UpdatedDylangroup110.ipynb

{
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    {
      "cell_type": "code",
      "execution_count": 132,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 278
        },
        "id": "xKKAK-bBLsN0",
        "outputId": "de9018de-7df8-48f8-f630-8a3f88858c3a"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "import copy\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.svm import SVC\n",
        "from sklearn.metrics import classification_report, f1_score, accuracy_score\n",
        "from sklearn.model_selection import StratifiedKFold\n",
        "from sklearn.preprocessing import MinMaxScaler, OneHotEncoder\n",
        "from sklearn.compose import ColumnTransformer\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.linear_model import LinearRegression\n",
        "from scipy.stats import pearsonr"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 133,
      "metadata": {
        "id": "uTkgjOvZqEZj"
      },
      "outputs": [],
      "source": [
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 134,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "    }\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>OSF_ID</th>\n",
              "      <th>Maternal_Age</th>\n",
              "      <th>Household_Income</th>\n",
              "      <th>Maternal_Education</th>\n",
              "      <th>Edinburgh_Postnatal_Depression_Scale</th>\n",
              "      <th>PROMIS_Anxiety</th>\n",
              "      <th>Gestational_Age_At_Birth</th>\n",
              "      <th>Delivery_Date(converted to month and year)</th>\n",
              "      <th>Birth_Length</th>\n",
              "      <th>Birth_Weight</th>\n",
              "      <th>Delivery_Mode</th>\n",
              "      <th>NICU_Stay</th>\n",
              "      <th>Language</th>\n",
              "      <th>Threaten_Life</th>\n",
              "      <th>Threaten_Baby_Danger</th>\n",
              "      <th>Threaten_Baby_Harm</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>38.3</td>\n",
              "      <td>$200,000+</td>\n",
              "      <td>Masters degree</td>\n",
              "      <td>9.0</td>\n",
              "      <td>13.0</td>\n",
              "      <td>39.71</td>\n",
              "      <td>Dec2020</td>\n",
              "      <td>49.20</td>\n",
              "      <td>3431.0</td>\n",
              "      <td>Vaginally</td>\n",
              "      <td>No</td>\n",
              "      <td>English</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>27</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>34.6</td>\n",
              "      <td>$200,000+</td>\n",
              "      <td>Undergraduate degree</td>\n",
              "      <td>4.0</td>\n",
              "      <td>17.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>English</td>\n",
              "      <td>2</td>\n",
              "      <td>33</td>\n",
              "      <td>92</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>34.3</td>\n",
              "      <td>$100,000 -$124,999</td>\n",
              "      <td>Undergraduate degree</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>French</td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>28.8</td>\n",
              "      <td>$100,000 -$124,999</td>\n",
              "      <td>Masters degree</td>\n",
              "      <td>9.0</td>\n",
              "      <td>20.0</td>\n",
              "      <td>38.57</td>\n",
              "      <td>Dec2020</td>\n",
              "      <td>41.00</td>\n",
              "      <td>2534.0</td>\n",
              "      <td>Vaginally</td>\n",
              "      <td>No</td>\n",
              "      <td>French</td>\n",
              "      <td>53</td>\n",
              "      <td>67</td>\n",
              "      <td>54</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>36.5</td>\n",
              "      <td>$40,000-$69,999</td>\n",
              "      <td>Undergraduate degree</td>\n",
              "      <td>14.0</td>\n",
              "      <td>20.0</td>\n",
              "      <td>39.86</td>\n",
              "      <td>Oct2020</td>\n",
              "      <td>53.34</td>\n",
              "      <td>3714.0</td>\n",
              "      <td>Caesarean-section (c-section)</td>\n",
              "      <td>No</td>\n",
              "      <td>English</td>\n",
              "      <td>23</td>\n",
              "      <td>32</td>\n",
              "      <td>71</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   OSF_ID  Maternal_Age    Household_Income    Maternal_Education  \\\n",
              "0       1          38.3           $200,000+        Masters degree   \n",
              "1       2          34.6           $200,000+  Undergraduate degree   \n",
              "2       3          34.3  $100,000 -$124,999  Undergraduate degree   \n",
              "3       4          28.8  $100,000 -$124,999        Masters degree   \n",
              "4       5          36.5     $40,000-$69,999  Undergraduate degree   \n",
              "\n",
              "   Edinburgh_Postnatal_Depression_Scale  PROMIS_Anxiety  \\\n",
              "0                                   9.0            13.0   \n",
              "1                                   4.0            17.0   \n",
              "2                                   NaN             NaN   \n",
              "3                                   9.0            20.0   \n",
              "4                                  14.0            20.0   \n",
              "\n",
              "   Gestational_Age_At_Birth Delivery_Date(converted to month and year)  \\\n",
              "0                     39.71                                    Dec2020   \n",
              "1                       NaN                                        NaN   \n",
              "2                       NaN                                        NaN   \n",
              "3                     38.57                                    Dec2020   \n",
              "4                     39.86                                    Oct2020   \n",
              "\n",
              "   Birth_Length  Birth_Weight                  Delivery_Mode NICU_Stay  \\\n",
              "0         49.20        3431.0                      Vaginally        No   \n",
              "1           NaN           NaN                            NaN       NaN   \n",
              "2           NaN           NaN                            NaN       NaN   \n",
              "3         41.00        2534.0                      Vaginally        No   \n",
              "4         53.34        3714.0  Caesarean-section (c-section)        No   \n",
              "\n",
              "  Language Threaten_Life Threaten_Baby_Danger Threaten_Baby_Harm  \n",
              "0  English             2                    3                 27  \n",
              "1  English             2                   33                 92  \n",
              "2   French                                                        \n",
              "3   French            53                   67                 54  \n",
              "4  English            23                   32                 71  "
            ]
          },
          "execution_count": 134,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Import the dataframe\n",
        "file_path = 'Pregnancy During the COVID-19 Pandemic.csv'\n",
        "df = pd.read_csv(file_path)\n",
        "\n",
        "# Display the first few rows to understand the structure of the dataset\n",
        "df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 135,
      "metadata": {
        "id": "G1tvblwwMsO6"
      },
      "outputs": [],
      "source": [
        "#Drop any row with null value for the values we are most interested in\n",
        "df = df.dropna(subset=['PROMIS_Anxiety', 'Birth_Length', 'Birth_Weight', 'NICU_Stay', 'Edinburgh_Postnatal_Depression_Scale'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 136,
      "metadata": {
        "id": "Zh1QiWMeNKFH"
      },
      "outputs": [],
      "source": [
        "#drop language and id column\n",
        "df = df.drop('Language', axis=1)\n",
        "df.drop(columns=['OSF_ID'], inplace=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 137,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-KtJURdRNLy3",
        "outputId": "ea293705-896a-4253-b600-cdf8fedccda0"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Maternal_Age                                   3\n",
              "Household_Income                              20\n",
              "Maternal_Education                            14\n",
              "Edinburgh_Postnatal_Depression_Scale           0\n",
              "PROMIS_Anxiety                                 0\n",
              "Gestational_Age_At_Birth                       0\n",
              "Delivery_Date(converted to month and year)     0\n",
              "Birth_Length                                   0\n",
              "Birth_Weight                                   0\n",
              "Delivery_Mode                                  0\n",
              "NICU_Stay                                      0\n",
              "Threaten_Life                                  0\n",
              "Threaten_Baby_Danger                           0\n",
              "Threaten_Baby_Harm                             0\n",
              "dtype: int64"
            ]
          },
          "execution_count": 137,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Checking for missing values\n",
        "missing_values = df.isnull().sum()\n",
        "\n",
        "missing_values"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 138,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 330
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        "id": "JratzxKLNNwc",
        "outputId": "5aaad2f4-9e70-43a9-848b-a63d71c61068"
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      "outputs": [
        {
          "data": {
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>mat_age</th>\n",
              "      <th>income</th>\n",
              "      <th>mat_edu</th>\n",
              "      <th>depression</th>\n",
              "      <th>anxiety</th>\n",
              "      <th>birth_age_wk</th>\n",
              "      <th>birth_date</th>\n",
              "      <th>birth_length_cm</th>\n",
              "      <th>birth_weight_g</th>\n",
              "      <th>delivery</th>\n",
              "      <th>NICU</th>\n",
              "      <th>life_danger</th>\n",
              "      <th>baby_danger</th>\n",
              "      <th>virus_baby_harm</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>38.3</td>\n",
              "      <td>$200,000+</td>\n",
              "      <td>Masters degree</td>\n",
              "      <td>9.0</td>\n",
              "      <td>13.0</td>\n",
              "      <td>39.71</td>\n",
              "      <td>Dec2020</td>\n",
              "      <td>49.20</td>\n",
              "      <td>3431.0</td>\n",
              "      <td>Vaginally</td>\n",
              "      <td>No</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>27</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>28.8</td>\n",
              "      <td>$100,000 -$124,999</td>\n",
              "      <td>Masters degree</td>\n",
              "      <td>9.0</td>\n",
              "      <td>20.0</td>\n",
              "      <td>38.57</td>\n",
              "      <td>Dec2020</td>\n",
              "      <td>41.00</td>\n",
              "      <td>2534.0</td>\n",
              "      <td>Vaginally</td>\n",
              "      <td>No</td>\n",
              "      <td>53</td>\n",
              "      <td>67</td>\n",
              "      <td>54</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>36.5</td>\n",
              "      <td>$40,000-$69,999</td>\n",
              "      <td>Undergraduate degree</td>\n",
              "      <td>14.0</td>\n",
              "      <td>20.0</td>\n",
              "      <td>39.86</td>\n",
              "      <td>Oct2020</td>\n",
              "      <td>53.34</td>\n",
              "      <td>3714.0</td>\n",
              "      <td>Caesarean-section (c-section)</td>\n",
              "      <td>No</td>\n",
              "      <td>23</td>\n",
              "      <td>32</td>\n",
              "      <td>71</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>33.1</td>\n",
              "      <td>$100,000 -$124,999</td>\n",
              "      <td>College/trade school</td>\n",
              "      <td>1.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>40.86</td>\n",
              "      <td>Nov2020</td>\n",
              "      <td>55.88</td>\n",
              "      <td>4480.0</td>\n",
              "      <td>Vaginally</td>\n",
              "      <td>No</td>\n",
              "      <td>27</td>\n",
              "      <td>76</td>\n",
              "      <td>72</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>29.2</td>\n",
              "      <td>$70,000-$99,999</td>\n",
              "      <td>Masters degree</td>\n",
              "      <td>14.0</td>\n",
              "      <td>17.0</td>\n",
              "      <td>41.00</td>\n",
              "      <td>Oct2020</td>\n",
              "      <td>47.00</td>\n",
              "      <td>3084.0</td>\n",
              "      <td>Vaginally</td>\n",
              "      <td>No</td>\n",
              "      <td>68</td>\n",
              "      <td>69</td>\n",
              "      <td>81</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    mat_age              income               mat_edu  depression  anxiety  \\\n",
              "0      38.3           $200,000+        Masters degree         9.0     13.0   \n",
              "3      28.8  $100,000 -$124,999        Masters degree         9.0     20.0   \n",
              "4      36.5     $40,000-$69,999  Undergraduate degree        14.0     20.0   \n",
              "8      33.1  $100,000 -$124,999  College/trade school         1.0      7.0   \n",
              "13     29.2     $70,000-$99,999        Masters degree        14.0     17.0   \n",
              "\n",
              "    birth_age_wk birth_date  birth_length_cm  birth_weight_g  \\\n",
              "0          39.71    Dec2020            49.20          3431.0   \n",
              "3          38.57    Dec2020            41.00          2534.0   \n",
              "4          39.86    Oct2020            53.34          3714.0   \n",
              "8          40.86    Nov2020            55.88          4480.0   \n",
              "13         41.00    Oct2020            47.00          3084.0   \n",
              "\n",
              "                         delivery NICU life_danger baby_danger virus_baby_harm  \n",
              "0                       Vaginally   No           2           3              27  \n",
              "3                       Vaginally   No          53          67              54  \n",
              "4   Caesarean-section (c-section)   No          23          32              71  \n",
              "8                       Vaginally   No          27          76              72  \n",
              "13                      Vaginally   No          68          69              81  "
            ]
          },
          "execution_count": 138,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.rename(columns={'Maternal_Age': 'mat_age', \n",
        "                   'Household_Income': 'income', \n",
        "                   'Maternal_Education': 'mat_edu',\n",
        "                  'Edinburgh_Postnatal_Depression_Scale': 'depression', \n",
        "                  'PROMIS_Anxiety': 'anxiety', \n",
        "                  'Gestational_Age_At_Birth': 'birth_age_wk',\n",
        "                  'Delivery_Date(converted to month and year)': 'birth_date', \n",
        "                  'Birth_Length': 'birth_length_cm', \n",
        "                  'Birth_Weight': 'birth_weight_g', \n",
        "                  'Delivery_Mode': 'delivery', \n",
        "                  'NICU_Stay': 'NICU', \n",
        "                  'Threaten_Life': 'life_danger', \n",
        "                  'Threaten_Baby_Danger': 'baby_danger', \n",
        "                  'Threaten_Baby_Harm': 'virus_baby_harm'}, inplace=True)\n",
        "df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 139,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "42E8TnlMNPig",
        "outputId": "0f65d99a-e564-4b50-9127-f3cc41555727"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(           mat_age   depression      anxiety  birth_age_wk  birth_length_cm  \\\n",
              " count  5173.000000  5176.000000  5176.000000    5176.00000      5176.000000   \n",
              " mean     32.521322     9.738022    18.389104      39.33868        50.499834   \n",
              " std       4.140823     5.307232     5.950169       1.62486         4.433899   \n",
              " min      18.500000     0.000000     7.000000      24.86000        20.000000   \n",
              " 25%      29.700000     6.000000    14.000000      38.57000        49.000000   \n",
              " 50%      32.400000    10.000000    19.000000      39.57000        50.800000   \n",
              " 75%      35.300000    13.000000    23.000000      40.43000        53.310000   \n",
              " max      49.000000    28.000000    35.000000      42.86000        70.000000   \n",
              " \n",
              "        birth_weight_g  \n",
              " count     5176.000000  \n",
              " mean      3412.676005  \n",
              " std        534.564742  \n",
              " min        314.000000  \n",
              " 25%       3119.000000  \n",
              " 50%       3431.000000  \n",
              " 75%       3742.000000  \n",
              " max       5968.000000  ,\n",
              " Maternal_Age                                   3\n",
              " Household_Income                              20\n",
              " Maternal_Education                            14\n",
              " Edinburgh_Postnatal_Depression_Scale           0\n",
              " PROMIS_Anxiety                                 0\n",
              " Gestational_Age_At_Birth                       0\n",
              " Delivery_Date(converted to month and year)     0\n",
              " Birth_Length                                   0\n",
              " Birth_Weight                                   0\n",
              " Delivery_Mode                                  0\n",
              " NICU_Stay                                      0\n",
              " Threaten_Life                                  0\n",
              " Threaten_Baby_Danger                           0\n",
              " Threaten_Baby_Harm                             0\n",
              " dtype: int64)"
            ]
          },
          "execution_count": 139,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Summary statistics for numerical columns\n",
        "summary_statistics = df.describe()\n",
        "\n",
        "\n",
        "summary_statistics, missing_values"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 140,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ENIvLyKKVEby",
        "outputId": "6ced880a-46e4-4f95-86da-34d2f6762ce2"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "income\n",
              "124999.0    1031\n",
              "99999.0      922\n",
              "149999.0     764\n",
              "174999.0     667\n",
              "200000.0     600\n",
              "69999.0      555\n",
              "199999.0     385\n",
              "39999.0      178\n",
              "20000.0       54\n",
              "Name: count, dtype: int64"
            ]
          },
          "execution_count": 140,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "#this function converts the income into integer values, taking the maximum of those values with ranges\n",
