{
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# <a href=\"https://miptstats.github.io/courses/ad_fivt.html\">Введение в анализ данных</a>\n",
        "\n",
        "## Время ожидания автобуса"
      ],
      "metadata": {
        "id": "GsCEZqgXFR09"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "from random import choices, shuffle\n",
        "import scipy.stats as sps\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "import seaborn as sns\n",
        "\n",
        "sns.set(style=\"whitegrid\", font_scale=1.3, palette=\"Set2\")"
      ],
      "metadata": {
        "id": "VFtVDr98FxWF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 1. Когда придет мой автобус? Или каково среднее время ожидания автобуса.\n",
        "\n",
        "Вы приходите на автобусную остановку. Согласно расписанию автобус ходит каждые 10 минут. Вы засекаете время, и получается, что автобусы обычно приходят через 9-11 минут.\n",
        "\n",
        "**Почему так не везет?**\n",
        "\n",
        "Казалось бы, если автобус ходит каждые 10 минут, то в среднем его нужно ждать 5 минут.\n",
        "Однако, не все так просто. В действительности автобусы приезжают не точно по расписанию, а случайным образом. Оказывается, справедливо следующее утверждение.\n",
        "\n",
        "**Если автобусы приходят с одинаковой интенсивностью в 10 минут и независимо друг от друга, то среднее время ожидания составляет 10 минут.**\n",
        "\n",
        "Это называют *парадоксом времени ожидания*. Далее мы попробуем экспериментально проверить это утверждение.\n",
        "\n",
        "Для точности вычислений возьмем достаточно большую выборку &mdash; миллион автобусов, которые приходят *с интенсивностью* раз в 10 минут и сгенерируем интервалы прибытия между автобусами."
      ],
      "metadata": {
        "id": "tVQswQzCFW3h"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "count = 1000000  # количество автобусов\n",
        "tau = 10  # средний интервал между автобусами\n",
        "\n",
        "bus_arrival_times = sps.uniform(scale=count * tau).rvs(size=count)\n",
        "bus_arrival_times = np.sort(bus_arrival_times)"
      ],
      "metadata": {
        "id": "1Ga46hKsFqi2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Посмотрим на гистограмму интервалов прибытия автобусов, сравнивая ее с графиком плотность **равномерного распределения**."
      ],
      "metadata": {
        "id": "vO_O25CCFscJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "grid = np.linspace(-10, count * tau + 10, 1010)\n",
        "\n",
        "plt.figure(figsize=(8, 5))\n",
        "plt.hist(bus_arrival_times, bins=50, density=True, label=\"Данные\")\n",
        "plt.plot(\n",
        "    grid,\n",
        "    sps.uniform(scale=count * tau).pdf(grid),\n",
        "    lw=5,\n",
        "    alpha=0.5,\n",
        "    label=\"Плотность $U[0, max]$\",\n",
        "    color=\"#FF3300\",\n",
        ")\n",
        "plt.xlabel(\"Время прибытия автобуса\")\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 499
        },
        "id": "K8QcKLmQFq6I",
        "outputId": "8135b6da-1965-484c-f5f5-28c3b63dd82b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Прежде чем перейти к ответу на наш исходный вопрос, посмотрим, сколько в среднем автобусов приезжает на остановку в течение часа?\n",
        "Поскольку у нас может не получится ровное количество часов, посчитаем для первых 100 000 часов."
      ],
      "metadata": {
        "id": "NiKwc4zoFuxC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "hours_count = 100000  # количество часов\n",
        "\n",
        "# количество автобусов за каждый интервал длиной в 1 час\n",
        "hist = np.histogram(bus_arrival_times, bins=60 * np.arange(hours_count))[0]\n",
        "# количество интервалов, за которые приехало одинаковое количество автобусов\n",
        "x, y = np.unique(hist, return_counts=True)"
      ],
      "metadata": {
        "id": "G41zzV0CGCQP"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Посмотрим на гистограмму распределения количества автобусов за час и сравним его с графиком дискретной плотности **пуассоновского распределения** с параметром 6. Число 6 есть теоретически ожидаемое количество автобусов в час если их интенсивность движения есть в среднем 1 автобус за 10 минут."
      ],
      "metadata": {
        "id": "v9Ow-9w5GEPI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "grid = np.arange(25)\n",
        "\n",
        "plt.figure(figsize=(8, 5))\n",
        "plt.bar(x, y / y.sum(), label=\"Данные\")\n",
        "plt.plot(\n",
        "    grid,\n",
        "    sps.poisson(mu=60 / tau).pmf(grid),\n",
        "    marker=\"o\",\n",
        "    ms=10,\n",
        "    lw=5,\n",
        "    alpha=0.5,\n",
        "    label=\"Дискр.\\nплотность $Pois(60/\\\\tau)$\",\n",
        "    color=\"#FF3300\",\n",
        ")\n",
        "plt.xlabel(\"Количество автобусов в час\")\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 481
        },
        "id": "5v0tTU_KGGAa",
        "outputId": "c71f7858-464a-4bba-baba-78fb3dd53430"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Теперь рассмотрим средние длины интервалов между прибытиями автобусов. Посчитаем также средний интервал. Как видим, он составляет примерно 10 минут."
      ],
      "metadata": {
        "id": "rcCqzu3KGIJB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "intervals = np.diff(bus_arrival_times)\n",
        "intervals.mean()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6Z-7mgfuGIuT",
        "outputId": "a4ff1cdf-7729-4d4d-854f-c0103b89eaaa"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "np.float64(9.999991940498665)"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Посмотрим на гистограмму длин интервалов между прибытиями автобусов и сравним ее с плотностью **экспоненциального распределения**."
      ],
      "metadata": {
        "id": "6cBxkP3HGKlL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "grid = np.linspace(-1, 70, 1000)\n",
        "\n",
        "plt.figure(figsize=(8, 5))\n",
        "plt.hist(intervals, bins=50, density=True, label=\"Данные\", range=(0, 70))\n",
        "plt.plot(\n",
        "    grid,\n",
        "    sps.expon(scale=tau).pdf(grid),\n",
        "    lw=5,\n",
        "    alpha=0.5,\n",
        "    label=\"Плотность $Exp(\\\\tau)$\",\n",
        "    color=\"#FF3300\",\n",
        ")\n",
        "plt.xlabel(\"Время между прибытиями автобусов\")\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 481
        },
        "id": "1RjfdC-cGMKn",
        "outputId": "8612067e-14cd-4925-90fe-1c09e001a4ec"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x500 with 1 Axes>"
            ],
            "image/png": 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MJLnq26lIaxbp4IiIiIiInBuHK7g4/4K7EN+zXSMRERERMcl1eWKNBn6F+XUdBhEREZFTYZLrdGyfPNYoL6cW4iAiIiJyXUxy6wEmuURERESGmOTWA43y7xouPUZERETUwDHJrQdkqgrIiwrqOgwiIiIip8Ekt57wu5tV1yEQEREROQ0muc7GzmEHvnezazgQIiIiItfFJNeF5Pk3NlvmxySXiIiIasDrr7+O3r17o7S01K7zk5KS0Lp1a/zyyy81HJltuOOZC8nzbwT/grsQmejt9VAWA8UFgLdfHURGRES1KSYmxuq6ly9frsVIqCZNmjQJ8fHxFuvMmzcP48ePd1BEwNmzZ7Ft2zbMnTsXnp6edl2jdevWePTRR7Fo0SIMGTIEXl5eNRyldZjkupBKiQQFvv7wL8gzXSHzJtDyAccGRUREDuHl5YUpU6aYLNNoNPj6668dHBFVhyAIuHjxItzd3fHCCy+YrdenTx8HRgX8+9//hp+fH55++ulqXeeFF17AqFGjsGbNGkybNq2GorMNk1wXczcgkEkuEVED5O3tjZdeeslkmVqtZpLrYlJTU1FUVITOnTub/b06WkpKCo4ePYqxY8fCzc2tWtd64IEH0KJFC/z888+YOnUqxGLHj5Blkutsqph4djegMZqnXjFdmHmzFgIiInIyGSnA2SPav3nqyrqOxjyJFAhpBnToBYRF11kYmzZtwltvvWW23NTwhu3bt2Pt2rW4dOkSKisrERkZiWHDhmHy5Mn65Keq65q6j/j4eEyaNAkzZ840mdjNmTMHmzdvxt69exEeHm5zTPc6e/YsVq5ciZMnTyIvLw/+/v5QKBQYNWoUhgwZUivx2+LChQsAgPbt21tVXze04ZtvvkG/fv30x9VqNV555RXs3r0bs2fPxvPPPw8AOHHiBCZMmIDJkyejdevW+Pnnn5GcnAyVSoX27dtjzpw56Nixo8F9bNy4EYIgYMiQIQbHc3Nz0atXrypj9Pf3x7FjxyASiQAAQ4YMwZIlS3D48GE89NBDVj3OmsQk18XctTD5DHezgLJSwMO+MTRERE4vIwXYsda5k1sdtRq4eUUb82MT6jTRBYABAwagTZs2+v9v3rwZGRkZRvW++OILfPPNN2jUqBGGDx8OT09PHDhwAJ9//jkOHjyIlStXQiaToU2bNpg5c6bBuUuXLkVYWBhGjBhRo7FbG5PO+vXr8d5770EikaB///6IjIxEbm4uzp07h59++glDhgwxiF+j0aCyshLLly+vlfhNOX/+PABtj6c1Zs+ejdGjR+PLL79E37599Ynke++9h927d+OFF17QJ7jAX0n0/v378cMPP+Dhhx9Gly5dcO7cOSQkJOBvf/sbdu/ejUaNGunPOXLkCKRSKTp06GBw36WlpQa/6xs3bmDr1q3o3LkzevbsicrKSkilUoSHh+vjAoAuXbror8skl6pU4e6BErk3vJTFxoWCANxJAyIVjg+MiMgRzh5xjQT3XupK4NzROkty1Wo1AOCRRx4xSN4SEhKMktxTp07hm2++QVhYGH755Rc0bqztWPnHP/6BmTNn4o8//sB3332HqVOnok2bNgZJM/BXkluTX7/bEhMAXL16FfPnz4ePjw9+/PFHtGjRwuB6t2/fBgCD+NVqNcrKyvRJriOGD+iS3BMnTuDmTeNvYkUiEaZPnw6JRAJAmwwPGTIE//vf/7B9+3Y8/vjj+Pe//43169dj9OjReP311w3Ov3jxIgBtL+zatWvRqVMnfdkbb7yBLVu24JdffsGLL74IAFAqlUhKSkKLFi3g4eFhcK3w8HCDNvnhhx+wdetWPPHEE3j66adRVlYGDw8Pfaw6ugT++PHj9jRRtTHJdSV/fjrKbRRoOskFtF/fMcklovoq53ZdR2Cf7Ft1dtcqlQoArBpjuXHjRgDA9OnT9ckkAEilUrz55pvYv38/Nm7cqE8oHcHWmH766SdUVlbi73//u1GCCwBNmzZ1TOAW6CadAdpeZ1OaNWtm1FP+6quvYvfu3Vi8eDGysrKwbNkyDBo0CO+//77R+brrz5s3zyDBBYDRo0djy5YtuHbtmv7YnTt3oFarERQUVGX8ly5dAgCjDzn38/Hxgbu7u/6DhaMxyXVBdwMao1l6qunC22aOExHVB4FNtUMAXE1QaJ3ddXGxtlPE3d29yrq6xCguLs6oLDo6GiEhIUhNTUVJSUm1loVKSEjAkiVLjI7rkqfqxHT69GkAQO/eve2Oryr3xu/p6YkmTZogLi4OwcHBVp1//fp1lJSUoGfPnvj++++tvt+IiAiMHz8eq1atwieffIJevXrhX//6l9GkrvLycly/fh3h4eF4/PHHja7j7+8PAKis/Otbkfz8fACAr69vlXEkJSVBLBZDoai6U83Pzw+5ublV1qsNTHKdjRU7nt0NsPApK/sWoKoAZNWbFUlE5JQ69NKOcXWlIQsSKfBAzzq7e13yEhAQUGXdoqIiAEBgYKDJ8qCgINy6dQvFxcXVTnITEhKsqmtrTLr6TZo0sTu+qpiKXyaT4dlnn8Xs2bOrPF83VKF169Y237duDK1IJMLHH39ssoc+KSkJlZWV6N27t8EYWR3dMJV7e7V1QxTKy8st3r9arcaVK1fQrFkzyOVy/XAYc8rLy636gFUbuOOZC1J6ylHqITddqNFwlQUiqr/CorWTuCIVgJs7IJE4783NXRtnHU86S0tLAwCj1QpM8fHxAQDk5OSYLM/O1u6u6e3tXa2YZs6cicuXLxvdTE34sjUmXf07d+5UK0ZL7o3/1KlT+O6779CoUSN8++23OHr0aJXn65JcWzb5ALQrWnz55ZcIDAyEIAhYs2aNyXq6SWdhYWEmy/ft2wfAsLdblzwXFBRYjCElJQXl5eVWJegajQaFhYUGw0wciT25rkgkQm6jQITfMpPM3koFIlo6NCQiIocJi67zlQpcyZkzZxAUFGRVz2abNm1w4cIFxMfHo1mzZgZlN27cQGZmJiIjIx26g5WtMXXq1Annz5/HoUOHTI7JrWlyuRy9evXClClT8Mknn+DcuXPo2dNyz70uCbWlJ3fv3r2YO3cuWrdujZUrV2L06NH44Ycf8MwzzyA01HA4jG7Yh26oyr3u3LmDLVu2oGXLlgZDQIKDg9GoUSOkpKRYjOP69esAgFatWlUZc0pKCgRBqHLsbm1hT64LEfDXVw65jSwMWeC4XCIiAvDLL7/gzp07ePTRR62q/9RTTwEA/vOf/+Du3bv642q1Gp988gk0Go2+jqPYGtO4ceMglUrx9ddfG0ys0snMzKyVOFNTUwFUPaZVo9Hg0qVLkEqlVifhCQkJePXVVxEeHo5vv/0WjRo1wksvvYTy8nJ8+eWXRvV1SfTOnTtRVlamP15YWIhXX30V5eXlmDt3rsFYXpFIhO7du+Pu3bv63n9TdImzNR90dOOjTY2ndgT25Looi0kux+USETVoV69exccff4zDhw8jNDQUM2bMsOq8Ll264Pnnn8e3336LoUOHYvDgwfDw8MCBAwdw5coVdOvWzezWwrXF1phatmyJefPmYd68eXjyySfRv39/REVFIS8vD+fOnYO3t7fZr/mtdf78efz0008AtGvInj59Grt27UKjRo2q/EBx7do1KJVK+Pr64ptvvjFb75lnnkFAQAAuXryI6dOnw9fXFytWrNCPTR4+fDi+/fZbbN26FVOmTNH3lqpUKly5cgVt27ZFfn4+nnjiCfTr1w+lpaXYs2cP7t69i3/+858me5sfeeQR7Nq1C4cPH8bYsWNNxhUVFQUAWLFiBbKystChQwezWw8fPnwYEokEAwYMsNgmtYVJrrOxYuIZ8Ne4XM8ypXGhRqNdLze89r+mISIi53Pz5k1kZGTgueeew/PPP2/TmMjZs2ejTZs2WLt2LTZv3qzfXeyVV17Bc889V+3tXu1ha0xPP/00WrVqhZUrVyIhIQF79+6Fv78/YmJiMHr06GrHs2/fPv24Vg8PDwQHB2PcuHH429/+ZrC5gim6XtbCwkIsXbrUZB0PDw9MmzYNN27cwPPPPw+xWIwVK1YgIiJCX0csFuOVV17BjBkzsHDhQqxcuRKA9gNORUUFOnfujGeeeQYfffQR1q9fD7FYjE6dOuHFF19EbGysyfsdNGgQPv74Y/z6669mk9wuXbpgxowZ+Pnnn/Hdd99h2rRpJpPc4uJi7N27F/369auzZdtEgmBlVlXPnTt3DoD1O4/UmtOHgfjfAACqShVkUhkO3L6CAlUZLrdsiyst/xrX0unscf24XD+ZB/o0vWd8TOeHgNi6+eTkDJRKJS5duoQ2bdpALjczSY9MYttVD9vPfve2nVgsRkpKCqKjo40Wpidjus0MTC3IT1WrT+23YcMGvPPOO/jwww/tSui//vprLFq0CP/973+tmhhnru3Wrl2L999/H2vXrkW3bt2suu+ysrIqX/e25Gsck+vCcgNML6cCQDv5jIiIiBoU3brC9ixPBgBTpkxBSEgIFi9ebHcM5eXl+OabbzBo0CCrE9zawOEKruS+pe4sj8vN4LhcIiKiBubixYuQSqVWbdRgiqenJz777DMcP34cpaWl8PT0tPkaGRkZGD16tMkl4RyJSa4LU8q9UObhCY+yUuNCjsslIiJqUDQaDS5fvozo6OhqbcAQFxdXrRURmjdvjpdeesnu82sKk1ynY8MQaZEIuY2CEGZpvVwmuURERA2CWCzGqVOn6joMp8ExuS4uh+NyiYiIiIwwyXVxVo3LJSIiImpgmOS6EOH+mWf4a1yuSbpxuUREREQNDJNcV/fnuFyzOGSBiIiIGiAmuc7Gjr05OC6XiOor7ldE1HDU9OudSW49wHG5RFTf6HZOUqlUdRwJETmK7vVeU7vOMcmtB5RyL5R7mtk+VKMBbt9wbEBERNUkk8ng7u6OgoIC9uYSNQCCIKCgoADu7u6QyWQ1ck2uk+tCBJHxxDMAgEiE/MZNALWZE2+lAM1a1VpcRES1ITAwEBkZGUhPT4efnx9kMhlE5v4ONnBqtRrl5eUAaq4XrCFh+9mvum0nCAJUKhUKCgpQXFyMsLCwGouNSW49kR8UAmTmmC5Mv+7YYIiIaoCvry8AICcnBxkZGXUcjXPTaDSorKyEVCqFWMwvaW3F9rNfTbWdu7s7wsLC9K/7msAk19nY+bVcXmBT80lubiZQWgJ4elUjMCIix/P19YWvry9UKhXUanNfV1FpaSmuX7+OZs2awdPTzLKSZBbbz3410XYSiaTGhijci0luPVHhKQf8A4F8M4luRgrQsr1jgyIiqiEymaxW3gTrC41GA0DbG+bh4VHH0bgetp/9nLnt2Cdfn4Q1N1+WwSELRERE1HAwya1PwqLNlzHJJSIiogaESW590jQKMDfzuCgfKMxzZDREREREdcauMblJSUlYunQpjh8/jtLSUkRERGDEiBGYPHkypFLrL3n8+HEsW7YMZ8+ehUqlQvPmzTFu3DiMHj3aZP3c3FysWLECBw4cQEZGBtzc3BAWFoYhQ4Zg7Nix8Pb2tufhOJfqrAfp4QkEhQJZZmYhZ1wHfLvaf30iIiIiF2FzT25iYiLGjBmDffv2oU+fPpg4cSJEIhEWLlyIWbNmWb1o965duzBp0iScOnUKgwcPxtixY5Gfn4+5c+fio48+Mqqfnp6OYcOGYcWKFQgICMDYsWMxdOhQKJVKLFy4EOPHj0dZWZmtD6f+4bhcIiIiItt6ctVqNd5++22UlZVh+fLl6Nu3LwBg1qxZmDJlCvbs2YOtW7di+PDhFq9TVFSEd999F1KpFGvXrkWbNm0AADNmzMCoUaOwevVqDB48GF27/tXruGLFCuTm5uKll17CzJkz9cc1Gg2ef/55HD58GDt37sSTTz5py0OqN3xlHtAIAsRhzYFTB01XykjR9hSLRNq6XFSdiIiI6imbenLj4+ORkpKCuLg4fYILAG5ubpg1axYAYN26dVVeZ+fOncjPz8fQoUP1CS4A+Pj4YNq0aSavk5aWBgDo37+/4QMQi9GvXz8AwN27d215OC7H7I5nAORSN4hFIqzMT8O+rOs4cPuK8S3lDL7840esSDrCBJeIiIjqNZuTXADo3bu3UVnXrl0hl8tx+vRpVFRUWLzOsWPHzF5HdywhIcHgeKtW2m1p9+3bZ3Bco9Fg//79EIvF6NGjh3UPpB67VV6CFC8vFKjKTN4q0pJxW1lQ12ESERER1SqbhiukpKQAACIjI43KJBIJwsPDkZycjLS0NLRo0cLsdVJTU81eJygoCHK5HJmZmSgtLdXvnvH888/jjz/+wKJFixAfH4+2bdtCpVLhyJEjyMrKwocffoi2bdva8nCMCIIApVJZrWtUl7isFBKVCoB2eIhMavvi59mNgxGYm2WyLPBuNm79+e/S0lKrx1C7mtLSUoOfZD22XfWw/ezHtrMf26562H72c3TbCYIAkZXfRtuU5BYXFwPQDiswxctLu21sYWFhta7j7e0NpVKJoqIifZLbuHFj/Pzzz3jrrbewd+9efW+wSCTCqFGj0KtXL1seikkqlQqXLl2q9nWqw/vGDfjkZAMAZFKZXbuH5DQONlvWKC8Ht//cGjMlJaXev6B1H6jIdmy76mH72Y9tZz+2XfWw/eznyLZzc3Ozqp7LbOubnp6O6dOno7y8HMuXL0fXrl1RWlqKvXv34tNPP8Uff/yBdevWISIiwu77kMlkaNmyZQ1GbTtxWTYkd28CgN37tBf6+EElc4NMZTxsRKJWwzdPu/VvdHR0ve7JTU1NRVRUFPchtxHbrnrYfvZj29mPbVc9bD/7Obrtrl69anVdm5Jc3Tq0RUVFJstLSkoAAL6+vtW6jqme3rfeegvJycnYsmULWrdurb/O2LFjUV5ejo8//hhfffUVPvnkExsekSGRSAS5XG73+TXC3QPQ7c9u59wwQSxGTqMgNL1jer3cgJzbANAgXsienp51/zt1UWy76mH72Y9tZz+2XfWw/eznqLazdqgCYOPEs+ho7baxN27cMCpTq9VIT0+HRCKpsjc1KirK7HWys7OhVCoREhKiT8KKi4uRkJAAf39/fYJ7r7i4OADAhQsXbHk4rseGX2xO4yCzZQFZt2siGiIiIiKnZVOSq0smDx06ZFR28uRJKJVKdOrUqcqxErpVEExdR3csNjZWf0z150Ss4uJikys35OVpt6uVyWyfpOV8amb4QHbjJmbLvAvuAqUlNXI/RERERM7I5iQ3KioK8fHx2L9/v/54RUUFFi1aBAAYO3as/nhpaSmuXbuGW7duGVxn8ODB8Pf3x7Zt2wwmehUVFWHZsmUAgDFjxuiPBwQEoEWLFqisrMTXX39tcK3y8nL9MS4h9hellzeUnl4my0SCwN3PiIiIqF6zaUyuRCLBggULMHnyZMyYMQNDhgxBUFAQ9u/fjytXrmDgwIEYNmyYvv7Zs2cxadIkxMbGYs2aNfrjPj4+mD9/Pl599VVMmDABjz/+OLy8vLB7925kZGRg4sSJ6Natm8F9z507F1OnTsV//vMfHDlyBJ07d0ZZWRkOHjyIjIwMREZG4oUXXqhmc9QvWYFNEJVmJplNuwq0fMCxARERERE5iM2rK3Tp0gXr16/HkiVLsH//fiiVSkRERGD27NmYPHmy1QOCBw8ejEaNGmHZsmXYvn07Kisr0aJFC0yfPh2jRo0yqt+rVy9s2LABK1aswPHjx7F27VqIxWJERETgxRdfxPPPP1/lhLeGJjuoiiT3zy1+iYiIiOobu5YQa926Nb766qsq68XFxeHy5ctmy2NjYw3G3lpzvwsXLrS6fn1j62jdnEbB0IjFEGs0xoWlJUBuJhDYtEZiIyIiInImNo3JJdeilkpx17+x+Qpp1q81R0RERORKmOQ6mxrenCE70PwqC0i/VqP3RUREROQsmOTWcxaT3MybQEW544IhIiIichAmufVcoY8fyt09TBdqNMCtFMcGREREROQATHJdiGDPSggiEbIs9eZyXC4RERHVQ0xyGwCLQxZ0S4kRERER1SNMcp1NLSSc2Y2DzRcW5QMFd2v8PomIiIjqEpPcBkDl5o58vwDzFdI5ZIGIiIjqFya5DQTH5RIREVFDwiTXpdi/BW92YIj5wlupQGWl3dcmIiIicjZMchuIfL8AqKQy04WVKuB2qkPjISIiIqpNTHKdTS2tdCCIxZZXWbiRXCv3S0RERFQXmOQ2IFlBFpLcm8lcSoyIiIjqDSa5DUiWpXG5RflAXrbDYiEiIiKqTUxyXYhg/7wzAECFuwfy/BuZr3CTQxaIiIiofmCS28BkBVnozeW4XCIiIqonmOQ6m1oeF3snqKmFwjSgTFmr909ERETkCExyG5hCHz+Ue8pNFwoCkH7NsQERERER1QImuQ2NSITcJmHmyzlkgYiIiOoBJrkupZozz/50N9hCkpt2BdBoauR+iIiIiOoKk9wGKD8wBJBITReWl2nH5hIRERG5MCa5Tqf2N2TQSKVAWLT5ChyyQERERC6OSW5D1Uxhvozr5RIREZGLY5LbUEVaSHLzsoHCPMfFQkRERFTDmOS6EEFUMxPPAADefkDjJubL2ZtLRERELoxJbkNmachC6mXHxUFERERUw5jkOpta3vEMAHxlHtAIguUk93YqUFYKANq6RERERC7EzDpSVJ/JpW4Qi0RYcfcGIu+mwa28zGS9pN3fQaLohL+17uXgCImIiIiqh0luA3a7rAhl/v5olp5qukJqEm6HW1hqjIiIiMhJcbiCC6mNQQOZwaFmy4Jz7kBcWVkL90pERERUu5jkNnA5jYOhNrP7mURdCf+cTAdHRERERFR9THKdjYMneWkkEmQFml9KLDCTW/wSERGR62GSS7jdxPyQhcaZ6YBG48BoiIiIiKqPSS4hKyjE7EYTsopy4A57c4mIiMi1MMl1JTW549k9KmVuyGkUZL5CalKt3C8RERFRbWGSSwCATAtDFpCa5PCxwkRERETVwSTX2dRRMnknuKn5wsI84G6W44IhIiIiqiYmuQQAKPOQI98vwHwFDlkgIiIiF8Ikl/QsbQzBJJeIiIhcCZNcFyLUzrwzPYvjcnNua4ctEBEREbkAJrmkV+zlgxK5t/kK1y86LhgiIiKiamCS62zqchUDkQi3m4SZL79+wXGxEBEREVUDk1wycDvEQpKbfQsoyndYLERERET2YpJLBgp8/aH09DJfgUMWiIiIyAUwyXUptTzzDNAOWbDUm8shC0REROQCmOSSkVsh4eYLszI4ZIGIiIicHpNcp1P32+cW+Pqj1FNuvgKHLBAREZGTY5JLxkQiy725KUxyiYiIyLkxySWTLC4ldicdKC5wXDBERERENmKS60IEkQMmnv0p3y+AQxaIiIjIZTHJJdNEItzixhBERETkopjkOpu63PHsPhaXEuOQBSIiInJiTHLJrHy/RijjxhBERETkgpjkknkiEXJCm5kvv3becbEQERER2YBJrgupi4EM2aGR5guzMoCCu44LhoiIiMhKTHLJoiL/xoCPv/kKV885LBYiIiIiazHJdTZONPEMACASAS0fMF9+9ZzzxUxEREQNHpNcqpqlJDc/B8i947hYiIiIiKzAJJeq1igYaNzEfDmHLBAREZGTkdpzUlJSEpYuXYrjx4+jtLQUERERGDFiBCZPngyp1PpLHj9+HMuWLcPZs2ehUqnQvHlzjBs3DqNHjzZ7TkFBAb799lvs3bsXGRkZkMlkCAsLQ+/evTF79mx7Ho7rcOCOZ0ZaPmC+x/bqOSBuYN3GR0RERHQPm3tyExMTMWbMGOzbtw99+vTBxIkTIRKJsHDhQsyaNQuCleMzd+3ahUmTJuHUqVMYPHgwxo4di/z8fMydOxcfffSRyXOuXr2KoUOHYsWKFQgPD8fEiRMxYsQING3aFDt27LD1oZAVfGUe0AgC0KK9+UolhUDmTQDQ1iUiIiKqYzb15KrVarz99tsoKyvD8uXL0bdvXwDArFmzMGXKFOzZswdbt27F8OHDLV6nqKgI7777LqRSKdauXYs2bdoAAGbMmIFRo0Zh9erVGDx4MLp27ao/R6lUYvr06aioqMDPP/+MBx4wHCeqUqlseSjOy8mSRLnUDWKRCCsyLiK4vAB+d7NM1ru16zuU9HgUf2vdy8EREhERERmzqSc3Pj4eKSkpiIuL0ye4AODm5oZZs2YBANatW1fldXbu3In8/HwMHTpUn+ACgI+PD6ZNm2byOuvWrcPNmzfx2muvGSW4ACCTyWx5KGSj28oCnG/UCAWqMpM3t9TLyCzmmrlERETkHGzqyY2PjwcA9O7d26isa9eukMvlOH36NCoqKuDm5mb2OseOHTN7Hd2xhIQEg+Pbtm2DSCTCY489hrS0NBw4cAAlJSUIDw9Hnz594O3tbctDITvcCglDu6QzEJnobZapKhCQfbsOoiIiIiIyZlOSm5KSAgCIjDTeBUsikSA8PBzJyclIS0tDixYtzF4nNTXV7HWCgoIgl8uRmZmJ0tJSeHp6QqVS4fLly2jUqBHWrVuHL7/8Emq1Wn+On58fPvnkE/Tv39+Wh2NEEAQolcpqXaO6JOXlEP859EKtVkMm/auHWkDdTuyqcPdATuNgBOWYnoAWnJ4KACgtLbV6bHZtKS0tNfhJ1mPbVQ/bz35sO/ux7aqH7Wc/R7edIAgQWTnR3aYkt7i4GIB2WIEpXl5eAIDCwsJqXcfb2xtKpRJFRUXw9PREQUEBKisrkZ+fjy+//BKzZs3CiBEjIBaLsXXrVnz++eeYNWsWNm/ejJYtW9rykAyoVCpcunTJ7vNrgl96GuQ52QAAmVQGDw+POo3nfhlNI8wmuY0z0wFVBVJSUpzmD4XuAxXZjm1XPWw/+7Ht7Me2qx62n/0c2XaWRgvcy64lxBxNo9EA0PZsjhs3Di+++KK+bMqUKcjKysLKlSuxatUqfPDBB3bfj0wmq1aSXBMkudchVuYCgEFvtbPIbBIKzQUxxH/+Tu4lUVcC1y8iOjrGKXpyU1NTERUVBU9PzzqNxdWw7aqH7Wc/tp392HbVw/azn6Pb7urVq1bXtSnJ1Y17LSoqMlleUlICAPD19a3Wde7v6b23x3fAgAFG9QcMGICVK1fi3LnqbUogEokgl8urdY1qc3MDdJPonHDZ2UqpDJnBoQjNTDdd4coZeMZ0cmhMlnh6etb979RFse2qh+1nP7ad/dh21cP2s5+j2s7aoQqAjasrREdHAwBu3LhhVKZWq5Geng6JRIKIiAiL14mKijJ7nezsbCiVSoSEhOg/EXh6eqJp06YATCfQfn5+AICysjLrHwzZLT20mfnCjBSgKN9hsRARERGZYlOSGxcXBwA4dOiQUdnJkyehVCrRqVOnKsdK9OjRw+x1dMdiY2MNjvfs2RMAcOXKFaNzdMfCwsKqegiuzUl6drMDm6DCzd18hStnHRcMERERkQk2J7lRUVGIj4/H/v379ccrKiqwaNEiAMDYsWP1x0tLS3Ht2jXcunXL4DqDBw+Gv78/tm3bZjDRq6ioCMuWLQMAjBkzxuCccePGQSQS4f/+7/+Ql5dn8pzHH3/clodDdhLEYsu9ucmnnW5TCyIiImpYbBqTK5FIsGDBAkyePBkzZszAkCFDEBQUhP379+PKlSsYOHAghg0bpq9/9uxZTJo0CbGxsVizZo3+uI+PD+bPn49XX30VEyZMwOOPPw4vLy/s3r0bGRkZmDhxIrp162Zw3x06dMC0adPwn//8B8OHD8fDDz8MsViMffv24fbt2xg4cCCefPLJ6rUGWS09tBmapxr3qgMACu4Cd9KAEAuJMBEREVEtsnl1hS5dumD9+vVYsmQJ9u/fD6VSiYiICMyePRuTJ0+2ekDw4MGD0ahRIyxbtgzbt29HZWUlWrRogenTp2PUqFEmz3nllVegUCiwevVqbN26FWq1GtHR0ZgyZQqeeeYZiMU2dUw7KdfoAS309Uehjx98iwpMV0g+wySXiIiI6oxdS4i1bt0aX331VZX14uLicPnyZbPlsbGxRmNvqzJkyBAMGTLEpnOodqSHRaJtkpnxt9fOA70GA1Jut0xERESOVx+6PhsMwYZlMxwhvWmE+ZgqyoFU8x9wiIiIiGoTk1yyW4W7B7KCQsxXSD7tsFiIiIiI7sUkl6rF4ioL6deAEtMbfhARERHVJia5zsbFlt66E9QUKpmZdZEFAbhyxrEBEREREYFJLlWTRiJBRki4+QpJp1wucSciIiLXxyTXhQjOsuXZfdLDIs0XFuQCt423byYiIiKqTUxyqdry/QJQ5O1rvkJSouOCISIiIgKTXKoJIhFuhkeZL79+ESgrdVg4RERERExynY2Ljl9ND20Gjbkd59SVwFUzm0YQERER1QImuVQjVG7uyGlqYTmxpESXTeCJiIjI9TDJdSXOOe9MLzOypfnC3DtA9i3HBUNEREQNGpNcqjH5jZsAfo3MV7h00nHBEBERUYPGJJdqjkgEtO5ivvzaeaCi3HHxEBERUYPFJNfZuPq4VUUnwNwENFUFcP2CQ8MhIiKiholJLtUsuTfQTGG+nEMWiIiIyAGY5LoQZ93xzEibrubLsjKAnNuOi4WIiIgaJCa5VGN8ZR7QCAIQ3gLwsrAD2oXjAKCtS0RERFQLpHUdANUfcqkbxCIRVlw+CjeJCpG3r5isp85KwY0AH0zu8LCDIyQiIqKGgkmus6kHvZu3lQXIDmwMvwvlEJl6PCpASD4NMMklIiKiWsLhClQryjzkyAwONVveNDW5XiT0RERE5JyY5FKtSW3W3GyZvLgQyLjuwGiIiIioIWGS60pELrK6wp9yGwWh2NvHfIU/J6ARERER1TQmuVR7RCKkRrQwX37jMlBc4Lh4iIiIqMFgkut06tc41fSwZlBLzMxvFATg4gnHBkREREQNApNcqlWVUhnSQpuZr5CUCFRWOi4gIiIiahCY5FKtu2FhAhpKS4CUi44LhoiIiBoEJrkuxFUHMhT5+OFuQKD5CufjuZwYERER1SgmueQQKc0sTEDLygDupDkuGCIiIqr3mOQ6m3rao5nZJBRl7h7mK5w75rhgiIiIqN5jkksOIYjFlsfmplwCivIdFg8RERHVb0xyyWFSI5pDIzbzlBME7dhcIiIiohrAJNeFCC6249n9VG7uSA+NNF8hKRGoKHdcQERERFRvMcklh7oe1dJ8YUU5cPmU44IhIiKieotJrrOppxPPdIq9fZEd2MR8hfPxgEbjuICIiIioXmKSSw53PdJCb25hHnDjsuOCISIionqJSS45XHZgE5T4+JmvwOXEiIiIqJqY5LoU1554picSIaN5a/Plt29oN4ggIiIishOTXKoTWWHRgIfcfIXThxwXDBEREdU7THKdTT2feKajkUqBtt3MV0hNAvJzHBcQERER1StMcqnutIsFJFLTZYIAnDni2HiIiIio3mCSS3VH7g207my+/MoZoLjQcfEQERFRvcEk14UI9WTeGQD4yjygEQSgQy/A3E5uajVwXrvSgqaBDOMgIiKimmHmu2Ki2iWXukEsEmHFrUvwE1Ug+FaqyXrq7FTckMswucPDjg2QiIiIXBp7cp1NA+uxvK0sQHzTUBSoykzeikuLILp0oq7DJCIiIhfDJJfqXKFfALIbB5stD7ueBFSqHBgRERERuTomueQUrjaPMVvmVl4GXD7tuGCIiIjI5THJdSXmJmjVA7mNgpDvF2C+wulD2oloRERERFZgkkvOQSTCtWiF+fLiAiD5tMPCISIiItfGJNfpNKyJZ/fKDA5FidzbfIVTB9mbS0RERFZhkktOQxCLcaVFa/MVivKBK2cdFg8RERG5Lia55FQymkagRO5lvsKpA4BG47iAiIiIyCUxyXUhDWEgQ5W9uYV57M0lIiKiKjHJJaeT0bQZlJ6WenMPsjeXiIiILGKSS05HEItxxcK6uSjIBa6dd1xARERE5HKY5DqbBratrznpYZEo9ZSbr5DIsblERERkHpNcckqCWIyr0RZ6c/NzODaXiIiIzGKS60KEerzjmSlpYZEo9bDQm3tyH1BZ6bB4iIiIyHUwySWnpZFIcLW5hV3QivKBpESHxUNERESug0kuObWb4dEos7gL2gGgUuW4gIiIiMgl2JXkJiUlYebMmYiLi0OHDh3w+OOP49tvv0WljV8dHz9+HH/729/QvXt3dOrUCSNHjsQvv/xi1bkVFRUYOnQoYmJi0KdPH3sehnPixDMDgliMGzEPmK+gLAbOJzguICIiInIJNie5iYmJGDNmDPbt24c+ffpg4sSJEIlEWLhwIWbNmgXByiRt165dmDRpEk6dOoXBgwdj7NixyM/Px9y5c/HRRx9Vef4XX3yBjIwMW8MnF3QnLBrwDzRf4fQhoLzMcQERERGR07MpyVWr1Xj77bdRVlaGr776CgsXLsTs2bOxadMmdOvWDXv27MHWrVurvE5RURHeffddSKVSrF27Fh988AHmzJmDLVu2ICoqCqtXr8bJkyfNnh8fH4/vv/8eb7zxhi3h1wMNa+KZnlgMdO9vvry8FDh31HHxEBERkdOzKcmNj49HSkoK4uLi0LdvX/1xNzc3zJo1CwCwbt26Kq+zc+dO5OfnY+jQoWjTpo3+uI+PD6ZNm2bxOsXFxXjrrbfQs2dPjBs3zpbwyZVFtwECm5ovP3sUKC1xXDxERETk1GxOcgGgd+/eRmVdu3aFXC7H6dOnUVFRYfE6x44dM3sd3bGEBNPjLD/88EMUFBRYNaSB6hGRyHJvrqpCu0EEEREREQCpLZVTUlIAAJGRkUZlEokE4eHhSE5ORlpaGlq0aGH2OqmpqWavExQUBLlcjszMTJSWlsLT01Nf9ttvv2Hz5s348MMPERoaakvoVhEEAUqlssavawtJeRnEKu1qAWq1GjKprE7jcQa+Mg9oBAHiiJZASDMg86bpihePA+1iofELQHl5OQCgtLTUgZHWD7o2Y9vZh+1nP7ad/dh21cP2s5+j204QBIis3DfApiS3uLgYgHZYgSleXl4AgMLCwmpdx9vbG0qlEkVFRfokNycnB//85z/Rp08fjB492pawraZSqXDp0qVauba1Gt26BfecbACATCqDh4dHncbjDORSN4hFIqxIOgKlXIyOt6+YrVv569foP/mfuH37NoC/PlCR7dh21cP2sx/bzn5su+ph+9nPkW3n5uZmVT2bkty6NHfuXKjVanz44Ye1dh8ymQwtW7astetbQ3LzFMSCthdSrVYblAkNdN6Zzm1lAdI83CD390dwdqbJOn6pSUBmGkJDQ3Ht2jVERUUZfBtAVSstLUVqairbzk5sP/ux7ezHtqsetp/9HN12V69etbquTUmut7d2Uf6ioiKT5SUl2ok/vr6+1brO/T29v/76K/744w98+umnaNKkiS0h20QkEkEut7CNrCO4uQOyP4coNPCk1pxLivZmk1wAwLHdcH9UOynR09Oz7n+nLoptVz1sP/ux7ezHtqsetp/9HNV21g5VAGyceBYdHQ0AuHHjhlGZWq1Geno6JBIJIiIiLF4nKirK7HWys7OhVCoREhKi/0Rw4cIFAMCbb76JmJgYgxsA3Llzx+D/VL8V+fjhZniU+Qp30iBKTXJYPEREROR8bOrJjYuLw7Jly3Do0CFMnTrVoOzkyZNQKpXo2rVrlWMlevTogW3btuHQoUN4/PHHDcoOHToEAIiNjdUf69y5s9kJYRs2bICnp6fRdVwWdzyzyuWWbRF2Ow2S+4Z06IhP/gG0fsjBUREREZGzsDnJjYqKQnx8PPbv369fK7eiogKLFi0CAIwdO1Zfv7S0FLdu3YKnp6fBagiDBw/Gv/71L2zbtg2TJk3Sr5VbVFSEZcuWAQDGjBmjrz9kyBAMGTLEZEwbNmyAr68vlxRrYMo9PHEtqhUU10z32IoK78Ir9SLQrr2DIyMiIiJnYFOSK5FIsGDBAkyePBkzZszAkCFDEBQUhP379+PKlSsYOHAghg0bpq9/9uxZTJo0CbGxsVizZo3+uI+PD+bPn49XX30VEyZMwOOPPw4vLy/s3r0bGRkZmDhxIrp161Zzj7Le4CDde12LjkFkeirczWzp6518EhgwFOD4KiIiogbH5tUVunTpgvXr12PJkiXYv38/lEolIiIiMHv2bEyePNnqAcGDBw9Go0aNsGzZMmzfvh2VlZVo0aIFpk+fjlGjRtn8QKjhUUuluNyyLTpcSDRZLlaVQ3zqADBgpIMjIyIiorpm1xJirVu3xldffVVlvbi4OFy+fNlseWxsrMHYW3tYuj7Vf2lhkWh+4wq8i02v1CFOSgQ69gICQxwcGREREdUlm1ZXIEfgxDNbCGIxLsZ0MFsuEgTgyA5O6CMiImpgmOSSy8sKbIKsIAs9tbdvANcvOi4gIiIiqnNMcl2IYMMCyA2KSIQLMR2gEVt4Oh/bDagqHBcTERER1SkmuVQvlHj7ICXSwpbMxQXAmcOOC4iIiIjqFJNcqjeutGiNCg8L+2afPgwU5jkuICIiIqozTHKdDSdI2a1SKkNKm07mK6grOQmNiIiogWCSS/XKnfDmQJNw8xVuJAMplxwXEBEREdUJJrkuhP2PVhCJgF6PQQQRZFKZ6U3ijuwAKsodHhoRERE5DpNcqld8ZR7QBIVC2j4WQUFB2kT3fiVFwIk/AAAaDl0gIiKql+za8YzIWcmlbhCLRPje3xsRuTchM9NjK2ReRYaoAuN7DndwhEREROQI7Ml1NuxZrBHp6nIcad4SBaoyk7fCilL4xe8BNJq6DpWIiIhqAZNcqrfSQyOR2yjQbLlPfi5w8YQDIyIiIiJHYZLrSrjjmW1EIpxt28XyTmjH92o3iiAiIqJ6hUku1Wsl3j64Fq0wX6GiHDiwlcNEiIiI6hkmuVTvXWkegxK5l/kKaVeBK2cdFxARERHVOia5zoY9ijVOI5HiXNvOlisd2aFdWoyIiIjqBSa51CDkBDZBWlik+QrlZcCh//FDBhERUT3BJNeFCJx4Vi0XYzqg3N3DfIXUJOD6RccFRERERLWGSS41GCo3N5ytatjC4e1AaYljAiIiIqJawySXGpQ7TUJxKyTcfIXSEg5bICIiqgeY5DobJle17nybjqiQuZmvcP0iV1sgIiJycUxyqcGpcPfA+badLFc6vB0oyndEOERERFQLmORSg3QrJBw5IRHmK1SUA/t+Zc86ERGRi2KSSw2TSIQrHWIBD7n5OrdSgbNHHRYSERER1RwmudRgqTw8gb7DLVc6vhe4m+WYgIiIiKjGMMl1Ovx63KGiWgMxFpYVU6uB3zcClZWOi4mIiIiqjUkuUa/BgI+/+fLcO8CxXQ4Lh4iIiKqPSa4L4Y5nNctX5gGNIABu7sDDIwBL7XvhOJBySVufiIiInJ60rgMgqityqRvEIhFWJB3BbWUBouUyRFy9YLa+5y+L0X36Asu9vkREROQU2JNLDd5tZQHSSvJwKDwCNz09UKAqM3krVRYCezdqx+kSERGRU2OS62z4dXid0UgkSOwYC7XEwhccd9KAk/scFhMRERHZh0ku0T1KvHxwrqrd0E4fAtKuOiQeIiIisg+TXBcigBPPHCE9LBLpoc3MVxAE7bAFbvtLRETktJjkEplwrm1nlMi9zVcoLwV+W8/1c4mIiJwUk1wiE9RSKU52jIVGbOElkn0LOLLDcUERERGR1ZjkOhtOPHMahX4BuBjTwXKlSyeBy6cdEg8RERFZj0kukQWpzZojo2mE5UoHtwE5mY4JiIiIiKzCJNeVcN6Z44lEONuuC4q8fc3XUVcCu9cBpSWOi4uIiIgsYpJLVAW1VIoTnXugUiYzX6koXzsRjRtFEBEROQUmuURWKPHyweVOPS1Xun0DOLyd46qJiIicAJNcZ8MEyWnlNm0GdHzQcqVLJ4ELCY4JiIiIiMxikktkJV+ZBzTd+wNh0ZYrHtkJpF+Dhh9YiIiI6oy0rgMgchVyqRvEEgm+j4hA00tH4VlSZLau54//QvepHwL+jR0YIREREemwJ9eFcFtf55CuLsfu9p2QK1SiQFVm8laqLAR2/giUKes6XCIiogaJSS6RHYq9fZHYMc5ypYJcYOdPQKXKMUERERGRHpNcIjtlBYXgYswDlivdSQN+3wRoNI4JioiIiAAwyXU+nKzkUq5HtUJaWKTlSimXgKO7+LslIiJyICa5RNUhEuFc2864G1DFBLPz8cDZo46JiYiIiJjkuhQRJ545I41EguOde6JE7m254rHdwNXzjgmKiIiogWOSS1QDVG7uiO/WGxVu7pYr7tsMpF11TFBEREQNGJNcohqilHshoUsvqCUS85XUamD3z0DmTccFRkRE1AAxyXU6nJzkyvL9G+FkxzgIloaWVKqAHWuBnNuOC4yIiKiBYZJLVMOygpvi6gPdLVeqKAf+twbIz3FMUERERA0Mk1wXInDemcu4HaUAuvSxXKlMCWxbDRTlOyQmIiKihoRJLlEt8JV5QNO1H9Au1nLFkkJg2ypomOgSERHVKGldB0BUH8mlbhCLxVjR2A++UjWapF03W9c7xwNdtq0Chk0GvP0cFyQREVE9xp5cZ8NdseqV26WF2NsyBpcDGqFAVWbyVqwqAwrzgK3fA8UFdR0yERFRvcAkl6iWCWIxEjvGIqdxsOWKTHSJiIhqDJNcFyKAM89clW5XtDz/Krb/ZaJLRERUI+wak5uUlISlS5fi+PHjKC0tRUREBEaMGIHJkydDKrX+ksePH8eyZctw9uxZqFQqNG/eHOPGjcPo0aON6p48eRJ79+5FfHw8MjIyUFxcjODgYPTs2RNTp05FZGSkPQ+FyGHUUiniu/ZCjxOH4F+QZ75iYR7w3++AxycBfo0cFyAREVE9YnNPbmJiIsaMGYN9+/ahT58+mDhxIkQiERYuXIhZs2ZBsHJM6a5duzBp0iScOnUKgwcPxtixY5Gfn4+5c+fio48+Mqr/8ssv47vvvoObmxuGDRuGiRMnIjg4GBs2bMCTTz6JU6dO2fpQiByuUuaGY916I98vwHLFonzgvyuBu1kOiYuIiKi+saknV61W4+2330ZZWRmWL1+Ovn37AgBmzZqFKVOmYM+ePdi6dSuGDx9u8TpFRUV49913IZVKsXbtWrRp0wYAMGPGDIwaNQqrV6/G4MGD0bVrV/05zz77LJ544gk0adLE4FrLli3Dv//9b7z77rvYunWrLQ/HOXHiWb2nS3Sr7NFVFmt7dIc8AwSHOS5AIiKiesCmntz4+HikpKQgLi5On+ACgJubG2bNmgUAWLduXZXX2blzJ/Lz8zF06FB9ggsAPj4+mDZtmsnrTJ061SjBBYAXXngBHh4eSE5ORl6ehYSByIlY3aNbXgpsWwVkpDgmMCIionrC5iQXAHr37m1U1rVrV8jlcpw+fRoVFRUWr3Ps2DGz19EdS0hIsComkUgEiUQCAPqf9RbnndUrukS3KCDQckVVBbBjLZB62TGBERER1QM2DVdISdH2Jpma5CWRSBAeHo7k5GSkpaWhRYsWZq+Tmppq9jpBQUGQy+XIzMxEaWkpPD09Lca0c+dOlJSUoFOnTvD19bXh0RgTBAFKpbJa16guaUUFRCoVAO3wEJlUVqfxUO2qlLkh9aEh0KSlQ5yZZr6iuhLYvQ54aCg0rTujvKzc6vHvtiotLTX4SbZh+9mPbWc/tl31sP3s5+i2EwQBIpF1vX42JbnFxcUAtMMKTPHy8gIAFBYWVus63t7eUCqVKCoqspjkpqWl4YMPPoBEIsGbb75ZZfxVUalUuHTpUrWvUx1BmZmQFmuHXcikMnh4eNRpPFT73D29IX58EvZ9/yHE6Vct1vXe9DW6PDIeKV4hKC0rq9W4dB9GyT5sP/ux7ezHtqsetp/9HNl2bm5uVtVz2W19c3Nz8cILL+Du3buYO3cuunTpUu1rymQytGzZsgais5/0YhOIPLS/FrVaXaexkANJZTjcqTsCS/MRmpluue6pg2gRGQP1g0OBWhiiU1paitTUVERFRVX5TQoZY/vZj21nP7Zd9bD97Ofotrt61XJn0L1sSnK9vb0BaFdHMKWkpAQAqhw2UNV1qurpzc3NxbPPPouUlBS8/fbbmDhxYtXBW0EkEkEul9fItezm5gbI/hyiwDG4DYogluBUh+6olErRLD3VYl33G5eBygrgkTGAe+309nt6etb968GFsf3sx7azH9uueth+9nNU21k7VAGwceJZdHQ0AODGjRtGZWq1Gunp6ZBIJIiIiLB4naioKLPXyc7OhlKpREhIiMlPBFlZWZg4cSKuXr2Kd999F88++6wtD8Glccez+k8Qi3G2XRdcj2pVdeWMFGDLCu3mEURERGTApiQ3Li4OAHDo0CGjspMnT0KpVKJTp05VjpXo0aOH2evojsXGxhqVZWZmYuLEibh+/Trmz5+PCRMm2BI+kWsQiXAx5gFcjHmg6rp52cDm/wNupdZ6WERERK7E5iQ3KioK8fHx2L9/v/54RUUFFi1aBAAYO3as/nhpaSmuXbuGW7duGVxn8ODB8Pf3x7Zt2wwmehUVFWHZsmUAgDFjxhick5GRgQkTJuDmzZv4+OOPjcqJ6hWRCNejFUjsGAuNuIqXaZkS+N9q4OIJx8RGRETkAmwakyuRSLBgwQJMnjwZM2bMwJAhQxAUFIT9+/fjypUrGDhwIIYNG6avf/bsWUyaNAmxsbFYs2aN/riPjw/mz5+PV199FRMmTMDjjz8OLy8v7N69GxkZGZg4cSK6detmcN8TJ05ERkYG2rVrh4yMDCxZssQovhEjRiA8PNzWNnAu3PGM7nGraQTK3D3QPfEoZJUq8xU1GuDgNuDuHaDn4FqZkEZERORKbF5doUuXLli/fj2WLFmC/fv3Q6lUIiIiArNnz8bkyZOtHhA8ePBgNGrUCMuWLcP27dtRWVmJFi1aYPr06Rg1apRR/YyMDADAhQsXcOHCBZPXjI2Ndf0kl+g+dxsF4XCPfog7cRh+ao3lyheOa4cwDBgFyL0dEyAREZETsmsJsdatW+Orr76qsl5cXBwuXza/S1NsbKzJsbemWLpOg2HDjEKqX4q9fXGoRz8MunCm6sq3UoFN3wADRwMhzWo9NiIiImdk05hcIqo75R6eSOk7DJoW7aquXFIEbP0emjNHOASGiIgaJJfdDIKoIfLwkEM8YCC2FWfD9/Rhi3W9ZR7ocmw3kJUO9H0CcHN3UJRERER1j0mu02GvG1VBJMLpZtGoqFSiy9njkKgrLde/fhHIvQM88jTQuIljYiQiIqpjHK5A5KLuNAnFoR79oPT0qrpyQa52Pd3z8Ry+QEREDQKTXBfC1ITuV+Tjh4M9++NOUEjVldWVwOEdwM4fgdKS2g+OiIioDjHJJXJxKjc3HO/SC0mtrJiQBgA3rwAb/gOkXa3dwIiIiOoQk1yi+kAkwtUWrXGs20OosGaCmbIY2P4DcGQnYGmTCSIiIhfFJNfZcLwkVUNOYDAO9OqPPP/G1p1w7hiw8RsgM612AyMiInIwJrlE9UyZhxxHYvsgrWU76zYQyc8B/rsSOLabvbpERFRvcAkxFyJwxzOykiAWI7djT2gCIiHet1m7OYTFEwQIZw5DdCMZorhBjgmSiIioFjHJJaqn5FI3iMOb4/sHOsP36G4EWhiS4C3zQJfACCA/B5L/rYKvTwjQsgUAueMCJiIiqkFMconquXR1OdLaPoBIH2+0vXwWErXaYn2RIMDr+llIN38D9HsSiIpxTKBEREQ1iGNynQ0nnlFtEIlwo1lzHOzZH/l+AdadUlwI7PoJ2P0zUFxYywESERHVLCa5RA1IsbcvDsf1wyVFe2jEVr78Uy4B65dqV2LQaGo3QCIiohrCJNelcOIZVZ8gFuNa8xgc7NkfBb4menVFgEwqM3y6qSq0a+puXAakX3dYrERERPZikkvUQBX5+OFQj35IatUOMpk7hD83jpZJZQgKCtImuve7mwX8bzU0u34CCu46OGIiIiLrceIZUQMmiMW42qI1mraORc87WUg8fxjFqjKL53jLPNAF0G4L/EBPoPNDgDW7rBERETkQk1xnw4lnVAfK/AKAuMeRqClBozNHIFNVVH2SWg2cPgQknwa69gNiOgMSSW2HSkREZBUOVyAiLZEImZEt8UfvR5AR2sz685TFwMFtwC9fA9cu8IMaERE5BSa5LkTgvDNygAp3D5zq0B3Huj2EErm39ScW5AJ7fgE2/x8npxERUZ1jkktEJuUEBmNf70dwsXUHVEptGNmUfQv432pg22rgTnrtBUhERGQBx+QSkVmCWIzrUa2Q3jQCra9cRLP0FOtPzriuvYW30I7ZDYmotTiJiIjuxyTX2XA8IzmhCncPnG3fBakR0Xj05g0IECCyct1mIf0qROnXgLDmQNe+QNPIWo6WiIiISS4R2aDQLwA3+nWEyD0AR7d9C1VelsX63jIPdAn8swdX17MbFg10ekj7U8SB5kREVDuY5LoSJgTkDEQioEU7/P7gAIgun4bi2iW4l1teW9dARor21jgE6NgLaN6OS48REVGNY5JLRHYRxBLcbNYc6aHNEH3jKlqkJENWqbL+ArmZwO+bgIS9wAM9gNZduKkEERHVGCa5RFQtaqkUV1u0xo2IaLS8fhlRadchUautv0BxAXB0F5C4H2jdFWgfC3j71V7ARETUIDDJdTqceEauSeXmjkutO+B6tALNU5IRlXYd7hKp9ZPUysuAM4ehOXsY4sjWQLvu2slqHKZDRER2YJJLRDWq3N0Dl1p3wLVoBR7LK4SoSInE29dQrKp63K5+olpqkvbmH6hNdlt1BNw9HBA9ERHVF0xyXYhg5ZJNRM6gwt0Dt9rHANGdcXHjIngln4a0stK2i+TnAId3aMfttnxAO243KJS9u0REVCUmuURUuzzkSG3TCbebNkVkWgqib16DR1mpbddQVQCXTmpvjYKBmM5Aqw6Ap1ftxExERC6PSS4ROUSlzA3XmsfgelQrhN1OQ/PUK/AtKrD9QneztBPV4n8DImO0CW9ES0DMXcqJiOgvTHKJyKEEsRjpYZFID22GoJw7aJF6BYG52k0lbJqoptFASLkIUcolQO6tXW+35QNAcBiHMxAREZNcp8NtfamhEImQHRSC7KAQ+BQVIOrmNYQUFEEEERJz0qqcqGawm5qyGDgfr735BmiT3ZYPAAFBDnggRETkjJjkuhCBnVNUTxX5+OFcuy7w8g1Bd4kXsnaugkqZb9/FCvOAxAPaW+MQoGV7ILot4NeoRmMmIiLnxiSXiJyG2s0daBOHE+W5KL2ZjKi0a2iSdRsie7/hyM3U3uL3AI2bAFFtgOg22slrHNJARFSvMcklIucjEiEnMBg5gcFwLytF+K2biMi4Ae+SIvuvmXtHezu5T9urG9UGiGqtHcPLSWtERPUOk1wicmrlHp641jwG16IVCMjPRbP0VIRmZtg2SQ0wrFtwFzhzWHvzkAPNWgERrYDwFoCHZy0+GiIichQmuc6GE8+ITBOJkBcQiLyAQJxv0wmPlKkhUouRePYgiissr7trMEntfmVKIPmM9iYWA00i/kp6OayBiMhlMcl1KXyzJQIAtVSK3KgWQJsHsSfAG8L18wi7lQa/wrzqXVijAW7f0N7i9wBevkB4cyA0GghrDnj51MwDICKiWsckl4hcWoWnHGlRrXA9qhW8SooQdjsNYbfS4KUs1texe2hDSSFw+bT2BgD+gdpkN7w50DQKcPeo8cdDREQ1g0kuEdUbJV4+SG7ZFskt2sC3MB9Ns26h6Z0MhGnE9q2/e7/8HO3tQoJ2GENQKNA0Eghppr15yGvhURERkT2Y5BJR/SMSodAvAIV+Abjcqh0q3f3QReaL2/t/gSY7v2buQxCArAzt7cwR7bGAIEgCmsCzRAWEhwCenhzTS0RUR5jkOhtOPCOqceU+/kCbB3FKKEZ2Tgaa3slAcHYmGuflQKzR1Nwd5WVDnHUL/jnZkKWfB/wbaXt4g8K0S5UFNgVkbjV3f0REZBaTXBcisEeIqNrKPOVIiWqFlKhWkFRWIjA3C02yMxGckwmPstLqLU12v5Ii4NoF7Q3Q9uo2Cv4r6Q0K0/6f6/QSEdU4JrlE1GCppVLcaRKKO01CAUGAT3Eh+lYIEIk8cPLiEZSU27E0mQiQSWWmF0MRhL82pUhK1B6TyrTbDwc2BQJDtP8OCAak/PNMRFQd/CtKRAQAIhGKfPxwJzASaPMgdocEQpl+BYF3sxGYmwXfogKjU0z1+sqkMgQFBZm8C5O9vpUq4E6a9qYjFmtXcmgc8tctMIQT24iIbMAkl4jIBLVMhqzgpsgKbgoAcCsv0ye8gbnZkJeWQCaW1MyqDffTaIC7WdrblbN/HZd7AwFBgH+Q9mejYO1PJr9EREaY5DobTjwjckoV7h641TQCt5pqE1XP0hL0EdzQRd4Yd45no1KZX/tBKIu1t4wUw+OeXtqEV5f8+jUG/BtrN7PgWH4iaqCY5BIR2aHU0wt3/xzacNJLgsy8TDTKz0WjvFw0ysuBX2G+wcoNNTqhzSiYEm3ie3/yK5ECfo20Sa9f47/+7dtI2yvMBJiI6jEmuURENUDl5o47waG4ExwKABCrK+FXWAD/grvwL8hDYCVqZ2iDJerKv4Y93E/mpk12fQMAH3/tzdv/r3+7uVf//omI6hCTXCKiWqCRSJEX0Bh5AY0BANLASPSM7oyj+35C6e1U+BfcRUD+XbipKozOrdVeXx1VBZCbqb2Z4iH/K+G9NwH28gW8fQF3bnRBRM6NSS4RkaN4yJEXHIo0L0/t/wUBHuWl8C0sgF9hPvyK8uFbWACZqJYmtNmiTKm9Zd8yXS6RAl4+2qRXd/P2Nfy/pxfXACaiOsMk1+lw4hlRgyESocxDjjIPuX4VBwBQ+oagS3ALnD68CWVZafAtKoBPcaHJ3dkc0utriroSKMzT3swRibSJrqe3dgyw3Bvw8Prr37rjnl7anmEiohrEJNeFcMczooZB7eYONI3EreatkdakCQBApNHAs1QJn5JCeBcXwae4ED7FRWgqiK3u9W3k7oV2jZparHO/aiXFgvDXihC5VdSVSCCVyBCYXwRJWgvA1187ZMLUzd1T+1MisS8uImoQmOQSEbkAQSyG0ssbSi9v3An+63hZ42boFt4Oxw5vQHFWOryUxfAuKYaXshju5YZJr7fM3eqEGLA9Ka5WQqxWQ1RWBllBNsTpAGSyqs9xczed/Lp5AO4ehj8N/u3O8cREDQCTXCIiVyYSAb4BuNskHGneXgZF0koV5CXF8FYWw6ukGB5ST8AnGHm5N6G0Ism1JSl2aEKsU1GuvVkaMmGKSKRNdN08tInx/YmwzA2QuWt/urkD0j9/ytwMy2RuTJaJnBiTXCKieqpSKkOhXwAK/QIAAP5/rut7tFkYMvPuwLO0BPIyJeTKEshLldr/lyohLy2BRK3WX6dYVYaCKifAWZ8Qm50sJ9Jui1wTQ4YtEgSgvEx7K8q3/zoiESCVmUiE702I3bR17LkxgSaqFruS3KSkJCxduhTHjx9HaWkpIiIiMGLECEyePBlSqfWXPH78OJYtW4azZ89CpVKhefPmGDduHEaPHm2yfmVlJb7//nts3rwZN2/ehFwuR/fu3fHSSy8hJibGnofifLjjGRE5gMrNDSo3N30CbEAQIFNVoJdHALqEtML1kztQdPcOPMtL4VFWCo/yMqOhEDrWJMTmJsvJpDIEBQUZh1NTk+VqmiBol2IzsQxcjZBITSe/Eom2THeTSiFRVcL31i2Ii28BXt5/lYklgFRqWF8iNXkNiCXam+TPn2IxE21yaTYnuYmJiZgyZQrUajUee+wxBAcHY//+/Vi4cCFOnTqFpUuXQmTFi2LXrl145ZVX4OnpiccffxxeXl7YvXs35s6di+TkZLzzzjsG9QVBwKxZs7Bnzx60atUKkyZNQlZWFnbs2IGDBw/iu+++Q5cuXWx9OK6Ff2yIyBFEIqjc3KFsFAS0aIf0gptIKwk1rKLRwOPPpNezrBSCux+6BEUhW1SOkoJceJYp4V5eBpGJD+4ysfVLpNXmMAinTZ511JXaW3lplVXFKhW8crIhKbpj3Xhma4nF9yW+EhPHzPzfqjpiQCT+66dIZHjs/nJd4m2p3Ny