPP0h3We/rDO0x/rOqf0hW08fQkKCrJ3EYhe6P79+xIYGCg3btx47jFjx46V6tWrp2Kpkg8DB4iIiIiIiIiIiIjIKXEymYiIiFLLqlWrXhg0ICKSLVu2VCpN8uNWBURERERERERERERERERERC8QFxf3r8eYzeZUKEnKYMYBIiIiIiIiIiIiInJKTGOePnCrAiIicgSVK1eWX3/99YXHhISEpFJpkh8DB4iIiIiIiIiIiIjIKXEymYiIiFLL8ePH//UYX1/fVChJymDgABERERERERERpUmcUCQiSnuYZSLtY4YJInJUSil7FyFFMXCAiIiIiIiIiIjSJE4upQ+cYErf2M7TB+t2zvZOREQp7fHjx/Lo0SMRETEajRIbGyuxsbGSKVOmlzrXWTFwgIiIiIiIiIiIiIiIiIiI0iWz2SwJCQmSkJAghw4dkgkTJrz2Z3l7eydjyVIXAweIiIiIiIiIiIiIiIiIiChdCA4OlubNm6fIZ7u6uqbI56YGBg78vz/++EOuXr2q/ds6jcTVq1dl2bJlNsd36dIllUpGRERERERERERERElh2noiIiJ6VRERESn22Xny5Emxz05pDBz4f4sWLZLly5cn+d6BAwfkwIEDNq8xcICIiIiIiIiIiIjIvho1aiSxsbH2LgalMHd3dwaJEBFRsvH395eff/5ZLl68KCaTSftjNpvFaDRKbGysxMTESGxsrPYnJiZG9u7d+6+ffevWLQkICEiFb5H8GDhARERERERERERERERERETpRvbs2SV79uyvdM7Bgwdl+PDhLzwmOjr6vxTLrhg48P+WLVuWaDsCIiIiIiIiIiIiIiIiIiJKP86ePSvXr18Xd3d30ev1EhcXp2UgaN++vURERMiDBw/k3r17cu/ePUlISNDOjYuLs2PJ/xsGDhARERERERERERERERERUbq3f/9+GTVq1Gufny1btmQsTepi4AARERERERERERERERE5nO3bt9u7CESUzuTNm/eVz3FxcZGcOXNK1apVpVChQilQqtTBwAEiIiIiIiIiIiIiIiJyOI0aNZLY2Fh7F4NSSVBQkL2LQCRvvPGG7NmzR0SebjsQGhoqoaGhEhISIitXrpQzZ84kOichIUFu3bolt27dksqVK0uZMmVSudTJg4EDRERERERERERERERE5HCYcYCI7MnNzU2yZ88u2bNnFxGRS5cuJRk4YM3FxSU1ipYiGDhAREREREREREREREREDocZB9IXZhwgR/fPP//86zEAUqEkKUNn7wIQERERERERERERERERERE5sipVqvzrMXFxcalQkpTBwAEiIiIiIiIiIiIiIiIiIqIXyJUrlxQuXPiFxzhz4AC3KiAiIiIiIiIiIiIip8T9z4mIiCi5AZCEhASJjY3V/sTExMiECRPk1q1bLzy3ZMmSqVTK5MfAASIiIiIiIiIiIiJyStz/PH1wd3dnkAgREaWK8+fPy6effvra5//zzz+SMWPGZCxR6mHgABERERERERERERE5JU4mExER0eu4fv26HD9+XJRSAkBERJRS8ueff/6nz82dO3dyFM8uGDhAREREREREREREREROgVkm0j5mmCCilHbjxg3p1q1binw2Mw4QEREREREREREREaUyTiKnD9YTyZxQJiKi/8rX11dcXFwkISEh2T87MjIy2T8ztTBwgIiIiIiIiIiIiIiIiIiI0oWMGTPKb7/9ZvMaAElISJDo6GiJioqSyMjIRP+9b98+OX369As/27LtgTNi4AAREREREREREREROSWuPiciIqKkAJAZM2bIpk2bUvz/5ePjIz4+PlK+fHkpW7Zsiv//UgoDB4iIiIheEgekiIiIiJwL+29ERETOjfdyInpdkZGRqRI0ICISEREhERERcvfuXWnVqpXkyZMnVf6/yY2BA0REREQviXtnpg/We2cSEREREZFj43Na+sDntPSLbTx9CQoKsncRKA3x8fGRKVOmyJIlS0Sn04mIiE6nE6VUor+ffU1ExGQyiclkkoSEBDEajWI0GuXSpUv/+v+NiopK0e+Vkhg4QEREREREREREaRInG9IHTigSERERUVIqVKggFSpUeOXz7t+/L/fv35fQ0FAJCQmR0NBQCQ0NFQBy+fLlF54bHBz8usW1OwYOEBEREREREREREZFTYtAIERERJadDhw5JYGDgK52j1+vF09NTypUrJ6VLl06hkqU8Bg4QEREREREREVGaxAlFIiIiIiJ6FQ8fPnzlc0wmk0RERMiFCxckKipKPD09U6BkKY+BA0RERERERERElCZxq4L0gVsVpG9s5+kD2zkREaWW+vXrS2xsrDx+/FhiY2MlLi5OYmNjJSYmRmJjY7U/UVFREhISYnPuw4cP5eLFi+Ln52en0v83DBwgIiIiIiIiIiIiIiIiIqJ0Lz4+Xu7evSvBwcFa0IDlj3XwgNFoTPJ8pVQqlzj5MHCAiIiIiIiIiIjSJK5OJUr72M6JiIgoOf3++++yadOm1z4/ICAgGUuTuhg4QEREREREREREaRJTmKcPTGFOlL7w2p728bpORPZUpEiR/3T+33//LWXKlEmewqQyBg4QEREREREREREREZFT4IQyERGlpDfffFN27Ngh0dHRibYnWLBggVy8ePGF57u4uKRSSZMfAweIiIiIiIiIiIiIyClx9Xn6wBXoRESUmlxdXcXV1TXR65988on069fvheeGh4enVLFSnM7eBSAiIiIiIiIiIiIiIiIiInJkERER/3qMXq9PhZKkDGYcICIiIiIiIiIiIiIiIiIiegYAiYuLk9jYWDlx4sS/Hu/m5pYKpUoZDBwgIiIiIiIiIiIiIiIiIqJ0ISEhQWbOnClbt25N9s82GJx3+t15S05ERESUyrifIhEREZFzYf+NKO1jOyciIqJXdeXKlRQJGmjUqJGUKFEi2T83tTBwgIiIiOglNWrUSGJjY+1dDEph7u7uHHwkIiJKI9h/Sx/Yf0vf2M7TB7ZzIiJKTkWLFpV27drJTz/9lKyfu337dnn//felePHiyfq5qYWBA0REREQviYMUREREREREjoXPaURERPSq9Hq99OjRQ3r06JHovVWrVsm8efNe63PLli0r+fPn/6/FsxsGDhARERG9JK5kSR+4koWIiIiIiIiIiCh9qlixoqxbt04ePHjwyueePHlSQkJCJGfOnClQspTHwAEiIiIiIiIiIiIiIiIiIkr38ufPLz///LOIiMTFxUloaKiEhITIw4cPZeLEif+6sCwsLIyBA0REREREREREREREqYmZ4dIHZoYjIqLkFBsbK0OGDJEzZ84k+2cHBwcn+2emFgYOEBEREb0kDlIQERERORf234jSPrZzIiIielV///13igQNiIhkyZIlRT43NTBwgIiIiOglcSVL+sCVLERERGkH+2/pA/tv6RvbefrAdk5ERMmpaNGiMnDgQNmyZcsrnRcfHy/Xr19/4TEPHz6UokWL/pfi2Q0DB4iIiIiIiIiIiIjIKXEymYiIiF6VUkreffddeffdd1/pvCVLlvxr4AAzDhARERERERERERERpTJmHEgfmHGAiIhSk9lslsjISAkNDZWQkBDt7ytXrvzruWFhYal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            "text/plain": [
              "<Figure size 2500x1000 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "msno.matrix(df)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UrwrBpRjM47L"
      },
      "source": [
        "\n",
        "\n",
        "  The `embarked` Column has very few missing values and does not seem to be correlated with any other column: therefore, the missingness in this column can be attributed as to the Missing Completely at Random (MCAR) mechanism.\n",
        "        \n",
        "  Both the `age` and the `cabin` columns have a lot of missing values: this could be a case of MAR as we cannot directly observe the reason for missingness of data in these columns.\n",
        "\n",
        "  The `missingno` package also allows to us sort the graph by a particular column. So let's sort the values by `age` and `cabin` column to check whether there is a pattern in the missing values.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 847
        },
        "id": "dZ77ENmH6eR8",
        "outputId": "7948c4a8-3f01-4449-9fe3-7f540143baf1"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "execution_count": 15,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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5cgEg69evl/3790vPnj1jPdtDQ0PFwcFBIiMjZdeuXbJy5Up58+aNRa6NKDFg4AARUTxwcHAQd3d3cXR0jJXmzrTBM2bMGClfvrxER0dLq1atGDxARERERERERBRDZGTkO/ddunRJOnfuLMmSJdMDAfLnzy9169aVe/fuSZs2beTAgQPSq1cvfWApJCREP9bX11dOnjwpTZo0kRQpUnz6iyEiIjPR0dFSvnx52bFjhxgMBpk2bZpZ8EBERISIiLRu3VqyZ88uy5cvl06dOsmhQ4fE19fXLGjAyclJRES6desmd+/elS5duoiLi4tlLowoEWDgABFRPFBKiZeXlwQFBcnUqVMlMjJSDAaDHkRgNBoFgLi6ukrGjBn1bS1atJDVq1dbsuhERERERERERFZj3rx58ttvv0lwcHCc+03XsBb5O8hg5MiRUrx4cXnx4oVkzpxZhgwZop/j7OwsIiLDhg2TadOmSZEiRaRXr156MAERESUcg8EgUVFRUrZsWdm9e7cePNCjRw8REbG3t5fIyEhJkSKFNGrUSF6/fi3Xrl2T6tWry6RJk0TkbRYZLWhg2LBhsnjxYilXrpw0atTIYtdFlBgwcICI6D+KiooSpZT06dNH3N3dZefOnbJp0yZ93T0teECLlCxWrJgUL15cWrVqJSIiefLksVjZiYiIiIiIiIisxfnz56Vjx47yzTffyPz5882CB7QlIbUU1AbD265tOzs7ERHJmDGjfPfdd1KoUCG5ffu2lClTRlauXCn+/v6ya9cuady4sYwYMUK8vb1l8eLFkj59+gS+OiIiEhEBoD/Dy5cvLwcPHhSDwSDTp0+XLl26iMjbZ7uzs7N0795dqlWrJnZ2dnL//n359ddf5e7duxIVFSWPHz+WLl26yIgRI8THx0dmz54tPj4+lrw0IpunEDOnNhERxaIFAWiioqIkKipKHBwc9G23bt2SAQMGyOrVq6VatWoyYMAAqVSpkhgMBrN19CpVqiQRERFy4MABefHihSRPnjzBr4eIiIiIiIiIyBqNGTNGfvzxR3n9+rX88ssv0qFDB3Fzc9P3b9myRb766iuZMGGC+Pn5mZ0bGhoqR44ckVGjRsmuXbvM9imlpESJEjJv3jzJlStXglwLERGZM+1nP3r0qOzdu1fCw8Nl3bp1curUKQEgXbt2lWnTpunn3LlzR4YNGyYbNmyQgIAAcXd3Fw8PDwkKCpKXL19K7ty5ZfXq1ZygRxQP7CxdACIia2famDl8+LDs2LFD9u3bJ0opKVWqlJQvX16qVq0qWbJkkZ49e8rNmzdlx44dEhQUJK1bt5YePXroQQNDhgyRvXv3SqdOnSQyMlK8vLwseWlERERERERERFYhKipKjEaj9O/fX+zs7GTIkCEyYMAAERGz4IHw8HAReZvKOiYnJyepWLGilCtXTqZPny7Xr1+X+/fvS6pUqaRGjRpSsmRJ8fb2TriLIiIiM1o/+9q1a6Vdu3YSHBwshQsXFi8vL8mSJYvcunVLZsyYIQaDQaZMmSIiIpkyZZIxY8ZI/fr1ZeHChXL69GkJDg6WEiVKSIUKFaRNmzbMIkMUT5hxgIjoA61Zs0batWsnr169EicnJwkNDRURkZQpU0q7du3kl19+ERGRPXv2yMiRI+XQoUMSHh4u+fLlk/Tp00tgYKAcPXpUfHx8ZN++fZI9e3ZLXg4RERERERERkVWJjo7W01ePGzdOhgwZIuHh4fLLL79I27ZtxdPTU5YsWSKtWrWS8ePHy//+9z+JiooSERGj0SgAJDw8XJ/AIfJ26ci4ggyIiMgy9u7dK9WqVRN7e3uZMmWKtGvXTkRELl68KPv27ZOePXtKdHS0dO/eXQ8eMBUQECBhYWHi4+Nj9t4gov+OGQeIiD7A9u3bpVGjRuLg4CCjR4+Wxo0by8OHD+Xy5cvyzTffyNixY+XixYuyadMmqVSpkqRMmVI2b94ss2fPlgsXLsiFCxckefLkUqxYMVm0aBGDBoiIiIiIiIiIYlBK6ZkH+vTpIwD0zAPR0dHSq1cvfSKHh4eHiLwNGDA93zRoQOTvzAQxl6EkIiLL2Lx5s0RGRsqwYcP0oAERkbx580revHklW7Zs8sUXX8i0adMEgEydOlVERF8O2NPTU7Q50XyuE8UvBg4QEf2Dx48fy7Bhw0REZPLkydKpUycREcmWLZu4uLiIj4+PvHjxQjJmzKifU6BAASlQoIB07dpVzpw5I0+ePJE8efKIj4+PpEqVyhKXQURERERERERktbSBfaPRKCdPnpQiRYpI3759RSklQ4YMkW+++UaSJ08uQUFBIiIye/ZsuXXrlrx69UqUUuLu7q4HHnh4eMjTp08lW7Zs0qFDBxHh4BIRkTWIjIyUQ4cOiYhI0aJF9W12dm+HKwFItWrVZPHixdKqVSuZPn26REVFyYwZM8TR0VEPLtOe6Xy2E8UvBg4QEf2DJ0+eyMmTJ6VFixZ60ICIyNmzZ6Vnz55y8eLFd6ZN8vLykooVKyZgaYmIiIiIiIiIbI/putcNGzaU1q1by4IFC6RPnz4iIvL9999Lly5dJEeOHCIicvDgQTl48OA7f56dnZ2cPXv20xeciIg+mNFoFC8vLxF5u+SAiOhBAyJ/vwu+/PJLadq0qSxbtkxmzZoldnZ2MmXKFLMsM0QU/xg4QET0Dlqk+7lz5yQ8PFyyZcum7zt79qx069ZNjhw5Eito4Pnz53Lr1i0pVqyYJYpNRERERERERGSTzp8/L7179xZXV1fJkyePvl0LHvjxxx/l4sWLUrlyZWnRooX4+PhIcHCwhISEiIuLi0REREhwcLDY29tLmTJlJHfu3Ja6FCIiioNSSgoWLCibN2+WxYsXS/ny5SVt2rSxjnN3d5fcuXMLAHF0dJRp06aJi4uLjBkzxgKlJko6GDhA9A/iWv+Ma6IlTjHrVfu7m5ub2XF//vmn+Pn5xQoa0NZYWrJkiezYsUPGjBkjuXLlSrgLICIiIiIiIiKyITH7Ys6ePSv37t2TWbNmSceOHUXk7xTWffr0kcjISPnpp5/kwIEDUq9ePalRowZnnxIR2QjtmV+rVi1ZsWKFHDt2TLZu3SotW7YUJycn/bjw8HBxcHCQvHnzSpkyZaRTp07Sr18/ad68uQVLT5Q0KACwdCGIrJXpx8vt27fl0aNHUqJECQYNJHL79+8Xd3d3KVSokIiInD59WkqWLCkZM2aUKVOmyMiRI+XAgQNxBg2IiHz22WcSGBgohw8fFh8fH4tdBxERERERERGRLVi0aJGEhISInZ2dTJ48WU6ePCkiItHR0WIwGPT/ioiMGzdOBg8eLBEREfLLL79I+/btxd3d3ZLFJyKifyE8PFz69+8vkyZNkkyZMsmYMWOkSpUq4uXlpQeLiYg0aNBADh8+LA8fPtSDCYjo0zJYugBE1io6OloPENi0aZO0aNFCmjZtKpMmTbJwyehT8vf3lwoVKsj+/fv1bYULF5Yvv/xSrl+/Li1atJADBw5Ir1699KCBkJAQPWigR48ecubMGWnSpIkkT57cItdARERERERERGQLAMitW7ekbdu20q1bNxk7dqwAkIiICBERPVhACx4QebtswciRI8XBwUEGDBggCxYskJcvX1rsGoiI6MNFR0eLg4ODjB49WurWrSt37tyRXr16yfjx4+XMmTN60MCgQYNk3bp1UrZsWQkJCRF7e3sLl5woaWDgAFEcAOgfJgsXLpSGDRvKkSNH5KuvvpJSpUoJE3UkXjdu3BARkZkzZ0pAQIC+ffTo0VKoUCF5/vy5ZMuWTX744Qd9n7Ozs4iIDBs2TKZPny5FixYVPz8/RkASEREREf1H/PYiIkrclFKSJUsWmTBhgoiIXL58WSIjI+XatWsiYv4eiBk8MGLECHF1dZVevXrJkiVL+M4gIrIBBoNBoqKixNXVVRYtWiStWrWSoKAgGTlypBQvXlxKliwp+fLlkx9//FF8fHxk1KhR4uzszCzQRAmEgQNEcdBeQkuXLpV27dqJu7u7LFiwQKZMmSLFixcXpZT+oSLCzqzEpGPHjlKxYkU5f/68zJs3T6/nrFmzyqBBgyRv3rxy48YNKVu2rKxfv14OHDgge/fulSZNmsiIESPE29