        "def standardize_income(income):\n",
        "  if pd.isna(income):\n",
        "    return np.nan\n",
        "\n",
        "  if 'Less than' in income:\n",
        "    return 20000\n",
        "\n",
        "  # Handle open-ended values like '$200,000+'\n",
        "  elif '+' in income:\n",
        "    return 200000\n",
        "\n",
        "  # Check if income is a range\n",
        "  else:\n",
        "    parts = income.replace('$', '').replace(',', '').replace(' ', '').split('-')\n",
        "    #convert to int\n",
        "    int_parts = [int(x) for x in parts]\n",
        "    # Calculate maximum for ranges\n",
        "    return int_parts[1]\n",
        "\n",
        "# Assuming 'df' is your dataframe\n",
        "df['income'] = df['income'].apply(standardize_income)\n",
        "\n",
        "df.income.value_counts()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 142,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hxSfol7iajKj",
        "outputId": "daa274d5-67d3-4aca-880c-7b45c6241647"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "503"
            ]
          },
          "execution_count": 142,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "\n",
        "# Convert 'Yes'/'No' to 1/0 in NICU_Stay\n",
        "df['NICU'] = df['NICU'].map({'Yes': 1, 'No': 0})\n",
        "nicu_stay_yes_count = df['NICU'].sum()\n",
        "nicu_stay_yes_count"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 143,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 652
        },
        "id": "6rA5bESZpPct",
        "outputId": "0e5bd32d-4039-49b2-e332-13654727e1a9"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
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              "    }\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>mat_age</th>\n",
              "      <th>income</th>\n",
              "      <th>mat_edu</th>\n",
              "      <th>depression</th>\n",
              "      <th>anxiety</th>\n",
              "      <th>birth_age_wk</th>\n",
              "      <th>birth_date</th>\n",
              "      <th>birth_length_cm</th>\n",
              "      <th>birth_weight_g</th>\n",
              "      <th>delivery</th>\n",
              "      <th>NICU</th>\n",
              "      <th>life_danger</th>\n",
              "      <th>baby_danger</th>\n",
              "      <th>virus_baby_harm</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>8818</th>\n",
              "      <td>30.8</td>\n",
              "      <td>149999.0</td>\n",
              "      <td>College/trade school</td>\n",
              "      <td>16.0</td>\n",
              "      <td>25.0</td>\n",
              "      <td>37.14</td>\n",
              "      <td>2020-04-01</td>\n",
              "      <td>50.00</td>\n",
              "      <td>2722.0</td>\n",
              "      <td>Vaginally</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>47</td>\n",
              "      <td>98</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1070</th>\n",
              "      <td>36.7</td>\n",
              "      <td>149999.0</td>\n",
              "      <td>Undergraduate degree</td>\n",
              "      <td>8.0</td>\n",
              "      <td>14.0</td>\n",
              "      <td>38.29</td>\n",
              "      <td>2020-04-01</td>\n",
              "      <td>51.00</td>\n",
              "      <td>3500.0</td>\n",
              "      <td>Vaginally</td>\n",
              "      <td>0</td>\n",
              "      <td>22</td>\n",
              "      <td>16</td>\n",
              "      <td>29</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4968</th>\n",
              "      <td>30.9</td>\n",
              "      <td>174999.0</td>\n",
              "      <td>Undergraduate degree</td>\n",
              "      <td>7.0</td>\n",
              "      <td>15.0</td>\n",
              "      <td>31.29</td>\n",
              "      <td>2020-04-01</td>\n",
              "      <td>54.61</td>\n",
              "      <td>3345.0</td>\n",
              "      <td>Caesarean-section (c-section)</td>\n",
              "      <td>0</td>\n",
              "      <td>60</td>\n",
              "      <td>67</td>\n",
              "      <td>87</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9832</th>\n",
              "      <td>38.6</td>\n",
              "      <td>200000.0</td>\n",
              "      <td>Undergraduate degree</td>\n",
              "      <td>11.0</td>\n",
              "      <td>28.0</td>\n",
              "      <td>36.14</td>\n",
              "      <td>2020-04-01</td>\n",
              "      <td>51.00</td>\n",
              "      <td>3374.0</td>\n",
              "      <td>Vaginally</td>\n",
              "      <td>0</td>\n",
              "      <td>85</td>\n",
              "      <td>94</td>\n",
              "      <td>95</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3273</th>\n",
              "      <td>30.7</td>\n",
              "      <td>124999.0</td>\n",
              "      <td>Masters degree</td>\n",
              "      <td>16.0</td>\n",
              "      <td>27.0</td>\n",
              "      <td>37.29</td>\n",
              "      <td>2020-04-01</td>\n",
              "      <td>48.26</td>\n",
              "      <td>2977.0</td>\n",
              "      <td>Vaginally</td>\n",
              "      <td>0</td>\n",
              "      <td>47</td>\n",
              "      <td>55</td>\n",
              "      <td>98</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9182</th>\n",
              "      <td>27.8</td>\n",
              "      <td>69999.0</td>\n",
              "      <td>College/trade school</td>\n",
              "      <td>9.0</td>\n",
              "      <td>20.0</td>\n",
              "      <td>40.71</td>\n",
              "      <td>2021-09-01</td>\n",
              "      <td>45.72</td>\n",
              "      <td>3345.0</td>\n",
              "      <td>Vaginally</td>\n",
              "      <td>0</td>\n",
              "      <td>50</td>\n",
              "      <td>70</td>\n",
              "      <td>65</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>160</th>\n",
              "      <td>38.6</td>\n",
              "      <td>200000.0</td>\n",
              "      <td>Masters degree</td>\n",
              "      <td>1.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>38.86</td>\n",
              "      <td>2022-09-01</td>\n",
              "      <td>51.00</td>\n",
              "      <td>3091.0</td>\n",
              "      <td>Caesarean-section (c-section)</td>\n",
              "      <td>0</td>\n",
              "      <td>13</td>\n",
              "      <td>20</td>\n",
              "      <td>50</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9272</th>\n",
              "      <td>33.1</td>\n",
              "      <td>174999.0</td>\n",
              "      <td>Undergraduate degree</td>\n",
              "      <td>7.0</td>\n",
              "      <td>18.0</td>\n",
              "      <td>38.14</td>\n",
              "      <td>2022-09-01</td>\n",
              "      <td>50.00</td>\n",
              "      <td>3573.0</td>\n",
              "      <td>Vaginally</td>\n",
              "      <td>0</td>\n",
              "      <td>70</td>\n",
              "      <td>40</td>\n",
              "      <td>73</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5715</th>\n",
              "      <td>29.9</td>\n",
              "      <td>200000.0</td>\n",
              "      <td>Doctoral Degree</td>\n",
              "      <td>11.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>41.00</td>\n",
              "      <td>2022-09-01</td>\n",
              "      <td>51.00</td>\n",
              "      <td>3771.0</td>\n",
              "      <td>Vaginally</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>10</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5523</th>\n",
              "      <td>35.7</td>\n",
              "      <td>149999.0</td>\n",
              "      <td>Masters degree</td>\n",
              "      <td>4.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>40.86</td>\n",
              "      <td>2022-09-01</td>\n",
              "      <td>54.50</td>\n",
              "      <td>3770.0</td>\n",
              "      <td>Vaginally</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>31</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5176 rows × 14 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "      mat_age    income               mat_edu  depression  anxiety  \\\n",
              "8818     30.8  149999.0  College/trade school        16.0     25.0   \n",
              "1070     36.7  149999.0  Undergraduate degree         8.0     14.0   \n",
              "4968     30.9  174999.0  Undergraduate degree         7.0     15.0   \n",
              "9832     38.6  200000.0  Undergraduate degree        11.0     28.0   \n",
              "3273     30.7  124999.0        Masters degree        16.0     27.0   \n",
              "...       ...       ...                   ...         ...      ...   \n",
              "9182     27.8   69999.0  College/trade school         9.0     20.0   \n",
              "160      38.6  200000.0        Masters degree         1.0     11.0   \n",
              "9272     33.1  174999.0  Undergraduate degree         7.0     18.0   \n",
              "5715     29.9  200000.0       Doctoral Degree        11.0      9.0   \n",
              "5523     35.7  149999.0        Masters degree         4.0     10.0   \n",
              "\n",
              "      birth_age_wk birth_date  birth_length_cm  birth_weight_g  \\\n",
              "8818         37.14 2020-04-01            50.00          2722.0   \n",
              "1070         38.29 2020-04-01            51.00          3500.0   \n",
              "4968         31.29 2020-04-01            54.61          3345.0   \n",
              "9832         36.14 2020-04-01            51.00          3374.0   \n",
              "3273         37.29 2020-04-01            48.26          2977.0   \n",
              "...            ...        ...              ...             ...   \n",
              "9182         40.71 2021-09-01            45.72          3345.0   \n",
              "160          38.86 2022-09-01            51.00          3091.0   \n",
              "9272         38.14 2022-09-01            50.00          3573.0   \n",
              "5715         41.00 2022-09-01            51.00          3771.0   \n",
              "5523         40.86 2022-09-01            54.50          3770.0   \n",
              "\n",
              "                           delivery  NICU life_danger baby_danger  \\\n",
              "8818                      Vaginally     0           4          47   \n",
              "1070                      Vaginally     0          22          16   \n",
              "4968  Caesarean-section (c-section)     0          60          67   \n",
              "9832                      Vaginally     0          85          94   \n",
              "3273                      Vaginally     0          47          55   \n",
              "...                             ...   ...         ...         ...   \n",
              "9182                      Vaginally     0          50          70   \n",
              "160   Caesarean-section (c-section)     0          13          20   \n",
              "9272                      Vaginally     0          70          40   \n",
              "5715                      Vaginally     0           0          10   \n",
              "5523                      Vaginally     0           5           5   \n",
              "\n",
              "     virus_baby_harm  \n",
              "8818              98  \n",
              "1070              29  \n",
              "4968              87  \n",
              "9832              95  \n",
              "3273              98  \n",
              "...              ...  \n",
              "9182              65  \n",
              "160               50  \n",
              "9272              73  \n",
              "5715              10  \n",
              "5523              31  \n",
              "\n",
              "[5176 rows x 14 columns]"
            ]
          },
          "execution_count": 143,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Let's check the data types of the 'birth_date' column in the DataFrame.\n",
        "df.dtypes\n",
        "#sort the data frame chronological dates\n",
        "df = df.sort_values(by='birth_date')\n",
        "#convert date format\n",
        "df['birth_date'] = pd.to_datetime(df['birth_date'], errors='coerce')\n",
        "\n",
        "# Display the sorted DataFrame\n",
        "df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 144,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "mat_edu\n",
              "UG     2137\n",
              "M      1162\n",
              "C/T    1048\n",
              "PhD     517\n",
              "HS      275\n",
              "<HS      23\n",
              "Name: count, dtype: int64"
            ]
          },
          "execution_count": 144,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Clean the mother education column's values\n",
        "mapping = {\n",
        "    'Undergraduate degree': 'UG',\n",
        "    'Masters degree': 'M',\n",
        "    'College/trade school': 'C/T',\n",
        "    'Doctoral Degree': 'PhD',\n",
        "    'High school diploma': 'HS',\n",
        "    'Less than high school diploma': '<HS'\n",
        "}\n",
        "\n",
        "# Replace values in the 'mat_edu' column using the mapping\n",
        "df['mat_edu'] = df['mat_edu'].replace(mapping)\n",
        "\n",
        "df.mat_edu.value_counts()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 145,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "delivery\n",
              "V    3665\n",
              "C    1511\n",
              "Name: count, dtype: int64"
            ]
          },
          "execution_count": 145,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Similar cleaning for the delivery mode\n",
        "mapping = {'Vaginally': 'V', 'Caesarean-section (c-section)': 'C'}\n",
        "df['delivery'] = df['delivery'].replace(mapping)\n",
        "df.delivery.value_counts()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 146,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "mat_age                   float64\n",
              "income                    float64\n",
              "mat_edu                    object\n",
              "depression                float64\n",
              "anxiety                   float64\n",
              "birth_age_wk              float64\n",
              "birth_date         datetime64[ns]\n",
              "birth_length_cm           float64\n",
              "birth_weight_g            float64\n",
              "delivery                   object\n",
              "NICU                        int64\n",
              "life_danger                 int64\n",
              "baby_danger                 int64\n",
              "virus_baby_harm             int64\n",
              "dtype: object"
            ]
          },
          "execution_count": 146,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Convert all the survey number responses to actual integers\n",
        "df['life_danger'] = df['life_danger'].astype(int)\n",
        "df['baby_danger'] = df['baby_danger'].astype(int)\n",
        "df['virus_baby_harm'] = df['virus_baby_harm'].astype(int)\n",
        "df.dtypes"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 147,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Classify births as either problematic or not depending on the birth outcomes\n",
        "df['prob_preg'] = ((df['birth_weight_g'] < 2500) | (df['delivery'] == 'C') | (df['NICU'] == 1) | (df['birth_age_wk'] < 37)).astype(int)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 148,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 688
        },
        "id": "zcLra_w9gwpO",
        "outputId": "b7aa90c8-aeb8-44e4-d563-22eeddc6c6f4"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Number of rows in the prevaccine subset: 2923\n",
            "Number of riws in the postvaccine subset: 65\n"
          ]
        },
        {
          "data": {
            "text/html": [
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>mat_age</th>\n",
              "      <th>income</th>\n",
              "      <th>mat_edu</th>\n",
              "      <th>depression</th>\n",
              "      <th>anxiety</th>\n",
              "      <th>birth_age_wk</th>\n",
              "      <th>birth_date</th>\n",
              "      <th>birth_length_cm</th>\n",
              "      <th>birth_weight_g</th>\n",
              "      <th>delivery</th>\n",
              "      <th>NICU</th>\n",
              "      <th>life_danger</th>\n",
              "      <th>baby_danger</th>\n",
              "      <th>virus_baby_harm</th>\n",
              "      <th>prob_preg</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>7014</th>\n",
              "      <td>30.2</td>\n",
              "      <td>69999.0</td>\n",
              "      <td>HS</td>\n",
              "      <td>19.0</td>\n",
              "      <td>22.0</td>\n",
              "      <td>37.29</td>\n",
              "      <td>2022-08-01</td>\n",
              "      <td>50.0</td>\n",
              "      <td>2778.0</td>\n",
              "      <td>V</td>\n",
              "      <td>1</td>\n",
              "      <td>50</td>\n",
              "      <td>50</td>\n",
              "      <td>75</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10105</th>\n",
              "      <td>35.9</td>\n",
              "      <td>124999.0</td>\n",
              "      <td>C/T</td>\n",
              "      <td>10.0</td>\n",
              "      <td>19.0</td>\n",
              "      <td>37.71</td>\n",
              "      <td>2022-08-01</td>\n",
              "      <td>46.0</td>\n",
              "      <td>2609.0</td>\n",
              "      <td>V</td>\n",
              "      <td>0</td>\n",
              "      <td>95</td>\n",
              "      <td>83</td>\n",
              "      <td>90</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5770</th>\n",
              "      <td>37.5</td>\n",
              "      <td>39999.0</td>\n",
              "      <td>PhD</td>\n",
              "      <td>7.0</td>\n",
              "      <td>22.0</td>\n",
              "      <td>41.57</td>\n",
              "      <td>2021-12-01</td>\n",
              "      <td>52.5</td>\n",
              "      <td>3154.0</td>\n",
              "      <td>V</td>\n",
              "      <td>0</td>\n",
              "      <td>74</td>\n",
              "      <td>70</td>\n",
              "      <td>72</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9176</th>\n",
              "      <td>35.7</td>\n",
              "      <td>199999.0</td>\n",
              "      <td>C/T</td>\n",
              "      <td>12.0</td>\n",
              "      <td>18.0</td>\n",
              "      <td>38.86</td>\n",
              "      <td>2021-12-01</td>\n",
              "      <td>50.0</td>\n",
              "      <td>3825.0</td>\n",
              "      <td>C</td>\n",
              "      <td>1</td>\n",
              "      <td>50</td>\n",
              "      <td>50</td>\n",
              "      <td>75</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3807</th>\n",
              "      <td>38.3</td>\n",
              "      <td>200000.0</td>\n",
              "      <td>PhD</td>\n",
              "      <td>0.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>39.71</td>\n",
              "      <td>2021-12-01</td>\n",
              "      <td>55.0</td>\n",
              "      <td>4366.0</td>\n",
              "      <td>V</td>\n",
