AgroCode2022 Final Notebook.ipynb

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      "cell_type": "markdown",
      "source": [
        "# FEDOT HACK TEAM - Кейс: Эффективные протоколы лечения коров"
      ],
      "metadata": {
        "id": "r09AOYJpDijB"
      }
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    {
      "cell_type": "markdown",
      "source": [
        "Участники:\n",
        "* Стебеньков Андрей\n",
        "* Сидоренко Денис\n",
        "* Гетманов Андрей\n",
        "* Пётр Шевченко\n",
        "* Иванчик Елизавета"
      ],
      "metadata": {
        "id": "XJoZLJyYDo0H"
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      "cell_type": "markdown",
      "source": [
        "# Решение кейса"
      ],
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        "id": "PdaGHnxCDg6l"
      }
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    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sb\n",
        "import plotly.express as px\n",
        "\n",
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\")\n",
        "\n",
        "pd.set_option('display.max_rows', 100)\n",
        "plt.rcParams[\"figure.figsize\"] = (12, 12)"
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      "execution_count": null,
      "outputs": []
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        "damp = pd.read_csv('damp.csv', encoding='cp1251', sep=';', index_col=False)"
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      "execution_count": null,
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        "damp['Дата события'] = pd.to_datetime(damp['Дата события'], format=\"%d.%m.%Y\")\n",
        "damp['Дата рождения'] = pd.to_datetime(damp['Дата рождения'], format=\"%d.%m.%Y\")\n",
        "damp['Текущий возраст'] = np.round((damp['Дата события'] - damp['Дата рождения'])\n",
        "                                   / np.timedelta64(1, 'Y')).astype('int32')\n",
        "\n",
        "damp"
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              "        Номер животного Ушная бирка животного Пол  Номер лактации  \\\n",
              "0                   146             598621616   F               6   \n",
              "1                   146             598621616   F               6   \n",
              "2                   146             598621616   F               6   \n",
              "3                   291             530073354   F               6   \n",
              "4                   291             530073354   F               6   \n",
              "...                 ...                   ...  ..             ...   \n",
              "592302           912269                912269   F               1   \n",
              "592303           912269                912269   F               1   \n",
              "592304           912269                912269   F               1   \n",
              "592305           912270                912270   F               1   \n",
              "592306           912270                912270   F               1   \n",
              "\n",
              "       Результат отела  Легкость отела Дата рождения  \\\n",
              "0                   FA               1    2010-03-29   \n",
              "1                   FA               1    2010-03-29   \n",
              "2                   FA               1    2010-03-29   \n",
              "3                   MA               1    2009-07-30   \n",
              "4                   MA               1    2009-07-30   \n",
              "...                ...             ...           ...   \n",
              "592302              FA               1    2019-12-31   \n",
              "592303              FA               1    2019-12-31   \n",
              "592304              FA               1    2019-12-31   \n",
              "592305              MA               1    2019-12-31   \n",
              "592306              MA               1    2019-12-31   \n",
              "\n",
              "        Дней в сухостое предыдущей лактации  Дней стельности при событии  \\\n",
              "0                                        62                          277   \n",
              "1                                        62                          277   \n",
              "2                                        62                          277   \n",
              "3                                        68                            0   \n",
              "4                                        68                            0   \n",
              "...                                     ...                          ...   \n",
              "592302                                    0                          239   \n",
              "592303                                    0                          239   \n",
              "592304                                    0                          239   \n",
              "592305                                    0                          273   \n",
              "592306                                    0                          273   \n",
              "\n",
              "        Номер группы животного  Предыдущий номер группы животного  Событие  \\\n",
              "0                           18                                 18  РАСЧКОП   \n",
              "1                           18                                 18   ВАКЦИН   \n",
              "2                           18                                 18   ВАКЦИН   \n",
              "3                           23                                 23  ПЕРЕВОД   \n",
              "4                           23                                 23  ПРОДАНА   \n",
              "...                        ...                                ...      ...   \n",
              "592302                       5                                  7  РАСЧКОП   \n",
              "592303                       5                                  7  ПЕРЕВОД   \n",
              "592304                       7                                  7   СУХОСТ   \n",
              "592305                      17                                 17   ВАКЦИН   \n",
              "592306                      17                                 17     СУХ2   \n",
              "\n",
              "        Дни доения при событии Дата события       Примечание события  \\\n",
              "0                          336   2019-03-28                      КАН   \n",
              "1                          349   2019-04-10                 КОГЛАВАК   \n",
              "2                          349   2019-04-10                    ЛЕПТО   \n",
              "3                          448   2019-04-08                 F001T023   \n",
              "4                          458   2019-04-18  ПРОДАНА ; Прочее ; УВЗ8   \n",
              "...                        ...          ...                      ...   \n",
              "592302                     274   2022-08-20                      КАН   \n",
              "592303                     274   2022-08-20                 F005T007   \n",
              "592304                     279   2022-08-25                        -   \n",
              "592305                     313   2022-08-23                     СКОУ   \n",
              "592306                     313   2022-08-23                        -   \n",
              "\n",
              "        Текущий возраст  \n",
              "0                     9  \n",
              "1                     9  \n",
              "2                     9  \n",
              "3                    10  \n",
              "4                    10  \n",
              "...                 ...  \n",
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              "      <td>2009-07-30</td>\n",
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              "      <td>0</td>\n",
              "      <td>23</td>\n",
              "      <td>23</td>\n",
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      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 592307 entries, 0 to 592306\n",
            "Data columns (total 16 columns):\n",
            " #   Column                               Non-Null Count   Dtype         \n",
            "---  ------                               --------------   -----         \n",
            " 0   Номер животного                      592307 non-null  int64         \n",
            " 1   Ушная бирка животного                592307 non-null  object        \n",
            " 2   Пол                                  592307 non-null  object        \n",
            " 3   Номер лактации                       592307 non-null  int64         \n",
            " 4   Результат отела                      331357 non-null  object        \n",
            " 5   Легкость отела                       592307 non-null  int64         \n",
            " 6   Дата рождения                        592307 non-null  datetime64[ns]\n",
            " 7   Дней в сухостое предыдущей лактации  592307 non-null  int64         \n",
            " 8   Дней стельности при событии          592307 non-null  int64         \n",
            " 9   Номер группы животного               592307 non-null  int64         \n",
            " 10  Предыдущий номер группы животного    592307 non-null  int64         \n",
            " 11  Событие                              592307 non-null  object        \n",