tbFi9eLOnTp7fwlRARERER2Q7tmyoyMlLCwsJERMy+uc6cOSMPHz60SNmIiOjT0Z71fn5+epbPCxcuyKpVq0REYvXBxQwe+PrrryVt2rRSvnx5DioREdkIo9EoUVFRkixZMpkxY4aMGzdOGjZsKJGRkXLs2DEJCAiQChUqiL+/v+TMmdPSxSVKUhQ44kkUp9OnT0utWrXk4cOHsmzZMmnSpImIiERERDAtTiIVFRUlRqNR1q1bJ61bt5bSpUvLli1bxGg0iojImzdv5NChQzJy5EizpQw0JUqUkPnz50uuXLkSuuj0LwEw61CI+W8iIiIiSnjh4eEyffp0uX79ugwaNEi8vb1FRGTZsmXSokULGTRokAwaNEicnJwsXFIiIvovtO5opZQAkOjoaL3vZcaMGdK9e3cREZk8ebL06NFDRN4GGGjZQWP++8WLF1wukojIwmL2r8Z8bscl5jEXL16U0NBQ8fb2Fjc3N3F3d/9k5SWiuNlZugBE1ur69evy8OFD6d27tx40ICJib28vjx8/ltGjR8vTp08lLCxMBgwYIAUKFBAXFxcLlpj+K+0jtUiRIpI5c2bZsWOHTJ06Vfz8/ERExMXFRapWrSrlypWTqVOnyvXr1+Wvv/4Sb29vqVGjhpQuXVp8fHwseQn0gQDI69evBYC4uLjodU9ERETWi4F+iV9ISIjs3btX1q5dKzdv3pRNmzbJ6tWrpUWLFpIsWTLJmTMngwaIiGyU6Xvc9H2ulBKj0Sjh4eHi4OAgXbt2FaWUdOvWTXr27ClKKenevbueaUAbYDL9N4MGiIgsw/TZDkDevHkj0dHRH9zfqj3TtQl9efPm/aTlJaJ/xsABone4cuWKiIhkyZJF33bjxg3ZtGmTjB8/Xu7cuaOn1Dl//ryMHTtWateuzQ5NK/eu+tEaJyIiGTNmlOHDh0ujRo1k3bp10rRpU0mVKpWeHs/R0VH69OkjIsxAYYv8/f1l3rx5sm/fPomIiBBvb2/p3r27VKhQQXLkyGHp4hEREZEJbUDAdKDgzJkzEhQUJA8fPpSyZctK6tSp2R5LJDw8PGTYsGFy6dIl2bJlixQrVkxOnjwpKVKkkIkTJ0rz5s0tXUQi+oQ+ZGYi2SbTvpjTp0/L3r175ciRI5IhQwYpWbKk1KxZU1xcXCQsLEwcHR2lS5cuopSSrl27iq+vr4jIO4MHiIjIMkyf7Xv37pW5c+fq/a2pU6eWHj16SPny5fWlBkyP157lQUFB4uHhIUajke0AIivBpQqIYtBeYKtXr5bGjRtLzpw5Zdq0aXLv3j2ZMGGCnD59WnLkyCHVqlWTxo0by9ixY2XTpk3y+eefy969e8XR0dHSl0D/b+7cuaKUkvbt28fat3z5crly5YpUrlxZypYtq2+Pjo4WpZQ8fPhQWrduLXv27JFNmzbJl19++d6gEAaM2IbVq1dL8+bNJTIyUnLmzClKKbl586YAkGrVqsk333wj5cqVs3QxiYiIkrQpU6ZIZGSk9OrVS0TernlvZ/c25v3333+Xvn37yuvXr+XNmzdSuHBhqV69ugwePFhcXV0tWWyKB1owb2BgoBQoUEAeP34sBoNBxo8fL926dRMRDiwS2TrTb+eAgAAJDw8XEdGXJqHEx7TON2zYIF27dpXHjx+bHfPVV1/JwoULxdPTUw8eEBGZNWuWdO3aVUTetg+0JQz4LiAish5r1qyR5s2bS0REhOTIkUMMBsMH9beePn1afvrpJ2nUqJE0bNiQfetEVoKBA5SkmX68PHnyRNzc3MTZ2VlERMLCwqR9+/aybNkys3M6deok3bp1k3z58omjo6PcunVLSpcuLeHh4XL06FHJnj17gl8HxXb69GkpUqSIiIgsXrxYWrRooe+7cOGCFChQQEREvLy8pFGjRtKrVy9Jnz692bpJY8eOlQEDBkjx4sVl3bp1kiZNmoS9CIpXu3fvlpo1a4qjo6P88ssv0r59ewkLC5MzZ87IsGHDZO/evZI+fXrZuXMnMw8QEVk5AAJADAYDg/cSmWPHjknJkiXF1dVVfvvtN+ncubO+b8WKFdKsWTMREalVq5bcvn1bHjx4IAEBAVK3bl1ZuHChuLm5WaroFI9OnTolRYsW1WeW1qtXT+bMmSNeXl6WLhoR/Qem7+wtW7bI2LFj5fLly+Lm5ialS5eWoUOHSoYMGbiUXCK1ceNGqVu3roiIDBo0SBo3bixRUVFSr149uXfvnuTPn1/27t0rXl5e7wwe+PXXX/UMkERkPfhNlnTt2bNHatasKQ4ODvLzzz9Lhw4d4uxv3bFjh555QETk1atX0rRpU9m6davkzZtXRo8eLXXq1LHglRCRhqGZlGRpM8tFRHbs2CFt2rSRKlWqyNOnT0VExNHRUebOnSsTJkyQdu3aSb9+/WTZsmUyc+ZMKVKkiP4B4+rqKgAkX758kjlzZktdDsVQuHBh+f7770VEZP369RIdHa3vy5cvnyxbtkwGDBgg4eHhMmvWLKlbt6507dpVzp07p894+Prrr6VMmTJy6dIlOXnypIi8nQVFtic4OFifvfjTTz9J9+7dxcnJSTw8PMTV1VUCAwMFgNSrV49BA0REVso03lkppc8yM011SLYvR44cMmLECAkPD5eBAwfKjBkzRETk9evXMnHiREmRIoUsWbJENm7cKNu3b5d58+ZJzpw5Zf369dKsWTMJDg628BXQfwVAnjx5IvXq1ZMRI0ZI/vz5Zd26ddK6dWt59uyZpYtHRP+B9s5ev369fPXVV+Lv7y8RERHy119/yYIFC6RFixaydetWCQ0NtXBJKb6dOHFC/Pz8xNXVVWbNmiUjR46UggULyunTp+XJkydib28v58+fl4oVK0pAQIA4OjpKWFiYiIh07txZZs2aJSIi/fr1k4CAAOE8OCLrYRo0cPv2bRHht1lS8erVK5k8ebJERETIjz/+KD169ND7W5MlSyZBQUECQOrWrWsWNCAikixZMvHz85M0adLInTt39El+RGQFQJQERUdH639fuHAh3N3doZRC5cqVsXv3bgBAZGTkO88PDQ3V/96+fXsopfDtt98iKirq0xWaPphp3a1atQohISEAgNu3b8c69uzZs2jTpg1y5swJpRSSJUuGHj16YM2aNQCAsWPHQimFatWqJUzh6ZO4f/8+fHx8UKlSJbPtp06dQqlSpaCUQs+ePc328X4mIrIepm23nTt34ttvv0XlypXRpUsXjBgxAi9evLBg6Si+BQYGYvTo0TAYDEiePDnmzp2L4OBgpEyZEqNGjYp1/Llz55AvXz4opfDll1/i5cuXFig1xaeIiAjcv38fAHDlyhXkzZsXSil89dVXePr0qdmx7/tuIyLrc+fOHWTPnh2enp6YMmUKbt26hcOHD6NixYpQSiFv3rxm3/Fk+968eQNfX18opfDLL7/o28ePHw9HR0fY2dlhy5YtKFmyJJRSKFCggN62M+1/mz9/Pk6fPp3g5SeiDzNu3Di4urrixo0bli4KJRCtv7VixYpm20+fPo3SpUtDKYUePXqY7YvZ37pz505cv379k5eViD4cAwcoSVuwYAGUUvD09MSMGTPiPEbrqDbtsNb06tULSil8/vnnePLkySctK/07MTsQFy5cCKUU5s2bp2/TGirBwcG4desWfH19kT59eiiloJRCr169MGnSJKRIkQKOjo5YuHBhQl4CxaM9e/ZAKYWGDRvqvxunT5/WgwZ8fX3Njn/8+DF27dqFoKAgSxSXiIjeYd68efp72vRP3rx5MW/ePDx+/NjSRaR4EhAQYBY80Lt3b2TOnFkfMIjZ1jt//jyDB2xQXN9YERERsbZdvHhRr1/T4AHTY3fu3IkHDx58usIS0Ucxvc+joqLw559/QimFSZMmmR13+/ZtdOvWDfb29gweSGSePXuGYsWKoVatWvq2uXPnwsPDA3Z2dti5cycA4M8//9QndeTNmxfPnz8HAP4e2IDo6Og43+mUtNSsWRNKKQwbNgwRERH8nUgCtP7WBg0a6O3yM2fO6P2tMYMGnjx5wv5WIhvApQooydq/f7/06NFDXFxcZPbs2dKlSxcRET1NvUZLtaT99969e3L06FGpXLmyTJw4UXLmzCmrVq2SVKlSMQ2TFYm5JuLVq1dFRKRbt27y+++/i4joKY6TJUsmmTNnlsmTJ8uaNWtkzJgx4u7uLhMnTpSff/5Z7OzsJDw8XI4ePSqRkZEJeyEUL9KnTy/u7u7y+vVrMRqNcuHCBenWrZscOXJEevToIZMnTxYR0VNibt++Xdq3by9nz561ZLGJiMjEtm3bpHPnzuLu7i4///yzrFq1SqZNmybFixeXS5cuycCBA2XKlCny4MEDSxeV4oGnp6d0795dfvjhBwkMDJT58+fL48eP5dGjRyIisVIU58uXT5YvXy558+aVrVu3ctkCGwCTtLYnTpyQyZMnS/PmzaVdu3Yyd+5cfakwEZE8efLIypUrJW/evLJ582Zp3769PHnyROzs7ERE5IcffpBq1arJ5MmTubQYkQUFBgbG2qbd58uXLxdfX185fvy4pEmTRjp06CAib5cDBCCZMmWSIUOGSKdOneTatWsyZMgQ2bx5M5ctSAScnZ1l0KBBMnz4cBEROXPmjMyYMUNev34tq1evlipVqkhUVJTkzp1bKleuLEopuXTpklSuXFlevHghTk5OFr4CiktERITe7lZK6X2iBw4ckCNHjliyaJTAtHb5wIEDJWXKlLJr1y4Reft7EbPNTolL+vTpxcPDQ16/fi12dnZy8eJFvb+1e/fuMmXKFBH5u7/1jz/+kPbt28uZM2csWWwi+icWDVsgsgAt2nHIkCFQSmH8+PGxjnn58iVmzZqFYcOGYc2aNXq6nKioKPzvf/+DUgoODg6oU6eOPquFKTKth2lEq2m9/PDDD1BKwWAwYPHixWbnxKy/U6dO4aeffkKOHDmglIKzszPOnTv3aQtOn8yjR4+QNWtWKKUwePDgONNlmaZArFKlCtzd3XH+/HlLFJeIiBA7hWGvXr1gNBqxcuVKs+3BwcHo0aMHvLy8kCJFCowbNw6vX79OyKLSJ/TixQs980DMd3dcywqZZh4oXbo0goODE7K49BHWrl2L5MmTx8okkjNnTkyePNnsWNPMA1WrVsXy5cvRo0cPvb1+9uxZC10FEU2YMAGZMmWK87v5wYMHSJs2LZRSKFKkCDJmzIg3b96YHaN9xz948AA9evRg5oFEJiwsTK/jOXPmQCmF3r17A4i9nKi9vb3+/V6mTBlERUVx5rKViYiIwJw5c9CxY0ds3rxZ375kyRIopdCyZUs9YwQlHQ8ePNCXHBk3bpyli0MJ4NGjR8iWLZve31qmTBkopdC9e3f9GNP+1qpVq8LNzY197ERWjoEDlORo6bMqV64MpRT27Nmj77t79y4WL16sr6GplIKLiwuqVaump0V99eoVevfujUWLFiEwMBAAgwas1cqVK7Fo0SKzbSNGjHhn8EBcAgICMGzYMFy7du1TFZM+IdMOhnnz5sFgMMDe3h5KKfj5+enHmXZa9e7dG0opdOnSBa9evUrwMhMRkbk1a9Zg/fr1qFKlCpo2bapvj4qK0tMhBgcHY+DAgVBKIXv27Lh48SKAuNOgk3UyravQ0FCzDqbnz5/jl19+0YMHJkyYoO+LK3jgwoUL8PHxgdFo5HJiVm7r1q1QSsHOzg7Dhw/H8ePHsXbtWgwdOhT29vawt7dHnz59APxd15cuXdLTn2p/MmXKhAsXLljyUoiStPDwcFSoUAFKKRQsWBD37t2LdcyaNWuQL18+/Xvs999/j/Wejit4oGDBgvj9998ZPGDl3tXmimv5z+bNm0MphTlz5gB4+3wPDw8HAKxatQq5cuXCiRMnUK1aNZw4ceITl5w+xvPnz/W2d4UKFXDy5EmsXLkSSim4ublh2bJlli4iWcjGjRuhlEKNGjXw7Nkzfo/ZuHfV3/v6W3v27KkfZ9rf2qdPHyil0LlzZ/a3Elk5Bg5QkqU1cAcNGoQbN25g3759+Oqrr+Ds7AwvLy80aNAAfn5+KFasGJRS+Pbbb/XOKtO1u+LqrCTLO3v2LJRSSJcuHR49emS270ODBxgQYnuuXbuGvXv34sCBA7HWuL179y46dOigr5W8adOmWOd///33UEohf/78uHPnTkIVm4iI3mHv3r16h2SGDBnQuHFjREVFmb2jtbZYcHAwvvjiCyil9OPINpjW1b59+9CrVy8MGTIEV69e1be/ePECP//8MwwGA7y8vDB9+vQ4z9dcuXJFbwvwd8E63b17F4ULFzYbPNIcPXoU2bNnj9X5qNXl7du3MWbMGHTo0AEDBw7ErVu3ErLoRBSHoKAg1K5dG1WqVEFYWJi+3fSdvWbNGuTJkwdKKTRr1gw3btyI9XNMgwf8/PyglEKJEiXw8uXLT38R9FFMB5bu3r2LP//8E9u2bcO5c+fM1rHWAj61TJ5ff/11rJ/VrFkzuLm5mf0OkXU6evQomjZtCjs7Oz0bUMqUKbF8+XL9GA4a2z7TOvyQNvXDhw9RpkwZGAwG/PHHH5+yaPSJmdb9tWvXsG/fvo/ubx08eLDe33r79u1PXnYi+m8YOEBJjvbS279/P4oWLQqj0WiWGrNJkybYuXOnfvysWbP0VKcAOx5tSaVKleDg4IAFCxYAgNmH57/NPEDWb+3atXr6S6UUUqdOjSVLluDZs2f6MQcOHECTJk1gMBjg7u4OPz8/LF++HLNmzcKXX34JpRR8fHxw6dIlC14JERFpzpw5o3dIKqVQs2ZNfZ9pm0wblDhx4gTc3NxQrFgxzmKwEaYdUosXL4anpyeUUvjiiy9w4sQJs/0BAQH48ccfPzh4AGAgqDU7dOgQjEYjOnfubLb9zJkzekYBX1/fd56v1bk2EEVElqM9a9+8eaPPHN+8ebOeitj0Wbxu3To9MKhHjx6xBiCAv98N9+/fR79+/ZhRxIqsWrUKf/zxh16npu/pjRs3Ik+ePHBwcND7W6pVq4apU6ea/Yw9e/ZAKQWj0Yhx48YhIiICoaGh+O6776CUQt26ddmOsxEPHz5E2bJlYTAY4ODggIEDB+r7+H62bTNmzMC+ffsAmE+gA94+B7Zt22Z2vOlz/ueff9aDv5n9y/atW7cO6dKl0/tbvb29P6i/dcWKFZg9ezZq1aqln6dlBiQi68bAAUq0/imqNTo6Gtu2bUOHDh2QOnVq1KlTJ9bHDPB2rXuDwYDmzZt/qqJSPNMaq7Nnz4ZSClWqVNH3aZ0YAIMHEpPt27fr6YvLly+vr6nm6OiI7777zmwmy+nTpzFo0CD9eO1PsmTJUL16dS5LQURkZc6fP4/OnTvrz2vT9pppey86Oho3btyAh4cHjEYjLl68yIBPG/L7779DKQUPDw/MmjXrnXUXEBCA0aNH/6vgAbJOEydOhFIKkyZN0reZBg306NHD7Pjnz5+bBXdyFiORdTF9Bvv7+0MphSxZsuiD/qaDShs2bNDXRPb19X1v8AADwKyHVq9Zs2bF7t27zepGS0+ulELlypVRpUoVpEmTRt/Wr18/s4Hk8ePH64GhRYoU0ZcM9fHxwZUrVyxxefQRdu3apfenGAwGVK1a1WwyFt/VtklbSsrb2xuHDx8G8HddavuUUmjdujVmzJgR6/yXL1+iZMmS8Pb2xp9//gmAz3JbtWPHDhiNRiilUK5cuQ/qbzXta9WeD9WqVTPLJkdE1o2BA5QomX6wXr9+HYcPH8aiRYtw5MiRONOdPX/+3KwBox0THR2NJk2aQCmld0qyQ9J2/PXXX/pMhilTpujbTevaNHhgyZIlligm/QfR0dGIjIxEkyZN4OnpqWeXAIBffvkFGTNmhJ2dHfr164fr16+bnXvy5ElMmjQJ33//PUaPHo2jR4/ixYsXCX0JREQE807F58+fIzg42Gz/2bNn0aVLF30t8/Xr1+v7IiMj9cDAwMBA+Pj4oEiRImbrKZJ1O3bsGHx8fGBnZ4eVK1f+4/GmwQPJkyfHzJkzE6CU9LFM72/T+1LL7Ka1048fPx5n0EBoaCgAYOzYsWjfvn2ca6eTdTGt88ePH+P69es4dOhQrLXKOaCUeAUFBaFSpUpQSiFfvnw4f/48gH8fPEDW5enTp3ofWZ48ebBr1y5EREQgMDAQxYsXh4eHB+bOnasff+rUKfz666/64NH//vc/fV9AQADmzZsHT09PGAwGeHp6omTJkrh8+bIlLo0+0vPnz1GqVCkMGjQIrVu3hsFgQIUKFcxS1PNZb3tevnyJFi1aQCmFDBky4NChQwDe9olfuHABQ4YMQd68efU17StXrozp06frKejDw8PxzTffQCnFiXg2Sutvbdq0KTw8PDB//nx9X8z+1pjLDp08eRITJ07U+1uPHDmC58+fJ/QlENF/wMABSnRMG6QrV65E/vz54ezsDKUUnJycUK5cOVy+fDnWDDXt36aBAX379tUj6kzT75B10Oos5n+Bv+tx2bJlcHBwQIMGDcyyDcQVPKCUwooVKxKi6BQPtPp+8+YN0qRJg/79+8c6ZvHixcidO/c7G7NERGR5pm2vXbt2oXnz5vjyyy/x119/mR137tw5dOjQQV8b0TRYTOPr6wulFJo2bcrAARsyc+ZMKKUwaNAgfds/BetqyxY4OjpCKYVFixZ96mLSfzR//nyMGjVKn3G6e/duPTvYyZMnUbZs2VhBAyEhIQDett1z5cqFAgUKICAgwBLFpw9k+k22bds2lCxZEsmSJdO/t+rUqYN58+bp32YMzE98tG/toKAgfTk4Bg/YPu1eff78OVq1aqUHD/j7++Py5cuws7PDiBEj4jx39erV+jPghx9+MNt3584dHD16FOfOnWMgv43RnuPae11bYsxgMKBixYqxggf4vLctr169Qps2bfTggYMHD5rtv3r1KtauXYsiRYro/e7p0qXD9OnTcfXqVQQHByNdunRIlSqV2ZIHZP1i9rd+/fXXsY5hfytR4sbAAUpUTBsg8+fP1z9M2rZti3HjxqFGjRpQSiFnzpz4448/zAaSNUFBQbh58ybq1aunp9bTZrWwkWudHj16ZPZv03q6fPkyChcuDKWU2exEwLzD4ptvvoG9vT3XTrRBUVFRKFGihD6AFBkZafY7sHjxYuTKlUtvzN66dUvfZ/o7wA8Y68dnMFHiE3N9e3d3dyil0KBBA+zevTvWMefPn0e7du30Np6fnx/mzp2L3bt36223rFmz6kEHfLZbn5h1EhkZqdfdunXrAHz4mrgvXrzA999/j/Tp03MWuhV43/12/PhxKKXw22+/6dtev36NIkWK6B3NSin06tVL328a/KMtVzJs2DCumWwj1q5daxYs0LFjR5QoUQJubm5IlSoV+vbtqweGUOKj9bX82+CBdu3axfq+J+sRV/BAvnz5MHPmTHh7e2PPnj0A4n6Pa0tJpkyZUp+9zHZa4mCa2fXPP/9Es2bN4gwe0KxcuRJHjx5NyCLSRwoODkbr1q3jDB7Q7t8nT55g8+bNejYSbTmxgQMHomHDhlBKYfjw4Za6BPpAcT2Po6OjUaJECT3bAPtbiZIOBg6QTXvXi2fz5s1wcnJC6tSpY6Uud3Bw0AMCNm3aZBY88PjxY3z33XdIkSIFlFKoWrUq7t+/D4BrMVmrJUuWwNnZGT/88APOnj2rbzf93fjtt9/0+ow5g8G0Xp8+ffrpC0zxYu/evejfvz/at2+Pbt26oWDBgpg9ezYAxJk95H2NWbJ+/v7+nH2SxNy+fRvHjx/H/PnzcerUKf1dTImXFvDp7u6OqVOnxtpv+l4/d+6cPoiolIKdnR08PDyQLl061KtXTx9AZtvN+pjWo5Z+/s2bN6hVqxaUUli8eHGs42IKCgoyCxIICgpCUFAQANa5JS1duhSrVq2Kc1k44O+1r7X2mlZXe/fuRdq0aaGUQuHCheM8d9iwYVBKoVSpUnwf2IgjR47Ay8sLjo6OmDNnjr49PDwcQ4cO1dc1X758uQVLSf/GuwYU3kf7HvuQ4IFNmzbBy8sLTk5OePjwYTyWnOJbXMEDWh/amjVr3nleUFCQHihomvKarJ/pvX7r1i0cO3YMixcvxu7du+Nse8UMHti2bZu+b+TIkXB2dkazZs0YPGbFTPvTwsLC9KxvGTJkwP79+/VjYtb/1q1b8d1338HV1dVsjfvkyZPj+PHjCXoN9OFM7/F9+/ZhwIABZv2ts2bNMjuO/a1EiR8DB8gmPXnyRP97zI/V+/fvo3LlyrE+RoYPHw6lFNzc3NCgQQMopZA9e3Zs3rxZDx6IjIzE+PHj0aJFC4wdO1Zff4edkNbDtL7v3LmDXr166Q3R7Nmzo0uXLrh79y5ev36tHxcYGIjSpUsjRYoUZutyaTiL2basWbMGBoPB7CNEKYVChQrh+vXrZsfG1Zh1cXFBt27dcOfOnYQuOn2E9evXw2g06h2MlPitWLEChQoVgouLC5RScHV1Rfbs2d/bEUm2bdeuXXByckKyZMnMlgyKKzOU5vTp0+jSpQuUUihevDh+/fVXAH/PeGLbzbrNmjULBQsW1DNDDB48GEopDB48GEDcg1Ha7MXff/8d3377bawZqZzJYjmHDh2CUgqpUqXChg0bzO5d0+XDlFJYuHCh2bnBwcGYMmUKfHx8oJRC3bp1cfToUZw9exanT59Gy5YtoZRCmjRpuO61DdDq+/vvv4dSCj/++KPZ/nPnzqFkyZKxlqQg6/fy5UsAf9ex6TP38OHDGD9+PJo0aYL+/ftj4cKFePz4sdn5HxI8sHXrVly9evVTXwrFA+334NmzZ2jZsqWeMap+/frvzRihBYK1a9cOkZGRfHfbANM62rp1KwoWLAgnJye9H6ZkyZI4ePCgWR8c8DZ4oHnz5jAajShUqBB++ukndO3aVf++Y7ZP62Xaj3bp0iXs2LEDc+bMQaZMmWJlHtCW/o357XXy5EmMHj0aefLkgdFohIODAyZOnAiA32nWbO3atTAajexvJSIGDpDt+fXXX1GnTh2cPHlS3xazIauUMltbbezYsTAajUiWLBmuXbsGAKhUqZK+bMHmzZv1SNeoqCgEBATonZMcVLZO69atQ9myZXHp0iXs3bsX7dq106Pcc+TIAV9fX5w4cQLA298PLXCkevXq75wJRdbv5MmTSJ06Nezt7fH9999jxYoVqFOnDnx8fODk5IR+/frFyipheg8vWbIEqVOnRqpUqWJ1ZpF1iY6ORmhoKOrWrQulFDZu3GjpIlECWLBggf5x2qZNG3Tv3l0P9jMYDPjpp5+YHSYR6tevH5RSmDx5cqx9wcHBWLp0KcaOHYt9+/aZPbvPnDmjr7tZrFgxbN++Xd/HjmjrtX79ej0AUJttvGTJEv0+N10DVatH0w7G/PnzI2vWrLh9+3bCF57i9ObNG3Tp0gUGgwEZMmTA+vXrYwX+zJs3L86lw4C3s1YXL16MLFmyQCkFJycnODk56R2XRYoUwaVLlxLqcug/Cg8P14O2Td/Zp0+fRqlSpeIMGtAykJB1Gj9+PHLkyKH3pUREROjP5zVr1iBZsmSxBhlq1KiBmTNnmv2cDwkeINuh1dvTp0/RokULODg4IG3atFiwYEGse1r799KlS6GUQrdu3RK8vPTfmC4/06pVK0yaNAnNmjWDUgq5cuXCwoUL9QxQmtOnT6Nr165mgQbZsmVj0IAVM/2GWrhwIdKkSQOlFDJmzKgHCGnBAx+y5EhwcDCGDx8Oo9GIXLly6UFoZH1OnToFb29v2NvbY9CgQVi+fPlH9bemTJmSSw4RJQIMHCCbcuvWLRQqVAhKKbRu3RqnTp3S92kNlbt376Jt27a4ceMGgLfr5aVNmxaurq44cuSIfvydO3eQIUMGfS3czZs3s8PCRuzatQsGgwH29vZYtGgRgLeN0XPnzqF+/fp6FKy9vT369++Pffv2ISIiArlz54a7uzvWrl0LgIMKtmjNmjVQSulpsgDg1atXGDt2LLJmzQp3d3cMHjz4vY3ZFStWxIqUJes1YcIEKKUwbdo0SxeFPrE9e/bAxcUF7u7uWLJkidm+Pn366J0U2tqplDiEhYWhUKFCsLe3NwsKvX//PlatWoWCBQvqdZ8sWTL4+fnpAxcAcP78ebRt2xZKKZQoUQJbt27V9/E9bx1MZ6dGRkaiVq1acHd3j5VFRMsgEXMWk+nP0WaqderUieltrYQ2cBQSEgJfX1+9DmMGD0ybNg1KKaxbt+6dP+vevXvo27cv6tati1KlSqFJkyaYM2eOnpmCrI/pPWq61nGePHmQLl06fampdwUNREVF4fnz55g6dSpOnz6dsIWnD/LmzRuULl1az/Bj+h3l7+8PR0dH2NnZoX///pg0aRKGDRuGLFmywMHBASlTpsTgwYPNfk9MgwcKFy6MM2fOWOKy6F94X3tKm3Dz7NkzfdmCnDlzYv369QgMDARgnkFKO2bChAn/+LPJeuzbtw9eXl7w8PAw+y6fMGGCHhSQMWNGzJkzJ1bwwIMHD7B+/Xq0adMGP/74I9OY24gVK1ZAKQUfHx/Mnz8fL168wP379zFhwgRUq1btg4IHtG+AyMhI/bk/Y8aMdx5PCStmHWj9raZBfx/T32r6rU5EtouBA2RztmzZgkqVKsFoNKJly5ZmwQNax9Xr16/1F2DPnj1hMBj0tJhRUVGIiIhASEgIypcvr6fGdXFxwc6dOxP8euifxWzMNGrUCC4uLmZLUWgNlVevXuHUqVP43//+BxcXF31GW6dOnVC7dm09LR7ZltWrV+OHH35At27dULZsWX27lj3izZs3mDFjBrJkyQIPD49/bMyS7dBmpTRu3BjA351TlHhoz/i+ffvGGSRy/PhxfPbZZ1BKoX///pYoIn1iXbp0gZ2dHWbNmoWAgAAcOHAADRo0gJubG5IlS4bq1aujVatWSJ48ORwcHDB37lyz82MGD5iuo0rW48iRI3j8+DHy5s2LAQMG6Nu1AYW//voL9evXh1IKLi4uWLRoES5evIiAgADcv38f7du3h1IKBQsW1Ne/ZqejddDaWDGDB9atW6enLh4zZozZ+tfR0dGIiorS61D7PdAyTXBGmm3ZunUrhgwZotd3jRo1YDQacfPmTdy+fTvOoAEt+Of06dMwGo344YcfLFJ2+mcPHjxAzZo1oZTCZ599hitXrgAAevToAaUUFixYYHb8n3/+CT8/P/09/ssvv5jtDwoKQp06dfQ058wIaL1M37Pnz5/HunXrsH79erN+OI1p8EDmzJkxcuRIs6Unvv32W30f01jbjmfPnunts0mTJunbf/31Vzg4OMDe3h6tWrWCs7MzsmTJgjlz5uhBI6bYZrMdL168QJkyZaCUwu+//262LywsDFeuXEHz5s3fG/Cr0frp161bp08CJOuyZs0a/PDDD+jevTvKlCmjb2d/K1HSxsABshkxlyMoX758nMEDpm7evIkUKVLAzc0NFy9e1LdrA0+tWrVCyZIl0aRJE6RMmZKzWazc6dOncffuXeTPnx89e/bUt8e1ziIA7N69G6NGjYKbmxuUUnBwcNDXU3v+/Dk/XGzEtWvX4OLiAm9vbxQrVgxlypRBRESEfh9r9fghjVmyPTdu3ICrqytKlSpl6aLQJxQaGopcuXIhffr0ePr0qX5fHz58WM809O2335qd8+bNGwDshEoMFi1ahBQpUsDJyQnZsmWDvb29vk7uli1b9ONGjBgBpRS++OILREZGmqU21oIH7O3tkTNnTuzYscMSl0LvoK1v37BhQ3h5eemdkDE7ma5fv64POiil4O3tjVy5csHb2xtKKeTJkwd3794FwNTW1uZdwQNapq/Ro0dDKQV/f/9/9XP5jLd+mzdvhlIKadKkwa5duwD8PUBYunRpFCtWDEopdO/eXT/HNNNfgwYNYGdnh82bNyd42enDPXz4UJ9lWqRIEdy6dQstWrRArVq19GNMZ5bfu3cPQ4YMgbOzM/Lnz68PLGkDEYGBgWjcuDEzTdiIdevWwcvLy2w5ilGjRunvZM3z58/197idnR2MRiNKlSqF5MmT69kITPvmyPrt378fSin06dNH3zZt2jS4uLjAzs4Ox48fB/B2go9Wx+8KHiDbcPv2bbi6uiJXrlz6N3fMNvuDBw/0yVkZMmTA/v37AcRut2nnnTt3Ds7OzihdujQng1iRa9euwdXV1ay/NTw8nP2tRMTAAbIt/zZ44OXLl3qaxPv37wP4u5MiOjoa2bNnR5cuXQBAT6PITkjrtHXrViil0L59e3h6emL27NkA4q6vmA3ac+fOoX///siePTuUUlxPzcY8e/YMY8eO1ddWy5Qpk36/xgwaidmYHTZsmH7vk/Xbt28f5s2bh8uXL+PZs2cA3tZxxowZkTp1ajx+/Fiva0Y0Jy4RERHIkSMHsmXLpqe3PHToUJxBA1FRUXj27Bl69eqFw4cPW6rI9B4xO4w+ZOBv5syZqF27NlxdXVGpUiWMGzcu1vlax6Wvr2+cP+PChQto0KABPDw8cPPmzY+/AIpX0dHRWL16NVKkSAFHR0cYDAZMnz4dgPkgk6mff/4ZNWrUgKurK5ycnFCiRAn07dtXXy+T7XXrFFfwQLp06eDv74/vvvsOSik0bdoU06ZNw6RJkzBu3DjMmjULCxYswOzZs7Fs2TJMmTLFbMkRsj7aMzk6OhoBAQEoVaoUPD09MWfOHP2YR48eoWTJkvoAY5s2bfR9psuMaL8X9evXx/PnzxPuIuijmAYPFCpUCNmyZUOHDh0AxP2uv3TpEurWrQulFAYNGqRvjzkgQdZtz549cHR0hFIKX331lV6n2r0dM/jj2bNnaNmyJTw8PKCUQvPmzdG3b1+sXLkS9+7ds9BV0McKDg5GgwYNcPbsWQBvlw7Nnj07jEYj/vjjD/24Bw8eIHPmzPpysPPmzWPwgI26evUqnJ2dkT179nfWYXR0NLZt24a0adPqS1W8b9mCgQMHQimFZs2aMXDAirC/lYjehYEDZHM+NHggMjISAQEB+OKLL6CUgp+fn9nP6dWrlx4lHdfPJusRFRWFxYsXw9vbG8mSJTNbF+ufOo5N058GBgYyJZ6NevbsGSZNmoQMGTLog0Zap+O7GrM5c+aEUgqjR4/mAIMN0NbQU0rB0dERKVKkQIkSJdCxY0ekT58eSils2rTJbIZaTHyG2x7TQJDixYvDyckJf/75Jw4cOBBn0IB23586dQqenp5m6c7JumjPXdP78vDhw1i6dCm++eYbTJw4EWfOnDFLRx4aGopHjx7pqa4B84FlLU3qkiVLYv1szaVLl5hBygqFh4dj48aNyJIlC5RSKFq0aJwzmEz/Hhoairt37+LGjRuIiIjQf6f4TrcepvdgzFTjMTMPaEvO/NMfNzc3s9TWZL0ePXqEu3fvws3NDSNGjNC3a0sDLly4EHny5IFSCp07d471HdavXz99gOnGjRsJXXz6SA8fPkTlypWhlILRaETVqlURFBT0zmeztmZyhgwZ8OzZMwb/2hDtGd+1a1ckS5bMbKnIlStXokiRInpAWMzggadPn6JFixZwcHBAunTp9NnIZFu0+9o04+OQIUOglMLEiRMBvP090drrLVu2hKOjI3x8fODo6IhFixbxG90GvXz5EtmyZdOXonnX8z0yMhL58uXT23BOTk44ceJErOP27t0LZ2dnJEuWjG08KxSzv7VHjx4f3N86atQofpsRJVIMHCCb90+ZB/bt2wej0QilFGrXro2uXbuiatWqUEohf/78ePz4sWUKTu8U14fFq1evsHz5chQoUEBPVatFq3/Ihwg/VmyTab09ffoUkyZNgre3N9zc3DBq1Ch9EDmuxuz48eNRuHBhXL58OeELTv/aokWLMHbsWLRv3x5Vq1ZFunTpzAYSDAYDkidPjmLFiqFhw4b45ptvsHbtWuzZswehoaF6VDTZBtPZRlonlLb+9Zdffhln0IBp0EjlypXh5OSEnTt3Jlyh6R+NGDECLVq00P9tOui/cOFCuLm56csQKKXg7u6OHj166KmtNdqz3DT4QBtgqlKlSpyzUvmet35hYWHYuHGjngGqffv2epCIaYcT69I2mNbT8ePHMXDgQHTq1MlsFpkWPODs7KzPVB05ciSmTp2KsWPHYujQofjxxx/x008/4bvvvsN3332H8+fPW+Jy6F9atWoV8uXLh++//x5OTk76oKHpoPDLly8xdepU5M6dG0op+Pj4oHv37mjdujWKFy+uDyYzG5ztefjwIapUqaIH+xw4cACAef1rz4inT58iXbp0cHNzw8OHDy1SXvr3tPqLiIhA7ty50blz51jH7Nq1C+XKldNnEcfsi3v27Bnq1q0LJycnXL9+PSGKTf9RXMGcptseP36MnDlzwmAwmA0Qa+04Pz8/ZM6cGT179kSaNGlw5cqVBCo5/RembTqtLgcMGAClFBo1ahRnULY2sNy6dWu0bNkStWvXhtFojHMG+s2bNzFixAi+762A6f1s+v31Mf2thQoVwqVLlxKw9ESUkBg4QDbBtBETEBCAV69eme3ftm0bKlSoAIPBgBYtWsT6YNm5cydSp05t1lldoEABrpFq5WI2Tl+9eoWlS5eiYMGCUEqhU6dOeuAHO5kTn7g+VJ89e4aJEyciZcqUSJEiBUaPHv3OxmxISAgCAgISttD0r8WcdaT9+9GjRzhy5Ag2bNigd0h5eXnB1dVVf45rQWHu7u6oXLlyrNmOZJ2mT58Ob29vrF+/3mz7qVOnkClTJr1++/fvr+/T3vvR0dF6xqBmzZohODg4QctO73b37l39nuzatavZPm22ocFggK+vL/r37486derA1dUVBoMBBQsWxKpVq/TjtefAmzdv8ODBAzRo0ABKKWTJkkXvjOKMRevzrraYaV1pwQNaKtvu3bvrwQOsU9thWtfr16/Xg/2KFy+ur1OvfV+FhISge/fucHBwQNasWbFx40aLlJniT3BwMDp16qRnC3B2dtZTWMfVubx37140bNjQLCA0S5YsaNu2LZeVsRFxPd8fPnyImjVrQikFb29vfYBQu/e14MGgoCCkSZMGefPmNVumgqyPaT2bBn82aNAAo0eP1rebHrd7926UL19ezzwQsy/uxYsXTGVt5UzrU8ssEBwcbJYBTBMaGopixYrB2dlZXzLOtD+1VKlSqF27NiIiIvDkyZNPX3j6KB/Sf3r48GH4+PhAKYX//e9/ZksWmD7LM2XKhBYtWiAkJEQP7o6rj53LE1hOXPUd81kO/B08oPW3vi94gP2tRIkfAwfI6pl2Im7fvh316tVDzpw59UF/TczggZMnT5rtP3fuHJYtW4ahQ4di2bJleiOWQQPWaeXKlcidOzdWrlxptv3Vq1dYtmwZcuXKBUdHR/Tq1YvBA4nIqVOnMHPmTHTr1g29e/fG0aNHY80kf/78+QcHD5Dt0TqpTOtw5cqVesq0e/fuYefOnRg9ejT69OmD4sWLo2jRorE6qcg6RUREoGPHjnrWn5jBA9oAsxY4cO3aNX1fWFgYunbtCqUU8uXLp89a4/1uPXbu3Kmvj2g6M61GjRpIliyZWXAA8Pbe1paUypkzpz7gCLwdkOjcuTMyZswIpRQqVKigdzyz7WZ9TNvrf/31F06dOoUtW7bg1q1bsQICtGULtEAhBg/Yro0bN0IpBWdnZ31QKa6ZTCEhIejZsyeUUkiXLh3Wrl3LYD8bd/nyZfj5+cHT01N/5se1RI2p48ePw9/fHxs3bsSjR4/05UrI+vxT20qr64cPH+rv8ezZs+sBJKa0Wav169dnnVsx0zrftWsXOnXqhK+++gr169dH7ty5zbKAxTw+ZvBAzGULyHqZ1uOBAwfQo0cPFCtWDLly5ULVqlUxefJkswHj169fo1u3blBKoWPHjmYDh4MGDYJSCn369EnIS6B/ybSddvHiRWzZsgXfffcdFi1aFCsLnOm3eZs2bXDw4EGz/X369IFSCmPHjo3z55Plmd7jR44cwXfffYfKlSujZMmS6NixI5YuXWp2fMzgAfa3EiVdDBwgq2b6Mpo/f76+vn3lypWxevVqAOaNkn/KPBATGzTWKTg4GB06dIBSCsWKFcPatWvN9r9+/RrLli1Djhw5GDyQiKxbt06PaNb+ODs7o3v37jh27JjZsR8SPEDW7fbt29i/fz9mzZqF7du3m611F7MO9+zZA6UUqlevHuvnBAUFxTkbgqzP5cuXsWPHDvz666/Imzcv7O3tkS9fvlizT1euXAknJycopZA5c2Z88cUX+Oqrr/R1FvPkyaOvk8wBZOuh3be7d+9GqlSpzAaECxYsCF9fX/1Y01lsJ06cQOPGjaGUQt26dXH79m1933fffYcKFSrgxx9/xLNnzwCwzq2Radtr9erVKFasmJ59wtXVFb6+vnoaa837ggdYx7bhwoUL+nPZdN3rmOIKHkifPj3Wr1/P4AEbd+nSJfj5+cHNzQ1p06bFggUL9OeB6XOBbXPbYlp3N2/exM6dOzFhwgTs2LEDDx480Pdps0cfPnyIGjVqQCkFT09PjBo1CkuWLMHJkyfRrl07fZkK02BQsl5r1641+x7X/qRNmxaHDh0yOzau4AE7Ozt88cUXcQaRkPVavXq1ntnPxcUFdnZ2et03adIEf/zxh17f+/bt0zMNVa1aFe3atUOtWrWglELGjBmZScaKmd6zS5cuRbZs2czqWssucOTIEf3dbfptnj59elSvXh1+fn56sFCePHnw6NEjS10SfaA1a9bAw8Mjzuf7//73P7Olwj4keICIEj8GDpBNWLFiBZRSSJUqFWbNmhVrv2nj533BAxxUth2XLl2Cr68vlFIoXLgwgwcSuQ0bNuiNVm1929WrVyNHjhxwdnZG7dq1Yw06PH/+XG/M+vj44PvvvzdbA52s16ZNm5A/f34YDAa93jNlyoQhQ4bEefzdu3eRPHlyZMqUiR+lNmrRokX6TPRUqVIhbdq0et3nzJkTmzZtMjve398fzZs31zultHdBz5499d8BDi5a3u+//272bNbev6bBA3Xr1kXGjBkxdepUAIhzRuq2bduQP39+ODo6Yt26dWb/j8ePH+uDE+yosD6m9Th37lz9fm3ZsiW+/vpr1KxZE0ajESVKlIgVJBQzeKBnz56xliMjy4t532l1rtV3375933lszO1a8IDBYICLi4tZlhGyHabv38uXL6NHjx5wdHRE4cKFsXLlyjiDB8g2mNbZ5s2bkS9fPrPBhdKlS5v1x5gGD2iZB7TlIfPkyQMnJydUrVqVayDbiIsXLyJjxoxwdnbGDz/8gP3796Nnz57Inj273r9mOrgEmP/O+Pv7o2DBgnB3d8e9e/cSuvj0kXbu3Amj0Qh3d3f89ttv+Ouvv3Do0CFMnjwZKVOmhFIKpUqVwtq1a/X63r59O/Lnz69P7jIYDMiRIwcuXrxo4auhD7Fw4UIopWBnZ4euXbuiR48eaNGihf6sr1y5MlatWqW333bt2oU6dero33dKKTg4OKB48eL6vc5vc+v1xx9/6AHdo0ePxtmzZ7Fy5UoMHTpUr88GDRrg+PHj+jnsbyUiBg6Q1bt8+TJy584NpRRWrFihb39XJxbwd/CA0WhE69atY81WJttw5coVPQ3ahwQP9O3bl4OKNujo0aNInz49kiVLhtmzZ+vbp0yZokc2K6VQpUqVOIMHpkyZoq+xqs1IJetlOoulY8eOGDx4MHr27AkHBwc9BV5QUFCs80qVKoUUKVLg+vXrFig1/Rda8J+Pjw8WLFiAV69e4c2bN5gxYwbq1Kmjp7eNGTwQHByMJ0+e4OjRozhw4ABev36tf6yyY8LytICvwoULx9nO2r17N1KkSKHf7927dwdgXnembbevv/5azywSGRnJdTBtzMaNG+Hk5ARvb28sXLhQ3/7tt99CKQWj0RhnkJAWPKDNXI+ZCpksY86cOZg+fbr+b9N7Vft7/fr1oZTSU5z+U2CP6Zqo7dq1g5ubm74mOlkf0zoPCwvDixcv8OzZszifzVeuXEH37t3h4OCAzz77jMEDiYBpeurOnTtj6tSpGDRoEIxGIzw8PDBy5Ej9WNPggerVq+vfZQsWLMDLly/N0pyTdYl5fx46dAhKKT3YU/P777+jRIkSsLOzQ8eOHXHhwoV3/px9+/bh1q1bn6zMFH+io6Px/PlzPehnxowZsY7ZtWsXqlatqrfRTZehuHDhAjZs2IDvv/8ey5cvj7WcLFmnQ4cOwdPTE+7u7rGWkdu4cSOKFCkCpRTKlCmDHTt26PuePn2KK1euYM6cOZgxYwZ2796N58+fA+C3uTV79OgRypUrB6UU5syZE2v/mjVrkD17diil0LZtW7PlR0z7W7NkyYKnT58mYMmJyNIYOEBWb/369TAajWYpbt/VAREzeKBKlSpQSsHX19csLS5Zhw/pSPrQ4IG8efNCKYVvvvmGMxJtSEhICPz8/KCUwm+//aZv//XXX+Hg4AB7e3tMnz4d9erV0z9W9+3bZ/Yznj59ipkzZ7Lz2QacOHEC3t7esLe3NwsSAd4uR6MFD5imPNY+QrXfgZhrsJF1e/LkCcqUKQOlFBYvXmy2LyoqCpcvX0aXLl2glEKOHDnMZiS/a+CYgxDW4fLly6hbty6UUhgxYgSAvwcOTWenaDOVihUrpgcFmXYuacceOXIEzs7OKF68OOvYxty8eRMlS5aEnZ2d2fN79OjRUErBzc0NjRo10jOMbNiwwez88PBwrFq1CiVKlDBbqoIs48yZM/qA4fuWIGjVqhWUUvjjjz8AvD9wIDAw0Gx/aGgoHj58GH+Fpnhl+gzes2cPmjVrhjRp0iB16tT4/PPPsW3bNn2wQHP16lUGDyQS+/fvR8qUKeHh4WE2kDh16lS9ra6Uwg8//KDviyt4oHz58gledvo4K1asQNeuXdGnTx8ULFhQ3246u3T9+vX4/PPPPyh4gGzHo0ePkCFDBuTPn1/fFhkZafbOPnjwoJ59ZODAgZYoJsUD7R797bffoJTCqFGj9H2m9X348GFUqlQJSim0bt1a70v/p6xSZJ1u3ryJVKlSoVSpUvq2iIgIs3rbvHmzvlTJmDFjzM5/+vQpZsyYgcuXLydYmYnIOjBwgKyW9hLr3r07lFL46aefzLZ/iA0bNqBhw4b6Wshknc6ePfvejmLT4IHPPvsMa9asMdv/+vVrLFiwAEWLFmVqNBtz//595MmTBy1atNC3zZkzB+7u7rCzs8PBgwcBAMuWLUOmTJng6OiIL7/8MlbwAD9WrFvMj1RtkFHz559/6mvk9ezZM86fMX36dCilMGXKlE9eXoo/t27dQooUKZAvXz68efMGwNv71bRz8datW2jZsqWe1tY0VT07Ia3btWvXMHnyZP3f2pJBwN/P5d27dyN16tRQSqFevXr6fm2QQfvvsWPH4ODggKpVqyZE0SkerV+/HkopDBs2TN82ZswYGI1GuLm54dSpUwgMDETt2rWhlELevHljLUkRERGBkJAQ/e9kWT/88IO+rnFUVJRZUJD2XNaCvlq2bKnXXUxakNDSpUvRrFkzzkizQg8ePHjnPbd69Wp9oLhIkSIoU6YM3NzckDx5cgwePDhWFqiYwQOrVq3ie9zGPHv2TM8mMn78eH37+PHj9aBuLZNMzDa99nv0119/oX79+mZLRpL1un//PjJnzgx7e3uUKFECefPmRXBwcJxLS61bt+69wQNke44dO6a3zV69evXOzGBaFhIHBwecOHHCEkWl/0irTy24K2bGKNP63r59u74MxfuCSMn67dq1C0opVKhQAYD5d5Zpnc+ZM0dfQvTmzZtm+9jfSpQ0MXCArN7IkSOhlMKECRMAvD8F0qNHj2LNfmBaY+u2detWGAwG9OrV672pzS5duoTWrVtDKYUSJUrESqn15s2bONObk/WbPn06jhw5AuBtEEmxYsVgZ2cXa93btm3bQikFR0dH1KxZE/7+/pYoLn2kqKgoVKlSBc7OzmbrnJ4+fRolS5bUs8OYMr2nJ0+eDKUUO6hszMmTJ2E0GpExY0bcv3//ncdt3bpVz0aRN29es8wDHHSwDRMnTkSePHlw6NAhfZvWybBnzx49eKB58+Zxdj60a9cOSin07t0b0dHRrHcbsnLlSnTo0EFfLmjFihXw9vaGq6ur/n6PiIjA0qVL4eDgAAcHB+TNmzfWsgVkXdatW6cHBNy8eVPfbhrskzp1amTJkgVr166NNchk+u1VtWpVpEiRggG+VmbSpEnImjWr2XNb88cff8DJyQmurq5mWcHatGkDpRQ8PT3h5+eHa9eumZ2nBQ+4uroic+bMsYKEyLqdOXMG7u7u6NSpk75txowZSJYsGezs7PTvr+HDh+vBA0OHDtWP1Z4P7HuxHW/evMHSpUtRoEAB/Vv77NmzABBn1hDT4IEuXbrox5Jtunz5Mry8vODo6Ihz584BiHt5ItNv+T179liiqBRPtMBPLW296XeZad1rmcPat28fax/ZjoMHD0IpBXd39zgnVWr1//jxYxQoUABubm44c+ZMQheTiKwQAwfI6k2aNAlKKRQsWBB//fVXnMdoH6arV69GlixZmPrSRkRHR2P27NnIkCEDPD090b9///cGD+zevRs+Pj4wGAwoVapUrGULyLaYfqBof9eiXPv27Qvg7e+I1gG1bt06uLi4oHLlyvrMVW0GM1m/qKgoVKpUCS4uLvraiKdPn0apUqViBQ1ERETg1atXGDp0qL6kwZMnT3Dv3j2LlJ0+3qtXr/DZZ5/Bzc1NDwZ4V8S6lhJRm/Wye/fuhCwq/QdBQUH6+qilSpXC0aNH9X1xZR6oVKkSlixZguPHj+P69evo0KGDnsaebTjbZDozpX379nBwcNBnMpnObMmTJw+MRqM+8Lhz506LlJc+3IIFC2KtfRwVFYVnz56hffv2+iymHTt26OlsTQcN+/fvD6UUWrRogeDg4AQvP8Vt8eLF+lIiMd+3165dQ5EiRWBnZ4fp06fr26dNmwZ7e3vY29sjc+bMcHJygp+fH65evWp2/tWrV9G6dWukTp0aN27cSJDrofhx+vRpfPvtt3qQ765du5ArVy7Y2dlh27ZtAN5+n509exb58uWDwWCA0Wg0W7aArF/MtvirV6+wYsUKFC1aFEop1K5dWw/4fVfwgLYUmZ+fH8LCwhKu8BTvtCwjzZo1w6NHjwAgztnG2hJl8+bNs0QxKZ4MHTpUzyQU8z4H/q7v1atX6992kZGRDBywYdoyzt999x0CAwPjPCYiIkLPAhpzWTkiSpoYOEBWS+twevnypR7RPGTIELx48QLA3w0brTMyOjpaT7mkpTcn62La0NTqNyQkBAsWLECuXLng6uoaZ/CA6XlNmjTRI+EzZszI2Wo2zrRjOSIiAh07doz1MWoaOKCUwoIFC9CiRQvOPLcBMdPederUCUajEbt378aFCxdQokSJWEED2uzG27dvI3369OjcuXPCF5ziRUREBMLDw9GjRw8opVCrVi19n+m9r9V5mzZtUK9ePf342rVrx5rJSNYpOjoaly9f1t/RxYsXjzN4wDTzgJubG5ycnJApUyZ94FF7/3Omou2ImeL8xo0bMBgMyJo1K+7fv6/XvZYBrFy5cmjRogU6dOiAFClSvDMomBJGXJ3AMQeUfvzxR73tPXfuXLN9p0+fRrly5aCUQtGiRTF69Gi8ePECQUFBCAgIQM+ePaGUQpYsWcyyFpBlnTlzBqlSpYKDgwPWr18fa/+0adNizSSfOHEinJycYGdnh927d2PNmjVIlSoV3N3d0atXL1y5csXsZ1y/fh0PHjz41JdCH8n03n/8+LHZrPFHjx7p7+Fhw4bBYDDoWSdM388tWrTQU5crpTB27NgEKj19jJjPe+1ZrwV8vXr1CitXrtQDQnr06KEHc8YVPLBixQpUqVKF3+RW7n2DvVobbseOHciSJQs8PDwwatQoPH36FMDb3xHTe7506dLw9vbWMxOQbdHq+9GjR3pw4Ndff61n7dV+V7RnwvHjx/Ulqch6ve8eDwsLQ3R0NGbOnAlPT09kzpwZv//+ux7IazpRKyoqCp999hkyZMjANjsRAWDgAFlYzBfc69ev9UaKJiwsDJMmTUKKFCmQNm1a/Pzzz2Zr6Gr+97//QSmFpk2b4tWrV5+03PTvmdb1pUuXMGfOHH1ttNDQUMyfP98seCBmCiWtw9nPzw+VK1dG586d4eXlxVksNuTy5cvYv38/tm3bZpaq1rSDulevXlBKoU+fPrHu4yZNmiBNmjQIDg6O9Zwg66Pd56brm86bNw9KKeTOnVsPGujevbu+X7vPgb+DhJYtW5aQxab/IDw8HC9evNBnqmguX76sDxa3aNHCbJ/pDKVcuXKhfv36uHr1Kr788ku4uLhg8eLFAJga0ZrEHGww7VS+evUqGjZs+I/BA6lSpYJSCp999hk2b96MS5cu6UuTMGjA+nxIe12r3xs3bsDe3h5Zs2bFkydPAPz9bI+OjkaaNGnQtm1bPHnyRA8GZp1bVmhoKG7fvg3AvE22bds27Ny5E5GRkRg7diyUUjAYDLGCB06cOIGvvvpKXws3bdq0yJQpE3x8fPSgAS5RYF22b98OpRTatGljtl1LTTt06FCUKlVKv/eXLVsGb29v2NnZ6VlCwsLC0K1bNyilkDx5cvzvf//D9evXE/ZC6KOYPtP37NmD6tWrw9XVFSNGjDA77uXLl8iSJQvs7e1x+PBh/Vztmd6rVy9UrVoVkyZNgqenJ1PWWzHTOj948CB69+6NokWLokyZMqhfvz7279+P8PBwREVFYdWqVciZMyccHBzg6+v73uABZpGxTnHV1fXr13HkyBGsWbMGf/zxB8LDw/W23LNnzzBo0CC4ubnB29sbfn5+sfrjBg8eDKUUatasqbffyLLiqucPabO/efMGv/32G1KkSAEfHx8MHz5cv89N24EtW7aEUgq//vprrH1kWXHV/ZUrV+LsbwXeBoto9Zk7d2789ttvsfpsBg0aBKUU6tevj5cvX376iyAiq8fAAbIY0xfchg0b0L59e+TIkQMlS5ZEhw4dsHnzZr1B+vDhQ/Tp0wfu7u7w8PBArVq1sH//fpw5cwanTp1C48aN9RS32swGDjJYD9O6+OOPP5A/f34opVCnTh29vsLCwsyCBwYMGKB3Ypo2dEuWLIl69erh1q1bnKVmQ9avX4+0adPqM1LSpUuHAQMG6Pu1j5D9+/cjXbp0yJw5M+bNm4eHDx8iOjoaAwcO1H9nuDyB9du2bRvy5cun15n2IRocHIzSpUvrgw9dunTRz9FmnQPA999/D6UUvvjiC33GA1m3LVu2oGXLlsiYMSPSpUuHxo0bY+fOnXoqvN27d8PNzQ1KKTRs2BCXL182Cxro06eP3jERHR2tL1NUuHBhs4ASsizTDqPt27ejXr16+Pzzz80ChD4keGDXrl3w9vaGUgrff/99nD+frMM/tde3bNmCgIAAs3O0NJeDBg3Cs2fP9O1akO/MmTPj/PmU8CIjIzF16lQUL17cLFBvxYoVUEqhTJkyesD2Tz/99M7ggevXr2PevHkoWbIkvL29YTAY8Pnnn8PX1xe3bt1KyEuiD/DHH39AKYXSpUvrncsrVqyAs7OzPmv86NGjiIqKQkhICBo3bgx7e3v8/vvvAP7+Nrtz544eCObm5oZ27dpxlpoNWb16NVxcXKCUQseOHbFx48ZYGaEKFiwIOzs77N27F4B5wGfJkiVRtGhRANCD/8i6mda5p6enHtjr7OyMb775Rg/+WbVqFXLnzv1BwQNkHaZOnao/owHzoMyNGzcic+bMcHR01JeFK1euHEaOHKnX7f3799GnTx/9dyJ79uwYPnw4fv75Z30pA29v71jZZchyTOsiOjr6X/WxP3jwAL1794a7uzuSJUuGOnXq4MiRI7hz5w5evHiB7t27QymFQoUKsT/GSrzvHt+wYQPSpUun3+Mx+1uBtxk9GzRoAHt7e3h6eqJEiRKYOXMmZs+ejebNm0MpBR8fn1jLTxFR0sXAAbK4+fPn641XOzs7fWAxTZo0aNmypZ629q+//sKIESOQJ08e/XillL5OatGiRZni1sqtXr1ar9+vv/4aV69eNUtxaxo84OLigs6dO+P8+fP6fm0w8aeffrJE8ekjbdmyRb9fK1eujBIlSuj3batWrcw6mp4/f46vv/4arq6ucHV1RcaMGVGgQAE2Ym3IypUr9fu8f//+uHbtmtl9fvv2bWTNmlXvsL5y5Yo+6BQWFqYPLGXMmJEZRWzE3LlzYTAYoJRC/vz5UahQIbi4uCBr1qwYNmyY3tmwdetWuLu7QymFPHnyoG7duujXrx8qVqyob9OCyW7cuIFUqVIhW7ZsjHi3EqadUYsWLdJnFzdv3hw7duww23/t2jWz4IEjR47o+7TggK1bt6JgwYK8z23E+9rrrVq10oM5o6KisHTpUmTIkAHJkydHnTp1MHHiRNSqVUvvgNQyEZB1mDBhgr4Uwd69e/U1bVOnTo05c+aYHfu+4AHg7Xv89u3bOHPmDMLCwhj4ZaWePn2KL7/8EkajEUOGDMHEiRP1zAFLliwxO1ZLVVypUiW8fPnSbBmqhw8fIm3atKhbty7y5csHb29vfRCKrNuuXbtgb28PDw8PTJs27Z3H9e3bF0opVK1a1SyjgPZd3q9fPw4i24gdO3bAaDTC2dkZ48aNw4MHD/D06VNMmzYN2bJlg5OTExo2bIinT5/izZs3WLly5XuDB8h6PHz4UG+jrVq1ymzf5s2b9X01a9ZEw4YNkSpVKtjb20MphRo1augTdh49eoTp06ejVKlSsfpcixcvjkuXLlni8igOQ4YMMcvOaBo48G/62IcOHYqcOXPqbTsvLy+kSJECSinkypVLP5bB3Zb1vnt869at+r5KlSrh888/N+tv1SZyAMC9e/fw7bff6pN8tD4c7RuNGcKIyBQDB8ii9u/fDxcXF6RMmRLTp0/HiRMn4O/vj9atWyNz5sxQSqFs2bJ6mqzAwEBcuHABPXr0QOXKlZE9e3Y0atQIY8eO1QcmGDRgnbSUmB4eHpg9e3as/VojNzQ0FAsWLNAHi9OnT49OnTqhcuXKUEohU6ZMnLlkI6KiovDmzRt8+eWXcHd3x7x58wC8TY3m7++P5MmTQymFJk2amDVmHz16hNGjR6NYsWL6B442wEzW7Z/uc+2D89y5c8ibN68eEFKyZEnUqFFD/2jNli0bP1psxMqVK6GUgpeXF6ZPnw7gbbpLbdA4bdq0+Prrr/VZx2fOnEHlypX1FNamnVFax4R2nKurK0qWLMn3upVZsGCBXuemM8cB887kmJkH4goe0AYVTYOLyPp8aHtdu4efP3+OCRMmxAr2zZs3LzsgrdSAAQOglNIHElKlSmU2gGxaX+8KHjCdicxZqdZv9erVKFSokN65nCJFCixfvlzfr9X5pk2boJRCtWrV9EwDWl2/evUKqVOnxuDBg7FhwwYuVWAjHj9+jJo1a0IpZRY0YHqfa3+/evUqqlatqrfPfX19Ub16df27nBkmbMOjR4/0/pSpU6ea7Tt16pT+vu7Tp4++PSQkRA8ecHV1Rdu2bWOltibr4e/vr7e3VqxYAQB48eIFKlSoADc3N8yfP18/9q+//sKkSZOQO3duPfvAvXv3ALxtkwcHB2Py5MkYOXIk+vXrh7Vr17LurcyuXbti1TcA7N2791/1sQcEBODYsWNo3bo18uTJA2dnZ5QrVw5+fn56sBC/xa1DXPf469evP6i/1TQ73KtXr3D9+nUMHjwYvr6+6NixIxYuXKhP4CAi0jBwgBJUzE7CX375JVZDB3jb4bhixQp89tlnUErhq6++0tNkaiIiIvQ0S1qnFDshrdOTJ09QtmzZWOlpTQcKXr58qX+sREVFYfv27XpKNO1P7ty5GeVsQ7QPDQ8PD/Tr10/frtX7n3/+iWzZssXZmA0JCUFgYCC2bt2Ky5cvMz2aDfi393lgYCC6deumP+e1KOeePXvGWlORrNOpU6eQLVs2uLq6YvHixfp2bS1sZ2dn+Pj4wMXFBf369dPf40+ePMGFCxcwffp0TJ8+Hdu2bYu1VmbHjh31mWxRUVEcfLIS/v7+cHNzg5OTk1nbLebamZrr16/r7/LixYvj2LFjCVVU+g/io73++vVrXL58Gd9++y0GDBiA3377Tc80wA5I62Fa17Vq1YKDgwMMBgN69+4d5zGadwUP8FvM+pm+T319fWE0GmEwGGItD6Udd+3aNaRJkwY5cuTA3r17zZ73vXv3hlIK27dvT7gLoP/s6tWr8PHxQYUKFfRt77p3o6KisHfv3ljf5bly5eJ3uQ25cuUKUqZMibp165ptP3nypL6MnJ+fX6zzXr16hVWrVsHb2xupU6dmRhErt2/fPv0eXbduHQDAzc3N7J2uPcNfv36NnTt3olChQlBKoVOnTmaTOcj67d+/X69vLfPAf+ljDwgI0Cdpab8nbLNbF9N7fO3atQA+rr+ViOhDMHCALGLKlClYsWIFxowZg88//1zfbtooiYiIwLZt25ArVy44Ojpi/PjxiIqKMuusME2VSNbr5s2b8PDwQPny5c22h4SE4Pr162jSpAkKFy6MZMmSYeDAgThz5ox+zLp16zBv3jysWrWKEZA2ZO7cuVBK4ZdffkGFChVw6NAhAH9/gGj37Lsas+x4tj0fcp8XKlQIyZIlQ//+/fVlJ0JDQ3H27FmcP38eERERZjMWybppHRMTJkzQt/34449Q6u1axwcOHMDixYuRPHlypEiRAn379v2gFOWDBg3SZ7ZpqTPJOowYMQJKKX0NbFOBgYGYNm0aBg0ahCVLluD+/fsA3i470aBBA71OT548mdDFpo/0Me31cePGITIy8p1tc3ZAWqcrV66YDQo6OTnpgw7vYho8YDqTkWzDn3/+CaUUHBwckD59etjZ2aFv3764du2a2XFBQUFo3749lFIoX748Jk2ahCNHjqBr165QSqFw4cIcTLQxixYtglIKtWrVAvDurD8xv8cWLlyIKVOmYOnSpfwutzFLly6FUgo9e/bUt50+fVpPSe/r62t2/L179/RsEsHBwVi3bh0zitgI04HFUaNGoXjx4ti8eTOA2IG+4eHhWLJkCdKlS4esWbPq2cFi3vvsb7VeMQeSf/nll//Ux86MUdbPtM5/++23f9XfqgUHxZyYwfomorgwcIASnOnaPHny5DFr1MQUHBysd0pVr149AUtJ8enMmTNQSqFixYp48+YNgLcDCcOHD0emTJn0dVS1NbhatWoVK/qVbMuePXvMOqBnzJgR65h/asySbfm393nLli3jHETmR4ttCAsLQ5s2bdCoUSN925w5c+Du7o5kyZLhxIkTAN52PDZv3hxKKWTMmBH9+vXD8+fPAZh3SgUGBuLkyZOoXbs2lFJIly4dl6uwMpGRkahQoQKUUti1a5e+/c6dO1i8eHGstPTNmzfHuXPnAPyd6tjLy4sDTDYiPtrr7JCyHQEBAfjxxx+xdu1aDB06FEopODo6YvXq1e89T6t3pZRZ5hmyfn/99Rd8fX2xatUqrF+/Hnny5IGdnR369OkTK/38lStX8MUXX+hLWdjZ2enLEXHWue3ZsmULlFKoXLmyvu1dz+jAwEBs3LgxoYpGn8jOnTuhlELLli0BvF02rmTJkrGCBkJCQgC8zR7m4eHBJSJtlOnAolIKw4cPf+exT5480ZcuMZ21TLbDtL5z587NPvYkIOY9zv5WIvoUGDhAFmE6qJglSxb8+eef7/xYvXXrFjw8PKCUwtmzZxO4pBQfgoOD9RTm3bt3x+DBg/WBxCJFimDUqFF49uwZli5digwZMkApBX9/f7OfwQ5n27N37179PjdNjxfXQIJpY7ZmzZoICgpK8PLSfxMf9znZlgcPHuD48eMA3q6hWa1aNTg7O2Pr1q0A/o54P3jwoP4s8PLyQrdu3WKlytu9ezcqVqyoz2iMOeORrMP3338Pg8GAYcOG4c6dO9i5cydq1qypr6VZv359DBgwQL/3R48eDeDts/7WrVt49uwZAGaVsRVsryctoaGh+t8HDBjwzuAB09nJ0dHRGD58OOzs7BjsZYNev36t/10LAHtX8MDNmzcxZswYVKhQARUrVkSHDh1w48aNhC4yxYNr164hefLkMBqNsdJZa7RZqvfu3YO3tzemTJmi7+N3ue25efMmkidPjowZM2LatGl6poEePXrox5i+A8qXL4/UqVMz85cNM10PvUWLFnj58iWAuO/fZcuWwWg0okmTJgldTIonpn1vbLMnDab3OPtbiehTYOAAWYxpZ6TWsRyTlrK6RIkSUErpqbPI9ly6dAn58+fX69xgMKB79+64deuW2Udq69atoZTCggULLFhaii+mjdmFCxfq2+NqzJ48eRJeXl5QSukprsm28D5PurZv3w6lFOrXr4/Q0FB9YCkqKgp37txBypQp0a1bN6RIkQI5cuSIM9J9w4YNWL58OTPOWLFr167pnQ4pU6aE0WiEUgpNmzbFzp079YCAGTNmQCmF4sWL6x2VGgYN2Ba215MW0/vzm2++iRU8YBo0MGPGDOzevRuRkZF6JhmyPaZt8qVLl743eMAUlx2xXdHR0ejfvz+MRiPq1auH06dP6/uioqLM7vNmzZpBKYXff//dEkWlePTtt9/Czs5O/96OK9MAAPj6+kIphb59+5p9v5HtMe2LMQ3+0Wj3+urVq6GUQt26dRO4hBSf2GZPetjfSkSfEgMHyKJMoyJjprfUPlKio6ORO3dupE+fni83G/fo0SOsXLkS8+bNw969e/Xtpo2acuXKwcfHh2voJSKm9/nKlSv17XE1Zs+cOcOZxjaO93nSpK2X27ZtW32b9h5/8OABDAYDxo0bh9WrV+Ovv/4C8PfvhOlAFWexWb8rV66gY8eOSJ8+PerWrYupU6fq+0yf5UoptG/f3lLFpHjE9nrS8q7ggVWrVunbR48eDaUUSpcubTZrnWzThwYPmAYL8H1t227fvo3ChQvrs5HjygL2/fffQymFChUqMKgzEbhz5w6KFy8Oo9GIHDly4MqVK7GOGTx4sJ4t7t69exYoJcU30zbcu4L227dvD6UURo4cmcClo/jGNnvSw/5WIvpUGDhAFmf6kps4cWKsj9JevXrp6y+9evXKQqWkT8U0il3rnKxfv36sGYpk20zX4DJNicn1j5MG3ueJ35EjR6CUQoECBXDw4EGzfe3atYuVCpEzFW1fYGCg2axEbQYLADRu3BhKKcyaNQsAn++JAdvrSUtcwQNOTk7o1asXGjRooGcdOXPmjAVLSfHpYzMPkO26ePEicuTIAaUU8uXLh379+mH79u1YsWIFGjVqBKUU0qRJg6tXr1q6qBRPzp8/jzx58kAphYoVK2L48OE4fvw41q9fj4YNG0IphdSpU+Py5cuWLirFI9O+mKFDh2LXrl148+YNgoOD9aWJMmXKhFu3blm6qBQP2GZPetjfSkSfggIAIbKw/fv3S4UKFUREpGbNmpIpUybJnz+/LF26VA4dOiTZsmWTvXv3Stq0aQWAKKUsXGL6r2LWY79+/WTcuHGSKVMm2b17t2TJksWCpaNPwfQ+X758uTRu3FhEYv8uUOLB+zzpCAkJkQ4dOsjy5culTp068tVXX0nJkiVlzJgxsmjRIilfvrysX79ePDw8LF1UikfaPR4dHS0Gg0EAyNdffy3jxo2TChUqyKpVqyRFihSWLibFE7bXkxbtvhYRGTlypEydOlUeP34sIiK5c+eWNWvWSO7cuS1ZRIpnpvftsmXLZMSIEXL79m1p1aqVDBo0SDJlymThElJ8u3btmgwcOFA2bdokkZGRZvuKFi0qixcvlly5clmodPQpXLt2Tfr16ydbt26VqKgoUUrp936ZMmVk9uzZkjNnTksXk+LZvn37pGLFiiIi4uHhIenSpZMnT57Is2fPJG/evLJq1Sq+0xMRttmTHva3ElF8Y+AAWQ3ThqyIyBdffCF3796VMmXKyNChQyVNmjQSFRUlRqPRcoWkeHX//n3Zt2+fTJ8+XQ4cOCA5cuSQdevWSZ48eSxdNPpETBuzK1eulIYNG1q4RPSp8T5POu7cuSMdO3YUf39/iY6OFkdHRwkLC5Ns2bLJnj17JH369GYDUZR4vHz5Up4/fy69evWSTZs2SdasWWXv3r2SLl061nkiw/Z60mJ6//r7+8uNGzdE5G29p02b1pJFo0/EtIN5+fLl0rt3b4mMjJTz58+Lt7e3hUtHn0JwcLAcOXJEFi5cKKGhoeLh4SHly5eX6tWri4+Pj6WLR59AcHCwHD58WJYtWyaRkZHi4eEh1apVk9KlS0vKlCktXTz6RPbu3SuVKlUSEZEqVaqIr6+vKKWkRIkSvNcToZht9po1a8q9e/fYZk/E2N9KRPGJgQNkVUwbslOnTpVu3bpJRESE2Nvbs0GTCF25ckXKli0r0dHRUr16dfnxxx8lc+bMli4WfWKmjdm1a9dK3bp1LVwi+pR4nyctDx8+lMWLF8vcuXMlQ4YMkilTJhk5cqT4+PjwPZ5IvXnzRnr27Cnbtm2TR48eSZUqVWTBggWSNm1a1nkixfZ60sKZSkmPaZ2vXr1aChcuLNmyZbNwqYiI6L/y9/eXypUri4jItm3bpHr16hYuEX1Kpm32cePGSa9evSQyMlLs7OzYZk+k2N9KRPGFgQNkdUwbNqtWrZIGDRqI9mvKTqvE59q1a3L37l0pVqwYU1gnIbt375aqVavK2bNnJX/+/JYuDn1ivM+TntevX4urqysHE5OI48ePy7Bhw6R69erSpk0b8fLyYp0ncmyvEyVuDBhJekzrnPWfNLDOk6Zdu3ZJtWrV2BeTRGiZB9zd3eXx48dib2/PbHCJHPtbiSg+MHCArBLX5iFK/N68eSMuLi6WLgYRfQLa+5rv7aQjJCREHBwcxGg0cnmCJILtdSIiIiLbw76YpOXYsWOSKlUqyZIli6WLQgmE9zgR/VcMHCCrxbV5iIiIiIisF9vrRERERETWT1umgIiI6J8wcICsGtfmISIiIiKyXmyvExERERERERElDswhSlatXLlysnPnThERyZYtm4VLQ0REREREptheJyIiIiIiIiJKHJhx4P89efJEjh07JseOHZPjx4/L8ePH5fnz5yIi0rZtW5k/f75lC5jEcW0eIiIiIiLrxfY6EREREREREZFt48I2/8/b29vSRaD3YCckEREREZH1YnudiIiIiIiIiMi2camCOGTIkEGqV69u6WIQERERERERERERERERERF9csw48P+GDBkixYsXl+LFi4u3t7fcvn1bsmTJYuliERERERERERERERERERERfVIMHPh/w4cPt3QRiIiIiIiIiIiIiIiIiIiIEhyXKiAiIiIiIiIiIiIiIiIiIkrCGDhARERERERERERERERERESUhDFwgIiIiIiIiIiIiIiIiIiIKAlj4AAREREREREREREREREREVESZmfpAiQVFStWtHQRKIE4OTnJtm3bRESkZs2aEhoaauES0afGOk9aWN9JD+s86WGdJz2s86SF9Z30sM6THtM6p6SH93nSwGd70sLnetLGezxp8ff3t3QRiGT69OmyfPny9x4zYcIEKViwYAKVKOEwcICIiIiIiIiIiIiIiIisDoNGiOhTCAkJkfnz58vp06dj7bt69eo/np86depPUCrLY+AAEREREREREREREdkkDioSERHRv7Vu3TpZsWLFR5/v4OAQj6WxHgwcICIiIiIiIiIiIiKbxDTmiR+XKkjaeI8nLVyqgBJK2bJlZc6cORIVFfVR5z958kSSJ08ez6WyPAYOEBERERERERERERERERFRkpAhQwbZuXPnO/cDkPDwcAkNDZWQkBAJDQ2V0NBQ+f777+X58+cSHR2dgKVNOAwcICIiIiIiIiIiIiIiIiIiEhGllDg6Ooqjo6N4eHjo27UlCn766Sfx9PQUJycn/Y+Li4u0bNlSUqVKZali/2cMHCAiIiIiIiIiokSJ6Y2TBqYxJyIiIqL4cv78eVm+fLm8fv3aLNtASEiIBAUFiYjIvXv35N69e7HO3bNnj6xZs0aMRmNCFzteMHCAiIiIiIiIiIgSJQ4mExER2Ta+y4kooS1dulQOHTr0UedWq1bNZoMGRBg4QEREREREREREREQ2ioOKRIkbswclLf7+/pYuAtE/DvwbDAbx8fERb29vcXV1NTuvTp06n7p4nxQDB/7fgQMH5Pr16/q/nz17pv/9+vXrMn/+fLPj27Vrl0AlIyIiIiIiIiKij8HBhqSBSxUkbbzPEz/e40RE9Ck8fPhQAgMDzZYiCA0NFS8vr/eeFx0dLQ8ePJAHDx7E2nfixAlZv369zWYdYODA/5s9e7YsWLAgzn0HDx6UgwcPmm1j4AARERERERERkXXjQBMREREREcW0efNmGTt2bLz/3AYNGths0IAIAweIiIiIiIiIiIiIyEYxQIgoceM9TkSfwuPHj//T+WPGjJE0adKIl5eXODs7i1IqnkpmWQwc+H/z58+PtRwBERERERERERHZLqYwTxqYxjxp432e+PEeJyKi+JY/f/7/dL6Hh4ekS5cunkpjPRg4QEREREREREREiRIHmogSP97nRERE9G/dvXv3P53v6ekZPwWxMgwcICIiIiIiIiIiIiKbxIwDiR8zDiRtvMeTFn9/f0sXgZKIGjVqyLRp0yQ6Ovqjzk+szyUGDhARERERERERERGRTeKAMlHixnuciD6F9evXf1DQgLe3t6RMmVK8vLzE09NTvLy8JEeOHJIhQ4YEKGXCY+AAERERERERERERERERERElCYGBgR903OPHj+Xx48cf/HO7d+8uTZo0+chSWR4DB4iIiIiIiIiIKFFieuOkgWnMkzbe54kf7/Gkjfd40sKlCiihVKtWTVavXh3vP3fBggXSsGFDMRqN8f6zEwIDB4iIiIiIiIiIKFHiQBNR4sf7PGnhIHLSYBoswnuciD6FrFmzyg8//CCBgYESGhpq9mfVqlX/eH7y5MnFxcVFDAaDGI1G/U/fvn1tNmhAhIEDRERERERERERERERkAziITERE8WHlypUya9asjz5/zpw54unpGX8FshIGSxeAiIiIiIiIiIiIiIiIiIjIFgCwdBE+CWYcICIiIiIiIiIiIiKbxNT1iZ9p2npKeniPJy3+/v6WLgIlEY0bN5bMmTPHuVSB6Z+QkBCzf9+/f19ERBo0aCDZs2cXpZT+M+3t7eV///uf5MqVy1KX9Z8xcICIiIiIiIiIiBIlDjYkDRxUJCIiIqJ/w87OTpRSEhQUJFFRURIdHS1KKXF0dBQ7OztxdnaWiIgICQsLMwsi0AIHRESuX78e6+f26dNHNm7cKEajMSEvJ94wcICIiIiIiIiIiBIlDiYTEREREVFM69evlwkTJsT7z02ZMqXNBg2IMHCAiIiIiIiIiIgSKWYcSBqYcYCIiIiI/o1s2bJ9kp/79OlTiYqKstngAQYOEBERERERERFRosTBZKLEj/c5ERER/Vv58uWTb775Rq5evSoiIgDk9evX8uLFCzlx4sQ/nm8wGMRoNMb6M3ToUJsNGhBh4AARERERERERERERERFZIQYHEdGnsHr1apk6dep7j8mSJYuULVtWvLy8xNPTU/+vj4+PODk5JVBJExYDB4iIiIiIiIiIiIjIJnFJksSPy5EQEVF8K1iw4D8ec+vWLbl161ac+zp27CgZM2Y0CypwdXUVpVR8FzVBMXCAiIiIiIiIiIgSJQ4oJg0cVCQiIiKifyNr1qzi6+sr169fFxcXFxERCQkJkWfPnsnTp0/lyZMnEhIS8s7z58yZE+f2smXLyogRI2w2gICBA0RERERERERElChxMJko8eN9TkRERP/WmjVrZPr06fH+c69duybR0dFiNBrj/WcnBAYOEBERERERERERERERkdVh9qCkxd/f39JFoCTi888//0+BA3369JHUqVOLvb2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            "text/plain": [
              "<Figure size 2500x1000 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "sorted = df.sort_values('age')\n",
        "msno.matrix(sorted)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aYA0wmN2Ng8c"
