GAM_optim.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyO4nUJfL1yJtK/tRiuofoZI",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/budennovsk/AuthorBooksComments/blob/master/GAM_optim.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install pygam\n",
        "!pip install pytimetk\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "SI5gzXSd_JCl",
        "outputId": "05f00788-7b59-4fd6-a09e-a97d2f381361"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "      Successfully uninstalled pyarrow-14.0.2\n",
            "  Attempting uninstall: polars\n",
            "    Found existing installation: polars 0.20.2\n",
            "    Uninstalling polars-0.20.2:\n",
            "      Successfully uninstalled polars-0.20.2\n",
            "  Attempting uninstall: plotly\n",
            "    Found existing installation: plotly 5.15.0\n",
            "    Uninstalling plotly-5.15.0:\n",
            "      Successfully uninstalled plotly-5.15.0\n",
            "  Attempting uninstall: matplotlib\n",
            "    Found existing installation: matplotlib 3.7.1\n",
            "    Uninstalling matplotlib-3.7.1:\n",
            "      Successfully uninstalled matplotlib-3.7.1\n",
            "  Attempting uninstall: holidays\n",
            "    Found existing installation: holidays 0.55\n",
            "    Uninstalling holidays-0.55:\n",
            "      Successfully uninstalled holidays-0.55\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "cudf-cu12 24.4.1 requires pyarrow<15.0.0a0,>=14.0.1, but you have pyarrow 16.1.0 which is incompatible.\n",
            "ibis-framework 8.0.0 requires pyarrow<16,>=2, but you have pyarrow 16.1.0 which is incompatible.\u001b[0m\u001b[31m\n",
            "\u001b[0mSuccessfully installed adjusttext-0.8 antropy-0.1.6 arch-7.0.0 dill-0.3.8 holidays-0.33 matplotlib-3.9.2 multiprocess-0.70.16 pandas-flavor-0.6.0 pathos-0.3.2 plotly-5.24.0 polars-1.6.0 pox-0.3.4 ppft-1.7.6.8 pyarrow-16.1.0 pytimetk-1.0.1 stochastic-0.7.0 supersmoother-0.4 tsfeatures-0.4.5\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.colab-display-data+json": {
              "pip_warning": {
                "packages": [
                  "pyarrow"
                ]
              },
              "id": "110b32b3ae4e4060b053dfbbb6e6f0c4"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 581
        },
        "id": "OHyaGlzX_FhW",
        "outputId": "96213076-c3a5-488d-d4a8-2b3f28aaffd1"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1324.0, 557.8988835091549)"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ],
      "source": [
        "import numpy as np\n",
        "from pygam import LinearGAM, s\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Данные\n",
        "prices = np.array([1324, 1706, 3735, 11195, 13751, 4755, 10556, 4733, 10701, 21422, 22981, 7994, 21457, 23159, 19329, 24965, 35538, 30202, 27719, 19388, 9006, 41747, 25271, 43175])\n",
        "sales = np.array([584, 540, 504, 488, 447, 559, 483, 535, 466, 450, 432, 553, 452, 441, 450, 441, 456, 462, 468, 468, 533, 425, 486, 468])\n",
        "# Обучение модели\n",
        "gam = LinearGAM(s(0)).fit(prices, sales)\n",
        "\n",
        "# Прогнозирование на следующем месяце для различных цен\n",
        "price_range = np.linspace(min(prices), max(prices), 100)\n",
        "sales_pred = gam.predict(price_range)\n",
        "\n",
        "# Поиск цены, при которой продажи максимальны\n",
        "optimal_price = price_range[np.argmax(sales_pred)]\n",
        "optimal_sales = max(sales_pred)\n",
        "\n",
        "# Построение графика\n",
        "plt.figure(figsize=(10, 6))\n",
        "plt.plot(price_range, sales_pred, label='Прогнозируемые продажи')\n",
        "plt.scatter(prices, sales, color='red', label='Исходные данные')\n",
        "plt.axvline(optimal_price, color='green', linestyle='--', label=f'Оптимальная цена: {optimal_price:.2f}')\n",
        "plt.xlabel('Цена')\n",
        "plt.ylabel('Продажи')\n",
        "plt.title('Прогнозирование продаж в зависимости от цены')\n",
        "plt.legend()\n",
        "plt.grid(True)\n",
        "plt.show()\n",
        "\n",
        "# Вывод оптимальной цены и объема продаж\n",
        "optimal_price, optimal_sales\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "file_path_1 = '/content/res_Kod_TT_2.xlsx'\n",
        "\n",
        "\n",
        "# Чтение данных из Excel файла\n",
        "# Если файл содержит несколько листов, вы можете указать название или индекс листа с помощью параметра sheet_name\n",
        "df_80k = pd.read_excel(file_path_1, engine='openpyxl')"
      ],
      "metadata": {
        "id": "6nPrPqGfyepj",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 321
        },
        "outputId": "c0dc3a3c-9972-42c9-fee6-babdb5b96619"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "FileNotFoundError",
          "evalue": "[Errno 2] No such file or directory: '/content/res_Kod_TT_2.xlsx'",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-4-f1842967cff6>\u001b[0m in \u001b[0;36m<cell line: 6>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;31m# Чтение данных из Excel файла\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;31m# Если файл содержит несколько листов, вы можете указать название или индекс листа с помощью параметра sheet_name\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m \u001b[0mdf_80k\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_excel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfile_path_1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mengine\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'openpyxl'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/io/excel/_base.py\u001b[0m in \u001b[0;36mread_excel\u001b[0;34m(io, sheet_name, header, names, index_col, usecols, dtype, engine, converters, true_values, false_values, skiprows, nrows, na_values, keep_default_na, na_filter, verbose, parse_dates, date_parser, date_format, thousands, decimal, comment, skipfooter, storage_options, dtype_backend, engine_kwargs)\u001b[0m\n\u001b[1;32m    502\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mio\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mExcelFile\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    503\u001b[0m         \u001b[0mshould_close\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 504\u001b[0;31m         io = ExcelFile(\n\u001b[0m\u001b[1;32m    505\u001b[0m             \u001b[0mio\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    506\u001b[0m             \u001b[0mstorage_options\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mstorage_options\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/io/excel/_base.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, path_or_buffer, engine, storage_options, engine_kwargs)\u001b[0m\n\u001b[1;32m   1578\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstorage_options\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mstorage_options\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1579\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1580\u001b[0;31m         self._reader = self._engines[engine](\n\u001b[0m\u001b[1;32m   1581\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_io\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1582\u001b[0m             \u001b[0mstorage_options\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mstorage_options\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/io/excel/_openpyxl.