            " 12  Дни доения при событии               592307 non-null  int64         \n",
            " 13  Дата события                         592307 non-null  datetime64[ns]\n",
            " 14  Примечание события                   592307 non-null  object        \n",
            " 15  Текущий возраст                      592307 non-null  int32         \n",
            "dtypes: datetime64[ns](2), int32(1), int64(8), object(5)\n",
            "memory usage: 70.0+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2eff72d6"
      },
      "source": [
        "## Подготовка датафрейма"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "8431cf90",
        "outputId": "49c3dd19-b77b-4193-9a92-b9f8ae1a87b3",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "604122    185\n",
              "512017    181\n",
              "610033    171\n",
              "606120    165\n",
              "712071    163\n",
              "         ... \n",
              "507026      1\n",
              "505141      1\n",
              "504139      1\n",
              "504089      1\n",
              "834         1\n",
              "Name: Номер животного, Length: 6997, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ],
      "source": [
        "# ушная бирка не нужна\n",
        "damp.drop('Ушная бирка животного', axis=1, inplace=True)\n",
        "\n",
        "# быков убираем, телок с 0 лактацией убираем, пол убираем\n",
        "damp = damp[~(damp['Пол'] == \"M\")]\n",
        "damp = damp[~(damp['Номер лактации'] == 0)]\n",
        "damp.drop(\"Пол\", inplace=True, axis=1)\n",
        "\n",
        "damp['Номер животного'].value_counts(ascending=False)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "damp.info()"
      ],
      "metadata": {
        "id": "_zKfLpHmDh49",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "cd79f5e1-7bc6-443a-97cf-518c2372e35a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 331420 entries, 0 to 592306\n",
            "Data columns (total 14 columns):\n",
            " #   Column                               Non-Null Count   Dtype         \n",
            "---  ------                               --------------   -----         \n",
            " 0   Номер животного                      331420 non-null  int64         \n",
            " 1   Номер лактации                       331420 non-null  int64         \n",
            " 2   Результат отела                      331357 non-null  object        \n",
            " 3   Легкость отела                       331420 non-null  int64         \n",
            " 4   Дата рождения                        331420 non-null  datetime64[ns]\n",
            " 5   Дней в сухостое предыдущей лактации  331420 non-null  int64         \n",
            " 6   Дней стельности при событии          331420 non-null  int64         \n",
            " 7   Номер группы животного               331420 non-null  int64         \n",
            " 8   Предыдущий номер группы животного    331420 non-null  int64         \n",
            " 9   Событие                              331420 non-null  object        \n",
            " 10  Дни доения при событии               331420 non-null  int64         \n",
            " 11  Дата события                         331420 non-null  datetime64[ns]\n",
            " 12  Примечание события                   331420 non-null  object        \n",
            " 13  Текущий возраст                      331420 non-null  int32         \n",
            "dtypes: datetime64[ns](2), int32(1), int64(8), object(3)\n",
            "memory usage: 36.7+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9a61033d"
      },
      "source": [
        "## Гипотеза. Есть линейная зависимость между событиями.\n",
        "\n",
        "**Результат:** Гипотеза не подтвердилась, но не исключает нелинейных зависимостей\n",
        "\n",
        "**Вывод:** Проверим зависимость через другие методы"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "b52f7de2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 721
        },
        "outputId": "b98e952f-70c1-4950-f012-9380ffd2c587"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f60afae0d90>"
            ]
          },
          "metadata": {},
          "execution_count": 24
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x864 with 2 Axes>"
            ],
            "image/png": 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Ztqy+HDNS/501XSEhISpZqpT+r+MrPruMz8kz6T75vX9ZsXShPv0w8xJULVrpH4/967zXbv9eb2W9dl/v1ue8P3K+GDlUYWGlsy7j8/tvmzS4bw+lpaYqrlJlvfRGD58upAny8aLRFUsX6pNB72deKqmV2v3zXxr72ce6qmp13Zp5jPslvKVtv21WRGTGMa5YuYrmzJ6hSeNGZrw/BAXpocc7qG7Dxlq/+ld1fv4JXXrFVQrKPC/jn//3gm6+rYHP5njyjNNnv/svq5Yt0qiP+yvd6VTj5q304CNP6ctRQ3Xl1dV1c714nTlzWoPffVt/bN2s8IiyevntdxVXqYpOnjyhj/p0087tf0iyuv3Olrqv3WPat2eX+nbtJCnj1IwGTe7Sg4+c16AsVDUrh/v8BMSwuwb4PMc5OfsVnxwHVwnkWklNrbWJ52yPlfRjcUsgiyp/SyCLI39IIAOBrxPIQODrBDIQ+EMCGQhIIDP4KoF09VbyvqTvjDHxxpiIzFsjSTMk9fN6dAAAAP7KGN/ffCTfy/hYa8cYY5Il9ZBUU5KVtF5SV2vtrEKIDwAAAH7G5VcZZiaK5yWLxpibrbUrvBIVAACAvwvgb6Jx67uwjTHVJT2UeTsk6SZvBAUAAAD/5TKBNMZcqrNJY6qkSyTdZK3905uBAQAAwD/lm0AaY5ZKipQ0QdID1trfjDF/kDwCAICAx1cZ5ilRUoSkWEl/XbHY50vWAQAA4DuuVmHfZ4wpK6m1pG7GmKsklTPG3GKt/blQIgQAAPBHLKLJm7X2sKRRkkZlXkC8raSBxpiLrbUXeTtAAAAA+Be3UmdrbaK19kNrbT1J9b0UEwAAAPyYq0U001w8v+UFjAUAAKDooIWdp9sk7ZT0paTlkgJ3uREAAAAkuU4g4yTdoYxrQD4s6TtJX1pr13s7MAAAAL/GZXxyZ611WmtnW2v/KelWSVslzTPGdCyU6AAAAOB3CvJNNKUk3a2MKuSlkgZLmuLdsAAAAOCvXC2iGSOppqSZkrpba9cVSlQAAAD+jkU0eXpE0nFJL0r6tznb6zeSrLU20ouxAQAAwA+5+iaawE2tAQAA8sMiGgAAAKBgSCABAADgFpersAEAAJCLAF5EE7gzBwAAgEeoQAIAAHiCRTQAAABAwZBAAgAAwC20sAEAADxgaGEDAAAABUMCCQAAALfQwgYAAPAALWwAAACggKhAAgAAeCJwC5BUIAEAAOAeEkgAAAC4hRY2AACAB1hEAwAAABQQFUgAAAAPUIEEAAAACogEEgAAAG6hhQ0AAOABWtgAAABAAVGBBAAA8AAVSAAAAKCASCABAADgFlrYAAAAngjcDjYVSAAAALiHCiQAAIAHWEQDAAAAFBAJJAAAANxCCxsAAMADgdzC9noCuXp2X2//ioBX+67XfB1Csbd70SBfhxAQoiOCfR1CQNh98KSvQyjWwkrwOkbxRwUSAALMFY4wX4dQrG1LIkFH8UcCCQAA4IFAbmGziAYAAABuoQIJAADgASqQAAAAKJaMMXcZYzYbY7YaY/6Ty+MXG2N+Msb8aoxZY4xp4WqfJJAAAADFlDEmWNJHkppLqi7pIWNM9XOGdZE00Vp7vaR2kj52tV8SSAAAAE8YP7i5doukrdba3621ZyRNkNTqnDFWUmTmz2Ul7XG1UxJIAACAIsoY08EYszLbrcM5QypL2pnt/q7Mbdl1k/SIMWaXpJmSXnD1e1lEAwAA4AF/WERjrR0uafjf3M1DkkZba/sbY26TNNYYU9Nam57XE6hAAgAAFF+7JV2U7X6VzG3ZPSVpoiRZa5dKCpUUnd9OSSABAACKrxWSrjLGXGaMKamMRTLTzhmzQ1ITSTLGVFNGApmc305pYQMAAHjAH1rYrlhr04wxHSV9LylY0khr7XpjTA9JK6210yS9KulTY8zLylhQ87i11ua3XxJIAACAYsxaO1MZi2Oyb+ua7ecNkuq5s08SSAAAAA8UhQqkt3AOJAAAANxCAgkAAAC30MIGAADwROB2sKlAAgAAwD1UIAEAADzAIhoAAACggEggAQAA4BZa2AAAAB6ghQ0AAAAUEBVIAAAAD1CBBAAAAAqIBBIAAABuoYUNAADgAVrYAAAAQAGRQAIAAMAttLABAAA8EbgdbCqQAAAAcA8VSAAAAA+wiAYAAAAoIBJIAAAAuIUWNgAAgAdoYQMAAAAFRAUSAADAA1QgAQAAgAIigQQAAIBbaGEDAAB4InA72K4rkMaYisaYCpk/X2GMecAYc7n3QwMAAIA/yjeBNMY8KmmJpGXGmOckzZJ0t6TpxpiWhRAfAACAXzLG+PzmK65a2C9IqikpTNJOSVdYa/dkViS/kzTNy/EBAADAz7hKIIOstcclHTfGbLPW7pEka+0BY0xJ74cHAAAAf+POIpoef/1gjCnh5nMBAACKFa4DmbcxxpjKkmStnZhtex1JX3gtKgAAAPitfKuI1trBeWxfZIxZ5p2QAAAA/B8VyDwYYxZl+3nsOQ//7JWIAAAA4NdctbDLZPu5xjmPBW7aDQAAEMBcLYSxHj7ml1YtX6xPB7+v9PR03XH3fWrzyJM5Hk89c0YDer2tbVs2KiKyrF7r1kexFSspce8ePfdoa1W++BJJ0jXVr9Xznbr4YgpF3rB32qt5w5pKPnBUN7V519fhFFnWWg18/10tWbRAoaFherv7u7qmWvXzxm3asF4J3d7U6VOnVLd+Q73c+c2slsukCeM0eeKXCg4KUt368er4UqfCnoZfs9aqT+9eWrRgvkLDQpXQ6z1Vq37u39HSh4MGavq0b3Xk8BEtW/lr1vYxo0dpyteTFBwSrPLlK6h7z3dVqVLlwpwCAtjK5Yv1yaC+Sk9P15333K+2uXze9evVRVs3Z3zevdG9j2IrVtbmDWv14fsJkiRrpfZPPqO6DRtr144/9d47r2U9f++e3Xr0qWd1X9tHCnVe/iaQW9iuEshyxpj7lVGpLGeMaZ253Ugq69XILjCn06lhA99TwoChinLE6pUO7VWnfrwuvvSKrDE/fPetwiMiNPzLaVowZ7ZGDxuk17v3kSTFVa6iwSO/8lX4xcbY6cs07Kv5+izhMV+HUqQtXbxAO3ds16Sps7V+7Rr17d1dI8ac//rs27uH3ujSQzWuraVXXvg/LVuyULfVa6hVK5Zrwby5GjthikqWLKkDB/b7YBb+bdHCBdqx/U9Nn/WD1q5ZrZ49uumLCZPOGxff6Ha1e7i97m1+Z47tVatV0/iJXyssLEwTJ4zXwP7v6/3+HxRW+AhgTqdTHw/orV4DhynaEauX/tVet9aL18WXnf28+/67KQqPiNSICdM1/8fZGjlskN7o3leXXH6lBn06XsEhITqQkqznn2irOnUbqsrFl2rIqIlZ+3+sdTPd1rCxr6YIP+CqhT1fUktJ92T+fG/m7R5JC7wb2oX128Z1qlj5IsVVqqISJUqoYZM7tXzRvBxjli+apyZ33StJqhffVKt/+VnWFrlCq19b/Ms2HTh8wtdhFHkL5s1V83tayRijmrVq69jRo0pJTs4xJiU5WcePH1PNWrVljFHze1pp/k9zJEnfTJ6gR594WiVLZlzOtUKFqEKfg7/7ae4c3dvyPhljVKv2dTp69IiSk5POG1er9nVyOGLO235LnVsVFhYmSbq29nVK2rfP6zEDkrRl4zpVqnyRKmb7vFt6zufdsoXz1DTz865+o6ZavSrj8y40NEzBIRm1pTNnzuRaYVu9arniKlVRbFwlr8/F7xk/uPmIq1XYTxRWIN62PyVJ0TGxWfejHLHasmFdLmPiJEnBISEqUyZcRw4fkiQl7t2tF59qp7DSZfTo08+rRu0bCi944BzJSUmKjY3Luu+IiVVycqKiHY6zY5ITFZPtNR8TE6vkpIwEaOf2P7X6l1X65KNBKlmylF54ubOq17i28CZQBCQlJSo27uwxjo2NU1JiYq7JoitTvp6seg0aXsjwgDztTz77WSZJ0Y5Ybd64NueYlCQ5sn3elc78vCtbrrw2rV+rD957R0mJe9WpS6+shPIv8+d8r0ZNm3t/IvBrrlZh1zHGrDbGHDPGLDXGVCuswPxJhahojZw0S4NGTNDTHV9Vvx5v6sTxY74OC/CY0+nUkSOH9dnnE9TxpU7q8vorVNu9ZMb0qdqwfp0ef/JpX4cCFEjVGtdq2Nhv9MHwLzRx3AidOX0667HU1FQtXzxf9W+/w4cRwh+4amF/JKmTpChJAyQV6AQeY0wHY8xKY8zKr8aO/JshXhhR0TFKSUrMur8/OVFR2ao1Z8dktJmcaWk6fvyYIsuWU4mSJRVZtpwk6cprqiuuchXt3rm98IIHJE3+arwea3e/Hmt3v6IdDiUmnm2JJiclyuGIzTHe4YhVUrbXfFJSohwxGdUzR0ycGjW+Q8YY1ahZS0FBQTp06GDhTMSPTRj/hdq2bqW2rVvJEe1QYra2c2LiPsXExubz7PMtW7pEnw0fpkFDhmadLgB4W5Tj7GeZJKUkJyoqOmflPCo6RsnZPu9OZH7eZXfxpZcrNKy0/vxja9a2lcsW6Yqrq6o8p71IylhE4+ubr7hKIIOstf+11p621k6S5HAxXpJkrR1urb3JWnvTPx590vUTCsFVVWtoz64d2rdnt1JTU7Vgzve6pV6jHGPq1IvXnNnTJUmL5/+oWjfcLGOMDh86IKfTKUnat2eX9uzaobhKVQp7CghwD/7jYY2ZMEVjJkxRw0ZNNGvGVFlrtW7NapUJj8jRvpakaIdDZcqEa92a1bLWataMqWrYKOOk94a3N9aqlRmXct2x/U+lpqaqXLnyhT4nf9Pu4faa+M1UTfxmqm5v0lTTp30ra63WrP6fwsMj3Gpfb9y4QQndu2rQkKGKiuLDFoXn6lw+726tH59jTJ368fox8/Nu0byzn3f79uyWMy1NkpS4b492bf8zx7mO83+crfgmdxXeZOC3CrIKu3Ve962133gnrAsvOCREz7z0ut7p9JzS09PVtEUrXXLZFRo34mNddU111anfSHfcfZ8G9OqiDg+1VHhEpF7r9p4kad3/ftEXI4cqJCRExgTp+VffUkRkkVqE7jc+7/24Gtx4laLLhWvr7AQlDJupz79d6uuwipy69RtqyaIFatPqLpUKDVWXbr2yHnus3f0aM2GKJKnzG2+r5ztv6vTp07q1bgPdVi/jPLx7W7VWr25d1L5NS4WUKKG3u78b0JejyE2DhvFatGC+7ml+h0JDw9Sj59nLTrVt3UoTv5kqSRrYr69mzpyhU6dO6o7GDdX6gTZ69vkXNLBfX504cUKdX35RkhRXsaIGfzTMJ3NBYAkOCdGzL/9HXV59Vunp6Wp2dytdctmVGvvZx7qqanXdWr+R7rz7fvXr+ZaeanevIiIj9Xq3jCuOrF/zqyZ9MTLr8+65V95Q2cw/Lk+dPKlfVy7TC525jB0kk995T8aYUfk811prXZYXtySe4MQqL6t912uuB+Fv2b1okK9DCAilSwX7OoSAEOqqdIC/ZVvSSV+HEBCuiAnz+V+9V7w6y+c5zrb+zX1yHDxehW2Mce9kIAAAABQLbv0daowpJ+kBSQ9LqiaJi0ABAICAFMhn/rhMII0xYZJaKSNpvF5ShKT7VMQuJA4AAIALw9V1IMdL2iLpDkkfSrpU0kFr7Txrbbr3wwMAAIC/cVWBrC7poKSNkjZaa53GGJ+fMAoAAOBrgXz1inwrkNba6yS1VUbb+kdjzCJJESygAQAACFwuz4G01m6S9I6kd4wxNyrjXMgVxphd1tq63g4QAADAHwVwAdK9VdjW2lWSVhljOklq4J2QAAAA4M/yTSCNMYNdPJ+V2AAAAAHGVQWylaSuhREIAABAURLIi2hcJZAHrLWfF0okAAAAKBJcJZBcsgcAACAXAVyAzP8yPgAAAMC5XFUgaxtjjuSy3Uiy1tpIL8QEAAAAP5ZvAmmtDS6sQAAAAIqSoKDA7WHTwgYAAIBb3LqQOAAAADKwiAYAAAAoIBJIAAAAuIUWNgAAgAcC+ZtoqEACAADALSSQAAAAcAstbAAAAA8EcAebCiQAAADcQwUSAADAAyyiAQAAAAqIBBIAAABuoYUNAADgAVrYAAAAQAFRgQQAAPBAABcgqeL5ev0AACAASURBVEACAADAPSSQAAAAcAstbAAAAA+wiAYAAAAoICqQAAAAHgjgAiQVSAAAALiHBBIAAABuoYUNAADgARbRAAAAAAVEBRIAAMADAVyApAIJAAAA95BAAgAAwC20sAEAADzAIhoAAACggKhAAgAAeCCAC5BUIAEAAOAeEkgAAAC4hRY2AACAB1hEAwAAABSQ1yuQpUsGe/tXBLzdiwb5OoRir3L9F30dQkA4uGKIr0Mo9lKOnvZ1CMXeiTNOX4cAeB0tbAAAAA8EcAebFjYAAADcQwUSAADAAyyiAQAAAAqIBBIAAABuoYUNAADggQDuYFOBBAAAgHuoQAIAAHiARTQAAABAAZFAAgAAwC20sAEAADwQwB1sKpAAAABwDxVIAAAAD7CIBgAAAMWSMeYuY8xmY8xWY8x/8hjT1hizwRiz3hgz3tU+qUACAAAUU8aYYEkfSbpD0i5JK4wx06y1G7KNuUrSG5LqWWsPGmNiXO2XBBIAAMADRaSFfYukrdba3yXJGDNBUitJG7KN+Zekj6y1ByXJWpvkaqe0sAEAAIooY0wHY8zKbLcO5wypLGlntvu7Mrdld7Wkq40xi40xy4wxd7n6vVQgAQAAPOAPBUhr7XBJw//mbkIkXSWpkaQqkhYYY6611h7K6wlUIAEAAIqv3ZIuyna/Sua27HZJmmatTbXW/iFpizISyjyRQAIAABRfKyRdZYy5zBhTUlI7SdPOGfOtMqqPMsZEK6Ol/Xt+O6WFDQAA4IGisIjGWptmjOko6XtJwZJGWmvXG2N6SFpprZ2W+VgzY8wGSU5Jna21+/PbLwkkAABAMWatnSlp5jnbumb72Up6JfNWICSQAAAAHigCBUiv4RxIAAAAuIUEEgAAAG6hhQ0AAOCBorCIxluoQAIAAMAtJJAAAABwCy1sAAAADwRwB5sKJAAAANxDBRIAAMADQQFcgqQCCQAAALeQQAIAAMAttLABAAA8EMAdbCqQAAAAcA8VSAAAAA/wTTQAAABAAZFAAgAAwC20sAEAADwQFLgdbCqQAAAAcA8VSAAAAA+wiCYPxpgoY8wnxpj3jDFljTGjjDFrjTEjjDGRhRUkAAAA/IerFvYwSeslHZK0XNIWSXdLWi1psHdDAwAAgD9y1cK+3FrbRpKMMf+21vbO3D7YGLPKu6EBAAD4rwDuYLusQKZm+3nTOY8F8GEDAAAIXK4qkE5jTIy1Nsla2/ivjcaY6pJOeDc0AAAA/2UCuJbmKoG8R9LxXLbvl/SPCx8OAAAA/F2+CaS19qAxpqQx5glJNTI3r5c03lp72uvRAQAAwO+4uoxPdUkbJDWStCPz1kjS+szHAAAAAlKQ8f3NV1y1sD+U9Ky19r/ZNxpjmkr6SNLt3goMAAAA/slVAln53ORRkqy1PxpjPvRSTBeUtVYfDeij5UsXqlSpUL32doKurnp+8XTLpg3qm9BFp0+fVp3bGuj5V16XMUZHDh9WQpfOSty7R7EVK6lrr36KiIzU4gU/adQnQxQUFKTg4GA999Jruva6G3wwQ/9jrdXA99/VkkULFBoapre7v6trqp1/zDdtWK+Ebm/q9KlTqlu/oV7u/GbWVf0nTRinyRO/VHBQkOrWj1fHlzoV9jSKrGHvtFfzhjWVfOCobmrzrq/DKbKsterTu5cWLZiv0LBQJfR6T9Wq1zhv3IeDBmr6tG915PARLVv5a9b2MaNHacrXkxQcEqzy5Suoe893ValS5cKcgt/w1vvwjj//UN+eb2vr5o168pkX1Lb945KkM6dP66Vnn1DqmTNyOp1q2LipHv/X84U8a9/69eclGvVRP6WnO9WkxX26/6EncjyeeuaMPuzTVb9v2ajwyLJ65e33FBNXSampqRo+sJe2bdkgY4L0xPOdVPO6m3I8970uLytx724NHDGxMKfkl/gmmnweN8aUOnejMSZUReRrEH9euki7dm7XmEkz9MobXTWob89cx33Qt6deeeMdjZk0Q7t2btfPSxdJkr4cM0I33FxHYybP0A0319GXY0ZIkm64qY4+HTdZw8dOUqe3eqh/726FNSW/t3TxAu3csV2Tps7Wf7p0V9/e3XMd17d3D73RpYcmTZ2tnTu2a9mShZKkVSuWa8G8uRo7YYrGT56uhx97ItfnI3djpy9Tq+c/8nUYRd6ihQu0Y/ufmj7rB3XtlqCePbrlOi6+0e36YsKk87ZXrVZN4yd+rclTpuuOZndqYP/3vRyx//LW+3BEZKQ6vvIftXn4nzn2U6JkSfUf8lnme/RErVi6WBvWrfbuJP2I0+nUZ4Pf01u9B2vgyMlaNPd77fzz9xxj5sz6VmXCIzVk7FTd80B7jfs047tBfvxuiiRpwGcT1bXvxxozbKDS09Oznrds4VyFhoUV3mTgt1wlkGMkfW2MueSvDcaYSyVNlDTWe2FdOIsX/KRmLe6VMUbVa9bWsWNHtT8lOceY/SnJOnH8mKrXrC1jjJq1uFeLF/wkSVqy8Cc1a9FSktSsRUstXjBXkhRWunTWXx6nTp0M6KX851owb66a39NKxhjVrFVbx44eVUpyzmOekpys48ePqWatjGPe/J5Wmv/THEnSN5Mn6NEnnlbJkiUlSRUqRBX6HIqyxb9s04HDXGXr7/pp7hzd2/I+GWNUq/Z1Onr0iJKTk84bV6v2dXI4Ys7bfkudWxWW+UF7be3rlLRvn9dj9lfeeh8uXyFKVavXVEhIznqGMUZhpUtLktLS0pSWlhZQ79FbN61XXOWLFFupikqUKKF6tzfTiiXzcoxZsWS+GjW7R5J0W3wTrf3lZ1lrtWv776p5/c2SpLLlK6h0eIS2bdkgSTp58oRmTB6nB9o/XZjTgZ/KN4G01vaUNFvSQmNMijEmRdJ8Sf+11vYojAD/rpTkJDli4rLuO2JilXLOh0BKcpIcjtis+9HZxhw8cEBR0Q5JUoWoaB08cCBr3KJ5c/T4P1rqrVefV6cuReJwFIrkpCTFxuY85snJiTnHJCcqJubsMY+JiVVyUsYx37n9T63+ZZWeeuwfevbpx7Rh/drCCRzIJikpUbFxZ1/HsbFxSkpMzOcZeZvy9WTVa9DwQoVW5HjzfTgvTqdTHR5toweaN9KNt9ymajVrXYipFAkHUpIUne1YRjlideCchP1ASrKiM9+Dg4NDVLpMuI4eOaRLr7haK5bMl9OZpsS9u/X7lo3an5Txup8waqjubfOISoWGFt5k/Jwxvr/5iqtV2K2ttUOstRdLukzSZdbaS6y1+Z7/aIzpYIxZaYxZ+cXozy5kvD5ljMnxj1W/URON/mqaevT5QKM/GeK7wIoZp9OpI0cO67PPJ6jjS53U5fVXZK31dViAR2ZMn6oN69fp8Sep2lwI574P5yU4OFjDx07SV9P+q00b1umPbb95P7hioHHzlopyxOr1Zx/VqI/765oatRUUFKQ/tm5W4p5dqlO/seudICC4Oo+xi6RvJMlae7SgO7XWDpc0XJJ2HTxd6J/8306eoJlTv5YkXVOthpKTzraOkpMSFX1OuynaEZOjQpaSbUz5ChW0PyVZUdEO7U9JVrnyFc77fbWuv0l797ytw4cOqmy58t6Ykt+b/NV4TZuScR5YtRrXKjEx5zHPXlmQJIcjVklJZ495UlKiHDEZx9wRE6dGje+QMUY1atZSUFCQDh06qPK5HHvgQpow/gt9MzljYUCNmtcqMVvbOTFxn2JiY/N6aq6WLV2iz4YP04jR47JOyQgUhf0+nJfwiEhdd+PNWrFssS674qq/M6Uio0J0jFKyHcv9yYmqkFnBPTvGoZSkREU5YuV0punE8WOKiCwnY4yeeO7VrHFvvvCEKla5RBvWrNK2LRv07MP3ZPyRf+iAur7SQT0GDC+0ecG/uDoHski678F2Gj52koaPnaR68Y31w8zpstZqw7rVKhMekdUK+UtUtEOly4Rrw7rVstbqh5nTVa9hxhWK6jZopB9mTpMk/TBzmuo2yNi+e+eOrKrYlk0bdCY1VZFlyxXiLP3Lg/94WGMmTNGYCVPUsFETzZoxVdZarVuTccyjHTmPebTDoTJlwrVuTcYxnzVjqho2yvjLtuHtjbVq5c+SpB3b/1RqaqrKBWhijsLV7uH2mvjNVE38Zqpub9JU06d9K2ut1qz+n8LDI3I91zEvGzduUEL3rho0ZKiiogLvPN7CeB/Oy6GDB3Ts6BFJ0ulTp7Tq56W66JLLvDBL/3Rl1erau3unEvfuVmpqqhb/9INurhufY8xNt8Vr3g8zJElL589RzetvljFGp0+d1KmTJyVJq1cuU3BwsC669HLd2bKNPp34vYaOn6Geg0aoYpVLSB4lBRnj85uvuKpAVjXGrMllu5FkrbV+f1JJnboNtHzJQj364N0KDQ1V5y4JWY91eLSNho/NqJq92PmtrMtH3HJbfd1yW31JUrvHnlLCW500a9oUxcZV1Nu9+kmSFvz0o/47a7pCQkJUslQpvZ3QN6CX82dXt35DLVm0QG1a3aVSoaHq0q1X1mOPtbtfYyZkrPLr/Mbb6vnOmzp9+rRurdtAt9XLOEfs3lat1atbF7Vv01IhJUro7e7vcmzd8Hnvx9XgxqsUXS5cW2cnKGHYTH3+7VJfh1XkNGgYr0UL5uue5ncoNDRMPXqevSRS29atNPGbqZKkgf36aubMGTp16qTuaNxQrR9oo2eff0ED+/XViRMn1PnlFyVJcRUravBHw3wyF1/z1vvwgf0pevbxdjpx/LhMUJC+njBOIyd8q/0pKeqb0EVOp1PWpiu+yZ26rX78+YEVU8HBIXr6hdfU8/WOSk93qnHzVrro0is0YdRQXXFNdd1cN15NWrTS4N5vq+OjrRQeUVYvd8l4fR8+dFA9X+8oE2RUITpG/34jwcVvQ6Ay+Z1bZoxZL6lFXo9ba7e7+gW+aGEHmtIli8QVlYq0yvVf9HUIAeHgCs4l9raUo3wLrbcdPJ7q6xACwrVVwn1eWXhg5Cqf5zhfP3mjT46Dq8zjTEGSRAAAAAQOV+dALi6UKAAAAFBkuEog0/76wRiTo4dnjBntjYAAAACKgozLSvn25iuuEsjsV7795zmP+f0CGgAAAFx4rs6BNHn8DAAAENAC+QIhrhLIIGNMeWVUKv/6+a/DFezVyAAAAOCXXCWQZSWt0tmk8RfvhgMAAAB/l28Caa29tJDiAAAAKFJ8+U0wvpbvIhpjzN15bI8wxnzonZAAAADgz1ytwv7AGPNk9g3GmIclrZGU5LWoAAAA/Jzxg5uvuDoHsqGk74wxVSRNkPSxpFRJTa2127wdHAAAAPxPvhVIa+1eSfGSGiij6viZtbY5ySMAAEDgctXClrX2qKTmkiZKam+MCfV6VAAAAH7O199C48tvosm3hW2MOSrJ/nVXUhlJB4wxTknWWhvp5fgAAADgZ1xdxieisAIBAAAoSoIC9yo+rlvYAAAAQHYkkAAAAHCLq8v4AAAAIBe+XMTia1QgAQAA4BYSSAAAALiFFjYAAIAHAriDTQUSAAAA7qECCQAA4AEW0QAAAAAFRAIJAAAAt9DCBgAA8ABfZQgAAAAUEBVIAAAAD7CIBgAAACggEkgAAAC4hRY2AACABwK3gU0FEgAAAG6iAgkAAOCBIBbRAAAAAAVDAgkAAAC30MIGAADwQAB3sKlAAgAAwD1UIAEAADzAN9EAAAAABUQCCQAAALfQwgYAAPBAAHewqUACAADAPVQgAQAAPMA30QAAAAAFRAIJAAAAt9DCBgAA8EAAd7CpQAIAAMA9JJAAAABwCy1sAAAADwTyVxl6PYE8eSbd278i4EVHBPs6hGLv4Iohvg4hIJS/uaOvQyj2ti8Y6OsQir0SwTT3UPxRgQQAAPBAIP+pEMhzBwAAgAdIIAEAAOAWWtgAAAAeCORFNFQgAQAA4BYqkAAAAB4ICtwCJBVIAAAAuIcEEgAAAG6hhQ0AAOABWtgAAABAAVGBBAAA8ACX8QEAAAAKiAQSAAAAbqGFDQAA4AEW0QAAAAAFRAUSAADAAwG8hoYKJAAAANxDAgkAAAC30MIGAADwQFAA97CpQAIAAMAtJJAAAAAeCPKDW0EYY+4yxmw2xmw1xvwnn3EPGGOsMeamgswdAAAAxZAxJljSR5KaS6ou6SFjTPVcxkVIelHS8oLslwQSAACg+LpF0lZr7e/W2jOSJkhqlcu4BEl9JJ0qyE5JIAEAADxgjO9vBVBZ0s5s93dlbss2D3ODpIustd8VdO4kkAAAAEWUMaaDMWZltlsHN58fJGmApFfdeR6X8QEAACiirLXDJQ3PZ8huSRdlu18lc9tfIiTVlDTPZJQ04yRNM8a0tNauzGunJJAAAAAeKCLXgVwh6SpjzGXKSBzbSXr4rwettYclRf913xgzT1Kn/JJHiRY2AABAsWWtTZPUUdL3kjZKmmitXW+M6WGMaenpfqlAAgAAeKBoFCAla+1MSTPP2dY1j7GNCrJPKpAAAABwCwkkAAAA3EILGwAAwANBRaSF7Q1UIAEAAOAWKpAAAAAeKCKX8fEKKpAAAABwCwkkAAAA3EILGwAAwAMB3MGmAgkAAAD3UIEEAADwAJfxyYUxpmNhBgIAAICiIb8W9pOFFgUAAACKDFrYAAAAHjAK3B52fglkLWPMkVy2G0nWWhvppZgAAADgx/JLINdaa68vtEgAAACKEBbRAAAAAAWUXwI5SZKMMdGFFAsAAACKgHxb2MaYZElpxhinpLbW2iWFFBcAAIBfo4Wdu16SGlhrK0p6QFLvwgkJAAAA/iy/BDLNWrtJkqy1yyVFFE5IAAAA8Gf5tbBjjDGv5HXfWjvAe2EBAAD4N2MCt4edXwL5qXJWHc+9XySsWr5Ywwf3VXp6uprdfb/aPJLzC3ZSz5zRgF5dtHXLRkVEltXr3footmLlrMeTEvfqucda6+HHn1Hrh/4pSTp29IgG9+2hHX9slWT04n+6qVrN2oU5Lb9mrVWf3r20aMF8hYaFKqHXe6pWvcZ54z4cNFDTp32rI4ePaNnKX7O2jxk9SlO+nqTgkGCVL19B3Xu+q0qVKp/3/EDGMfa9Ye+0V/OGNZV84KhuavOur8MpMqy1GtSvt5YtXqhSoaF6s1svXVO1+nnjNm9cr3e7ddHp06d0a70GerHTGzLG6LOhH2rh/LkKCgpS+fIV9Ga3Xop2xEiSfl35swYP6KO0tDSVLVdeQ4aPLuTZ+Y9fli/Wp0PeV7ozXXfcfZ8ebH/+Z9/A3m9r2+aNiihbVp279lFsxUqSpD+3bdHH/XvqxInjCjJB6jdsnEqWKqXU1FQNH/Se1v1vpYwJ0iNPP6+68U19MT34gTwTSGtt98IMxBucTqeGDuytngOGKcoRq5c7tFed+vG6+NIrssb88N0UlYmI1KdfTtf8ObM1etggvd69b9bjnw3prxvr1Mux3+GD++rGOnX1ZkI/paam6vSpk4U2p6Jg0cIF2rH9T02f9YPWrlmtnj266YsJk84bF9/odrV7uL3ubX5nju1Vq1XT+IlfKywsTBMnjNfA/u/r/f4fFFb4RQLH2PfGTl+mYV/N12cJj/k6lCJl2eKF2rVzh76cMlMb1q1R/94JGv75l+eN6987Qa916abqNWup84vPavmSRbq1XgM99OgTevrZFyRJkyeM0+hPh6rTm+/o6NEj6t+np/p/+Ili4yrq4IH9hT01v+F0OvXJoPfUvd9QRTli1emZ9rqlXs7Pvv/O/Fbh4RH6ZPw0LZgzW58PH6TX3ukjZ1qaBvTqopffTNBlV16jI4cPKTgkI1WYNO4zlS1XQUPHTVV6erqOHTnsqyn6DRbR5MEY09wYs8AYk5J5m2+MaVFYwf1dWzauU8XKFymuUhWVKFFCDZvcqWWL5uUYs2zRPDW5615JUv34plr9y8+y1kqSli6cq7iKlXL8pzt+7KjWr/5Fze6+X5JUokQJhUfwpTzZ/TR3ju5teZ+MMapV+zodPXpEyclJ542rVfs6OTIrB9ndUudWhYWFSZKurX2dkvbt83rMRQ3H2PcW/7JNBw6f8HUYRc6i+T/prhYtZYxRjWtr69jRo0pJSc4xJiUlWcePH1eNa2vLGKO7WrTUwnlzJUllwsOzxp08eVLKbCH+OHum4m9vqti4ipKk8hWiCmlG/ue3TesUl+2zr0HjO/Xz4nk5xixfPE+NMz/76sU31ZpVGZ99v65cqksvv0qXXXmNJCmybDkFBwdLkn6cOTWrkhkUFKTIcuULb1LwO3kmkMaYf0lKkNRN0uWZt+6SuhljOhRKdH/T/pQkOWLisu5HO2K1/5wP2exjgkNCVLpMuI4cPqSTJ05o8vjReujxZ3KMT9y7W5HlyuuD3l3176f+ocF9uuvUSSqQ2SUlJSo27uxxj42NU1Jiokf7mvL1ZNVr0PBChVZscIxRVCUnJyom22vXERurlKScr92UpEQ5YmNzjElOPjtm+EeD9MDdTfTfWd/pqWc6SpJ27vhTR48e0QsdHtdTj7TV7BlTvTwT/7U/OUnRjrPHL8oRq/3JOZP0A8lJinac/ewrEx6uo4cPac/OHTLG6J3Oz+nlfz2kb74