      },
      "source": [
        "With this, it is clear that, here, there is no relation between the missingness in `age` and `cabin` columns. To reinforce this conclusion further, we can also draw a (correlation) Heatmap between the different variables in the dataset.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "28SJCihBN2Ay"
      },
      "source": [
        "**Finding reason for missing data using a Heatmap**\n",
        "\n",
        "The heatmap function shows that there are no strong correlations between missing values of different features: this is good; low correlations further suggest that the data are MAR."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "Kg4zqSBM66sL",
        "outputId": "6d1cb324-a2f8-4007-c819-1f8e2af5ac44"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "execution_count": 14,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 2000x1200 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "msno.heatmap(df)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RvAxRte9Ogbg"
      },
      "source": [
        "\n",
        "**Finding reason for missing data using Dendrogram**\n",
        "\n",
        "A dendogram is a tree diagram of missingness. It groups highly correlated variables together.\n",
        "\n",
        "Let's read the dendrogram below from a top-down perspective:\n",
        "\n",
        "  - Cluster leaves which are linked together at a distance of zero fully predict one another's presence — one variable might always be empty when another is filled, or they might always both be filled or both empty, and so on (`missingno` documentation)\n",
        "  - the missingness of `embarked` tends to be more similar to `age` than to `cabin` and so on. However, in this particluar case, the correlation is high since `embarked` column has a very few missing values.\n",
        "\n",
        "This dataset doesn't have that many missing values, but, if you use the same methodology on datasets that a lot more of missing values, some interesting pattern will definitely emerge.\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 856
        },
        "id": "EYJjXxDN7P6J",
        "outputId": "cb805098-5ee9-483a-f389-88ddbddb0b82"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "execution_count": 15,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 2500x1000 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "msno.dendrogram(df)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nKME2WroP0fy"
      },
      "source": [
        "**Handling missing data**\n",
        "\n",
        "![missing_data_handling.webp](data:image/webp;base64,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)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qK3c79KwQSaR"
      },
      "source": [
        "![missing_data_handling2.png](data:image/png;base64,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)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bG31CsVVQlGp"
      },
      "source": [
        "**Deletions**\n",
        "\n",
        "Deletion means to delete the missing values from a dataset. This is however not recommended as it might result in loss of information from the dataset. We should only delete the missing values from a dataset if their proportion is very small and it is also only recommended in the case of random variables that are MCAR.\n",
        "\n",
        "Deletions are further of three types:\n",
        "\n",
        "*Pairwise Deletion*\n",
        "\n",
        "Parwise Deletion is used when values are missing completely at random i.e MCAR. During Pairwise deletion, only the missing values are deleted. All operations in `pandas` such as `mean`, `sum` etc intrinsically skip missing values."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3rKnRyW77wmR",
        "outputId": "8e8af2bc-7e4c-4e5f-aa36-78ab79b2e3af"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "pclass          1\n",
              "survived        1\n",
              "name            1\n",
              "sex             1\n",
              "age           264\n",
              "sibsp           1\n",
              "parch           1\n",
              "ticket          1\n",
              "fare            2\n",
              "cabin        1015\n",
              "embarked        3\n",
              "boat          824\n",
              "body         1189\n",
              "home.dest     565\n",
              "dtype: int64"
            ]
          },
          "execution_count": 16,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.isnull().sum()\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "r3f0nZBo-LeO",
        "outputId": "330cca3b-938b-4434-8780-9c718e76d6a2"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "29.8811345124283"
            ]
          },
          "execution_count": 17,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_1 = df.copy()\n",
        "df_1['age'].mean() #pandas skips the missing values and calculates mean of the remaining values."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TTWt6snyRbdj"
      },
      "source": [
        "\n",
        "*Listwise Deletion/ Dropping rows*\n",
        "\n",
        "During Listwise deletion, complete rows (which contain the missing values) are deleted: it is also called Complete Case deletion as a result. Like Pairwise deletion, listwise deletions are also only used for MCAR values.\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mVgvYBzK-ZET",
        "outputId": "9051011b-3667-4c4a-aad5-f3b7cbc79874"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "execution_count": 18,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "#Drop rows which contains any NaN or missing value for Age column\n",
        "df_1.dropna(subset=['age'],how='any',inplace=True)\n",
        "df_1['age'].isnull().sum()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NYmtYb50RybE"
      },
      "source": [
        "  The `age` column doesn't have any missing values anymore following listwise deletion.\n",
        "  A major disadvantage of Listwise deletion is that a major chunk of data and therefore a lot of information is lost. For this reason, it is advisable to use it only when the number of missing values is very small. And again, it is only really advisable to use deletion when the missingness mechanism is MCAR as you would otherwise also exercise undue influence on the relationships between the variables in your data through deletion!"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5fiPUjGTSmP2"
      },
      "source": [
        "\n",
        "*Dropping complete columns*\n",
        "\n",
        "If a column contains a lot of missing values, say more than 80%, and the feature is not significant, you might want to delete that feature. Again, however, it is not a good methodology to delete data.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5ec7jq42SzM_"
      },
      "source": [
        "**Imputations Techniques for non Time Series Problems**\n",
        "\n",
        "Imputation simply means replacing missing data with substitute values. There are many ways in which the missing values can be imputed depending upon the nature of the problem and data. Depending on the nature of the problem, imputation techniques can be broadly they can be classified as follows:\n",
        "\n",
        "*Basic Imputation Techniques*\n",
        "\n",
        "- Imputation with a constant value\n",
        "- Imputation using statistics (mean, median or most frequent) of  each column in which the missing values are located\n",
        "\n",
        "To demonstrate basic imputation, we will use the `The SimpleImputer` class from `sklearn`."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tgkgxMu5-nA9",
        "outputId": "e0651315-1057-4b75-b553-82b3d297709f"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "pclass       0\n",
              "survived     0\n",
              "name         0\n",
              "sex          0\n",
              "age          0\n",
              "sibsp        0\n",
              "parch        0\n",
              "ticket       0\n",
              "fare         0\n",
              "cabin        0\n",
              "embarked     0\n",
              "boat         0\n",
              "body         0\n",
              "home.dest    0\n",
              "dtype: int64"
            ]
          },
          "execution_count": 19,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "from sklearn.impute import SimpleImputer\n",
        "df_constant = df.copy()\n",
        "#setting strategy to 'constant'\n",
        "mean_imputer = SimpleImputer(strategy='constant') # imputing using constant value\n",
        "df_constant.iloc[:,:] = mean_imputer.fit_transform(df_constant)\n",
        "df_constant.isnull().sum()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fJpARNFKTrMh"
      },
      "source": [
        "Showing first columns of new dataset after imputation"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "uWJntTBQ_kSf",