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, filepath_or_buffer, storage_options, engine_kwargs)\u001b[0m\n\u001b[1;32m    551\u001b[0m         \"\"\"\n\u001b[1;32m    552\u001b[0m         \u001b[0mimport_optional_dependency\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"openpyxl\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 553\u001b[0;31m         super().__init__(\n\u001b[0m\u001b[1;32m    554\u001b[0m             \u001b[0mfilepath_or_buffer\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    555\u001b[0m             \u001b[0mstorage_options\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mstorage_options\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/io/excel/_base.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, filepath_or_buffer, storage_options, engine_kwargs)\u001b[0m\n\u001b[1;32m    570\u001b[0m         )\n\u001b[1;32m    571\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mExcelFile\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_workbook_class\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 572\u001b[0;31m             self.handles = get_handle(\n\u001b[0m\u001b[1;32m    573\u001b[0m                 \u001b[0mfilepath_or_buffer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"rb\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstorage_options\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mstorage_options\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mis_text\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    574\u001b[0m             )\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/io/common.py\u001b[0m in \u001b[0;36mget_handle\u001b[0;34m(path_or_buf, mode, encoding, compression, memory_map, is_text, errors, storage_options)\u001b[0m\n\u001b[1;32m    870\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    871\u001b[0m             \u001b[0;31m# Binary mode\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 872\u001b[0;31m             \u001b[0mhandle\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhandle\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mioargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmode\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    873\u001b[0m         \u001b[0mhandles\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhandle\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    874\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: '/content/res_Kod_TT_2.xlsx'"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_80k\n"
      ],
      "metadata": {
        "id": "i0Q1Nd0zy_f2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_23k=df_80k[(df_80k[\"SALES VOL\"]>=100) & (df_80k[\"SALES VOL\"]<=101) ]\n",
        "\n",
        "print(df_23k.shape)\n",
        "df_4k_kod_TT =df_23k['Kod_TT'].unique()\n",
        "print(df_4k_kod_TT.shape)\n"
      ],
      "metadata": {
        "id": "JzaZCpq3yyxB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_23k.describe()"
      ],
      "metadata": {
        "id": "9QDuddHR0hl4"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_23k.info()"
      ],
      "metadata": {
        "id": "VVHrZMVEBP0D"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_4k_kod_TT"
      ],
      "metadata": {
        "id": "u9oCWzSJ0aCm"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Данные\n",
        "import pandas as pd\n",
        "from pygam import s,ExpectileGAM\n",
        "from scipy.interpolate import interp1d\n",
        "\n",
        "# prices = np.array([584, 540, 504, 488, 447, 559, 483, 535, 466, 450, 432, 553])\n",
        "# sales = np.array([1324, 1706, 3735, 11195, 13751, 4755, 10556, 4733, 10701, 21422, 22981, 7994])\n",
        "\n",
        "prices = np.array([1324, 1706, 3735, 11195, 13751, 4755, 10556, 4733, 10701, 21422, 22981, 7994, 21457, 23159, 19329, 24965, 35538, 30202, 27719, 19388, 9006, 41747, 25271, 43175])\n",
        "sales = np.array([584, 540, 504, 488, 447, 559, 483, 535, 466, 450, 432, 553, 452, 441, 450, 441, 456, 462, 468, 468, 533, 425, 486, 468])\n",
        "\n",
        "# # Исходные данные\n",
        "# prices = np.array([584, 540, 504, 488, 447, 559, 483, 535, 466, 450, 432, 553])\n",
        "# sales = np.array([1324, 1706, 3735, 11195, 13751, 4755, 10556, 4733, 10701, 21422, 22981, 7994])\n",
        "\n",
        "# # Создаем интерполяционные функции\n",
        "# f_prices = interp1d(np.linspace(0, 1, len(prices)), prices, kind='cubic')\n",
        "# f_sales = interp1d(np.linspace(0, 1, len(sales)), sales, kind='cubic')\n",
        "\n",
        "# # Генерируем 1000 значений\n",
        "# x_new = np.linspace(0, 1, 1000)\n",
        "# prices_new = f_prices(x_new)\n",
        "# sales_new = f_sales(x_new)\n",
        "\n",
        "# X = pd.DataFrame({'Price':prices})\n",
        "# y = pd.DataFrame({'Sales':sales})\n",
        "\n",
        "# ________________________________\n",
        "# Создаем значения X, которые распределены по нормальному закону\n",
        "# prices = np.random.normal(50, 10, 100)\n",
        "\n",
        "# # Генерация зависимой переменной y как логарифмическая функция от X с добавлением шума\n",
        "# sales = np.log(prices) * 1000 + np.random.normal(0, 50, 100)\n",
        "\n",
        "# Создаем DataFrame\n",
        "X = pd.DataFrame({'Price': prices})\n",
        "y = pd.DataFrame({'Sales': sales})\n",
        "\n",
        "# # Построение графика\n",
        "# plt.figure(figsize=(10, 6))\n",
        "# plt.scatter(X['Price'], y['Sales'], alpha=0.5)\n",
        "# plt.xlabel('Price')\n",
        "# plt.ylabel('Sales')\n",
        "# plt.title('Данные, распределенные по логарифмической функции')\n",
        "# plt.grid(True)\n",
        "# plt.show()\n",
        "# print(X.shape)\n",
        "#______________________________\n",
        "# Генерация данных\n",
        "# np.random.seed(42)\n",
        "# prices = np.random.normal(50, 10, 50)\n",
        "\n",
        "# # Инвертированная зависимость: чем выше цена, тем ниже продажи, с ещё большей дисперсией\n",
        "# sales = (10000 / prices) ** 2 + np.random.normal(0, 10000, 50)  # Увеличил разброс шума\n",
        "\n",
        "# # Создание DataFrame\n",
        "# X = pd.DataFrame({'Price': prices})\n",
        "# y = pd.DataFrame({'Sales': sales})\n",
        "\n",
        "# # Построение графика\n",
        "# plt.figure(figsize=(10, 6))\n",
        "# plt.scatter(X['Price'], y['Sales'], alpha=0.5, color='blue')\n",
        "# plt.xlabel('Price')\n",
        "# plt.ylabel('Sales')\n",
        "# plt.title('Данные с убывающей зависимостью продаж от цены и увеличенной дисперсией')\n",
        "# plt.grid(True)\n",
        "# plt.show()\n",
        "#_______________\n",
        "# Путь к вашему Excel файлу\n",
