MP1.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# ECE471 Fall 2022 MP1 Checkpoint 1\n",
        "Dante Howard (danteah2) \\\\\n",
        "Guifu Liu (guiful2) \\\\\n",
        "Sean Lee (sl84)"
      ],
      "metadata": {
        "id": "ujvRI3xlNXXF"
      },
      "id": "ujvRI3xlNXXF"
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Setup\n",
        "Fetch and import dataset collected from Carla client, as well as libraries used. "
      ],
      "metadata": {
        "id": "pXwxDcG7OVDa"
      },
      "id": "pXwxDcG7OVDa"
    },
    {
      "cell_type": "code",
      "source": [
        "!git clone https://github.com/seanseungbeomlee/ECE_471_MP1_AV_Analysis"
      ],
      "metadata": {
        "id": "hqYC7U4nVUZS",
        "outputId": "a8ce6a88-97da-4ced-dba0-4f9ac62e4141",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "id": "hqYC7U4nVUZS",
      "execution_count": 93,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "fatal: destination path 'ECE_471_MP1_AV_Analysis' already exists and is not an empty directory.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "29975330",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2022-09-08T23:08:56.806091Z",
          "start_time": "2022-09-08T23:08:56.386039Z"
        },
        "id": "29975330"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "from matplotlib import pyplot as plt\n",
        "import scipy\n",
        "import os"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "136bcd7e",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2022-09-08T23:12:58.590554Z",
          "start_time": "2022-09-08T23:12:58.522467Z"
        },
        "id": "136bcd7e"
      },
      "outputs": [],
      "source": [
        "os.chdir('/content/ECE_471_MP1_AV_Analysis')\n",
        "clear_night_ctl = pd.read_csv('campaign_results/route_highway_epoch24_clear-night_fi_ghost_cutin/routes_fi_route_highway-0911_024337-data_fi_ghost_cutin-single_ctl.csv')\n",
        "clear_night_cvip = pd.read_csv('campaign_results/route_highway_epoch24_clear-night_fi_ghost_cutin/routes_fi_route_highway-0911_024337-data_fi_ghost_cutin-single_cvip.csv')\n",
        "clear_night_traj = pd.read_csv('campaign_results/route_highway_epoch24_clear-night_fi_ghost_cutin/routes_fi_route_highway-0911_024337-data_fi_ghost_cutin-single_traj.csv')\n",
        "\n",
        "clear_sunset_ctl = pd.read_csv('campaign_results/route_highway_epoch24_clear-sunset_fi_ghost_cutin/routes_fi_route_highway-0911_023451-data_fi_ghost_cutin-single_ctl.csv')\n",
        "clear_sunset_cvip = pd.read_csv('campaign_results/route_highway_epoch24_clear-sunset_fi_ghost_cutin/routes_fi_route_highway-0911_023451-data_fi_ghost_cutin-single_cvip.csv')\n",
        "clear_sunset_traj = pd.read_csv('campaign_results/route_highway_epoch24_clear-sunset_fi_ghost_cutin/routes_fi_route_highway-0911_023451-data_fi_ghost_cutin-single_traj.csv')\n",
        "\n",
        "clear_noon_ctl = pd.read_csv('campaign_results/route_highway_epoch24_clear-noon_fi_ghost_cutin/routes_fi_route_highway-0911_023016-data_fi_ghost_cutin-single_ctl.csv')\n",
        "clear_noon_cvip = pd.read_csv('campaign_results/route_highway_epoch24_clear-noon_fi_ghost_cutin/routes_fi_route_highway-0911_023016-data_fi_ghost_cutin-single_cvip.csv')\n",
        "clear_noon_traj = pd.read_csv('campaign_results/route_highway_epoch24_clear-noon_fi_ghost_cutin/routes_fi_route_highway-0911_023016-data_fi_ghost_cutin-single_traj.csv')\n",
        "\n",
        "rain_noon_ctl = pd.read_csv('campaign_results/route_highway_epoch24_rain-noon_fi_ghost_cutin/routes_fi_route_highway-0911_023309-data_fi_ghost_cutin-single_ctl.csv')\n",
        "rain_noon_cvip = pd.read_csv('campaign_results/route_highway_epoch24_rain-noon_fi_ghost_cutin/routes_fi_route_highway-0911_023309-data_fi_ghost_cutin-single_cvip.csv')\n",
        "rain_noon_traj = pd.read_csv('campaign_results/route_highway_epoch24_rain-noon_fi_ghost_cutin/routes_fi_route_highway-0911_023309-data_fi_ghost_cutin-single_traj.csv')"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Task 1\n",
        "## Task 1.1 List the first 5 rows of the csv firles for the clear-night weather condition"
      ],
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        "id": "cqkIpwZmN66Y"
      },
      "id": "cqkIpwZmN66Y"
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          "end_time": "2022-09-08T23:14:31.040876Z",
          "start_time": "2022-09-08T23:14:31.032513Z"
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
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      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         ts  agent_id  throttle     steer  brake\n",
              "0  15547837         0       0.9 -0.001013    0.0\n",
              "1  15547838         0       0.9 -0.010019    0.0\n",
              "2  15547839         0       0.9 -0.006061    0.0\n",
              "3  15547840         0       0.9 -0.007578    0.0\n",
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              "      <td>0.0</td>\n",
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              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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          "metadata": {},
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      ],
      "source": [
        "clear_night_ctl.head()"
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          "data": {
            "text/plain": [
              "         ts  agent_id        cvip      cvip_x     cvip_y      cvip_z\n",
              "0  15547837         0  500.491189  198.767441 -95.832657 -499.819366\n",
              "1  15547838         0    5.595580  195.567444 -90.832657    0.100000\n",
              "2  15547839         0    5.592365  195.567444 -90.832657    0.095407\n",
              "3  15547840         0    5.589578  195.567444 -90.832657    0.084691\n",
              "4  15547841         0    5.587154  195.567444 -90.832657    0.069311"
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              "      <td>-499.819366</td>\n",
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              "      <td>0.100000</td>\n",
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              "      <th>2</th>\n",
              "      <td>15547839</td>\n",
              "      <td>0</td>\n",
              "      <td>5.592365</td>\n",
              "      <td>195.567444</td>\n",
              "      <td>-90.832657</td>\n",
              "      <td>0.095407</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>15547840</td>\n",
              "      <td>0</td>\n",
              "      <td>5.589578</td>\n",
              "      <td>195.567444</td>\n",
              "      <td>-90.832657</td>\n",
              "      <td>0.084691</td>\n",
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              "      <th>4</th>\n",
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              "      <td>5.587154</td>\n",
              "      <td>195.567444</td>\n",
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              "                                                     [key], {});\n",
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          "metadata": {},
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      ],
      "source": [
        "clear_night_cvip.head()"
      ]
    },
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      "id": "7bf9aef3",
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              "         ts  agent_id           x         y         z    v\n",
              "0  15547837         0  192.362411 -86.26268  0.539326  0.0\n",
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          "metadata": {},
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      ],
      "source": [
        "clear_night_traj.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "c009641e",
      "metadata": {
        "id": "c009641e"
      },
      "source": [
        "## Task 1.2 Summarizing information for each weather condition"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "6c294f61",
      "metadata": {
        "id": "6c294f61"
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      "source": [
        "### Durations"
      ]
    },
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      "id": "fb141b54",
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      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              Duration\n",
              "Clear night      926.0\n",
              "Clear sunset     757.0\n",
              "Clear noon       751.0\n",
              "Rain noon        404.0"
            ],
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              "      <th>Duration</th>\n",
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              "    <tr>\n",
              "      <th>Clear noon</th>\n",
              "      <td>751.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Rain noon</th>\n",
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              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-06ebb9f8-f0cd-4562-93c9-0396e8d90b56 button.colab-df-convert');\n",
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              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-06ebb9f8-f0cd-4562-93c9-0396e8d90b56');\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",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 53
        }
      ],
      "source": [
        "def get_duration(ctl):\n",
        "  times = ctl['ts']\n",
        "  return times.iloc[-1] - times.iloc[0]\n",
        "\n",
        "clear_night_duration = get_duration(clear_night_ctl)\n",
        "clear_sunset_duration = get_duration(clear_sunset_ctl)\n",
        "clear_noon_duration = get_duration(clear_noon_ctl)\n",
        "rain_noon_duration = get_duration(rain_noon_ctl)\n",
        "\n",
        "duration_df = pd.DataFrame()\n",
        "duration_df.loc['Clear night', 'Duration'] = clear_night_duration\n",
        "duration_df.loc['Clear sunset', 'Duration'] = clear_sunset_duration\n",
        "duration_df.loc['Clear noon', 'Duration'] = clear_noon_duration\n",
        "duration_df.loc['Rain noon', 'Duration'] = rain_noon_duration\n",
        "# print('clear-night: %f, clear-sunset: %f, clear-noon: %f, rain-noon: %f' % (clear_night_duration, clear_sunset_duration, clear_noon_duration, rain_noon_duration))\n",
        "duration_df"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "7a0056c8",
      "metadata": {
        "id": "7a0056c8"
      },
      "source": [
        "### Mean and Standard Deviations"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "b235d811",
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        "id": "b235d811",
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          "height": 269
        }
      },
      "outputs": [
        {
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          "data": {
            "text/plain": [
              "          clear-night mean  clear-night std  clear-sunset mean  \\\n",
              "throttle             0.616            0.341              0.610   \n",
              "steer                0.003            0.066              0.000   \n",
              "brake                0.122            0.327              0.057   \n",
              "cvip                39.506           39.506             20.107   \n",
              "x                  189.727            2.851            192.940   \n",
              "y                  -25.546           40.433            -31.128   \n",
              "v                    5.668            2.588              6.884   \n",
              "\n",
              "          clear-sunset std  clear-noon mean  clear-noon std  rain-noon mean  \\\n",
              "throttle             0.282            0.608           0.283           0.637   \n",
              "steer                0.006            0.001           0.006          -0.001   \n",
              "brake                0.231            0.059           0.235           0.047   \n",
              "cvip                20.107           19.409          19.409           7.095   \n",
              "x                    0.398          192.916           0.403         192.646   \n",
              "y                   40.475          -31.366          40.570         -63.407   \n",
              "v                    3.307            6.936           3.279           6.390   \n",
              "\n",
              "          rain-noon std  \n",
              "throttle          0.275  \n",
              "steer             0.003  \n",
              "brake             0.212  \n",
              "cvip              7.095  \n",
              "x                 0.269  \n",
              "y                21.537  \n",
              "v                 3.882  "
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              "      <th></th>\n",
              "      <th>clear-night mean</th>\n",
              "      <th>clear-night std</th>\n",
              "      <th>clear-sunset mean</th>\n",
              "      <th>clear-sunset std</th>\n",
              "      <th>clear-noon mean</th>\n",
              "      <th>clear-noon std</th>\n",
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              "      <th>rain-noon std</th>\n",
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              "      <th>throttle</th>\n",
              "      <td>0.616</td>\n",
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              "      <td>0.282</td>\n",
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              "      <td>0.003</td>\n",
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              "      <td>0.006</td>\n",
              "      <td>0.001</td>\n",
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              "      <td>-0.001</td>\n",
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              "      <th>brake</th>\n",
              "      <td>0.122</td>\n",
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              "      <td>0.057</td>\n",
              "      <td>0.231</td>\n",
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              "      <td>0.212</td>\n",
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              "      <th>cvip</th>\n",
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              "      <td>39.506</td>\n",
              "      <td>20.107</td>\n",
              "      <td>20.107</td>\n",
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              "      <td>7.095</td>\n",
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              "      <td>0.398</td>\n",
              "      <td>192.916</td>\n",
              "      <td>0.403</td>\n",
              "      <td>192.646</td>\n",
              "      <td>0.269</td>\n",
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              "      <th>y</th>\n",
              "      <td>-25.546</td>\n",
              "      <td>40.433</td>\n",
              "      <td>-31.128</td>\n",
              "      <td>40.475</td>\n",
              "      <td>-31.366</td>\n",
              "      <td>40.570</td>\n",
              "      <td>-63.407</td>\n",
              "      <td>21.537</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>v</th>\n",
              "      <td>5.668</td>\n",
              "      <td>2.588</td>\n",
              "      <td>6.884</td>\n",
              "      <td>3.307</td>\n",
              "      <td>6.936</td>\n",
              "      <td>3.279</td>\n",
              "      <td>6.390</td>\n",
              "      <td>3.882</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-98bb7e81-05b4-4c29-82da-96823da2de3e')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\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",
              "    [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-98bb7e81-05b4-4c29-82da-96823da2de3e 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-98bb7e81-05b4-4c29-82da-96823da2de3e');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 54
        }
      ],
      "source": [
        "summary_df = pd.DataFrame()\n",
        "\n",
        "# Helper function to get the information for each condition's dataframe\n",
        "def add_summary(ctl, cvip, traj, df_name):\n",
        "\n",
        "  # Getting values from ctl dataframe\n",
        "  ctl_means = ctl.mean(axis=0)\n",
        "  ctl_stds = ctl.std(axis=0)\n",
        "  summary_df.loc['throttle', df_name + ' mean'] = ctl_means['throttle']\n",
        "  summary_df.loc['throttle', df_name + ' std'] = ctl_stds['throttle']\n",
        "  summary_df.loc['steer', df_name + ' mean'] = ctl_means['steer']\n",
        "  summary_df.loc['steer', df_name + ' std'] = ctl_stds['steer']\n",
        "  summary_df.loc['brake', df_name + ' mean'] = ctl_means['brake']\n",
        "  summary_df.loc['brake', df_name + ' std'] = ctl_stds['brake']\n",
        "\n",
        "  # Getting values from cvip dataframe\n",
        "  summary_df.loc['cvip', df_name + ' mean'] = cvip.mean()['cvip']\n",
        "  summary_df.loc['cvip', df_name + ' std'] = cvip.mean()['cvip']\n",
        "\n",
        "  # Getting values from traj dataframe\n",
        "  traj_means = traj.mean(axis=0)\n",
        "  traj_stds = traj.std(axis=0)\n",
        "  summary_df.loc['x', df_name + ' mean'] = traj_means['x']\n",
        "  summary_df.loc['x', df_name + ' std'] = traj_stds['x']\n",
        "  summary_df.loc['y', df_name + ' mean'] = traj_means['y']\n",
        "  summary_df.loc['y', df_name + ' std'] = traj_stds['y']\n",
        "  summary_df.loc['v', df_name + ' mean'] = traj_means['v']\n",
        "  summary_df.loc['v', df_name + ' std'] = traj_stds['v']\n",
        "\n",
        "add_summary(clear_night_ctl, clear_night_cvip, clear_night_traj, 'clear-night')\n",
        "add_summary(clear_sunset_ctl, clear_sunset_cvip, clear_sunset_traj, 'clear-sunset')\n",
        "add_summary(clear_noon_ctl, clear_noon_cvip, clear_noon_traj, 'clear-noon')\n",
        "add_summary(rain_noon_ctl, rain_noon_cvip, rain_noon_traj, 'rain-noon')\n",
        "\n",
        "summary_df = summary_df.round(3)\n",
        "summary_df\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "72f882e7",
      "metadata": {
        "id": "72f882e7"
      },
      "source": [
        "## Task 1.3 Visualize campaign results"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "f423810f",
      "metadata": {
        "id": "f423810f"
      },
      "outputs": [],
      "source": [
        "import seaborn as sns\n",
        "sns.set_theme()\n",
        "\n",
        "def plot_feature(feature, ctl=False, cvip=False, traj=False, histogram=False, scatter=False):\n",
        "\n",
        "  # Getting y feature and ts values from the dataframes\n",
        "  clear_night_ts = clear_night_ctl['ts']\n",
        "  clear_sunset_ts = clear_sunset_ctl['ts']\n",
        "  clear_noon_ts = clear_noon_ctl['ts']\n",
        "  rain_noon_ts = rain_noon_ctl['ts']\n",
        "\n",
        "  if ctl:\n",
        "    clear_night_y = clear_night_ctl[feature]\n",
        "    clear_sunset_y = clear_sunset_ctl[feature]\n",
        "    clear_noon_y = clear_noon_ctl[feature]\n",
        "    rain_noon_y = rain_noon_ctl[feature]\n",
        "\n",
        "  elif cvip:\n",
        "\n",
        "    clear_night_y = clear_night_cvip[feature]\n",
        "    clear_sunset_y = clear_sunset_cvip[feature]\n",
        "    clear_noon_y = clear_noon_cvip[feature]\n",
        "    rain_noon_y = rain_noon_cvip[feature]\n",
        "  \n",
        "  else:\n",
        "\n",
        "    clear_night_y = clear_night_traj[feature]\n",
        "    clear_sunset_y = clear_sunset_traj[feature]\n",
        "    clear_noon_y = clear_noon_traj[feature]\n",
        "    rain_noon_y = rain_noon_traj[feature]\n",
        "\n",
        "\n",
        "  # Setting the timestamp data to start at 0\n",
        "  clear_night_ts = clear_night_ts - clear_night_ts[0]\n",
        "  clear_sunset_ts = clear_sunset_ts - clear_sunset_ts[0]\n",
        "  clear_noon_ts = clear_noon_ts - clear_noon_ts[0]\n",
        "  rain_noon_ts = rain_noon_ts - rain_noon_ts[0]\n",
        "\n",
        "  if histogram:\n",
        "\n",
        "    plt.hist(clear_night_ts, clear_night_y)\n",
        "    plt.hist(clear_sunset_ts, clear_sunset_y)\n",
        "    plt.hist(clear_noon_ts, clear_noon_y)\n",
        "    plt.hist(rain_noon_ts, rain_noon_y)\n",
        "\n",
        "  elif scatter:\n",
        "\n",
        "    plt.scatter(clear_night_ts, clear_night_y)\n",
        "    plt.scatter(clear_sunset_ts, clear_sunset_y)\n",
        "    plt.scatter(clear_noon_ts, clear_noon_y)\n",
        "    plt.scatter(rain_noon_ts, rain_noon_y)\n",
        "\n",
        "  else:\n",
        "\n",
        "    plt.plot(clear_night_ts, clear_night_y, label='clear-night')\n",
        "    plt.plot(clear_sunset_ts, clear_sunset_y, label='clear-sunset')\n",
        "    plt.plot(clear_noon_ts, clear_noon_y, label='clear-noon')\n",
        "    plt.plot(rain_noon_ts, rain_noon_y, label='rain-noon')\n",
        "\n",
        "  plt.xlabel('Timestamp')\n",
        "  plt.ylabel(feature)\n",
        "  plt.title(feature + ' of car for various weather conditions')\n",
        "  plt.legend()\n",
        "  plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "8ba99e71",
      "metadata": {
        "id": "8ba99e71",
        "outputId": "5ab41bc7-304f-46f1-829a-21520cf66e36",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 355
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plot_feature('throttle', ctl=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "dcfc1a92",
      "metadata": {
        "id": "dcfc1a92",
        "outputId": "ebd7f1fa-9668-4c95-b102-eac9a58af06a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 355
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plot_feature('steer', ctl=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "2ff48361",
      "metadata": {
        "id": "2ff48361",
        "outputId": "b52590bd-276d-4175-a420-5b0b2ab960f4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 355
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plot_feature('brake', ctl=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "21a9be14",
      "metadata": {
        "id": "21a9be14",
        "outputId": "7457abca-491f-4be2-9980-c8ddbd58d78c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 355
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ],
            "image/png": 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uKIp20/KSk80oL3e2fCBFFbZb58M265zYbp0P26xjyLKExMT4RstO+7CmKELTsFZ/Dep82G6dD9usc2K7dT5ss+jCYVAiIiKiKMawRkRERBTFTvthUCIiojNBKBREZWUpgkF/ROcpKZGhKEob1Yp+Ta83IDExFTpd6yMYwxoREdFpoLKyFLGxJsTHZ0CSpP/6PHq9jGCQYU0LQgi4XDWorCxFSoq91a/jMCgREdFpIBj0Iz7eGlFQI21JkoT4eOsp934yrBEREZ0mGNSi33/TRu02DDp+/HgYDAYYjUYAwL333osxY8bgu+++Q15eHnw+H7KysvDUU08hOTkZAJotIyIiIjoTtGvP2tq1a7Fp0yZs2rQJY8aMgaIouO+++5CXl4dt27YhNzcXK1asAIBmy4iIiKhzuvLKafjllwMdcu0ff9yPRYseafE4h6MQU6dOaLSstrYWb7zxaltXrVkdOgy6b98+GI1G5ObmAgCuvvpqfPTRRy2WRYu/PbMAb99/U0dXg4iI6IwVDAZbfWzv3n2wcOFjEV3P6azFm2++FtE5TlW73g167733QgiBoUOH4u6774bD4UBmZqZanpSUBEVRUFVV1WyZzWZr9TWTk81t+h5OFFNUilhPAKmpFs2uQdphu3U+bLPOie3WPkpKZOj1bdMH01bn2bt3N9atexputwsAcMcdfwQA6HThupaVlWLlyuUoLi6Cz+fDRRdNxuzZvwcArF27Grt27UAgEIDNZsPDDy+E3Z6JwsJC3HDDtZg6dRq2b/8Wl112OS6//Er1mjt2bMeaNSvQt28/7N27B5IkYcmSJ3DWWWdjx47tWLduNTZufAMA8M47b+N///ctWCwWnH/+KPzlL+9g27bPoNPJACS8+OJz+OqrL+D1evHQQ3kYNGgwVq9eDqfTiRtumInY2Fi8+OLGU/5cZFk+pb8X7RbW3njjDdjtdvj9fixduhSLFy/GRRddpPl1y8udmm+bUVpaq+n5qe2lplrYbp0M26xzYru1H0VRGiy58cVeB/61x3HK55EkQLTwa3P0ADtG9W9+6Ymammo88MC9WLp0Ofr3H4hQKASXKxzaQqFwXR99dAFmz74RgwYNQSAQwLx5t6JXrxwMG3YeZs68HrfdNg8AsHnze3jmmaexaNETCIUUVFdXoVevHLX8xPcdCin45ZeDePDBPNx770N49dWX8PLLG7Bw4WMIhRQIET7+wIGf8eqrr+CVV95EYmIi1qxZAUAgGFTUa/Tp0x833XQbPv74Qzz77NN4/vmXcddd9+PGG6/DK6+8edK1W0tRlJP+Xsiy1GQHU7uFNbs93KgGgwEzZ87ErbfeilmzZqGwsFA9pqKiArIsw2azwW63N1kWNXjTDRERUaP27duL7Oyz0L//QACATqeD1WpVyz0eD3bt2oGqqir1Obfbhfz8fAwbdh6+/voLvPvuO/B43AiFQg3ObTAYMX580x0+3bp1x7nn9gYA9O3bH1988f9OOmbXrh0YOXIUEhMTAQBTp07HJ598qJbHxZkwatQY9RzPPLPmVD+CNtMuYc3tDn/QFosFQgh88MEHyMnJQb9+/eD1erF9+3bk5ubi7bffxpQpUwCg2bKowr1uiYgoCo3q33LvV2Paa1FcIRRIkoQNG16DXt8wjhQVObBu3Sq8+OJryMzMwt69uxvcGBAXF6sugbFy5TLs3bsbALB48eMAwmGunizLJ4W91jAYYn51jtbPjWtr7RLWysvLcccddyAUCkFRFPTo0QMLFy6ELMtYvnw5Fi5c2GB5DgDNlhEREVF069evP/LzD2Hfvj3o129Ag2FQADCZ4jFw4GC8/vpGzJ59IwCguLgIer0eLpcLen0MkpOToSgK3nvv/5q8zj33PNDg57KyslbVb9CgIXjzzdfUufAffbSlVa+Lj4+H1+tFMBg8KWRqpV2u0rVrV7z33nuNlg0ZMgSbN28+5bJowZFQIiKik1mtCVi6dDnWrVsNr9cDSZJx++3zGhyTl7cEa9euwqxZVwEIB7gHH8xDjx49MW7cRFx77W+RkGDDyJGjsHv3rjat3znnnIuZM2fhlltugMkUj9zcYYiPb/mmRKs1AZMmXYzrr78aFosV69e/3Kb1aowkREvTCDs3LW8w+HzBLTC5Ahi+6iVNzk/a4aTnzodt1jmx3dpPUdFhZGR0j/g8Z9LeoG63CyZTPADgpZdeQEHBMeTlLdH8uo21VVTcYHBaYrcaERFRp/X8889g797dCAYDyMzMwv33P9zRVWoUw1qkTut+SSIiotPXr+e7RStu5E5EREQUxRjWiIiIiKIYw1pEOGmNiIiItMWwFiHGNSIiItISw1okmNSIiIhIYwxrEeCNoERERKfmyiun4ZdfDnR0NTT10ksvIBAItNn5GNYixcRGRETUYYLBjtuzsymvvPJim4Y1rrNGRER0Ggr89AUC//nnKb9OkiS0tLlRTK8LEHPuqBbPtW/fHjz77NNwu90AcNJ2U2VlZVizZjmKi4vg8/kwceJkzJo1BwDwzDNr8N13OxEIBGCz2fDgg3nIyLDD4SjEjTdeh4svnoadO7/F9On/g8suu1I955Ej+Vi6dBG8Xi8UJYSLL56GmTOvw9Klj6J37xxccUV4a6sTf1669FEYDAYcPXoEJSXF6Nu3Px55ZBEkScKmTe/iz39+EzExBgihYPHiJ9G9ezaOHMnH00+vQnV1FQKBAH77299h6tTpWLlyGQDg1lvnQJJkrFv3AiwWS+sboBEMaxGS2LVGRER0kpqaajz00H1YunQ5+vcfeNJG7gDw2GN5mD37RgwaNASBQADz5t2KnJw+GDbsPFx77WzMnftHAMDmze/h+efXYtGiJwAA1dXVyMnpo5af6N13/4LRoy/AddfdUFePmlbV95dfDmLNmucgyzJuuOEabN/+bwwbdh6ee+5pvPHG/yElJQV+vx+KoiAYDOLRRx/BwoWPoXv3bLjdLvz+99ehX78BuOeeB/DXv76D559/GSaTKZKPUMWwFgneYEBERFEq5txRrer9+rW22ht03769yM4+C/37DwQA6HQ6WK1Wtdzj8WDXrh2oqqpSn3O7XcjPz8ewYefh66+/wLvvvgOPx41QKNTg3AaDEePHX9TodQcNGoznnlsLr9eLIUNyMWRIbqvqO2bMWBiNRgBAr169UFBwDMOGAUOGDMPSpQsxatQYjBw5GllZXXDo0C84fPgQFi58SH19IBBAfv4hdO+e3arrnQqGNSIiImp3QiiQJAkbNrwGvb5hHCkqcmDdulV48cXXkJmZhb17d2PRokfU8ri4WEhSuMdk5cpl2Lt3NwBg8eLHMXbsBPTrNwDffPM1Xn99I7ZufR95eUug0+mgKMdHw/x+X4NrGo0G9bEs69SA+PjjT+GHH77Hjh3bceedt+Deex9EenoGEhJs2Ljxzbb9UJrAGwwixVFQIiKik/Tr1x/5+Yewb98eAEAoFGowJGkyxWPgwMF4/fWN6nPFxUUoLy+Dy+WCXh+D5ORkKIqC9977vyavc889D2DjxjexceOb6NYtG8eOHUVSUjIuuWQabrjhJuzf/z0AICurK378Mfy4rKwMO3fuaPE9BINBFBYWoE+ffrjuutkYPvw8/Pzzf9CtW3fExsbio4+2qscePpwPl8upvrf6x22BPWsR4TgoERFRY6zWBCxduhzr1q2G1+uBJMkn3WCQl7cEa9euwqxZ4Un/JlM8HnwwDz169MS4cRNx7bW/RUKCDSNHjsLu3btadd3PPvsEH3/8EWJi9JAkCfPm3QMAmD79MjzyyAO49trfoGvXbujTp2+L51IUBUuXPgqnsxaSJCM9PR233DIXer0ey5atxtq1K/HWW/8fQiEFSUlJWLz4SQDA1VdfgzvvvAVGY2yb3GAgiZZu+ejkysudDbo929LfH70N1kovhj79sibnJ+2kplpQWlrb0dWgU8A265zYbu2nqOgwMjK6R3yetpqzRk1rrK1kWUJysrnR4zkMSkRERBTFGNaIiIiIohjDWgQEp6wRERGRxhjWInVaz/gjIiKijsawRkRERBTFGNYiwnFQIiIi0hbDWoQY14iIiFrvyiun4ZdfDnR0NToVhrVIMKkRERF1qGAw2NFV0Bx3MIgUbzAgIiJq1L59e/Dss0/D7XYDwEk7GJSVlWHNmuUoLi6Cz+fDxImTMWvWHADAM8+swXff7UQgEIDNZsODD+YhI8MOh6MQN954HS6+eBp27vwW06f/Dy677Er1nDt3bsfatavQp09ffP/9XgASFi16HNnZZwEAXn99I7Zt+wAAkJPTF3/8430wmUxwu91Ysya8DygATJkyFddccz0AYO7cm5GT0xf79u1BWVkZxo+fiFtvvUPTz+5EDGtERESnoX87duArx7en/DpJAlra22ikfRhG2Ic2e0xNTTUeeug+LF26HP37D0QoFILL5WpwzGOP5WH27BsxaNAQBAIBzJt3K3Jy+mDYsPNw7bWzMXfuHwEAmze/h+efX4tFi54AAFRXVyMnp49a/muHDh3EQw/l4f77H8arr76EV199CQsXPoavvvoC27Z9gPXrX4bJFI/HHluIjRs34Lbb7sTGjRugKApee+1/4Xa78Ic/zMHZZ/fEyJGjAIT3LX322Rfhdrtx1VUzcOmlM9C1a7fWfKQRY1gjIiKiNrdv315kZ5+F/v0HAgB0Oh2sVqta7vF4sGvXDlRVVanPud0u5OfnY9iw8/D111/g3XffgcfjRigUanBug8GI8eMvavLa3bp1x7nn9gYA9O3bH1988f8AANu3f4MJEyYhPj68rdP06Zfj6adXqGXz5t0LSZIQH2/GxImTsH37N2pYGzduAmRZhtlsRvfuZ6Gg4BjDWmfBaWtERBSNRtiHttj71Zj22htUCAWSJGHDhteg1zeMI0VFDqxbtwovvvgaMjOzsHfvbixa9IhaHhcXC0kK/wZeuXIZ9u7dDQBYvPhxAOEwV0+W5ZPC3n9Di3O2Fm8wiAijGhERUWP69euP/PxD2LdvDwAgFAqhpqZGLTeZ4jFw4GC8/vpG9bni4iKUl5fB5XJBr49BcnIyFEXBe+/9X5PXueeeB7Bx45vYuPFNdOuW3WydcnOH47PPPoHb7YIQAlu2vIdhw0aoZVu3boIQAm63C59++rFa1tHYs0ZERERtzmpNwNKly7Fu3Wp4vR5IknzSDQZ5eUuwdu0qzJp1FYBwgHvwwTz06NET48ZNxLXX/hYJCTaMHDkKu3fvirhOI0eOwsGDP+MPf7gBANC7dx9cf/3vAQCzZ9+I1auXq3WZPPkSnHfe+RFfsy1IQrQ0jbBzKy93QlG0eYufLZmLxBI3Bq97WZPzk3ZSUy0oLa3t6GrQKWCbdU5st/ZTVHQYGRndIz5Pew2DnskaaytZlpCcbG70eA6DRuq0jrpERETU0RjWIsCcRkRERFpjWIuEBEiMbERERKQhhjUiIiKiKMawRkRERBTFGNYiJHEUlIiIiDTEsBYRLopLRERE2mJYIyIiog61YcN6fPrpxx1djajFHQwiwY41IiKiFgWDwZP2/zzRjTfe0o616XzaPaw988wzWLduHTZv3oxzzz0X3333HfLy8uDz+ZCVlYWnnnoKycnJANBsGRERETWt5ssvUP2vf57y6yRJQkubGyWMvgDW80c1e8zo0bm44Yab8NVXX2DEiJEYP/4irFz5JLxeD/x+P6ZP/x/89rczAQBLlz6K3r1zcMUVV+Gll17AkSOH4XI5UVhYgKysLliyZBliY2NPukZzx7rdbqxZ8xR++OF7AMCUKVNxzTXXAwCOHTuKp556HFVVldDpdLj55tvVraVGj87FzTffhn/+83NUV1fj9tvvxNixE075c2xL7ToM+v333+O7775DVlYWAEBRFNx3333Iy8vDtm3bkJubixUrVrRYFk14gwEREVHjjEYjNmx4DTfddCvsdjvWrHkOL7/8Bv70p1fx/vt/RX7+oUZf95///ICFC5fijTf+gmAwiI8//rDJazR17MaNG6AoCl577X+xfv3L+PDDrfjqqy8AAIsWPYKLLpqMV199GwsWLMGSJQtQWVmpnjM+Ph4bNryGBQsWYc2ajs8e7daz5vf7sXjxYqxcuRKzZvle8ZQAACAASURBVM0CAOzbtw9GoxG5ubkAgKuvvhoTJkzAE0880WxZtGBOIyKiaGU9f1SLvV+Nacu9QS+++FL1sdfrxTPPPIkDB36CJMkoKyvFgQM/ITv7rJNeN3z4ebBYLACAPn36oaDgWJPXaOrY7du/wbx590KSJMTHmzFx4iRs3/4NBg4chAMHfsIll0wHAJx11tno2bMXvv9+L0aPvgAAMGHCZABA3779UVZWCp/PB6PR2AafyH+n3cLa008/jenTp6NLly7qcw6HA5mZmerPSUlJUBQFVVVVzZbZbLZWX7epTVHbgiSFJ62lplo0uwZph+3W+bDNOie2W/soKZGh17fNgFlbncdiMavnevHF55CSkoK8vEXQ6/W4887bEAwGoNfLkCQJsixBr5chyxJiY2PV1+n1OgQCfuj1MubMmYVAwA+TyYQXXni52WMlCdDpJLVMlsPn1unqj5Uhy+HHvz7WZAqfs/5nSRJt9pnU1+VU/l60S1jbtWsX9u3bh3vvvbc9LtdAebkTiqJRH1jdaUtLa7U5P2kmNdXCdutk2GadE9ut/SiK0iY9Ym3ZsxYMHq9TTU0NzjqrJwAZP/30E777bhcmTpyMYFCBEAKKIhAMKlCU448BNPj5T3/a2ODczR07dOhwvP/+e+jbdwA8Hjc++eQj3H77H2E0xqFnz3OxefP7mDp1OvLzD+HAgZ/Qu3c/9Twn1ruxnyOlKMpJfy9kWWqyg6ldwtq3336LgwcPYsKE8AS9oqIi/P73v8d1112HwsJC9biKigrIsgybzQa73d5kGREREXUu11//eyxZkoetWzeha9duGDRosKbXmz37RqxevRyzZl0FAJg8+RL1JoKFCx/DU089jj//+U3odDo88shiJCYmalqfSEiipVs+NDB+/HisX78ePXv2xKRJk/Dkk08iNzcXzz33HI4ePYonnngCiqI0WXYqtOxZ+9vjdyLtWC0GPPeKJucn7fD/9jsftlnnxHZrP0VFh5GR0T3i87Rlzxo1rrG26vCetabIsozly5dj4cKFDZbnaKksWkjg3aBERESkrQ4Ja5999pn6eMiQIdi8eXOjxzVXRkRERHQm4HZTERDcwoCIiKJIB8xsolP037QRwxoREdFpQK83wOWqYWCLYkIIuFw10OsNp/Q67g0aCXasERFRlEhMTEVlZSmczqqIziPLMhSFNxhoRa83IDEx9dReo1Fdzhi8wYCIiKKBTqdHSoo94vPwDt7ow2FQIiIioijGsBYRjoMSERGRthjWIiRxO3ciIiLSEMNaJNixRkRERBpjWIsUO9aIiIhIQwxrRERERFGMYY2IiIgoijGsRULipDUiIiLSFsNahBjXiIiISEsMa5HiDQZERESkIYY1IiIioijGsBYhDoMSERGRlhjWIsGkRkRERBpjWCMiIiKKYgxrEWHXGhEREWmLYS0CQv0PERERkTYY1iLEvjUiIiLSEsNaBCTuYEBEREQaY1gjIiIiimIMaxHgdDUiIiLSGsNahCQmNiIiItIQw1oEJN5eQERERBpjWIuAkHg3KBEREWmLYY2IiIgoijGsEREREUUxhrU2EAwGO7oKREREdJpiWIsEF8UlIiIijTGstQFFhDq6CkRERHSaYliLAPvViIiISGsMaxFQ18NVOrIWREREdDpjWGsDiuANBkRERKQNhrUISLzBgIiIiDTGsNYGhMINQomIiEgbDGsRYEQjIiIirTGstQGh8A4DIiIi0gbDGhEREVEU07fXhW677TYcO3YMsizDZDJhwYIFyMnJwaFDhzB//nxUVVXBZrNh2bJlyM7OBoBmy6JC3Q0G7FkjIiIirbRbz9qyZcvw/vvv47333sOcOXPw0EMPAQAWLlyImTNnYtu2bZg5cyby8vLU1zRXFk2EYFgjIiIibbRbWLNYLOpjp9MJSZJQXl6O/fv349JLLwUAXHrppdi/fz8qKiqaLYsaXLmDiIiINNZuw6AA8PDDD+OLL76AEAIbNmyAw+FAeno6dDodAECn0yEtLQ0OhwNCiCbLkpKS2rPaLVJC7FkjIiIibbRrWFu6dCkA4L333sPy5csxb948za+ZnGzW7NyyFO6YTE4xw5ZsaeFoijapqWyzzoZt1jmx3Toftll0adewVu+yyy5DXl4eMjIyUFxcjFAoBJ1Oh1AohJKSEtjtdgghmiw7FeXlTigaLVorRPi8ZWU1CCixmlyDtJGaakFpaW1HV4NOAdusc2K7dT5ss44hy1KTHUztMmfN5XLB4XCoP3/22WdISEhAcnIycnJysGXLFgDAli1bkJOTg6SkpGbLooXgpDUiIiLSWLv0rHk8HsybNw8ejweyLCMhIQHr16+HJEl49NFHMX/+fDz33HOwWq1YtmyZ+rrmyqJCXVYTIe5lQERERNpol7CWkpKCP//5z42W9ejRA++8884pl0UTBbzBgIiIiLTBHQwiwmFQIiIi0hbDWhtQuIMBERERaYRhjYiIiCiKMay1BcEbDIiIiEgbDGsRkCTOWSMiIiJtMaxFoL4/TQmFOrQeREREdPpiWGsTHAYlIiIibTCsRYLDoERERKQxhrU2wKU7iIiISCut3sHA7/fj+eefx9atW1FSUoK0tDRccskluPXWW2E0GrWsY9RivxoRERFprdVh7dFHH8WhQ4fw8MMPIysrCwUFBXjhhRdQXFyMJ554Qss6Rj3BpTuIiIhII60Oa59++ik++eQTWK1WAEDPnj0xcOBATJo0SbPKRT3OWSMiIiKNtXrOWkpKCjweT4PnfD4fUlNT27xSnY0QnLNGRERE2mh1z9qMGTNw44034rrrrkN6ejqKiorwxhtvYMaMGfjqq6/U40aOHKlJRaOawnXWiIiISButDmtvv/02AGD9+vUnPV9fJkkSPv300zasXpTjMCgRERFprNVh7bPPPtOyHp0a7y8gIiIirXCdNSIiIqIo1mzP2sUXX4wPP/wQAHDhhRc2uXH5559/3uYV60wE56wRERGRRpoNa0uWLFEfP/XUU5pXptPhnDUiIiLSWLNhLTc3V33cs2dPJCUlaV6hzohz1oiIiEgrrZ6zNm7cONx0003YtGkT3G63lnUiIiIiojqtDmt///vfMXbsWLz99tsYNWoU7r77bnz22WcIBoNa1i+61Q+DciN3IiIi0kirw1pSUhKuueYavPXWW9iyZQt69+6N1atXY/To0VrWr1NQwLBGRERE2vivlu4oLy9HWVkZKisr1b1Cz0y8wYCIiIi01epFcQ8cOIAtW7Zg69at8Hq9uPjii/Hcc89hwIABWtYvqtWPgooQ7zAgIiIibbQ6rP3ud7/DpEmTsHjxYowYMQKyzPV0iYiIiLTW6sQ1depUXHHFFRg5cqQa1Hbu3ImlS5dqVrloJ9RhUC6KS0RERNpodVj76KOP0K9fvwbP9evXD1u2bGnzShERERFRWKvDmiRJUH61REUoFDrpuTOJunIHV8UlIiIijbQ6rOXm5uLpp59Ww5miKFi3bl2DXQ7OWArDGhEREWmj1TcYPPzww/jDH/6A0aNHIzMzEw6HA6mpqVi/fr2W9YtyXLqDiIiItNXqsJaRkYG//vWv2LNnDxwOB+x2OwYMGHBm3xWqDoOeuUPBREREpK1WhzUAkGUZgwYNwqBBg7SqDxERERGd4AzuFmsL4a41wZ41IiIi0gjDGhEREVEUY1iLSH3PWgdXg4iIiE5bDGttQXAHAyIiItIGw1oEJK7cQURERBpjWIuAqEtrgoviEhERkUYY1oiIiIiiGMNaBKT6cVDeYUBEREQaaZewVllZiZtuugmTJ0/GtGnTMHfuXFRUVAAAvvvuO0yfPh2TJ0/GnDlzUF5err6uuTIiIiKiM0G7hDVJknDjjTdi27Zt2Lx5M7p27YoVK1ZAURTcd999yMvLw7Zt25Cbm4sVK1YAQLNl0aN+6Q72rBEREZE22iWs2Ww2jBgxQv150KBBKCwsxL59+2A0GpGbmwsAuPrqq/HRRx8BQLNl0UYo3MGAiIiItHFKe4O2BUVR8NZbb2H8+PFwOBzIzMxUy5KSkqAoCqqqqpots9lsrb5ecrK5Tet/Ir1eBwCINxuRmmrR7DqkDbZZ58M265zYbp0P2yy6tHtYW7JkCUwmE6699lp88sknml+vvNwJRaOlNYKhcI+a0+lFaWmtJtcgbaSmWthmnQzbrHNiu3U+bLOOIctSkx1M7RrWli1bhsOHD2P9+vWQZRl2ux2FhYVqeUVFBWRZhs1ma7aMiIiI6EzRbkt3rFq1Cvv27cOzzz4Lg8EAAOjXrx+8Xi+2b98OAHj77bcxZcqUFsuiRt3KHVr13BERERG1S8/azz//jBdeeAHZ2dm4+uqrAQBdunTBs88+i+XLl2PhwoXw+XzIysrCU089BQCQZbnJMiIiIqIzRbuEtXPOOQf/+c9/Gi0bMmQINm/efMpl0UCq71oTvBuUiIiItMEdDNoC11kjIiIijTCsRaJ+uykiIiIijTCsRULiDgZERESkLYY1IiIioijGsBYJiTcYEBERkbYY1oiIiIiiGMNaBOpvL1DAOWtERESkDYa1tqBwGJSIiIi0wbAWES7dQURERNpiWIuEunRHB9eDiIiITlsMa0RERERRjGEtAhKX7iAiIiKNMawRERERRTGGtYjU96xx0hoRERFpg2GtDSgcBiUiIiKNMKxFgit3EBERkcYY1iKh3mDQsdUgIiKi0xfDGhEREVEUY1iLQP3SHQKcs0ZERETaYFgjIiIiimIMa5Gon7OmcNIaERERaYNhrU0wrBEREZE2GNaIiIiIohjDWgSkuoXWFO5gQERERBphWCMiIiKKYgxrkVAXxeXSHURERKQNhjUiIiKiKMawFgFJqvv4OGWNiIiINMKw1ga4gwERERFphWEtEvVz1oiIiIg0wrAWCXUHg46tBhEREZ2+GNaIiIiIohjDWgTqF8UVXLqDiIiINMKwRkRERBTFGNYiIOT6nrUOrggRERGdthjW2gSHQYmIiEgbDGsR4IdHREREWmPeiEjdMKjCcVAiIiLShr6jK0BEREQUjQJBBcUVbkgSkJVq7rB6MKxFQt3BgD1rREREnZXHF0RRhRuFZS4UlrvgKHOjsNyF0ioPhABiDTo8c9cFkDto5yKGNSIiIjoj1Lr9cJSHg1hhmQuOcjcc5S5U1PjUY3SyhIwkE7qlmTEiJx2ZKfE4O9PaYUENaKewtmzZMmzbtg0FBQXYvHkzzj33XADAoUOHMH/+fFRVVcFms2HZsmXIzs5usSxaSPUNx7U7iIiIooIiBKpqfWooc5S5UFgXymrdAfU4Q4wMe3I8enW1wZ4cj8yUeNiTTUi1xUGvi64p/e0S1iZMmIBZs2bhmmuuafD8woULMXPmTMyYMQObNm1CXl4eXnvttRbLoo1gWCMiImpXvkAIxRVuFFW41R6yorqf/YHjS2rFx+phT4nH4HNSGoSyJGtsh/aWnYp2CWu5ubknPVdeXo79+/fjlVdeAQBceumlWLJkCSoqKiCEaLIsKSmpParcOp2kkYmIiDojRREoq/GiuMJd9+VBUaUbReUulJ8wdCkBSE6IRUayCb26JsKebEJGkgn2lHhYTTHHR8I6qQ6bs+ZwOJCeng6dTgcA0Ol0SEtLg8PhgBCiybJTDWvJydrdvWE0xgAADAYdUlMtml2HtME263zYZp0T263zac82E0KgvNqLwjInCkpdKCx1orDUhcIyJ4rKXQiGjo9exRn1yEyNR78eqeiSbkaXNDOyUs3ITDXDGKNrtzq3t9P+BoPycicUjdZB8/mCMAPw+4IoLa3V5BqkjdRUC9usk2GbdU5st85HizYTQqDWE1B7x4or3XVDmB6UVDUctozRy0hLjENGogn9z05CRqIJ6Unhr6Z6yWqq3G1a344gy1KTHUwdFtbsdjuKi4sRCoWg0+kQCoVQUlICu90OIUSTZdFEkrl0BxERUT23NxgOYpV1oawi/LiowgOPL6gep5MlpNjikJ4Yhz7ZiUhPjAsHskQTEq3GTjOXrL10WFhLTk5GTk4OtmzZghkzZmDLli3IyclRhzmbKyMiIqL2FVIUVDv9qKjxobzGi4oab913n/rY5T0eyCQASdZYZCTF4by+6XU9ZOFQlpIQC50cXXdcRjNJtMOtjI899hg+/vhjlJWVITExETabDVu3bsXBgwcxf/581NTUwGq1YtmyZTj77LMBoNmyU6HlMOjXm19H0qa/oeSS0Rh9+Y2aXIO0waGZzodt1jmx3ToPjy+I8movApKEQ0cr1SBWXuNFZY0XlbV+KL+KDCajHknWWCRbjUhKiEWKNbauhywOaYlxiNGfvvPI2lpzw6DtEtY6UnuEtdIpozDqyps0uQZpg79AOh+2WefEdosOQgi4fUGUVYV7wMqqvSiv9qKs2oPymvDjE3vFAECvk5BoMSLZGosk9euEny1GxBlP+6nv7SYq56ydDjikTkRE0cLlDaCk0lMXwsIBLBzMwoHM4ws1ON4Yo0NKQiySE2LRIysBKdbw457dk6BTFFjiDZw7FiUY1iIgSeHxdsEbDIiIqB24vAEUV3hQUulGceXx78UV7pN6xuKMeqQkxCIlIQ69uyUiOSFWDWcpCXGIj9U3emcle0OjD8MaERFRFBFCoMblR0GZCwWlLhSU1e9j6WpkAr8RaYkmDMtJR5otPE8spS6UmWJjOu5NUJtiWItE/Y0sp/e0PyIi0ojXH8TREieOljjVYFZQ6mwQysxxMchKicew3mlIq7ujMi3RhDRbLCfwnyEY1oiIiNqByxvAkWInDhfV4khxLQ4X16Ko3K1OpIkz6pGVEo/c3mnITIlHl5R4ZKaaT4vtkigyDGsRkOq71tizRkREJ6hx+XG4uBaHi2rV72XVXrU8yWpE93QLhueko3u6Bd3SzUi0GBnKqFEMa5Go+zslhNL8cUREdFoSQqDKeUIwqwtnlbXHNxlPs8Uh227FhYMy0T3Dgm7pFlhNhg6sNXU2DGtEREQtOHHSf+EJXwVlxyf9SwAykk3o1c1W11tmQfd0Myf6U8QY1iIhcRiUiOh0EVIUVNb4UFrlQWm1F6VVHpTVff/10hgmox6Zqcfnl2VnWNA1zYxYA3+tUtvjnyoiIjojCCHg9ARQWhVeKLa0yoPSqvpQ5kFFjQ+hE3a8kSUJSVYjUm1xaijLSolHZko8EuINnF9G7YZhLQL1KztzUVwioujgD4RQWu1FWZWnQc9YfUDz+huu4m81xSDFFoezMxMwPCcWqbY4pCaEvydajdxsnKICwxoREXUqbm8QRRVuOMpdKKn0oLTag7K6HrJql7/BsYYYGakJcUi1xaF3NxtSbHFItYXDWEpCLIctqVPgn9KIsAuciEgLQghU1vrgKA+HMkeFG46y8Pdq5/FAJklAkiUWqbZY9O+R3KBnLMUWxzXK6LTAsBYJWV27o2PrQUTUSQWCCkoq3Q1DWbkbReVu+ALHhyzjjHpkJpvQ76wk2JPjYU82ISPJhFRbHPQ6DlXS6Y1hjYiINOf1B0/Y4zIcxhzlLpRWeaGc8D+8yVYjMpLjMWagPRzKkkywJ5tg5YR+OoMxrEWgfgcDwZ41IiIA4eUvSio9OFbqwrESJ0prvDh4rAqlVcdX79frZGQkxaFr3Qr+9mQT7MnxyEgywWjgXpf1RCgA4amBcFdDeGqgeKrDj/1uGPqMh2xN6+gqUjthWCMiov9KjduPoyVOHKv/qtuIPBgK7+oiSUBWqhndM6wY3d+OLqlmZKbGIzUhDrJ8ZvaSCSUI4amFqA9enhoo7moITzUC7irUeqtR66tBTdCNWsUPp15GrU6GDGByuRMxAoAxHvou/RnWziAMa5Go/7eGPWtEdBoLhhQUVbjVYHa07uvEOy8T4g3okmbGhKFZ6JJqRpdUM+zJJmRl2lBaWtuBtdeeUEIQ3tq68HViAKtB0F2FWm8lany1qA24UKv4UauX4dSFv2p1Mpx1P7t1MmBC+AuxdV+AUdIjPTYRhvEzEW9OhaTnVlVnGoY1IiJS1br9DQLZ0VInCstcCIbC/1Oq10nITI5Hv7OS0CXNjK5pZnRJM592e10KRQkHME/NCb1g1VDqA5inCjX+mnAAC/nCwUt/QgCrC2FunQzEIfz1qwBmjTHBYrDAHpuABGMCLAYzLAYLrAYLrOpjMwy60+uzpVPHsBYBWd1uqmPrQUR0qoIhBUXlbhwtrestq/te5Ty5t2xibhK6ppnRNdWMjGRTp737UggFwuusC2A1EO4qtRcs6K6G01sZDmB+F2oVL2rl471eJwYwV4MAZqz7AgySHla9CRaDGfbYBFhjbbAYzL8KXwxgdOoY1iKh3pnEtEZE0UkIgWqXv0EgO1rigqPcpW6tVN9b1ic7CV1Sw71lXdPMsMZHf6AQQgA+V3jyvToZP9wTFqgLYLW+WtQEnKgNeeGUpQZDj/WP3bIEESvVdXwZ6r4Ag6SDRW+C1WBGRqwNFmMCrAYzrEaL2vNlNYQfGxnASCMMa0REp5HKWh/yHTX4xVGDQ44aHCl2wukJqOWJFiO6ppkxoEcyuqTFo2uqGelJ0dVbps4BU3vAqiG8NVDcNQi5q1HrrUSt34nagBM1QS+cOqnBMGT9Y7csQRiluo6vmLqv+gAWB6vBgnRjAnrGJiDBcEL4MlpgibHAYjAjVm/syI+CCADDWmQkLt1BRB3H7Q0ivygcyg45anHIUYPKWh+A8N7FXdLiMficFLWnLCvVDHNcTIfUVQT9J0zCr1uGou6uyKCnGk5PNWr99ZPwfccDmE5Xd0ekBKdOB5fuxB6w4wEsRtLBqo+DJcaMtNgE9Ii1qUOO9T1f9UOSDGDU2TCsERF1AoGggqMlzrpgFv5ylLvV8rTEOPTqasNZdivOyrSiW5oZhhjt1iwTQgABb13wqlHvgqz/ql+G4ueQC1VeJ5wIntT7pU7Cl2UI9S7I43PAYiQ5PASpBrCEugDWMHxZDWYYdUYumkunLYa1CEhcuoOINKAIgaJyd4NgdqTYqc4xs8YbcLbdivP6pOOsTCuyM6xt0mMWDmAeCHfN8TlgdfO/FHc1PN5q1Piq4fQ7URtwwyUpJwWv8GMdPHoJMNedOMGkXsMo6WGJCU/CTzcm4ByjVR1+PB7Awo9jGcCIADCsERF1KCEEKmp8yC8KzzPLd9Qiv6gGHl94X8xYgw7ZGRZMGt4VZ2VYcXamFYmW/y7EiIAXirMCwlUBxVkOpbYcblcZKt3lqPZWoTroggsKXHVDkE5d+M7H+iAWipHqRh1lHE9igEk2wBxjgtVgQZfYBFgM1rrAZUaX1DQIj47LUBBFgGEtEnL9hFz2rBFRyxQhUFLpweGiWhwursWR4locLqqFyxsEAOhkCV3TzDivbwbOtluRbbfCnmRq9Wr/IhSAqC2HUlOCYHUxqmsdqHQWo8pTiaqgCzUIolqvQ7VeRo1ORo1eh4AsnbD8V7gHLAYyLPo4mGPiYTNa0TU2Qe31shjMsMSYYTaYYTHEwxwTD73c9K+S1FTLab8oLpHWGNYiINdtYcCoRkS/FgwpKCxz4UixUw1mR0qc8PnDPWZ6nYSsVDOG9kpF93QLumVY0C3Nghh983dlCiUEUVOKYGUBaiuPory2AOXucpT7a1Ch+FCpl1ERo0OVXoegLIX/lbcAgBE6xMKmi0OCwYLucYmwmVKQGGtDgjEBNmNC3VwwM4w6bppOFE0Y1oiIIuQPhHC01IkjRbU4XBfOCkqd6qr/xhgduqabMbq/Hd3SzeiebkFmSnyzy2WIoA9KVREClcdQUn4IxbWFKPJWoETxoCRGRmmMDr761xsBGGWYJAuS9fHoEpuEgeZ0pFgykBSbiARjAhKNCYiPMTGEEXVCDGuRUHcwYN8a0ZkiEFRwrNSJfEcNDhXVIt9Ri8IyF5S6fwfiY/Xolm7BxNyuajBLT2x6KFP4XAhVFsJdcRhFlfkochajxF+FYuFHqUGH8hgdFEkCdADigQTJjHRDAobHpyMtoSuSzelIiUtCUmwi4vSx7fhJEFF7YVgjImpC/VBmflFtOJw5anGs9Phdmea4GGTbLRh0TjK6p1vRPcOMZGtso71Xwu9BqLIAlWUHUVR5GEWuIpT4qlEih1Bi0KFGX7fMRgwg6yWk6GywxyZikDkT9qRsZJjtSDelIpaBjOiMw7AWAfUfZPasEZ0Walx+/HysGgcKqnDgWDUOFzsRDCkAAJNRj2y7BZOHd0N2hgXZdkujwUwoCoKVhago/gEFFb+g0FWE4kANSqQQSk4cuowBjDExSNPZ0MuUggxrF2TYspFhTkdqXDJ0snZrpBFR58KwRkRnJCEEiirc+PlYNX4+Fg5nxZUeAIBeJ+MsuwUThmYhO8OKbLsFaba4RoJZEM7Swygo2oeCqnwUukvhUNwoipHhrQ9lesCqi0F6TCKGmdKQkdAV9qSzkWFOR4LByjlkRNQihrVIsGeNqNMIBBUcLqrFzwVV+PloNQ4UVKt7ZprjYnBOlwRcMCgT53SxoXt643dlumqKcfjodhwp/wlH3KU4JvlQfsIuAbExQIZsxRBTKrISs5GVci6yLFkwxZhOOhcRUWsxrEWifm9QLt5BFHWcngAOFFTjQF3P2SFHrTqkmZ4Yh0E9U9CzSwLO6ZKAjKST75L0BX04XLQXh4v24XDNURwL1qL0hH8xbXoZXWOSMcKciS4pPdElNQdJcUnsKSOiNsewRkSdnhACxZUedTjzQEG1um+mTpbQPSM8pNkzy4ZzuiTAGn/yKvo13mocPLYDB0r346C7CAXwhe/CBJAQVNBFNiE3zo7uqb3RPWswrHG2dn2PRHTmYliLgHorPjvWiNpVIKggv6hG7Tk7UFCNWnd4SDM+Vo8eWQk4v18GemYlINtuhfFXG5oLIVDqdODno9txsOIn/OItR6lct1itItA1CIw1JOLsxB7IzhoMW2pPSFLz0N6mZgAAEClJREFUi9USEWmFYY2IopoQAuU1XvynsAa7fyzBgYJq5BfVqAvOpifGYcDZyejZJQE9u9hgTzZB/tVQZEgJ4VjFQfxcsAMHqw7hUKAatXL49XEhBdkhHYbHpaNnSm9075oLozW93d8nEVFTGNYiIIE3GJxOFKEgEApACfgQCnghhARJAiRZd/xLkiHJOsg6HSSE/wzIksx5Sm0kGFJQXOmBo8yFgjIXDjlqcMhRo/aa6XXhIc2JQ7uGw1lW40OaLr8TvxTuxqHSH/CLswCHFRf8dU2UGAjhHMTi7LhMnJPeD/YuQ6GLNZ90DiKiaMGwRpoIBIPw+9zwOl3w+Vzwu1zw+z3we9wI+jwI+r0I+r0IBfxQAn6EAn6IoB9KMAiEghChUPhLCUIKhSAUBZKiACd8SYqAJMLfIQRkRdQ9JyApgKTUPScAWRGQRd1zApBOeCwrgFz3Gt0p5G4BQEjh74oMKLLU8Pv/3969x0ZR93scf89lLy23XqCwIMKRR0k9JlILFi+oDxhrilAOqBgFb3i/IIl6UjHeNVpMRCIoaPAPcoxGgoJBNJjQiGCEEtEjkYMGK1QpFEsL9MJud+Z3/mhZWy99fBDYKf28ks3u7MzO/Ha/299+dn6zHcvCtE8bu/22ZUH7NLaFcWxwbCzHAcfBcl1s18EOhXDaL244ghOJEgpHcMJR3GiUUCSTUKQX4Wgm4Whv3Ixe2NFMrGgGVigSyPCY9HwONSU42JSg4XCchqYEBxvjNDTGaWhMUFvfwv6GltQ/nLWAwf17ce6I/vxHrA/nnR2jV8j63SmaDh9p4Kd92/n5wE5+OryHXYl6atuHNC1jGNTqM8bpwxl9T+cfsVHkDj4Hy/19wBMRCarAh7WqqirKyspoaGggKyuL8vJyhg8fnu5mAWA7bcfB5H65gy3/e+vxW/Hx/pw1HVZpOmziD+9v/6DsEFr+1W0L87v1Ov4fP41w++VY+RZ4Nhjbwrc6BqSj4aht2rQHJS9kYywLLAv/aECyfr0+ehvLxlh2e5CyoX1vmTF++4bbnqAxpn1PavtzNgZjTFuQNJ0DZFtoNKkgaBmD7fnYSbDaA6LtGWwfHM/g+AbXA7uLwOi1X+JdvUaA54Dntj1nz7HwHQuTutgY1wbHAdfBchyskIvthrBDYexwGCcUwQlHcCNR3GgmoUgmbjQDN6M3bkZvnGgmCSdKwomQMC6JpKE5nuRwcyuHmxOdrg81JWhojHO4ufV3h3daQN9eIXJ6uQzLCTF2RC6xrAgDs0Lk9QmDlaA50cThRDUNP/8f3/60mwOtB9nvtnAg2UytSXC4w+FovTyf0z2XwugAzsj5B8MHjyIjd5iONxORbs0yJthjeDfeeCPTpk2jtLSUVatWsWLFCpYtW/aXH19X14jvn5inmIjH+Wz+f2MfaT2Oaz1R5WiLTsbqMN0hTRnazshgfl200zKGtiE/0zGBpfbeWL9/jN0eBhwHHLctEDgulutiuWEcN9S+9yiCHQ7jhqK4kSihSJRQuO3ajWQSiUaJZPQmnBHFDWXguMfn+8WAAX3Yv//wcVnXX+H7hlbPpzXZfvF8Wlu9TvclWj3irQmOHGkhET9EsqWRZLwJL9GMn2jGb41jWuOQjGO8BFayFbwklt+K5XtYnofle9i+h+372J7B6XDt+KYtFHpt4dA9ekm2hcRj+Y5ggKQDnmORdNqCofntijq9h/hNsO/wfm//8mB12Ntp+23TrmdSIdaz4X/+K5csx6W/ncHgjP4M6TuUIQPOol/uGdihyDE8EzlRTvbfmvx9qll62LZFbu4fH5IR6LBWV1dHcXExmzZtwnEcPM+jqKiItWvXkpOT8xfXceLCGuhN3V31pLr5xuB1DIXt1/HWJC3JOC2JBEdaj9DS0oR3pBETb8RLNGMSLZhECySPYFrjWK2tWMkkVjKB5Xk4yWRbGEwmsTwf2/NxPB+M+TXHdwxmR7Xv6Twa7K32aevofY4NtgO2he26uE4INxKhV3YWocxsskecQ5+z/vOkvoZy7HrS39qpQjVLj67CWqCHQWtqahg4cCBO+3Cj4zjk5eVRU1Pzl8OaSE9nWxa26xByu/e5JvUBIiI9VaDD2vHwZyn1eBowoM8J34Ycf6pb96OadU+qW/ejmgVLoMNaLBZj3759eJ6XGgatra0lFov95XVoGFT+iOrW/ahm3ZPq1v2oZunR1TBooH8ilZubS35+PqtXrwZg9erV5OfnawhUREREeoxA71kDePLJJykrK+PVV1+lb9++lJeXp7tJIiIiIidN4MPaiBEjWL58ebqbISIiIpIWgR4GFREREenpFNZEREREAkxhTURERCTAFNZEREREAizwPzD4u2z7eJ8VPT3bkONPdet+VLPuSXXrflSzk6+r1zzQ5wYVERER6ek0DCoiIiISYAprIiIiIgGmsCYiIiISYAprIiIiIgGmsCYiIiISYAprIiIiIgGmsCYiIiISYAprIiIiIgGmsCYiIiISYAprx6iqqorp06dTXFzM9OnT+fHHH9PdJAHq6+u5/fbbKS4uZtKkSdx3330cOHAAgK+++orJkydTXFzMrbfeSl1dXepxXc2Tk2fhwoWMHDmS7777DlDNgi4ej/PEE09wxRVXMGnSJB577DGg6/5RfWf6VVRUMGXKFEpLS5k8eTJr164FVLdAM3JMZs6caVauXGmMMWblypVm5syZaW6RGGNMfX29+eKLL1LTL7zwgnnkkUeM53nm8ssvN5WVlcYYYxYtWmTKysqMMabLeXLybNu2zcyaNcv885//NDt27FDNuoFnnnnGPPfcc8b3fWOMMfv37zfGdN0/qu9ML9/3zejRo82OHTuMMcZs377djBo1yniep7oFmMLaMfjll19MYWGhSSaTxhhjksmkKSwsNHV1dWlumfzWxx9/bG666Sbz9ddfm4kTJ6bur6urM6NGjTLGmC7nyckRj8fNtddea6qrq1NhTTULtsbGRlNYWGgaGxs73d9V/6i+M/183zfnn3++2bJlizHGmM2bN5srrrhCdQs4N9179rqjmpoaBg4ciOM4ADiOQ15eHjU1NeTk5KS5dXKU7/u8/fbbjB8/npqaGgYPHpyal5OTg+/7NDQ0dDkvKysrHU3vcRYsWMDkyZM57bTTUvepZsFWXV1NVlYWCxcuZNOmTfTq1YsHHniAaDT6p/2jMUZ9Z5pZlsXLL7/MPffcQ2ZmJk1NTbz++utdfq6pbumnY9bklPXMM8+QmZnJjBkz0t0U6cLWrVvZtm0b119/fbqbIv8Gz/Oorq7m7LPP5r333uOhhx7i/vvvp7m5Od1Nky4kk0mWLFnCq6++SkVFBa+99hpz5sxR3QJOe9aOQSwWY9++fXieh+M4eJ5HbW0tsVgs3U2TduXl5ezatYvFixdj2zaxWIw9e/ak5h84cADbtsnKyupynpx4lZWV7Ny5kwkTJgCwd+9eZs2axcyZM1WzAIvFYriuy1VXXQXAueeeS3Z2NtFo9E/7R2OM+s402759O7W1tRQWFgJQWFhIRkYGkUhEdQsw7Vk7Brm5ueTn57N69WoAVq9eTX5+vnYHB8RLL73Etm3bWLRoEeFwGIBzzjmHI0eOsGXLFgDeeecdrrzyyn85T068O+64gw0bNrBu3TrWrVvHoEGDWLp0KbfddptqFmA5OTkUFRWxceNGoO3XgnV1dQwfPvxP+0f1nek3aNAg9u7dyw8//ADAzp07qaurY9iwYapbgFnGGJPuRnRHO3fupKysjEOHDtG3b1/Ky8s544wz0t2sHu/777/nqquuYvjw4USjUQBOO+00Fi1axJdffskTTzxBPB5nyJAhvPjii/Tv3x+gy3lyco0fP57Fixdz1llnqWYBV11dzdy5c2loaMB1XebMmcOll17aZf+ovjP9PvjgA9544w0sywJg9uzZXH755apbgCmsiYiIiASYhkFFREREAkxhTURERCTAFNZEREREAkxhTURERCTAFNZEREREAkxhTUROGRMnTmTTpk3pboaIyHGlMxiISLdRUFCQut3S0kI4HE6dr/Cpp57iww8/POltGjlyJGvXrmXYsGEnfdsi0jMorIlIt7F169bU7fHjx/Pss89y4YUXprFFIiInnoZBReSUMX78eD7//HMAXnnlFWbPns1DDz1EQUEBkyZNoqqqiiVLlnDBBRdw6aWXsmHDhtRjDx8+zNy5c7n44osZN24c8+fPx/M8AHbt2sWMGTMoLCykqKiIOXPmAHDDDTcAUFpaSkFBAWvWrOHgwYPceeedjB07ljFjxnDnnXeyd+/e1HZmzpzJ/Pnzue666ygoKOCuu+6ivr6eBx98kPPOO49p06bx008/pZYfOXIky5YtY8KECRQVFVFeXo7v+yf8tRSR4FBYE5FTVkVFBaWlpVRWVpKfn8+sWbPwfZ/169dz77338vjjj6eWLSsrw3Vd1q5dy8qVK9m4cSPLly8HYMGCBVx00UVUVlayfv16ZsyYAcBbb70FwKpVq9i6dSslJSX4vs/UqVOpqKigoqKCSCTC008/3alda9asYd68eaxfv57du3dz3XXXMW3aNDZv3syIESNYtGhRp+U/+eQTVqxYwfvvv8+6detYsWLFiXzZRCRgFNZE5JQ1evRoxo0bh+u6XHnlldTX13PHHXcQCoUoKSnh559/5tChQ/zyyy98+umnzJ07l8zMTHJzc7n55ptTx8C5rsuePXuora0lEokwevToP91mdnY2xcXFZGRk0Lt3b+6++24qKys7LTN16lROP/10+vTpwyWXXMLQoUO58MILU+389ttvOy1/++23k5WVxeDBg7nxxhtTJ9QWkZ5Bx6yJyCkrNzc3dTsajZKdnZ36QUI0GgWgubmZ2tpakskkF198cWp53/eJxWIAPPzwwyxYsICrr76afv36ccstt3D11Vf/4TZbWlp4/vnn+eyzzzh48CAATU1NeJ6X2nbHE85HIpFO09FolObm5k7rPNoOgCFDhlBbW/vvvxgi0m0prIlIjzdo0CDC4TBffPEFrvv7bnHAgAE8++yzAGzZsoVbbrmFMWPG/OEvQN98802qqqp49913GTBgANu3b2fKlCkYY465fTU1NZx55pkA7Nmzh7y8vGNel4h0PxoGFZEeLy8vj4suuogXXniBxsZGfN9n9+7dbN68GYCPPvoo9SOBfv36YVkWtt3Wffbv35/q6urUupqamohEIvTt25eGhgYWLlz4t9u3dOlSDh48SE1NDcuWLaOkpORvr1NEug+FNRERYN68ebS2tlJSUsKYMWOYPXs2+/fvB+Cbb77hmmuuoaCggLvvvptHH32UoUOHAnDfffdRVlbG6NGjWbNmDTfddBPxeJyxY8cyffp0xo0b97fbNmHCBKZOncqUKVO47LLL/nQIVkROTZb5O/vmRUTkhNI/3RUR7VkTERERCTCFNREREZEA0zCoiIiISIBpz5qIiIhIgCmsiYiIiASYwpqIiIhIgCmsiYiIiASYwpqIiIhIgCmsiYiIiATY/wMFu5DZM0mp4gAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plot_feature('cvip', cvip=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "5e4c476d",