cLUk6dvSoJOmLkR/p5X89pD7vdNahAK7yIv8K5MuSmllr51prj2Te5kpqnvlYnowxHYwxK40xKyeMHXEh4y0040cN031t2iusdOkc251Op7b9tkkt7murwSO+UqnQUE36YqSPoizeZkyfqg3r1+nxJ5/2dSjFFscYRVGH51/U19/N0R3N79Y3E8dLkpxpTm3euEF9B32s/kM+0ecjPtGO7X/6NtAiyOl0asPaX/XqW7303ocjtWzhXK1etVzpzjTtT05U1Rq1NfDTL1W1Ri2NGjrQ1+H6nDG+v/lKfotojLX2wLkbrbX7Xa06stYOlzRckn5LPGn/VoR/Q1R0jJKTzrbmUpITFXVOO++vMdExsXKmpenE8WOKLFtOmzeu1eL5/9WoYR/o+LGjMiZIJUqWUv1GTRXtiNE11a+VJNVrdIcmk0Bqwvgv9M3kiZKkGjWvVWK2lmhi4j7FZKsmFMSypUv02fBhGjF6nEqWLHlBYy2qOMYoqr6Z+KWmfztZklS1es0cp0wkJyYqOibnazc6JlbJ2SrqyYmJcjjOf303a36POv/7WT31fx3liI1VZLmyCgsrrbCw0qp9/Y3a9ttmXXzJpd6ZlB+LcsQoJVvFdn9yoqIcjhxjKjhilJJ89rPv+LFjiihbTlGOGNWofUNWe/rGW+tr22+bVOuGW1QqNFS3NWwiSarb6A79d+a3hTcp+J38KpBHjDHnLS3O3HbUeyFdOFdXraE9u3Zo357dSk1N1YI536tOvfgcY+rUi9ec2dMlSYvm/6haN9wsY4z6DhmlkRNnaeTEWWr5YHu1feQp3ftAO5WPilZ0TJx27fhTkrR61XJdfOnlhT01v9Pu4faa+M1UTfxmqm5v0lTTp30ra63WrP6fwsMjcj0PLy8bN25QQveuGjRkqKKiAvc8pnNxjFFUtW77kEaN/1qjxn+tBo0aa/bMabLWav3a1QoPD1d0dM7kJjraoTJlymj92tWy1mr2zGmqH3+7JGnnju1Z4xbOm6uLL71MklQ//nat+d+vSktL06lTJ7Vh3VpdEqDvzVddU0N7d+1Q4t6Mz76Fc7/XLXUb5RhzS914zc387Fuc7bPvhlvqavvvW3X61Ek509K07n+rdPEll8sYo5tva6h1/1spSVqz6mdddElgHt/sgozx+c1XzF8LRs57wJj6kr6QNErSqszNN0n6p6RHrLWLCvILfFmBlKQVSxfq0w/fV3p6uu5o0Ur/eOxfGjfiY111TXXVqd9IZ06fVv9eb+n33zYrPCJSr3fro7hKVXLs44uRQxUWVjrrMj6//7ZJg/v2UFpqquIqVdZLb/Tw6UKai6LCfPa7c2OtVe+ePbR48UKFhoapR893VaNmRsW2bev/b+++46ssz8ePf64EFGRDArhxI9raKi6GuOquW+usWC2tq1qrRVscLe5i3QsVa7XOVv2qperPrSgqigNx4QaVIS4EgST3749ziEmAJCeSnCTn8/aV1+sZ9znnem4fTq5c93M/zx7ccVfm2qSLRl3A2LH3M3PGDEp79mTvffbjqGOOY9gRQ3nnnbcpzf5S6b3iilx6xdX5OpxmqbX2cbdNj813CPV247lDGbzJOpR07ciM2V8z8uqx3HjPs/kOq04fPpnfYceUEhddcDbPPfM07dq159QzRtK334YAHH7QPtxwy38AeHPypO9v4zNgMCf88U9EBCNOPoGPPvyAKAp6r7gSJ516OqXZCuYt/xzD2PvuoSiK2G3Pfdj/oEPzcoyz5yzMy+dWNWH8U1x/+SgqKirYbuc92P/QI/nXmCtZe71+bD4w87vvonNG8N47b9Gpc2dOOv28yt99jz/0X/59yxiCYJMtBjH0tycAMOOzT7jonBF8O2cOXbp243fDz6S014p5O8a+K66Q9znQFz/1fl5zHIATBq+Rl35YagIJEBG9gGOARTeYmwxckVKq95TNfCeQhaC5JZBSQ7WkBLKlyncCWQiaQwJZCEwgM/KVQNZ2DSQppenA6U0UiyRJUovhfSCXICKOqrK8bkQ8ExHfRMQrEbF504QnSZKk5qa2STS/rrJ8IXAxUAqcBFzemEFJkiQ1d/m+hU8+b+NTWwJZNaxVUkp3pJS+Syn9P6BtI8clSZKkZqq2BDItZRmgohFikSRJUgtQ2ySavhHxKplK5FrZZbLr3vxJkiQVtCLyOIacZ7UlkOsvYVsAqwKnNk44kiRJau6WmkCmlCpv9x8RPwUOAvYD3gf+0/ihSZIkNV/5nMSSb0tNICNiXeDA7M8s4HYyNx7fpolikyRJUjNU2xD2m8BTwG4ppSkAEfH7JolKkiRJzVZtCeTewAHAYxHxAHAbFPDVopIkSVX4JJolSCndk1I6AOgLPAacAPSMiKsiYoemClCSTliwzgAAIABJREFUJEnNS63PwgZIKX0L3ALcEhHdyEykGQ481MixSZIkNVtFBTyLprYbiS8mpfRFSml0Smm7xgpIkiRJzVtOCaQkSZJU5xC2JEmSFlfAI9hWICVJkpQbE0hJkiTlxCFsSZKkBnAWtiRJklRPViAlSZIaoIALkFYgJUmSlBsTSEmSJOXEIWxJkqQGKOQqXCEfuyRJkhrACqQkSVIDRAHPorECKUmSpJyYQEqSJCknDmFLkiQ1QOEOYFuBlCRJUo6sQEqSJDWAz8KWJEmS6skEUpIkSTlxCFuSJKkBCncA2wqkJEmScmQFUpIkqQEKeA6NFUhJkiTlxgRSkiRJOXEIW5IkqQGigMewrUBKkiQpJ1YgJUmSGqCQq3CFfOySJElqABNISZIk5cQhbEmSpAZwEo0kSZJUTyaQkiRJyolD2JIkSQ1QuAPYViAlSZKUIyuQkiRJDVDIk2gaPYEsssbZ6KZ9MY+Vu7XPdxit2qxv5uc7hILw4ZMX5TuEVm/1rX6f7xBavVtuHJHvEApC3xVXyHcIBc0KZCvRzv+TkiSpiZh2SJIkNUAhD7IW8rFLkiSpAaxASpIkNUAhT6KxAilJkqScmEBKkiQpJw5hS5IkNUDhDmBbgZQkSVKOrEBKkiQ1QAHPobECKUmSpNyYQEqSJCknDmFLkiQ1QFEBT6OxAilJkqScWIGUJElqACfRSJIkSfVkAilJkqScOIQtSZLUAOEkGkmSJKl+TCAlSZKUE4ewJUmSGsBZ2JIkSVI9WYGUJElqAJ9EI0mSJNWTCaQkSZJy4hC2JElSAziJRpIkSaonK5CSJEkNYAVSkiRJqicTSEmSpFYsInaKiLciYkpEnLKE/SdGxOSIeDUiHomI1et6TxNISZKkBohm8F+dMUYUA1cAOwP9gAMjol+NZhOB/imlHwP/Bi6o631NICVJklqvzYApKaX3UkoLgNuAPao2SCk9llKam10dD6xS15s6iUaSJKkBiprBJJqIGAYMq7JpdEppdJX1lYGPq6xPBTav5S2PAP5X1+eaQEqSJLVQ2WRxdJ0N6yEiDgH6A0PqamsCKUmS1HpNA1atsr5Kdls1EbE98GdgSEppfl1v2uAEMiI2BjoCpJSebOj7SJIktUT1mcTSDLwArBMRa5BJHA8ADqraICJ+ClwD7JRSmlGfN601gYyIX9ay+6/AY0CKiHEppfL6fKAkSZKaRkqpLCKOBR4EioExKaXXI+KvwISU0r3A38gUBe+MzN3RP0op7V7b+9ZVgRxFZrbOklLs9imlw3M8DkmSpFahpTyJJqU0FhhbY9vpVZa3z/U960ogp6WUfrekHRExKNcPkyRJUstX130gUwP3SZIkqZWqqwK5bkR8DVQA3wFfAJOAZ4EVGjk2SZKkZquFTKJpFLUmkCmljouWI6It0AvoC+xPZkbPVtl2zsKWJEkqEPW+jU9KaSGZu5dPBR6OiNnA4TgLW5IkFaDm8CSafKnrNj59U0pvZpeXr3FjyXtSSuMbNTpJkiQ1O3VNormlyvKzNfZduYxjkSRJUgtQ1xB2LGV5SeuSJEkFo5An0eRyG5+at+3xNj6SJEkFqK4K5CoRcSmZauOiZbLrKzdqZJIkSWqW6kogT66yPKHGvprrkiRJBaOlPMqwMdR1H8gbmyoQSZIktQx13cbn3tr2p5R2X7bhLHsTxo/jmksuoKKigh1324v9D/1Vtf0LFyxg1FkjmPLWG3Tq3IVT/3o+vVZcmbcmv8ZlF4wEICU4+Fe/ZcCQbZk5/TMuPGsEX3wxmwB22n0f9tz/4DwcmQpBSokr/n4+zz37FMsv344/njaSdfv2W6zd229O5oKRI5g/fz6bbzmYY04cTkTw9VdfMXLEyUz/9BN6rbgSp589ik6dO/PRB+9zwVmnMeWtN/jVb49j/4OHArBg/nxOOOpwFi5YQHl5OVttuz1Df31MEx91/qSUuGTUuYwf9xTLt2vHn848m/WW0N9vvfE655w5gvnzv2OLgYM5/qRTiQiuu+oynnriUYqKiujWrTt/OvNsSkp7AjBxwvNc+vfzKSsro0vXblw++h9NfHQtz9VnHMzOW23IzNnf0H+/c/IdTov11sTnuO+Gy0gVFWy63a5svVf131lP3Xc7LzzyX4qKi+nQuSv7Hj2cbqW9ATh1/23ovdqaAHQt6clhp5zb5PE3ZwVcgKxzCHt94MimCKQxlJeXc+Xfz+Xsi66mpGcvTjjyYLYYNITV1lirss2D999Nx06duf72+3ji4QcYc9UlnPrXC1h9zbW55LpbKG7ThtmzZnLM0P3ZfOBWFBcXc+Sxf2Dt9dZn7txv+d2vDmTjTbeo9p7SsvL8s08z9eMP+eed9/PG669yyQVnccWYWxZrd/EFZ3HiqWew/gY/5tTfH83zzz7N5gMGc+s/r2fjTTfnwF8ewa3/vJ5b/3k9w479PZ06d+bYE09h3BOPVnuftsstx4WXX0f7FVagrGwhxw87jM22HES/DTdqqkPOq/HjnmLqxx9x691jmTzpVS48dySjb7x1sXYXnjuSP444k34b/piTjz+K5555mi0GDubAQw/nyKOOA+Dft93MP669ipP+dAbffPM1F55/Fhdedg29eq/IF7M/b+pDa5Fuum88V9/+BNeN/GW+Q2mxKsrL+b/rL+aI0y6kS/dSLj/1N6zffyC9Vu1T2WalNdbh2PNHs9zy7Rj/4D3876arOejEMwFou9zyHD/q+vwEr2atrlnY36SUnljaT5NE+AO8/cYkVlplVVZceRXatm3LVtvvyLNPP16tzfinH2f7nX8OwKCtt+eVF58npUS7du0pbpPJrxcsWEBkL3ToXlLK2uutD8AKK3RgtT5rMmvWjKY7KBWUcU8+xg67/JyIoN+GGzFnzjd8PmtmtTafz5rJ3G/n0G/DjYgIdtjl54x78jEAnnnqMXbYJTNQsMMuuzPuyUzC2K17D/r225A2bar/DRkRtF8h85j7srIyysrKCuo2FU8/8Rg77bI7EcEGP9qIOd98w6wa/T1r1ky+/fZbNvhRpr932mV3nno8068dOlY+/ZV58+ZVXiD18ANjGbLN9vTqvSKQ6X/VbdxL7zL7q7n5DqNF+3jKG/TovTI9eq1Em7Zt2Wjgtkye8HS1NmttuDHLLd8OgFXX7cdXs2cu6a2kauqqQHaNiL2AMmAeMAN4J6U0r9EjWwY+nzmDkp69K9dLSnvx1uTXFmtTmm1T3KYNK3ToyNdffUmXrt148/XXuPjcM5gx/VNOGnF2ZUK5yPRPp/Hu22/St9+PGv9gVJBmVTk/AUp79mLWzBn0KCmt3qa0V+V6SbYNwBezZ1e27d6jhC9mz67zM8vLyzlq6AFMm/oRe+xzAOtv+ONldTjN3syZ0+nZu0p/9+rFrBnTKana3zOmU9qrV7U2M2dOr1wffcUlPDj2Xjp06MQl14wB4OOPPqCsrIzjhg1l7ty57HfAwey02x5NcEQqdF/PnkWXHj0r17t0L+Xjd95YavsJj4xl3Z9uXrletnABlw0fRlFxMVvveRAbbDa4UeNtaYoKeBZNXRXIJ4A9gF8AxwHXAB9FxNiI6F3rK1uBvhv8iKtvvouLr/0Xd9x8PQvmf/8kx3lz53L2n09i2PEns0KHjrW8i9Q8RES9ZgwWFxcz+qY7uf3e/8ebkyfx/rvvNH5wrciwY47nP/99hJ/tvCt33ZG53KC8rJy33pjMBZdcyYWXX8ON11/DRx9+kN9ApRomPvkQU997iyG7H1C5bfiVt3Pc+aM54PjTuO8fl/P5Z9PyGKGak1oTyJTS4SmloSmlQ1JKe6SUtgR6knms4dVLe11EDIuICREx4bZ/5u/aiR6lPZk147PK9Vkzp9OjtOdibWZm25SXlTH32zl07tK1WpvV+qxJu/Yr8MH7UwAoK1vI2SP+wNY77MLAIds18lGo0Nzz79sYduh+DDt0P3r0KKk8PwFmzpheOSljkZLSntUqYLOqtOnWvXvlkPfns2bStVv3esfRsVNnfrLJprwwftwPOZxm7647buXwg/bh8IP2oUdJKTM+q9Lf06dT0rNXtfYlPXsxc/r0am2qVoAX2WHn3XjikYeBTJVysy0H0L79CnTt2o2NfroJ777zViMdkfS9zt1L+Orz7y+z+mr2TDr3KFms3TuvTuDRu27isOHn0KbtcpXbu/TIVN979FqJNfv9hE/e9w/KqqIZ/ORLXRXIxaSUEnA2cHctbUanlPqnlPof8Msjfkh8P8i6fTfgk48/4rNPprFw4UKefPhBthg4pFqbzQcO4eH/3QfA048/zI833pSI4LNPplFeVgbA9M8+YeqHH9Cr90qklLj43L+w6uprsPcBhzb5Man123PfAxh9052MvulOBg7ZlofG3kdKicmTXqFDx07Vhq8BepSUskKHjkye9AopJR4aex8Dt9oGgAGDt+ahsZmbKTw09l4GDN6m1s/+8ovZzPnmawDmf/cdLz7/LKuuvkYjHGXzsff+B3LDLf/hhlv+w+Ctt+WBsfeSUuL1116hY8eO1YavAUpKSunQoQOvv5bp7wfG3sugIZl+/fijDyvbPfX4o6zWJ9N3g4Zsw6svT6SsrIzvvpvH5EmvsXqfNZvuIFWwVlm7L59/OpXZ0z+lbOFCXhn3KP36D6zWZtr7b3P36As5bPi5dOzSrXL73DnfULZwAQDffv0lH771Gj1X6dOU4asZq+s2PjsCnVJK/66xa2/gk0aLahkpbtOGo048hREnHkVFRQU77LoHq6+5NjdddyXr9O3HFoO2Zsfd9mLUyD9zxC9+TqfOnRl+5vkAvP7qRO68eQxt2rQhioo4+g+n0qVrN15/ZSKPPng/fdZah2OH7g/AYb85jk239LoQLXubDxjMc888xaH77kq7du04ecTIyn3DDt2P0TfdCcDxJ/+58jY+m205iM22HATAAb88gpF/Pon/3Xs3vXqvyGlnjwJg9uezOGroAcz99luiqIj/3HYzY267h89nzeKCkSMoLy8npQqGbLcjWw4asnhgrdSWA7di/LinOGDPnWnXrj2nnvF9fx9+0D7ccMt/ADjxlBHf38ZnwGC2GJj593/NZRfx0YcfEEVB7xVX4qRTTwegzxprsfmWAxl64N4URRG77bkPa669TtMfYAtz47lDGbzJOpR07ciUB0Yy8uqx3HjPs/kOq0UpLm7D7kecwJizT6KiooL+2+xCr1XX4KHbrmeVtfrSb9OB/O+mq1nw3Tz+deEZwPe365k57UPuumYUUVREqqhg6z0PrjZ7W4UtMgXFpeyMGAfsmVKaWWN7CXBfdki7Vu/OnOczs5vAWqXt8x1Cqzb1i/l1N9IPtlybnAdFlKPVt/p9vkNo9W65cUS+QygIe/24d95nsIx/98u85zhbrNU1L/1Q17f18jWTR4CU0iygQ+OEJEmSpOasrtv4dI6INimlsqobI6ItYMlLkiQVrEK6T25NdVUg7wKujYjKamNEdCQzA/uuxgxMkiRJzVNdCeQIYDrwYUS8GBEvAe8DM7P7JEmSVGBqHcLODl2fEhF/AdbObp7SUp5EI0mS1FgK+EE0dV4DSUT0BI4BNshuej0irkgp+QBoSZKkAlTrEHZEDAReyK7+M/sD8Hx2nyRJUkHK91No8lkArasCeSGZ+0BOrLLt3oi4m8xzsTdf8sskSZLUWtU1iaZzjeQRgJTSy0CnxglJkiRJzVldFciIiG4ppS9qbOxOA56jLUmS1GoU8CSaupLAi4CHImJIRHTK/mwN/C+7T5IkSQWmrtv4jI6IT4CRVJmFDZyVUrqvsYOTJElqrgr5STR13sYnpXQ/cH8TxCJJkqQWoNYEMiIurW1/Sul3yzYcSZIkNXd1VSD3AE5vikAkSZJaEp9Es3SzU0o3NkkkkiRJahHqmoWdmiQKSZIktRh1TqKRJEnS4gp4BLvOBHKjiPh6CdsDSCmlzo0QkyRJkpqxuu4DWdxUgUiSJLUoBVyC9HGEkiRJyokJpCRJknLiJBpJkqQGKORHGVqBlCRJUk6sQEqSJDVAIT+JxgqkJEmScmICKUmSpJw4hC1JktQABTyCbQVSkiRJubECKUmS1BAFXIK0AilJkqScmEBKkiQpJw5hS5IkNYBPopEkSZLqyQqkJElSA/gkGkmSJKmeTCAlSZKUE4ewJUmSGqCAR7CtQEqSJCk3JpCSJEnKiUPYkiRJDVHAY9hWICVJkpQTK5CSJEkN4JNoJEmSpHoygZQkSVJOHMKWJElqAB9lKEmSJNWTFUhJkqQGKOACpBVISZIk5cYEUpIkSTmJlFKjfsCkaXMa9wNE+7bF+Q6h1Zu7oDzfIRSEtsX+TdvY3pj5db5DaPUOOuysfIdQEOZNvDzvI8hvfPpt3nOc9VfskJd+8NtakiRJOXESjSRJUgP4JBpJkiSpnkwgJUmSlBOHsCVJkhrAJ9FIkiRJ9WQFUpIkqQEKuABpBVKSJEm5MYGUJElSThzCliRJaogCHsO2AilJkqScWIGUJElqAJ9EI0mSJNWTCaQkSZJy4hC2JElSA/gkGkmSJKmeTCAlSZKUE4ewJUmSGqCAR7CtQEqSJCk3ViAlSZIaooBLkFYgJUmSlBMTSEmSJOXEIWxJkqQG8FGGkiRJUj1ZgZQkSWoAn0QjSZIk1ZMJpCRJknLiELYkSVIDFPAIthVISZIk5cYKpCRJUkMUcAnSCqQkSZJyYgIpSZKknDiELUmS1AA+iUaSJEmqJyuQkiRJDeCTaCRJkqR6MoGUJElqxSJip4h4KyKmRMQpS9i/fETcnt3/XET0qes9TSAlSZIaIJrBT50xRhQDVwA7A/2AAyOiX41mRwBfpJTWBi4Czq/rfU0gJUmSWq/NgCkppfdSSguA24A9arTZA7gxu/xvYLuI2q/wrHUSTURsXNv+lNJLtYYsSZLUSjWHSTQRMQwYVmXT6JTS6CrrKwMfV1mfCmxe420q26SUyiLiK6AHMGtpn1vXLOwLqyxvAkzg+4ppArat4/WSJElqJNlkcXSdDZexWhPIlNI2i5YjYmJKyYRRkiSp5ZgGrFplfZXstiW1mRoRbYAuwOe1vWku10CmHNpKkiS1cvmeQlOvMfQXgHUiYo2IWA44ALi3Rpt7gcOyy/sCj6aUas37vJG4JElSK5W9pvFY4EGgGBiTUno9Iv4KTEgp3QtcD9wUEVOA2WSSzFrVNYnmMr6vPK4SEZfWCOp3uR+KJEmSmkpKaSwwtsa206ssfwfsl8t71lWBnFBl+cVc3liSJKk1aw6zsPOlrkk0N9a2X5IkSYWnriHsmhdZVpNS2n3ZhiNJktQyFHABss4h7PWBI5siEEmSJLUMdSWQ36SUnmiSSBrJxOefYczlo6ioKGe7XfZk74MOr7Z/4YIFXHre6bz39ht06tyFE08/j569V6KsbCFXjRrJe++8SXl5OVvvsCt7H/QrAH574G60X2EFioqKKS4u5oKrb87HoTUrE54bxzWXXEBFRQU77rYX+x/yq2r7Fy5YwKizRzDlrUw/n/qX8+m14sq8Nfk1LvvbSABSgoN/9VsGbLUtUz/6gPPO+GPl6z/9ZBqHHnEUe+5/SJMeV3My8flnuOGK78/lvQ5c/Fy+7PzMudyxcxdOPC1zLi9cuJDRF53Nu29PJqKIw485iQ1/0r/aa88b8XumfzqNi66/oykPqdl56blxXHv536gor+Bnu+7Jvgcvfh5fdO5pvPvWG3Tq0oWTTz+fXiuuBMAH777NlReexdy531IURYy6+maWW375TP9fch6TXp5ARBGHHHkMA4Zsn4/Da3bemvgc991wGamigk2325Wt9zq42v6n7rudFx75L0XFxXTo3JV9jx5Ot9LeAJy6/zb0Xm1NALqW9OSwU85t8vhbg6vPOJidt9qQmbO/of9+5+Q7HLUgdSWQXSNiL6AMmAfMAN5JKc1r9MiWgfLycq695DxO/9uV9CjtxfCjDmXTAUNYtc+alW0e+d89dOzUmStu/j+efvRBbhp9KX84/TyefeJhFi5cyEXX38H87+Zx/OH7MWjbnejZO/PL4i9/v4bOXbrl69CalfLycq78+7mcfdHVlJT24oRfH8wWA4ew2hprVbZ58L9307FTZ66/7T6eePgBxlx9Caf+5QJWX3NtLrn2ForbtGH2rJkcc/j+bD5gK1ZZrQ+X33BH5fv/cu8d2HKrwr2PfXl5Odddeh6nX3Al3Ut7ccrRh9J/y8XP5Q4dO3P5TZlz+eZrL+XE087j4f/eDcDfr7uDr76YzdmnHsd5V95EUVHmNrDjn3qUdu3b5+W4mpPy8nKuueQ8/jLqKnqU9uKk3x7MZgOHsFqf78/j/zf2Hjp27MQ1t9zLk488wI2jL+GPZ5xPeVkZfz97BL//00jWWHs9vv7qS4rbZL5e77z5Orp07c5VN/8fFRUVzPn6q3wdYrNSUV7O/11/MUecdiFdupdy+am/Yf3+A+m1ap/KNiutsQ7Hnj+a5ZZvx/gH7+F/N13NQSeeCUDb5Zbn+FHX5yf4VuSm+8Zz9e1PcN3IX+Y7lBapkCfR1HUj8SfIPGD7F8BxwDXARxExNiJ6N3ZwP9SUN1+n98qr0nulVWjbti2Dtt2BF555vFqb58c9wdY77AbAlkO247WXnidz78zgu3nzKC8vY8H8+bRp25b2K3Ro8mNoCd5+YxIrrbwqK2b7eavtduTZpx+v1mb8U4+z/U4/B2DQ1tvzyouZfm7Xrn3lL9oFCxawpGe3v/Lic/ReaRV6ZZP3QrToXO6V7eOB2yx+Lr/wzJLP5akfvseGP90UgC7durNCx068+/ZkAObNm8v9/76ZfQ72SpV33pxU7fti8LY78vy4x6u1eW7c42ybPY8HDtmeV7Pn8cQJz9JnzXVYY+31AOjcpSvFxcUAPDz2/yormUVFRXTu6h+eAB9PeYMevVemR6+VaNO2LRsN3JbJE56u1matDTdmueXbAbDquv34avbMfITaqo176V1mfzU332GoBaprFvbhNbdF5jf8COBqYM9GimuZmD1rBiU9e1Wudy/pxTtvTKrRZmZlm+LiNqzQoSPffP0lWw7ZjheeeYIj992R+fO/Y+jRf6BT5y4ARAR/PfkYIoKf/Xwfdtht76Y7qGbo85kzKOn5/d8TJaW9eOuN16q3mTWD0myb4jaZfv76qy/p0rUbb77+GhefdwYzpn/KSSPOrkwoF3nikQfZevudG/9AmrHZs2ZQUvr9udyjtP7ncp+11uWFZ55g0LY7MmvGdN57+w0+nzGddfpuyG03XMXP9zuE5du1a9LjaY4+n7l4H789uUYfz5xBSen353GHjh355qsv+eTjj4gIzjj5aL7+8gsGb7sjex84lDnffAPAv8ZcwaSXX6T3Sqvwm+NPoWv3Hk13YM3U17Nn0aVHz8r1Lt1L+fidN5bafsIjY1n3p5tXrpctXMBlw4dRVFzM1nsexAabDW7UeKUliQKeRpPLowwByD7a5mzg7qW1iYhhETEhIibcefOYHxJf3kx583WKioq49s4HuOpf93HfHTfz2SdTATjrkusZNfoWRpx3GQ/ccwevv/JSnqNt2fpu8COuvukuLh79L+64+XoWzJ9fuW/hwoU8N+4JBm3zszxG2LJtu/PulZdw3HDlhay3wUYUFRXx/pS3mP7JVDYfVLiXBiwr5eXlTH5tIn/489mcd9kYxj/1KK+8+BwV5WV8PnM6fTfYiIuuvZW+G/yYG666KN/htjgTn3yIqe+9xZDdv384xvArb+e480dzwPGncd8/Lufzz2o+2ldSY6o1gYyIQ6osD1y0nFKqADot7XUppdEppf4ppf771ZhM0ZS6l/Rk1ozpleuzZ02nR2lpjTallW3Ky8uY++0cOnXuylOPPMBPNh1AmzZt6dKtO3033Khy2K9Haeav5i7durP5oG2Y8mb1KkWh6VHak1kzPqtcnzVzOj1KelZvU9KTmdk25WWZfu7cpWu1Nqv1WZN27Vfgg/enVG6bMP5p1lq3L90KvGLTvaQns2Z+fy5/PnM63Uvqdy4XF7fh8KP/wKjRt3LKyL/z7ZxvWHGV1Xl78qu8+/ZkjjpoN0YcfwSfTv2Q008c1qTH1Zz0KF28jxf7vijtyayZ35/H386ZQ6cuXelR2pMNNtqYzl27sXy79myyxSDefedNOnXpyvLt2rHlVtsBMGDrn/FuLVW2QtK5ewlffT6jcv2r2TPp3KNksXbvvDqBR++6icOGn0ObtstVbu/SI/P/pkevlViz30/45P13Gj9oSZXqqkCeWGX5shr78pcZ1tPaffvx6bSPmf7pNBYuXMjTjz5E/y2HVGuz6YAhPP7Q/QA8+8QjbPjTTYkISnr2ZtLEFwD4bt483n7jNVZedQ2+mzePeXO/rdz+yoTxrLbG2k17YM3Mun034JOpH/HZJ5l+fvKRB9liUPV+3nzQEB5+4D4Ann78YX68caafP/tkGuVlZQBM/+wTpn74QbVrHZ94+AGGbLdT0x1MM1XzXB732ENsOqB6H/ffcsnn8vzv5vHdvMy8t1cmjKe4uJhV+6zJjrvvx7V3PMhVt9zPWZdcz4qrrM5f/z66yY+tuVhnvQ34dOpHlX381KMPstmArau12WzAEB7Nnsfjnvj+PN54swF8+N4U5n83j/KyMia9/CKrrb4mEcGmW27FpJczD/V69cXnWXX1NWt+dEFaZe2+fP7pVGZP/5SyhQt5Zdyj9Os/sFqbae+/zd2jL+Sw4efSscqkxblzvqFs4QIAvv36Sz586zV6rtKnKcOXMqIZ/ORJXbOwYynLS1pvdoqL23DkcX9k5PBjqSgvZ9ud92C1Ndbi1huuYu11+7HpwCFst8seXHrOaRxzyB507NSF35+WuY3BTnvuzxXnn8nxh+8HJLbZcXf6rLUOn30ylQtOPwnIDFsN3m4nfrrZgDweZf4Vt2nDUb8/hRF/OIpZs0y4AAAMU0lEQVSKigp22HUPVl9jbW667krW6duPLQZtzY677sWos/7MEQf8nE6dOzP8zPMBeP3Vidz5rzG0adOGiCKOPvFUumQnGXw3bx4TJ4znuJNH5PPwmoVF5/JZw4+loiJzLq/aZy1uu+Eq1lqvH5sOyJ7L557GsYdmz+URmXP5qy+/4KzhxxJFQfeSnvzu1JF5PprmqbhNG4YdP5wzTz6aiooKtst+X/xrzJWsvV4/Nh+4NT/bZU8uOmcEvzlodzp17sxJp58HQMdOndljv0P4w28PIQg22WIQ/bfMXJN32G+O56JzRnDd5aPo0rUbvxt+Zh6PsvkoLm7D7kecwJizT6KiooL+2+xCr1XX4KHbrmeVtfrSb9OB/O+mq1nw3Tz+deEZwPe365k57UPuumYUUVREqqhg6z0PrjZ7W/V347lDGbzJOpR07ciUB0Yy8uqx3HjPs/kOSy1AZC5pXMrOiJdSShvXXF7S+tJMmjZn6R+gZaJ92+J8h9DqzV1Qnu8QCkLb4pwvy1aO3pj5db5DaPUOOuysfIdQEOZNvDzvhazpXy/Me47Tq3PbvPRDXRXIvhHxKplq41rZZbLrjsNIkiQVoPo8ylCSJEmqVFcCeW1KaYcmiUSSJKkF8Uk0S1dax35JkiQVmLoqkF0iYqmPWUkp3bWM45EkSWoRCvlJNHUmkMBuLPmWPQkwgZQkSSowdSWQH6aUmv0NwyVJktR06roGcr2qjzBcJCIGRsRajRSTJElS85fvp9DkcQS9rgTyOWBJd539Grh42YcjSZKk5q6uIexOKaXXam5MKb0WEX0aJSJJkqQWoHCn0NRdgexWy772yzIQSZIktQx1JZAvRMSva26MiCOBFxsnJEmSJDVndQ1hnwDcHREH833C2B9YDtirMQOTJElqzgr5STS1JpAppenAgIjYBtgwu/m/KaVHGz0ySZIkNUt1VSABSCk9BjzWyLFIkiSpBahXAilJkqTqCvlRhnVNopEkSZKqsQIpSZLUAIU8icYKpCRJknJiAilJkqScmEBKkiQpJyaQkiRJyomTaCRJkhrASTSSJElSPZlASpIkKScOYUuSJDWAT6KRJEmS6skKpCRJUgM4iUaSJEmqJxNISZIk5cQhbEmSpAYo4BFsK5CSJEnKjRVISZKkhijgEqQVSEmSJOXEBFKSJEk5cQhbkiSpAXwSjSRJklRPJpCSJEnKiUPYkiRJDeCjDCVJkqR6sgIpSZLUAAVcgLQCKUmSpNyYQEqSJCknDmFLkiQ1RAGPYVuBlCRJUk6sQEqSJDWAT6KRJEmS6skEUpIkSTlxCFuSJKkBfBKNJEmSVE+RUsp3DM1ORAxLKY3OdxytmX3c+OzjpmE/Nz77uPHZx8qVFcglG5bvAAqAfdz47OOmYT83Pvu48dnHyokJpCRJknJiAilJkqScmEAumdeBND77uPHZx03Dfm589nHjs4+VEyfRSJIkKSdWICVJkpQTE0hJkiTlpFUkkBGRIuLmKuttImJmRNxfo909ETF+Ca//ZURMiojXImJiRJwUEVdExMsRMTki5mWXX46IfSPiHxGxb5XX3xURH0TEj6q0mx0R72eXH27cHsiPiCivcrwvR8RHEXF5jTYvR8RtNbb9o0rfvBQRWy5h+8sR8Ux2+/ER8WL2Z0JE/LLpjjK/ImJOleUVI2JKRPw8u/5B9pxd1F+X1nHeRkSMiIh3IuLtiHgsIjbIvlf3iHgmIp7P/j/5T0SU5uu4m1pE7Jn9HumbXe9To/9ejohjqrQflO2rN7M/w6rsOzMipmVfMykido+IP1d5n6r/bn6Xj+NtTHV9L0TEsCr99nxEDKqyr21EnJc9R1+KiGcjYucq+6t+R0xZ9B0fEUMj851f9XP7VXndnCrbZ0bE0CbqjiYVEb0j4raIeDf7fflYRMxd0u+kpZzjv6zyXrX1dc3v+ccjon9TH6/yLKXU4n+AOcDLQPvs+s7Z9furtOkKfAy8AaxZZfvOwEvAStn15YFfV9nfB5hU4/P+AeybXd4A+AD4YGltWusPMKfG+lDg8irr6wOvAdOADkvpvx2AV2vrM2BVoCi73Cnb3yvn+/ibso+zxz2xxrn5AVCylNct6bw9FhgLrFCl798F2mXP+95V2h4PjM738TdhP98OPAX8ZWn9V6Vtb+AjYOPsegnwIrBrdv1M4KTs8vrArEXnb9X/p631p7bvBWC3bF+VZNc3zvZl7+z6ecCNwPLZ9V7A/lXe60Zgn+zy1mS/42t+99QWE3A5MDTf/dQI/R7As8Bvq2zbCBicXa72/VrbOZ5rXwOPA/3z3Qf+NO1Pq6hAZo0Fds0uHwjcWmP/3sB9wG3AAVW2n0rmy/4TgJTS/JTStTl87p+BUQ2KuPU7ELgJeAjYYyltngTWru1NUkofp5QqsqvLA2XA/GUVZHMXEW2Bu4B7czw3axoOHJtSmguQUnoIeAY4OHvef1al7QrAlz/gs1qMiOgIDAKOoPp3w9IcA/wjpfQSQEppFvBH4JSaDVNKb5A5X0uWWcAt23Dg5Gyfke3DG4FjImIF4NfAcSml+dn901NKd1R5fRsy/ZmrirqbtHjbAAtTSlcv2pBSeiWl9FQD36+hfa0C0ZoSyNuAAyKiHfBj4Lka+xcllbdmlxfZkMxfxDmLiHWAnwB31NW2QP2CzP+Xmn1e1c/JVCkX+VuV4ZR/LdoYEb+IiNfJVDMvXvQLqECMAYaw+B9FAI9V6a/fL+0NIqIzmSrwezV2TSBTRV/U7rGI+BQ4HDj3h4feIuwBPJBSehv4PCI2yW5fq0rf/nfR8DaZ/qr5nVGtHxeJiM3JJC8zGyn2lqa2vlsb+Cil9HUtr+8EzFvKvl/UGI5tDxARywHf/cC4W4KG/C5bq0afDa6yr959DTh8XYDa5DuAZSWl9GpE9CGTqIytui8iegHrAE+nlFJELIyIDVNKk37gx54K/I3C+Os2J9nrYWallD6KiGnAmIjonlKanW3yt4gYQeYX6xFVXnpySunfNd8vpXQ7cHtErEImaXowpfROYx9HM9AB6EFm2OgKYLsa+7dZlsl0SmmbiAjgT8BF2c9t7Q4ELsku35Zdvxx4N6X0E8j8AQPcAGxZz/f8fUQcAnwD/CKl5P3Slo2ewIyl7Ls9pXTsErb3Ar5ovJBatMpzfAnq3dcR8fiyDkzNX2uqQALcS2Y4uWalZn+gG/B+RHxA5tqPRRWx14FNyN1qZIa9bmpIoAXgQKBvtr/fBToD+1TZf3JK6ScppZ/lksinlKYCT5Op/BaC+cB+KaVbgLKIOLghb5Kt6nwbEWvW2LUJmX8DVdsm4GZg04Z8VksSEd2BbYHrsufqyWS+L6JG03vJjGwATGbx74ya/XhR9vwe/AOGEFuj2vpuCrBatlq+mIgoIlOlzPUPx77ADy0WtAQN/V22mB/Q1yogrS2BHEPmIvjXamw/ENgppdQnpdSHzD+yRdc6nUumGtYbMsMdEXFkPT7rBDK/JLxGpIbsl8/+wI+q9PkeLH0Yu673G5wdhlr0C38LMhNKCkFZSunb7PIxwNkR0aWB7/U34NIqQ3vbk/kj6Jbs7M2qQ7B7As83NOgWZF/gppTS6tlzdVXgfTITt6oaBCwa/r8CGBoRi6qTPYDzgQuaKOaW7ALg/Gyfke3DocCV2WtzrwcuqfLvvTQi9ouIYjLXm0+o8u+hvvYnM7mktXsUWD6q3xHgxzWGpev0A/taBaTVDGFDZXXq0qrbssPaqwPjq7R7PyK+iojNU0pjs0PcD2eH7hKZRLQuberZrhANBqYtmpiU9STQLyJWrOO1i4a2F9kMGABcERHlwALgTymlKcs04hYgpTQlIm4AziGTTEJmOL88u/xqSqm2WxxdRqYS/1r2NZ8Be6SU5mV/oY+OiA5k/g28BrS6W8wswYFkkr+q/kPm8pS1std3BZnJBMMAUkqfZoenr42ITtn9F6eU7mu6sFumlNK9EbEy8ExEJDJD/IeklD7NNhkBnAVMjojvgG+B04FDyVTEf1XL2/8iqtwSCDiazB+uRwLvZP+fAawM7B4Rk1JKE5bVseVb9vKsvYCLI2I4mes+PyBT7FiaRef4ImOAr6m7ryUfZShJap0i4h9kZsw/Xp/tkuqvtQ1hS5IkqZFZgZQktUrZe6iWV7mPbK3bJdWfCaQkSZJy4hC2JEmScmICKUmSpJyYQEqSJCknJpCSJEnKyf8HSksfaMvWXGQAAAAASUVORK5CYII=\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "events = [\n",
        " 'МАСТИТ',\n",
        " 'ПАРЕЗ',\n",
        " 'КЕТОЗ',\n",
        " 'АБОРТ',\n",
        " 'ПОСЛЕД',\n",
        " 'СТЕЛН']\n",
        "\n",
        "cow_unique = pd.DataFrame([], columns=events)\n",
        "\n",
        "for event in events:\n",
        "    damp_tmp = damp[damp['Событие'] == event]\n",
        "    damp_tmp['tmp'] = 1\n",
        "    number_cows = damp_tmp.groupby(by='Номер животного')['tmp'].sum()\n",
        "    cow_unique[event] = number_cows\n",
        "\n",
        "# calculate the correlation matrix\n",
        "corr = cow_unique.corr()\n",
        "sb.heatmap(corr, cmap=\"Blues\", annot=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "b4e66707"
      },
      "source": [
        "## Гипотеза. Существуют вспышки заболеваний в течение года, возможно они имеют связь с проведенной вакцинацией"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "21d59ad7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "outputId": "677061a9-27ac-4b9e-e526-8af46c60d2ae"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "events = [\n",
        " 'МАСТИТ',\n",
        " 'БОЛЕЗНЬ',\n",
        " 'ПАРЕЗ',\n",
        " 'МЕТРИТ',\n",
        " 'КЕТОЗ']\n",
        "\n",
        "ts_events = pd.DataFrame([], index = damp['Дата события'].unique(), columns=events)\n",
        "\n",
        "for event in events:\n",
        "    count_events = damp[damp['Событие']==event].groupby(by='Дата события').count()['Событие']\n",