        "file_path = '/content/ALL_res_Sales_vol_price_Kod_TT_share_vol.xlsx'\n",
        "\n",
        "\n",
        "# Чтение данных из Excel файла\n",
        "# Если файл содержит несколько листов, вы можете указать название или индекс листа с помощью параметра sheet_name\n",
        "df_kod_TT = pd.read_excel(file_path, engine='openpyxl')\n",
        "# df_kod_TT = df_kod_TT[df_kod_TT['PRICE PER UNIT'] >= 400]\n",
        "# items = [\n",
        "#     \"7224\",\n",
        "#     \"5207\",\n",
        "#     \"5257\",\n",
        "#     \"D084\",\n",
        "#     \"3874\",\n",
        "#     \"5184\",\n",
        "#     \"J502\",\n",
        "#     \"5216\",\n",
        "#     \"6910\",\n",
        "#     \"5194\"\n",
        "# ]\n",
        "# df_kod_TT = df_kod_TT[(df_kod_TT['Kod_TT'] == '7224') | (df_kod_TT['Kod_TT'] == '5207')]\n",
        "# print(df_kod_TT)\n",
        "# 'S848','S849'\n",
        "# 314B\n",
        "# 31CW\n",
        "# my_list = ['30CP', '30WL', '312M', '314B', '31CW', '31KC', '33IC', '3460', '346O', '35BE']\n",
        "# my_list = ['Y038', 'X957', '8245', '3874', 'D525', '31I7', '4600', 'J162', '4075']\n",
        "my_list = ['4151', 'J919', '5187', 'Q198', '35A9', '346B', 'S634', '5087', '4538', '3893']\n",
        "df_kod_TT = df_kod_TT[df_kod_TT['Kod_TT'].isin(my_list)]\n",
        "# df_kod_TT[\"SALES VOL\"] = df_kod_TT[\"SALES VOL\"].apply(lambda x: 20 if x >= 30 else x)\n",
        "# df_kod_TT = df_kod_TT[df_kod_TT['PRICE PER UNIT'] >= 400]\n",
        "\n",
        "\n",
        "\n",
        "X = pd.DataFrame({'Price': df_kod_TT['PRICE PER UNIT'].tolist()})\n",
        "y = pd.DataFrame({'Sales': df_kod_TT['SALES VOL'].tolist()})\n",
        "\n",
        "# X = X[X['Price'] >= 400]\n",
        "# y = y[y['Sales'] >= 15]\n",
        "\n"
      ],
      "metadata": {
        "id": "X77OTdq2_jtM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_kod_TT"
      ],
      "metadata": {
        "id": "XUt_0YRk0TCW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_kod_TT[\"SALES VOL\"] = df_kod_TT[\"SALES VOL\"].apply(lambda x: 20 if x >= 30 else x)\n",
        "df_kod_TT"
      ],
      "metadata": {
        "id": "HJVHskGTiyue"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_kod_TT.describe()"
      ],
      "metadata": {
        "id": "KlNxth4U0pvk"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "quantiles = [0.05,0.5,0.95]\n",
        "gam_res = {}\n",
        "for q in quantiles:\n",
        "  gam  = ExpectileGAM(s(0),expectile=q)\n",
        "  gam.fit(X,y)\n",
        "  gam_res[q]=gam\n",
        "gam_res\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PG6EBi2p_260",
        "outputId": "476c8f46-a701-409a-f24a-63b98bda0e86"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{0.05: ExpectileGAM(callbacks=[Deviance(), Diffs()], expectile=0.05, \n",
              "    fit_intercept=True, max_iter=100, scale=None, \n",
              "    terms=s(0) + intercept, tol=0.0001, verbose=False),\n",
              " 0.5: ExpectileGAM(callbacks=[Deviance(), Diffs()], expectile=0.5, \n",
              "    fit_intercept=True, max_iter=100, scale=None, \n",
              "    terms=s(0) + intercept, tol=0.0001, verbose=False),\n",
              " 0.95: ExpectileGAM(callbacks=[Deviance(), Diffs()], expectile=0.95, \n",
              "    fit_intercept=True, max_iter=100, scale=None, \n",
              "    terms=s(0) + intercept, tol=0.0001, verbose=False)}"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "oSw-DdIOVXf7"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(10,6))\n",
        "plt.scatter(X,y,alpha=0.5,label='Data Points')\n",
        "XX = np.linspace(X.min(),X.max(),1000).reshape(-1,1)\n",
        "for q,gam in gam_res.items():\n",
        "   plt.plot(XX,gam.predict(XX),label=f'{int(q*100)}th Quantile Gam')\n",
        "# plt.xlim(0, XX.max())\n",
        "# plt.ylim(0, gam.predict(XX).max())\n",
        "plt.xlabel('Price')\n",
        "plt.ylabel('Quantile Demanded')\n",
        "plt.title('Сыр плавленый Карат Дружба Блочки 90 г 45,0%') #'Сыр плавленый Карат Дружба Ванночки 200 г 45,0%','Сыр плавленый Карат Дружба Ванночки 400 г 45,0%','Сыр плавленый Карат Волна Блочки 90 г 45,0%'\n",
        "plt.grid(True,which='both',linestyle='--')\n",
        "plt.legend()\n",
        "plt.tight_layout()\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 607
        },
        "id": "YEu8v1gD_4Lc",
        "outputId": "0efc0d97-74f2-4382-c0ae-46e6efbf8a72"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "gam_res"
      ],
      "metadata": {
        "id": "uSB_wfi1CoUE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import statsmodels.api as sm\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Исходные данные\n",
        "prices = np.array([1324, 1706, 3735, 11195, 13751, 4755, 10556, 4733, 10701, 21422, 22981, 7994, 21457, 23159, 19329, 24965, 35538, 30202, 27719, 19388, 9006, 41747, 25271, 43175])\n",
        "sales = np.array([584, 540, 504, 488, 447, 559, 483, 535, 466, 450, 432, 553, 452, 441, 450, 441, 456, 462, 468, 468, 533, 425, 486, 468])\n",
        "# Создаем DataFrame\n",
        "X = pd.DataFrame({'Price': prices})\n",
        "y = pd.DataFrame({'Sales': sales})\n",
        "\n",
        "# Добавляем константу для свободного члена\n",
        "X = sm.add_constant(X)\n",
        "\n",
        "# Создаем модель линейной регрессии\n",
        "model = sm.OLS(y, X)\n",
        "\n",
        "# Обучаем модель\n",
        "results = model.fit()\n",
        "\n",
        "# Выводим результаты\n",
        "print(results.summary())\n",
        "\n",
        "# Предсказываем значения\n",
        "y_pred = results.predict(X)\n",
        "\n",
        "# Строим график\n",
        "plt.scatter(X['Price'], y, label='Data')\n",
        "plt.plot(X['Price'], y_pred, color='red', label='Regression line')\n",
        "plt.xlabel('Price')\n",
        "plt.ylabel('Sales')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "Rmn8K6HcD2j4"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "from pygam import s,ExpectileGAM\n",
        "from scipy.interpolate import interp1d\n",
        "import plotly.express as px\n",
        "import pytimetk as tk\n",
        "import textwrap\n",
        "import numpy as np\n",
        "\n",
        "\n",
        "# Исходные данные\n",
        "prices = np.array([1324, 1706, 3735, 11195, 13751, 4755, 10556, 4733, 10701, 21422, 22981, 7994, 21457, 23159, 19329, 24965, 35538, 30202, 27719, 19388, 9006, 41747, 25271, 43175])\n",
        "sales = np.array([584, 540, 504, 488, 447, 559, 483, 535, 466, 450, 432, 553, 452, 441, 450, 441, 456, 462, 468, 468, 533, 425, 486, 468])\n",
        "# Создаем DataFrame\n",
        "data = pd.DataFrame({'Price': prices, 'Sales': sales})\n",
        "\n",
        "# Создаем scatter plot с линией LOWESS\n",
        "fig = px.scatter(data, x='Price', y='Sales', trendline='lowess')\n",
        "\n",
        "# Показываем график\n",
        "fig.show()"
      ],
      "metadata": {
        "id": "rEa_3taCegyS"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "all_gam_results = pd.DataFrame()\n",