              "      <td>0</td>\n",
              "      <td>20</td>\n",
              "      <td>50</td>\n",
              "      <td>50</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3886</th>\n",
              "      <td>31.9</td>\n",
              "      <td>69999.0</td>\n",
              "      <td>M</td>\n",
              "      <td>5.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>41.43</td>\n",
              "      <td>2021-10-01</td>\n",
              "      <td>50.0</td>\n",
              "      <td>3487.0</td>\n",
              "      <td>V</td>\n",
              "      <td>0</td>\n",
              "      <td>25</td>\n",
              "      <td>25</td>\n",
              "      <td>15</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>160</th>\n",
              "      <td>38.6</td>\n",
              "      <td>200000.0</td>\n",
              "      <td>M</td>\n",
              "      <td>1.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>38.86</td>\n",
              "      <td>2022-09-01</td>\n",
              "      <td>51.0</td>\n",
              "      <td>3091.0</td>\n",
              "      <td>C</td>\n",
              "      <td>0</td>\n",
              "      <td>13</td>\n",
              "      <td>20</td>\n",
              "      <td>50</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9272</th>\n",
              "      <td>33.1</td>\n",
              "      <td>174999.0</td>\n",
              "      <td>UG</td>\n",
              "      <td>7.0</td>\n",
              "      <td>18.0</td>\n",
              "      <td>38.14</td>\n",
              "      <td>2022-09-01</td>\n",
              "      <td>50.0</td>\n",
              "      <td>3573.0</td>\n",
              "      <td>V</td>\n",
              "      <td>0</td>\n",
              "      <td>70</td>\n",
              "      <td>40</td>\n",
              "      <td>73</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5715</th>\n",
              "      <td>29.9</td>\n",
              "      <td>200000.0</td>\n",
              "      <td>PhD</td>\n",
              "      <td>11.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>41.00</td>\n",
              "      <td>2022-09-01</td>\n",
              "      <td>51.0</td>\n",
              "      <td>3771.0</td>\n",
              "      <td>V</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>10</td>\n",
              "      <td>10</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5523</th>\n",
              "      <td>35.7</td>\n",
              "      <td>149999.0</td>\n",
              "      <td>M</td>\n",
              "      <td>4.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>40.86</td>\n",
              "      <td>2022-09-01</td>\n",
              "      <td>54.5</td>\n",
              "      <td>3770.0</td>\n",
              "      <td>V</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>31</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>65 rows × 15 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "       mat_age    income mat_edu  depression  anxiety  birth_age_wk  \\\n",
              "7014      30.2   69999.0      HS        19.0     22.0         37.29   \n",
              "10105     35.9  124999.0     C/T        10.0     19.0         37.71   \n",
              "5770      37.5   39999.0     PhD         7.0     22.0         41.57   \n",
              "9176      35.7  199999.0     C/T        12.0     18.0         38.86   \n",
              "3807      38.3  200000.0     PhD         0.0      9.0         39.71   \n",
              "...        ...       ...     ...         ...      ...           ...   \n",
              "3886      31.9   69999.0       M         5.0     11.0         41.43   \n",
              "160       38.6  200000.0       M         1.0     11.0         38.86   \n",
              "9272      33.1  174999.0      UG         7.0     18.0         38.14   \n",
              "5715      29.9  200000.0     PhD        11.0      9.0         41.00   \n",
              "5523      35.7  149999.0       M         4.0     10.0         40.86   \n",
              "\n",
              "      birth_date  birth_length_cm  birth_weight_g delivery  NICU  life_danger  \\\n",
              "7014  2022-08-01             50.0          2778.0        V     1           50   \n",
              "10105 2022-08-01             46.0          2609.0        V     0           95   \n",
              "5770  2021-12-01             52.5          3154.0        V     0           74   \n",
              "9176  2021-12-01             50.0          3825.0        C     1           50   \n",
              "3807  2021-12-01             55.0          4366.0        V     0           20   \n",
              "...          ...              ...             ...      ...   ...          ...   \n",
              "3886  2021-10-01             50.0          3487.0        V     0           25   \n",
              "160   2022-09-01             51.0          3091.0        C     0           13   \n",
              "9272  2022-09-01             50.0          3573.0        V     0           70   \n",
              "5715  2022-09-01             51.0          3771.0        V     0            0   \n",
              "5523  2022-09-01             54.5          3770.0        V     0            5   \n",
              "\n",
              "       baby_danger  virus_baby_harm  prob_preg  \n",
              "7014            50               75          1  \n",
              "10105           83               90          0  \n",
              "5770            70               72          0  \n",
              "9176            50               75          1  \n",
              "3807            50               50          0  \n",
              "...            ...              ...        ...  \n",
              "3886            25               15          0  \n",
              "160             20               50          1  \n",
              "9272            40               73          0  \n",
              "5715            10               10          0  \n",
              "5523             5               31          0  \n",
              "\n",
              "[65 rows x 15 columns]"
            ]
          },
          "execution_count": 148,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "\n",
        "# Now, define your date ranges for the prevaccine and postvaccine periods\n",
        "# Vaccine started 2020-12-14, so 40 weeks past this date puts us at 2021-9-20\n",
        "# But also, to keep it relatively equal, we will only look at the 12 months leading up to the vaccine start\n",
        "# As our post dataframe only ranges 12 months\n",
        "prevaccine_start = pd.to_datetime('2020-10-1')\n",
        "prevaccine_end = pd.to_datetime('2021-9-19')\n",
        "postvaccine_start = pd.to_datetime('2021-9-20')\n",
        "\n",
        "\n",
        "# Filter the DataFrame for prevaccine and postvaccine periods\n",
        "prevaccine_df = df[(df['birth_date'] <= prevaccine_end) & (df['birth_date'] >= prevaccine_start)]\n",
        "postvaccine_df = df[(df['birth_date'] >= postvaccine_start)]\n",
        "\n",
        "# Number of rows in the prevaccine subset\n",
        "num_rows_prevaccine = prevaccine_df.shape[0]\n",
        "\n",
        "# Number of rows in the postvaccine subset\n",
        "num_rows_postvaccine = postvaccine_df.shape[0]\n",
        "\n",
        "# Print the results\n",
        "print(f\"Number of rows in the prevaccine subset: {num_rows_prevaccine}\")\n",
        "print(f\"Number of riws in the postvaccine subset: {num_rows_postvaccine}\")\n",
        "prevaccine_df.birth_date.value_counts().sort_index()\n",
        "postvaccine_df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 149,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "image/png": 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Q0FAALl26BGiTgoxER0cbfIH4+fkZFXtYWBiNGjVKs71q1ar6/dntmt+xY0eGDRuGSqXCxcWFatWqpdu49dlYw8LCAKhcuXKaY6tUqcK+ffsMtqnV6jQNIytVqgSgfw+fdefOHR48eMD333/P999/n+4xt2/fNnic3v8zgK+vb5rtGo2G6OhofZXQ/v37CQwM5MCBA8THxxscHx0drT+XsXRVTs8zbMLkyZNp0qQJVlZWFCtWjKpVq6bbO+bZ/59Lly6hKEqa+1ZHlxDq/h+frba0trbOsBrk2fdYd0/fv39fvy08PJzJkyezfv16g+1AmsRR1/7m2XM+/bwrV67g4+ODu7t7ujFl5NKlS5w6dSrN+XV098/777/PqlWraNu2LSVLluTVV1+la9eumQ7dAE8SmYcPH2bYMeDZhMja2pouXbqwcuVKEhMTsbOzY+3atSQnJxskO5cuXeLcuXNZxq5j7OeJOWT389TU11TYSLJTyOh6Bnz//fdMmDAhzf6Mfsk926Dxael1+8yoK6iSjXExdKU2n3/+ObVr1073mGcH1nv616+llCpVipYtW2Z5nCVi1b2nvXr1yjCJrFmzpsHjjP5Ps/q/vnLlCgEBAVSpUoU5c+bg6+uLra0tf/31F19++WWaUrncUqNGjWz9/2g0GlQqFZs3b073tRs7yGN6snovU1NTadWqFVFRUYwfP54qVarg5OTEjRs36Nu3b5r3Mie7ZGs0Glq1asW4cePS3a9LuD09PQkODmbr1q1s3ryZzZs3s3jxYnr37s3SpUszPH/VqlVZt24dp06dStPOT+fUqVMABqUu3bt357vvvmPz5s106tSJVatWUaVKFWrVqmUQe40aNZgzZ0665302gc/Nv9Hsfp6a+poKG0l2CqGPP/6Yn3/+Wd9Y8Gm6X5IPHjww2K77lZoTdCU3OoqicPnyZf2Xbfny5QFwdXU16svJFGXKlOHChQtptp8/f16/P7fprnnhwgV9EbTOhQsX0sSk0Wi4evWq/ssF4OLFiwAZliAUL14cFxcXUlNTzf6ePmvDhg0kJiayfv16g1+t6RWrG1sFpbsnTp8+nePxp3dtRVHw8/MzeM+fpft/unz5Mq+88op+e0pKCqGhoWmSSWP8999/XLx4kaVLlxo03DW1iuhp5cuXZ+vWrURFRZlUulO+fHliY2ONev9tbW3p0KEDHTp0QKPR8P777/Pdd98xadKkNCVfOu3bt2f69OksW7Ys3WQnNTWVlStXUrRoUV566SX99qZNm+Lt7c1vv/3Gyy+/zM6dO/UN3p+O/eTJkwQEBGSrZ2VeVBBfkzlJm51CqHz58vTq1YvvvvuOyMhIg32urq4UK1ZMX9+us2DBghyLZ9myZfriaIDff/+diIgI/Vg09erVo3z58nzxxRfExsamef6dO3eyfe127dpx+PBhDhw4oN8WFxfH999/T9myZXOtnv5p9evXx9PTk2+//dagy+jmzZs5d+4cr732WprnfP311/p1RVH4+uuvsbGxISAgIN1rWFlZ0aVLF9asWcPp06fT7H+e9zS9a+ni0omOjmbx4sVpjnVyckqTaKenbt26+Pn5MXfu3DTHZ6f00BSdO3fGysqKoKCgNNdSFEVftVC/fn08PDz44YcfDNovrVixIk31k7HSey8VRXmuKukuXbqgKApBQUFp9mX2Xnbt2pUDBw6wdevWNPsePHigf83PVl2r1Wp9opdZl+gXX3yRli1bsnjxYjZu3Jhm///+9z8uXrzIuHHjDEpe1Go1b775Jhs2bGD58uWkpKQYVGHpYr9x4wY//PBDmvM+evSIuLi4DOPKqwriazInKdkppP73v/+xfPlyLly4QLVq1Qz2DRgwgBkzZjBgwADq16/P3r179SUFOcHd3Z2XX36Zfv36cevWLebOnUuFChUYOHAgoP3w+vHHH2nbti3VqlWjX79+lCxZkhs3brBr1y5cXV313VRNNWHCBH755Rfatm3LiBEjcHd3Z+nSpYSEhLBmzRqLDO5nY2PDzJkz6devH82aNaNHjx76rudly5Zl9OjRBsfb29uzZcsW+vTpQ6NGjdi8eTObNm3io48+yrD+HrTdnHft2kWjRo0YOHAg/v7+REVFcfz4cbZv305UVJRZXs+rr76q/2U/ePBgYmNj+eGHH/D09EzTUL5evXosXLiQTz75hAoVKuDp6ZmmdAu098TChQvp0KEDtWvXpl+/fnh7e3P+/HnOnDmT7hewuZQvX55PPvmEiRMn6ruSu7i4EBISwh9//MGgQYMYO3Ystra2TJkyheHDh9OiRQu6du1KaGgoS5YsoXz58tn69V2lShXKly/P2LFjuXHjBq6urqxZsybbyRPAK6+8wjvvvMO8efO4dOkSbdq0QaPR8M8///DKK68YNH5/2ocffsj69etp3749ffv2pV69esTFxfHff//x+++/ExoaSrFixRgwYABRUVG0aNGCUqVKERYWxvz586ldu7a+bVxGli1bRkBAAB07duTtt9+mSZMmJCYmsnbtWnbv3k23bt348MMP0zyvW7duzJ8/n8DAQGrUqJHmOu+88w6rVq3ivffeY9euXbz00kukpqZy/vx5Vq1axdatWzPscp9XFcTXZE6S7BRSFSpUoFevXunWmU+ePJk7d+7w+++/6xsWbt68GU9PzxyJ5aOPPuLUqVNMnz6dhw8fEhAQwIIFC3B0dNQf07x5cw4cOMC0adP4+uuviY2NxcvLi0aNGjF48OBsX7tEiRL8+++/jB8/nvnz55OQkEDNmjXZsGFDuiUouaVv3744OjoyY8YMxo8fj5OTE2+88QYzZ85M01jTysqKLVu2MGTIED788ENcXFwIDAw0GOcmPSVKlODw4cNMnTqVtWvXsmDBAjw8PKhWrVq6VZzZVblyZX7//Xc+/vhjxo4di5eXF0OGDKF48eJpenJNnjyZsLAwZs2axcOHD2nWrFm6yQ5A69at2bVrF0FBQcyePRuNRkP58uX1SXJOmjBhApUqVeLLL7/Ul4j4+vry6quvGgyAN2zYMBRFYfbs2YwdO5ZatWqxfv16RowYka0RdG1sbNiwYQMjRoxg+vTp2Nvb88YbbzBs2DCDNimmWrx4MTVr1uSnn37iww8/xM3Njfr16/Piiy9m+BxHR0f27NnDZ599xurVq1m2bBmurq5UqlSJoKAgfaNzXRvBBQsW8ODBA7y8vOjWrRtTpkzJ8seEt7c3hw8fZvbs2axevZo1a9ZgbW1NzZo1WbJkCb179043aXzxxRfx9fXl2rVraUp1QJssr1u3ji+//JJly5bxxx9/4OjoSLly5Rg5cmSm1ZN5VUF8TeakUnK6zFcIIYSeRqOhePHidO7cOd0qByGE+UmbHSGEyCEJCQlp2r0sW7aMqKgog+kihBA5S0p2hBAih+zevZvRo0fz1ltv4eHhwfHjx/npp5+oWrUqx44dM+ss7kKIjEmbHSGEyCFly5bF19eXefPm6bt29+7dmxkzZkiiI0QukpIdIYQQQhRo0mZHCCGEEAWaVGOh7R1x8+ZNXFxcZORJIYQQIp9QFIWHDx/i4+OT6VAGkuwAN2/eLPTzhgghhBD51bVr1yhVqlSG+yXZ4cmMudeuXcPV1dXC0YgCJy4OfHy06zdvQjoznwuRrpQ4WPv43ul8E6zl3hHiaTExMfj6+uq/xzMiyQ5PJh90dXWVZEeY39MzFru6SrIjjJdiBbqBxF1dJdkRIgNZNUGRBspCCCGEKNAk2RFCCCFEgSbJjhBCCCEKNGmzI0ROU6uhfv0n66LA02g0JCUlPf+JUpKh+Gva9YRksE54/nMKkY/Y2Nhg9XS7x2ySZEeInObgAEeOWDoKkUuSkpIICQlBo9GY54SlPtf+ey3CPOcTIp8pUqQIXl5ezzUOniQ7QghhJoqiEBERgZWVFb6+vpkOciaEyJyiKMTHx3P79m0AvL29s30uSXaEEMJMUlJSiI+Px8fHB0dHx6yfIITIlIODAwC3b9/G09Mz21VakuyINFI1CodDorj9MAFPF3sa+rljpZZpNLItPh78/bXrZ8+CfAkWWKmpqQDmm9FcSYXoM9p1t2qgev62C0LkN7ofDsnJyZLsCPPYcjqCoA1niYh+0hDS282ewA7+tKme/SLEQk1RICzsyboo8Mw6x16qGRo6C5GPmePvyaIVytOnT6dBgwa4uLjg6elJp06duHDhgsExCQkJDB06FA8PD5ydnenSpQu3bt0yOCY8PJzXXnsNR0dHPD09+fDDD0lJScnNl1IgbDkdwZCfjxskOgCR0QkM+fk4W05n3UAyVaNw4Mo9/gy+wYEr90jVyJe7EEIIy7Joyc6ePXsYOnQoDRo0ICUlhY8++ohXX32Vs2fP4vR4SP3Ro0ezadMmVq9ejZubG8OGDaNz587s378f0BYbv/baa3h5efHvv/8SERFB7969sbGx4bPPPrPky8tXUjUKQRvOkl5qogAqIGjDWVr5e2VYpWWOUiGpQhNCCGFuKkXJO+Xqd+7cwdPTkz179tC0aVOio6MpXrw4K1eu5M033wTg/PnzVK1alQMHDvDCCy+wefNm2rdvz82bNylRogQA3377LePHj+fOnTvp1p0nJiaSmJiof6ybSCw6OrrQzo114Mo9evxwMMvjxrepTNNKxSnuYoeHk50+EdGVCj17M+nSlIW96maZ8BTYKrS4OHB21q7HxsrcWAVYQkICISEh+Pn5YW9vn+3z6JP+mHg8uUbDUjZYFaub79rslC1bllGjRjFq1ChLhyLyscz+rmJiYnBzc8vy+ztPtdmJjo4GwN3dHYBjx46RnJxMy5Yt9cdUqVKF0qVL65OdAwcOUKNGDX2iA9C6dWuGDBnCmTNnqFOnTprrTJ8+naCgoBx+NfnL7YfGDVY2c8sFZm7RVjWqVeDuZEcxZ1uu3o177lKh9JIlXRWaMcmSEAVBukm/s5rAjpG0qVHSgpEJkX/lmWRHo9EwatQoXnrpJapXrw5AZGQktra2FClSxODYEiVKEBkZqT/m6URHt1+3Lz0TJ05kzJgx+se6kp3CzNPFuF+hpd0diU9K5V5cIhoF7sYmcjc2MdPnKEBEdAKNPt1OKXdHirvYaRdnu8clRLZ8vO70cyVLQhQEGSb9sRqGrAhmYS91nkj6k5KSzNfjzMySk5OxsbGxdBgij8kzI14NHTqU06dP8+uvv+b4tezs7HB1dTVYCruGfu54u2Wc8KjQVintGtucox+35NInbTn8vwA2jXiZ95qVM+oad+OSCL72gG1nb7HyUDhf7bjEx+tOM2TFce7FZdzjRJcsHQ6JMvFV5REqlbbrub+/dl0UGoqiEJ+UYtTyMCGZwPVnMkz6AaasP8vDhGSjzmdKC4XmzZszbNgwhg0bhpubG8WKFWPSpEn6c5QtW5Zp06bRu3dvXF1dGTRoEABr1qyhWrVq2NnZUbZsWWbPnp3m3A8fPqRHjx44OTlRsmRJvvnmG6PjUqlULFy4kLZt2+Lg4EC5cuX4/fff9ftDQ0NRqVT89ttvNGvWDHt7e1asWAHAjz/+SNWqVbG3t6dKlSosWLDA4Nz//vsvtWvXxt7envr167Nu3TpUKhXBwcFGxyfyjzxRsjNs2DA2btzI3r17KVWqlH67l5cXSUlJPHjwwKB059atW3h5eemPOXz4sMH5dL21dMeIrFmpVQR28Oe9n4+n2af7eg7s4K8vWbG2UuPpYo+niz0xj1L4ds/VLK8xtWM1Srjac+dhonaJ1f57MfIhYVHxWT7f2Kq2PMfREc6csXQUwgIeJafiP3mrWc6lAJExCdSY8rdRx5+d2hpHW+M/4pcuXUr//v05fPgwR48eZdCgQZQuXZqBAwcC8MUXXzB58mQCAwMBbTODrl27MmXKFLp168a///7L+++/j4eHB3379tWf9/PPP+ejjz4iKCiIrVu3MnLkSCpVqkSrVq2MimvSpEnMmDGDr776iuXLl9O9e3f+++8/qlatqj9mwoQJzJ49mzp16ugTnsmTJ/P1119Tp04dTpw4wcCBA3FycqJPnz7ExMTQoUMH2rVrx8qVKwkLC5N2RQWcRZMdRVEYPnw4f/zxB7t378bPz89gf7169bCxsWHHjh106dIFgAsXLhAeHk7jxo0BaNy4MZ9++ql+dEWAbdu24erqir9uIDdhlOaVPbG3UZOQbDinj1cWjYR1pUKR0Qnp/ipVPT5Hz0Zl0q2GMrZxtLFVbUII0/n6+vLll1+iUqmoXLky//33H19++aU+2WnRogUffPCB/viePXsSEBDApEmTAKhUqRJnz57l888/N0h2XnrpJSZMmKA/Zv/+/Xz55ZdGJztvvfUWAwYMAGDatGls27aN+fPnG5TUjBo1is6dO+sfBwYGMnv2bP02Pz8/zp49y3fffUefPn1YuXIlKpWKH374AXt7e/z9/blx44b+tYqCx6LJztChQ1m5ciV//vknLi4u+jY2bm5uODg44ObmRv/+/RkzZgzu7u64uroyfPhwGjduzAsvvADAq6++ir+/P++88w6zZs0iMjKSjz/+mKFDh2JnZ2fJl5fv7L5wm4RkDV6udszpWps7sYlGdf/WlQoN+fk4KjBIeNIrFXqWsclSQz/3bL4yISzDwcaKs1NbG3Xs4ZAo+i7OesLYJf0aGPW34GBjWs+tF154wWDwtsaNGzN79mz9qND169c3OP7cuXN07NjRYNtLL73E3LlzSU1N1Y90q/th+vR5586da3Rc6T3/2aqmp2OLi4vjypUr9O/f3yB5SUlJwc3NDdD+aK5Zs6ZBz56GDRsaHZPIfyya7CxcuBDQ1hc/bfHixfpfBl9++SVqtZouXbqQmJhI69atDTJ6KysrNm7cyJAhQ2jcuLG+mHLq1Km59TIKjHUnbgLQsXZJXqxQzKTntqnuzcJeddP0IsmqVAgyT5Z0MkuW8rz4eGjQQLt+5IhMF1GIqFQqo6uSmlQsblTS36RicYv8LTjl4SETno4tNjYWgB9++IFGjRoZHJfdqQZE/mfxaqys2Nvb880332TaqK1MmTL89ddf5gyt0Il+lMzO89qZZTvWzl731jbVvWnl75WtQQEzSpYAvuhaK0/0QMk2RdHOiaVbFyIdz1tC+rwOHTpk8PjgwYNUrFgxwwShatWq+sFddfbv30+lSpUMnnPwoGEV9cGDBw3a22Tl4MGD9O7d2+BxekOK6JQoUQIfHx+uXr1Kz5490z2mcuXK/PzzzyQmJuprAI4cybpUTeRfeaKBsrC8LacjSErVUKmEM1W9XbJ9Hiu1isblPbL1XINkKSaBL/6+wLX7j0hK0WT9ZCEKgAxLSF3UBL5eM0eT/vDwcMaMGcPgwYM5fvw48+fPT7d3lc4HH3xAgwYNmDZtGt26dePAgQN8/fXXaXo97d+/n1mzZtGpUye2bdvG6tWr2bRpk9FxrV69mvr16/Pyyy+zYsUKDh8+zE8//ZTpc4KCghgxYgRubm60adOGxMREjh49yv379xkzZgxvv/02//vf/xg0aBATJkwgPDycL774AjDzvGYiz5BkRwDwx4kbgLZUx5J/7E8nS5ExCUzffJ41x67To2Fpi8UkRG4yTPqfHkE5Z3uX9u7dm0ePHtGwYUOsrKwYOXKkvot5eurWrcuqVauYPHky06ZNw9vbm6lTpxo0TgZtUnT06FGCgoJwdXVlzpw5tG5tXDsm0CYuv/76K++//z7e3t788ssvWXY+GTBgAI6Ojnz++ed8+OGHODk5UaNGDX2PK1dXVzZs2MCQIUOoXbs2NWrUYPLkybz99tvPNfK1yLvy1HQRlmLscNMFVUT0I16csRNFgX3jX6FU0bzRpuRWTAKNp+9Ao8CeD5tTxiPvthnIlEwXUWiYa7oIPSUVok5o193r5Nh0Ec2bN6d27domNRzODSqVij/++INOnTrl+LVWrFhBv379iI6OxsHBIcevJ4xnjuki8syggsJy1gffRFGgYVn3PJPoAJRwteflisUBWHv8hoWjEUIUJMuWLWPfvn2EhISwbt06xo8fT9euXSXRKaAk2RGsC37cC6uOj4UjSatLXW1j6bUnrqPRFPpCSCEKjBUrVuDs7JzuUq1atRy/fmRkJL169aJq1aqMHj2at956i++//z7HryssQ9rsFHIXbz3kXEQMNlYqXquR93o8vervhbOdNdeiHnE07H7+HGtHpYIyZZ6sC2EKq5yfg2r37t05fo1nvf7662m6huvo5rbKyVYW48aNY9y4cTl2fpG3SLJTyK173DC5WSVPijjmvYn9HGytaFfDi1VHr7Pm2PX8mew4OkJoqKWjEPmRygqK1LR0FDnCxcUFF5fs9/wUwhRSjVWIaTQKfz6uwuqUB6uwdLrU1c6Xtum/CBKSUy0cjRBCiPxGkp1C7GjYfW48eISznTUtq5awdDgZalDWnVJFHYhNTOHvs7csHY4QQoh8RpKdQmxdsLYKq3U1L+xNnEcnN6nVKjrX0TZUXnPsuoWjyYZHj7TTRTRooF0XwliKBqLPahdFBtcUIrsk2SmkklI0/PVfBABv1Mne9BC5qfPjqqx/Lt3hdkxCFkfnMRoNHD2qXTTyhSVMoUBKvHZJd8YsIYQxJNkppPZcvMOD+GQ8XeyyPb1DbipbzIl6ZYqiUZ6USAkhhBDGkGSnkNIlDB1q+eSb2cR1DZXXHLuRo11ShRC5a8qUKdSuXTtXrtW8eXP9tBGi8JBkpxB6mJDM9scNfTtlc4ZzS3ithje21mou3HrI2YgYS4cjRIHQoUMH2rRpk+6+f/75B5VKxalTp3I0hrFjx7Jjxw6znnP37t2oVCoePHhgsH3t2rVMmzbNrNdKzx9//MELL7yAm5sbLi4uVKtWzSDJMneC17dv31yZViO/kmSnENpyOpLEFA3lijtRvWT+mQvMzdGGVo97ja05JlVZQphD//792bZtG9evp238v3jxYurXr0/Nmjk71o+zszMeHrlTne7u7p7j4/vs2LGDbt260aVLFw4fPsyxY8f49NNPSU5ONvu1UlNT0UhbwCxJslMI6cfWsfAM59nRpZ62JGr9yRskp8ofuMgn4uIyXhISsjj20eMlLm1vvozOaYL27dtTvHhxlixZYrA9NjaW1atX079/f+7du0ePHj0oWbIkjo6O1KhRg19++cXgeI1Gw6xZs6hQoQJ2dnaULl2aTz/9VL//+vXr9OjRA3d3d5ycnKhfvz6HDh0C0pZy6EopvvjiC7y9vfHw8GDo0KEGycLy5cupX78+Li4ueHl58fbbb3P79m0AQkNDeeWVVwAoWrQoKpVKPxv7s9VY9+/fp3fv3hQtWhRHR0fatm3LpUuX9PuXLFlCkSJF2Lp1K1WrVsXZ2Zk2bdoQERGR4Xu6YcMGXnrpJT788EMqV65MpUqV6NSpE998843+nEFBQZw8eRKVSoVKpdK//3PmzKFGjRo4OTnh6+vL+++/T2xsbJp41q9fj7+/P3Z2drz77rssXbqUP//8U38+S4yKnZdJslPI3I5J4N8rd4H8VYWl06RicYo523I3Nom9F+9YOhzjFSumXUTh5Oyc8dKli+Gxnp5P9rm4QZmm2sXFDdq2NTy2bNn0z2kCa2trevfuzZIlSwzawq1evZrU1FR69OhBQkIC9erVY9OmTZw+fZpBgwbxzjvvcPjwYf3xEydOZMaMGUyaNImzZ8+ycuVKSpTQlsTGxsbSrFkzbty4wfr16zl58iTjxo3LtERi165dXLlyhV27drF06VKWLFlikJAlJyczbdo0Tp48ybp16wgNDdUnNL6+vqxZswaACxcuEBERwVdffZXudfr27cvRo0dZv349Bw4cQFEU2rVrZ5BYxcfH88UXX7B8+XL27t1LeHg4Y8eOzTB2Ly8vzpw5w+nTp9Pd361bNz744AOqVatGREQEERERdOvWDQC1Ws28efM4c+YMS5cuZefOnWmmtYiPj2fmzJn8+OOPnDlzhnnz5tG1a1d9EhYREcGLL76YYXyFkiKU6OhoBVCio6MtHUqO+2HvFaXM+I3KG9/ss3Qo2TZ1wxmlzPiNyvs/H7N0KEIYePTokXL27Fnl0aNHhjsg46VdO8NjHR0zPrZZM8NjixVL/zgTnTt3TgGUXbt26bc1adJE6dWrV4bPee2115QPPvhAURRFiYmJUezs7JQffvgh3WO/++47xcXFRbl37166+wMDA5VatWrpH/fp00cpU6aMkpKSot/21ltvKd26dcswniNHjiiA8vDhQ0VRFGXXrl0KoNy/f9/guGbNmikjR45UFEVRLl68qADK/v379fvv3r2rODg4KKtWrVIURVEWL16sAMrly5f1x3zzzTdKiRIlMowlNjZWadeunQIoZcqUUbp166b89NNPSkJCQoavOSOrV69WPDw89I918QQHBxsc16dPH6Vjx45Zni8/yvDvSjH++9vkkp1Hjx4RHx+vfxwWFsbcuXP5+++/zZF7iRz2ZHqI/Feqo9P58Uzo287eIjre/HXgQphdbGzGy+MSCL3btzM+dvNmw2NDQ9M/zkRVqlThxRdfZNGiRQBcvnyZf/75h/79+wPadiHTpk2jRo0auLu74+zszNatWwkPDwfg3LlzJCYmEhAQkO75g4ODqVOnDu7uxs9tV61aNaysngx26u3tra+mAjh27BgdOnSgdOnSuLi40KxZMwB9TMY4d+4c1tbWBhOSenh4ULlyZc6dO6ff5ujoSPny5TOM5VlOTk5s2rSJy5cv8/HHH+Ps7MwHH3xAw4YNDb4/07N9+3YCAgIoWbIkLi4uvPPOO9y7d8/geba2tjnejqqgMTnZ6dixI8uWLQPgwYMHNGrUiNmzZ9OxY0cWLlxo9gCF+Vy5E8t/N6KxUufNGc6N5e/tShUvF5JSNWz876alwxEia05OGS/29sYf6+Bg3LHZ0L9/f9asWcPDhw9ZvHgx5cuX1ycQn3/+OV999RXjx49n165dBAcH07p1a5KSkgBweDauZ2S1Pz26mc91VCqVvtorLi6O1q1b4+rqyooVKzhy5Ah//PEHgD4mc0ovFsWI4S/Kly/PgAED+PHHHzl+/Dhnz57lt99+y/D40NBQ2rdvT82aNVmzZg3Hjh3Tt/N5+nU5ODjku/aWlmZysnP8+HGaNGkCwO+//06JEiUICwtj2bJlzJs3z+wBCvP58/EM500rFsPD2c7C0WSfSqXSj7mz9ng+6JX16BE0b65dZLoIYQpFAzEXtEsOTxfRtWtX1Go1K1euZNmyZbz77rv6L9T9+/fTsWNHevXqRa1atShXrhwXL17UP7dixYo4ODhk2H28Zs2aBAcHExUVZZZYz58/z71795gxYwZNmjShSpUqaUpabG1tAW2pVEaqVq1KSkqKvqE0wL1797hw4QL+/v5miVWnbNmyODo6Eve4AbmtrW2a2I4dO4ZGo2H27Nm88MILVKpUiZs3jftBl975xBMmJzvx8fH6bnt///03nTt3Rq1W88ILLxAWFmb2AIV5KIrCugJQhaXTsbYPahUcC7tPyF3Tep/kOo0G9uzRLtJFVJhEgeSH2iWHp4twdnamW7duTJw4kYiICH1jX9AmM9u2bePff//l3LlzDB48mFu3nkzKa29vz/jx4xk3bhzLli3jypUrHDx4kJ9++gmAHj164OXlRadOndi/fz9Xr15lzZo1HDhwIFuxli5dGltbW+bPn8/Vq1dZv359mrFzypQpg0qlYuPGjdy5c8egR9PTr6tjx44MHDiQffv2cfLkSXr16kXJkiXp2LFjtmIDbe+ycePGsXv3bkJCQjhx4gTvvvsuycnJtGrVCtAmPyEhIQQHB3P37l0SExOpUKECycnJ+te1fPlyvv32W6OuWbZsWU6dOsWFCxe4e/dujnRzz89MTnYqVKjAunXruHbtGlu3buXVV18F4Pbt27i65p8xWwqb4+EPCI+Kx9HWilb+eXeGc2N5utrTtFJxAP44ng8nBxUiD+rfvz/379+ndevW+Pj46Ld//PHH1K1bl9atW9O8eXN94vK0SZMm8cEHHzB58mSqVq1Kt27d9KUttra2/P3333h6etKuXTtq1KjBjBkzDNrkmELXVX716tX4+/szY8YMvvjiC4NjSpYsSVBQEBMmTKBEiRIMGzYs3XMtXryYevXq0b59exo3boyiKPz1119pqq