      "metadata": {
        "id": "5e4c476d",
        "outputId": "726c8f58-0af1-4200-d7c6-959f50cf04db",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 355
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plot_feature('x', traj=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "cfe47bbf",
      "metadata": {
        "id": "cfe47bbf",
        "outputId": "d02b1bed-5e84-4818-c6a7-4f6d873ae234",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 355
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plot_feature('y', traj=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "a3c73e01",
      "metadata": {
        "id": "a3c73e01",
        "outputId": "16fbc35d-57a7-4597-cb99-821436d9881a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 355
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plot_feature('v', traj=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "9d59227d",
      "metadata": {
        "id": "9d59227d"
      },
      "source": [
        "## Task 1.4. Which features will change during an accident?"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "c24f4486",
      "metadata": {
        "id": "c24f4486"
      },
      "source": [
        "Based on my intuition, when an accident occurs there is initially a sudden push on the breaks and a sharp decrease in speed as the driver makes an attempt to not have a collision. There could also be erratic movement of the steering wheel from side to side as the car tries to avade the accident.\n",
        "\n",
        "From the visualization of the data it seems that an accident occurs during the rain-noon weather condition as there are two sudden pressings of the break and a decrease in speed. One of the brake pressing takes much more time than those in clear sunset or clear noon. The data also stops before the other weather conditions such that the car was in an accident and stopped collecting data.\n",
        "\n",
        "It also looks like an accident, or a near miss occurs in the clear-night weather conditon do to the erratic steering data. It seems as if the AV started swerving back and forth to avoid an accident."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "6667e795",
      "metadata": {
        "id": "6667e795"
      },
      "source": [
        "# Task 2\n",
        "## Task 2.1 Probability of an accident"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "f7be8d4b",
      "metadata": {
        "id": "f7be8d4b"
      },
      "source": [
        "From the 5 simulated scenarios only 1 resulted in an accident. Therefore the marginal probability of an accident ocurring is 0.2."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "19f0c2ae",
      "metadata": {
        "id": "19f0c2ae"
      },
      "source": [
        "## Task 2.2 Weather condition for accidents\n",
        "\n",
        "From the completion records the accident happened during the rain-noon weather condition. This was what we predicted from the simulation data. It makes sense that the accident occurs during the weather event of rain as it lowers visibility. While we would expect noon to have the most visibility time wise, since it was the only rain simulation in the data it seems the weather has a larger effect than time."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "988234a8",
      "metadata": {
        "id": "988234a8"
      },
      "source": [
        "## Task 2.3 Abnormal AV behavior\n",
        "\n",
        "Density Plot"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "7e9a6003",
      "metadata": {
        "id": "7e9a6003"
      },
      "outputs": [],
      "source": [
        "abnormal = 0\n",
        "normal = 0\n",
        "# Helper function for creating the density plot of a certain feature\n",
        "def get_density_plot(feature, ctl=False, cvip=False, traj=False):\n",
        "\n",
        "  if ctl:\n",
        "    clear_night_y = clear_night_ctl[feature]\n",
        "    clear_sunset_y = clear_sunset_ctl[feature]\n",
        "    clear_noon_y = clear_noon_ctl[feature]\n",
        "    rain_noon_y = rain_noon_ctl[feature]\n",
        "  elif cvip:\n",
        "    clear_night_y = clear_night_cvip[feature]\n",
        "    clear_sunset_y = clear_sunset_cvip[feature]\n",
        "    clear_noon_y = clear_noon_cvip[feature]\n",
        "    rain_noon_y = rain_noon_cvip[feature]\n",
        "  else:\n",
        "    clear_night_y = clear_night_traj[feature]\n",
        "    clear_sunset_y = clear_sunset_traj[feature]\n",
        "    clear_noon_y = clear_noon_traj[feature]\n",
        "    rain_noon_y = rain_noon_traj[feature]\n",
        "\n",
        "  abnormal = clear_night_y + rain_noon_y\n",
        "  normal = clear_noon_y + clear_sunset_y\n",
        "\n",
        "  sns.set(rc = {'figure.figsize': (10,5)})\n",
        "  sns.kdeplot(abnormal, label='abnormal', fill=True)\n",
        "  sns.kdeplot(normal, label='normal', fill=True)\n",
        "\n",
        "  plt.xlabel(feature)\n",
        "  plt.ylabel('Density')\n",
        "  plt.title(feature)\n",
        "  plt.legend()\n",
        "\n",
        "  \n",
        "  plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "1e22f979",
      "metadata": {
        "id": "1e22f979",
        "outputId": "ecfa7a94-0971-402e-f3e0-ae338880ca58",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 355
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "get_density_plot('steer', ctl=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "c96dbb3f",
      "metadata": {
        "id": "c96dbb3f",
        "outputId": "74ce7a82-ce1d-47d1-f9a1-2bce94480bb1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 355
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ],
            "image/png": 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uM2bMBOALX7iC0tJ99PX1AlBcPIUFC04+7DkXAzB37jwGBgZYvPgSAObNm09dXS0Apmnypz+t4utfX87XvvZ/2LRpA/v374vJnzMSUqAJIYQQNjBbq1Ccbp7b0M6J031kp7sjuk9RFc6cn8e7W+vp6Q0d8jU1Zxp6zY5oxI0Jl8sFMDyPzDCMMe/xeLxjPufg71VVxTB0AF5/fTXbtm3hV7/6LU8++QxXX/0lQqHQEc+yixRoQgghhA306m00a3k4HApzijPHdW9mWgpzirN45aPqQ4Y6tZzpGDXbJjipvU488STKyvZRVVUJwKuvvsScOSV4vanH9dxgsIfMzCy83lSCwSCvv756AtJOHFkkIIQQQtggVLWFDS05nHJKzvBqzfGYPy2bNeurqWrsYXpBOgCqfwqhzS9ihQdRnCnjep6S6ovKlhhKqu+47s/OzubHP76LlSv/HcMwyMrKZsWKnxx3rqVLl/Hee++yfPkXyc72ccopCxkcHDzu504UxUqysyHa2oKYZmz+SIFAOi0tPTF512QlbRwb0s6xIe0cG4nQztZAkM4//oDXvctYdELhMT+nsqGb2pYgyy+eO/zZ4LqnSVl0DY6pp4x6X2NjFfn50475vQAOh4quJ+6ChFg7vM1VVcHvTxv1ehniFEIIIWKsu2wrtbqPE2YEjus50/LS6eoNUdscHP5M9U9Dr9l+vBGFzaRAE0IIIWKsdvMHDKQVjr6lRoQUVaFkajbv72gc/kwLTEevlQIt0UmBJoQQQsRQd+8gWcFysqbNHfviCMzIT6els5/Gtj4AlMw8rP4ezGDbUe5SEnq/tEQz1Nbjm2coBZoQQggRQ+s/3olTAy3NPyHP0zSVkilZrN/dBICiqGiB6Ri1O0e9x+Vy09nZiq6HSbKp6HHFsix0PUxnZysuV2TbqBwkqziFEEKIGDFMk7pdW5ifXXhMKzdHMyM/g5fWVdI/aOBJ0VCzi9Eb9uCcd96I12dnBwgGu2hvb8I0x95rbCSqqmKa0gs3FlXV8HjSSEsb31YqUqAJIYQQMbJ5XytznU0o2UUT+lyXS6PAn8quynZOLwmg+ooJbX5x1OsVRSE9PYv09KxjfmcirJZNZDLEKYQQQsTImo+rmeVoIpx+7FtrjGZGQTrbyoaOf1LSc7AGejD7uib8PSI2pEATQgghYqC6qYf+jjZSrAEMz/Ft3jqS/GwvfYM6Te39KIqC6ivGaNo/4e8RsSEFmhBCCBED726t53MFvUO9ZxM4/2yYojAjP2O4F03NKsRoiJ/Dv8X4SIEmhBBCRJlumKzf3UyJq4lwan7U3jOjIINdVR3ohjnUg9a4N2rvEtEVkwLt3nvv5cILL6SkpIR9+0au5g3DYOXKlVx00UVcfPHFPPvss7GIJoQQQkTdjop2stNTyAhWRmX+2UGpHidZqSlU1vegZuVjdtRj6fFzvqSIXEwKtMWLF/PUU09RVDT6qpUXX3yR6upqXnvtNZ555hkeeughamtrYxFPCCGEiKr3tzVwUpEbbaAD3ZsT1XcVBVLZU92BojlRM/Mwmsuj+j4RHTEp0BYtWkRBQcFRr3nllVe49tprUVUVn8/HRRddxOrVq2MRTwghhIiavoEwOyvbOSmjEz0tH1Qtqu+bEkilrL4bw7RQs4tkHlqCipt90BoaGigs/KTbt6CggMbGxqPcMbKjnQwfDYFAekzfNxlJG8eGtHNsSDvHRjy185p1VcyekkWOsRclq4C0tJSovi8tLYXsjBTagiGmFs+gv2pn1Nojnto52cRNgTZR2tqCmGZsjq2QTfqiT9o4NqSdY0PaOTbirZ1Xf1DBSTN9mA37GfTPIRSM/pywfJ+HjbuaCCzMYaC+lObmbpQJXjkab+2caFRVOWqnUtys4iwoKKC+vn749w0NDeTnR2+lixBCCBFtHT2D1LYEmZGfhqu7hnBqXkzeWxxIo7SuC8vpRXGkYHU3xeS9YuLETYG2dOlSnn32WUzTpL29nTfeeIMlS5bYHUsIIYQ4Zpv3tzC7KBP3YDuWloLl9MbkvWkeF94UjdqWXtSsAoyWipi8V0ycmBRod999N+eddx6NjY3ccMMNXHbZZQDceOONbN++HYArr7yS4uJiLrnkEq677jq+973vMWXKlFjEE0IIIaJi/e5mZhdl4uysIpyaG9N3F+Wksbe6AyUjV1ZyJiDFsqzYTNiKEZmDllykjWND2jk2pJ1jI17auacvxA9//SHfvXIBOXv/CopCf/6pMXt/V3CQtTsauPHsdIzy9aRevWJCnx8v7ZyoEmYOmhBCCJFMtuxvZUZBOk6HiqurMmbzzw7KTHVhGhbdWhZmRy2WacT0/eL4SIEmhBBCRMH6Pc3MKcoCI4Sztxk9NRDbAIpCgT+V8uYQiicTs6Mutu8Xx0UKNCGEEGKC9Q3olNZ2MbMwA1d3HbrHD2rsd7bK93kpq+tCzcyXhQIJRgo0IYQQYoJtK2tlal4aKU4NZ1cVeowXCByU5/NQ1xbESs/FaCqzJYM4NlKgCSGEEBNs074WZhZmAODqrCQc6+HNA5wODX+6hyYjA7NFVnImEinQhBBCiAmkGyY7K9uZVZgJgKu71rYeNBg6VWBfpwuzqwlLD9mWQ4yPFGhCCCHEBCqt7SI7PYU0jxMl3I862I3hzrYtT4HfS1lDL0p6DmZbtW05xPhIgSaEEEJMoK2lrcwoGBredPbUoXtzQLHvn9ustBTChknYk4PRWmVbDjE+UqAJIYQQE2hzaevw8KazuxbDm2NvIEUhP9tDm5GG0VJpbxYRMSnQhBBCiAnS1NFH34BOXrYHAFdX9VAPms0CWR4qgh7Mtkq7o4gISYEmhBBCTJBtpW3MKsxAURRgqAct1mdwjiQv28vudg2zsxHL1O2OIyIgBZoQQgCWZWGF+kiy44lFjG3e/8n2Goo+iGOgw9YFAgd5PU4UpwsjJQOzs8HuOCICsd/WWAgh4oRlmeil6wjv/xCztRIrPACKipqZi2P66bhOWoLi8tgdUySI/kGd8oZulp45FTiwQMCTA6pmc7IheVleesgitbUazTfF7jhiDFKgCSEmJb1+N4Mf/C9YJo4Zi3DMPQfFkwHhQcxgK0b1VoI738C18ApcCy4eHrISYjR7qjsoyknF5RwqyJzdtXEx/+yg3CwPDU1eAq2VOOeeY3ccMQYp0IQQk87gtjWEt76Mc/7nUQtKDi2+XG40XzGarxizp4Xw1tWYrZW4z/8Gig1nKYrEsb2sjWl56cO/d8bJAoGDcrM9bC1N4UQ5kzMhyBw0IcSkYZkmA++vIrzzDVxnLUcrnHfUnjE1PYDrs9djdrfQ/+oDQ0OgQoxiR0U70/Mzhn8/dIKAPUc8jcSd4qDP6UNvq5W5lglACjQhxKQxuO5PGI37SDlrOao3M6J7FIcL1+lXgaLS/9aj8g+bGFFTRx+DYYNAlnvoAyOEo78N3eO3N9hhMrKy0NGwelrtjiLGIAWaEGJSCO18C71yM67Tr0Zxucd1r6KqOE+6BKunhdDmF6KUUCSyHeXtzCj41PYawUZ0d3bcLBA4KC/bQ5uZidEmJwrEOynQhBBJT6/dQWjj33Cdcc24i7ODFM2B67QrCO98E716ywQnFIlu2+Hzz7rr7D9BYAS5WR7qBj3ocqJA3JMCTQiR1My+LgbeehTnwstRU49vPyrFnY5z4eUMvPN7rIHgBCUUiU43TPbVdDI9/1MFWk8tusdnY6qRuVwaQS2bvroyu6OIMUiBJoRIWpZlMfDuH9CKF6D5p07IMzVfMWpBCQPrnp6Q54nEt7+2C3+mG0/KJ6t8nd11cbWC89OUjABWZ53dMcQYpEATQiSt8L61mF2NOOacPaHPdc49F6NmG3rD3gl9rkhM28vbmJaX9skHlokz2BC3BZo3y48a7sca7LU7ijgKKdCEEEnJ7O1gcN3TuE75Aoo2sfuXKc4UnCdcyMC7f8Ay5FzDyW7nYdtraP3tWJoby3Fs8x2jLZDlpdVMx2irtTuKOAop0IQQSWlw3TM4ppyMmhGdg6rV/LkoKWmEdr8TleeLxBDsD9Pc2U+h3zv8mbOnHj01PnvPAFI9TjqtNDprZR5aPJMCTQiRdIzG/Rj1u3DM/mzU3qEoCs655xLe/CKWPhi194j4truqg6m5aWjaJ/+cOrtr0d3xt0Dg08JuHz115XbHEEchBZoQIqlYlsnA+3/EUXIeisMV1XepWfmoWQWEdr4Z1feI+LWjvI0pgbRDPou3MzhHombkonTU2B1DHIUUaEKIpKLvXQuWiVZ0Qkze55h7NqGtr2CF+mPyPhFfdlW2M70g/ZDPnD31cV+gef35pIfbsCzT7ihiFFKgCSGShqWHGNzwd5zzLzjqGZsTSU0PoOVMJ7T9tZi8T8SP5s5+BsMm/oxPFgMooV5UvR8zJeMod9ovPSONfstFZ0O93VHEKKRAE0IkjdDuf6Ck56BmF8X0vY5ZnyG88w0sPRTT9wp77apoZ3p++iE/DAxtrxGAGP2AcMwUhaAji4ZS2SomXkmBJoRICpY+SHjLizjnTuyeZ5FQ03NQMvMIl34Y83cL++yoaGNq3uHzz+ow4vAEgZHo7mz6GmShQLySAk0IkRRCO99EzSpEzcy35f2O6acT3voqlmXZ8n4RW6Zpsaeq85DzNwGcPXXoHr9NqcZHSw+gdcuJAvFKCjQhRMKzwoOEtr6Cw4bes4PUnGlYWBi1O2zLIGKnqqmHVI+DdO+hK4WdPXXo3sQo0FL8+fiMVgbDht1RxAikQBNCJLzQnn+gZRejpgdsy6AoCo7ppxPa+optGUTs7K5qZ0ruob1nmAbO3ua4PCR9RN5s0tV+qmpa7E4iRiAFmhAioVmmTnjbqzhmnmF3FLTC+ZjtNZidjXZHEVG2s6Lj0PM3AUdfK4YrDbTo7r83YRSVXi2TxopSu5OIEUiBJoRIaHrpRyieTNTsQrujoGgOtKIFcvxTkgvrJuX1XUzJPWyBQLAe3RPf+58dTnf7CDZU2R1DjCBmBVpFRQXXX389S5Ys4frrr6eysvKIa9ra2rjpppu4/PLLufTSS7nzzjvRdTmIWAgxMssyCW15CcesM+2OMkybchLh/WuxjLDdUUSUlNd34c9043Y5Dvnc0V2P4cm2KdWx0dL9aF21srglDsWsQLvjjjtYvnw5a9asYfny5axYseKIa379618za9YsXnzxRV544QV27tzJa6/J5o9CiJEZNduwLFBzptsdZZia5kNNy0Gv3Gx3FBElOyvbmXr4/DMSawXnQUpGgAKtg+ZOOQkj3sSkQGtra2PXrl0sW7YMgGXLlrFr1y7a29sPuU5RFHp7ezFNk1AoRDgcJi8vLxYRhRAJKLRtNY4Zp8fs1IBIaVNOIrzrLbtjiCjZVTl0QPrhhjapTawCzfD4yde6KK3tsjuKOExMCrSGhgby8vLQNA0ATdPIzc2loaHhkOu++93vUlFRwbnnnjv83+mnnx6LiEKIBGO012G016EVzrM7yhG0/LkY7TWY3c12RxETbCCkU9scpOiwA9KVcD9qqBczJdOmZMfGdKbiwKC6qmHsi0VMOca+JHZWr15NSUkJTzzxBL29vdx4442sXr2apUuXRvwMv//In2qiKRA4sptbTCxp49hItHZuWf8WaXMXke6Lz9zdM0/BWfMRvguWH/J5orVzoopWO2/Y3cSU/HQCOYf+W6O21GOlB0hLd49yZ/wyUnPobaoiEDh/3PfK93P0xKRAKygooKmpCcMw0DQNwzBobm6moKDgkOtWrVrFPffcg6qqpKenc+GFF/LRRx+Nq0BrawtimrGZ7BgIpNPS0hOTd01W0saxkWjtbA0ECe78APcF36Szs8/uOCMyc+bSt+kF9BOWDQ/BJlo7J6potvMHW+ooyPYe8X2X2lABrmyCwcGovDeaUj0+UlobqK7twJMSeVkg38/HR1WVo3YqxWSI0+/3M3/+fF566SUAXnrpJebPn4/Pd+hmfsXFxbz77rsAhEIhPvzwQ+bMmROLiEKIBBLa8w+0/NkoKal2RxmVkpELmobRtN/uKGIC7apqP2L/MwBHT13ibFB7GMPrY6a7m8pGKbbiScxWcd55552sWrWKJUuWsGrVKlauXAnAjeU08s8AACAASURBVDfeyPbt2wG4/fbb2bhxI5dffjlXXXUV06dP57rrrotVRCFEArBMk/DON3FMW2h3lKNSFAWt8ATC+963O4qYID19IVq7Bsj3H/mDgaunDiPBVnAeZHj8FDo6Ka+XhQLxJGZz0GbNmsWzzz57xOe//e1vh389depUHnvssVhFEkIkIKN2G4rLg5pVMPbFNtOKTmDwvSewzvlnFM1pdxxxnPZWdzIlkIamHrZq2LJwBBsTbgXnQbrHR5bRTmlNJ5xldxpxkJwkIIRIKKGdb6JNPdnuGBFRPRmoGbno1dvsjiImwM6KdooDR/aeaQMdWFoKliPxFggAQ7kdLtob62XD2jgiBZoQImGYPa0YTWVohfPtjhIxrXA+4X1r7Y4hJsCuqg6m5Y+0QW19wm1QezjD6ydX6aC9O/EWOSQrKdCEEAkjtPsdtKITEmq4UCsowajfjRWSndoTWXv3AL39YXKzPEd8zdGTeEc8Hc7wZDMnrZcymYcWN6RAE0IkBMvU0fe8i2PqKXZHGRfFmYLmn4peJUc/JbLdB3rPRjq1IhGPeDqc7vFR5GinrK7b7ijiACnQhBAJQa/cjJKWjZqeY3eUcVPz5xIu/cjuGOI4jDb/DMDZ04DuTcwtNg4yPH78VjtlddKDFi+kQBNCJITw7n+gFS+wO8Yx0fJmYzTuxRzotTuKOAaWZbGnuoNpeSPsmm/qOAY6MNyJXaDp7mw8oXbqW7rRDdPuOAIp0IQQCcAMtmG0lKEVlNgd5ZgcHObs3f+x3VHEMWjq6Me0LLLTU474mjPYhJGSCapmQ7IJpDkxXWlMTx+ktiVodxqBFGhCiAQQ3vseWuH8hFoccDg1fy7BnbKaMxHtrmxnWt7I888cwcRfwXmQ4fFRkt4n89DihBRoQoi4Zlkm4T3v4ig+ye4ox0XLm81AzW6sQRnmTDQ7KtqZkjvymYnOJFjBeZDhzqbY2Snz0OKEFGhCiLhm1O0GhwslM8/uKMdFcaaQkjcTvWqL3VHEOJiWxd7qzpHnn5Ece6AdpHuyCdBGeYP0oMUDKdCEEHEtvPsdHMULRhxeSjQpxSWEy2UeWiKpaQridTtI97pG/PrQCs5kKdD8pA220BkcpG8gbHecSU8KNCFE3LIGe9Frt6MVnWB3lAnhLp47tGmtLru1J4pdle1MzRt5eFMJ9aIYg5iukXvXEo3hzsIx0EFRdgoVjT12x5n0pEATQsStcNl6tMAMFNeRu7cnIjXFi5pdiF6zw+4oIkI7KtqZmjvK8GawAd2TA0nQuwuA6sBIyWBOxgAVcqKA7aRAE0LErfDed9GKTrQ7xoTScmehV8gwZyLQDZPy+u6jLBBowPAk9v5nh9M9Pqa5eyiVlZy2kwJNCBGXjM56zJ5W1MAMu6NMKC1/Dnr1NixTtzuKGEN5fTf+jBQ8KY4Rvz50xFNyrOA8yHBnkad2UNHQjWVZdseZ1KRAE0LEpfDetTiKTkBRk+uvKcWTgZqajVG/x+4oYgw7K9uZMsrqTTjQg+ZNvKPHjkb3+EkbaMKyoKNH5kraKbn+5hNCJAXLNNH3v590w5sHqXmzCVdssDuGGMOuinamjjK8iWXi6G1ET7IhTsPjw9nbSIHfS3m9DHPaSQo0IUTcMep3obhSUTMCdkeJCi1/DkbVZhlCimP9gzo1LUGKAyMXaFp/B5aWguVwxzhZdBkpmWiD3RRlOSmThQK2irhAe+ONN9B1mTMhhIi+8N61SbO1xkiUVB+oDsy2KrujiFHsremkKCcVp2PkfyaHVnAmx/5nh1A1dHc2M7y9cuSTzSIu0H7xi19w7rnnctddd7F169ZoZhJCTGJWeAC9egta4Ty7o0SNoihoubMIV26yO4oYxc6KttGHNwFHT33SreA8yPBkU+DspKa5B8M07Y4zaUVcoL3wwgs8/vjjpKSk8P3vf58lS5bwq1/9itra2mjmE0JMMnrFRlTfFJSUVLujRJWaNxtDCrS4taO8nal5GaN+3dVTl3Tzzw4y3Nl4+5tJ87poaO2zO86kNa45aPPmzeNHP/oR//jHP7jjjjtYvXo1F198MV/+8pd54YUXMKXSFkIcp/De99CK5tsdI+rU7CLMYDtmsM3uKOIwHT2DdPeFycsefYNkRxId8XQ43ePD2VNPgc9LhZzLaZtxLxKorq7m4Ycf5s4772RwcJCbb76Za6+9lqeeeoqbb745GhmFEJOEGWzHaKtCy5ttd5SoU1R1aNNaOTw97uyqbGd6fjqqOsoJAaaOY6ADw51ce6AdpHt8OIKN5Pm8slDARiPvvjeCp556iueff56qqiouvfRS7rvvPk499dThry9ZsoSzzz47KiGFEJNDuPRDtPwSFM1pd5SY0HJnoldsxHXiYrujiE/ZUdE26ukBAM5gE0ZKJqhaDFPFjpmSgRruoyhT5a3tHXbHmbQiLtDeffddbrjhBhYvXozL5Tri6x6Ph4ceemhCwwkhJhd93/s45l9gd4yYUXNnENq2GivUnzTnjSY6y7LYVdnBPy2eM+o1jmB90g5vAqCoGB4fhc5umjr6CYUNXM7kLEbjWcRDnGeeeSaXXnrpEcXZY489Nvzrc889d+KSCSEmFaOtBivUh+ortjtKzCiOFFRfMXrdTrujiAPqWnpxaCpZaSmjXuPsqU/a4c2DdI8Pd18TgUw31U1Bu+NMShEXaA8//PCInz/yyCMTFkYIMXmF972PVjgfRRll3k+S0gIz0Cs32x1DHLCjoo3p+aMf7wTg7K5L7h40hs7kdPY0kOf3Ui4LBWwx5hDnhx9+CIBhGKxbt+6Qna9ra2tJTU3upfBCiOizTBO97ENcZ3zJ7igxp+bOYvDDp7AsE0WRw13stq2sjXlTj9475gw20lv82Rglsofh8eFu20u+30tZXRecMcXuSJPOmAXav//7vwMQCoW4/fbbhz9XFIVAIMCPf/zj6KUTQkwKRsMecHpQ05Pr4OlIqKlZKC4vZkslWu5Mu+NMaqGwQXlDN0vOmDrqNWooiGKEMF1H72VLdLrHjyPYSMFcLx/vabY7zqQ0ZoH21ltvAfDDH/6Q++67L+qBhBCTT3j/B2iFyb/32Wi0wEzCVZulQLPZvppO8n1eUlyjT4gf2v8sB5J8KN50paEYIXLcBj19YYL9YdI8k2N1dbyIuD9dijMhRDRYegi9ciOOSbA57WjU3JkYVTIPzW7by9uYmjvG/LNg8h7xdAhFQff4cfU1UeCXDWvtcNQetEsvvZRXX30VgPPPP3/UybvvvPPOhAcTQkwOevVW1Mx8FHdyDxkdzfCpAr0dqKnJvTownm0vb+fiRUdfRezsTt4jng5neHxDCwV8Myir6+Kkmcm9MCLeHLVA+8lPfjL865/97GdRDyOEmHzC+95HK0jeg9Ejoajq0GrOqi24Tvi83XEmpfbuAbp7Q+Rle496nbOnjt7is2KUyl6GOxtHTwMFvgWU1smJArF21AJt0aJFw78+88wzox5GCDG5WIO9GA17cE6izWlHo0qBZqsdFe1MLzjK8U4ApoGzt3lyDHECutePp3ErBTO8vLGxFsuyJt02OHaKeA7aY489xu7duwHYsmULF1xwARdeeCGbN8u8CSHEsQmXf4wWmI7iHH1T0MlCy52J0bAHSw/ZHWVS2lbWxrS8ow+zO/paMVxpWNqRp+kkI93jw9nbSJrbgaJAW9eA3ZEmlYgLtMcff5zi4qGx+f/+7//m61//Ot/5zne45557ohZOCJHchoY3J+/igE9TXB7UjFyMhr12R5l0DNNkd1XH2BvUBuvRPZNnKxjL6QVFRQv1UOBPlQ1rYyziAq2np4f09HSCwSB79+7lK1/5Ctdeey0VFRUR3V9RUcH111/PkiVLuP7666msrBzxuldeeYXLL7+cZcuWcfnll9Pa2hppRCFEAjGDbZgdtaiytcSwg8OcIrbK6rrJTHWR7j16z5ijux7DM7kWceieHJzBBvJ9HsrqpECLpYgPSy8oKGDTpk2UlpayaNEiNE0jGAyiaZEdoHrHHXewfPlyrrzySp5//nlWrFjBk08+ecg127dv55e//CVPPPEEgUCAnp6eEQ9mF0IkvnDpR2j5c1G0iP8aSnpa3ixCm17Asv5Z5vrE0LayVmYUjL2K2NVTx2DmtBgkih+6JxtnsIECXxEb9sqGtbEUcQ/aD3/4Q26++WZ+/etf893vfheAt99+m5NOOmnMe9va2ti1axfLli0DYNmyZezatYv29vZDrnv88cf5xje+QSAQACA9PZ2UFJmbIkQy0if55rQjUdIDYOiYXQ12R5lUtpS2MaMgY8zrhjapnVxbTRiebBw99eT7vNQ0BzFM0+5Ik0bEP7qef/75rF279pDPli5dytKlS8e8t6Ghgby8vOHeNk3TyM3NpaGhAZ/vk9UwZWVlFBcX8+Uvf5m+vj4uvvhivvOd74zrJ0m/Py3iaydCIDB5926KFWnj2IhlO4daaugd7ME3swRFnVznT2ZlHX0bh67iubjb9pA1pyRGiZJTpN/PrZ39dAUHOWFW4OgrOEP9aHofXn9u0p8i8GmKkY+zfC15uelkZ7jpN2DGpxZTyN/P0TOusYWenh4qKiro7e095POzzpqYPWEMw2Dv3r089thjhEIh/uVf/oXCwkKuuuqqiJ/R1hbENK2xL5wAgUA6LS09MXnXZCVtHBuxbueB9W+g5pfQ1T25VoVlZXnp7Ow76jVG5hS6dq4jPOvCGKVKPuP5fv7Hljqm56fT3d1/1OtcHWW4PD6CvZNrla1COr7uJjo7guRmudm4s4E059APVfL38/FRVeWonUoRF2h/+9vfuOuuu/B6vbjd7uHPFUXhzTffPOq9BQUFNDU1YRgGmqZhGAbNzc0UFBQccl1hYSFLly7F5XLhcrlYvHgx27ZtG1eBJoSIb5ZloZd+iGvhMrujxCU1ZxqhLS9jhfpQXEfvbRPHb8v+VqZHMLzp7J5cKzgPsjQXltOD1tdGvs9LWX03559aZHesSSHisYUHHniABx98kA8++IC33npr+L+xijMAv9/P/PnzeemllwB46aWXmD9//iHDmzA0N23t2rVYlkU4HGbdunXMmze5dxgXItmYzWWgKCgZeXZHiUuKw4XqK0av3Wl3lKQX1k321nRGNP/M2V076eafHaR7/AdWcqZSXi8nCsRKxAWaYRice+65x/yiO++8k1WrVrFkyRJWrVrFypUrAbjxxhvZvn07AJdddhl+v58vfOELXHXVVcyePZsvfelLx/xOIUT8CR9YHCCrFEenBabLdhsxsK+2k5xMN96UsQeTnD216N7J14MGQ0c+OXsayM1y09I5wEBItzvSpBDxEOeNN97II488wne/+13UY5jUO2vWLJ599tkjPv/tb387/GtVVbntttu47bbbxv18IUT8s0yDcPl6Us5abneUuKbmziL04dNYlomiTK5FFLG0ZX9LRL1nB494miyHpB9O9/pwBuvRNJW8bA9VjT2UTJ1c+8HZIeIC7fHHH6e1tZXf/e53ZGVlHfK1d955Z6JzCSGSkFG3E9WTiZoqf7kfjZqaDQ4XZms1WmC63XGSkmVZbNrXylXnzhjzWkdfM4YrHSbJEU+H0z1DZ3ICB+ahdUmBFgMRF2g/+9nPoplDCDEJhGXvs4ipuTPQq7dIgRYlNc1BAHIy3WNceWCBwCQd3oShIU7HQAcYYfJ9Xkpr5USBWIi4QDvzzDOjmUMIkeQsfRC9agvu879pd5SEoOXORC/9iJTTZRV7NGze38KsosyI5kI6e2oxJunwJgCqhu7OwtnbRGFOgPe2yUbKsRDx5IZQKMQDDzzA4sWLOf300wFYu3Ytq1atilo4IUTy0Ku2omYXorhju5l0olJ9UzC7GjH7pbciGjbta2V2YQTzzwBnd92k7kEDMDx+nD31ZKa6CBsm7ZNsD0M7RFyg3XPPPezbt4/7779/+CeOOXPm8Kc//Slq4YQQySO8/320Atk2J1KKqqHlTMeo2W53lKTT3j1Aa9cAxYEIfliwLJw9UqDpB458UhSFopxUyuvlB4doi3iI84033uC1117D6/UOr+LMy8ujqakpauGEEMnBGghiNOzFOf/zdkdJKGpgOnrVZpxzz7E7SlLZUtrKrMKMox/tdIAa6kaxLExnagySxS/D4yelbS9wYKFAneyHFm0R96A5nU4Mwzjks/b29iNWdAohxOHCFRvQAjNQnCl2R0koWu4s9NqdWKbsOzWRNu5tYVbEw5v16KmBSXX+5kh0jx9nz9DcswKfl1LZsDbqIi7Qli5dyo9+9CNqamoAaG5u5q677uKyyy6LWjghRHII73sfrVCGN8dLcaehpmZjNO63O0rS6BsIU17fHdHxTsDQ8OZkXiBwgOlKQzEN1MEe8v1eapqCGIZpd6ykFnGB9oMf/IApU6ZwxRVX0N3dzZIlSwgEAnzve9+LZj4hRIIzg22YHXWogZl2R0lIau4M9KqtdsdIGpv3tzItP50UpxbR9c7umkl5BucRFAXdm4Mz2IDb5SAj1UVVoxyUHk0Rz0Grrq5mxowZfOtb38IwDC666CJKSkqimU0IkQTCpevQCuaiaBH/dSM+RcudRWj7Gjjr/9gdJSms393MnKLMiK93ddcxEDghiokSh3FgocCgfy4F/lT2VrWzaI4Ur9EyZg+aZVncdtttXH755Tz66KO8/fbbPPvss1x99dXcdtttWJYVi5xCiASly+a0x0XJzIfBXszuZrujJLz+QZ19NZ3MjrBAU/QB1MFuDLfsmg8H56HVA0MLBXZXttucKLmNWaA988wzrF+/nmeeeYa3336bZ555hnfeeYenn36aDRs28PTTT8cipxAiARnttVgDQVTfFLujJCxFUVADM9GrZZjzeG0tbWVqXhoprgiHNw9uryHnoQKge/04e+oAKMzxsqeqw+ZEyW3M77rnn3+eH//4x5x88smHfH7yySdz++238/zzz0ctnBAisYX3f4hWOC+i3drF6LTcmeiVm+yOkfA+2t00ruFNZ3ctxiTf/+zTDI8PZ28zWCaBTA/t3QME+8N2x0paYxZoZWVlnHHGGSN+7YwzzqCsrGzCQwkhEp9lmeilH6AVyfyd46XmTMdoLscK9dsdJWH1D+rsre5kdvE45p911Uz6DWo/zdJcGK40HL0tqKrClLx0ymW7jagZs0AzDIO0tJF3W05LS8M0ZZmtEOJIRuN+0Jwo6QG7oyQ8xZmC6itCr9tpd5SEtb28jeJAKm5X5ItVnN016F75/v00w+PHGRyah1YcSKNUNqyNmjG/U3VdZ926daMuBjh881ohhIADRzsVzpfhzQmiBWagV27COWOR3VES0oc7GplTHPnG6ooRwtHfLnugHUb3+HB219Gfv5Cpeems3ykHp0fLmAWa3+/n9ttvH/XrPp988wohDmUZYfSKjaSc+1UAdMOko2eQYF+YYH+Y/pBBWDcJ6waKMjQR3qmpuF0abpeDdK+TzFQXaR6XzM8+QM2bzeD7T2GZJooqjTIewf4we2o6uWBhUcT3OHoa0D1+UCNbUDBZ6N4cUjqGpjYV56Xz5zf3YZpWRMdmifEZs0B76623YpFDCJEkDNOkfss6HGoGr37cSktHDcH+MGkeJ163A49Lw+nUcKgK2oFCwwIMwyRsmITCJn2DYXoHdEJhg6y0FHKy3BT6UikIpJKX7cGhTb4CRfVmoaR4MVvK0fJm2x0noWzY08zMgoyIN6cFcHXXonv9UUyVmHRvDmnV7wEM/z9d39pLcW4EB8+LcZGdI4UQx20wZLB5fwub9rWws7KDr3vfQvcUkpPpZm5RFmle5zH9hK0bBt3BMF29g9S0BNla1kpXb4gCfyrT89OZVZRJIMszaY5J1HJnEa7aLAXaOL2/vYGTZ41vsr+zu1oWCIzAdKWjGDrqYA/gpTAnldL6LinQokAKNCHEMbEsi/21Xby9uY6tpa0UBVKZXZTJNy6axuz1DbTPX4zlcB/XOxyahi9Tw5fpZsaBz8Jhg+bOfpo6+tlS2goKzCnK5ITpPgr8qUldrKm5M9F3vQ1nXmt3lITR2tlPQ1sfV52bPq77XF21BKecFaVUCUxR0FNzhjaszcujwOdlf00XF5wa+fCxiIwUaEKIcTFMk3U7m1izvpr+kMEps/x887L5pLqdAHhr1xHKKD7u4mw0TqdGUSCNokAaWDl09YaoaQny4vuVWMCCGT5OmuUnM9UVlffbSc0uxOzvwuxpQZXVsRFZt6uRkqlZaOMZFjd1HH3NQ3PQxBGGThSoAxZSmJPKqx9V2x0pKUmBJoSIiGGafLijiRferyDV4+SzJ+QxoyDjiFWa3rr1DOTMi00oRSEzLYXMtBQWTPfR3jNIRUMPj7+yh3yfl9NLAswqzEyahQaKoqLlzkKv2oJrwcV2x4l7lmXxwY4mLjxtfL07zp56dHcWaM4oJUtsutePs7sGCwhkeugMDg7PMxUTRwo0IcSYdlS08b+v7yfFqXLRomKm5o48XKT1t+MMNtI9a0mMEwKKgi/DjS/Dzamz/dQ0B3l3az1vbKzh9Lm5nDzbP65J4vFKy52FXrFBCrQIVDb2MBg2KMpJHdd9rq4a9NTcKKVKfLo3B0/TdkKAqioU5aRSVtfFKbNlzt5EkgJNCDGqtq4B/rhmL7UtQc4/tZDZRZlH3dfMU7+BQd8s27cm0DSV6QUZTC/IoK2rn301XXyws5FTZ+dwxrxcvO7E/atPDUwntPUVrMFelJTxFR6TzTub61gwwzfuvficXVWyQe1RGG4fjoEOQnoIgAJ/KvtrO6VAm2BJ0vEvhJhIpmXx1qZa7nxsPZlpLr5+6TzmFGcd/R86yyK1/mMGfHNjFzQC/kwPZy3I5+LTi2nvHuC3L+7krY219CboGYKKw4Xqn4pes83uKHFtIKSzYW8zC2aMf69OV1eV9KAdjaqhu7NRu4dOFCjKSWVfjZwoMNGkQBNCHKKjZ5Cf/Wkzb2+u4/oLZ3PWifkR7Tvm7K4BU0dPy49ByvFL87o4vSSXpWdOpbsvzO9e2sU7m+roH0y801C0vFnoFRvtjhHXPt7TTHEgjXTv+BaLKPogWn+HLBAYg+7NQe2oBaAwJ5Xqph50Q45+nEhSoAkhhm0pbeXOP6wnkOnmny6cQ06mJ+J7vXUfM+ibQ7zvc+FxOzltboClZ06lIzjIb17cyQc7GgnrifOPi5Y3G712B5aRmL2AsfCPzfUsmDH+IsvZU4vhzbF9mD7eGR4fakcVAClODV+Gm6rGHptTJRcp0IQQGKbFX94p5YlX93D5OdM5e0HB+DaWNXU8jZsY8JdEL+QE87idnF6Sy8WnF1PTFOQ3L+xkW1kbVgLUaUpKKmp6AKN+j91R4lJ1YzctXf3MKswY972urhrZoDYCQz1on2yvUZjjZX+tDHNOJCnQhJjkegfCrPzdh+ys7OArl8ylODD+HcHdrXsw3NmY7swoJIyuNK+Lsxbkc86CfDbtbeGxV3dT3RS0O9aY1NxZhCs22B0jLq1ZV8WJ033HdHqFs7OSsMw/G5PuDaB21XPwJ5pCfyr7ajptTpVcpEATYhJr7uznJ09swJPi4Evnz8LrPrZ9jLy1HzHonzPB6WLLl+nhwtOKKJmSxcsfVvL3d8vpCobsjjUqLX8ORtVmrETo8ouhgZDOmxtqjnlFoWyxERnLkYKVkoajtxmA4kAapXVdWJZlc7LkIQWaEJNUeX039zy5gVNm+Vl2zky0Y+htAFBCvaS072cwOwnOh1QUpuSls/TMqXhcDh5fvYe1Wxvicn6amuYDpxuzqczuKHHl/e2NzCzMOKaTJJRQL2o4iOHOikKy5GOm5w4tDgIyUl1oqkJzR7/NqZKHFGhCTELbytp44M9buGjRFBbOOb79nryNWwhlTsNypExQOvtpmsoJM3wsPWMKda29/O7lXZTWdhFvnQNa/hwZ5vwU07J4fUMNn11QcEz3u7oP9J4ly9ETUWalBnB11Qz/viiQKvPQJpB8FwoxyXy8p5nfvbSLqz43k9lFxz9nzFv3EYP++Nr7bKJ43E7OWpDPorm5vLmxlr++UxpXw55a/lz0ig0yrHTArop2FAWmF4x/cQCAs6taNqgdBzM9F+enCrQCfyr7amUe2kSRAk2ISWTttnpWvbaXL54/c9zH34zEEWxC628nlDllAtLFrzyflyVnTCHN6+Lx1Xv4cEcjhml/UaSkB8CyMNvksGqANetrOG1OYNwnBxyU0lFBODVvglMlLzM9F2fwk4UCxQFZKDCRpEATYpJ4f3sDf3mnjOs+P5u8bO+EPHO492wSDAmpmsoJ031cvKiYsvouHn9lN7XN9q72VBTlwDDnx7bmiAcNbb1UNnUzf1r2sT3AsoZOEIjTjZbjktOD5fDg6GsFhg5O7+4N0dUbP73MiSz5/1YVQvDhjgb+/HYp135+Nv4M98Q81DLx1m9gIGfexDwvQaR5XJx3ciHzpmbz3NoKVq+rZiBk32kEav4c9HKZh/bi+5WcPjcQ0akXI3H0tWBpTkyXnG86HmFvYHiYU1UVpuSmSS/aBIlZgVZRUcH111/PkiVLuP7666msrBz12vLyck455RTuvffeWMUTImlt2NPMn94s5doLZk1ccQaktO7FdKVieMZ/1mHCO7Da89Izp9If0vndS7vYXdlhyyICNasQQn0YHfWxf3mcaO7sZ1t5GwtnH/v8MVdnJWHpPRs33ZszvJIThs7l3FPdYWOi5BGzAu2OO+5g+fLlrFmzhuXLl7NixYoRrzMMgzvuuIOLLrooVtGESFo7K9t5YvUevnjezHEd2xQJb926hDo5IBqcTo3TS3I5e0E+721r4C/vlMZ8eEdRFNT8uejl62P63njy8geVnDo7hxTXsR/P5OqokP3PjoGeGsDV9ckcyCm5aeyVAm1CxKRAa2trY9euXSxbtgyAZcuWsWvXLtrb24+40nTwvwAAIABJREFU9je/+Q0XXHAB06dPj0U0IZJWWX0Xv35uB1ecM4M838TMOTtICfXibt3LoC8J9j6bADmZHi5ZVEy618Xjr+5hw+7mmB4ZpRWUoJd9FLsXxpH27gE27G3mtLnHt/rS1VlBOFV60MZL9wZw9nyyUCA320tb1yDBfjkn9ng5YvGShoYG8vLy0LShn240TSM3N5eGhgZ8vk+GR/bs2cPatWt58skn+dWvfnVM7/L7x39MzfEIBNJj+r7JSNp4/Opagvzyb9v54ufnMG96ZEOQWVmRF3GOfR9h+meQmpV4RztF05mZHkqm+3h/Wz27azr50ufnUBA4dE7TeNo5UlbmbFq2vkQmHbgCUyf8+fHsr+9VsGh+PoV5h26tMa52DvfjGOjAnVskh6SPU2p2FjjdZDt6sdKHVsBOK8iguXuQGVMn4fSHCRSTAi0S4XCY//iP/+A///M/hwu5Y9HWFsSM0fL3QCCdlpaemLxrspI2Hr/u3hB3P7mBs07IIz/LTWdn35j3ZGV5I7ruoMD+fxAsPINwcPB4oiYlDTjvpAIqGrr57fPbOXmmn3NOLsDpUMfdzuOSN5emDW/jXnRNdJ4fh5o7+3l7Qw03XDrvkHYdbzuntO3F6c0l2KcDehSSJqe0tBSCwUEUby4DNfvoLxz6YTovy81H2xuYmRfbDpNEo6rKUTuVYlKgFRQU0NTUhGEYaJqGYRg0NzdTUPDJbs8tLS1UV1dz0003AdDd3Y1lWQSDQX7yk5/EIqYQCW8wbPDAs1uZOyWLk2cd21mEY3F216KFewlnFEfl+UlBUZhRmEmBP5XNpa38/uXdLDlzCguj0Ht2kFZQQnjH/9/enUdJVd8J/3/f2rfeqtfqBXphaxZlUwRFEUFIgiJOkOjEmWwkOfMknskz+sTMnJ+JxpMnzORJxhiTSTQxkzgxigYim7grsggKsnUDve/7vtR+7+8PlNg2Sjfddauq+/M6J+dQt27V/fQ316pPfZfP9xW0RRsuuw5YvHn+jQoWzkjHab+8PWQ/ZOmqIuSS+WeXK+RIw9JdjTd7EXB+X879J5uiHFX80yVBS01Npbi4mJ07d7J+/Xp27txJcXHxkOHN7Oxs3nnnb3MoHn30UQYHB/nud7+rR4hCxD1V03j8hdMk2M1cOzdyc2kc9Qfxpc6CSZIEjIXNamLpnCya2vvZc6iGs/U9XD/Pg8M2/h+9huRsCPpQuxowuid+8lzV1MuZ2i6++rniMb+XpbsKf0rROEQ1OYVcWTga/1bqxZPqoLlzEK8/hN0aMwN1cUe3VZw/+MEPeOqpp1izZg1PPfUUDz74IACbN2/m5MmTeoUhxIS1/a1K2np83HxVXuR6UMIBHE3H8KVN7tWbo+VJc7F2yfm5Yb/dVcqJ8vZxL8lxvmjtDIIVh8b3jWOQpmn8+dUyls3NwmIa45wxTcXSUyslNsYg6EjD1N8M6vnhYZPRgCdV9uUcK91S26KiIrZu3Trs+OOPP37R87/97W9HOiQhJoyDp5t5+2QTX1w947ILdY6EveUkIWc6qvXy9jqczExGI1fPzsKTYufds22crOxgzZKppCWNX206Y3Yxgfd3oi3+uwk9zPl+WTtdfX7mFaSO+b1MfU2oJjuaOXLDzxOe0ULYloy5r5Fg0vkfInkZTkprOrmiaOz/H01WspOAEHGuqqmXP718jg3LC3HYxjYX51KcHw5visuWkmDjpoW5eFKd/Onlc7z5fgPB0PjU5FCSMkFRUFsrxuX9YpEvEOKPL53jpkW5GAxjT0KtXRUEE7LHIbLJ7eP10KZkJnC6WuqhjYUkaELEsZ6BAI8+f5Kbr8ojPXl8C9F+nHGgDXN/E/6UwoheZzJQDArTcpNZc/UUmju9PLGrhPJxGA5SFAVjdjHBsoPjEGVs2vZWJXkZTqZmjk/5HWtnGUGX59Inik8VcmZg7q6+8NjjdtDW5ZV6aGMgCZoQcSoUVvnFX04wpyCF6bnJEb/e+d6zGVInahzZP1hEsGh6Oq+8V89zr1fQ0z+2nQiM2cUEK99BU6O3P2ik1DT3ceB0CzdcOU49XpqGpatSetDGQdCZibWn5sJjo9FAboZTdhUYA0nQhIhTT79ShkFRWDZHh8nNaghHw2F8abMjf61JKCvVyZqr8nA5zPz+xTO8faKJ0GUOexqcKRjsyYQbTo9zlNEVCqv8bncp11/hGbehfNNAKxjMqFYphj1WYbsbg68HJei9cCwv3cXp6uE7BomRkQRNiDh04FQTJyra+eySqbpMBre1niJsSyFsT4n4tSYro9HA7Hw3axbn0dA2wOO7SjhX13NZqz2N2TMJlh0Y/yCj6Pk3K7BajMwtGL/q9BaZfzZ+FAMhZwaWj2ycPiUzgVKZh3bZJEETIs7Utfbz9Ctl3HptwZg2hx4NZ90BfOmyOEAPDruZpXOzWDwjgzeONfD0q2W0dXkv/cKPMGYXE6o5jhb0RShKfZ2u7uTAqWbWjnMJmfPzz6S8xngJOdMxd/9tmDMj2U7vYJCuPtlx5HJIgiZEHBn0hfjF8ydYsSAn4osCPmQcbMfSWy+FPHWW6Xaw5qo8slLsPP1aOS8eqmXAN7JtiBSrE4M7l1DVu5c+Ocb1DgZ4YkcJn1kyZXxXKWsa1s5KAtKDNm6CzkysXZUXHhsMClMzXZyReWiXRRI0IeKEpmk8sauEvEwXc0a4Afp4cNYdOF+Y1iAVwfX24WrPzy6ZQiAc5omdJRw41UwweOn5aabcOQRK39QhysgJhlR+8fxJZuenkJ81vrX3jN4O0FRUa9K4vu9kFnJ5sPRUg/a3+zM33UVJlcxDuxySoAkRJ/YeqaO1c5AV83P0u6gawtHwDr70OfpdUwxjMRuZPy2d1YtyqWvp5zc7TvN+WTuq+skT1AyZ01C7G1F7W3WMdPxomsbv95RiNCpcN2/8y2BYOysIJmbLlmXjSLU4UY3W84svPjA163w9NG28t86YBCRBEyIOlDf0sPtgDeuW5Ud0p4CPszcfJ+xII2yLfBkPcWkuh4Wlc7O4dm4Wxys6eGJnKWdqui66kEAxGM/vLHDubf0DHQe7DtZQ3dzHZ5ZMichCGGt7KcEEHX/sTBIhlwdLV9WFx+4EK5qm0dQxGMWo4pMkaELEuH5vkF9tP8XqxXkku6y6XttVuw+v9J7FHHeSnRvnZzN/Wir7TzXzu92lF13xacqdS+js22ja+OxUoJeXjtTx+rEGbruucOx7bV6MpmLtLCOQmDf+7z3JBV2ZWLr/Ng9NURQKPAmckmHOUZMETYgYpmoaj+84zfTcJKbn6jtXxtTXiNHbSSA5X9frihFSFLJSnaxelMvsqSm8dbyB3+8p5Wxt94VEzZCUCSYL4cYz0Y11FF46Usved2q548ZpJDgis3WZua8RzWiV+mcREHR5sH6kBw1galYiJyraoxRR/JIETYgYtvedWjr7/Cy/Qv+VZs7at/GlFYMiHxMxTVHISXdx8+I8Zk1JYf/JJp7YWcLJik7CqoYpdx7BkteiHeUlaZrGC/ur2Hu4jk0rp5HktETsWtb2swQTcyP2/pNZ2O7GEBjA4O+7cGxqpouKhh4CwYm3u0UkySevEDGqoqGHPe/Usm5pPsZx2BR6NJSgF0fzMXzpsnNA3PggUVu1KJcrp6Xyfnkb/7X9FEf70gjVnUIdHPten5ESCIb5r7+e5nBpC19YOY3ECCZnALaOMwQkQYsMRSHoysLykX05bRYTGSkOztV3Ry+uOCQJmhAxqN8b5JfbT7F6cW5EexI+ibP+EIGkqagWp+7XFmOkKGS5ndwwP4flV3io6QhQ6kvj4AvPU9vSd+nX66ypY4Af/fE9Bn0hNt04nQRHhO/3cABzT630oEVQyJWJ5SP10ACmZiZwskLmoY2GFDYSIsZomsZvd5VQlJOkyybowwNQcdbuo69gpf7XFuMqOcHGktlZqF1X4ap6lS3PTifZZWXFghyuLs7Ebo3eV4Cqarx4uJbdh2q4dm4W86el6bJtmbWrkpAjHc2o/w+fySLoysLRdGzIsQJPAi+/W8+dTI9SVPFHEjQhYswr79bT1uXlzpui80FmaytFM1oIOTOjcn0x/gzJHixWG9++wkhpKINDJS0881o5cwvcLJ2bxdwCN+ZIrJa8CE3TOF7ewXNvlmMxGfni6hm6rk62tp+V8hoRFnRmYupvQgkHLiTCmSkOegcCdPb6cCfaohxhfJAETYgYUtXUywv7q/j71TMw6ljv7KOcNW/izZgrBTwnEkXBl16Mq/5tihZupig7Ca8/xNm6bl54u5rHd5Qwr9DNghnpzMl3R2SYMRhSOXqujZeO1DLgDXHtvCym5STp0mv2Ubb2UvqnXKfrNScdo5mQMx1LdzX+1BnA+W2f8j0JnKzs4AY9i23HMUnQhIgRg74Qv9x+ipsW5epe7+xDpv5mzH2N9BXcFJXri8jxpc4gteEwxsEOwo5U7FYT86elMX9aGgPeIGUNPbxxrJH/fvEsmcl2Zk5JYeaUZAo8iSS7LJeVSPkDYc7WdXG8vJ0jZ9pIT7YxrzCVGbnJGHRe+AJg9HZiDPQRcmbofu3JJujKxtJRdiFBAyjwJHL0XLskaCMkCZoQMUDTNJ7cXcqUTBezpqRELQ5X9Rvnt3Uy6DPcJXRktOBLm4mz5i16izcMecppN19I1sJhlcaOQerb+tlzqIamjkEUBXLSXWSm2MlIsZPssuK0mbFbjSiKgqZpBEIqvQMBegcCNLQPUNvaT2vXINmpTqZmJXDnTdNJSYjOD48P2VpPE0iaKqVjdBBMyMbecpyPLksp9CTyyrv1BIJhLGb5jLkUSdCEiAGvH2ugvq2fv18949InR4gh0I+95Tidc++KWgwisrwZ80gpeY6+6Z9BM118HpDRaCAvw0Vehgs4/+Oh3xuivcdLd7+futZ+ztZ24w+G8QfDaBoYFAWjUcFhM+Gwms4vRLgym7QkW0x9EdtaT+JPKYp2GJNC0JVFYvkelJAfzXQ+MbdbTWS5HZTUdDF/WlqUI4x9kqAJEWW1LX1se6uSO2+arus+mx/nrH0bf0oRmtketRhEZKnWRAIJOTgaDjMw9foRvUZRFBIc5ohV9deLEvJh6amhL39FtEOZHIxmQs4MLN1V+NNmXThcmJ3IsXNtkqCNgPTzChFFXn+Ix7ad4sYFOdFd2RQO4qx9G2/mFdGLQejCmzkPV81bEGf7c46Vtf0MQZcHpLyGboIJ2Vg7y4ccK8pJ5Hh5O+rHN44Vw0iCJkSUaJrG7/ecISfNwex8d1RjMdW8Q8iRTtge3ThE5IVcHjSDCVvb6WiHoit76ykCyVOjHcakEkzIxtpxbsgxd8L5Ye+a5tgrmhxrJEETIkpeP9ZAbUsfNy6IckVzTcVc+iLerCujG4fQh6IwmDWfhIqXYbL0Ymgq1vZSAkn50Y5kUgm6sjD1N6OEfEOOF2UncqysLUpRxQ9J0ISIgurmXra9Vckt1+ZjNkX3P0Nb6ykwmKR45yQSSCnEEOjH8rHhp4nK0lWJanGhWhOiHcrkYjB9sO1T1ZDDhdlJHDvXHqWg4ockaELobNAX5LG/nK935k6IckVtTSOh8mWCUxZLYdrJRDHgzZpPQuXL0Y5EF/amo/iTC6MdxqQUTMjB1n5myLGcNCfdA35au71Riio+SIImhI5UTeM3O0rI9yREtd7ZhyxdFRiCg6hp06IditCZL3Um5v5mzD110Q4lstQw9pbj+N1yj0dDIGkK1vbSIccMBoUZucm8e6YlSlHFB0nQhNDR7oM1dPb6WHFldrRDASCh4mW8mVdK79lkZDDizbyShIoXox1JRFk7y1Gtiai2pGiHMimFHOkYgwMYvV1Djs/ITeZwaWuUoooPkqAJoZOS6k5efreOW5blR22fzY8yd9dg7m/Clzoz2qGIKPFmzMHSU4u5pzbaoUSMvek9KU4bTYpCIDEP68eGOfMyXHT0+GiTYc5PFP1vCSEmgfYeL79+4TSfXTI1IhtRX47Eir0MZi2QbZ0mM4OJQc9CEst2RTuSyFBD2FtPyfBmlAUS87C1lQw5ZjAoTM9N4t0z0ov2SSRBEyLCAsEwjz5/ksUz05maFRuryMy9dZh76/GlF0c7FBFlvrTZmPtbsHRVRjuUcWdrP0PI7ka1uKIdyqQWSMrD2lkGanjIcRnm/HSSoAkRQZqm8eSeMyQ6LCyemRHtcC5IKN97vu6ZQXZ7m/QMRgY8i0g8t3PC1UWzN76L3y3Dm9GmmR2EbclYeqqHHM/LTKCtx0t7jwxzXowkaEJE0N4jdVQ39XLzVXkoMTIR39TbgKWnBm/6nGiHImKEP20mRn8P1vaSS58cJwyBfmztZ/C7p0c7FAEEEnOxtQ1dzWk0KEzPSeKI9KJdlCRoQkTIiYoO9hyq4bblhVEvRvtRiWW78GbOl94z8TeKgYHcpSSd2T5sGCpeORqOEEgpQDNFudagACCYNGXYPDSAWVNTOHCqOQoRxT7dvjWqqqrYtGkTa9asYdOmTVRXVw8757HHHuNzn/sct9xyC7fffjv79u3TKzwhxlVj+wCP7zzNrcvySXLGxqIAAHN3NZbeerwZ0nsmhgokTUU1O3DWvh3tUMZO03DWH8CbJnMsY0XQlYXR143R2znk+JQMF/3eIHWt/VGKLHbplqB9//vf56677mLv3r3cddddPPDAA8POueKKK3juuefYsWMHP/rRj/jOd76Dz+e7yLsJEbt6BwP859bj3HBlNjnpsTU5OencTgazF0vvmRhOURjIW0ZC5UsogYFoRzMmhvZy0DRCLk+0QxEfUgz4kwuwtZ4celhRmJ2fwoGTTVEKLHbpkqB1dHRQUlLCunXrAFi3bh0lJSV0dg7NpJcvX47dbgdg5syZaJpGd3e3HiEKMS4CwTCPbD3O9Nwk5hakRjucIawd5zB6O6XumfhEYXsqgZSiuC+7YS5/E1/aLCnAHGMCKfnYm08MOz57qpuDp5tR1Ym1SGWsdEnQmpqayMzMxGg8X2/JaDSSkZFBU9MnZ8zbt29nypQpZGVl6RGiEGOmahq/eeE0DquZ6+bF2C93TSXx7AsMZl8ldc/EpxrIuRp7y0ks3dXRDuWyGAL9GBtPyg+RGBRIzMPc14AhMHQ4MzXJhsthoaSm8xNeOTnF5DjH4cOHeeSRR/jd73436tempuo7pJSeHht1rSayeGhjTdP4zfaTdA8G+dLnZmOKgZ0CPspYcxiTomKeMgfzJ/QquFxWnaOanGK/na2Ep99AaumzeNf8f3GX0JtPvkI4YwbOlORohzIpjO5+tqKl5uMeKCeUsWzIMwtnZfDuuXZuvDp/XOOLZ7okaB6Ph5aWFsLhMEajkXA4TGtrKx7P8F6GY8eOcd999/HLX/6SwsLCUV+ro6Nft27S9PQE2tr6dLnWZBUvbbzjQBWHTzfzhZXT6O+LsXmT4QCZx56np+BGQgOBi57iclnp7/frHNjkEzft7JhKonKK0LGd9BetjnY0I6aE/GSWvU5w4R3x0c5x7nLuZ6szD3PlYbpT5g85np/u5InDtdTUdeGwxWTf0bgzGJRP7VTS5Wd+amoqxcXF7Ny5E4CdO3dSXFyM2+0ect6JEyf4zne+w89//nPmzJFVZiI+vHGsgTeONfL5G4qwWWLvg8VV8yYhRyqhhNjYoF3EAUWhf+r1JFS/jqk/fiZvO+oPEXR50BzuS58soiKQPBVrVyVKaGhi57CZKfAkcqhESm58SLdxmB/84Ac89dRTrFmzhqeeeooHH3wQgM2bN3Py5PlVHQ8++CA+n48HHniA9evXs379es6ePatXiEKM2sHTzWzbV8nnbyjCZTdHO5xhDP4+EqpeZyD3mmiHIuKMak1kIHcJ7uN/BDUU7XAuTQ3jqn4Db9b8S58rokYz2Qi6si5aE+2KwlRee68ebYLtaHG5dPu5X1RUxNatW4cdf/zxxy/8+/nnn9crHCHG7MiZVp5+pYw7biwiJSE25xUlnv0rvrRZqDaZjyNGz5c2G0t3LYllu+mdeWu0w/lU9uZjqBYXIZcsLIt1fvc0HI1H8HoWDDk+JdOFP6RS0dDLtNykKEUXO2JrJrMQceLouTb+uPcsn7+hkLQke7TDuShLVyW2jnPn654JcTkUhb78G3A0HMbSURbtaD6ZGiKxbBeD2YuiHYkYgUBKIZauymGrORVF4YrCVF49Wh+lyGKLJGhCjNI7JS08uecMt19fSEaKI9rhXJwaJrlkKwO5S9GMsbOTgYg/mtlBX8FNuE/8EYMvNutSumr2Eba5CSbmRjsUMQKa0UIgeSr25uPDnptb4OZ4eTt9gxdf0DSZSIImxCjsO97In145xx0rishyx2hyBjhr30YzmPG7p0U7FDEBBJPy8KXPIfXY72JuPpohMICr6hUGcpdEOxQxCn73dOyNR4Ydt1tNTM9N4u0T8bM4JVIkQRNiBDRNY8+hGv7yViV33DiN9OTYHNYEMHo7SazYS/+U5VJJXYybQc9CNKOZ5JLnIYYmcSdU7CWQUkTYLis340kgMQ/zQCvGwY5hz82fls7L79YTCqtRiCx2SIImxCWoqsafXjnHG+83cudN00lNtEU7pE+maSSf+jODWfMJ21OiHY2YSBSFvoKbsHRV4Kp6NdrRAGDurcPe9B4DMs8y/hiM5xcLNL037ClPqoMkl4UjZ1qjEFjskARNiE/hD4R5bNtJyut7uPOmaSQ6Y3s+l6PhMEZ/D97MK6MdipiANKOF3umfxVW7D3vD8OEpXakhUk48xUDuMjRz7E43EJ/MlzoTR/0h0Ib3lC2emc7ugzWTuuRG7FXVFJOWFvQTbj5LuK2acHs1Wl87g4EBwn4vGAwoBhOKIwlDQgYGdw7GrBkYM4pQzJEpcdHe4+WRrSdITbTxdzcUxdz2TR9n9HaReO4Femasi7vteUT8UC0ueqZ/lqSz29HMdnwZc6MSR2L5i6iWBPypM6JyfTF2IWcGmtGCtf0s/vTiIc8VehLZd7yJkuou5hRMzuFrSdBEVGlBH6GKwwQrDhFuqcCQ7MGQlIkhJQdDzhwS09z0eTVQw2hqGM3XhzbYg9rdTKjqPdTeFoyZ0zAVLcFcsBjF6hyXuEqrO/mvF05z1awMFs1IR4n1uVyaSsqJP+LLuIKwIz3a0YgJLmxPpXfaZ0k+9TTdc76AL3Oerte3dFXiqD9E15w7ZJ5lPFMUfOmzcda+PSxBUxSFRTPT2XWoWhI0IfSk9rTgP76bUMVhjKl5GLOLMc+9eVhvmCnBgRIeBEABcCSB+29L6bVQgHBrBaHyQ/gP/hlTwSIsc1dhTMu/vLhUjRf2V/H6sQY+s2QK+VmJl/kX6stV9SpKOMDgxwo/ChEpIVcmvdM/R/LpP9OjhfBm6XPvGQc7cL//JP35N8rQ5gTgc08ntf4gRm/XsHmzs6emsP9UM1VNvRR44uOzeDxJgiZ0pXY343/vL4TqT2OaMh/bDV9GsSVc9vspJgum7GLILkbzDxCqO4l3z88wpORgWXwbpqyRD3909Ph4fGcJ/mCYu2+eGZNbN12MuacWV/UbdBd/HpTYHoYVE0vImUHPjFtIKt2GcbCd/oJVEe3RUoJeUo/+hsGsBQSSp0bsOkJHRjN+9wwc9Qfpm/7ZoU8ZDVwzO5Pn3qjgvjsn349P+TQXulB9ffje/iMD2x8Csx3bis2YZ143puTs4xSrE/O0a7DeuBlDah6+V37F4K5/J9xR96mv0zSNfSca+cGTh8ly29kYo/tqXowh0H++N2HK9ajW8WtLIUYq7Eiju/h2HI3vknzqTxCOTIFRJeQj9dhvCTkz8WVeEZFriOjwZszGWX/oojX25hW4ae4c5GxtVxQiiy5J0EREaZpKoPRNBp75HupgN7Ybvop52jURm9gPoBiMmKbOx7riqxiSsxncuQXvG0+genuHndva7eX/PfM+uw7WsHFFEdfMzsJgiJM5LWoY9/H/JpBSSMBdFO1oxCSmWlx0z7wNo7+XjIP/D3Nvw7i+v8HfR9rhX6CabPRPuW5c31tEX9ieStiegqPp6LDnjEYDS+ec70WbbCs6JUETEaN2NzP4wo8InnoJ69WfxzJ3FYpVvzkjisGIqWARthVfg1CAgWe/h//Uy2iqSjAUZseBKh76/RHSk+3cffPM2N226RMklu1CCQUYyJEK6iIGGM30FazCm3EFaUd+SUL5HpRx6E0z9TaQ/s4jBBOy6Z96gwzjT1CDWQtIqHz5oiU3Zk910zMQ4FRVZxQiix6ZgybGnaapBE69TODoC5inLcWYvwAlih+qitmKefaNGPPmEjz9Kr3HX+MPPUsIJk3hi6tnkOyKXG9epDjqDmBvPkb3rNvlC0vEDkXBnzaTYIIHZ/0hMut/RM+MdecXEIy29IsaIqHiJZx1+xnIXYo/bVZkYhYxIZiQg2YwY2s5iS9raB1Hg0Hh2nkennmtjNn5KRgNk+MzTxI0Ma7U/k58r/0aLTCIddnfY3DGRjV7TYPqPitvdV5JnlrLl6178adeRa8tm3jrNLe2nSaxbDfds25DM8fullNi8lKtifQV3Yy5rxFX9RskndtBf951eLMXXXJLJiU4iLP+EM6afYTtbrpnb0S1uHSKXESNojCYNZ+EypfOzzH82GKTGblJHC9v5/WjDaxanBelIPUlCZoYN8HKI/je/m9M+YswF10d1V6zD2kaVDT0cPB0M15/mDn5KeRl5NITWoir7gAZ+39M95xNcfPr3NJdTcrJP9E77TOotuRohyPEpwomZNMz6zaMg23YW0+TUPMGqtmBP2UaIWc6YVsKoKGEA5gG27B0VmLubySQXEBf4SpCrsxo/wlCR4HkApwNh7F2nB32mawoCisX5vDMa+VcXZwZ87u6jAdFm2Cz7jo6+lFVff6k9PQE2tr6dLlWLNNCAXwH/odw3Qks89dhSMket/dOTnbQ3T046tcFQyrymcNqAAAVOElEQVSlNV0cLm1BAWbmpZCX4UL52AIAc08tCTVv4XcX0TNrQ0z/Urd0VeE+9gR9BSsJJo1viQGXy0p/v39c31MMN+nbWdMwDbZh6m/GGOjDEBgAFDSjGdXsIOjyEHJlohnH9uU76dtZJ5FoZ2tHGba207Qt/d8Xnb7x+rEGrGYDX/nc7HG9bjQYDAqpqZ/8nSM9aGJMwt2N+F7+BYojGet1/xjR1Zkj0dnr43h5BycrO0hLsnFFUSpZKY5PrM0UTJpC55w7cDYcIePtH9Mz6za8nkUxV53c0lWJ+9hv6Su4iWDSlGiHI8TlURRCzgxCzoxoRyJilN89DXvrCexNx/BmLxr2/LI5WfxuTyll9d1Mz53YowiSoInLFiw7gO/A/2CesRzjlCuith2SPxDmXF03x8vb6eoPkJ/lYtXiXFz2Ef4KN1oYmHItfve085OS69+ha84dhJ2xsWWSrfk4KaefpbdQkjMhxASnKPTnLiXx3A68mfPgY72pVouRmxbm8sSOEh766hKslom777AkaGLUtFAA3/6nCDecxrrkDgyJ+v8aDoVUKpv6KKnupLq5l4wUB4XZSWSnOS+7jlnIlUn37M9jbzlBxqGfMZC3jL7C1WimKPUKahquqtdw1bxJz4x1hGIkYRRCiEgKJWQTcqThqnmL/sJVw56fkZdMeUMPz7xWxj+sjY/5w5dDEjQxKmp3E96XH0VxpGC97m4UHZOXYFClqrmXM7VdVDX2kpJgIzfDxbpr8rGM168oxYA3az5+9zSc9QfJfPv/0jPjFryeBbqWs1ACA6Sc+jOmwVa6i2+P6blxQggx3gZzryH5zDa8nkXD9ugEWLkwh/9+8SwLZ3YwtyA1ChFGniwSGIPJtkggcG4//oN/0nVI02w1c7SkmXN13dS19ZOaaCMnzUlOugu7NfK/L8x9jTjrD6JhoHfmLfhTZ0Z8fpq14xwpJ/+EP7mAgdxrRl8/6jLIpGp9SDvrQ9pZH5FuZ0fjuxh93XQs+sZFP3erm3vZe7iOB750FSkJ8VfP8lKLBCRBG4PJkqBpQR++t/9AuOkcloW3RHRIU9Ogo8dLeUMPZfU9dPT6yEpxkJ3mJDvVOX49ZaMMytJVibPxCKrZTl/RGnzps8e9R83o7SLxzHasPdX0T1lOIDl/XN//08gXmj6knfUh7ayPiLezGia59Hn6ClfjzbnqoqccPN1MfdsA3/viQkzG6Jd2Gg1J0CJoMiRo4bZqvK/+EkNSFuY5N6GYxr/2TCisUd/aT3lDN+UNPYRVjexUJzlpTgpyU/B6I7P58qh9kKg5mo+hqEEG8q5lMOfqMQ8/GgfacFW/jqP5GN6MeQxmLQCjvpu1yxeaPqSd9SHtrA892tk00EZi2U5ar/0/qNakYc9rmsZf91fjcTv4x8/E13w0SdAiaCInaJqqEjixh8Dx3Zhnr8SUM741ZwZ9ISoaeilr6Ka2pY8kpwXPB0lZktNyoTs7Jj9oNQ3TQAv2ttNYuqoIJOXhzZpPwD2dkCN9REOgBn8fttZT2JuPYultwJsxB2/GPDRzdPYDjcl2noCknfUh7awPvdrZ0XgE00Ab7Vf9r4tO+fAHw/zPy+e4+aq8uNplQOqgiVFTe1vxvv4bCAWwXns3BsfwXy2jpWnQ3uOlvP780GVnn48stwNPqoN5BW6slji6FRWFkCuLPlcWTAli6anF3nKSxPK9oKkEE3MI2dMIO9xoBguawYQh7Mfg78Xk7cDcU4chOEAgaSr+lCL68lfq3mMmhBDxYtCziMSy3SSefYHe4g3Dnreajfzd9YX8+bVy7FYT187zRCHK8RdH34oi0jRNJVjyOv53/4KpaAmmgkVj2q4prJ4fuiyr76asoQdN1fCkupg1JZmMZDuGOJsvcFFGMwF3EQF3EWgahkAfJm8nRn8P5t56FDUMmopmMKOabAQdGXjT55zf4ibGiuEKIURMUgz0Fa4iueQ5gsn551fVf0ySy8rf3VDIM6+V47CaWDAj/ssSSYImgA/KZ7z5Wwj4sF7zBQwJaZf1Pv5AmKqmXs7VdVPV1EeCw0x2qpPr5nqGDF1OSIqCak0kYE2MdiRCCDGhaCYbfUVrSCp9jrA1gYB72rBz0pLsbFheyO92l3J3WOXq4vjey1UStElOC/nxH91BsOQ1zNOXYcxfMOpes96BAOUNPZyr7aapc5CMZDueNCdrr56C3Sa3mBBCiLELOdPpK1yN+/0n6Vj4dYLJw/ck9qQ62LhiGn96+Ry9A4G4mpP2cfLtOUlpmkao6l38B5/GkJyFbfk/othH1vOjadDSOUh5fQ/nGrrpGwiSk+ZkSmYCVxdnYDJN3K03hBBCRE8wMZf+/BtJPfo4HQs3XzRJy0ixc+dN03nuzUpaugbZtHJ63JXgAFnFOSbxuooz3FyG7+DTaIFBzMUrMKYNv8E/LhhUqWnpo7y+h4rGHkxGA540BzmpLtKSbCiXub3SpchqLH1IO+tD2lkf0s76iGY7W7qrcFW/QffcL+DLmHfRc7z+EHsO1xIOa/yvDXNxJ9p0jvLTySpOcUG4tQL/kedRuxoxTV+GMXfOpw5ndvX5qWzspbyhh8b2AVITbXhS7ayYn0OCc/zroQkhhBAjEUguoHe6k+TTz9I/0Ep//o3DiofbrSY2XFfA4TOt/ODJI2xaOY1lc7N02QVnPEgP2hjEQw+apmmE604SOL4LtbsZ07RrMObNQ7lYLZlAmNqWfiqbeqhq6iMUVvG4HWSlOslKsWM26z90Kb+E9SHtrA9pZ31IO+sjFtrZ4O8lsfIVVIuTznlfRLVdvCxUc+cgLx2pI8ll4R/XziLLHZ2akx8lhWojKJYTNM3XT+Dc2wRLXwcUTAWLMWbPGpKYBYJhGtoGqG3to6a5n/ZeL+lJdjLddjxuZ0ysuoyFD4DJQNpZH9LO+pB21kfMtLOm4mh8D1vbKfqKPsNA3tKLFrRVVY33zrXyTmkrC2ekc9t1BVEd9pQELYJiLUHTwkFCtScIlu0n3FCCMXMaxrwrMLhzURSFvoEAjR2DNLT1U9vST2efD3eijfRkOxnJdtKSbBhjbCJlzHwATHDSzvqQdtaHtLM+Yq2djYPtuOoOoIT99M5Yhy99zkX3TPb6Qxw508rxinbmT0vj5qumMDUrQfd4JUGLoFhI0NTBHsINpwlVvUeo4TSGpEwMnln4Uwpp7T+/2rKxfYDmzkFCqkZ6ku18UpZkx51ojbmE7ONi7QNgopJ21oe0sz6knfURk+2saVi6q3A0HUVRw/QVrMCbtRDNbB92qtcf4kRlB++XtZPssrJ0biZXF2eS7LLqEmrMJGhVVVXcf//9dHd3k5yczJYtW8jPzx9yTjgc5uGHH2bfvn0oisLXv/51Nm7cOKrrTOQETdM0tP52wi0VhJvLCDWVovZ34nfl0G3xUKNm0Nir0dHrR1U13IlWklwW3C4b7kQrLrs56kOWoxWTHwATkLSzPqSd9SHtrI+YbmdNw9zXiK31FJbeOnypM+iZswnV4hx2qqpq1LT0caa2i7L6HrLcDuYVpXLN7Ew8qcPPHy8xs4rz+9//PnfddRfr16/nr3/9Kw888AB/+MMfhpyzY8cOamtreemll+ju7ua2225j6dKl5Obm6hVmTNA0FW2gC39nM4NtDQTb6qC7AetAI2FNoUNx0xxOpNqbR6fhChJUK4kOCwkOM9NyLCyaYcFuNcVdMiaEEEKMC0UhmJhDMDEHJeQjqWwXlu4qfBlzh51qMCgUeBIp8CSyerFKQ9sAJ6s6qGrs5X9vmh+F4M/TJUHr6OigpKSEJ598EoB169bxwx/+kM7OTtxu94Xzdu/ezcaNGzEYDLjdblatWsWLL77I1772tRFfyxChelwjvZ6maaiaRjCkEQqrhEIqAd8gYZ+XoN9LyOcl7PcS9g+i+b1ogQEUfz+mYD+W8AC28AA2zYtPM9On2vAanHiNTgKWmQQSlmCwOXHaTKRbTUy1mTAZJ3ZRWLPVgiUkiWakSTvrQ9pZH9LO+oifdrZhdOdgsdvRLJ/+nWnFyIwpydisRsobeiKaU1zqvXVJ0JqamsjMzMT4QTJhNBrJyMigqalpSILW1NREdnb2hccej4fm5uZRXSslJXLdkRfzad2THzkr4nEIIYQQ4pMsG9XZV0coitGI7RniQgghhBCTkC4JmsfjoaWlhXA4DJxfDNDa2orH4xl2XmNj44XHTU1NZGVl6RGiEEIIIUTM0CVBS01Npbi4mJ07dwKwc+dOiouLhwxvAqxdu5atW7eiqiqdnZ288sorrFmzRo8QhRBCCCFihm5lNioqKrj//vvp7e0lMTGRLVu2UFhYyObNm7nnnnuYN28e4XCYhx56iP379wOwefNmNm3apEd4QgghhBAxY8IVqhVCCCGEiHeySEAIIYQQIsZIgiaEEEIIEWMkQRNCCCGEiDGSoAkhhBBCxBhJ0EbB6/Xyz//8z6xevZq1a9fy+uuvX/S8d955hyuvvJL169ezfv36UW/4PhlVVVWxadMm1qxZw6ZNm6iurh52Tjgc5sEHH2TVqlWsXr2arVu36h9oHBtJGz/66KMsXbr0wr374IMP6h9onNuyZQsrV65k5syZnDt37qLnyL08NiNpY7mXx66rq4vNmzezZs0abrnlFr71rW/R2dk57LyRfjeKUdLEiD366KPav/3bv2mapmlVVVXasmXLtP7+/mHnHTp0SNuwYYPe4cW1u+++W9u+fbumaZq2fft27e677x52zrZt27SvfOUrWjgc1jo6OrTly5drdXV1eocat0bSxj//+c+1H//4x3qHNqEcOXJEa2xs1G688Ubt7NmzFz1H7uWxGUkby708dl1dXdqhQ4cuPP7xj3+sfe973xt23ki/G8XoSA/aKOzZs+dCXbb8/Hzmzp3LW2+9FeWo4l9HRwclJSWsW7cOgHXr1lFSUjLsl9ru3bvZuHEjBoMBt9vNqlWrePHFF6MRctwZaRuLsVu8ePGwXVI+Tu7lsRlJG4uxS05OZsmSJRcez58/f8huPx+S78bIkARtFBobG8nJybnw+NM2c6+urmbDhg1s3LiRbdu26RViXGpqaiIzMxOj0QiA0WgkIyODpqamYedlZ2dfePxp7S+GGmkbA+zatYtbbrmFr3zlKxw7dkzvUCcFuZf1Iffy+FFVlaeffpqVK1cOe240341i5EzRDiCWbNiw4aK/DgAOHDgw4veZM2cOb775JgkJCdTV1fHlL3+ZzMxMli1bNl6hChERX/jCF/jmN7+J2Wxm//79/NM//RO7d+8mJSUl2qEJMSpyL4+vH/7whzgcDr74xS9GO5RJQ3rQPmLbtm288847F/2f0WgkOzubhoaGC+d/0mbuLpeLhIQEAPLy8li1ahVHjx7V7e+INx6Ph5aWFsLhMHB+AnVra+uwIQyPxzMkgf6k9hfDjbSN09PTMZvNAFx77bV4PB7Kysp0j3eik3s58uReHj9btmyhpqaG//zP/8RgGJ42jPS7UYyOJGijsHbtWp555hng/BDmyZMnWb58+bDzWltb0T7YQau7u5v9+/cza9YsXWONJ6mpqRQXF7Nz504Adu7cSXFxMW63e8h5a9euZevWraiqSmdnJ6+88gpr1qyJRshxZ6Rt3NLScuHfpaWlNDQ0UFBQoGusk4Hcy5En9/L4+OlPf8qpU6d47LHHsFgsFz1npN+NYnRkL85RGBwc5P7776e0tBSDwcB9993HqlWrAHjkkUfIyMjgzjvv5KmnnuLpp5/GZDIRDoe57bbb+NrXvhbl6GNbRUUF999/P729vSQmJrJlyxYKCwvZvHkz99xzD/PmzSMcDvPQQw+xf/9+ADZv3nxhYqq4tJG08Xe/+11Onz6NwWDAbDZzzz33cMMNN0Q79Ljy8MMP89JLL9He3k5KSgrJycns2rVL7uVxNJI2lnt57MrKyli3bh35+fnYbDYAcnNzeeyxx1i/fj2/+c1vyMzM/NTvRnH5JEETQgghhIgxMsQphBBCCBFjJEETQgghhIgxkqAJIYQQQsQYSdCEEEIIIWKMJGhCCCGEEDFGEjQhxIRVX1/PzJkzCYVCul535syZ1NTU6HpNIcTEIgmaEGJCWbly5ai2ZhuNRx99lHvvvXfIsbvvvputW7dG5HpCiMlLEjQhhPiA3j1tQgjxSSRBE0JMGPfddx+NjY1885vfZMGCBezZsweAHTt2sGLFCpYsWcKvfvWrC+c/+uij3HPPPdx7770sXLiQbdu20dLSwje/+U2uvvpqVq9ezbPPPgvAW2+9xa9//Wv27NnDggULuPXWW/nZz37Gu+++y0MPPcSCBQt46KGHhsUUCATYsmULK1asYNmyZTzwwAP4fD59GkQIEbdM0Q5ACCHGy3/8x3/w3nvv8fDDD7Ns2TLq6+v5yU9+wnvvvceLL75IdXU1n//857n55pspKioC4NVXX+WRRx7h3//93wkEAnz1q19l+vTp7Nu3j8rKSr785S+Tl5fH9ddfzze+8Q1qamr4yU9+cuGaR48e5dZbb2Xjxo0XjeknP/kJtbW1bN++HZPJxL333stjjz3Gv/zLv+jSJkKI+CQ9aEKICe9b3/oWNpuNWbNmMWvWLM6cOXPhufnz57Nq1SoMBgNdXV0cPXqUe++9F6vVSnFxMRs3buSvf/3rZV1X0zSeffZZ/vVf/5Xk5GRcLhff+MY32LVr13j9aUKICUp60IQQE15aWtqFf9vtdgYHBy88zsrKuvDv1tZWkpKScLlcF45lZ2dz6tSpy7puZ2cnXq+X22+//cIxTdNQVfWy3k8IMXlIgiaEmNQURbnw74yMDHp6eujv77+QpDU1NZGZmTns3JFISUnBZrOxa9euC+8hhBAjIUOcQogJJS0tjbq6ust6rcfjYcGCBfz0pz/F7/dz5swZnnvuOW699VYAUlNTaWhoGNID9mnXMxgMbNy4kR/96Ed0dHQA0NLSwr59+y4rPiHE5CEJmhBiQvn617/Or371KxYvXszevXtH/fqf/vSnNDQ0sHz5cr71rW/x7W9/m2XLlgGwdu1aAJYsWcKGDRsA+Id/+Af27t3LVVddxcMPPzzs/e677z6mTp3KHXfcwcKFC/nSl75EVVXVGP5CIcRkoGiapkU7CCGEEEII8TfSgyaEEEIIEWMkQRNCCCGEiDGSoAkhhBBCxBhJ0IQQQgghYowkaEIIIYQQMUYSNCGEEEKIGCMJmhBCCCFEjJEETQghhBAixkiCJoQQQggRY/5/6jI7gbkQ93UAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "get_density_plot('throttle', ctl=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "82af465e",
      "metadata": {
        "id": "82af465e",
        "outputId": "3a8c348e-b8fe-4e15-b236-a54eeb90ad34",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 355
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "get_density_plot('brake', ctl=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "af8cdc95",
      "metadata": {
        "id": "af8cdc95",
        "outputId": "8afe4085-7cf7-42ec-f792-8e657dcb38ff",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 355
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "get_density_plot('cvip', cvip=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "c8f6da4b",
      "metadata": {
        "id": "c8f6da4b",
        "outputId": "2a2ed168-664c-4e22-9c5c-230c7abef7df",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 355
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "get_density_plot('x', traj=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "61da2e78",
      "metadata": {
        "id": "61da2e78",
        "outputId": "9584eea0-f9f6-4d99-da96-c674282527fd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 355
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "get_density_plot('y', traj=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "77b6b121",
      "metadata": {
        "id": "77b6b121",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 355
        },
        "outputId": "ba8466e6-6d4a-4248-8561-c153f7b5790c"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "get_density_plot('v', traj=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "$H_0: \\mu_a = \\mu_n$ \\\\\n",
        "\n",
        "$H_a: \\mu \\neq \\mu_n$ \\\\\n",
        "\n",
        "\n",
        "where $\\sigma_a$ is the standard deviation of steer values in abnormal runs\n",
        "and $\\sigma_n$ is the standard deviation of steer values in normal runs\n"
      ],
      "metadata": {
        "id": "AXIbrtLLxAif"
      },
      "id": "AXIbrtLLxAif"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "d42292cc",
      "metadata": {
        "id": "d42292cc",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "18bdf5a9-4c30-44f8-88ae-422f219e6bdd"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Ttest_indResult(statistic=-1.1201087079414076, pvalue=0.26286597683500523)"
            ]
          },
          "metadata": {},
          "execution_count": 71
        }
      ],
      "source": [
        "abnormal = pd.concat([clear_night_ctl.steer, rain_noon_ctl.steer], axis=0)\n",
        "normal = pd.concat([clear_noon_ctl.steer, clear_sunset_ctl.steer], axis=0)\n",
        "scipy.stats.ttest_ind(normal, abnormal, equal_var=False)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The t-test gives a p-value of 0.263 which is greater than the significance level of 0.05. Thus we fail to reject the null hypothesis and conclude that the population means for steering under normal and abnormal conditions are equal."
