ПР_3.ipynb

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      "cell_type": "markdown",
      "metadata": {
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        "<a href=\"https://colab.research.google.com/github/offthetab/statistics_homework/blob/main/%D0%9F%D0%A0_3.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "source": [
        "#Вариант 4\n",
        "1 и 4 файлы – нормальное распределение\n",
        "\n",
        "3 и 6 файлы – показательное распределение"
      ],
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        "id": "Pe6Q8Y55mgTm"
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    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from math import log2, sqrt, e, pi\n",
        "from scipy. stats import norm, chi2\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "from sklearn.metrics import r2_score"
      ],
      "metadata": {
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      "execution_count": null,
      "outputs": []
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    {
      "cell_type": "markdown",
      "source": [
        "## Функция 'open_file' для чтения данных из файлов.\n",
        "## Функция 'show_hist' для отрисовки гистограммы распределения, расчета: интервалов, абсолютных и относительных частот"
      ],
      "metadata": {
        "id": "V1WJNi8W4cgU"
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    {
      "cell_type": "code",
      "source": [
        "def open_file(path: str):\n",
        "  with open(path, 'r') as f:\n",
        "    data = f.read()\n",
        "  data = np.array(data.split(\"\\n\"))[:-1]\n",
        "  data = data.astype(np.float64)\n",
        "  print(\"Длина списка:\", len(data))\n",
        "  return data\n",
        "\n",
        "def show_hist(data):\n",
        "  m = round(1 + log2(len(data)))  # кол-во интервалов\n",
        "  h = (max(data) - min(data)) / m  # шаг\n",
        "\n",
        "  intervals = [(min(data) - 0.01) + (i * h) for i in range(m + 1)]\n",
        "  intervals[-1] = intervals[-1] + 0.01\n",
        "\n",
        "  int = []\n",
        "  for i, num in enumerate(intervals):\n",
        "    if(i != len(intervals) - 1):\n",
        "        int.append([round(num, 3), round(intervals[i+1], 3)])\n",
        "    else:\n",
        "        break\n",
        "\n",
        "  # Абсолютные частоты\n",
        "  absolute_freq = [0]*len(int)\n",
        "  for num in data:\n",
        "    for index, interval in enumerate(int):\n",
        "      if (num >= interval[0] and num < interval[1]):\n",
        "        absolute_freq[index] += 1\n",
        "        break\n",
        "\n",
        "  # Относительные частоты\n",
        "  relative_freq = np.array(absolute_freq) / len(data)\n",
        "\n",
        "\n",
        "  print(\"Интервалы:\", int)\n",
        "  print(\"Абсолютные частоты:\", absolute_freq)\n",
        "  print(\"Относительные частоты:\", relative_freq)\n",
        "\n",
        "  x = np.arange(len(relative_freq))\n",
        "  hist_outpt = ['{a} - {b}'.format(a=round(i[0], 2), b=round(i[1], 2)) for i in int]\n",
        "  plt.figure(figsize=(12, 6))\n",
        "  plt.bar(x, height=relative_freq)\n",
        "  plt.title(\"Гистограмма распределения относительных частот\\n для рассчитанных интервалов выборки\")\n",
        "  plt.xlabel('X')\n",
        "  plt.ylabel('Относительная частота')\n",
        "  plt.xticks(x, hist_outpt)\n",
        "  plt.show()\n",
        "\n",
        "\n",
        "  return int, absolute_freq, relative_freq\n",
        "\n",
        "def normal_dest_func(x):  # Значение функции распределения в точке стандартного нормального распределения (мат ожидание = 0 и дисперсия = 1)\n",
        "  return norm.cdf(x)"
      ],
      "metadata": {
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      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Проверка гипотезы о нормальности распределения. H0 (нулевая гипотеза) - выборка имеет нормальное распределение. Альтернаятивная гипотеза - выборка имеет распределение отличное от нормального.  "
      ],
      "metadata": {
        "id": "vDlR_GMU46_4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def Normality_distribution_test(data, int, absolute_freq, relative_freq) -> None:\n",
        "  print(\"Проверка гипотезы о нормальности распределения\")\n",
        "\n",
        "  # Выборочная средняя\n",
        "  sample_mean = round(data.mean(), 3)\n",
        "  print(\"\\nВыборочное среднее для всей выборки:\", sample_mean)\n",
        "\n",
        "  # Несмещенное стандартное отклонение\n",
        "  std = np.sqrt(np.sum((data - sample_mean)**2) / (len(data) - 1))\n",
        "  std = round(std\n",
        "  print(\"Несмещенное стандартное отклонение\", std)\n",
        "\n",
        "  # Расчет теоритических частот интервалов\n",
        "  theor_freqs = np.zeros(len(absolute_freq))\n",
        "  for i, interval in enumerate(int):\n",
        "    f1 = round((interval[1] - sample_mean) / std, 3)  # z статистики для правых границ интервала\n",
        "    f2 = round((interval[0] - sample_mean) / std, 3)  # z статистики для левых границ интервала\n",
        "\n",
        "    theor_freq = round((normal_dest_func(f1) - normal_dest_func(f2)) * len(data), 3)  # теоритическая часота на данном интервале\n",
        "    theor_freqs[i] = theor_freq\n",
        "\n",
        "    print(\"\\n\", i, interval, \"ni:\", absolute_freq[i], \"- эмпирическая частота на интервале;\",\"Ф_left:\", f1,\"Ф_right:\", f2, \" Теор частота\", theor_freq)\n",
        "\n",
        "  print(\"\\nКритерий согласия Пирсона\")\n",
        "  chi_our = np.sum((np.array(absolute_freq) - theor_freqs) ** 2 / theor_freqs)\n",
        "  chi_normal = chi2.ppf(0.95, len(theor_freqs) - 2 - 1)\n",
        "  print(\"Кси нашего распределения:\", chi_our)\n",
        "  print(\"Кси нормального распределения:\", chi_normal)  # 6 - число интервалов 2 - число параметров распределения (мат одидание и дисперсия) k = 6 - 2 -1 для 1 распределения\n",
        "\n",
        "  print(\"\\nВывод о принятии или отвержении нулевой гипотезы\")\n",
        "  if (chi_our > chi_normal):\n",
        "    print(\"Нулевая гипотеза отвергается\")\n",
        "    print(\"Распределение отлично от нормального\")\n",
        "  else:\n",
        "    print(\"Нулевая гипотеза принимается\")\n",
        "    print(\"Распределение является нормальным\")\n"
      ],
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      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Проверка гипотезы о показателном распределении. H0 (нулевая гипотеза) - выборка имеет показательное распределение. Альтернаятивная гипотеза - выборка имеет распределение отличное от показательного.  "
      ],
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      }
    },
    {
      "cell_type": "code",
      "source": [
        "def Exponential_distribution_test(data, int, absolute_freq, relative_freq) -> None:\n",
        "  print(\"Проверка гипотезы о показательном распределении\")\n",
        "\n",
        "  # Выборочная средняя\n",
        "  sample_mean = round(data.mean(), 3)\n",
        "  print(\"\\nВыборочное среднее для всей выборки:\", sample_mean)\n",
        "\n",
        "  # Несмещенное стандартное отклонение\n",
        "  std = np.sqrt(np.sum((data - sample_mean)**2) / (len(data) - 1))\n",
        "  std = round(std, 3)\n",
        "  print(\"Несмещенное стандартное отклонение\", std)\n",
        "\n",
        "  # Расчет теоритических частот интервалов\n",
        "  theor_freqs = np.zeros(len(absolute_freq))\n",
        "  for i, interval in enumerate(int):\n",
        "\n",
        "    # вычисление Pi\n",
        "    a = e ** (- interval[0] / sample_mean)\n",
        "    b = e ** (- interval[1] / sample_mean)\n",
        "    theor_freq = (a - b) * len(data)\n",
        "\n",
        "    theor_freqs[i] = theor_freq\n",
        "\n",
        "    print(\"\\n\", i, interval, \"ni:\", absolute_freq[i], \"- эмпирическая частота на интервале;\", \" Теор частота\", theor_freq)\n",
        "\n",
        "  print(\"\\nКритерий согласия Пирсона\")\n",
        "  chi_our = np.sum((np.array(absolute_freq) - theor_freqs) ** 2 / theor_freqs)\n",
        "  chi_normal = chi2.ppf(0.95, len(data) - 1 - 1)\n",
        "  print(\"Кси нашего распределения:\", chi_our)\n",
        "  print(\"Кси показательного распределения:\", chi_normal)  # длина выборки - число интервалов 2 - число параметров распределения k = 32 - 1 -1 для 1 распределения\n",
        "\n",
        "  print(\"\\nВывод о принятии или отвержении нулевой гипотезы\")\n",
        "  if (chi_our > chi_normal):\n",
        "    print(\"Нулевая гипотеза отвергается\")\n",
        "    print(\"Распределение отлично от показательного\")\n",
        "  else:\n",
        "    print(\"Нулевая гипотеза принимается\")\n",
        "    print(\"Распределение является показательным\")"
      ],
      "metadata": {
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      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Функции анаморфоз для нормального и показательного распределения. Каждая функция преобразует систему координат для нормального или показательного распределения. Выводит графики полученых точек (столбцов интервального ряда), строит линейную регрессию по точкам, производит расчет метрики R2 и производит поиск параметров распределения по построенным прямым."
      ],
      "metadata": {
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      }
    },
    {
      "cell_type": "code",
      "source": [
        "def Normal_anamorphosis(data, int, relative_freq) -> None:\n",
        "  print(\"Анаморфоза для нормального распределения\")\n",
        "\n",
        "  # Удаление столбцов с нулевыми частотами\n",
        "  index_to_delete = []\n",
        "  for i, num in enumerate(relative_freq):\n",
        "    if num == 0:\n",
        "      index_to_delete.append(i)\n",
        "\n",
        "  int = np.delete(int, index_to_delete, axis=0)\n",
        "  relative_freq = np.delete(relative_freq, index_to_delete)\n",
        "\n",
        "  # Расчет ln(w)\n",
        "  y = np.log(relative_freq)\n",
        "\n",
        "  # Расчет центров интервалов\n",
        "  centres_of_intervals = []\n",
        "  for i, interval in enumerate(int):\n",
        "    centres_of_intervals.append(round((interval[0]+interval[1]) / 2, 3))\n",
        "\n",
        "  centres_of_intervals = np.array(centres_of_intervals)\n",
        "\n",
        "  # Расчет средней от выборки и среднего квадратичного отклонения средних интервалов от среднего\n",
        "  mean = data.mean()\n",
        "  mae = (centres_of_intervals - mean) ** 2\n",
        "\n",
        "  print(\"log(w):\", y)\n",
        "  print(\"mae:\", mae)\n",
        "\n",
        "  # Расчет регрессионной прямой\n",
        "  X_new = np.c_[mae, np.ones(len(mae))]\n",
        "  W = np.linalg.inv(X_new.T @ X_new) @ X_new.T @ y\n",
        "\n",
        "  # Поиск параметров нормального распределения по построенным прямым (sigma или std)\n",
        "  std = np.sqrt(-1/(2*W[0]))\n",
        "  print(\"\\nПоиск параметров нормального распределения по построенным прямым (sigma или std)\")\n",
        "  print('sigma (std) =', std)\n",
        "\n",
        "  # Построение графика анаморфозы и регрессии\n",
        "  fig, ax = plt.subplots()\n",
        "  plt.figure(figsize=(5,5))\n",
        "  ax.scatter(x=mae ,y=y)\n",
        "  ax.plot(mae, X_new@W, color='g')\n",
        "  ax.set_title(f'Анаморфоза нормального распределения; R2 = {r2_score(y, X_new@W).round(3)}')\n",
        "  ax.set_xlabel(\"(x-mean)^2\")\n",
        "  ax.set_ylabel(\"ln(w)\")\n",
        "  ax.grid()\n",
        "  plt.show()\n"
      ],
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      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def Exponential_anamorphosis(data, int, relative_freq):\n",
        "  print(\"Анаморфоза для показательного распределения\")\n",
        "\n",