        "    ts_events[event] = count_events\n",
        "\n",
        "px.bar(ts_events.groupby(pd.Grouper(freq='2W')).sum(),\n",
        "      labels={'value': 'Количество событий',\n",
        "              'index': 'Таймлайн, частота - 2 недели'},\n",
        "      title = 'Заболеваемость (высота столба = сумма выявленных заболеваний за период)')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "88a6053a"
      },
      "source": [
        "## Гипотеза. Заболевание Кетозом зависит от количества дней в сухостое"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "5d18d59c",
        "outputId": "2b8f85e0-1a80-4ebb-927c-77fe04499ba3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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              "                            \n",
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            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "data = damp[(damp['Событие']== 'КЕТОЗ') & (damp['Номер лактации'] != 1)]\n",
        "px.histogram(data, x='Дней в сухостое предыдущей лактации', color='Текущий возраст',\n",
        "            title='Рапределение заболевших КЕТОЗОМ особей по количеству дней в сухостое') \\\n",
        ".update_layout(\n",
        "    yaxis_title='Количество заболевших КЕТОЗОМ'\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Заполнение таблицы статистика"
      ],
      "metadata": {
        "id": "_tSAx4O_FbN0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import re\n",
        "\n",
        "years = list(range(2020, 2023))\n",
        "months = list(range(1, 13))\n",
        "\n",
        "advanced_index = ['Мастит случаев за период', 'Мастит голов за период', 'Передняя левая', 'Передняя правая', 'Задняя левая', 'Задняя правая']\n",
        "\n",
        "target = ['МВ', 'КМ5', 'КМ7', 'КМ1', 'КМ6', 'КМ3', 'КМ8', 'КМ4', 'ТМ1', 'КМ2', 'ТМ5', 'СКМ2', 'ТМ2',\n",
        "          'ТМ3', 'СКМ5', 'ТМ4', 'СКМ3', 'СКМ4', 'СКМ6', 'СКМ1', 'ТМ6', 'ТМ9']\n",
        "heads_total = ['МВ_heads', 'КМ5_heads', 'КМ7_heads', 'КМ1_heads', 'КМ6_heads', 'КМ3_heads', 'КМ8_heads',\n",
        "               'КМ4_heads', 'ТМ1_heads', 'КМ2_heads', 'ТМ5_heads', 'СКМ2_heads', 'ТМ2_heads',\n",
        "               'ТМ3_heads', 'СКМ5_heads', 'ТМ4_heads', 'СКМ3_heads', 'СКМ4_heads', 'СКМ6_heads', \n",
        "               'СКМ1_heads', 'ТМ6_heads', 'ТМ9_heads']\n",
        "heads_1_time = ['МВ_heads_1_time', 'КМ5_heads_1_time', 'КМ7_heads_1_time', 'КМ1_heads_1_time', 'КМ6_heads_1_time', 'КМ3_heads_1_time', 'КМ8_heads_1_time',\n",
        "               'КМ4_heads_1_time', 'ТМ1_heads_1_time', 'КМ2_heads_1_time', 'ТМ5_heads_1_time', 'СКМ2_heads_1_time', 'ТМ2_heads_1_time',\n",
        "               'ТМ3_heads_1_time', 'СКМ5_heads_1_time', 'ТМ4_heads_1_time', 'СКМ3_heads_1_time', 'СКМ4_heads_1_time', 'СКМ6_heads_1_time', \n",
        "               'СКМ1_heads_1_time', 'ТМ6_heads_1_time', 'ТМ9_heads_1_time']\n",
        "heads_2_times = ['МВ_heads_2_times', 'КМ5_heads_2_times', 'КМ7_heads_2_times', 'КМ1_heads_2_times', 'КМ6_heads_2_times', \n",
        "                 'КМ3_heads_2_times', 'КМ8_heads_2_times', 'КМ4_heads_2_times', 'ТМ1_heads_2_times', 'КМ2_heads_2_times', \n",
        "                 'ТМ5_heads_2_times', 'СКМ2_heads_2_times', 'ТМ2_heads_2_times', 'ТМ3_heads_2_times', 'СКМ5_heads_2_times',\n",
        "                 'ТМ4_heads_2_times', 'СКМ3_heads_2_times', 'СКМ4_heads_2_times', 'СКМ6_heads_2_times', 'СКМ1_heads_2_times',\n",
        "                 'ТМ6_heads_2_times', 'ТМ9_heads_2_times']\n",
        "heads_more_2_times = ['МВ_heads_more_2_times', 'КМ5_heads_more_2_times', 'КМ7_heads_more_2_times', 'КМ1_heads_more_2_times', \n",
        "                      'КМ6_heads_more_2_times', 'КМ3_heads_more_2_times', 'КМ8_heads_more_2_times', 'КМ4_heads_more_2_times',\n",
        "                      'ТМ1_heads_more_2_times', 'КМ2_heads_more_2_times', 'ТМ5_heads_more_2_times', 'СКМ2_heads_more_2_times',\n",
        "                      'ТМ2_heads_more_2_times', 'ТМ3_heads_more_2_times', 'СКМ5_heads_more_2_times', 'ТМ4_heads_more_2_times',\n",
        "                      'СКМ3_heads_more_2_times', 'СКМ4_heads_more_2_times', 'СКМ6_heads_more_2_times', 'СКМ1_heads_more_2_times',\n",
        "                      'ТМ6_heads_more_2_times', 'ТМ9_heads_more_2_times']\n",
        "idx_names = advanced_index + target + heads_total + heads_1_time + heads_2_times + heads_more_2_times\n",
        "\n",
        "print(idx_names)\n",
        "col_names = ['Index']\n",
        "\n",
        "for year in years:\n",
        "        for month in months:\n",
        "            col_names.append(str(month) + '.' + str(year))\n",
        "            \n",
        "output = pd.DataFrame([], columns=col_names)\n",
        "\n",
        "output['Index'] = advanced_index + target + heads_total + heads_1_time + heads_2_times + heads_more_2_times\n",
        "output.head(50)"
      ],
      "metadata": {
        "id": "ScoGNcG5Dh2d"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Смотрим на кодировку части вымени в примечениях\n",
        "unique_km1_descriptions = damp[damp['Примечание события'].str.startswith('КМ5')]['Примечание события'].unique()\n",
        "unique_km1_descriptions"
      ],
      "metadata": {
        "id": "Z06NvVHvDhz1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Формализуем интерпретацию примечания, проверяем\n",
        "from functools import partial\n",
        "\n",
        "def is_ill_part(part, description):\n",
        "    prefix = description.split('_')[0]\n",
        "    if prefix == 'МВ' or prefix not in target:\n",
        "        return 0\n",
        "    description = description[len(prefix) + 1:]  # remove prefix\n",
        "    # Unify\n",
        "    description = description.replace('?', '')\n",
        "    if not description:\n",
        "        return 0\n",
        "    # description = description.replace('-', '')\n",
        "    # description = description.replace('_', '')\n",
        "    if len(description) == 2:\n",
        "        if ',' in description:\n",
        "            parts = description.replace(',', '')  # КМ6_,3\n",
        "        else:\n",
        "            # parts = map(str, range(int(description[0]), int(description[1]) + 1))\n",
        "            parts = description\n",
        "    elif ',' in description:\n",
        "        parts = description.replace(',', '')\n",
        "        # if len(parts) == 2:\n",
        "        #     parts = map(str, range(int(parts[0]), int(parts[1]) + 1))\n",
        "    elif '-' in description:\n",
        "        parts = description.replace('-', '')\n",
        "        # if len(parts) == 2:\n",
        "        #     parts = map(str, range(int(parts[0]), int(parts[1]) + 1))\n",
        "    elif '_' in description:\n",
        "        parts = description.replace('_', '')\n",
        "        # Этот разделитель влияет на категории 2 и 3!\n",
        "        if len(parts) == 2:\n",
        "            parts = map(str, range(int(parts[0]), int(parts[1]) + 1))\n",
        "    else:\n",
        "        parts = description\n",
        "    return int(part in parts)\n",
        "\n",
        "# 1 -> ['Передняя левая']\n",
        "# 2 -> ['Передняя правая']\n",
        "# 3 -> ['Задняя левая']\n",
        "# 4 -> ['Задняя правая']\n",
        "\n",
        "is_front_left_ill = partial(is_ill_part, '1')\n",
        "is_front_right_ill = partial(is_ill_part, '2')\n",
        "is_back_left_ill = partial(is_ill_part, '3')\n",
        "is_back_right_ill = partial(is_ill_part, '4')\n",
        "\n",
        "list(zip(unique_km1_descriptions, map(is_back_left_ill, unique_km1_descriptions)))"
      ],
      "metadata": {
        "id": "kS1lMIGDDhxO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Применяем интерпретацию к датафрейму, конструируем новые признаки\n",
        "damp['Передняя левая'] = damp['Примечание события'].map(is_front_left_ill)\n",
        "damp['Передняя правая'] = damp['Примечание события'].map(is_front_right_ill)\n",
        "damp['Задняя левая'] = damp['Примечание события'].map(is_back_left_ill)\n",
        "damp['Задняя правая'] = damp['Примечание события'].map(is_back_right_ill)"
      ],
      "metadata": {
        "id": "_u3rqlmDDhuk"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def get_value_from_df(month, year, reg):\n",
        "    return damp[(damp['Дата события'].dt.month == month) & (damp['Дата события'].dt.year == year)]['Примечание события'].str.contains(fr\"{reg}\")\n",
        "\n",
        "def get_sub_df(month, year):\n",
        "    return damp[(damp['Дата события'].dt.month == month) & (damp['Дата события'].dt.year == year)]\n",
        "\n",
        "def cases_n_times(df):\n",
        "    length = []\n",
        "    for number in sorted(df['Номер животного'].unique()):\n",
        "        length.append(len(df[df['Номер животного'] == number]))\n",
        "    num_1_time = len([el for el in length if el == 1]) # голов 1 раз\n",
        "    num_2_times = len([el for el in length if el == 2]) # голов 2 раза\n",
        "    num_more_2_times = len([el for el in length if el > 2]) # голов > 2 раз\n",
        "    return num_1_time, num_2_times, num_more_2_times\n",
        "\n",
        "for year in years:\n",
        "    for month in months:\n",
        "        cases = []\n",
        "        heads_mastit = []\n",
        "        num_heads_total = []\n",
        "        num_1_total = []\n",