        "outputId": "e28fceaa-6b57-4c29-934c-cba51702d8bc"
      },
      "outputs": [
        {
          "data": {
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              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
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              "  }\n",
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              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "    (() => {\n",
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              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "  pclass survived                                             name     sex  \\\n",
              "0    1.0      1.0                    Allen, Miss. Elisabeth Walton  female   \n",
              "1    1.0      1.0                   Allison, Master. Hudson Trevor    male   \n",
              "2    1.0      0.0                     Allison, Miss. Helen Loraine  female   \n",
              "3    1.0      0.0             Allison, Mr. Hudson Joshua Creighton    male   \n",
              "4    1.0      0.0  Allison, Mrs. Hudson J C (Bessie Waldo Daniels)  female   \n",
              "5    1.0      1.0                              Anderson, Mr. Harry    male   \n",
              "6    1.0      1.0                Andrews, Miss. Kornelia Theodosia  female   \n",
              "7    1.0      0.0                           Andrews, Mr. Thomas Jr    male   \n",
              "8    1.0      1.0    Appleton, Mrs. Edward Dale (Charlotte Lamson)  female   \n",
              "9    1.0      0.0                          Artagaveytia, Mr. Ramon    male   \n",
              "\n",
              "      age sibsp parch    ticket      fare          cabin embarked  \\\n",
              "0    29.0   0.0   0.0     24160  211.3375             B5        S   \n",
              "1  0.9167   1.0   2.0    113781    151.55        C22 C26        S   \n",
              "2     2.0   1.0   2.0    113781    151.55        C22 C26        S   \n",
              "3    30.0   1.0   2.0    113781    151.55        C22 C26        S   \n",
              "4    25.0   1.0   2.0    113781    151.55        C22 C26        S   \n",
              "5    48.0   0.0   0.0     19952     26.55            E12        S   \n",
              "6    63.0   1.0   0.0     13502   77.9583             D7        S   \n",
              "7    39.0   0.0   0.0    112050       0.0            A36        S   \n",
              "8    53.0   2.0   0.0     11769   51.4792           C101        S   \n",
              "9    71.0   0.0   0.0  PC 17609   49.5042  missing_value        C   \n",
              "\n",
              "            boat           body                        home.dest  \n",
              "0              2  missing_value                     St Louis, MO  \n",
              "1             11  missing_value  Montreal, PQ / Chesterville, ON  \n",
              "2  missing_value  missing_value  Montreal, PQ / Chesterville, ON  \n",
              "3  missing_value          135.0  Montreal, PQ / Chesterville, ON  \n",
              "4  missing_value  missing_value  Montreal, PQ / Chesterville, ON  \n",
              "5              3  missing_value                     New York, NY  \n",
              "6             10  missing_value                       Hudson, NY  \n",
              "7  missing_value  missing_value                      Belfast, NI  \n",
              "8              D  missing_value              Bayside, Queens, NY  \n",
              "9  missing_value           22.0              Montevideo, Uruguay  "
            ]
          },
          "execution_count": 20,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_constant.head(10)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "I41AIT96_pxI",
        "outputId": "ca96c7bd-034e-4233-bc1a-93bf8610afed"
      },
      "outputs": [
        {
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            "text/plain": [
              "      pclass  survived                                               name  \\\n",
              "15       1.0       0.0                                Baumann, Mr. John D   \n",
              "37       1.0       1.0      Bradley, Mr. George (\"George Arthur Brayton\")   \n",
              "40       1.0       0.0                          Brewe, Dr. Arthur Jackson   \n",
              "46       1.0       0.0                              Cairns, Mr. Alexander   \n",
              "59       1.0       1.0  Cassebeer, Mrs. Henry Arthur Jr (Eleanor Genev...   \n",
              "...      ...       ...                                                ...   \n",
              "1297     3.0       0.0                             Wiseman, Mr. Phillippe   \n",
              "1302     3.0       0.0                                  Yousif, Mr. Wazli   \n",
              "1303     3.0       0.0                              Yousseff, Mr. Gerious   \n",
              "1305     3.0       0.0                              Zabour, Miss. Thamine   \n",
              "1309     NaN       NaN                                                NaN   \n",
              "\n",
              "         sex  age  sibsp  parch      ticket     fare cabin embarked boat  \\\n",
              "15      male  NaN    0.0    0.0    PC 17318  25.9250   NaN        S  NaN   \n",
              "37      male  NaN    0.0    0.0      111427  26.5500   NaN        S    9   \n",
              "40      male  NaN    0.0    0.0      112379  39.6000   NaN        C  NaN   \n",
              "46      male  NaN    0.0    0.0      113798  31.0000   NaN        S  NaN   \n",
              "59    female  NaN    0.0    0.0       17770  27.7208   NaN        C    5   \n",
              "...      ...  ...    ...    ...         ...      ...   ...      ...  ...   \n",
              "1297    male  NaN    0.0    0.0  A/4. 34244   7.2500   NaN        S  NaN   \n",
              "1302    male  NaN    0.0    0.0        2647   7.2250   NaN        C  NaN   \n",
              "1303    male  NaN    0.0    0.0        2627  14.4583   NaN        C  NaN   \n",
              "1305  female  NaN    1.0    0.0        2665  14.4542   NaN        C  NaN   \n",
              "1309     NaN  NaN    NaN    NaN         NaN      NaN   NaN      NaN  NaN   \n",
              "\n",
              "      body         home.dest  \n",
              "15     NaN      New York, NY  \n",
              "37     NaN   Los Angeles, CA  \n",
              "40     NaN  Philadelphia, PA  \n",
              "46     NaN               NaN  \n",
              "59     NaN      New York, NY  \n",
              "...    ...               ...  \n",
              "1297   NaN               NaN  \n",
              "1302   NaN               NaN  \n",
              "1303   NaN               NaN  \n",
              "1305   NaN               NaN  \n",
              "1309   NaN               NaN  \n",
              "\n",
              "[264 rows x 14 columns]"
            ]
          },
          "execution_count": 21,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df[df[\"age\"].isnull()] #rows where was NULL in original dataset"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "V9S3kgmgT6w3"
      },
      "source": [
        "Showing an example of row where `age` value was imputed"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Dataframe row 15 prior to imputation: the value of `age` is `NaN` here"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "pclass                       1.0\n",
              "survived                     0.0\n",
              "name         Baumann, Mr. John D\n",
              "sex                         male\n",
              "age                          NaN\n",
              "sibsp                        0.0\n",
              "parch                        0.0\n",
              "ticket                  PC 17318\n",
              "fare                      25.925\n",
              "cabin                        NaN\n",
              "embarked                       S\n",
              "boat                         NaN\n",
              "body                         NaN\n",
              "home.dest           New York, NY\n",
              "Name: 15, dtype: object"
            ]
          },
          "execution_count": 18,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.iloc[15]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "After imputation with a constant value, the value of `age` has been replaced by `missing_value` instead"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1cLjJ49r_28G",
        "outputId": "8ef31185-5835-43dc-f462-0bfb2fd8f05f"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "pclass                       1.0\n",
              "survived                     0.0\n",
              "name         Baumann, Mr. John D\n",
              "sex                         male\n",
              "age                missing_value\n",
              "sibsp                        0.0\n",
              "parch                        0.0\n",