        "  # Удаление столбцов с нулевыми частотами\n",
        "  index_to_delete = []\n",
        "  for i, num in enumerate(relative_freq):\n",
        "    if num == 0:\n",
        "      index_to_delete.append(i)\n",
        "\n",
        "  int = np.delete(int, index_to_delete, axis=0)\n",
        "  relative_freq = np.delete(relative_freq, index_to_delete)\n",
        "\n",
        "  # Расчет ln(w)\n",
        "  y = np.log(relative_freq)\n",
        "\n",
        "  # Расчет центров интервалов\n",
        "  centres_of_intervals = []\n",
        "  for i, interval in enumerate(int):\n",
        "    centres_of_intervals.append(round((interval[0]+interval[1]) / 2, 3))\n",
        "\n",
        "  centres_of_intervals = np.array(centres_of_intervals)\n",
        "\n",
        "  print(\"log(w):\", y)\n",
        "  print(\"centres\", centres_of_intervals)\n",
        "\n",
        "  # Расчет регрессионной прямой\n",
        "  X_new = np.c_[centres_of_intervals, np.ones(len(centres_of_intervals))]\n",
        "  W = np.linalg.inv(X_new.T @ X_new) @ X_new.T @ y\n",
        "\n",
        "  # Поиск параметров показательного распределения по построенным прямым (lambda)\n",
        "  lambd = -1/W[0]\n",
        "  print(\"\\nПоиск параметров показательного распределения по построенным прямым (lambda)\")\n",
        "  print('lambda =', lambd)\n",
        "\n",
        "  # Построение графика анаморфозы и регрессии\n",
        "  fig, ax = plt.subplots()\n",
        "  plt.figure(figsize=(5,5))\n",
        "  ax.scatter(x=centres_of_intervals ,y=y)\n",
        "  ax.plot(centres_of_intervals, X_new@W, color='g')\n",
        "  ax.set_title(f'Анаморфоза показательного распределения; R2 = {r2_score(y, X_new@W).round(3)}')\n",
        "  ax.set_xlabel(\"X\")\n",
        "  ax.set_ylabel(\"ln(w)\")\n",
        "  ax.grid()\n",
        "  plt.show()\n"
      ],
      "metadata": {
        "id": "TS-goX1jpbhT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Файл '1.txt' - нормальное распределение\n"
      ],
      "metadata": {
        "id": "7ULYq-cI2px2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# нормальное распределение\n",
        "data = open_file('1.txt')\n",
        "\n",
        "# Отрисовка гистограммы\n",
        "int, absolute_freq, relative_freq = show_hist(data) # show_hist вычисляет интервалы, частоты и рисует гистограмму"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 654
        },
        "id": "m7pMDO0wmgub",
        "outputId": "fcc7d170-622d-40fc-bd2b-0a3fc9beeb5b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Длина списка: 32\n",
            "Интервалы: [[-12.242, -5.982], [-5.982, 0.278], [0.278, 6.539], [6.539, 12.799], [12.799, 19.059], [19.059, 25.33]]\n",
            "Абсолютные частоты: [1, 3, 9, 11, 3, 5]\n",
            "Относительные частоты: [0.03125 0.09375 0.28125 0.34375 0.09375 0.15625]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Проверка гипотезы о нормальности распределения\n",
        "Normality_distribution_test(data, int, absolute_freq, relative_freq)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YNaT5Hy9zGMw",
        "outputId": "f58bfcba-0a46-45be-eaf7-29ef8397c801"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Проверка гипотезы о нормальности распределения\n",
            "\n",
            "Выборочное среднее для всей выборки: 8.28\n",
            "Несмещенное стандартное отклонение 8.646\n",
            "\n",
            " 0 [-12.242, -5.982] ni: 1 - эмпирическая частота на интервале; Ф_left: -1.65 Ф_right: -2.374  Теор частота 1.302\n",
            "\n",
            " 1 [-5.982, 0.278] ni: 3 - эмпирическая частота на интервале; Ф_left: -0.926 Ф_right: -1.65  Теор частота 4.088\n",
            "\n",
            " 2 [0.278, 6.539] ni: 9 - эмпирическая частота на интервале; Ф_left: -0.201 Ф_right: -0.926  Теор частота 7.78\n",
            "\n",
            " 3 [6.539, 12.799] ni: 11 - эмпирическая частота на интервале; Ф_left: 0.523 Ф_right: -0.201  Теор частота 8.933\n",
            "\n",
            " 4 [12.799, 19.059] ni: 3 - эмпирическая частота на интервале; Ф_left: 1.247 Ф_right: 0.523  Теор частота 6.217\n",
            "\n",
            " 5 [19.059, 25.33] ni: 5 - эмпирическая частота на интервале; Ф_left: 1.972 Ф_right: 1.247  Теор частота 2.621\n",
            "\n",
            "Критерий согласия Пирсона\n",
            "Кси нашего распределения: 4.853195008659293\n",
            "Кси нормального распределения: 7.814727903251179\n",
            "\n",
            "Вывод о принятии или отвержении нулевой гипотезы\n",
            "Нулевая гипотеза принимается\n",
            "Распределение является нормальным\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Проверка гипотезы о показательном распределении\n",
        "Exponential_distribution_test(data, int, absolute_freq, relative_freq)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JGxO3rcizopN",
        "outputId": "faf11cf8-099a-4568-b833-d76fcc9ae604"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Проверка гипотезы о показательном распределении\n",
            "\n",
            "Выборочное среднее для всей выборки: 8.28\n",
            "Несмещенное стандартное отклонение 8.646\n",
            "\n",
            " 0 [-12.242, -5.982] ni: 1 - эмпирическая частота на интервале;  Теор частота 74.45986236507015\n",
            "\n",
            " 1 [-5.982, 0.278] ni: 3 - эмпирическая частота на интервале;  Теор частота 34.96059508968744\n",
            "\n",
            " 2 [0.278, 6.539] ni: 9 - эмпирическая частота на интервале;  Теор частота 16.4165473130573\n",
            "\n",
            " 3 [6.539, 12.799] ni: 11 - эмпирическая частота на интервале;  Теор частота 7.706186940748854\n",
            "\n",
            " 4 [12.799, 19.059] ni: 3 - эмпирическая частота на интервале;  Теор частота 3.618229644315619\n",
            "\n",
            " 5 [19.059, 25.33] ni: 5 - эмпирическая частота на интервале;  Теор частота 1.7008371828070554\n",
            "\n",
            "Критерий согласия Пирсона\n",
            "Кси нашего распределения: 112.95488490339952\n",
            "Кси показательного распределения: 43.77297182574219\n",
            "\n",
            "Вывод о принятии или отвержении нулевой гипотезы\n",
            "Нулевая гипотеза отвергается\n",
            "Распределение отлично от показательного\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Анаморфоза для нормального распределения\n",
        "Normal_anamorphosis(data, int, relative_freq)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 611
        },
        "id": "nyhPpL-nzGe4",
        "outputId": "dd11d3b9-7987-4c24-db79-c716e480d17c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Анаморфоза для нормального распределения\n",
            "log(w): [-3.4657359  -2.36712361 -1.26851133 -1.06784063 -2.36712361 -1.85629799]\n",
            "mae: [302.46664759 123.9118126   23.73217761   1.93052048  58.51380548\n",
            " 193.61140981]\n",
            "\n",
            "Поиск параметров нормального распределения по построенным прямым (sigma или std)\n",
            "sigma (std) = 8.871297930967508\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x500 with 0 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Анаморфоза для показательного распределения\n",
        "Exponential_anamorphosis(data, int, relative_freq)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 594
        },
        "id": "IhtoX0K50Mkw",
        "outputId": "68b156f8-ed49-4ffd-f24e-4210d3f10f2d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Анаморфоза для показательного распределения\n",
            "log(w): [-3.4657359  -2.36712361 -1.26851133 -1.06784063 -2.36712361 -1.85629799]\n",
            "centres [-9.112 -2.852  3.408  9.669 15.929 22.194]\n",
            "\n",
            "Поиск параметров показательного распределения по построенным прямым (lambda)\n",
            "lambda = -26.570703408454726\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x500 with 0 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Файл '4.txt' - нормальное распределение"
      ],
      "metadata": {
        "id": "nAlmNWH73IqR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# нормальное распределение\n",
        "data = open_file('4.txt')\n",
        "\n",
        "# Отрисовка гистограммы\n",
        "int, absolute_freq, relative_freq = show_hist(data) # show_hist вычисляет интервалы, частоты и рисует гистограмму"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 672
        },
        "id": "WL9tr0Ypmgxs",
        "outputId": "b99ed46a-fd76-421f-d5a7-755d344d38e1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Длина списка: 128\n",
            "Интервалы: [[-13.819, -9.922], [-9.922, -6.026], [-6.026, -2.129], [-2.129, 1.768], [1.768, 5.664], [5.664, 9.561], [9.561, 13.458], [13.458, 17.365]]\n",
            "Абсолютные частоты: [3, 7, 24, 29, 32, 22, 9, 2]\n",
            "Относительные частоты: [0.0234375 0.0546875 0.1875    0.2265625 0.25      0.171875  0.0703125\n",
            " 0.015625 ]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Проверка гипотезы о нормальности распределения\n",
        "Normality_distribution_test(data, int, absolute_freq, relative_freq)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KUnHHqSr3eNt",
        "outputId": "16eee5be-469f-46c5-fbb6-47d0811332dc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Проверка гипотезы о нормальности распределения\n",
            "\n",
            "Выборочное среднее для всей выборки: 1.887\n",
            "Несмещенное стандартное отклонение 5.898\n",
            "\n",
            " 0 [-13.819, -9.922] ni: 3 - эмпирическая частота на интервале; Ф_left: -2.002 Ф_right: -2.663  Теор частота 2.403\n",
            "\n",
            " 1 [-9.922, -6.026] ni: 7 - эмпирическая частота на интервале; Ф_left: -1.342 Ф_right: -2.002  Теор частота 8.596\n",
            "\n",
            " 2 [-6.026, -2.129] ni: 24 - эмпирическая частота на интервале; Ф_left: -0.681 Ф_right: -1.342  Теор частота 20.242\n",
            "\n",
            " 3 [-2.129, 1.768] ni: 29 - эмпирическая частота на интервале; Ф_left: -0.02 Ф_right: -0.681  Теор частота 31.243\n",
            "\n",
            " 4 [1.768, 5.664] ni: 32 - эмпирическая частота на интервале; Ф_left: 0.64 Ф_right: -0.02  Теор частота 31.602\n",
            "\n",
            " 5 [5.664, 9.561] ni: 22 - эмпирическая частота на интервале; Ф_left: 1.301 Ф_right: 0.64  Теор частота 21.051\n",
            "\n",
            " 6 [9.561, 13.458] ni: 9 - эмпирическая частота на интервале; Ф_left: 1.962 Ф_right: 1.301  Теор частота 9.184\n",
            "\n",
            " 7 [13.458, 17.365] ni: 2 - эмпирическая частота на интервале; Ф_left: 2.624 Ф_right: 1.962  Теор частота 2.629\n",
            "\n",
            "Критерий согласия Пирсона\n",
            "Кси нашего распределения: 1.5053317236636825\n",
            "Кси нормального распределения: 11.070497693516351\n",
            "\n",
            "Вывод о принятии или отвержении нулевой гипотезы\n",
            "Нулевая гипотеза принимается\n",
            "Распределение является нормальным\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Проверка гипотезы о показательном распределении\n",
        "Exponential_distribution_test(data, int, absolute_freq, relative_freq)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AWHRdwcA3eao",
        "outputId": "9caf9ae7-ef3e-42d3-fe89-ae3b3cc7371c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Проверка гипотезы о показательном распределении\n",
            "\n",
            "Выборочное среднее для всей выборки: 1.887\n",
            "Несмещенное стандартное отклонение 5.898\n",
            "\n",
            " 0 [-13.819, -9.922] ni: 3 - эмпирическая частота на интервале;  Теор частота 169347.7639762239\n",
            "\n",
            " 1 [-9.922, -6.026] ni: 7 - эмпирическая частота на интервале;  Теор частота 21470.81500479744\n",
            "\n",
            " 2 [-6.026, -2.129] ni: 24 - эмпирическая частота на интервале;  Теор частота 2724.047041851573\n",