5M0axZM65evUrv3r2pUqUKbdu2JTIykr///pvKlSsD0KVLF9q0acMrr7xC8eLF+eWXX6hVqxZz5sxh5syZVK9enRUrVjB9+nSjrjlw4EAqV65M/fr1KV68OPv37892/AWRSjGm4vEpv//+O2+//Tapqam0aNGCbdu2ATB9+nT27t3L5mcb0OUDMTExuLm5ER0dXWATtsl/nmbZgTDeqFOSL7vVtnQ4ZrH+5E1G/HKCkkUc+GfcK6jz6rQXcXFPugPHxma7TYXI+xISEggJCcHPzw/7Z9viZIeSClEntOvudUCVveRAiPwss78rY7+/rU296JtvvsnLL79MREQEtWrV0m8PCAjgjTfeMPV0Ihckp2rYeEo7AFbH2j5ZHJ1/vOpfAhc7a248eMTh0CheKJf3JzQVQgiR+7I1qKCXlxcuLi5s27aNR48bXDZo0IAqVaqYNThhHvsu3SUqLolizra8XKHgDGxnb2PFazW1vcrWSlWWEEKIDJic7Ny7d4+AgAAqVapEu3bt9ENm9+/fnw8++MCkc+3du5cOHTrg4+ODSqVi3bp1Bvt1w14/u3z++ef6Y8qWLZtm/4wZM0x9WQWabobz9jV9sLYqWINmd37cK+uv/yJ5lCQ9EYQQQqRl8jff6NGjsbGxITw8HEdHR/32bt26sWXLFpPOFRcXR61atfTjCDxLN+y1blm0aJG22/Ezw6tPnTrV4Ljhw4eb+rIKrLjEFP4+o+01UZCqsHQalC2Kr7sDsYkp/H020tLhZMzRUbsIYSqVWrsIIbLN5DY7f//9N1u3bqVUqVIG2ytWrGhy1/O2bdvS9tm5Xp7i5eVl8PjPP//klVdeoVy5cgbbdRPBGSsxMZHExET945iYGKOfm9/8fTaSR8mplPFwpLZvEUuHY3YqlYrOdUrx1Y5L/H7sOh3z4nxfTk4mT84oBKBtkOxe19JRCJHvmfxzIS4uzqBERycqKgo7u5wbqO7WrVts2rRJP3z502bMmIGHhwd16tTh888/JyUlJdNzTZ8+HTc3N/3i6+ubU2Fb3LoT+XeGc2PpBhjcf/kukdEJWRwthBCisDE52WnSpIl+ugh4MoT3rFmzeOWVV8wa3NOWLl2Ki4sLnTt3Ntg+YsQIfv31V3bt2sXgwYP57LPP0swQ+6yJEycSHR2tX65du5ZjcVtCqkbhwJV7/HwwjH8uaWcGLwgDCWaktIcjDcoWRaPAn8H5YERlIYQQucrkaqxZs2YREBDA0aNHSUpKYty4cZw5c4aoqKgcHcRo0aJF9OzZM00f+zFjxujXa9asia2tLYMHD2b69OkZljTZ2dnlaCmUJW05HUHQhrNEPFXCYWOl4kJkDH7FCu74Lp3rluJI6H3WHL/OoKbl8lYpVkIC6NqZrVmTdi4kITKiaCD2inbduby03REim0z+y6levToXL17k5ZdfpmPHjsTFxdG5c2dOnDhhMCusOf3zzz9cuHCBAQMGZHlso0aNSElJITQ0NEdiycu2nI5gyM/HDRIdgORUhSE/H2fL6QgLRZbzXqvpja21mou3YvnlcDh/Bt/gwJV7pGpydoh9o6Smwl9/aReZu0aYRIGkaO2Sw9NFCFGQmVyyA+Dm5sb//vc/c8eSoZ9++ol69eoZDGKYkeDgYNRqNZ6enrkQWd6RqlEI2nA204/DoA1naeXvhVVeHWn4Obja21CjpCvHwh7w0R+n9du93ewJ7OBPm+r5d5Z3IYQwxbp16xg7diwhISEMHz6cuXPnWiyWKVOmsG7dOoKDgzM8pm/fvjx48CDN8DPmZFTJzqlTp9A8nsDw1KlTmS6miI2NJTg4WP8m6CZFCw8P1x8TExPD6tWr0y3VOXDgAHPnzuXkyZNcvXqVFStWMHr0aHr16kXRokVNiiW/OxwSlaZE52kKEBGdwOEQ88w6nNdsOR3BsbAHabZHRicU+FItIZ5X37599eOU2draUqFCBaZOnZplZ4/CLjU1lS+//JIaNWpgb29P0aJFadu2bbaadDRv3pxRo0aZJa7Bgwfz5ptvcu3atTQTpD7txIkTvPXWW5QoUQJ7e3sqVqzIwIEDDWa0f15jx45lx44dZjtfdhlVslO7dm0iIyPx9PSkdu3aqFQq0ptSS6VSmTTF/NGjRw0aNeva3/Tp04clS5YA8Ouvv6IoCj169EjzfDs7O3799VemTJlCYmIifn5+jB492qAdT2Fx+6FxvZCMPS4/0ZVqpUcBVBTsUi0hzKFNmzYsXryYxMRE/vrrL4YOHYqNjQ0TJ05Mc2xSUhK2trYWiDLvUBSF7t27s337dj7//HMCAgKIiYnhm2++oXnz5qxevTrNZKm5ITY2ltu3b6eZzPVZGzdupEuXLrRu3ZoVK1ZQvnx5bt++zerVq5k0aRK//fabWeJxdnbGWTc3oAUZVbITEhJC8eLF9etXr14lJCQkzXL16lWTLt68eXMURUmz6BIdgEGDBhEfH4+bm1ua59etW5eDBw/y4MEDHj16xNmzZ5k4cWKBbXycGU8X4xq9GntcflLYS7VEPpASl/GSmpDFsY8eL4/XjTlvNtjZ2eHl5UWZMmUYMmQILVu2ZP369YC25KdTp058+umn+Pj46GfuvnbtGl27dqVIkSK4u7vTsWNHg/aSKSkpjBgxgiJFiuDh4cH48ePp06ePQRLQvHlzRowYwbhx43B3d8fLy4spU6YYxDZnzhxq1KiBk5MTvr6+vP/++8TGxur3L1myhCJFirB161aqVq2Ks7Mzbdq00Y/wr7No0SKqVauGnZ0d3t7e+pnQ3333Xdq3b29wbHJyMp6envz000/pvl+rVq3i999/Z9myZQwYMAA/Pz9q1arF999/z+uvv86AAQOIezy+lu79e9qoUaNo3ry5fv+ePXv46quv9CVsGbU7vX//Pr1796Zo0aI4OjrStm1bLl26BMDu3btxcXEBoEWLFqhUKnbv3p3mHPHx8fTr14927dqxfv16WrZsiZ+fH40aNeKLL77gu+++0x+7Z88eGjZsqH/PJkyYoC/x+/777/Hx8dHX/Oh07NiRd999F9BWY9WuXVu/LzU1lTFjxujviXHjxqVbeGJuRiU7ZcqU0fduKVOmTKaLsIyGfu54u9mTUbmFCm37lYZ+7rkZVq4ozKVaIp9Y5Zzx8o/hiPCs8Xyyb7Ub7GiqXVa7we5nBmH9s2z65zQDBwcHkpKS9I937NjBhQsX2LZtGxs3biQ5OZnWrVvj4uLCP//8w/79+/VJhu55M2fOZMWKFSxevJj9+/cTExOTbruMpUuX4uTkxKFDh5g1axZTp05l27Zt+v1qtZp58+Zx5swZli5dys6dO9MMMRIfH88XX3zB8uXL2bt3L+Hh4YwdO1a/f+HChQwdOpRBgwbx33//sX79eipUqADAgAED2LJli0FytHHjRuLj4+nWrVu678/KlSupVKkSHTp0SLPvgw8+4N69ewavITNfffUVjRs3ZuDAgfqZADIa/61v374cPXqU9evXc+DAARRFoV27diQnJ/Piiy9y4cIFANasWUNERAQvvvhimnNs3bqVu3fvZjhMS5EiRQC4ceMG7dq1o0GDBpw8eZKFCxfy008/8cknnwDw1ltvce/ePXbt2qV/blRUFFu2bKFnz57pnnv27NksWbKERYsWsW/fPqKiovjjjz+Mep+eh8m9saZPn86iRYvSbF+0aBEzZ840S1DCdFZqFYEd/AHSJDy6x4Ed/AtkNU5hLtUSwtwURWH79u1s3bqVFi1a6Lc7OTnx448/Uq1aNapVq8Zvv/2GRqPhxx9/pEaNGlStWpXFixcTHh6uL02YP38+EydO5I033qBKlSp8/fXX+i/Sp9WsWZPAwEAqVqxI7969qV+/vkE7j1GjRvHKK69QtmxZWrRowSeffMKqVasMzpGcnMy3335L/fr1qVu3LsOGDTM4xyeffMIHH3zAyJEjqVSpEg0aNNC3kXnxxRepXLkyy5cv1x+/ePFi3nrrrQyrYC5evEjVqlXT3afbbmzbFzc3N2xtbXF0dMTLywsvLy+srKzSHHfp0iXWr1/Pjz/+SJMmTahVqxYrVqzgxo0brFu3DltbW33nHF0pWXrVjbqSoKwm716wYAG+vr58/fXXVKlShU6dOhEUFMTs2bPRaDT6NkorV67UP+f333+nWLFiGY67N3fuXCZOnEjnzp2pWrUq3377bbo1N+Zmcm+s7777zuCF6VSrVo3u3bszfvx4swQmTNemujcLe9Xl43WnuRv75BeZVwHvkaQr1YqMTki3N5oK7XtgsVItJyfIhWJakYd1jc14n+qZL7UutzM50TO/TzuGZjeiNDZu3IizszPJycloNBrefvttg+qkGjVqGHxxnjx5ksuXL+urTXQSEhK4cuUK0dHR3Lp1i4YNG+r3WVlZUa9evTTVHjVr1jR47O3tze3bT96H7du3M336dM6fP09MTAwpKSkkJCQQHx+vH9Hf0dHRYPiTp89x+/Ztbt68SUBAQIavf8CAAXz//feMGzeOW7dusXnzZnbu3Jnpe5Yb1S9PO3fuHNbW1jRq1Ei/zcPDg8qVK3Pu3Dmjz2Ns3OfOnaNx48YG45a99NJLxMbGcv36dUqXLk3Pnj0ZOHAgCxYswM7OjhUrVtC9e3fU6rRlKdHR0URERBjEb21tTf369XP8vTQ52YmMjMTbO+2XZvHixdPUj4rc16a6Nyjw3orj+Lo7MKtLLRr6uRfIEh0dXanWkJ+Po8JwNJKCXqol8glrEwb0zKljs/DKK6+wcOFCbG1t8fHxwdra8OvBycnwWrGxsdSrV48VK1akOZeujaexbGxsDB7rRuYHCA0NpX379gwZMoRPP/0Ud3d39u3bR//+/UlKStInO+mdQ/cF6uDgkGUMvXv3ZsKECRw4cIB///0XPz8/mjRpkuHxlSpVyjDB0G2vVKkSoK2Ge/bLPDk5OcuYcoourvPnz9O4cePnOleHDh1QFIVNmzbRoEED/vnnH7788ktzhGlWJldj+fr6ptutbv/+/Zm2/Ba559ZD7SSn/t6uNC7vUSi+5HWlWl5uhlVVXm72LOxVt8CWaglhLk5OTlSoUIHSpUunSXTSU7duXS5duoSnpycVKlQwWHTzDpYoUYIjR47on5Oamsrx48dNiuvYsWNoNBpmz57NCy+8QKVKlbh586ZJ53BxcaFs2bKZdoH28PCgU6dOLF68mCVLltCvX79Mz9m9e3cuXbrEhg0b0uybPXs2Hh4etGrVCki/MODZcWdsbW2z7M1ctWpVUlJSOHTokH7bvXv3uHDhAv7+/pk+92mvvvoqxYoVY9asWenuf/Dggf56unZBOvv378fFxUU/Gbi9vT2dO3dmxYoV/PLLL1SuXJm6ddOfvNbNzQ1vb2+D+FNSUjh27JjRsWeXycnOwIEDGTVqFIsXLyYsLIywsDAWLVrE6NGjGThwYE7EKEx0M1rbY8PbLetfMwVJm+re7Bvfgq971AFArYK/Rze1fKKTkABvvaVdEqSRtDCBooGHV7SLosn6+FzUs2dPihUrRseOHfnnn38ICQlh9+7djBgxguvXrwMwfPhwpk+fzp9//smFCxcYOXIk9+/fN2k6lwoVKpCcnMz8+fO5evUqy5cv59tvvzU53ilTpjB79mzmzZvHpUuXOH78OPPnzzc4ZsCAASxdupRz587Rp0+fTM/XvXt33njjDfr06cNPP/1EaGgop06dYvDgwfp2NbrSsBYtWnD06FGWLVvGpUuXCAwM5PTp0wbnK1u2LIcOHSI0NJS7d++mqeoDqFixIh07dmTgwIHs27ePkydP0qtXL0qWLEnHjh2Nfi907a82bdrE66+/zvbt2wkNDeXo0aOMGzeO9957D4D333+fa9euMXz4cM6fP8+ff/5JYGAgY8aMMaim6tmzJ5s2bdJP65SZkSNHMmPGDNatW8f58+d5//339clVTjI52fnwww/p378/77//PuXKlaNcuXIMHz6cESNGpDseg8h9Nx9ov1BLFilcyQ5oq7Ta1/LBx80ejQKnb8RYOiTtFBG//65dZLoIYRIFku5rlzw2XYSjoyN79+6ldOnS+sam/fv3JyEhAVdXVwDGjx9Pjx496N27N40bN8bZ2ZnWrVunmeMwM7Vq1WLOnDnMnDmT6tWrs2LFCqZPn25yvH369GHu3LksWLCAatWq0b59e31DXZ2WLVvi7e2d5Rg1oK0mW7VqFR999BFffvkllStXpkmTJoSFhbF7926DruatW7dm0qRJjBs3jgYNGvDw4UN69+5tcL6xY8diZWWFv78/xYsXNxhc92mLFy+mXr16tG/fnsaNG6MoCn/99VeaarysdOzYkX///RcbGxvefvttqlSpQo8ePYiOjtb3tipZsiR//fUXhw8fplatWrz33nv079+fjz/+2OBcLVq0wN3dnQsXLvD2229net0PPviAd955hz59+tC4cWNcXFx44403TIo9O1RKNlsFxcbGcu7cORwcHKhYsWK+HtsmJiYGNzc3oqOj9X+k+dmbC//laNh9vn67Du1rFs6qxWErj7PxVAQftq7M0FcqWDaYuDjQ9eiIjdU2WBYFUkJCAiEhIfj5+Zn0hZ4hJRWiTmjX3eukbcycz2g0GqpWrUrXrl0zHdnXUmJjYylZsiSLFy+mc+fOlg5HPJbZ35Wx39/ZmhsLtKMiNmjQILtPFzlIN8BeYavGelq9MkXZeCqCo6EykKAQlhIWFsbff/9Ns2bNSExM5OuvvyYkJCTLX/+5TaPRcPfuXWbPnk2RIkV4/fXXLR2SMDOTk524uDhmzJjBjh07uH37dpp6RVNHURbmlapRiIwpvNVYOvXKaOdGOx7+AI1GQV0IGmkLkdeo1WqWLFnC2LFjURSF6tWrs3379gzHp7GU8PBw/Pz8KFWqFEuWLDGqgbbIX0z+Hx0wYAB79uzhnXfewdvb26SGZiLn3X6YQKpGwVqtorhL/q1afF5VvV2xt1ET/SiZq3djqeDpkvWThBBmlVHv3bymbNmyuT5mjshdJic7mzdvZtOmTbz00ks5EY94TjcfaHtilXC1LxRdzjNiY6WmVqkiHAqJ4ljYfUl2hBCiEDO5N1bRokVxdy948ysVFLqeWD5FZGoEXVXWsbD7Fo5EFDZSSiCE+aTXDd9UJpfsTJs2jcmTJ7N06VL9yJUi74gopGPspCfPJDuOjtpeWLp1UWDZ2NigUqm4c+cOxYsXf/5qfkUBx8ftWxKSQJoNiEJEURSSkpK4c+cOarU63Xm+jGVysjN79myuXLlCiRIlKFu2bJq+/aaOjinM60nJjiQ7dUtrk50rd+K4H5dEUafs/6E8F5VKupsXElZWVpQqVYrr168TGhpq6XCEKBAcHR0pXbp0uvNtGcvkZOfpgZJE3qNrsyPVWFDUyZbyxZ24cieOE9fu06JKCUuHJAoBZ2dnKlasaNG5j4QoKKysrLC2tn7uUlKTk53AwMDnuqDIWTLGjqF6ZYpy5U4cx8IsmOwkJsLgwdr1776DfDwApzCOlZUVVlZmGAAwNREOP753Gn4HVnLvCJEd2S8TEnmSlOwY0rXbORpqwXY7KSmwdKl2SUmxXBwi/1FSIGSpdlHk3hEiu0wu2UlNTeXLL79k1apVhIeHk5SUZLA/KkpGrLWUhORU7sVp/z98pGQHeJLsnLz+gORUDTZWkt8LIURhY/Inf1BQEHPmzKFbt25ER0czZswYOnfujFqtZsqUKTkQojBW5OMqLAcbK4o4mjYpXEFVrpgzbg42JCRrOBeRByYFFUIIketMTnZWrFjBDz/8wAcffIC1tTU9evTgxx9/ZPLkyRw8eDAnYhRG0lVheRexl5GtH1OrVdQtXQTIA13QhRBCWITJyU5kZCQ1atQAtL0OoqOjAWjfvj2bNm0yb3TCJDcfl+xIFZahPDPejhBCCIswOdkpVaoUERERAJQvX56///4bgCNHjmAnvUwsShonp6+ublJQSXaEEKJQMjnZeeONN9ixYwcAw4cPZ9KkSVSsWJHevXvz7rvvmj1AYTwZPTl9tUoVwUqt4mZ0gj4hFEIIUXiY3BtrxowZ+vVu3bpRunRpDhw4QMWKFenQoYNZgxOmkXmx0udkZ01VbxdO34jhePj93B9d2tERbt9+si6EsawcofPtJ+tCiGwxOdl5VuPGjWncuLE5YhHP6Uk1lpTsPKt+GXdO34jhWNh92tf0yd2Lq1RQvHjuXlMUDCoV2Mu9I8TzytagI5cuXeL777/nk08+YerUqQaLKfbu3UuHDh3w8fFBpVKxbt06g/19+/ZFpVIZLG3atDE4Jioqip49e+Lq6kqRIkXo378/sbpJFwsZGT05Y3WlkbIQQhRaJpfs/PDDDwwZMoRixYrh5eVl0MVZpVIxefJko88VFxdHrVq1ePfdd+ncuXO6x7Rp04bFixfrHz/bCLpnz55ERESwbds2kpOT6devH4MGDWLlypUmvrL8LSYhmdhE7QirUo2Vlq5H1pmbMcQnpeBo+9yFmsZLTIQxY7Trc+bIdBHCeKmJcPzxvVN3jkwXIUQ2mfyJ/8knn/Dpp58yfvz4575427Ztadu2babH2NnZ4eXlle6+c+fOsWXLFo4cOUL9+vUBmD9/Pu3ateOLL77Axyf96orExEQSExP1j2Ni8v9gc7oqrCKONrn7RZ5P+LjZ4+VqT2RMAqeuR/NCOY/cu3hKCixYoF2fNUuSHWE8JQUuPb536swC5N4RIjtMrsa6f/8+b731Vk7Ekq7du3fj6elJ5cqVGTJkCPfu3dPvO3DgAEWKFNEnOgAtW7ZErVZz6NChDM85ffp03Nzc9Iuvr2+OvobcEPFAqrAyo1KpZLwdIYQopExOdt566y392Do5rU2bNixbtowdO3Ywc+ZM9uzZQ9u2bUlNTQW0Axx6enoaPMfa2hp3d3ciIyMzPO/EiROJjo7WL9euXcvR15Ebbjwu2SkpVVgZkvF2hBCicDK5vqNChQpMmjSJgwcPUqNGDWxsDOdgGjFihNmC6969u369Ro0a1KxZk/Lly7N7924CAgKyfV47O7sCNwCijLGTNX3JTvh9FEWRKTWEEKKQMDnZ+f7773F2dmbPnj3s2bPHYJ9KpTJrsvOscuXKUaxYMS5fvkxAQABeXl7c1o1f8lhKSgpRUVEZtvMpqPTVWFKykyF/b1fsrNU8iE/m6t04yhd3tnRIQgghcoHJyU5ISEhOxGGU69evc+/ePby9vQHtGD8PHjzg2LFj1KtXD4CdO3ei0Who1KiRxeK0hCfVWFKykxFbazW1ShXhcGgUx8LuS7IjhBCFRLbG2TGX2NhYgoODCQ4OBrSJVHBwMOHh4cTGxvLhhx9y8OBBQkND2bFjBx07dqRChQq0bt0agKpVq9KmTRsGDhzI4cOH2b9/P8OGDaN79+4Z9sQqqGSMHePUKyvtdoQQorAxuWRnjG68kGeoVCrs7e2pUKECHTt2xN3dPctzHT16lFdeeSXNufv06cPChQs5deoUS5cu5cGDB/j4+PDqq68ybdo0g/Y2K1asYNiwYQQEBKBWq+nSpQvz5s0z9WXlaxqNQmS0TBVhjHqlLdAjy8EBdCWiDpKMChNYOcDrIU/WhRDZolIURTHlCa+88grHjx8nNTWVypUrA3Dx4kWsrKyoUqUKFy5cQKVSsW/fPvz9/XMkaHOLiYnBzc2N6OhoXF1dLR2OyW4/TKDhpztQqeDiJ22xsbJogV2eFhWXRN1p2wAIntyKIo62Fo5ICCFEdhn7/W3yt2LHjh1p2bIlN2/e5NixYxw7dozr16/TqlUrevTowY0bN2jatCmjR49+rhcgjKdrnOzpYieJThbcnWwpV8wJgBPhDywbjBBCiFxh8jfj559/zrRp0wwyKDc3N6ZMmcKsWbNwdHRk8uTJHDt2zKyBiozJBKCmyfV5spKS4MMPtUtSUu5cUxQMqUlw4kPtkir3jhDZZXKyEx0dnaa7N8CdO3f00y4UKVKEJPlQzzU3de11pHGyUXJ9JOXkZPjiC+2SnJw71xQFg5IM577QLorcO0JkV7aqsd59913++OMPrl+/zvXr1/njjz/o378/nTp1AuDw4cNUqlTJ3LGKDEQ80A0oKI2TjaFLdoKvPSAlVWPhaIQQQuQ0k3tjfffdd4wePZru3buTkqKdZdva2po+ffrw5ZdfAlClShV+/PFH80YqMnQzWqqxTFGhuDMu9tY8TEjhfORDqpd0s3RIQgghcpDJyY6zszM//PADX375JVevXgW0Ixs7Oz8ZoK127dpmC1Bk7eYD6XZuCrVaRd3SRdlz8Q7Hwu5LsiOEEAVctrvuREZGEhERQcWKFXF2dsbEHuzCjGReLNPVlxnQhRCi0DA52bl37x4BAQFUqlSJdu3aERERAUD//v354IMPzB6gyFxSiobbDxMBqcYyRa43UhZCCGExJic7o0ePxsbGhvDwcBwdHfXbu3XrxpYtW8wanMjarZgEFAVsrdR4OMkAecaq5VsEtUo7p5hu9GkhhBAFk8ltdv7++2+2bt1KqVKlDLZXrFiRsLAwswUmjKOfE6uIPWq1ysLR5B9OdtZU9XblzM0YjoXd57Wa3jl3MQcHOH36yboQxrJygHann6wLIbLF5JKduLg4gxIdnaioKIM5q0TuuCndzrMt16qy1GqoVk27qGWEa2EClRqKVNMuKrl3hMguk/96mjRpwrJly/SPVSoVGo2GWbNmGUzqKXKHvtu5NE42mT7ZCZd2O0IIUZCZXI01a9YsAgICOHr0KElJSYwbN44zZ84QFRXF/v37cyJGkQmZKiL76j6eAf3MjWgSklOxt7HKmQslJcFnn2nXP/oIbKVtlTBSahKceXzvVPsIrOTeESI7TC7ZqV69OhcvXuTll1+mY8eOxMXF0blzZ06cOEH58uVzIkaRCd0koN4yxo7JShV1wNPFjhSNwqnr0Tl3oeRkCArSLjJdhDCFkgyng7SLTBchRLaZVLKTnJxMmzZt+Pbbb/nf//6XUzEJE8i8WNmnUqmoV6Yom09HcizsPg393C0dkhBCiBxgUsmOjY0Np06dyqlYRDZINdbzkfF2hBCi4DO5GqtXr1789NNPORGLMFF8UgrRj7RF21KNlT26ZOd4+H0ZBVwIIQookxsop6SksGjRIrZv3069evVwcnIy2D9nzhyzBScyp5sTy8XOGld7GwtHkz9V83HD1lpNVFwSIXfjKFfcOesnCSGEyFdMTnZOnz5N3bp1Abh48aLBPpVKBrXLTfoxdqRUJ9tsrdXUKuXGkdD7HAu7L8mOEEIUQCYnO7t27cqJOEQ2yASg5lG3TFGOhN7nePh93qrva+lwhBBCmJlJyc5vv/3G+vXrSUpKIiAggPfeey+n4hJGuPG4GksaJz+feqVzuJGyvT0cPvxkXQhjqe2h9eEn60KIbDE62Vm4cCFDhw6lYsWKODg4sHbtWq5cucLnn3+ek/GJTEToemLJVBHPpe7jRsoXb8US/SgZNwczt3+ysoIGDcx7TlE4qK3AQ+4dIZ6X0b2xvv76awIDA7lw4QLBwcEsXbqUBQsW5GRsIgtPJgGVkp3nUczZjrIe2vneTsjUEUIIUeAYnexcvXqVPn366B+//fbbpKSkEBERkSOBiaw9GWNHSnael65053hOVGUlJcHnn2uXpCTzn18UXKlJcPZz7ZIq944Q2WV0spOYmGjQzVytVmNra8ujR49yJDCROUVRZBJQM8rRSUGTk2HcOO0i00UIUyjJEDxOu8h0EUJkm0kNlCdNmoSjo6P+cVJSEp9++ilubm76bTLOTu54EJ9MQrIGAC9ps/Pc6pfRThURHP6AlFQN1lYmj7cphBAijzL6E71p06ZcuHCBEydO6JcXX3yRq1ev6h8HBwebdPG9e/fSoUMHfHx8UKlUrFu3Tr8vOTmZ8ePHU6NGDZycnPDx8aF3797cvHnT4Bxly5ZFpVIZLDNmzDApjvzoxuMqrGLOtjk3W3chUtHTGRc7a+KSUjkf+dDS4QghhDAjo0t2du/ebfaLx8XFUatWLd599106d+5ssC8+Pp7jx48zadIkatWqxf379xk5ciSvv/46R48eNTh26tSpDBw4UP/YxcXF7LHmNfrGyVKFZRZqtYrapYvwz6W7LPk3lC51S9HQzx0rtfEDZaZqFA6HRHH7YQKeLvYmP18IIUTOMHlQQXNq27Ytbdu2TXefm5sb27ZtM9j29ddf07BhQ8LDwyldurR+u4uLC15eXkZfNzExkcTERP3jmJgYEyO3PN2AgtI42Ty2nI7gRPgDAH4/dp3fj13H282ewA7+tKnubdTzgzac1SehwJPn+7nmVNhCCCGMkK8aJkRHR6NSqShSpIjB9hkzZuDh4UGdOnX4/PPPSUlJyfQ806dPx83NTb/4+ua/UXN11VhSsvP8tpyOYMjPx4lNNLxvIqMTGPLzcbaczrzHoe75Tyc6Tz9/25lIs8cshBDCePkm2UlISGD8+PH06NEDV9cnv5RHjBjBr7/+yq5duxg8eDCfffYZ48aNy/RcEydOJDo6Wr9cu3Ytp8M3uwj96MlSsvM8UjUKQRvOkt5857ptQRvOkqpJf0Z0Y57/2eZzZohUCCFEdlm0GstYycnJdO3aFUVRWLhwocG+MWPG6Ndr1qyJra0tgwcPZvr06djZ2aV7Pjs7uwz35RdPxtiRkp3ncTgkKk2JzNMUtO2jhvx8LN1eb5HRCVk+PyxOw5mf11GtpJtMFyFMo7aHgF1P1oUQ2ZLnkx1dohMWFsbOnTsNSnXS06hRI1JSUggNDaVy5cq5FGXukwbK5nH7YcaJytP+Pnsr29fQqK24XK021WqXzPY5RCGltoISzS0dhRD5nsnJzuLFi3F2duatt94y2L569Wri4+MNRll+XrpE59KlS+zatQsPD48snxMcHIxarcbT09NsceQ1qRqFyBjtl3RJKdl5Lp4uxv1a7ly3JKWKOqbZfv1+PGuP3zDbdYQQQpifycnO9OnT+e6779Js9/T0ZNCgQSYlO7GxsVy+fFn/OCQkhODgYNzd3fH29ubNN9/k+PHjbNy4kdTUVCIjtQ093d3dsbW15cCBAxw6dIhXXnkFFxcXDhw4wOjRo+nVqxdFixY19aXlG7cfJpCqUbBWqyjukr+r4yytoZ873m72REYnpNvuRoV20MbP36yVbjfyVI3CgSv3Mnw+QClnaxpt/gVUKhg0CGzMPNGoKLg0yXD5e+16hUGglntHiOwwuYFyeHg4fn5+abaXKVOG8PBwk8519OhR6tSpQ506dQBt+5s6deowefJkbty4wfr167l+/Tq1a9fG29tbv/z777+Atu3Nr7/+SrNmzahWrRqffvopo0eP5vvvvzf1ZeUrNx83Ti7hai/juDwnK7WKwA7+gDaxeZrucWAH/wzf58yer/N2bS/Uw4fDsGEyN5YwjSYJjg7TLhq5d4TILpNLdjw9PTl16hRly5Y12H7y5Emjqpme1rx5cxQlo9/DZLoPoG7duhw8eNCkaxYEMsaOebWp7s3CXnXTjJPjZeQ4Oxk9385aTWKKhkX7Qng/x6IXQgiRFZOTnR49ejBixAhcXFxo2rQpAHv27GHkyJF0797d7AGKtG7KGDtm16a6N638vbI9AnJ6z69Zyo1By49y/GzWbXqEEELkHJOTnWnTphEaGkpAQADW1tqnazQaevfuzWeffWb2AEVaumosbynZMSsrtYrG5U0rnczq+T/0rs/ghfH6x+ciYqhawSnb1xBCCGE6k9vs2Nra8ttvv3H+/HlWrFjB2rVruXLlCosWLcLW1jYnYhTP0JXsSE+svM/R1pqF79TTPx6w9AiXbslEo0IIkZuyPc5OpUqVqFSpkjljEUaSMXbyF2e7J39mUXHJvP3jIX4b9ALlijtbMCohhCg8jEp2xowZw7Rp03BycjIYsTg9c+bMMUtgImPSQDn/qlTCmZP3E3n7h0OsGtyY0h5px+4RQghhXkYlOydOnCA5OVm/LiwnITmVu7HaLqg+UrKTP9jZwcaNAPz00st0X3SUy7djefvHg/w2uLFUR4qMqe2g2cYn60KIbFEpWfXvLgRiYmJwc3MjOjo6y+koLC30bhzNv9iNvY2ac1PboFLJODv5ze2YBLp+d4DQe/GU8XBk1eDGlHCVUjohhDCVsd/fJjdQfvfdd3n4MG0Dy7i4ON59911TTydMdDP6yQSgkujkT56u9qwc+AK+7g6E3Yvn7R8Ocudhon405j+Db3Dgyr0MZ1oXQghhGpNLdqysrIiIiEgz99Tdu3fx8vIiJSXFrAHmhvxUsvP7seuMXX2SlysU4+cBjSwdjjBGcjKsWKFd79lTP13Etah4un13gJvRCfi42ZOiUbj9MFH/NG8jBzUUBZgmGUIf3ztle8p0EUI8w+wlOzExMURHR6MoCg8fPiQmJka/3L9/n7/++qtAT76ZV0ToBxSUao98IykJ+vXTLk9NF+Hr7siKgS/gam/NzegEg0QHIDI6gSE/H2fL6YjcjljkFZokONhPu8h0EUJkm9Fdz4sUKYJKpUKlUqXb5VylUhEUFGTW4ERaT1djifyvtLsjttbp/+ZQ0M63FbThLK38vWQeNCGEyCajk51du3ahKAotWrRgzZo1uLu76/fZ2tpSpkwZfHx8ciRI8YRu9GTpdl4wHA6J0veuS4+CdlylwyFRzzW6sxBCFGZGJzvNmjUDICQkBF9fX9Rqk9s2CzOIkJKdAuX2w4SsDzLhOCGEEGmZPIJymTJlAIiPjyc8PJykJMNfpTVr1jRPZCJd+nmxZIydAsHTxbgSOmOPE0IIkZbJyc6dO3fo168fmzdvTnd/amrqcwcl0heTkExsora3m1RjFQwN/dzxdrMnMjqB9LpFqgAvN+0M7EIIIbLH5LqoUaNG8eDBAw4dOoSDgwNbtmxh6dKlVKxYkfXr1+dEjOKxiMelOkUcbXC0zfa0ZiIPsVKrCOzgD2gTm/QEdvCXxslCCPEcTP7G3LlzJ3/++Sf169dHrVZTpkwZWrVqhaurK9OnT+e1117LiTgFT2Y7lyqsfMbODlaterL+jDbVvVnYqy5BG87qJ3nVGdjET8bZKczUdvDyqifrQohsMTnZiYuL04+nU7RoUe7cuUOlSpWoUaMGx48fN3uA4gl9t3MZYyd/sbaGt97K9JA21b1p5e/F4ZAobj9MYM+F26w9cZPt528zrk0VrK2kQ0ChpLaG0pnfO0KIrJn8CVq5cmUuXLgAQK1atfjuu++4ceMG3377Ld7e8gs0J0Xou51LyU5BZKVW0bi8Bx1rlySoY3XcnWy5eieO349dt3RoQgiRr5mc7IwcOZKICO2IroGBgWzevJnSpUszb948PvvsM7MHKJ7QV2NJ4+T8JSUFVq/WLkZOp+Jib8PQVyoAMHf7JRKSpeF/oaRJgfDV2kWT/6biESKvMLkaq1evXvr1evXqERYWxvnz5yldujTFihUza3DCkK4aq6SU7OQviYnQtat2PTZWW61lhJ6NSrNoXwg3Hjxi6b+hDG5WPkfCS9Uo+uozTxdtzy9pEJ1HaBJh3+N7p2ustlpLCGGy5/7LcXR0pG7duuaIRWRBxtgpXOxtrBjdqhJjV59kwe4rdG9YGjcH804EueV0RJqG0TIBqRCioDE52VEUhd9//51du3Zx+/ZtNBqNwf61a9eaLTjxhEajEBmtS3akGquweKNOSb7fe4WLt2L5ds8VxrepYrZzbzkdwZCfj6cZ30c3AenCXnUl4RFCFAjZGmfnnXfeISQkBGdnZ9zc3AwWkTPuxSWRlKpBpdIOMicKByu1ig9baxOcxftDuBVjnmkjUjUKQRvOpjuQoW5b0IazpGrSO0IIIfIXk0t2li9fztq1a2nXrl1OxCMyoGuc7Olih410Qy5UWlb1pF6ZohwLu89XOy7x2Rs1nvuch0Oi0ozp8zSZgFQIUZCY/K3p5uZGuXLlciIWkQndBKDSXqfwUalU+uqr345cI+Ru3HOfUyYgFUIUJiYnO1OmTCEoKIhHjx4998X37t1Lhw4d8PHxQaVSsW7dOoP9iqIwefJkvL29cXBwoGXLlly6dMngmKioKHr27ImrqytFihShf//+xMbGPndseY2ucbL0xCqcGvq506KKJ6kahS/+vvDc55MJSIUQhYnJyU7Xrl25f/8+np6e1KhRg7p16xospoiLi6NWrVp888036e6fNWsW8+bN49tvv+XQoUM4OTnRunVrEhKe/Nrs2bMnZ86cYdu2bWzcuJG9e/cyaNAgU19Wnvdkqgj58sl3bG1h8WLtYmub7dN82LoyKhVsOhXBf9ejnyukemWKYmed8Z+/Cu29JhOQWpjaFl5YrF3U2b93hCjsTG6z06dPH44dO0avXr0oUaIEKlX2x+No27Ytbdu2TXefoijMnTuXjz/+mI4dOwKwbNkySpQowbp16+jevTvnzp1jy5YtHDlyhPr16wMwf/582rVrxxdffIGPj0+2Y8trdO0rZPTkfMjGBvr2fe7TVPV2pVPtkvxx4gaztp5nef9G2TqPoihM23iWxBRNpsfJBKR5gNoGyvW1dBRC5HsmJzubNm1i69atvPzyyzkRj15ISAiRkZG0bNlSv83NzY1GjRpx4MABunfvzoEDByhSpIg+0QFo2bIlarWaQ4cO8cYbb6R77sTERBITE/WPY2Jicu6FmMmNxyU7PjJ6cqE2plUlNp66yT+X7rL/8l1eqmD6QJ7f773K8oNhqFQw8GU/NpyKSNNYuUUVT+l2LoQoMEyuxvL19cXV1TUnYjEQGRkJQIkSJQy2lyhRQr8vMjJSPympjrW1Ne7u7vpj0jN9+nSD7vK+vr5mjt78pIFyPpaSAps2aRcjp4vIiK+7Iz0blQFg5pbzKIppXcM3nLzJ9M3nAfhfu6p89Jo/+8a34JeBL/BV99pMaKttCL374h0u3nr4XLEKM9CkwI1N2kWmixAi20xOdmbPns24ceMIDQ3NgXByx8SJE4mOjtYv165ds3RImUpO1XD7obYkSqqx8qHERGjfXrs8VaKYXcNaVMDR1opT16PZfDrjpP5Zh0Oi+GDVSQD6vVSWAU20vSqfnoD0vWbledW/BKkahSnrz5icTAkz0yTCnvbaRfP8944QhZXJyU6vXr3YtWsX5cuXx8XFBXd3d4PFXLy8vAC4deuWwfZbt27p93l5eXH79m2D/SkpKURFRemPSY+dnR2urq4GS14WGZ2AooCtlRoPJ2mkWNgVc7bTJypfbL1ASmrmbW8ALt9+yMBlR0lK1dC6Wgk+fs0/w2MntffHzlrNv1fu8dd/xidTQgiRV5ncZmfu3Lk5EEZafn5+eHl5sWPHDmrXrg1o29YcOnSIIUOGANC4cWMePHjAsWPHqFevHgA7d+5Eo9HQqFH2Gm/mRbr2FF5u9qilwagABjbx4+eDYVy9G8fqY9fp0bB0hsfefphA38VHiH6UTJ3SRfiqe51MGx77ujsypHl55m6/xCebzvJKleI42soElEKI/CtbvbHMJTY2lsuXL+sfh4SEEBwcjLu7O6VLl2bUqFF88sknVKxYET8/PyZNmoSPjw+dOnUCoGrVqrRp04aBAwfy7bffkpyczLBhw+jevXsB64kljZOFIRd7G4a+UoFpG88yd/tFOtUuiYOtVZrj4pNS6L/kKNfvP6KshyM/9q6PvU3a4571XrPy/H7sOtfvP+KbXZf1U1YIIUR+lK15B65cucLHH39Mjx499NVImzdv5syZMyad5+jRo9SpU4c6deoAMGbMGOrUqcPkyZMBGDduHMOHD2fQoEE0aNCA2NhYtmzZgr39ky/9FStWUKVKFQICAmjXrh0vv/wy33//fXZeVp6l74kljZPFU3q9UJqSRRy4FZPI0gOhafanpGoYtvIE/92Ixt3JliX9GuLhbGfUue1trJjUXlvV9cPeEELNMGqzEEJYisnJzp49e6hRowaHDh1i7dq1+tGKT548SWBgoEnnat68OYqipFmWLFkCaIfJnzp1KpGRkSQkJLB9+3YqVapkcA53d3dWrlzJw4cPiY6OZtGiRTg7O5v6svK0iAcyxo5Iy87aitGttH8P3+y8xPazt/gz+AYHrtwjJVVD4Poz7Dx/GztrNT/0rk/ZYk4mnf9V/xI0rVScpFQNUzeezYmXIIQQucLkZGfChAl88sknbNu2DdunRoNt0aIFBw8eNGtwQks/erJUY4lnvFGnJD5u9jxMTGXAsqOM/DWYHj8cpM7Ubaw4FI5KBV91r0O9MkVNPrdKpSKwgz82Vip2nr/NjnO3sn6SEELkQSa32fnvv/9YuXJlmu2enp7cvXvXLEEJQzd1oydLNVb+ZGsLX3/9ZN2Mtp2N1N8fT3uYqB2T5a16pWhTPeOeiVkpX9yZd1/247s9VwnacJaXKhQzqs2PMBO1LdT/+sm6ECJbTC7ZKVKkCBEREWm2nzhxgpIlS5olKGHoSQNlSXbyJRsbGDpUu9jYmO20qRqFoA2ZVy/9c+kuqZrnGytneIuKlHC1Izwqnh//ufpc5xImUttApaHaRW2+e0eIwsbkZKd79+6MHz+eyMhIVCoVGo2G/fv3M3bsWHr37p0TMRZq8UkpPIhPBqQaSxg6HBKVZpqHZ0VEJ3A4JOq5ruNsZ81H7aoC8PWuy/oG80IIkV+YnOx89tlnVKlSBV9fX2JjY/H396dp06a8+OKLfPzxxzkRY6F283HjZGc7a1zt5ZddvpSaCrt3a5fUVLOd9vbDzBMdU4/LzOu1fGjo505CsoZPN0lj5VyjSYVbu7WLxnz3jhCFjcnJjq2tLT/88ANXrlxh48aN/Pzzz5w/f57ly5djZSV1+eYmY+wUAAkJ8Mor2iXh+RMPHU8X4+4JY4/LjEqlIuj1aqhV8Nd/key/LO3zcoUmAXa8ol005rt3hChssj0saunSpSldOuNRW4V56HtiSeNk8YyGfu54u9lrpxNJZ78K7ajbDf3MM41LVW9Xejcuy5J/Qwlcf4bNI5tgY5WtobqEECJXGZXsjBkzxugTzpkzJ9vBiLRuyhg7IgNWam3X8CE/H0cFBgmPbjKIwA7+mU4NYarRrSqx4eRNLt+OZem/ofo5uoQQIi8zKtk5ceKEUSdTqWTeJnPTV2O5STWWSKtNdW8W9qpL0IazBo2VvdzsCezgT5vq3ma9npuDDePbVGHcmlPM3X6J12v54Okq96YQIm8zKtnZtWtXTschMqAr2fGWkh2RgTbVvWnl78XhkChuP0zA00VbdWXOEp2nvVmvFCsOh3Py2gNmbD7PnG61c+Q6QghhLs81lfG1a9cA8PX1NUswIq2b0kBZGMFKraJxeY9cuZZarWLq69XotGA/a0/coFsDXzQKuZJoCSFEdpic7KSkpBAUFMS8efP082I5OzszfPhwAgMDsTHjoGmFnaIo+gbKMnqyyEtq+RahW31ffj1yjZ4/HiLlqYELvXOoCk0IIbLL5GRn+PDhrF27llmzZtG4cWMADhw4wJQpU7h37x4LFy40e5CF1YP4ZBKSNYC2DYbIp2xsYNasJ+sFRL0yRfn1yDWDRAcgMjqBIT8fZ2GvupLwPC+VDdSe9WRdCJEtKkVRTBpL3s3NjV9//ZW2bdsabP/rr7/o0aMH0dHRZg0wN8TExODm5kZ0dDSurq6WDkfvzM1oXpu3j2LOthz9uJWlwxFCL1Wj8PLMnRmO4Kzr9r5vfAup0hJC5Bhjv79NHiTDzs6OsmXLptnu5+dnMAu6eH76xslShSXymKymqlAwz1QVQghhDiYnO8OGDWPatGkkJibqtyUmJvLpp58ybNgwswZX2MnoyQVEaiocOaJdzDhdhCXl5lQVhZomFe4d0S4yXYQQ2WZUm53OnTsbPN6+fTulSpWiVq1aAJw8eZKkpCQCAgLMH2EhJiU7BURCAjRsqF2PjQUnJ8vGYwbGTkHh5iDtTJ6LJgG2Pr53usaCOv/fO0JYglHJjpubm8HjLl26GDyWruc5Q98TS0p2RB6T1VQVOuN/P8VHr1Xl9Vo+MuioEMJijEp2Fi9enNNxiHQ8qcaSkh2Rt2Q1VYUCeDjZcuthIiN/DWb5gTCmvF6N6iXd0j+hEELkoGzP4nfnzh327dvHvn37uHPnjjljEo9JNZbIy3RTVTw7LIKXmz3f9qrL/gktGPtqJRxsrDgadp8OX+9jwppT3I190t4vVaNw4Mo9/gy+wYEr90jVmNQ5VAghjGLyODtxcXEMHz6cZcuWodFox4CxsrKid+/ezJ8/H0dHR7MHWRglpWj0JTuR0Y9I1RSRLrwiz8lqqophLSrSpV4pZmw+z5/BN/n1yDU2nYpgZMuKlHC157O/zhn06pIBCYUQOcHkcXYGDx7M9u3b+frrr3nppZcA2LdvHyNGjKBVq1b5clDBvDbOzpbTEUz+8wy3Hz75BSxfAvlYXBw4O2vXC0gD5ew4GhrFlA1nOH0jJsNjdOm8DEj4WEocrHp873SNBevCee8IkRFjv79NTnaKFSvG77//TvPmzQ2279q1i65du+bLKq28lOxsOR3BkJ+Pp2n0KV8C+ZgkO3qpGoVVR8P53x+nyajGSgYkfIokO0Jkytjvb5OrseLj4ylRokSa7Z6ensTHx5t6OvGUVI1C0Iaz6fZuUdB+CQRtOEsrfy/5EshPbGwgMPDJeiFmpVZR1sM5w0QHDAckzK3JTfMslQ1UD3yyLoTIFpOTncaNGxMYGMiyZcuwt9c2THz06BFBQUH6ubJE9pgyKm2h/xLIT2xtYcoUS0eRZ8iAhCawsoWaUywdhRD5nsnJzty5c2nTpk2aQQXt7e3ZunWr2QMsTORLQBQGxg5I6OEk088IIczD5K7nNWrU4NKlS0yfPp3atWtTu3ZtZsyYwaVLl6hWrZrZAyxbtiwqlSrNMnToUACaN2+eZt97771n9jhyg7FfAsYeJ/IIjQbOnNEuj3swFma6AQmzqoj9ZNM5Tt/IfxMLm5WigQdntIsi944Q2WVSyU5ycjJVqlRh48aNDBw4MKdiMnDkyBFSn5pP6PTp07Rq1Yq33npLv23gwIFMnTpV/zi/dn/PalRaXcPNhn7uuR2aeB6PHkH16tr1Qt5AGYwbkNDJ1orzkQ/p+M1+Bjctx4iAitjbWFkmYEtKfQR/Pb53pIGyENlmUsmOjY0NCQm5W4VSvHhxvLy89MvGjRspX748zZo10x/j6OhocIyle1Rll+5LID26X8GBHfylcbLI97IakHDPuFdoX9ObVI3Cgt1XeG3ePxwLu2+haIUQ+Z3JXc8/++wzLl68yI8//oi1tclNfp5LUlISPj4+jBkzho8++gjQVmOdOXMGRVHw8vKiQ4cOTJo0KdPSncTERINZ22NiYvD19c0TXc8BlvwbwpT1Zw22yTg7+Zh0Pc9QqkbJcEBCgC2nI5n052nuPExEpYJ3X/Jj7KuVcbC1Mur5+Z50PRciUznW9fzIkSPs2LGDv//+mxo1auD0zAf32rVrTY/WSOvWrePBgwf07dtXv+3tt9+mTJky+Pj4cOrUKcaPH8+FCxcyjWP69OkEBQXlWJzPq7S7o/7fD16tVDA/xIVAW5qZWc/CNtW9eKGcO9M2nmPN8ev8tC+E7eduMaNzTaIfJRG04ayMwCyEyJLJJTv9+vXLdH9OThraunVrbG1t2bBhQ4bH7Ny5k4CAAC5fvkz58uXTPSavl+z8tC+EaRvP0q6GFwt61rN0OOJ5ScmOWey6cJv/rf2Pm5kMz1DgBt+Ukh0hMpVjJTuWmgE9LCyM7du3Z1ly1KhRI4BMkx07Ozvs7OzMHqO5hN6NA6CMh3ywCaHzSmVPto5uymd/neOXw9fSPUYG3xRCpMfoBsoajYaZM2fy0ksv0aBBAyZMmMCjR49yMjYDixcvxtPTk9deey3T44KDgwHw9s6/v+pC72mTHT9JdoQw4GJvw+u1SmZ6zNODbwohBJhQsvPpp58yZcoUWrZsiYODA1999RW3b99m0aJFORkfoE20Fi9eTJ8+fQwaRV+5coWVK1fSrl07PDw8OHXqFKNHj6Zp06bUrFkzx+PKKbpkp2wxSXYKBBsbGDv2ybp4LoVq8E2VDVQd+2RdCJEtRic7y5YtY8GCBQwePBiA7du389prr/Hjjz+iVps8NqFJtm/fTnh4OO+++67BdltbW7Zv387cuXOJi4vD19eXLl268PHHH+doPDkpKUXDjfvaErOyHvlzvCDxDFtb+PxzS0dRYBSqwTetbKGO3DtCPC+jk53w8HDatWunf9yyZUtUKhU3b96kVKlSORKczquvvkp67ah9fX3Zs2dPjl47t4VHxaNRtIOqFXfJu+2KhLAUGXxTCGEqo4tkUlJS9BN/6tjY2JCcnGz2oAqzsHtPGierVNK4skDQaCA0VLvIdBHP7enBNzP6Cykwg28qGogN1S4yXYQQ2WZ0yY6iKPTt29egF1NCQgLvvfeewVg7OTnOTmEQ8rgnlp+01yk4Hj0CPz/tunQ9NwvdCMzPjrNjb6NmbrfaBaPbOWini1j/+N6RrudCZJvRyU6fPn3SbOvVq5dZgxFPGieXkfY6QmSqTXVvWvl7cTgkiuBr95m55QJJKRrqlC5q6dCEEHmM0cmOpcbXKWzC7sUD0hNLCGPoRmBuXN6DXefvcDg0il8PX2Nky4qWDk0IkYfkbDcqYTKpxhIie3o1LgPAysNhJKdK+xYhxBOS7OQhiSmp3Hyg7XYu1VhCmKZNNS+KOdtyKyaRHeduWTocIUQeIslOHnLt6W7nztLtXAhT2Fqr6dbAF4DlB8MsHI0QIi+RZCcPCb37pL2OdDsXwnQ9GpZGrYL9l+9x5U6spcMRQuQRRiU7devW5f79+wBMnTqV+Pj4HA2qsJJpIgooa2t4/33tYm3y3LvCBKWKOtKiiicAKw6GWzgaM1BZQ8X3tYtK7h0hssuoZOfcuXPExWm/iIOCgoiNlV9MOUHXOFmmiShg7Ozgm2+0i51UT+a0Xi9oGyqvPnaN+KQUC0fznKzsoME32sVK7h0hssuonwq1a9emX79+vPzyyyiKwhdffIGzs3O6x06ePNmsARYm+m7nMtu5ENnWtGJxSrs7Eh4Vz4aTN+nWoLSlQxJCWJhRyc6SJUsIDAxk48aNqFQqNm/ebDD7uI5KpZJk5zlIt/MCSlHg7l3terFiIO2xcpRaraLXC6X57K/zLD8YRtf6vvm3DZyiQOLje8dO7h0hssuoZKdy5cr8+uuvAKjVanbs2IGnp2eOBlbYJCSncjNa1+1ckp0CJT4edH8vMl1Ernirni9f/H2R0zdiOHk9mtq+RSwdUvakxsPax/eOTBchRLaZ3BtLo9FIopMDrkXFoyjgbGdNMWdbS4cjRL5W1MmW9jW182MtPyDd0IUo7LLV9fzKlSsMHz6cli1b0rJlS0aMGMGVK1fMHVuhEqqfJsIx/xa5C5GHvPO4ofKGUze5H5dk4WiEEJZkcrKzdetW/P39OXz4MDVr1qRmzZocOnSIatWqsW3btpyIsVAI1ffEkmJqIcyhtm8Rqvm4kpSi4fdj1y0djhDCgkxOdiZMmMDo0aM5dOgQc+bMYc6cORw6dIhRo0Yxfvz4nIixUAi5J8mOEOakUqn0pTs/HwpDo1EsHJEQwlJMTnbOnTtH//7902x/9913OXv2rFmCKozCZEBBIczu9do+uNhbE3Yvnn8u37V0OEIICzE52SlevDjBwcFptgcHB0vD5eegmyrCr5gMKCiEuTjaWvNmvVKANFQWojAzefzxgQMHMmjQIK5evcqLL74IwP79+5k5cyZjxowxe4CFgXQ7L+CsraFPnyfrIlf1bFSGxftD2Xn+FjcePKJkEQdLh2Q8lTX49XmyLoTIFpP/eiZNmoSLiwuzZ89m4sSJAPj4+DBlyhRGjBhh9gALg/DH3c5d7KzxcJJu5wWOnR0sWWLpKAqtCp7OvFjeg3+v3OOXQ+GMbV3Z0iEZz8oOGi+xdBRC5HsmV2OpVCpGjx7N9evXiY6OJjo6muvXrzNy5EjpMp1N+p5YMtu5EDlC11D51yPhJKVoLByNECK3ZWucHR0XFxdcXFzMFUuhJbOdF3CKAnFx2kWRHkGW0NK/BJ4udtyNTWLLmUhLh2M8RYGUOO0i944Q2fZcyY4wj5C7uglApXFygRQfD87O2iU+3tLRFEo2Vmp6NNROCPrzwXzUUDk1HlY5a5dUuXeEyC5JdvKAMBljR4gc16NhaazUKg6HRHEh8qGlwxFC5CJJdvKAp9vsCCFyhpebPa/6lwDyWemOEOK5mZTsJCcnExAQwKVLl3IqHgNTpkxBpVIZLFWqVNHvT0hIYOjQoXh4eODs7EyXLl24detWrsRmLtpu5wmAVGMJkdN6PW6ovPb4dWITUywcjRAit5iU7NjY2HDq1KmciiVd1apVIyIiQr/s27dPv2/06NFs2LCB1atXs2fPHm7evEnnzp1zNb7nFfZ4AlAXe2vcpdu5EDnqxfIelCvuRFxSKutO3LB0OEKIXGJyNVavXr346aefciKWdFlbW+Pl5aVfihUrBkB0dDQ//fQTc+bMoUWLFtSrV4/Fixfz77//cvDgwVyL73npemL5SbdzIXKcSqWiV6PH82UdDEORHk5CFAomDyqYkpLCokWL2L59O/Xq1cPJybCdyZw5c8wWHMClS5fw8fHB3t6exo0bM336dEqXLs2xY8dITk6mZcuW+mOrVKlC6dKlOXDgAC+88EKG50xMTCQxMVH/OCYmxqwxm0LXXkdGThYid3SpV4pZW89zPvIhS/8NpaiTLZ4u9jT0c8dKLT84hCiITE52Tp8+Td26dQG4ePGiwT5zl0w0atSIJUuWULlyZSIiIggKCqJJkyacPn2ayMhIbG1tKVKkiMFzSpQoQWRk5uNoTJ8+naCgILPGml36kh1pr1NwWVnBm28+WRcW5eZgQ73SRdl/5R5TNjyZvNjbzZ7ADv60qe5tweieobIC3zefrAshssXkZGfXrl05EUe62rZtq1+vWbMmjRo1okyZMqxatQoHh+zPbzNx4kSDebxiYmLw9fV9rlizSzcBqPTEKsDs7WH1aktHIR7bcjqC/VfupdkeGZ3AkJ+Ps7BX3byT8FjZQxO5d4R4Xtnuen758mW2bt3Ko0faCSxzo+67SJEiVKpUicuXL+Pl5UVSUhIPHjwwOObWrVt4eXlleh47OztcXV0NFkuR0ZOFyD2pGoWgp0pznqb7BAvacJZUjbTlEaIgMTnZuXfvHgEBAVSqVIl27doREREBQP/+/fnggw/MHuDTYmNjuXLlCt7e3tSrVw8bGxt27Nih33/hwgXCw8Np3LhxjsZhLo+SUonQdzuXZEeInHY4JEr/N5ceBYiITuBwSFTuBSWEyHEmJzujR4/GxsaG8PBwHB2ftDPp1q0bW7ZsMWtwY8eOZc+ePYSGhvLvv//yxhtvYGVlRY8ePXBzc6N///6MGTOGXbt2cezYMfr160fjxo0zbZycl4RFaUt1XO2tKepoY+FoRI6JiwOVSrvExVk6mkLt9sOME53sHJfjUuJgpUq7pMi9I0R2mdxm5++//2br1q2UKlXKYHvFihUJCzPvqKTXr1+nR48e3Lt3j+LFi/Pyyy9z8OBBihcvDsCXX36JWq2mS5cuJCYm0rp1axYsWGDWGHKSrr2OdDsXInd4utib9TghRP5gcrITFxdnUKKjExUVhZ2dnVmC0vn1118z3W9vb88333zDN998Y9br5hZdex3pdi5E7mjo5463mz2R0Qmk1ypHhXZaiYZ+7rkdmhAiB5lcjdWkSROWLVumf6xSqdBoNMyaNYtXXnnFrMEVdDInlhC5y0qtIrCDP6BNbJ6lAIEd/GW8HSEKGJNLdmbNmkVAQABHjx4lKSmJcePGcebMGaKioti/f39OxFhgPRk9WcbYESK3tKnuzcJedQnacDZNY+V6ZYrknW7nQgizMTnZqV69OhcvXuTrr7/GxcWF2NhYOnfuzNChQ/H2lg8JU+jH2JFqLCFyVZvq3rTy9+JwSBS3HybwKCmVCWv/41jYA46F3ademaKWDlEIYUYmJzsAbm5u/O9//zN3LIXKo6RUImOk27kQlmKlVtG4vIf+8fHw+6w6ep1PNp1l7ZAXpdOAEAVItpKd+/fv89NPP3Hu3DkA/P396devH+7u0qjPWLoqLDcHG4rKbOcFm5UVtGv3ZF3kSR+8WpmNpyI4Ef6Ajaci6FDLx9IhaaeI8Gn3ZF0IkS0mN1Deu3cvZcuWZd68edy/f5/79+8zb948/Pz82Lt3b07EWCCFycjJhYe9PWzapF3spUtzXlXC1Z73mpUHYMbm8yQkp1o4IrTTRTTfpF2s5N4RIrtMTnaGDh1Kt27dCAkJYe3ataxdu5arV6/SvXt3hg4dmhMxFkgh+vY60jhZiLxiYJNyeLnac+PBIxbvD7V0OEIIMzE52bl8+TIffPABVk8Vx1tZWTFmzBguX75s1uAKMn23c2mvI0Se4WBrxYetKwOwYNdl7sYmWjgiIYQ5mJzs1K1bV99W52nnzp2jVq1aZgmqMHjS7VySnQIvLg6cnLSLTBeR571RpyTVS7ryMDGFudsvWjaYlDj4zUm7yHQRQmSbUQ2UT506pV8fMWIEI0eO5PLly/o5qA4ePMg333zDjBkzcibKAkhmOy9k4uMtHYEwklqt4uPX/On+/UFWHgqnd+OyVCrhYrmAUuXeEeJ5qRRFSW/UdANqtRqVSkVWh6pUKlJT80CjPhPFxMTg5uZGdHQ0rq6uOX69+KQU/CdvBSB4ciuKOEpvrAItLg6cnbXrsbHaEh6R5w1efpStZ27RvHJxlvRraJkgUuJg1eN7p2ssWMu9I8TTjP3+NqpkJyQkxGyBiSeDCRZxtJFER4g8akLbquw8f5vdF+6w9+IdmlYqbumQhBDZZFSyU6ZMmZyOo1DRdzuXxslC5Fl+xZx454WyLNofwqebzvFShWIyZ5YQ+VS2BhW8efMm+/bt4/bt22g0GoN9I0aMMEtgBVmIPtmRbudC5GUjAiqw5vh1Ltx6yKqj1+jRsLSlQxJCZIPJyc6SJUsYPHgwtra2eHh4GAyprlKpJNkxgsx2LkT+UMTRlpEBFZm68Syz/75Ah1o+ONtl6zeiEMKCTP6rnTRpEpMnT2bixImo1Sb3XBdA6D1tmx3pdl5IqNXQrNmTdZGv9HqhDMsPhhFyN46Fuy/zYesquXh1NXg2e7IuhMgWk/964uPj6d69uyQ6z0EGFCxkHBxg927t4uBg6WiEiWyt1Uxoq01wfvwnhBsPHuXexa0doOVu7WIt944Q2WVyxtK/f39Wr16dE7EUCnGJKdx+qB2VVZIdIfKHV/1L0MjPncQUDZ9vOW/pcIQQJjK5Gmv69Om0b9+eLVu2UKNGDWxsbAz2z5kzx2zBFUS6wQSLOtrg5miTxdFCiLxApVIxqb0/Hb7ex7rgm/R9yY/avkUsHZYQwkjZSna2bt1K5cra+WOebaAsMhf2uL2ONE4uROLioGxZ7XpoqAwqmE9VL+lG5zqlWHP8Op9sPMvq9xrn/GdeShz8WVa73jFUBhUUIptMTnZmz57NokWL6Nu3bw6EU/CFSHudwunuXUtHIMzgw9aV2fTfTY6G3eevUxG4O9tx+2ECni72NPRzz5lxeBLl3hHieZmc7NjZ2fHSSy/lRCyFgjROFiL/8nKzZ1DT8szbcYkRvwaT+tQUOt5u9gR28KdNdW8LRiiESI/JDZRHjhzJ/PnzcyKWQuFJNZYMKChEflTucRV06jNzBUZGJzDk5+NsOR1hibCEEJkwuWTn8OHD7Ny5k40bN1KtWrU0DZTXrl1rtuAKIt3oyTLGjhD5T6pGYWYGvbEUQAUEbThLK38vmVpCiDzE5GSnSJEidO7cOSdiKfBiE1O487jbeRmpxhIi3zkcEkVEdEKG+xUgIjqBwyFRNC7vkXuBCSEyZXKys3jx4pyIo1DQtddxd7LFzUG6nQuR39x+mHGik53jhBC5I08Pgzx9+nQaNGiAi4sLnp6edOrUiQsXLhgc07x5c1QqlcHy3nvvWSjizOnb68gEoIWLWg3162sXGXk8X/N0sTfrcVlTg3t97ZK3P66FyNNMLtnx8/PLdGyJq1evPldAT9uzZw9Dhw6lQYMGpKSk8NFHH/Hqq69y9uxZnJ4aq2TgwIFMnTpV/9jRMW8mE6H3pCdWoeTgAEeOWDoKYQYN/dzxdrMnMjoBJZPjVh0Jp4yHIz5FnnOKB2sHaCP3jhDPy+RkZ9SoUQaPk5OTOXHiBFu2bOHDDz80V1wAbNmyxeDxkiVL8PT05NixYzRt2lS/3dHRES8vL7NeOyeEyGznQuRrVmoVgR38GfLzcVSQYcLzR/BNNp+JZFCTcgxuVh4nmSldCIsy+S9w5MiR6W7/5ptvOHr06HMHlJno6GgA3N3dDbavWLGCn3/+GS8vLzp06MCkSZMyLd1JTEwkMTFR/zgmJiZnAn5G2D1JdoTI79pU92Zhr7oEbThr0FhZN86OTxEHPtl4jsOhUczbeZlfjlzjw1cr06VeKYMeWqkahcMhUTk/KKEQApWiKJmVxhrt6tWr1K5dO8cSB41Gw+uvv86DBw/Yt2+ffvv3339PmTJl8PHx4dSpU4wfP56GDRtm2gV+ypQpBAUFpdkeHR2Nq6trjsQPUP+T7dyNTWTDsJepUcotx64j8pj4ePD3166fPQt5tJpVmCazZEVRFLaeiWT65vP6tnpVvV35+LWqvFShGFtOR2SYLBkMSpgSD5se3zuvnQVruXeEeFpMTAxubm5Zfn+bLdmZNWsWCxYsIDQ01BynS2PIkCFs3ryZffv2UapUqQyP27lzJwEBAVy+fJny5cune0x6JTu+vr45muw8TEimxpS/ATg15VVc7aU3VqERFwfOztr12FiZG6sQSUxJZfmBML7acYmHCSkA1CzpyqkbaX8U6sp0Fvaq+yThSYmDVY/vna6xMjeWEM8wNtkxuRqrTp06Bg2UFUUhMjKSO3fusGDBguxFm4Vhw4axceNG9u7dm2miA9CoUSOATJMdOzs77OzszB5nZnS/7jycbCXREaKQsLO2YkCTcnSuW4p5Oy6x/EBouokOyKCEQuQkk5OdTp06GTxWq9UUL16c5s2bU6VKFXPFBWgTqeHDh/PHH3+we/du/Pz8snxOcHAwAN7eeWt+mlBpryNEoeXuZMuU16tRo5QbH6w6meFxMiihEDnD5GQnMDAwJ+JI19ChQ1m5ciV//vknLi4uREZGAuDm5oaDgwNXrlxh5cqVtGvXDg8PD06dOsXo0aNp2rQpNWvWzLU4jaEbULCMjLEjRKFlbWRpjQxKKIR55en+kAsXLgS0Awc+bfHixfTt2xdbW1u2b9/O3LlziYuLw9fXly5duvDxxx9bINrMhdzVVmP5yRg7QhRauT8ooRACTEh21Gp1poMJAqhUKlJSUp47KJ2s2k77+vqyZ88es10vJ0m3cyFEVoMSqgAvN23PLiGE+Rid7Pzxxx8Z7jtw4ADz5s1Do9GYJaiCKFRmOy+8VKonXc+z+MEgCrasBiVUgMAO/k81TlaBm/+TdSFEthid7HTs2DHNtgsXLjBhwgQ2bNhAz549DaZsEE88TEjmbmwSIG12CiVHRzhzxtJRiDwio0EJAWyt1VQs4fJkg7UjvCb3jhDPK1ttdm7evElgYCBLly6ldevWBAcHU716dXPHVmCEPm6vU8zZFhfpdi5Eodemujet/L30gxIWc7bj652XOHA1imErT/DH+y9ib2Nl6TCFKDBMmkY3Ojqa8ePHU6FCBc6cOcOOHTvYsGGDJDpZkAlAhRDPslKraFzeg461S/JShWLM7V4HDydbzkXE8Nlf5ywdnhAFitHJzqxZsyhXrhwbN27kl19+4d9//6VJkyY5GVuB8aTbuSQ7hVJ8PFSrpl3i4y0djcijSrjaM7trLQCWHQhjy+nIx9NFVNMuKXLvCJFdRldjTZgwAQcHBypUqMDSpUtZunRpusdlNidVYRWib5ws7XUKJUXRzomlWxciA80rezK4aTm+23uVcb+fpMbQupSMfnzvZDjHuhAiK0YnO717986y67lIn26qCOl2LoTIytjWlTkUEkXwtQeMXX2SX4pYOiIh8j+jk50lS5bkYBgFm64aS9rsCCGyYmOlZn6POrSb9w/B16KhiKUjEiL/M6mBsjBdTEIy9+K03c6lZEcIYQxfd0dmdslbU94IkZ9JspPDdKU6xZztcLbL07NzCCHykHY1vOnWoJT+scyXJUT2SbKTw0Ift9eRxslCCFNNaFv1yfqaU2g00khZiOyQZCeHSbdzgUoFZcpoF2nkL0xgb2NNsn1pbiR58u+VKBbuuWLpkITIlyTZyWG6ZEfmxCrEHB0hNFS7OEoJnzCBtSM2ncP4t/pREhR75my7yNHQKEtHJUS+I8lODpPRk4UQz+vNeqV4o05JUjUKI345wb3YRA5cucefwTc4cOUeqVK9JUSmpMVsDtO12ZEJQIUQ2aVSqZjWqTrB1x4QcjeOF2fsJDFFo9/v7WZPYAd/2lT3tmCUQuRdUrKTg6IfJRMl3c7Fo0fQoIF2efTI0tGI/CTlEWxpAFsa4GyVTI8GvgAGiQ5AZHQCQ34+zpbTEZaIUog8T0p2ckiqRmHDyZsAuDnY4CAzGBdeGg0cPfpkXQijaSBKe++kalJZ/G9oukcpgAoI2nCWVv5eWKmlIbwQT5OSnRyw5XQEL8/cycfrTgPaEp6XZ+6UX11CiGw7GhpFRHTGY+0oQER0AodDpAGzEM+SZMfMtpyOYMjPx9N8KEkxsxDiedyJTTTqOBl8UIi0JNkxo1SNQtCGs+nOTazbFrThrPScEEKYrLiznVHH7b5wm3tGJkai4EvVKNJzD2mzY1aHQ4wvZm5c3iP3AhNC5Hv1y7rj7WZPZHRCuj+odP44cZPNpyPpVt+XAU3K4euetidoqkbhcEgUtx8m4OliT0M/d2nnUwBtOR1B0IazBt9LhbXnniQ7ZmRs8bEUMwshTGWlVhHYwZ8hPx9HBQYJjy5NGdTUjwNXozh1PZqlB8L4+VA4r9fyYXCzclTxcgXkC7Cw0DWpeDYx1jWpWNirbqH6/5Zkx4w8XezNepwoQIoVs3QEIr+ye3LvtKnuzcJeddMkK15PJSuKoq22WLjnCv9cussfJ27wx4kbBFTxpE7pIsz++6J8ARZwWTWpKIw991SKohTOCrynxMTE4ObmRnR0NK6urtk+T6pG4eWZOzMsZlah/VDaN75FobnBhBDmZ2w11H/Xo/l2zxX+Oh1BVp/0xn4+PW8VWGGsQsvt9+zAlXv0+OFgluf9ZeALWTapyOv/38Z+f0uyg/mSHXhSdAjpFzPLLychRG4LuRvHtI1n2Hn+TpbHZvYF+LxVYIWxCi0337MH8UnsvXSXZf+GcjTsfpbnrlu6CD0blaFJpWLp1jjkh/9vSXZMYM5kBwrnH7QQIm/7M/gGI38NzvK49jW9ebNeKar5uFHc5UkPsIzagBj7Q+55n6+T3ZICS5Qo5fR7tqBnXcp4OLHrwm12nb/N8fD7ZLezVTUfV5pVKk6zSsWpW6YoO87dyhP/31kpdMnON998w+eff05kZCS1atVi/vz5/2/vzqOiuNI2gD/VDbSyuLOIopgoBIWgBiFxX3BDY5wYx4lMRA+GY9REjZ5McL6MoibExBXNcZkkbiERNUYyYzS4LwwqLhgXEDUqLihgUMJiQ3e/3x/YZbeINAhVXe37O4cDdHfB00Vx++1b995CcHCwRdvWdrEDPJ9dtawSJSXA4MHlX+/YAdSvL28ephy6EmD/w2On9w7ArubHjqWnNky5N9DA37Mh/Jq7IP5oFvKLy574uKpOgRlP8Vc2W9XSU2g1fSMpxxvQZ33OVW0PACoBFYobX3cX9PRthh9P3ER+UWmlQyqaODlgVBcvHLqYhzM375vd7+Sghs5AFS5LUlvZa3NIx3NV7CQkJGDMmDFYuXIlQkJCsGTJEmzevBkXLlyAm5tbldvXRbHDmKioCHB2Lv+6sBBw4uukMQvpioBND4+dvxYCdjU/dqoaUwgALvXs0NvHFeeyC3Alr6jKcT6PG9DeHR4NK54OuX3/AZLO36ly+6/HBKGfnxsEoeILYE17Cuqyh6FMb0CxVo+iUh2KtDoUlepR/PDz6ev5WL7vcpU/41n3mYNahZ4+zdDb1w29fV3RsnH5UgPVGVKRV6jFoYu5OHAhFwcv5onXdKzr7JaMGarKc1XshISEoEuXLli+fDkAwGAwwMvLC++//z4+/vjjKrfnYofVKS52WE3VYrEDVO8FsEirQ3p2Ac7evI8dZ2/jqESXoXBQq9DM2QGuLhrxo6mTA9anXEPBA12l27k30GDbpG5mPQV6A+GN5cnI+bPyRRYbO9rjX0Pbo6TM8LBg0aG4VI8ibfnnQq0OxaU6FGn14ueiUh2KtXqU6uW/1t2CkS/jrVe8nnhfTXq0DAbCV/svYWFSZp3kNbX0bx3xRscWz/QzLH39VvzU89LSUpw4cQLR0dHibSqVCqGhoUhJSXniNlqtFlrto4O/oKCgznMyxpjcLJm6buSksUOQdxMEeTeBr0cDi06BvdnJU+xZMHUjvxhbT92yKGOp3oBb9x/g1lNO3zzJnQItXovdW61tACC/uAzTNp2u9namHNQqOGrUcHKwg6ODGo4aO+j1Bpy9VfVry7PusxaNKm5rNMi/Ofq396jWkAqVSkBQ6yZV/t7ayC7lMiyKL3by8vKg1+vh7u5udru7uzsyMjKeuE1sbCxiYmKkiMcYY1alJi+AwW2evnqzcQzGlyM7VjqGI+X3P6rcfs/0XsgvLkPun1qzj2NX7yL50l2Lnp/pGTBLz1u0c3OGdzMnOD0sVJwc1HB0sIOzxs6siHHS2JV/PPY4B7uKV16ydCmSZ91nwW2eXpioVUK1TxVJ9feuKnttUnyxUxPR0dH48MMPxe8LCgrg5fXkbkDGGLM11X0BtGT15lmvt6+0YLJ0e0cHOzg62KFFI/OB2CmXm1hU7Dw+BsTSQdlz3vCv9Uv4SLXP6mLii5KzV0bxFwJt1qwZ1Go17twxHwx1584deHh4PHEbjUaDBg0amH0wxhirnPEU2OMDUj0a1rNokO+zbG/saajspVFA+ViUx3sKarpdbZFznz0rJWd/EpsZoBwcHIxly5YBKB+g3KpVK0yePJkHKDP5FRUBxlmBOTk8QJlZTlcE/Pjw2BmR88wDlGuDXCvq1nTBVmtY6NXaVyGuy9/NKyjXooSEBERERGDVqlUIDg7GkiVLsGnTJmRkZFQYy/MkXOwwxpj1U9I6O0waz1WxAwDLly8XFxXs2LEj4uLiEBISYtG2XOwwxpgyKGkFZVb3nrti51lwscMYY4wpj6Wv34ofoMyY1XvwABgypPzjQfXWDmHPOf0DYP+Q8g89HzuM1dRzOfWcMUnp9cAvvzz6mjFLkR649cujrxljNcI9O4wxxhizaVzsMMYYY8ymcbHDGGOMMZvGxQ5jjDHGbBoXO4wxxhizaTwbC4BxqaGCggKZkzCbVFT06OuCAp6RxSynKwKKH35dUADY8bHDmCnj63ZVSwbyooIAbty4wVc9Z4wxxhTq+vXraNmyZaX3c7GD8guH3rp1Cy4uLhCE2r1AmZeXF65fv664lZk5uzyUmp1zS4+zS49zS6+q7ESEP//8E56enlCpKh+Zw6exAKhUqqdWhM+qQYMGijvAjDi7PJSanXNLj7NLj3NL72nZGzZsWOX2PECZMcYYYzaNix3GGGOM2TQuduqQRqPBrFmzoNFo5I5SbZxdHkrNzrmlx9mlx7mlV1vZeYAyY4wxxmwa9+wwxhhjzKZxscMYY4wxm8bFDmOMMcZsGhc7jDHGGLNpXOwwxhhjzKZxsfOcKiwslDtCjfDkQVZdubm5fNwwm8dt+tNxsVND169fx86dO+WOUW3Xrl3DwIED8Y9//ANA+XXBlCIvLw+5ubnQP7xquJJewHQ6HQBl7W+g/CK58fHxyM/PlztKtV29ehVhYWGYMGECBEFQ1L7PycnBlStXUFRUBEBZx7pxPxv/T5WC23TpSdqmE6u2zMxMEgSB6tevT5mZmXLHsYjBYKCoqCiys7OjBg0aUIcOHUiv18sdy2ITJ04kDw8PCgwMpH79+tHFixfljmSxDz74gAYMGCB3jGrLysqiZs2akSAIlJSUJHcci5ke6y1atCBXV1d68OCB3LEsNnnyZGrcuDH5+/uTn58fJScnU2lpqdyxLDJt2jR6++235Y5RbdymS0/qNp17dmqgrKwMAwcORNOmTTFv3jy541Rp0aJFaNSoEdLS0nDy5El89tlnsLe3x507d+SOZpEZM2YgJSUFGzduxPTp01FaWoo333wThw4dkjvaU6Wnp2PIkCFITEzErl27EB8fD0A577wcHBzQrVs3tGrVCgsXLkROTo7ckaq0cOFC8VhPTU3FypUr4erqirNnz8odzSKxsbE4fPgwtm3bhri4OPj6+iIyMhIJCQlyR3uqU6dOoX///vjuu++QkJCAX3/9FYByene4TZeWLG16nZZSNur777+nYcOGUUpKCqlUKtq3b5/ckSqVmZlJPXv2pDVr1oi3HThwgARBoOvXrxNR+TsEa2QwGKioqIi6dOlCs2fPFm8vLi6mTp060ejRo+nSpUsyJny6H3/8kSIjI2nv3r00depU8vDwUMw7dCKiHTt20BtvvEHXrl0jQRBozZo1Yg+JNR4zhYWFNHDgQPr222/F206fPk12dnaUmppKRGT173z79OlDEyZMEL/X6/U0YsQICg0NpePHj8uY7OlWrVpFERERtH37dvr73/9O/v7+4n3WeKw8jtt0acjZpnPPjoVM343b29ujdevWePXVV9GvXz/MmjULAMTz69akdevW2L9/P8aOHQug/Jxoo0aN8MILL2Dfvn0AAEEQZExYOUEQkJ+fj+vXr6Nz584AgNLSUtSvXx/R0dE4c+YMtm/fLnPKRx7vsenVqxemT5+OPn36YMqUKVCpVPjXv/71xMfKyTSL6dcNGzaEnZ0dWrVqhdGjR+OLL74AEaGwsNBqjhnTvE5OTtixYwfGjRsn3teyZUu0bdsWe/bsAQCoVNbb5P3xxx/Iz89H+/btAZT3iqhUKkyaNAkPHjzAd999J3PCyr3xxhuYPn06wsLCEBkZibt372Lx4sUArOtYN8VtuvTkbNOt9z9fZrGxsZg2bRpWrVqF0tJSs0byt99+Q0FBAQAgPj4eKSkpGDx4MHr16oW0tDSZEpd7PLeDg4PZ4ExBEODq6gqtVgutVgvAegY/bt26VdyvQHmuFi1awNvbGxs3bgTw6MVq5MiR4j93bm6uLHlNzZkzB5GRkZg7dy7u3r0LAGjatCn8/PwAAF5eXoiOjsaiRYuQlZUFlUplFfv98dymx3lqaipKSkoAAN999x2uXr2K0NBQvPTSS0hOTpYrsuhJ+1wQBPHUifG5ODo6Wt1MlR9++AHnz58XvyciNGnSBK1atcLPP/8M4NELVp8+fRAUFIQzZ87g3LlzsuQ19XgbAwDu7u4ICAgAAHTs2BERERGYP38+/vzzT6jVaqsoeLhNl55Vtel10l+kYBkZGdS+fXsKCAigUaNGUePGjal379505MgR8THjxo2jrVu3EhFRfHw8OTs7k1qtpvXr18sV26LcRI+68bt3704RERFEJH+X5759+8jX15cEQaBVq1aJtxtzffPNN2Rvby8OHCwpKSEioqSkJKpXrx7duHFD+tAPZWVlUefOnSkgIIAmTZpEHh4eFBQURJs3byYi832bm5tLQUFBNHz4cLniiqrKTUQ0Z84cWrZsGRER/fzzz+Ti4kJqtZq+/PJLuWITkeX73HisDxs2jMLCwszuk8uBAwfI39+fBEGgmTNnUnFxMRER6XQ6Iir/XxAEgQ4cOEBERGVlZUREdPz4cXJ2dhZPx8mhqjbGdN+eOnWK/P39KSoqiojkPX3Ibbr0rLFN556dx2zfvh0NGzbEyZMnsXHjRpw/fx75+flYtGgRLly4AACws7PDli1b0LNnT0yePBkzZsxA06ZN8fvvv1tl7suXLwMo77ZVqVQoLS2Fj48PcnNzZT8lkZ6ejpUrVyI0NBTvvvsuPv30U2RnZwMwf2cbEhKCiRMnAgDq1asHAPD29oZGoxH/LnLYu3cvDAYDDh06hOXLl+PSpUvw9PTE0qVLcfr0aQiCIE47b9asGWbNmoXExEQcPHgQAJCUlITMzEyryn3y5EkA5adVtm7dil69eiEiIgJz586Fl5cXLly4gPv370ue2ZLsxn1uPAUEAEFBQbh16xby8vJkPdZv3LiBdevWITQ0FNHR0VixYgVOnz4NAFCr1QCA4OBgDBs2DFOnTkVJSQns7OwAAC+99BKA8un0cqmqjTE91v38/DBhwgSxB0ulUuHAgQOyLGHAbbq0rLVN52LHhE6nw7lz5+Dm5iY2Ph4eHvjnP/+JrKwsbNiwAQBQXFyM7du3w9fXF6dOncKsWbMwa9YsxMTEICMjw+pyf/PNNwDKuwsNBgMcHBzQrFkzZGdnw9nZWdYuzyZNmqB///6YNGkSFixYAL1ej4ULF5o9xtvbGzNnzsShQ4fw5Zdfil2c+/fvR7t27dClSxc5ogMof/Gxt7eHk5MTgPJxI9OnT4dGo8H8+fMBlDekxn3cr18/jBo1ChEREXj11VcxfPhw3Lt3z6pyL1iwAADQuHFjnDlzBj4+PkhNTcWUKVOwbNkyfPPNNzh69KjkmS3JbtznarVa3OcuLi4oKSmBXq+X9Vhv0KCBOKbl008/haurK5YuXWr293d0dMT8+fNx5coVREdHi+3Jf//7X7z44ovo1q2bLNktbWOMx7pGo0FYWBi6d++O8PBwdO/eHWFhYZLP6OM2XXpW26bXel+RwoWHh9OAAQNIp9OJXctERJMmTaKePXvSlStX6MKFC5SWlmbWVajX6+mLL74grVYrR+yn5u7bty+dPHmSiB51i//666+kUqmsYjaTaRf3t99+SxqNhtLS0io87t///je5u7uTn58fvfXWW6TRaGjevHlkMBhk67b9+OOP6bXXXqObN2+a3b5gwQLq2LEj7dq1i4gePceLFy9S//79SRAEGj9+PBUUFEiemejpuQMCAujIkSN0584dOnfuXIV9u2zZMlnXrbF0nxuP9fT0dBIEgU6fPi151qfZtWsXCYJA27ZtE/ex8XNiYiL5+/uTu7s7DRs2jBwcHGjmzJmyng6ytI0xZszMzKSQkBASBIEiIyNlO9a5TZeeNbbpXOw8ZHrOXKVS0alTp4jo0YG0f/9+euGFF8zGNFgDS3K3bduWNm3aZLbdli1bKDIykvLy8mQ/v0tkfo45JCSEhg0bJj4HU8nJyRQXF0dTp0594j+PVIz/zMYX0p9++sns/rS0NAoJCaHPP/9cvC0jI4O6dOlCHTp0oLNnz0oZV2RJ7uDgYJo/f36FbU0bXDnUZJ8TEaWkpFBUVBRlZ2dbxbFO9Oi5DB48mEJCQsQpw6YyMzNp/fr1NGvWLFkLtZq0MampqeTj40MdO3akc+fOSZ6ZiNt0uVlbm/5cFTvGwYBPYvwjlJSUUK9evSg0NJSIzP9gL774Is2ZM6fC7XWtNnMb/5Gkym9JdiNjpoMHD5JKpaKff/6ZiMoz5+Tk1F3IKjxpX5lmHzlyJHXq1Ilyc3PNHhMSEkLvv/+++H1BQYGkBVpt5Jar0ayN7HL1glSV3fT7a9eukUqlori4ODHvkwqfuvakF6HH77O0jSEiysvLo8OHD9dR2orZnnafNbbptZlb6jbdkuxG1tSmPxdjdsrKyvDee+/hzTffxJgxY3DkyBHxnKZx6qSdnR30ej3u37+PmJgYHDhwACtXrhQfl5+fDycnJzRp0gSANOsY1EVu4/nfus5vaXadTieu+mnM1KNHD7z99tuIiYnBnj17MGTIEMTFxaGsrKxOM5tmX7BgAX766SezXMCjFWHt7OxQWlqKS5cuYcGCBcjIyMDixYvFgbs6nQ4ajQaNGzcWt3VxcUFgYKCicks10LEusku1po6l2XU6HdLT08Xv9Xo9WrVqhalTp2Lx4sVISEjAwIEDER0djQcPHkiSvbS0FB999BGioqLw4Ycfmg3INQ42rm4bQ0Ro2rRpnY4vqovcUhzrdZFbqjbd0uzW2KYDsP0xO9nZ2dSpUyfq2rUrffXVVxQYGEiBgYEVurqXLl1KDg4OtHbtWiIimjdvHrm5udH48ePp4MGDNG3aNGrTpg2lp6dz7lrMrtFoaM2aNRXelfzvf/8jQRBIEAQaOHAg/fHHH5Jk/+WXX8jPz48EQaDw8HBxXMjj+ZYuXUqOjo7i6Z7Vq1dT27ZtaeDAgZSYmEjTpk2j5s2b07Fjxzg3ZxezL1iwQFxF2/iYrKws8VgfMmQI3b17V5LsmzZtIk9PT+rTpw998skn5OnpSf3796fk5OQK2a2pjeHc0rfp1clubW26kc0XO1u2bKEOHTqI8/bv3btHs2fPpnr16onjJkaNGkWenp60bt06sz9QXFwc9ejRgwICAigwMJCOHj3KuWs5+/r1682y63Q6WrduHdnb21NISIg4CE8KhYWFNH78ePrggw8oNjaWgoKCaMWKFWaP0Wq1NGHCBHJzc6MNGzaYnSr5z3/+Q2FhYfTaa69RUFBQhfUwODdnf/wFID4+nuzs7KhLly7i2AwpnDp1igYPHkyxsbHibVlZWdSmTRv6/vvviaj8/zY8PNyq2hjOLX2bXt3s1tSmm7LZYsfYIK5YsYI8PT3N7svOzqZ+/fpRz549iYjoyJEjdP/+/QrbGr/+/fffJUhs/ruVltv099cku1FRUREtWbLEbCEqqRgMBkpOTqaMjAwiIhoxYgS9/vrrZoNDDQYDZWZmVrrfiYhu374tTWCTTErMTfR8ZjfS6/X0448/ynKsHz16lKZPny72RBl7mzp37kz/93//R0TlY0aOHTtmVW0M55Y2N1HNsxvJ2aabsqliZ/PmzbRr1y66deuWeNvq1aupc+fOdPDgQbPH7t69m+zt7enXX38lInlX+FRqbiLby24qKSmJOnXqRLNnz7aK2Q1GSs1NxNnlYsz++HR9U/fu3SNfX1/asWOHhMmejnNLT8nZn8Ymip3169eTm5sbBQcHk6urK3Xr1o22bNlCREQnT56k9u3b0+eff262XsLt27dp2LBh9M4778gVW7G5iWwvu3GpeL1eb/ZCNXHiROrVqxft3r2biORdhl2puYk4u1yelt1gMJi94bh27Rq1a9fOKtZp4dzSU3J2Syi62CkrK6MlS5aQn58fff3116TVaik5OZnGjBlDgwcPFqc9R0VFUXBwMO3bt89s+xEjRtDYsWM5dzXYcnbThfJM13QxTmcuLCwkvV5PFy5cICLp1p1Ram7OrozsxoJs7dq11LZtW7PlIoyDpaWc1sy5pZ8Gr9Ts1aHoqedFRUXIzc1FREQExo0bBwcHB3Tt2hXt27dHQUGBOMU5JiYGZWVlWL16NW7evCluX1JSYjY1mHM/39mN0ycBiFclf+mll/CXv/wFx48fx9y5c9GlSxeEh4dDr9eLUz45N2dXcnbj9ODExEQMHToU9evXR1paGgYMGIC5c+eCiCRbgoBzS5tb6dmrRb46q2YyMzMrXF3X+I7J+O4qPj6eOnbsaHYKZfPmzdSjRw9q3bo1LVy4kN555x1yc3OjQ4cOcW7OXiG76f2pqalkb29PgiBQVFSUJMvHKzU3Z1dm9sLCQurbty/98MMP9N5775Farabw8HBxMCrntp3cSs9eU4opdhISEsjb25t8fX0pODiYvv76a7P7Tc8njh49WjxVYvqHunHjBkVFRdHw4cMpLCxMnEXBuTm7MfvjK4CuWLGCBEGgAQMG0OXLlzn3U3B25WZPS0sT10B59dVX6fz585zbxnIrPfuzUkSxk5SURN7e3vTVV1/Rzp076cMPPyR7e3tavXo1lZSUEBGJFw4rKSmhl19+mTZs2FDpzzNuw7krx9nLnT59mhISEjg3Z7fp7AcPHqTevXuLF1Hl3LaVW+nZa4NVFzvGbraYmBh65ZVXzLrJJk6cSEFBQeJocaObN2+St7c3ZWZmElF5d920adOkC03KzU3E2fl4qR7OruzsU6dOlS40cW6pcxMpO3ttsuoBysaBTufPn8eLL74Ie3t78Voa8+bNQ7169ZCYmIjbt2+L2+zevRteXl5o3rw5pkyZgvbt2+PatWsoKysTry3CuTm7NWVXam7OrvzsWVlZKCsrg8Fg4Nw2mFvp2WuVjIVWBUlJSfT+++/T4sWLzZbDXr16Nbm4uIgDqIyV6erVq8nHx0ec3mwwGGjkyJHUuHFjatq0KXXo0IFSU1M5N2e3quxKzc3ZOTvntu7cSs9el6yi2Ll16xYNHTqU3NzcKDw8nAICAqhhw4biH+rChQvUokUL+uSTT4jIfACsh4cHLV68mIjKl6UeOnQotWzZkjZu3Mi5ObtVZVdqbs7O2Tm3dedWenYpyF7sFBUVUUREBI0aNcrsuh/BwcHiSPCCggKaN28e1a9fn7Kysojo0XnIXr160fjx48Xtjh8/zrk5u9VlV2puzs7ZObd151Z6dqnIPmbH0dERGo0GY8eORZs2bcQFjMLCwpCeng4igouLC0aPHo3OnTvjr3/9K65duwZBEJCVlYWcnBwMHz5c/HmvvPIK5+bsVpddqbk5O2fn3NadW+nZJSNTkWXGdHS4cZ7/6NGj6d133zV73I0bN6ht27bk7e1Nb731Fnl6elLfvn1lueIxkXJzE3F2OSg1NxFnl4tSs3Nu6Sk5uxQEIgmnEFRD9+7d8e677yIiIkIc/a1SqXDp0iWcOHECR48eRWBgICIiImROak6puQHOLgel5gY4u1yUmp1zS0/J2Wud3NXWk1y+fJnc3d3NzhtKteT6s1BqbiLOLgel5ibi7HJRanbOLT0lZ68Lso/ZMUUPO5kOHz4MZ2dn8bxhTEwMpkyZgpycHDnjVUqpuQHOLgel5gY4u1yUmp1zS0/J2euSndwBTBkXPzp27BhGjBiBXbt2ISoqCsXFxdiwYQPc3NxkTvhkSs0NcHY5KDU3wNnlotTsnFt6Ss5ep2TqUapUSUkJtW3blgRBII1GQ59//rnckSyi1NxEnF0OSs1NxNnlotTsnFt6Ss5eV6xygHL//v3Rrl07LFq0CPXq1ZM7jsWUmhvg7HJQam6As8tFqdk5t/SUnL0uWGWxo9froVar5Y5RbUrNDXB2OSg1N8DZ5aLU7JxbekrOXhessthhjDHGGKstVjUbizHGGGOstnGxwxhjjDGbxsUOY4wxxmwaFzuMMcYYs2lc7DDGGGPMpnGxwxhjjDGbxsUOY4wxxmwaFzuMMas3duxYCIIAQRBgb28Pd3d39O/fH99++y0MBoPFP2ft2rVo1KhR3QVljFklLnYYY4owaNAgZGdn4+rVq9ixYwf69OmDKVOmYOjQodDpdHLHY4xZMS52GGOKoNFo4OHhgRYtWqBz586YOXMmEhMTsWPHDqxduxYAsGjRIgQEBMDJyQleXl6YOHEiCgsLAQD79+/HuHHjcP/+fbGXaPbs2QAArVaLGTNmoEWLFnByckJISAj2798vzxNljNU6LnYYY4rVt29fBAYGYuvWrQAAlUqFuLg4nDt3DuvWrcPevXvx0UcfAQC6du2KJUuWoEGDBsjOzkZ2djZmzJgBAJg8eTJSUlKwceNG/Pbbbxg5ciQGDRqEixcvyvbcGGO1h6+NxRizemPHjsW9e/ewbdu2Cvf97W9/w2+//Ybz589XuG/Lli2YMGEC8vLyAJSP2Zk6dSru3bsnPiYrKwsvvPACsrKy4OnpKd4eGhqK4OBgfPbZZ7X+fBhj0rKTOwBjjD0LIoIgCACA3bt3IzY2FhkZGSgoKIBOp8ODBw9QXFwMR0fHJ25/5swZ6PV6+Pj4mN2u1WrRtGnTOs/PGKt7XOwwxhQtPT0dbdq0wdWrVzF06FC89957+PTTT9GkSRMcPnwYkZGRKC0trbTYKSwshFqtxokTJ6BWq83uc3Z2luIpMMbqGBc7jDHF2rt3L86cOYNp06bhxIkTMBgMWLhwIVSq8uGImzZtMnu8g4MD9Hq92W2dOnWCXq9HTk4OevToIVl2xph0uNhhjCmCVqvF7du3odfrcefOHezcuROxsbEYOnQoxowZg7Nnz6KsrAzLli3D66+/juTkZKxcudLsZ3h7e6OwsBB79uxBYGAgHB0d4ePjg/DwcIwZMwYLFy5Ep06dkJubiz179uDll1/GkCFDZHrGjLHawrOxGGOKsHPnTjRv3hze3t4YNGgQ9u3bh7i4OCQmJkKtViMwMBCLFi3C/Pnz4e/vj/j4eMTGxpr9jK5du2LChAkYNWoUXF1d8cUXXwAA1qxZgzFjxmD69Onw9fXF8OHDkZqailatWsnxVBljtYxnYzHGGGPMpnHPDmOMMcZsGhc7jDHGGLNpXOwwxhhjzKZxscMYY4wxm8bFDmOMMcZsGhc7jDHGGLNpXOwwxhhjzKZxscMYY4wxm8bFDmOMMcZsGhc7jDHGGLNpXOwwxhhjzKb9P8JTA6DErrxRAAAAAElFTkSuQmCC",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "problematic_pregnancies_by_date = df.groupby('birth_date')['prob_preg'].sum()\n",
