Risk_EDA.ipynb

{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[],"authorship_tag":"ABX9TyPCVscCflu0zV+zStRmV40L"},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"},"gpuClass":"standard"},"cells":[{"cell_type":"code","execution_count":1,"metadata":{"id":"1jzgAiK3rAod","executionInfo":{"status":"ok","timestamp":1674583067382,"user_tz":300,"elapsed":2020,"user":{"displayName":"Mona Ascha","userId":"01195351992176878996"}}},"outputs":[],"source":["#Import packages\n","import numpy as np\n","import pandas as pd\n","import seaborn as sns\n","from google.colab import files\n","import io\n","from google.colab import drive\n"]},{"cell_type":"code","source":["#Load data\n","drive.mount('/content/drive')\n","df =pd.read_csv(\"drive/My Drive/Capstone Project/Cervical_Cancer/kag_risk_factors_cervical_cancer.csv\")\n","df.head()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":387},"id":"wO4iQSYwrDoa","executionInfo":{"status":"ok","timestamp":1674583068136,"user_tz":300,"elapsed":757,"user":{"displayName":"Mona Ascha","userId":"01195351992176878996"}},"outputId":"60f87461-136a-467a-dc00-1362f81f06ea"},"execution_count":2,"outputs":[{"output_type":"stream","name":"stdout","text":["Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"]},{"output_type":"execute_result","data":{"text/plain":["   Age Number of sexual partners First sexual intercourse Num of pregnancies  \\\n","0   18                       4.0                     15.0                1.0   \n","1   15                       1.0                     14.0                1.0   \n","2   34                       1.0                        ?                1.0   \n","3   52                       5.0                     16.0                4.0   \n","4   46                       3.0                     21.0                4.0   \n","\n","  Smokes Smokes (years) Smokes (packs/year) Hormonal Contraceptives  \\\n","0    0.0            0.0                 0.0                     0.0   \n","1    0.0            0.0                 0.0                     0.0   \n","2    0.0            0.0                 0.0                     0.0   \n","3    1.0           37.0                37.0                     1.0   \n","4    0.0            0.0                 0.0                     1.0   \n","\n","  Hormonal Contraceptives (years)  IUD  ... STDs: Time since first diagnosis  \\\n","0                             0.0  0.0  ...                                ?   \n","1                             0.0  0.0  ...                                ?   \n","2                             0.0  0.0  ...                                ?   \n","3                             3.0  0.0  ...                                ?   \n","4                            15.0  0.0  ...                                ?   \n","\n","  STDs: Time since last diagnosis Dx:Cancer Dx:CIN Dx:HPV Dx Hinselmann  \\\n","0                               ?         0      0      0  0          0   \n","1                               ?         0      0      0  0          0   \n","2                               ?         0      0      0  0          0   \n","3                               ?         1      0      1  0          0   \n","4                               ?         0      0      0  0          0   \n","\n","  Schiller Citology Biopsy  \n","0        0        0      0  \n","1        0        0      0  \n","2        0        0      0  \n","3        0        0      0  \n","4        0        0      0  \n","\n","[5 rows x 36 columns]"],"text/html":["\n","  <div id=\"df-9b1965cb-21ab-4615-a81f-63cd1983af4a\">\n","    <div class=\"colab-df-container\">\n","      <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>Age</th>\n","      <th>Number of sexual partners</th>\n","      <th>First sexual intercourse</th>\n","      <th>Num of pregnancies</th>\n","      <th>Smokes</th>\n","      <th>Smokes (years)</th>\n","      <th>Smokes (packs/year)</th>\n","      <th>Hormonal 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858.000000  858.000000  858.000000  \n","mean     0.020979    0.027972    0.040793    0.086247    0.051282    0.064103  \n","std      0.143398    0.164989    0.197925    0.280892    0.220701    0.245078  \n","min      0.000000    0.000000    0.000000    0.000000    0.000000    0.000000  \n","25%      0.000000    0.000000    0.000000    0.000000    0.000000    0.000000  \n","50%      0.000000    0.000000    0.000000    0.000000    0.000000    0.000000  \n","75%      0.000000    0.000000    0.000000    0.000000    0.000000    0.000000  \n","max      1.000000    1.000000    1.000000    1.000000    1.000000    1.000000  "],"text/html":["\n","  <div id=\"df-2bde0681-2837-4439-a23e-af8e89b1e612\">\n","    <div class=\"colab-df-container\">\n","      <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","        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<td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","    </tr>\n","    <tr>\n","      <th>50%</th>\n","      <td>25.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","    </tr>\n","    <tr>\n","      <th>75%</th>\n","      <td>32.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","    </tr>\n","    <tr>\n","      <th>max</th>\n","      <td>84.000000</td>\n","      <td>3.000000</td>\n","      <td>1.000000</td>\n","      <td>1.000000</td>\n","      <td>1.000000</td>\n","      <td>1.000000</td>\n","      <td>1.000000</td>\n","      <td>1.000000</td>\n","      <td>1.000000</td>\n","      <td>1.000000</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-2bde0681-2837-4439-a23e-af8e89b1e612')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 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Visit the ' +\n","            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n","            + ' to learn more about interactive tables.';\n","          element.innerHTML = '';\n","          dataTable['output_type'] = 'display_data';\n","          await google.colab.output.renderOutput(dataTable, element);\n","          const docLink = document.createElement('div');\n","          docLink.innerHTML = docLinkHtml;\n","          element.appendChild(docLink);\n","        }\n","      </script>\n","    </div>\n","  </div>\n","  "]},"metadata":{},"execution_count":3}]},{"cell_type":"code","source":["#Let's inspect the data\n","print(df.columns)\n","\n","#Let's rename the columns so they are usable \n","df.rename({\n","           'Age': 'age', \n","           'Number of sexual partners': 'num_sex_partners',\n","           'First sexual intercourse': 'first_sex',\n","           'Num of pregnancies': 'num_preg',\n","           'Smokes': 'smoker',\n","           'Smokes (years)': 'smoke_years',\n","           'Smokes (packs/year)': 'pack_years',\n","           'Hormonal Contraceptives': 'contracept', \n","           'Hormonal Contraceptives (years)': 'contracept_years',\n","           'IUD': 'iud',\n","           'IUD (years)': 'iud_years',\n","           'STDs': 'stds',\n","           'STDs (number)': 'stds_num',\n","           'STDs:condylomatosis': 'std_condy', \n","           'STDs:cervical condylomatosis': 'std_cerv_condy',\n","           'STDs:vaginal condylomatosis': 'std_vag_condy',\n","           'STDs:vulvo-perineal condylomatosis': 'std_vulvperi_condy',\n","           'STDs:syphilis': 'std_syphilis',\n","           'STDs:pelvic inflammatory disease': 'std_pid',\n","           'STDs:genital herpes': 'std_herpes',\n","           'STDs:molluscum contagiosum': 'std_molluscum', \n","           'STDs:AIDS': 'std_aids',\n","           'STDs:HIV': 'std_hiv',\n","           'STDs:Hepatitis B': 'std_hep_b',\n","           'STDs:HPV': 'std_hpv',\n","           'STDs: Number of diagnosis': 'std_num_dx',\n","           'STDs: Time since first diagnosis': 'std_time_first_dx',\n","           'STDs: Time since last diagnosis': 'std_time_last_dx',\n","           'Dx:Cancer': 'cancer', \n","           'Dx:CIN': 'cin', \n","           'Dx:HPV': 'hpv', \n","           'Dx': 'dx', \n","           'Hinselmann': 'hinselmann',\n","           'Schiller': 'schiller', \n","           'Citology': 'cytology',\n","           'Biopsy': 'biopsy'\n","           }, \n","          axis=1, \n","          inplace=True)\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"hweLYFO4GClG","executionInfo":{"status":"ok","timestamp":1674583068502,"user_tz":300,"elapsed":10,"user":{"displayName":"Mona Ascha","userId":"01195351992176878996"}},"outputId":"5c9f0bfb-42af-4c9b-b662-588a968eda3a"},"execution_count":4,"outputs":[{"output_type":"stream","name":"stdout","text":["Index(['Age', 'Number of sexual partners', 'First sexual intercourse',\n","       'Num of pregnancies', 'Smokes', 'Smokes (years)', 'Smokes (packs/year)',\n","       'Hormonal Contraceptives', 'Hormonal Contraceptives (years)', 'IUD',\n","       'IUD (years)', 'STDs', 'STDs (number)', 'STDs:condylomatosis',\n","       'STDs:cervical condylomatosis', 'STDs:vaginal condylomatosis',\n","       'STDs:vulvo-perineal condylomatosis', 'STDs:syphilis',\n","       'STDs:pelvic inflammatory disease', 'STDs:genital herpes',\n","       'STDs:molluscum contagiosum', 'STDs:AIDS', 'STDs:HIV',\n","       'STDs:Hepatitis B', 'STDs:HPV', 'STDs: Number of diagnosis',\n","       'STDs: Time since first diagnosis', 'STDs: Time since last diagnosis',\n","       'Dx:Cancer', 'Dx:CIN', 'Dx:HPV', 'Dx', 'Hinselmann', 'Schiller',\n","       'Citology', 'Biopsy'],\n","      dtype='object')\n"]}]},{"cell_type":"code","source":["print(df.columns)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"_rsdK6kqKGUq","executionInfo":{"status":"ok","timestamp":1674583068502,"user_tz":300,"elapsed":7,"user":{"displayName":"Mona Ascha","userId":"01195351992176878996"}},"outputId":"2e1150f7-4dd1-441b-8435-958cf55afc9c"},"execution_count":5,"outputs":[{"output_type":"stream","name":"stdout","text":["Index(['age', 'num_sex_partners', 'first_sex', 'num_preg', 'smoker',\n","       'smoke_years', 'pack_years', 'contracept', 'contracept_years', 'iud',\n","       'iud_years', 'stds', 'stds_num', 'std_condy', 'std_cerv_condy',\n","       'std_vag_condy', 'std_vulvperi_condy', 'std_syphilis', 'std_pid',\n","       'std_herpes', 'std_molluscum', 'std_aids', 'std_hiv', 'std_hep_b',\n","       'std_hpv', 'std_num_dx', 'std_time_first_dx', 'std_time_last_dx',\n","       'cancer', 'cin', 'hpv', 'dx', 'hinselmann', 'schiller', 'cytology',\n","       'biopsy'],\n","      dtype='object')\n"]}]},{"cell_type":"code","source":["df.shape"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Ztp-GM9xMJJl","executionInfo":{"status":"ok","timestamp":1674583068503,"user_tz":300,"elapsed":6,"user":{"displayName":"Mona Ascha","userId":"01195351992176878996"}},"outputId":"b9ee1103-0fe8-466a-9c0c-20d3ddcc38d9"},"execution_count":6,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(858, 36)"]},"metadata":{},"execution_count":6}]},{"cell_type":"code","source":["#Describing numeric values\n","df.describe().T"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":363},"id":"yc2-AF3KKLR3","executionInfo":{"status":"ok","timestamp":1674583068730,"user_tz":300,"elapsed":5,"user":{"displayName":"Mona Ascha","userId":"01195351992176878996"}},"outputId":"e0fa055f-d08b-4931-f2f4-37fb56d8373e"},"execution_count":7,"outputs":[{"output_type":"execute_result","data":{"text/plain":["            count       mean       std   min   25%   50%   75%   max\n","age         858.0  26.820513  8.497948  13.0  20.0  25.0  32.0  84.0\n","std_num_dx  858.0   0.087413  0.302545   0.0   0.0   0.0   0.0   3.0\n","cancer      858.0   0.020979  0.143398   0.0   0.0   0.0   0.0   1.0\n","cin         858.0   0.010490  0.101939   0.0   0.0   0.0   0.0   1.0\n","hpv         858.0   0.020979  0.143398   0.0   0.0   0.0   0.0   1.0\n","dx          858.0   0.027972  0.164989   0.0   0.0   0.0   0.0   1.0\n","hinselmann  858.0   0.040793  0.197925   0.0   0.0   0.0   0.0   1.0\n","schiller    858.0   0.086247  0.280892   0.0   0.0   0.0   0.0   1.0\n","cytology    858.0   0.051282  0.220701   0.0   0.0   0.0   0.0   1.0\n","biopsy      858.0   0.064103  0.245078   0.0   0.0   0.0   0.0   1.0"],"text/html":["\n","  <div id=\"df-5f72ebe1-3a88-4d6c-9d28-407a1d099743\">\n","    <div class=\"colab-df-container\">\n","      <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>count</th>\n","      <th>mean</th>\n","      <th>std</th>\n","      <th>min</th>\n","      <th>25%</th>\n","      <th>50%</th>\n","      <th>75%</th>\n","      <th>max</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>age</th>\n","      <td>858.0</td>\n","      <td>26.820513</td>\n","      <td>8.497948</td>\n","      <td>13.0</td>\n","      <td>20.0</td>\n","      <td>25.0</td>\n","      <td>32.0</td>\n","      <td>84.0</td>\n","    </tr>\n","    <tr>\n","      <th>std_num_dx</th>\n","      <td>858.0</td>\n","      <td>0.087413</td>\n","      <td>0.302545</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>3.0</td>\n","    </tr>\n","    <tr>\n","      <th>cancer</th>\n","      <td>858.0</td>\n","      <td>0.020979</td>\n","      <td>0.143398</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","    </tr>\n","    <tr>\n","      <th>cin</th>\n","      <td>858.0</td>\n","      <td>0.010490</td>\n","      <td>0.101939</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","    </tr>\n","    <tr>\n","      <th>hpv</th>\n","      <td>858.0</td>\n","      <td>0.020979</td>\n","      <td>0.143398</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","    </tr>\n","    <tr>\n","      <th>dx</th>\n","      <td>858.0</td>\n","      <td>0.027972</td>\n","      <td>0.164989</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","    </tr>\n","    <tr>\n","      <th>hinselmann</th>\n","      <td>858.0</td>\n","      <td>0.040793</td>\n","      <td>0.197925</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","    </tr>\n","    <tr>\n","      <th>schiller</th>\n","      <td>858.0</td>\n","      <td>0.086247</td>\n","      <td>0.280892</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","    </tr>\n","    <tr>\n","      <th>cytology</th>\n","      <td>858.0</td>\n","      <td>0.051282</td>\n","      <td>0.220701</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","    </tr>\n","    <tr>\n","      <th>biopsy</th>\n","      <td>858.0</td>\n","      <td>0.064103</td>\n","      <td>0.245078</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-5f72ebe1-3a88-4d6c-9d28-407a1d099743')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n","  </svg>\n","   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Ascha","userId":"01195351992176878996"}},"outputId":"645dc902-3ac9-4439-d278-eb8ef3cab1a5"},"execution_count":8,"outputs":[{"output_type":"execute_result","data":{"text/plain":["                    count unique   top freq       mean       std   min   25%  \\\n","age                 858.0    NaN   NaN  NaN  26.820513  8.497948  13.0  20.0   \n","num_sex_partners      858     13   2.0  272        NaN       NaN   NaN   NaN   \n","first_sex             858     22  15.0  163        NaN       NaN   NaN   NaN   \n","num_preg              858     12   1.0  270        NaN       NaN   NaN   NaN   \n","smoker                858      3   0.0  722        NaN       NaN   NaN   NaN   \n","smoke_years           858     31   0.0  722        NaN       NaN   NaN   NaN   \n","pack_years            858     63   0.0  722        NaN       NaN   NaN   NaN   \n","contracept            858      3   1.0  481        NaN       NaN   NaN   NaN   \n","contracept_years      858     41   0.0  269        NaN       NaN   NaN   NaN   \n","iud                   858      3   0.0  658        NaN       NaN   NaN   NaN   \n","iud_years             858     27   0.0  658        NaN       NaN   NaN   NaN   \n","stds                  858      3   0.0  674        NaN       NaN   NaN   NaN   \n","stds_num              858      6   0.0  674        NaN       NaN   NaN   NaN   \n","std_condy             858      3   0.0  709        NaN       NaN   NaN   NaN   \n","std_cerv_condy        858      2   0.0  753        NaN       NaN   NaN   NaN   \n","std_vag_condy         858      3   0.0  749        NaN       NaN   NaN   NaN   \n","std_vulvperi_condy    858      3   0.0  710        NaN       NaN   NaN   NaN   \n","std_syphilis          858      3   0.0  735        NaN       NaN   NaN   NaN   \n","std_pid               858      3   0.0  752        NaN       NaN   NaN   NaN   \n","std_herpes            858      3   0.0  752        NaN       NaN   NaN   NaN   \n","std_molluscum         858      3   0.0  752        NaN       NaN   NaN   NaN   \n","std_aids              858      2   0.0  753        NaN       NaN   NaN   NaN   \n","std_hiv               858      3   0.0  735        NaN       NaN   NaN   NaN   \n","std_hep_b             858      3   0.0  752        NaN       NaN   NaN   NaN   \n","std_hpv               858      3   0.0  751        NaN       NaN   NaN   NaN   \n","std_num_dx          858.0    NaN   NaN  NaN   0.087413  0.302545   0.0   0.0   \n","std_time_first_dx     858     19     ?  