              "ticket                  PC 17318\n",
              "fare                      25.925\n",
              "cabin              missing_value\n",
              "embarked                       S\n",
              "boat               missing_value\n",
              "body               missing_value\n",
              "home.dest           New York, NY\n",
              "Name: 15, dtype: object"
            ]
          },
          "execution_count": 22,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_constant.iloc[15]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cSib2135UDum"
      },
      "source": [
        "Simple imputation with most frequent value in column `age` (instead of constant)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "id": "uBAV5Q1F_8uF"
      },
      "outputs": [],
      "source": [
        "\n",
        "df_most_frequent = df.copy() #creating a copy of the original dataframe `df`\n",
        "#setting strategy to 'most_frequent' to impute by the most_frequent\n",
        "mean_imputer = SimpleImputer(strategy='most_frequent')# strategy can also be mean or median\n",
        "df_most_frequent.iloc[:,:] = mean_imputer.fit_transform(df_most_frequent)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "F1E8UvYdUPdk"
      },
      "source": [
        "Showing same example of row after imputation"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0SJgYPMqATwY",
        "outputId": "3edb4581-062b-4eb0-cf84-272c80f858f6"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "pclass                       1.0\n",
              "survived                     0.0\n",
              "name         Baumann, Mr. John D\n",
              "sex                         male\n",
              "age                         24.0\n",
              "sibsp                        0.0\n",
              "parch                        0.0\n",
              "ticket                  PC 17318\n",
              "fare                      25.925\n",
              "cabin                C23 C25 C27\n",
              "embarked                       S\n",
              "boat                          13\n",
              "body                         1.0\n",
              "home.dest           New York, NY\n",
              "Name: 15, dtype: object"
            ]
          },
          "execution_count": 24,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_most_frequent.iloc[15]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JyXGgVhCUWKe"
      },
      "source": [
        "Showing that imputation does not leave NULL values in `age` column"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qIuPti-3AXUH",
        "outputId": "db7f3188-3354-46e5-dc6e-44b0e20c83aa"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "execution_count": 25,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_most_frequent['age'].isnull().sum()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "x5IOwDlmUxSe"
      },
      "source": [
        "\n",
        "**Advanced Imputation Techniques**\n",
        "\n",
        "Advanced imputation techniques uses machine learning algorithms to impute the missing values in a dataset unlike the previous techniques where we used other column values to predict the missing values. Here are two examples of such techniques:\n",
        "\n",
        "- [Nearest neighbors imputation](https://scikit-learn.org/stable/modules/impute.html#nearest-neighbors-imputation)\n",
        "- [Multivariate feature imputation](https://scikit-learn.org/stable/modules/impute.html#multivariate-feature-imputation)\n",
        "\n",
        "*K-Nearest Neighbor Imputation*\n",
        "\n",
        "The `KNNImputer` class of the `sklearn` library is an implementation of imputing missing values by using the k-Nearest Neighbors approach: each missing feature value is imputed using values from `n_neighbors` nearest neighbors that have a value for the feature. The feature of the neighbors are averaged uniformly or weighted by distance to each neighbor.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "id": "Vj80cULoA3LE"
      },
      "outputs": [],
      "source": [
        "#creating a copy of the original dataframe\n",
        "df_knn = df.copy(deep=True)\n",
        "\n",
        "# initializing a KNN imputation (2 neighbours)\n",
        "# For a full explanation of the parameters used in the imputation, check the documentation available at this link: https://scikit-learn.org/stable/modules/generated/sklearn.impute.KNNImputer.html\n",
        "knn_imputer = KNNImputer(n_neighbors=2, weights=\"uniform\")\n",
        "#KNN imputation performed for `age` column in the dataframe\n",
        "df_knn['age'] = knn_imputer.fit_transform(df_knn[['age']])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0BU56AucV9je"
      },
      "source": [
        "Showing an example of row after KNN imputation"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "utEvJLJDBAul",
        "outputId": "8b3acba4-c2b9-4df9-e318-21c786ce3aef"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "pclass                       1.0\n",
              "survived                     0.0\n",
              "name         Baumann, Mr. John D\n",
              "sex                         male\n",
              "age                    29.881135\n",
              "sibsp                        0.0\n",
              "parch                        0.0\n",
              "ticket                  PC 17318\n",
              "fare                      25.925\n",
              "cabin                        NaN\n",
              "embarked                       S\n",
              "boat                         NaN\n",
              "body                         NaN\n",
              "home.dest           New York, NY\n",
              "Name: 15, dtype: object"
            ]
          },
          "execution_count": 27,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_knn.iloc[15]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2Ol7Xe3nBRyc",
        "outputId": "eeaaa0af-5e02-4ebc-cd6f-e2a11c7850b5"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "execution_count": 28,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_knn['age'].isnull().sum()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ECSCcNZVWESi"
      },
      "source": [
        "\n",
        "*Multivariate feature imputation - Multivariate imputation by chained equations (MICE)*\n",
        "\n",
        "A strategy for imputing missing values consists in modeling each feature with missing values as a function of other features in a round-robin fashion. It performs multiple regressions over random sample of the data, then takes the average of the multiple regression values and uses that value to impute the missing value. In `sklearn`, it is implemented as follows:\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "id": "32WgTNL9BcgS"
      },
      "outputs": [],
      "source": [
        "#creating a copy of original dataframe\n",
        "df_mice = df.copy(deep=True)\n",
        "\n",
        "#initializing multiple imputatiom\n",
        "mice_imputer = IterativeImputer()\n",
        "#performing multiple imputation on the `age` column\n",
        "df_mice['age'] = mice_imputer.fit_transform(df_mice[['age']])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SzTUhrRRWfXu"
      },
      "source": [
        "Showing an example of row after multiple imputation"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "prk5muXqB8Gg",
        "outputId": "9b81192f-4cdb-42b0-c3f3-10176fc70097"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "pclass                       1.0\n",
              "survived                     0.0\n",
              "name         Baumann, Mr. John D\n",
              "sex                         male\n",
              "age                    29.881135\n",
              "sibsp                        0.0\n",
              "parch                        0.0\n",
              "ticket                  PC 17318\n",
              "fare                      25.925\n",
              "cabin                        NaN\n",
              "embarked                       S\n",
              "boat                         NaN\n",
              "body                         NaN\n",
              "home.dest           New York, NY\n",
              "Name: 15, dtype: object"
            ]
          },
          "execution_count": 22,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_mice.iloc[15]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6XQiS-xxCC6E",
        "outputId": "c810dbf4-ae7c-4292-953e-5d2e01600043"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "execution_count": 23,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_mice['age'].isnull().sum()\n",
        "\n"
      ]
    }
  ],
  "metadata": {
    "colab": {
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    },
    "kernelspec": {
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      "codemirror_mode": {
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