        "quantiles"
      ],
      "metadata": {
        "id": "OqaEiKNE5usF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Путь к вашему Excel файлу\n",
        "file_path = '/content/ALL_res_Sales_vol_price_Kod_TT.xlsx'\n",
        "\n",
        "# Чтение данных из Excel файла\n",
        "# Если файл содержит несколько листов, вы можете указать название или индекс листа с помощью параметра sheet_name\n",
        "df_kod_TT = pd.read_excel(file_path, engine='openpyxl')\n",
        "\n"
      ],
      "metadata": {
        "id": "rIhcPsBpxTh2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 211
        },
        "outputId": "48c36186-c937-4795-fb7f-ebd2a8146275"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "name 'pd' is not defined",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-1-9d1ccca39430>\u001b[0m in \u001b[0;36m<cell line: 6>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;31m# Чтение данных из Excel файла\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;31m# Если файл содержит несколько листов, вы можете указать название или индекс листа с помощью параметра sheet_name\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m \u001b[0mdf_kod_TT\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_excel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfile_path\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mengine\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'openpyxl'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      7\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mNameError\u001b[0m: name 'pd' is not defined"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "\n",
        "# Чтение файла Excel\n",
        "df = pd.read_excel('/content/df_Сыр плавленый Hochland Чизбургер Слайсы 150 г 45,0%.xlsx')\n",
        "\n",
        "# Вывод первых нескольких строк DataFrame"
      ],
      "metadata": {
        "id": "E-myaIFR0rW9"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "my_list = ['301A','301G','303T','304L','304M','304N','305K','308N','309H','309Q']\n",
        "df_kod_TT = df[df['Kod_TT'].isin(my_list)]\n",
        "df_kod_TT\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "3sHlGUe5utTM",
        "outputId": "9a0037cb-c8a0-4159-f94b-aeee19c0134e"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       Unnamed: 0  SALES VOL  PRICE PER UNIT Kod_TT  SHARE VOL\n",
              "7               7      18.45      652.256369   301A   0.112445\n",
              "11             11       4.95      726.585859   301G   0.067577\n",
              "25             25      13.50      695.510370   303T   0.116149\n",
              "28             28      10.95      660.441096   304L   0.087804\n",
              "29             29      18.75      662.961067   304M   0.154105\n",
              "...           ...        ...             ...    ...        ...\n",
              "73651       73651       3.75      649.880000   304N   0.038929\n",
              "73656       73656       7.80      661.126923   305K   0.062231\n",
              "73669       73669      26.40      644.411364   308N   0.174200\n",
              "73672       73672      10.05      655.665672   309H   0.063551\n",
              "73678       73678       5.70      649.229825   309Q   0.066713\n",
              "\n",
              "[120 rows x 5 columns]"
            ],
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              "      <th></th>\n",
              "      <th>Unnamed: 0</th>\n",
              "      <th>SALES VOL</th>\n",
              "      <th>PRICE PER UNIT</th>\n",
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              "      <td>304L</td>\n",
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              "    <tr>\n",
              "      <th>29</th>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>73651</th>\n",
              "      <td>73651</td>\n",
              "      <td>3.75</td>\n",
              "      <td>649.880000</td>\n",
              "      <td>304N</td>\n",
              "      <td>0.038929</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>73656</th>\n",
              "      <td>73656</td>\n",
              "      <td>7.80</td>\n",
              "      <td>661.126923</td>\n",
              "      <td>305K</td>\n",
              "      <td>0.062231</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>73669</th>\n",
              "      <td>73669</td>\n",
              "      <td>26.40</td>\n",
              "      <td>644.411364</td>\n",
              "      <td>308N</td>\n",
              "      <td>0.174200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>73672</th>\n",
              "      <td>73672</td>\n",
              "      <td>10.05</td>\n",
              "      <td>655.665672</td>\n",
              "      <td>309H</td>\n",
              "      <td>0.063551</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>73678</th>\n",
              "      <td>73678</td>\n",
              "      <td>5.70</td>\n",
              "      <td>649.229825</td>\n",
              "      <td>309Q</td>\n",
              "      <td>0.066713</td>\n",
              "    </tr>\n",
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              "<p>120 rows × 5 columns</p>\n",
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              "type": "dataframe",
              "variable_name": "df_kod_TT",
              "summary": "{\n  \"name\": \"df_kod_TT\",\n  \"rows\": 120,\n  \"fields\": [\n    {\n      \"column\": \"Unnamed: 0\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 23226,\n        \"min\": 7,\n        \"max\": 73678,\n        \"num_unique_values\": 120,\n        \"samples\": [\n          26954,\n          26973,\n          29\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SALES VOL\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4.6304100767633525,\n        \"min\": 1.95,\n        \"max\": 26.4,\n        \"num_unique_values\": 82,\n        \"samples\": [\n          7.95,\n          18.45,\n          2.1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"PRICE PER UNIT\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 36.4882025356394,\n        \"min\": 573.4535211267607,\n        \"max\": 733.2157894736843,\n        \"num_unique_values\": 120,\n        \"samples\": [\n          662.5333333333332,\n          657.4965277777778,\n          662.9610666666666\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Kod_TT\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 10,\n        \"samples\": [\n          \"309H\",\n          \"301G\",\n          \"304N\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SHARE VOL\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.03063503957542516,\n        \"min\": 0.02078891257995735,\n        \"max\": 0.1741999340151765,\n        \"num_unique_values\": 120,\n        \"samples\": [\n          0.0937882288976485,\n          0.1505331381977838,\n          0.1541053669762472\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Предположим, что df_kod_TT уже загружен\n",
        "# Если нет, то загрузите его, например:\n",