787        NaN       NaN   NaN   NaN   \n","std_time_last_dx      858     19     ?  787        NaN       NaN   NaN   NaN   \n","cancer              858.0    NaN   NaN  NaN   0.020979  0.143398   0.0   0.0   \n","cin                 858.0    NaN   NaN  NaN    0.01049  0.101939   0.0   0.0   \n","hpv                 858.0    NaN   NaN  NaN   0.020979  0.143398   0.0   0.0   \n","dx                  858.0    NaN   NaN  NaN   0.027972  0.164989   0.0   0.0   \n","hinselmann          858.0    NaN   NaN  NaN   0.040793  0.197925   0.0   0.0   \n","schiller            858.0    NaN   NaN  NaN   0.086247  0.280892   0.0   0.0   \n","cytology            858.0    NaN   NaN  NaN   0.051282  0.220701   0.0   0.0   \n","biopsy              858.0    NaN   NaN  NaN   0.064103  0.245078   0.0   0.0   \n","\n","                     50%   75%   max  \n","age                 25.0  32.0  84.0  \n","num_sex_partners     NaN   NaN   NaN  \n","first_sex            NaN   NaN   NaN  \n","num_preg             NaN   NaN   NaN  \n","smoker               NaN   NaN   NaN  \n","smoke_years          NaN   NaN   NaN  \n","pack_years           NaN   NaN   NaN  \n","contracept           NaN   NaN   NaN  \n","contracept_years     NaN   NaN   NaN  \n","iud                  NaN   NaN   NaN  \n","iud_years            NaN   NaN   NaN  \n","stds                 NaN   NaN   NaN  \n","stds_num             NaN   NaN   NaN  \n","std_condy            NaN   NaN   NaN  \n","std_cerv_condy       NaN   NaN   NaN  \n","std_vag_condy        NaN   NaN   NaN  \n","std_vulvperi_condy   NaN   NaN   NaN  \n","std_syphilis         NaN   NaN   NaN  \n","std_pid              NaN   NaN   NaN  \n","std_herpes           NaN   NaN   NaN  \n","std_molluscum        NaN   NaN   NaN  \n","std_aids             NaN   NaN   NaN  \n","std_hiv              NaN   NaN   NaN  \n","std_hep_b            NaN   NaN   NaN  \n","std_hpv              NaN   NaN   NaN  \n","std_num_dx           0.0   0.0   3.0  \n","std_time_first_dx    NaN   NaN   NaN  \n","std_time_last_dx     NaN   NaN   NaN  \n","cancer               0.0   0.0   1.0  \n","cin                  0.0   0.0   1.0  \n","hpv                  0.0   0.0   1.0  \n","dx                   0.0   0.0   1.0  \n","hinselmann           0.0   0.0   1.0  \n","schiller             0.0   0.0   1.0  \n","cytology             0.0   0.0   1.0  \n","biopsy               0.0   0.0   1.0  "],"text/html":["\n","  <div id=\"df-81afd5f5-ab44-49eb-98fe-d94c48390369\">\n","    <div class=\"colab-df-container\">\n","      <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>count</th>\n","      <th>unique</th>\n","      <th>top</th>\n","      <th>freq</th>\n","      <th>mean</th>\n","      <th>std</th>\n","      <th>min</th>\n","      <th>25%</th>\n","      <th>50%</th>\n","      <th>75%</th>\n","      <th>max</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>age</th>\n","      <td>858.0</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>26.820513</td>\n","      <td>8.497948</td>\n","      <td>13.0</td>\n","      <td>20.0</td>\n","      <td>25.0</td>\n","      <td>32.0</td>\n","      <td>84.0</td>\n","    </tr>\n","    <tr>\n","      <th>num_sex_partners</th>\n","      <td>858</td>\n","      <td>13</td>\n","      <td>2.0</td>\n","      <td>272</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>first_sex</th>\n","      <td>858</td>\n","      <td>22</td>\n","      <td>15.0</td>\n","      <td>163</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>num_preg</th>\n","      <td>858</td>\n","      <td>12</td>\n","      <td>1.0</td>\n","      <td>270</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>smoker</th>\n","      <td>858</td>\n","      <td>3</td>\n","      <td>0.0</td>\n","      <td>722</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>smoke_years</th>\n","      <td>858</td>\n","      <td>31</td>\n","      <td>0.0</td>\n","      <td>722</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>pack_years</th>\n","      <td>858</td>\n","      <td>63</td>\n","      <td>0.0</td>\n","      <td>722</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>contracept</th>\n","      <td>858</td>\n","      <td>3</td>\n","      <td>1.0</td>\n","      <td>481</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>contracept_years</th>\n","      <td>858</td>\n","      <td>41</td>\n","      <td>0.0</td>\n","      <td>269</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>iud</th>\n","      <td>858</td>\n","      <td>3</td>\n","      <td>0.0</td>\n","      <td>658</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>iud_years</th>\n","      <td>858</td>\n","      <td>27</td>\n","      <td>0.0</td>\n","      <td>658</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>stds</th>\n","      <td>858</td>\n","      <td>3</td>\n","      <td>0.0</td>\n","      <td>674</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>stds_num</th>\n","      <td>858</td>\n","      <td>6</td>\n","      <td>0.0</td>\n","      <td>674</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>std_condy</th>\n","      <td>858</td>\n","      <td>3</td>\n","      <td>0.0</td>\n","      <td>709</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>std_cerv_condy</th>\n","      <td>858</td>\n","      <td>2</td>\n","      <td>0.0</td>\n","      <td>753</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>std_vag_condy</th>\n","      <td>858</td>\n","      <td>3</td>\n","      <td>0.0</td>\n","      <td>749</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>std_vulvperi_condy</th>\n","      <td>858</td>\n","      <td>3</td>\n","      <td>0.0</td>\n","      <td>710</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>std_syphilis</th>\n","      <td>858</td>\n","      <td>3</td>\n","      <td>0.0</td>\n","      <td>735</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>std_pid</th>\n","      <td>858</td>\n","      <td>3</td>\n","      <td>0.0</td>\n","      <td>752</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>std_herpes</th>\n","      <td>858</td>\n","      <td>3</td>\n","      <td>0.0</td>\n","      <td>752</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>std_molluscum</th>\n","      <td>858</td>\n","      <td>3</td>\n","      <td>0.0</td>\n","      <td>752</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>std_aids</th>\n","      <td>858</td>\n","      <td>2</td>\n","      <td>0.0</td>\n","      <td>753</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>std_hiv</th>\n","      <td>858</td>\n","      <td>3</td>\n","      <td>0.0</td>\n","      <td>735</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>std_hep_b</th>\n","      <td>858</td>\n","      <td>3</td>\n","      <td>0.0</td>\n","      <td>752</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>std_hpv</th>\n","      <td>858</td>\n","      <td>3</td>\n","      <td>0.0</td>\n","      <td>751</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>std_num_dx</th>\n","      <td>858.0</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>0.087413</td>\n","      <td>0.302545</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>3.0</td>\n","    </tr>\n","    <tr>\n","      <th>std_time_first_dx</th>\n","      <td>858</td>\n","      <td>19</td>\n","      <td>?</td>\n","      <td>787</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>std_time_last_dx</th>\n","      <td>858</td>\n","      <td>19</td>\n","      <td>?</td>\n","      <td>787</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>cancer</th>\n","      <td>858.0</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>0.020979</td>\n","      <td>0.143398</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","    </tr>\n","    <tr>\n","      <th>cin</th>\n","      <td>858.0</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>0.01049</td>\n","      <td>0.101939</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","    </tr>\n","    <tr>\n","      <th>hpv</th>\n","      <td>858.0</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>0.020979</td>\n","      <td>0.143398</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","    </tr>\n","    <tr>\n","      <th>dx</th>\n","      <td>858.0</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>0.027972</td>\n","      <td>0.164989</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","    </tr>\n","    <tr>\n","      <th>hinselmann</th>\n","      <td>858.0</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>0.040793</td>\n","      <td>0.197925</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","    </tr>\n","    <tr>\n","      <th>schiller</th>\n","      <td>858.0</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>0.086247</td>\n","      <td>0.280892</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","    </tr>\n","    <tr>\n","      <th>cytology</th>\n","      <td>858.0</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>0.051282</td>\n","      <td>0.220701</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","    </tr>\n","    <tr>\n","      <th>biopsy</th>\n","      <td>858.0</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>0.064103</td>\n","      <td>0.245078</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-81afd5f5-ab44-49eb-98fe-d94c48390369')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n","  </svg>\n","   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Visit the ' +\n","            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n","            + ' to learn more about interactive tables.';\n","          element.innerHTML = '';\n","          dataTable['output_type'] = 'display_data';\n","          await google.colab.output.renderOutput(dataTable, element);\n","          const docLink = document.createElement('div');\n","          docLink.innerHTML = docLinkHtml;\n","          element.appendChild(docLink);\n","        }\n","      </script>\n","    </div>\n","  </div>\n","  "]},"metadata":{},"execution_count":8}]},{"cell_type":"code","source":["#Looks like some cells have \"?\" which is NA\n","#We have to convert these to NAs\n","df = df.replace('?', np.nan)\n"],"metadata":{"id":"_5NSryVpK4k5","executionInfo":{"status":"ok","timestamp":1674583069098,"user_tz":300,"elapsed":5,"user":{"displayName":"Mona Ascha","userId":"01195351992176878996"}}},"execution_count":9,"outputs":[]},{"cell_type":"code","source":["#Looking at missing values\n","df.isnull().sum()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Pcw4V67NMFLD","executionInfo":{"status":"ok","timestamp":1674583069677,"user_tz":300,"elapsed":583,"user":{"displayName":"Mona Ascha","userId":"01195351992176878996"}},"outputId":"099dde0e-f2dc-4481-fb56-af679ae20adc"},"execution_count":10,"outputs":[{"output_type":"execute_result","data":{"text/plain":["age                     0\n","num_sex_partners       26\n","first_sex               7\n","num_preg               56\n","smoker                 13\n","smoke_years            13\n","pack_years             13\n","contracept            108\n","contracept_years      108\n","iud                   117\n","iud_years             117\n","stds                  105\n","stds_num              105\n","std_condy             105\n","std_cerv_condy        105\n","std_vag_condy         105\n","std_vulvperi_condy    105\n","std_syphilis          105\n","std_pid               105\n","std_herpes            105\n","std_molluscum         105\n","std_aids              105\n","std_hiv               105\n","std_hep_b             105\n","std_hpv               105\n","std_num_dx              0\n","std_time_first_dx     787\n","std_time_last_dx      787\n","cancer                  0\n","cin                     0\n","hpv                     0\n","dx                      0\n","hinselmann              0\n","schiller                0\n","cytology                0\n","biopsy                  0\n","dtype: int64"]},"metadata":{},"execution_count":10}]},{"cell_type":"code","source":["#Two columns have 787 missing values, let's drop those\n","df.drop(['std_time_first_dx', 'std_time_last_dx'], axis=1, inplace = True)"],"metadata":{"id":"siDP7OKnMMRX","executionInfo":{"status":"ok","timestamp":1674583069677,"user_tz":300,"elapsed":3,"user":{"displayName":"Mona Ascha","userId":"01195351992176878996"}}},"execution_count":11,"outputs":[]},{"cell_type":"code","source":["#Impute missing values with median \n","import missingno as msno\n","msno.matrix(df)\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":472},"id":"NquiodolMafV","executionInfo":{"status":"ok","timestamp":1674583071100,"user_tz":300,"elapsed":1426,"user":{"displayName":"Mona Ascha","userId":"01195351992176878996"}},"outputId":"558219db-d369-4cae-ffd1-d820336e4998"},"execution_count":12,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<matplotlib.axes._subplots.AxesSubplot at 0x7f7b31c12040>"]},"metadata":{},"execution_count":12},{"output_type":"display_data","data":{"text/plain":["<Figure size 1800x720 with 2 Axes>"],"image/png":"iVBORw0KGgoAAAANSUhEUgAABcAAAAK4CAYAAABTZV7AAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nOyddZgl1dGH3986K7CLO8HdCU6CE3wJrrtYFvjw4LC4u7u7u0uQEBIsyOIJkAR3W5bVqe+Pqt7pvczAyr1zR+p9nn5mbtut23369Dm/U6dKZkaSJEmSJEmSJEmSJEmSJEmStDc61duAJEmSJEmSJEmSJEmSJEmSJKkFKYAnSZIkSZIkSZIkSZIkSZIk7ZIUwJMkSZIkSZIkSZIkSZIkSZJ2SQrgSZIkSZIkSZIkSZIkSZIkSbskBfAkSZIkSZIkSZIkSZIkSZKkXZICeJIkSZIkSZIkSZIkSZIkSdIuSQE8SZIkSZIkSZIkSZIkSZIkaZekAJ4kSZIkSZIkSZIkSZIkSZK0S1IAT5IkSZIkSZIkSZIkSZIkSdolKYAnSZIkSZIkSQdHUuf4q3rbkiRJkiRJkiTVJAXwJEmSJEmSJOnASNoQeFBSFzOzetuTJEmSJEmSJNUkBfAkSZIkSZIk6aBI6gQsBywGzBTr0gs8SZIkSZIkaTekAJ4kSZIkSZIkHRQzawDOBnoCO8a69AJPkiRJkiRJ2g0pgCdJkiRJkiRJB6Apz25JXc3sY+BaoL+kOVresiRJkiRJkiSpHSmAJ0mSJEmSJEnHoBOApJklTQ5gZqNi233AvMCisU/2E5IkSZIkSZJ2QTZsk6QKlD2qJPWtpy1JkiRJkiQFkvaTtBuAmY2R9Dvgf8D1knaQNFlsuxv4C3CIpCkiNEqSJEmSJEmStHlSAE+SSUSSiliZkjYAzpa0cZ3NSpIkSZKkgyNpWmA/YC9JA2P1s8BBwBjgEuAJSadLmgp4EugHzBPHZ18hSZIkSZIkafNkozZJJpGS+D0AuBr4BhhaV6OSJEmSJOnQxAD958CqwHDgIEk7mNkIMzsZ2A5YAngb6A+8BCwOzBHbSC/wJEmSJEmSpD2gTPKeJJOOpPWA64DBwHVm9lWsH+sdniRJkiRJ0lKE93YnMxstaT7gDnyA/iIzu7S0X3egG3AAsACwEfAVsLqZvdLylidJkiRJkiRJdelSbwOSpJ2wHvAEcImZ/QQg6QBgDkmjgGPN7LMUxJMkSZIkaQnCe7tB0ubArLjIvQhwqKSRZnZ17DrazEYAgyOPyYXAvcDvgRTAkyRJkiRJkjZPhkBJkgmkIuHl/PHvbEBPM/tJ0pqSnsU9qRYFdgLOg8ZwKUmStG3K9UCSJElrRdJWwFVAX+BMYE9gSuDwCN1WJMbsGod8b2aPALcBO0jqlfVdkiRJkiRJ0tZJD/AkmUBKMb+3Aw6TtCVwGXCDpK+B74D3gcXweOB/BnaTNI2ZfVEns5MkqRIViW8XA4ab2Vt1NitJkmQcJE0H7AvcAJxgZkNj/V+B+4Bjojq72sxGSepUivn9JTAKb/bk4H2SJEmSJEnSpkkBPEnGkwrRa3rgCOAC4B3gdWAVYCngXTO7J/brDPQDXgWG1cPuJEmqR0U9sDVwGPCWpF3N7NP6WpckSTIOnfAZaveWxO+uZvaGpE2BvwL7SOphZhcX4rek3wDrAM+YWbZdkiRJkiRJkjZPCuBJMp6URK/1gNHAs8BNZvZD7PJ0LMR+U+EdyAHAgWb2Y8tanCRJtSnVA9sAlwBHA4+k+J0kSStkFPAxMI+kPtFeGS2ps5m9IOlFYHF8NtvTZvZGHDcdcIOZDYZM6J0kSZIkSZK0fZTt2SQZfyTNCDyDJ5MaAqxQeFVV7Lc6sCWeHPMsMzs+1mcnMknaOJIWxhPEnYc/3yNi/dy4x+W/zWxMHU1MkqQD8UttC0lnATsDWwEPlOqrfsDlwCPAN2Z2QzPHl8OiJEmSJEmSJEmbJJNgJskEYGYfAwfh04bnBJaEsaFOiP8nB5YApgAOKInfnVL8TpK2RxMJ4KYBBNxlZiMk9ZN0HR5T903gAkl9W9rOJEk6HhVhmRaWtJqkJSV1i132Bv6Oz1gZKGm6mKG2ER627ZFC/G4q2WWK30mSJEmSJEl7ID3Ak6QZfsWjamM89MHUwPJm9m5MKR4T26cAuhVJL9ODKknaPpHw9stYXgQOASYDNgO6AucCPYDjgT+a2Z11MjVJkg6GpAHAmUB3vB66AjjXzF6KvCUXABvi9dd3+Ey2o4pB+iRJkiRJkiRpz6QAniRNUOFRNT8wE2DAp2b2eqzvD5yEe3qvECJ4FzMb3dy5kiRpm8TzfiOe/PYUYDBwKC6EvwYMMrMGSbMDTwD7mtltdTI3SZJ2TkU7ZUHgMeB04BVgLuAc4CHgcDN7PvbbJrZ1AV4oBulykD5JkiRJkiRp76QAniS/QHhUnYCHC+oLfAucZGZnxPYNcRG8N7Cymf07Be8kaV9I6oI/56OAY4u4/5JmAYaa2TfxuQewBXAksIWZ/aM+FidJ0lGQtAgejm01YNciMXcM2t0OPIx7ev+9meNT/E6SJEmSJEnaPRkDPEmaQdIG+JTh84E/ACsBdwCnSRoIYGZ3AQfgwtjrEf87SZJ2QohIzwHLAUNK4rfM7IOS+L0AsB0eBuW8FL+TJKk1kn4L/A1vhww1sx8kdYqQbHficb7XBA6VtGxT50jxO0mSJEmSJOkIdKm3AUnS2ogkUJ2ArYG7gfPN7OvYdiLwHj7FGAAzuzs8P7ub2fd1MDlJkhogqRPQE4/1PzNwbawfZ5aHpCXx0AMzA0eY2WlN7ZckSdPkszJxmNnzkm4HtsUv40xm9pGkzuHZfVfMVLsL6CdpIzP7vL5WJ0mSJEmSJEnLkyFQkqQJJPUEXgeuN7NDY919wCLAumb2qqQ1gZFm9kTFsTmdOEnaCZJ6AevgIVB+wJ//Dyv2mQNYFvjCzB6JdVkPJMkEImktYJSZ/aXetrRWisGCch0j6VxgJzzx5VFm9qmkzoBFboJNgBnN7Ow6mp4kSZIkSZIkdSM9wJOkCcxsmKQvgDkAJN0DLAysF+L39MBmwLuS/m5mI0rHpuiVJG2M5jxQzexHSQ8AAs4GbpC0cdmL0szek/R+KSGdsh5IkvEnZltMB9wKnAX8JcJ4jKmvZa2DivppqrheSPrOzEaY2e4xcL85YJKOLkTwEMpvbeZcSZIkSZIkSdIhyBjgSYcmwp00tb4z8CywoqR/AosDa5jZK7FtA+B3wCtl8TtJkrZHWRCStKykLSQdKmkhSb0i7vd9wJ7APMDtkqYtn6MsKKW4lCQThpk1mNkneJih7SRNk+K3U1E/bQHcD/wL+DdwYcxGw8x2AO7EE/EeKmmGuIbj1EdZPyVJkiRJkiQdkRTAkw5LRadyIUlrS1pQUr/oNB4LDAUWA64ys7cl/QbYETgTuMjM7q+T+UmSVIlSPbAt7oE6GNgdF5r2lzSlmf2Ii+B7ALMD98RMkCRJJpHCoxkXcLsB/SvWt7Q9PevxvU1Rqp+2BK4CngGOwhPu9gdOjRAnhQh+C7ADcIKknil4JwXNOX0kSZIkSZJ0BDIGeNLhkbQdcArQCw9zcDtwSoQ6mQFPhDlj7P4jPnB0sZmdHMfndOIkaeNI2hi4EheWTsc9vd8APgGuAU4ys28iJvgGse/W5dACSZKMH7/03pT0DDDazH7XwmYV378ZML+ZHdVaYvlHW+Q+XPze38x+ivUb4O2Xr4FdzezlWH898KSZXVQnk5NWRoXTxxzASHx85aP6WpYkSZIkSdIypACedDgqOgGLA4/hMUcfA9YDtgbeBg40sxclTQksg4dBeQv4yMyejeNbRec4SZKJJ2Z2XAE8ambHSVoMeAr3pJwOWBs4GR8Y+1pSb2AGM/tXnUxOkjaHpOWBN83sm/i8KbAhcBHwvJkNj/Ub4qFQBprZbXWw83JgYzxp5I8t/f1NIWle4DlgbzO7ohwfXdLWwNXADmZ2VRPH5iB9MpaY6XQUMAXwX+AMM7umvlYlSZIkSZLUngyBknQ4SuL3osBkwA3AaWb2tJkdBJyGJ788RdLiZva1mT1gZseb2e0l8TsT3SVJ++BLYAjwoKRZgbuAW8xsR2Aj4B18YGywpKnNbGghftcrREOStCUidvXTwDaS+kYujfmAPwA3As9I+qOk2YFHcY/mFePYlg7bcB3wE55QssW/v5nv6wyMAeaMtscYSV0AzOw6vI5aL47vXD5Xit9JgaSV8DbuDXhS52+BqyTtWVfDkiRJkiRJWoDsuCcdhnKnMjy/n8Vj/HY3s6GSugGY2Zm4R/hvgJNDKP8Z2alMkrZLIVzHLI6hwCFm9iKwHfApcJykbmY2Ck841w8YBMxbPk8OgiXJeHE7LnQfD2wLdDGzY4DpgcNwT9TLgQfxZ/BZYCdJC9TqXVu0CUp/izbx48CHhADeku/6ihlqi0paODa9jYvcmwKLRr01OvabHmjAQzZRTh6a7ZSOTRODKTMADwAnmNlRwF74gM+ZkvZqafuSJEmSJElakhTAk3ZPpYempD7A+/iU4eF4rF/MbKSk7vH/WcAZwPzAZZKmalGjkySpOhViQPG/AYQIDrAo0MPM3os6YXLgO2BLYH0z+1uLGZwk7YAQa0fiwvfNeMzqHSVNZ2ajzewKM9sI2AxPQns8sDKel6NIhtm5yZNPvE1lz+gZoXEwK/6eCCwrqX81v3d8bZK0DT5gcJqkOUPUHgRMjie/XC32mxxYI37DSy1la9L6qShPs0uaE5gLeL1435nZEPx5ux44Q9IedTM4SZIkSZKkxqQAnrRrIpzBZpJmMDOTtAM+7XMoMBjvjK8o6UoAMxtREsHPAc4BzjWzr+ryA5IkqQoVYsB6wMWSHgKukbSgpB6x6/PAjJIGSpoZ97hcA/jczB6L4/PdmSTjQQw6FULzori36WjgUGATSf2Kfc3sYTM7FB94Phz4KzAwBPQxVAFJ+0mau1QX/AF4WdKlkn5XzAQD/gl8Bawe+9X8mS/ZtAVwGe4Rv7+ZvRvbX8EHEWYB7pT0Bu4xfz4exu3OWtuYtB1K5Wk74O+xHAusFbMGiv3eBI7Dkz2fJenAOpibJEmSJElSczIJZtKukTQI7xwejsf0PBXYA7jIzEZLmg6ffr0NcHvE/EVSdzMbUXGujKWZJG0cSQPwpHv34TkAZgRmA47G64q58RBIywHfA72B48zshLoYnCTtAEnbAyfgyWX7AnMC0wIHA9eY2XexX+eIby1geeBeYM9qJOmTNA8eWuVz4A9m9r6k3+He1NsBXYBPcJHwCWAT3Nt6MTN7Z1K/fzxtnBn/zQ8BR5rZT7F+bMJtSVMD++P11kfAM0WyUGVi7g5PxWDvUnhM/fOBb4Df4gle/wxcVSSkjX0XwIXwJyMUYJIkSZIkSbsiBfCk3SPpXGAXfMbDYWZ2fKzvZGYN4QlzKDE928z+VN5eL7uTJKkukpYA7sBFrYvN7DtJvfFEYA8C25vZF5LmA5bB4xO/Zmb3xfFZJyTJBCJpGVzQPQm4xMy+lDQvcAyeuPEA4LpCjCsEPEkzAS/i3s2nVMGObsCq+AAXwNpm9l5s640L3pviwvuXeNztpYAzzOyklnj+Jc2P/+atyh7dlQPwTQ3IZ/2UlIn48XPjZX7vcProhQ8AbwIcxM9F8H7lz0mSJEmSJO2JnMadtEvkdImPj9NY1icPr28AC2+zT3GvlyuBbSXdCJncLknaIXPH34cLj1PgJuADYHCI3z3M7C0zu8rMTkrxO0kmmXni7wNm9iWAmb0N7AQ8CRwFbC5pythWCLvT4O/u0ZNqQLzrR5rZg8Bucc7bJM0e3znUzK40s3WBrfBwEEvjXupbtODz3xfogYdpQ1KnCo/e1SWt3NRstKyfkgJJywKP4YM9I0P87mRmPwLb47H2T8TbvOUwRGMHoepgdpIkSZIkSU1JATxpl5gzOhJYrYY3+K/APc32KmKCAw3RKShE8OvwDnmSJG2YZjrwCwJdI5Yuku4HFgb6m9lLklYBLpfUs/LAFJeSZKKZjFJ7s0hoaWbf43Gu++GJ+HYoxeAGF6pfMrMzJuXLQ0AeE/+vhsf0/xaPSX5TIYKX7HrAzI4GlgT2AWYFdpgUGyaAj4H/AbtLmsXMGkridw9gLWBQJL9MEqDJ9937wDP4s7UY+DtMUhczG4W3iW8CzsTLU/fywRnuL0mSJEmS9kgK4Em7otwJkLQOcDvwAnBDxPe+Ap/2uYek6UMob5C0NNAT2NXMLqg8V9LxqChLU1QIM0krpyQarSRptlj9V2BaSVtKuhUXvzc0s1dC9P4tMF0sSZJMBE28O5/E42vvAFCR0PJz4B940skRZjaytG03M1srzjnR7dVSXbAtHl+7H3AbLgDOBdwnafZS7PFixscHuED/OZ4ToOaY2X/xgfgNgH0jbnkR93tzXLh8KAYPkgQYp4xPGSL3Z/gMizuBZSSdHQNBo0si+E7A3cAPlTlvkiRJkiRJ2iNdfn2XJGk7lDoB0+GC9pnA9cQUajPbMfq3B+EhUG7EwyJcCWwbnc9MeNnBqZhy3h/oD9wh6YEKgSZppYSQtQDwCC4a/Rd4D58WfimeFHcxM/swPCs3A/YDDjGz9+tjdZK0TSremb0ljS4SOJrZ25KOBY6V9JmZHRfHdAWWwBNPblUW4UKAHl069yTNwJA0Cx5q5Tw83NFP4fW6NnA6cLekDYpnv/g+M/tB0jPAXJJ6RQiJmlCEWTGzQyVNBQwCNpL0Ah6KZRHgZDO7MvbPdkoyFklr4gM2m0t6NmLt7wN0xmN+jwH2KYvgkjbKMpQkSZIkSUchBfCk3RFhDO7GY2hebGbDY30XMxsdIngDcDCwDTAVcLqZ3VWcIzsEHZuS+D0QF0duA74sxO8UHlo/cX9elzQEF8BvMLN3JZ2FxxaeARcKPsWniA8CTjSzSyHvcZJMCKU6c1NgX1wEfwvY08w+wWNqzwQcLWklPMFkV/zZPKgQv4vnrix4V+k57IontX07xO/OZjZC0gO4uHwhcLWkARaJMcOe+YABwNm1FL9hbIiKzmY2xsx2CeF7BXyQ4B/AeWZ2U9iVOQmSSr7EB3YvxcMJPW+e12Iv4Gw8ln2Dmf05RPDyQH++75IkSZIkafco2ztJe0PSWsBgPL7nuWZ2cGlKdqdSLNBB+FTof5vZrbEuO5UJAJLWxqfIHw5cY2ZflbZ1De+pLC+tlEJIiuf8KGA7M3s4tq2MhxPYCGgAXgJuN7PLYnve1ySZQGK2zI3ALUA3YCXge2CAmT0rT3K5Jp6LY2pcsLvSzM6O42smwkmaHvgPcLSZHR9tgs4hBHYGXseTdX4GzFuEGJG0EO41u+PE2ijpj8DfIizF+OzfuRwmRlK38syjrJ+S5pC0KHA1PgNyW+CFKOPTAmcAfwBuNbNBdTQzSZIkSZKkLqQAnrQbyp1CeaKro/G4nZuVBG5REsGbOz7peBTCRunvRcB0Zta/tM9heMzYLvhU9FfrZW8yfkT87xdxoW2/0noBUwACRpnZ0Fif9UCSTCCS+uLv3K+BY/GBpaWBi4DewNZm9o/Ytxeeg6a7mX0Z66ry3DUnUIfIfTGwYdjyUGnbjMDN+Eyfj8zs5mbOPcE2yuOOXwgsYmbvTsixSfJrSOpdvLtK6xYDrsJF8O1wEXxUiOCXA3cUg71JkiRJkiQdiUyCmbRZSl7dQGPMzvj/MdwL/BngfEkbx3rDO+Y/I0Wvjk1JNJkzYtN2xhMmziVptZiOvgceM34l4MzwaExaCZI2lrSrpDmLdeZx/c8BBoR33NjdzexbM/sGGBbHT3Ks4STpaETYk0uBhYDnI9RYA/AsHj5kKHCtpGXCu/lHM/sB+CqOr8pzVxHSYX5JS0paGMYm3rwB+B9ed28c+00FrIqLhY8W4reaSLo5MTaa2TXAkhF+aaEQ25Nkkonn7uoI0zMWM3sZGAgYPgC1ZMwi+BzYNMXvpK3QVD2cJEmSJJNCvliSNklFR3dlSYdLuk7SAEmTA5jZX/DQB28BF8Y05IzvnTSLpD8BDwILA68AfXDv4fNwz8YlzGwFXEiZA08qlbQCQsjaDL9X90q6QtKC4W16Bx6SYanYt3PFgFmR8C7rhiSZcCYD/gisjOfUAPx5CjFuAPAdHhpl+fL28t9JpdQm2A64D3gKuE/SBbH9UeBEPOnmTZJewpPiXoKHhRhSOlc1BPmuca63JC0CvAr8OcKxjO85VPq/86TalLQreuIJugdLmrdi26vABfig1CXAMtFu/gl+7kCSJK0NSVviseuzvCZJkiRVIwXwpE1S6ugOxMXINXGB6wrgmIjbiZk9gk/LHgJcKWmruhictEoqxIXpcaHmSuBlMzsHn0WwN3Cgma1pZh+FR8pw4D2gazbMWwdm9pWZbY53+G8BVgQeAe4EegBvArtL6tFUCKQkScafcr1nZlcDGwOjgO2L929p+8vATviA4Ry1tCXi+58BXAvsgtcBAyXdGbbcDOwD/An4GJ8ltpOZHV95rkm1ycxGxf9zRbisS4C9gL0kzTCe5yjaOjsBm6RHZMekooz3DY/uq4Ct8YHfo8oieLzj/oMP4E8OzFUeaMrB3qS1UirrewEzQpbXJEmSpHp0qbcBSTKxSNoAOB043sxOlbQg3gnfAw9dcYyZvWFmj0an8SQ8FmmSAOMMpKyCC6Zd8FiwxfY7y/tLmg5PIrUvcEARvzapL6W47Z3M7A3gcEnHArsCa+Ai11fANHgM4Jsy1neSTBhlQbZSkDCzOyRtj8cePkLSEfEsFttfkvTbWtSZpXp8WlzsuwE4xcx+kHQf8A5wrKQ7zay/mb0CvCLpivLvqGadUCFc7yFpoJkNktQAHBjbzjazT5o6vkL83h33hNx2Yu2rOF8XMxs9MedJWp6Ke7cRsAHwjKSrzeyGEAyviu3HmNnrMVtgPuAJPPHrD3UyP0kmlE74YOnzwG8ldcPztKQIniRJkkwyKYAnbRJJUwPbAFeE+L0A8A882dSrwLnACEmnmdkQM3tY0ptm9kEdzU5aGdFxnBq4Ea8P/2Nm78S2cUQCSevhYurmwAlmdmFxjmyY15eSKFckwe1kZiOBs4Cz5Elx18QTgm0C3JTid5KMPxUi3OrA2sBvgHdxz+b/mtn1Mdh8RRwy2MzeLM5hjQkvq15nSloG+DvwIXB9IfiZ2deSLo7djpF0q5ltEp8LoaWwr9pxyKfBvRivBd6P79hVkvELInjFOfYAzsS91K+rgk0rAwtIutfM/jcx50taltK9G4Dns7gaeNvMRsT266NMXQ3MKOlJfDbGfsCexbOQbZWktdFUmSzN0HsHb7P1M7PPsvwmSZIk1SAF8KStMgyP1fxqeOXeB9wKHIQnuVwHF8gVIvirhfidjaikIMrBF5I2x5O4LS5pVzO7wMxGV3gELg3MDOxvntisqh6DWS6rR+U9MbPHJD2B5wM4U9KKZvZ0XYxLkjZISYQbiItwr+Gxv9fEQzAcK+l6M7s2xLhLgC4hgg9p6lxV5hvcC3YrYCZJPYCR8XXfhAjeAJwk6REzW8NqEAqpYpBgCeBz4GYz+7Z4X5jZbjH4eiAwRtL5ZvZxHNeU+D3IzC6vgk3bAacAd+GOAimAtxEkrYDPeDwWuNDMvi9vD0/wT/G4338CfgCONLMrS/tk+yJpVZiZyXOyjJG0BZ5H4iPc+7sb8CU+c++zyvKbbeakrVCU8SbWZxlOkjqQAnjSJjGzYZLuNrMvJQ3CE2wdX3QKJL0DLA5sC9yEd/aKY/NlkwCNAraZPSFpG9wT/P8kfWFmt5pZQ9FwMbPDJU1rZp+Xj62SHWXRY8FY/XZOU29kUq93dLDewt97U1bPsiTpGEhaDjgVOA4XuL8HZsc9nE/CRejbzOy6EHivBi7Gc3DUFDN7R9IpuEf39sDTZnZR2K0QwS8DeuGiSs2QNDM+G21y3DP+/dK24n2yq6QxwCFAb0mHAD+V3gO70yh+X1oFmzbHxdEj8ISf/6nY3mE74pW/vTVdi5Ity+DJW28ui99lW83scXk4NwE9zOy92CfDfSWtCklHAvOY2VbRNpseWB9PVN4bnzkzBdAZOCUGVf+Gz6T5F16vfl4X45NkAiiL35L2BqYjBuMrBzKTJGkZMplO0qqJTnTxf29JfeSxDcdOp8a9cmcAiuz2vYG+eMdyZjO7v2WtTlojFWVpOkkzAXMW68zsH/h0y97AoZI2ifVjJHWN/wvxWzUSv7cE7sAHbmauxvkn0qau9fruSiR1L3fgJc0uqedEnm407hU6edUMTJKOw29xz9KbzJPOjjIPGbUy8CmwvyJJo5ldC8xX7fdvRT3eR1I/eagRzGOOnwRcD5wvaedYb1HPfo2Hrzq38lzVxMw+BA4HvgCWlLRprG8AGkptmN2Ba4D3zWxY6T2wLx7ze+cqid9TAbvhgxHnFuK3pA0lbS9p+eIaTep3tTUq3r8LSZqxtYjfMI7DxoK4qP0faCy75YFzSZOb2Sdm9nFJ/K5aWyVJqoGkPnjbe21J5wGY2admtrWZzWtmMwGr4flahgLz4P27HfHwWs/iM4+SpNVTEr9vBQ4GtgD2Bp6TtExHfO8mSb1JATxp1ZQa95sBtwH/xBPY7V7a7X9AD2CQpDXxGM3rA0NL04qzrHdgKjq5m+OzAl4C7pN0erGfmT1FxBwEDoxyh5mNKp+vmh3kiunpl+HelLc15aFXre9sCkmdJc0gqZuZjQpxaR1J3Wv5vb9i00zAYGD5+DwIuBOYcSLO1QM4GXguxLkkSSaMmfABwpHgdZI8V8IwPDTDkrhwAbhXduxXlfdvRT2+CZ6w+BXgMUmnSuplZv/CvZyvBy6UtGPYUnjJjijZN8n1eHP1spldj9dd7+HhYdYqfWdZBB9gZmdVHg7sahMR9kTSxZLOrFjdA5gXGGJmw0MsfRi4HBfan5a0fmsSfluCJgafbwBOldS3njaV/u9cWvc1ntx9MRi37MpnHOwELFd5vo52T5PWj3lM+gPxtu6mksYO8pXam0OAh/CwddeY2cZ4+Z4LWD7bcElrp6i/4//18ZwpGwIr4GFah+GzjldMETxJWpYUBZNWT3RMrsKlOVEAACAASURBVAVGAH/FPQfOkk9nxswuwUWxP+OxLU8GzjSzO4pzpAdMx6bUyd0a9yB5Bhe6HwT2lnRp0fAOEXxbXOw5UdJctbZP0qLAkcBRwClm9nysnzeWnuGhV5M6OxpfOwIXAfNJmgIXbjaqxfdNAD2BHYDTJZ0InI/fv/+M7wlKnnLDgc3NbK1Yn++/JGmCChFuytKz8gIwFbBqfO5kjWGafsLDj3xbeb5qvX9L9fi2uMD9NXALXh/sDtwhadbwfj0WD8FyScWAedWoEFAXkLRSDBrOEfbejovgY4CTK0XwynOVfucZFuFbJtCePsCbwN0Vm77C33knSbofuAePq7s+sDrwIrBLDIJ2mI54RXm6HBcjzjWzn5XhlqCiPP0O2FLSbLHuAnzgabCkKUvHFHH4/4i3kZOk1SKpk3xG38f44NtdwCaSzgAfoJSHjGgIz9nvgWXj8M/N7IOYrZltuKRVU/L83g6fPfcK8GKU/aeAAfi7+WpghY707k2SeqN0DkhaM9GhOxJ/SZxjZj9ImhXYGjgaOM/M9o5918MHdb43sydiXcY+TACQtCze0LjUzE6WNCfe8X8D91y8EY+3Ojz2Xw2Y1cyuqIEtlTFHV8MFnZXMY9lOhcd/XQEP1/EssKXVMF6cpKWAR4F/A9PjnjdbWZ3jLMbz/hrQHRfA97fxjI1eISgcBIwwszOyXkhaA03UA3WPPVzxzGyED4K9iCe+7IUPRq8OrGdmj8d+3YA9gEFAf/NQJLWybw584PJ64FQzGxoDdmvicbefNLM/xr7zAscDT5jZOTW0aWB8T2dcWH4TuMvMDontm+HtmJ+Aw8zsgRra0j1EpI2Bdc1sh1i/PJ4gtB/wkpmdGusnI5Jimtl+tbKrtSJpCXzA4AzgfDMrQunNimvkH9TBpm1pTFZ6qZk9L6kLMBBvF7yAP4df4+Lg/wFHmdnJLW1rkkwM8pw7qwIr4jP6euL9uT1iezczGynpVGA1M1u8ftYmycQhaWngH/HxfPOwZ+XtC+NOPVPgyYufqHcbMEk6Ajl6mrRaQtC+E++AvxvT5jCz/+GhIs4FdghPGczsXjO7O8XvpJKYijYb8DBwbggjL+ChULbAy9K2eLKdyQDM7LFC/K72yHxJYNogym9X3NNlV3kytKfxsB8nA1fhCbA2qKYNTdj0ArAxsAQwGe4JNzbhZy2/+1fohYddGI0PCCxTbPil+1Ih5O2OC1QjIWeEJK2DUvmcs/y5npRsGoDHp/4SD53REO/g0/DwUQ9KOkXSfnjIkWOBS2opfge98fwIr5nZ0LD5O9yr+Qigv6QtYv3bwA41Fr83wAfmzgDWBeYAXgYOknRA2HFz2DYFcEGIq9W04cDSd42Id9h8wEBJ58f6Z6LzPbAkfvfBPYcXprGT3tGYCRiOhx37SR7660rgPuAdScdJ6tVSxshnPF6MJ5s9tZgNFoO+1+Az12YFzsIHgdYEDinE7/SKrR/pwTl+xODcZbiTxT7AKsAjwHaSLgAws5Gx+xBgQXl4vizbSZvCzJ7DNYyhwDohiJe3D8EHNjvhg5s9WtrGJOmIdKm3AUnyC6wCLIR7fhZCXFdgtJl9LulyPFP4LE0d3FFErtbgNdjaMU9k+RLwAS6Cnol7ER5iZl9JugLYBfekmk1S/3L5qcX1ldQfuB3YBve8fghYD/gOeBLYMzxgZsdnPAyvtg1NMA/uET89cIikL4C/m1lDS5ezYgDLzN4M7/QeeILQMyT9GXi6wnu2nCizLH7vgd/vnWwiYuomSS2R5604R9K6ZvbvetsDY72FT8O9li8qBp/BQ0TFMzUAD0/UFXgHONDMzo7jq1JXNHOeKfG6oPDU7WqejHO4pDuB43ARurD3u2raVGFfF2BTXHy/3My+ivUzAq/jA66FHbfI8xBYDOJXy4YpcM97SfrBzC4IIfdSPCTGCTEAvIs5o+K45YC18GRcp5jZrdWyqY3RBS8vq0iaFheYe+IC3VR40rIH8EHpmiJpenwmxZn4jMcRsX59fFD6QzO7XdKD+KDFCOCrwks9nT5alsrrne3wX0ceanAA8ARwYTGrUdJO+PtmR0k/mdm+cchIYD8z+6QO5ibJeCMP3TOmcr2Z3RUOBTfi/arDzOy10vbXwuFveDEDKUmS2pICeNJqMbM/S/oBj6F5pqR1zOyj6HSOxmONfgP0qaed9UDS2kB3M7vTzCxF8EaauxbmCdnekTQ17h13XiFYAN1wb5R7gE9q3YmUNB2wKO41ebf5NP4/Aye4qWOTt/YCVsa9Bz6ugR2VHbgLYmBpUeBe3KtxX0nPlMTlIlZ61eONVty7HpJGm9lIM/tnbN8Av0enAfsSooSkNYDJJd0FNJRsLcSEQSl+J62U4cDc+MyLViGA4yGhvgJuL4vfRQfPzF4CXgrv4h8Zt86qighXMYg1FzDGzN7HZ+68iMezfjnaBEXHczQeFmJo5flq9H7sgc/Uuackft+PJ2pbz8xekbQCMIWZ3W9m1zT1+yYFM/tO0u/xhKD7xbU418w+k3Q17ll2HJ54c7doL8yDD/bOAxxsZheETe1WQP2FdsEd8nwyl+EzG17AB0tHSZoP2BKfddASdMbLzmXhyT8//g5eCncEmUzSJmZ2Jx4WbSzx+9rlvWttyBP/jsaT1SJpXTyp3W/wQa+noo5Mfk4X/H33opl9H17znc3sA0nHAmvgeXl6m9mfzOyG4sD2XD8lbZuy+B0Dlp2Bby1mpMd7Zhs8dBVNiOBvtrzVSdJxyelESaskPJYwsyPxztsMwOXyhECjJfUG/oCHR2gtokGLEF7wtwGHS9oQvHOf0y9/JposKmlNSVvIQ54UzABMB/SSJ+Tpigs+n+JT+C8qzlUjGzfEpy5vhYf2GRqNpxFm9lFJSJob97A8O+x6psp2dDH37O4maUFJy4aH4kjzaXsb4h2604Gl41pNi0/PXrmathSU7t2m+BT0VyQ9Kqm/pKnN7Fk8FMxvgNMk7SBpe9x7fhozG10Sv3fHxYNBZnZpLexNWj+tuV4M2/6Gx/rdW9I0dTapYGFgMvNkkuVEskUHbxG55/U7ZvYR8EmxXw3E7y3x993O8f4fioeFmhJvE8xuPsOnNx6CpC/ufd0SjAE+w0ObIOk+fNZaIX7PCOwEzC2Pkz6WSRW/JR0madq4Vh8Am+Hi/z5R92EewupK4NCwowiH8g5er2/f0cRvSUtHm2CPGDjAzHYGFsNjpg8I8bs7sBI+mPJZC5k6BngOv4d34fXCDED/WN4iElOrIhxEOkC0DJKOAAaoMTH5AHxm2gp4u/IU4CJJO9bTztaKmf2Ih1taTtJcUW4bog38Pt7uexe/xqtXHNsu66ek7VNqG92A95FuBW6SdF5pn1vwGb9rA0dKWqwetiZJkh7gSSslOrSFt9nhIYjvBTwr6cnYbWXgaDN7tG6GtjDypGRT41POFwEGS2ows3vSE3wcAXV74CTcI3B64D1JD5jZHmY2RNK5wGG41+VwPPTIYSHmjHOuGjAznvinCzCOsFQgT4p5MB7r8wgzOz3WVyu0QKcYSOqDh4KZDxdx3gdOl3S3mf1d7nF9F56k5QG8kzcjULPOXYjf1+Px//8Xtt2Cx849zcz+IWkdPHHZufgU2cFmdmHpHMXAwc5mdlmtbK0HFWJOXzP7tt42tTbK16jyealXHamK6bElO8ZIegw4EY9H/EUrECM/BqaS5yd42kohkORhGnbBB53ugsZrXK3rWirfWwOX4qLSPWb239h+rnwmz07Ac5KewgfDlwdONLPHqmFHQXNlxjzUyD+BbeXJrKYB/mBmb0SbZS28rr/FGmPaVsOexfFQHQ+Zh4NTeFD+EQ+rtY8kzD3BP5fHtAY4Sp4kcweLWTWl39duxaWKdsHpeFz73wBfSXoBWN/MXi32j8Hn3+MDqEdYDb15JW0OrG5mO5vZp5IuwcXuyYErzez42K8bPivj4/hN7fZ+tVbksfU3IeLGS3oOd1I4GLjKzL6UtBI+6+wASV+b2R31s7jV8ldgfTznzRlm9iGMnfE4JT7A+biZ/a2ONibJr6JxPb8Pwp2p/gQMw/Nr7Bzt9K1hbCi0BrxPM1zSDtVsGyRJMp6YWS65tNoFnxpX/H8kHh/5f3icxPlL2zrV29YWuBZb4y/VU/DBgIPxEDCv4x5nxX6qt611vk7r4dPy98UHCaYALgQagGNjn15Rnv4FPAX8X62vX/m8eMLNEcCbwIpN7DtFlPHVS+uqUsaL8+BhX56MZQc8lu3duKB8ATBj7Ldw2Pkm7p3TNdZ3roY9FbZNjjcMj8Y9UIv1Z+Mxfw8Fesa6fngnarnSfp3xqeK7AlvWuyzWuAxthIepWbOjP/O/cL3WwpPFDQbWbOo6toANfSo+rxhlt0fF+jfxRHyt4brNFe/a24A5S+t7ANsD/wFWrbENc8c1ObF45mN9l9L/60fd/io+tXib0rZq1ZflZ27heN5+X1yXqEcfjvfLAXis5pmB3fD30P41uDbdgKnj/1WBGUrbZgGex70ody+tnxY4JOys6b1rjUvUBd/hSffmwGfA7h/X4/zSfsvFO/H98r2rRZ0Rz9NpYcN5pfXdK8r55Ljn4KdA/3pfy4684ALtY/hAxP/hM3gWLpcR3LHiv/F+7pHv5yav43l4G/gafPbFXMDOwBfASqX92n3fLpe2vwALxvtkTxr7WNPhbc8G4LqK/TekpGHkkksuLbvU3YBccvm1hXFF8BOiY3IbMF2s61ZvG1vgGsyMZ0M/G+hVWr8Q7s00BNigtL5DNrijU3sxPiV1mtL6R4HXgMUr9u/LuOJK1Rrbv3YPcC/qr3EP7GVL67tUHl+t+1nqoHWNTtrjwDIV+5yJi+CDSg25yXBvdJVtrPK92xSfrv847kVZ+exfE9dr9ubufen/dl0n4CLkt7h37Nr1tqc1LniIoZ+ibvwR+BCPd1xsr3kdic9CKQ8mzQt8jw9cXoOL4ZPFtmPi3bZAS9nXjM3FM79VXLen8E7d+vjg61A84WWt7VglnvdVmrOx9Ln7L22vkj0D45n7Ee/Qvg0cFNtmw0XwYXEP38YF6ANqWd7w2ThfRxmfrrS+ORF8emDJepSrei+40PwoMH1p3UO4A8FSpXWz4wOo69SyPJXOPRMRox24oLS+c/xdChdRvgUOrfd1zGWsCP5U3LPPizKFD8AX922b2L5Ive1tTQvjtumOxR2aGuK9OCzLeC5tbcEHwhrwAdZtY13RV5o66u8xwNX1tjWXXHLxJWOAJy1OZUzWX4vRahEOJf4/GM+kvDRwoaRZrONMH5oZ+MA8hh4Rg/U1fErmHMD+EbICM6vKVPQ2SHc8TMc3ZvYFjE1KNi+wtZm9JGlFSRsDmNm3ZjYs9qvaNPCKMBVLyWNV7ytplWIf89AcBwG/xaemLxPrR8dfK+1blftpZiZPIvsP4DrcE75IMFkkt9wbF3P2JcJkmdlPZva/OL5TYWO1iHims+LT+n+Pe04Uz34RO3cwLtz3j2PGqTds3GSe7bZOkLQWLuYcBfzZzB6I9fk+x6+DpL7AAOBwvD5YAY+te6CkI6HF8iZ0wgeS9pDHz38PmB/3Wp4D9zS9IeLIXoULmusU9tXYtiYpPUc3AhvjAt2J+KDzGsAhZnYS1LzMzYAPvBXvu7HfZR6SZdEI0QI+YNfUb5hoymUjYnWehg8ArAqsBrwBHC3pZDP7r5mtiYvkp+EzVTY3s5ML22t0P7/Cy1cf4O4IT4N5TPA/4uL4HpL2ivWfmtmLhU01sKdVIs/zsTzwo5l9GuvuBxbAZwq9IOn3knY0j0N8oZndH/vVJDxMUb7Mw66dC5wMDFLEjI1330J4HbY+/twdF8d2mHvXGjGzr/EZWPfgAtcgSZObh0IonvNvcFGsZ32sbJ1U9OcOw993G+MzZtbOMp60QZ7Ckyj3BhYutx3M7EvcCWIwsI2ki+tjYpIkZTIGeNLilITBJfAkgN+NxzHlmOAHSzJ8uvGTuMdqe6cLPro8gyIDvZmNim3P453xFYCukt41s9frFeu2HsgTEg2Ljx8BU0Sn907cS35986Rk0+Hes+9ELNQRxTmqea1KZXwA7uXyPe6NerKkPSwSj5nZxdFWOgY4QdLhZvZ0texohk7ATfh0+NmAFSQ9aWYjStfkBjzu98LAixW/rWpiQFFGQ9C6BC/jZwObSXo8RPdC3OqNx3T/PuzoKGW7iL1cxIVeB78n5xZ1gKTBeMP7G+Bmq3IM5NZOua6LsjQM9/h+2My+B16WdBge93/fiI98ZCGC16IsxXn3l/QjLmJ1Ac4OcfJgSf3wcBrb4IL4+/iU8D9Jui2EuLoRZe3BEH9nwb0bv7LGJL21jlP+Kh7qYyN88GLsPZI0BR626UtJz5nZ8Gp/eakOXxj31H8cOCfKE5L+g79rdpD0ppldYWY3V56nWgJqU+U06ux78HrxHOAuSRuG0F3EBL8HT5j9uJXiXNf43rUqzJNavoHXkVPjM42KdsGrsW5j4CdJvc0TrRbHVqVuKNXjGwOjzeyu0vvvE0lnxa4HSPrJzPYzs9cknQp8Z2avxHnqnR8gAczsK3mukZvwkIQfSLrDzL4JZ4K5cRF82C+dpyMS/blOZtZg7sTzWnl7lvGktVLh3FTU30MknYkP2O8DDDGza4pjzHMDXAqMwsMiJUlSZ3KENakL8kQxT+Lx8caLaDR1jf8PATY2s44gfmNm/8G9cncElq3YPBnw79i2IC7wdiSBcEPgEXBPZTwm47q4SLggHk7j5fB8XhtPnvpmWfyukV0b42LueWa2IO4B0Ak4T9IhxX5mdjEej3xlYKoa2FHpKT0SuAg4MOzZCfc8pXRN+uBTrodSZSq8I8pe7t/jXqcH4cLgUZIWjWP64LM+ugAfVNum1kzpGs0Rf6fGk6fOK2lNSc/j8eL74rHl9wqBsMNQ6pCsF0LSVXjoAGK9zOwNPLb8vcCeko4pH1tNKrzXTsA9h3cHdpI0a3zvN2Z2E57bYX683hqCx0Kdr7C7ijZN0Myr2KeTmf1gZm+Y2ZCS+F3TpIlx/tfw+vNASbuV7nEfYANgS+D9WojfJTuWAZ7Gn6+fzOx7SZ3DvveB8/FBu981d45qlC/5bK/i988raSVJv5HUK37/Q2HjTLgIXvYE3xDYrSx+t1d+pUz/E68T/o4P7K5eahesh5ep58vidxXs2Uee1LmYcdIPD69yh6R1igG42P4J/l5+GR+kOz/WP1USv9t1stK2hpl9BWyGD9adB1wraR8aZ2id3hGeu0rG593yS+U4y3jSGpE74ZXf572Kf8zsdeB4PAn15ZK2LR9rZp8Dp5nZmy1ibB2pZrs1SWqGtYI4LLl0vAVPejIKWJzxjI1Z3g/4Xen/CY7POL7fWcfrMx+ejGk1YLZYNyXwLJ58ZyM8kVrhCfdf3Jv3CjzuZ5+JuS5tbcGFwO2BH2iMG90ND+/RgAteUwK/wQWCHynFZa2hXbPgsb2LpJsLhI3X4J3cBmCfimMWqoEdRTzxzvhU3HJc9B6459KouF6rxT4r4rMKHqt2Gap4htfAp31fgXcWJ4/1ncKukbhX7E3AzbhH7+B6l7l6LPggRQMugm8BDMcTor2Hi1+zxH774TkBZqinvXW6RtvEdXkHHxBswGdW9K7Yb1480WoDFfHva2DTVvjg3EPxfSPwcCIzlvYpYsZ2wsN+PAQ8Qyk3QZVtWpfGmLWt+h2BD2BeH9fu4agrbol6/JAWsuHG+P7/ADPFunKCwiuinupTg+9eq+LzdvgA4DDgM9zre+bY1hNvF3wY5Wf6Js7Xqu/3JF6r8rtluagzd6QUQx4PNdKAT1efE29n7RPlqapx7fF3/ke4d2s5SfkSwF1xD9ervC9RZ/0r7FyNVt5WzWVs2/zeuGevRx2/cWl7R3nuJirhZ5bxXFr7wrix64/GZ1Z9EPX1MqVtC+B9llF42M26297C16nIIdO93M7NJZfWttTdgFza/1I0bioa+XPgSSE2mpBzxP97RENz5SrYtinQv97XqMKmgdFx+iJ+5ytEYhh82u4Dsf5fsW0EsH9svwV4qN6/oYWv1+z4oMBppXWzAtfGtfkQFy/eAfYr7VPLxFazRSNpLiJ2O3B5NAoWK93bA0vH/Ow5mUQbCmGtDy5wvwS8iYvJc+OieCc8wd3wuFYv4FP9HyISSdbiOkUZHxrf9QIu2r6Bx7PvEh2pXWOfH/BBntVb4t61hqWivpsdeAKfQTBlrFsT9/Zet7RfDzzczmPA1PX+DS18vfrhQuRe+CyKhXCRa2SU754V+xczQ2pp00Z4J2gwPqi0JnBJPPcn0YQIHv8fFvVF1Qcx8Nj6XwJXTkx5LP3tXE27fuW7Z8PFzBei/roJ2K60vRb102Z4fP3i8wVx3y5h3CSKPfGEy48APapsw+bxncfH59/i3ubH4WGQrsTbCbcDs5bs6Y+/X94iEqx2pAUfEP8JT07YEO+QG0rbz4ln4Ft8EPE1SoPR1SxPuPf9P3EP4f6l9YsB91ESwWN9r6jH9qMiYXeV7PmZ0NjUulwm6tpOFfXASMJpJda367ZK6XduhA9W3o/Pzpl5PI8rt3XmqnY9mksuk7owrnZxE+5gcTpwcLxr7gfWKO2zAI1OWJvV2/46XK+uuLPe7pXXL5dcWstSdwNy6TgL7nHTGffQ7Rmdgv2b2K9TxedK8XsMsOMk2tIJmAYX13at97Up2bVhdIoOAJbBPZRujhfprsX1wMXBc/FplxvE+hWiQ3xkvX9Hja6NKj/TKPIegosDC1fssxbuNbcesGhzZaxG9s4Rf4/CPfJmKW27Dvhf3Nela3WtokP9Vnz/CfgUvbfxGQP9Y5/J8ERqQ/FkLkuUju9aA9uWx4WH/YGpYt1ccS2uotETvA+wCy4inlbs25EW3ANwUNyXuZsrt3hYlIG4qLNbve1u4Wu0Hj6r4gVg2dL6aYCLo/zsRTMe1dWuC6Je6o57CN0H9Cpt6xLP4BhcyJylieM3x5MbLlKDa9UdFyleodFz+BcFMMZ9/65Yx/vcO+zvXqt7F+fsgw8AvkhpEAIXJr/DvXfnwUOR7YB3gKv+zOHv/mvxRJZHACsBZ5XLMe599gGe66Isgm/OJLaR2srCzwcLP8TbT3PiM7+Owdt5D5T2+y3wB3xgap5qlic8lni53GwQz1tTIvi9UX52xkOgDcQH839fTZuauE5L4e+WDjdAUssFfw9vWoPztupBCnzA8CfgL3h7cxTu8DHPrxxXLpP7R9mfvd6/J5dcmlrwmR1vER7f+GBrAx7r/2nGnW20SDwD89fb7jpdq2fwcGMpfufSKpe6G5BLx1jwLPYNuPA1BDgjPj+Ae4PP0cxxVRe/K87/Ep6YrO6NTHzU9EZcoJiytP7JuGZNegQBk+PeoO8BdzZ17drTgocwmKli3Sq4x1cxSNDtF46v2nWpPBelqfGldbdEQ6BnfJ4KF1h2b+6eVss2PEHs34C5S+v3iWdvMxqF7snDntF4Qr6palV+cDFySPmZB+7GwwgsXrFvr9h/OC5ydpjQHvHbi0GStyiFs6nYb3NcnPsEOLi5stleF1xkHh3XaupYV0zDnBoXwYdFue/Vgnb9Dbi3ifXT4AMa3+FieHlgbEZc0LypBvYU12TBeJ4OHo9jyu/fPZnEATsqZrkwnp2jCjtqXq6BAbiIs0bF+kvjGnwf9dWNlEJqVds2fGDwGryD/RFwRqzvVtrn6Cj7t9EYLq1LrWxqrQseh31Q1AfTltZPAfwpyvwp41PGJsGG5aJ8XApMV1rfnAg+Py6SNOAi/XfUILRPxfOzNT4Afg41CLvWHpZq1De1eO6AfrU690TaUzigXIgPOhXOC8dHnXUjMO94XOM98BmIHWrwPpe2s+CDqQ8D28fn/aKNsBYelrABb9eVPcGb7Ye216XUttse13vWjc+tos7KJZdiqbsBuXSMBfcqXRvYFxckHsS9FRtw4fI7XCS8H5+6Pk4MaxrF750m8vubrHxxD5y//Np+LXSN+uChOk4urbsfF0QWic/LFy9YXODsFtf1r8BlpePa5ahr/P7hePiXXSh5SeJC8zs0hu6o6b2saMCvH52Av+MJ7zYsbTs9yveaePKtLfHBiuVrfb/wECNX0SiebomLhQfG58lL+/bERa5huId6VcNo0Ch+XQa8Wlp/Py7gFGV8HeCE0vYeNIpv29e7DLbkggtgf4vfvgsl79fSPgPwAZVtal2e6r009Uzjs3mujmt0OdC3YvvUNIpMi7aEnfG9N+OzLfrG57IoeTEed7iBCq/q8juuGveR0iwOGmdgXYbHqp37F45rSqCYqPdvE+ee85fuaR3LV7nN8TjuxVQZQ/5CXAC/gpK4U8M6fJ4o3z8At5XWl0XwI/GY4I/invKt5prW8F5Ven5/hnuQPlVsp7EzPhXe5qxZbP34nh7A/8U79BLGQwSPbavjbYiValme8DwJw/AZcz8Tv9vre2MiytPPZhtO6jmrYN8f8HZvXUW1iutUOHU8SsxELW07Cu/j3UCFJ3gT75aqOjblkkstFjy82Mz47JkvcJG3GAS6Hg+t9RwlT/D2vtCE01esnxIfaL2sJe3JJZfxXepuQC7tbxmfRh8+lXmf6AxsgYf0OB8XciuTAxYeq5PcQMLFtf64t9D0eAyvFygJgXW4XlsAq+KeSs8BF8X6+3BhcNH4PBMe9/P/GFdM6Q0sUPrcrjsxeDzY83AB6S1c4F0QF3ffAHYY33JYJXsG4NM/H8Tjwb6LD2ScFNv7RAehAe+kDyNiulfZjnE6cFGe3icGVHDPrwbgoPjcFRdxynHIewAHRuPuZ0nUJsGescmRovz+hE8RvBUf4FkstvXGxZx7GHca+WTAmvUue/VY8Kn8r0W5WpvGwYzy9S3PGGnXz3/8xg2A35Z/My44f4lPU528Yv9pgVVrYEezdQyNMZuvqVjfExfAt6AkllHUMQAAIABJREFUQvFzz/6Juo8V5WJzPN54pci+MS5ob93MdzclUEy0+F1xvsWiHvrT+FzHCdlnQm0q1UmTldYXz9e+UQ+uHJ/LoVeuxgfuz6KUWLhW5Qmf9XRNXLfy4GDZplOJd18V7anab6vVQuN09H3iXTK8qBuIPBfx/6HxPM5UY3u6A7vhsaB/SQTf8BfOUbWwJ6UyPic+6HU4pTjLUU+tWNzrjvD+aOo6lf5fJ+rna/CcGlNOxDnmpRlhaBJsXAcPU7dLva9X2LMJ3q69CW+f/D7Wl+vSo6IOvZXoo1T73ZJLLtVemqsDS3Xp0fhgar/StlvwGeVvUcoB0BEWvE27RhPr98Zngizb0jblksuvLXU3IJf2tVQ0bhbHY1rvjE8fqoztvXM06JqN7YvHwXweGFQF2+bBRaTh0ZH8b/xtwBOPHYCH0pi13Gmp8fVaK75/z/h8ER7/9bmwr2g0dsbjjb4DrD0+138ibGmxxGYTaV9l+VkSOAgXef8VDY8RxABCC9m0GD41vRzPeg7cQ/D5ohzhXpd742FG1m/uN03qvcNF7RloFHEuCPsOjI7GoaVjlsSTK+7Pz8XqfpNoT/l86+LxjtfChcrFcRHge3yK3EylazQQ9+IbML7loK0vFddq6ig/MzCuh+cceALAf+Md4eL+Npsvob0uuDdnUX6WLJcLvKP9BXAyMEUty0/FfVsFF+AOKZXnHvg7ZSQ+oLMCsDTuyf8l44ZCqIW352x4CKTP8E7IeYw7PfemqDf7/cLvqrb4vQA+APYh/t7btqn9fuUc2wGbT4I9W1HKFYEPBrwQ92+K0vqu+Lvl9vK60v+X4e/qq4E+VS5P/aIO6FR61ueN7/qWSIwZ65uaFVKNcB4n4En9Wm0MUzz025dEmyjK1pf4u7c8O6w7/i58hVJ4lBra1R136hjJz8OhrB92/JPxTAI/Ed9/CvEOpVG0WRKPJ18MGMyMh835GB+Qfp8aDw609iXqlpF4ArfX8AGTf8U9G6+welGPPAfMVWXbpoxyXfck97gT0SjcSeflqMv/QeNgU3lg7pjY/oeKc+xDFWcV5ZJLNRbGTUi+dLx3Z6jY5xy8nVmU9+nwtufSdLC8CtFGKZK7P4z3NSeLbQtGHbpXsW+97c0ll2KpuwG5tM+FRiHrE7zz/QMuMpdjEa8UlePi8flncSvxTmhVGpK4iDwZ7km9CO4ddwGNMXY/xTuXI3FhfrkaX6Pp8Vh5J9M4Tb4v8FjYtE/8/hnxztSPwH41sqXcgH8A2LfeZWgC7+uupZdwA02MRtfou/+ID6rMX1p3Bx7ipPDcn7mZY6slxBUCSW88Gdr5hMcnLpb+K67JWcX+0TD5O560qBDPaxGzciDe6b6Scad37xrX6B1cGF8H90z7kQ4Ux7riudsC70R+iwsklzFuiJpCBH8LT/xY9QSlbWHBvRpXwj1wPgGWKm0rRPBP8MSpfVvAnoH4e+xjfHbH5zTGPewb2z+Msj0Mfx9WfQZIhU1/Cptmxz08C2/mj/FOyvK4uD2EkghdcY4ixmVVpqbj04Xfw0XVO0t19S96glc8I3viIZwmKtEcPtOqARf/5491q+EJLz/EBwv3LMoU7sn7MaXp/YzbQb4TuKDK925L3JPsa1xcOpPG2L/z4yL498Cx5XJfgzI0IK71jcB8tSyvE2lfD/wddhDjeuLtHffyPWAjfIBjj3j29qmBHc157feieRF8Pbwu/y9VTvqHtxf/GuV809L6+eJ5fwDPwfMO/i7ZBHdG+QI4ooXuXc0HISbCpsmiHjgIH4DqjAtaT+Hvk6Vjv2YHnaOcNTAJDjvN1IFFG2/dyvtah+s0JT6YOhjvn/QG/oy/b54s7VcWwZepOMfik3qdcsmllku8Zz/C222PAf/P3nlHyVFcXfw3ypmsQJBIQmSECCIHk3PO0QQDJolkggkGbDBgg7ABA04YYxAiGEzGfEY2YLIRwYDJGQQiSwiEmP3+uK/VNb09q9Vu18zs6t1z6uxOdff0nerqqlevXtg6OLaLzTH3Wd+/Ccl9ubnM2smjW97/dW6bijUjMrbYEHlBv4s8188DFkHeNK9TcEhNL17aW+pOwEvnKyakTUHWb6NMYPqFCTzHksZonhct5A6u8j3tsWaeZXJCq18YhavYEyUCXA0tQHeO3EY7I2uOJ5PfT7qbvIxNuFOQcvUFm0CiJNuickF/st1zGxrcIjzL3T5vas/zrAjtlLcwGWMCUiIIJPGsE+X3akgRNyzS70/u28/6yf0ozmfohnoEWui+g9zA/4gslJ7ElKgxnjWKa/qFCYjNvDyQ9d6dSDExGVmjHxYcn2OsBVB4mi/RZtiqSPmdWFTMHZy3uL2fH8XqU41Uqr2/SAm+lvXjPCX4rUh5t3ZMTiiswOMoUeuSyMr7HqQMCJVP81l/3wVzFU+4RuC0CFqYnUGQ9BNt/B6FlKofoM2vMnBrzvcthcIlFJKUzJ7VFDT/D7a69dBmYZnKuOe5sXhJld/tshhEG/EfESh2rT99D23UfYRCaZxsz+t1AotrO7/ZeFmtr84mt+2QwvRPSH66HS0onyZN8Lo0GsO/AcYW3b/Dfok25b5B4YUaJmEiMl44DrgZs+YP3yXrK18Y9/fRuHpUkc8qp38uBAzHwnlZXTfScChZJfhOwJ6R2mcUSixdBvawuu7WLg+hufYCUvlhIJIRoichRJsS9+W9Qw3Qn/6a7edojTARWYVXjQ1ubVtYLGtgeTLGP8g79SHjOXdR/Xg2OO2CNv3uJ/BERWH+jkAyTKgE75W5PnxHG2Y88eIlLEh2ehlt2p9q8+/7wO52vGTnPIsU3/8h8DgqkEc4vlyANuvrqgQP5oxeSM4eTaoQ74fk4EutzcpIfpoK7GvnzDHrOi+NXepOwEvnKZi7LlpEjqMyLu2NyCInjHm6ALJ23LVgHtl4fpeYwPYzMnFgTfD/jCqW1bEGaxRzMbGAO6vKOXsghcFuBBYUETkNt7Y6tt6TbDuf+RVIgdFut/Qq91oDs26xZzMNxfZMEpYm8ax7oVhxtwGLRPztXVG8ysepTC4XWg5saULJCya0/ITUqqjoWJWJ8uSXaKEdLvpLGV7dUFiERQiswuYkIQlZmb1Amph0GFLePIKE679TaQk+nCpWu521IAXqIpm6UAn+LjAq7D/AlpE5rY1c5u8Ahgb1w5Dy6Qu00ZnrOh+jjwObIMXp4/ZeJYuV7EbhCSg5WRk4Led7+lGgNRNS/n+MPE9CJchI4EFywqFkxvQiQrGEG70/Rpb448gsXNHG3flIeZokoC0jN+dqyvkilN9DbFz+KaY4QmP7wWhT9e/YnIY2KG4m2DAs8FmFz6cfsmIuIwXuskXfrw385kZWzGVknb9owpvmSvCXrX+FSukYm717og3lz61MsLGpm/XlRAl+BTm5NYroPzl9fA0kd5exUCvWnwZQuana197Pt6iB5xxSopaBzerdl3L604dYYttMX0o8atYM6qLFskYbGN8hY46jqJTrjkOeOSOzPGvQVkkemTKZMHU2VhyBPFcerhGfTu0h6KU2JTsn2Bx8GKn8tDHSIXyM5U2x+kVsLq4axrUdnMLx5wKkpxid5Vrjdpq5ZkQbdGVkSLFqThsuCOxqc+GnwN31fs5evISl7gS8dL6C3HevCj4nVrEr2ueNSZMUrRSRx/5o5/E+ZEn1AUqSeEZwTlfgn5gbMxEVv9iinnS3dBSyinuHID4eLYQ2iCXwIWu3JEljVOv32AXtzD9NO5M4VvnuxaydjgzqHrG6d7GYxKSLyg+JnN0eWZc+Q85GSrY/A/0yn6MJU8iL4f4Wji9CZZzrUvh3Tig2HuyNNg17IOX2J8DlaGF+qfWt28kJ58EcsFFg/eQ7m0cWyhzrisIJfI0sGFfJuT6Gonk+m1vKwISc40OREvxjWwREDVdj/agX8qQoA/8N2yivLVDioqXC74jI7wjjlYT6Ct/7XUmVKt/PckHKpXZZfpOzEYAsuz5BGwHNFLvIkuk8pNg8NfLz2xaFWngZS3JHutjsTprAcavgmtwY9wVy2gdtpNxh9y6jDYO6xwQH1iRNCnpsUF/K9PHjkAXaP4DlInHZDW2WXIg2K0625/g2Fuff3s1DjO91RI4TizwJLkWbg8m7tWtwPHkfVrA2+oIg9Fhkbgsia+obY/fhNvanH1ld2I+2RErndXOuTZTf7ZbzMuPefiic3TfIAv3X2OaF9ee7yVhY16ittkVGHxMIcinYsb7IG6oM7FQDLocQQfnoZc4pGZkgSQj8N5rHrN8QKcEnY5bgNeI3CnmE7kEd10akeov+NnbfiPQ6ZSQjNZO97fz5ScMEbl4rvl68zKrUnYCXjlvyBmO0M/gwMN4+32ELgSQkxLzInfanVC6CC1VSoIQ/HyBruCQ54aImxN6NZby3+nHAf6r9pgI55blNr2nC5GPARrHaoxXcliG1FrqIjKK0nn1qNq8fhDYUxkfiNxBZBV5Pao23otV9iBaTB6G4rV9QmXQy1ubFcGQdcGxe30GWOevZu1kTAQopJm9BYRT6JXUZzj8js4CakwqpEqK/CdclZIl2I2m4g3msP39uQmbVRFydtSAF4N5IWXkTOTH1kXVsGSkLahJf1sbul21O2S47viPFfWJZWGhCtBwuidfFYHvnyjYWJUrUFq2VY803QR9fHoUWuYI07FLyd32k4LmZ5pb8idKwqLACi2U+h0rwMI9DqHwOPdkKD1ljnw9CoaDKBHGqgzYq2fGfzuq7CuK3tfXrk5GSdCUUG3k6kt9qogRv6bcha7gklvye4TVUKi/HIEva5ygwyaPdZwGkzL0Y6BP2MyTTvYTJmmjDaQxwROQ228Oe3U/t/yPst2eV4GuijdXnsCRlVh+rj2c9MKaQJnmvibw7i/60RtCfvh/U90T5ED4IxyY7liSxb/P4NKv3F1lX/gHJl++ivEW3IEOj1WrQZtsDF2bqdrGx4GYyG0tInik8HEQOr6tRyJUVYoyBXuasAvwFrdk+svHzbDIblUhOT2TNKAmMM/cbiww73iY1IKyb0QvKk/AMFhMdeRkejbz7n6UyKf1MLz5kTPApQRiygviEc4qPAV5mq9SdgJeOWahcUI+0gbCPfT7IJoiXULza5a2+O3Ag8AqwXWR++6CwAmHSzRvRQihRxg+xv5cQOemjCZG3oM2BPyD3+UQxuDayYHyMTIiWSFyaTaA2WY1AVpafIYVOTcKgZPpSf2QJO1vJ64KJdjGCxDoxJkW0oPyWIPYoUnbdihaTn6EYjWFc22hCC7IKehW4PXs/e677Wh8fFOHeLS0oN0eWm9n4uT2BQ4H/UYMFXKOUWfVFZCnxKpUKsNEoUdplWAy9zlyqtRGyoNwdLXhvJlCCIyXU35DL6ja1fG72fCYhJVizeONo07XWnAbZPPc2sq7uMqtrasCpLwqh8Rby0EkUuz3QAuoatKD6ijRW5DwoFnYh/R5Zd5XJxDmmuhK8arK7Attsg2Du2sn695vApiGP4JnGTp6aeKn9CSWZnStzfD809/2ZSBbVee2NwkTtbfNG6C23Kmms65aU4KcDF0XguABVktra2DANOD+vTxXVnzLtNAB4wt6nXkH9mig3QRmTvdGYuiuwVh6/Atuo2Sa38XweyUk1celvQ3/6PXAm8gL5Ajgp7zuBDQritBXyBLvR7rkwab6kvmhz80q0hkis+jeO3GZ9kGJwOs3luN2oogSP2Z/se0ei0IKb16r/eOlchUrl6TFIL7G//f9P5L23Dc29aDe1vjeiBhwPJvXqqwgRV6c229n4rBPU9URJ6V+zuWeVLD8kyz0NnFcUf1K5qQ8NEJrNS8crdSfgpeOVjNC2J3IxvQSL0YoStd2AXMR/ZXXLIVfBr4ATasDxFLSTmyz+76AyDMtaKD5zHwL39BgTC7LCmYF2jhOL+I/QwnvegM+XyBIumptQZtJfFCmMw/i1iyM3r4/yJv/IfWlnpID/H5YclMD6rpXfsTYWu7Yo4TvoQ4nSZhDa7f4DQaZ7OzYPWqj0zF5f5LPLOZZYSv4sUz8CWaePK7pvZ9p9UbQoWTuoG4Qs48po4bY2EpROsbHhuJh9q5FKpq1WQRtMR9j7l2wcDkEL7bH2uQ/avLiZIIN6jDGqEUqmjVZAysE1grreSAn+BVJSrIzc6fckk4SowHcu5DTCxuntgLlIBfC1bLx8hBaSbkbilO1LSYiRBdE88wZSVEQNL5ThtDGy8ByPNqKHW/38yCL+HeTCfxwK9fM1ZkGJNg/DDaABBXJcEim5vyaTeJtUCX4NkRW7mf40FRgT1O1qfelh0rjNA5Gy7hsKtjqzcblZqAKkmHyUynwNJRuTrkMhH66mBonsUCKyT5FBxVT7fzxpHo4k4eN3ZHIj5L1zRb4DaOP7M2zeRV5WXYL/nwZuiNQuYwhiQ1vd/Ehxc5p9DmXbTUkVp81c+Itol2x/QnP+58haeQdM1kTGMOfaWDCq2rOqY3+6EW30fITGz1D5lDzfwhSv9n5/a+/942iOe5FMQnM7dxngcAKr/cjttQTwWxsfz8sc283a8DYihrPM3PMKtL6cSLp+6pQykZf4BSWWvIjAMwclmv4HVdbBBN4+BfIIZahwnb6TjQdPAxvmnV/DtjoIbeomnjtJWJSeSJ4ro82DFTLHf2nHliqYT1fkxXSr/d/pQ0J6Ka7UnYCXjlvQ4vZr5Ca7eubYKKQgLCM38XeQxccJwTntHqwyk0aodDwAWSesZQNkGIalFwq/cCtBYrUYEwqKFXs3WmD3De5/BxK8DyYVvNey9tot0vMKJ9UrkCvTZyiR1PHAgnZsiWDy3yo7+UfsS9ORe+fFyPrmO2QRs2grn/9xSEhfo0BeWyPX7/kz9b9FlnoD7HNujN+i+hSpS35vtCg6ico45MNQjMgyUjKdBJyDFPVPkRMKoZ18wnbfC21YfIw2w/5JGsJjMWR9NwVtfk2x8eD4vO/q7AUtvj+wd+5L66/nYoKhtVUZxde8hRptGDZSQVamb9lvn0QQzsj6//bWbp8ii/nPgVMic9rH+u2XSFHxBtp0GmjH17Tx8iFy4sTWuC8tY8eHWDu+gjZhoy8O0IbNR8hF9v+M2x3AynZ8HuA0pOSZijxmjrBj29m17baYrzamoA3eP6K5JasEP5lUmTNPDdpqXuSldl2mfg97lmXgXpTE8X/A6QXffzCy5G7mdYaSgH5OunkRKsLPsveyTBCTPFIbbWY8jsc2eEjjyZ8TnDeKNNzQUlTOT+1OVtrSdUiW+g5YPzwXhR57gNQIpEjF+5ZInh6Zc+wp4J7gc6gEvxWNm2W0AVPYmJDXn9DmzQHI+ONz68c/sL4/AK0LxsbsQ23sT6NJkwTvENQXLgtbWzxq489caHNgBPII/QBLTFrt3kU9w2z/pHIzJxk385Tge9RiLAjud5vd71PSBLhzjAzppW0FyY4VGyYo6fQUJEdukDm2JDVaB1O5Nu9O8/Xm3sbjHwQeJ7Xu98iAaQa2IYi80xIl90pIzzIZeCxz3XAihbtERhTvk3rU+1jgpVWl7gS8dMyCLLpfQjG2Q1fLUSgcylz2eVVkqbINgbBehNBG5cJmM+AE0uSaXUyA/A7FzUssvxOrykkEcf4itdHOKA7j/2FuppmJ7t/IymPuoK7wxI05vK61iepQtBg5k9TlM4lXuSRafJeJnLgCLVifR1aDA4L6N+0Z5sbtzDz/I5Fi6pACeXVHyoePkLD9c2AzO7YIWpycXYPnFSYfeRItcF9ASriHkvYxTgfZeznZzvsjQebuCNz2QIrKM+y9P8H6zOPAsOC8Ych6dxMCq8EixoGOUpAQPRVt1IxCFgvJpsUZds5Q4Chk3XQ3cFhwfacU7DLv8doo1unpaM74pb17D2auWQwp6c4CdonZRsCOyPr2TKR42gqFE/gWKXMTq+vRSLnyAhZeq159iXSzdTCa62YqMyNy2gZtgh1nn4dYG01GC7eRVp+MZwsG/6+LxvvrC+bUTJGNNngTJfiBmWM/o8A5pAVeye/eCxkRbJw5vqM9s9ftXVg0OFak0rJPcL+9gvpF0DzyWNKXrL4Xikt6KLZhHrmdxqIk5oODunuQInWVzLmjga0jcAjHp5VRkrTQ02l95IEyGW2Yd0GhUfZHG1NRkopj86vxSTZQu9r7/x5wYthnkDHGP5AHVpRQe0F/2olM6CIkK1xlY8LTSDa+GI33hRktFNifViNVtu4Zic9OaL6/g0zcbLSB8l8yc18N2minpH8TWFUiJfhVaN45M3NN1LnF7hFu5Fxiz+VSIoT289K5ivXje5ExwMCgfnG0+VQGLsm5Lvo6mEqdwC/QmvNT5Pm5b3BsHxsr7wfWi91eVer7oDw8X2IbvsGxHZDn2HZIpjnS6qNsHATj0hJIxo2+FvfSuUrdCXjpmAUpKj4ARtvnQchi4g0bHO+gSuIvig/FsC9afPwWWDOo3xi5FL5v5+xPGs/vlFh87Du7m3BWRjumGwfHkrh+qyGr590SHqS7z7Fi522FLBk3JVWMLpUIAFRuZiyNLIYKdVvK4bQmUpxsEtTdaX0pUZosmLkmq/xupswo8DkuhSwrn0XWAuPRps7tyEp3vhh9KMOjDxLU7kfK5B4mKJWRJf+w4Nz+KCTJPEF/iqH8HokWsifb5yWR0uZOtMEykSC8Ts71nVKhm/c7kTXVldZ35guO3WDv4wqZa3oTJKKNNR40UkHK/02QRUeygdoHKds+B/49i+uLTqTcBSkh7kSWjf0yx6+z8WC9oG4dMpbFdehLSc6NXvZ3QeCAyM9uoN3/p/Z5JSQHXIEUbtOR8m2FzG+ZBynC/kulpX8RG+SbI6V6M+WajVW32LyRq9yqxfiEkoO+DvzcPoeJwXdHC7t7iOhVYH08iXu8i9V1Q1Znk+zZ7II2f45Gi/Mw3nYsWaUbUsCH/SIJIZcYNGya977F4IQ8Uz5GCsCvgCuCY9uSzsfPozn5EyLEbA9/G7IU/hB5DCXW+kPsXXsduMDGsaH2PF8BVo3cTmF/2jXn+DpIkfouaUiWqAqdtvYntOFxq3Fcs2A+/YPv/pQghAipbL6b9bXouYHsfgsgw4ky6doutARf2vpVmSC2fcz+FHx3Nsn01cbjLGqU+NpLxyxoHbcn8oh7mEol+MIofN37BF61wfER1GYdPB6teS8ATkRGRO8ReMjYb3jX3tF1IvEIvY0PRslAT0Khl0oof8KTSPbdH60DN7R2/b2NIZOAs2LwytSVUH6Eu5G3VWJYMEesLb20r9SdgJeOWYDvocXlhcgy9nm0+N7HBszPgb3t3GhJSpCV9TSkkByWOVZCsWTvIXWVv43KJIkxBbb5kTVOEgN5vszxdUy4jZYQNPv7UKK490njZC9JmgCst9WFSorc0B4FcUssJ/dAFpaJ0usuE1SSkDWjbHIbkTzX4DsS5fdBEfhl225JZB3zBFrgJou3zWK1UfJ7kafF/5Fafd2AFm5Hm0D0OFWUzUUJA9nvQTGaf402AJZEm1BX2ufDSUN5DCuSR0copBsPM/+ixfe44Jy7qFx8r48EyexCr9O3G1I0lJG3xSWZY6ES/F9BfS1CevSwueN3QV2yQOiPrGRvbakPROI1q760nvWlHpnrorjLW90JSC4YZO3yB9K8CUns7Qdpbmm5FQUkDM4Zn0ahBeVzZEK02fFtgjG82cI3wjPbAXnoLJepv9jGzsT7KrQI2wsZGtxPxHkGefRdhzYq9rC63siz7t9IxpqOlK0nx26rgNcfkXX1XEipGsoF8yGL3guB/pF5LIeUx8cja/mfI5nlRlKDhiVRkvfxaFNnu+D6mHLmGcbtLmBpqxsGXI9C3E1DMsI0LDZ4DZ7bcijvyEwDD6tP5sO+yMvg92SS0jZIfwo9EUcT5CUomM9KaJOwTH4i1a2sn9UkrJbdc2Mkb3+NKf2RAjFRgl+M1noziKSEy/A5ESm7/4XkytCD8Bpru7NxJbiXFgqKU70jUio/QqUSfCha171OvhI82jrYvj9Rzm9EariwjvXt86k0TDvQ3r9hEXgk73h/tMZ9FSmzp1i7HYs2VFdDa9AZNj59igy0elhbvkJqAV5k6K++yGt9qUz9hkT01PHSOUvdCXjpuMUExRdtMrmUVDEwN1KO7xf5/nMhC45fUxmjcn8bqMNF9aLAQtTYqtKE6guQkvYM0ph1fVEM2c9iCbdUWgotbH9PAz6y/xdEyu9xpPGz9kTC78IR+ISK632Bq+3/RWySvRxZ5b1JmhipF1JyP0xzF9GjbQIu3PK7Gm/7PAAln7vIeN9OwQvw4F1K4sPvA+xv/19qbbSCtU+SYOQBbGMjcnvsiKwre5OGHBqHXPaSmMg9UVbwMlLM9YvNqxELaYz4fig2+s32ObGSDxff41BMwh714FrndhqK5pMvk/eJSo+YPkjwnQY8G4lDs1jBaC77L3CHfe6SGVf/ihQqsaxg8zj1bZS+hKyCt8vU7YnkgpWC8et8tPE6Cdgx+9uCa4uw/N4S2NL+H4UU4C8iy6WwPVdHSpWbgaMit1MvpKBMFpLnYFadKHTUaygEWJew3wftWaYAxXNemwfHlkeL2plK8OQa5JmxPjbeF/WsWsHpSNKEhW9jSR+RS/sBSGHRLIln0ZzQfH8X6fw2NzImmIpcwnu18F1Fe6Yk40C4UXIKkglCJfh8KLTQ2Uhxv22Nn91yQX/aLcM9yrtfZH8qkuMsOK2ANp++NQ6JLN4LGfZMIrDaL7ofVTm2EZK5ZyrBrb4f8BcUeqsWVvs3IcXgnUgu+AaN2TsH51xtfeyX2CaiFy95hZaV4MNIleCHR7p/d4KQYkH9OciqOwldtwzabB5HGlYqTPJeWHLwHC49kHz5AJLhBiP9SRKa5Rg7rxfKW7YvsAWp7PIXlNth0YL4hLLQsUgWesnaZiXMAxuFA73exqhO7zHrpf2l7gS8dLwzl9kAAAAgAElEQVRCpQJgQSyxhH3ui5JzvUlk6wCkFHkBuNA+r4YsTj+2Ca5MJlZdcG3NrCqRm/eFxucxpLC/ygTbUyPdM5w0xmGJa1BM9k9skppsfxPL6yHIQuavBHHJC+AyLPi/u01Wd5K6y/cDfoWUX1+QJnHrhzYzPiGwErLrE8v1aOEGqvyWrFX4ScgytbB4qKSLxAHI9W60vVc9UTKR/yHLwOS81UiVzddG7ku7ICXOkaTuZj1QeJiLg/OGIze5I4Hda/mMGqWgTYIXSZUm+9kz+h+ykEgUlj3QwvdVInqDNHpBC5ALrI1OC+pDJfgY4NAI9w77+Fr2fiXP7VDjdGjmmp5IwXMjUqAUHdqrJU771rMvocXOQGSxf6XVJePBmcDUTDuNNV7L1YjTb62uG1JeJkrwdYL+dCTwJyLEkM08uyHB/+uhhNgfIgXc3Sip60vA7S18R7vDL2S/DylwD6QyPNvS1p+ntzRuU5yyMgz5MhpZyG9LED4P5Swp27MagjYMjkLecz+K/OxGorAYx5JJ1ojm50QJHhoRxAgrEnLqb+9U/8w5oRJ8RIM8u2ZK8LzfVGA7NWJ/ag2n5ZHnwAzrS2chA4tPiJDgOdOfQk7Dg/qNkPfH1zZGrYwMMd4hchgd+94fIeX32klfB35oz24MlVaxtyDlnCvAvcwseWMMMtxpSQn+MFrT/aBgLt2RscSlZBTYSCfwrP2frG3DOeVAFIZz3mq/q42cQsPBRC5a3eaRXTLn9kRK5nfIifmPrLBvQfJXs+TMbeSXyJRdSY0plkSGfM8jOeohu/dlSPezePh7vHipVupOwEvHLHlCD4qVfCRSYp4Y+f4lm8guQTu2E5Ey4FETJkegxeV9RAzBMht857EJrIxcmo8kSNhUhBCJdmTDOIJdrY1ewJQhxuMPaNH2AqmL+qJI+f0+poAu6HefgXbV187UPwL8JPg8FFl7TEJhak5CcXc/JnANpVJwX6yOzzOZmBdGC6e9Cv7ebmgH/u8ESiO0KJmW9B17D35ogsfqsfs6sio9F7NKCPrU62gBNzdSDOyNLAbCRd4cJZDYOz6V1JthqL17XyNrpd5o8T3Gzit88d3Rir1PF9k4eWpQn2f1GEOBsh9aGF2FJflBi6LrjNOPUQLOxVF8xKnE93TK47RQI/QlpKiZknnP10cywB+BNew9+IQIFqiz4JTERO6CLMEn2vzyJ+A3dk7hSWapnKN2QuFLjsmcszAK/fUwUs5/Yv1rj8x52Q3XIuSE/ayPfIDmkhnWHom3yrJIaflVlk+Bz+hIguTWxukLtHguo7kjTG77BxTOZiqSUZ4Djo3Zn4J2+sw4fUlmIwLNdYdYG95FhPk305+2R5viSZzxjalUBCZK8NuJFLO2lc9up+B4ogSfo/pTG9ppeSTzlo3TD6n0xIjVx1vitC6SLcv2HkwhgkK+CrcbUIiTJMTQCDRmXUsarnGB4PzoSXm9dJxCpaw4AG0cJhbWiRL8fZorwRdDORRyc5i1g08/5An3DVpDhWGWjiL1AJmM1lKJ8nuwvQu/I1h3FcBnFJIps/m1vmfv+zZBXeKRvISNmcdlrpkHOMLezUKMHIJ79kOhsi4kE+YIeYWOR3LwROP9GyKHrPHSOUrdCXjpHAVZVj2HlKrHBPVRXVGQ++ApaFf1sMyx64Gr7P+6K99QTPCfonAoR5DuaLabG1J2T0BWt0ncvhIKE/MuQQZrpIi7nnT39K9I2foOBe3cBvc6Allm3A2sFdQ/jSlpSJXwC6IYfw+gsAN/JLBCS/pS0c+SdriTmbAwLRQW2vFdiZKvFzCv/f61MucMRwvxy0yAWQ25q4WJUmIswndCFpT/JicBGkrW9C2yBP+XCUlRN8EavSDF22vAdUHdymjTrozi+L1nff34bJvOqYVKJXgt4w3vgJQ0JwCLZI4tjwTrMqmH0YfET6bcEqeR9epLwVi1vPXxsaSKirlRjORP0GLvE+CkGjy/PE49g+MLIKuql5Al