        "        num_2_total = []\n",
        "        num_more_2_total = []\n",
        "        \n",
        "        df_month = get_sub_df(month, year)\n",
        "        front_left_sum = df_month['Передняя левая'].sum()\n",
        "        front_right_sum = df_month['Передняя правая'].sum()\n",
        "        back_left_sum = df_month['Задняя левая'].sum()\n",
        "        back_right_sum = df_month['Задняя правая'].sum()\n",
        "\n",
        "        for t in target:\n",
        "            cases.append(get_value_from_df(month, year, fr\"{t}\").sum())\n",
        "            num_heads_total.append(len(df_month[df_month['Примечание события'].str.contains(fr\"{t}\")].drop_duplicates('Номер животного'))) # голов всего 63\n",
        "            num_1_time, num_2_times, num_more_2_times = cases_n_times(df_month[df_month['Примечание события'].str.contains(fr\"{t}\")])\n",
        "            num_1_total.append(num_1_time)\n",
        "            num_2_total.append(num_2_times)\n",
        "            num_more_2_total.append(num_more_2_times)\n",
        "\n",
        "        heads_mastit.append(df_month[df_month['Событие'] == 'МАСТИТ'].groupby(by='Номер животного')['Номер животного'].count().count())\n",
        "\n",
        "        output[str(month) + '.' + str(year)] = [sum(cases)] + heads_mastit + [front_left_sum, front_right_sum, back_left_sum, back_right_sum] + cases + num_heads_total + num_1_total + num_2_total + num_more_2_total       \n",
        "    #     break\n",
        "    # break\n",
        "\n",
        "output.head(200)"
      ],
      "metadata": {
        "id": "btgY7lcDDhsQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "output.to_excel('statistics_output.xlsx', encoding='cp1251')"
      ],
      "metadata": {
        "id": "A1lbra-wDhoW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Байесовские сети"
      ],
      "metadata": {
        "id": "cuxJVV9fIWdV"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "[Ссылка на ноутбук с расчетом Байесовских сетей (эту библиотеку не запустить в Colab)](https://niuitmo-my.sharepoint.com/:u:/g/personal/sidorenko_den95_niuitmo_ru/EVt-sTMMUrNGqt9DPFTuODMB9f-XEAErqp4dCCEStRTPuw?e=fI5kjh)"
      ],
      "metadata": {
        "id": "a6JROI4tdrHJ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Поиск наиболее эффективных протоколов"
      ],
      "metadata": {
        "id": "s4A0QbF4RPlo"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Подготовка данных, фильтр по событию МАСТИТ и ЗДОРОВА (при условии, что здорова \n",
        "#                                                        после мастита)\n",
        "mastit = damp[(\n",
        "    (damp['Событие']== 'МАСТИТ')|\n",
        "    ((damp['Событие']== 'ЗДОРОВА')&(damp['Примечание события']=='МАСТИТ'))\n",
        "    )]\n",
        "\n",
        "# Отсеиваем мастит, которых лечился с помощью продоя\n",
        "# MB in Russian\n",
        "mastit = mastit[mastit['Примечание события']!='МВ']\n",
        "# MB in English\n",
        "mastit = mastit[mastit['Примечание события']!='MB']\n",
        "\n",
        "# Датасет может содержать протоколы лечения [СКМ*_*, КМ*_*, TM*_*]\n",
        "# КМ, ТМ - клинический и травматический мастит\n",
        "# СКМ - субклинический мастит, обозначает улучшение состояния животного или легкую форму заболевания\n",
        "\n",
        "# Во время предобработки данных исходим из того, что нам необходимо понять, является ли \n",
        "# успешным лечением протокол при переходе из одного события в другое\n",
        "# Например, цепочка событий [КМ1_1, КМ1_2, СКМ1_2, КМ2_2, ЗДОРОВА] фактически означает\n",
        "# набор успехов/неудач вида [успех, успех, неудача, успех]\n",
        "# Такой подход свидетельствует о том, что мы можем упростить весь набор аббревиатур следующим образом:\n",
        "\n",
        "mastit['Примечание события'] = mastit['Примечание события'].str.replace(r'.*КМ1.*', 'КМ1', regex=True)\n",
        "mastit['Примечание события'] = mastit['Примечание события'].str.replace(r'.*КМ2.*', 'КМ2', regex=True)\n",
        "mastit['Примечание события'] = mastit['Примечание события'].str.replace(r'.*КМ3.*', 'КМ3', regex=True)\n",
        "mastit['Примечание события'] = mastit['Примечание события'].str.replace(r'.*КМ4.*', 'КМ4', regex=True)\n",
        "mastit['Примечание события'] = mastit['Примечание события'].str.replace(r'.*КМ5.*', 'КМ5', regex=True)\n",
        "mastit['Примечание события'] = mastit['Примечание события'].str.replace(r'.*КМ6.*', 'КМ6', regex=True)\n",
        "mastit['Примечание события'] = mastit['Примечание события'].str.replace(r'.*КМ7.*', 'КМ7', regex=True)\n",
        "mastit['Примечание события'] = mastit['Примечание события'].str.replace(r'.*КМ8.*', 'КМ8', regex=True)\n",
        "\n",
        "mastit['Примечание события'] = mastit['Примечание события'].str.replace(r'.*ТМ1.*', 'КМ1', regex=True)\n",
        "mastit['Примечание события'] = mastit['Примечание события'].str.replace(r'.*ТМ2.*', 'КМ2', regex=True)\n",
        "mastit['Примечание события'] = mastit['Примечание события'].str.replace(r'.*ТМ3.*', 'КМ3', regex=True)\n",
        "mastit['Примечание события'] = mastit['Примечание события'].str.replace(r'.*ТМ4.*', 'КМ4', regex=True)\n",
        "mastit['Примечание события'] = mastit['Примечание события'].str.replace(r'.*ТМ5.*', 'КМ5', regex=True)\n",
        "mastit['Примечание события'] = mastit['Примечание события'].str.replace(r'.*ТМ6.*', 'КМ6', regex=True)\n",
        "mastit['Примечание события'] = mastit['Примечание события'].str.replace(r'.*ТМ9.*', 'ТМ9', regex=True)\n",
        "\n",
        "mastit['Примечание события'] = mastit['Примечание события'].replace('МАСТИТ', 'well')\n",
        "\n",
        "mastit['event'] = mastit['Примечание события'].apply(lambda x: [x])\n",
        "\n",
        "df = mastit.groupby('Номер животного')['event'].sum()\n",
        "\n",
        "# После упрощения и перезаписи всех исходов событий мы получим трансформированные цепочки событий\n",
        "# Например, цепочка событий из первого примера [КМ1_1, КМ1_2, СКМ1_2, КМ2_2, ЗДОРОВА] \n",
        "# перейдет в состояние [КМ1, КМ1, КМ1, КМ2, ЗДОРОВА], чтобы неизменять результат \n",
        "# набора успехов/неудач и сохранить для этого случая его как [успех, успех, неудача, успех]\n",
        "# составим алгоритм, который будет учитывать введённые закономерности:\n",
        "\n",
        "success = pd.DataFrame(columns=mastit.columns)\n",
        "fail = pd.DataFrame(columns=mastit.columns)\n",
        "\n",
        "# рассматриваем каждую корову\n",
        "for cow in df.index:\n",
        "\n",
        "  # создаем временный датафрейм для конкретной коровы\n",
        "    tmp = mastit[mastit['Номер животного'] == cow]\n",
        "    tmp.index = [x for x in range(len(tmp))]\n",
        "\n",
        "    # проходимся по всем событиям, которые были у коровы\n",
        "    for i in range(len(tmp) - 1):\n",
        "\n",
        "      # если корова выздоровела - эта строка нас не интересует\n",
        "        if tmp['Примечание события'][i] == 'well':\n",
        "            continue\n",
        "\n",
        "      # но если следующий протокол соответствует рассматрвиаемому или следующий - выздоровление, то\n",
        "      # сохраняем такое событие как успешное в отдельный датафрейм с упешно завершенными протоколами\n",
        "        elif tmp['Примечание события'][i] == tmp['Примечание события'][i + 1] or \\\n",
        "        tmp['Примечание события'][i + 1] == 'well':\n",
        "            success = success.append(tmp.loc[i])\n",
        "\n",
        "      # если следующий протокол не соответствует данному и не является \"ЗДОРОВА\", то\n",
        "      # сохраняем такое событие как неудачное\n",
        "        else:\n",
        "            fail = fail.append(tmp.loc[i])\n",
        "\n",
        "# Можем сохранить данные в CSV формате\n",
        "# ===============================\n",
        "# success.to_csv('success.csv')\n",
        "# fail.to_csv('fail.csv')\n",
        "# ===============================\n",
        "\n",
        "# В итоге мы получили 2 датафрейма с успешными и неуспешными событиями, \n",
        "# посчитаем статистики для каждого датафрейма и найдем вероятности успеха применения каждого протокола по стаду\n",
        "\n",
        "probability = pd.DataFrame(success['Примечание события'].value_counts())\n",
        "probability['success'] = probability['Примечание события']\n",
        "probability['fail'] = fail['Примечание события'].value_counts()\n",
        "probability['prob_success'] = np.round(probability['success']/(probability['fail']+probability['success']), 2)\n",
        "probability.drop('Примечание события', axis=1, inplace=True)\n",
        "probability = probability.sort_values('prob_success', ascending = False)\n",
        "\n",
        "display(probability)"
      ],
      "metadata": {
        "id": "4y1UUnQBJgWU",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 328
        },
        "outputId": "4ee1cc15-f115-4114-fb57-ec120c8faeee"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "     success  fail  prob_success\n",
              "ТМ9      237    69          0.77\n",
              "КМ3      896   365          0.71\n",
              "КМ1     1111   668          0.62\n",
              "КМ2      351   212          0.62\n",
              "КМ5      642   454          0.59\n",
              "КМ6      301   207          0.59\n",
              "КМ4      381   334          0.53\n",