        "# df_kod_TT = pd.read_excel('/content/df_2_Сыр плавленый Hochland Ассорти Синее Сегменты 140 г 50,0%.xlsx')\n",
        "\n",
        "# Построение scatter plot\n",
        "plt.figure(figsize=(10, 6))\n",
        "plt.scatter(df_kod_TT['PRICE PER UNIT'], df_kod_TT['SALES VOL'], alpha=0.5)\n",
        "\n",
        "# Добавление заголовков и меток осей\n",
        "plt.title('Scatter Plot между SALES VOL и SHARE VOL')\n",
        "plt.xlabel('PRICE PER UNIT')\n",
        "plt.ylabel('SALES VOL')\n",
        "\n",
        "# Отображение графика\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "kZGPyN7SvkDv",
        "outputId": "d21b2a1c-3962-4a2f-b153-16d56b1a8fa0"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Построение гистограммы\n",
        "plt.figure(figsize=(10, 6))\n",
        "plt.hist(df_kod_TT['PRICE PER UNIT'], bins=10, color='skyblue', edgecolor='black')\n",
        "\n",
        "# Добавление заголовков и меток осей\n",
        "plt.title('Гистограмма PRICE PER UNIT')\n",
        "plt.xlabel('Цена за единицу')\n",
        "plt.ylabel('Частота')\n",
        "\n",
        "# Отображение графика\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "bwNsUXZHvmS0",
        "outputId": "b0039045-724b-45f9-8b7f-1a81a0d1bbb3"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "\n",
        "# Предположим, что df_kod_TT уже загружен\n",
        "# Если нет, то загрузите его, например:\n",
        "# df_kod_TT = pd.read_excel('/content/df_2_Сыр плавленый Hochland Ассорти Синее Сегменты 140 г 50,0%.xlsx')\n",
        "\n",
        "# Получаем границы интервалов\n",
        "counts, bin_edges = np.histogram(df_kod_TT['PRICE PER UNIT'], bins=10)\n",
        "\n",
        "# Отрезаем по одному интервалу с каждого края\n",
        "filtered_data = df_kod_TT[(df_kod_TT['PRICE PER UNIT'] > bin_edges[1]) & (df_kod_TT['PRICE PER UNIT'] < bin_edges[-2])]\n",
        "\n",
        "# Построение гистограммы с отфильтрованными данными\n",
        "plt.figure(figsize=(10, 6))\n",
        "plt.hist(filtered_data['PRICE PER UNIT'], bins=10, color='skyblue', edgecolor='black')\n",
        "\n",
        "# Добавление заголовков и меток осей\n",
        "plt.title('Гистограмма PRICE PER UNIT (без крайних интервалов)')\n",
        "plt.xlabel('Цена за единицу')\n",
        "plt.ylabel('Частота')\n",
        "\n",
        "# Отображение графика\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "d-8tvzOnxFkk",
        "outputId": "dcf3c339-3575-4431-87fc-731b99b76a62"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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YMN1888369ttvVapUKad2nTp1UlBQkKpUqaL77rtPpUuX1uLFi/McJmefKe/yI3d5iYqK0ooVK5xu33//fb4eu2fPHgUFBSkoKEh169bVjBkz1K1bN82ZM8epnY+PT5bnWLFihdPkDnaPPPKIgoKCFBYWpi5duigpKUkff/yxbrnllivarytlP5r022+/ZTs051o5deqUpJyPlI0ZM8ZpgouHHnpIISEhjvfe9u3btXfvXv3jH//QqVOnlJCQoISEBKWkpKhjx4768ccfnWYbvHjxohISEnT48GFNmzZNycnJjiGNeUlLS9M999yjkydPaunSpTkebRw6dKg8PT0d9x9//HF5eHg4HUGxvqfOnDmjhIQEtWnTRqmpqdqzZ0+22zXGaPz48erdu7datmyZY43218B+O3/+fK77tWXLFi1YsEBTp051fO5cvj+RkZEaOXKkHnroIdWsWVMvv/xyrtvMzldffaVNmzZl+3dvd/r0aafa8/v5crkVK1YoMTFRffv2ddqeu7u7WrZsqdWrV2d5jHWoon3oYkZGhn744QendmfPnlVCQoL++usvvf/++4qLi1PHjh0d66/08zsjI8NxVDQ7x48f1/bt2zVw4ECniYwaNWqkO++8M9sjc/b93b17t2bNmqXSpUurVatWWdqVLVs2x9kwgWuF4XbANZSUlKSXX35ZY8aMUUhIiNM6+4Ums5se+kodPnxYkpyGKNiFh4fr559/vuJt3nzzzVnCWu3atSVdGloVGhqqzMxMTZ8+XTNnztTBgwedzlPI7ouZp6en+vfvrzlz5ui+++7TnDlz1Lt371yHcnXr1k0eHh6OcyPmz5+vhQsXZpmtTJLWrVuniRMnav369UpNTXVal5SUlG2wutyXX34pf39/eXp6qnLlyqpZs2a27aKiolS7dm0lJSVpzpw5+vHHH3P9AmFn39czZ87k2dbq1ltvVYsWLa7oMXbVq1fXBx984JjOu1atWgoODs7Szt3dPcs5Nzl54YUX1KZNG509e1Zff/21Pv/882y/uBZUbj8U9OvXTy+99JImT56se++9t9CeMz/MZedC2esMDw93Wu7u7q5atWo5hiHu3btXkjRgwIAct52UlOQIYXv37nUMpfXy8tLMmTPzfb2YQYMGOc6Vsp+HlJ1atWo53ffz81PFihWznNM0YcIErVq1Kss1n3IKBp9++ql27dql+fPn67PPPsu2zezZs7M9z6patWo51vvPf/5Tbdq00d13353tOU327dasWVN79+7VL7/84hTy8uPixYt67rnn1K9fPzVq1CjHdtl91haE/e/Cfu7j5S7/bHRzc9NNN93ktMz6uWw1cuRIjRw50nF/0KBBGj16tOP+lX5+JyYm5joDZG7/D9WtW1fLli1TSkqK09BH+5BD+75++umnqlKlSpbHG2Ou+MdD4GoRkoBr6NVXX5Wbm5vGjh3r+CX6RvLyyy/r+eef1yOPPKKXXnpJ5cqVk5ubm0aNGuX0q7jVI488oqZNmyomJkYLFixwHFXKiT1YzZ07V6mpqSpfvrw6dOiQJSTt379fHTt2VHh4uN566y1VqVJFXl5e+u677zRt2rQc67lc27ZtVaFChTzbWUPLvffeq9atW+sf//iHYmJicv0iYf8yvWPHjmwnpbgWSpcune/wk18NGzZ0bPPee+9VamqqhgwZotatW2f7JcfKfrTz3Llz2a5PTU3NdRp1+9GkgQMHatGiRQXcgytj/9L4999/Ox0tzO+XcPvf3+uvv55jv1v/bqpWraoVK1bozJkz+uabbzR69GhVqVJFd999d57PtXXrVi1atEgjRozQ0KFDtWrVqnzVeLnExES1a9dO/v7+mjx5smrWrCkfHx9t3bpV48aNy/Y9lZGRoeeff16DBw92fHnPTo8ePbIEnQkTJjiOUl9u+fLl+uGHH7R+/fpca16zZo3jqPOOHTsc05Hn1+zZs3Xo0CEtW7Ys13b2H1Ps/vzzzwKd92h/DT/++ONsp86//DyyKzF27Fh17txZFy9e1K5duxwTPMydO1fSlX9+x8XF5RpiC8I+GUNKSoq+/PJL9enTR998843jvFe7v//+O0uoB641QhJwjRw7dkzTp0/X1KlTVaZMmSwhyf5r4M6dO6/6uez/ccXExGT5RTImJqZA/7Ht27cvy693f/75pyQ5Tr794osvdMcdd2T5RTgxMTHHoNGwYUM1bdrUMaPfHXfcobVr1+ZayyOPPKLGjRvryJEjGjBgQLa/KC5ZskTp6elavHix0/DF7IarFDZ3d3dNnTpVd9xxh959913985//zLFt165d5e7urk8++eSKJ28ozl555RV9/fXXmjJlimbNmpVrW+vf6+WBKjU1VUeOHFHnzp1z3Ub//v3173//W5MmTdI999xzdcXngz3cHjx4UA0bNnQst1+7KiYmxukX/szMTO3du9cx4Yj9iKS/v3++AmupUqUc7Xr27KlDhw7ppZdeyldI+vDDD3XPPffI3d1dd999t2bPnq3Bgwdnabd3717dcccdjvtnz57V8ePHddddd0m6FDhOnTqlr776Sm3btnW0y22mx5kzZ+rEiRNZZjy7XOXKlbO8Dm+//Xa2IckYo3/+85/q2bNntkOx7I4fP66RI0eqc+fO8vLy0jPPPKPIyMh8f/6lpqZq0qRJeuKJJ/J8zOU/plw+AUp+2f8ugoOD8/V3kZmZqQMHDjgF0Ms/l+3q1avn2GZkZKTS09P13HPPacqUKQoLC7uiz+/z589r37596tKlS461Wd/Xl9uzZ48qVKiQZQIN6z736NFDGzdu1BtvvOEUki5cuKAjR44UyfscsOKcJOAamTRpkkJCQvTYY49luz4oKEht27bVnDlzFBsb67Tu8iE9eWnRooWCg4M1a9Ysx6+o0qWZ1Hbv3q1u3bpdcf3Hjh3T119/7bifnJys//znP2rSpInjF093d/cstS5YsCDLtOOXe+SRR/T77787ppTNS/369dW8eXP98ccfjml/L2c/H8RaT1JSkuNX02utffv2uvXWW/X2229nO1OfXZUqVTRkyBAtX75cM2bMyLI+MzNTb775po4ePXotyy10NWvWVO/evTVv3rwcjwbYdezYUV5eXoqOjs7yi/X777+vCxcuqGvXrrluw340afv27XkejSwMzZs3l5eXlzZv3uy0vGPHjvL29tY777zjtC+ffvqp4uPjHaGmefPmqlmzpt544w2dPXs2y/Zzm4r94sWL+vvvv53e27mxn7vUrVs3Pfjggxo7dqzi4+OztHv//fedzgOKjo52eu2ze09lZGRo5syZ2T7vmTNnNGXKFI0ePbpQLyj8+eef6/fff3dcEDknQ4YMUWZmpmbPnq33339fHh4eGjx4cL4/T6dPn66UlBTHJRiKQmRkpPz9/fXyyy9ne05Wdn8X7777ruPfxhi9++678vT0dDrfKDv2I7f2acCv5PN70aJFOnfuXI7DAiWpYsWKatKkiT766CMlJiY6lu/cuVPLly93hO+cXLx4URkZGVn+zv/44w+lpaXptttuy/XxQGHjSBJwjSxfvlyffvqpvLy8cmzzzjvvqHXr1mrWrJmGDh2qGjVq6NChQ/r222+zXE8oN56ennr11Vc1aNAgtWvXTn379nVMAV69enWncej5Vbt2bQ0ePFibNm1SSEiI5syZo/j4eKfQcffdd2vy5MkaNGiQbrvtNu3YsUOffvppljHzlxsyZIjuv//+fJ0jZLdq1Sqlp6c7nRBsZf/1uHv37ho2bJjOnj2rDz74QMHBwTp+/Hi+n+dqjB07Vvfff7/mzZuXYziWpDfffFP79+/Xk08+qa+++kp33323ypYtq9jYWC1YsEB79uzRgw8+6PSY77//PtsT5W+77bY8X+/8unDhgj755JNs1/Xs2TPPaZTHjh2r+fPn6+233871pPfg4GC98MILmjBhgtq2bat77rlHpUqV0i+//KL//ve/6ty5s7p3755nvfZzk67kvVJQPj4+6ty5s3744QenqZbLlSunCRMm6Pnnn1dkZKR69OihAwcO6N1331Xjxo316KOPSrp0LsmHH36orl27qn79+ho0aJAqVaqkv/76S6tXr5a/v7+WLFki6dJRivbt26tq1ao6e/asvvjiC23btk1vvPHGFdc9ffp01a1bVyNHjtT8+fOd1mVkZKhjx47q06ePYmJiNHPmTLVu3drxi/1tt92msmXLasCAAXryySdls9n08ccf5xg6tm7dqgoVKujZZ5+94jpzs3z5cg0ZMiTX84Dmzp2rb7/9VvPmzXMMh5wxY4b69++v6OhoPfHEE/l6nilTphR4Wv2C8Pf3V3R0tB566CE1a9ZMDz74oIKCghQbG6tvv/1Wt99+u1Mo8vHx0dKlSzVgwAC1bNlS33//vb799ls999xzTuf3SNL69evl4eHhGG43Y8YMNW3a1HHEKT+f36mpqZo4caJmzpyp2267Lc8jvK+//rq6du2qiIgIDR482DEFeEBAQLZHF+2fNykpKVq4cKEOHTqU5fpxK1asUKlSpbIMwQOuOVdMqQdcj650CvAmTZo4TfWb07SsO3fuND179jSBgYHGx8fH1KlTxzz//PPZbjunKcDt/ve//5mmTZsab29vU65cOdOvXz9z9OhRpzb5nQK8W7duZtmyZaZRo0bG29vbhIeHO03RbcylKWSffvppU7FiRePr62tuv/12s379+izTAdun2L388bmtz6vO7NYvXrzYNGrUyPj4+Jjq1aubV1991cyZM8dpSvac5DUttV1ufwcXL140NWvWNDVr1nRMB335FOB2Fy5cMB9++KFp06aNCQgIMJ6enqZatWpm0KBBTtOD5zYFeHZ/T5fL6fkvl9sU4NbXL6++bN++vfH39zeJiYl5Pucnn3xiWrVqZUqXLu34G5s0aZLT1NLGOE8Bfjnr63MtpwA3xpivvvrK2Gw2Exsbm2VdVFSUCQ8PN56eniYkJMQMGzbMaRpku23btplevXqZ8uXLG29vb1OtWjXTp08fs3LlSkebxx9/3NSoUcPxPm7VqpX56KOP8qzv8ssP2H300UdGklm8eLFTu7Vr15qhQ4easmXLGj8/P9OvX78sNa9bt860atXK+Pr6mrCwMPPss8+aZcuWZZkyu127dkaSmTZtmtPjs5vqWVc4Bbivr6/566+/nNpaPwuPHDliAgICnKa0t+vZs6cpXbq0OXDgQHYvmTHm//6mK1as6Jjq31rrtZwC3FpDZGSkCQgIMD4+PqZmzZpm4MCBZvPmzY429r/p/fv3m86dO5tSpUqZkJAQM3HiRKepvO2vm/3m5uZmKleubAYMGOD0/0F+Pr+PHj1qqlSpYkaNGmWSkpLytV8//PCDuf32242vr6/x9/c33bt3N3/88YdTG/vraL/5+vqaevXqmWnTpmW5BEbLli1N//79c3ztgGvFZkwRX7YcQLFXvXp1NWjQQN98842rSwGKjYsXL6pevXrq06eP44Kc1yP7hac3bdpU4BkTUfQGDhyoL774Itvhmjeq7du3q1mzZtq6dWuRTXQD2HFOEgAA+eDu7q7JkycrKiqqRH1RBVzllVde0X333UdAgktwThIAAPn0wAMP5HrxYwCF5/PPP3d1CSjBOJIEAAAAABackwQAAAAAFhxJAgAAAAALQhIAAAAAWNzwEzdkZmbq2LFjKlOmjGw2m6vLAQAAAOAixhidOXNGYWFhcnPL+XjRDR+Sjh07pipVqri6DAAAAADFxJEjR1S5cuUc19/wIalMmTKSLr0Q/v7+Lq4GAAAAgKskJyerSpUqjoyQkxs+JNmH2Pn7+xOSAAAAAOR5Gg4TNwAAAACABSEJAAAAACwISQAAAABgQUgCAAAAAAtCEgAAAABYEJIAAAAAwIKQBAAAAAAWhCQAAAAAsCAkAQAAAIAFIQkAAAAALAhJAAAAAGBBSAIAAAAAC0ISAAAAAFgQkgAAAADAgpAEAAAAABaEJAAAAACwICQBAAAAgAUhCQAAAAAsPFxdAAAARSE2NlYJCQmuLqNYqlChgqpWrerqMgCg2CAkAQBueLGxsQqvW1fnUlNdXUqx5FuqlPbs3k1QAoD/j5AEALjhJSQk6Fxqqvr8O1rBNWq5upxi5cTBvZo/4XElJCQQkgDg/yMkAQBKjOAatVSpbmNXlwEAKOaYuAEAAAAALAhJAAAAAGBBSAIAAAAAC0ISAAAAAFgQkgAAAADAgpAEAAAAABaEJAAAAACwICQBAAAAgAUhCQAAAAAsCEkAAAAAYEFIAgAAAAALQhIAAAAAWBCSAAAAAMCCkAQAAAAAFoQkAAAAALAgJAEAAACABSEJAAAAACwISQAAAABgQUgCAAAAAAtCEgAAAABYEJIAAAAAwIKQBAAAAAAWhCQAAAAAsCAkAQAAAIAFIQkAAAAALAhJAAAAAGBBSAIAAAAAC0ISAAAAAFgQkgAAAADAgpAEAAAAABaEJAAAAACwICQBAAAAgAUhCQAAAAAsCEkAAAAAYEFIAgAAAAALQhIAAAAAWBCSAAAAAMCCkAQAAAAAFoQkAAAAALAgJAEAAACABSEJAAAAACwISQAAAABgQUgCAAAAAAtCEgAAAABYEJIAAAAAwIKQBAAAAAAWhCQAAAAAsHBpSHrxxRdls9mcbuHh4Y71aWlpGj58uMqXLy8/Pz/17t1b8fHxLqwYAAAAwI3O5UeS6tevr+PHjztuP//8s2Pd6NGjtWTJEi1YsEBr167VsWPH1KtXLxdWCwAAAOBG5+HyAjw8FBoammV5UlKSZs+erc8++0wdOnSQJM2dO1d169bVhg0b1KpVq6IuFQAAAEAJ4PIjSXv37lVYWJhuuukm9evXT7GxsZKkLVu26Pz58+rUqZOjbXh4uKpWrar169fnuL309HQlJyc73QAAAAAgv1waklq2bKl58+Zp6dKlio6O1sGDB9WmTRudOXNGcXFx8vLyUmBgoNNjQkJCFBcXl+M2p06dqoCAAMetSpUq13gvAAAAANxIXDrcrmvXro5/N2rUSC1btlS1atU0f/58+fr6Fmib48eP15gxYxz3k5OTCUoAAAAA8s3lw+2sAgMDVbt2be3bt0+hoaHKyMhQYmKiU5v4+Phsz2Gy8/b2lr+/v9MNAAAAAPKrWIWks2fPav/+/apYsaKaN28uT09PrVy50rE+JiZGsbGxioiIcGGVAAAAAG5kLh1u98wzz6h79+6qVq2ajh07pokTJ8rd3V19+/ZVQECABg8erDFjxqhcuXLy9/fXyJEjFRERwcx2AAAAAK4Zl4ako0ePqm/fvjp16pSCgoLUunVrbdiwQUFBQZKkadOmyc3NTb1791Z6eroiIyM1c+ZMV5YMAAAA4Abn0pD0+eef57rex8dHUVFRioqKKqKKAAAAAJR0xeqcJAAAAABwNUISAAAAAFgQkgAAAADAgpAEAAAAABaEJAAAAACwICQBAAAAgAUhCQAAAAAsCEkAAAAAYEFIAgAAAAALQhIAAAAAWBCSAAAAAMCCkAQAAAAAFoQkAAAAALAgJAEAAACABSEJAAAAACwISQAAAABgQUgCAAAAAAtCEgAAAABYEJIAAAAAwIKQBAAAAAAWhCQAAAAAsCAkAQAAAIAFIQkAAAAALAhJAAAAAGBBSAIAAAAAC0ISAAAAAFgQkgAAAADAgpAEAAAAABaEJAAAAACwICQBAAAAgAUhCQAAAAAsCEkAAAAAYEFIAgAAAAALQhIAAAAAWBCSAAAAAMCCkAQAAAAAFoQkAAAAALAgJAEAAACABSEJAAAAACwISQAAAABgQUgCAAAAAAtCEgAAAABYEJIAAAAAwIKQBAAAAAAWhCQAAAAAsCAkAQAAAIAFIQkAAAAALAhJAAAAAGBBSAIAAAAAC0ISAAAAAFgQkgAAAADAgpAEAAAAABaEJAAAAACwICQBAAAAgAUhCQAAAAAsCEkAAAAAYEFIAgAAAAALQhIAAAAAWBCSAAAAAMCCkAQAAAAAFoQkAAAAALAgJAEAAACABSEJAAAAACwISQAAAABgQUgCAAAAAAtCEgAAAABYFJuQ9Morr8hms2nUqFGOZWlpaRo+fLjKly8vPz8/9e7dW/Hx8a4rEgAAAMANr1iEpE2bNum9995To0aNnJaPHj1aS5Ys0YIFC7R27VodO3ZMvXr1clGVAAAAAEoCl4eks2fPql+/fvrggw9UtmxZx/KkpCTNnj1bb731ljp06KDmzZtr7ty5+uWXX7RhwwYXVgwAAADgRubykDR8+HB169ZNnTp1clq+ZcsWnT9/3ml5eHi4qlatqvXr1+e4vfT0dCUnJzvdAAAAACC/PFz55J9//rm2bt2qTZs2ZVkXFxcnLy8vBQYGOi0PCQlRXFxcjtucOnWqJk2aVNilAgAAACghXHYk6ciRI3rqqaf06aefysfHp9C2O378eCUlJTluR44cKbRtAwAAALjxuSwkbdmyRSdOnFCzZs3k4eEhDw8PrV27Vu+88448PDwUEhKijIwMJSYmOj0uPj5eoaGhOW7X29tb/v7+TjcAAAAAyC+XDbfr2LGjduzY4bRs0KBBCg8P17hx41SlShV5enpq5cqV6t27tyQpJiZGsbGxioiIcEXJAAAAAEoAl4WkMmXKqEGDBk7LSpcurfLlyzuWDx48WGPGjFG5cuXk7++vkSNHKiIiQq1atXJFyQAAAABKAJdO3JCXadOmyc3NTb1791Z6eroiIyM1c+ZMV5cFAAAA4AZWrELSmjVrnO77+PgoKipKUVFRrikIAAAAQInj8uskAQAAAEBxQkgCAAAAAAtCEgAAAABYEJIAAAAAwIKQBAAAAAAWhCQAAAAAsCAkAQAAAIAFIQkAAAAALAhJAAAAAGBBSAIAAAAAC0ISAAAAAFgQkgAAAADAgpAEAAAAABaEJAAAAACwICQBAAAAgAUhCQAAAAAsCEkAAAAAYEFIAgAAAAALQhIAAAAAWBCSAAAAAMCCkAQAAAAAFoQkAAAAALAgJAEAAACABSEJAAAAACwISQAAAABgQUgCAAAAAAtCEgAAAABYEJIAAAAAwIKQBAAAAAAWhCQAAAAAsCAkAQAAAIAFIQkAAAAALAhJAAAAAGBBSAIAAAAAC0ISAAAAAFgQkgAAAADAgpAEAAAAABaEJAAAAACwICQBAAAAgAUhCQAAAAAsCEkAAAAAYEFIAgAAAAALQhIAAAAAWBCSAAAAAMCCkAQAAAAAFoQkAAAAALDwKOgDU1JStHbtWsXGxiojI8Np3ZNPPnnVhQEAAACAKxQoJG3btk133XWXUlNTlZKSonLlyikhIUGlSpVScHAwIQkAAADAdatAw+1Gjx6t7t276++//5avr682bNigw4cPq3nz5nrjjTcKu0YAAAAAKDIFCknbt2/X008/LTc3N7m7uys9PV1VqlTRa6+9pueee66wawQAAACAIlOgkOTp6Sk3t0sPDQ4OVmxsrCQpICBAR44cKbzqAAAAAKCIFeicpKZNm2rTpk2qVauW2rVrpxdeeEEJCQn6+OOP1aBBg8KuEQAAAACKTIGOJL388suqWLGiJGnKlCkqW7asHn/8cZ08eVLvv/9+oRYIAAAAAEWpQEeSWrRo4fh3cHCwli5dWmgFAQAAAIArFehIUocOHZSYmFjIpQAAAACA6xUoJK1ZsybLBWQBAAAA4EZQoJAkSTabrTDrAAAAAIBioUDnJElSz5495eXlle26VatWFbggAAAAAHClAoekiIgI+fn5FWYtAAAAAOByBQpJNptNY8eOVXBwcGHXAwAAAAAuVaBzkowxhV0HAAAAABQLBQpJEydOZKgdAAAAgBtSgYbbTZw4UZJ08uRJxcTESJLq1KmjoKCgwqsMAAAAAFygQEeSUlNT9cgjjygsLExt27ZV27ZtFRYWpsGDBys1NbWwawQAAACAIlOgkDR69GitXbtWixcvVmJiohITE7Vo0SKtXbtWTz/9dGHXCAAAAABFpkDD7b788kt98cUXat++vWPZXXfdJV9fX/Xp00fR0dGFVR8AAAAAFKkCD7cLCQnJsjw4OPiKhttFR0erUaNG8vf3l7+/vyIiIvT999871qelpWn48OEqX768/Pz81Lt3b8XHxxekZAAAAADIlwKFpIiICE2cOFFpaWmOZefOndOkSZMUERGR7+1UrlxZr7zyirZs2aLNmzerQ4cO6tGjh3bt2iXp0rC+JUuWaMGCBVq7dq2OHTumXr16FaRkAAAAAMiXAg23e/vtt9WlSxdVrlxZjRs3liT99ttv8vHx0bJly/K9ne7duzvdnzJliqKjo7VhwwZVrlxZs2fP1meffaYOHTpIkubOnau6detqw4YNatWqVUFKBwAAAIBcFSgkNWzYUHv37tWnn36qPXv2SJL69u2rfv36ydfXt0CFXLx4UQsWLFBKSooiIiK0ZcsWnT9/Xp06dXK0CQ8PV9WqVbV+/focQ1J6errS09Md95OTkwtUDwAAAICSqUAh6ccff9Rtt92mIUOGXHUBO3bsUEREhNLS0uTn56evv/5a9erV0/bt2+Xl5aXAwECn9iEhIYqLi8txe1OnTtWkSZOuui4AAAAAJVOBzkm64447dPr06UIpoE6dOtq+fbs2btyoxx9/XAMGDNAff/xR4O2NHz9eSUlJjtuRI0cKpU4AAAAAJUOBjiQZYwqtAC8vL918882SpObNm2vTpk2aPn26HnjgAWVkZCgxMdHpaFJ8fLxCQ0Nz3J63t7e8vb0LrT4AAAAAJUuBQpIkrV+/XmXLls12Xdu2bQtcUGZmptLT09W8eXN5enpq5cqV6t27tyQpJiZGsbGxVzSDHgAAAABciQKHpJ49e2a73Gaz6eLFi/naxvjx49W1a1dVrVpVZ86c0WeffaY1a9Zo2bJlCggI0ODBgzVmzBiVK1dO/v7+GjlypCIiIpjZDgAAAMA1U+CQFBcXp+Dg4Kt68hMnTujhhx/W8ePHFRAQoEaNGmnZsmW68847JUnTpk2Tm5ubevfurfT0dEVGRmrmzJlX9ZwAAAAAkJsChSSbzVYoTz579uxc1/v4+CgqKkpRUVGF8nwAAAAAkJcCzW5XmBM3AAAAAEBxUqAjSZmZmYVdBwAAAAAUCwU6kjR16lTNmTMny/I5c+bo1VdfveqiAAAAAMBVChSS3nvvPYWHh2dZXr9+fc2aNeuqiwIAAAAAVylQSIqLi1PFihWzLA8KCtLx48evuigAAAAAcJUChaQqVapo3bp1WZavW7dOYWFhV10UAAAAALhKgSZuGDJkiEaNGqXz58+rQ4cOkqSVK1fq2Wef1dNPP12oBQIAAABAUSpQSBo7dqxOnTqlJ554QhkZGZIuXdNo3LhxGj9+fKEWCAAAAABFqcAXk3311Vf1/PPPa/fu3fL19VWtWrXk7e1d2PUBAAAAQJEqUEiy8/Pz0y233FJYtQAAAACAyxU4JG3evFnz589XbGysY8id3VdffXXVhQEAAACAKxRodrvPP/9ct912m3bv3q2vv/5a58+f165du7Rq1SoFBAQUdo0AAAAAUGQKFJJefvllTZs2TUuWLJGXl5emT5+uPXv2qE+fPqpatWph1wgAAAAARaZAIWn//v3q1q2bJMnLy0spKSmy2WwaPXq03n///UItEAAAAACKUoFCUtmyZXXmzBlJUqVKlbRz505JUmJiolJTUwuvOgAAAAAoYgWauKFt27ZasWKFGjZsqPvvv19PPfWUVq1apRUrVqhjx46FXSMAAAAAFJkChaR3331XaWlpkqR//etf8vT01C+//KLevXtrwoQJhVogAAAAABSlKwpJycnJlx7k4SE/Pz/H/SeeeEJPPPFE4VcHAAAAAEXsikJSYGCgbDZbnu0uXrxY4IIAAAAAwJWuKCStXr3a6b4xRnfddZc+/PBDVapUqVALAwAAAABXuKKQ1K5duyzL3N3d1apVK910002FVhQAAAAAuEqBpgAHAAAAgBvVVYWkI0eOKDU1VeXLly+segAAAADApa5ouN0777zj+HdCQoL++9//qkOHDgoICCj0wgAAAADAFa4oJE2bNk2SZLPZVKFCBXXv3p3rIgEAAAC4oVxRSDp48OC1qgMAAAAAigUmbgAAAAAAC0ISAAAAAFgQkgAAAADAgpAEAAAAABaEJAAAAACwICQBAAAAgAUhCQAAAAAsCEkAAAAAYEFIAgAAAAALQhIAAAAAWBCSAAAAAMCCkAQAAAAAFoQkAAAAALAgJAEAAACABSEJAAAAACwISQAAAABgQUgCAAAAAAtCEgAAAABYEJIAAAAAwIKQBAAAAAAWhCQAAAAAsCAkAQAAAIAFIQkAAAAALAhJAAAAAGBBSAIAAAAAC0ISAAAAAFgQkgAAAADAgpAEAAAAABaEJAAAAACwICQBAAAAgAUhCQAAAAAsCEkAAAAAYEFIAgAAAAALQhIAAAAAWBCSAAAAAMCCkAQAAAAAFoQkAAAAALAgJAEAAACABSEJAAAAACwISQAAAABgQUgCAAAAAAuXhqSpU6fqlltuUZkyZRQcHKx7771XMTExTm3S0tI0fPhwlS9fXn5+furdu7fi4+NdVDEAAACAG51LQ9LatWs1fPhwbdiwQStWrND58+fVuXNnpaSkONqMHj1aS5Ys0YIFC7R27VodO3ZMvXr1cmHVAAAAAG5kHq588qVLlzrdnzdvnoKDg7Vlyxa1bdtWSUlJmj17tj777DN16NBBkjR37lzVrVtXGzZsUKtWrbJsMz09Xenp6Y77ycnJ13YnAKCYiY2NVUJCgqvLKFZ2797t6hIAANcRl4akyyUlJUmSypUrJ0nasmWLzp8/r06dOjnahIeHq2rVqlq/fn22IWnq1KmaNGlS0RQMAMVMbGyswuvW1bnUVFeXAgDAdavYhKTMzEyNGjVKt99+uxo0aCBJiouLk5eXlwIDA53ahoSEKC4uLtvtjB8/XmPGjHHcT05OVpUqVa5Z3QBQnCQkJOhcaqr6/DtawTVqubqcYiNm3UqtmDnV1WUAAK4TxSYkDR8+XDt37tTPP/98Vdvx9vaWt7d3IVUFANen4Bq1VKluY1eXUWycOLjX1SUAAK4jxWIK8BEjRuibb77R6tWrVblyZcfy0NBQZWRkKDEx0al9fHy8QkNDi7hKAAAAACWBS0OSMUYjRozQ119/rVWrVqlGjRpO65s3by5PT0+tXLnSsSwmJkaxsbGKiIgo6nIBAAAAlAAuHW43fPhwffbZZ1q0aJHKlCnjOM8oICBAvr6+CggI0ODBgzVmzBiVK1dO/v7+GjlypCIiIrKdtAEAAAAArpZLQ1J0dLQkqX379k7L586dq4EDB0qSpk2bJjc3N/Xu3Vvp6emKjIzUzJkzi7hSAAAAACWFS0OSMSbPNj4+PoqKilJUVFQRVAQAAACgpCsWEzcAAAAAQHFBSAIAAAAAC0ISAAAAAFgQkgAAAADAgpAEAAAAABaEJAAAAACwICQBAAAAgAUhCQAAAAAsCEkAAAAAYEFIAgAAAAALQhIAAAAAWBCSAAAAAMCCkAQAAAAAFoQkAAAAALAgJAEAAACABSEJAAAAACwISQAAAABgQUgCAAAAAAtCEgAAAABYEJIAAAAAwIKQBAAAAAAWhCQAAAAAsCAkAQAAAIAFIQkAAAAALAhJAAAAAGBBSAIAAAAAC0ISAAAAAFgQkgAAAADAgpAEAAAAABaEJAAAAACwICQBAAAAgAUhCQAAAAAsCEkAAAAAYEFIAgAAAAALQhIAAAAAWBCSAAAAAMCCkAQAAAAAFoQkAAAAALAgJAEAAACABSEJAAAAACwISQAAAABgQUgCAAAAAAtCEgAAAABYEJIAAAAAwIKQBAAAAAAWhCQAAAAAsCAkAQAAAIAFIQkAAAAALAhJAAAAAGBBSAIAAAAAC0ISAAAAAFgQkgAAAADAgpAEAAAAABaEJAAAAACwICQBAAAAgAUhCQAAAAAsCEkAAAAAYEFIAgAAAAALQhIAAAAAWBCSAAAAAMCCkAQAAAAAFoQkAAAAALAgJAEAAACABSEJAAAAACwISQAAAABgQUgCAAAAAAuXhqQff/xR3bt3V1hYmGw2mxYuXOi03hijF154QRUrVpSvr686deqkvXv3uqZYAAAAACWCS0NSSkqKGjdurKioqGzXv/baa3rnnXc0a9Ysbdy4UaVLl1ZkZKTS0tKKuFIAAAAAJYWHK5+8a9eu6tq1a7brjDF6++23NWHCBPXo0UOS9J///EchISFauHChHnzwwaIsFQAAAEAJUWzPSTp48KDi4uLUqVMnx7KAgAC1bNlS69evz/Fx6enpSk5OdroBAAAAQH4V25AUFxcnSQoJCXFaHhIS4liXnalTpyogIMBxq1KlyjWtEwAAAMCNpdiGpIIaP368kpKSHLcjR464uiQAAAAA15FiG5JCQ0MlSfHx8U7L4+PjHeuy4+3tLX9/f6cbAAAAAORXsQ1JNWrUUGhoqFauXOlYlpycrI0bNyoiIsKFlQEAAAC4kbl0druzZ89q3759jvsHDx7U9u3bVa5cOVWtWlWjRo3Sv//9b9WqVUs1atTQ888/r7CwMN17772uKxoAAADADc2lIWnz5s264447HPfHjBkjSRowYIDmzZunZ599VikpKRo6dKgSExPVunVrLV26VD4+Pq4qGQAAAMANzqUhqX379jLG5LjeZrNp8uTJmjx5chFWBQAAAKAkK7bnJAEAAACAKxCSAAAAAMCCkAQAAAAAFoQkAAAAALAgJAEAAACABSEJAAAAACwISQAAAABgQUgCAAAAAAtCEgAAAABYEJIAAAAAwIKQBAAAAAAWhCQAAAAAsCAkAQAAAIAFIQkAAAAALAhJAAAAAGBBSAIAAAAAC0ISAAAAAFgQkgAAAADAgpAEAAAAABaEJAAAAACwICQBAAAAgAUhCQAAAAAsCEkAAAAAYEFIAgAAAAALQhIAAAAAWBCSAAAAAMCCkAQAAAAAFoQkAAAAALAgJAEAAACABSEJAAAAACw8XF0AAAAAcCOIjY1VQkKCq8solipUqKCqVau6uox8IyQBAAAAVyk2NlbhdevqXGqqq0splnxLldKe3buvm6BESAIAAACuUkJCgs6lpqrPv6MVXKOWq8spVk4c3Kv5Ex5XQkICIQkAAAAoaYJr1FKluo1dXQauEhM3AAAAAIAFIQkAAAAALAhJAAAAAGBBSAIAAAAACyZuAAAAwBXhekBZ7d6929UloBARkgAAAJBvXA8IJQEhCQAAAPnG9YCyF7NupVbMnOrqMlBICEkAAAC4YlwPyNmJg3tdXQIKERM3AAAAAIAFIQkAAAAALAhJAAAAAGBBSAIAAAAACyZuAHBd4hod2eM6HQAAXD1CEoDrDtfoAAAA1xIhCcB1h2t05IzrdAAAcPUISQCuW1yjIyuu0wEAwNVj4gYAAAAAsCAkAQAAAIAFIQkAAAAALDgnCQAAMH18NipUqKCqVau6ugwALkBIAgCgBDuTEC+bm5v69+/v6lKKHd9SpbRn926CElACEZIAACjBzp1JlsnMZEr9y5w4uFfzJzyuhIQEQhJQAhGSAAAAU+oDgAUTNwAAAACABSEJAAAAACwISQAAAABgQUgCAAAAAAsmbihisbGxSkhIcHUZxRLXowAAAEBxQEgqQrGxsQqvW1fnUlNdXUqxxPUoAAAAUBwQkopQQkKCzqWmci2KbHA9CgAAABQXhCQX4FoUAAAAQPHFxA0AAAAAYHFdhKSoqChVr15dPj4+atmypX799VdXlwQAAADgBlXsQ9L//vc/jRkzRhMnTtTWrVvVuHFjRUZG6sSJE64uDQAAAMANqNiHpLfeektDhgzRoEGDVK9ePc2aNUulSpXSnDlzXF0aAAAAgBtQsZ64ISMjQ1u2bNH48eMdy9zc3NSpUyetX78+28ekp6crPT3dcT8pKUmSlJycfG2LzYezZ89Kkv7a/bsyUlNcXE3xcvLwfknSli1bHK8TLnFzc1NmZqaryyhWYmJiJPFeys7JQ3sl8dpcjtclZ7w22eP/pZzxGZw93ks5s7+fzp496/Lv5PbnN8bk2s5m8mrhQseOHVOlSpX0yy+/KCIiwrH82Wef1dq1a7Vx48Ysj3nxxRc1adKkoiwTAAAAwHXkyJEjqly5co7ri/WRpIIYP368xowZ47ifmZmp06dPq3z58rLZbC6s7PqVnJysKlWq6MiRI/L393d1OSgA+vD6Rv9d/+jD6xv9d/2jD69vhdl/xhidOXNGYWFhubYr1iGpQoUKcnd3V3x8vNPy+Ph4hYaGZvsYb29veXt7Oy0LDAy8ViWWKP7+/nywXOfow+sb/Xf9ow+vb/Tf9Y8+vL4VVv8FBATk2aZYT9zg5eWl5s2ba+XKlY5lmZmZWrlypdPwOwAAAAAoLMX6SJIkjRkzRgMGDFCLFi1066236u2331ZKSooGDRrk6tIAAAAA3ICKfUh64IEHdPLkSb3wwguKi4tTkyZNtHTpUoWEhLi6tBLD29tbEydOzDKMEdcP+vD6Rv9d/+jD6xv9d/2jD69vrui/Yj27HQAAAAAUtWJ9ThIAAAAAFDVCEgAAAABYEJIAAAAAwIKQBAAAAAAWhKQS7K+//lL//v1Vvnx5+fr6qmHDhtq8ebNjvTFGL7zwgipWrChfX1916tRJe/fuddrG6dOn1a9fP/n7+yswMFCDBw/W2bNni3pXSqzc+vD8+fMaN26cGjZsqNKlSyssLEwPP/ywjh075rQN+tB18noPWj322GOy2Wx6++23nZbTf66Tn/7bvXu37rnnHgUEBKh06dK65ZZbFBsb61iflpam4cOHq3z58vLz81Pv3r2zXEAd105efXj27FmNGDFClStXlq+vr+rVq6dZs2Y5bYM+dJ3q1avLZrNluQ0fPlxS/vomNjZW3bp1U6lSpRQcHKyxY8fqwoULrtidEie3/jt9+rRGjhypOnXqyNfXV1WrVtWTTz6ppKQkp21c0/4zKJFOnz5tqlWrZgYOHGg2btxoDhw4YJYtW2b27dvnaPPKK6+YgIAAs3DhQvPbb7+Ze+65x9SoUcOcO3fO0aZLly6mcePGZsOGDeann34yN998s+nbt68rdqnEyasPExMTTadOncz//vc/s2fPHrN+/Xpz6623mubNmztthz50jfy8B+2++uor07hxYxMWFmamTZvmtI7+c4389N++fftMuXLlzNixY83WrVvNvn37zKJFi0x8fLyjzWOPPWaqVKliVq5caTZv3mxatWplbrvtNlfsUomTnz4cMmSIqVmzplm9erU5ePCgee+994y7u7tZtGiRow196DonTpwwx48fd9xWrFhhJJnVq1cbY/LumwsXLpgGDRqYTp06mW3btpnvvvvOVKhQwYwfP95Fe1Sy5NZ/O3bsML169TKLFy82+/btMytXrjS1atUyvXv3djz+WvcfIamEGjdunGndunWO6zMzM01oaKh5/fXXHcsSExONt7e3+e9//2uMMeaPP/4wksymTZscbb7//ntjs9nMX3/9de2KhzEm7z7Mzq+//mokmcOHDxtj6ENXym//HT161FSqVMns3LnTVKtWzSkk0X+uk5/+e+CBB0z//v1zXJ+YmGg8PT3NggULHMt2795tJJn169cXWq3IXn76sH79+mby5MlOy5o1a2b+9a9/GWPow+LmqaeeMjVr1jSZmZn56pvvvvvOuLm5mbi4OEeb6Oho4+/vb9LT04u8/pLO2n/ZmT9/vvHy8jLnz583xlz7/mO4XQm1ePFitWjRQvfff7+Cg4PVtGlTffDBB471Bw8eVFxcnDp16uRYFhAQoJYtW2r9+vWSpPXr1yswMFAtWrRwtOnUqZPc3Ny0cePGotuZEiqvPsxOUlKSbDabAgMDJdGHrpSf/svMzNRDDz2ksWPHqn79+lm2Qf+5Tl79l5mZqW+//Va1a9dWZGSkgoOD1bJlSy1cuNDRZsuWLTp//rzT52x4eLiqVq3q+JzFtZOf9+Btt92mxYsX66+//pIxRqtXr9aff/6pzp07S6IPi5OMjAx98skneuSRR2Sz2fLVN+vXr1fDhg0VEhLiaBMZGank5GTt2rWryPehJLu8/7KTlJQkf39/eXh4SLr2/UdIKqEOHDig6Oho1apVS8uWLdPjjz+uJ598Uh999JEkKS4uTpKc/vDs9+3r4uLiFBwc7LTew8ND5cqVc7TBtZNXH14uLS1N48aNU9++feXv7y+JPnSl/PTfq6++Kg8PDz355JPZboP+c528+u/EiRM6e/asXnnlFXXp0kXLly9Xz5491atXL61du1bSpf7z8vJy/GhhZ/2cxbWTn/fgjBkzVK9ePVWuXFleXl7q0qWLoqKi1LZtW0n0YXGycOFCJSYmauDAgZLy1zdxcXHZfs+xr0PRubz/LpeQkKCXXnpJQ4cOdSy71v3ncdVbwHUpMzNTLVq00MsvvyxJatq0qXbu3KlZs2ZpwIABLq4O+XElfXj+/Hn16dNHxhhFR0e7olxcJq/+27Jli6ZPn66tW7fm+KsaXCev/svMzJQk9ejRQ6NHj5YkNWnSRL/88otmzZqldu3auax2XJKfz9AZM2Zow4YNWrx4sapVq6Yff/xRw4cPV1hYmNMRCrje7Nmz1bVrV4WFhbm6FBRAbv2XnJysbt26qV69enrxxReLrCaOJJVQFStWVL169ZyW1a1b1zHrUmhoqCRlmQUmPj7esS40NFQnTpxwWn/hwgWdPn3a0QbXTl59aGcPSIcPH9aKFSscR5Ek+tCV8uq/n376SSdOnFDVqlXl4eEhDw8PHT58WE8//bSqV68uif5zpbz6r0KFCvLw8MjzczYjI0OJiYlObayfs7h28urDc+