1TJ7jxL9/qjabkly1Wo23334bZWVlWL58Ofr27QsAmDVrFqZMmYI9e/Zg69atGD58uMXrFBUV4d1334VUKsXatWvRpk0bAMCMGTMwatQorF69GoMHD0bXrl315/z3v//Fnj170L17d6xcuRJubm4AgKFDh2Lq1Kl45513sG3bNkhcabZtZSWgUVddj4jIyQhiMUo9vVDq6YU8AGF/DoW45CEgreTPMbJ/9gi7V5TD/c/eX4+Kckjd/YDgKNxUl6CkMFdfbootwyCCPX3Q2j+kzpPnekWj0d4qVXUdSe0RiyGtrERIdjakx5sAbm5/JdIi0X23+45BdE8yfc//zdXX1RGLbb8GcM85MKxrT9m9x6tRJiorg8ft20DzKEAur8FfTPXZlOTGx8cjJSUFcXFx+gQXANzc3DBr1ixMnDgR69atqzLJ3blzJ/Lz8zFy5Eh9ggsAPj4+mDZtGubMmYN169YZJLnr1q0DoE2odQkuAPTt2xexsbFISEhAQkICevbsactDqhu3bwDHfwey0gE1k1wiqqf+7BFWubmj2NtXf7jxnwnxeV93fUIs0mj0CbFbRTncKirgpiqHyDMAXUJa4trFShQV3dUfd6uogERdaXB33jLtVsQ1PYbYJXqTHTWeuT7SaCBSqyHSqCGqVLENbSRVqRCQkw3pjdNA32FA+7i6DknP5iQXAHr37m1U1rVrV8jlcpw+fRoVFRUGiej9jh07ZvY6umMJCQn6Y+Xl5Thz5gzkcrnJIQkPPfQQEhISEB8f7/xJblYGsP2H+v2pmIjIRoJYjAp3D1S4exgcD/4zIb58bw/xn8RqNWSqCripKuBWUY4uXo3RJbwtrp/7A3lFdyFVqSCr/POmqoBM9de/dWo6Ia7N3mTAfFJsajxzvRq6Udv4IaHaRIIAHN4B+AcC4S3qOhwAgEgQrJ/p9PLLL2PXrl1YvHgxBg0aZFQ+bNgwJCcnY/v27WjRwvwDfOqpp3D+/Hls3LgR7du3Nyrv3LkzlEolTp8+DU9PT1y5cgVDhw6FQqHA1q1bjerv2rULL7/8Mh577DF8+eWX1j4cA4mJiRAEAbKaHMtkSpkSIisnKQgQIBKJUKGuhEYQUOblY7aum1gCudQNRapyqAXjXZFYt3p1nSWO+lzXWeJgXeeKo9bqiiSQS2UoriiFRqMBIGjfpAVABEE/CVgkCJCKxHCXSFBWqYJGo/mz3LCejkQkhkwi0f/dtkRXV6VRWzXnWCwSQSoWW1XfnroEbjpqBwECNBoNxCIxRCIRBJmbdr3qWqJSqSASiayah2VTT25xcTEA7bACU7y8tGs0FhYWVus63t7eUCqVKCoqgqenJ4qKivTHzdUHoK9nD91kOWsmzVWLp5f2ZgVdJLo+cQ9zFe/h8+fXddZgXdvrOksc9bmus8TBus4VR23V9Xa3/s3Ymr/BOua/yzRma9eKLfVruduGCCIAkvv+X6v3JxJZnatxndw/de7cua5DICIiIqIaYtP3E1X1mJaUlAAAfH19TZZbe537e3p1P3XHq6pPRERERA2bTUludHQ0AODGjRtGZWq1Gunp6ZBIJIiIMLGTzT2ioqLMXic7OxtKpRIhISHw9PQEADRr1gwSiQTp6elQm1iNQHcd3XWJiIiIqGGzKcmNi9MuC3Ho0CGjspMnT0KpVKJTp04WV1YAgB49epi9ju5YbGys/pi7uzs6duwIpVKJxMREo3MOHjxoEB8RERERNWw2J7lRUVGIj4/H/v379ccrKiqwaNEiAMDYsWP1x0tLS3Ht2jXcunXL4DqDBw+Gv78/tm3bhkuXLumPFxUVYdmyZQCAMWPGGJyju+6iRYtQUfHX6gT79+9HQkICoqOjDRJjIiIiImq4bFpCDNAutTV58mRoNBoMGTIEQUFB2L9/P65cuYKBAwcabOsbHx+PSZMmITY2FmvWrDG4zs6dO/Hqq68abeubkZGBiRMnYu7cuQb1BUHAjBkzsHfvXrRq1Qp9+/ZFdnY2tm/fDrFYjO+//77+b+tLRERERFaxOckFgKSkJCxZsgQnTpyAUqlEREQERo4cicmTJ0Mq/WvBBktJLqDd8GHZsmU4c+YMKisr0aJFC4wbNw6jRo0yuTyESqXCqlWrsGnTJqSlpUEul6Nbt254+eWXERMTY+vDICIiIqJ6yq4kl4iIiIjImXGLEyIiIiKqd5jkEhEREVG9wySXiIiIiOodJrlEREREVO8wySUiIiKieodJLhERERHVO9Kqq5CjJSUlYenSpTh+/DhKS0sRERGBESNGGK1D3BBt2bIFiYmJuHjxIi5fvozy8nLMnDkTL730ksn6giBgw4YN+Omnn3D9+nXIZDJ06NAB06ZNQ/fu3R0cfd3Ky8vDnj17sG/fPiQnJ+POnTuQyWRQKBQYOXIknnrqKYjFxp97f/vtN3z33Xf63QnbtGmD5557DgMHDnT0Q6hTFRUV+OKLL3D+/HncuHED+fn58PLyQlhYGIYNG4ann34acrnc4Bw+/8zbsmUL3njjDQDAhx9+iNGjRxuUs+3+0r9/f2RkZJgs69ixI9avX29wjG1n2v79+/Hjjz/izJkzKC4uRuPGjdGmTRtMmzYNnTp10tdj+2lt2rQJb731lsU6YrHYYOdawLneM7hOrpNJTEzElClToFar8dhjjyE4ONjsjnINke6PvZ+fH/z8/HDz5k2LSe6HH36INWvWICwsDI8++ihKSkrwv//9D6Wlpfjyyy8xaNAgBz+CuvPTTz/hvffeQ1BQEOLi4hAaGoqcnBz89ttvKCoqwqBBg7Bo0SKD59f333+PBQsWoFGjRhgyZAgAYPv27bh79y7eeustTJ48uY4ejeMVFhbiwQcfRPv27dG8eXM0btwYRUVFOHbsGK5fv46WLVvi559/hre3t/4cPv9Mu337NoYNGwa1Wg2lUmkyyWXb/aV///4oLCzEs88+a1QWEhLCtrPCxx9/jFWrViEsLAy9e/eGv78/cnJycPr0aUyYMAETJkzQ12X7aV26dAl79uwxWXbixAkcO3YM/fr1wzfffKM/7nTvGQI5jcrKSmHQoEGCQqEQ9u3bpz9eXl4ujB8/XlAoFMKWLVvqMMK6d/jwYSEtLU0QBEHYuHGjoFAohMWLF5use/z4cUGhUAiPPvqoUFhYqD9+8eJFoX379kJsbKxQVFTkkLidwZEjR4Q9e/YIlZWVBsezsrKEvn37CgqFQti5c6f++M2bN4V27doJcXFxwu3bt/XHb9++LcTFxQnt2rUTbt686bD465pGoxHKy8tNlv3jH/8QFAqFsHz5cv0xPv9M02g0wrPPPisMGDBA+OSTTwSFQiGsX7/eoA7bztDDDz8sPPzww1bVZdsZ+/HHHwWFQiG8/fbbQkVFhVH5vcfYftZ5+umnBYVCIezZs0d/zBnfMzgm14nEx8cjJSUFcXFx6Nu3r/64m5sbZs2aBQBYt25dXYXnFHr16oXw8HCr6uraavr06fDx8dEfb9OmDYYOHYr8/Hzs2LGjVuJ0Rj179sSAAQMgkUgMjgcFBWHs2LEAtFtt62zYsAEqlQrPPPMMQkJC9MdDQkLwzDPPQKVSYePGjY4J3gmIRCK4ubmZLBs8eDAA4MaNG/pjfP6Ztnr1ahw7dgwLFiwwGt6hw7azH9vOUHl5ORYtWoSwsDDMmzcPMpnMqM69x9h+Vbt8+TJOnz6NJk2aoF+/fvrjzviewSTXicTHxwMAevfubVTWtWtXyOVynD59GhUVFY4OzSXp2vPBBx80KtO18b1JXUOmG+t9bwJs6fnI9jO0f/9+AEDr1q31x/j8M3bt2jV8/vnnmDRpksWxjWw7YxUVFfj111+xbNkyrFmzBidOnIBgYrQh287QkSNHkJeXh0ceeQQikQh79+7F8uXLsWbNGly8eNGoPtuvarox4KNGjXL694yGPYvJyaSkpAAAIiMjjcokEgnCw8ORnJyMtLQ0tGjRwtHhuRSlUomsrCzI5XIEBQUZlevaODU11cGROZ/Kykps2bIFAPDQQw/pj+vaxtTzsaG335IlSwAABQUFOHnyJC5evIhevXrh6aefBsDnnymVlZWYPXs2mjZtitdee81sPbadadnZ2XjzzTcNjrVs2RILFy5E27ZtAbDtTDl37hwAbW/tk08+iatXrxqUDxw4EJ999hm8vLzYflYoKyvDf//7X0gkEqOx4M74nsEk14kUFxcDgMFXJPfy8vICoJ0AQ5YVFRUBMN+WuslBbEvg888/R3JyMvr06WOQ5Fp6Pjb09lu6dKnB/5988km8++67+uEMfP4Z++qrr3Dp0iX8+OOP8PDwMFuPbWds5MiR6N69O1q2bAlPT0+kpqZixYoV2LZtG6ZMmYJff/0VTZs2ZduZkJubCwBYuXIlFAoFfv75Z7Rq1QrXr1/H+++/jz179mD+/Pn47LPP2H5W2LFjBwoLC9GvXz80bdrUoMwZ3zM4XIGoAVu9ejVWrlyJ6OhofPrpp3Udjsu4fPkykpKScODAAXz66ac4cuQInnrqKaSnp9d1aE7pzJkz+OabbzBlyhR07ty5rsNxOTNnzkRcXBwaN24MuVyOtm3b4vPPP8eQIUOQn5+PFStW1HWITks3pEMikeDrr79Gp06d4OXlhQceeAD/+c9/IJfL8d///hd37typ40hdw88//wwAGDNmTB1HYh0muU5E90lH92nyfiUlJQAAX19fh8XkqnSfJM21pe4TZ0Nuyx9++AEfffQRWrRogTVr1qBRo0YG5Zaej2w/7US0Jk2a4Mknn8TSpUuRkpKCDz74AACff/eqrKzEG2+8gaioKLzyyitV1mfbWU83PCYxMREA284UXZu0bdsWoaGhBmWBgYHo2LEjBEHAuXPn2H5VuHLlCk6dOoWQkBCDyfE6zviewSTXiURHRwMwnKGto1arkZ6eDolEgoiICEeH5nLkcjmCg4OhVCqRnZ1tVK5r46ioKAdH5hy+//57fPDBB1AoFFizZo3J8We6tjH1fGzo7Xe/jh07wtfXVz+pgs+/vyiVSqSmpuLatWt44IEHEBMTo7/phn3MnTsXMTExWLJkCdvOBgEBAQC0bQzweWeK7n3V3BAEXdJVXl7O9quCrhf3/glnOs74nsEk14nExcUBAA4dOmRUdvLkSSiVSnTq1MnsMkZkSNeehw8fNirTtXFsbKxDY3IGy5cvx4IFC9CmTRusWrUKjRs3NlnP0vOxIbefKSUlJSguLjb4w8/nn5abmxtGjRpl8qabMNW1a1eD/7PtrHPmzBkAMFhWkW1nqEePHgCA69evm1yNQjcRLSwsDADbz5zy8nL9hLNRo0aZrOOU7xkOXZWXLKqsrBQeffRRbgZhpZrYDOLe4w3B0qVLBYVCIYwYMULIy8uzWNcZF/auS1euXBFKSkqMjpeXlwtz5swRFAqF8Nprr+mP8/lXtcWLF9u9GURDaburV68KSqXS6Pjly5eFuLg4QaFQCL/++qv+ONvO2NSpUwWFQiGsWbPG4PiGDRsEhUIhDBw4UL9JDtvPtM2bNwsKhUJ48cUXzdZxxvcMrq7gRCQSCRYsWIDJkydjxowZGDJkCIKCggy29R02bFhdh1mnfvnlF5w8eRLAX19/7NmzR7+ve/PmzTF16lQAQLdu3TBx4kSsWbMGTzzxhMH2jJWVlXj//ffNfoVVH23evBmLFy+GRCJBt27dsGbNGqM6YWFhGDlyJAAgIiICr7/+OhYsWIARI0YYbNGYl5eHt956q0ENndmxYwdWrlyJLl26ICwsDH5+fsjOzsbhw4eRlZWF6OhogyWe+PyzH9vuL9u3b8d3332H7t27IzQ0FB4eHkhJScHBgwdRWVmJESNGYPjw4fr6bDtj8+bNw6VLl/DBBx/gjz/+gEKhwPXr17Fv3z54enpiwYIF+m9h2H6m6dbG1Y0DN8UZ3zNEgmCi/57qVFJSEpYsWYITJ05AqVQiIiICI0eOxOTJk/WL9jdUc+bMwebNm82Wx8bGGiRvgiBgw4YN+PHHH3H9+nVIpVJ07NgR06dPt7gYfX20ZMkSo6Wv7nd/+wHAb7/9hpUrVyIpKQmAdsOD5557Do888kitxeqMzp07h19++QWnTp3CnTt3UFxcDG9vb7Ro0QIDBw7E+PHj4enpaXAOn3+W6Z6TH374odGam2w7rYSEBKxbtw4XLlxAbm4uSktL4efnh3bt2mHUqFEYNGiQ0TlsO2PZ2dlYunQp9u3bh9zcXPj6+qJHjx74+9//jpYtWxrUZfsZunbtGoYMGYKQkBD8/vvvJsfj3suZ3jOY5BIRERFRvcOJZ0RERERU7zDJJSIiIqJ6h0kuEREREdU7THKJiIiIqN5hkktERERE9Q6TXCIiIiKqd5jkEhEREVG9wySXiIiIiOodJrlEREREVO807D1iiYjqmaSkJJw4cQLp6ekoLi6GUqkEAHzxxRd1HBnVd8XFxTh69CguX76MnJwclJaWoqCgAOPGjUPfvn3rOjxqgJjkklPq378/MjIyDI6JRCL4+PigVatWeOKJJzB69GiIxfwygggALl26hH/+8584d+6cUZmXlxeysrIQHBxcB5FRfadWq7F06VJ89913KC0tNSgTiUSIiopikkt1gkkuObXevXsjKCgIAKBSqZCRkYHExEScPHkS+/btw9dffw2RSFTHURLVrfPnz2PixIlQKpV46qmnMH78eCgUCri5udV1aNQAvPHGG9i2bRvCwsLw8ssvo1evXvxARU6BSS45talTpyIuLs7g2JkzZzBx4kT8/vvv2Lt3LwYOHFhH0RHVPZVKhddffx3l5eX4/PPPMXTo0LoOiRqQX3/9Fdu2bUPnzp3xf//3f/Dx8anrkIj0+F0vuZyOHTti0KBBAID4+Pg6joaobv3+++9ISUnBpEmTmOCSw61YsQJyuRyLFy9mgktOh0kuuaTAwEAA2rFg94qPj0dMTIz+q9tPP/0U/fv3xwMPPID+/fvjs88+Q3Fxsdnrnjx5Ei+//DJ69+6N9u3b48EHH8SsWbNw6dIlo7rp6emIiYlBTEwM4uLiUFFRYfKaTzzxhL6eqaT84sWLmDVrFh566CG0a9dOX1d3W7JkidXtMnHiRP15O3fuNFnn+++/19eZM2eOyTrJycmYM2cO+vXrh/bt2yMuLg5Tp041GX9CQgI6dOiABx98EDdu3DAo0/0+7r8fQRDw2muvISYmBu+9957JGDZt2mTUFvfe7r2mIAgYNGgQYmJiTI5J1dH9Lk6ePGm2js69v9+qbunp6Qbn9u/fX398+/btePrpp9G5c2d069YN06ZNw8WLF83e38SJE03Gc+/v9l4HDx4EAHTu3Bn//Oc/8fDDD+t/Z3/729/wxx9/WLyeNR8UlyxZYvRctLZtYmJisGnTJoPz+vfvb/J+3nzzTbNtCgBHjx7Fiy++iF69eqFt27YW7+f+NrPn9aB7Dpp6Dd69exdxcXEWf2emFBcXY926dZg2bRoGDhyIDh06oEuXLhg1ahRWrVqFyspKo3NMPRfbtm2Lhx56yOTv+N7HXdXt/sdcXFyMpUuXYtiwYejYsSM6d+6Mp556CqtXr4ZKpTKoe+fOHSQnJyMuLg6HDx/GuHHj0KVLFzzwwAMYNGgQPv30U9y9e9fgnK1btyImJgYvvvii2Tbas2cPYmJiMGXKFKOykydP4pVXXkGfPn3Qvn179OrVC2PHjsXy5ctRVlZmUFej0WDTpk0YP348unXrZjEu4K+/V/fe2rdvj4cffhgzZ87EiRMnzMZMzonDFcgl6RKZFi1amCxXqVR49tlncfXqVfTo0QPt2rVDfHw8VqxYgaNHj+KHH36Al5eXwTnLly/HF198AZFIhPbt26Nr165IS0vDzp07sXfvXixevNjsm3N+fj62b9+OJ5980uD4yZMnkZSUZPZxXLx4EWPHjkV5eTnCw8MxYMAAyOVyANqJRJbOrcpPP/2EwYMHGxwTBAE//fSTxfO2bNmCd955ByqVCq1bt0aHDh2QlZWFQ4cO4cCBA5g3bx7GjRunrx8bG4svvvgCL7/8Mp577jmsW7dOP47anA8//BD/+9//MGTIELz77rsW6zZr1gxdu3bV///GjRtITEw0qCMSiTB+/Hh8/PHHWLduHR544AGj65w6dQpJSUlQKBQG16uKXC7Xf3Nwv127dulXLzBl1apVWL16NTp37oz+/fsjOTkZf/zxBw4fPozly5ejZ8+eVsWwZcsWJCQkmCy7c+cOAGD27NkoLy9HVFQUHn30UWRnZ+Po0aM4dOgQpk6din/84x9W3Ze1RowYYfD/7OxsHDp0yOj3BWh/h1U5ceIEfv31V7Pl+/btw/Tp06HRaNCyZUvExcXB3d0dgPZ1dvPmTYvXt/f1YM7nn3+O/Px8m89LSkrCvHnzEBQUhOjoaHTo0AG5ubk4deoUPv74Yxw9ehT/+c9/TM41uPe5qFKpkJKSgkOHDuHQoUN4//33MWbMGADAQw89hLCwMINzN2/ebPK5fO/vKjc3F5MmTcLVq1cREBCAvn37QqVS4dixY/joo4/w22+/4dtvv9W3u+65d+TIEfzxxx9wc3NDXFwcvL29kZiYiJUrV2LHjh1YtWoVIiMjAQCDBg3CggULcODAAdy6dQuhoaFGj1P3O7n37wwAfPXVV1i8eDEAoE2bNujWrRsKCgpw/fp1fP755xgyZAjCw8MB/PVBeseOHVbFda/AwEA89NBDAICKigokJSXht99+w969e7Fs2TJOonMlApETevjhhwWFQiEcO3ZMf6yiokJITU0VPvjgA0GhUAh9+/YVioqKDM47duyYoFAoBIVCITz22GNCVlaWviwvL0946qmnBIVCIXz88ccG5/3xxx+CQqEQ+vXrJ5w/f96gbO/evULbtm2Frl27Cnl5efrjaWlp+jgGDBggPP3000aP47XXXhM6d+4sjBgxwujxCIIgvPXWW4JCoRBmzZolqNVqg7LFixcLCoVCWLx4sXWNJgjCM888IygUCv3Pa9euGZQfOnTIoPzNN980KL948aLQrl07oWvXrsKRI0cMyk6dOiV069ZNaNeundF1BUEQNmzYICgUCmHYsGFCYWGhIAh//T7uvZ8lS5YICoVCeO6554Ty8nKzj+WXX34xGePGjRtNHi8sLBQ6deokdOrUyeh5IQiCMHv2bEGhUAhr1641e5/30v1+H374YbN1dM/TtLQ0k8dbt24t7N6926Ds66+/FhQKhdC7d2+htLTU6P6eeeYZg/pFRUXCgw8+KHTu3Fno1q2boFAoDMp1v0uFQiF88cUXgkaj0ZedPHlS6NSpk6BQKIR9+/aZPO/+56Qp1jwXTf2uTTHVpiqVShg6dKjQpk0boXfv3ibbdNKkSYJCoRA++eQTo2u++eabgkKhEDZu3GhwvLqvB91z7f7HferUKSEmJkbo16+fyd+ZJbdv3xaOHj1q8HsSBEHIzs7W/53Ytm2bQZml5+LKlSsFhUIhjBgxwuL9VvVcFgRBeOmll/SvzXtfQ3fu3BEef/xxQaFQCJ999pn++L1/b/v27Sukpqbqy8rLy4XXXntNUCgUwqhRowzu54svvtA/X+9348YNISYmRujdu7egUqn0x3fu3CkoFAqhW7duRn+bNBqNcPToUf3fHUEQhDVr1tgcl+7xmPp96t53Zs6cabb9yPlwuAI5tUmTJhl8bfToo49izZo1GDZsGH7++Wd4e3ubPffNN9806FH09/fHP//5TwDA+vXrDb7aWrp0KQBgwYIFaNeuncF1+vfvj3HjxqGoqAj//e9/je5HLBZjzJgxOH36tMGwhrt372LXrl144oknjHqNdXQ9IQMHDqzR5dCefvppSKVSo16qH3/8Ef7+/hgyZIjJ85YtWwaVSoW33nrLqJexU6dO+Pvf/w6VSoWff/7Z6NynnnoKs2fPxuXLlzFt2jSUl5cb1fnxxx+xZMkSdOzYEUuWLLE4+193vlRq3RdOPj4+GDZsGJRKpVGPYH5+Pnbu3Am5XI7hw4dbdb2a8Oijj+KRRx4xODZt2jS0bNkSWVlZZr9Cv9eXX36J7OxsvPTSSxbHPEZHR2PWrFkGPYBdunTBc889BwD47rvv7HwUte+HH35AcnIyxo8fj6ioKJN1dK+VRx991Obr2/t6MEWj0eD999+HSCTCm2++aXMsISEh6NGjh1FPbWBgoL63fdeuXVZfLzY2Vh9XdWRkZGD37t2QyWSYP3++wd/W4OBg/d/OH3/80eRr+7XXXjPoFXVzc8O8efPg4+ODs2fPGnzVP27cOEgkEmzcuNFoeMa6desgCIL+d6bz1VdfAQDeeecdo79NIpEIPXr0MHh96J7vtsRlSU21MzkWk1xyar1798aIESMwYsQIPPnkk3jwwQfh6+uL7du3Y/HixWbHwfr5+Zn8Sqljx46IjIyEUqnEhQsXAGiT0XPnziEgIMBoJQedbt26AdCu7GDKqFGj4ObmZvAm+ssvv0ClUhl95XaviIgIAMDu3buNxhdXR3BwMAYMGIBff/1Vv25lZmYm/vjjD4wcOVL/deO9NBoNDh06BIlEYjaRqKodnn/+eQwdOhQnTpzAq6++avCGsH37dnzwwQfw8vLCN998ox+WYY4ubg8Pj6of8J8mTJgAAEZJ+KZNm1BeXo5hw4ZZ/GBU04YNG2Z0TCQS6SeIVfUGm5SUhB9//BEKhaLKcZ/Dhw83+UHpqaeeAgAkJibW6HOspmRlZWHJkiUIDAzErFmzzNbTvVas+WBwP3teD+b89NNPuHDhAkaPHo327dvbHAug/So9ISEBX3/9Nd577z289dZbmDNnDtatWwcASE1NrfIalZWVSE5OxsKFCwEAjz32mF2x6Jw4cQKCIKBbt276r/zvFRcXh/DwcCiVSpw/f96gTCaT4fHHHzc6x9fXV/8h7/jx4/rjISEhGDBgALKzs7Fnzx798YqKCmzatAkSiQRPP/20/nhWVhYuX74MT09Pk/dzv8zMTKSnp9sclykVFRU4e/asPsm+f8gLOTeOySWnZmoJseLiYsyaNQsbNmyAWCzGBx98YHSeqXFeOmFhYbhx44a+Z0g3wSUvLw+tW7e2GI+pyQoAEBAQgMceewxbt27FG2+8Ablcjp9//hldu3aFQqEwe73Jkydj69at2LVrFx555BG0b9/eYExudYwbNw67du3Ctm3bMHr0aKxbtw4ajQZjx441OfEqPz9fPylPl8yaY64d7t69i8OHDwMA9u7di1u3bgEAzp49i23btkGj0aCkpAT79u0zGtN5v6ysLADaHnhrxcTEoHv37jh+/DhOnDiBbt26QRAEfdI7fvx4q69VE0wlCwD04yUzMzPNnisIAubPnw+1Wo133323yh5tc/cVEhICmUyG8vJy5Ofno3HjxgblkyZN0v9bJpMhKCgI3bt3x9SpU9GyZUuL91kTPv30UxQXF+Of//ynxZ7qF198EUePHsX333+PAwcOoHXr1gZjcqti6+vBlNzcXHz55ZcICAjAa6+9ZnESqznZ2dmYMWOG2Q+KAMxeNyMjw2jioZeXF9599139Bzx76f4emnseAdoPGunp6fq6ut7o0NBQSCQSs+fce32dCRMmYPfu3Vi3bp0+cdyxYwfy8vIwcOBANGnSRF/39u3b+thkMpnVj8WeuADtZNr72zkwMBBffvlltT9MkGMxySWX4+3tjTfffBOHDh3Cxo0bMXv2bPj6+tp9PV1vo7+/Px5++GGLdZs3b262bPz48diyZQt+/fVXhIaGIiMjA6+99prF60VFRWHr1q1YsWIF1q1bZ7TLW3X07NkTzZs3x08//YQnn3wSGzZswIMPPojIyEiTb+q6Xj6ZTFblUlQBAQEmj3/wwQfIy8vDP//5Txw+fBi///47AODatWsICAjAv//9b7z88stYsGCBwUYfpqSlpQGw/KZryvjx43H8+HGsW7cO3bp1w9GjR5GamorOnTtX+SHGmWzevBmJiYkYPnw4unfvbraeLb2Qptz7eyguLsb58+exZcsW/Pbbb1i3bp3Rm31Nio+Px7Zt29CtWzejSZv369atGzZv3oxvv/0Wv/76K65fv27Tfdn6ejBl4cKFKCwsxIcffgh/f3+7kty5c+fizJkz6NatG1566SXExMTAx8cHUqkUKSkpFnsK7504ptFokJubi4SEBHz22Wfw9vbGE088YXM81VGd516PHj3QsmVLHDt2DKmpqYiKitL3ZN//7ZejN/y5d+KZWq1GZmYmTp48iffeew++vr548MEHHRoP2Y9JLrkk3adwtVqNGzduGM2m1/UgmqJLJHU78jRt2hSA9mvxTz75xO6YOnXqhLZt2+Knn35C06ZN0bhxY6vGD4aGhqJjx4744Ycf0KNHD3z//fcQiURYsmSJfqywvcaNG4ePPvoIn3/+ObKzs80u1wVoE1d3d3dUVlbi/ffft3m3rN9++w3bt29HbGwsJkyYgNGjR+O5557DiRMnIJfLsXz5cnTo0AFz5szB22+/jXnz5uHrr782ez3dChqmVkqw5NFHH0VwcDB27dqFt99+Wz+EZOzYsTZdpyZkZGSYTKx1z8F7e6vuVVhYiIULF8LHxwdvvPGGxfvQXUP3oeB+mZmZUKlUcHd3h5+fn1H5/d+WVFZW4pVXXsFvv/2GNWvW4MMPP7R4//ZSqVR4//33IZVKq1xhQ6dVq1b6MbsjR47EggULAABz5szB5s2bqzzfltfD/XSrP3Tq1AmjRo2y+rx7KZVKHDhwABKJBMuWLTPqua5qhYiAgACjv1FpaWl46qmnMHfuXMTGxur/ntmqqufRvWW6urqft27dglqtNtlrev859xo/fjzef/99rFu3DiNGjEBiYiIiIyONksiQkBAA2m/dVCpVlb251Y2refPmRu189uxZjBs3Dv/4xz/wxx9/wNPT02IM5Bw4Jpdc0r1vBqbGdhYUFODAgQNGx8+ePYsbN25ALpfrJ5g1adIECoUCmZmZFr9CtMb48eNx9epVHDx4UD9Otyq3bt3C+++/D19fX3z66ac12msxYsQIyOVyfPfddwgJCbHYUy2VStGrVy+o1WqDcXLWKCgowPz58+Hp6YmPP/4YIpEI7u7ueOmllwBolw3q0KEDAO0Y0d69e2Pv3r3Ytm2byeudPn0a2dnZCA0NNbtMnKXHMWbMGFRUVGDZsmX4/fff4e/vXydfM5p6fIIgYPv27QBgtof23//+N+7evYuXXnqpyuXYdENLtm7danJSjG7t2K5du1o1iU8qlep7E3VfE9eGVatW4erVq5gwYYLVvcXnz5/HV199hfDwcLzzzjs236ctr4d7qdVq/WSzd9991+7XaFFRETQaDby8vEwOzTD3erAkIiIC7du3148dtVe3bt0gEolw4sQJk2sUJyQkID09HXK5XD8WOSQkBGFhYVCpVPjf//5ndE5RUZH+b4lu4ta9dJNyN2/ejFWrVgEAxowZY9S+wcHBUCgUKC0t1b92LAkJCUF4eLhVcVn6luReHTp0QHh4OPLy8nDt2jWrzqG6xySXXE5xcTE+++wzAEBkZKTZIQSfffYZcnJy9P8vLCzERx99BECbaN37Sfzll18GoJ2Ja2o90oqKCvz+++9V/nEbOnQoBgwYgH79+unXrLREo9HgzTffRFFREebNm6fvsagpPj4+mD59Ovr164eZM2eaHZ+m8/e//x1SqRTz5883meiq1WocO3YMp0+fNjj+0UcfITs7G6+88oq+lx0w/zWjbgLahx9+aDS+t7KyUr8WpqVJe5aMGTMGMplMv7i+rZOLasquXbuM2vH//u//kJycjKCgIJPr76akpOiHCTzzzDNV3segQYMQGBiIlJQULF68GIIg6MvOnDmDlStXAoDJhfXNOXToEADLY9uro6ioCF999RWCgoL0r72qlJaW4vXXX4darcann35q1wRCW18POps3b8bly5cxduxYo9VXbBEYGAhfX18UFhYaJbRbtmzB1q1bbb5mWlqafiKYLePX7xcWFoZHHnkElZWVmDdvHkpKSvRlOTk5+rkP48ePN3gt6Z6jX3zxhUHnQ0VFBd5//30UFhaiU6dOJtem9vb2xogRI5Cfn4+NGzfCzc0NI0eONBnf3//+dwDavzXHjh0zKo+Pj0dRUZH+/5MnT64yrg4dOlQ5/0Dn7Nmz+uS/Ou1MjsXhCuTUli9frv8aUhAE5OTk4Ny5cygoKICXlxc++eQTk4lUp06doFar8eijj6JHjx6QSqWIj49Hfn4+WrdujVdeecWg/iOPPILXX38dX3zxBSZOnIiWLVsiKioK7u7uuHPnDi5evAilUon/+7//s9iz6OnpafEr+Pt9++23SEhIwOOPP15rW7JOnTrV6rodOnTAggUL8M4772DGjBlo1qwZmjdvDm9vb+Tk5ODSpUsoKCjAe++9h06dOgHQLtK/ZcsWdO7c2WASkyWhoaF4/fXXMX/+fMyfPx+LFi0CoN3paPHixbh8+TIAbc/d/Tsy6XZVO3nyJObMmYM33ngDjRo1MqgTFBSERx55BNu3b4dIJKqToQqANiGYMWMGOnfujLCwMCQnJyM5ORlubm749NNPTX7lmZ2dre8xtCYJk8vl+Ne//oXp06fjP//5D3bu3Il27dohJycHx48fh1qtxvTp09GnTx+T569fv16/a1pJSQlOnz6NixcvQi6X27STly0KCwsBwGipKks++eQTpKSkYOrUqVYnJqbY8nrQyczMRKNGjfDqq6/afb8AIJFI8OKLL2LhwoX4xz/+gbVr1yI0NBRXr15FUlISpk6diuXLl5s9Py8vT/960P09TEhIQEVFBbp3716tdgGA9957D9euXcOhQ4cwcOBAdO/eHZWVlTh27BhKSkoQGxtr9KHk2WefRXx8PPbt24ehQ4cabLqQmZmJ0NBQ/Otf/zJ7n+PHj8cPP/wAQLtygbnx/o899hiSk5Px9ddf49lnn0Xbtm3RvHlzFBQU4Nq1a7h16xb27t2r7yGfMGECTpw4gZ07d5qMKyQkxGxc169f17fzvWNy1Wo1hg0bZvM8Aao7THLJqel6lHQ8PT0RFhaGJ554As8995zZ8Wdubm5YtmwZFi9ejN27dyM7OxuBgYEYOXIkZsyYYfKN9YUXXkCvXr2wZs0aJCQk4MCBA3Bzc0NQUBD69OmDAQMGVPtN5F6XLl3C4sWL0aRJE8ybN6/Grltdw4cPR4cOHbBq1SocOXIEx44dg1gsRlBQELp06YL+/fvrxxoXFRXh3Xffhbu7Oz766COb1vodN24cduzYgZ07d2LXrl0YNGgQLl26pE9wAcvrhd68eRM3b97EzJkzjZJcAOjVqxe2b9+OXr16mdzVyBEmT56MTp064fvvv8fevXshkUjQt29fvPzyyxaXn3riiSdseq717NkTmzZtwvLly3H06FHs2rULcrkcPXv2xMSJE9GvXz+z597bo6hbXeGJJ56o9dUVYmNjrV6zeP/+/Vi3bh3atGmjHwLjaK+//nq1JrjqPP/88wgNDcXKlStx+fJlXLlyBW3atMHy5cvRokULi0muUqk0GHssl8vRokULDBo0CJMnT7a6Z9qcxo0bY/369fjuu++wa9cu7Nu3DxKJBNHR0XjiiScwbtw4oyFYEokEX3/9NdavX4/NmzfjxIkTqKysRGhoKP72t7/h+eefN/n61GnRogVCQkKQmZlZ5Tc3s2bNQo8ePbBmzRqcOnUKV65cga+vL5o1a4YJEyYYDO0Ri8X497//jT59+mDDhg04efIkKioqEBoaiueeew4vvPCC2bhycnL07SwSiSCXy/HAAw9g2LBhdn+7RHVDJNz73RaRi4uPj8ekSZMQGxuLNWvW1HU4ZCPdZLt7E11zNm3ahLfeegt79+412bMyZcoUHDlyBEuXLjXakKG29e/fHxkZGWZjIyKtkydPYvz48YiJiTG52Q5RdXBMLhHVO8ePH8eRI0cQHh6O/v3713U4RGSCIAhYsmQJAO2wB6KaxuEKROQ0Bg4cqN8ooSpdu3bFggULDMbwvfPOOygpKcH+/fsBAP/4xz+q/RUuEdWsvXv3Ys+ePUhKSsLFixehUCgcvsYvNQxMconIabRp0wZt2rSxqm5kZKTRWNsNGzZAIpEgLCwMU6ZMwZAhQ2ojTCKqhosXL2LTpk3w8fFB//798c4771i1vB2RrTgml4iIiIjqHY7JJSIiIqJ6h0kuEREREdU7THKJiIiIqN5hkktERERE9Q6TXCIiIiKqd5jkEhEREVG9wySXiIiIiOodJrlE/79RMApGwSgYBaNgFAw7AADInUQ9QWDA1gAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Проведем опрос миллиона пассажиров с целью выяснить, сколько времени они ждали автобус."