      ],
      "metadata": {
        "id": "cIQfad0V2LZ9"
      },
      "id": "cIQfad0V2LZ9"
    },
    {
      "cell_type": "markdown",
      "source": [
        "The results can be shocking because the two distributions should be different. However, this results does not contradict our observation because while the variances of steering data under normal and abnormal weather conditions differ, for the normal case, the steering data is trends at 0 with little variance. On the other hand, for abnormal conditions, the steering data varies greatly, but is symmetric about 0. Thus we expect the means for both conditions to be roughly equal."
      ],
      "metadata": {
        "id": "kS9EkkxG2ipz"
      },
      "id": "kS9EkkxG2ipz"
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Task 2.4 Features that are indicators for abnormal AV behavior"
      ],
      "metadata": {
        "id": "--qNGsJ34Ctn"
      },
      "id": "--qNGsJ34Ctn"
    },
    {
      "cell_type": "markdown",
      "source": [
        "The following are better features for indicating abnormal AV behaviors:\n",
        "\n",
        "* Steering data for abnormal conditions has much higher variance than that for normal conditions. This larger spread can be indicative of sporatic driving with volatile steering which can lead to accidents. \\\\\n",
        "* cvip data for abnormal conditions has higher probability when the two cars are close, unlike for normal conditiosn. The closer two cars are together, the higher the chance of an accident.\n",
        "* Both x and y have different spreads between when they are under abnormal and normal conditions. Intuitively this does not make sense because the car should not be varying its position drastically on the same road under different weather conditions."
      ],
      "metadata": {
        "id": "kChxPIQG4MqA"
      },
      "id": "kChxPIQG4MqA"
    },
    {
      "cell_type": "code",
      "source": [
        "indicators_df = pd.DataFrame()\n",
        "indicators_df['steer'] = pd.concat([clear_night_ctl.steer, rain_noon_ctl.steer, clear_noon_ctl.steer, clear_sunset_ctl.steer], axis=0)\n",
        "indicators_df['cvip'] = pd.concat([clear_night_cvip.cvip, rain_noon_cvip.cvip, clear_noon_cvip.cvip, clear_sunset_cvip.cvip], axis=0)\n",
        "indicators_df['x'] = pd.concat([clear_night_traj.x, rain_noon_traj.x, clear_noon_traj.x, clear_sunset_traj.x], axis=0)\n",
        "indicators_df['y'] = pd.concat([clear_night_traj.y, rain_noon_traj.y, clear_noon_traj.y, clear_sunset_traj.y], axis=0)\n",
        "\n",
        "indicators_df.corr() "
      ],
      "metadata": {
        "id": "_onQvxqCxcDG",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "outputId": "d3c0ab8f-bed8-4c7d-dcb3-1f3ab03ff189"
      },
      "id": "_onQvxqCxcDG",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          steer      cvip         x         y\n",
              "steer  1.000000 -0.125259  0.242422 -0.072079\n",
              "cvip  -0.125259  1.000000 -0.604496  0.646726\n",
              "x      0.242422 -0.604496  1.000000 -0.347289\n",
              "y     -0.072079  0.646726 -0.347289  1.000000"
            ],
            "text/html": [
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              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-81bb4e0c-8ea9-4ed5-a5ac-ee879b86d334 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-81bb4e0c-8ea9-4ed5-a5ac-ee879b86d334');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 72
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.heatmap(indicators_df.corr(), cmap='vlag')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 343
        },
        "id": "zLxwl1i_8CFm",
        "outputId": "b9f5404e-78ab-4e43-9a49-3942165f3e67"
      },
      "id": "zLxwl1i_8CFm",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f56c913eb50>"
            ]
          },
          "metadata": {},
          "execution_count": 73
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x360 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Since there is relatively strong correlation between x and cvip or y and cvip, we can simply use less fields to predict abnormal AV behaviour while keeping most of the predicting power while using all the features.\n",
        "\n",
        "\n",
        "If we do not consider correlations between certain features (and remove some highly correlated ones), then these features may contribute much more during prediction than it should have."
      ],
      "metadata": {
        "id": "87ea0i_29F3E"
      },
      "id": "87ea0i_29F3E"
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Task 2.5 Kolmogorov-Smirnov two-sample test "
      ],
      "metadata": {
        "id": "g4Rg7g-P9xVc"
      },
      "id": "g4Rg7g-P9xVc"
    },
    {
      "cell_type": "markdown",
      "source": [
        "$H_0$: the distribution of indicator values between in abnormal condition and that in normal condition are identical, so $F(x)=G(x)$ for all $x$ \\\\\n",
        "$H_a$: the distribution of indicator values between in abnormal condition and that in normal condition are not identical $F(x)\\neq G(x)$ (two-sided)\n"
      ],
      "metadata": {
        "id": "6ta6V35OAkT8"
      },
      "id": "6ta6V35OAkT8"
    },
    {
      "cell_type": "code",
      "source": [
        "def ks_2samp(feature, ctl=False, cvip=False, traj=False):\n",
        "  if ctl:\n",
        "    abnormal = pd.concat([clear_night_ctl[feature], rain_noon_ctl[feature]], axis=0)\n",
        "    normal = pd.concat([clear_noon_ctl[feature], clear_sunset_ctl[feature]], axis=0)\n",
        "  elif cvip:\n",
        "    abnormal = pd.concat([clear_night_cvip[feature], rain_noon_cvip[feature]], axis=0)\n",
        "    normal = pd.concat([clear_noon_cvip[feature], clear_sunset_cvip[feature]], axis=0)\n",
        "  elif traj:\n",
        "    abnormal = pd.concat([clear_night_traj[feature], rain_noon_traj[feature]], axis=0)\n",
        "    normal = pd.concat([clear_noon_traj[feature], clear_sunset_traj[feature]], axis=0)\n",
        "  return scipy.stats.ks_2samp(normal, abnormal)\n",
        "ks_2samp('steer', ctl=True)"
      ],
      "metadata": {
        "id": "HsPxM6Xo8Flx",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "62bd2be3-e667-47e3-a36d-06036ecde480"
      },
      "id": "HsPxM6Xo8Flx",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "KstestResult(statistic=0.255447169023328, pvalue=3.216072714961863e-41)"
            ]
          },
          "metadata": {},
          "execution_count": 74
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Since our p-value is much less than the significance level of 0.05, we reject the null hypothesis and conclude that the probability distributions between the steering data for normal and abnormal conditions are different."
      ],
      "metadata": {
        "id": "4xcfM5C__xKg"
      },
      "id": "4xcfM5C__xKg"
    },
    {
      "cell_type": "code",
      "source": [
        "ks_2samp('cvip', cvip=True)"
      ],
      "metadata": {
        "id": "TY3uF4x_AfAC",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "44f4f7df-222b-44dd-ba30-17968c05fc42"
      },
      "id": "TY3uF4x_AfAC",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "KstestResult(statistic=0.24501024203010957, pvalue=6.542134262242544e-38)"
            ]
          },
          "metadata": {},
          "execution_count": 75
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Since our p-value is much less than the significance level of 0.05, we reject the null hypothesis and conclude that the probability distributions between the cvip data for normal and abnormal conditions are different."
      ],
      "metadata": {
        "id": "hj5lORhOBT6T"
      },
      "id": "hj5lORhOBT6T"
    },
    {
      "cell_type": "code",
      "source": [
        "ks_2samp('x', traj=True)"
      ],
      "metadata": {
        "id": "rTUlvOWV_F-W",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "dd714677-7a4e-41dc-8718-ea09b4a0c866"
      },
      "id": "rTUlvOWV_F-W",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "KstestResult(statistic=0.5475548396078197, pvalue=1.0606363957036516e-199)"
            ]
          },
          "metadata": {},
          "execution_count": 76
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Since our p-value is much less than the significance level of 0.05, we reject the null hypothesis and conclude that the probability distributions between the x data for normal and abnormal conditions are different."
      ],
      "metadata": {
        "id": "3W7m3YIoBbh6"
      },
      "id": "3W7m3YIoBbh6"
    },
    {
      "cell_type": "code",
      "source": [
        "ks_2samp('y', traj=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tWb9o4EXBYse",
        "outputId": "28b13144-35d9-4899-8ccb-79d92d9a99b5"
      },
      "id": "tWb9o4EXBYse",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "KstestResult(statistic=0.1277708171747907, pvalue=1.5569290301442607e-10)"
            ]
          },
          "metadata": {},
          "execution_count": 77
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Since our p-value is much less than the significance level of 0.05, we reject the null hypothesis and conclude that the probability distributions between the y data for normal and abnormal conditions are different."
      ],
      "metadata": {
        "id": "fne9ZtQwBgpm"
      },
      "id": "fne9ZtQwBgpm"
    },
    {
      "cell_type": "code",
      "source": [
        "ks_2samp('throttle', ctl=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2xIMo-eEBfO_",
        "outputId": "758096e3-973c-42d2-a16e-7c212dbd61af"
      },
      "id": "2xIMo-eEBfO_",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "KstestResult(statistic=0.13137541515024959, pvalue=4.108169360250713e-11)"
            ]
          },
          "metadata": {},
          "execution_count": 78
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Since our p-value is much less than the significance level of 0.05, we reject the null hypothesis and conclude that the probability distributions between the throttle data for normal and abnormal conditions are different."
      ],
      "metadata": {
        "id": "W80UHM_mB0F9"
      },
      "id": "W80UHM_mB0F9"
    },
    {
      "cell_type": "code",
      "source": [
        "ks_2samp('brake', ctl=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Si_c3H4OBngl",
        "outputId": "6de1c4e2-b4f6-437c-87f5-226be0360c79"
      },
      "id": "Si_c3H4OBngl",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "KstestResult(statistic=0.041483205059364, pvalue=0.16863859937978443)"
            ]
          },
          "metadata": {},
          "execution_count": 79
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Since our p-value is greater than the significance level of 0.05, we fail to reject the null hypothesis and conclude that the probability distributions between the brake data for normal and abnormal conditions are equal."
      ],
      "metadata": {
        "id": "T3-gPM4UB5Px"
      },
      "id": "T3-gPM4UB5Px"
    },
    {
      "cell_type": "code",
      "source": [
        "ks_2samp('v', traj=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4xcfs3BLBqfq",
        "outputId": "135758de-f733-4251-bedd-3162f8d06304"
      },
      "id": "4xcfs3BLBqfq",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "KstestResult(statistic=0.31729610405107095, pvalue=7.068817815005532e-64)"
            ]
          },
          "metadata": {},
          "execution_count": 80
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Since our p-value is much less than the significance level of 0.05, we reject the null hypothesis and conclude that the probability distributions between the v data for normal and abnormal conditions are different."
      ],
      "metadata": {
        "id": "z86uD6HpCAQf"
      },
      "id": "z86uD6HpCAQf"
    },
    {
      "cell_type": "markdown",
      "source": [
        "The major difference between KS test and t-test is that t-test tests on whether the means of two distributions are equal or not, while KS-test catches difference of distance between two distributions, in both shape and mean. Two sample t-test may assume the distribution is approximately normal and variability of two distributions is similar. KS-tests does not assume similarity between two distributions. \n",
        " \n",
        "\n",
        "To conclude, KS test can find more types of discrepancy between two distributions than the t-test can."
      ],
      "metadata": {
        "id": "1dn-rGkRCYBN"
      },
      "id": "1dn-rGkRCYBN"
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Task 2.6 Assumptions of KS two-sample test"
      ],
      "metadata": {
        "id": "9alFj_h9Drgq"
      },
      "id": "9alFj_h9Drgq"
    },
    {
      "cell_type": "markdown",
      "source": [
        "In 2.4, we assume that all the features data are independent from when they were measured (i.e. values at each time point as an individual sample independent from each other). $v$, for example, could have its value at the current timestamp be dependent on its values previous timestamp(s). This is because the change of $v$ is determined by both current $v$ and acceleration. This assumption is shown to fail for both v and throttle, since they were intially not predicted to be indicator variables. Thus, suggesting that both v and throttle are dependent on time."
      ],
      "metadata": {
        "id": "7-h-hJ3bHA_I"
      },
      "id": "7-h-hJ3bHA_I"
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Task 2.7 Dynamic-time-wrapper (DTW)"
      ],
      "metadata": {
        "id": "vvOiG6n6I8cw"
      },
      "id": "vvOiG6n6I8cw"
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install git+https://github.com/wannesm/dtaidistance.git#egg=dtaidistance"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "b4rSgnk-I5S3",
        "outputId": "20f7e2a0-f4f9-4f21-a427-e14c16a8e99f"
      },
      "id": "b4rSgnk-I5S3",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting dtaidistance\n",
            "  Cloning https://github.com/wannesm/dtaidistance.git to /tmp/pip-install-yp9n0ugg/dtaidistance_9c894489ca724109b3c8eeb533ebecab\n",
            "  Running command git clone -q https://github.com/wannesm/dtaidistance.git /tmp/pip-install-yp9n0ugg/dtaidistance_9c894489ca724109b3c8eeb533ebecab\n",
            "  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
            "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
            "    Preparing wheel metadata ... \u001b[?25l\u001b[?25hdone\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from dtaidistance) (1.21.6)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from dtaidistance import dtw\n",
        "\n",
        "dtw1 = dtw.distance(clear_night_ctl['steer'], clear_noon_ctl['steer'])\n",
        "dtw2 = dtw.distance(clear_sunset_ctl['steer'], clear_noon_ctl['steer'])\n",
        "print('dtw of steering data between clear-noon and clear-night: ' + str(dtw1))\n",
        "print('dtw of steering data between clear-sunset and clear-noon: ' + str(dtw2))"
      ],
      "metadata": {
        "id": "4pADUy5PHAFB",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2f88d2d5-2f34-4974-b887-f96f3c3e2b38"
      },
      "id": "4pADUy5PHAFB",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "dtw of steering data between clear-noon and clear-night: 1.719544523494614\n",
            "dtw of steering data between clear-sunset and clear-noon: 0.061802231504329126\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Temporally, the steering data between clear-night and clear-noon are less similar than the steering data between clear-sunset and clear-noon."
      ],
      "metadata": {
        "id": "utfEOjx2PCvo"
      },
      "id": "utfEOjx2PCvo"
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Task 3\n",
        "## Task 3.1a\n",
        "The assumption of at most one disengagement per mile allows us to treat the occurrence of a disengagement in a mile as a random variable with a Bernoulli distribution."
      ],
      "metadata": {
        "id": "QS8gvGgtuJs3"
      },
      "id": "QS8gvGgtuJs3"
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Tasks 3.1b/c"
      ],
      "metadata": {
        "id": "udQjPZ_xUPmb"
      },
      "id": "udQjPZ_xUPmb"
    },
    {
      "cell_type": "code",
      "source": [
        "disengagements = pd.read_csv('mp1_av_disengagements.csv')\n",
        "total_miles = 505229"
      ],
      "metadata": {
        "id": "pdNecL0gUsp-"
      },
      "id": "pdNecL0gUsp-",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "$P(D)$ is"
      ],
      "metadata": {
        "id": "TpKXyQRBSNWu"
      },
      "id": "TpKXyQRBSNWu"
    },
    {
      "cell_type": "code",
      "source": [
        "p = len(disengagements) / total_miles # Probability of disengagement per mile"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Js4U7lHSTllq",
        "outputId": "8ade15a1-8008-4d1e-e82c-d4ad79ba5427"
      },
      "id": "Js4U7lHSTllq",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.002026803687040926"
            ]
          },
          "metadata": {},
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let D be disengagement per mile \\\\\n",
        "$P(D | \\text{cloud}) = \\frac{P(\\text{cloud} | D)*P(D)}{P(cloud)}$"
      ],
      "metadata": {
        "id": "8AJC4SqpPKpV"
      },
      "id": "8AJC4SqpPKpV"
    },
    {
      "cell_type": "code",
      "source": [
        "# Bayes rule for conditional probability\n",
        "P_clear = 0.72\n",
        "P_cloudy = 1-P_clear\n",
        "print(p, len(disengagements))\n",
        "p_cloudy_disengagement = 1.0 * disengagements['Weather'].value_counts()['cloudy'] / len(disengagements) * p / P_cloudy\n",
        "p_clear_disengagement = 1.0 * disengagements['Weather'].value_counts()['clear'] / len(disengagements) * p / P_clear\n",
        "\n",
        "print('Probability of a disengagement on a given mile on a cloudy day: %f' % p_cloudy_disengagement)\n",
        "print('Probability of a disengagement on a given mile on a clear day: %f' % p_clear_disengagement)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "L_RQfKhdQOb5",
        "outputId": "7af3a646-77e6-4698-8adf-b7b0afbdc365"
      },
      "id": "L_RQfKhdQOb5",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.002026803687040926 1024\n",
            "Probability of a disengagement on a given mile on a cloudy day: 0.005903\n",
            "Probability of a disengagement on a given mile on a clear day: 0.000520\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Task 3.1d"
      ],
      "metadata": {
        "id": "QugHIUDqU3ym"
      },
      "id": "QugHIUDqU3ym"
    },
    {
      "cell_type": "code",
      "source": [
        "automatic_disengagements = disengagements[disengagements.TypeOfTrigger == 'automatic']"
      ],
      "metadata": {
        "id": "8LgDZnbXU7sG"
      },
      "id": "8LgDZnbXU7sG",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "n_automatic = disengagements['TypeOfTrigger'].value_counts()['automatic']\n",
        "p_automatic = n_automatic / 505229 # Probability of automatic disengagement per mile\n",
        "\n",
        "p_cloudy_automatic = automatic_disengagements['Weather'].value_counts()['cloudy'] / n_automatic * p_automatic / P_cloudy\n",
        "p_clear_automatic = automatic_disengagements['Weather'].value_counts()['clear'] / n_automatic * p_automatic / P_clear\n",
        "print('Probability of an automatic disengagement on a given mile on a cloudy day: %f' % p_cloudy_automatic)\n",
        "print('Probability of an automatic disengagement on a given mile on a clear day: %f' % p_clear_automatic)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ayrLtpHNaGhZ",
        "outputId": "13e5f6fe-1915-4809-d38b-cb873f016375"
      },
      "id": "ayrLtpHNaGhZ",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Probability of an automatic disengagement on a given mile on a cloudy day: 0.002806\n",
            "Probability of an automatic disengagement on a given mile on a clear day: 0.000264\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Task 3.1e\n",
        "\n"
      ],
      "metadata": {
        "id": "2Xz16d1wV3aO"
      },
      "id": "2Xz16d1wV3aO"
    },
    {
      "cell_type": "code",
      "source": [
        "import scipy.integrate as integrate\n",
        "\n",
        "mean = p_cloudy_disengagement\n",
        "std = np.sqrt(p_cloudy_disengagement * (1- p_cloudy_disengagement) )\n",
        "\n",
        "n = 10000\n",
        "Sn = 100\n",
        "alpha = (Sn - n * mean) / (std * np.sqrt(n))\n",
        "\n",
        "result =  integrate.quad(lambda x: np.exp((-x**2)/2), alpha, np.Inf)\n",
        "result = (1/np.sqrt(2*np.pi)) * result[0]\n",
        "print( 'Likelihood that there are 100 or more disengagements in 10,000 miles: %E' % result)\n",
        "print('It is very unlikely there will be 100 or more disengagements.')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PSo4Na5IV5ZK",
        "outputId": "4a2e2312-a4ee-4b22-8cf3-3bfb297ea084"
      },
      "id": "PSo4Na5IV5ZK",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Likelihood that there are 100 or more disengagements in 10,000 miles: 4.420338E-08\n",
            "It is very unlikely there will be 100 or more disengagements.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Disengagement is a binomial distribution with n = 10000, p = 0.0059. \\\\\n",
        "So $\\mu = 10000*0.0059 = 59$ \\\\\n",
        "$\\sigma = \\sqrt{10000*0.0059*(1-0.0059)} = 7.658$ \\\\\n",
        "Since 12000 is a large number, we can use the Central Limit Theorem:"
      ],
      "metadata": {
        "id": "pejWVq3OcDDu"
      },
      "id": "pejWVq3OcDDu"
    },
    {
      "cell_type": "code",
      "source": [
        "from scipy.stats import norm\n",
        "z = (100 - 59) / 7.658\n",
        "p_100_more = 1-norm.cdf(z)\n",
        "\n",
        "print( 'Likelihood that there are 100 or more disengagements in 10,000 miles: %E' % p_100_more)\n",
        "print('It is very unlikely there will be 100 or more disengagements.')"