            "\n",
            " 3 [-2.129, 1.768] ni: 29 - эмпирическая частота на интервале;  Теор частота 345.3958348192936\n",
            "\n",
            " 4 [1.768, 5.664] ni: 32 - эмпирическая частота на интервале;  Теор частота 43.79113074013667\n",
            "\n",
            " 5 [5.664, 9.561] ni: 22 - эмпирическая частота на интервале;  Теор частота 5.555872011628386\n",
            "\n",
            " 6 [9.561, 13.458] ni: 9 - эмпирическая частота на интервале;  Теор частота 0.7044573834896691\n",
            "\n",
            " 7 [13.458, 17.365] ni: 2 - эмпирическая частота на интервале;  Теор частота 0.08939030117497766\n",
            "\n",
            "Критерий согласия Пирсона\n",
            "Кси нашего распределения: 193955.0398483384\n",
            "Кси показательного распределения: 153.1979027439562\n",
            "\n",
            "Вывод о принятии или отвержении нулевой гипотезы\n",
            "Нулевая гипотеза отвергается\n",
            "Распределение отлично от показательного\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Анаморфоза для нормального распределения\n",
        "Normal_anamorphosis(data, int, relative_freq)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 628
        },
        "id": "fV7So-XI3elr",
        "outputId": "b2572c05-7c87-45cf-ffb9-b9eb162f0f2e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Анаморфоза для нормального распределения\n",
            "log(w): [-3.75341798 -2.90612011 -1.67397643 -1.48473443 -1.38629436 -1.76098781\n",
            " -2.65480569 -4.15888308]\n",
            "mae: [189.25195117  97.23710048  35.56795302   4.27202356   3.34565287\n",
            "  32.77691419  92.60429595 182.92867061]\n",
            "\n",
            "Поиск параметров нормального распределения по построенным прямым (sigma или std)\n",
            "sigma (std) = 5.936781370090277\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x500 with 0 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Анаморфоза для показательного распределения\n",
        "Exponential_anamorphosis(data, int, relative_freq)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 611
        },
        "id": "HLnE_jDK3etk",
        "outputId": "ed9126bc-6d6a-4479-ff7e-79247c0cce88"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Анаморфоза для показательного распределения\n",
            "log(w): [-3.75341798 -2.90612011 -1.67397643 -1.48473443 -1.38629436 -1.76098781\n",
            " -2.65480569 -4.15888308]\n",
            "centres [-11.87   -7.974  -4.077  -0.18    3.716   7.612  11.51   15.412]\n",
            "\n",
            "Поиск параметров показательного распределения по построенным прямым (lambda)\n",
            "lambda = 187.10999770848176\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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zVZ3+y8/P1y233KKjR49q5cqVdicKSmc+Xfv4+NjWf+zYMQUHB6tly5ZOf9aZmZlKS0vTxx9/rPXr1+vGG2+s9rrKY4zRhx9+qP79+8sYY/d6TUhIUFZWlsM6CwsLFRAQUKn1l6zL2WhmiZKfeenH2e1LXoePPfaY3fSS0SBnV+uU5fPPP5evr6/++te/OqzLGKMvvviiwnXUqlVLDz30kO25v7+/HnroIR05csR2WLmy7+8PP/xQFotFkydPdtjO2YcQq2LAgAG65JJLbM/bt2+vDh062PoyMzNTa9as0Z133qmcnBxb3x87dkwJCQnatWuXwyHu/Px8u/exMykpKTpx4oQGDRpk9zP19fVVhw4dHA4fFhYWSlK5r6nly5eruLhYd955p906IyMjddlllzmss6ioyOE1lZubW27dubm5evbZZzVmzBg1adLEbt6pU6dUXFzs8L52ZuHChTLGVHqUaP78+YqIiFDDhg117bXXavXq1Xr88ccdXutn27lzp0aPHq1OnTpp6NChFW4nISFBKSkplXpUJC8vz+nPq+S1Ud7pDCXzKrN8Xl6ewyHD0m3L287SpUs1f/58jRs3TpdddlmZ7Zzh8FkFioqK9O6776p79+7au3evbXqHDh30yiuvaPXq1brpppvslpk8ebLTX3Ilw9YlFi1apFdeecXhGHTpwzulDR8+XPPmzdP48eO1cOFCde7c2eEHfvToUeXm5qply5YOy7dq1UrFxcX6448/7IZcz15HcHCwoqKibMffS44Rl/4lW5ZLLrnE1n7//v2S5LSWuLg4ffvtt3bTVq9erT59+kiSQkJC9MEHH6hjx462+QcOHNAzzzyjTz/91OGQQVZWVoW1VUZ1+m/48OHasGGDAgMDbecRlVZyzsibb76pvXv32gVkZ79o4+PjdfjwYUlnzuN47bXXqr2uivb1xIkTmjdvnsPhihJHjhyxe37ixIlKnS906tQpRURE2J7HxMRo3Lhx+tvf/mbXbtWqVXbtnNm/f798fHwcrvaMjIxUvXr1bK+zyti/f7+io6Nth3dLtGrVyja/ItHR0apTp47dtMsvv1zSmXN6Sl6zlXl/79mzR9HR0QoLC6v0PlSGsz8El19+ue2cjd27d8sYo0mTJmnSpElO13HkyBG793xGRoZCQ0PL3e6uXbskST169HA6/+xDLiWXTJf3mtq1a5eMMWX+cSv9gU46Exgqek2d7dVXX1V+fr6efPJJh0DSoEEDXXbZZXr77bfVtWtXXXXVVfLx8XE4n606br31Vo0ZM0aFhYXavHmzXnjhBeXm5to++DiTnp6ufv36KTQ01HZ+T0WioqIUFRV1zvVKZz6cOtv3kg8z5V3pVTKvMsvXrl3bFpqdtS1rO998841GjBihhIQEPf/88+XsiXOEogqsWbNGaWlpevfdd/Xuu+86zF+yZIlDKHrwwQc1cOBAu2kjR460e7548WINGzZMAwYM0N///nc1bNhQvr6+mjZtmsNJdiWGDBmixx9/XBs2bNCiRYv09NNPn+PeVU55n/SdqcyJz85cd911SklJ0fHjx7V48WLdf//9iomJ0bXXXquioiL17t1bmZmZeuKJJxQXF6c6dero4MGDGjZsmIqLi6u1TVfYunWrPvnkE40ZM0YPPvig1qxZYzf/hRde0KRJk3T//ffrueeeU1hYmHx8fPToo486rXvZsmXKzs7Wli1b9OKLL+qSSy7R1KlTq7Wu8pS0HzJkSJmfNkvOPSqRnp6upk2bVrjuwMBArVixQtKZc6X+9a9/6dFHH1VUVJTuvPNOW7sOHTrY9q3EG2+8oU8++cRhnecycuJu1Xl/u1PJz378+PF2l36XdnYI3bdvX4WfukvW+8477ygyMtJhfsl5diVKzvtw1rb0Oi0Wi7744gunAeDsQNWsWTO99dZbdtOWLVtWZvDPyMjQjBkzNHHixDLD6XvvvafBgwc79NXZ4biqGjdurF69ekmS+vbtq/DwcI0ZM0bdu3e3G5kvkZWVpT59+ujEiRP65ptvbOcjViQvL6/SHxzL+1lIZwLW2aOI0pmTzCWVW1NYWJgCAgJsbctbPioqSkVFRTpy5IgaNmxoa1dYWKhjx4453c6PP/6oW265RW3atNEHH3zg8HqrDEJRBZYsWaKGDRtq1qxZDvOWL1+ujz76SHPmzLFLrZdddpnthV7i7DfPBx98oBYtWmj58uV2v+ydjTCVaNCggW655RbbUH3JcHJpERERCgoKUmpqqsPyO3fulI+Pj8NJcLt27bIdGpTOnHyclpamvn37SpLtE8ahQ4cq/LRx8OBB24u15I9namqqwyfH1NRUhz+uDRo0sPVbUlKSWrZsqRkzZui9997Tzz//rN9++02LFi2yO9m0KjcMq4zq9N/bb7+tW265Rb6+vrr55ps1f/58jRgxwjb/gw8+UPfu3TV//ny75U6cOOH0JMAbbrhB0pnDjCVXdk2YMEHBwcFVXldF+1q3bl0VFRU5vF6dsVqt2r17txITEyts6+vra7fOfv36KSwsTCtXrrQLReHh4Q7bPvuqw6ZNm6q4uFi7du2yjehIZ04SP3HiRKVCWul1ffXVV7aTOUvs3LnTNr8ihw4d0qlTp+ze0yU30Ss5bFLZ93dsbKy+/PJLZWZmunS0qGTEprTffvvNVl/JoWE/P79K/eyPHj2qAwcOaNCgQeW2KzlBuGHDhpVa7/bt2yXJ7ufqbJ3GGDVv3tw2IleeOnXqOGx727ZtZbafOnWq6tat6zCKWdrVV1+tt956SzfccIOeffZZdezYUTNmzNB///vfCuupioceekgzZ87U008/rdtuu83utZOfn6/+/fvrt99+01dffaX4+PhKr/e9997T8OHDK9XWVHAV5lVXXaW1a9cqOzvbbuRv48aNtvll8fHxUdu2bZ1epbxx40a1aNHC9r4sWc/3339v+1tU8ry4uNhhO3v27FFiYqIaNmyozz//vFIj2k5rrNZSF4m8vDwtX75cN998s+644w6Hx5gxY5STk1PmJazlKfnEU/oFuHHjRq1fv77c5e6//3799NNPGjhwoNMfuq+vr2666SZ98skndpefHj58WEuXLlWXLl0chrDnzZtnN7w/e/ZsnT592nYoq+ScltJX+ZS8ATZv3myb9ttvv2n79u229tdee60aNmyoOXPm2A2XfvHFF9qxY4f69etX5n7m5+fr1KlTtuWc9Zcxxu7QkitUp/9Kh5i7775bf//7322Hv0rWefYvmmXLljn9tHW2jIwMFRcX234+57Kus/n6+iopKUkffvih08u1jx49avf8k08+UV5eXpmHRspTUnNlhvrPVvIL8R//+Ifd9FdffVWSyn0dOVtXUVGR3njjDbvpM2fOlMVisb3my3P69GnNnTvX9rywsFBz585VRESE2rVrJ6ny7++kpCQZY5ScnOywnYr+OJXn448/tntNbNq0SRs3brTtX8OGDdWtWzfNnTvX6af2s3/2JVd33nrrreVuNyEhQSEhIXrhhRecXpZ+9nrfe+89RUVFlRuKbr/9dvn6+io5OdmhT4wxOnbsWLk1lWffvn2aPXu2pkyZUu5hn+zsbN1777265ZZb9PTTT6tXr14uOxxVWq1atTRu3Djt2LHDbrS0qKhId911l9avX69ly5apU6dOVVqvK88puuOOO1RUVGQ38lZQUKAFCxaoQ4cOdh8aDxw4YPvAUXr5zZs32wWj1NRUrVmzxu4IS48ePRQWFqbZs2fbLT979mwFBQXZve/T09N10003ycfHR19++WWVD5+WxkhROT799FPl5OTolltucTq/Y8eOioiI0JIlS3TXXXdVad0333yzli9frttuu039+vXT3r17NWfOHMXHx+vkyZNlLpeYmKijR4+Wm4KnTp2qlJQUdenSRQ8//LBq1aqluXPnqqCgQC+99JJD+8LCQvXs2VN33nmnUlNT9eabb6pLly62/W7RooXuvvtuTZs2TSdPntQll1xie0N8//33uvvuu9WhQwe99tprCg0NtV3i7ufnp+nTp2v48OHq2rWrBg0aZLskv1mzZho7dqyk/z8k3KdPH0VHRyszM1PvvPOO0tLSbJdcxsXFKTY2VuPHj9fBgwcVEhKiDz/80OnlyOeqqv1X2muvvaZWrVrpkUcesZ2/cfPNN+vZZ5/V8OHD1blzZ/38889asmSJ3YnckvTwww/Lz89PLVu2lI+Pj7799lstXbpUN998s+rXr1+ldVXWiy++qLVr16pDhw4aOXKk4uPjlZmZqa1bt+qrr75SZmamcnNzNXnyZL355pvq3Lmzw+FiZ4qKirRy5UpJZw6fLViwQKdOnbK7pURlXXnllRo6dKjmzZunEydOqGvXrtq0aZMWLVqkAQMG2I1yVqR///7q3r27nnrqKe3bt09XXnmlVq1apU8++USPPvpopS6Fjo6O1vTp07Vv3z5dfvnleu+997Rt2zbNmzfPdn5LZd/f3bt317333qt//vOf2rVrlxITE1VcXKxvvvlG3bt315gxY6rcX9KZQ19dunTRqFGjVFBQoH/84x9q0KCBHn/8cVubWbNmqUuXLmrbtq1GjhypFi1a6PDhw1q/fr3+/PNP272vZs2apaeffloRERHas2eP3eG/06dP6/fff1dKSop69+6tkJAQzZ49W/fee6+uueYa3X333YqIiNCBAwf0P//zP7r++uv1xhtv6Pvvv9ekSZO0cuVKzZkzp9xDo7GxsZo6daomTpyoffv2acCAAapbt6727t2rjz76SA8++KDGjx9frX5at26dWrVqVeEoyujRo5WXl2d3OwJnhg0bdk6X5Jes45lnntH06dNt75dx48bp008/Vf/+/ZWZmanFixfbLVPepemSa88p6tChgwYOHKiJEyfqyJEjuvTSS7Vo0SLt27fPYQT7vvvu07p16+zC7MMPP6y33npL/fr10/jx4+Xn56dXX31VjRo1sruVRu3atfXcc89p9OjRGjhwoO2O1osXL9bzzz9vN7KamJio33//XY8//ri+/fZbu/NVGzVqpN69e1d+B6t0rdpFpn///iYwMNCcOnWqzDbDhg0zfn5+JiMjo0qX5BcXF5sXXnjBNG3a1AQEBJirr77afPbZZw6XT5e3zvLmb9261SQkJJjg4GATFBRkunfvbr777ju7NiWXaa5bt848+OCDpn79+iY4ONgMHjzYHDt2zK5tTk6OGTJkiAkKCjJxcXHmyy+/NJLMk08+aYYNG2Zq165t2rRp47ANY4x57733zNVXX20CAgJMWFiYGTx4sN3lwnl5eeauu+4yjRs3Nv7+/qZhw4ame/fuZsWKFXbr2b59u+nVq5cJDg424eHhZuTIkebHH380ksyCBQuc9s/Z+1qZS/Kr2n9nX35bcifVTz/91Bhz5jL6cePGmaioKFO7dm1z/fXXm/Xr15uuXbvavSZmz55t2rZta+rUqWOCg4NNfHy8SU5ONidPnrS1qey6SivvknxjjDl8+LAZPXq0iYmJMX5+fiYyMtL07NnTzJs3zxhjzJ9//mliYmLMo48+arKyshyWP7v/Si5LLnkEBweba665xrzzzjt2y1X2knxjzly6nZycbJo3b278/PxMTEyMmThxot3l5OWts7ScnBwzduxYEx0dbfz8/Mxll11mZsyYYXcJfFlKLvn+/vvvTadOnUxgYKBp2rSpeeONN+zaVfb9bcyZy/dnzJhh4uLijL+/v4mIiDB9+vQxW7ZssbVRFS/JnzFjhnnllVdMTEyMCQgIMDfccIP58ccfHZbfs2ePue+++0xkZKTx8/Mzl1xyibn55pvNBx98YLftih5nv/bWrl1rEhISTGhoqAkMDDSxsbFm2LBh5vvvvzfGnLmr8XXXXWeWLFniUFNZ76sPP/zQdOnSxdSpU8fUqVPHxMXFmdGjR5vU1FSHn8/ZyrokX5L56KOP7Nqe/TP6z3/+YywWi1m5cqVDu7MvyU9KSjK1a9c2x48fd6jhbGX9TI0xZsqUKUaS7U7NJbeCKOvhbnl5eWb8+PEmMjLSBAQEmOuuu86hf0rXfbY//vjD3HHHHSYkJMQEBwebm2++2ezatcvptubNm2datmxp/P39TWxsrJk5c6bDe7Uqr82K8N1nF7GSGytu3rzZ4QZZFeG7z3Ax6tatmzIyMpwebjwf7Nu3T82bN9eMGTOqPXpyNovForVr1zq9a7B05vfIwoUL9fXXX7tke96sUaNGuu+++zRjxgxPl4Jq4pwiAADO0a+//qq8vDy7LyqF9+GcIgBAmQYPHuxwj7XSYmNjq3bOxgWqdevWDjd2hfchFAEAynT2Sb1nu+GGG2xXYALejnOKAAAAxDlFAAAAkghFAAAAkjinqELFxcU6dOiQ6tat61XfvQQAwMXMGKOcnBxFR0eX+yW7pRGKKnDo0CGH77oCAADe4Y8//lDjxo0r1ZZQVIGSL6f7448/HL7zyltZrVatWrVKN910k+1rCVA99KXr0JeuQT+6Dn3pOp7oy+zsbMXExNh9+XNFCEUVKDlkFhISckGFoqCgIIWEhPBGP0f0pevQl65BP7oOfek6nuzLqpz6wonWAAAAIhQBAABIIhQBAABIIhQBAABIIhQBAABIIhQBAABIIhQBAABIIhQBAABIIhQBAABI4o7WAOARRcVGm/Zm6khOvhrWDVT75mHy9eFLpwFPIhQBgJut/CVNySu2Ky0r3zYtKjRQk/vHK7FNlAcrAy5uHD4DADda+UuaRi3eaheIJCk9K1+jFm/Vyl/SPFQZAEIRALhJUbFR8ortMk7mlUxLXrFdRcXOWgCoaYQiAHCTTXszHUaISjOS0rLytWlvpvuKAmBDKAIANzmSU3Ygqk47AK5FKAIAN2lYN9Cl7QC4FqEIANykffMwRYUGqqwL7y06cxVa++Zh7iwLwP8hFAGAm/j6WDS5f7wkOQSjkueT+8dzvyLAQwhFAOBGiW2iNHvINYoMtT9EFhkaqNlDruE+RYAHcfNGAHCzxDZR6h0fyR2tgfMMoQgAPMDXx6JOsQ08XQaAUghFADzi7O/+urpxXU+XBOAiRygC4HbOvvuraf0APRbnwaIAXPS85kTr559/Xp07d1ZQUJDq1atXqWWGDRsmi8Vi90hMTKzZQgGUq6zv/jqcfeb5VzsOe6IsAPCeUFRYWKiBAwdq1KhRVVouMTFRaWlptsd//vOfGqoQQEUq891fL36xk+/+AuARXnP4LDk5WZK0cOHCKi0XEBCgyMjIGqgIQFVV9N1fkpSefea7vzgJGYC7ec1IUXV9/fXXatiwoVq2bKlRo0bp2LFjni4JuGjx3V8AzmdeM1JUHYmJibr99tvVvHlz7dmzR08++aT69Omj9evXy9fX1+kyBQUFKigosD3Pzs6WJFmtVlmtVrfUXdNK9uNC2R9Poi+rJjyolgJ8nR8aC/Axtn/Dg2rRp9XEa9J16EvX8URfVmdbFmOMxw7eT5gwQdOnTy+3zY4dOxQX9/8vSVm4cKEeffRRnThxosrb+/333xUbG6uvvvpKPXv2dNpmypQptkN1pS1dulRBQUFV3iYAAHC/3Nxc3XPPPcrKylJISEillvFoKDp69GiFh7NatGghf39/2/NzCUWSFBERoalTp+qhhx5yOt/ZSFFMTIwyMjIq3annO6vVqpSUFPXu3Vt+fn6eLser0ZdV99WOwxr73jZJsjvhOtDH6Nlri+XX9Cr1bh3tkdouBLwmXYe+dB1P9GV2drbCw8OrFIo8evgsIiJCERERbtven3/+qWPHjikqquzvFgoICFBAQIDDdD8/vwvuTXEh7pOn0JeV1+eKxrL4+DrcpygyJFDSKfVuHU1fugCvSdehL13HnX1Zne14zTlFBw4cUGZmpg4cOKCioiJt27ZNknTppZcqODhYkhQXF6dp06bptttu08mTJ5WcnKykpCRFRkZqz549evzxx3XppZcqISHBg3sCwNl3f13duK6+XPmFp0sDcBHzmlD0zDPPaNGiRbbnV199tSRp7dq16tatmyQpNTVVWVlZkiRfX1/99NNPWrRokU6cOKHo6GjddNNNeu6555yOBAFwr7O/+4uTWQF4mteEooULF1Z4j6LSp0fVrl1bX375ZQ1XBQAALhQX/H2KAAAAKoNQBAAAIEIRAACAJEIRAACAJEIRAACAJEIRAACAJEIRAACAJEIRAACAJEIRAACAJEIRAACAJC/6mg/gQlJUbOy+DLV98zD5+lg8XRYAXNQIRYCbrfwlTckrtistK982LSo0UJP7xyuxTZQHKwOAixuHzwA3WvlLmkYt3moXiCQpPStfoxZv1cpf0jxUGQCAUAS4SVGxUfKK7TJO5pVMS16xXUXFzloAAGoaoQhwk017Mx1GiEozktKy8rVpb6b7igIA2BCKADc5klN2IKpOOwCAaxGKADdpWDfQpe0AAK5FKALcpH3zMEWFBqqsC+8tOnMVWvvmYe4sCwDwfwhFgJv4+lg0uX+8JDkEo5Lnk/vHc78iAPAQQhHgRoltojR7yDWKDLU/RBYZGqjZQ67hPkUA4EHcvBFws8Q2UeodH8kdrXFR467uOB8RigAP8PWxqFNsA0+XAXgEd3XH+YrDZwAAt+Gu7jifEYoAAG7BXd1xviMUAQDcgru643xHKAIAuAV3dcf5jlAEAHAL7uqO8x2hCADgFtzVHec7QhEAwC24qzvOd4QiAIDbcFd3nM+4eSMAwK24qzvOV4QiAIDbcVd3nI84fAYAACBCEQAAgCRCEQAAgCRCEQAAgCRCEQAAgCRCEQAAgCRCEQAAgCRCEQAAgCRCEQAAgCRCEQAAgCRCEQAAgCRCEQAAgCRCEQAAgCRCEQAAgCRCEQAAgCRCEQAAgCRCEQAAgCRCEQAAgCQvCUX79u3TiBEj1Lx5c9WuXVuxsbGaPHmyCgsLy10uPz9fo0ePVoMGDRQcHKykpCQdPnzYTVUDAABv4hWhaOfOnSouLtbcuXP166+/aubMmZozZ46efPLJcpcbO3asVqxYoWXLlmndunU6dOiQbr/9djdVDQAAvEktTxdQGYmJiUpMTLQ9b9GihVJTUzV79my9/PLLTpfJysrS/PnztXTpUvXo0UOStGDBArVq1UobNmxQx44d3VI7AADwDl4RipzJyspSWFhYmfO3bNkiq9WqXr162abFxcWpSZMmWr9+fZmhqKCgQAUFBbbn2dnZkiSr1Sqr1eqi6j2rZD8ulP3xJPrSdehL16AfXYe+dB1P9GV1tuWVoWj37t16/fXXyxwlkqT09HT5+/urXr16dtMbNWqk9PT0MpebNm2akpOTHaavWrVKQUFB1a75fJSSkuLpEi4Y9KXr0JeuQT+6Dn3pOu7sy9zc3Cov49FQNGHCBE2fPr3cNjt27FBcXJzt+cGDB5WYmKiBAwdq5MiRLq9p4sSJeuyxx2zPs7OzFRMTo5tuukkhISEu354nWK1WpaSkqHfv3vLz8/N0OV6NvnQd+tI16EfXoS9dxxN9WXKkpyo8GorGjRunYcOGldumRYsWtv8fOnRI3bt3V+fOnTVv3rxyl4uMjFRhYaFOnDhhN1p0+PBhRUZGlrlcQECAAgICHKb7+fldcG+KC3GfPIW+dB360jXoR9ehL13HnX1Zne14NBRFREQoIiKiUm0PHjyo7t27q127dlqwYIF8fMq/cK5du3by8/PT6tWrlZSUJElKTU3VgQMH1KlTp3OuHQAAXFi84pL8gwcPqlu3bmrSpIlefvllHT16VOnp6XbnBh08eFBxcXHatGmTJCk0NFQjRozQY489prVr12rLli0aPny4OnXqxJVnAADAgVecaJ2SkqLdu3dr9+7daty4sd08Y4ykM8crU1NT7U6smjlzpnx8fJSUlKSCggIlJCTozTffdGvtAADAO3hFKBo2bFiF5x41a9bMFpBKBAYGatasWZo1a1YNVgcAAC4EXnH4DAAAoKYRigAAAEQoAgAAkEQoAgAAkEQoAgAAkEQoAgAAkEQoAgAAkEQoAgAAkEQoAgAAkEQoAgAAkEQoAgAAkEQoAgAAkEQoAgAAkEQoAgAAkEQoAgAAkEQoAgAAkEQoAgAAkEQoAgAAkEQoAgAAkEQoAgAAkEQoAgAAkEQoAgAAkEQoAgAAkEQoAgAAkEQoAgAAkEQoAgAAkEQoAgAAkEQoAgAAkEQoAgAAkEQoAgAAkEQoAgAAkEQoAgAAkEQoAgAAkEQoAgAAkEQoAgAAkEQoAgAAkEQoAgAAkEQoAgAAkEQoAgAAkEQoAgAAkCTV8nQBF6vvD32vvcf3KsgvqNyHn6+fp0sFAOCiQCjykH/98C/N/n52he1q+dSqMDjV8atTYRu7oCU/HS08qozcDIUGhSqwVqB8LAwaAgAuboQiD4mtH6sbm96oXGuuw+NU4SkZGUnS6eLTyi7IVnZBtuuL2P7//1u7Vu0qBavKPkoHNka9AADnM0KRh4zrPE7jOo9zOs8Yo8KiQqeBqdKP02fCVVnzTxaclNVYbdvMO52nvNN5OpZ3rMb2uTKjXkF+QQqqVY3w5X8mfDHqBQCoLkLRechisSigVoACagWofu36Ll+/1WrV559/roTEBJ22nD638FXB45T1lIpNsaQaHvUqxZWjXmUdmmTUCwAuPISii5ivj68C/QIV7B9cY9swxshabC3zMGFlR70qapN/Ot+2TbeOetUKkrEahR8Mt41WVWbUy2nbsx6MegGAexGKUKMsFov8ff3l7+uveoH1amw7xaZYeda8ao1kuWLU6/DRwzWyXzV1rlfpUTBGvQDgDEIRLgg+Fh/V8a+jOv51amwbzka9svKytHrdal113VUqNFU/D8xZKPPYqFcNnOtV8qjtV5tRLwDnPUIRUEnORr2sVqv+rPOnujfrLj8/14y4VHfU6+zDjeUdnvTEuV6BtQLLHbEK9A3UsfRjSlmVouCA4GqFLz8fP1kslhrdDwAXLkIRcJ7x1KhXpUe3SsJWFc/1yj+dr/zT+crMyyy3tq8yv6r2fvlafM/5JHpGvYCLl1eEon379um5557TmjVrlJ6erujoaA0ZMkRPPfWU/P39y1yuW7duWrdund20hx56SHPmzKnpkoHz2vlyrtcp6ynb/Jz8HP3w6w9q3LyxCooLqnWuV5EpUk5hjnIKc2psn6SyR70qe7ixMifaM+oFuJ9XhKKdO3equLhYc+fO1aWXXqpffvlFI0eO1KlTp/Tyyy+Xu+zIkSP17LPP2p4HBQXVdLkA/k9VRr2sVqs+z/hcfbv1rdKhyHMZ9arKSffVGfU6F1UZ9Sr9CPAJ0J6MPTr+y3GFBIaUO0LGqBdgzytCUWJiohITE23PW7RoodTUVM2ePbvCUBQUFKTIyMiaLhGAh5wvo16VPdfL2eHIvNN5tsOSRaZI0rmPes3+s+KvEZLOfdSrMg9/X39GveAVvCIUOZOVlaWwsLAK2y1ZskSLFy9WZGSk+vfvr0mTJpU7WlRQUKCCggLb8+zsMyefWq1WWa3WshbzKiX7caHsjyfRl67jDX3pb/GXv7+/6vnXq5H1Ox31Op3rEMbyTueV2eZk4UntP7hfwfWDlV+U73SZvNN5tm16YtTr7FtNVPjcr7ZDMHO2jK+Pr0vr9obXpLfwRF9WZ1sWY4ypgVpq1O7du9WuXTu9/PLLGjlyZJnt5s2bp6ZNmyo6Olo//fSTnnjiCbVv317Lly8vc5kpU6YoOTnZYfrSpUs59AbgglBsimU1VhUUFyi/OF8FxQW2R2Fx4f9/bgrs5lVlfn5xvopV7Nb98rP4KcAnwPbwt/jbPS/zYSm1jI+TZUrNr2WpxaiXl8jNzdU999yjrKwshYSEVGoZj4aiCRMmaPr06eW22bFjh+Li4mzPDx48qK5du6pbt256++23q7S9NWvWqGfPntq9e7diY2OdtnE2UhQTE6OMjIxKd+r5zmq1KiUlRb1793bZZeQXK/rSdehL1zif+tFaZHU4dGgb9Sr1/Yx2I19nj4xVMFJWetTLHXwsPnaHFGv71a7c8/+bdvbz0m1KzveqiVEvT/PE6zI7O1vh4eFVCkUePXw2btw4DRs2rNw2LVq0sP3/0KFD6t69uzp37qx58+ZVeXsdOnSQpHJDUUBAgAICAhym+/n5efwXjKtdiPvkKfSl69CXrnE+9KOfn5+CAmt2hL3YFCv/dL794URXnXBfmKvsvGwVmALbuV7FplgnC0/qZOHJGt2vAN+AczqPq1In5fsGuH3Uy52vy+psx6OhKCIiQhEREZVqe/DgQXXv3l3t2rXTggUL5ONT9Ssmtm3bJkmKioqq8rIAgPOPbeTGz/Xhq+TLs/v27Sv56NwCVwUn2pc8L1FQVKCCogIdzz/u8v0qYZGl0gHqXO7rVcvHe05f9opKDx48qG7duqlp06Z6+eWXdfToUdu8kivLDh48qJ49e+rf//632rdvrz179mjp0qXq27evGjRooJ9++kljx47VjTfeqCuuuMJTuwIA8EJ+vn4K9Q1VaGBojW3DGON01Ksq39FYmYe1+MwJyEZGp6yndMp6qsb2SZL8ff0V5BcknyIfhe0PU5C/Y3h64OoH1Du2d43WURleEYpSUlK0e/du7d69W40bN7abV3JKlNVqVWpqqnJzzyRtf39/ffXVV/rHP/6hU6dOKSYmRklJSXr66afdXj8AABWxWCyq7Vdbtf1qq4Ea1Nh2rEVWxysYz/VQo5NHicKiQhUWFUqSMo87v8qxdwvPByLJS0LRsGHDKjz3qFmzZip9znhMTIzD3awBALjY+fn6yc/XTyEBNXfx0NmjXll5WUpZm6JrOlzj9MuzOzXuVGO1VIVXhCIAAOA9zh71igyK1J6gPeoc09njFwCUh/u7AwAAiFAEAAAgiVAEAAAgiVAEAAAgiVAEAAAgiVAEAAAgiUvyAQDwiKJio017M3UkJ18N6waqffMw+fq497vIYI9QBACAm638JU3JK7YrLSvfNi0qNFCT+8crsQ3fz+kpHD4DAMCNVv6SplGLt9oFIklKz8rXqMVbtfKXNA9VBkIRAABuUlRslLxiu4yTeSXTkldsV1GxsxaoaYQiAADcZNPeTIcRotKMpLSsfG3a6/yLU1GzCEUAALjJkZyyA1F12sG1qnWidXFxsdatW6dvvvlG+/fvV25uriIiInT11VerV69eiomJcXWdAAB4vYZ1A13aDq5VpZGivLw8TZ06VTExMerbt6+++OILnThxQr6+vtq9e7cmT56s5s2bq2/fvtqwYUNN1QwAgFdq3zxMUaGBKuvCe4vOXIXWvnmYO8vC/6nSSNHll1+uTp066a233lLv3r3l5+fn0Gb//v1aunSp7r77bj311FMaOXKky4oFAMCb+fpYNLl/vEYt3iqLZHfCdUlQmtw/nvsVeUiVRopWrVql999/X3379nUaiCSpadOmmjhxonbt2qUePXq4pEgAAC4UiW2iNHvINYoMtT9EFhkaqNlDruE+RR5UpZGiVq1aVbqtn5+fYmNjq1wQAAAXusQ2UeodH8kdrc8z1b6j9Y033qhu3bqpa9euuv766xUYyElhAABUlq+PRZ1iG3i6DJRS7Uvyb7rpJm3YsEG33nqr6tWrpy5duujpp59WSkqKcnNzXVkjAABAjav2SNHTTz8tSTp9+rQ2b96sdevW6euvv9ZLL70kHx8f5edzjwUAAOA9zvkLYX///Xf9/PPP+vHHH/XTTz+pbt26uvHGG11RGwAAgNtUOxTdc889WrdunQoKCnTjjTeqa9eumjBhgq644gpZLJwoBgAAvEu1Q9G7776r8PBwPfDAA+rRo4e6dOmioKAgV9YGAADgNtU+0frYsWN6++23VVhYqIkTJyo8PFydO3fWk08+qVWrVrmyRgAAgBpX7VBUv3593XLLLXr11Ve1ZcsW/fTTT7r88ss1Y8YM9enTx5U1AgAA1LhqHz47duyY7Yqzr7/+Wtu3b1e9evXUv39/de3a1ZU1AgAA1Lhqh6KGDRsqPDxcN9xwg0aOHKlu3bqpbdu2rqwNAADAbaodin766Se1bt3albUAAAB4TLXPKSIQAQCAC0mVQlFiYqI2bNhQYbucnBxNnz5ds2bNqnZhAAAA7lSlw2cDBw5UUlKSQkND1b9/f1177bWKjo5WYGCgjh8/ru3bt+vbb7/V559/rn79+mnGjBk1VTcAAIBLVSkUjRgxQkOGDNGyZcv03nvvad68ecrKypIkWSwWxcfHKyEhQZs3b1arVq1qpGAAAICaUOUTrQMCAjRkyBANGTJEkpSVlaW8vDw1aNBAfn5+Li8QAADAHc75C2FDQ0MVGhrqiloAAAA85pxC0a5du7R27VodOXJExcXFdvOeeeaZcyoMAADAnaodit566y2NGjVK4eHhioyMlMVisc2zWCyEIgAA4FWqHYqmTp2q559/Xk888YQr68FFoqjYaNPeTB3JyVfDuoFq3zxMvj6WihcEAKCGVDsUHT9+XAMHDnRlLbhIrPwlTckrtistK982LSo0UJP7xyuxTZQHKwMAXMyqfUfrgQMHatWqVa6sBReBlb+kadTirXaBSJLSs/I1avFWrfwlzUOVAQAudtUeKbr00ks1adIkbdiwQW3btnW4HP+vf/3rOReHC0tRsVHyiu0yTuYZSRZJySu2q3d8JIfSAABuV+1QNG/ePAUHB2vdunVat26d3TyLxUIogoNNezMdRohKM5LSsvK1aW+mOsU2cF9hAADoHELR3r17XVkHLgJHcsoORNVpBwCAK1UpFD322GOVamexWPTKK69UqyBcuBrWDXRpOwAAXKlKoeiHH36oVLvS9ywCSrRvHqao0EClZ+U7Pa/IIiky9Mzl+QAAuFuVQtHatWtrqg5cBHx9LJrcP16jFm+VRbILRiUxenL/eE6yBgB4RLUvyQeqI7FNlGYPuUaRofaHyCJDAzV7yDXcpwgA4DHn/IWwQFUltolS7/hI7mgNADivEIrgEb4+Fi67BwCcV7zm8Nktt9yiJk2aKDAwUFFRUbr33nt16NChcpfJz8/X6NGj1aBBAwUHByspKUmHDx92U8UAAMCbeE0o6t69u95//32lpqbqww8/1J49e3THHXeUu8zYsWO1YsUKLVu2TOvWrdOhQ4d0++23u6liAADgTbzm8NnYsWNt/2/atKkmTJigAQMGyGq1OnzFiCRlZWVp/vz5Wrp0qXr06CFJWrBggVq1aqUNGzaoY8eObqsdAACc/7wmFJWWmZmpJUuWqHPnzk4DkSRt2bJFVqtVvXr1sk2Li4tTkyZNtH79+jJDUUFBgQoKCmzPs7OzJUlWq1VWq9WFe+E5JftxoeyPJ9GXrkNfugb96Dr0pet4oi+rsy2vCkVPPPGE3njjDeXm5qpjx4767LPPymybnp4uf39/1atXz256o0aNlJ6eXuZy06ZNU3JyssP0VatWKSgoqNq1n49SUlI8XcIFg750HfrSNehH16EvXcedfZmbm1vlZSzGGGc3F3aLCRMmaPr06eW22bFjh+Li4iRJGRkZyszM1P79+5WcnKzQ0FB99tlnTu+gvXTpUg0fPtxu1EeS2rdvr+7du5e5XWcjRTExMcrIyFBISEhVd/G8ZLValZKSot69e5c50obKoS9dh750DfrRdehL1/FEX2ZnZys8PFxZWVmV/vvt0ZGicePGadiwYeW2adGihe3/4eHhCg8P1+WXX65WrVopJiZGGzZsUKdOnRyWi4yMVGFhoU6cOGE3WnT48GFFRkaWub2AgAAFBAQ4TPfz87vg3hQX4j55Cn3pOvSla9CPrkNfuo47+7I62/FoKIqIiFBERES1li0uLpYkh5GgEu3atZOfn59Wr16tpKQkSVJqaqoOHDjgNEQBAICLm1ecU7Rx40Zt3rxZXbp0Uf369bVnzx5NmjRJsbGxtoBz8OBB9ezZU//+97/Vvn17hYaGasSIEXrssccUFhamkJAQPfLII+rUqRNXngEAAAdeEYqCgoK0fPlyTZ48WadOnVJUVJQSExP19NNP2w51Wa1Wpaam2p1YNXPmTPn4+CgpKUkFBQVKSEjQm2++6andAAAA5zGvCEVt27bVmjVrym3TrFkznX3OeGBgoGbNmqVZs2bVZHkAAOAC4DV3tAYAAKhJhCIAAAARigAAACQRigAAACQRigAAACQRigAAACQRigAAACQRigAAACQRigAAACQRigAAACQRigAAACQRigAAACQRigAAACQRigAAACQRigAAACQRigAAACQRigAAACQRigAAACQRigAAACQRigAAACQRigAAACQRigAAACQRigAAACQRigAAACQRigAAACQRigAAACQRigAAACQRigAAACQRigAAACQRigAAACQRigAAACQRigAAACQRigAAACQRigAAACQRigAAACQRigAAACQRigAAACQRigAAACQRigAAACQRigAAACQRigAAACQRigAAACQRigAAACQRigAAACQRigAAACQRigAAACQRigAAACQRigAAACQRigAAACQRigAAACR5USi65ZZb1KRJEwUGBioqKkr33nuvDh06VO4y3bp1k8VisXv85S9/cVPFAADAm3hNKOrevbvef/99paam6sMPP9SePXt0xx13VLjcyJEjlZaWZnu89NJLbqgWAAB4m1qeLqCyxo4da/t/06ZNNWHCBA0YMEBWq1V+fn5lLhcUFKTIyEh3lAgAALyY14Si0jIzM7VkyRJ17ty53EAkSUuWLNHixYsVGRmp/v37a9KkSQoKCiqzfUFBgQoKCmzPs7OzJUlWq1VWq9U1O+BhJftxoeyPJ9GXrkNfugb96Dr0pet4oi+rsy2LMcbUQC014oknntAbb7yh3NxcdezYUZ999pkaNGhQZvt58+apadOmio6O1k8//aQnnnhC7du31/Lly8tcZsqUKUpOTnaYvnTp0nLDFAAAOH/k5ubqnnvuUVZWlkJCQiq1jEdD0YQJEzR9+vRy2+zYsUNxcXGSpIyMDGVmZmr//v1KTk5WaGioPvvsM1kslkptb82aNerZs6d2796t2NhYp22cjRTFxMQoIyOj0p16vrNarUpJSVHv3r0rHGlD+ehL16EvXYN+dB360nU80ZfZ2dkKDw+vUijy6OGzcePGadiwYeW2adGihe3/4eHhCg8P1+WXX65WrVopJiZGGzZsUKdOnSq1vQ4dOkhSuaEoICBAAQEBDtP9/PwuuDfFhbhPnkJfug596Rr0o+vQl67jzr6sznY8GooiIiIUERFRrWWLi4slyW5UpyLbtm2TJEVFRVVrmwAA4MLlFZfkb9y4UW+88Ya2bdum/fv3a82aNRo0aJBiY2Nto0QHDx5UXFycNm3aJEnas2ePnnvuOW3ZskX79u3Tp59+qvvuu0833nijrrjiCk/uDgAAOA95RSgKCgrS8uXL1bNnT7Vs2VIjRozQFVdcoXXr1tkOdVmtVqWmpio3N1eS5O/vr6+++ko33XST4uLiNG7cOCUlJWnFihWe3BUAAHCe8opL8tu2bas1a9aU26ZZs2Yqfc54TEyM1q1bV9OlAQCAC4RXjBQBAADUNEIRAACACEUAAACSCEUAAACSCEUAAACSCEUAAACSCEUAAACSCEUAAACSCEUAAACSCEUAAACSCEUAAACSCEUAAACSCEUAAACSCEUAAACSCEUAAACSCEUAAACSCEUAAACSCEUAAACSCEUAAACSpFqeLuBiVFRstGlvpo7k5Kth3UC1bx4mXx+Lp8sCAOCiRihys5W/pCl5xXalZeXbpkWFBmpy/3gltonyYGUAAFzcOHzmRit/SdOoxVvtApEkpWfla9TirVr5S5qHKgMAAIQiNykqNkpesV3GybySackrtquo2FkLAABQ0whFbrJpb6bDCFFpRlJaVr427c10X1EAAMCGUOQmR3LKDkTVaQcAAFyLUOQmDesGurQdAABwLUKRm7RvHqao0ECVdeG9RWeuQmvfPMydZQEAgP9DKHITXx+LJvePlySHYFTyfHL/eO5XBACAhxCK3CixTZRmD7lGkaH2h8giQwM1e8g13KcIAAAP4uaNbpbYJkq94yO5ozUAAOcZQpEH+PpY1Cm2gafLAAAApXD4DAAAQIQiAAAASYQiAAAASYQiAAAASYQiAAAASYQiAAAASYQiAAAASYQiAAAASYQiAAAASYQiAAAASYQiAAAASYQiAAAASYQiAAAASYQiAAAASYQiAAAASYQiAAAASYQiAAAASYQiAAAASV4YigoKCnTVVVfJYrFo27Zt5bbNz8/X6NGj1aBBAwUHByspKUmHDx92T6EAAMCreF0oevzxxxUdHV2ptmPHjtWKFSu0bNkyrVu3TocOHdLtt99ewxUCAABv5FWh6IsvvtCqVav08ssvV9g2KytL8+fP16uvvqoePXqoXbt2WrBggb777jtt2LDBDdUCAABv4jWh6PDhwxo5cqTeeecdBQUFVdh+y5Ytslqt6tWrl21aXFycmjRpovXr19dkqQAAwAvV8nQBlWGM0bBhw/SXv/xF1157rfbt21fhMunp6fL391e9evXspjdq1Ejp6ellLldQUKCCggLb8+zsbEmS1WqV1WqtVv3nm5L9uFD2x5PoS9ehL12DfnQd+tJ1PNGX1dmWR0PRhAkTNH369HLb7NixQ6tWrVJOTo4mTpxY4zVNmzZNycnJDtNXrVpVqREqb5KSkuLpEi4Y9KXr0JeuQT+6Dn3pOu7sy9zc3CovYzHGmBqopVKOHj2qY8eOldumRYsWuvPOO7VixQpZLBbb9KKiIvn6+mrw4MFatGiRw3Jr1qxRz549dfz4cbvRoqZNm+rRRx/V2LFjnW7P2UhRTEyMMjIyFBISUsU9PD9ZrValpKSod+/e8vPz83Q5Xo2+dB360jXoR9ehL13HE32ZnZ2t8PBwZWVlVfrvt0dHiiIiIhQREVFhu3/+85+aOnWq7fmhQ4eUkJCg9957Tx06dHC6TLt27eTn56fVq1crKSlJkpSamqoDBw6oU6dOZW4rICBAAQEBDtP9/PwuuDfFhbhPnkJfug596Rr0o+vQl67jzr6szna84pyiJk2a2D0PDg6WJMXGxqpx48aSpIMHD6pnz57697//rfbt2ys0NFQjRozQY489prCwMIWEhOiRRx5Rp06d1LFjR7fvAwAAOL95RSiqDKvVqtTUVLtjiDNnzpSPj4+SkpJUUFCghIQEvfnmmx6sEgAAnK+8MhQ1a9ZMZ58K5WxaYGCgZs2apVmzZrmzPAAA4IW85j5FAAAANYlQBAAAIEIRAACAJEIRAACAJEIRAACAJEIRAACAJEIRAACAJEIRAACAJC+9eSMAAPBuRcVGm/Zm6khOvhrWDVT75mHy9bFUvGANIhQBAAC3WvlLmpJXbFdaVr5tWlRooCb3j1dimyiP1cXhMwAA4DYrf0nTqMVb7QKRJKVn5WvU4q1a+UuahyojFAEAADcpKjZKXrFdxsm8kmnJK7arqNhZi5pHKAIAAG6xZf9xhxGi0oyktKx8bdqb6b6iSiEUAQAAt8g4WVCpdkdyyg5ONYlQBAAA3CI8OKBS7RrWDazhSpwjFAEAALdo17S+okIDVdaF9xaduQqtffMwd5ZlQygCAABu4etj0eT+8ZLkEIxKnk/uH++x+xURigAAgNsktonS7CHXKDLU/hBZZGigZg+5xqP3KeLmjQAAwK0S20Spd3wkd7QGAADw9bGoU2wDT5dhh8NnAAAAIhQBAABIIhQBAABIIhQBAABIIhQBAABIIhQBAABIIhQBAABIIhQBAABIIhQBAABI4o7WFTLGSJKys7M9XInrWK1W5ebmKjs7W35+fp4ux6vRl65DX7oG/eg69KXreKIvS/5ul/wdrwxCUQVycnIkSTExMR6uBAAAVFVOTo5CQ0Mr1dZiqhKhLkLFxcU6dOiQ6tatK4vFs19U5yrZ2dmKiYnRH3/8oZCQEE+X49XoS9ehL12DfnQd+tJ1PNGXxhjl5OQoOjpaPj6VO1uIkaIK+Pj4qHHjxp4uo0aEhITwRncR+tJ16EvXoB9dh750HXf3ZWVHiEpwojUAAIAIRQAAAJIIRRelgIAATZ48WQEBAZ4uxevRl65DX7oG/eg69KXreEtfcqI1AACAGCkCAACQRCgCAACQRCgCAACQRCgCAACQRCi66Dz//PPq3LmzgoKCVK9ePadtDhw4oH79+ikoKEgNGzbU3//+d50+fdq9hXqhZs2ayWKx2D1efPFFT5flFWbNmqVmzZopMDBQHTp00KZNmzxdkteZMmWKw+svLi7O02V5hf/93/9V//79FR0dLYvFoo8//thuvjFGzzzzjKKiolS7dm316tVLu3bt8kyx57GK+nHYsGEOr9HExETPFFsGQtFFprCwUAMHDtSoUaOczi8qKlK/fv1UWFio7777TosWLdLChQv1zDPPuLlS7/Tss88qLS3N9njkkUc8XdJ577333tNjjz2myZMna+vWrbryyiuVkJCgI0eOeLo0r9O6dWu719+3337r6ZK8wqlTp3TllVdq1qxZTue/9NJL+uc//6k5c+Zo48aNqlOnjhISEpSfn+/mSs9vFfWjJCUmJtq9Rv/zn/+4scJKMLgoLViwwISGhjpM//zzz42Pj49JT0+3TZs9e7YJCQkxBQUFbqzQ+zRt2tTMnDnT02V4nfbt25vRo0fbnhcVFZno6Ggzbdo0D1blfSZPnmyuvPJKT5fh9SSZjz76yPa8uLjYREZGmhkzZtimnThxwgQEBJj//Oc/HqjQO5zdj8YYM3ToUHPrrbd6pJ7KYqQIdtavX6+2bduqUaNGtmkJCQnKzs7Wr7/+6sHKvMOLL76oBg0a6Oqrr9aMGTM47FiBwsJCbdmyRb169bJN8/HxUa9evbR+/XoPVuaddu3apejoaLVo0UKDBw/WgQMHPF2S19u7d6/S09PtXqOhoaHq0KEDr9Fq+Prrr9WwYUO1bNlSo0aN0rFjxzxdkh2+EBZ20tPT7QKRJNvz9PR0T5TkNf7617/qmmuuUVhYmL777jtNnDhRaWlpevXVVz1d2nkrIyNDRUVFTl9zO3fu9FBV3qlDhw5auHChWrZsqbS0NCUnJ+uGG27QL7/8orp163q6PK9V8nvP2WuU34lVk5iYqNtvv13NmzfXnj179OSTT6pPnz5av369fH19PV2eJELRBWHChAmaPn16uW127NjBSZfVUJW+feyxx2zTrrjiCvn7++uhhx7StGnTzvtb28P79enTx/b/K664Qh06dFDTpk31/vvva8SIER6sDDjj7rvvtv2/bdu2uuKKKxQbG6uvv/5aPXv29GBl/x+h6AIwbtw4DRs2rNw2LVq0qNS6IiMjHa78OXz4sG3exeZc+rZDhw46ffq09u3bp5YtW9ZAdd4vPDxcvr6+ttdYicOHD1+UrzdXqlevni6//HLt3r3b06V4tZLX4eHDhxUVFWWbfvjwYV111VUequrC0KJFC4WHh2v37t2EIrhORESEIiIiXLKuTp066fnnn9eRI0fUsGFDSVJKSopCQkIUHx/vkm14k3Pp223btsnHx8fWj3Dk7++vdu3aafXq1RowYIAkqbi4WKtXr9aYMWM8W5yXO3nypPbs2aN7773X06V4tebNmysyMlKrV6+2haDs7Gxt3LixzKt4UTl//vmnjh07Zhc2PY1QdJE5cOCAMjMzdeDAARUVFWnbtm2SpEsvvVTBwcG66aabFB8fr3vvvVcvvfSS0tPT9fTTT2v06NEcAirH+vXrtXHjRnXv3l1169bV+vXrNXbsWA0ZMkT169f3dHnntccee0xDhw7Vtddeq/bt2+sf//iHTp06peHDh3u6NK8yfvx49e/fX02bNtWhQ4c0efJk+fr6atCgQZ4u7bx38uRJuxG1vXv3atu2bQoLC1OTJk306KOPaurUqbrsssvUvHlzTZo0SdHR0bYgjzPK68ewsDAlJycrKSlJkZGR2rNnjx5//HFdeumlSkhI8GDVZ/H05W9wr6FDhxpJDo+1a9fa2uzbt8/06dPH1K5d24SHh5tx48YZq9XquaK9wJYtW0yHDh1MaGioCQwMNK1atTIvvPCCyc/P93RpXuH11183TZo0Mf7+/qZ9+/Zmw4YNni7J69x1110mKirK+Pv7m0suucTcddddZvfu3Z4uyyusXbvW6e/FoUOHGmPOXJY/adIk06hRIxMQEGB69uxpUlNTPVv0eai8fszNzTU33XSTiYiIMH5+fqZp06Zm5MiRdrd/OR9YjDHGA1kMAADgvMJ9igAAAEQoAgAAkEQoAgAAkEQoAgAAkEQoAgAAkEQoAgAAkEQoAgAAkEQoAgAAkEQoAnCRKSoqUufOnXX77bfbTc/KylJMTIyeeuopD1UGwNO4ozWAi85vv/2mq666Sm+99ZYGDx4sSbrvvvv0448/avPmzfL39/dwhQA8gVAE4KL0z3/+U1OmTNGvv/6qTZs2aeDAgdq8ebOuvPJKT5cGwEMIRQAuSsYY9ejRQ76+vvr555/1yCOP6Omnn/Z0WQA8iFAE4KK1c+dOtWrVSm3bttXWrVtVq1YtT5cEwIM40RrARetf//qXgoKCtHfvXv3555+eLgeAhzFSBOCi9N1336lr165atWqVpk6dKkn66quvZLFYPFwZAE9hpAjARSc3N1fDhg3TqFGj1L17d82fP1+bNm3SnDlzPF0aAA9ipAjARedvf/ubPv/8c/34448KCgqSJM2dO1fjx4/Xzz//rGbNmnm2QAAeQSgCcFFZt26devbsqa+//lpdunSxm5eQkKDTp09zGA24SBGKAAAAxDlFAAAAkghFAAAAkghFAAAAkghFAAAAkghFAAAAkghFAAAAkghFAAAAkghFAAAAkghFAAAAkghFAAAAkghFAAAAkghFAAAAkqT/B9AUT00f5Oi7AAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x500 with 0 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Файл '3.txt' - показательное распределение"