      ],
      "metadata": {
        "id": "IOLLvn-2GPLs"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "n_passengers = 1000000  # количество пассажиров\n",
        "\n",
        "# сгенерируем для каждого пассажира время его прибытия на остановку\n",
        "passenger_times = sps.uniform(scale=bus_arrival_times.max()).rvs(size=n_passengers)\n",
        "# найдем время прибытия следующего автобуса поиском по отсортированному массиву\n",
        "i = np.searchsorted(bus_arrival_times, passenger_times, side=\"right\")\n",
        "# вычислим интервал ожидания\n",
        "wait_times = bus_arrival_times[i] - passenger_times\n",
        "\n",
        "wait_times.mean()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9-uULktqGQyu",
        "outputId": "64a9ea1e-57a5-4e16-9f2b-4192765c6711"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "np.float64(10.010549715321439)"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Посмотрим на гистограмму времени ожидания прибытия автобуса и сравним ее с плотностью **ТОГО ЖЕ САМОГО экспоненциального распределения**."
      ],
      "metadata": {
        "id": "0C6Pb6UHGTkl"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "grid = np.linspace(-1, 70, 1000)\n",
        "\n",
        "plt.figure(figsize=(8, 5))\n",
        "plt.hist(wait_times, bins=50, density=True, label=\"Данные\", range=(0, 70))\n",
        "plt.plot(\n",
        "    grid,\n",
        "    sps.expon(scale=tau).pdf(grid),\n",
        "    lw=5,\n",
        "    alpha=0.5,\n",
        "    label=\"Плотность $Exp(\\\\tau)$\",\n",
        "    color=\"#FF3300\",\n",
        ")\n",
        "plt.xlabel(\"Время ожидания автобуса\")\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 481
        },
        "id": "GtFSx7KEGag8",
        "outputId": "04f73393-a762-4969-ab68-d72cfa454ff3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x500 with 1 Axes>"
            ],
            "image/png": 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JSa4LqFRVo8xMkqvNz0ZOeaGDIyIiIiJybixXcHFirRYB5aXNHQYRERGRU2GS63Rsr6sNKuFILhEREdGtmOS6ASa5RERERMaY5LqBoNLiOkuPEREREbVkTHLdgExVC3lFWXOHQUREROQ0mOS6iYDi/OYOgYiIiMhpMMl1NnaWHfgXFzRyIERERESui0muCykJbG32XACTXCIiImoEzz//PIYMGQKlUmnX9ampqejSpQu2bt3ayJHZhptBuJCSwCAElhVDZGK010tRCVSWAb4BzRAZERE1pbi4OKvbXrp0qQkjocY0a9YsJCUlWWzzyiuv4OGHH3ZQRMCZM2ewc+dOLFmyBN7e3nbdo0uXLrj33nuxYsUKjBkzBj4+Po0cpXWY5LoQtUSCMv9ABJaVmG5w8zrQqadjgyIiIofw8fHBnDlzTJ7TarX4+OOPHRwRNYQgCLhw4QI8PT3x5JNPmm03dOhQB0YFvP/++wgICMBDDz3UoPs8+eSTmDx5MjZu3Ih58+Y1UnS2YZLrYopbBTPJJSJqgXx9fbFw4UKT5zQaDZNcF5OZmYmKigr07dvX7NfV0TIyMvD7779j2rRp8PDwaNC9evbsiY4dO2Lz5s2YO3cuxGLHV8gyyXU29Uw8K27VGh0yr5g+efN6EwRERORkcjKAM0d0/+dp1M0djXkSKRDWHug1GAiPabYwvv32W7z00ktmz5sqb9i1axe+/PJLXLx4EWq1GlFRURg/fjxmz55tSH7qu6+p50hKSsKsWbOwYMECk4ndiy++iO3bt2P//v2IiIiwOaZbnTlzBuvXr8eJEydQUlKCwMBAxMbGYvLkyRgzZkyTxG+L8+fPAwB69OhhVXt9acOaNWtw1113GY5rNBo888wz2Lt3LxYvXownnngCAHD8+HHMmDEDs2fPRpcuXbB582ZcvnwZKpUKPXr0wIsvvojevXsbPcc333wDQRAwZswYo+NFRUUYPHhwvTEGBgbi6NGjEIlEAIAxY8Zg5cqVOHz4MO68806rXmdjYpLrYootTD5DcT5QrQS87KuhISJyejkZwE9fOndyq6fRANev6GIePaNZE10AGDFiBLp27Wr49/bt25GTk1On3XvvvYc1a9YgKCgIEyZMgLe3N3777Te8++67OHjwINavXw+ZTIauXbtiwYIFRteuWrUK4eHhmDRpUqPGbm1Melu2bMG///1vSCQSDB8+HFFRUSgqKsLZs2fx9ddfY8yYMUbxa7VaqNVqrF27tkniN+XcuXMAdCOe1li8eDGmTJmCDz74AMOGDTMkkv/+97+xd+9ePPnkk4YEF/gziU5MTMQXX3yBu+++G/369cPZs2eRnJyMxx9/HHv37kVQUJDhmiNHjkAqlaJXr15Gz61UKo2+1teuXcOOHTvQt29f3HHHHVCr1ZBKpYiIiDDEBQD9+vUz3JdJLtWr1tMLVXJf+Cgq654UBCAvC4iKdXxgRESOcOaIayS4t9KogbO/N1uSq9FoAAD33HOPUfKWnJxcJ8k9efIk1qxZg/DwcGzduhWtW+sGVv7+979jwYIF+PXXX/Hpp59i7ty56Nq1q1HSDPyZ5Dbmx++2xAQAV69exauvvgo/Pz989dVX6Nixo9H9bty4AQBG8Ws0GlRXVxuSXEeUD+iT3OPHj+P69bqfxIpEIjz11FOQSCQAdMnwmDFj8OOPP2LXrl0YO3Ys3n//fWzZsgVTpkzB888/b3T9hQsXAOhGYb/88kv06dPHcO6FF17A999/j61bt+Kvf/0rAEChUCA1NRUdO3aEl5eX0b0iIiKM+uSLL77Ajh07MHHiRDz00EOorq6Gl5eXIVY9fQJ/7Ngxe7qowZjkupI/3h0VBQWbTnIB3cd3THKJyF0V3mjuCOxTkNtsT61SqQDAqhrLb775BgDw1FNPGZJJAJBKpfjHP/6BxMREfPPNN4aE0hFsjenrr7+GWq3G008/XSfBBYC2bds6JnAL9JPOAN2osynt27evM1L+7LPPYu/evfjwww+Rn5+P1atX47777sNrr71W53r9/V955RWjBBcApkyZgu+//x5paWmGY3l5edBoNAgJCak3/osXLwJAnTc5t/Pz84Onp6fhjYWjMcl1QcWtWqN9dqbpkzfMHCcicgfBbXUlAK4mpF2zPXVlpW5QxNPTs962+sQoISGhzrmYmBiEhYUhMzMTVVVVDVoWKjk5GStXrqxzXJ88NSSmU6dOAQCGDBlid3z1uTV+b29vtGnTBgkJCQgNDbXq+vT0dFRVVeGOO+7AZ599ZvXzRkZG4uGHH8bnn3+Ot99+G4MHD8Z///vfOpO6ampqkJ6ejoiICIwdO7bOfQIDAwEAavWfn4qUlpYCAPz9/euNIzU1FWKxGLGx9Q+qBQQEoKioqN52TYFJrrOxYsez4lYW3mUV5AKqWkDWsFmRREROqddgXY2rK5UsSKRAzzua7en1yUurVq3qbVtRUQEACA4ONnk+JCQEubm5qKysbHCSm5ycbFVbW2PSt2/Tpo3d8dXHVPwymQyPPvooFi9eXO/1+lKFLl262Pzc+hpakUiEt956y+QIfWpqKtRqNYYMGWJUI6unL1O5dVRbX6JQU1Nj8fk1Gg2uXLmC9u3bQy6XG8phzKmpqbHqDVZT4I5nLkjhLYfSS276pFbLVRaIyH2Fx+gmcUXFAh6egETivA8PT12czTzpLCsrCwDqrFZgip+fHwCgsLDQ5PmCAt3umr6+vg2KacGCBbh06VKdh6kJX7bGpG+fl5fXoBgtuTX+kydP4tNPP0VQUBA++eQT/P777/Ver09ybdnkA9CtaPHBBx8gODgYgiBg48aNJtvpJ52Fh4ebPH/gwAEAxqPd+uS5rKzMYgwZGRmoqamxKkHXarUoLy83KjNxJI7kuiKRCEVBwYjINZPM5mYCkZ0cGhIRkcOExzT7SgWu5PTp0wgJCbFqZLNr1644f/48kpKS0L59e6Nz165dw82bNxEVFeXQHaxsjalPnz44d+4cDh06ZLImt7HJ5XIMHjwYc+bMwdtvv42zZ8/ijjssj9zrk1BbRnL379+PJUuWoEuXLli/fj2mTJmCL774Ao888gjatTMuh9GXfehLVW6Vl5eH77//Hp06dTIqAQkNDUVQUBAyMjIsxpGeng4A6Ny5c70xZ2RkQBCEemt3mwpHcl2IgD8/cigKslCywLpcIiICsHXrVuTl5eHee++1qv2DDz4IAPjf//6H4uJiw3GNRoO3334bWq3W0MZRbI1p+vTpkEql+Pjjj40mVundvHmzSeLMzMwEUH9Nq1arxcWLFyGVSq1OwpOTk/Hss88iIiICn3zyCYKCgrBw4ULU1NTggw8+qNNen0Tv3r0b1dXVhuPl5eV49tlnUVNTgyVLlhjV8opEIgwcOBDFxcWG0X9T9ImzNW909PXRpuqpHYEjuS7KYpLLulwiohbt6tWreOutt3D48GG0a9cO8+fPt+q6fv364YknnsAnn3yCcePGYdSoUfDy8sJvv/2GK1euYMCAAWa3Fm4qtsbUqVMnvPLKK3jllVdw//33Y/jw4YiOjkZJSQnOnj0LX19fsx/zW+vcuXP4+uuvAejWkD116hT27NmDoKCget9QpKWlQaFQwN/fH2vWrDHb7pFHHkGrVq1w4cIFPPXUU/D398e6desMtckTJkzAJ598gh07dmDOnDmG0VKVSoUrV66gW7duKC0txcSJE3HXXXdBqVRi3759KC4uxr/+9S+To8333HMP9uzZg8OHD2PatGkm44qOjgYArFu3Dvn5+ejVq5fZrYcPHz4MiUSCESNGWOyTpsIk19lYMfEM+LMu17taUfekVqtbLzei6T+mISIi53P9+nXk5OTgsccewxNPPGFTTeTixYvRtWtXfPnll9i+fbthd7FnnnkGjz32WIO3e7WHrTE99NBD6Ny5M9avX4/k5GTs378fgYGBiIuLw5QpUxocz4EDBwx1rV5eXggNDcX06dPx+OOPG22uYIp+lLW8vByrVq0y2cbLywvz5s3DtWvX8MQTT0AsFmPdunWIjIw0tBGLxXjmmWcwf/58LF++HOvXrwege4NTW1uLvn374pFHHsGbb76JLVu2QCwWo0+fPvjrX/+K+Ph4k89733334a233sJ3331nNsnt168f5s+fj82bN+PTTz/FvHnzTCa5lZWV2L9/P+66665mW7ZNJAhWZlVu7uzZswCs33mkyZw6DCT9DABQqVWQSWX47cYVlKmqcalTN1zp9GddS58zxwx1uQEyLwxte0t9TN87gfjmeefkDBQKBS5evIiuXbtCLjczSY9MYt81DPvPfrf2nVgsRkZGBmJiYuosTE916TczMLUgP9XPnfpv27Zt+Oc//4k33njDroT+448/xooVK/DDDz9YNTHOXN99+eWXeO211/Dll19iwIABVj13dXV1vT/3tuRrrMl1YUWtTC+nAkA3+YyIiIhaFP26wvYsTwYAc+bMQVhYGD788EO7Y6ipqcGaNWtw3333WZ3gNgWWK7iS25a6s1yXm8O6XCIiohbmwoULkEqlVm3UYIq3tzfeeecdHDt2DEqlEt7e3jbfIycnB1OmTDG5JJwjMcl1YQq5D6q9vOFVrax7knW5RERELYpWq8WlS5cQExPToA0YEhISGrQiQocOHbBw4UK7r28sTHKdjg0l0iIRioJCEG5pvVwmuURERC2CWCzGyZMnmzsMp8GaXBdXyLpcIiIiojqY5Lo4q+pyiYiIiFoYJrkuRLh95hn+rMs1SV+XS0RERNTCMMl1dX/U5ZrFkgUiIiJqgZjkOhs79uZgXS4RuSvuV0TUcjT2zzuTXDfAulwicjf6nZNUKlUzR0JEjqL/eW+sXeeY5LoBhdwHNd5mtg/VaoEb1xwbEBFRA8lkMnh6eqKsrIyjuUQtgCAIKCsrg6enJ2QyWaPck+vkuhBBVHfiGQBAJEJp6zaAxsyFuRlA+85NFhcRUVMIDg5GTk4OsrOzERAQAJlMBpG5/wdbOI1Gg5qaGgCNNwrWkrD/7NfQvhMEASqVCmVlZaisrER4eHijxcYk102UhoQBNwtNn8xOd2wwRESNwN/fHwBQWFiInJycZo7GuWm1WqjVakilUojF/JDWVuw/+zVW33l6eiI8PNzwc98YmOQ6Gzs/lisJbms+yS26CSirAG+fBgRGROR4/v7+8Pf3h0qlgkZj7uMqUiqVSE9PR/v27eHtbWZZSTKL/We/xug7iUTSaCUKt2KS6yZqveVAYDBQaibRzckAOvVwbFBERI1EJpM1yS9Bd6HVagHoRsO8vLyaORrXw/6znzP3Hcfk3Ul4B/PncliyQERERC0Hk1x3Eh5j/hyTXCIiImpBmOS6k7bRgLmZxxWlQHmJI6MhIiIiajZ21eSmpqZi1apVOHbsGJRKJSIjIzFp0iTMnj0bUqn1tzx27BhWr16NM2fOQKVSoUOHDpg+fTqmTJlisn1RURHWrVuH3377DTk5OfDw8EB4eDjGjBmDadOmwdfX156X41wash6klzcQ0g7INzMLOScd8O9v//2JiIiIXITNI7kpKSmYOnUqDhw4gKFDh2LmzJkQiURYvnw5Fi1aZPWi3Xv27MGsWbNw8uRJjBo1CtOmTUNpaSmWLFmCN998s0777OxsjB8/HuvWrUOrVq0wbdo0jBs3DgqFAsuXL8fDDz+M6upqW1+O2/CXeUErCFbX5Wq5uDoRERG5MZtGcjUaDV5++WVUV1dj7dq1GDZsGABg0aJFmDNnDvbt24cdO3ZgwoQJFu9TUVGBpUuXQiqV4ssvv0TXrl0BAPPnz8fkyZOxYcMGjBo1Cv37/znquG7dOhQVFWHhwoVYsGCB4bhWq8UTTzyBw4cPY/fu3bj//vtteUluQy71gFgkwubqErS9ccVkG1XRdfzeygdtfQLxeJfBDo6QiIiIyHFsGslNSkpCRkYGEhISDAkuAHh4eGDRokUAgE2bNtV7n927d6O0tBTjxo0zJLgA4Ofnh3nz5pm8T1ZWFgBg+PDhxi9ALMZdd90FACguLrbl5bgcszue3eKqtxdKNLUoU1XXeSiqylB2IwM3FGUOiJaIiIio+dic5ALAkCFD6pzr378/5HI5Tp06hdraWov3OXr0qNn76I8lJycbHe/cWbct7YEDB4yOa7VaJCYmQiwWY9CgQda9EDcmSCQobhVs9nxIUb4DoyEiIiJqHjaVK2RkZAAAoqKi6pyTSCSIiIjA5cuXkZWVhY4dO5q9T2Zmptn7hISEQC6X4+bNm1AqlYbdM5544gn8+uuvWLFiBZKSktCtWzeoVCocOXIE+fn5eOONN9CtWzdbXk4dgiBAoVA06B4NJa5WQqJSAdCVh8ikti9+XtA6FMFmktng4gLk/vF3pVJpdQ21q1EqlUZ/kvXYdw3D/rMf+85+7LuGYf/Zz9F9JwgCRFZ8sg3YmORWVlYC0JUVmOLjo9s2try8vEH38fX1hUKhQEVFhSHJbd26NTZv3oyXXnoJ+/fvN4wGi0QiTJ48GYMHN7zGVKVS4eLFiw2+T0P4XrsGv8ICAIBMKrNr95DC1qFmzwWVFOLGH1tjZmRkuP0PtP4NFdmOfdcw7D/7se/sx75rGPaf/RzZdx4eHla1c5ltfbOzs/HUU0+hpqYGa9euRf/+/aFUKrF//3785z//wa+//opNmzYhMjLS7ueQyWTo1KlTI0ZtO3F1ASTF1wHA7n3ay/0CoJJ5QKaqWzYi0WjgX6Lb+jcmJsatR3IzMzMRHR3NfchtxL5rGPaf/dh39mPfNQz7z36O7rurV69a3damJFe/Dm1FRYXJ81VVVQAAf3//Bt3H1EjvSy+9hMuXL+P7779Hly5dDPeZNm0aampq8NZbb+Gjjz7C22+/bcMrMiYSiSCXy+2+vlF4egH6/dmtG42vQxCLURgUgrZ5ptfLbVV4AwBaxA+yt7d3839NXRT7rmHYf/Zj39mPfdcw7D/7OarvrC1VAGyceBYTo9s29tq1a3XOaTQaZGdnQyKR1DuaGh0dbfY+BQUFUCgUCAsLMyRhlZWVSE5ORmBgoCHBvVVCQgIA4Pz587a8HNdjwxe2sHWI2XOt8m80RjRERERETsumJFefTB46dKjOuRMnTkChUKBPnz711kroV0EwdR/9sfj4eMMx1R8TsSorK02u3FBSotuuViazfZKW82mc8oGC1m3MnvMtKwaUVY3yPERERETOyOYkNzo6GklJSUhMTDQcr62txYoVKwAA06ZNMxxXKpVIS0tDbm6u0X1GjRqFwMBA7Ny502iiV0VFBVavXg0AmDp1quF4q1at0LFjR6jVanz88cdG96qpqTEc4xJif1L4+ELh7WPynEgQjHY/IyIiInI3NtXkSiQSLFu2DLNnz8b8+fMxZswYhISEIDExEVeuXMHIkSMxfvx4Q/szZ85g1qxZiI+Px8aNGw3H/fz88Oqrr+LZZ5/FjBkzMHbsWPj4+GDv3r3IycnBzJkzMWDAAKPnXrJkCebOnYv//e9/OHLkCPr27Yvq6mocPHgQOTk5iIqKwpNPPtnA7nAv+cFtEJ1lJpnNugp06unYgIiIiIgcxObVFfr164ctW7Zg5cqVSExMhEKhQGRkJBYvXozZs2dbXRA8atQoBAUFYfXq1di1axfUajU6duyIp556CpMnT67TfvDgwdi2bRvWrVuHY8eO4csvv4RYLEZkZCT++te/4oknnqh3wltLUxBST5IrCDbV+RIRERG5CruWEOvSpQs++uijetslJCTg0qVLZs/Hx8cb1d5a87zLly+3ur27sbVatzAoFFqxGGKttu5JZRVQdBMIbtsosRERERE5E5tqcsm1aKRSFAe2Nt8gy/q15oiIiIhcCZNcZ9PImzMUBJtfZQHZaY36XERERETOgkmum7OY5N68DtTWOC4YIiIiIgdhkuvmyv0CUOPpZfqkVgvkZjg2ICIiIiIHYJLrQgR7VkIQiZBvaTSXdblERETkhpjktgAWSxb0S4kRERERuREmuc6mCRLOgtah5k9WlAJlxY3+nERERETNiUluC6Dy8ERpQCvzDbJZskBERETuhUluC8G6XCIiImpJmOS6FPu34C0IDjN/MjcTUKvtvjcRERGRs2GS20KUBrSCSiozfVKtAm5kOjQeIiIioqbEJNfZNNFKB4JYbHmVhWuXm+R5iYiIiJoDk9wWJD/EQpJ7/TKXEiMiIiK3wSS3Bcm3VJdbUQqUFDgsFiIiIqKmxCTXhQj2zzsDANR6eqEkMMh8g+ssWSAiIiL3wCS3hckPsTCay7pcIiIichNMcp1NE9fF5oW0tXAyC6hWNOnzExERETkCk9wWptwvADXectMnBQHITnNsQERERERNgEluSyMSoahNuPnzLFkgIiIiN8Ak16U0cObZH4pDLSS5WVcArbZRnoeIiIiouTDJbYFKg8MAidT0yZpqXW0uERERkQtjkut0mn5DBq1UCoTHmG/AkgUiIiJycUxyW6r2sebPcb1cIiIicnFMcluqKAtJbkkBUF7iuFiIiIiIGhmTXBciiBpn4hkAwDcAaN3G/HmO5hIREZELY5LbklkqWci85Lg4iIiIiBoZk1xn08Q7ngGAv8wLWkGwnOTeyASqlQCga0tERETkQsysI0XuTC71gFgkwrria4gqzoJHTbXJdql7P4Uktg8e7zLYwRESERERNQyT3BbsRnUFqgMD0T4703SDzFTciLCw1BgRERGRk2K5ggtpiqKBm6HtzJ4LLcyDWK1ugmclIiIialpMclu4wtah0JjZ/UyiUSOw8KaDIyIiIiJqOCa5zsbBk7y0Egnyg80vJRZ8k1v8EhERkethkku40cZ8yULrm9mAVuvAaIiIiIgajkkuIT8kzOxGE7LaGiCPo7lERETkWpjkupLG3PHsFmqZBwqDQsw3yExtkuclIiIiaipMcgkAcNNCyQIyUx1eK0xERETUEExynU0zJZN5oW3NnywvAYrzHRcMERERUQMxySUAQLWXHKUBrcw3YMkCERERuRAmuWRgaWMIJrlERETkSpjkuhChaeadGVisyy28oStbICIiInIBTHLJoNLHD1VyX/MN0i84LhgiIiKiBmCS62yacxUDkQg32oSbP59+3nGxEBERETUAk1wyciPMQpJbkAtUlDosFiIiIiJ7McklI2X+gVB4+5hvwJIFIiIicgFMcl1KE888A3QlC5ZGc1myQERERC6ASS7VkRsWYf5kfg5LFoiIiMjpMcl1Os2/fW6ZfyCU3nLzDViyQERERE6OSS7VJRJZHs3NYJJLREREzo1JLplkcSmxvGygssxxwRARERHZiEmuCxFEDph49ofSgFYsWSAiIiKXxSSXTBOJkMuNIYiIiMhFMcl1Ns2549ltLC4lxpIFIiIicmJMcsms0oAgVHNjCCIiInJBTHLJPJEIhe3amz+fds5xsRARERHZgEmuC2mOQoaCdlHmT+bnAGXFjguGiIiIyEpMcsmiisDWgF+g+QZXzzosFiIiIiJrMcl1Nk408QwAIBIBnXqaP3/1rPPFTERERC0ek1yqn6Ukt7QQKMpzXCxEREREVmCSS/ULCgVatzF/niULRERE5GSk9lyUmpqKVatW4dixY1AqlYiMjMSkSZMwe/ZsSKXW3/LYsWNYvXo1zpw5A5VKhQ4dOmD69OmYMmWK2WvKysrwySefYP/+/cjJyYFMJkN4eDiGDBmCxYsX2/NyXIcDdzyro1NP8yO2V88CCSObNz4iIiKiW9g8kpuSkoKpU6fiwIEDGDp0KGbOnAmRSITly5dj0aJFEKysz9yzZw9mzZqFkydPYtSoUZg2bRpKS0uxZMkSvPnmmyavuXr1KsaNG4d169YhIiICM2fOxKRJk9C2bVv89NNPtr4UskXHHubPVZUDN687LhYiIiKietg0kqvRaPDyyy+juroaa9euxbBhwwAAixYtwpw5c7Bv3z7s2LEDEyZMsHifiooKLF26FFKpFF9++SW6du0KAJg/fz4mT56MDRs2YNSoUejfv7/hGoVCgaeeegq1tbXYvHkzevY0rhNVqVS2vBTn5WSTuPxlXtAKAsR+gUBYe/PJ7NWzQNsoXVuO6BIREVEzs2kkNykpCRkZGUhISDAkuADg4eGBRYsWAQA2bdpU7312796N0tJSjBs3zpDgAoCfnx/mzZtn8j6bNm3C9evX8dxzz9VJcAFAJpPZ8lLISnKpB8QiEdalHsF6dTl+u3HF5GP/wW+x/sJBJrhERETkFGwayU1KSgIADBkypM65/v37Qy6X49SpU6itrYWHh4fZ+xw9etTsffTHkpOTjY7v3LkTIpEIo0ePRlZWFn777TdUVVUhIiICQ4cOha+vry0vhWx0Q1GGvAB/BKtrIDI12qyqRvW1VKDbnY4PjoiIiOg2NiW5GRkZAICoqLq7YEkkEkRERODy5cvIyspCx44dzd4nMzPT7H1CQkIgl8tx8+ZNKJVKeHt7Q6VS4dKlSwgKCsKmTZvwwQcfQKPRGK4JCAjA22+/jeHDh9vycuoQBAEKhaJB92goSU0NxH+UXmg0Gsikf45QC2jeUdJaTy8Utg5FSKHpCWih2ZkAAKVSaXVtdlNRKpVGf5L12HcNw/6zH/vOfuy7hmH/2c/RfScIAkRWfmpsU5JbWVkJQFdWYIqPjw8AoLy8vEH38fX1hUKhQEVFBby9vVFWVga1Wo3S0lJ88MEHWLRoESZNmgSxWIwdO3bg3XffxaJFi7B9+3Z06tTJlpdkRKVS4eLFi3Zf3xgCsrMgLywAAMikMnh5eTVrPLfLaRtpNsltfTMbUNUiIyPDaf6j0L+hItux7xqG/Wc/9p392HcNw/6znyP7zlK1wK3sWkLM0bRaLQDdyOb06dPx17/+1XBuzpw5yM/Px/r16/H555/j9ddft/t5ZDJZg5LkxiApSodYUQQARqPVzuJmm3bQnhdD/MfX5FYSjRpIv4CYmDinGMnNzMxEdHQ0vL29mzUWV8O+axj2n/3Yd/Zj3zUM+89+ju67q1evWt3WpiRXX/daUVFh8nxVVRUAwN/fv0H3uX2k99YR3xEjRtRpP2LECKxfvx5nzzZsUwKRSAS5XN6gezSYhwegn0TnhHO41FIZboa2Q7ub2aYbXDkN77g+Do3JEm9v7+b/mroo9l3DsP/sx76zH/uuYdh/9nNU31lbqgDYuLpCTEwMAODatWt1zmk0GmRnZ0MikSAyMtLifaKjo83ep6CgAAqFAmFhYYZ3BN7e3mjbti0A0wl0QEAAAKC6utr6F0N2y27X3vzJnAygotRhsRARERGZYlOSm5CQAAA4dOhQnXMnTpyAQqFAnz596q2VGDRokNn76I/Fx8cbHb/jjjsAAFeuXKlzjf5YeHh4fS/BtTnJyG5BcBvUeniab3DljOOCISIiIjLB5iQ3OjoaSUlJSExMNByvra3FihUrAADTpk0zHFcqlUhLS0Nubq7RfUaNGoXAwEDs3LnTaKJXRUUFVq9eDQCYOnWq0TXTp0+HSCTC//3f/6GkpMTkNWPHjrXl5ZCdBLHY8mju5VNOt6kFERERtSw21eRKJBIsW7YMs2fPxvz58zFmzBiEhIQgMTERV65cwciRIzF+/HhD+zNnzmDWrFmIj4/Hxo0bDcf9/Pzw6quv4tlnn8WMGTMwduxY+Pj4YO/evcjJycHMmTMxYMAAo+fu1asX5s2bh//973+YMGEC7r77bojFYhw4cAA3btzAyJEjcf/99zesN8hq2e3ao0Nm3VF1AEBZMZCXpdshjYiIiKgZ2Ly6Qr9+/bBlyxasXLkSiYmJUCgUiIyMxOLFizF79myrC4JHjRqFoKAgrF69Grt27YJarUbHjh3x1FNPYfLkySaveeaZZxAbG4sNGzZgx44d0Gg0iImJwZw5c/DII49ALLZpYNpJucYIaLl/IMr9AuBfUWa6weXTTHKJiIio2di1hFiXLl3w0Ucf1dsuISEBly5dMns+Pj6+Tu1tfcaMGYMxY8bYdA01jezwKHRLNVN/m3YOGDwKkHK7ZSIiInI8dxj6bDEEG5bNcITstpHmY6qtATLNv8EhIiIiakpMcslutZ5eyA8JM9/g8imHxUJERER0Kya51CAWV1nITgOqTG/4QURERNSUmOQ6GxdbeisvpC1UMjPrIgsCcOW0YwMiIiIiApNcaiCtRIKcsAjzDVJPulziTkRERK6PSa4LEZxly7PbZIdHmT9ZVgTcqLt9MxEREVFTYpJLDVYa0AoVvv7mG6SmOC4YIiIiIjDJpcYgEuF6RLT58+kXgGqlw8IhIiIiYpLrbFy0fjW7XXtoze04p1EDV81sGkFERETUBJjkUqNQeXiisK2F5cRSU1w2gSciIiLXwyTXlTjnvDODm1GdzJ8sygMKch0XDBEREbVoTHKp0ZS2bgMEBJlvcPGE44IhIiKiFo1JLjUekQjo0s/8+bRzQG2N4+IhIiKiFotJrrNx9brV