fO6bnnntNbb72l7t27q1GjRhoxYoQeeOABvfHGG5Low+Li8OHD+uGHH/Too486luWnb0JDQ7P9nmNfh6KRXf/ZnTlzRl26dFGZMmX09ddfy9PT07HuWvcfIamEuv322xUTE+O07M8//1S1atUkSTVq1FBoaKhWrlzpWJ+cnKyNGzcqIiJCkhQREaHExERt2bLF0WbVqlXKzMxUy5Yti2AvSra8+lD6v4C0d+9e/fDDDypfvrxTe/rQdfLqv4ceeki///67tm/f7riFhYVp7NixWrZsmST6z5Xy6j8vLy/dcsstubZp3ry5PD09nT5nY2JiFBsb6/icxbWTVx+eP39e58+fl5ub81cld3d3x5FC+rB4mDt3roKDg9WtWzfHsvz0TUREhHbs2OH0Y5P9x8TLAzSunez6T7r0vbNz587y8vLS4sWL5ePj47T+mvffVU/9gOvSr7/+ajw8PMyUKVPM3r17zaeffmpKlSplPvnkE0ebV155xQQGBppFixaZ33//3fTo0SPbKcCbNm1qNm7caH7++WdTq1Ytph8uInn1YUZGhrnnnntM5cqVzfbt252m2bTO+kIfukZ+3oOXu3x2O2PoP1fJT/999dVXxtPT07z//vtm7969ZsaMGcbd3d389NNPjjaPPfaYqVq1qlm1apXZvHmziYiIMBEREa7YpRInP33Yrl07U79+fbN69Wpz4MABM3fuXOPj42NmzpzpaEMfutbFixdN1apVzbhx47Ksy6tv7FNId+7c2Wzfvt0sXbrUBAUFMQV4Ecqp/5KSkkzLli1Nw4YNzb59+5y+w1y4cMEYc+37j5BUgi1ZssQ0aNDAeHt7m/DwcPP+++87rc/MzDTPP/+8CQkJMd7e3qZjx44mJibGqc2pU6dM3759jZ+fn/H39zeDBg0yZ86cKcrdKNFy68ODBw8aSdne7NeQMIY+dKW83oOXyy4k0X+uk5/+mz17trn55puNj4+Pady4sVm4cKHT+nPnzpknnnjClC1b1pQqVcr07NnTHD9+vKh2ocTLqw+PHz9uBg4caMLCwoyPj4+pU6eOefPNN52mKKYPXWvZsmVGUpbvJ8bkr28OHTpkunbtanx9fU2FChXM008/7ZhiGtdeTv23evXqHL/DHDx40NHuWvafzRhjrv54FAAAAADcGDgnCQAAAAAsCEkAAAAAYEFIAgAAAAALQhIAAAAAWBCSAAAAAMCCkAQAAAAAFoQkAAAAALAgJAEAAACABSEJAAAAACwISQBQAg0cOFD33ntvluVr1qyRzWZTYmJikdcEAEBxQUgCAAAAAAtCEgAgV4mJiXr00UcVFBQkf39/dejQQb/99ptj/YsvvqgmTZo4PebyI1KnTp1S3759ValSJZUqVUoNGzbUf//731yfd/78+apZs6Z8fHxUvnx53XfffTp58qRj/VtvvaWGDRuqdOnSqlKlip544gmdPXv2qvbFzmazZblt377dsb569ep6++23HfdXrlwpm83mdHSuffv2GjVqlNN2L3+tLj+id+rUKZUtW1aBgYE5PkbK+vp26NBBI0aMcGpz8uRJeXl5aeXKlbm9JACAbBCSAAC5uv/++3XixAl9//332rJli5o1a6aOHTvq9OnT+d5GWlqamjdvrm+//VY7d+7U0KFD9dBDD+nXX3/N8THh4eGaN2+eYmJitGzZMh06dEjjxo1zrHdzc9M777yjXbt26aOPPtKqVav07LPPFtq+zJ07V8ePH8+1RknKzMzU008/LT8/vzxehbxNmjRJFy5cuOLHPfroo/rss8+Unp7uWPbJJ5+oUqVK6tChw1XXBQAlDSEJAJCjn3/+Wb/++qsWLFigFi1aqFatWnrjjTcUGBioL774It/bqVSpkp555hk1adJEN910k0aOHKkuXbpo/vz5OT6mUaNGatOmjapVq6batWurfPnyunjxomP9qFGjdMcdd6h69erq0KGD/v3vf+e6vfzuiz1oBAUFKTQ0VEFBQbnu20cffaT09HT16NEjvy9Htv7880/NmTNHo0ePvuLH9urVS5K0aNEix7J58+Zp4MCBstlsV1UXAJREHq4uAABQfP322286e/asypcv77T83Llz2r9/v+P+jh07nI6kWMOM/f7LL7+s+fPn66+//lJGRobS09NVqlSpXJ//p59+UteuXZWamqrWrVvrzTffdKz74YcfNHXqVO3Zs0fJycm6cOGC0tLSlJqamu1287svp06dkiT5+/vnWpskpaamasKECZo1a5a+/PLLLOtnzpypDz/80HE/IyND9erVy3Zbzz77rIYNG6abbropz+e9nI+Pjx566CHNmTNHffr00datW7Vz504tXrz4ircFACAkAQBycfbsWVWsWFFr1qzJss563kydOnWcvpBv3LhR/fv3d9x//fXXNX36dL399tuO84hGjRqljIyMXJ+/RYsW2rZtmw4fPqynnnpKH3zwgcaPH69Dhw7p7rvv1uOPP64pU6aoXLly+vnnnzV48GBlZGRkG5Lyuy8HDhyQJNWoUSPX2uz7VadOHXXv3j3bkNSvXz/961//ctx/55139OOPP2Zpt3btWv3000+aO3eu09GgK/Hoo4+qSZMmOnr0qObOnasOHTqoWrVqBdoWAJR0hCQAQI6aNWumuLg4eXh4qHr16jm28/Ly0s033+y4f/ToUaf169atU48ePRzBKTMzU3/++WeOR1XsfH19VatWLdWqVUtDhw51hKQtW7YoMzNTb775ptzcLo0cz22o3ZXsy9q1a1W9enVVrlw51+0dP35c0dHRWrt2bY5tAgICnF6XcuXKZWljjNHTTz+t559/XmXLls31OXPTsGFDtWjRQh988IE+++wzvfvuuwXeFgCUdIQkACihkpKSnGZsk6R9+/ZJujR8rmnTpurUqZMiIiJ077336rXXXlPt2rV17Ngxffvtt+rZs6datGiRr+eqVauWvvjiC/3yyy8qW7as3nrrLcXHx+cakj7//HPVrFlTISEh2rt3r2bNmuV4vptvvlnnz5/XjBkz1L17d61bt06zZs3KtYb87Mv27dsVFRWl/v37Ky4uTpIcM+qdOnVKFy9elLu7uyQpKipKvXv3VtOmTfP1GuRk5cqVqlixooYPH55jG2OM0tLSHPftR+CsEzVIl44mjRgxQqVLl1bPnj2vqi4AKMmYuAEASqg1a9aoadOmTrchQ4ZIktq2bavNmzfLZrPpu+++U9u2bTVo0CDVrl1bDz74oA4fPqyQkJB8P9eECRPUrFkzRUZGqn379goNDc32YrZWu3fvVu/evVWrVi09/PDDuv322zVjxgxJUuPGjfXWW2/p1VdfVYMGDfTpp59q6tSpuW4vP/vStGlTHT9+XK+//roqVqyoihUr6tZbb5V0KWQdOXLEsb3MzExNmTIl369BTlJSUvTKK6/I09Mzxza///67fH19HbfIyEhJUmhoqFO7vn37ysPDQ3379pWPj89V1wYAJZXNGGNcXQQAoHipXr265s2bp/bt27u6lCJls9mU03+LTZo00cKFC3MdqlfUAgMDHddKkqRDhw6pZs2a2rRpk5o1a+a6wgDgOsdwOwBAFvXq1SuU6/5cb3I7OlahQgXHULviwn606Pz58zp16pQmTJigVq1aEZAA4CpxJAkAgOvcmjVrdMcdd6h27dr64osv1LBhQ1eXBADXNUISAAAAAFgwcQMAAAAAWBCSAAAAAMCCkAQAAAAAFoQkAAAAALAgJAEAAACABSEJAAAAACwISQAAAABgQUgCAAAAAIv/B4VGvBfUEnMvAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "bin_edges[1]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5upnKk38xZjK",
        "outputId": "9bc35dd0-9dbd-4bae-e2cd-304b8e17a524"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "589.4297479614531"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "bin_edges[-2]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QpdUy7cjxr_L",
        "outputId": "9ba6cac1-2932-4ffe-bbba-9e9e676303ce"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "717.2395626389919"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "18/20"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "quhGmJPlxv1V",
        "outputId": "e44f06b4-048e-481e-f04b-6a602c85a1a9"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Построение scatter plot\n",
        "plt.figure(figsize=(10, 6))\n",
        "plt.scatter(filtered_data['PRICE PER UNIT'], filtered_data['SALES VOL'], alpha=0.5)\n",
        "\n",
        "# Добавление заголовков и меток осей\n",
        "plt.title('Scatter Plot между SALES VOL и SHARE VOL')\n",
        "plt.xlabel('PRICE PER UNIT')\n",
        "plt.ylabel('SALES VOL')\n",
        "\n",
        "# Отображение графика\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "uy9mnnotyyTI",
        "outputId": "0e3dbbc1-b96d-4b55-bce8-bf1ae8abe038"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "counts, bin_edges = np.histogram(df_kod_TT['PRICE PER UNIT'], bins=10)"
      ],
      "metadata": {
        "id": "JmgnAV-OzF6I"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "counts"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1_ORDZis0lDR",
        "outputId": "e28c7dee-1897-4f27-a1ef-8c5102e2c61c"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 7,  1,  0,  2, 19, 59,  2,  4,  5, 21])"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "bin_edges"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yVCr2XEu0nCy",
        "outputId": "a8c3fb1b-0919-49ab-84cc-c5de22a2380d"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([573.45352113, 589.42974796, 605.4059748 , 621.38220163,\n",
              "       637.35842847, 653.3346553 , 669.31088213, 685.28710897,\n",
              "       701.2633358 , 717.23956264, 733.21578947])"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Вычисление процентилей\n",
        "lower_percentile = df_kod_TT['SALES VOL'].quantile(0.05)\n",
        "upper_percentile = df_kod_TT['SALES VOL'].quantile(0.95)\n",
        "\n",
        "# Фильтрация данных\n",
        "df_filtered = df_kod_TT[(df_kod_TT['SALES VOL'] >= lower_percentile) & (df_kod_TT['SALES VOL'] <= upper_percentile)]\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Построение scatter plot\n",
        "plt.figure(figsize=(10, 6))\n",
        "plt.scatter(df_filtered['PRICE PER UNIT'], df_filtered['SALES VOL'], alpha=0.5)\n",
        "\n",
        "# Добавление заголовков и меток осей\n",
        "plt.title('Scatter Plot между SALES VOL и SHARE VOL (без выбросов)')\n",
        "plt.xlabel('PRICE PER UNIT')\n",
        "plt.ylabel('SALES VOL')\n",
        "\n",
        "# Отображение графика\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "VLCy1aFb0o6_",
        "outputId": "3f357d0b-919b-4bf8-8e9f-da4f8a509959"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "1k2vh_da2jmw"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}