      ],
      "metadata": {
        "id": "_AcXY11f3Jkw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# показательное распределение\n",
        "data = open_file('3.txt')\n",
        "\n",
        "# Отрисовка гистограммы\n",
        "int, absolute_freq, relative_freq = show_hist(data) # show_hist вычисляет интервалы, частоты и рисует гистограмму"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 654
        },
        "id": "bRDNhpNNnKw8",
        "outputId": "03948ce8-6eac-45ba-faac-8651ab824a41"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Длина списка: 32\n",
            "Интервалы: [[0.013, 1.435], [1.435, 2.857], [2.857, 4.28], [4.28, 5.702], [5.702, 7.124], [7.124, 8.556]]\n",
            "Абсолютные частоты: [17, 6, 3, 5, 0, 1]\n",
            "Относительные частоты: [0.53125 0.1875  0.09375 0.15625 0.      0.03125]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 1 Axes>"
            ],
            "image/png": 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nzp01dOhQ5cuXz97uznayfft2e9+01zQ2NjbTUwEKFy6s0aNHq2PHjgoJCdHo0aNdmlf9+vU1c+ZMjRw5Ug8//LDy5MmjU6dOOfU7efKkBg4cqBkzZji9Vmk/Nh4/flxnzpzJ9s+PSpUqaeHChTp//rzDawwg9yCkA/jPef/99+Xl5aWXX3453Yv+/Nu99957euutt9S9e3e9/fbbKly4sLy8vPT888877VVN0717d9WsWVM7duzQrFmz7HvVM5IW7OPj43XhwgUVKVJE9957r1NI3717t5o0aaKKFStq+PDhKlWqlHx9ffX9999rxIgRGdbjrhUrVqhNmzZq1KiRxo4dq+LFi8vHx0fx8fG3HGo8KW05H3vssQwDdfXq1R2e79u3T7fffnume9/Spjt06NB0L+YnOYaskydPKjk5WaGhoTed5rRp09LtlyeP41cDf39/ffvttw5tK1ascLhewPWSk5P11ltvqUePHvYfW240YcIEde7cWR07dsywzn9S3bp19c477zi0jRkzRl9//bVT36wckZEeV14vV6SmpqpatWoaPnx4usNLlSrl8PzIkSPKnz+/SyHwo48+0h133KHLly9r+fLlGjZsmCQ5nA/uznYSFhamiRMnSpJOnDih0aNHq0uXLipbtmym2+7ChQslXTuS5uDBgw7X/MjIE088oYULF+qFF17QCy+8kGG/hx56SCtXrtTLL7+siIgI5c+fX6mpqWrevLnH3scAwFWEdAD/KX///bdGjRqluLg4FShQwCmkp+2x3bx5c5bnVbp0aUnSjh07nPZK7tixwz7cHX/++aeMMQ5BYOfOnZJkv5jX7Nmzdc8992jy5MkO454+fdp+AagbVatWTTVr1rRf0f6ee+7Rjz/+mGkt3bt3V40aNXTgwAHFxMSkG06+/fZbXb58Wd98843D6QPpHTKdkdTUVO3Zs8choNy4zF999ZX8/f21cOFCh8Nz4+PjHaYVHh6u1NRUbd26NcNQeyvS9j5fb+fOnQoICLDvESxQoIBSUlIc9jpm5OrVq9q4caOaN2+eab/w8HBJ1/ZuuzLdrVu3Ssr8QlJp0wwODnZpmt7e3k790vbCpmfs2LE6duyYBgwYkGGfsLAwTZ8+XdWqVVP37t3Vrl07ffrpp+keqXG97Pifk6SiRYs6LeONF1orXbq0UlNTtWvXLofX9+jRozp9+rR93mmv7+bNm532uqfHldfLFeHh4dq4caOaNGni0g8JW7dudfmCY3Xq1LEf3t2yZUtt3LhRCxYscOjjznaSL18+h74NGzZUyZIltWjRIkVHR6c7zoIFCzRp0iS98sor+uyzzxQTE6PffvvN6UelG/n7+2v+/PnauXOnDhw4IGOMjh49qscee8ze59SpU0pISNDAgQPVv39/e/uN//fFihVTYGCgxz8/brR9+3YVLVqUvehALsY56QD+UwYOHKiQkBA99dRT6Q4vVqyYGjVqpClTpmj//v0Ow8wNt/y6mdq1ays4OFjjx493OPz6hx9+0LZt2xyuOO6qv//+2+Eq0WfOnNGnn36qiIgI+x4mb29vp1pnzZrldNu3G3Xv3t1+myFXvsRXqVJFtWrV0tatW9W1a9d0+3h7e0tyfO2SkpKcwvPNjBkzxv63MUZjxoyRj4+PmjRpYp+PzWZzOLR/3759TkGqXbt28vLy0qBBg5z2frm7fq+3atUqh3PKDxw4oK+//lrNmjWTt7e3vL299cADD+irr75K9wv88ePHHZ4vWrRISUlJNz2ntlatWgoPD9ewYcN07ty5m053xowZ8vX11d13353hNKOiohQYGKj33ntPV65cuek03XH27Fm9++67euGFFzLdI3r16lU9+uijqlKlikaMGKGmTZs6nfKQnuz4n3PV/fffL0kaOXKkQ3vanuu0eTdr1kwFChRQXFyc03ntN26Drr5ernjooYd06NAh+x7q6128eFHnz5+3Pz9w4IB++eWXDE95uJnU1FT7/74npP2vZjTN06dP6/HHH1edOnX03nvvadKkSVq3bp3TXQsyc8cdd6hJkyZq2rSpGjRo4DAsvfcxyXlde3l5qV27dvr222/TvS2dO+8xxYsXV0REhD755BOHHzM2b96sRYsW2bc3ALkTe9IB/KcsWrRIn332mXx9fTPsM3r0aN19992688479cQTT6hMmTLat2+f5s+f73Q/8cz4+Pjo/fffV7du3RQZGanOnTvbbwcVFhaW6aGVGbnjjjvUo0cPrVmzRiEhIZoyZYqOHj3qEHpbtWqlQYMGqVu3bqpfv742bdqkzz777KYhp2fPnurYseNNzxG/3tKlS3X58mWHCxtdr1mzZvL19VXr1q315JNP6ty5c5o4caKCg4N1+PBhl+bh7++vBQsWKCYmRnXr1tUPP/yg+fPn6/XXX7fvpW7ZsqWGDx+u5s2b65FHHtGxY8f00UcfqVy5cvrjjz/s0ypXrpzeeOMNvf3222rYsKE6dOggPz8/rVmzRiVKlFBcXJzLy369qlWrKioqyuEWbNK1H4XSDB48WMuWLVPdunXVs2dPVa5cWSdPntS6deu0ZMkSnTx5UpI0c+ZMvfTSS/Lz89PFixc1ffp0+zSSkpKUkpKiefPm2X9wmDRpklq0aKEqVaqoW7duKlmypA4dOqRly5YpMDBQ3377rXbt2qXY2Fh98cUXeu211zK93kBgYKDGjRunLl266M4779TDDz+sYsWKaf/+/Zo/f74aNGjg8KOJO9atW6eiRYvqlVdeybTfwIEDtWnTJq1fv14+Pj4uTz87/udcVaNGDcXExGjChAk6ffq0IiMjtXr1an3yySdq166d7rnnHknXXt8RI0bo8ccf1//+9z898sgjKlSokDZu3KgLFy7ok08+sU/T1dfLFV26dNGXX36pp556SsuWLVODBg2UkpKi7du368svv9TChQtVu3ZtjRs3TnFxcQoICNBzzz3n0rRXrVqlxMRE++HuCQkJeumll2651nPnztn3xJ88eVKjR4+Wj49Phj+y9OnTRydOnNCSJUvk7e2t5s2b6/HHH9c777yjtm3bqkaNGrdci3RtnTVq1EhDhgzRlStX7Hv19+7d69T3vffe06JFixQZGWm/1d3hw4c1a9Ys/fzzzy4dgp9m6NChatGiherVq6cePXrYb8EWFBSU5SMrAPzL5cQl5QHAVe7egi0iIsLhNjgZ3c5n8+bNpn379qZgwYLG39/fVKhQwbz11lvpTvtmt7yZOXOmqVmzpvHz8zOFCxc2jz76qDl48KBDH1dvwdayZUuzcOFCU716dePn52cqVqzocIs0Y67dgu3FF180xYsXN3nz5jUNGjQwq1atcrrVT9qtt24cP7PhN6szveHffPONqV69uvH39zdhYWHm/fffN1OmTMnwVlHpTW/37t2mWbNmJiAgwISEhJjY2FinW6hNnjzZlC9f3v66xMfH22/bdaMpU6bY10mhQoVMZGSkWbx4sX24u7dg69Wrl5k+fbp9/jVr1nS4rVSao0ePml69eplSpUoZHx8fExoaapo0aWImTJjgMG/9/9u6ZfQoXbq0w3TXr19vOnToYIoUKWL8/PxM6dKlzUMPPWQSEhKMMdduFVW1alUzatQop9tA3XgLtuvbo6KiTFBQkPH39zfh4eGma9euDreac/fWWpLMiBEjHPreuI5WrFhhvL29zccff5xuv8xuwZbGlf+568kDt2AzxpgrV66YgQMHmjJlyhgfHx9TqlQp069fP3Pp0iWn8b/55htTv359kzdvXhMYGGjq1KljvvjiC/twV1+v62V2CzZjrt2i7v333zdVqlSxb/u1atUyAwcONElJScYYY+rUqWM6duxotm/f7jR+RrdgS3v4+vqacuXKmf79+zvc0vFWtpO0R8GCBU2DBg3MDz/8YIxxfs/++uuvjSTzwQcfOEz7zJkzpnTp0qZGjRomOTk53dcjI+l9Lhw8eND+mRAUFGQ6duxo/v7773S3nb/++stER0ebYsWKGT8/P1O2bFnTq1cvh9fk+uXN6BZsxhizZMkS06BBA/t20rp1a7N169YM+3MLNiB3sBmTheP/AAAeExYWpqpVq+q7777L6VL+MV27dtXs2bPTPZTbKmw2m3r16nXLe5dvFBYWpgEDBmR4CsHy5cvVtWtXhyvbAwCA3INz0gEAAAAAsAhCOgAA/6D27dvbrwCenpCQELVv3/4frAgAAFgJF44DAOAfNGLEiEyHV6pU6aZ9AADAfxfnpAMAAAAAYBEc7g4AAAAAgEUQ0gEAAOA2Y4xOnjypXbt25XQpAPCfQkgHAACAS86ePas333xTFSpUkK+vr4oUKaI77rhDO3bsyOnSAOA/gwvHAQAA4KZOnDihyMhI7d+/X88++6waNGggX19f+fj4KCwsLKfLA4D/DEI6AAAAburll1/W4cOHtWrVKlWpUiWnywGA/yyu7g4AAIBMHTt2TMWLF9f48ePVs2fPnC4HAP7TOCcdACxkwIABstlsTo/GjRs79d23b1+6fW02m0O/Y8eOqUePHrr99tvl7e1t75M/f/6b1hMWFqZWrVpp0aJFioiIkL+/vypXrqw5c+Y49Dt58qReeuklVatWTfnz51dgYKBatGihjRs3Ok3z0qVLGjBggO644w75+/urePHi6tChg3bv3m3vk5qaqlGjRqlatWry9/dXsWLF1Lx5c/3++++SpOXLl8tms2n58uUO0+7atavDYbdpr9HUqVMd+vXq1Us2m01du3aVJE2dOjXD1zLtkTaNP/74Q127dlXZsmXl7++v0NBQde/eXSdOnHCYR9q6TExMdGj//fffnWrq2rVruutj9uzZTsvZuHFjVa1a1alvRst87NgxFStWTI0bN9b1v8v/+eefypcvnzp16pThtK5fjuudO3dOoaGhLtc2bNgw2Ww27du3T9K17Sqz1/r6dZiamqqRI0eqSpUq8vf3V0hIiJ588kmdOnXKYR6ubqs3ruuAgABVq1ZNkyZNcujn6nq+fv7pLcuN2+jYsWNVpUoV+fn5qUSJEurVq5dOnz7t0Kdx48YO0yhatKhatmypzZs3pzvvWxnXZrOpd+/eGU4n7XVKW2dr1qxRamqqkpOTVbt2bfn7+6tIkSLq3Lmz9u/f7zT+0qVL1bBhQ+XLl08FCxZU27ZttW3bNoc+advW9u3b9dBDDykwMFBFihRRnz59dOnSJad6BwwYYH9+9epV3X///SpcuLC2bt2aYT9JGjp0aIbvowBgRRzuDgAWNG7cOHto69evX6Z9n3jiCTVs2FCSNGfOHM2dO9dheExMjJYsWaJnn31WNWrUkLe3tyZMmKB169a5VMuuXbvUqVMnPfXUU4qJiVF8fLw6duyoBQsW6L777pMk7dmzR/PmzVPHjh1VpkwZHT16VB9//LEiIyO1detWlShRQpKUkpKiVq1aKSEhQQ8//LD69Omjs2fPavHixdq8ebPCw8MlST169NDUqVPVokULPf7447p69apWrFihX3/9VbVr13b9hUzHn3/+qYkTJzq0NWrUSNOmTbM/f/fddyVJb7zxhr2tfv36kqTFixdrz5496tatm0JDQ7VlyxZNmDBBW7Zs0a+//uoUaHNacHCwxo0bp44dO+rDDz/Uc889p9TUVHXt2lUFChTQ2LFj3Z7mBx98oKNHj95yTSNHjtS5c+ckSdu2bdN7772n119/XZUqVZIkhx8snnzySU2dOlXdunXTc889p71792rMmDFav369fvnlF/n4+Nj7urKtphkxYoSKFi2qM2fOaMqUKerZs6fCwsLUtGlTSbe2nhs2bKgnnnjCYbmuN2DAAA0cOFBNmzbV008/rR07dmjcuHFas2aN07JUrFhRb7zxhowx2r17t4YPH677778/3UB8o6yMm5G0Hyd69+6tWrVqafDgwTp+/LhGjx6tn3/+WevXr1fRokUlSUuWLFGLFi1UtmxZDRgwQBcvXtSHH36oBg0aaN26dU7nrz/00EMKCwtTXFycfv31V40ePVqnTp3Sp59+mmE9jz/+uJYvX67FixercuXKGfY7ffq04uLibnm5ASBHGACAZbz++utGkklMTLS3ValSxURGRjr13bVrl5FkPvnkE3tbbGysuf6t/eLFi8bLy8s8+eSTDuPGxMSYfPny3bSe0qVLG0nmq6++srclJSWZ4sWLm5o1a9rbLl26ZFJSUhzG3bt3r/Hz8zODBg2yt02ZMsVIMsOHD3eaV2pqqjHGmKVLlxpJ5rnnnsuwz48//mgkmaVLlzotV+nSpR1qkGTi4+PtbQ899JCpWrWqKVWqlImJiUl3uSMjI9N9zY0x5sKFC05tX3zxhZFkfvrpJ3tb2ro4fvy4Q981a9Y41ZTR+pg1a5aRZJYtW+ZQW5UqVdKtzZj0l9kYYzp37mwCAgLMzp07zdChQ40kM2/evAync+NypDl27JgpUKCAadGihcu1pc1v7969TsOWLVvmNJ00K1asMJLMZ5995tC+YMECp3ZXt9X4+HinWnbu3GkkmSFDhtjbXF3PaUqWLGm6deuW4XIdO3bM+Pr6mmbNmjn8r4wZM8ZIMlOmTLG3pbf9pb03HDt2zGne13N1XEmmV69eGU7nxtcp7XnlypUdXpu05XzxxRftbRERESY4ONicOHHC3rZx40bj5eVloqOj7W1p21abNm0c5v3MM88YSWbjxo0O9cbGxhpjjOnXr5/x9vZOd/u9vp8xxrzyyismODjY1KpVK8P/aQCwGg53BwALSTvE09/f/6Z9k5OTJUl+fn4Z9jl//rxSU1NVpEiRW66pRIkSat++vf15YGCgoqOjtX79eh05csReg5fXtY+UlJQUnThxQvnz51eFChUc9th/9dVXKlq0qJ599lmn+aTtmfzqq69ks9kUGxubYZ/g4GBJ0sGDB91alrVr12rWrFmKi4uz1+uuvHnz2v++dOmSEhMTddddd0lSukcnnDx5UomJifZHUlJShtO+vl9iYqLOnj2bbr+UlBR7n7Tt4GbGjBmjoKAgPfjgg3rrrbfUpUsXtW3b1qVxr/f2228rKChIzz333E1rS3tcuHDB7flI0qxZsxQUFKT77rvPYXq1atVS/vz5tWzZMof+rmyraU6dOqXExETt2bNHI0aMkLe3tyIjI+3D3V3PycnJmf4vLlmyRMnJyXr++ecdtr2ePXsqMDBQ8+fPd+h/5coVJSYm6vjx41q1apXmzp2r6tWr2/dWZ8bVcdOW68SJE0pNTb3pdKVrp4pc/9o0btxYtWrVstd/+PBhbdiwQV27dlXhwoXt/apXr6777rtP33//fbrTvF7a+0N6fceMGaO4uDiNHj36ptvvoUOH9OGHH+qtt95y6fQeALAKDncHAAtJTEyUj4+PAgICbto37TzWzL58FilSROXLl9ekSZMUGRmpiIgIeXl56fLlyy7XVK5cOadDe++44w5J185/Dg0NtZ9DPnbsWO3du1cpKSkONaTZvXu3KlSooDx5Mv742b17t0qUKOHwBf9GZcuWVWhoqIYNG6YaNWrYD6e/2XK99tpratiwoVq1apXp+biZOXnypAYOHKgZM2bo2LFjDsPSC+AVKlRwabrnz59XsWLFXOq7fft2e18vLy+VK1dOsbGxeuSRRzIcp3Dhwho9erQ6duyokJAQjR492qV5XW/v3r36+OOPNW7cuAx/SLq+tqzatWuXkpKS7D/K3OjG19+VbTXNnXfeaf/bz89PY8aMUZ06dext7q7npKSkTP8X//rrL0nO24Ovr6/Kli1rH55m5cqVDq9j+fLlNW/ePJdOp3B13MmTJ2vy5Mn2OurWravhw4ene0pJ2rgVK1Z0GlapUiXNnj070+VM67dw4UKdP39e+fLlc6jveuHh4fLy8rKfD5/mhx9+sF+X4uTJk+kv/HViY2NVokQJPfnkk/b6AODfgJAOABayb98+3X777S59EU/bM3h98EjPzJkz9eijjyoqKsqh/fovyVn13nvv6a233lL37t319ttvq3DhwvLy8tLzzz/v8h46d/j6+mrixIl65JFHVKNGDYdhpUuXTnecRYsWacmSJVq1alWW5v3QQw9p5cqVevnllxUREaH8+fMrNTVVzZs3T3dZv/rqKwUGBtqf79y502nPoXTt6Ilvv/3WoW3FihUaNGiQU9+wsDD7efUnTpzQ6NGj1aVLF/uPFxlZuHChpGt7kQ8ePKiCBQu6tMxp3njjDZUvX14xMTFasWJFun2ury3NrFmzNGHCBLfmJV27aFxwcLA+++yzdIdn5ceA6dOnKyQkRJcuXdLSpUvVq1cv+fv72y8m6M56PnnypJKTk2/6v+iO6tWr64MPPpAk+7nfjRs31rp16246H1fHbdu2rXr37i1jjPbu3atBgwapVatW2rVrl9M0r997nt0yev9bvXq1evbsqXz58umdd95Rx44dM/wRbNu2bZo6daqmT5/ucK4/APwbENIBwCKuXr2qjRs3qnnz5i7137p1q2w220331NasWVMTJ05Uw4YNNWjQIN11110aOnSofvnlF5fm8+eff8oY4/DFeefOnZJkvwDU7Nmzdc8999j3yqU5ffq0wyG24eHh+u2333TlypUMvziHh4dr4cKFOnnyZKZ701u1aqVDhw7pjz/+0MWLFyVdu4rzjh07nPoaY/Taa6+pffv29kOWb8WpU6eUkJCggQMHqn///vb29EJNmkaNGjm8BhkFY29vb/tFy9LceNXvNPny5XPo27BhQ5UsWVKLFi1SdHR0uuMsWLBAkyZN0iuvvKLPPvtMMTEx+u233zI9quF669ev14wZMzRv3jx5e3tn2O/G2iRpw4YNLs3jRuHh4VqyZIkaNGjgUkh0ZVtN06BBA3tbq1attGXLFsXFxalr165ur+e0q4unXfguPWk/Hu3YsUNly5a1tycnJ2vv3r1Or1mhQoUc2ho3bqwSJUooPj7+pheTdHXc2267zaFf/vz59eijj2r9+vVO0yxTpoy9/nvvvddh2Pbt2+2v5fXLeaPt27eraNGiTj8Q7tq1yz596dp6TE1NdVpn9913n8aNG6dLly5p3rx5euKJJ+x3erhRv379FBERcdO7FwCAFXFOOgBYxKJFi5SUlOTSecJXr17VV199pTp16tz0XMszZ86oS5cuatOmjd588001bdpUxYsXd7muv//+2+GK8WfOnNGnn36qiIgI+145b29vh9t7Sdf2nh46dMih7YEHHlBiYqLGjBnjNJ+08R944AEZYzRw4MAM+6QpUKCAGjRooKZNm2a6XDNmzNAff/yR5as8p4XTG+