2D6AuQloqlog/bxDwyEiIqKWiUkuNS65L9A+1vx5liwQERGRAzDJdSHOuuNZHV37mz+XnwMU3nBcLERERNQiMcmlRuMv84JWEICIjoCPhR3Qzh8DAF1bIiIioiYgbe4AyH3IpR4Qi0RYd+l3eEhUiLpxxWQ7TX4GrrXyw+xedzs4QiIiImopmOQ6GzcY3byhKENBcGsEnK+ByNTrUQHC5VMAk1wiIiJqIixXoCZR7SXHzdB2Zs+3zbzsFgk9EREROScmudRkMtt3MHtOXlkO5KQ7MBoiIiJqSZjkuhKRi6yu8IeioBBU+vqZb/DHBDQiIiKixsYkl5qOSITMyI7mz1+7BFSWOS4eIiIiajGY5Dod96pTzQ5vD43EzPxGQQAuHHdsQERERNQiMMmlJqWWypDVrr35BqkpgFrtuICIiIioRWCSS03umoUJaFBWARkXHBcMERERtQhMcl2IqxYyVPgFoLhVsPkG55K4nBgRERE1Kia55BAZ7S1MQMvPAfKyHBcMERERuT0muc7GTUc0b7Zph2pPL/MNzh51XDBERETk9pjkkkMIYrHl2tyMi0BFqcPiISIiIvfGJJccJjOyA7RiM99ygqCrzSUiIiJqBExyXYjgYjue3U7l4YnsdlHmG6SmALU1jguIiIiI3BaTXHKo9OhO5k/W1gCXTjouGCIiInJbTHKdjZtOPNOr9PVHQXAb8w3OJQFareMCIiIiIrfEJJccLj3KwmhueQlw7ZLjgiEiIiK3xCSXHK4guA2q/ALMN+ByYkRERNRATHJdimtPPDMQiZDToYv58zeu6TaIICIiIrITk1xqFvnhMYCX3HyDU4ccFwwRERG5HSa5zsbNJ57paaVSoNsA8w0yU4HSQscFRERERG6FSS41n+7xgERq+pwgAKePODYeIiIichtMcqn5yH2BLn3Nn79yGqgsd1w8RERE5DaY5LoQwU3mnQGAv8wLWkEAeg0GzO3kptEA53QrLWhbSBkHERERNQ4znxUTNS251ANikQjrci8iQFSL0NxMk+00BZm4Jpdhdq+7HRsgERERuTSO5DqbFjZieUNRhqS27VCmqjb5qFRWQHTxeHOHSURERC6GSS41u/KAVihoHWr2fHh6KqBWOTAiIiIicnVMcskpXO0QZ/acR001cOmU44IhIiIil8ck15WYm6DlBoqCQlAa0Mp8g1OHdBPRiIiIiKzAJJecg0iEtJhY8+cry4DLpxwWDhEREbk2JrlOp2VNPLvVzdB2qJL7mm9w8iBHc4mIiMgqTHLJaQhiMa507GK+QUUpcOWMw+IhIiIi18Ukl5xKTttIVMl9zDc4+Rug1TouICIiInJJTHJdSEsoZKh3NLe8hKO5REREVC8mueR0ctq2h8Lb0mjuQY7mEhERkUVMcsnpCGIxrlhYNxdlRUDaOccFRERERC6HSa6zaWHb+pqTHR4FpbfcfIMU1uYSERGReUxyySkJYjGuxlgYzS0tZG0uERERmcUk14UIbrzjmSlZ4VFQelkYzT1xAFCrHRYPERERuQ4mueS0tBIJrnawsAtaRSmQmuKweIiIiMh1MMklp3Y9IgbVFndB+w1QqxwXEBEREbkEu5Lc1NRULFiwAAkJCejVqxfGjh2LTz75BGobPzo+duwYHn/8cQwcOBB9+vTBAw88gK1bt1p1bW1tLcaNG4e4uDgMHTrUnpfhnDjxzIggFuNaXE/zDRSVwLlkxwVERERELsHmJDclJQVTp07FgQMHMHToUMycORMikQjLly/HokWLIFiZpO3ZswezZs3CyZMnMWrUKEybNg2lpaVYsmQJ3nzzzXqvf++995CTk2Nr+OSC8sJjgMBg8w1OHQJqqh0XEBERETk9m5JcjUaDl19+GdXV1fjoo4+wfPlyLF68GN9++y0GDBiAffv2YceOHfXep6KiAkuXLoVUKsWXX36J119/HS+++CK+//57REdHY8OGDThx4oTZ65OSkvDZZ5/hhRdesCV8N9CyJp4ZiMXAwOHmz9cogbO/Oy4eIiIicno2JblJSUnIyMhAQkIChg0bZjju4eGBRYsWAQA2bdpU7312796N0tJSjBs3Dl27djUc9/Pzw7x58yzep7KyEi+99BLuuOMOTJ8+3ZbwyZXFdAWC25o/f+Z3QFnluHiIiIjIqdmc5ALAkCFD6pzr378/5HI5Tp06hdraWov3OXr0qNn76I8lJ5uus3zjjTdQVlZmVUkDuQd/mRe0gOXRXFWtboMIAFpBgKiFLbdGRERExqS2NM7IyAAAREVF1TknkUgQERGBy5cvIysrCx07djR7n8zMTLP3CQkJgVwux82bN6FUKuHt7W049/PPP2P79u1444030K5dO1tCt4ogCFAoFI1+X1tIaqohVulWC9BoNJBJZc0ajzOQSz0gFomwriofoTVlCCjON9lOyEtDlgfwyMDRqKmpAQAolUpHhuoW9H3GvrMP+89+7Dv7se8ahv1nP0f3nWDDQJZNSW5lZSUAXVmBKT4+PgCA8vLyBt3H19cXCoUCFRUVhiS3sLAQ//rXvzB06FBMmTLFlrCtplKpcPHixSa5t7WCcnPhWVgAAJBJZfDy8mrWeJzJDWU5rkZFY3DedbNtvE/+BgwcjdzcXAB/vqEi27HvGob9Zz/2nf3Ydw3D/rOfI/vOw8PDqnY2JbnNacmSJdBoNHjjjTea7DlkMhk6derUZPe3huT6SYgF3SikRqMxOifwE3gUB4UgPyQMoQU3TZ4Pyb0O3MxCu3btkJaWhujoaKNPA6h+SqUSmZmZ7Ds7sf/sx76zH/uuYdh/9nN03129etXqtjYlub6+ukX5KyoqTJ6vqtJN/PH392/QfW4f6f3uu+/w66+/4j//+Q/atGljS8g2EYlEkMstbCPrCB6egOyPEgUmtSZdjO1hNskFABzdC897dZMSvb29m/9r6qLYdw3D/rMf+85+7LuGYf/Zz1F9Z8ucG5smnsXExAAArl27VuecRqNBdnY2JBIJIiMjLd4nOjra7H0KCgqgUCgQFhZmeEdw/vx5AMA//vEPxMXFGT0AIC8vz+jf5N4q/AJwPSLafIO8LIgyUx0WDxERETkfm0ZyExISsHr1ahw6dAhz5841OnfixAkoFAr079+/3lqJQYMGYefOnTh06BDGjh1rdO7QoUMAgPj4eMOxvn37mp0Qtm3bNnh7e9e5j8vijmdWudSpG8JvZEFyW0mHnvjEr0CXOx0cFRERETkLm5Pc6OhoJCUlITEx0bBWbm1tLVasWAEAmDZtmqG9UqlEbm4uvL29jVZDGDVqFP773/9i586dmDVrlmGt3IqKCqxevRoAMHXqVEP7MWPGYMyYMSZj2rZtG/z9/bmkWAtT4+WNtOjOiE0zPWIrKi+GT+YFoHsPB0dGREREzsCmJFcikWDZsmWYPXs25s+fjzFjxiAkJASJiYm4cuUKRo4cifHjxxvanzlzBrNmzUJ8fDw2btxoOO7n54dXX30Vzz77LGbMmIGxY8fCx8cHe/fuRU5ODmbOnIkBAwY03qt0GyzSvVVaTByisjPhaWZLX9/LJ4AR4wDWVxEREbU4Nq+u0K9fP2zZsgUrV65EYmIiFAoFIiMjsXjxYsyePdvqguBRo0YhKCgIq1evxq5du6BWq9GxY0c89dRTmDx5ss0vhFoejVSKS526odf5FJPnxaoaiE/+Box4wMGRERERUXOzawmxLl264KOPPqq3XUJCAi5dumT2fHx8vFHtrT0s3Z/cX1Z4FDpcuwLfStMrdYhTU4Deg4HgMAdHRkRERM3JptUVyBE48cwWgliMC3G9zJ4XCQJw5CdO6CMiImphmOSSy8sPboP8EAsjtTeuAekXHBcQERERNTsmuS5EsGEB5BZFJML5uF7Qii18Ox/dC6hqHRcTERERNSsmueQWqnz9kBFlYUvmyjLg9GHHBURERETNikkuuY0rHbug1svCvtmnDgPlJY4LiIiIiJoNk1xnwwlSdlNLZcjo2sd8A42ak9CIiIhaCCa55FbyIjoAbSLMN7h2Gci46LiAiIiIqFkwyXUhHH+0gkgEDB5tuc2Rn4DaGsfEQ0RERM2CSS65n9BwCHF9IZPKTO+EXFUBHP/V4WERERGR4zDJJbfiL/OCVhDgMWQMQiKjdImuKeeSgMIb0LI+l4iIyC3Zta0vNSEmXQ0il3pALBJhXeYpaAPliDt52mxb8ZYPMGTeMl2JAxEREbkVJrnklm4oypDVqhVkfr5oXVxosk1AfjZw4TjQI97B0REREVFTY7mCK+GIo21EIpzp1s/yTmjH9us2iiAiIiK3wiSX3FqVrx/SYmLNN6itAX7bwTIRIiIiN8Mkl9zelQ5xqJL7mG+QdRW4csZxAREREVGTY5LrbDii2Oi0EinOdutrudGRn3RLixEREZFbYJJLLUJhcBtkhUeZb1BTDRz6kW8yiIiI3ASTXBcicOJZg1yI64UaTy/zDTJTgfQLjguIiIiImgyTXGoxVB4eOFNf2cLhXYCyyjEBERERUZNhkkstSl6bdsgNizDfQFnFsgUiIiI3wCTX2TC5anLnuvZGrczDfIP0C1xtgYiIyMUxyaUWp9bTC+e69bHc6PAuoKLUEeEQERFRE2CSSy1SblgECsMizTeorQEOfMeRdSIiIhfFJJdaJpEIV3rFA15y821yM4EzvzssJCIiImo8THKpxVJ5eQPDJlhudGw/UJzvmICIiIio0TDJdTr8eNyhorsAcRaWFdNogF++AdRqx8VEREREDcYkl1osf5kXtIIADB4F+AWab1iUBxzdAwC69kREROT0pM0dAFmPO541LrnUA2KRCOvST0AREoheV45DZC6JvXEF+TVlmDziYccGSURERHZhkkst3g1FGbK8PKAOb49OGZfMtgs6+jMQP8byqC8RERE5BZYrEP3hcqeuKPMPNHteqqoF9n+jq9MlIiIip8Yk19mw5rPZaCUSpPSOh0Zi4QOOvCzgxAGHxURERET2YZJLdIsqHz+crW83tFOHgKyrDomHiIiI7MMk14UI4MQzR8gOj0J2u/bmGwiCrmyB2/4SERE5LSa5RCac7dYXVXJf8w1qlMDPW7h+LhERkZNikktkgkYqxYne8dCKLfyIFOQCR35yXFBERERkNSa5zoYTz5xGeUArXIjrZbnRxRPApVMOiYeIiIisxySXyILM9h2Q0zbScqODO4HCm44JiIiIiKzCJNeVcN6Z44lEONO9Hyp8/c230aiBvZsAZZXj4iIiIiKLmOQS1UMjleJ430FQy2TmG1WU6iaicaMIIiIip8Akl8gKVT5+uNTnDsuNblwDDu9iXTUREZETYJLrbJggOa2itu2B3n+x3OjiCeB8smMCIiIiIrOY5BJZyV/mBe3A4UB4jOWGR3YD2WnQ8g0LERFRs5E2dwBErkIu9YBYIsFnkZFoe/F3eFdVmG3r/dV/MXDuG0BgawdGSERERHocyXUh3NbXOWRrarC3Rx8UCWqUqapNPpSKcmD3V0C1ornDJSIiapGY5BLZodLXHym9Eyw3KisCdn8NqFWOCYqIiIgMmOQS2Sk/JAwX4npabpSXBfzyLaDVOiYoIiIiAsAk1/lwspJLSY/ujKzwKMuNMi4Cv+/h15aIiMiBmOQSNYRIhLPd+qK4VT0TzM4lAWd+d0xMRERExCTXpYg48cwZaSUSHOt7B6rkvpYbHt0LXD3nmKCIiIhaOCa5RI1A5eGJpAFDUOvhabnhge1A1lXHBEVERNSCMcklaiQKuQ+S+w2GRiIx30ijAfZuBm5ed1xgRERELRCTXKfDyUmurDQwCCd6J0CwVFqiVgE/fQkU3nBcYERERC0Mk1yiRpYf2hZXew603Ki2BvhxI1Ba6JigiIiIWhgmuS5E4Lwzl1HVuRe0fe+03KhaAezcAFSUQsvlxYiIiBqVtLkDIHJHcqkHxAMHY0/6SXinnrTY1mPddQx64lXAN8BB0REREbk/juQSNRWRCMc6d8P5kFCUqarNPmpLC4AdnwGVZc0dMRERkdtgkuts+LG1exGJcLpHf9wMbWe5XXkJE10iIqJGxCSXqIkJYjFSesejsHWo5YZMdImIiBoNk1wXIoAzz1yVfle0ksB6tv9loktERNQo7Jp4lpqailWrVuHYsWNQKpWIjIzEpEmTMHv2bEil1t/y2LFjWL16Nc6cOQOVSoUOHTpg+vTpmDJlSp22J06cwP79+5GUlIScnBxUVlYiNDQUd9xxB+bOnYuoqCh7XgqRw2ikUiT1H4xBxw8hsKzEfMPyEuCHT4Gxs4CAIMcFSERE5EZsHslNSUnB1KlTceDAAQwdOhQzZ86ESCTC8uXLsWjRIghW1pTu2bMHs2bNwsmTJzFq1ChMmzYNpaWlWLJkCd5888067f/2t7/h008/hYeHB8aPH4+ZM2ciNDQU27Ztw/3334+TJy3PYCdyBmqZB44OGILSgFaWG1aUAj+sB4rzHRIXERGRu7FpJFej0eDll19GdXU11q5di2HDhgEAFi1ahDlz5mDfvn3YsWMHJkyYYPE+FRUVWLp0KaRSKb788kt07doVADB//nxMnjwZGzZswKhRo9C/f3/DNY8++igmTpyINm3aGN1r9erVeP/997F06VLs2LHDlpfjnDjxzO3pE916R3QVlboR3TGPAKHhjguQiIjIDdg0kpuUlISMjAwkJCQYElwA8PDwwKJFiwAAmzZtqvc+u3fvRmlpKcaNG2dIcAHAz88P8+bNM3mfuXPn1klwAeDJJ5+El5cXLl++jJISCwkDkROxekS3Rgns/BzIyXBMYERERG7C5iQXAIYMGVLnXP/+/SGXy3Hq1CnU1tZavM/Ro0fN3kd/LDk52aqYRCIRJBIJABj+dFucd+ZW9IluRatgyw1VtcBPXwKZlxwTGBERkRuwqVwhI0M3mmRqkpdEIkFERAQuX76MrKwsdOzY0ex9MjMzzd4nJCQEcrkcN2/ehFKphLe3t8WYdu/ejaqqKvTp0wf+/v42vJq6BEGAQqFo0D0aSlpbC5FKBUBXHiKTypo1HmpaapkHzgwajrE3i6DOTocAM+UqahWwayM0g0dDiOtnde27PZRKpdGfZBv2n/3Yd/Zj3zUM+89+ju47QRAgElk36mdTkltZWQlAV1Zgio+PDwCgvLy8Qffx9fWFQqFARUWFxSQ3KysLr7/+OiQSCf7xj3/UG399VCoVLl682OD7NETIzZuQVurKLmRSGby8vJo1Hmp6PnJ/aMeMhvTnLUDWVYttZUk/Q1NdhfPebaBSq5s0Lv2bUbIP+89+7Dv7se8ahv1nP0f2nYeHh1Xt7FpCzBkUFRXhySefRHFxMZYsWYJ+/fo1+J4ymQydOnVqhOjsJ73QBiIv3ZdFo9E0ayzkGHKpB8QyD6yPjkJA9gWE5F4z29ZX5oV+p48gLioOmr+MA5qgREepVCIzMxPR0dH1fpJCdbH/7Me+sx/7rmHYf/ZzdN9dvWp5MOhWNiW5vr6+AHSrI5hSVVUFAPWWDdR3n/pGeouKivDoo48iIyMDL7/8MmbOnFl/8FYQiUSQy+WNci+7eXgAsj9KFFiD26LkVlciOa4bempq0D4702Jbz2uXAHUtcM9UwLNpRvu9vb2b/+fBhbH/7Me+sx/7rmHYf/ZzVN9ZW6oA2DjxLCYmBgBw7VrdkSaNRoPs7GxIJBJERkZavE90dLTZ+xQUFEChUCAsLMzkO4L8/HzMnDkTV69exdKlS/Hoo4/a8hJcGnc8c3+CWIwz3fshPbpz/Y1zMoDv1+k2jyAiIiIjNiW5CQkJAIBDhw7VOXfixAkoFAr06dOn3lqJQYMGmb2P/lh8fHydczdv3sTMmTORnp6OV199FTNmzLAlfCLXIBLhQlxPXIjrWX/bkgJg+/8BuZlNHhYREZErsTnJjY6ORlJSEhITEw3Ha2trsWLFCgDAtGnTDMeVSiXS0tKQm5trdJ9Ro0YhMDAQO3fuNJroVVFRgdWrVwMApk6danRNTk4OZsyYgevXr+Ott96qc57IrYhESI+JRUrveGjF9fyYViuAHzcAF447JjYiIiIXYFNNrkQiwbJlyzB79mzMnz8fY8aMQUhICBITE3HlyhWMHDkS48ePN7Q/c+YMZs2ahfj4eGzcuNFw3M/PD6+++iqeffZZzJgxA2PHjoWPjw/27t2LnJwczJw5EwMGDDB67pkzZyInJwfdu3dHTk4OVq5cWSe+SZMmISIiwtY+cC7c8Yxukds2EtWeXhiY8jtkapX5hlotcHAnUJwH3DGqSSakERERuRKbV1fo168ftmzZgpUrVyIxMREKhQKRkZFYvHgxZs+ebXVB8KhRoxAUFITVq1dj165dUKvV6NixI5566ilMnjy5TvucnBwAwPnz53H+/HmT94yPj3f9JJfoNsVBITg86C4kHD+MAI3WcuPzx3QlDCMmA3JfxwRIRETkhOxaQqxLly746KOP6m2XkJCAS5fM79IUHx9vsvbWFEv3aTFsmFFI7qXS1x+HBt2F+86frr9xbibw7Rpg5BQgrH2Tx0ZEROSMbKrJJaLmU+PljYxh46Ht2L3+xlUVwI7PoD19hCUwRETUIrnsZhBELZGXlxziESOxs7IA/qcOW2zrK/NCv6N7gfxsYNhEwMPTQVESERE1Pya5ToejblQPkQin2segVq1AvzPHINHUs71v+gWgKA+45yGgdRvHxEhERNTMWK5A5KLy2rTDoUF3QeHtU3/jsiLderrnkli+QERELQKTXBfC1IRuV+EXgIN3DEdeSFj9jTVq4PBPwO6vAGVV0wdHRETUjJjkErk4lYcHjvUbjNTOVkxIA4DrV4Bt/wOyrjZtYERERM2ISS6ROxCJcLVjFxwdcCdqrZlgpqgEdn0BHNkNWNpkgoiIyEUxyXU2rJekBigMDsVvg4ejJLC1dRecPQp8swa4mdW0gRERETkYk1wiN1PtJceR+KHI6tTdug1ESguBH9YDR/dyVJeIiNwGlxBzIQJ3PCMrCWIxinrfAW2rKIgPbNdtDmHxAgHC6cMQXbsMUcJ9jgmSiIioCTHJJXJTcqkHxBEd8FnPvvD/fS+CLZQk+Mq80C84EigthOTHz+HvFwZ06ghA7riAiYiIGhGTXCI3l62pQVa3nojy80W3S2cg0WgsthcJAnzSz0C6fQ1w1/1AdJxjAiUiImpErMl1Npx4Rk1BJMK19h1w8I7hKA1oZd0lleXAnq+BvZuByvImDpCIiKhxMcklakEqff1xOOEuXIztAa3Yyh//jIvAllW6lRi02qYNkIiIqJEwyXUpnHhGDSeIxUjrEIeDdwxHmb91o7pQ1erW1P1mNZCd3rQBEhERNQImuUQtVIVfAA4Nugupnbsbj+qKAJlUZvo9VXE+8OMGYM8moKzYYbESERHZihPPiFowQSzG1Y5dENi5D4YWlkB8MwsyqQwhISGWr8u8CFHWFaDnHUDfOwFrdlkjIiJyICa5zoYTz6gZiILaQDx4Er7b/Rn8Th6ErLbGbFvDcmMaDXDqEHD5FND/LiCuLyCROCxmIiIiS5jkEpGOSIRzbcKQFz8E3S+dRXjudeuuU1QCB3cCZ34HBg4HOnSzbqc1IiKiJsSaXBciMG8gB6j19MLJXgNxdMCdqJL7Wn9hWRGwbyuw/f84OY2IiJodk1wiMqkwOBQHhtyDC116QS214UOfglzd5LSdG4C87KYLkIiIyAKWKxCRWYJYjPTozshuG4kuVy6gfXaG9RfnpOseER11NbthkU0WJxER0e2Y5DobTjwjJ1Tr6YUzPfohMzIG916/BgECRFau2yxkX4UoOw0I7wD0Hwa0jWriaImIiJjkEpENygNa4dpdvSHybIXfd34CVUm+xfaGlRiAP0d2w2OAPnfq/uQENSIiaiJMcl0JEwJyBiIR0LE7fvnLCIgunUJs2kV41lRbf31Ohu7ROgzoPRjo0J1LjxERUaNjkktEdhHEElxv3wHZ7doj5tpVdMy4DJlaZf0Nim4Cv3wLJO8Heg4CuvTjphJERNRomOQSUYNopFJc7dgF1yJj0Cn9EqKz0iHRaKy/QWUZ8PseICUR6NIf6BEP+AY0XcBERNQiMMl1Opx4Rq5J5eGJi116IT0mFh0yLiM6y8a1cmuqgdOHgTNHgKg4oPtA3WQ1lukQEZEdmOQSUaOq8fTCxS69kBYTi+GFxdAqaiBWWV/GIAhaiDJTgcxUIDBYl+x27g14ejVh1ERE5G6Y5LoQwcolm4icQa2nF4p63wFxTF/s2vkJvC6lQFpPsmu0GgMAlBYCh3/S1e126qmr2w1px9FdIiKqF5NcImpaXnKkdOiMG0H+iMrKQMz1NHhVK227h6oWuHhC9wgKBeL6Ap17Ad4+TRMzERG5PCa5ROQQapkH0jrEIT26M8JvZKFD5hX4V5TZfqPifN1EtaSfdbW7cX2ByE6AmLuUExHRn5jkEpFDCWIxssOjkN2uPUIK89Ax8wqCiyxvKmGSVgtkXNQ95L669XY79QRCw1nOQERETHKdDrf1pZZCJEJBSBgKQsLgV1GG6Otp6FdSatuWwfq2ikrgXJLu4d9Kl+x26gm0CmniF0FERM6KSa4LETg4RW6qwi8AZ7v3g49/GAZLfHDkl81QF+dZvKbOJDW98hIg5Tfdo3UY0KkHENMNCAhqouiJiMgZMcklIqeh8fAEuibgl5oiKK9fRnRWGtrk34DI3k84im7qHkn7gNZtgOiuQExX3eQ1ljQQEbk1JrlE5HxEIhQGh6IwOBSe1UpE5F5HZM41+FZV2H/Pojzd48QB3ahudFcguouuhpeT1oiI3A6TXCJyajVe3kjrEIe0mFi0Ki1C++xMtLuZA0+J1L76XQAoK9btrnb6MOAlB9p3BiI7AxEdAS/vJnw1RETkKExynQ0nnhGZJhKhpFUwSloF41zXPrinWgORRoyUMwdRWWt53V2z9bsAUK0ALp/WPcRioE3kn0kvyxqIiFwWk1yXwl+2RACgkUpRFN0R6PoX7GvlCyH9HMJzsxBQXtKwG2u1wI1rukfSPsDHH4joALSLAcI7AD5+jfMCiIioyTHJJSKXVustR1Z0Z6RHd4ZPVQXCb2QhPDcLPorKht+8qhy4dEr3AIDAYF2yG9EBaBsNeHo1/DmIiKhJMMklIrdR5eOHy5264XLHrvAvL0Xb/Fy0zctBqFqwv373VqWFusf5ZF0ZQ0g7oG0UENZe9/CSN/IrIiIiezHJJSL3IxKhPKAVygNa4VLn7lB7BiBB5o9Dv++ENj/b4qUW63dvJQhAfo7ucfqI7lirEEhatYF3lQqICAO8vVnTS0TUTJjkOhtOPCNqdDV+gUDXv+CAUImCwhzd6G7BTbQuKYRYq228JyopgDg/F4GFBZBlnwMCg3QjvCHhuqXKgtsCMo/Gez4iIjKLSa4LETgiRNRg1d5yZER3RkZ0Z0jUagQX5aNNwU2EFt6EV7WyYUuT3a6qAkg7r3sAulHdoNA/k96QcN2/uU4vEVGjY5JLRC2WRipFXpt2yGvTDhAE+FWWY1itAJHICycuHEFVjR1Lk4kAmVRmejEUQfhzU4rUFN0xqUy3/XBwWyA4TPf3VqGAlP89ExE1BP8XJSICAJEIFX4ByAuOArr+BXvDgqHIvoLg4gIEF+XDv6KsziWmRn1lUhlCQkJMPoXJUV+1CsjL0j30xGLdSg6tw/58BIdxYhsRkQ2Y5BIRmaCRyZAf2hb5oW0BAB411YaEN7ioAHJlFWRiCUQQIaUwC5Wqaov3s3pCG6Bbr7c4X/e4cubP43JfoFUIEBii+zMoVPcnk18iojqY5DobTjwjckq1nl7IbRuJ3La6RNVbWYWhggf6yVsj71gB1IrSpg9CUal75GQYH/f20SW8+uQ3oDUQ2Fq3mQVr+YmohWKSS0RkB6W3D4r/KG044SPBzZKbCCotQlBJEYJKChFQXmq0ckOjTmirE0yVLvG9PfmVSIGAIF3SG9D6z7/7B+lGhZkAE5EbY5JLRNQIVB6eyAtth7zQdgAAsUaNgPIyBJYVI7CsBMFqNE1pgyUa9Z9lD7eTeeiSXf9WgF+g7uEb+OffPTwb/vxERM2ISS4RURPQSqQoadUaJa1aAwCkwVG4I6Yvfj/wNZQ3MhFYVoxWpcXwUNXWubZJR331VLVA0U3dwxQv+Z8J760JsI8/4OsPeHKjCyJybkxyiYgcxUuOktB2yPLx1v1bEOBVo4R/eRkCyksRUFEK//IyyETWT2gL8vRB96C2VodgdUJcrdA9CnJNn5dIAR8/XdKrf/j6G//b24drABNRs2GS63Q48YyoxRCJUO0lR7WX3LCKAwAo/MPQL7QjTh3+FtX5WfCvKINfZbnJ3dl8ZZ6OL4MAdKUQ5SW6hzkikS7R9fbV1QDLfQEvnz//rj/u7aMbGSYiakRMcl0Idzwjahk0Hp5A2yjkduiCrDZtAAAirRbeSgX8qsrhW1kBv8py+FVWoK2gGymtVFWjrJ4k19YyCMDOUgjDxcKfK0IU1dNWIoFUIkNwaQUkWR0B/0BdyYSph6e37k+JxL64iKhFYJJLROQCBLEYCh9fKHx8kRf65/Hq1u0xIKI7zh3ehsr8bPgoKuFbVQkfRSU8a4yTXlvW9QWasBTCFI0GoupqyMoKIM4GIJPVf42Hp+nk18ML8PQy/tPo756sJyZqAZjkEhG5MpEI8G+F4jYRyPL1MTolVasgr6qEr6ISPlWV8JJ6A36hKCm6DoUVSa4tpRCNkhBb2hLZlNoa3cNSyYQpIpEu0fXw0iXGtyfCMg9A5qn708MTkP7xp8zD+JzMg8kykRNjkktE5KbUUhnKA1qhPKAVACDwj3V9f28fjpslefBWVkFerYBcUQW5UqH7t1IBubIKEo3GcB9rSiEaIyE2tyVyg0aITREEoKZa96gotf8+IhEglZlIhG9NiD10bex5MIEmahC7ktzU1FSsWrUKx44dg1KpRGRkJCZNmoTZs2dDKrX+lseOHcPq1atx5swZqFQqdOjQAdOnT8eUKVNMtler1fjss8+wfft2XL9+HXK5HAMHDsTChQsRFxdnz0txPtzxjIgcQOXhAZWHhyEBNiIIkKlqMdirFfqFdUb6iZ9QUZwH7xolvKqV8KqprlMKodfYCXGjTpZrbIKgW4rNxDJwjUIiNZ38SiS6c/qHVAqJSg3/3FyIK3MBH98/z4klgFRq3F4iNXkPiCW6h+SPP8ViJtrk0mxOclNSUjBnzhxoNBqMHj0aoaGhSExMxPLly3Hy5EmsWrUKIit+KPbs2YNnnnkG3t7eGDt2LHx8fLB3714sWbIEly9fxj//+U+j9oIgYNGiRdi3bx86d+6MWbNmIT8/Hz/99BMOHjyITz/9FP369bP15bgW/mdDRI4gEkHl4QlFUAjQsTuyy64jq6qdcROtFl5/JL3e1UoIngHoFxKNAlENqsqK4F2tgGdNNUQW3rg3xWS5Jl9f2JE0at2jRllvU7FKBZ/CAkgq8qyrZ7aWWHxb4isxcczMv61qIwZE4j//FImMj91+Xp94Wzpv7l4mr3Hirz81mE1Jrkajwcsvv4zq6mqsXbsWw4YNAwAsWrQIc+bMwb59+7Bjxw5MmDDB4n0qKiqwdOlSSKVSfPnll+jatSsAYP78+Zg8eTI2bNiAUaNGoX///oZrfvjhB+zbtw8DBw7E+vXr4eHhAQAYN24c5s6di3/+85/YuXMnJK4021atBrSa+tsRETkZQSyG0tsHSm8flAAI/6MU4qKXgKyqP2pk/xgR9qytgecfo79etTWQegYAodEo1lShqrzIcN4UWybLhXr7oUtgWPOuL2xjW6en1eoealVzR9J0xGJI1WqEFRRAeqwN4OHxZyItEt32uO0YRLck07f821x7fRux2PZ7ALdcA+O29py79XgDzomqq+F14wbQIRqQyxvxC9NwNiW5SUlJyMjIQEJCgiHBBQAPDw8sWrQIM2fOxKZNm+pNcnfv3o3S0lI88MADhgQXAPz8/DBv3jy8+OKL2LRpk1GSu2nTJgC6hFqf4ALAsGHDEB8fj+TkZCQnJ+OOO+6w5SU1jxvXgGO/APnZgIZJLhG5qT9GhFUenqj09Tccbv1HQnzO39OQEIu0WkNC7FFbA4/aWnioaiDyboV+YZ2QdkGNiopiw3GP2lpINGqjp/OV6bYibuySCWdJni2yddIe/UmrhUijgUirgUitYh/aSKpSoVVhAaTXTgHDxgM9Epo7JAObk1wAGDJkSJ1z/fv3h1wux6lTp1BbW2uUiN7u6NGjZu+jP5acnGw4VlNTg9OnT0Mul5ssSbjzzjuRnJyMpKQk509y83OAXV+497tiIiIbCWIxaj29UOvpZXQ89I+E+NKtI8R/EGs0kKlq4aGqhUdtDfr5tEa/iG5IP/srSiqKIVWpIFP/8VDVQqb68+961ibEtrRtqhUpAPNJsalJey22zMMefJPQYCJBAA7/BAQGAxEdmzscAIBIEKyf6fS3v/0Ne/bswYcffoj77ruvzvnx48fj8uXL2LVrFzp2NP8CH3zwQZw7dw7ffPMNevToUed83759oVAocOrUKXh7e+PKlSsYN24cYmNjsWPHjjrt9+zZg7/97W8YPXo0PvjgA2tfjpGUlBQIggBZY9YymVKtgMjKSQoCBIhEItRq1NAKAqp9/My29RBLIJd6oEJVA41Qd1cktm1YW2eJw53bOkscbOtccTRZW5EEcqkMlbVKaLVaAILul7QAiCAYJgGLBAFSkRieEgmq1Spotdo/zhu305OIxJBJJIb/ty3Rt1VpNVbNORaLRJCKxVa1b+q2botzv20mQIBWq4VYJIZIJIIg89CtV91EVCoVRCKRVfOwbBrJraysBKArKzDFx0e3RmN5eXmD7uPr6wuFQoGKigp4e3ujoqLCcNxcewCGdvbQT5azZtJcg3j76B5W0EeiHxP3MtfwFn5/jDhYg21tb+sscbhzW2eJg22dK46mauvraf0vY2v+D9Yz/1lmXbYOrdjSvqnaEumJAEhu+3eTPp9IZHWuxnVy/9C3b9/mDoGIiIiIGolNnznUN2JaVVUFAPD39zd53tr73D7Sq/9Tf7y+9kRERETUstmU5MbExAAArl27VuecRqNBdnY2JBIJIiMtL9wdHR1t9j4FBQVQKBQICwuDt7c3AKB9+/aQSCTIzs6GxsRqBPr76O9LRERERC2bTUluQoJuWYhDhw7VOXfixAkoFAr06dPH4soKADBo0CCz99Efi4+PNxzz9PRE7969oVAokJKSUueagwcPGsVHRERERC2bzUludHQ0kpKSkJiYaDheW1uLFStWAACmTZtmOK5UKpGWlobc3Fyj+4waNQqBgYHYuXMnLl68aDheUVGB1atXAwCmTp1qdI3+vitWrEBt7Z+rEyQmJiI5ORkxMTFGiTERERERtVw2LSEG6Jbamj17NrRaLcaMGYOQkBAkJibiypUrGDlypNG2vklJSZg1axbi4+OxceNGo/vs3r0bzz77bJ1tfXNycjBz5kwsWbLEqL0gCJg/fz7279+Pzp07Y9iwYSgoKMCuXbsgFovx2Wefuf+2vkRERERkFZuTXABITU3FypUrcfz4cSgUCkRGRuKBBx7A7NmzIZX+uWCDpSQX0G34sHr1apw+fRpqtRodO3bE9OnTMXnyZJPLQ6hUKnz++ef49ttvkZWVBblcjgEDBuBvf/sb4uLibH0ZREREROSm7EpyiYiIiIicmRtvW0JERERELRWTXCIiIiJyO0xyiYiIiMjtMMklIiIiIrfDJJeIiIiI3A6TXCIiIiJyO9L6m5CjpaamYtWqVTh27BiUSiUiIyMxadKkOusQt0Tff/89UlJScOHCBVy6dAk1NTVYsGABFi5caLK9IAjYtm0bvv76a6Snp0Mmk6FXr16YN28eBg4c6ODom1dJSQn27duHAwcO4PLly8jLy4NMJkNsbCweeOABPPjggxCL677v/fnnn/Hpp58adifs2rUrHnvsMYwcOdLRL6FZ1dbW4r333sO5c+dw7do1lJaWwsfHB+Hh4Rg/fjweeughyOVyo2v4/Wfe999/jxdeeAEA8MYbb2DKlClG59l3fxo+fDhycnJMnuvduze2bNlidIx9Z1piYiK++uornD59GpWVlWjdujW6du2KefPmoU+fPoZ27D+db7/9Fi+99JLFNmKx2GjnWsC5fmdwnVwnk5KSgjlz5kCj0WD06NEIDQ01u6NcS6T/zz4gIAABAQG4fv26xST3jTfewMaNGxEeHo57770XVVVV+PHHH6FUKvHBBx/gvvvuc/AraD5ff/01/v3vfyMkJAQJCQlo164dCgsL8fPPP6OiogL33XcfVqxYYfT99dlnn2HZsmUICgrCmDFjAAC7du1CcXExXnrpJcyePbuZXo3jlZeX4y9/+Qt69OiBDh06oHXr1qioqMDRo0eRnp6OTp06YfPmzfD19TVcw+8/027cuIHx48dDo9FAoVCYTHLZd38aPnw4ysvL8eijj9Y5FxYWxr6zwltvvYXPP/8c4eHhGDJkCAIDA1FYWIhTp05hxowZmDFjhqEt+0/n4sWL2Ldvn8lzx48fx9GjR3HXXXdhzZo1huNO9ztDIKehVquF++67T4iNjRUOHDhgOF5TUyM8/PDDQmxsrPD99983Y4TN7/Dhw0JWVpYgCILwzTffCLGxscKHH35osu2xY8eE2NhY4d577xXKy8sNxy9cuCD06NFDiI+PFyoqKhwStzM4cuSIsG/fPkGtVhsdz8/PF4YNGybExsYKu3fvNhy/fv260L17dyEhIUG4ceOG4fiNGzeEhIQEoXv37sL169cdFn9z02q1Qk1Njclzf//734XY2Fhh7dq1hmP8/jNNq9UKjz76qDBixAjh7bffFmJjY4UtW7YYtWHfGbv77ruFu+++26q27Lu6vvrqKyE2NlZ4+eWXhdra2jrnbz3G/rPOQw89JMTGxgr79u0zHHPG3xmsyXUiSUlJyMjIQEJCAoYNG2Y47uHhgUWLFgEANm3a1FzhOYXBgwcjIiLCqrb6vnrqqafg5+dnON61a1eMGzcOpaWl+Omnn5okTmd0xx13YMSIEZBIJEbHQ0JCMG3aNAC6rbb1tm3bBpVKhUceeQRhYWGG42FhYXjkkUegUqnwzTffOCZ4JyASieDh4WHy3KhRowAA165dMxzj959pGzZswNGjR7Fs2bI65R167Dv7se+M1dTUYMWKFQgPD8crr7wCmUxWp82tx9h/9bt06RJOnTqFNm3a4K677jIcd8bfGUxynUhSUhIAYMiQIXXO9e/fH3K5HKdOnUJtba2jQ3NJ+v78y1/+Uuecvo9vTepaMn2t960JsKXvR/afscTERABAly5dDMf4/VdXWloa3n33XcyaNctibSP7rq7a2lp89913WL16NTZu3Ijjx49DMFFtyL4zduTIEZSUlOCee+6BSCTC/v37sXbtWmzcuBEXLlyo0579Vz99DfjkyZOd/ndGy57F5GQyMjIAAFFRUXXOSSQSRERE4PLly8jKykLHjh0dHZ5LUSgUyM/Ph1wuR0hISJ3z+j7OzMx0cGTOR61W4/vvvwcA3HnnnYbj+r4x9f3Y0vtv5cqVAICysjKcOHECFy5cwODBg/HQQw8B4PefKWq1GosXL0bbtm3x3HPPmW3HvjOtoKAA//jHP4yOderUCcuXL0e3bt0AsO9MOXv2LADdaO3999+Pq1evGp0fOXIk3nnnHfj4+LD/rFBdXY0ffvgBEomkTi24M/7OYJLrRCorKwHA6COSW/n4+ADQTYAhyyoqKgCY70v95CD2JfDuu+/i8uXLGDp0qFGSa+n7saX336pVq4z+ff/992Pp0qWGcgZ+/9X10Ucf4eLFi/jqq6/g5eVlth37rq4HHngAAwcORKdOneDt7Y3MzEysW7cOO3fuxJw5c/Ddd9+hbdu27DsTioqKAADr169HbGwsNm/ejM6dOyM9PR2vvfYa9u3bh1dffRXvvPMO+88KP/30E8rLy3HXXXehbdu2Ruec8XcGyxWIWrANGzZg/fr1iImJwX/+85/mDsdlXLp0Campqfjtt9/wn//8B0eOHMGDDz6I7Ozs5g7NKZ0+fRpr1qzBnDlz0Ldv3+YOx+UsWLAACQkJaN26NeRyObp164Z3330XY8aMQWlpKdatW9fcITotfUmHRCLBxx9/jD59+sDHxwc9e/bE//73P8jlcvzwww/Iy8tr5khdw+bNmwEAU6dObeZIrMMk14no3+no303erqqqCgDg7+/vsJhclf6dpLm+1L/jbMl9+cUXX+DNN99Ex44dsXHjRgQFBRmdt/T9yP7TTURr06YN7r//fqxatQoZGRl4/fXXAfD771ZqtRovvPACoqOj8cwzz9Tbnn1nPX15TEpKCgD2nSn6PunWrRvatWtndC44OBi9e/eGIAg4e/Ys+68eV65cwcmTJxEWFmY0OV7PGX9nMMl1IjExMQCMZ2jraTQaZGdnQyKRIDIy0tGhuRy5XI7Q0FAoFAoUFBTUOa/v4+joaAdH5hw+++wzvP7664iNjcXGjRtN1p/p+8bU92NL77/b9e7dG/7+/oZJFfz++5NCoUBmZibS0tLQs2dPxMXFGR76so8lS5YgLi4OK1euZN/ZoFWrVgB0fQzw+84U/e9VcyUI+qSrpqaG/VcP/Sju7RPO9JzxdwaTXCeSkJAAADh06FCdcydOnIBCoUCfPn3MLmNExvT9efjw4Trn9H0cHx/v0Jicwdq1a7Fs2TJ07doVn3/+OVq3bm2ynaXvx5bcf6ZUVVWhsrLS6D9+fv/peHh4YPLkySYf+glT/fv3N/o3+846p0+fBgCjZRXZd8YGDRoEAEhPTze5GoV+Ilp4eDgA9p85NTU1hglnkydPNtnGKX9nOHRVXrJIrVYL9957LzeDsFJjbAZx6/GWYNWqVUJsbKwwadIkoaSkxGJbZ1zYuzlduXJFqKqqqnO8pqZGePHFF4XY2FjhueeeMxzn91/9PvzwQ7s3g2gpfXf16lVBoVDUOX7p0iUhISFBiI2NFb777jvDcfZdXXPnzhViY2OFjRs3Gh3ftm2bEBsbK4wcOdKwSQ77z7Tt27cLsbGxwl//+lezbZzxdwZXV3AiEokEy5Ytw+zZszF//nyMGTMGISEhRtv6jh8/vrnDbFZbt27FiRMnAPz58ce+ffsM+7p36NABc+fOBQAMGDAAM2fOxMaNGzFx4kSj7RnVajVee+01sx9huaPt27fjww8/hEQiwYABA7Bx48Y6bcLDw/HAAw8AACIjI/H8889j2bJlmDRpktEWjSUlJXjppZdaVOnMTz/9hPXr16Nfv34IDw9HQEAACgoKcPjwYeTn5yMmJsZoiSd+/9mPffenXbt24dNPP8XAgQPRrl07eHl5ISMjAwcPHoRarcakSZMwYcIEQ3v2XV2vvPIKLl68iNdffx2//vorYmNjkZ6ejgMHDsDb2xvLli0zfArD/jNNvzauvg7cFGf8nSESBBPj99SsUlNTsXLlShw/fhwKhQKRkZF44IEHMHv2bMOi/S3Viy++iO3bt5s9Hx8fb5S8CYKAbdu24auvvkJ6ejqkUil69+6Np556yuJi9O5o5cqVdZa+ut3t/QcAP//8M9avX4/U1FQAug0PHnvsMdxzzz1NFqszOnv2LLZu3YqTJ08iLy8PlZWV8PX1RceOHTFy5Eg8/PDD8Pb2NrqG33+W6b8n33jjjTprbrLvdJKTk7Fp0yacP38eRUVFUCqVCAgIQPfu3TF58mTcd999da5h39VVUFCAVatW4cCBAygqKoK/vz8GDRqEp59+Gp06dTJqy/4zlpaWhjFjxiAsLAy//PKLyXrcWznT7wwmuURERETkdjjxjIiIiIjcDpNcIiIiInI7THKJiIiIyO0wySUiIiIit8Mkl4iIiIjcDpNcIiIiInI7THKJiIiIyO0wySUiIiIit8Mkl4iIiIjcTsveI5aIiMgJFBcX48iRI7h69SpKSkqgUChQUlKC559/Hl26dGnu8IhcEpNcIicxfPhw5OTkGB0TiUTw8/ND586dMXHiREyZMgViMT+AIXIXSqUS//nPf7Bt2zaoVCqjc2KxGHfeeSeTXCI7McklcjJDhgxBSEgIAEClUiEnJwcpKSk4ceIEDhw4gI8//hgikaiZoySihlKpVHjyySdx7NgxxMXFYcGCBRgwYACCgoKaOzQit8Akl8jJzJ07FwkJCUbHTp8+jZkzZ+KXX37B/v37MXLkyGaKjogay5o1a3Ds2DHce++9ePfdd+Hh4dHcIRG5FX7uSeQCevfujfvuuw8AkJSU1MzREFFDqVQqbNy4EWFhYXjnnXeY4BI1AY7kErmI4OBgAIBGozE6npSUhFmzZiE+Ph5r1qzBypUrsWfPHhQUFCAkJASjRo3C008/DV9fX5P3PXHiBD7//HOkpKSgtLQUAQEBGDBgAObNm4euXbsatc3OzsaIESMAAIGBgTh48KDJX84TJ05EamoqAGDDhg11RqYvXLiANWvWICUlBcXFxVCr1UbnFyxYgIULF1rdN5WVlfjss8+wZ88eXL9+HWKxGB06dMDEiRMxffp0yGQyQ1u1Wo0FCxbg119/xWOPPYZ//OMfRveaOXMmkpOTsX//fkRERBiOnzlzBo8++ig8PDzw1VdfoWPHjkZ9Eh8fj40bN9aJTX8/ALh06ZLJ+G9tY4qpPtyzZw9+/fVXnDlzBnl5eVCpVGjXrh3uvvtuPPnkkyY/8tbXfd/+2vRefPFFbN++HcuWLcMDDzxQ57ipOI4ePYpHH30UgOmvW2FhIVavXo2DBw/ixo0bqKmpMTpvrt9MKSoqwo4dO/Dbb78hMzMTBQUF8PLyQmxsLKZMmYL777+/zjX6n49byWQyhISEoHv37pg9ezYGDBhQp4+scfvrLSoqwv/93//h119/RW5uLjw9PREXF4cpU6Zg4sSJRmVGZ86cQWlpKaZNm4bNmzfjhx9+QHp6OgRBQPv27TFq1CjMnj0bPj4+hmtWr16N999/HzNmzMDSpUtNxvTZZ59h2bJlmDJlCt544w2jcwcOHMCmTZtw9uxZlJWVITAwEDExMbjnnnuM+ignJwc7duzAoUOHcP36dRQXF8PHxwc9evTAI488grvvvtuq/iFqbkxyiVzE2bNnAcCQXN1OpVLh0UcfxdWrVzFo0CB0794dSUlJWLduHX7//Xd88cUXRr8wAWDt2rV47733IBKJ0KNHD/Tv3x9ZWVnYvXs39u/fjw8//BDDhw83+XylpaXYtWtXncTixIkThgTXlAsXLmDatGmoqalBREQERowYAblcDgC4ePGixWtNKSoqwqxZs3D16lW0atUKw4YNg0qlwtGjR/Hmm2/i559/xieffAJPT08AgFQqxQcffIDHH38c69evR6tWrTB37lyLz5GWlmZo83//939mvwa3+/777y0mr7e7tR4bAA4ePIjCwkKTbZ999ll4eXmhU6dO6Ny5MxQKBc6fP4/169djz5492LZtW5PXdqpUKrz22mtmzysUCkybNg1ZWVnw9/fH4MGDERgYCAAoKCjAoUOHbHq+Q4cOYdmyZWjXrh2ioqLQp08f5OXl4eTJkzh+/DhOnz6NV155xeS1wcHBuPPOOwEAtbW1SE1Nxc8//4z9+/dj9erVGDZsGADgvvvuQ0lJidFr2LNnj9H1ere+CczIyMCsWbOQn5+PsLAwjBgxApWVlUhKSsLx48dx8OBB/Pe//zUkunl5eQCA7du3o6amBj4+Prjjjjsgk8mQlJSEDz/8ELt378bnn39u+DpOmTIFq1atwg8//IDnn3/e8HNzq02bNgEApk+fbjgmCAL+9a9/YevWrRCLxejVqxcGDRqEoqIiXLlyBW+++aZRkvv9999jxYoViI6ORufOneHn54ecnBwcOnQIhw4dwuLFi/HEE09Y+VUjaj5McomcmEqlQm5uLjZu3Ihjx46hbdu2mDhxosm2J0+eRMeOHbF3715DolRaWoonnngCZ8+exYcffoiXXnrJ0P7AgQN499130a5dO6xatQrdu3c3nPvll1+wcOFCvPDCC9i3b58hMdFr27YtpFIpvv766zpJ7ldffQUfHx9ER0fj/PnzdeL84osvUFNTg9GjR+O9994zWi1i5cqVNie5r776Kq5evYohQ4ZgxYoVhhHr/Px8PPbYY0hOTsaHH36IxYsXG67x8vLC6tWr8cgjj+Ddd99F69at8eCDD5q8/40bN/D444+jsrISa9asQa9evayKq7KyEsuXL4ePjw8kEgnKy8vNttVqtQDq1mPPnDnTbJL77rvv4u6774aXl5fhWE1NDV577TVs27YNH3zwgcUEtDF89tlnSEtLQ7t27ZCbm1vn/N69e5GVlYWoqChs27YN/v7+hnNJSUk2J7k9evTA1q1b63wNrl+/jkcffRRfffUVJk6ciD59+tS5tkOHDnj77beNjr3xxhvYuHEjtm3bZkhybx/Zz87Oxp49e0xef6vnn38e+fn5mDRpEl577TXDJxzp6el49NFHsXPnTvTv3x8PP/wwAKC6uhqA7mvWpUsXrF+/Hq1btwag+955+umnkZSUhNdeew0ffPABAKB169YYNWoUduzYgR9//BFTpkwxiuH3339HRkYGevXqZfTz/Omnn2Lr1q1o27YtPv74Y3Tr1s1wTqPRIDEx0eg+Q4YMwX333VfnzdzZs2cxZ84cvP/++xg7dizatm1rtj+InAFrcomczKxZsxAXF4e4uDj06NED9957LzZu3Ijx48dj8+bNZssOAN0v6FtHAgMDA/Gvf/0LALBlyxbDL1YAWLVqFQBg2bJlRr8QAd1HttOnT0dFRQV++OGHOs8jFosxdepUnDp1ChcvXjQcLy4uxp49ezBx4sQ6o8Z6+hGskSNHNng5tJycHOzduxcymQyvvvqqUd+EhoYaXvtXX31V52NyPz8/fPLJJ4iMjMS//vUv7N+/v879S0pK8PjjjyMvLw/vvPMO/vKXv1gd2wcffICCggIsXLgQfn5+FtvqY5NKrR93GD16tFGCCwCenp7417/+BalUir1791p9L3vcvHkTH3/8MUJDQ/H444+bbJOfnw8AGDRokFGCa6+OHTuafJPRvn17PPXUUwB0ZRzWio+PB/Dnmwx7HTt2DOfOnUNgYCCWLFliVMLToUMHPPvsswB0yaYpS5cuNSS4AODr64vXX38dEokEe/bsMXoD8cgjjwAAvv766zr3MTWKq1KpsGbNGgDA8uXLjRJcAJBIJHU+renVq5fJTyt69uyJGTNmQK1W45dffjH5WoicCUdyiZzMrR9ZC4KAgoICnD17Frt27YKnpydeeeUVk3WwAQEBhtGoW/Xu3RtRUVG4du0azp8/j/79+6O4uBhnz55Fq1at6tRY6g0YMAAbN27E6dOnTZ6fPHkyPvzwQ3z99deGEcOtW7dCpVJh+vTpeP31101eFxkZCUA3yjd69GhIJJL6O8WM48ePQxAEDBgwwGSNaUJCAiIiIpCdnY1z586hf//+RudDQkKwZs0ajB07Fs8++yzWr19vOKdUKvHcc88hLS0N8+bNw5gxY6yOKzU1FV999RViY2Mxc+bMemtOlUolAMDb29vq5wB0ZRQHDx5EVlYWFAoFBEEAoKs5LSkpQVlZGQICAmy6p7XeeustKBQKvPHGG3XeQOjpvya///47SktL63wiYA+VSoUjR47g9OnTKCwshEqlMvycAEBmZma999CXK3z00UcAgFGjRjUopmPHjgHQvXEz9SZ0woQJWLp0Ka5fv468vDy0adPGcK59+/Z1vi8BICoqCgMGDDCUO0yYMAEA0KdPH3Tv3h3nz5/H2bNn0bNnTwC68o/9+/cjICDA6Hv13LlzKC0tRVRUFAYOHGj1a6qursZvv/2Gc+fOoaSkxLCGr75/MzIyrL4XUXNhkkvkZEwtIVZZWYlFixZh27ZtEIvFJhPIdu3amb1neHg4rl27ZhhFzc7OBqAbqaxvofni4mKTx1u1aoXRo0djx44deOGFFyCXy7F582b0798fsbGxZu83e/Zs7NixA3v27ME999yDHj16GNXk2kL/ekwluHqRkZHIzs42tL3drl27IAgCampqMG/ePMOI4+LFiw3x7N27F/Pnz7dqBrwgCHj11Veh0WiwdOlSq0Zn9SOe1iaBarUaS5cuxTfffGOxXWVlpckkVz950F6HDh3Cnj17MGjQIIwdOxbffvutyXYjR45E165dcfHiRYwcORIDBgwwqsm1VVpaGp5++mmLiWxlZaXJ48nJyYiLizM6FhwcjA8++ACjR4+2OZZb1fd9KJVK0bZtW6MkV1+bW9/3blJSUp3v3RkzZuDll1/Gpk2bDEmu/g3m/fffbzTCf+PGDQBATEyM1a/nxIkTeOaZZwzfl6ZUVVVZfT+i5sIkl8gF+Pr64h//+AcOHTqEb775BosXL27Qx7/6j2cDAwPrnSndoUMHs+cefvhhfP/99/juu+/Qrl075OTk4LnnnrN4v+joaOzYsQPr1q3Dpk2brJ7J3hRSU1OxZs0aREZGYunSpViwYIEhnosXL+KRRx6Bp6cn1q1bh5UrV+Lvf/97vffcvn07UlJSMGHCBKtGzsrKylBeXg6ZTIawsDCr4v7888/xzTffoE2bNnjppZfQt29fBAUFGZLwIUOGoKCgwDCye7v77rvP5KSlEydO4Pr16xafu7a2Fq+//jpkMpnZGf56Hh4e+Prrr/H5559j48aN+PXXX616feYsWrQImZmZGDlyJJ588knExMTA19cXEokEhw4dMls2ARhPPNNoNLh58yZOnDiBf//73/D397epFKUx6CdC2mPs2LF45513sGvXLrz44ovw8fHB1q1bAQDTpk1rUFwKhQILFy5EUVERHnroIUyfPh3t27eHXC6HWCzG5s2bsXTpUrPfW0TOhEkukYvQf8yv0Whw7do1wwiOnqmJP3r6xC00NBQADBNGvLy8LE6mqU+fPn3QrVs3fP3112jbti1at26Ne++9t97r2rVrh969e+OLL77AoEGD8Nlnn0EkEmHlypWGWmFr6D/2zcrKMttGf+7Wj4gB3Wjoyy+/DLVajTfeeAODBg3Ce++9h7/97W/QaDQYO3Ys/vnPf0KlUuGXX37BunXrcO+999bp91uVl5dj+fLl8PPzwwsvvGDVazhz5gwA3Ux9a2uU9XWnr776ap03KQqFwuxkNb0XXnjB7BJi9SW569atQ2ZmJp544gmrVpnw9vbGqFGjsGbNGgQHB2PHjh0ICgoyubSXJWlpabhy5QqCg4Px4Ycf1ilzuXbtmsXrTU0cO3PmDKZPn46///3v+PXXX20uF9Gr7/tQrVYbRlT1bfU/i/Z873p5eeHBBx/EunXr8N133yE8PBy5ubkYNGhQnTel+k94rCnjAHQlQEVFRejevbvJT4zq62ciZ8KJZ0Qu4tbkw9QoXFlZGX777bc6x8+cOYNr165BLpcbJpi1adMGsbGxuHnzptmaW2s9/PDDuHr1Kg4ePIjJkydb9ZF+bm4uXnvtNfj7++M///mP3dsUDxgwACKRCMePHzeUYNwqOTkZ2dnZkMvl6NGjh9G5Tz75BOfPn8dDDz2EQYMGAdB9vN6vXz8AwHPPPQexWAxPT0+8+eabEAQBL7/8sqE20ZT3338fxcXFWLhwodEEQEt+/vlnAKizPJUlZWVlAGBydvuPP/7YZKNsubm5WLNmDcLCwjB//nyrrlGr1Xj++ecN9bv2Lmumf80hISEm67h37dpl8z179eqFiIgIlJSUIC0tza64ABhG7Pfv32+yXGLHjh1QqVRo3769IWHt3r07vL29kZWVhRMnTtS55vr16zhx4gTEYrHJTwSmT59uGFk1NeFMr3v37ggMDERmZiaOHz9e72ux9L1VW1tr+H4lcgVMcolcQGVlJd555x0Augkp5koI3nnnHaNRvPLycrz55psAgAcffNBopOpvf/sbAF0yZ2ot19raWvzyyy/1/vIfN24cRowYgbvuugtTp06t97VotVr84x//QEVFBV555RWrP6I3JTw8HPfccw/UajVeeeUVozrBwsJCw0jUww8/bPTxcFpaGj766CO0bdu2zoirqYS7f//+mDFjBi5fvoz//e9/JmPJyMjApk2bEBcXZ5gBX5+0tDT88MMPkEqldZaDskRfX/nVV18ZJbQXL17Ee++9Z/V9bPXBBx9AqVTixRdfNPlGy5RVq1bh7NmzmDp1aoM2EYiOjoZYLMaVK1eMkjVBELB69WqrErjbnTlzxvDmqCGT4gYOHIju3bujtLQUb775ptEboczMTLz//vsAgDlz5hiOe3t7G5ate/31141q3ysrK7F06VKo1WqMHj3aZMIZGRmJYcOG4cqVK0hMTERISIjJ7b5lMplhjefFixfXWaJPo9EYrZSg/7/l6NGjSE9PNxxXqVR466236h3pJ3ImLFcgcjJr167F9u3bAeh+gRcWFhp2KPLx8cHbb79tMhHr06cPNBoN7r33XgwaNAhSqRRJSUkoLS1Fly5d8Mwzzxi1v+eee/D888/jvffew8yZM9GpUydER0fD09MTeXl5uHDhAhQKRb2bH3h7e+Pjjz+2+vV98sknSE5OxtixYzFu3DirrzPn3//+N9LS0nDo0CGMHDkSAwcOhFqtxtGjR1FVVYX4+HhDQg/ofqm/9NJLqK2txWuvvWZxSbZbPffcczhw4ADWrl2Le++9t86EvYKCAohEIixdutSqFSOWLVuGrVu3QqlUIjg4GCtWrKjTRp9krF27FqdPnzYkK3PnzsWhQ4ewefNmJCcno2vXrigqKsLx48cxevRonDx5sklqnW/evIm//OUvVk/USklJwdq1axEVFYUXX3yxQc8dFBSEadOm4auvvjLs8BcUFITz588jKysLjz32mNHqGLdLT083xHBrTa5Go8H48eMtTgCzxrvvvotZs2bh22+/xe+//46+ffuisrISR48eRW1tLcaNG1dnpPXZZ5/F6dOncfbsWdxzzz1ISEiAVCpFcnKyYVKopbrnhx9+2FDnPHnyZLOTHB977DFcuXIF27dvx6RJk9C7d2+Eh4ejuLgYly9fRmFhoWE3vu7du2PYsGFITEzExIkTcccdd0Aul+PkyZMoKyuzarUQImfBJJfIydy+QL63tzfCw8MxceJEPPbYY2YXYPfw8MDq1avx4YcfYu/evSgoKEBwcDAeeOABzJ8/32Qy9+STT2Lw4MHYuHEjkpOT8dtvv8HDwwMhISEYOnQoRowYYbTlaUNdvHgRH374Idq0aWN2ZypbtW7dGlu2bMGnn36KPXv24MCBA5BIJIiJiTFs63trCcVnn32G06dPY9KkSRg6dKjVzyOXy/HGG29g9uzZeOmll7B169Y6ScXEiROt7q+ff/7ZMPJcWFhoeGNjyqFDh1BbW2tIcvv164fNmzfj/fffx/nz5/Hrr7+iffv2eOGFFzBr1iyTI3qNQSaTYcmSJVa1raqqMoySv/POO1aP/Fryr3/9C506dcLmzZtx6tQpeHp6ok+fPnj77bdRW1trMcm9tY9FIhHkcjl69uyJ8ePHm/yY31YxMTH47rvvsHbtWvz666/4+eef4eHhgZ49exq2HL79zamvry+++OILbNiwAT/++COOHDli2NZ31qxZmD17tsV+S0hIgEwmg1artfgpikgkwttvv40RI0Zg06ZNOHfuHM6dO4dWrVqhU6dOePrpp43ar1q1Cp988gl27tyJo0ePwtfXF/Hx8Vi4cGGDy5uIHEkkcIokkUvTT+CJj4/nCIsLGT58OOLj462a+Ddz5kwA4NeXjPzwww9YvHgxhg8fbraMhqglY00uERGRi6mtrTXsZPboo482czREzonlCkREzeCFF15AcHCwVW2ffPLJJo6GXMU333yD48ePIyUlBZmZmRg6dKhhdRAiMsYkl4ioGdiylawttcPk3o4dO4bt27cjMDAQEyZMwD//+c/mDonIabEml4iIiIjcDmtyiYiIiMjtMMklIiIiIrfDJJeIiIiI3A6TXCIiIiJyO0xyiYiIiMjtMMklIiIiIrfDJJeIiIiI3A6TXCIiIiJyO/8PBbGKLgIAq3gAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Оказывается, оставшееся время ожидания автобуса не зависит от того времени, которое уже прошло с момента прибытия предыдущего автобуса. Это свойство называется **свойством отсутствия памяти**. Среди всех абсолютно непрерывных распределений таким свойством обладает только экспоненциальное распределение. Среди всех дискретных распредений &mdash; только геометрическое.\n",
        "\n",
        "Если мы считаем, что у нас есть несколько маршрутов автобусов, причем все автобусы приходят независимо друг от друга с одинаковой интенсивностью, то количество автобусов, которое придется пропустить, имеет **геометрическое распределение**.\n",
        "\n",
        "А сколько времени проходит между каждым пятым прибывающим автобусом? Посчитаем эти интервалы времени."