        "\n",
        "# Plot the number of problematic pregnancies over time\n",
        "problematic_pregnancies_by_date.plot(marker='o', linestyle='-')\n",
        "\n",
        "# Add a red line at the specific date\n",
        "plt.axvline(x=pd.to_datetime('2020-12-14'), color='red', linestyle='--', label='Vaccination Start')\n",
        "plt.axvline(x=pd.to_datetime('2021-9-20'), color='orange', linestyle='--', label='Pregnancy Out of Covid')\n",
        "\n",
        "# Add labels and title\n",
        "plt.xlabel('Date')\n",
        "plt.ylabel('Number of Problematic Pregnancies')\n",
        "plt.title('Number of Problematic Pregnancies Over Time')\n",
        "\n",
        "# Add legend\n",
        "plt.legend()\n",
        "\n",
        "# Show the plot\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 150,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "1056\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "21"
            ]
          },
          "execution_count": 150,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Get the number of problematic pregnancies in the pre and post-covid dataframes\n",
        "\n",
        "num_prob_preg_pre = prevaccine_df['prob_preg'].sum()\n",
        "num_prob_preg_post = postvaccine_df['prob_preg'].sum()\n",
        "print(num_prob_preg_pre)\n",
        "num_prob_preg_post"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 151,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "prop_prob_preg_pre_covid = num_prob_preg_pre / len(prevaccine_df)\n",
        "prop_prob_preg_post_covid = num_prob_preg_post / len(postvaccine_df)\n",
        "\n",
        "plt.bar(['Pre-COVID', 'Post-COVID'], [prop_prob_preg_pre_covid, prop_prob_preg_post_covid])\n",
        "plt.title('Proportion of Problematic Pregnancies')\n",
        "plt.xlabel('Time Period')\n",
        "plt.ylabel('Proportion')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 152,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Since there is such a large difference in observations for the months post covid, we will plot ratios of each month as opposed to purely count\n",
        "monthly_ratio = df.groupby('birth_date')['prob_preg'].mean()\n",
        "\n",
        "# Plot the monthly ratio of problematic pregnancies over time\n",
        "plt.plot(monthly_ratio.index, monthly_ratio, marker='o', linestyle='-')\n",
        "\n",
        "# Add a red line at the specific date\n",
        "plt.axvline(x=pd.to_datetime('2020-12-14'), color='red', linestyle='--', label='Vaccination Start')\n",
        "plt.axvline(x=pd.to_datetime('2021-9-20'), color='orange', linestyle='--', label='Pregnancy Out of Covid')\n",
        "# Add labels and title\n",
        "plt.xlabel('Date')\n",
        "plt.ylabel('Ratio of Problematic Pregnancies')\n",
        "plt.title('Ratio of Problematic Pregnancies Over Time')\n",
        "\n",
        "# Add legend\n",
        "plt.legend()\n",
        "plt.xticks(rotation=45)\n",
        "# Show the plot\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 153,
      "metadata": {},
      "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>mat_age</th>\n",
              "      <th>income</th>\n",
              "      <th>mat_edu</th>\n",
              "      <th>depression</th>\n",
              "      <th>anxiety</th>\n",
              "      <th>birth_age_wk</th>\n",
              "      <th>birth_date</th>\n",
              "      <th>birth_length_cm</th>\n",
              "      <th>birth_weight_g</th>\n",
              "      <th>delivery</th>\n",
              "      <th>NICU</th>\n",
              "      <th>life_danger</th>\n",
              "      <th>baby_danger</th>\n",
              "      <th>virus_baby_harm</th>\n",
              "      <th>prob_preg</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>8084</th>\n",
              "      <td>26.3</td>\n",
              "      <td>99999.0</td>\n",
              "      <td>C/T</td>\n",
              "      <td>9.0</td>\n",
              "      <td>24.0</td>\n",
              "      <td>39.57</td>\n",
              "      <td>2021-10-01</td>\n",
              "      <td>53.00</td>\n",
              "      <td>3317.0</td>\n",
              "      <td>V</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>62</td>\n",
              "      <td>83</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4392</th>\n",
              "      <td>30.7</td>\n",
              "      <td>124999.0</td>\n",
              "      <td>C/T</td>\n",
              "      <td>14.0</td>\n",
              "      <td>25.0</td>\n",
              "      <td>41.29</td>\n",
              "      <td>2021-10-01</td>\n",
              "      <td>51.31</td>\n",
              "      <td>3544.0</td>\n",
              "      <td>V</td>\n",
              "      <td>0</td>\n",
              "      <td>79</td>\n",
              "      <td>100</td>\n",
              "      <td>100</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6471</th>\n",
              "      <td>27.2</td>\n",
              "      <td>39999.0</td>\n",
              "      <td>PhD</td>\n",
              "      <td>12.0</td>\n",
              "      <td>16.0</td>\n",
              "      <td>39.57</td>\n",
              "      <td>2021-10-01</td>\n",
              "      <td>50.00</td>\n",
              "      <td>3490.0</td>\n",
              "      <td>C</td>\n",
              "      <td>0</td>\n",
              "      <td>66</td>\n",
              "      <td>55</td>\n",
              "      <td>63</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6639</th>\n",
              "      <td>34.8</td>\n",
              "      <td>39999.0</td>\n",
              "      <td>PhD</td>\n",
              "      <td>10.0</td>\n",
              "      <td>13.0</td>\n",
              "      <td>39.00</td>\n",
              "      <td>2021-10-01</td>\n",
              "      <td>49.00</td>\n",
              "      <td>3220.0</td>\n",
              "      <td>C</td>\n",
              "      <td>0</td>\n",
              "      <td>85</td>\n",
              "      <td>90</td>\n",
              "      <td>100</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2180</th>\n",
              "      <td>28.4</td>\n",
              "      <td>174999.0</td>\n",
              "      <td>C/T</td>\n",
              "      <td>3.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>40.71</td>\n",
              "      <td>2021-10-01</td>\n",
              "      <td>48.26</td>\n",
              "      <td>2807.0</td>\n",
              "      <td>V</td>\n",
              "      <td>1</td>\n",
              "      <td>56</td>\n",
              "      <td>74</td>\n",
              "      <td>51</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10105</th>\n",
              "      <td>35.9</td>\n",
              "      <td>124999.0</td>\n",
              "      <td>C/T</td>\n",
              "      <td>10.0</td>\n",
              "      <td>19.0</td>\n",
              "      <td>37.71</td>\n",
              "      <td>2022-08-01</td>\n",
              "      <td>46.00</td>\n",
              "      <td>2609.0</td>\n",
              "      <td>V</td>\n",
              "      <td>0</td>\n",
              "      <td>95</td>\n",
              "      <td>83</td>\n",
              "      <td>90</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>160</th>\n",
              "      <td>38.6</td>\n",
              "      <td>200000.0</td>\n",
              "      <td>M</td>\n",
              "      <td>1.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>38.86</td>\n",
              "      <td>2022-09-01</td>\n",
              "      <td>51.00</td>\n",
              "      <td>3091.0</td>\n",
              "      <td>C</td>\n",
              "      <td>0</td>\n",
              "      <td>13</td>\n",
              "      <td>20</td>\n",
              "      <td>50</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9272</th>\n",
              "      <td>33.1</td>\n",
              "      <td>174999.0</td>\n",
              "      <td>UG</td>\n",
              "      <td>7.0</td>\n",
              "      <td>18.0</td>\n",
              "      <td>38.14</td>\n",
              "      <td>2022-09-01</td>\n",
              "      <td>50.00</td>\n",
              "      <td>3573.0</td>\n",
              "      <td>V</td>\n",
              "      <td>0</td>\n",
              "      <td>70</td>\n",
              "      <td>40</td>\n",
              "      <td>73</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5715</th>\n",
              "      <td>29.9</td>\n",
              "      <td>200000.0</td>\n",
              "      <td>PhD</td>\n",
              "      <td>11.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>41.00</td>\n",
              "      <td>2022-09-01</td>\n",
              "      <td>51.00</td>\n",
              "      <td>3771.0</td>\n",
              "      <td>V</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>10</td>\n",
              "      <td>10</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5523</th>\n",
              "      <td>35.7</td>\n",
              "      <td>149999.0</td>\n",
              "      <td>M</td>\n",
              "      <td>4.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>40.86</td>\n",
              "      <td>2022-09-01</td>\n",
              "      <td>54.50</td>\n",
              "      <td>3770.0</td>\n",
              "      <td>V</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>31</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>65 rows × 15 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "       mat_age    income mat_edu  depression  anxiety  birth_age_wk  \\\n",
              "8084      26.3   99999.0     C/T         9.0     24.0         39.57   \n",
              "4392      30.7  124999.0     C/T        14.0     25.0         41.29   \n",
              "6471      27.2   39999.0     PhD        12.0     16.0         39.57   \n",
              "6639      34.8   39999.0     PhD        10.0     13.0         39.00   \n",
              "2180      28.4  174999.0     C/T         3.0     11.0         40.71   \n",
              "...        ...       ...     ...         ...      ...           ...   \n",
              "10105     35.9  124999.0     C/T        10.0     19.0         37.71   \n",
              "160       38.6  200000.0       M         1.0     11.0         38.86   \n",
              "9272      33.1  174999.0      UG         7.0     18.0         38.14   \n",
              "5715      29.9  200000.0     PhD        11.0      9.0         41.00   \n",
              "5523      35.7  149999.0       M         4.0     10.0         40.86   \n",
              "\n",
              "      birth_date  birth_length_cm  birth_weight_g delivery  NICU  life_danger  \\\n",
              "8084  2021-10-01            53.00          3317.0        V     0            2   \n",
              "4392  2021-10-01            51.31          3544.0        V     0           79   \n",
              "6471  2021-10-01            50.00          3490.0        C     0           66   \n",
              "6639  2021-10-01            49.00          3220.0        C     0           85   \n",
              "2180  2021-10-01            48.26          2807.0        V     1           56   \n",
              "...          ...              ...             ...      ...   ...          ...   \n",
              "10105 2022-08-01            46.00          2609.0        V     0           95   \n",
              "160   2022-09-01            51.00          3091.0        C     0           13   \n",
              "9272  2022-09-01            50.00          3573.0        V     0           70   \n",
              "5715  2022-09-01            51.00          3771.0        V     0            0   \n",
              "5523  2022-09-01            54.50          3770.0        V     0            5   \n",
              "\n",
              "       baby_danger  virus_baby_harm  prob_preg  \n",
              "8084            62               83          0  \n",
              "4392           100              100          0  \n",
              "6471            55               63          1  \n",
              "6639            90              100          1  \n",
              "2180            74               51          1  \n",
              "...            ...              ...        ...  \n",
              "10105           83               90          0  \n",
              "160             20               50          1  \n",
              "9272            40               73          0  \n",
              "5715            10               10          0  \n",
              "5523             5               31          0  \n",
              "\n",
              "[65 rows x 15 columns]"
            ]
          },
          "execution_count": 153,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "postvaccine_df = postvaccine_df.sort_values(by='birth_date')\n",
        "postvaccine_df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 154,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_crJguhu_tod",
        "outputId": "3e5ae4b6-a1b5-4578-ea7c-f8266a9aebb4"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Pre-vaccine period:\n",
            "Regression coefficients: [-7.78318695  4.04060493]\n",
            "Intercept: 3423.739573303964\n",
            "\n",
            "Post-vaccine period:\n",
            "Regression coefficients: [-3.23921234 -0.86587632]\n",
            "Intercept: 3443.0196095597166\n"
          ]
        }
      ],
      "source": [
        "\n",
        "# Define a function to perform linear regression and print results\n",
        "def perform_linear_regression(data):\n",
        "    X = data[['depression', 'anxiety']]\n",
        "    y = data['birth_weight_g']\n",
        "\n",
        "    model = LinearRegression()\n",
        "    model.fit(X, y)\n",
        "\n",
        "    # Extracting model coefficients and intercept\n",
        "    coefficients = model.coef_\n",
        "    intercept = model.intercept_\n",
        "\n",
        "    print(f'Regression coefficients: {coefficients}')\n",
        "    print(f'Intercept: {intercept}')\n",
        "\n",
        "# Applying the function to the prevaccine and postvaccine subsets\n",
        "print(\"Pre-vaccine period:\")\n",
        "perform_linear_regression(prevaccine_df)\n",
        "\n",
        "print(\"\\nPost-vaccine period:\")\n",
        "perform_linear_regression(postvaccine_df)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GMOs7J_-_vDq"
      },
      "source": [
        "Pre-vaccine period:\n",
        "\n",
        "The coefficients for depression and anxiety are -7.78318695 and 4.04060493, respectively, and the intercept is about 3423.74 grams.\n",
        "This suggests that, on average, an increase of one unit in depression scores is associated with a decrease of approximately 7.78 grams in birth weight. Similarly, an increase of one unit in anxiety scores is associated with an increase of approximately 4.04 grams in birth weight during the pre-vaccine period.\n",
        "\n",
        "\n",
        "Post-vaccine period:\n",
        "\n",
        "The coefficients for depression and anxiety are -3.23921234 and -0.86587632, respectively, with an intercept of about 3443.02 grams.\n",
        "This implies that, on average, an increase of one unit in depression scores is associated with a decrease of approximately 3.24 grams in birth weight. Conversely, an increase of one unit in anxiety scores is associated with a decrease of approximately 0.87 grams in birth weight during the post-vaccine period.\n",
        "\n",
        "\n",
        "Analysis:\n",
        "\n",
        "The impact of depression on birth weight appears to be stronger in the pre-vaccine period compared to the post-vaccine period. This could be due to various factors, including changes in the external environment, healthcare practices, or the psychological impact of the pandemic over time. However, one explanation may be that the depression was more severe in the pre df.\n",
        "Interestingly, while anxiety was associated with an increase in birth weight in the pre-vaccine period, it's associated with a decrease in the post-vaccine period. This could suggest that the nature of anxiety or its effects may have changed after the vaccine became available. It might be that post-vaccine anxiety is less related to stress that affects birth weight or that the composition of the population with higher anxiety changed post-vaccine.\n",
        "It is also important to note that the intercepts, which indicate the expected birth weight when depression and anxiety scores are zero, are higher in the post-vaccine period."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 155,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "UqZFTQvt_1JE",
        "outputId": "922a24da-159a-4c7b-bcc2-f8dad3cdbce4"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 750x750 with 12 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 750x750 with 12 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Perform pairplots for each subset\n",
        "pairplot_pre = sns.pairplot(prevaccine_df[['life_danger', 'anxiety', 'depression']])\n",
        "pairplot_pre.figure.suptitle('Pre-Vaccine Pairplot of Life_danger, Anxiety, and Depression', y=1.02)\n",
        "\n",