sYOXJklqabVWl7djMKz6dPn9bjjz+uOnXq6L333tOkSZO0bt06p6uPZ+a1115TgwYN1KZNG4/U7IqHHnpIKSkpevvtt52GXb161elHDFe21YxcvHjRfrqEu+t5xowZ8vX11d13353h9Js2bSpfX1+NHj3aYbqTJ09WUlKSWrZsedP6pJuf0pGVcTPbjmrWrKnQ0FCNHz/eYTorVqzQ77//rlatWkmSihcvroiICH3yyScO62fz5s1atGiR7r//fqdpf/TRRw7PP/zwQ0lSixYtHNrr168vb29v5cuXT+PHj9dPP/3kdNSGJK1atUpff/21Bg8ebLm7LQCAK9iTDgAWMHPmTL300kvy8/PTxYsXNX36dPuwpKQkpaSkaN68eWrXrp2WLFmit956S3/88YfT4dHp6dWrly5evOh0H2hX3XHHHerRo4fWrFmjkJAQTZkyRUePHlV8fLy9T6tWrTRo0CB169ZN9evX16ZNm/TZZ5857DGUpOjoaH366afq27evVq9erYYNG+r8+fNasmSJnnnmGbVt21b33HOPunTpotGjR2vXrl32w4tXrFihe+6555bOJV+0aJF69uzp8vnhGQkMDFSjRo00ZMgQXblyxb73eu/evVmarrvOnTunBQsWSLp2qPXo0aPl4+OTYdDr06ePTpw4oSVLlsjb21vNmzfX448/rnfeeUdt27Z1OmUgPYsWLXL56AtPiYyM1JNPPqm4uDht2LBBzZo1k4+Pj3bt2qVZs2Zp1KhRevDBB+39XdlW08ybN09Fixa1H+6+YsUKPf/885JcX8+7du1SbGysvvjiC7322msOpzXcqFixYurXr58GDhyo5s2bq02bNtqxY4fGjh2r//3vf3rssccc+h89etT+PpCYmKiPP/5YefLksYfhzLg67v79+7VgwQL74e7vvvuuSpcurZo1azodNZAnTx4NGTJE0dHRatiwoR599FH7ofS33XabXn31VXvfoUOHqkWLFqpXr5569OhhvwVbUFCQ0z3MpWvXOmjTpo2aN2+uVatWafr06emeynK9qKgoPfbYY3rllVfUunVrhx/oFi1apPvuu8/p6AQA+NfImYvKAwCul3b7qMweabcWe/bZZ02jRo3MggULnKZz4+2yvvjiC2Oz2Zz6unMLtpYtW5qFCxea6tWrGz8/P1OxYkUza9Ysh36XLl0yL774oilevLjJmzevadCggVm1alW6t4O6cOGCeeONN0yZMmWMj4+PCQ0NNQ8++KDZvXu3vc/Vq1fN0KFDTcWKFY2vr68pVqyYadGihVm7dm2m9WZ0C7a8efOaQ4cOOS3brdyC7eDBg6Z9+/amYMGCJigoyHTs2NH8/fffTrd+ys5bsF2/XRQsWNA0aNDA/PDDDw7LnDb9r7/+2kgyH3zwgcO0z5w5Y0qXLm1q1KhhkpOT013W65ejbdu2Du3p3TrN07dgSzNhwgRTq1YtkzdvXlOgQAFTrVo188orr5i///7b3sfVbTXtVmJpD19fX1OuXDnTv39/c+nSJXs/V9bzF198YapWrWpGjRplvz3gzZZrzJgxpmLFisbHx8eEhISYp59+2pw6dcqhT0br+Pvvv8/wNXJ33Ov72Gw2Exoaajp06GC2bdvm8DrduM6+/PJLU7NmTePn52cKFy5sOnfubP766y+nOpYsWWIaNGhg8ubNawIDA03r1q3N1q1bHfqkbVtbt241Dz74oClQoIApVKiQ6d27t7l48aJTvdf/fxljTGJioilWrJhp3769Qz+bzeb0XpHZ/zQAWI3NmBuO5QIA/OPCwsI0YMAA+0WrbrR8+XJ17drV6WrH/0RdVatW1XffffePzhdwF9vqv8+AAQM0cOBAHT9+3KVbywFAbsE56QAAAAAAWAQhHQAsoH379goPD89weEhIiNq3b/8PVgQAAICcwIXjAMACRowYkenwSpUq3bQPAAAA/v04Jx0AAAAAAIvgcHcAAAAAACyCkA4AAAAAgEXkunPSU1NT9ffff6tAgQKy2Ww5XQ4AAAAA4D/OGKOzZ8+qRIkS8vLKfF95rgvpf//9t0qVKpXTZQAAAAAAcpkDBw7otttuy7RPrgvpBQoUkHTtxQkMDMzhagAAAAAA/3VnzpxRqVKl7Hk0M7kupKcd4h4YGEhIBwAAAAD8Y1w55ZoLxwEAAAAAYBGEdAAAAAAALIKQDgAAAACARRDSAQAAAACwCEI6AAAAAAAWQUgHAAAAAMAiCOkAAAAAAFgEIR0AAAAAAIsgpAMAAAAAYBGEdAAAAAAALIKQDgAAAACARRDSAQAAAACwCEI6AAAAAAAWQUgHAAAAAMAiCOkAAAAAAFgEIR0AAAAAAIsgpAMAAAAAYBGEdAAAAAAALIKQDgAAAACAReTJ6QKQubDX5ud0CfCgfYNb5nQJAAAAACyMPekAAAAAAFgEIR0AAAAAAIsgpAMAAAAAYBGEdAAAAAAALIKQDgAAAACARRDSAQAAAACwCEI6AAAAAAAWQUgHAAAAAMAiCOkAAAAAAFgEIR0AAAAAAIsgpAMAAAAAYBGEdAAAAAAALIKQDgAAAACARRDSAQAAAACwCEI6AAAAAAAWQUgHAAAAAMAiCOkAAAAAAFgEIR0AAAAAAIsgpAMAAAAAYBGEdAAAAAAALIKQDgAAAACARVgipH/00UcKCwuTv7+/6tatq9WrV2fYd+rUqbLZbA4Pf3//f7BaAAAAAACyR46H9JkzZ6pv376KjY3VunXrVKNGDUVFRenYsWMZjhMYGKjDhw/bH3/99dc/WDEAAAAAANkjx0P68OHD1bNnT3Xr1k2VK1fW+PHjFRAQoClTpmQ4js1mU2hoqP0REhLyD1YMAAAAAED2yNGQnpycrLVr16pp06b2Ni8vLzVt2lSrVq3KcLxz586pdOnSKlWqlNq2bastW7Zk2Pfy5cs6c+aMwwMAAAAAACvK0ZCemJiolJQUpz3hISEhOnLkSLrjVKhQQVOmTNHXX3+t6dOnKzU1VfXr19fBgwfT7R8XF6egoCD7o1SpUh5fDgAAAAAAPCHHD3d3V7169RQdHa2IiAhFRkZqzpw5KlasmD7++ON0+/fr109JSUn2x4EDB/7higEAAAAAcE2enJx50aJF5e3traNHjzq0Hz16VKGhoS5Nw8fHRzVr1tSff/6Z7nA/Pz/5+flluVYAAAAAALJbju5J9/X1Va1atZSQkGBvS01NVUJCgurVq+fSNFJSUrRp0yYVL148u8oEAAAAAOAfkaN70iWpb9++iomJUe3atVWnTh2NHDlS58+fV7du3SRJ0dHRKlmypOLi4iRJgwYN0l133aVy5crp9OnTGjp0qP766y89/vjjObkYAAAAAABkWY6H9E6dOun48ePq37+/jhw5ooiICC1YsMB+Mbn9+/fLy+v/dvifOnVKPXv21JEjR1SoUCHVqlVLK1euVOXKlXNqEQAAAAAA8AibMcbkdBH/pDNnzigoKEhJSUkKDAzM6XJuKuy1+TldAjxo3+CWOV0CAAAAgH+YOzn0X3d1dwAAAAAA/qsI6QAAAAAAWAQhHQAAAAAAiyCkAwAAAABgEYR0AAAAAAAsgpAOAAAAAIBFENIBAAAAALAIQjoAAAAAABZBSAcAAAAAwCII6QAAAAAAWAQhHQAAAAAAiyCkAwAAAABgEYR0AAAAAAAsgpAOAAAAAIBFENIBAAAAALAIQjoAAAAAABZBSAcAAAAAwCII6QAAAAAAWAQhHQAAAAAAiyCkAwAAAABgEYR0AAAAAAAsgpAOAAAAAIBFENIBAAAAALAIQjoAAAAAABZBSAcAAAAAwCII6QAAAAAAWAQhHQAAAAAAiyCkAwAAAABgEYR0AAAAAAAsgpAOAAAAAIBFENIBAAAAALAIQjoAAAAAABZBSAcAAAAAwCII6QAAAAAAWAQhHQAAAAAAiyCkAwAAAABgEYR0AAAAAAAsgpAOAAAAAIBFENIBAAAAALAIQjoAAAAAABZBSAcAAAAAwCII6QAAAAAAWAQhHQAAAAAAiyCkAwAAAABgEYR0AAAAAAAsgpAOAAAAAIBFENIBAAAAALAIQjoAAAAAABZBSAcAAAAAwCII6QAAAAAAWAQhHQAAAAAAiyCkAwAAAABgEYR0AAAAAAAsgpAOAAAAAIBFENIBAAAAALAIQjoAAAAAABZBSAcAAAAAwCII6QAAAAAAWAQhHQAAAAAAiyCkAwAAAABgEYR0AAAAAAAsgpAOAAAAAIBFENIBAAAAALAIQjoAAAAAABZBSAcAAAAAwCII6QAAAAAAWAQhHQAAAAAAiyCkAwAAAABgEYR0AAAAAAAsgpAOAAAAAIBFENIBAAAAALAIQjoAAAAAABZhiZD+0UcfKSwsTP7+/qpbt65Wr17t0ngzZsyQzWZTu3btsrdAAAAAAAD+ATke0mfOnKm+ffsqNjZW69atU40aNRQVFaVjx45lOt6+ffv00ksvqWHDhv9QpQAAAAAAZK8cD+nDhw9Xz5491a1bN1WuXFnjx49XQECApkyZkuE4KSkpevTRRzVw4ECVLVs20+lfvnxZZ86ccXgAAAAAAGBFORrSk5OTtXbtWjVt2tTe5uXlpaZNm2rVqlUZjjdo0CAFBwerR48eN51HXFycgoKC7I9SpUp5pHYAAAAAADwtR0N6YmKiUlJSFBIS4tAeEhKiI0eOpDvOzz//rMmTJ2vixIkuzaNfv35KSkqyPw4cOJDlugEAAAAAyA55croAd5w9e1ZdunTRxIkTVbRoUZfG8fPzk5+fXzZXBgAAAABA1uVoSC9atKi8vb119OhRh/ajR48qNDTUqf/u3bu1b98+tW7d2t6WmpoqScqTJ4927Nih8PDw7C0aAAAAAIBskqOHu/v6+qpWrVpKSEiwt6WmpiohIUH16tVz6l+xYkVt2rRJGzZssD/atGmje+65Rxs2bOB8cwAAAADAv1qOH+7et29fxcTEqHbt2qpTp45Gjhyp8+fPq1u3bpKk6OholSxZUnFxcfL391fVqlUdxi9YsKAkObUDAAAAAPBvk+MhvVOnTjp+/Lj69++vI0eOKCIiQgsWLLBfTG7//v3y8srxO8UBAAAAAJDtbMYYk9NF/JPOnDmjoKAgJSUlKTAwMKfLuamw1+bndAnwoH2DW+Z0CQAAAAD+Ye7kUHZRAwAAAABgEYR0AAAAAAAsgpAOAAAAAIBFENIBAAAAALAIQjoAAAAAABZBSAcAAAAAwCII6QAAAAAAWAQhHQAAAAAAi8hzKyP9/vvv+vLLL7V//34lJyc7DJszZ45HCgMAAAAAILdxe0/6jBkzVL9+fW3btk1z587VlStXtGXLFi1dulRBQUHZUSMAAAAAALmC2yH9vffe04gRI/Ttt9/K19dXo0aN0vbt2/XQQw/p9ttvz44aAQAAAADIFdwO6bt371bLli0lSb6+vjp//rxsNpteeOEFTZgwweMFAgAAAACQW7gd0gsVKqSzZ89KkkqWLKnNmzdLkk6fPq0LFy54tjoAAAAAAHIRty8c16hRIy1evFjVqlVTx44d1adPHy1dulSLFy9WkyZNsqNGAAAAAAByBbdD+pgxY3Tp0iVJ0htvvCEfHx+tXLlSDzzwgN58802PFwgAAAAAQG7hdkgvXLiw/W8vLy+99tprHi0IAAAAAIDcyu1z0r29vXXs2LHsqAUAAAAAgFzN7ZBujMmOOgAAAAAAyPXcDumSZLPZPF0HAAAAAAC5ntvnpEtSaGhohsNSUlJuuRgAAAAAAHKzWwrps2fPdriAHAAAAAAAyDq3Q7rNZlODBg0UHBycHfUAAAAAAJBrceE4AAAAAAAswu2QvmzZMg51BwAAAAAgG7h9uHtkZKRSUlL01Vdfadu2bZKkKlWqqE2bNvL29vZ4gQAAAAAA5BZuh/Q///xTLVu21MGDB1WhQgVJUlxcnEqVKqX58+crPDzc40UCAAAAAJAbuH24+3PPPaeyZcvqwIEDWrdundatW6f9+/erTJkyeu6557KjRgAAAAAAcgW396T/+OOP+vXXXx3OSy9SpIgGDx6sBg0aeLQ4AAAAAAByE7f3pPv5+ens2bNO7efOnZOvr69HigIAAAAAIDdyO6S3atVKTzzxhH777TcZY2SM0a+//qqnnnpKbdq0yY4aAQAAAADIFdwO6aNHj1Z4eLjq1asnf39/+fv7q0GDBipXrpxGjRqVHTUCAAAAAJAruH1OesGCBfX111/rzz//tN+CrVKlSipXrpzHiwMAAAAAIDdxO6QPGjRIL730ksqVK0cwBwAAAADAg9w+3H3gwIE6d+5cdtQCAAAAAECu5nZIN8ZkRx0AAAAAAOR6bh/uLknDhg1T/vz50x3Wv3//LBUEAAAAAEBudUsh/Zdffkn3nug2m42QDgAAAADALbqlkD537lwFBwd7uhYAAAAAAHI1t89JBwAAAAAA2cPtkB4ZGZnuoe4AAAAAACBr3D7cfdmyZdlRBwAAAAAAuZ7be9IfeOABvf/++07tQ4YMUceOHT1SFAAAAAAAuZHbIf2nn37S/fff79TeokUL/fTTTx4pCgAAAACA3MjtkH7u3Ll0z0n38fHRmTNnPFIUAAAAAAC5kdshvVq1apo5c6ZT+4wZM1S5cmWPFAUAAAAAQG7k9oXj3nrrLXXo0EG7d+/WvffeK0lKSEjQF198oVmzZnm8QAAAAAAAcgu3Q3rr1q01b948vffee5o9e7by5s2r6tWra8mSJYqMjMyOGgEAAAAAyBXcDumS1LJlS7Vs2dLTtQAAAAAAkKu5fU46AAAAAADIHm7vSU9JSdGIESP05Zdfav/+/UpOTnYYfvLkSY8VBwAAAABAbuL2nvSBAwdq+PDh6tSpk5KSktS3b1916NBBXl5eGjBgQDaUCAAAAABA7uB2SP/ss880ceJEvfjii8qTJ486d+6sSZMmqX///vr111+zo0YAAAAAAHIFt0P6kSNHVK1aNUlS/vz5lZSUJElq1aqV5s+f79nqAAAAAADIRdwO6bfddpsOHz4sSQoPD9eiRYskSWvWrJGfn59nqwMAAAAAIBdxO6S3b99eCQkJkqRnn31Wb731lsqXL6/o6Gh1797d4wUCAAAAAJBbuH1198GDB9v/7tSpk26//XatWrVK5cuXV+vWrT1aHAAAAAAAuYnbIf1G9erVU7169TxRCwAAAAAAuZrbIf2bb77JdHibNm1uuRgAAAAAAHIzt0N6u3btHJ7bbDYZY+x/p6SkeKQwAAAAAAByG7cvHJeamurwCAgI0J9//qnU1FQCOgAAAAAAWeB2SL+RzWbzRB0AAAAAAOR6WQrp+/bt0/nz51WgQAFP1QMAAAAAQK7l9jnpHTp0kCRdvHhRv/76q5o0aaJixYp5vDAAAAAAAHIbt0N6UFCQJCk0NFStW7dW9+7dPV4UAAAAAAC5kdshPT4+PjvqAAAAAAAg18vyheMAAAAAAIBnENIBAAAAALAIQjoAAAAAABZBSAcAAAAAwCII6QAAAAAAWITbV3e/8847Mx2+bt26Wy4GAAAAAIDczO096Rs2bFCTJk3Utm1btWnTRlu2bFHDhg3Vtm1btW3b9paK+OijjxQWFiZ/f3/VrVtXq1evzrDvnDlzVLt2bRUsWFD58uVTRESEpk2bdkvzBQAAAADAStzeky5JL7/8soKDgyVJH3zwgfr06aOyZcveUgEzZ85U3759NX78eNWtW1cjR45UVFSUduzYYZ/H9QoXLqw33nhDFStWlK+vr7777jt169ZNwcHBioqKuqUaAAAAAACwArf3pOfLl0/nzp2TJF29elWXLl3SK6+8Ym9z1/Dhw9WzZ09169ZNlStX1vjx4xUQEKApU6ak279x48Zq3769KlWqpPDwcPXp00fVq1fXzz//fEvzBwAAAADAKtwO6dWqVdMbb7yhX3/9Va+88opCQ0Pl7e2t2rVra+vWrW5NKzk5WWvXrlXTpk3/ryAvLzVt2lSrVq266fjGGCUkJGjHjh1q1KhRun0uX76sM2fOODwAAAAAALAitw93/+CDD9SxY0fNnDlTQUFBmj59ulq2bKnhw4erQYMGOnXqlMvTSkxMVEpKikJCQhzaQ0JCtH379gzHS0pKUsmSJXX58mV5e3tr7Nixuu+++9LtGxcXp4EDB7pcEwAAAAAAOcXtkF6vXj0dPHhQx48fV+HCheXt7S1J6tu3r+rUqePxAtNToEABbdiwQefOnVNCQoL69u2rsmXLqnHjxk59+/Xrp759+9qfnzlzRqVKlfpH6gQAAAAAwB23dOE4SSpWrJhT29133+3WNIoWLSpvb28dPXrUof3o0aMKDQ3NcDwvLy+VK1dOkhQREaFt27YpLi4u3ZDu5+cnPz8/t+oCAAAAACAn3HJI37p1q/bv36/k5GSH9jZt2rg8DV9fX9WqVUsJCQlq166dJCk1NVUJCQnq3bu3y9NJTU3V5cuXXe4PAAAAAIAVuR3S9+zZo/bt22vTpk2y2WwyxkiSbDabJCklJcWt6fXt21cxMTGqXbu26tSpo5EjR+r8+fPq1q2bJCk6OlolS5ZUXFycpGvnmNeuXVvh4eG6fPmyvv/+e02bNk3jxo1zd1EAAAAAALAUt0N6nz59VKZMGSUkJKhMmTJavXq1Tpw4oRdffFHDhg1zu4BOnTrp+PHj6t+/v44cOaKIiAgtWLDAfjG5/fv3y8vr/y5Cf/78eT3zzDM6ePCg8ubNq4oVK2r69Onq1KmT2/MGAAAAAMBKbCZtV7iLihYtqqVLl6p69eoKCgrS6tWrVaFCBS1dulQvvvii1q9fn121esSZM2cUFBSkpKQkBQYG5nQ5NxX22vycLgEetG9wy5wuAQAAAMA/zJ0c6vZ90lNSUlSgQAFJ1wL733//LUkqXbq0duzYcQvlAgAAAAAA6RYOd69atao2btyoMmXKqG7duhoyZIh8fX01YcIElS1bNjtqBAAAAAAgV3A7pL/55ps6f/68JGnQoEFq1aqVGjZsqCJFimjmzJkeLxAAAAAAgNzC7ZAeFRVl/7tcuXLavn27Tp48qUKFCtmv8A4AAAAAANx3y/dJv17hwoU9MRkAAAAAAHI1t0N6hw4dMh0+Z86cWy4GAAAAAIDczO2ruwcFBdkf8+fPl5eXl0MbAAAAAAC4NW7vSY+Pj7f/PXv2bA0ZMoSrugMAAAAA4AFu70kHAAAAAADZg5AOAAAAAIBFuH24++jRo+1/X716VVOnTlXRokXtbc8995xnKgMAAAAAIJexGWOMOyOUKVMm44nZbNqzZ0+Wi8pOZ86cUVBQkJKSkhQYGJjT5dxU2Gvzc7oEeNC+wS1zugQAAAAA/zB3cqjbe9L37t17y4UBAAAAAICMeeSc9JSUFE9MBgAAAACAXC1LIX3btm2qXr26/Pz8VLlyZW3atMlTdQEAAAAAkOtkKaS//PLLKl68uL755hvVqFFDffr08VRdAAAAAADkOm6fk369devW6bvvvtOdd96pO++8UxUrVvRUXQAAAAAA5DpZ2pN+9uxZFSxYUJJUqFAhnT171hM1AQAAAACQK7m9J/2bb76x/52amqqEhARt3rxZV65c8WhhAAAAAADkNm6H9Hbt2jk8f/LJJ+1/22y2LBcEAAAAAEBu5XZIT01NzY46AAAAAADI9Txyn3QAAAAAAJB1bu9J79u3b6bDhw8ffsvFAAAAAACQm7kd0keOHKl69erJ19fXaRjnpAMAAAAAcOtu6T7pc+fOVXBwsKdrAQAAAAAgV3P7nHSbzcYecwAAAAAAsoHbe9KNMeratavy58+vfPnyqUSJEqpZs6ZatGihgICA7KgRAAAAAIBcwe096dHR0SpWrJjy5Mmj48eP64cfflCXLl1Uvnx5bdu2LTtqBAAAAAAgV3B7T/rUqVOd2s6fP6/OnTvr5Zdf1nfffeeJugAAAAAAyHU8cp/0fPnyaejQoSpQoIAnJgcAAAAAQK6UpZB+6dIl+98VKlTQF198keWCAAAAAADIrdwO6ampqXr77bdVsmRJ5c+fX3v27JEkvfXWW5o8ebLHCwQAAAAAILdwO6S/8847mjp1qoYMGSJfX197e9WqVTVp0iSPFgcAAAAAQG7idkj/9NNPNWHCBD366KPy9va2t9eoUUPbt2/3aHEAAAAAAOQmbof0Q4cOqVy5ck7tqampunLlikeKAgAAAAAgN3I7pFeuXFkrVqxwap89e7Zq1qzpkaIAAAAAAMiN3L5Pev/+/RUTE6NDhw4pNTVVc+bM0Y4dO/Tpp59yj3QAAAAAALLA7T3pbdu21bfffqslS5YoX7586t+/v7Zt26Zvv/1W9913X3bUCAAAAABAruD2nnRJatiwoRYvXuzpWgAAAAAAyNXc3pNetmxZnThxIjtqAQAAAAAgV3M7pO/bt08pKSnZUQsAAAAAALma2yFdkmw2m6frAAAAAAAg17ulc9Jr164tb2/vdIft2bMnSwUBAAAAAJBb3VJIf/HFFxUUFOTpWgAAAAAAyNXcDuk2m00PP/ywgoODs6MeAAAAAAByLbfPSTfGZEcdAAAAAADkem6H9Pj4eA51BwAAAAAgG7h9uHtMTEx21AEAAAAAQK7ndkgvXLhwpsNPnjx5y8UAAAAAAJCbuR3SjTFKTU3VCy+8oDJlymRHTQAAAAAA5Epuh/Tdu3drwIAB+uCDD/TUU0/pzTff5Bx1AAAAAAA8wO0LxxUuXFijR4/W2rVr9eeff6pcuXL68MMPlZKSkh31AQAAAACQa7gd0tPccccdmjt3rr766it9+umnqly5subNm+fB0gAAAAAAyF3cPty9Q4cOTm0lS5bU9u3b9cADD7BHHQAAAACAW+R2SM/o/PMHH3wwy8UAAAAAAJCbuR3S4+Pjs6MOAAAAAAByPbfPSd+7d6927drl1L5r1y7t27fPEzUBAAAAAJAruR3Su3btqpUrVzq1//bbb+ratasnagIAAAAAIFdyO6SvX79eDRo0cGq/6667tGHDBk/UBAAAAABAruR2SLfZbDp79qxTe1JSEld2BwAAAAAgC9wO6Y0aNVJcXJxDIE9JSVFcXJzuvvtujxYHAAAAAEBu4vbV3d9//301atRIFSpUUMOGDSVJK1as0JkzZ7R06VKPFwgAAAAAQG7h9p70ypUr648//tBDDz2kY8eO6ezZs4qOjtb27dtVtWrV7KgRAAAAAIBcwe096ZJUokQJvffee56uBQAAAACAXO2WQvrp06c1efJkbdu2TZJUpUoVde/eXUFBQR4tDgAAAACA3MTtw91///13hYeHa8SIETp58qROnjyp4cOHKzw8XOvWrcuOGgEAAAAAyBXc3pP+wgsvqE2bNpo4caLy5Lk2+tWrV/X444/r+eef108//eTxIgEAAAAAyA3cDum///67Q0CXpDx58uiVV15R7dq1PVocAAAAAAC5iduHuwcGBmr//v1O7QcOHFCBAgU8UhQAAAAAALmR2yG9U6dO6tGjh2bOnKkDBw7owIEDmjFjhh5//HF17tw5O2oEAAAAACBXcPtw92HDhslmsyk6OlpXr16VJPn4+Ojpp5/W4MGDPV4gAAAAAAC5hdsh3dfXV6NGjVJcXJx2794tSQoPD1dAQIDHiwMAAAAAIDdx+3D3NAEBAapWrZqqVauW5YD+0UcfKSwsTP7+/qpbt65Wr16dYd+JEyeqYcOGKlSokAoVKqSmTZtm2h8AAAAAgH8Lt/ekd+jQIdPhc+bMcWt6M2fOVN++fTV+/HjVrVtXI0eOVFRUlHbs2KHg4GCn/suXL1fnzp1Vv359+fv76/3331ezZs20ZcsWlSxZ0q15AwAAAABgJW7vSQ8KCrI/5s+fLy8vL4c2dw0fPlw9e/ZUt27dVLlyZY0fP14BAQGaMmVKuv0/++wzPfPMM4qIiFDFihU1adIkpaamKiEhwe15AwAAAABgJW7vSY+Pj7f/PXv2bA0ZMkRly5a9pZknJydr7dq16tevn73Ny8tLTZs21apVq1yaxoULF3TlyhUVLlw43eGXL1/W5cuX7c/PnDlzS7UCAAAAAJDdbvmcdE9ITExUSkqKQkJCHNpDQkJ05MgRl6bx6quvqkSJEmratGm6w+