      ],
      "metadata": {
        "id": "3AsTZdlMGeTD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "intervals = np.diff(bus_arrival_times[::5])\n",
        "intervals.mean()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lbkSTOKGGhHn",
        "outputId": "9e12467e-520b-4f2a-bf30-9a1f0e4a1dd3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "np.float64(50.00007057551471)"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "grid = np.linspace(-1, 180, 1000)\n",
        "\n",
        "plt.figure(figsize=(8, 5))\n",
        "plt.hist(intervals, bins=50, density=True, label=\"Данные\", range=(0, 180))\n",
        "plt.plot(\n",
        "    grid,\n",
        "    sps.gamma(a=5, scale=tau).pdf(grid),\n",
        "    lw=5,\n",
        "    alpha=0.5,\n",
        "    label=\"Плотность $\\\\Gamma(5, \\\\tau)$\",\n",
        "    color=\"#FF3300\",\n",
        ")\n",
        "plt.xlabel(\"Время между прибытиями каждого 5-го автобуса\")\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 481
        },
        "id": "TaGgvcjGGkNZ",
        "outputId": "372aecec-6ace-4348-de6a-b916124ccb34"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Этот пример также показывает, что экпоненциальное распределение является частным случаем гамма-распределения."
      ],
      "metadata": {
        "id": "zLN502YkGnGX"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gbgNvwvoEZyA"
      },
      "source": [
        "\n",
        "\n",
        "### 2. Парадокс времени ожидания: а что в реальной жизни?\n",
        "\n",
        "В предыдущем примере мы посмотрели на то, какие эффекты возникают в случае если автобусы прибывают на остановку с одинаковой интенсивностью независимо друг от друга. Выполняется ли это в реальности?\n",
        "\n",
        "Исследуем данные о запланированном и фактическом времени прибытия для автобусов RapidRide маршрутов C, D и E на автобусной остановке 3rd&Pike в центре Сиэтла. Ниже приведена схема общественного транспорта Сиэтла, на которой красными линиями отмечены рассматриваемые автобусные маршруты.\n",
        "\n",
        "Сами данные и идея исследования взяты из <a target=\"_blank\" href=\"https://habr.com/ru/post/428610/\">статьи на Хабре</a>.\n",
        "\n",
        "<a href=\"https://seattletransitmap.com/app/\"><img src=\"https://miptstats.github.io/courses/ad_fivt/bus.png\n",
        "\" alt=\"Схема автобусных маршрутов города Сиэтл\"></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "crP_8Az7EZyC"
      },
      "source": [
        "Загрузим данные с помощью библиотеки `pandas`, с которой вы уже должны были <a href=\"https://thetahat.ru/courses/python/08\">познакомиться</a>. Напечатаем также несколько первых строк таблицы. В данных есть следующая информация:\n",
        "* `OPD_DATE` &mdash; дата события;\n",
        "* `VEHICLE_ID` &mdash; номер транспортного средства;\n",
        "* `RTE` &mdash; номер маршрута;\n",
        "* `DIR` &mdash; направление движения (на север или на юг);\n",
        "* `TRIP_ID` &mdash; идентификатор рейса;\n",
        "* `STOP_ID` &mdash; идентификатор остановки;\n",
        "* `STOP_NAME` &mdash; наименование остановки;\n",
        "* `SCH_STOP_TM` &mdash; время прибытия по расписанию (**sch**edule);\n",
        "* `ACT_STOP_TM` &mdash; время прибытия по факту (**act**ual);"
      ]
    },
    {
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        "id": "TiVPUIrOEZyD",
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          "height": 293
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      "outputs": [
        {
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          "data": {
            "text/plain": [
              "     OPD_DATE  VEHICLE_ID  RTE DIR   TRIP_ID  STOP_ID  \\\n",
              "0  2016-03-26        6201  673   S  30908177      431   \n",
              "1  2016-03-26        6201  673   S  30908033      431   \n",
              "2  2016-03-26        6201  673   S  30908028      431   \n",
              "3  2016-03-26        6201  673   S  30908019      431   \n",
              "4  2016-03-26        6201  673   S  30908252      431   \n",
              "\n",
              "                 STOP_NAME SCH_STOP_TM ACT_STOP_TM  \n",
              "0  3RD AVE & PIKE ST (431)    01:11:57    01:13:19  \n",
              "1  3RD AVE & PIKE ST (431)    23:19:57    23:16:13  \n",
              "2  3RD AVE & PIKE ST (431)    21:19:57    21:18:46  \n",
              "3  3RD AVE & PIKE ST (431)    19:04:57    19:01:49  \n",
              "4  3RD AVE & PIKE ST (431)    16:42:57    16:42:39  "
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 39157,\n  \"fields\": [\n    {\n      \"column\": \"OPD_DATE\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"num_unique_values\": 63,\n        \"samples\": [\n          \"2016-04-11\",\n          \"2016-04-15\",\n          \"2016-03-26\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"VEHICLE_ID\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 151,\n        \"min\": 2601,\n        \"max\": 7080,\n        \"num_unique_values\": 115,\n        \"samples\": [\n          6212,\n          6088,\n          6115\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"RTE\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 673,\n        \"max\": 675,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          673,\n          675,\n          674\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"DIR\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"N\",\n          \"S\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"TRIP_ID\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2412323,\n        \"min\": 0,\n        \"max\": 31012569,\n        \"num_unique_values\": 1660,\n        \"samples\": [\n          30905884,\n          30905940\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"STOP_ID\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 73,\n        \"min\": 431,\n        \"max\": 578,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          578,\n          431\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"STOP_NAME\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"3RD AVE & PIKE ST (578)\",\n          \"3RD AVE & PIKE ST (431)\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SCH_STOP_TM\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"num_unique_values\": 1356,\n        \"samples\": [\n          \"18:18:57\",\n          \"12:43:21\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ACT_STOP_TM\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"num_unique_values\": 27633,\n        \"samples\": [\n          \"18:48:09\",\n          \"11:48:28\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 4
        }
      ],
      "source": [
        "df = pd.read_csv(\"arrival_times.csv\")\n",
        "df.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "B7ncxU3LEZyD"
      },
      "source": [
        "#### Обработка и первичный анализ данных"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ra_zNVO1EZyD"
      },
      "source": [
        "В данных всего 39 157 записей."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "ExecuteTime": {
          "end_time": "2021-03-20T13:00:28.899963Z",
          "start_time": "2021-03-20T13:00:28.895571Z"
        },
        "id": "RqjIJ-KTEZyE",
        "outputId": "a31b346e-aade-4a2e-cd95-c84ff248dfe5",
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        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(39157, 9)"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ],
      "source": [
        "df.shape"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dsHYbkNuEZyE"
      },
      "source": [
        "Проверим, есть ли пропуски."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "ExecuteTime": {
          "end_time": "2021-03-20T13:00:28.926792Z",
          "start_time": "2021-03-20T13:00:28.901500Z"
        },
        "id": "WAZy-8clEZyE",
        "outputId": "31adf408-e783-4328-c90f-f5181aed6472"
      },
      "outputs": [
        {
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>39152</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>39153</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>39154</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>39155</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>39156</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>39157 rows × 9 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "       OPD_DATE  VEHICLE_ID    RTE    DIR  TRIP_ID  STOP_ID  STOP_NAME  \\\n",
              "0         False       False  False  False    False    False      False   \n",
              "1         False       False  False  False    False    False      False   \n",
              "2         False       False  False  False    False    False      False   \n",
              "3         False       False  False  False    False    False      False   \n",
              "4         False       False  False  False    False    False      False   \n",
              "...         ...         ...    ...    ...      ...      ...        ...   \n",
              "39152     False       False  False  False    False    False      False   \n",
              "39153     False       False  False  False    False    False      False   \n",
              "39154     False       False  False  False    False    False      False   \n",
              "39155     False       False  False  False    False    False      False   \n",
              "39156     False       False  False  False    False    False      False   \n",
              "\n",
              "       SCH_STOP_TM  ACT_STOP_TM  \n",
              "0            False        False  \n",
              "1            False        False  \n",
              "2            False        False  \n",
              "3            False        False  \n",
              "4            False        False  \n",
              "...            ...          ...  \n",
              "39152        False        False  \n",
              "39153        False        False  \n",
              "39154        False        False  \n",
              "39155        False        False  \n",
              "39156        False        False  \n",
              "\n",
              "[39157 rows x 9 columns]"
            ]
          },
          "execution_count": 4,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.isna()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ipPcZMvIEZyE"
      },
      "source": [
        "На первый взгляд не видно. Посчитаем долю пропусков по столбцам, усреднив логические константы. В данном случае сначала произошло преобразование к 0 и 1."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "ExecuteTime": {
          "end_time": "2021-03-20T13:00:28.952108Z",
          "start_time": "2021-03-20T13:00:28.928655Z"
        },
        "id": "D5ZEHNUvEZyF",
        "outputId": "9d669bc0-983a-490a-e2f4-6a0d519470c7"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "OPD_DATE       0.000000\n",
              "VEHICLE_ID     0.000000\n",
              "RTE            0.000000\n",
              "DIR            0.000000\n",
              "TRIP_ID        0.000000\n",
              "STOP_ID        0.000000\n",
              "STOP_NAME      0.000000\n",
              "SCH_STOP_TM    0.006129\n",
              "ACT_STOP_TM    0.000000\n",
              "dtype: float64"
            ]
          },
          "execution_count": 5,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.isna().mean()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ONQmv171EZyF"
      },
      "source": [
        "Теперь уже видно, что для 0.6% рейсов не задано время прибытия.\n",
        "\n",
        "Посмотрим, что это за рейсы. Для этого сначала с помощью функции `any` определим для каждого рейса, есть для него *хотя бы один* пропуск."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "ExecuteTime": {
          "end_time": "2021-03-20T13:00:28.968451Z",
          "start_time": "2021-03-20T13:00:28.953994Z"
        },
        "id": "RyFXw5ESEZyF",
        "outputId": "9692ddce-ac47-4b37-e402-bf2b91121953"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0        False\n",
              "1        False\n",
              "2        False\n",
              "3        False\n",
              "4        False\n",
              "         ...  \n",
              "39152    False\n",
              "39153    False\n",
              "39154    False\n",
              "39155    False\n",
              "39156    False\n",
              "Length: 39157, dtype: bool"
            ]
          },
          "execution_count": 6,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.isna().any(axis=1)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1SiIVEPREZyG"
      },
      "source": [
        "Теперь мы можем извлеть все строки таблицы с пропусками."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "ExecuteTime": {
          "end_time": "2021-03-20T13:00:29.004313Z",
          "start_time": "2021-03-20T13:00:28.970940Z"
        },
        "id": "LbdisPwuEZyG",
        "outputId": "78b2106b-4a2e-41ce-cf9b-9ea0d145de6f"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>OPD_DATE</th>\n",
              "      <th>VEHICLE_ID</th>\n",
              "      <th>RTE</th>\n",
              "      <th>DIR</th>\n",
              "      <th>TRIP_ID</th>\n",
              "      <th>STOP_ID</th>\n",
              "      <th>STOP_NAME</th>\n",
              "      <th>SCH_STOP_TM</th>\n",
              "      <th>ACT_STOP_TM</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>383</th>\n",
              "      <td>2016-04-01</td>\n",
              "      <td>6206</td>\n",
              "      <td>673</td>\n",
              "      <td>S</td>\n",
              "      <td>0</td>\n",
              "      <td>431</td>\n",
              "      <td>3RD AVE &amp; PIKE ST (431)</td>\n",
              "      <td>NaN</td>\n",
              "      <td>16:00:57</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>471</th>\n",
              "      <td>2016-03-31</td>\n",
              "      <td>6207</td>\n",
              "      <td>673</td>\n",
              "      <td>S</td>\n",
              "      <td>0</td>\n",
              "      <td>431</td>\n",
              "      <td>3RD AVE &amp; PIKE ST (431)</td>\n",
              "      <td>NaN</td>\n",
              "      <td>15:28:05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>588</th>\n",
              "      <td>2016-03-30</td>\n",
              "      <td>6208</td>\n",
              "      <td>673</td>\n",
              "      <td>S</td>\n",
              "      <td>0</td>\n",
              "      <td>431</td>\n",
              "      <td>3RD AVE &amp; PIKE ST (431)</td>\n",
              "      <td>NaN</td>\n",
              "      <td>14:52:36</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>599</th>\n",
              "      <td>2016-03-30</td>\n",
              "      <td>6200</td>\n",
              "      <td>673</td>\n",
              "      <td>S</td>\n",
              "      <td>0</td>\n",
              "      <td>431</td>\n",
              "      <td>3RD AVE &amp; PIKE ST (431)</td>\n",
              "      <td>NaN</td>\n",
              "      <td>15:12:06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>685</th>\n",
              "      <td>2016-03-29</td>\n",
              "      <td>6213</td>\n",
              "      <td>673</td>\n",
              "      <td>S</td>\n",
              "      <td>0</td>\n",
              "      <td>431</td>\n",
              "      <td>3RD AVE &amp; PIKE ST (431)</td>\n",
              "      <td>NaN</td>\n",
              "      <td>17:56:29</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37327</th>\n",
              "      <td>2016-05-10</td>\n",
              "      <td>6215</td>\n",
              "      <td>674</td>\n",
              "      <td>N</td>\n",
              "      <td>0</td>\n",
              "      <td>578</td>\n",
              "      <td>3RD AVE &amp; PIKE ST (578)</td>\n",
              "      <td>NaN</td>\n",
              "      <td>15:25:47</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37328</th>\n",
              "      <td>2016-05-10</td>\n",
              "      <td>6215</td>\n",
              "      <td>674</td>\n",
              "      <td>N</td>\n",
              "      <td>0</td>\n",
              "      <td>578</td>\n",
              "      <td>3RD AVE &amp; PIKE ST (578)</td>\n",
              "      <td>NaN</td>\n",
              "      <td>17:44:05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37570</th>\n",
              "      <td>2016-05-12</td>\n",
              "      <td>6218</td>\n",
              "      <td>674</td>\n",
              "      <td>N</td>\n",
              "      <td>0</td>\n",
              "      <td>578</td>\n",
              "      <td>3RD AVE &amp; PIKE ST (578)</td>\n",
              "      <td>NaN</td>\n",
              "      <td>18:27:55</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37670</th>\n",
              "      <td>2016-05-13</td>\n",
              "      <td>6212</td>\n",
              "      <td>674</td>\n",
              "      <td>N</td>\n",
              "      <td>0</td>\n",
              "      <td>578</td>\n",
              "      <td>3RD AVE &amp; PIKE ST (578)</td>\n",
              "      <td>NaN</td>\n",
              "      <td>19:04:25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>39107</th>\n",
              "      <td>2016-05-27</td>\n",
              "      <td>6099</td>\n",
              "      <td>674</td>\n",
              "      <td>N</td>\n",
              "      <td>0</td>\n",
              "      <td>578</td>\n",
              "      <td>3RD AVE &amp; PIKE ST (578)</td>\n",
              "      <td>NaN</td>\n",
              "      <td>14:51:34</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>240 rows × 9 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "         OPD_DATE  VEHICLE_ID  RTE DIR  TRIP_ID  STOP_ID  \\\n",
              "383    2016-04-01        6206  673   S        0      431   \n",
              "471    2016-03-31        6207  673   S        0      431   \n",
              "588    2016-03-30        6208  673   S        0      431   \n",
              "599    2016-03-30        6200  673   S        0      431   \n",
              "685    2016-03-29        6213  673   S        0      431   \n",
              "...           ...         ...  ...  ..      ...      ...   \n",
              "37327  2016-05-10        6215  674   N        0      578   \n",
              "37328  2016-05-10        6215  674   N        0      578   \n",
              "37570  2016-05-12        6218  674   N        0      578   \n",
              "37670  2016-05-13        6212  674   N        0      578   \n",
              "39107  2016-05-27        6099  674   N        0      578   \n",
              "\n",
              "                     STOP_NAME SCH_STOP_TM ACT_STOP_TM  \n",
              "383    3RD AVE & PIKE ST (431)         NaN    16:00:57  \n",
              "471    3RD AVE & PIKE ST (431)         NaN    15:28:05  \n",
              "588    3RD AVE & PIKE ST (431)         NaN    14:52:36  \n",
              "599    3RD AVE & PIKE ST (431)         NaN    15:12:06  \n",
              "685    3RD AVE & PIKE ST (431)         NaN    17:56:29  \n",
              "...                        ...         ...         ...  \n",
              "37327  3RD AVE & PIKE ST (578)         NaN    15:25:47  \n",
              "37328  3RD AVE & PIKE ST (578)         NaN    17:44:05  \n",
              "37570  3RD AVE & PIKE ST (578)         NaN    18:27:55  \n",
              "37670  3RD AVE & PIKE ST (578)         NaN    19:04:25  \n",
              "39107  3RD AVE & PIKE ST (578)         NaN    14:51:34  \n",
              "\n",
              "[240 rows x 9 columns]"
            ]
          },
          "execution_count": 7,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df[df.isna().any(axis=1)]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EdzhmiXJEZyG"
      },
      "source": [
        "Давайте удалим пропуски из данных, чтобы они не мешали нашему дальнейшему анализу. Параметры функции предписывают удалять *строки*, в которых есть *хотя бы один* пропуск в любой из колонок."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "ExecuteTime": {
          "end_time": "2021-03-20T13:00:29.028490Z",
          "start_time": "2021-03-20T13:00:29.006728Z"
        },
        "id": "T8SkkFl3EZyG"
      },
      "outputs": [],
      "source": [
        "df = df.dropna(axis=0, how=\"any\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-AtGBmHfEZyH"
      },
      "source": [
        "Посмотрим на диапазон дат в датасете. Всего у нас есть данные за 2 месяца &mdash; с 26 марта 2016 по 27 мая 2016."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "ExecuteTime": {
          "end_time": "2021-03-20T13:00:29.039930Z",
          "start_time": "2021-03-20T13:00:29.030168Z"
        },
        "id": "9TIy_OkSEZyH",
        "outputId": "44084e18-bddb-40a7-8abb-d35955717db0"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "('2016-03-26', '2016-05-27')"
            ]
          },
          "execution_count": 9,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df[\"OPD_DATE\"].min(), df[\"OPD_DATE\"].max()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ek83jUt0EZyH"
      },
      "source": [
        "Уникальные идентификаторы автобусных маршрутов."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "ExecuteTime": {
          "end_time": "2021-03-20T13:00:29.046247Z",
          "start_time": "2021-03-20T13:00:29.041998Z"
        },
        "id": "T8VezVALEZyH",
        "outputId": "53d8b81a-4bc9-43e8-b74f-34cff66ef7da"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([673, 675, 674])"
            ]
          },
          "execution_count": 10,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df[\"RTE\"].unique()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ftsijXkXEZyI"
      },
      "source": [
        "Направления движения."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "ExecuteTime": {
          "end_time": "2021-03-20T13:00:29.069411Z",
          "start_time": "2021-03-20T13:00:29.047863Z"
        },
        "id": "tAY0LMEoEZyI",
        "outputId": "2781ec54-4350-4f4c-90a3-34bbfcc56976"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array(['S', 'N'], dtype=object)"
            ]
          },
          "execution_count": 11,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df[\"DIR\"].unique()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6HcLoTNuEZyI"
      },
      "source": [
        "Идентификаторы автобусных остановок."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "ExecuteTime": {
          "end_time": "2021-03-20T13:00:29.078389Z",
          "start_time": "2021-03-20T13:00:29.070771Z"
        },
        "id": "q4UuM4g3EZyI",
        "outputId": "f6480945-b423-42cd-953f-5c5173e8ad3c"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([431, 578])"
            ]
          },
          "execution_count": 12,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df[\"STOP_ID\"].unique()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "76p9eY8JEZyI"
      },
      "source": [
        "Как видим остановки две. Если мы составим с помощью функции <a href=\"https://miptstats.github.io/courses/python/10_pandas2.html#4.1-Функция-pd.crosstab\">`pd.crosstab`</a> таблицу, в которой показано, сколько рейсов было на каждой остановке по каждому из направлений, то мы сможем догадаться, что два идентификатора означают одну и ту же остановку по разные стороны движения."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "ExecuteTime": {
          "end_time": "2021-03-20T13:00:29.113705Z",
          "start_time": "2021-03-20T13:00:29.079811Z"
        },
        "id": "tBp-3QZ_EZyJ",
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      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th>DIR</th>\n",
              "      <th>N</th>\n",
              "      <th>S</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>STOP_ID</th>\n",
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            "text/plain": [
              "DIR          N      S\n",
              "STOP_ID              \n",
              "431          0  19318\n",
              "578      19599      0"
            ]
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          "execution_count": 13,
          "metadata": {},
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        }
      ],
      "source": [
        "pd.crosstab(columns=df[\"DIR\"], index=df[\"STOP_ID\"])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "L_d-COhqEZyJ"
      },
      "source": [
        "Также с помощью этой функции мы можем составить таблицу с количеством автобусов каждого маршрута за каждые сутки."