        "pairplot_post = sns.pairplot(postvaccine_df[['life_danger', 'anxiety', 'depression']])\n",
        "pairplot_post.figure.suptitle('Post-Vaccine Pairplot of Life_danger, Anxiety, and Depression', y=1.02)\n",
        "\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Sm9P-nPEx3nr"
      },
      "source": [
        "Pre-Vaccine Pairplot:\n",
        "\n",
        "Life_danger Distribution: The histogram suggests a right skewed distribution with a concentration of lower scores and fewer high scores.\n",
        "Anxiety Distribution: The histogram for anxiety shows a normal-like, right skewed distribution with a peak around the middle of the distribution, indicating that most individuals reported lower anxiety levels.\n",
        "Depression Distribution: Similar to anxiety, depression scores also show a right skewed distribution with a peak near the middle of the scores, around 13-14.\n",
        "Scatterplots: The scatterplots do show a linear relationship between anxiety and depression. There is some density observed in the lower score regions for all plots, which is consistent with the histograms.\n",
        "\n",
        "Post-Vaccine Pairplot:\n",
        "\n",
        "Life_danger Distribution: The distribution remains right skewed, however the it appears bimodal with a larger proporiton of the group having lower responses to general life danger.\n",
        "Anxiety Distribution: The distribution of anxiety seems to have shifted slightly towards the lower end, with the peak of the histogram moving downwards. Still right skewed.\n",
        "Depression Distribution: The distribution of depression has also shifted, showing a lower maximum depression score, with a peak around 5-6, as opposed the 13-14 from pre.\n",
        "Scatterplots: The scatterplots still show linear correlations between anxiety and depression. However the fewer data of the post df makes this harder to see.\n",
        "\n",
        "\n",
        "From a statistical standpoint, there seems to be a slight improvement in the distribution of the scores, with fewer individuals reporting higher levels of life_danger, anxiety, and depression post-vaccine. It's important to note that correlation does not imply causation, and any inference about the effect of the vaccine on these psychological variables would require a proper statistical analysis considering other factors and potential confounders."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 156,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "mat_age                   float64\n",
              "income                    float64\n",
              "mat_edu                    object\n",
              "depression                float64\n",
              "anxiety                   float64\n",
              "birth_age_wk              float64\n",
              "birth_date         datetime64[ns]\n",
              "birth_length_cm           float64\n",
              "birth_weight_g            float64\n",
              "delivery                   object\n",
              "NICU                        int64\n",
              "life_danger                 int64\n",
              "baby_danger                 int64\n",
              "virus_baby_harm             int64\n",
              "prob_preg                   int64\n",
              "dtype: object"
            ]
          },
          "execution_count": 156,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "prevaccine_df.dtypes"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 157,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "m0ZlcFH2e_AD",
        "outputId": "6e6ead9c-708d-4529-b087-5e540950a950"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Pre-vaccine Anxiety Correlation: 0.3598213052110457, P-value: 4.626079112087198e-90\n",
            "Pre-vaccine Depression Correlation: 0.2950326667581357, P-value: 8.600694158875852e-60\n",
            "Post-vaccine Anxiety Correlation: 0.45193043980236713, P-value: 0.00015741927767761843\n",
            "Post-vaccine Depression Correlation: 0.3689289241906914, P-value: 0.002493381471236656\n"
          ]
        }
      ],
      "source": [
        "\n",
        "# Calculate the Pearson correlation coefficient and p-value for pre-vaccine\n",
        "correlation_pre_anxiety, p_value_pre_anxiety = pearsonr(prevaccine_df['life_danger'], prevaccine_df['anxiety'])\n",
        "correlation_pre_depression, p_value_pre_depression = pearsonr(prevaccine_df['life_danger'], prevaccine_df['depression'])\n",
        "\n",
        "# Calculate the Pearson correlation coefficient and p-value for post-vaccine\n",
        "correlation_post_anxiety, p_value_post_anxiety = pearsonr(postvaccine_df['life_danger'], postvaccine_df['anxiety'])\n",
        "correlation_post_depression, p_value_post_depression = pearsonr(postvaccine_df['life_danger'], postvaccine_df['depression'])\n",
        "\n",
        "# Print the results\n",
        "print(f\"Pre-vaccine Anxiety Correlation: {correlation_pre_anxiety}, P-value: {p_value_pre_anxiety}\")\n",
        "print(f\"Pre-vaccine Depression Correlation: {correlation_pre_depression}, P-value: {p_value_pre_depression}\")\n",
        "print(f\"Post-vaccine Anxiety Correlation: {correlation_post_anxiety}, P-value: {p_value_post_anxiety}\")\n",
        "print(f\"Post-vaccine Depression Correlation: {correlation_post_depression}, P-value: {p_value_post_depression}\")\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-ZrLIeK-jz62"
      },
      "source": [
        "The results of the Pearson correlation coefficient and p-value for both pre-vaccine and post-vaccine data give us a quantitative measure of the strength and direction of the linear relationship between life_danger and the psychological variables of anxiety and depression.\n",
        "\n",
        "##### Pre-Vaccine Data:\n",
        "\n",
        "Anxiety Correlation: There is a moderate positive correlation (r ≈ 0.360) between life_danger and anxiety levels, suggesting that individuals with higher life_danger scores tend to report higher anxiety levels. The p-value is about 4.62e-90, which is extremely small, much less than the standard significance level of 0.05, indicating that this result is statistically significant.\n",
        "\n",
        "Depression Correlation: There is a weak to moderate positive correlation (r ≈ 0.0.295) between life_danger and depression levels. However, the p-value is  8.60e-60, which is also extremely small, suggesting that this result is statistically significant.\n",
        "\n",
        "##### Post-Vaccine Data:\n",
        "\n",
        "Anxiety Correlation: The correlation between life_danger and anxiety levels post-vaccine is lower (r ≈ 0.452) than in the pre-vaccine data. The p-value is about 0.0002 < .05, which strongly suggests that the correlation is statistically significant.\n",
        "\n",
        "Depression Correlation: Similar to anxiety, there is a moderate positive correlation (r ≈ 0.369) between life_danger and depression levels post-vaccine. The  small p-value of about 0.002 < 0.05, again indicates a statistically significant result.\n",
        "\n",
        "##### Overall Interpretation:\n",
        "\n",
        "The correlations for both anxiety and depression with life_danger are positive in both the pre-vaccine and post-vaccine scenarios, which means that as the perception of life being threatened increases, so do the reported levels of anxiety and depression.\n",
        "\n",
        "Post-vaccine, the correlation coefficients are both higher. This could imply an increase in the strength of the relationship between life_danger and the psychological measures after vaccination, possibly suggesting an overall unhelpful effect of the vaccine on psychological well-being. However, the change in the strength of the correlation is moderate/small and could be influenced by other factors.\n",
        "\n",
        "The p-values in the post-vaccine data are much smaller than in the pre-vaccine data, this is perhaps due to the discrepency between the size of the two populations we are comparing, as it may be harder to see the true relationship in the post df, since we have fewer observations.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 158,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "NAFF2dHwFr4_",
        "outputId": "3184d025-eee3-45cb-a9d6-cf71b8b0b6f1"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "def logistic_regression_plot(df, outcome):\n",
        "    # Perform logistic regression\n",
        "    X = df[['life_danger']].values  # 2D array for sklearn\n",
        "    y = df[outcome].values  # Outcome\n",
        "    log_reg = LogisticRegression()\n",
        "    log_reg.fit(X, y)\n",
        "    X_min = df['life_danger'].min()  # Get the minimum value\n",
        "    X_max = df['life_danger'].max()  # Get the maximum value\n",
        "\n",
        "    # Generate predicted probabilities for a range of 'life_danger' values\n",
        "    X_test = np.linspace(X_min, X_max, 300).reshape(-1, 1)  # Reshaped for sklearn\n",
        "    y_prob = log_reg.predict_proba(X_test)[:, 1]  # Probability of the outcome\n",
        "\n",
        "    # Plotting the results\n",
        "    plt.figure(figsize=(10, 6))\n",
        "    plt.scatter(X, y, alpha=0.2, label='Actual Outcomes')  # Plot the actual data\n",
        "    plt.plot(X_test, y_prob, color='red', label='Predicted Probability')  # Plot the predicted probabilities\n",
        "    plt.title(f'life_danger Score vs. {outcome} Probability')\n",
        "    plt.xlabel('life_danger Score')\n",
        "    plt.ylabel(f'{outcome} Probability')\n",
        "    plt.legend()\n",
        "    plt.show()\n",
        "\n",
        "    return log_reg\n",
        "\n",
        "log_reg_pre = logistic_regression_plot(prevaccine_df, 'NICU')\n",
        "log_reg_post = logistic_regression_plot(postvaccine_df, 'NICU')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nPBAtkZWzYK5"
      },
      "source": [
        "Pre-Vaccine Analysis:\n",
        "\n",
        "The scatter plot shows the actual outcomes (NICU Stay or not) against the life_danger scores before vaccination.\n",
        "The red line represents the predicted probability of a NICU Stay based on the life_danger scores.\n",
        "As observed, there's a general trend where higher life_danger scores tend to correspond to higher predicted probabilities of NICU Stay. This suggests that individuals with higher life_danger scores were more likely to experience a NICU Stay before vaccination.\n",
        "However, there's a considerable amount of variability in the data, as indicated by the spread of points around the predicted probability line.\n",
        "\n",
        "\n",
        "Post-Vaccine Analysis:\n",
        "\n",
        "Similarly, the scatter plot shows the actual outcomes against the life_danger scores after vaccination.\n",
        "The red line represents the predicted probability of a NICU Stay based on the life_danger scores post-vaccination.\n",
        "Compared to the post-vaccine plot, there seems to be a slight shift downwards in the predicted probability line pre-vaccination, especially for higher life_danger scores.\n",
        "This suggests that post-vaccination, individuals with the same life_danger scores might have higher predicted probabilities of experiencing a NICU Stay compared to pre-vaccination.\n",
        "\n",
        "\n",
        "Overall, these plots provide a visual representation of how life_danger scores relate to the likelihood of NICU Stay before and after vaccination. It appears that vaccination may have no to hurtful impact on reducing the predicted probability of NICU Stay, especially for individuals with higher life_danger scores. However, further analysis and consideration of other factors are necessary to draw definitive conclusions about the vaccine's effect on NICU Stay probability. As, again the size of the post population is not large."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 159,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bRWplOD1yCTV",
        "outputId": "ebe50408-4574-4714-be50-b19e874c039e"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Optimization terminated successfully.\n",
            "         Current function value: 0.306235\n",
            "         Iterations 6\n",
            "Optimization terminated successfully.\n",
            "         Current function value: 0.298914\n",
            "         Iterations 7\n",
            "Pre-Vaccine Logistic Regression Summary:\n",
            "                         Results: Logit\n",
            "================================================================\n",
            "Model:              Logit            Method:           MLE      \n",
            "Dependent Variable: NICU             Pseudo R-squared: 0.006    \n",
            "Date:               2024-03-20 19:50 AIC:              1796.2490\n",
            "No. Observations:   2923             BIC:              1814.1901\n",
            "Df Model:           2                Log-Likelihood:   -895.12  \n",
            "Df Residuals:       2920             LL-Null:          -900.25  \n",
            "Converged:          1.0000           LLR p-value:      0.0059311\n",
            "No. Iterations:     6.0000           Scale:            1.0000   \n",
            "----------------------------------------------------------------\n",
            "                 Coef.  Std.Err.    z     P>|z|   [0.025  0.975]\n",
            "----------------------------------------------------------------\n",
            "const           -2.7427   0.1731 -15.8407 0.0000 -3.0820 -2.4033\n",
            "baby_danger      0.0055   0.0036   1.5355 0.1247 -0.0015  0.0125\n",
            "virus_baby_harm  0.0028   0.0035   0.8069 0.4197 -0.0040  0.0097\n",
            "================================================================\n",
            "\n",
            "P-values:\n",
            "const              1.629708e-56\n",
            "baby_danger        1.246518e-01\n",
            "virus_baby_harm    4.197075e-01\n",
            "dtype: float64\n",
            "\n",
            "Post-Vaccine Logistic Regression Summary:\n",
            "                        Results: Logit\n",
            "===============================================================\n",
            "Model:               Logit            Method:           MLE    \n",
            "Dependent Variable:  NICU             Pseudo R-squared: 0.029  \n",
            "Date:                2024-03-20 19:50 AIC:              44.8588\n",
            "No. Observations:    65               BIC:              51.3820\n",
            "Df Model:            2                Log-Likelihood:   -19.429\n",
            "Df Residuals:        62               LL-Null:          -20.010\n",
            "Converged:           1.0000           LLR p-value:      0.55963\n",
            "No. Iterations:      7.0000           Scale:            1.0000 \n",
            "---------------------------------------------------------------\n",
            "                 Coef.  Std.Err.    z    P>|z|   [0.025  0.975]\n",
            "---------------------------------------------------------------\n",
            "const           -2.7387   1.0469 -2.6160 0.0089 -4.7906 -0.6868\n",
            "baby_danger      0.0244   0.0235  1.0398 0.2985 -0.0216  0.0705\n",
            "virus_baby_harm -0.0135   0.0222 -0.6079 0.5433 -0.0571  0.0301\n",
            "===============================================================\n",
            "\n",
            "P-values:\n",
            "const              0.008896\n",
            "baby_danger        0.298454\n",
            "virus_baby_harm    0.543280\n",
            "dtype: float64\n"
          ]
        }
      ],
      "source": [
        "import statsmodels.api as sm\n",
        "import pandas as pd\n",
        "\n",
        "# Copy the DataFrames to avoid SettingWithCopyWarning\n",
        "prevaccine_df = prevaccine_df.copy()\n",
        "postvaccine_df = postvaccine_df.copy()\n",
        "\n",
        "# Define the function to perform logistic regression and obtain p-values\n",
        "def logistic_regression_with_p_values(df):\n",
        "    # Prepare the data\n",
        "    X = df[['baby_danger', 'virus_baby_harm']]\n",
        "    y = df['NICU']\n",
        "\n",
        "    # Initialize and fit the logistic regression model\n",
        "    model = sm.Logit(y, sm.add_constant(X))\n",
        "    result = model.fit()\n",
        "\n",
        "    # Get the summary of the logistic regression model\n",
        "    summary = result.summary2()\n",
        "    p_values = result.pvalues\n",
        "\n",
        "    return summary, p_values\n",
        "\n",
        "# Perform logistic regression on prevaccine_df\n",
        "prevaccine_summary, prevaccine_p_values = logistic_regression_with_p_values(prevaccine_df)\n",
        "\n",
        "# Perform logistic regression on postvaccine_df\n",
        "postvaccine_summary, postvaccine_p_values = logistic_regression_with_p_values(postvaccine_df)\n",
        "\n",
        "# Show the summary and p-values for prevaccine_df\n",
        "print(\"Pre-Vaccine Logistic Regression Summary:\")\n",
        "print(prevaccine_summary)\n",
        "print(\"P-values:\")\n",
        "print(prevaccine_p_values)\n",
        "\n",
        "# Show the summary and p-values for postvaccine_df\n",
        "print(\"\\nPost-Vaccine Logistic Regression Summary:\")\n",
        "print(postvaccine_summary)\n",
        "print(\"P-values:\")\n",
        "print(postvaccine_p_values)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "46t4MkJj0_Jl"
      },
      "source": [
        "Pre-Vaccine Model:\n",
        "\n",
        "Pseudo R-squared: The value is 0.006, indicating a weak model that explains only a small variance in the dependent variable.\n",
        "\n",
        "Coefficients: Both predictors have small positive coefficients.\n",
        "\n",
        "- baby_danger: Coefficient = 0.0055, p-value = 0.1402\n",
        "- virus_baby_harm: Coefficient = 0.0028, p-value = 0.5391\n",
        "\n",
        "Interpretation: The p-values for both variables are greater than the common alpha level of 0.05, which suggests that neither is statistically significant in predicting the likelihood of NICU stay in the pre-vaccine data set.\n",
        "\n",
        "\n",
        "Post-Vaccine Model:\n",
        "\n",
        "Pseudo R-squared: The value is slightly higher at 0.029, but it still indicates a weak model.\n",
        "\n",
        "Coefficients: Similar to the pre-vaccine model, the coefficients are small and positive.\n",
        "\n",
        "- baby_danger: Coefficient = 0.0244, p-value = 0.2985\n",
        "- virus_baby_harm: Coefficient = -0.0135, p-value = 0.5433\n",
        "\n",
        "Interpretation: Again, since the p-values are above 0.05, there is no statistically significant association between these predictors and the likelihood of NICU stay for the post-vaccine period.\n",
        "\n",
        "\n",
        "Overall Analysis:\n",
        "\n",
        "The results are consistent across both pre-vaccine and post-vaccine models, showing no significant predictors among the variables analyzed.\n",
        "The Pseudo R-squared values in both models are low, indicating that other variables not included in the model may better explain the variance in NICU stay.\n",
        "\n",
        "The constant term (const) is significant in both models, indicating that there are other factors not captured by the two predictors that are influencing the NICU stay.\n",
        "\n",
        "Conclusion:\n",
        "\n",
        "- The variables baby_danger and virus_baby_harm do not have a statistically significant impact on the likelihood of a newborn's stay in the NICU, based on the data and models presented.\n",