      ],
      "metadata": {
        "id": "oSxfcdxQb_OH",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e791b612-8e96-4881-b2c6-993d43a1b75d"
      },
      "id": "oSxfcdxQb_OH",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Likelihood that there are 100 or more disengagements in 10,000 miles: 4.304439E-08\n",
            "It is very unlikely there will be 100 or more disengagements.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Task 3.2a\n",
        "There is a geometric distribution of miles to the next disengagment. The probability models the first occurrence of disengagement requires x miles.\n",
        "\n",
        "There parameters of the distribution would be k, the number of miles till the next disengagement, and p, the probability of disengagement per mile \n"
      ],
      "metadata": {
        "id": "g5d5f2zGJf8T"
      },
      "id": "g5d5f2zGJf8T"
    },
    {
      "cell_type": "code",
      "source": [
        "p_g =  p_cloudy_disengagement\n",
        "p_g"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XcOhaEdVLDDY",
        "outputId": "74228178-08bd-4158-b719-1f3487945dd6"
      },
      "id": "XcOhaEdVLDDY",
      "execution_count": 94,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.00590255677552725"
            ]
          },
          "metadata": {},
          "execution_count": 94
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "$p(k) = (1-p)^{k-1}p$"
      ],
      "metadata": {
        "id": "A94rR2JJuqWv"
      },
      "id": "A94rR2JJuqWv"
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Task 3.2b: \n",
        "The binomial distribution describes the probabilities for repeated Bernoulli trials (3.1a) has parameter n = 10000, p = 0.002026 "
      ],
      "metadata": {
        "id": "Cm7P764lNgmu"
      },
      "id": "Cm7P764lNgmu"
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Task 3.2c: \n",
        "When X ∼ Binom(n, p) with large n, small p, such that\n",
        "np ≃ λ. Then X is approximately Poisson distributed. It has parameter λ "
      ],
      "metadata": {
        "id": "5lJveUZjOvJ_"
      },
      "id": "5lJveUZjOvJ_"
    },
    {
      "cell_type": "code",
      "source": [
        "n = 10000\n",
        "lamda = p_cloudy_disengagement * n\n",
        "lamda"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1xhE4s3HO7I0",
        "outputId": "1e48bc91-54c8-4203-bd38-e4fdb813613f"
      },
      "id": "1xhE4s3HO7I0",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "59.0255677552725"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Task 3.2d"
      ],
      "metadata": {
        "id": "fIMbSX0zwdCW"
      },
      "id": "fIMbSX0zwdCW"
    },
    {
      "cell_type": "code",
      "source": [
        "from scipy.stats import binom\n",
        "r_values = list(range(100))\n",
        "dist = [binom.pmf(r, n, p_cloudy_disengagement) for r in r_values ]\n",
        "plt.bar(r_values, dist)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "f6YkKKVNQZHZ",
        "outputId": "63b9a805-2b46-43f2-a3ef-29bc3ab90603"
      },
      "id": "f6YkKKVNQZHZ",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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x/hNgy0DZ2cb1Snp3K26g91Dph87nDZsJevqWaqiqJ4EzSzU0paoeqqrPddv/Te8//2p6fb2lq3YL8Ob5aeHcSLIGuAr4o24/wOvpLbkBjfU5ycXA6+jd0UZVPVlVX6fxcaZ3J+Azu+dxngU8RGPjXFX/QO/uxH5nG9etwJ9Wz2eBS5J8/7m+Z0tBvxp4oG//RFfWrG6V0FcCh4EXVNVD3aGvAi+Yp2bNld8Ffh14uttfCXy9qk53+62N9zrgJPDH3XTVHyV5Ng2Pc1U9CPwO8O/0Av4x4E7aHuczzjauY8m1loJ+SUnyHOCjwK9U1X/1H+seVmvmvtkkPwV8rarunO+2XEDLgVcBH6qqVwLfYGCapsFxfh69M9h19Fa7fTbfOcXRvLkY15aCfskst5Dke+mF/J9X1ce64v888ytd9/1r89W+OfCjwJuS3E9vSu719OavL+l+xYf2xvsEcKKqDnf7B+kFf8vj/OPAV6rqZFU9BXyM3ti3PM5nnG1cx5JrLQX9KEs1LHrd3PQ+4N6q+kDfof5lKK4F/uZCt22uVNV7qmpNVU3QG9c7qurngE/TW3ID2uvzV4EHkrykK7qC3hPmzY4zvSmbVyd5Vvfv/Eyfmx3nPmcb10ngbd3dN68GHuub4hldVTXzBbwR+FfgPuA357s9c9TH19D7te4LwF3d1xvpzVl/Cvgy8Eng+fPd1jnq/48BH++2X0Rv7aRp4C+BFfPdvjH39RXAVDfWfw08r/VxBt4LfAm4G9gPrGhtnIEP07sG8RS939yuO9u4AqF3N+F9wBfp3ZF0zu/pEgiS1LiWpm4kSUMY9JLUOINekhpn0EtS4wx6SWqcQS9JjTPoJalx/weQFKrxceS4MwAAAABJRU5ErkJggg==\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from scipy.stats import poisson\n",
        "\n",
        "x = np.arange(0, 100, 0.5)\n",
        "y = poisson.pmf(x, mu=lamda, loc=0)\n",
        "plt.plot(x, y)\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "-_95QboLPDuB",
        "outputId": "22c6d30f-9198-4309-a117-d00ff9c67ff9"
      },
      "id": "-_95QboLPDuB",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Both plots look like normal distribution"
      ],
      "metadata": {
        "id": "dQq70zUwwoFM"
      },
      "id": "dQq70zUwwoFM"
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Task 3.2e\n",
        "Under poisson distribution "
      ],
      "metadata": {
        "id": "rT3xmgViyifE"
      },
      "id": "rT3xmgViyifE"
    },
    {
      "cell_type": "code",
      "source": [
        "p_100_more_2 = 1-poisson.cdf(100, lamda)\n",
        "print( 'Likelihood that there are 100 or more disengagements in 10,000 miles: %E' % p_100_more_2)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FSbGGLnPy2FS",
        "outputId": "8ab7e647-9f00-498a-a0a0-ded086d6cd79"
      },
      "id": "FSbGGLnPy2FS",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Likelihood that there are 100 or more disengagements in 10,000 miles: 4.308829E-07\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The result is very close to that of 3.2.c, but not equal. This is because poisson distribution is an approximation, and n is large enough (as well as p is small enough) to reach similar probability of 10000 Bernoulli trials."
      ],
      "metadata": {
        "id": "6LQU9mZxzjkJ"
      },
      "id": "6LQU9mZxzjkJ"
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Tasks 3.3a/b"
      ],
      "metadata": {
        "id": "55jNMMk1jM42"
      },
      "id": "55jNMMk1jM42"
    },
    {
      "cell_type": "code",
      "source": [
        "cloudy_automatic = automatic_disengagements[automatic_disengagements['Weather'] == 'cloudy']\n",
        "\n",
        "reaction_time_a = cloudy_automatic[cloudy_automatic['ReactionTime'] > 0.4]\n",
        "p_reaction_a = (len(reaction_time_a) /  len(cloudy_automatic))\n",
        "print('Conditional probability that reaction time is > 0.4s given weather is cloudy: %f' % p_reaction_a)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "us_hp3R-jQNR",
        "outputId": "ec65b1bd-049b-4ef3-a9c9-fd923652572f"
      },
      "id": "us_hp3R-jQNR",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Conditional probability that reaction time is > 0.4s given weather is cloudy: 0.612091\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "clear_automatic = automatic_disengagements[automatic_disengagements['Weather'] == 'clear']\n",
        "reaction_time_b = clear_automatic[clear_automatic['ReactionTime'] > 0.7]\n",
        "p_reaction_b = (len(reaction_time_b) /  len(clear_automatic))\n",
        "print('Conditional probability that reaction time is > 0.7s given weather is clear: %f' % p_reaction_b)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rS0CD32V79lh",
        "outputId": "bb944347-9fdf-45d9-8847-7d1582f86d3d"
      },
      "id": "rS0CD32V79lh",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Conditional probability that reaction time is > 0.7s given weather is clear: 0.385417\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Task 3.4\n",
        "\n"
      ],
      "metadata": {
        "id": "jZQgyLzRlkc_"
      },
      "id": "jZQgyLzRlkc_"
    },
    {
      "cell_type": "code",
      "source": [
        "clear_slow = clear_automatic[clear_automatic['ReactionTime'] > 0.7]\n",
        "\n",
        "p_automatic_slow = ( len(reaction_time_a) + len(clear_slow) ) / total_miles\n",
        "\n",
        "print('Probability of an accident per mile due to automatic AV disengagement and slow reaction: %f' % (p_automatic_slow))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ub4f9GFUlqSm",
        "outputId": "b1621167-5df5-48e0-9b96-459ae4a19b27"
      },
      "id": "Ub4f9GFUlqSm",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Probability of an accident per mile due to automatic AV disengagement and slow reaction: 0.000554\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "$P(\\text{automatic disengagement } \\cap \\text{slow}) = P(\\text{slow }| \\text{ automatic disengagement}) \\cdot P(\\text{automatic disengagement})$"
      ],
      "metadata": {
        "id": "qyLDk_j6EKxd"
      },
      "id": "qyLDk_j6EKxd"
    },
    {
      "cell_type": "code",
      "source": [
        "p_automatic_disengagements = len(automatic_disengagements) * 1.0 / total_miles\n",
        "p_automatic_slow = (p_reaction_a + p_reaction_b) * p_automatic_disengagements\n",
        "p_automatic_slow\n",
        "print('Probability of an accident per mile due to automatic AV disengagement and slow reaction: %f' % (p_automatic_slow))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JRSKtJN_8Uba",
        "outputId": "15947996-e05c-4d6a-a432-3e4d3a0759a9"
      },
      "id": "JRSKtJN_8Uba",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Probability of an accident per mile due to automatic AV disengagement and slow reaction: 0.000973\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Task 3.5a"
      ],
      "metadata": {
        "id": "Ox5KcFsEws5g"
      },
      "id": "Ox5KcFsEws5g"
    },
    {
      "cell_type": "code",
      "source": [
        "posterior = len(reaction_time_a[reaction_time_a.Cause == \"Software Froze\"]) / len(reaction_time_a)\n",
        "print(\"P(SF|auto disengagement, rt>0.4s, cloudy): %f\" % posterior)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XSxsAlRGHIkN",
        "outputId": "cb85a97f-b3fb-47a7-8c6f-3d8e668bb1b4"
      },
      "id": "XSxsAlRGHIkN",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "P(SF|auto, rt>0.4s, cloudy): 0.053498\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Task 3.5b"
      ],
      "metadata": {
        "id": "HmSiVnG-zSaa"
      },
      "id": "HmSiVnG-zSaa"
    },
    {
      "cell_type": "code",
      "source": [
        "posterior = len(reaction_time_b[reaction_time_b.Cause == \"Software Froze\"]) / len(reaction_time_b)\n",
        "print(\"P(SF|auto disengagement, rt>0.7s, clear): %f\" % posterior)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RYjxO2ToI9RB",
        "outputId": "2353594c-c1c7-40b2-9f97-91f360692754"
      },
      "id": "RYjxO2ToI9RB",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "P(SF|auto disengagement, rt>0.7s, clear): 0.243243\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Task 3.6a"
      ],
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        "id": "3jT3VMmeC_SP"
      },
      "id": "3jT3VMmeC_SP"
    },
    {
      "cell_type": "markdown",
      "source": [
        "<img src='https://github.com/seanseungbeomlee/ECE-471-MP1-AV-Analysis/blob/8d65a88241d91a3d6e3d1162dff15cb50499cef6/naive_bayes_diagram.png?raw=true' width = 300px>"
      ],
      "metadata": {
        "id": "pOxenBfqFPoG"
      },
      "id": "pOxenBfqFPoG"
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Task 3.6b\n",
        "\n",
        "*   Prior (2 parameters): P(Software Froze), P(Hardware Fault), P(Other)\n",
        "*   Conditional Probability (6): P(Location|C) and P(Weather|C) for all three causes (C)\n",
        "\n",
        "A total of 9 parameters for prior and conditional probability. \n"
      ],
      "metadata": {
        "id": "8UOTMqxlGEFz"
      },
      "id": "8UOTMqxlGEFz"
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Task 3.6c\n",
        "Prior:"
      ],
      "metadata": {
        "id": "5MBoIVjPKbBs"
      },
      "id": "5MBoIVjPKbBs"
    },
    {
      "cell_type": "code",
      "source": [
        "cause = disengagements['Cause'].unique()\n",
        "cause\n",
        "# Preprocess 'Other' cause\n",
        "disengagements.loc[(disengagements['Cause']!= 'Software Froze') & (disengagements['Cause'] != 'Hardware Fault'),'Cause'] = 'Other'\n",
        "cause = disengagements['Cause'].unique()\n",
        "cause"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HjoUTMdjJ_zV",
        "outputId": "16a4280f-0713-4d15-bedb-9ea0f8612961"
      },
      "id": "HjoUTMdjJ_zV",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Other', 'Software Froze', 'Hardware Fault'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 97
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "prior = pd.DataFrame()\n",
        "for c in cause:\n",
        "  prior.loc[c, 'P'] = (disengagements['Cause']==c).sum() * 1.0 / len(disengagements)\n",
        "prior"
      ],
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          "base_uri": "https://localhost:8080/",
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      "id": "LHO_LMfQ2xZ6",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                       P\n",
              "Other           0.802734\n",
              "Software Froze  0.095703\n",
              "Hardware Fault  0.101562"
            ],
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        "location = disengagements['Location'].unique()\n",
        "weather = disengagements['Weather'].unique()\n",
        "location_cpt = pd.DataFrame()\n",
        "\n",
        "for c in cause:\n",
        "  for l in location:\n",
        "    location_cpt.loc[c, l] = ((disengagements['Location']==l) & (disengagements['Cause'] == c)).sum() / (disengagements['Cause']==c).sum()\n",
        "location_cpt"
      ],
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        {
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          "data": {
            "text/plain": [
              "                urban-street   highway\n",
              "Other               0.993917  0.006083\n",
              "Software Froze      0.938776  0.061224\n",
              "Hardware Fault      0.913462  0.086538"
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        "location_cpt = pd.DataFrame()\n",
        "\n",
        "for c in cause:\n",
        "  for w in weather:\n",
        "    location_cpt.loc[c, w] = ((disengagements['Weather']==w) & (disengagements['Cause'] == c)).sum() / (disengagements['Cause']==c).sum()\n",
        "location_cpt"
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          "data": {
            "text/plain": [
              "                  cloudy     clear\n",
              "Other           0.913625  0.086375\n",
              "Software Froze  0.387755  0.612245\n",
              "Hardware Fault  0.442308  0.557692"
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 100
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "\n",
        "*   $P(\\text{other} | \\text{cloudy}, \\text{urban-street}) = 0.802734 \\times 0.913625 \\times 0.993917 = 0.728$ \n",
        "*   $P(\\text{software froze} | \\text{cloudy}, \\text{urban-street}) = 0.095703 \\times 0.387755 \\times 0.938776 = 0.035$ \n",
        "*   $P(\\text{hardware fault} | \\text{cloudy}, \\text{urban-street}) = 0.101562 \\times 0.442308 \\times 0.913462 = 0.041$ \n"
      ],
      "metadata": {
        "id": "uqh0l1i4HZKQ"
      },
      "id": "uqh0l1i4HZKQ"
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Task 3.6d\n",
        "The most possible root cause given the day was cloudy and the Location was urban-street is **other**."
      ],
      "metadata": {
        "id": "DdRe0vPJJ8Go"
      },
      "id": "DdRe0vPJJ8Go"
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Task 4"
      ],
      "metadata": {
        "id": "6yt4IXgnC97_"
      },
      "id": "6yt4IXgnC97_"
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Task 4.1a"
      ],
      "metadata": {
        "id": "aYbf3B8neW36"
      },
      "id": "aYbf3B8neW36"
    },
    {
      "cell_type": "code",
      "source": [
        "simulation = pd.read_csv('part3_data.csv')\n",
        "simulation.drop(simulation.index[simulation['status'] == 'Failed -- Simulator crashed'], inplace = True)\n",
        "accident_p = len(simulation[simulation['status'] == 'Failed -- Accident']) * 1.0 / len(simulation)\n",
        "print(\"The probability of accident P(acc=1) across all weather conditions is\", accident_p)\n",
        "\n",
        "weather = simulation['weather'].unique()\n",
        "print(weather)"
      ],
      "metadata": {
        "id": "TS6nSRhcQKRA",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e3147cbd-a514-4f72-ab7f-edb31b089869"
      },
      "id": "TS6nSRhcQKRA",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The probability of accident P(acc=1) across all weather conditions is 0.0898989898989899\n",
            "['clear-noon' 'cloudy-noon' 'rain-noon' 'snow-noon']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Task 4.1b\n",
        "\n",
        "P (Accident = 1 | Weather condition = ?)"
      ],
      "metadata": {
        "id": "cz_2RnmHedjA"
      },
      "id": "cz_2RnmHedjA"
    },
    {
      "cell_type": "code",
      "source": [
        "acc = simulation[simulation['status'] == 'Failed -- Accident']\n",
        "p_a_given_w = {}\n",
        "\n",
        "for weather_condition in ['clear', 'cloudy', 'rain', 'snow']:\n",
        "\n",
        "  weather_simulations = simulation[simulation['weather'].str.startswith(weather_condition, na=False)]\n",
        "  weather_accidents = weather_simulations[weather_simulations['status'] == 'Failed -- Accident']\n",
        "\n",
        "  p_a_given_w[weather_condition] = len(weather_accidents) / len(weather_simulations)\n",
        "  print(weather_condition, len(weather_accidents) / len(weather_simulations))\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aIpK7lolenUb",
        "outputId": "cb671591-5170-44f3-a3ae-e8fd9acd9e0b"
      },
      "id": "aIpK7lolenUb",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "clear 0.008130081300813009\n",
            "cloudy 0.04251012145748988\n",
            "rain 0.12474849094567404\n",
            "snow 0.18309859154929578\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Task 4.2 Probability of Accident\n",
        "\n",
        "Assumption: \\\\\n",
        "1. The AV is equally likely to drive on any weather condition\n",
        "2. Weather condition is mututally exclusive on a day (a day cannot be both clear and cloudy, for example)"
      ],
      "metadata": {
        "id": "Yqo-SBJUCILp"
      },
      "id": "Yqo-SBJUCILp"
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let A be accident, and C be weather where C = {Clear, Cloudy, Rain, Snow} \\\\\n",
        "$P(A) = \\sum_C P(A \\land C)$ By law of total probability \\\\\n",
        "$P(A) = \\sum_C {P (A|C)} {P(C)}$ By conditional probability "
      ],
      "metadata": {
        "id": "jWtu4NSWONhc"
      },
      "id": "jWtu4NSWONhc"
    },
    {
      "cell_type": "code",
      "source": [
        "california_weather = {'clear': 0.72, 'cloudy': 0.15, 'rain': 0.1, 'snow': 0.03}\n",
        "chicago_weather = {'clear': 0.56, 'cloudy': 0.1, 'rain': 0.25, 'snow': 0.09}\n",
        "\n",
        "def probability_projection(p_w, p_a_given_w):\n",
        "  total_probability = 0\n",
        "\n",
        "  for weather_condition in p_a_given_w:\n",
        "    total_probability += p_a_given_w[weather_condition] * p_w[weather_condition]\n",
        "\n",
        "  return total_probability\n",
        "\n",
        "print('Probability of accident in cut-in scenario for California: %f' \n",
        "      % (probability_projection(california_weather, p_a_given_w)))\n",
        "print('Probability of accident in cut-in scenario for Chicago: %f' \n",
        "      % (probability_projection(chicago_weather, p_a_given_w)))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RsxKKsJIibno",
        "outputId": "8baec994-7127-4caf-afb7-b8ef54323c3f"
      },
      "id": "RsxKKsJIibno",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Probability of accident in cut-in scenario for California: 0.030198\n",
            "Probability of accident in cut-in scenario for Chicago: 0.056470\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Task 4.3a\n",
        "In oder to make a reasonable comparison between the probabiities of an accident of the simulated dataset and the real dataset, we take only the accidents from the simulation dataset that occured during clear and cloudy weather conditions."
      ],
      "metadata": {
        "id": "YvgLg7gZSgec"
      },
      "id": "YvgLg7gZSgec"
    },
    {
      "cell_type": "code",
      "source": [
        "apm_dmv = 0.000973 # from task 3.4\n",
        "apm_simulation = len(acc[acc.weather.str.contains('clear') | acc.weather.str.contains('cloudy')]) / len(simulation[simulation.weather.str.contains('clear') | simulation.weather.str.contains('cloudy')])\n",
        "\n",
        "apm_dmv, apm_simulation"
      ],
      "metadata": {
        "id": "AJjQwvjrkTOZ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0c7346a8-1bc6-4feb-bddc-fa7a25863554"
      },
      "id": "AJjQwvjrkTOZ",
      "execution_count": 89,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(0.000973, 0.02535496957403651)"
            ]
          },
          "metadata": {},
          "execution_count": 89
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Task 4.3b\n",
        "Considering for every one mile, there are k cut-in scenarios (independent trials), the probability of having accident is p (the probability of success in each trial). Then using Binomial distribution with n = k, p, and k = 0, the probability of having no accident at all is \n",
        "\n",
        "$P(0) = {k \\choose 0}(1-p)^k = (1-p)^k$ \n",
        "\n",
        "Then\n",
        "\n",
        "$P(\\geq 1) = 1-P(0) = 1- (1-p)^k$"
      ],
      "metadata": {
        "id": "ibMZyo6Hft8s"
      },
      "id": "ibMZyo6Hft8s"
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Task 4.3c\n",
        "The simulated dataset has a higher probabilty of an accident per mile. In this isolated environment that is designed to produce accidents bewteen an AV and eagle vehicle, it is naturally going to have a higher rate of accidents than in real life."
      ],
      "metadata": {
        "id": "JPESVeD1gL4j"
      },
      "id": "JPESVeD1gL4j"
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Task 4.3d\n",
        "\n",
        "Simulation allows more weather condition and more flexible scenarios than what real-world data can generate. This allows us to have a wider perception and take account of possible undesirable road condition and accident in the future."
      ],
      "metadata": {
        "id": "c_E2pCvbgJla"
      },
      "id": "c_E2pCvbgJla"
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Task 4.4\n",
        "More involvement in setting up the simulation runs would be beneficial. In this case, we just had to run the bash and python script. Also, more consistency in the feature names would be nice. For example for task 4.3a, we were unsure whether we could equate \"sunny\" and \"clear\" weather conditions. We assumed they were the same, but clarity here would've saved us time."
      ],
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    }
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