      ]
    },
    {
      "cell_type": "code",
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        "id": "B8l95i21EZyJ",
        "outputId": "e35594d2-72ee-4fec-c891-c58eb7a64153",
        "colab": {
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          "height": 455
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      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RTE         673  674  675\n",
              "OPD_DATE                 \n",
              "2016-03-26  176  178  159\n",
              "2016-03-27  153  153  140\n",
              "2016-03-28  236  228  221\n",
              "2016-03-29  233  231  221\n",
              "2016-03-30  230  230  221\n",
              "...         ...  ...  ...\n",
              "2016-05-23  230  230  221\n",
              "2016-05-24  236  229  220\n",
              "2016-05-25  235  231  224\n",
              "2016-05-26  231  203  222\n",
              "2016-05-27  231  231  223\n",
              "\n",
              "[63 rows x 3 columns]"
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "daily_counts",
              "summary": "{\n  \"name\": \"daily_counts\",\n  \"rows\": 63,\n  \"fields\": [\n    {\n      \"column\": \"OPD_DATE\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"num_unique_values\": 63,\n        \"samples\": [\n          \"2016-05-26\",\n          \"2016-05-22\",\n          \"2016-03-26\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": 673,\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 31,\n        \"min\": 143,\n        \"max\": 241,\n        \"num_unique_values\": 28,\n        \"samples\": [\n          175,\n          171,\n          232\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": 674,\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 29,\n        \"min\": 152,\n        \"max\": 234,\n        \"num_unique_values\": 24,\n        \"samples\": [\n          226,\n          152,\n          178\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": 675,\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 34,\n        \"min\": 118,\n        \"max\": 228,\n        \"num_unique_values\": 25,\n        \"samples\": [\n          222,\n          138,\n          159\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 6
        }
      ],
      "source": [
        "daily_counts = pd.crosstab(columns=df[\"RTE\"], index=df[\"OPD_DATE\"])\n",
        "daily_counts"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qJVXE3SoEZyJ"
      },
      "source": [
        "И построить с ее помощью график автобусов за день по маршрутам. На этом графике мы четко наблюдаем некоторую периодичность, которую называют **недельной сезонностью**. Действительно, мы видим, что 7-дневные интервалы, из которых в течение пяти дней наблюдается достаточно большое количество автобусов по каждому из маршрутов, а в остальные два дня &mdash; существенно меньше.\n",
        "\n",
        "В целом стоит отметить, что исследование сезонностей в данных &mdash; важная часть работы с временн*ы*ми данными."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "ExecuteTime": {
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          "height": 329
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "with sns.axes_style(\"darkgrid\"):\n",
        "    daily_counts.plot(figsize=(15, 5), lw=2)\n",
        "    plt.ylabel(\"Количество автобусов\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-FUNQvKgEZyK"
      },
      "source": [
        "Для удобства дальнейшего анализа объединим дату и время в одну метку времени."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "ExecuteTime": {
          "end_time": "2021-03-20T13:00:29.465146Z",
          "start_time": "2021-03-20T13:00:29.407983Z"
        },
        "id": "cglvUlIXEZyK"
      },
      "outputs": [],
      "source": [
        "df[\"scheduled\"] = pd.to_datetime(df[\"OPD_DATE\"] + \" \" + df[\"SCH_STOP_TM\"])\n",
        "df[\"actual\"] = pd.to_datetime(df[\"OPD_DATE\"] + \" \" + df[\"ACT_STOP_TM\"])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_OeIk5ApEZyK"
      },
      "source": [
        "Однако, такой операцией мы могли привнести ошибки в данные.\n",
        "\n",
        "Давайте посчитаем для каждого автобуса время между прибытием по расписанию и фактическим временем прибытия."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "ExecuteTime": {
          "end_time": "2021-03-20T13:00:29.471018Z",
          "start_time": "2021-03-20T13:00:29.466722Z"
        },
        "id": "ZZ6QWB5dEZyK"
      },
      "outputs": [],
      "source": [
        "minute = np.timedelta64(1, \"m\")\n",
        "hour = 60 * minute\n",
        "\n",
        "diff_hrs = (df[\"actual\"] - df[\"scheduled\"]) / hour"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "L5tDuGzcEZyK"
      },
      "source": [
        "Посмотрим на гистограмму."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "ExecuteTime": {
          "end_time": "2021-03-20T13:00:30.268912Z",
          "start_time": "2021-03-20T13:00:29.472607Z"
        },
        "id": "lJW8tnO9EZyK",
        "outputId": "8cc2b072-1b44-42f0-8c29-be531d74bad4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 400
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.figure(figsize=(12, 5))\n",
        "plt.hist(diff_hrs, bins=48, log=True)\n",
        "plt.xticks(range(-24, 25, 3))\n",
        "plt.xlabel(\"Отклонения от расп., час\")\n",
        "plt.ylabel(\"Кол-во автобусов\");"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8HsvWSmZEZyL"
      },
      "source": [
        "Оказывается, что несколько автобусов приезжали на сутки раньше, а еще несколько &mdash; на сутки позже. Как такое могло произойти?\n",
        "\n",
        "Вспомним, что мы искусственно склеили дату с временем суток. У нас имеется две отметки времени суток для каждого рейса, но дата одна, и она соответствует времени прибытия по расписанию. Таким образом, возможны две следующие ситуации.\n",
        "* Время прибытия по расписанию &mdash; сразу после полуночи, а автобус пришел немного раньше. Тем самым при склейке даты и времени мы получили, что как будто он пришел на сутки позже.\n",
        "* Время прибытия по расписанию &mdash; непосредственно до полуночи, а автобус немного опоздал. И при склейке даты и времени мы получили, что как будто он пришел на сутки раньше.\n",
        "\n",
        "Исправим фактическое время прибытия руками для тех рейсов, где отклонение по модулю слишком большое."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "ExecuteTime": {
          "end_time": "2021-03-20T13:00:30.279022Z",
          "start_time": "2021-03-20T13:00:30.270405Z"
        },
        "id": "bjeUbnnQEZyL"
      },
      "outputs": [],
      "source": [
        "df.loc[diff_hrs > 20, \"actual\"] -= 24 * hour\n",
        "df.loc[diff_hrs < -20, \"actual\"] += 24 * hour"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JN_6HIMMEZyL"
      },
      "source": [
        "И теперь посчитаем время отклонения факта от расписания в минутах."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "ExecuteTime": {
          "end_time": "2021-03-20T13:00:30.293810Z",
          "start_time": "2021-03-20T13:00:30.280411Z"
        },
        "id": "Zw71YGDgEZyL"
      },
      "outputs": [],
      "source": [
        "df[\"minutes_late\"] = (df[\"actual\"] - df[\"scheduled\"]) / minute"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4qTlZ4AYEZyL"
      },
      "source": [
        "Переобозначим технические идентификаторы маршрутов на те, которые видят пассажиры. Также переобозначим направления движения."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "ExecuteTime": {
          "end_time": "2021-03-20T13:00:30.323484Z",
          "start_time": "2021-03-20T13:00:30.301798Z"
        },
        "id": "npJqGh8tEZyL"
      },
      "outputs": [],
      "source": [
        "df[\"route\"] = df[\"RTE\"].replace({673: \"C\", 674: \"D\", 675: \"E\"}).astype(\"category\")\n",
        "df[\"direction\"] = df[\"DIR\"].replace({\"N\": \"север\", \"S\": \"юг\"}).astype(\"category\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WQFqnFb5EZyL"
      },
      "source": [
        "Оставим только интересующие нас столбцы."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "ExecuteTime": {
          "end_time": "2021-03-20T13:00:30.332880Z",
          "start_time": "2021-03-20T13:00:30.327792Z"
        },
        "id": "o3MU7VGgEZyM"
      },
      "outputs": [],
      "source": [
        "df = df[[\"route\", \"direction\", \"scheduled\", \"actual\", \"minutes_late\"]]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8SG6C8smEZyM"
      },
      "source": [
        "И посмотрим на данные."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "ExecuteTime": {
          "end_time": "2021-03-20T13:00:30.350356Z",
          "start_time": "2021-03-20T13:00:30.335548Z"
        },
        "id": "zuvA1RFBEZyM",
        "outputId": "ab30d243-2266-4f7e-f1b0-ba7c885081a3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  route direction           scheduled              actual  minutes_late\n",
              "0     C        юг 2016-03-26 01:11:57 2016-03-26 01:13:19      1.366667\n",
              "1     C        юг 2016-03-26 23:19:57 2016-03-26 23:16:13     -3.733333\n",
              "2     C        юг 2016-03-26 21:19:57 2016-03-26 21:18:46     -1.183333\n",
              "3     C        юг 2016-03-26 19:04:57 2016-03-26 19:01:49     -3.133333\n",
              "4     C        юг 2016-03-26 16:42:57 2016-03-26 16:42:39     -0.300000"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-1ed69185-dd9c-4b9c-b892-c77d2ad06d14\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>route</th>\n",
              "      <th>direction</th>\n",
              "      <th>scheduled</th>\n",
              "      <th>actual</th>\n",
              "      <th>minutes_late</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>C</td>\n",
              "      <td>юг</td>\n",
              "      <td>2016-03-26 01:11:57</td>\n",
              "      <td>2016-03-26 01:13:19</td>\n",
              "      <td>1.366667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>C</td>\n",
              "      <td>юг</td>\n",
              "      <td>2016-03-26 23:19:57</td>\n",
              "      <td>2016-03-26 23:16:13</td>\n",
              "      <td>-3.733333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>C</td>\n",
              "      <td>юг</td>\n",
              "      <td>2016-03-26 21:19:57</td>\n",
              "      <td>2016-03-26 21:18:46</td>\n",
              "      <td>-1.183333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>C</td>\n",
              "      <td>юг</td>\n",
              "      <td>2016-03-26 19:04:57</td>\n",
              "      <td>2016-03-26 19:01:49</td>\n",
              "      <td>-3.133333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>C</td>\n",
              "      <td>юг</td>\n",
              "      <td>2016-03-26 16:42:57</td>\n",
              "      <td>2016-03-26 16:42:39</td>\n",
              "      <td>-0.300000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-1ed69185-dd9c-4b9c-b892-c77d2ad06d14')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-1ed69185-dd9c-4b9c-b892-c77d2ad06d14 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-1ed69185-dd9c-4b9c-b892-c77d2ad06d14');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-a25a0da6-ac7e-4ce8-aaf0-46c130d24d07\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-a25a0da6-ac7e-4ce8-aaf0-46c130d24d07')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-a25a0da6-ac7e-4ce8-aaf0-46c130d24d07 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 39157,\n  \"fields\": [\n    {\n      \"column\": \"route\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"C\",\n          \"E\",\n          \"D\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"direction\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"\\u0441\\u0435\\u0432\\u0435\\u0440\",\n          \"\\u044e\\u0433\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"scheduled\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"2016-03-26 00:00:25\",\n        \"max\": \"2016-05-27 23:59:58\",\n        \"num_unique_values\": 38900,\n        \"samples\": [\n          \"2016-05-16 08:48:57\",\n          \"2016-04-16 00:57:59\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"actual\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"2016-03-26 00:05:01\",\n        \"max\": \"2016-05-28 00:01:28\",\n        \"num_unique_values\": 38961,\n        \"samples\": [\n          \"2016-04-05 11:53:50\",\n          \"2016-05-24 12:00:06\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"minutes_late\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4.945534960216665,\n        \"min\": -12.866666666666667,\n        \"max\": 150.28333333333333,\n        \"num_unique_values\": 1876,\n        \"samples\": [\n          26.183333333333334,\n          14.916666666666666\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 15
        }
      ],
      "source": [
        "df.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Le17tgmWEZyM"
      },
      "source": [
        "#### Исследование отклонения от расписания\n",
        "\n",
        "Для каждого направления и каждого маршрута визуализируем гистограмму отклонения от расписания в минутах.\n",
        "\n",
        "Визуализацию выполним в виде таблицы с помощью класса <a target=\"_blank\" href=\"https://seaborn.pydata.org/generated/seaborn.FacetGrid.html\">`FacetGrid`</a>. Работа функции несколько похожа на функцию <a target=\"_blank\" href=\"https://miptstats.github.io/courses/python/10_pandas2.html#4.2-Функция-pd.pivot_table\">`pd.pivot_table`</a>, только вместо применения агрегирующией функции рисуется график. Функция построения каждого графика передается в метод `map`."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "ExecuteTime": {
          "end_time": "2021-03-20T13:00:32.188849Z",
          "start_time": "2021-03-20T13:00:30.353699Z"
        },
        "id": "-OJ1fLbNEZyM",
        "outputId": "41c41ecb-5811-464d-fb37-b9da5e59276e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 287
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1800x600 with 6 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "g = sns.FacetGrid(df, row=\"direction\", col=\"route\", height=3, aspect=2)\n",
        "g.map(plt.hist, \"minutes_late\", bins=np.arange(-10, 15))\n",
        "g.set_titles(\"{col_name} {row_name}\")\n",
        "g.set_axis_labels(\"Отклонения от расп., мин.\", \"Кол-во автобусов\");"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fuJjSKRwEZyM"
      },
      "source": [
        "Заметим, что **автобусы ближе к графику в начале маршрута и больше отклоняются от него к концу**. Если внимательно посмотреть на схему маршрутов, то можно увидеть, что исследуемая остановка находится на северной части маршрута С, и на южных частях машрутов D и Е. Тем самым, при следовании по маршруту C на юг наша остановка близка к началу маршрута, тем самым отклонения от расписания в основном небольшие. Аналогично с маршрутами D и E при движении на север. В остальных случаях наблюдается больший разброс отклонений.\n",
        "\n",
        "\n",
        "Однако нам хотелось бы еще посмотреть на интервалы между автобусами. Сначала посчитаем интервалы между автобусами по расписанию и фактические."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "ExecuteTime": {
          "end_time": "2021-03-20T13:00:32.239348Z",
          "start_time": "2021-03-20T13:00:32.190404Z"
        },
        "id": "7odj2E5oEZyM",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "710b657b-2c37-45e6-a2e3-4a80ec85ed3e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-17-5654ec461668>:8: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
            "  grouped = df.groupby([\"route\", \"direction\"])\n"
          ]
        }
      ],
      "source": [
        "def compute_headway(times):\n",
        "    \"\"\"Вычисляет интервалы между соседними моментами времени в минутах.\"\"\"\n",
        "    minute = np.timedelta64(1, \"m\")\n",
        "    return times.sort_values().diff() / minute\n",
        "\n",
        "\n",
        "# сгруппируем по маршруту и по направлению\n",
        "grouped = df.groupby([\"route\", \"direction\"])\n",
        "# для каждой группы посчитаем интервалы определенной выше функцией\n",
        "df[\"actual_interval\"] = grouped[\"actual\"].transform(compute_headway)\n",
        "df[\"scheduled_interval\"] = grouped[\"scheduled\"].transform(compute_headway)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oZJeBqD3EZyN"
      },
      "source": [
        "Визуализируем фактические интервалы между автобусами."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "ExecuteTime": {
          "end_time": "2021-03-20T13:00:34.446410Z",
          "start_time": "2021-03-20T13:00:32.242525Z"
        },
        "id": "cNVaPC2cEZyN",
        "outputId": "ba6ec85e-7fd5-42f2-a1ca-764ceda6d99c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 287
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1800x600 with 6 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "g = sns.FacetGrid(df.dropna(), row=\"direction\", col=\"route\", height=3, aspect=2)\n",
        "g.map(plt.hist, \"actual_interval\", bins=np.arange(40))\n",
        "g.set_titles(\"{col_name} {row_name}\")\n",
        "g.set_axis_labels(\"Актуальные интервалы, мин.\", \"Кол-во автобусов\");"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7olwkLUeEZyN"
      },
      "source": [
        "Видим, что **фактические интервалы даже по каждому отдельному маршруту, видимо, не подчиняются экспоненциальному распределению.**\n",
        "\n",
        "Однако, не все так просто. В прошлый раз мы рассматривали случай, при котором автобусы приходят на остановку независимо друг от друга с одинаковой интенсивностью. Но это не так, для этого достаточно посмотреть на графики временных рядов выше. Как мы ранее заметили, в выходные дни автобусы ходят реже. Кроме того, наверняка в час пик автобусы ходят чаще, чем в вечерне-ночное время.\n",
        "\n",
        "Посмотрим на интервалы между автобусами по расписанию."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "ExecuteTime": {
          "end_time": "2021-03-20T13:00:36.686019Z",
          "start_time": "2021-03-20T13:00:34.447983Z"
        },
        "id": "NfGkjn9dEZyN",
        "outputId": "6b313b6e-5283-46a8-fecf-1eef4c0c0be9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 287
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1800x600 with 6 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ],
      "source": [
        "g = sns.FacetGrid(df.dropna(), row=\"direction\", col=\"route\", height=3, aspect=2)\n",
        "g.map(plt.hist, \"scheduled_interval\", bins=np.arange(40))\n",
        "g.set_titles(\"{col_name} {row_name}\")\n",
        "g.set_axis_labels(\"Интервалы по расписанию, мин.\", \"Кол-во автобусов\");"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2pyc9_8fEZyN"
      },
      "source": [
        "Тут мы уже видим, что по расписанию есть **много разных значений запланированных интервалов между автобусами.**\n",
        "\n",
        "#### Исследование для однородных ожидаемых интервалов\n",
        "\n",
        "Тем не менее, есть частые интервалы по расписанию: 10, 12 и 15 минут. Например, есть почти 2000 автобусов в северную сторону с запланированным интервалом в 10 минут.\n",
        "Давайте посмотрим внимательнее на эти интервалы.\n",
        "\n",
        "Составим условие выбора строк из таблицы."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "ExecuteTime": {
          "end_time": "2021-03-20T13:00:36.706598Z",
          "start_time": "2021-03-20T13:00:36.697668Z"
        },
        "id": "yiUX2KGQEZyN",
        "outputId": "d16526d7-6813-438f-8669-66d518cc363e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 458
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0        False\n",
              "1         True\n",
              "2         True\n",
              "3         True\n",
              "4         True\n",
              "         ...  \n",
              "39152     True\n",
              "39153    False\n",
              "39154    False\n",
              "39155    False\n",
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              "</div><br><label><b>dtype:</b> bool</label>"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ],
      "source": [
        "df[\"scheduled_interval\"].isin([10, 12, 15])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RVDPEuS7EZyN"
      },
      "source": [
        "Составим подтаблицу с помощью данного условия."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "ExecuteTime": {
          "end_time": "2021-03-20T13:00:36.722856Z",
          "start_time": "2021-03-20T13:00:36.708119Z"
        },
        "id": "9tRQbdZ2EZyN"
      },
      "outputs": [],
      "source": [
        "subset = df[df[\"scheduled_interval\"].isin([10, 12, 15])]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "K0v0KSYNEZyO"
      },
      "source": [
        "Сгруппируем по маршруту, по направлению и по интервалам среди выбранных интервалов."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "ExecuteTime": {
          "end_time": "2021-03-20T13:00:36.735267Z",
          "start_time": "2021-03-20T13:00:36.724418Z"
        },
        "id": "1Iuo75umEZyO",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d6b732d9-2acf-434a-9a63-14a1ac8698c1"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-23-52883d3df64d>:1: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
            "  grouped = subset.groupby([\"route\", \"direction\", \"scheduled_interval\"])\n"
          ]
        }
      ],
      "source": [
        "grouped = subset.groupby([\"route\", \"direction\", \"scheduled_interval\"])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "ExecuteTime": {
          "end_time": "2021-03-20T13:00:36.744097Z",
          "start_time": "2021-03-20T13:00:36.737872Z"
        },
        "id": "SRKrMQpsEZyO"
      },
      "outputs": [],
      "source": [
        "def stack_sequence(data):\n",
        "    \"\"\"Перераспределение времени так, как будто события происходят последовательно.\"\"\"\n",
        "\n",
        "    # сортируем по ожидаемому времени прибытия\n",
        "    data = data.sort_values(\"scheduled\")\n",
        "    # переопределим время прибытия так, как будто они шли подряд\n",
        "    data[\"scheduled\"] = data[\"scheduled_interval\"].cumsum()\n",
        "    # соответствующе переопределим фактическое время на основе имеющегося отклонения\n",
        "    data[\"actual\"] = data[\"scheduled\"] + data[\"minutes_late\"]\n",
        "    # посчитаем фактические интервалы по скорректированному фактическому времени\n",
        "    data[\"actual_interval\"] = data[\"actual\"].sort_values().diff()\n",
        "    return data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GGWu7RgjEZyO"
      },
      "source": [
        "Применим эту функцию к нашим группам."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "ExecuteTime": {
          "end_time": "2021-03-20T13:00:37.539460Z",
          "start_time": "2021-03-20T13:00:36.749103Z"
        },
        "id": "U7552nKNEZyO",
        "outputId": "5fbb66e1-fa99-464a-9270-0f5594326d5d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 261
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-25-911cc68e75f3>:1: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n",
            "  sequenced = grouped.apply(stack_sequence).reset_index(drop=True)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  route direction  scheduled     actual  minutes_late  actual_interval  \\\n",
              "0     C     север       10.0  12.133333      2.133333              NaN   \n",
              "1     C     север       20.0  22.633333      2.633333        10.500000   \n",
              "2     C     север       30.0  34.350000      4.350000        11.716667   \n",
              "3     C     север       40.0  37.516667     -2.483333         3.166667   \n",
              "4     C     север       50.0  48.733333     -1.266667        11.216667   \n",
              "\n",
              "   scheduled_interval  \n",
              "0                10.0  \n",
              "1                10.0  \n",
              "2                10.0  \n",
              "3                10.0  \n",
              "4                10.0  "
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              "type": "dataframe",
              "variable_name": "sequenced",
              "summary": "{\n  \"name\": \"sequenced\",\n  \"rows\": 19308,\n  \"fields\": [\n    {\n      \"column\": \"route\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"C\",\n          \"D\",\n          \"E\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"direction\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"\\u044e\\u0433\",\n          \"\\u0441\\u0435\\u0432\\u0435\\u0440\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"scheduled\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5548.8271784791095,\n        \"min\": 10.0,\n        \"max\": 22605.0,\n        \"num_unique_values\": 3901,\n        \"samples\": [\n          7896.0,\n          5720.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"actual\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5548.892463050587,\n        \"min\": 9.183333333333334,\n        \"max\": 22606.766666666666,\n        \"num_unique_values\": 19110,\n        \"samples\": [\n          1540.2333333333333,\n          17580.516666666666\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"minutes_late\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4.175040053215538,\n        \"min\": -10.216666666666667,\n        \"max\": 68.1,\n        \"num_unique_values\": 1441,\n        \"samples\": [\n          7.05,\n          -7.266666666666667\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"actual_interval\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4.784144561157893,\n        \"min\": 0.0,\n        \"max\": 46.149999999999636,\n        \"num_unique_values\": 5509,\n        \"samples\": [\n          17.533333333333303,\n          10.299999999999272\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"scheduled_interval\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.00905015187956,\n        \"min\": 10.0,\n        \"max\": 15.0,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          10.0,\n          12.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 25
        }
      ],
      "source": [
        "sequenced = grouped.apply(stack_sequence).reset_index(drop=True)\n",
        "sequenced.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RnOUibLOEZyO"
      },
      "source": [
        "Теперь визуализируем все отклонения от расписания в рамках выбранного интервала."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "ExecuteTime": {
          "end_time": "2021-03-20T13:00:43.192797Z",
          "start_time": "2021-03-20T13:00:37.541026Z"
        },
        "id": "KaOES5UcEZyO",
        "outputId": "64f162ab-4225-4c01-8d33-33c02ae2b06a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 901
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1800x600 with 6 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1800x600 with 6 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1800x600 with 6 Axes>"
            ],
            "image/png": 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PP6z58+crKSlJ9913n3bv3q01a9aoUqVKOV6a/csvv2j06NEKDg7W8OHDdfvtt6tSpUoqUaKEJOm5557TggULZBiGw31Zf1FXrFjRqRqvfTHyqVOntGnTJk2fPl1Lly7V/Pnz7YZykKTw8HB17NjR4basL+x2176srMMa1K5dWw0aNMizPY7uriuIV155RVlZWRo1alSB7mq6/oXKVpmZmVq8eLFLNVzL2uayZcvm+4J1dwyJYnVtuy5duqR9+/bpp59+0vDhw/X++++rXr16kq60c9CgQfrrr7/Uo0cPPfjgg6pevbrCw8MVFBSkGTNmaOTIkW4/X6998fq5c+e0bds2LVmyRMuXL9eUKVPUqlUrV5teIC1atNBNN92k6dOnq0uXLpo9e7ZatWqlG2+8scAh8e2339bp06f12muvqWzZsvmGxL/97W/6+9//7nCeJ/+jAsD/kH3skX2uIPsUDtknb2QfAL5E9rFH9rmC7FM4ZJ+8kX3gb+gcBK6Tnp5uuyAfPnxYhw8fznXZlJQUPffccznGuA4LC9OUKVPUrFkzvfnmm+rVq5eGDRumlJQUp+/Cyk3Dhg3VoEEDbdy4Udu3b9e8efNkGIZ69OihYsWK2S27ZMkSGYahXr16qW/fvjm2tWfPnlz3c+7cOR04cEDFixdX9erVnarx+hcjnz9/Xr1799bPP/+s2bNnq3///nbLlytXLtcXcltf2O2ufVlVqlRJklSvXr1c910YX3/9tdatW6e77rqrwP9JuP4Fzlb79u1zS0i03lFXokQJl9p8+vRpnT17ViVLlswxzzrmfGRkZI55jtq1bNkyDRgwQEOGDNHChQslSRs2bNBff/2l+vXra/To0Tm2k9f5Kkk7d+6UJKeHdElISFC3bt1sXxuGoTFjxigxMVEff/yxx0OidOUusjFjxmjcuHE6evSoXn755QKvu2HDBs2fP1+NGjVS9+7dC7TOTTfdlOs5QEgEzIPsY4/sUzhkn6vIPvkj+wDwBbKPPbJP4ZB9riL75I/sA38S5OsCAH8zd+5cSVL79u21ffv2XP/UqFFDR48e1cqVK3Nso2PHjrbH5Zs1a6ZHH31Uhw4d0iuvvOLWWh9++GFJ0ueff6558+YpODhYCQkJOZY7deqUpKuB6Fp//vmntm7dmus+1qxZo+zsbDVo0EDFixcvVL0lSpSwDQNx8ODBQm3LXftq0aKFihcvrpUrV+Y6XIGrzp49q7feekvh4eG2YQv8QWRkpKKjo3Xo0KFcx5bPT0pKSo5phw8f1vr162WxWBzeAedI27ZtFRISoj///NM2LIT1fHU0LMjFixe1dOnSXLd36tQp/frrrwoNDc3xwm1nWSwW3XPPPZKu3E3qDV27dlV4eLg+/fRTh3eD5uby5cu2l1GPHDnSdpcrABQE2cce2cd1ZJ/8kX3skX0A+ALZxx7Zx3Vkn/yRfeyRfeBP6BwErpGdna3k5GRJjl+8e63OnTtLcnyXxfUX6MGDBysmJkYLFiyw3SXjDvfcc4/KlSunOXPm6OTJk2rbtq3DR+qtQwQkJyfbBaHjx4/rxRdfVFZWlsPtHzhwQG+99ZYk2d1Z46rs7GytXr1akhQVFVXo7bljXxUqVFDPnj114sQJDRw40DYW/bVOnz6tpKSkXI9Tbj755BMdPXpUAwYMcHhHlS899dRTkqRnn31W69atyzH/4sWLtpeYO/L+++/bXhAtXXkZ9CuvvKJLly7pjjvusLubLy/fffedLl68qNDQUNvwINbzdc2aNba7waQrQ1K8/vrrSk9Pd7itc+fO6aWXXlJWVpY6duyoiIiIAtWQl9TUVEnK8yXbVjExMYqJidHatWtd3l+pUqXUv39/3X777Ro4cGCOu0FzM2/ePG3fvl09e/YsdDjOz9ChQ9WpUyd9+eWXHt0PAO8g+9gj+1xB9smJ7JMT2QdAUUT2sUf2uYLskxPZJyeyDwINw4oC11izZo0OHDig0qVL6/bbb89z2c6dO2vixIlavny5Tp48qbJly+a6bEhIiN5++211795dr7zyipo2beqW0BASEqLu3bvro48+kuT4hdTSlYD32Wef6bffflP79u3VtGlTXbp0SevWrVPFihXVrl07LVu2zG6d559/XosWLdLFixfVvn37Aj+ufq2PPvrI9nLg06dPa+3atdq9e7f+9re/6f7773d6e57a19ChQ3X48GEtWbJEd911l+rWrasqVaooOztb+/bt0/bt25WVlaWuXbvmGEokL4cOHVLt2rXVp0+fQrXNE9q3b68hQ4Zo/Pjx6tWrl2rXrq0aNWooNDRUhw8f1pYtW5SZmamPPvpItWrVsls3KipKdevWVefOnXXrrbcqIiJCGzZs0NGjRxUVFaVRo0Y53GdaWpqGDRsmyX7seUnq16+fQkNDJUn169fXbbfdphUrVui+++5TixYtFB4erk2bNunUqVPq1auXEhMT7bb9+eef64MPPtDJkydVs2ZNh++EyM+iRYtsofT8+fPaunWrNmzYoODgYD322GN5rmsdz1+SU+eII08++aTT6xw6dEjly5fXM888U6h9F8TBgwe1a9cu2x1/AIo2ss9VZB+yD9mH7OMI2QcILGSfq8g+ZB+yD9nHEbKPedA5CFzDOrREp06d8n2B8I033qiGDRvql19+0YIFC/TII4/kuXxMTIwGDx6st99+Wy+++KI+/vhjtzwC3qpVK3300UeqUaOGbr31VofLlClTRrNnz9a7776rNWvW6IcfflCFChXUvXt3DRw4UK+//nqOdXbs2KEmTZqoa9euuu+++1yqNSkpyfbv4sWLq3LlynrwwQf1z3/+M9cXRbuqMPsKCQnRxIkTtXTpUs2ZM0ebN2/W1q1bVbJkSVWsWFHdu3dXu3btbCHGGS+99FKhh+XwlCeeeEItW7ZUYmKi1q1bpx9//FEhISGqUKGC2rRpo7Zt29qGSbmWxWLRhAkT9OGHHyolJUX79+9X2bJl1aNHDz311FO5Hu/09HTb3V9BQUEqVaqU4uLi1L17d9133312y06aNEkff/yxFixYoDVr1qhkyZKKi4vToEGDHA6JsXPnTlWpUkX9+vXTI4884nBc/PysWLFCK1askHQl6JUrV04dO3ZUv3791Lhx4zzX3bJli6QrLy9v1KiR0/t2hyFDhrjt3RYAzIPscxXZh+xD9iH7AAh8ZJ+ryD5kH7IP2QfmZjEMw/B1EQBcN2LECM2aNUvDhw93+NJpwF327duntm3bqkqVKlq+fLmvy/ErU6ZM0bhx4/TBBx/Y3nkAAPAMsg+8heyTO7IPAHgP2QfeQvbJHdkHgYh3DgJF2O7du5WcnKySJUu6fbgGAAW3Zs0aNWnShIAIAB5G9gH8A9kHALyD7AP4B7IPAhHDigJF0DvvvKODBw8qNTVVFy9e1FNPPWUbdx2A902dOtXXJQBAQCP7AP6F7AMAnkX2AfwL2QeBiM5BoAhauHChDhw4oEqVKqlXr175vjQXAACgKCP7AAAAMyH7AAA8jXcOAgAAAAAAAAAAACbBOwcBAAAAAAAAAAAAk6BzEAAAAAAAAAAAADAJOgcBAAAAAAAAAAAAk6BzEAAAAAAAAAAAADAJOgcBAAAAAAAAAAAAk6BzEAAAAAAAAAAAADAJOgcBAAAAAAAAAAAAk6BzEAAAAAAAAAAAADCJ/we92Q3vEUZHwAAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "for route in [\"C\", \"D\", \"E\"]:\n",
        "    g = sns.FacetGrid(\n",
        "        sequenced.query(f\"route == '{route}'\"),\n",
        "        row=\"direction\",\n",
        "        col=\"scheduled_interval\",\n",
        "        height=3,\n",
        "        aspect=2,\n",
        "    )\n",
        "    g.map(plt.hist, \"actual_interval\", bins=np.arange(40) + 0.5)\n",
        "    g.set_titles(\"{row_name} ({col_name:.0f} мин.)\")\n",
        "    g.set_axis_labels(\"Актуальные интервалы, мин.\", \"Кол-во автобусов\")\n",
        "    g.fig.suptitle(f\"{route} line\", y=1.05, fontsize=28)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aO9NNPHgEZyO"
      },
      "source": [
        "Видим, что для каждого маршрута и направления при фиксированном интервале **распределение фактических интервалов близко к нормальному**. Оно достигает максимума около запланированного интервала и имеет стандартное отклонение (корень из дисперсии), которое меньше в начале маршрута (на юг для C, на север для D/E) и больше в конце.\n",
        "\n",
        "Также видно, что фактические интервалы прибытия определенно не соответствуют экспоненциальному распределению, что является основным предположением, на котором основан парадокс времени ожидания.\n",
        "\n",
        "#### Симуляция пассажиров\n",
        "\n",
        "Теперь оценим, сколько в среднем придется ждать пассажиру автобуса при данных интервалах движения. Для этого оформим код из предыдущего примера в специальную функцию."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "ExecuteTime": {
          "end_time": "2021-03-20T13:00:43.197773Z",
          "start_time": "2021-03-20T13:00:43.194420Z"
        },
        "id": "b5yu0vVpEZyP"
      },
      "outputs": [],
      "source": [
        "def simulate_wait_times(bus_arrival_times, n_passengers=1000000):\n",
        "    \"\"\"\n",
        "    Моделирование времени ожидание пассажиров.\n",
        "\n",
        "    bus_arrival_times -- моменты времени прибытия автобусов.\n",
        "    n_passengers -- количество пассажиров для семплирования.\n",
        "    \"\"\"\n",
        "\n",
        "    bus_arrival_times = np.array(bus_arrival_times)\n",
        "\n",
        "    # сгенерируем для каждого пассажира время его прибытия на остановку\n",
        "    passenger_times = sps.uniform(scale=bus_arrival_times.max()).rvs(size=n_passengers)\n",
        "    # найдем время прибытия следующего автобуса поиском по отсортированному массиву\n",
        "    i = np.searchsorted(bus_arrival_times, passenger_times, side=\"right\")\n",
        "    # вычислим интервал ожидания\n",
        "    wait_times = bus_arrival_times[i] - passenger_times\n",
        "\n",
        "    return wait_times"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KalbBN0eEZyP"
      },
      "source": [
        "Произведем семплирование."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "ExecuteTime": {
          "end_time": "2021-03-20T13:00:45.381019Z",
          "start_time": "2021-03-20T13:00:43.199355Z"
        },
        "id": "RLp46qAYEZyP",
        "outputId": "b9083224-21d5-4b46-bd4f-b0506ed55486",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 700
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-35-5999c72c3141>:2: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
            "  grouped = sequenced.groupby([\"route\", \"direction\", \"scheduled_interval\"])\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                   actual\n",
              "route direction scheduled_interval       \n",
              "C     север     10.0                  7.0\n",
              "                12.0                  7.3\n",
              "                15.0                  8.3\n",
              "      юг        10.0                  5.9\n",
              "                12.0                  6.7\n",
              "                15.0                  7.8\n",
              "D     север     10.0                  5.4\n",
              "                12.0                  6.2\n",
              "                15.0                  7.8\n",
              "      юг        10.0                  6.8\n",
              "                12.0                  7.4\n",
              "                15.0                  8.5\n",
              "E     север     10.0                  5.5\n",
              "                12.0                  6.2\n",
              "                15.0                  7.8\n",
              "      юг        10.0                  7.0\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"pd\",\n  \"rows\": 18,\n  \"fields\": [\n    {\n      \"column\": \"actual\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 14,\n        \"samples\": [\n          \"7.4\",\n          \"5.5\",\n          \"7.0\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 35
        }
      ],
      "source": [
        "# сгруппируем по маршрутам, направлениям и интервалам\n",
        "grouped = sequenced.groupby([\"route\", \"direction\", \"scheduled_interval\"])\n",
        "# применим семплирование\n",
        "simulated = grouped[\"actual\"].apply(simulate_wait_times)\n",
        "\n",
        "# посчитаем средние значения при сэмплировании\n",
        "simulated = simulated.apply(\n",
        "    lambda times: \"{0:.1f}\".format(times.mean())\n",
        ")\n",
        "pd.DataFrame(simulated)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kWx0SeVPEZyP"
      },
      "source": [
        "Видим, что среднее время ожидания, возможно, на минуту или две больше половины интервала расписанию, но не равно этому интервалу, как подразумевает парадокс времени ожидания. Другими словами, **парадокс инспекции подтвержден, но парадокс времени ожидания не соответствует действительности.**\n",
        "\n",
        "**Вывод.**\n",
        "\n",
        "Мы подтвердили, что в реальном мире автобусные маршруты подчиняются некоторой разновидности парадокса инспекции, однако основное предположение, лежащее в основе парадокса времени ожидания, — что прибытие автобусов происходит независимо друг от друга и с одинаковой интенсивностью — не является действительным.\n",
        "\n",
        "На самом же деле в хорошо управляемой системе общественного транспорта есть специально структурированные расписания, чтобы избежать такого поведения: автобусы не начинают свои маршруты в случайное время в течение дня, а стартуют по расписанию, выбранному для наиболее эффективной перевозки пассажиров."
      ]
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