        "- Since the model does not explain much of the variability in NICU stays, it may be useful to consider additional variables that could have a stronger relationship with the outcome. This might include medical history, health indicators, or other socioeconomic factors."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 160,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Convert appropriate columns to be categorical data for logistical regression\n",
        "df['income'] = df['income'].astype('category')\n",
        "df['mat_edu'] = df['mat_edu'].astype('category')\n",
        "df['birth_date'] = df['birth_date'].dt.strftime('%b%Y').astype('category')\n",
        "df['delivery'] = df['delivery'].astype('category')\n",
        "df['NICU'] = df['NICU'].astype('category')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 161,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "mat_age             float64\n",
              "income             category\n",
              "mat_edu            category\n",
              "depression          float64\n",
              "anxiety             float64\n",
              "birth_age_wk        float64\n",
              "birth_date         category\n",
              "birth_length_cm     float64\n",
              "birth_weight_g      float64\n",
              "delivery           category\n",
              "NICU               category\n",
              "life_danger           int64\n",
              "baby_danger           int64\n",
              "virus_baby_harm       int64\n",
              "prob_preg             int64\n",
              "dtype: object"
            ]
          },
          "execution_count": 161,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.dtypes"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 164,
      "metadata": {
        "id": "DiTGF40-WC6T"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Classification Report: For Total Dataframe\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.76      0.90      0.82       623\n",
            "           1       0.79      0.56      0.65       409\n",
            "\n",
            "    accuracy                           0.77      1032\n",
            "   macro avg       0.77      0.73      0.74      1032\n",
            "weighted avg       0.77      0.77      0.76      1032\n",
            "\n",
            "Accuracy: 0.7655038759689923\n",
            "income_20000.0: 0.0027453660685435696\n",
            "income_39999.0: 0.0013884563075564668\n",
            "income_69999.0: 0.006779953146037762\n",
            "income_99999.0: -0.010843058774805691\n",
            "income_124999.0: 0.015348210182265861\n",
            "income_149999.0: 0.006246093585694998\n",
            "income_174999.0: 0.00013823711270496584\n",
            "income_199999.0: -9.394354695768081e-05\n",
            "income_200000.0: -0.012632450069260153\n",
            "mat_edu_<HS: -0.0034208290278221736\n",
            "mat_edu_C/T: 0.036526605527989905\n",
            "mat_edu_HS: 0.01935619665750409\n",
            "mat_edu_M: -0.03191302088516405\n",
            "mat_edu_PhD: -0.025310643719426887\n",
            "mat_edu_UG: 0.01383855545870846\n",
            "birth_date_Apr2020: 0.0008409715032275105\n",
            "birth_date_Apr2021: 0.0022640044808301873\n",
            "birth_date_Aug2020: -0.006004840232047336\n",
            "birth_date_Aug2021: -0.005739872929088156\n",
            "birth_date_Aug2022: 4.379194153681097e-06\n",
            "birth_date_Dec2020: 0.014225286750799124\n",
            "birth_date_Dec2021: -0.0004930895110750262\n",
            "birth_date_Feb2021: 0.0011145120008070578\n",
            "birth_date_Feb2022: -0.0003493913615131574\n",
            "birth_date_Jan2021: 0.000864615620849539\n",
            "birth_date_Jan2022: 0.0\n",
            "birth_date_Jul2020: -0.0007154612828509049\n",
            "birth_date_Jul2021: -0.0073097713490754045\n",
            "birth_date_Jul2022: 0.0022961285078696592\n",
            "birth_date_Jun2020: 0.006912969884360425\n",
            "birth_date_Jun2021: -0.0068357019379543475\n",
            "birth_date_Jun2022: -0.0004977171977063077\n",
            "birth_date_Mar2021: 0.007524385056255669\n",
            "birth_date_Mar2022: -0.0006873180643592945\n",
            "birth_date_May2020: 0.00027978962059588134\n",
            "birth_date_May2021: 0.0017961075723701195\n",
            "birth_date_May2022: 0.0005683411028097642\n",
            "birth_date_Nov2020: -0.006874065240806556\n",
            "birth_date_Nov2021: 0.00016776888671922333\n",
            "birth_date_Oct2020: 0.0011334641847695773\n",
            "birth_date_Oct2021: -0.002228323876256553\n",
            "birth_date_Sep2020: 0.00862549644094768\n",
            "birth_date_Sep2021: -0.0009315497324568679\n",
            "birth_date_Sep2022: -0.0008742540803893054\n",
            "delivery_C: 0.759162407802919\n",
            "delivery_V: -0.75008554379108\n",
            "NICU_0: -0.22650070335379227\n",
            "NICU_1: 0.23557756736550095\n",
            "mat_age: 0.006246093585694998\n",
            "depression: 0.00013823711270496584\n",
            "anxiety: -9.394354695768081e-05\n",
            "birth_age_wk: -0.012632450069260153\n",
            "birth_length_cm: -0.0034208290278221736\n",
            "birth_weight_g: 0.036526605527989905\n",
            "life_danger: 0.01935619665750409\n",
            "baby_danger: -0.03191302088516405\n",
            "virus_baby_harm: -0.025310643719426887\n"
          ]
        }
      ],
      "source": [
        "df.dropna(inplace=True)\n",
        "\n",
        "# Define the independent variables and dependent variable\n",
        "X = df.drop('prob_preg', axis=1)  # Independent variables\n",
        "y = df['prob_preg']  # Dependent variable\n",
        "\n",
        "categorical_cols = ['income', 'mat_edu', 'birth_date', 'delivery', 'NICU']\n",
        "numerical_cols = ['mat_age', 'depression', 'anxiety', 'birth_age_wk', 'birth_length_cm', 'birth_weight_g', 'life_danger', 'baby_danger','virus_baby_harm']\n",
        "\n",
        "# Create a ColumnTransformer to apply one-hot encoding to categorical columns\n",
        "preprocessor = ColumnTransformer(\n",
        "    transformers=[('encoder', OneHotEncoder(), categorical_cols)],\n",
        "    remainder='passthrough'\n",
        ")\n",
        "\n",
        "# Fit the ColumnTransformer on data and transform 'X'\n",
        "X_encoded = preprocessor.fit_transform(X)\n",
        "# Split the data into training and testing sets\n",
        "X_train, X_test, y_train, y_test = train_test_split(X_encoded, y, test_size=0.2, random_state=42)\n",
        "\n",
        "# Initialize and fit the logistic regression model\n",
        "log_reg = LogisticRegression()\n",
        "log_reg.fit(X_train, y_train)\n",
        "\n",
        "# Predict the target variable for the test set\n",
        "y_pred = log_reg.predict(X_test)\n",
        "\n",
        "# Evaluate the model's performance\n",
        "print(\"Classification Report: For Total Dataframe\")\n",
        "print(classification_report(y_test, y_pred))\n",
        "accuracy = accuracy_score(y_test, y_pred)\n",
        "print(\"Accuracy:\", accuracy)\n",
        "print(\"\\n\")\n",
        "print(\"Variable : Coefficient (Change in log-odds per 1 unit increase)\")\n",
        "\n",
        "# Display the coefficients and corresponding variable names\n",
        "coefficients = log_reg.coef_[0]\n",
        "variable_names = preprocessor.named_transformers_['encoder'].get_feature_names_out(input_features=categorical_cols)\n",
        "for coef, var_name in zip(coefficients, variable_names):\n",
        "    print(f\"{var_name}: {coef}\")\n",
        "\n",
        "non_categorical_coeffs = log_reg.coef_[0][len(categorical_cols):]  # Exclude coefficients of one-hot encoded variables\n",
        "# Get the names of the non-categorical variables\n",
        "non_categorical_var_names =  numerical_cols #Assuming original_feature_names contains the names of non-categorical variables\n",
        "\n",
        "# Display the coefficients of the non-categorical variables\n",
        "for coef, var_name in zip(non_categorical_coeffs, non_categorical_var_names):\n",
        "    print(f\"{var_name}: {coef}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 167,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Classification Report: For Pre-Vaccine Dataframe\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.68      0.93      0.78       353\n",
            "           1       0.74      0.32      0.45       229\n",
            "\n",
            "    accuracy                           0.69       582\n",
            "   macro avg       0.71      0.62      0.61       582\n",
            "weighted avg       0.70      0.69      0.65       582\n",
            "\n",
            "Accuracy: 0.6872852233676976\n",
            "income_20000.0: 0.000834427682072276\n",
            "income_39999.0: 0.001939696811289777\n",
            "income_69999.0: -0.0010047808139544271\n",
            "income_99999.0: -0.008187518134243996\n",
            "income_124999.0: 0.010578681166040967\n",
            "income_149999.0: -9.759004669804836e-05\n",
            "income_174999.0: -0.00371030450171144\n",
            "income_199999.0: 0.0005036263891226474\n",
            "income_200000.0: 0.0016360993273807738\n",
            "mat_edu_<HS: 0.0008043530352577902\n",
            "mat_edu_C/T: 0.007613046689619581\n",
            "mat_edu_HS: 0.005645921796367737\n",
            "mat_edu_M: -0.0063329767974941165\n",
            "mat_edu_PhD: -0.004606293455082924\n",
            "mat_edu_UG: -0.0006317133893709257\n",
            "birth_date_2020-10-01T00:00:00.000000000: 0.004511791902820682\n",
            "birth_date_2020-11-01T00:00:00.000000000: -0.0027187725278496736\n",
            "birth_date_2020-12-01T00:00:00.000000000: 0.00668853494956571\n",
            "birth_date_2021-01-01T00:00:00.000000000: 0.0017912520884913016\n",
            "birth_date_2021-02-01T00:00:00.000000000: 0.003726657288788437\n",
            "birth_date_2021-03-01T00:00:00.000000000: 7.784764524453986e-05\n",
            "birth_date_2021-04-01T00:00:00.000000000: -0.00298196833140932\n",
            "birth_date_2021-05-01T00:00:00.000000000: 0.0014335371828287841\n",
            "birth_date_2021-06-01T00:00:00.000000000: -0.0019447256635862855\n",
            "birth_date_2021-07-01T00:00:00.000000000: -0.00358529713200824\n",
            "birth_date_2021-08-01T00:00:00.000000000: -0.004168022190035189\n",
            "birth_date_2021-09-01T00:00:00.000000000: -0.0003384973335558971\n",
            "delivery_C: 0.23552083969351592\n",
            "delivery_V: -0.23302850181435522\n",
            "NICU_0: -0.06994692668249879\n",
            "NICU_1: 0.07243926456182052\n",
            "mat_age: -9.759004669804836e-05\n",
            "depression: -0.00371030450171144\n",
            "anxiety: 0.0005036263891226474\n",
            "birth_age_wk: 0.0016360993273807738\n",
            "birth_length_cm: 0.0008043530352577902\n",
            "birth_weight_g: 0.007613046689619581\n",
            "life_danger: 0.005645921796367737\n",
            "baby_danger: -0.0063329767974941165\n",
            "virus_baby_harm: -0.004606293455082924\n"
          ]
        }
      ],
      "source": [
        "prevaccine_df.dropna(inplace=True)\n",
        "\n",
        "# Define the independent variables and dependent variable\n",
        "X = prevaccine_df.drop('prob_preg', axis=1)  # Independent variables\n",
        "y = prevaccine_df['prob_preg']  # Dependent variable\n",
        "\n",
        "categorical_cols = ['income', 'mat_edu', 'birth_date', 'delivery', 'NICU']\n",
        "numerical_cols = ['mat_age', 'depression', 'anxiety', 'birth_age_wk', 'birth_length_cm', 'birth_weight_g', 'life_danger', 'baby_danger','virus_baby_harm']\n",
        "\n",
        "# Create a ColumnTransformer to apply one-hot encoding to categorical columns\n",
        "preprocessor = ColumnTransformer(\n",
        "    transformers=[('encoder', OneHotEncoder(), categorical_cols)],\n",
        "    remainder='passthrough'\n",
        ")\n",
        "\n",
        "# Fit the ColumnTransformer on data and transform 'X'\n",
        "X_encoded = preprocessor.fit_transform(X)\n",
        "# Split the data into training and testing sets\n",
        "X_train, X_test, y_train, y_test = train_test_split(X_encoded, y, test_size=0.2, random_state=42)\n",
        "\n",
        "# Initialize and fit the logistic regression model\n",
        "log_reg = LogisticRegression()\n",
        "log_reg.fit(X_train, y_train)\n",
        "\n",
        "# Predict the target variable for the test set\n",
        "y_pred = log_reg.predict(X_test)\n",
        "\n",
        "# Evaluate the model's performance\n",
        "print(\"Classification Report: For Pre-Vaccine Dataframe\")\n",
        "print(classification_report(y_test, y_pred))\n",
        "accuracy = accuracy_score(y_test, y_pred)\n",
        "print(\"Accuracy:\", accuracy)\n",
        "print(\"\\n\")\n",
        "print(\"Variable : Coefficient (Change in log-odds per 1 unit increase)\")\n",
        "\n",
        "# Display the coefficients and corresponding variable names\n",
        "coefficients = log_reg.coef_[0]\n",
        "variable_names = preprocessor.named_transformers_['encoder'].get_feature_names_out(input_features=categorical_cols)\n",
        "for coef, var_name in zip(coefficients, variable_names):\n",
        "    print(f\"{var_name}: {coef}\")\n",
        "\n",
        "non_categorical_coeffs = log_reg.coef_[0][len(categorical_cols):]  # Exclude coefficients of one-hot encoded variables\n",
        "# Get the names of the non-categorical variables\n",
        "non_categorical_var_names =  numerical_cols #Assuming original_feature_names contains the names of non-categorical variables\n",
        "\n",
        "# Display the coefficients of the non-categorical variables\n",
        "for coef, var_name in zip(non_categorical_coeffs, non_categorical_var_names):\n",
        "    print(f\"{var_name}: {coef}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 173,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Classification Report: For Pre-Vaccine Dataframe\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.75      0.60      0.67        10\n",
            "           1       0.20      0.33      0.25         3\n",
            "\n",
            "    accuracy                           0.54        13\n",
            "   macro avg       0.47      0.47      0.46        13\n",
            "weighted avg       0.62      0.54      0.57        13\n",
            "\n",
            "Accuracy: 0.5384615384615384\n",
            "\n",
            "\n",
            "Variable : Coefficient (Change in log-odds per 1 unit increase)\n",
            "income_39999.0: 0.01939530180382613\n",
            "income_69999.0: 0.011130035099891224\n",
            "income_99999.0: 0.002839566435756263\n",
            "income_124999.0: 0.01030094424448348\n",
            "income_149999.0: 0.004623636022109909\n",
            "income_174999.0: -0.0342770349922274\n",
            "income_199999.0: 0.00687593429752791\n",
            "income_200000.0: -0.019788937674101007\n",
            "mat_edu_C/T: -0.0073491212804785815\n",
            "mat_edu_HS: 0.037285310406731116\n",
            "mat_edu_M: -0.018096882109367447\n",
            "mat_edu_PhD: -0.012027663078334168\n",
            "mat_edu_UG: 0.001287801298682772\n",
            "birth_date_2021-10-01T00:00:00.000000000: -0.05646352497568199\n",
            "birth_date_2021-11-01T00:00:00.000000000: -0.0042030320693769475\n",
            "birth_date_2021-12-01T00:00:00.000000000: 0.0022421360079385297\n",
            "birth_date_2022-01-01T00:00:00.000000000: 0.011993565555725207\n",
            "birth_date_2022-02-01T00:00:00.000000000: -0.008695496709644592\n",
            "birth_date_2022-03-01T00:00:00.000000000: -0.02803258420693645\n",
            "birth_date_2022-05-01T00:00:00.000000000: 0.005793923440310836\n",
            "birth_date_2022-06-01T00:00:00.000000000: 0.008692106305648581\n",
            "birth_date_2022-07-01T00:00:00.000000000: 0.06747438288592811\n",
            "birth_date_2022-08-01T00:00:00.000000000: 0.011081971687477233\n",
            "birth_date_2022-09-01T00:00:00.000000000: -0.008784002684103424\n",
            "delivery_C: 0.2246978732852095\n",
            "delivery_V: -0.22359842804794025\n",
            "NICU_0: -0.08502160150500414\n",
            "NICU_1: 0.08612104674226656\n",
            "mat_age: -0.0342770349922274\n",
            "depression: 0.00687593429752791\n",
            "anxiety: -0.019788937674101007\n",
            "birth_age_wk: -0.0073491212804785815\n",
            "birth_length_cm: 0.037285310406731116\n",
            "birth_weight_g: -0.018096882109367447\n",
            "life_danger: -0.012027663078334168\n",
            "baby_danger: 0.001287801298682772\n",
            "virus_baby_harm: -0.05646352497568199\n"
          ]
        }
      ],
      "source": [
        "postvaccine_df.dropna(inplace=True)\n",
        "\n",
        "# Define the independent variables and dependent variable\n",
        "X = postvaccine_df.drop('prob_preg', axis=1)  # Independent variables\n",
        "y = postvaccine_df['prob_preg']  # Dependent variable\n",
        "\n",
        "categorical_cols = ['income', 'mat_edu', 'birth_date', 'delivery', 'NICU']\n",
        "numerical_cols = ['mat_age', 'depression', 'anxiety', 'birth_age_wk', 'birth_length_cm', 'birth_weight_g', 'life_danger', 'baby_danger','virus_baby_harm']\n",
        "\n",
        "# Create a ColumnTransformer to apply one-hot encoding to categorical columns\n",
        "preprocessor = ColumnTransformer(\n",
        "    transformers=[('encoder', OneHotEncoder(), categorical_cols)],\n",
        "    remainder='passthrough'\n",
        ")\n",
        "\n",
        "# Fit the ColumnTransformer on data and transform 'X'\n",
        "X_encoded = preprocessor.fit_transform(X)\n",
        "# Split the data into training and testing sets\n",
        "X_train, X_test, y_train, y_test = train_test_split(X_encoded, y, test_size=0.2, random_state=42)\n",
        "\n",
        "# Initialize and fit the logistic regression model\n",
        "log_reg = LogisticRegression()\n",
        "log_reg.fit(X_train, y_train)\n",
        "\n",
        "# Predict the target variable for the test set\n",
        "y_pred = log_reg.predict(X_test)\n",
        "\n",
        "# Evaluate the model's performance\n",
        "print(\"Classification Report: For Pre-Vaccine Dataframe\")\n",
        "print(classification_report(y_test, y_pred))\n",
        "accuracy = accuracy_score(y_test, y_pred)\n",
        "print(\"Accuracy:\", accuracy)\n",
        "print(\"\\n\")\n",
        "print(\"Variable : Coefficient (Change in log-odds per 1 unit increase)\")\n",
        "\n",
        "# Display the coefficients and corresponding variable names\n",
        "coefficients = log_reg.coef_[0]\n",
        "variable_names = preprocessor.named_transformers_['encoder'].get_feature_names_out(input_features=categorical_cols)\n",
        "for coef, var_name in zip(coefficients, variable_names):\n",
        "    print(f\"{var_name}: {coef}\")\n",
        "\n",
        "non_categorical_coeffs = log_reg.coef_[0][len(categorical_cols):]  # Exclude coefficients of one-hot encoded variables\n",
        "# Get the names of the non-categorical variables\n",
        "non_categorical_var_names =  numerical_cols #Assuming original_feature_names contains the names of non-categorical variables\n",
        "\n",
        "# Display the coefficients of the non-categorical variables\n",
        "for coef, var_name in zip(non_categorical_coeffs, non_categorical_var_names):\n",
        "    print(f\"{var_name}: {coef}\")"
      ]
    }
  ],
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