1hGRn93+Nlb/HNiqhfO2RxvFM4A/Ff3cqFSgLo42AU5Gi9llkTXVVOCG4LxlSRVfzay92slnbTRnPoEW93MjS88fISOKHa0fP4flkbHrVga2s+uXLrqPZ9ppUSQPnYTcuxPX6/E0T1g8F9p4ihrLGnl9TLF3/gdISfM5st7tG5x3ElLsPIh599Xp2YXK2zmqP7WjnVZCyp5vge0j9/FhLXAKZbwBKBb5XsCGRXOqwrMPymtzlX0eQWoVmygxxyCL9DkuZJyXlguVyu9foE3ol9EaeX2r7051S/BmCRfbwSW0sp4LzbffISV4kgerh3EsI6OnpI8PRYrvtyl4QxMZKZVRCKHBQf2qNv6cR/N8RAsjBX1evoJ+FBTqknRd2d/GowlozusdHg/OXxeFkUk8IpPNg7rrfbw0bqk7AS+dpyA3nY2Cz3VR5JiwubcN1A2VFAHtlI5FLqEnU+ACBVmcfADciylOkdvSJybAdiHdVR2MwlmMs/IjIllUo5A4k43XBlb3X2Df2fiOKPEhkQXMRdY2szVZ2oT/O+C+AtuqK9qUKKMwBkODY4lQcCBSLE2zZ/tE8FwLs2KkcrF0MNpImUGaJK175pptkRA3jiAue2cXQvJ+H6ky8AxkaRa6DJdQWIYDga2pTDo7Ryu/g3ZYGC1cysDPanC/viifxLhwTKZyIdXbBO0zkXXe92I+t1Zy6ooWLHXrSyiM1mtUhkLrB6yIFEyj682J5tZgc8fkhCxzJyPLrtDSq0+18RBZEn9NgR5Yme/f0J7H9cD8mWf1fRvbTw/ql8NiqRfMo4SUV68hC/hNkVV+yGkTNK89TwveVTHmFmunLZGVcPjeHWLj0Q00V4J3rwGnNzHLO6RQ/gzNLd8Yt/7B+WcD32+AZxcqd1ecU/pTO9tpBRSG6GsirV+sP22GLEBnySnn+lgbq+H79ldk1bkC2nS+gTQUyjDkcXEmbu3ppUpBG5bvIMOzscgQawZmLEcaE/xNZOCTJKIuahzohZLtHhfUZZXgSQjSdZDH+pcoFOBvje8kAs/uAtumB9pA+gpZboeh2i6x+p2pVOCvYmPafjV4dknIlXtR2JNkjduNKhsUyEBsKoE3jxcv1UrdCXhp3NLaSYAci9OiJpA2cF4H7QRWhM6odxtlrgljghdmOWz/Jy5U91pb9EK7x4Un0WkFrzBW/IHG6+9IOfCMPac1kBXzqijO7SZo8dm1aCGb1OK+K1qgdEE7y6fMzrPMtPeovN87O98TTOxJ+JOjkIX8p6RJJrM78asjq7TvB9e122oBWUkuHnzezp5JD6QEfxttXvTLuycW472tbdKRC4qfOzhTt5IJZKFVbjUFWKfeKGhDey4CXEHkBIV2r4HIoruqpTJV3E9j9fF2coqhhNsZLSjXoDIE0hJoI+68WVwfQ9Hcak7ZMTRWO9n3/hh5MiWL2xJa7P4NLWo3SNokmAdWQXJLjKSOw5FV8NfAI9nngRJJ3Yo8yObPub6oDeiZXlwoifXr9pvvtPpwsb0xUsY9Q40MGYJ2+gq4LYfTD5DcNo4ISokqnHogmeBKa7dlkeX3b43vHUhpsj853mxF9fF2PLu9q31XZ+xPRbQTkiXGW19rd/z/nD7+gfXxhnjv7L6no3VR4sG6prVbGW1oJu06CFnJv0wNkr576ZjFxsPXkNFCskbawPrTL0iNVHqjcGQvUFDixoDD8va9LxLkkKFSCX4eqWXzgsDFSBH+EPIeixLKyu7XHRmBJUrwBa1+SRRG6yvjt7210eMoj1n0sLJIpnsJ5U1JZKTNkPfzPcAOVhfKMQNs7Lqy3v3PS+OXuhPw0hiFSqVVNrnObCsGi+JDxhJ1FtcMQQuTfxAksCDOwrtNbRScPzeZpEAFPr9ECf535B47GTjRJrr9bCLbDyXj2r7oZ5fhFQrWCZd/mhDyPNqNn4oE3ekmEBwSgcepSKmcxDntgnaSXyQTz2w22nkfZDnfrQ3PvyuymB5pnwcAN6OwBr3RQvt9lBk8UdxXtfIuQiBB7ma/Ra7KCyEviu+Sfmq8DrZn9TipAFmYu2BHLUjRnSS5PQcpL5Ps7ZcjQXzxzDWdXuHd3t9I4NofmedA5A5+GYECI/N8T8+O+w3CqRC301lw6YVCjX2MPEHuQxuWiYX11chab1gN26fhOAXcfosUNIshT6v/IEuu8Ugh9jRBeBa7ZjRyPY6hAC8hGeC/aGNgSasPE/SdjWIBF54UNMMlVBAei1yWPwQWtvrQmvp7SBH3LgWHzphFO71ozyvhFLbTwTbW3wXMV6P+tDYyFOiDwpBdQ7q5coDxmY6sjqPNx+14djGVOA3Xn9rBaURQvwJBmKYCuSV9/IV6t1NwvxuQjHQ8poREMvGPUIiKB5FhzFEoV9An1GgDykvHLGjD+SlSq+5kU/waUtk8SSreiwJlOyrltFVRWMhXaFkJHnrw9GU2dB/t5NcT6QOmoVw3YXv9Cm2aJx7J95CGLI2db2ozu+8KyBvtfGurh5D89DVmIBZc0wPlgbuOOcj4ykvbSt0JeGmsgtyB/oaUp2OwxeOsBuLMgLoC7VwYBINs99bcP7hucSozmcdQfre5jUgV+8uTLtTbzDHT7qEAexBazD5uk8i/kcvsJ2gB/q4Jlku39d6z4lPl8yFIYfEGCiOwhLXFsjbJjSiSj92zK3KBm4SUJIkSvCeZuJCz0dZHWrvu3kZOK9skfhuyUHjTuA2y41ll80zr9aLbJ8NrDIoz+h+0OfEDAmt8JCgebP3IleBpu3VDVhJXBO/YeOvbieX8lnZuLIvhhhL4Mu/Lcu0Za1o7/reT799tXFqVypAZPdDC/A6CkEQ1asOG4mT3PZk0TNM9aCNvXfu8Tx36WSNyWhrNrzPQwvcO0vl+Z5R4bqng/AFo8/6WCFxCZVySkPQ5Kl2ee6E8Kk+F9RHbJ8vpI5tzhlh9KMtsThvn2YI5hUrwo4AxdehXKyCX/h2Duk2Qt8GNwJEN0E6N+Ow6NCfiWMw3Sjudi+SjtUnl3ITnvCj/zNNIRn4JhXBaNjYvLx2nkON1iuTvp+z/RW3eu57Ue/VQNOcWatSADImuIkguiTx2H6BlJfjMmOAR2yl3/YjWm/sjJfg4KuOhr4y8yVcO2rbQ9V7e+IY2Af6BZLhX7P3fGcl7w+15Hh72AdKcY8vXu096afxSdwJeGqcgxe5XyPXlKeRWeTsWk5IqSojM5HM8EtBnO46lTRJhrKwxKKlem5KcFCU0NlIbZb4zGxZjrsznQ2ySeAqFsuhmAuU8NvEWPfGHv3EztHs8Ae3cbhkcOwhZntxFEEIk5rOzSfNEpJS4n1QZ8Rqw2Wz+tiORYuPAdnI61J7PlygJS5L4JKts/hhZFvcIj0d8duPt9z1LsMNOaoGe8JpEjiXjnFysn82DhNmHkHfDzUiIu7Pa+FDAfUPl6A4UnMy2nf1pT2SJ+0cyGedn4ztqYdW4NFqIPwZsbXUL2Xj1OTVQLjU4p7CPdTEO99pY8XdSK6F5YvXzjsAp4DMIWX+vEdR1RwveR2keLqnN4bRmoz+VkPL2feRdsC1aVB5tY1XNlLo5nN60fj44aauca6I+w1ZwaiaL1rJfIWX3NGxTx/r8ycjirc3JvOeQZ+ecGowTUnJNQJtvvTLHsmP7cOQ52ztm+3jpWCXTT8Kk10nuqZPt78xcKii83jUoh1OhXoYo3v+dpFbmidHbrJTgX6M1cxQvQyrXb7vauz8zsWTQZokleK6sTvGyScKruz2XMCHxALTW3IZKg4HRKOHl1pnvGoqHRfLSylJ3Al7qX0jjY11sA3EycJ+CrM/+QRUFL80Vg9OxBBOzywEtxqYhS5Z97btOmZ0BN6KglgiNdWujvGdm/58N/B+K63cNgdWGTXAfImXA6jXqT/sjK+KHUbiTz5GQ/ZMMr0lIGb1BZD7Js+tpz+s9a4+hyBLudGSJPhxZqi6DEjatX+X5fQcc1A4+4fd9gZJY3QusnH2+SFhJNgzeJK4isBtaFN2BwqB8ijwdqvFK4qFGtxLqKCXoa4nQ+30Ur3ISCm+wYbYPFHDPcCz4E9rwOp8ahQ+ZBbd9kGLtdKpsdlW5LnxHDkVu/rWIO7geCl3xnT2zV9Em1cl53GrUhg3DKXsflDNgAxvnPwOOrkMfa3hOVjcIzY2fkbFcmtW1BXEKFV9HIOvPr21eOZ8gcVQN+1M2XvJbyFttSC15zCanunna2Bz9HyRzjrXnNoXKkH/+7JxTh+CEkl1PAX7UwjnL1bp9GqFQA3mnoxcqZd9zUdizue3z0jZWloEHg/OGoATHbxAhNBMWXtP+PwatK1ujBD8XGTstEIOT/e1vbfIaMuSbgbwqdiA1sNofyex/xsIjRXx+Ia9/Ih1GGekOjgzOS9qzJ/KqfRDpXLpmv8uLl9aWuhOY00u9XloqFQz9kAJ6PBYTOjh2PFogNVPwZr7jKLRAb7NVLLJOPtwG5W+TCaK1bZThsxsFKuYapY1yeN2AhNdrkBXFB8i6/OzgnIOt/lEiK8FRnMqPgZNIraxHoPAe7wLHZHh9l23PSH08yR7fC1kEvI9ikH+NLJ3L1ue+MMGgDByc+b7E8rvNyu+QG3I7Owv4CVJo/Q1YJTgnVNwfgVz7CxWKyVfUhMJbwmtUhnsiZLbLi6Ejlby2as151ueWQMrLX0Xkdx1Sjm5F5Di+reSzkr3zP6LSymRhYInWtJ/1+zKwbw1590dxWy9CXkibBMfqNV83FKecPt4vHAta+650dk7BvTcB/mrvQz03U0LF1xgU5/cZAuVurftThtORSFZ5EcW7rlcy9UbklGw+z49iyk5CG2OhTFXP/tQo7eScOgAnu+8CSGa5iXS9EM7/GwO/YTY8xzpTsWextv3vCvHKtgmTH96AYtr/ksACGFgfWQq/gdbHFyHP4w+JHEMehQxJPD8Xg1kqwQeQk4C6QD490SbX/ShXylBgC+Sp+hFpPrDeyACxTGC0FoFP0h49kAfKv5BMsod9fge4PDh/XpQU9DGky6hJLHIvnbfUncCcVGxg2Q2FYtiNVsaOjsxpV7SbdgdS4u5u9WHywuNNSHkAi62UEVLabRUbfNc2SMlYBm4N6lu0fM3wOdqu364ztlHwnYll8IbBZLA4cDdaaB8SnHu48YsaLxZZar5DmmQrEbKHoXAZT1IZXyxKLL9M22+FrHDXsc89gB8jBfjHyMJyOIoZuyyy/h6V+b7k+bVng6drHj/7fAypxXWoBB+YcCEVGAqZ8DNttBzKur1K5pxjSZXgK1ndTtbPlwjO67S778Ai1Z5ba9qXdEPhPBs/Bs7O97T03IK6Pe29Wz945+ZGsaPXrkEbHQ6skKnbGsVCH4gWu/2Rhfp/0fh80Sz6ZPLOHVAUp1ZcU/XdKqKPdxZOLXFwTs2u644srK6k0jurXv0pqwR/C4XgSkIgtDdxbXs5nQh8v71tMwdwGkJlnNlG6E+N2E7OqU6cWsnhF8gQZR8qEwHOhyx67yZybORGLShm9cv15tHIBfg1WtuuReqZHYZCWQEpTp9CStaLiZiUN7hvNxR6dDJSui9OcyX4CwQbmJH5bID0Aptn6gcjxfj/sDBESFm+BZE8joNxpxsKGXMfsuxO2mchlEj8bcwSHCXm/Y09v2RNNUfnnvLSvlJ3AnNKQYv/Z5G7yafIeuN9WhF/OCKnbVA4jltI3YUfJ03EFyrsTkKhLHbMfMcxJry0SbFLpcKjC1LE7YKsdKcTJIWieczrbjnfcSSy5D24LXwasY1a4DbW+tTMGFr2dxiyDPo/KpX0cxV5/yqckt86LHk2Ab+NkeJro+zzJ5ICFblzfYriD+8c1CfhUN5B4VD6Vbk+sbo6jQKU3yjm4fHAOSiefN/gnETZfAtKCDQYbbxcl22vgttoP2QBMNWez4VUKrePtePPWjtOB86IwKMXDZa8BCmtn6Ayjm+bngFwKhLU2+ziiCyC/kRzZfMpwHP2f29Sy5f37JmOjdhGi6MwPo9SGb8ved+PQlbgLyLXy1Os/5eBrfLalXZuGGY4NYSXgnOaczmhhV6/4HMRyso2c6K59eebaLOqXTJCEZwyde2e75yTc3JOtedU5V77oXXSbqRrqD4oGfwUZMG7PrA78rb9mAaTCWtZrJ0+AfaoN5dGLCis2H+o9IJZFK2Nx6MN3gGZa2Ksoaoll+yBlOCf0FwJvhpaUz1JhA0eMsphZCRTBkZnOSMjsW/J2QDLfk+B/HqgtdUE4OGgPtFjLGzH/xUcm29Wbe7FS2tL3QnMCQUp2/6JdrlWsRd/JbTo+ARTFtaY00CkhDgDhfboj9xeJqFd0gXsvHCQrEjSZL+hTBB/cDY5ZJMmnpMMcMh68UQySnA7tik5CzUKtrJuhDaaBb/foB3ScFJIJo/97L7LkCpfa+HWuLPd9xgyyWqQQuxrYK0a9fGN7P06jkrLkqQ9eiBh/G3kBhY7A3c/ZP36HlIol9Eud6g0PAZZ9X+I3PaeJycpUTt5hO/dKOvPJ9qzOwUJQtcRxF5E8b7/gTZ/jsz7rnZy6oIsNJ6kRrHqW8lrR3tOd2GCY1t+N2m8+fHt5LOqjYkPUBneIRFuLwKuRbkU/oyE75Pt2CrtuXcLnLoiL5S30AJ8WaRY64Wsu8rIbfGPpJthI5E1yhY533c47Y+zn+XU3oTDRSgpnJNzSsa6unOiUgl+KgWEZyqinQg2xRuhnRqVUx6/enNqxHZyTvXhlPP945GB0GQkE9yOrQVQkuIr0fqgjIzDngBWLJpHRyooKeBrwJ/rzaURC7Ak8CVwIFrrHoTCWD6H1hNlYD87t1lY0oI49Aj+3wZt3qwdzK+9qK4EHwUsWiCXuQgU/khnsZr9Pxqt7Y5I2oF0LbyEvXuHFMWllXyvsWf0FTAiqE/0GPvb8aUy19UtaoKXzlPqTmBOKDbIvW6DY0+r2wPt2J9onyuSp0XmsytSZj0E7BTU97HB+0OqKHizHCkgQYkNcpNMQNoiqJ8XWQ5OR/EzFwf2sgHxh5nvKCw+c6O1EVWsxZCiqIzCjvTMHDsaCZExEmqEv603mWR79qwmo1AZSdbtJI71m9TIogNZ7z5OpZtwyUryvvVA1t3TwudcIIduwX1PQfHglkIx4Q6xvj2OSmXzbiix6c+I6OqFhMdDkJtpuEGwFxKUxhGEqEEx6uYOPhedDXwna497CJTN9SpB229p79k9WBid5Jm24jsSQXcYcPzsXFuNE3JlfBsplRNl89zWZ95EbrMHBNfshCzPq8bdbkcbhXEY17T7/z3TnxfHYgzb5542Zr2KCefBsSR8VZvCnrTA6d7ZGXfynk9bn5lzck6NyokcK6p2jE2dtp2ck3NyTm0vSIZ6BikGh6HwaN+g9dV6wXkroZCEy2IxwYss4W8q8vcVwCu7fpyZiwEpd6djCdTn1EL1dfB1SGZ8ExlVnEWa8+lp4KoIXPqRyTFmPD5AxhvvobCbiSI3VILfBgyPwKk7Shh/D1qr9ULezzchmXsAipX+NbBl5tr10EZLlHxcdo/w3Qs3Dc6353clleEmuyD9z7sE6wcvXooqdScwJxSk7C6TJvlIlLgn2+f+yNJ4YI34bGv3LxMkYbBjSZzydwniQuZ8RyHKL6Sc+RJZ6Ta7F1LsHGfnTEYWBD/OnPND+y1FxtfeLmijQzLHkjZ6D2Urzk04V0QbUWldPjg7EaC45JONT6JsHgT8BQmX0cKeoE2Ce9Bu+0mksc8XRpsHU43HycCv0C5v1WzvBXPrisKH3BPUZeNuL2h/ewGrRuTSF23yXIc2akJBYE9ylM2Z62Mov9ewvv0M8Itsn7Ux61tkTdxsAyfblgXwSTKQJ8rmOwmUzbUuBJsk9vkgJNjeRODB0FI7ZJ7zytj43tZxgcpwRmsiL4K7qNykmJvKUAvzA79DMf7ma8t9W/n7NgVOR0r5MvJ2WjbnnVsaJb+dSrAhEBw/kHaEr5oFp79Xe8da+I6TgdsitpNzck715vTjhFNL45m3k3NyTs6pDRz3RWH1fkPgzQisQ6oE/16R96zCI6tkTqxfGyJ/DVon/JQg3J7VL4MUuX9Ca8+GUdzXsG3CdfBQMl7zSAewK7BuULcQWp+fVDCXEjJOKpNaU5+Awo5uh2T9K5AO5VbStU0vZARZBm6k4HUdUhhvgoxjnrO/dwMLB+dsaG3yDQpNuBbajHrE6qOEFSHjhU5zo7nLrF1+h3mvWDs+jjbz5rg+7yV+qTuBOaHYi/yVDX472Yt+ih0roQRJ95CxhovMaUNSBdg6mWO9kAJsOkHc5ILvX0K7qHfZoBcKRnshheq2aFeztwlLxwHbBud1M6HhZiIkb2mENgr+/z2KoTsVuABY2eqHIkF3BlI6/wXF1PqMiO6DwPYoZt+t9gxn2N817Hg/m9ReQbvQD2DCQva3ReR4JYrxvYR9DhWawwkSOwb1hQvDKAFLGQlEG+VwSbxBrqMdid/awCuxnHiCwEo+OL6bHb+DiOFhqBRu50FhM75DngRr1qo9qnDbD8XpuxlZd5SRUNliTPDMu3ssQey9AtrobBsz37HvvZ+chS4KK/UHe/+i9Su0wJ2KhP6TkFvjp0igDpXza6GNjdcpwBq+CE6zeG5HovH9KOfknJyTc3JOzsk5zRav3ZGM8gZwnNV1JfWuS5TgE4CNi7pvDo9Q3j4Vyb5/okbhGFvJ8ShkITwDhUYMPY9/grxUF83+noI5VPVkrmO7hH30dyiP2gwU4m/TKtcMs3fhLWDJCJxWRJ6ziQf2gfb8EgOiAcCZaM3wNyqV4FsQhPuIwC0JQ/oZmc0UO74BMriabue9jXQHibV6oUrw4F3vhzbB/oV0GWdSuY5KlOBfI8X3fXZu0nYNsVHlpfOUuhPojAWFyTgE2+VCSRmeRgv/bzArWKQEXgpZql4b8wVHyqzLMnWb24Dzj6wggJTOhbrpZCayxFr5Ddht6mwAACAASURBVCSwdUPub/cjwS2JkZwbk4pKgaZPZ2mjKrzGonABZwOXIMvce0mVzV2RB8EEmzj+SKTkYcEEfxayFkmyRm9v7TQBWDs4fxFgQWD+vGdXZJ/K1qG41dOQQjUMg9IdWWS/kH2mEZ/h5dY+N5GvbE4WCmdF5JC0S2hJ/LvkvuSEy7F2OiI2J/v/emAiig35H9KwI82EuBo9s82xMFXIcnkx5GJYDt+/nN+RXVR+Q0E5AJDg+hpK7rM9Shr1Ic2VzftZ3RPEVX4vanzOI53v+iJL/nfR3Lac1a+IrL83C66PseHUak4t9MUkn0SbE986J+fknJyTc3JOcxqnzH2ShNfPY7F8kbVqYhW6FqlhQe+i75/hMo7Uu/kNu+8+Me/ZApe85KNLodCWr6A18D0ol9HiSJ67ukbcDkRhM+qqAKdynX8F0qGcjpSnn9lz3DFzzRnIeOZdMgZOBXNbBhmcJTGs97P6RJHcD62rPkAGND0icgnXdD9AHp9vI+OdRXPasi/KR7cDlfHKi7ZKT9ac/YD/If3EjfYelu3zLsH551n9X6lcJxSaB8uLl6YmV4DHaVS4GIXsOD0QSDZBisu3kMVnf2QN/jBS9iS7ZDEUAr2QErUMXJg5loQcuJ8q1pZFc7LJ9d82wf4WWS48bALJozYwz4usBR8jE9860jNrmDbKfhdSaG8VfE5C2EygUtk8AMW0LnSipVJQnhdtVtyGhSoIJrmwndad1XcVzGsxYHnkbRGGgPgTEuz/Aqxuk/0J1udOiNCPqvYD41JGwluesnljIgkgwbObO3tvUkvwmbzynlORzy7nu89BitwNbWwcgKyaEmVzzSzBSePE/wYpkOfNHE8SY95B802xvEVlUXkJNgQ+RvNGIrD2RYr691Gi5UTZvAzyOGq22VJUf7LPS9i7dFimvgdpApu/Ycp5Ki3Ziwqj1VZOK+R9RxHPzTk5J+fknJyTc5oTOFXhmfVam4Jk4MWtLlSCjyaC0U6Gw3po3bkOMoJZEa0Loij+Z8ErWe93R2HqhmeOD0fy739RfqxnkcHOS7QirE07uR0LfF7L9qjCI1TYDkXGX5uRyr4bIJ3BE5gSHCWB/DUypincyprma/MVkRHRt8DYoD5RgvdFCvnpwLWR26uvtdGq1qe2QhtjzwKLZc7tNavfVmSbIU/UJwms8YHvoXXL48DqQf0l9k5eQJXwsl68FFHqTqAzFbTL9UN72aciZcVpmEIOKcGfsQltmk1ufyOS60mG28KkoRjGZo5taQP0g8D6Ee4dCluroZ3ZU1C4g7lQ3LOLCRJb2mB+AwpjUZNd6Hq2UXCfUGBLkiU+iYVPIBUYE2XzP6hRrGRkpfwIEiKfwUK/IEG7S8CrFu0U9qk9kHCYhKi4Htg8OH4xitdeRta4r2LumNnvaienRKjtCayLPApWIVCg0gplMwUpwTNttKs9k49s/LmQys2ThNcZ1CgXQXDvnmjT5IagLulPuxiv20K+NeL1B2S1lCQuDhdsv7a+dBu22UPxC92sO+pWyPVzg0wb9SINs1Kx0C2wLbIx/BIPnhForjvGPncPzpkPCeBlez/naTBOz2c5ITfWb9v63JyTc3JOzsk5Oac5gVMVnoNR/OX5M/UXIO/eq8lRghddqJTHTkOhT66hMgHfkqRGMjVRgpPKbf2RccfbwBfI83ELMlbwyKL3GnuGTxE5FASKY/0lUjrXNOyE9d0emboLkPXwW9hGQfAurEuqBN8u6FP9InDLbqYk79jypOFQjgp/S/CcT8a8HyK23WbGYWP73APF906U4ItY/WBkvFN43iv77p6Zuj72fC4P6pK18kbG+bTMNZfaO/kbPAGml0il7gQ6S0EK2xeQQvI04AgTOL5E8bsSJfiiwArIfX0EkVxPAl6hELCQTSZ5Ct6trX7Hgu8f7uIuh6xc/0ILGb6R9eeeyCJ0rxo8u7q2URUeVyHB6E20S5rEju5OpbL5G+RBsHpkPluj2FzXIVfFGWiDJ0l82S3gFbWdMhz3RML/GcBAFBriW2Qdv3Vw3vIoSdA6BJYmFGeFmghk/dEGQRKfeTpSyG8anHutTe5nUCXJbMFttJfxuBQp3hNPh4eo9CxIhLjzybEQiNGv7HMPFOvt9vCcoE3HW9+bQMRkpVlu9nymkrPBhDbwPszr5yhz+Te0YUGFhPfumbpEcF3b3rs9kz4XvHNLoY2NZFOsMC8QtIlzCpZEEyn3nyNN6nwVcgEdmfwG+7sgWuD9ANi74OdUOCdSy/62Kiick3NyTs7JOTmnTs+pCs8LkTfve0jptl7m+AUodMUfiBA+EhmhXU6Q8ButuRPjmNtpLn8mSvCvgcOL5pS5V6I07YWUgv9GcuYJSJH7PpIfm3k9I4vnQj3FyQktgZS7ZWpvcNILJZLcNlN/Pgp98iUW0oTK9ea6dt1rwDaRuIXK798ihfxpQd2ypJsUeUrwWuS86obWSHdiOZuQvmArZPT1KtJFPWbvZ9HexkvZGJTNl7aAtdfVQV0p6Ms3obGsd9jv7Z38nBobZHmZc0rdCXSGYi/zeTbAhMq1nihB4RSkFO9b5foYYU92BLZM+AX1oYL355lrhhV4/00yn0eQKgT/Wu23Iyv5n6FkIKdEfm51baNqfcD60ltIKEpCxLyHWXZmJv/tra2i8LJ79EUhKs4mjfm9P0pk8T8s2abxSpSW0fgEvFZGFhEn2OcVkYXJfTZxPgFs0cL1hQolNoE/hYSQLZBb51HI2+Pf4TtBqmyOanWCrDieQbGi+wT121pb3UulW9otFJgEKYdP2M9DofJy68fNYvahBLCP1aCf74ziL/a2z/2RYPYEgQshGtfPQRsLgzLfMQhtWrUpbjqyeL80eM/+iRTavdFGwV/RAnJk5rr1kFXH9zDrqoLapIQWGEk8yqPRBtOJVFrhPI02fda2urlsjHiGyjmx3e9cBE5d7Ds3JdgQck7OyTk5J+fknJxTq3iOQ7LPoSi28Oto7Zs1EEi8bH9D8Uq4E5BCLZR1uyPP4weRzLsxzdedS6A4zZMxy/qiC+marQsKZ/dPzIDI6nujtcP7mMEMOdbx7W0zpGgekanbDK3R+yFjoWeAfav0xSjKXOStcBKpZ0NoqX+C9aV/Y+sVKg1Avmf9f7EY3AIe11m/3i57LyqV4LE3UrpU+f90GydGBHXd0ebJQ8b9DiJEHUCK7r2DZ9M9OHYDMhhaNnNNCa057wvqwnVhdAMxL3NuqTuBzlKQleLjwefEbb4nUhRORju9iXIlZjzduVFIgXIwkYYK3iVssqiId52ckxUO2nD/C+z+iwR1g5DA9p5NrovmXDcQhat4lCBpXHv5NGIbtcBrIaTw2z2o2w95F1RTNudurBTEZ1tkyfEgGWt8FKPuBbT7PlM5X4t2su9eC1l0DERWHJOA39mxPUmtYbeNxSHzW3dHAsbamePbkFrPzx3Un04kz4/gHisiy4l9Eq6kQuNe1ka7xORQhddvUdiOZDwcZOPk4wSWQcil7mZ71tFyASDlciK8Hhzw2hQt6l5EyvHtbRz7Etg/uD4UQudvI4euyHtiClro3omsgsJkm0kYrQ+M2wLI8uL3aDwbEKFt+hqvr9AG5ok55+yEBOwyGr8ftz5/UqTnVTgn2jknOyfn5Jyck3NyTnMCp8x3nYo8ntewz8cZ14nIG26HzPlnEiFGs313svb+ITDU/k+U4M8hWW6N7O9H4SYLzZmSxw2FkbwbuCuHc28kb94S6f5J/qbngJWt7izSRI6fAA/Y5xuRonc0ZnhCRM/QDM8r0fqgb1B3CvKKvpt8JXjsBKp7IHl8M9I1X8WGAMq9k+R6KiTxfQ6P5PfmWe/3N46/r/LsRwTcY0Ud6GHjz7HYRhSwNFobP06QkNfqJ9Lc274i3JMXLzFK3Ql09BIMJr9CisAKS0H7ewISRF5BrmhFJ5XMS1i3kk0UU7B4yFQqac4xPmW0O1hUHOTf2AC8jn3OKrOOQxaxf8gbgIEhVFqkxlSg1qWNWuBzEbLufJ5MqAdSZfNLVFE2x+hLwPqkMQRPyD4P4/WsPfOVI7ZN7m8kTfp3LXAXFi8MLRj+Z+35NBEsh3Pa4ngkRC5sn8Od7DFoIZBn4Rwj5vcg+7u8vW/HBMcS4aKHPdursjxi9nUkpN1s7fEDUmvnLY3PW8hK6GRkHTCZCO6yObwGIcX8N8iKqYuVlZG1zmdo8fku8RS73dFCtmzj0vdyztkW+D8URmcSsvr6OK9vtbc/kc5va9v9vkUeFomLZdjHl0Cu2Xeg8X3v8Luck3NyTs7JOTkn59RxOeVwnAt5GB5un49FctKuKHTL/5AX69Yx7h/wCC1Ot0My1E2YUhspAFdFubdyleCxC7Jyvt64vQ4sERxL9AWnIkV0lBjcyIDjJWQwsRTKxbUwWuP+CK17PzeOydpvkvG9ngKthqvwG4Dk/mR9ECrBf4yU4PckbUeNlKVoo+B1mnt9ZteBKyEFfuHJSkmV30nepIeR1+jApC2QEeHLpJsWefqhGP0qzDtwi73zh6B1Zld7J19HluB/RmutZ5ACvHBdhhcvsyp1J9CRC5XK0kRpcTLQP3PemcBlaOd3IgXuooYDhk1mqwUD3xJIUTJTwWv1fVHMuOOBUQVyORUl81jLPm9uE+3xwTkDkSL1WxsAq8bIijEY1ruNMlyySe42sv5RRq6L3TLH97EJYzLBLmqkvrQuFrIDCd7PI6GxWVsY1/fIcZmLwGsEsLqVZNLsa/x+GZy3OLL+Pgk4IAKnMOHlekE7fIeF1QmfMYo9XsYStURuo52BW5EwW0JJVJ8GFspcMwQtTk6NwWkWfBdA2dMTZXPJynCUyPFNZOX8KOb1UFQbZd+7zDkD0cJxuvEKF1VrWFkpqCs0hrz9/317x6eiEFp5C91BKMnquWhTcYkieFTpT+sgt+tRyBvlY+OVxCPNLgBa/OycnJNzck7OyTk5p47FKYfjsvZ3XWTgtAoyhvkBqbyVhDwpA9tH4hHKRol18I+RHHkTJvtSqQR/znjHNPbIC2EyH3CJtcdJNNcX/AwpCqvmySqgPx2OlKQTyMmrgzY07kOetZujtc1lxFl35rXRPGj9/Q1SomaV4K8hfUph4f5awenPwCvB5+w7tjuBcVEETsmaswfaXDoG6S7esfY4DOk3hlCD8JqzaKv+yBJ+uvEqIWOi4ShU0n9ROJvLgt8VdWPFi5dsqTuBjljQblZPmiuUfokUuz8mzXK9og3U+yA3/plZegvmtA+yVp5if8+3+kVtIpuKFCvrIQHqXWDd4Pr2hj3pj8JRXG2ft0Vxjl9CSrYjg3MTJfh0FPu3mStPpOdW1zbK9qHg/2NJM1ivgxTNz5JjUY3CMzxCXIXXPsgK919IAOpqvN5ACTdH5ly/dA2e377Gaxqyxn0CKSTnQ9bfjyAXwl5IYHuKypAjRVnlhAkv70dC5B5IEf8KciEcnLlmG2S5sGbkNtrP+vHPSePbb4gsOO4nTZLU19roYyJa5lCpQM56F2SVzWEClGEoHNDcBXLJhqbZHIURyW5ELQD8EXntHERmgRKrP9n/R6NNt4HA3qQL3XnDexZ171n9LhsLXrc+PgzFiNybNNb+PJn2XMY5OSfn5Jyck3NyTp2HUw7Hy4zT4UHd/kjOXSGo+ynKXXJZDG5Uyk+XIfn7B/b5XOOTVYKvgmJtP0aksB6YEhStSQ5F+YA2DuoSBeEppEr7kWjd8jcKlvEy7TQUec2+h7wbE6/icNPiTaBf5D4UxvneChnvrGafeyEdQp4S/GdoA2No5P50LLCz/b8vWnsenXPNEtaX9o/UTuGa858oPOIeVrcN8ryfjNZ5V6PNjUcxj+jYzw/ogzZVTkabX/3RJsaVSCd2WOZZz0Plei9qKFAvXvJK3Ql0tIKEj6uRIvAjlLBsKTvWB/gFqevQRLQ79yTa/drBBql2u8ZQKSB9DykEf47i+t6E3EyuseNDUPzhMopd+zkREkwiIecDFOu8jBIBDrWJ4VWaK8GPs/OuJaJCp5HayO4Veg5ca5PVNciVsBupsvkp8pXghcf5Db57D5vkj832U+P1CrImbqYEz/bLgvv4pmjj4lzkbXGYvVcfIqHpKCTMvYMUvdHiDxufvmgX+x8oTEw/q9/BxoUHUUiPRdGGyqN2bsyQPqvb+3cClcJiX2Qt/Ja9A4+ghdOXwI8j8BhAJq44Eli3JF/Z/CfrcwdSRdlcAKfD7B5D0IZOP7QAesPGhiyvpdD4PRkpwWMtksKx4Dq0WXgNmkv6o8VvstCdmRwIjbFRN52CseA4KuP2dUEL8I+Qcn4dtPFSBn7onJyTc3JOzsk5OafOx8nuPx4p5fcgyOeEFGBfIUVuF7SmGWf1hVvFtsApDC1yDvlK8JUp3pBoNSpzw8yFjJneAmYgBeWVdqwHabzot1BC+gfQ2iFRLMbwJrjJ7vMsMiIqo42MkcE5u6K11aLJ9RRn8NGX5jHhr0NrlzIyPLs0aKM/kyrBw+Sm80XoQ9m1+Ut2/77IevkxJKMfHJy3MPJmf5mc3GYFcgvXnBvQ3HNgFWQg9wLyxJ+GGfAV1Y9a6OPPo7X358gA6yXS9e+VaKOn4vnl9U0vXmpZ6k6gIxWkNHkBKdiOIhU4biAIDYGUYL+wAfRUUhePG5DSboECOQ1GISouIlXCzYUUhZOB64Jzt0ZJ3NYL6to9MJLuGve3yWu68UkSyS1CdSX46cARkZ9b3duoCq+rkMC2LpnEeUigfRkp4QqN7dsCn2FoV/0MKuNBb4CsiIcb1xdsIm7mOheJ1xCkAL+cwCLB6v8PWcwPQbHQ/oIE4f2y/bNgTuejjYDhmfre1n9eQTvf09Bm2L9Jw/3E6k8HIKuNpYO6mWE/kKB2MYp7fwWwW3BekQLSZcnvRYLzYCQUPU9G2WzHV0SK6A+BI4jjPjiQ1MplUfu7DBLUnkfhh7JK8N8jwbtMYNEU6dnljgXWPnujhdM/kZvl741TNEuvWYwF69oz2wuN91OJuGHonJyTc3JOzsk5Oaf6crL7H4KUXeuTxiRO/s6DZKoXkKHY3WgDP2oOlyqcQu/DRAl+PbBIJA7d0PpjZvgJpBi9FyknF0dy9wfAODveExnRlZESOFx3Fu4ZDZyHNk3WwvQQSEfxDlKCr2h1SxqnLSJwuCDTRiejte4OKOHmVZk2SpTgU5CHZDMlagSOV5Evjy+PwpB+ZM/1BrRhUXgOnhxOzdacaF2X9aztb235pL1/RRql5fXxPyOd2CporbcRWqt8hDxS5kYbPV8hw7qaePx78TKrUncCHaXYIPxXG/Tmt7pxKFnFVGSht1pwfrjjuoYNqJ9TUDxb+94kpMo7wE+tLpn8EwXvR8YzeiIEpDz+1iavl2wyS5T/oRI8dJnrHfwfQ1HZUG0UfO96NsFuFdQtgJSYB6JYf+si5e7rRFbA2f2XsnbaEinehqJY0snO/ONo93c9ZEm8Rw04Jc9vEvDrnOMr2DsYxv8OXa1iPb97gRtbON4bLU6OBHYkdWGL5uqFlNsfVfvtVApOpWrnFcBjHtLs30ky3BFoU6Casvlu62cfUmDYkxxua1h/OiTg9Zrx2phKV8PfA3sCo2PxsXtVGwsORJus6yAL/jfQYvJV4gvbsxoLHkbj+5LI5XGTWP3JOTkn5+ScnJNzck715WTffSnyaOyTqU/WesujkIQvIsXY8rG4tIJTuBY4FRk0XE2O4rAgHqEn8d7A2cBOwfF5CdacVtcHhQKcBvyQSGsEZEH8d7QJUGHRjbxGy0hxuRIyGHmLCDG2rY3G2f12t757OOm6PK+NeqDEipOIuD6we+XJ4wORJ+h2aE1wpr1/SRzrETXo47lrzmr9GBkXvknBYWIyfXx/FPp3j8w5A60v/c/6XX+kP5sQ473z4qUtpe4EOkoBlkOuQ0kMr+vRjvIyaLdtuh0fnblumA0Wz1KwEhPttt2AFPBXWF0XUkFkLhR64DPgrhq00RJI2Tfafu9LwJqklq+L2ITxDnBCjZ5bQ7VRwGtbLBwOUhhuj0IyvEOqbB6OLK+fBharAacFkevUzWjD5iVkgbKTCQVlzMKEyLHFMs9vPNr9H291JSpd1R7Ie3axJlokjE0Ebs67F9rx3ifnutjZ0w9DAv4OWV5IoXpNwouIC6Tg3j+0PnOAfQ6VzeFibYlk7CTjCRGpP92e4bW08XoJLVpWQILda8AGwbWxFrqzGgsesbFgIPKEiP7utXIsOC3nupgLb+fknJyTc3JOzsk51YdTCbgHeDSsy5yzmf3tQW2sdVvDaVP7eyLxrdEHATfa85mKWXWTroMTw6sPgWutLlGCT0XhbgoPuYesdx8B7gnqQiv5W5CBxUQkk89VozaaAuyVcMy00UfAX4L+tGAN+tOs5PEHCNbjMd+34B6zWnP2JwjLYnWb2fhReN6p4Pl9Z2V7qw+9ezdFVt/72+d5STc5XAnupe6l7gQ6SkEJGTZBO8eHox3CxLpxbtJYWo9hMcGDaxcjktICKXSS3bgwLlW3gNvF2cGxBu21KhLWXkJWl8nkPxTtCu5fQy4N10bIumMa2hB4ECngL0GKrpHGNUly0buGvDayZ3Y7liTU6ntZHz82c34tJv/BKJxQmcB7IOB1D1okdK/FxGpjwF9MKFo/5/iGKDTLmva5JpM9Elo/RTHiRgf1SVLQN7HFSY34DEGbT1kl+Ks2fo5BXiN/sLFicI14DSQVvhNew4zD19aGH1MDd2K7d2vGgmYbKjXgNaux4Bjn5Jyck3NyTs7JOc0xnH6JZJRNco4tj+TxTRuM073Yer1GfAajsA/fAT8J6rOGV2XgTKsbiIx9JhEkNi2QUxckV74PbBjUJ16PN6HQNY8Dw2rYRjOA86q00U+tjX5fw2fXGnl8v+D8Rllz3oet+2x8uBmts2Lqnq6y5xd6YCdK7oXR5sZx2X5Yq2fpxUtLpe4EOlIhtaa8HrmihTtwt6L4XncS2dIzh9cgUkXTQUF9MpGEbmC1UsaVULiMRAk+mlQJHjWzdAdqo/VQ6IfzqHSTWw5tEmxe63ay+/elcie3Lwrn8X4dOYXP78dI2TscuaV9A3y/xnxWRLvbfw8FEmQ18JCNAzWf6El33Z9BsSN3BcaaIBItKegsnltW2TwECdlTkbL5JWoU574KrySWXTcUgugAgo2CWjzHWYwFL1LDjYsMr5bGAufknJyTc3JOzsk5zSGckIw7BSnc1grqBwG/Q/mBFnJODEa6gmprznmAQ6m0wp6fiB5+yADlc7RuWTOonw+FJdmMyCFGctpo3Cza6HQyRoU14NVw8jizueZEOqnVa/j8DsscW8PGqIPts1t9e2moUncCHa2gnbjbbMCZz+pWss9hDPB6KsEPCOrrNuiQKsEnovi16xDEHqs1twZto2ws5CEo2eML1MDdqxX8RqM41l9SBwVqzvNLBMoPUJzBx4GT6/EsgS1MIJmELHRuNeHoP0ROeDkLXmugBCifo82BxwkSzdaaE5XK5oOsritKiLl+vfo5VTbFMufUrK18LHBOzsk5OSfn5JycUyNzCmTf11GixwuR5+En1NiYocE5VTO86p45r2aJAZEn+xS0VrkQOAr4m7VT9FCbs9tG1Gl93ojyOK1bc9Y0yWTm+Y1FRlf7ohjp/8m2oxcvjVLqTqAjFqTUnW6Dz4XIFe2her/oNhAlu3GH15NLwKkErI7ipe/bAHwaro0Cbvshl7CPKTBZajv4LI7cwF4Ejgrq6+bChHacr0aK3bMyx+qhbF4B+CNSMt+Oss0n1gvREl62gtcAYCGUICnMYl6XZ5cRkg6sV7tU4ZWMB4fWm0/Ay8cC5+ScnJNzck7OyTk1HCdglMm8SV6X64Bl6tU+Dcwp1/CqzpySUDXvImOiJ+spZzZiG2X4NYw8TgOuOUnXUdNRSJSzkedFwsuV4F4ariSWuI7ZRKlUWhftMndFYT52bWpq+rZUKnVtamr6ro68BqPB8Z6mpqax9eIRolQqlYAFmpqaPqw3F2jYNloT+DXamT+8qanpv3WmRKlU6gqsgiaxf1tdl6ampnKdeQ1BO827AD9oamr6XT15lUqlLmgRMiOoq+s4kIdSqVRqquOAXyqVBqFY+7uimNZ/qReXEDYeXAXc3QjjgY8Fzsk5OSfn5Jyck3NqcE49UO6dMjCjqanp23pxSdCgnAYBv0Jrlu2amppuqzMlSqVSH5R8sz/wSVNT0+d15jMIret2QzG2/1xPPgkaVB5vuDWnraPOQfmmtmhqarrH6ruFPB2ORoErwNuBUqnUD+iJJo+mRnnRS6VSn6ampq/qzSMP9VbCBTwaqo1sQhuO+tJH9eaTh0Z5dtBMoBzT1NT0qzpTcrQCtnlxLkro9Hy9+SRopPHAx4K2wTm1Ds6pdXBOrYNzah2cU+vgnFqHRuTkqA6TfQ9HSTHrridoRDTi+qAjyOONglKptBBwGN7HHR0ArgAvCPXeic+DC0izhrdRx4Upwa8FPmhqatqr3nwcrUO9LRVago8HDofD4XA4HI4YaBRjuUZEI68PHK2H93FHo8MV4A6Ho8OiVCoNaGpq+qLePBwOh8PhcDgcDofD4XA4HI0JV4A7HI4OD7fcdTgcDofD4XA4HA6Hw+Fw5KFLvQmEKJVKO5dKpV+XSqUHSqXSF6VSqalUKl1Tb14Oh6Ox4cpvh8PhcDgcDofD4XA4HA5HHrrVm0AGpwIroWy77wBL15eOw+FwOBwOh8PhcDgcDofD4XA4OioaygIcOAZYChiAMsk6HA6Hw+FwOBwOh8PhcDgcDofD0SY0lAV4U1PT/cn/pVKpnlQcDofD4XA4HA6Hw+FwOBwOh8PRwdFoFuAOh8PhcDgcDofD4XA4HA6Hw+FwFIKGsgAvAhtssEFDJcMbO3YsAGPGjKkzkxTOqXVwTq2Dc2odnFPr4JxaB+fUOjin1sE5tQ7OqXVwTq2D6lcDZAAAIABJREFUc2odnFPr4JxaB+fUOjin1sE5tR4TJkzorCEeGkr/2GiYNm0a5557Lo8++igA06dP56CDDmKvvfbKOz16H+l0CnBH6zBy5EgmTJhQbxoVmDhxYr0pOBwOh8PhcDgcDofD4XA4HI524M033+SBBx6oqBsyZEid2LgCfI7FxIkTG2pXMNmpdDgcDofD4XA4HA6Hw+FwOBwdF0svvTS33HILr776Ki+//DKXX345b7/9dt34eAxwh8PhcDgcDofD4XA4HA6Hw+FwFIa55pqLUaNGscsuu9SbiluAOxwOh8PhcDgcDofD4XA4HA6HY/YwY8YMpkyZ0mL58ssv603TFeAOh8PhcDgcDofD4XA4HA6Hw+GYPWyyySatPnfo0KERmbQMD4HicDgcDofD4XA4HA6Hw+FwOByO2cJBBx1E7969q5bu3bvPPPf999+vG8+GsgAvlUrbA9vbx8H2d81SqXSV/T+5qanp+JoTczgcDofD4XA4HA6Hw+FwOBwOx0zstdde7LXXXi2e8/XXX7PFFlvw7bff1ohVczSUAhwYCeyXqVvcCsCbgCvAHQ6Hw+FwOBwOh8PhcDgcDoejwdGjR496U2isEChNTU0/aWpqKrVQFq03R4fD4XA4HA6Hw+FwOBwOh8PhcHQMNJoFuMPhcDgcDofD4XA4HA6Hw+FwODooZsyYwb/+9S9eeuklXnnlFQBKpVLd+LgC3NEwGDlyJBMmTKg3jQpMnDix3hQcDofD4XA4HA6Hw+FwOByODoOHHnqIs88+e+bn0aNHs/HGG9eNT0OFQHE4HA6Hw+FwOBwOh8PhcDgcDkfHxdChQ1lggQVmfn700Ud55pln6sbHLcAdDYOJEycyZsyYetOYibFjx9abgsPhcDgcDofD4XA4HA6Hw9GhsNhiizF+/Hi++uorXnnlFY4++mgmTZpUNz5uAe5wOBwOh8PhcDgcDofD4XA4HI5C0adPH5Zffvl603AFuMPhcDgcDofD4XA4HA6Hw+FwODonXAHucDgcDofD4XA4HA6Hw+FwOByOTglXgDscDofD4XA4HA6Hw+FwOBwOh6NTwhXgDofD4XA4HA6Hw+FwOBwOh8Ph6JRwBbjD4XA4HA6Hw+FwOBwOh8PhcDg6JVwB7nA4HA6Hw+FwOBwOh8PhcDgcjk4JV4A7HA6Hw+FwOBwOh8PhcDgcDoejU6JbvQk4HA6Hw+FwOBwOh8PhcDgcDoejc6CpqYm7776b1157jUmTJgFQKpXqxscV4DXAyJEjmTBhQr1pVGDixIn1puBwOBwOh8PhcDgcDofD4XA4Ohmefvppzj///Iq6YcOG1YmNK8BrgokTJzJmzJh605iJsWPH1puCw+FwOBwOh8PhcDgcDofD4egk+O677/j888/55JNP+Oabb1h55ZV56qmnZh5/++2368bNFeA1gFuAOxwOh8PhcDgcDofD4XA4HI7OhMMOO4wXX3yxxXMGDBjAqFGj2GSTTWrEqjlcAV4DuAV46+AbBQ5H7dGI7x3QcJx8LHA4HA6Hw+FwOBwOh6MS06ZNm+U5X3zxBRMmTGDVVVdlq622qgGr5nAFuKNh4BsFDkft0ajvXSNycjgcDofD4fh/9u49Rq67Pvz+Z3bsOHachDhxSN3EsQmB4ieBhZSUm0mLy0Utoqq4CBANPEApAYSWR48oQlDS/hBqUFVWabmI0rTllvBDJW0hCT/R1ktR4WkIZbgkIbHjOHFInNhxHO/N3p1zzvMH3WHX3t357m2+k/HrJR35eHZmvu8dslX98fH3AAC/9Pd///czft9sNmN0dDRGRkZieHg4Dhw4EA899FB8+tOfjoMHD+aJDANwAAAAAAAW6Nprr40bb7wx6blr1qxZ4Zq5GYDDPGwPka7bmmxZAQAAALByUoffEREbN25cwZL5GYDDPGwP0V43NwEAAACwMnbu3BllWcbY2FiMjIzMehw5ciS+8IUvxAMPPJCt0wAcAAAAAIAF6+vri/Xr18f69etn/XpZlvGFL3yhw1Uz9WVdHQAAAAAAVogBOAAAAAAAPckAHAAAAACAnmQADgAAAABATzIABwAAAACgJxmAAwAAAADQkwzAAQAAAADoSQbgAAAAAAD0JANwAAAAAAB6kgE4AAAAAAA9yQAcAAAAAICetCp3AAAAAAAAy6eqqijLsvXr9CP1saW+vqqqKIoi90dhAA4AAAAAsJyuuuqq+NnPfpY7o2ucccYZ2dY2AO+A/v7+GBoayp1xgm5rajQauRPoIX7u0mlKoymNpjSa0mhKoymNpjSa2vPnFgBS7N+/P3dCVzn11FOzrW0A3gGNRiMGBgZyZ7QMDg5GRHRlEywXP3ftaUqjKY2mNJrSaEqjKY2mNJrS+HMLAKluvPHGWR//+c9/Hm9605s6XJNfVVXZ1jYAB3qSK8DTaUqjKY2mNJrSaEqjKY2mNJracwU4AEuxevXq3AlZHDt2LNvaBuBAT3IFeHua0mhKoymNpjSa0mhKoymNpjSuAAdgqc4999zYuXNnR9ecfoPKqRtTzne+nEdRFPGnf/qnceTIkY5+z9MZgAMAAAAA9KharRb1ej3q9XrH1y7LsuNrHq8vdwAAAAAAAKwEA3AAAAAAAHqSATgAAAAAAD3JABwAAAAAgJ5kAA4AAAAAQE9alTsAAAAAAIDOKsty0UdRFFFVVdvzoihyf5sG4AAAAAAAnfDoo4/Ga17zmtwZHXfaaadlW9sAHOhJ/f39MTQ0lDvjBJrSaEqjKY2mNJrSaEqjKY2mNN3W1Gg0cicA8AQ2OjqaOyGLM844I9vaBuBAT2o0GjEwMJA7o2VwcDAiQlMbmtJoSqMpjaY0mtJoSqMpTTc3AcBibd68OXbu3Lmo105tLbLSR+r2JmVZRrPZbB1FUcTk5OSM88nJyfinf/qneOihh5b5k0xnAA4AAAAA0AFjY2PxkY98JG6//fbWY7VaLaqqap3P9vjxX0s132tSvjbf8HshVq9evaDnLycDcKAn2QIlnaY0mtJoSqMpjaY0mtJoSqOpPVugALAU+/bti9tuuy13Rkf92q/9Wvzu7/5utvUNwIGeZAuU9jSl0ZRGUxpNaTSl0ZRGUxpNaWyBAsBSnXfeefGkJz0pDh8+nDulY372s5/F9773vXjFK16RZX0DcKAnuQI8naY0mtJoSqMpjaY0mtJoSqOpPVeAA7AUhw4dOqmG31PWrFmTbW0DcKAnuQK8PU1pNKXRlEZTGk1pNKXRlEZTGleAA7BUW7dujZtvvjkmJiZaj02/uWVRFK2bSDabzZicnEw6n+9GlHOdL/W9FuL+++9f7o8ymQE4AAAAAEAH3HXXXfHOd74zd0bHXXjhhdnWNgAHAAAAAOiAzZs3x2/8xm/EnXfemfyaqqoiIqJWq816Pt/Xjn/eYk1doT79SvWqqpLf+8EHH1xyw2IZgAMAAAAAdMDatWvjz//8z3NnLJvp27fMdjSbzXjNa16z4C1TlpMBOAAAAAAAC1ar1aJer0e9Xo+IXwzEJycnY3x8PMbHx2N0dDRzoQE4AAAAAABtHDx4MK688soYHx9f8GvXrl27AkVpDMCBntTf3x9DQ0O5M06gKU23NTUajdwJAAAA9ICiKOLLX/5y7N27N/k17fb2rtVqSc+bMt+WJfMdu3btWvT3vWnTpkW/dqkMwAGgDX+hkk5TGk1pNKXRlEZTGk3t+YtxAJbijjvuiOuuuy53RsdNDelzMAAHelKj0YiBgYHcGS2Dg4MREZra0JRGUxpNaTSl0ZRGUxpNabq5CQAW69JLL41PfOIT8eijjy7pfcqybN2AsiiK1u+nn099baHns73XYt+3LMu4++67Y/fu3fHCF75wmT7FhTEABwAAAADokP7+/twJyaaG2VO/Hn8+29eOH4K/+c1vzvo9GIADAAAAACzB4cOH473vfW/s27cvd0pXOuWUU7KtbQAO87DvbzpNaTSl0ZRGUxpNaTSl0ZRGUxpNabqtyR7gAMxm3759ht/zePKTn5xtbQNwmId9pNvTlEZTGk1pNKXRlEZTGk1pNKXRlKabmwDgeJdeemns3LkzIn6xbcjUMX07kJTtQ1K3Fpnva8u1znKsWRRF3HzzzVn/csAAHAAAAABgmdRqtajVahERUa/XM9d01uTkZIyOjsbIyEiMjIzEkSNH4uabb87aZAAOAAAAAMCCvOc974nbb7896blnnHHGCtfMzQCcrtGN+23b349e140/dxH2+gQAAIBu99hjjyU/100wIbp3v23oZd36c9eNTQAAAMAvfelLX5rx+4mJidbWJ1PH8PBwfPSjH42DBw9mqjQABwAAAABgiU455ZTYsGFDbNiwofVYWZbx0Y9+NGNVRF/W1QEAAAAA6Emjo6O5E1wBDgAAAADA8jh06FC85S1vieHh4dZj69aty9ZjAN4B3XiTOTd0AwAAAACW29Te39OdfvrpmWoMwDuiW28yBwAAAACwnDZv3hw7d+6MQ4cOxU9+8pO4+uqr4+GHH87WYwAOAAAAAMC8RkZG4sMf/nDcfffdERFRq9XmfG5RFHH06NHW71evXr3ifXMxAAd6UjduPRQRmhJ1W5NtowAAADjZ7d69e9F/Pt60adMy16QzAAd6UrduPaRpft3cBAAAACez/v7+uPnmm+Po0aMxPj4e4+PjMTY21jqfOqZ/fXR0NG655Za47777snUbgAMAAAAAdECz2Ywf/vCHcezYsSiKIqqqirIsk87nOxb6XvO973J3Rcy/XcpKMwAHepItUNJpSqMpjaY0mtJoSqMpjaY0mtqzNRoAS/Gf//mfcfXVVy/re9Zqtejr61uxo16vL+n1q1atih07dizr97wQBuAAAAAAAB0wMTGx4NesWrVqzqNery9pQF2r1Vqvn+t8qe+7atWqOP3001fg00z8/LKtDLCC7AHenqY0mtJoSqMpjaY0mtJoSqMpjXuDALBUz3/+8+Oqq66KY8eOtd0+ZLmOZrO5Yu+d6vHHH483vvGNK/jJzs0AHAAAAACgA9avXx+ve93rcmcsm5Th+2tf+9qYnJzM1mgADgAAAADQAVVVxXe+853Yv3//sr/3cl3VnXqDy+W+SnylGIADPclNMNNpSqMpjaY0mtJoSqMpjaY0mtpzE0wAluLHP/5xfOQjH8md0XEXXXRRtrUNwIGeZA/w9jSl0ZRGUxpNaTSl0ZRGUxpNaewBDsBSPfOZz4z3ve998cADDyS/pqqqiIio1Wptz4///dSV3NOv6p7rPPV5RVFEWZZRVdWM87med+jQodizZ0+86EUvWq6PcUEMwAEAAAAAOqBWq8WrXvWq3BkdU5Zl7NixY8aAvtP6sq0MAAAAAAAryBXgAAAAAAAsmzvvvDP27t0b99xzT+4UA3AAAAAAAJbHT37yk3jve98747EtW7bkiQlboAAAAAAAsEy2bdsWf/AHfxCXXXZZbNu2LSIi7r///mw9rgAHAAAAAGBZ1Ov1eOtb3xoRv7wJZlmW2XoMwAEAAAAAekxVVVGWZZRlGVVVRVEUMx6b61jO5xZFkftjMAAHAAAAAFgp//Ef/xEf+chHcmdkdcYZZ2Rb2wC8A/r7+2NoaCh3xgyNRiN3Aqyobvy5iwhNiTSl0ZRGUxpNaTSl0ZRGU5pua/JnKQAWamxsLHdCdmvWrMm2tgF4BzQajRgYGMid0TI4OJg7AVZct/7caZqfpjSa0mhKoymNpjSa0mhK081NALAQr3jFK+IVr3jFsr9vp7czSX3uvn374stf/vIJrbkYgNM1uvGKXVd3AAAAANCNarVa1Ov1qNfruVNmOHDgwAkD8GPHjmWqMQCni3TrFbsAAAAAQJqNGzfGzp07o6qqeOSRR+L1r399DA8PZ+vpy7YyAAAAAAA9qVarxcaNG3NnGIADAAAAANCbbIECAAAAANAh+/fvb7slSFVVrRtNTr/p5HznnTym3wBzvhtnFkXRoU91bgbgAAAAAAAdcMcdd8S73/3u3Bkdd9FFF2Vb2xYoAAAAAAAdsHbt2twJWVRVlW1tA3AAAAAAgA44/fTTcydk8cgjj2Rb2xYoAAAAAAAdcM4558TOnTuzrT+1V/ds+3cv137jRVHEjTfeGHfffXccPHgwIiJGR0ezfc8G4Cep/v7+GBoayp0xQ6PRyJ0AAAAAAD2rVqtFvV5f0TUajUZ897vfnfHYhRdeuKJrzscA/CTVaDRiYGAgd0bL4OBg7gQAAAAAYBEmJibisccei0cffTRGRkbi13/91+O2225rfX3fvn3Z2gzA6RquSgcAAACAJ4a3ve1tsWfPnnmfc8YZZ8RznvOcePnLX96hqhMZgNM1uvGq9G4cykeEpgT+8gIAAADodVP7dx+/B/dSf5/ynHbD74iII0eOxNDQUDz72c+OV73qVR34RE5kAA7z6MahfERoasOWOgAAAMATzbe+9a342Mc+ljtjRaxZsybb2n3ZVgYAAAAAICIiNm7cmDthxQwPD2db2xXgAAAAAAAdUJZlfOUrX4m9e/fO+vWXvexlK7Z2VVWtLVOKopixfUqz2Wwdk5OTc/5+cnIyiqJo/ZpqfHx8xb6vdgzAgZ5k//Z0mtJoSqMpjaY0mtJoSqMpjab23GsGgKX46U9/Gp/97GdzZ3Tcli1bsq1tAA70JPu3t6cpjaY0mtJoSqMpjaY0mtJoSuNeMwAs1TOf+cz4i7/4izhw4MCyvN/xN6ecOqqqan1toefzHfO9fmJiIo4cOdI6qqpqde7Zsye2b9++LN/zQhmAAwAAAAB0yGWXXZY7YVlceeWVsW/fvqTnnnPOOStcMzcDcLpGN25Z4Z830uu68ecuwj91BgAAgG63fv365OdOTk6uYMn8DMDpGt26ZQX0sm79uevGJgAAAOCXPvWpT53w2MTERIyMjLSO4eHh+MAHPhCPP/54hsJfMAAHAAAAAGDJTjnllNiwYUNs2LAhIiLKssxcFNGXOwAAAAAAAFaCATgAAAAAAD3JABwAAAAAgJ5kAA4AAAAAQE8yAAcAAAAAoCetyh0AAAAAAEB3K8sybrrppti/f/8JX5uYmIjx8fEYHx+Po0ePts7HxsYiIqJWq3U6t8UAHOhJ/f39MTQ0lDvjBJrSdFtTo9HInQAAAABZ/dd//Vf85V/+5aJeu3Xr1mWuSWcADvSkRqMRAwMDuTNaBgcHIyI0tdHNTQAAAHAye97znhcf+MAH4sCBAzMeL4oijh071rrqe/oxNjYWe/bsiT179sT27duzdBuAAwAAAAAwr1qtFi9/+csX9JqyLGPHjh0rVJTGTTABAAAAAOhJrgAHAAAAAFghExMT8e///u9RVVVEzH1DyKnHZ/t6rVaLqqqiLMsTjqqqoiiK1vl8z5nr6ynPPX6d+d5v+nNzMwDvgG68GZ8butHruvHnLqL7bu4YoSmVpjSa0mhKoymNpjSa0mhqz5+lAFioT37yk/Ev//IvuTM64rLLLotarRb1ej36+vqiVqvF+eefHy984QuzNRmAn6S6cTjo/5FkObkJZnua0mhKoymNpjSa0mhKoymNpjRujg3AYrz1rW+NycnJGVdDT10N3u73U1d+T3/s+Oe2e592V31PXand7ms///nP236vF198caxduzbWrVsXa9eubZ1fcMEFbV+7UgzAO8Agrj3/jyQAAAAAvejMM8+M97///a3fTw3Dp29ZMn07kcWcz7VFyWLO5/ragQMH4qabbpr3e73hhhtmffyqq66K173udcv6uaYyAAcAAAAA6IC77747/uiP/ih3Rsdt3Lgx29p92VYGAAAAADiJnH/++XHOOefkzui4/fv3Z1vbABwAAAAAoAPuvffeOHjwYO6MjvvVX/3VbGvbAgUAAAAAoAMuvvji+L3f+73YvXt37pQZ2u0zPtvNMVNeM+X+++/P9r0ZgAMAAAAAdMApp5wSAwMDuTM6YurmmS996UujKIpsHQbgAAAAAAAr5PHHH4+/+Zu/aQ2Ba7XanM+tqmrG72d77vHPme25xz9nvqu5y7JsDavne87xz506b/ecdt/zSjMA74D+/v4YGhrKnTFDo9HInQArqht/7iJCUyJNaTSl0ZRGUxpNaTSl0ZSm25r8WQqAhbruuuvipptuyp2R1datW7OtbQDeAY1Go6v+acPg4GDuBFhx3fpzp2l+mtJoSqMpjaY0mtJoSqMpTTc3AcBCvPvd746nP/3pc165vRipV20v5sruuZ47Pj4et91226J677vvvti+ffuyff8LYQAO9CRXgKfT1J4rvQAAAFisU045JX7nd34nd8aS/eAHP1j0APyCCy5Y5pp0BuBAT3IFeHua0rjSCwAAACIuu+yy+OIXvxijo6NRlmUcPXo0xsfHZxzTHxsbG4uxsbH4zne+E3v37o0rrrgiS7cBOAAAAADASW761ifH3+hy6nzt2rVxyimnzPuc6edFUcR3vvOdrN+XATgAAAAAQAccPnw43vzmN8eRI0dyp3TU2rVrs61tAA70JHuAp9OURlMaTWk0pdGURlMaTWk0tefeIAAsxeHDh0+64XdExFlnnZVtbQNwoCfZA7w9TWk0pdGURlMaTWk0pdGURlMa9wYBYKm2bNkSO3fubPu8qqpmbEky11EURet5852nblmy3EdRFHH99dfHgw8+2IFPd3YG4AAAAAAAXaRWq0WtVou+vr7cKUtSlmVcf/31WRue2J8gAAAAAADMwQAcAAAAAICeZAAOAAAAAEBPMgAHAAAAAKAnuQkmAAAAAEAHFEUR1113Xdxzzz3ZGmq1WlRV1eopyzKqqmqdz3dMf17KeVmWERFZb+ZpAA70pP7+/hgaGsqdcQJNaTSl0ZRGUxpNaTSl0ZRGU5pua2o0GrkTAHgCu+OOO+LLX/5y7oyOu/DCC7OtbQAO9KRGoxEDAwO5M1oGBwcjIjS1oSmNpjSa0mhKoymNpjSa0nRzEwAs1qWXXhqf+cxn4tChQ4t6fVEUUVVVlGV5wnm7K7FTr9he7uP222+Pe++9N1784hcv86eZxgAcAAAAAKBDnv70p+dO6JiyLGPHjh1ZGwzAAQAAAAC6RFVVrSu7Z7vSO/Wq75W8snt6R7vn5WYADgAAAADQAffee2+89a1vzZ3RcZs3b862dr7bbwIAAAAAnEQ2bNgQZ511Vu6Mjjt27Fi2tV0B3gH9/f3uXJ7A5wQAAABAL1u3bl381m/9VuzevTtbQ61Wi6qqZpxP37ak2WxGs9mMoihicnJyzvOFOP3001fiW0liAN4BjUbDncsT+JxYTt34FyoRoSmRpjSa0mhKoymNpjSa0mhK021NLpIBYCnuuuuu+NrXvpY7o+OmBu45GIADPalb/0JF0/w0pdGURlMaTWk0pdGURlOabm4CgMW65JJL4rrrrovHH388+TXHX6FdFEXrKu3px+TkZOtrs50ffwX3XF9fiSvA77nnnnjRi1600I9rWRiAAwAAAAAso3vuuSe++93vxuHDh+Pw4cPx2GOPtc6Hh4ejqqqkq6KnPyfnVdRLtWnTpmxrG4B3QDduxdCN/2zP58Ry6sb/niK675/wRmhKpSmNpjSa0mhKoymNpjSa2vNnBABSvP3tb8+dsCT1ej1WrVoVq1atinq9HqtXr26dT3981apVsXr16hmPH/+8NWvWxLZt27J9LwbgHdCtWzF0G58Ty6lb/3vSND9NaTSl0ZRGUxpNaTSl0ZSmm5sAoJ1PfOIT8f3vfz/OOuusOPPMM2P16tWtLUyqqoqiKGZsbTLbY1OPT//9Ql4/13vO9r6Lec+JiYk4evRo284jR47Ehg0b4sorr8zyv4UBOAAAAADAMurv74/+/v7cGdmVZRk7duxwE0wAAAAAANqb2j98tqu557pqe66rv9t9bfp7LWadoihyf1wG4AAAAAAAS3HffffFW97yltwZXevss8/OtrYBOMzDjRTTaUqjKY2mNJrSaEqjKY2mNJrSaErTbU1uggnAbGq1Wu6ErrZu3bpsaxuAwzzcSLG9bm2yzxYAAADQKZs3b46dO3e2fd6uXbviHe94RweKusvq1auzrW0ADvQkf3nRnqY0mtJoSqMpjaY0mtJoSqMpzVQTACzWxo0b44wzzogjR47kTumohx56KNvaBuBAT7J9TTpNaTSl0ZRGUxpNaTSl0ZRGU3u2QAFgKR577LGTbvgdEXHWWWdlW9sAHOhJrgBvT1MaTWk0pdGURlMaTWk0pdGUxhXgACzV1q1bk7ZKqaoqyrKMoiiSzsuyjKqqluV8Mcdc71UURdxwww3x4IMPduDTnZ0BOAAAAABAF6nValGv16Ner+dOWZKyLOOGG27I2tCXdXUAAAAAAFghBuAAAAAAAPQkA3AAAAAAAHqSATgAAAAAAD3JTTABAAAAAFiQ73znO3HLLbfMeKxWq8Xk5GSMjo7GyMhIDA8PR0REX1++67ANwGEe/f39MTQ0lDvjBJraazQauRMAAAAATvDoo4/G6OjonF8vyzKqqoqiKKIsy2U5iqKIqqpOOE993vHnZVnGN77xjeTv+alPfepyfHSLYgAO82g0GjEwMJA7o2VwcDAiQlMbU00AAAAA3eTOO++Md73rXbkzOs4V4AAAAAAAPe4pT3lK7NixI+6+++7cKXOqqmrOK8TbHVVVzfqeR48e7fB38UsG4AAAAAAAHbBmzZr40Ic+NOvXpobOcw2fp2+LMnWeMqg+/rnzvW6ha8z23vv3749vfvObM763gwcPduLjnZUBOAAAAADACvnqV78an/rUp3JnZHXGGWdkWzvf5isAAAAAAD3u4osvzp2Q3WOPPZZtbVeAAwAAAACskP7+/ti5c2dEROzatSve8Y53ZC7qvPPOOy/b2q4ABwAAAADogE2bNsUll1yStaFWq7U9b/e1hZqcnFzS65fCFeBAT+rv74+hoaHcGSfotqZGo5E7AQAAAE4ap512WvzVX/1V7owlme2mmN8k6jDqAAAgAElEQVT85jfj2muvnfM14+PjHSycyQAc6EmNRiMGBgZyZ7QMDg5GRHRlEwAAAECqWq0W9Xo96vV667Hp57NZtSrfGNoWKAAAAAAALNrZZ58979dHRkY6VHIiV4ADAAAAAHRAWZbxta99Lfbt29exNadvWVJVVRRFMet5WZZzfi3lfNOmTVEURVRVFZOTk/HYY4+1GsbGxjr2/R7PABzoSfYAT6cpjaY0mtJoSqMpjaY0mtJoas89VABYip/85CfxyU9+MndGx23dujXb2gbgQE+yB3h7mtJoSqMpjaY0mtJoSqMpjaY07qECwFI94xnPiHPPPTceeeSR3Ckddd9998X27duzrG0ADvQkV4Cn05RGUxpNaTSl0ZRGUxpNaTS15wpwAJZi165dJ93wOyLioosuyra2ATjQk1wB3p6mNJrSaEqjKY2mNJrSaEqjKY0rwAFgcer1era1DcABAAAAADrgaU97Wvz+7/9+3HPPPQt63Y9//OMVKuqMoiiyrW0A3gHduBWDf7YHAAAAAJ21evXqeO9737vg1/31X/91/OM//uMKFK2sTZs2xdOe9rQ4//zzszUYgHdAt27FAAAAAAB0v/e85z3xnve8Z0nvUVVVlGUZZVlGVVVRFMWM86IoYnJyMprNZkxMTESz2YzJycnWY1Pn0x+bet7U+dTjw8PDsWvXrti1a1c8+OCDsXXr1rjyyiuX6dNYGANwAAAAAIAOOHDgQLzuda/LndFx69evz7a2ATjQk7px66GI0JRIUxpNaTSl0ZRGUxpNaTSl6bYm20kCsBQjIyO5E7I455xzsq3dl21lAAAAAICTyPj4eO6ELPr68o2hXQEO9KRu3Xtf0/w0pdGURlMaTWk0pdGURlOabm4CgMXatm1bfOUrX4nR0dF5nze1V3dZllEURWv/7vnO59rbe/p7zXakPm8xR1EUceutt8Y999wTL3rRizr0Kc9kAA4AAAAA0CHnnntu7oSOKcsyduzYkbXBFigAAAAAAPQkV4ADAAAAAHRAURTx+c9/Pu69996sHfNtWdJuu5WFbssSYQ9wgGXX398fQ0NDuTNOoCmNpjSa0mhKoymNpjSa0mhK021NjUYjdwIAT2B33HFHfP7zn8+d0XFbtmzJtrYBONCT3ASzPU1pNKXRlEZTGk1pNKXRlEZTGjfBBGCpLr744njyk58cDz/8cO6Ujrr//vuzrW0ADvQkV4Cn05RGUxpNaTSl0ZRGUxpNaTS15wpwAJZi7969J93wOyLiKU95Sra13QQTAAAAAKADJicncydkUa/Xs63tCnCgJ9kCpT1NaTSl0ZRGUxpNaTSl0ZRGUxpboACwVNu2bYuNGzfGgQMHcqecNAzAgZ5kC5R0mtJoSqMpjaY0mtJoSqMpjab2bIECwFL89Kc/fcINv/v6+mLVqlWxevXq1q/Tz+f62qpVq6Kvry9Wr14d5557brZ+A3CgJ7kCvD1NaTSl0ZRGUxpNaTSl0ZRGUxpXgAOwVM985jPj6quvjoMHD8aaNWtax+rVq6OqqijLMoqiWND5XF8riiKazWY0m80oiiImJydnnE99/fjz+V5z7NixGBsbW/BWLuedd15s2bJlZT7UNgzAAQAAAAA64JFHHomrr746d0bHPelJT8q2tgE40JNsgZJOUxpNaTSl0ZRGUxpNaTSl0dSeLVAAWIqyLHMnZLFqVb4xtAE40JNsgdKepjSa0mhKoymNpjSa0mhKoymNLVAAWKqiKHInZFGv17Ot3ZdtZQAAAACAk8iaNWtyJ2QxOjqabW1XgAMAAAAAdMDGjRtj586d2daffuPM2Y75brpZFEV89atfjQceeOCE1zWbzRgbG4vR0dEYGxs7YasXA3CAZWYP8HSa2rPXJwAAAL2gVqtFvV5f1JYk3/ve9+KWW25Z1Lpbt25d1OuWgwE40JPsAd6epjT2+gQAAICI5z3veXH11VfHwYMHI+IXw/SqqmJycjLGx8dnPcbGxuKuu+6KPXv2xPbt27N0G4ADAAAAADCvWq0WV1xxxYJeU5Zl7NixY4WK0rgJJgAAAAAAPckV4AAAAAAALIuqquK73/1u7N27N+6///7cOQbgdI9uvGmhG98BAAAAQLof/ehH8aEPfWjGY1u2bMkTEwbgdJFuvWkhAAAAAJDm0ksvjU2bNsWDDz7Yemzfvn3ZegzAO8CVzQAAAADAyeCnP/3pjOF3RMQFF1yQqcYAvCNc2QwAAAAAnAye9axnxcc//vG47777Yu/evXHTTTfF3r1744orrsjSYwAOAAAAAMCyee5znxvPfe5zoyzLuOmmm7K29GVdHQAAAAAAVogBOAAAAAAAPckAHAAAAACAnmQADgAAAABAT3ITTAAAAACADjl8+HCMj4+f8HhVVVFVVRRFEVVVRVmWyedlWbZeu9jz5XyvqfOyLDN8wjMZgAMAAAAAdMDPfvazuOqqq3JndNyWLVuyrW0ADgAAAADQAVu2bIkrrrgi7rrrrtwpbS30avSqquZ8rwceeKCD5TMZgAMAAAAAdMCpp54aV199de6MFTE1EJ9+NJvNeNWrXhXNZjNblwE4AAAAAABLUqvVol6vR71ebz3WDXuA9+UOAAAAAACAleAKcAAAAACAHlKW5Ql7eM93zLff91zPTVmjKIrcH4UBOAAAAADASvn2t7/ds/t+pzrzzDOzrW0A3gH9/f0xNDSUO2OGRqOROwFWVDf+3EWEpkSa0mhKoymNpjSa0mhKoylNtzX5sxQAC3Xs2LHcCdmtXr0629oG4B3QaDRiYGAgd0bL4OBg7gRYcd36c6dpfprSaEqjKY2mNJrSaEqjKU03NwHAQrzsZS+Ll73sZSvy3u22KVmObUwWsz3KsWPHYnR0NEZGRuL666+PgwcPrsj3n8IAHAAAAADgCahWq0W9Xo96vb7iax06dCje/e53x/79+xf82jVr1qxAURoDcKAn2QIlnab2/FNnAAAAFmtkZCT+4R/+Icqy7NiaVVXNuEo75SrudleC33777VFV1aJ6LrjggmX+DtMZgHdANw7iDHMA0nXj/x2P6L6/KIjQlEpTGk1pNKXRlEZTe/4sBcBCffazn42vf/3ruTOyqtVq2dY2AO+Abt2LGHpZt/7caZqfpjSa0mhKoymNpjSa0mhK081NALAQ73rXu+LCCy9c9NXT85nrKu6UK7oXcvX3YvcCL8sy9u3bF7t27YoXvOAFy/79pzAABwAAAABYIaeeemq8+tWvzp2RRVmWsWPHjqwNBuAAAAAAAE8As+3r3e7q7pxHURS5PzIDcAAAAACATti3b19ceeWVuTM67slPfnK2tfuyrQwAAAAAcBJZu3Zt7oQsRkZGsq1tAA4AAAAA0AFHjhzJnZDFWWedlW1tA3AAAAAAgA44++yzY9OmTbkzOu7gwYPZ1rYHOAAAAABAB6xduzae/exnx2mnnZY7ZVZFUbRutFkURTSbzTmPhZicnFyh4vYMwIGe1N/fH0NDQ7kzTqApjaY0mtJoSqMpjaY0mtJoStNtTY1GI3cCAE9gu3fvjptuuil3RsddcMEF2dY2AAd6UqPRiIGBgdwZLYODgxERmtrQlEZTGk1pNKXRlEZTGk1purkJABZr27Zt8cUvfjHpppBTV2KnHMdfud3ufDnfa773LYoibrvttti7d29cccUVHfiET2QADgAAAACwQsbHx+PrX/96FEURERG1Wm3O51ZVNeP3sz33+OdExLwD7aqqkoffKc+dek7q83MzAO+AbtyKwT/bo9d1489dRPf9E94ITak0pdGURlMaTWk0pdGURlN7/iwFwEJ9+tOfjq9//eu5M7J62tOelm1tA/AO6NatGKCXdevPnab5aUqjKY2mNJrSaEqjKY2mNN3cBAAL8Y53vCPWrVsXERH33HNP3HbbbZmLOq9er2db2wAcAAAAAGCFrF+/Pt75zndGRMTw8HC8973vjb179+aN6rDHH38829oG4HSNbtyywj9vpNd1489dhH/qDAAAQG86/fTT4+/+7u9yZyzIbPt7F0URr33ta+Po0aNJ7zG1/3kOBuB0jW7dsgJ6Wbf+3HVjEwAAAJyMarVa1Ov1E7YxueWWW1rnVVXF+Ph4jIyMzDiOHDkS11xzTTz88MOdzm4xAAcAAAAAYNFqtVqsW7cu1q1bF+eee27r8bIs45prrslYFtGXdXUAAAAAAFghBuAAAAAAAPQkA3AAAAAAAHqSATgAAAAAAD3JABwAAAAAgJ60KncAAAAAAMDJotFoxIEDB5b8PmVZto6iKKKqqhPOF3PM9V6Led+iKJbhE1saA3CgJ/X398fQ0FDujBNoSqMpjaY0mtJoSqMpjaY0mtJ0W1Oj0cidAMAT2E9+8pN43/velzuj457ylKdkW9sAHOhJjUYjBgYGcme0DA4ORkRoakNTGk1pNKXRlEZTGk1pNKXp5iYAWKxt27bF2972tti7d2+W9adfwV1VVRRFccJ56jHX649/r7Gxsdi7d29s3749y/dsAA4AAAAA0AH1ej3e9KY35c7omLIsY8eOHVGWZbYGA3AAAAAAgB6y2D3AU/b/bnfV9/Rze4ADAAAAAJwk9u/fH294wxtyZ3TcWWedlW1tA3CgJ7kJZjpNaTSl0ZRGUxpNaTSl0ZRGU3tuggnAUoyPj+dOyOKMM87ItnZftpUBAAAAAE4iExMTuROyqNfr2dZ2BTjQkxqNRgwMDOTOaBkcHIyI0NSGpjSa0mhKoymNpjSa0mhK081NALBYT3/60+OWW26JZrPZeqzdPtrLcSx0b+/l3AN8aGgo9uzZE9u3b8/ymRuAAwAAAAB0yKmnnpo7YUVNTEzEyMhIjIyMxJEjR7JvZ2YADgAAAADQIZOTk9FsNk+4wrsoihmPTb+Keq7HUp4/2zqpr53vsaIoYnx8PEZGRmJ4eLg19J6cnDzhez7ttNMyfNK/YAAO83AjxXTd1uTmRAAAAEC3aTQa8b73vS93xqxqtVr09fVFX19f1Ov1qNfrrd/P9li9Xo9169bF+vXr49xzz43169efcJx++ulx+umnx9Oe9rRs35cBOMzDPtLtdWuTv7xIpymNpjSa0mhKoymNpjSa0mhqz4UWACzFbFdHd4uqqlpHWZYnPB4RUZZla/hdFEU0m80YHR2NAwcOtAbo9Xp9xvmqVavi7W9/e1xyySVZvi8DcKAn+cuL9jSl0ZRGUxpNaTSl0ZRGUxpNadwEE4Cleu5znxs33nhjHDt2bFE3smw2m3Mek5OTrefMdj5965X5njv1frO9Zvr5QvzgBz8wAAcAAAAA6GX3339/vPnNb86d0XHnnXdetrX7sq0MAAAAAHASWb9+fe6ELIaHh7Ot7QpwAAAAAIAV8qUvfSk+97nP5c7I6qyzzsq2tivAAQAAAABWyLOf/ezcCdkdOHAg29quAAcAAAAAWCHbtm2LnTt35s5YsKqqoizL1q/zHVVVRVEUJzy32WzG29/+9jh27Fi278MAHAAAAACAGWq1WtTr9SW9R1mWy1SzeLZAAQAAAACgJxmAAwAAAADQkwzAAQAAAADoSQbgAAAAAAD0JANwAAAAAAB60qrcAQAAAAAALFxZlst+FEURVVUty3lRFLk/IgNwAAAAAIBO+PnPfx5vetObcmd03LnnnpttbVugAAAAAAB0wCmnnJI7IYujR49mW9sV4AAAAAAAHbBx48bYuXNnx9ed2pZkqVuj/PM//3Ps378/arVa9PX94trqsbGxGB0dbR1jY2NRluWM9YeHhzv+PU8xAAd6Un9/fwwNDeXOOIGmNN3W1Gg0cicAAADAotVqtajX61Gv1xf9Ht/73vfiG9/4xoJes2HDhrj00kvjN3/zNxe97lIZgHdANw7iDHPodY1GIwYGBnJntAwODkZEaGqjm5sAAADgZPa85z0vPvjBD8b+/ftbN7gsiiImJiZidHQ0RkZGYnh4OEZGRuLQoUPx2GOPxaFDh+Lb3/52bN++PTZv3pyl2wC8A7p1EAcAAAAAkKJWq8VLX/rS5OcfOnQodu3aFR/4wAfigQceWMGy+bkJJgAAAAAAy2rDhg3x3Oc+N3eGK8ABAAAAAJhpuW6cmZsBOAAAAABABxw5ciSuuuqqePDBB3OndNSpp56abW0DcKAndePNZyNCUyJNaTSl0ZRGUxpNaTSl0ZSm25oajUbuBACewA4ePNizw+81a9bEaaeddsJx5plnxite8YpsXQbgQE/q1pvPapqfpjSa0mhKoymNpjSa0mhK081NALBYT3nKU2Lnzp0zHpu+1UhRFDPOp47pX5v+a8rXF/va2d6j2WzG+Ph4HD16NMbHx084RkdH49ChQyd83+eff3687nWv69THPIMBOAAAAABAJrVaLer1etTr9Vi9evWi36csy6iqqjWwnjpP2cv7+OfO97p2azSbzRgbG4vR0dEYGRmJ66+/PsbHx5fxE1sYA3AAAAAAgBXyb//2b/HRj340d0ZWGzZsyLZ2X7aVAQAAAAB6XK1Wy52QXVVV2dZ2BTgAAAAAwAp5yUteEi95yUtWfJ3p25C02/5kIVulLGZ7lOnPveaaa+Kxxx5b8e9/LgbgAAAAAABPcNP3Eu8WZVnGNddck7XBFigAAAAAAPQkV4ADAAAAALAspm+PUhRF7hwDcAAAAACATpiYmIhPfOITcdddd2VrqNVqrZtSznY+297eCzlmu+Flzm1ZDMCBntTf3x9DQ0O5M06gKY2mNJrSaEqjKY2mNJrSaErTbU2NRiN3AgBPYLt3745vfvObuTM67oILLsi2tgE40JMajUYMDAzkzmgZHByMiNDUhqY0mtJoSqMpjaY0mtJoStPNTQCwWNu2bYsbbrghRkdH53xOVVWLuhK7KIrW644/n7oye/p56vNmO293BfjU84qiiO9///uxd+/euOKKKzr4Sf+SATgAAAAAQIc8+clPzp3QMWVZxo4dO7I2GIADAAAAAGQw15XYCz1PvXJ7Oa/uTn2v3AzAAQAAAAA6YPfu3fGHf/iHuTM6Luce4H3ZVgYAAAAAOImcd955cdFFF+XO6LhHHnkk29quAAcAAAAA6ID169fH5z73udwZC7aYm3KWZRnNZjPe8pa3xLFjx7K1G4ADAAAAADCnWq0W9Xo96vX6gl7XDXuA2wIFAAAAAICeZAAOAAAAAEBPMgAHAAAAAKAn2QMcAAAAAKDHTN24curXoiiSzqdesxznRVHk/hgMwAEAAAAAOuHhhx+O17/+9bkzOu7MM8/MtrYtUAAAAAAAOmB4eDh3QhZPetKTsq3tCnCgJ/X398fQ0FDujBNoSqMpjaY0mtJoSqMpjaY0mtJ0W1Oj0cidAMAT2N69e3MndNx5550Xz3nOc7KtbwAO9KRGoxEDAwO5M1oGBwcjIjS1oSmNpjSa0mhKoymNpjSa0nRzEwAs1iWXXBLbt2+P8fHxGY/Pt4/28Ufq82Y7cti/f39861vfile/+tVZ1jcABwAAAADogPPOOy/+7M/+LNv6qYPydkfqDTWLoogPf/jDMTIyku17NgAHAAAAADgJ9PX1RV9f524Lmeuq8+ncBBMAAAAAgJ5kAA4AAAAAQE8yAAcAAAAAoCcZgAMAAAAA0JPcBBMAAAAAIKOqqqKqqijLclFHURSt1y/1vCzLqKpqzvOFvlduBuAAAAAAAB2wZ8+eeNvb3pY7o+O2bNmSbW0DcAAAAACAFfK9730vPvjBD+bOyOrYsWPZ1jYAp2v09/fH0NBQ7owZGo1G7gQWqRv/e4oITYk0pdGURlMaTWk0pdGURlOabmvyZwSAJ7ZarfbiiPh/I+KyiNgUEf93VVV/P+3r/ysiXhsRF0TERET8d0R8uKqq7y52zUcffXQpyT3hBS94Qba1DcDpGo1GIwYGBnJntAwODuZOYAm69b8nTfPTlEZTGk1pNKXRlEZTGk1purkJgCe09RHx04j4/P8cx7srIt4dEfdGxNqIeF9EfLNWq11cVdXDi1nwla98Zbzyla+MiIiHHnoo3vjGNy7mbZ7Qbr311njJS16SZW0DcAAAAADgpFBV1c0RcXNERK1W+/tZvv7F6b+v1Wr/T0S8LSL6I+L/zPaeRVHErbfeGrt27YqLL744Lr/88qjX67Ou39fXt7Rv4AlqeHg429oG4AAAAAAAx6nVaqdExDsi4khEzLoHVlEU8f73vz/uvPPOOHr0aJx66qnxjGc8Iz7+8Y+3huBVVcX4+HgMDw/HyMhIXHvttVEURZRlGVVVLfr8+GO25zWbzZicnIyiKGJycjKazWbr/PivT503m80Zz5vrfCEOHz68xP81Fs8AHOhJ9gBPpymNpjSa0mhKoymNpjSa0mhqzx7gAL2vVqu9MiJuiIh1EfFQRLx0ru1Pbr311rjzzjtjfHw8IiLGx8fjv//7v+O3f/u3O9a7ElatWjXjqNfrcdppp7XOpz++evXqqNfrM877+vpax6pVq+LFL35xvu8l28rwBGCImq4bmwAAAAAWYWf8YsuTcyLiDyPif9dqtedXVfXQ8U/ctWtXHD16tNN9K27qKvDFqtVqUavVWgPwF7zgBbF169ZlLExnAA7zcCPF9jSl0ZRGUxpNaTSl0ZRGUxpNaTSl6eYmAHpbVVWjEbH7f47/r1ar7YqIt0fE/zr+uRdffHGceuqprSvAIyJOPfXU+JM/+ZN4/vOfP98ara1Kpm9hcvyWJsdvbzLX66Z/LeW9UteZ/vV268z2vkVRxPXXXx979+6NK664Ytn/t0phAA4AAAAAMLe+iFgz2xcuv/zyeMYznhF33HFHHDt2LNasWRPbtm2Lyy+/fN43rNVqc94os5eUZRnXX3991gYDcAAAAADgpFCr1dZHxFP/57d9EbG5Vqv1R8ShiDgcEe+PiK/HL/b+3hgR746I8yPif8/2fvV6PT7+8Y/HrbfeGrt3746nPvWpcfnll58Uw+0nCgNwAAAAAOBk8evxiz2+p/zp/xz/EBHvioj/KyLeGhFnR8SjEfH9iHhxVVU/nusN6/V6PP/5z593yxPyMQAHAAAAAE4KVVUNRURtnqf8fodS6BADcAAAAACAk1hVVdFsNqPZbMbExERMTk62jrl+P9tzZ3t+bgbgAAAAAAAnqVtvvTX++I//eEXXuOCCC1b0/efTl21lAAAAAACyuuiii+JXfuVXVnSNhx9+eEXffz6uAAcAAAAAOEmdffbZ8eUvf3lZ3qssyxlboUxMTMQb3vCGmJiYWJb3XwwDcAAAAAAAlqyvry/WrFkTa9asiYhfDMRzswUKAAAAAAA9yQAcAAAAAICeZAAOAAAAAEBPsgc4AAAAAEAHTE5Oxmc+85nYvXt3toZarRZVVc04L8uydRRF0Xps+vlij4iIer2e7fs1AAd6Un9/fwwNDeXOOIGmNJrSaEqjKY2mNJrSaEqjKU23NTUajdwJADyB3XXXXfG1r30td0bHbd68OdvaBuBAT2o0GjEwMJA7o2VwcDAiQlMbmtJoSqMpjaY0mtJoSqMpTTc3AcBiXXLJJXHdddfF448/nvyapV6BvZAruee6+vv487Iso6qqGeezPa8oivjxj38c9957b7z4xS9ewU92bgbgAAAAAAAdsnXr1twJHVOWZezYsSNrg5tgAgAAAADQkwzAAQAAAADoSbZAAQAAAADokPHx8Wg2mxHxi/29p/bSTtl7eyF7ci/2fLnfKzcDcAAAAACADrjrrrvine98Z+6MjrvwwguzrW0ADgAAAADQAZs3b47LL7887rjjjuTXVFUVERG1Wm3O85TnLcXU1ebHH6kefPDBJTcslgE4AAAAAEAHrF27Nq655prcGctmtqH49KPZbMZrX/vamJyczNZoAA4AAAAAwIL19fVFX1/fnF/vhj3A564DAAAAAIAnMFeAAwAAAAB0sdtvvz1uvPHGGY8dvwf4bOba//v411RVFVVVRVEUUZbljF+njumPT//abOfTf42Iea8SX2kG4ADQRn9/fwwNDeXOOIGmNJrSaEqjKY2mNJrSdFtTo9HInQDASeg973lP7oQl2bJlS7a1DcABoI1GoxEDAwO5M1oGBwcjIjS1oSmNpjSa0mhKoylNNzcBQKd99atfjR/+8IcLft3xV3VPv1J7tqu157riO+VK79neoyiK2LVrV+zZsye2b9++Ap9MewbgAAAAAABd7JxzzomXvvSluTMWrCzL2LFjR9YGN8EEAAAAAKAnuQIcAAAAAKADqqqKf/3Xf40HH3xwQa+J+MWNK9udTz1/aouThR5FUSS9fvrz5jqf+n1uBuBAT3LTwnSa0mhKoymNpjSa0mhKoymNpvbcBBOApfjRj34UH/vYx3JndNxFF12UbW0DcKAnuWlhe5rSaEqjKY2mNJrSaEqjKY2mNG6CCcBSPetZz4oPfOADC7oCfKGqqppxRfbUMTk5Gc1ms3VeFEU0m80TzpvN5oznzXW+EHv27IkXvehFK/Qdz88AHAAAAACgAw4cOBB//ud/njuj404//fRsa7sJJgAAAABABzSbzdwJWaxZsybb2q4ABwAAAADogE2bNsXOnTuX9T2n33SyqqooiuKE8+U+Um6CObX1yrXXXhsHDhxY1u95IQzAAQAAAACeoGq1WtTr9ajX67lTTlCWZVx77bVZG2yBAgAAAABATzIABwAAAACgJ9kCBQAAAACAZdFsNuNv//Zv///27j/Yr7q+8/jrc+9NCArosvzIwiICYaqI8e4yFYlWBGSR2m6txXFbt9tOt9Zat5rdKnTKj9q1oos/JmWc2VranXEbRLvtFu1gpyLmVtgyMEW+gAImBhBpjMEEMWACufd79o/7ozfJTe7n5t7c8803j8fMmXvu93u+5zy/d8ZxfOf4OXnggQeya9euJGl1eRYD8EUwPDyckZGRtjN20+l02k4AAAAAgMPOpk2bsn379lmPm/5wy4V6KGXNOed6rj3Pe//99+fpp88OoZsAABrUSURBVJ/e7bucdtppB+vPOSsD8EXQ6XSyevXqtjOmrFmzpu0EOOh68R+ekmiqpKmOpjqa6miqo6mOpjqa6vRak5uJAJiPBx98MO95z3vazlh0pZTWrm0ADvSlXv2HJ037p6mOpjqa6miqo6mOpjqa6vRyEwAcqBUrVuTNb35z1q9f33bKjGa6I7xpmoyNjc1pv9vt7nbeRx55JKtWrWrlOxmAAwAAAAAsgqVLl+b9739/2xkH3fTlUS655JKMjY211mIADgAAAADAgimlZHBwsNWlTyYNtB0AAAAAAAAHgwE4AAAAAAB9yQAcAAAAAIC+ZAAOAAAAAEBf8hBMAAAAAIBFcscdd+R73/vegp6zaZo0TZNut5uxsbF57Xe73TRNs9v+fM7VNgNwoC8NDw9nZGSk7Yy9aKqjqY6mOprqaKqjqY6mOprq9FpTp9NpOwGAQ9h9992Xq6++uu2MRXfGGWe0dm0DcKAvdTqdrF69uu2MKWvWrEkSTbPQVEdTHU11NNXRVEdTHU11erkJAA7UypUr8773vS//9E//VP2ZpmmSJKWUfe7v67jJu7EXetvXXd8zHffUU0/lkUceyete97oF+zvOhQE4AAAAAMAiKKXkLW95S9sZi6bb7eaiiy6aGsi3wUMwAQAAAADoSwbgAAAAAAD0JQNwAAAAAAD6kgE4AAAAAAB9yQAcAAAAAIC+NNR2AAAAAAAAh66mabJ9+/Zs27YtW7duzZYtW7Jx48Zs2LAhSTIw0N592Abgi2B4eDgjIyNtZ+ym0+m0nQAAAAAAHKLe+9735oEHHtjvMcccc0xe//rX5+KLL16kqr0ZgC+CTqeT1atXt50xZc2aNW0nAAAAAACHsK1bt856zI9+9KN87Wtfyxve8IYsX758Ear2ZgBOz3CnPAAAAAAcGm688cap/aZp8txzz+WZZ57JF7/4xfz5n//51HuvfOUrc84557SRmMQAHAAAAACAeSilZNmyZVm2bFlOOeWUJMnatWtz8sknt1xmAA4AAAAAwAL75Cc/mbPOOitnnnlmXvOa12Tp0qWtdBiA0zOslQ4AAAAAh7Zzzjknl1xySdavX5/Pfvaz6Xa7+c3f/M28/e1vb6XHABwAAAAAgAVx7LHH5nd/93eTJDt27MhP//RPZ+fOna31DLR2ZQAAAAAA+tYRRxzRdoIBOAAAAAAA/ckAHAAAAACAvmQADgAAAABAXzIABwAAAACgLxmAAwAAAADQl4baDgAAAAAA4NBy66235uabb97ttVJKmqbJzp078+yzz2b79u1JksHBwTYSkxiAw34NDw9nZGSk7Yy9aJpdp9NpOwEAAACgb1177bXVx55yyikHsWT/DMBhPzqdTlavXt12xpQ1a9YkiaZZTDYBAAAAcHD81V/9VZ544ol0u93s2LEjzzzzzF7b9u3bc/vtt+exxx7L+eef30qnATgAAAAAAHNy7LHH5thjj93vMd1uNxdddNEiFc3MQzABAAAAAOhLBuAAAAAAAPQlA3AAAAAAAPqSATgAAAAAAH3JABwAAAAAgL5kAA4AAAAAQF8yAAcAAAAAoC8ZgAMAAAAA0JcMwAEAAAAAWFBN0+TJJ59sOyNDbQcAAAAAANAfNm/enE984hNZv359fvSjHyVJjj766NZ6DMDpGcPDwxkZGWk7YzedTqftBAAAAAA4ZDzwwAP5x3/8x1x44YU5++yzc+aZZ+ass85qrccAHAAAAACABfVrv/ZrOfnkk9vOsAY4AAAAAAD9yR3gAAAAAAAHybPPPpvPf/7z6Xa7C37uUkqapkky/tDJsbGxdLvdA9rvdrsL8vkdO3Ys+PecDwNwoC/14prySTRV0lRHUx1NdTTV0VRHUx1NdXqtyXOCAJirT3/60/mbv/mbtjMW3Yknnth2QhIDcKBPdTqdrF69uu2MKWvWrEkSTbPQVEdTHU11NNXRVEdTHU11erkJAObi3e9+d0444YQkmbpbu5Sy389MP262/cm7ryfvwB4bG8vo6OiCb2NjY3P63t///vd7Yg1wA3AAAAAAgIPkjjvuyNe+9rWpgfVMmqbZ55IiNfvTP9MLBgcH88IXvrDtjCQG4ECfsgRKPU11NNXRVEdTHU11NNXRVEfT7CyBAsBcrVu3Lhs2bGg7Y1allAwNDVVtS5YsyeDg4F77k9upp56aF73oRW1/pSQG4ECfsgTK7DTV0VRHUx1NdTTV0VRHUx1NdSyBAsCBeNe73pWf/MmfnFqmpOahkm1uk0uo7Nq1K88///zU/lzvLj///POzfPnyg/RXrWcADgAAAABwkDzzzDO5/vrr285YdIODg20nJDEAB/qUJVDqaaqjqY6mOprqaKqjqY6mOppmZwkUAObqcP3vDmuAAxxElkCZnaY6mupoqqOpjqY6mupoqqOpjiVQADgQ73jHO/KOd7xjt9cmH1g514ddzvYQzIXY33Ob6+e/853v5J577slNN92U4eHhrFixotX1wA3AAQAAAAAWUSmlZ5YIWWgbN27MlVdembVr12bt2rVJkt/7vd/LxRdf3EqPATg9o1fv2AUAAAAA6pxxxhn53Oc+lx/+8IfZsGFDLr/88mzatKm1noHWrgwAAAAAQF968YtfnHPOOaftDANwAAAAAAD6kwE4AAAAAAB9yQAcAAAAAIC+ZAAOAAAAAEBfMgAHAAAAAKAvGYADAAAAANCXDMABAAAAAOhLBuAAAAAAAPQlA3AAAAAAAPqSATgAAAAAAH1pqO0AAAAAAAD6Q7fbndpGR0fbzjEABwAAAABo2+bNm3PNNddk586dbadMmT7MHhsbq/p9JoODg4tc/s8MwAEOY8PDwxkZGWk7Yy+a6miqo6mOpjqa6miqo6lOrzV1Op22EwDoU1/+8pezYcOGtjMOilNOOaW1axuAAxzGOp1OVq9e3XbGlDVr1iSJplloqqOpjqY6mupoqqOpTi83AcCBeuaZZ/I7v/M7Wb9+fdspi2rTpk2tXdsAHOhL7myup6mOpjqa6miqo6mOpjqa6mianTvAAZiPzZs3H3bD7yQ58cQTW7u2ATjQl9zZPDtNdTTV0VRHUx1NdTTV0VRHUx13gAMwXytWrMi6deuSJE3T7LaG9oFsY2NjU+fZ3363203TNLPu72+rPW7Pvi996Uv57ne/29rf3AAcAAAAAGCRlVIyODh4UB8QOX3IPt8B+IEO6L/0pS8dtO9XwwAcAAAAAGARbNmyJW9/+9vbzlh0xxxzTGvXHmjtygAAAAAAh5HR0dG2E1qxdOnS1q7tDnAAAAAAgEVw0kknTa0BvlAmlzmpWQt8IdcL39f+o48+ms9//vO7NR7MZV5mYwBOzxgeHvaEdwAAAACYg8m1xHvF3//93+/2+7nnnpuVK1e2VGMATg/pdDqe8A4AAAAAh7BVq1blqquuyvr167Nhw4bcddddue222/LLv/zLrfQYgAMAAAAAsCCWLFmSiy66KBdddFG63e7Uz7YYgAMAAAAALIJdu3blT/7kT/Ltb3976rVSypzOMf34Ukqappn1vZmuMZc1wOeznnhiDXCABdeLa8on0VRJUx1NdTTV0VRHUx1NdTTV6bUmzwkCYD6+9a1v5S//8i/bzlg0J510Ul72spflkksuaa3BABzoS726prym/dNUR1MdTXU01dFUR1MdTXV6uQkADtTZZ5+dP/uzP8vTTz+dJLvdvT3dnq/PdGf15Fb73kzv73ls7Xln++yzzz6bxx9/PJs2bcqmTZsyPDycn/3Znz3of9+ZGIADAAAAACyS008/ve2EA7LnoHtsbGxqm/7e5M/nnnsujz76aD7ykY9k27ZtrXUbgAMAAAAA9LAbb7wxf/qnf9p2xgFbtmxZa9c2AAeAWVhTvp6mOprqaKqjqY6mOppmZw1wANpwyy23tJ0wL8cdd1xr1zYAB4BZWFN+dprqaKqjqY6mOprqaKpjDXAADkTTNHn++ednXDu7aZq91umeafvgBz+417GTy4/s2rUro6Oj8/65r9f2fH1yGx0d3eca5nt64oknDvJfed8MwBdBL9456K4F+l0v/ucu6b07mBJNtTTV0VRHUx1NdTTV0VRH0+z8bykA5uqGG27ITTfd1HbGvJRSMjAwMLUNDQ1l6dKlGRgYSCklg4ODex0z/dizzz67tXYD8EXQq3cOAgAAAAAH14UXXnjIDcBLKRkaGsrQ0FAGBwczMDAw9XNy4D3T/uRx0/eHhoZyxBFHtPZdDMCBvtSr//Ckaf801dFUR1MdTXU01dFUR1OdXm4CgLlYsWJF1q1blyTpdrvZtm1bRkdHd1sCZb77B7It5Ln2dd6xsbF8/etfzz333NPaXeAG4AAAAAAAi6CUkoceeiibN2+u/szkOtullP3uH8yh9ny2thmAA33JGuD1NNXRVEdTHU11NNXRVEdTHU2zswY4APNx//3355prrmk7Y9GdeeaZrV3bABzoS5ZAmZ2mOprqaKqjqY6mOprqaKqjqY4lUACYr6OOOqrthFYsW7astWsbgMN+uIu4Xq81uTMHAAAA6DWTy5b0iw996EN57Wtfm1JK2yn7ZAAO++Eu4tn1apN/vKinqY6mOprqaKqjqY6mOprqaJqdGy0AmI/HHnus7YQkyZIlS7J06dIsWbIkQ0NDGRwcnNoGBgZ2+33JkiU54ogjsmzZsixdujTLli3LEUcckRNPPDGnn356tm3bNvX+0FDvjZt7rwh6iCFqvV5sYnb+kWd2mupoqqOpjqY6mupoqqOpjiVQAJivVatW5bd/+7fz3HPPZWxsbK+t2+3u8/f9vTfXc03+vmPHjt0elDlXf/zHf7zb7wMDA1MD8sntyCOPzLvf/e6sXLlyof6Mc2IADvthODg7TXV6uQkAAABYHC94wQvy1re+te2MGU0OwacPy0dHR7Nr164899xzU9vOnTt323/++ed3e236MTt37sztt9+ee++91wAcAAAAAKCfNU2T2267LZs2bZrTZ5KklLLP/enHNU0zNcyePtSefH1/+91ud07H7Wt/+nFtMwAH+pLla+ppqqOpjqY6mupoqqOpjqY6mmZnDXAA5uP+++/Phz/84bYzFt0ZZ5zR2rUNwIG+ZPma2Wmqo6mOpjqa6miqo6mOpjqa6lhCDoD5WrlyZa644oo53QE+V03TzHgX+EJv3/zmN6ubHnvssbzuda87aN95fwzAYT/cRVyv15rcmQMAAAD0mlJKXv3qV+fHP/7xjO/PZ3C9UEuW7O+46Q/MnMsA3B3g0KPcRTy7Xm3yjxf1NNXRVEdTHU11NNXRVEdTHU2zc6MFAPPx0EMP5bd+67fazlh0g4ODrV3bABzoS/7xYnaa6miqo6mOpjqa6miqo6mOpjqWQAFgvpYtW9Z2Qit27tzZ2rUHWrsyAAAAAMBh5Kijjmo7oRVbt25t7druAAcAAAAAWATHH3981q1bN+N7t9xySz7+8Y8vctHiaHPw7w5wAAAAAICWnXbaaW0nHDRPPfVUa9d2BzgAAAAAwCIYHR3NDTfckI0bN874/jnnnLOoPU3TpNvt7rWNjY1ldHQ0o6Oj2bVr1277Y2NjUz9rPf/88wfxW+yfATjQl4aHhzMyMtJ2xl401dFUR1MdTXU01dFUR1MdTXV6ranT6bSdAMAh7OGHH85f/MVftJ2x6M4444zWrm0JFAAAAACARTAwYBy72NwBDvSlTqeT1atXt50xZc2aNUmiaRaa6miqo6mOpjqa6miqo6lOLzcBwIFasWJFLr300mzYsKG1hlJKmqbZaz/J1BIok0ujjI2NHdD+9HMmycaNG3Peeect6vecZAAOAAAAALAIli5dmssvv7ztjINuciA+OjqaN73pTXNaL3yhGYADAAAAALBgSikZHBxMKaXtFGuAAwAAAADQnwzAAQAAAADoSwbgAAAAAAD0JWuAAwAAAAAcQiYfMjk2NpZut5umaab2J7fZ3tvzHDNtB/re5PttPvxykgE4AAAAAEALfvEXfzGbN29uO+OgKqXk5JNPbu36BuCLYHh4OCMjI21n7KbT6bSdsBd/JwAAAAAOJ2effXbfDcBPO+20vOpVr8o73/nODA0NZXBwMIODg631GIAvgk6nk9WrV7edMWXNmjVtJwAAAADAYe/KK6/MlVdeuWjXm2nZk8n9/b2253szLany5JNPZuPGjbn33ntz880357LLLmv1zu9JBuAAAAAAAIeBUspBuSN7+mD9K1/5Sj72sY8t6PnnwwAcAAAAAKAFt956a2666aYZ3yulpGmaRemYvNa+HnA52+/dbnevc7a57Ml0BuAAh7FeXHs/iaZKmupoqqOpjqY6mupoqtNrTZ4TBMBCuvbaa9tOOGiOO+64thOSJANtBwAAAAAA0F9KKW0nJHEHOMBhrVcf0qtp/zTV0VRHUx1NdTTV0VSnl5sAYKF8+MMfzhe+8IXdXptt2ZOFGiwf6FInk69t27Ztv+d/8skns3z58gVpnQ8DcKAvWdqjjv8LLwAAALRn1apVWbVqVdsZB+SCCy7Y7/t33nlnfuInfiKnnXZajjzyyEWq2psBONCX3Nk8O3cwAQAAAAfqU5/6VO64446UUjI2Npann34627Zty9atW7Nly5Zcf/31SZKBgYFcddVVsw7MDxYDcHpGrw4sAQAAAIDdveIVr8grXvGKGd9rmiZbtmzJhg0bcvXVV+fxxx9f5Lp/ZgAOAAAAAMCCKaXkxBNPzPHHH992SgbaDgAAAAAAgIPBABwAAAAAgL5kAA4AAAAAQF8yAAcAAAAAoC95CCYAAAAAAAtidHQ0n/nMZ/KNb3wjO3fuTJIMDLR3H7YB+GFqeHg4IyMjbWfsptPptJ0AAAAAAMzDQw89lLVr1+722kte8pKWagzAD1udTierV69uO2PKmjVr2k4AAAAAAObp9NNPz0/91E/l9ttvn3rtiSeeaK3HAPww5Q5wAAAAAGCh3XXXXbsNv88999xceumlrfUYgB+m3AEOAAAAACy0s846K+eee27Wr1+fp556KnfddVe++tWv5rLLLmulxwAcAAAAAIAFsXz58nz0ox9NkvzgBz/I2972tjz77LOt9bT3+E0AAAAAAPrWscce23aCATgAAAAAAP3JABwAAAAAgL5kAA4AAAAAQF8yAAcAAAAAoC/NaQBeSnlzKeXLpZQnSik7SimPlFL+TynlvD2Oe2kppdnP9rl9nP+EUsp1pZRvlFK2l1K2llLuKaV8oJRy9Hy+KAAAAAAAh5eh2gNLKf8jyeVJtia5OckPkqxI8nNJfqGU8p+aplm7x8fumzh2T9+Y4fwvTXJXkhOSjCT52yTLkvy7JNcl+Y+llNc0TbOjthkAAAAAgN5VSnlPkncleenES99M8odN09wyw7GfTvIbST7QNM3Ha85fNQAvpSxP8v4k30+ysmmaLdPeuyDJV5P89yR7DsA7TdN8sOYaST6Q8eH3B5um+YNp5x9M8uUkFyZ5W5L/XXk+AAAAAAB62xNJrkiyIeMrlvxKkptLKec0TXP/5EGllMuSvDrJprmcvHYJlFMnjr1r+vA7SZqmWZdke5Lj53LhGZw+8fOLe5x/LMnktH++1wAAAAAAYBGMjY0lSe67777ceeedU79P1zTNF5qm+dumab7dNM36pmmuzPi8eWrZ7VLKqUn+KMkvJdk1l4baJVA2JHk+yatLKcc1TfODaRd/fZKjM/NSJyeVUt6V5F9mfOmUO6dP7ffwzSRvSvLmJPdOO/9AkkuTdDN+pzkAAAAAAD1sbGwsV1xxRZLk3nvvzcMPP5yXv/zlue666zI4ODjjZyZWA3lbkqOS/MPEa0NJbsr4sigPlVLm1FE1AG+aZlsp5Yokn0zyYCnl5owPtM9I8u+T3JrxdVr2dPHENv1LjCT5laZpHt/j2OuS/EySD00sq/L1JEszvgb48iS/3jTNvQEAAAAAoKfdfffdefjhh6d+37FjRx588MHcfffdOe+883Y7tpTyyiR3ZvyZkM8k+fmmaR6YePsPkvygaZr/eSAdpWma+oNLeUuS/5XkX0x7+dtJfr9pms9OO+6EJP8l43eFPzLx8sokH0xywcRnhpumeXaP87944vw/P+3lJskNGZ/wf7c6FgAAAACAVlxwwQVXZ3wePH0Z7m6S31+3bt0fTj+2lLI0yUuSvCjJZUnemeQNSY5LcmPGZ8lPThz7WJJP1T4Es3oAXkq5PMm1Sa5P8qkkm5O8LMlHMn6X9seaprl8lnMMJbkjyblJVjdN80fT3ntpxtf/PjLJe5P8vyQvSPJzST6R8cn/eU3TPFoVDAAAAADAIaeU8pUk30ny3STXZHxwPmlw4vfvNU3zr2c9V80AvJTyhiTrkvx10zRv3eO9FyRZn+RfJTmzaZpH9j7Dbsf/esbv6P6/TdP8wrTXR5Kcn+RVe64TXkp5X5I1ST7TNM2vzhoMAAAAAMAhqZTy1SSbkvy3JCfs8fbfZXxN8BuapvnWbOeqfQjmz0z8XLfnG03T/LiUcnfGly35N/nnJU/25cmJny+cfKGUcnTGh9/b9vGQzMnrnlPZCwAAAABAjyulfDTJLRm/2/voJL+U8eVP3tw0zZYkW/Y4fleSzTXD76R+AH7ExM/j9/H+5OvPV5zrNRM/pw/Kl078PKaUsrRpmj3PM5fzAwAAAABwaFieZO3Ez6eT3J/k0qZp/m4hTj4w+yFJktsnfv5GKeXk6W+UUi5N8tokO5P8w8Rr/7aUste5SykXJfmvE7+unXy9aZqtSR7K+ED+6j0+syzJVRO/3lbZCwAAAABAj2ua5lebpjm1aZojmqY5oWmaN+5v+N00zUtrH4CZ1K8BPpDxtVXemGR7kr/O+EMwX57x5VFKpj3UcmI97zMzPhB/YuI0K5NcOLF/ddM0ez7p840Zv9V9aZK7Jj57ZJJLk5ya5NtJXjMxLAcAAAAAgP2qGoAnSSllSZL3JPkPSc5K8oIk25LcneT6pmm+PO3Y/5zxNcHPTnJckiVJvp/kziSfaprm9syglLIyyQcyvh748iRjGV8q5QtJrmua5odz/4oAAAAAAByOqgfgAAAAAABwKKldAxwAAAAAAA4pBuAAAAAAAPQlA3AAAAAAAPqSATgAAAAAAH3JABwAAAAAgL5kAA4AAAAAQF8yAAcAAAAAoC8ZgAMAAAAA0JcMwAEAAAAA6Ev/H41gwYG4vRauAAAAAElFTkSuQmCC\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":["df = df.fillna(df.median())\n","df.isnull().sum()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"H8HauYDsyhOg","executionInfo":{"status":"ok","timestamp":1674583071434,"user_tz":300,"elapsed":337,"user":{"displayName":"Mona Ascha","userId":"01195351992176878996"}},"outputId":"7173e207-ec6c-451c-b30a-c35db79e6387"},"execution_count":13,"outputs":[{"output_type":"execute_result","data":{"text/plain":["age                   0\n","num_sex_partners      0\n","first_sex             0\n","num_preg              0\n","smoker                0\n","smoke_years           0\n","pack_years            0\n","contracept            0\n","contracept_years      0\n","iud                   0\n","iud_years             0\n","stds                  0\n","stds_num              0\n","std_condy             0\n","std_cerv_condy        0\n","std_vag_condy         0\n","std_vulvperi_condy    0\n","std_syphilis          0\n","std_pid               0\n","std_herpes            0\n","std_molluscum         0\n","std_aids              0\n","std_hiv               0\n","std_hep_b             0\n","std_hpv               0\n","std_num_dx            0\n","cancer                0\n","cin                   0\n","hpv                   0\n","dx                    0\n","hinselmann            0\n","schiller              0\n","cytology              0\n","biopsy                0\n","dtype: int64"]},"metadata":{},"execution_count":13}]},{"cell_type":"code","source":["#Create some visualizations\n","sns.set(style='whitegrid', palette=\"deep\", font_scale=1.1, rc={\"figure.figsize\": [8, 5]})\n","sns.distplot(\n","    df['age'], norm_hist=False, kde=False, bins=20, hist_kws={\"alpha\": 1}\n",").set(xlabel='Age', ylabel='Count');"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":398},"id":"DiD45nOsyoCa","executionInfo":{"status":"ok","timestamp":1674583072059,"user_tz":300,"elapsed":629,"user":{"displayName":"Mona Ascha","userId":"01195351992176878996"}},"outputId":"96acad48-0aab-45e7-b11b-0d4ec83c23da"},"execution_count":14,"outputs":[{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.8/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n","  warnings.warn(msg, FutureWarning)\n"]},{"output_type":"display_data","data":{"text/plain":["<Figure size 576x360 with 1 Axes>"],"image/png":"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\n"},"metadata":{}}]},{"cell_type":"code","source":["print(\"The minimum age is:\", df['age'].min())\n","print(\"Old df shape\", df.shape)\n","#The minimum age is 13, we should get rid of anyone who is below 21\n","\n","df = df.loc[df['age'] >= 21]\n","print(\"New df shape\", df.shape)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"jwPM19NpzD63","executionInfo":{"status":"ok","timestamp":1674583072059,"user_tz":300,"elapsed":6,"user":{"displayName":"Mona Ascha","userId":"01195351992176878996"}},"outputId":"490dd4de-c5f7-47a6-e3e6-cced144ca855"},"execution_count":15,"outputs":[{"output_type":"stream","name":"stdout","text":["The minimum age is: 13\n","Old df shape (858, 34)\n","New df shape (634, 34)\n"]}]},{"cell_type":"code","source":["df.value_counts('cancer')\n","#We have 617 non cancer and 17 cancer "],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"On5IjMpnz0TR","executionInfo":{"status":"ok","timestamp":1674583072060,"user_tz":300,"elapsed":6,"user":{"displayName":"Mona Ascha","userId":"01195351992176878996"}},"outputId":"b9d941c0-3a07-48af-b525-e93dceebd675"},"execution_count":16,"outputs":[{"output_type":"execute_result","data":{"text/plain":["cancer\n","0    617\n","1     17\n","dtype: int64"]},"metadata":{},"execution_count":16}]},{"cell_type":"code","source":["#Some more visualizations\n","df['stds_num'] = df['stds_num'].astype(np.float16)\n","\n","sns.lmplot(x = 'age', y = 'stds_num', data = df, fit_reg=False, hue = 'cancer')\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":380},"id":"yRodGZV80fvr","executionInfo":{"status":"ok","timestamp":1674583074319,"user_tz":300,"elapsed":2263,"user":{"displayName":"Mona Ascha","userId":"01195351992176878996"}},"outputId":"19026567-05a5-40ec-f5d7-10c658b415b3"},"execution_count":17,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<seaborn.axisgrid.FacetGrid at 0x7f7b2ee5ba00>"]},"metadata":{},"execution_count":17},{"output_type":"display_data","data":{"text/plain":["<Figure size 414.805x360 with 1 Axes>"],"image/png":"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\n"},"metadata":{}}]},{"cell_type":"code","source":["df['smoke_years'] = df['smoke_years'].astype(np.float16)\n","sns.lmplot(x = 'age', y = 'smoke_years', data = df, fit_reg=False, hue = 'cancer')\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":380},"id":"IhAGwAIZ3UY9","executionInfo":{"status":"ok","timestamp":1674583076100,"user_tz":300,"elapsed":1784,"user":{"displayName":"Mona Ascha","userId":"01195351992176878996"}},"outputId":"c90dd604-48d3-4e59-d9e9-70323c42ade0"},"execution_count":18,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<seaborn.axisgrid.FacetGrid at 0x7f7b2d51d9d0>"]},"metadata":{},"execution_count":18},{"output_type":"display_data","data":{"text/plain":["<Figure size 414.805x360 with 1 Axes>"],"image/png":"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\n"},"metadata":{}}]},{"cell_type":"code","source":["df.columns"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"XCCvjxjN3fHf","executionInfo":{"status":"ok","timestamp":1674583076100,"user_tz":300,"elapsed":4,"user":{"displayName":"Mona Ascha","userId":"01195351992176878996"}},"outputId":"7f4f9d36-f80d-4233-8aa4-738aa763b9ce"},"execution_count":19,"outputs":[{"output_type":"execute_result","data":{"text/plain":["Index(['age', 'num_sex_partners', 'first_sex', 'num_preg', 'smoker',\n","       'smoke_years', 'pack_years', 'contracept', 'contracept_years', 'iud',\n","       'iud_years', 'stds', 'stds_num', 'std_condy', 'std_cerv_condy',\n","       'std_vag_condy', 'std_vulvperi_condy', 'std_syphilis', 'std_pid',\n","       'std_herpes', 'std_molluscum', 'std_aids', 'std_hiv', 'std_hep_b',\n","       'std_hpv', 'std_num_dx', 'cancer', 'cin', 'hpv', 'dx', 'hinselmann',\n","       'schiller', 'cytology', 'biopsy'],\n","      dtype='object')"]},"metadata":{},"execution_count":19}]},{"cell_type":"code","source":["df['num_sex_partners'] = df['num_sex_partners'].astype(np.float16)\n","sns.lmplot(x = 'age', y = 'num_sex_partners', data = df, fit_reg=False, hue = 'cancer')\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":380},"id":"CM094x6g3WFU","executionInfo":{"status":"ok","timestamp":1674583077993,"user_tz":300,"elapsed":1896,"user":{"displayName":"Mona Ascha","userId":"01195351992176878996"}},"outputId":"b1fa54dd-b9f0-40b3-f52d-4ab6b9983f95"},"execution_count":20,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<seaborn.axisgrid.FacetGrid at 0x7f7b2d621a30>"]},"metadata":{},"execution_count":20},{"output_type":"display_data","data":{"text/plain":["<Figure size 414.805x360 with 1 Axes>"],"image/png":"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\n"},"metadata":{}}]},{"cell_type":"code","source":["#df['first_sex'] = df['first_sex'].astype(np.float16)\n","#df['num_preg'] = df['num_preg'].astype(np.float16)\n","#df['contracept'] = df['contracept'].astype(np.float16)\n","#df['contracept_years'] = df['contracept_years'].astype(np.float16)\n","#df['smoke_years'] = df['smoke_years'].astype(np.float16)\n","#df['iud'] = df['iud'].astype(np.float16)\n","#df['iud_years'] = df['iud_years'].astype(np.float16)\n","df = df.astype(np.float16)"],"metadata":{"id":"MDwqEPTN3_XB","executionInfo":{"status":"ok","timestamp":1674583077993,"user_tz":300,"elapsed":4,"user":{"displayName":"Mona Ascha","userId":"01195351992176878996"}}},"execution_count":21,"outputs":[]},{"cell_type":"code","source":["#Let's build a baseline decision tree model\n","\n","#Get labels\n","labels = np.array(df['cancer'])\n","\n","#Drop cancer variable (and HPV variable)\n","df.drop('cancer', axis = 1, inplace = True)\n","df.drop('hpv', axis = 1, inplace = True)\n","df_list = list(df.columns)\n","\n","#Create dummies/one hot encoding\n","df_dum = pd.get_dummies(df)\n","df_list_dum = list(df_dum.columns)"],"metadata":{"id":"XzEfv5Up6Edd","executionInfo":{"status":"ok","timestamp":1674583089342,"user_tz":300,"elapsed":99,"user":{"displayName":"Mona Ascha","userId":"01195351992176878996"}}},"execution_count":22,"outputs":[]},{"cell_type":"code","source":["from sklearn.model_selection import train_test_split\n","\n","# Split the data into training and testing sets\n","train_df, test_df, train_labels, test_labels = train_test_split(df_dum, labels, test_size = 0.25, random_state = 42)\n","print('Training Features Shape:', train_df.shape)\n","print('Training Labels Shape:', train_labels.shape)\n","print('Testing Features Shape:', test_df.shape)\n","print('Testing Labels Shape:', test_labels.shape)\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"s8PWoOXz7ieF","executionInfo":{"status":"ok","timestamp":1674583137996,"user_tz":300,"elapsed":494,"user":{"displayName":"Mona Ascha","userId":"01195351992176878996"}},"outputId":"3387492f-449f-43a5-8c49-e1ad109f358b"},"execution_count":23,"outputs":[{"output_type":"stream","name":"stdout","text":["Training Features Shape: (475, 32)\n","Training Labels Shape: (475,)\n","Testing Features Shape: (159, 32)\n","Testing Labels Shape: (159,)\n"]}]},{"cell_type":"code","source":["# Import the model we are using\n","from sklearn.ensemble import RandomForestClassifier\n","\n","# Instantiate model with 1000 decision trees\n","rf = RandomForestClassifier(n_estimators = 1000, random_state = 42)\n","\n","# Train the model on training data\n","rf.fit(train_df, train_labels)\n","\n","y_pred=rf.predict(test_df)\n"],"metadata":{"id":"OUx414jX7xR9","executionInfo":{"status":"ok","timestamp":1674583321060,"user_tz":300,"elapsed":2185,"user":{"displayName":"Mona Ascha","userId":"01195351992176878996"}}},"execution_count":28,"outputs":[]},{"cell_type":"code","source":["#Import scikit-learn metrics module for accuracy calculation\n","from sklearn import metrics\n","# Model Accuracy, how often is the classifier correct?\n","print(\"Accuracy:\",metrics.accuracy_score(test_labels, y_pred))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Pun0XyxS8cDG","executionInfo":{"status":"ok","timestamp":1674583340000,"user_tz":300,"elapsed":3,"user":{"displayName":"Mona Ascha","userId":"01195351992176878996"}},"outputId":"89dd2a11-549e-4a1f-ad10-84e6537b9f84"},"execution_count":29,"outputs":[{"output_type":"stream","name":"stdout","text":["Accuracy: 0.9937106918238994\n"]}]},{"cell_type":"code","source":["feature_imp = pd.Series(rf.feature_importances_,index=df_dum.columns).sort_values(ascending=False)\n","feature_imp"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"g9ssZlcj8ttH","executionInfo":{"status":"ok","timestamp":1674583563607,"user_tz":300,"elapsed":430,"user":{"displayName":"Mona Ascha","userId":"01195351992176878996"}},"outputId":"edd13774-ac78-40bc-d8b7-172ecca33a72"},"execution_count":40,"outputs":[{"output_type":"execute_result","data":{"text/plain":["dx                    0.292668\n","contracept_years      0.089769\n","age                   0.084023\n","first_sex             0.080521\n","std_hpv               0.067950\n","num_sex_partners      0.051253\n","num_preg              0.045939\n","pack_years            0.035539\n","iud_years             0.034110\n","cytology              0.033238\n","smoke_years           0.031826\n","biopsy                0.025066\n","cin                   0.024859\n","schiller              0.024614\n","iud                   0.016733\n","contracept            0.016571\n","hinselmann            0.014087\n","stds_num              0.007758\n","stds                  0.007538\n","std_num_dx            0.005641\n","smoker                0.004418\n","std_condy             0.002137\n","std_vulvperi_condy    0.001887\n","std_syphilis          0.000912\n","std_hiv               0.000826\n","std_vag_condy         0.000113\n","std_pid               0.000002\n","std_hep_b             0.000000\n","std_aids              0.000000\n","std_molluscum         0.000000\n","std_herpes            0.000000\n","std_cerv_condy        0.000000\n","dtype: float64"]},"metadata":{},"execution_count":40}]},{"cell_type":"code","source":["import matplotlib.pyplot as plt\n","import seaborn as sns\n","%matplotlib inline\n","# Creating a bar plot\n","sns.barplot(x=feature_imp, y=feature_imp.index)\n","# Add labels to your graph\n","plt.xlabel('Feature Importance Score')\n","plt.ylabel('Features')\n","plt.title(\"Visualizing Important Features\")\n","plt.legend()\n","plt.show()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":322},"id":"kAHf2qpP9Z_J","executionInfo":{"status":"ok","timestamp":1674583588182,"user_tz":300,"elapsed":938,"user":{"displayName":"Mona Ascha","userId":"01195351992176878996"}},"outputId":"04080fc6-78a0-43c8-a955-bff06778fad1"},"execution_count":41,"outputs":[{"output_type":"stream","name":"stderr","text":["WARNING:matplotlib.legend:No handles with labels found to put in legend.\n"]},{"output_type":"display_data","data":{"text/plain":["<Figure size 432x288 with 1 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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":["#Generating on a model on selected features (sample code, need to change)\n","# Import train_test_split function\n","from sklearn.cross_validation import train_test_split\n","# Split dataset into features and labels\n","X=data[['petal length', 'petal width','sepal length']]  # Removed feature \"sepal length\"\n","y=data['species']                                       \n","# Split dataset into training set and test set\n","X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.70, random_state=5) # 70% training and 30% test"],"metadata":{"id":"MqbTgiBa9hFD"},"execution_count":null,"outputs":[]}]}