              "КМ7       11    24          0.31\n",
              "КМ8        4    16          0.20"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-c41f46e1-ffcd-44a3-b3e5-2704bcaf633d\">\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>success</th>\n",
              "      <th>fail</th>\n",
              "      <th>prob_success</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>ТМ9</th>\n",
              "      <td>237</td>\n",
              "      <td>69</td>\n",
              "      <td>0.77</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>КМ3</th>\n",
              "      <td>896</td>\n",
              "      <td>365</td>\n",
              "      <td>0.71</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>КМ1</th>\n",
              "      <td>1111</td>\n",
              "      <td>668</td>\n",
              "      <td>0.62</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>КМ2</th>\n",
              "      <td>351</td>\n",
              "      <td>212</td>\n",
              "      <td>0.62</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>КМ5</th>\n",
              "      <td>642</td>\n",
              "      <td>454</td>\n",
              "      <td>0.59</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>КМ6</th>\n",
              "      <td>301</td>\n",
              "      <td>207</td>\n",
              "      <td>0.59</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>КМ4</th>\n",
              "      <td>381</td>\n",
              "      <td>334</td>\n",
              "      <td>0.53</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>КМ7</th>\n",
              "      <td>11</td>\n",
              "      <td>24</td>\n",
              "      <td>0.31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>КМ8</th>\n",
              "      <td>4</td>\n",
              "      <td>16</td>\n",
              "      <td>0.20</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c41f46e1-ffcd-44a3-b3e5-2704bcaf633d')\"\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",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
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              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
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              "          document.querySelector('#df-c41f46e1-ffcd-44a3-b3e5-2704bcaf633d button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-c41f46e1-ffcd-44a3-b3e5-2704bcaf633d');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Поиск особей, резистентных к конкретному протоколу лечения"
      ],
      "metadata": {
        "id": "7ePy1e8tRhdH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# воспользуемся ранее полученным датафреймом с неуспешными событиями,\n",
        "# составим данные вида Номер_животного -> [список неудачных протоколов]\n",
        "df_fail = fail.groupby('Номер животного')['event'].sum()\n",
        "df_fail = pd.DataFrame(df_fail)\n",
        "df_fail['len'] = df_fail['event'].apply(lambda x: len(x))\n",
        "\n",
        "# отбросим особей, у которых менее 3 неудачных событий \n",
        "df_fail = df_fail[df_fail['len'] > 3]\n",
        "df_fail['cow'] = df_fail.index\n",
        "\n",
        "# условимся, что резистентность к лекарствам того или иного протокола лечения - это \n",
        "# случай, когда корова не вылечилась от мастита одним и тем же протоколом более 3 раз\n",
        "df_fail['resistance'] = df_fail['event'].apply(lambda x: set([res for res in x if x.count(res) >= 3]))\n",
        "df_fail['resistance'] = df_fail['resistance'].apply(lambda x: None if len(x)==0 else x)\n",
        "df_fail.dropna(axis=0, inplace=True)\n",
        "\n",
        "# на данном шаге около 200 коров являются резистентными к одному или нескольким протоколам, однако\n",
        "# некоторые из них уже были проданы или умерли,\n",
        "# найдем живых особей и отразим их возраст на момент последнего события в истории записей\n",
        "alive_cows = []\n",
        "age_alive_cows = []\n",
        "for cow in df_fail['resistance'].index:\n",
        "    tmp = damp[damp['Номер животного']==cow]\n",
        "    tmp['event'] = tmp['Событие'].apply(lambda x: [str(x)])\n",
        "    tmp = tmp.groupby(by='Номер животного')['event'].sum()\n",
        "    \n",
        "    if 'ПРОДАНА' in tmp.values[0] or \"ПАЛА\" in tmp.values[0]:\n",
        "        continue\n",
        "    else:\n",
        "        alive_cows.append(cow)\n",
        "        tmp = damp[damp['Номер животного']==cow]\n",
        "        age = tmp['Текущий возраст'].iloc[-1]\n",
        "        age_alive_cows.append(age)\n",
        "        \n",
        "resistance = df_fail.loc[alive_cows]\n",
        "resistance['age'] = age_alive_cows\n",
        "resistance.drop(['event', 'len', 'cow'], axis=1, inplace=True)\n",
        "\n",
        "# Только 2 живущих ныне животных обрели резистентность к лекарствам указанных протоколов лечений.\n",
        "# Это говорит о том, что никакой зависимости между эффективностью протоколов и индивидуальными\n",
        "# особенностями животных не существует. \n",
        "display(resistance)"
      ],
      "metadata": {
        "id": "wXJQTV8dJgTy",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 142
        },
        "outputId": "4ce1e64d-aea3-4b3f-e404-b3b5ec1bf1f0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "                resistance  age\n",
              "Номер животного                \n",
              "512017               {ТМ9}    7\n",
              "711136               {КМ1}    5"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-499e8c6e-5664-4a95-aee8-86b735966af8\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>resistance</th>\n",
              "      <th>age</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Номер животного</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>512017</th>\n",
              "      <td>{ТМ9}</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>711136</th>\n",
              "      <td>{КМ1}</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-499e8c6e-5664-4a95-aee8-86b735966af8')\"\n",
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              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
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              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-499e8c6e-5664-4a95-aee8-86b735966af8 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-499e8c6e-5664-4a95-aee8-86b735966af8');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
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              "  "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Собрание датасета для генеологического графа"
      ],
      "metadata": {
        "id": "w_7l3-6etL1o"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "ancestors = pd.read_csv('Предки.CSV', sep=';', encoding='cp1251')\n",
        "\n",
        "ancestors = ancestors.drop(25498)\n",
        "ancestors['Номер животного'] = ancestors['Номер животного'].astype('int64')\n",
        "ancestors['Номер Матери'] = ancestors['Номер Матери'].astype('int64')\n",
        "\n",
        "ancestors"
      ],
      "metadata": {
        "id": "5VO3NOlqtTqa"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "family_df = pd.DataFrame([])\n",
        "\n",
        "family_df['Номер животного'] = damp['Номер животного'].unique()\n",
        "\n",
        "mother, father, sex, mastit = [], [], [], []\n",
        "for cow in family_df['Номер животного']:\n",
        "  mother.append(ancestors[ancestors['Номер животного'] == cow]['Номер Матери'].values[0])\n",
        "  father.append(ancestors[ancestors['Номер животного'] == cow]['Код Отца'].values[0])\n",
        "  sex.append(damp[damp['Номер животного'] == cow]['Пол'].values[0])\n",
        "  \n",
        "  if damp[(damp['Номер животного'] == cow) & (damp['Событие'] == 'МАСТИТ')]['Пол'].values.size != 0:  \n",
        "    mastit.append(True)\n",
        "  else:\n",
        "    mastit.append(False)\n",
        "\n",
        "family_df['Пол'] = sex\n",
        "family_df['Однажды болел маститом'] = mastit\n",
        "family_df['Номер матери'] = mother\n",
        "family_df['Код отца'] = father\n",
        "\n",
        "family_df"
      ],
      "metadata": {
        "id": "hypVR4y-JgRT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "family_df = family_df[family_df['Номер матери'] != 0]\n",
        "ancestors = ancestors[ancestors['Номер матери'] != 0]"
      ],
      "metadata": {
        "id": "7Xz1XUHVtE_y"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Построение графа"
      ],
      "metadata": {
        "id": "bXGHAf1TuZMx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import networkx as nx\n",
        "G = nx.DiGraph()\n",
        "G.add_node(mother, sex='F')\n",
        "\n",
        "for n in family_df['Номер матери'].unique():\n",
        "  G.add_node(n, sex='F')\n",
        "\n",
        "for _, cow in family_df.iterrows():\n",
        "  if cow['Пол'] == 'F':\n",
        "    G.add_node(cow['Номер животного'], sex=cow['Пол'], mastit=cow['Однажды болел маститом'])\n",
        "    G.add_edge(cow['Номер матери'], cow['Номер животного'])"
      ],
      "metadata": {
        "id": "8LdE3BWDtzJE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "nx.write_gexf(G, 'female_cow_graph.gexf')"
      ],
      "metadata": {
        "id": "GuHe8WuZt3Mv"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}