Pi4hz29JcqVSrLdQMAAAAAkB1yNKRn1eDBgzVjxgzNnTtX/v7+6fbp16+fkpKS7I8DBw78w1UCAAAAAOAatw93Hz16tP3vq1evaurUqSpatKi97bnnnnN5WkWLFpW3t7eOHj3q0H706FGFhoZmOu6wYcM0ePBgLVmyRNWrV8+wn5+fn/z8/FyuCQAAAACAnOJ2SB8xYoT979DQUE2bNs3+3GazuRXSfX19VatWLSUkJKhdu3aSZL8IXO/evTMcb8iQIXr33Xe1cOFC1a5d291FAAAAAADAktwO6Xv37vVoAX379lVMTIxq166tOnXqaOTIkTp//ry6desmSYqOjlbJkiUVFxcnSXr//ffVv39/ff755woLC7Ofu54/f37lz5/fo7UBAAAAAPBPcvuc9EGDBunChQseK6BTp04aNmyY+vfvr4iICG3YsEELFiywX0xu//79Onz4sL3/uHHjlJycrAcffFDFixe3P4YNG+axmgAAAAAAyAk2Y4xxZwRvb28dPnxYwcHB2VVTtjpz5oyCgoKUlJSkwMDAnC7npsJem5/TJcCD9g1umdMlAAAAAPiHuZND3d6T7mamBwAAAAAALnL7nHTp2pXVMzr/u3///lkqCAAAAACA3OqWQvovv/wiX19fp3abzUZIBwAAAADgFt1SSJ87d+6/9px0ILfhugb/PVzbAAAA4L/L7XPSAQAAAABA9nA7pEdGRqZ7qDsAAAAAAMgatw93X7ZsmSQpMTFRklS0aFHPVgQAAAAAQC7l1p7006dPq1evXipatKhCQkIUEhKiokWLqnfv3jp9+nQ2lQgAAAAAQO7g8p70kydPql69ejp06JAeffRRVapUSZK0detWTZ06VQkJCVq5cqUKFSqUbcUCAAAAAPBf5nJIHzRokHx9fbV7926FhIQ4DWvWrJkGDRqkESNGeLxIAAAAAAByA5cPd583b56GDRvmFNAlKTQ0VEOGDNHcuXM9WhwAAAAAALmJyyH98OHDqlKlSobDq1atqiNHjnikKAAAAAAAciOXQ3rRokW1b9++DIfv3btXhQsX9kRNAAAAAADkSi6H9KioKL3xxhtKTk52Gnb58mW99dZbat68uUeLAwAAAAAgN3HrwnG1a9dW+fLl1atXL1WsWFHGGG3btk1jx47V5cuXNW3atOysFQAAAACA/zSXQ/ptt92mVatW6ZlnnlG/fv1kjJEk2Ww23XfffRozZoxKlSqVbYUCAAAAAPBf53JIl6QyZcrohx9+0KlTp7Rr1y5JUrly5TgXHQAAAAAAD3ArpKcpVKiQ6tSp4+laAAAAAADI1Vy+cBwAAAAAAMhehHQAAAAAACyCkA4AAAAAgEUQ0gEAAAAAsAhCOgAAAAAAFkFIBwAAAADAIgjpAAAAAABYBCEdAAAAAACLIKQDAAAAAGARhHQAAAAAACyCkA4AAAAAgEXkyekCAABA7hD22vycLgEetG9wy5wuAQD+k9iTDgAAAACARRDSAQAAAACwCEI6AAAAAAAWQUgHAAAAAMAiCOkAAAAAAFgEIR0AAAAAAIsgpAMAAAAAYBGEdAAAAAAALIKQDgAAAACARRDSAQAAAACwCEI6AAAAAAAWQUgHAAAAAMAiCOkAAAAAAFgEIR0AAAAAAIsgpAMAAAAAYBGEdAAAAAAALIKQDgAAAACARRDSAQAAAACwCEI6AAAAAAAWQUgHAAAAAMAiCOkAAAAAAFgEIR0AAAAAAIsgpAMAAAAAYBGEdAAAAAAALIKQDgAAAACARRDSAQAAAACwCEI6AAAAAAAWQUgHAAAAAMAiCOkAAAAAAFgEIR0AAAAAAIsgpAMAAAAAYBGEdAAAAAAALIKQDgAAAACARRDSAQAAAACwCEI6AAAAAAAWQUgHAAAAAMAiCOkAAAAAAFgEIR0AAAAAAIsgpAMAAAAAYBGEdAAAAAAALIKQDgAAAACARRDSAQAAAACwiBwP6R999JHCwsLk7++vunXravXq1Rn23bJlix544AGFhYXJZrNp5MiR/1yhAAAAAABksxwN6TNnzlTfvn0VGxurdevWqUaNGoqKitKxY8fS7X/hwgWVLVtWgwcPVmho6D9cLQAAAAAA2StHQ/rw4cPVs2dPdevWTZUrV9b48eMVEBCgKVOmpNv/f//7n4YOHaqHH35Yfn5+/3C1AAAAAABkrxwL6cnJyVq7dq2aNm36f8V4ealp06ZatWqVx+Zz+fJlnTlzxuEBAAAAAIAV5VhIT0xMVEpKikJCQhzaQ0JCdOTIEY/NJy4uTkFBQfZHqVKlPDZtAAAAAAA8KccvHJfd+vXrp6SkJPvjwIEDOV0SAAAAAADpypNTMy5atKi8vb119OhRh/ajR4969KJwfn5+nL8OAAAAAPhXyLE96b6+vqpVq5YSEhLsbampqUpISFC9evVyqiwAAAAAAHJMju1Jl6S+ffsqJiZGtWvXVp06dTRy5EidP39e3bp1kyRFR0erZMmSiouLk3TtYnNbt261/33o0CFt2LBB+fPnV7ly5XJsOQAAAAAA8IQcDemdOnXS8ePH1b9/fx05ckQRERFasGCB/WJy+/fvl5fX/+3s//vvv1WzZk3782HDhmnYsGGKjIzU8uXL/+nyASDXCHttfk6XAA/bN7hlTpcAAADSkaMhXZJ69+6t3r17pzvsxuAdFhYmY8w/UBUAAAAAAP+8//zV3QEAAAAA+LcgpAMAAAAAYBGEdAAAAAAALIKQDgAAAACARRDSAQAAAACwCEI6AAAAAAAWQUgHAAAAAMAiCOkAAAAAAFgEIR0AAAAAAIsgpAMAAAAAYBGEdAAAAAAALIKQDgAAAACARRDSAQAAAACwCEI6AAAAAAAWQUgHAAAAAMAiCOkAAAAAAFgEIR0AAAAAAIsgpAMAAAAAYBGEdAAAAAAALIKQDgAAAACARRDSAQAAAACwCEI6AAAAAAAWQUgHAAAAAMAiCOkAAAAAAFgEIR0AAAAAAIsgpAMAAAAAYBGEdAAAAAAALIKQDgAAAACARRDSAQAAAACwCEI6AAAAAAAWQUgHAAAAAMAiCOkAAAAAAFgEIR0AAAAAAIsgpAMAAAAAYBGEdAAAAAAALIKQDgAAAACARRDSAQAAAACwCEI6AAAAAAAWQUgHAAAAAMAiCOkAAAAAAFgEIR0AAAAAAIsgpAMAAAAAYBGEdAAAAAAALIKQDgAAAACARRDSAQAAAACwCEI6AAAAAAAWQUgHAAAAAMAiCOkAAAAAAFgEIR0AAAAAAIsgpAMAAAAAYBF5croAAAAAAPinhL02P6dLgIftG9wyp0vwKPakAwAAAABgEYR0AAAAAAAsgpAOAAAAAIBFENIBAAAAALAIQjoAAAAAABZBSAcAAAAAwCII6QAAAAAAWAQhHQAAAAAAiyCkAwAAAABgEYR0AAAAAAAsgpAOAAAAAIBFENIBAAAAALAIQjoAAAAAABZBSAcAAAAAwCII6QAAAAAAWAQhHQAAAAAAiyCkAwAAAABgEYR0AAAAAAAsgpAOAAAAAIBFENIBAAAAALAIQjoAAAAAABZhiZD+0UcfKSwsTP7+/qpbt65Wr16daf9Zs2apYsWK8vf3V7Vq1fT999//Q5UCAAAAAJB9cjykz5w5U3379lVsbKzWrVunGjVqKCoqSseOHUu3/8qVK9W5c2f16NFD69evV7t27dSuXTtt3rz5H64cAAAAAADPyvGQPnz4cPXs2VPdunVT5cqVNX78eAUEBGjKlCnp9h81apSaN2+ul19+WZUqVdLbb7+tO++8U2PGjPmHKwcAAAAAwLPy5OTMk5OTtXbtWvXr18/e5uXlpaZNm2rVqlXpjrNq1Sr17dvXoS0qKkrz5s1Lt//ly5d1+fJl+/OkpCRJ0pkzZ7JY/T8j9fKFnC4BHpQT2x3b0H8P2xE8ge0IWfVv+S4F3Ij3ov+ef8P7UVqNxpib9s3RkJ6YmKiUlBSFhIQ4tIeEhGj79u3pjnPkyJF0+x85ciTd/nFxcRo4cKBTe6lSpW6xauDWBY3M6QrwX8B2BE9gO0JWsQ0BsIp/0/vR2bNnFRQUlGmfHA3p/4R+/fo57HlPTU3VyZMnVaRIEdlsthysDGnOnDmjUqVK6cCBAwoMDMzpcvAvxXaErGIbgiewHcET2I7gCWxH1mKM0dmzZ1WiRImb9s3RkF60aFF5e3vr6NGjDu1Hjx5VaGhouuOEhoa61d/Pz09+fn4ObQULFrz1opFtAgMDeQNBlrEdIavYhuAJbEfwBLYjeALbkXXcbA96mhy9cJyvr69q1aqlhIQEe1tqaqoSEhJUr169dMepV6+eQ39JWrx4cYb9AQAAAAD4t8jxw9379u2rmJgY1a5dW3Xq1NHIkSN1/vx5devWTZIUHR2tkiVLKi4uTpLUp08fRUZG6oMPPlDLli01Y8YM/f7775owYUJOLgYAAAAAAFmW4yG9U6dOOn78uPr3768jR44oIiJCCxYssF8cbv/+/fLy+r8d/vXr19fnn3+uN998U6+//rrKly+vefPmqWrVqjm1CMgiPz8/xcbGOp2WALiD7QhZxTYET2A7giewHcET2I7+vWzGlWvAAwAAAACAbJej56QDAAAAAID/Q0gHAAAAAMAiCOkAAAAAAFgEIR0AAAAAAIsgpOdSH330kcLCwuTv76+6detq9erVNx1n1qxZqlixovz9/VWtWjV9//33DsPnzJmjZs2aqUiRIrLZbNqwYYNHar106ZK6du2qatWqKU+ePGrXrp1b41++fFkRERGZ1vTnn3+qQIECKliwYJbrzS1++ukntW7dWiVKlJDNZtO8efPcGv+XX35Rnjx5FBERkWGfwYMHy2az6fnnn89SrZK0fPlytW3bVsWLF1e+fPkUERGhzz777KbjrVmzRk2aNFHBggVVqFAhRUVFaePGjQ59jDEaNmyY7rjjDvn5+alkyZJ69913s1xzbhAXF6f//e9/KlCggIKDg9WuXTvt2LHjpuONHDlSFSpUUN68eVWqVCm98MILunTpkkOfQ4cO6bHHHlORIkWUN29eVatWTb///rvHaj9x4oRuu+022Ww2nT59OsN++/btU48ePVSmTBnlzZtX4eHhio2NVXJyskO/hQsX6q677lKBAgVUrFgxPfDAA9q3b5/H6s2tXH0fmThxoho2bKhChQqpUKFCatq0qdNn47lz59S7d2/ddtttyps3rypXrqzx48dnucZ9+/bJZrM5PX799dcMx5k6dWq649hsNh07dizLNUEaMGCA02tbsWLFTMdp3LhxuuukZcuWWarlVtf3u+++q/r16ysgICDd7zgbN25U586dVapUKeXNm1eVKlXSqFGjslRrbhYWFpbuOurVq1eG42zZskUPPPCAfdyRI0c69bnVz0pX3MpnT3rLOXjwYIc+fDfKGkJ6LjRz5kz17dtXsbGxWrdunWrUqKGoqKhM3+RXrlypzp07q0ePHlq/fr3atWundu3aafPmzfY+58+f1913363333/fo/WmpKQob968eu6559S0aVO3x3/llVdUokSJDIdfuXJFnTt3VsOGDbNSZq5z/vx51ahRQx999JHb454+fVrR0dFq0qRJhn3WrFmjjz/+WNWrV89KmXYrV65U9erV9dVXX+mPP/5Qt27dFB0dre+++y7Dcc6dO6fmzZvr9ttv12+//aaff/5ZBQoUUFRUlK5cuWLv16dPH02aNEnDhg3T9u3b9c0336hOnToeqfu/7scff1SvXr3066+/avHixbpy5YqaNWum8+fPZzjO559/rtdee02xsbHatm2bJk+erJkzZ+r111+39zl16pQaNGggHx8f/fDDD9q6das++OADFSpUyGO19+jRw6Xtc/v27UpNTdXHH3+sLVu2aMSIERo/frxDvXv37lXbtm117733asOGDVq4cKESExPVoUMHj9WbG7nzPrJ8+XJ17txZy5Yt06pVq1SqVCk1a9ZMhw4dsvfp27evFixYoOnTp2vbtm16/vnn1bt3b33zzTceqXfJkiU6fPiw/VGrVq0M+3bq1Mmh7+HDhxUVFaXIyEgFBwd7pB5IVapUcXiNf/7550z7z5kzx6H/5s2b5e3trY4dO2apjltd38nJyerYsaOefvrpdIevXbtWwcHBmj59urZs2aI33nhD/fr105gxY7JUb261Zs0ah3W0ePFiScp0/V+4cEFly5bV4MGDFRoamm6fW/msdEVWPnsGDRrksKzPPvusw3C+G2WRQa5Tp04d06tXL/vzlJQUU6JECRMXF5fhOA899JBp2bKlQ1vdunXNk08+6dR37969RpJZv369x2pOExMTY9q2bety/++//95UrFjRbNmyJcOaXnnlFfPYY4+Z+Ph4ExQU5LFacxNJZu7cuS7379Spk3nzzTdNbGysqVGjhtPws2fPmvLly5vFixebyMhI06dPH4/Ver3777/fdOvWLcPha9asMZLM/v377W1//PGHkWR27dpljDFm69atJk+ePGb79u3ZUmNuc+zYMSPJ/Pjjjxn26dWrl7n33nsd2vr27WsaNGhgf/7qq6+au+++O9vqHDt2rImMjDQJCQlGkjl16pRb4w8ZMsSUKVPG/nzWrFkmT548JiUlxd72zTffGJvNZpKTkz1Vdq6S1feRq1evmgIFCphPPvnE3lalShUzaNAgh3533nmneeONN7JUqyc+N48dO2Z8fHzMp59+mqVa8H8y+oxyx4gRI0yBAgXMuXPnPFPU/+fu+nbnO84zzzxj7rnnnixUhzR9+vQx4eHhJjU11aX+pUuXNiNGjLhpP1c+K11xq589N6uT70ZZx570XCY5OVlr16512CPt5eWlpk2batWqVRmOt2rVKqe92FFRUZmOk9OOHj2qnj17atq0aQoICEi3z9KlSzVr1qxb2huMWxMfH689e/YoNjY2wz69evVSy5Ytb+nICXckJSWpcOHCGQ6vUKGCihQposmTJys5OVkXL17U5MmTValSJYWFhUmSvv32W5UtW1bfffedypQpo7CwMD3++OM6efJkttb+X5WUlCRJma6X+vXra+3atfZDkffs2aPvv/9e999/v73PN998o9q1a6tjx44KDg5WzZo1NXHiRI/UuHXrVg0aNEiffvqpvLxu7WP0xm2vVq1a8vLyUnx8vFJSUpSUlKRp06apadOm8vHx8UjduU1W30cuXLigK1euOKyn+vXr65tvvtGhQ4dkjNGyZcu0c+dONWvWzCM1t2nTRsHBwbr77rvd3jv/6aefKiAgQA8++KBHasE1u3btUokSJVS2bFk9+uij2r9/v1vjT548WQ8//LDy5cvn0bqyc33f7LMRrklOTtb06dPVvXt32Ww2j07blc9KV2Tls2fw4MEqUqSIatasqaFDh+rq1av2YXw38oCc/pUA/6xDhw4ZSWblypUO7S+//LKpU6dOhuP5+PiYzz//3KHto48+MsHBwU59rbAnPTU11TRv3ty8/fbbGdaUmJhoSpUqZf8Vkj3pt04u7knfuXOnCQ4ONjt27DDGpL+X4osvvjBVq1Y1Fy9eNMaYbNuTPnPmTOPr62s2b96cab9NmzaZ8PBw4+XlZby8vEyFChXMvn377MOffPJJ4+fnZ+rWrWt++ukns2zZMhMREcFeiFuQkpJiWrZs6bBHPCOjRo0yPj4+Jk+ePEaSeeqppxyG+/n5GT8/P9OvXz+zbt068/HHHxt/f38zderULNV46dIlU716dTNt2jRjjDHLli1ze0/6rl27TGBgoJkwYYJD+/Lly01wcLDx9vY2kky9evXc3kOPazzxPvL000+bsmXL2qdhzLX1Hx0dbSSZPHnyGF9fX4c97bfq+PHj5oMPPjC//vqrWb16tXn11VeNzWYzX3/9tcvTqFSpknn66aezXAv+z/fff2++/PJLs3HjRrNgwQJTr149c/vtt5szZ864NP5vv/1mJJnffvvN47W5u75d/Y7zyy+/mDx58piFCxdmoToYc+17hre3tzl06JDL47iyJ92dz0pX3MpnzwcffGCWLVtmNm7caMaNG2cKFixoXnjhBftwvhtlHSE9l7FSSP/pp59Mvnz57I/p06fftH5XQ/qoUaNMgwYNzNWrVzOsqX379ubVV1+1Pyek3zpXQvrVq1dN7dq1zbhx4+xtN4b0/fv3m+DgYLNx40Z7282+XN/KdrR06VITEBBw0y/XFy5cMHXq1DHR0dFm9erVZtWqVeaBBx4wVapUMRcuXDDGGNOzZ08jyf7DgzHGrF271kjiMC83PfXUU6Z06dLmwIEDmfZbtmyZCQkJMRMnTjR//PGHmTNnjilVqpTDYcg+Pj6mXr16DuM9++yz5q677kp3mn/99ZfDdvTuu++m2++FF14wnTp1cqjFnZB+8OBBEx4ebnr06OHQfvjwYVO+fHnz8ssvm3Xr1pkff/zRREZGmiZNmrh8mCSuuZX3kRvFxcWZQoUKOUzDGGOGDh1q7rjjDvPNN9+YjRs3mg8//NDkz5/fLF68ON3p3Mr7U5ouXbq4fMrGypUrjSTz+++/uzx9uO/UqVMmMDDQTJo0yaX+TzzxhKlWrVqmfVx977neraxvV77jbNq0yRQtWtS+gwNZ06xZM9OqVSu3xnElpLvyWenqe4+nPnsmT55s8uTJYy5dumSM4buRJxDSc5nLly8bb29vp0AVHR1t2rRpk+F4pUqVcnrT6N+/v6levbpTX1dD+oULF8yuXbvsD1d+mXY1pLdt29Z4eXkZb29v+0OS8fb2NtHR0cYYY4KCghyGe3l52ftMnjz5pvPA/3ElpJ86dcr++qY9bDabvS0hIcHMnTvXqY8kY7PZjLe3t/1Hl+u5ux0tX77c5MuXz3z88cc3Xa5JkyaZ4OBgh3O1Ll++bAICAswXX3xhjLn2f5AnTx6nmiSZRYsW3XQeuKZXr17mtttuM3v27Llp37vvvtu89NJLDm3Tpk0zefPmta+r22+/3SkIjx071pQoUSLdaV65csVhOzpx4kS6/WrUqOHw3nL9+0b//v0zrfvQoUOmfPnypkuXLg7blDHGvPnmm6Z27doObQcOHDCSzKpVqzKdLhzdyvvI9YYOHWqCgoLMmjVrHNovXLhgfHx8zHfffefQ3qNHDxMVFZXutG7lcy7NmDFjTGhoqEt9u3fvbiIiIlyeNm5d7dq1zWuvvXbTfufOnTOBgYFm5MiRmfZz9b3nereyvm8W0rds2WKCg4PN66+/7tZ0kb59+/YZLy8vM2/ePLfGu1lId/Wz0tX3Hk999mzevNkhgPPdKOvyZOux9LAcX19f1apVSwkJCfZbmaWmpiohIUG9e/fOcLx69eopISHB4RY2ixcvVr169W65lrx586pcuXK3PH5mRo8erXfeecf+/O+//1ZUVJRmzpypunXrSrp2nn1KSoq9z9dff633339fK1euVMmSJbOlrtwsMDBQmzZtcmgbO3asli5dqtmzZ6tMmTJKTU116tOtWzdVrFhRr776qry9vZ2m6852tHz5crVq1Urvv/++nnjiiZv2v3Dhgry8vBzOJUt7npqaKklq0KCBrl69qt27dys8PFyStHPnTklS6dKlXaorNzPG6Nlnn9XcuXO1fPlylSlT5qbjpK2X66VtG8YYSdfWy423p9m5c2eG6yRPnjwubUdfffWVLl68aH++Zs0ade/eXStWrLCv//QcOnRI99xzj2rVqqX4+Hin+jNbprRtDa5p0qSJ2+8jaYYMGaJ3331XCxcuVO3atR2GXblyRVeuXEl3PWW0jrLyObdhwwYVL178pv3OnTunL7/8UnFxcbc0H7ju3Llz2r17t7p06XLTvrNmzdLly5f12GOPZdrP1fee62vw9PresmWL7r33XsXExHCLLA+Jj49XcHBwlm+9l8bdz0pX33s89dmzYcMGeXl52e80wHcjD8jhHwmQA2bMmGH8/PzM1KlTzdatW80TTzxhChYsaI4cOWLv06VLF4dfitPOURo2bJjZtm2biY2NNT4+PmbTpk32PidOnDDr16838+fPN5LMjBkzzPr1683hw4ezXPOWLVvM+vXrTevWrU3jxo3N+vXrHfbU//bbb6ZChQrm4MGD6Y7vyt59Dnd3z9mzZ+3rQZIZPny4Wb9+vfnrr7/sfV577TXTpUuXDKfhypVzPXVOetoh7v369TOHDx+2P67fazFnzhxToUIF+/Nt27YZPz8/8/TTT5utW7eazZs3m8cee8wEBQWZv//+2xhz7dywO++80zRq1MisW7fO/P7776Zu3brmvvvuy3LNucHTTz9tgoKCzPLlyx3WS9rpBMY4vx/FxsaaAgUKmC+++MLs2bPHLFq0yISHh5uHHnrI3mf16tUmT5485t133zW7du0yn332mQkICHDrcGNXpHe4+43vRwcPHjTlypUzTZo0MQcPHnRYzjQJCQnGZrOZgQMHmp07d5q1a9eaqKgoU7p0aYfXArcmvfeRG7erwYMHG19fXzN79myHdXT27FmH6VSpUsUsW7bM7Nmzx8THxxt/f38zduzYLNU3depU8/nnn5tt27aZbdu2mXfffdd4eXmZKVOm2Pvc+P6UZtKkScbf35/rF2SDF1980Sxfvtzs3bvX/PLLL6Zp06amaNGi5tixY/Y+N25Hae6++26HU2M8JbP1nd53ob/++susX7/eDBw40OTPn9/+uZ22XW/atMkUK1bMPPbYYw7b/fXLCPekpKSY22+/3eGUyuvduM1cvnzZvl6KFy9uXnrpJbN+/Xr7XWSMce2z8la48tlz43a1cuVKM2LECLNhwwaze/duM336dFOsWDH7kapprwHfjbKGkJ5Lffjhh+b22283vr6+pk6dOubXX391GB4ZGWliYmIc2r788ktzxx13GF9fX1OlShUzf/58h+Hx8fFGktMjNjY2y/WWLl063WmnSfuivHfv3nTHJ6R7XtprfuPj+u0mJibGREZGZjiNfzKkx8TEpFvv9fWlbcPXW7RokWnQoIEJCgoyhQoVMvfee6/TIWCHDh0yHTp0MPnz5zchISGma9euLh2yCJPuOpFk4uPj7X1ufD+6cuWKGTBggAkPDzf+/v6mVKlS5plnnnH60vrtt9+aqlWrGj8/P1OxYkWnC7V5Qnoh/cb3o4zeG2/c1r744gtTs2ZNky9fPlOsWDHTpk0bs23bNo/XnBul9z5y43aV0efM9Z9hhw8fNl27djUlSpQw/v7+pkKFCuaDDz7I8nUDpk6daipVqmQCAgJMYGCgqVOnjpk1a5ZDn/Ten4wxpl69euaRRx7J0vyRvk6dOpnixYsbX19fU7JkSdOpUyfz559/OvRJ7/vS9u3bs+2w3szWd3rfhTL67Fu2bJkx5trncHrDS5cu7fHac4uFCxc6nY99vRu3mbTvqJl9P3Hls/JW3eyz58btau3ataZu3bomKCjI+Pv7m0qVKpn33nvPfj56Gr4bZY3NmP9/bCAAAAAAAMhR3CcdAAAAAACLIKQDAAAAAGARhHQAAAAAACyCkA4AAAAAgEUQ0gEAAAAAsAhCOgAAAAAAFkFIBwAAAADAIgjpAAAAAABYBCEdAAAAAACLIKQDAIAMpaSkqH79+urQoYNDe1JSkkqVKqU33ngjhyoDAOC/yWaMMTldBAAAsK6dO3cqIiJCEydO1KOPPipJio6O1saNG7VmzRr5+vrmcIUAAPx3ENIBAMBNjR49WgMGDNCWLVu0evVqdezYUWvWrFGNGjVyujQAAP5TCOkAAOCmjDG699575e3trU2bNunZZ5/Vm2++mdNlAQDwn0NIBwAALtm+fbsqVaqkatWqad26dcqTJ09OlwQAwH8OF44DAAAumTJligICArR3714dPHgwp8sBAOA/iT3pAADgplauXKnIyEgtWrRI77zzjiRpyZIlstlsOVwZAAD/LexJBwAAmbpw4YK6du2qp59+Wvfcc48mT56s1atXa/z48TldGgAA/znsSQcAAJnq06ePvv/+e23cuFEBAQGSpI8//lgvvfSSNm3apLCwsJwtEACA/xBCOgAAyNCPP/6oJk2aaPny5br77rsdhkVFRenq1asc9g4AgAcR0gEAAAAAsAjOSQcAAAAAwCII6QAAAAAAWAQhHQAAAAAAiyCkAwAAAABgEYR0AAAAAAAsgpAOAAAAAIBFENIBAAAAALAIQjoAAAAAABZBSAcAAAAAwCII6QAAAAAAWAQhHQAAAAAAi/h/qDJUizNGPeEAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Проверка гипотезы о нормальности распределения\n",
        "Normality_distribution_test(data, int, absolute_freq, relative_freq)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OQXodKJG3lYV",
        "outputId": "fa6ee062-e527-44c7-9f26-158cef1f6f76"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Проверка гипотезы о нормальности распределения\n",
            "\n",
            "Выборочное среднее для всей выборки: 2.03\n",
            "Несмещенное стандартное отклонение 2.053\n",
            "\n",
            " 0 [0.013, 1.435] ni: 17 - эмпирическая частота на интервале; Ф_left: -0.29 Ф_right: -0.982  Теор частота 7.131\n",
            "\n",
            " 1 [1.435, 2.857] ni: 6 - эмпирическая частота на интервале; Ф_left: 0.403 Ф_right: -0.29  Теор частота 8.66\n",
            "\n",
            " 2 [2.857, 4.28] ni: 3 - эмпирическая частота на интервале; Ф_left: 1.096 Ф_right: 0.403  Теор частота 6.622\n",
            "\n",
            " 3 [4.28, 5.702] ni: 5 - эмпирическая частота на интервале; Ф_left: 1.789 Ф_right: 1.096  Теор частота 3.191\n",
            "\n",
            " 4 [5.702, 7.124] ni: 0 - эмпирическая частота на интервале; Ф_left: 2.481 Ф_right: 1.789  Теор частота 0.968\n",
            "\n",
            " 5 [7.124, 8.556] ni: 1 - эмпирическая частота на интервале; Ф_left: 3.179 Ф_right: 2.481  Теор частота 0.186\n",
            "\n",
            "Критерий согласия Пирсона\n",
            "Кси нашего распределения: 22.01230388180074\n",
            "Кси нормального распределения: 7.814727903251179\n",
            "\n",
            "Вывод о принятии или отвержении нулевой гипотезы\n",
            "Нулевая гипотеза отвергается\n",
            "Распределение отлично от нормального\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Проверка гипотезы о показательном распределении\n",
        "Exponential_distribution_test(data, int, absolute_freq, relative_freq)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fXfwwmK33lgs",
        "outputId": "6eed3454-27ef-4293-8ded-8ea91e4552cd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Проверка гипотезы о показательном распределении\n",
            "\n",
            "Выборочное среднее для всей выборки: 2.03\n",
            "Несмещенное стандартное отклонение 2.053\n",
            "\n",
            " 0 [0.013, 1.435] ni: 17 - эмпирическая частота на интервале;  Теор частота 16.014213145070052\n",
            "\n",
            " 1 [1.435, 2.857] ni: 6 - эмпирическая частота на интервале;  Теор частота 7.948506414651418\n",
            "\n",
            " 2 [2.857, 4.28] ni: 3 - эмпирическая частота на интервале;  Теор частота 3.9470822827863343\n",
            "\n",
            " 3 [4.28, 5.702] ni: 5 - эмпирическая частота на интервале;  Теор частота 1.9571830227336289\n",
            "\n",
            " 4 [5.702, 7.124] ni: 0 - эмпирическая частота на интервале;  Теор частота 0.9714296712501428\n",
            "\n",
            " 5 [7.124, 8.556] ni: 1 - эмпирическая частота на интервале;  Теор частота 0.48449502698444824\n",
            "\n",
            "Критерий согласия Пирсона\n",
            "Кси нашего распределения: 7.016161727929531\n",
            "Кси показательного распределения: 43.77297182574219\n",
            "\n",
            "Вывод о принятии или отвержении нулевой гипотезы\n",
            "Нулевая гипотеза принимается\n",
            "Распределение является показательным\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Анаморфоза для нормального распределения\n",
        "Normal_anamorphosis(data, int, relative_freq)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 611
        },
        "id": "cL1cKyOt3loT",
        "outputId": "240253ed-6343-408a-f864-81121044cef6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Анаморфоза для нормального распределения\n",
            "log(w): [-0.63252256 -1.67397643 -2.36712361 -1.85629799 -3.4657359 ]\n",
            "mae: [1.70479895e+00 1.35304594e-02 2.36642997e+00 8.76941912e+00\n",
            " 3.37598243e+01]\n",
            "\n",
            "Поиск параметров нормального распределения по построенным прямым (sigma или std)\n",
            "sigma (std) = 2.9063425332172645\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x500 with 0 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Анаморфоза для показательного распределения\n",
        "Exponential_anamorphosis(data, int, relative_freq)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 594
        },
        "id": "huk6Epc_3lzG",
        "outputId": "c3a92249-ab1e-4112-84b8-2c8b73f9c64f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Анаморфоза для показательного распределения\n",
            "log(w): [-0.63252256 -1.67397643 -2.36712361 -1.85629799 -3.4657359 ]\n",
            "centres [0.724 2.146 3.568 4.991 7.84 ]\n",
            "\n",
            "Поиск параметров показательного распределения по построенным прямым (lambda)\n",
            "lambda = 2.879326659621032\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x500 with 0 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Файл '6.txt' - показательное распределение"
      ],
      "metadata": {
        "id": "EyMdM4tv3KWF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# показательное распределение\n",
        "data = open_file('6.txt')\n",
        "\n",
        "# Отрисовка гистограммы\n",
        "int, absolute_freq, relative_freq = show_hist(data) # show_hist вычисляет интервалы, частоты и рисует гистограмму"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 672
        },
        "id": "77Ebaxy2nK5C",
        "outputId": "5ee07ac3-3c74-41fa-84d1-742f4149dff5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Длина списка: 128\n",
            "Интервалы: [[0.003, 1.018], [1.018, 2.034], [2.034, 3.05], [3.05, 4.065], [4.065, 5.081], [5.081, 6.096], [6.096, 7.112], [7.112, 8.137]]\n",
            "Абсолютные частоты: [66, 35, 9, 9, 5, 2, 1, 0]\n",
            "Относительные частоты: [0.515625  0.2734375 0.0703125 0.0703125 0.0390625 0.015625  0.0078125\n",
            " 0.       ]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Проверка гипотезы о нормальности распределения\n",
        "Normality_distribution_test(data, int, absolute_freq, relative_freq)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "o4nP_Z1c3mZp",
        "outputId": "1533bae7-7f80-4ee3-b29d-c865b21b33b2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Проверка гипотезы о нормальности распределения\n",
            "\n",
            "Выборочное среднее для всей выборки: 1.461\n",
            "Несмещенное стандартное отклонение 1.485\n",
            "\n",
            " 0 [0.003, 1.018] ni: 66 - эмпирическая частота на интервале; Ф_left: -0.298 Ф_right: -0.982  Теор частота 28.135\n",
            "\n",
            " 1 [1.018, 2.034] ni: 35 - эмпирическая частота на интервале; Ф_left: 0.386 Ф_right: -0.298  Теор частота 34.227\n",
            "\n",
            " 2 [2.034, 3.05] ni: 9 - эмпирическая частота на интервале; Ф_left: 1.07 Ф_right: 0.386  Теор частота 26.552\n",
            "\n",
            " 3 [3.05, 4.065] ni: 9 - эмпирическая частота на интервале; Ф_left: 1.754 Ф_right: 1.07  Теор частота 13.132\n",
            "\n",
            " 4 [4.065, 5.081] ni: 5 - эмпирическая частота на интервале; Ф_left: 2.438 Ф_right: 1.754  Теор частота 4.138\n",
            "\n",
            " 5 [5.081, 6.096] ni: 2 - эмпирическая частота на интервале; Ф_left: 3.121 Ф_right: 2.438  Теор частота 0.83\n",
            "\n",
            " 6 [6.096, 7.112] ni: 1 - эмпирическая частота на интервале; Ф_left: 3.805 Ф_right: 3.121  Теор частота 0.106\n",
            "\n",
            " 7 [7.112, 8.137] ni: 0 - эмпирическая частота на интервале; Ф_left: 4.496 Ф_right: 3.805  Теор частота 0.009\n",
            "\n",
            "Критерий согласия Пирсона\n",
            "Кси нашего распределения: 73.25797085393636\n",
            "Кси нормального распределения: 11.070497693516351\n",
            "\n",
            "Вывод о принятии или отвержении нулевой гипотезы\n",
            "Нулевая гипотеза отвергается\n",
            "Распределение отлично от нормального\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Проверка гипотезы о показательном распределении\n",
        "Exponential_distribution_test(data, int, absolute_freq, relative_freq)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vnKaTDX83mgk",
        "outputId": "ce5dc462-a3a7-448f-f2bd-fefdb21970b3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Проверка гипотезы о показательном распределении\n",
            "\n",
            "Выборочное среднее для всей выборки: 1.461\n",
            "Несмещенное стандартное отклонение 1.485\n",
            "\n",
            " 0 [0.003, 1.018] ni: 66 - эмпирическая частота на интервале;  Теор частота 63.96970754703211\n",
            "\n",
            " 1 [1.018, 2.034] ni: 35 - эмпирическая частота на интервале;  Теор частота 31.95606050822937\n",
            "\n",
            " 2 [2.034, 3.05] ni: 9 - эмпирическая частота на интервале;  Теор частота 15.941850371743397\n",
            "\n",
            " 3 [3.05, 4.065] ni: 9 - эмпирическая частота на интервале;  Теор частота 7.947455500723231\n",
            "\n",
            " 4 [4.065, 5.081] ni: 5 - эмпирическая частота на интервале;  Теор частота 3.970150538531809\n",
            "\n",
            " 5 [5.081, 6.096] ni: 2 - эмпирическая частота на интервале;  Теор частота 1.979230390474636\n",
            "\n",
            " 6 [6.096, 7.112] ni: 1 - эмпирическая частота на интервале;  Теор частота 0.9887243281709891\n",
            "\n",
            " 7 [7.112, 8.137] ni: 0 - эмпирическая частота на интервале;  Теор частота 0.4962581841074174\n",
            "\n",
            "Критерий согласия Пирсона\n",
            "Кси нашего распределения: 4.280344138949815\n",
            "Кси показательного распределения: 153.1979027439562\n",
            "\n",
            "Вывод о принятии или отвержении нулевой гипотезы\n",
            "Нулевая гипотеза принимается\n",
            "Распределение является показательным\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Анаморфоза для нормального распределения\n",
        "Normal_anamorphosis(data, int, relative_freq)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 628
        },
        "id": "h1rLUjGf3moL",
        "outputId": "f9c8006f-5d52-40aa-cdab-fd3c0858c907"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Анаморфоза для нормального распределения\n",
            "log(w): [-0.66237552 -1.2966822  -2.65480569 -2.65480569 -3.24259235 -4.15888308\n",
            " -4.85203026]\n",
            "mae: [9.04914808e-01 4.18995962e-03 1.16797711e+00 4.39627626e+00\n",
            " 9.68286295e+00 1.70298996e+01 2.64476708e+01]\n",
            "\n",
            "Поиск параметров нормального распределения по построенным прямым (sigma или std)\n",
            "sigma (std) = 1.9359077129707294\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x500 with 0 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Анаморфоза для показательного распределения\n",
        "Exponential_anamorphosis(data, int, relative_freq)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 611
        },
        "id": "n4qrm6KQ3mz6",
        "outputId": "5b9084cf-1230-4782-fab5-160fdd8851d2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Анаморфоза для показательного распределения\n",
            "log(w): [-0.66237552 -1.2966822  -2.65480569 -2.65480569 -3.24259235 -4.15888308\n",
            " -4.85203026]\n",
            "centres [0.51  1.526 2.542 3.558 4.573 5.588 6.604]\n",
            "\n",
            "Поиск параметров показательного распределения по построенным прямым (lambda)\n",
            "lambda = 1.506097476979313\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x500 with 0 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    }
  ]
}