loading_vector_results.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "loading_vector_results.ipynb",
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/CanopySimulations/canopy-python-examples/blob/master/loading_vector_results.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NtIv7oIVvn5i"
      },
      "source": [
        "# Upgrade Runtime\n",
        "This cell ensures the runtime supports `asyncio` async/await, and is needed on Google Colab. If the runtime is upgraded, you will be prompted to restart it, which you should do before continuing execution."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "E-AnefvPvmtR"
      },
      "source": [
        "!pip install ipython ipykernel --upgrade"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_61Bg2_KwIR2"
      },
      "source": [
        "# Set Up Environment"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zX-xTWzWG1bt"
      },
      "source": [
        "### Import required libraries"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gNnEvtkmwL0W"
      },
      "source": [
        "!pip install -q canopy"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0bBsqDyhwN_8"
      },
      "source": [
        "import canopy\n",
        "import logging\n",
        "import matplotlib.pyplot as plt\n",
        "import nest_asyncio\n",
        "\n",
        "logging.basicConfig(level=logging.INFO)\n",
        "nest_asyncio.apply()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nH5-pXaZw5jg"
      },
      "source": [
        "### Authenticate"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SAnwvDofwhw1"
      },
      "source": [
        "authentication_data = canopy.prompt_for_authentication()\n",
        "session = canopy.Session(authentication_data)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VSDal4PawY8-"
      },
      "source": [
        "# Set Up Example"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cc9N0PwfUKEa"
      },
      "source": [
        "Run a quick ApexSim exploration."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5r6ZPt3bUQJ9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 104
        },
        "outputId": "a98d878d-e1a4-457f-caa2-27267805ff43"
      },
      "source": [
        "logging.info('Loading configs...')\n",
        "\n",
        "default_car = await canopy.load_default_config(\n",
        "    session, 'car', 'Canopy F1 Car 2019')\n",
        "\n",
        "default_weather = await canopy.load_default_config(\n",
        "    session, 'weather', '25 deg, dry')\n",
        "\n",
        "default_exploration = await canopy.load_default_config(\n",
        "    session, 'exploration', 'Automated Test Monte Carlo')\n",
        "\n",
        "# We'll run a very small exploration for this example\n",
        "default_exploration.data.design.numberOfPoints = 3\n",
        "\n",
        "logging.info('Creating study...')\n",
        "\n",
        "study_id = await canopy.create_study(\n",
        "    session,\n",
        "    'apexSim',\n",
        "    'Loading Vector Results Example',\n",
        "    [\n",
        "        default_car,\n",
        "        default_weather,\n",
        "        default_exploration,\n",
        "    ])\n",
        "\n",
        "logging.info('Waiting for study to complete...')\n",
        "\n",
        "wait_result = await canopy.wait_for_study(\n",
        "    session,\n",
        "    study_id,\n",
        "    timeout_seconds=300)\n",
        "    \n",
        "logging.info('Sim Version: {}'.format(wait_result.document.sim_version))    \n",
        "logging.info('Succeded Simulations: {}/{}'.format(\n",
        "    wait_result.succeeded_simulation_count, \n",
        "    wait_result.simulation_count))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "INFO:root:Loading configs...\n",
            "INFO:root:Creating study...\n",
            "INFO:root:Waiting for study to complete...\n",
            "INFO:root:Sim Version: 1.3371\n",
            "INFO:root:Succeded Simulations: 3/3\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FAFirfukWLfF"
      },
      "source": [
        "# Example: Loading Vector Results"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BOcIhzzUY32z"
      },
      "source": [
        "## Loading for a single job\n",
        "We can load the vector results for an individual job by passing a list of channel names to the `load_study_job` function. Note we are specifically loading the `ApexSim` vector results here. If we also wanted the `StraightSim` results we can fetch them in a second call."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vkHGACInWNkr",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "693badf4-3d18-4ef3-ff0f-674683b7ba90"
      },
      "source": [
        "job_0 = await canopy.load_study_job(\n",
        "    session,\n",
        "    study_id,\n",
        "    'ApexSim',\n",
        "    job_index=0,\n",
        "    channel_names=[\n",
        "        'vCar',\n",
        "        'hRideF',\n",
        "        'hRideR'\n",
        "    ])\n",
        "    \n",
        "job_0.vector_data"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "INFO:canopy.load_study_job:Loading job index 0\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>vCar</th>\n",
              "      <th>hRideF</th>\n",
              "      <th>hRideR</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>9.893382</td>\n",
              "      <td>0.027693</td>\n",
              "      <td>0.143110</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>11.928808</td>\n",
              "      <td>0.027240</td>\n",
              "      <td>0.143004</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>13.963180</td>\n",
              "      <td>0.026509</td>\n",
              "      <td>0.142673</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>15.980705</td>\n",
              "      <td>0.025901</td>\n",
              "      <td>0.142306</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>17.988016</td>\n",
              "      <td>0.025314</td>\n",
              "      <td>0.141898</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>19.989450</td>\n",
              "      <td>0.024715</td>\n",
              "      <td>0.141450</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>21.987408</td>\n",
              "      <td>0.024109</td>\n",
              "      <td>0.140945</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>23.983580</td>\n",
              "      <td>0.023514</td>\n",
              "      <td>0.140329</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>25.978909</td>\n",
              "      <td>0.022895</td>\n",
              "      <td>0.139640</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>27.975653</td>\n",
              "      <td>0.022405</td>\n",
              "      <td>0.138623</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>29.970448</td>\n",
              "      <td>0.021682</td>\n",
              "      <td>0.137865</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>31.965170</td>\n",
              "      <td>0.020926</td>\n",
              "      <td>0.137049</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>33.959953</td>\n",
              "      <td>0.020142</td>\n",
              "      <td>0.136179</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>35.955013</td>\n",
              "      <td>0.019341</td>\n",
              "      <td>0.135242</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>37.950569</td>\n",
              "      <td>0.018537</td>\n",
              "      <td>0.134226</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>39.946686</td>\n",
              "      <td>0.017729</td>\n",
              "      <td>0.133129</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>41.944988</td>\n",
              "      <td>0.017009</td>\n",
              "      <td>0.131774</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>43.941723</td>\n",
              "      <td>0.016164</td>\n",
              "      <td>0.130570</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>45.941841</td>\n",
              "      <td>0.015264</td>\n",
              "      <td>0.129229</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>47.942772</td>\n",
              "      <td>0.014333</td>\n",
              "      <td>0.127830</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>49.943840</td>\n",
              "      <td>0.013373</td>\n",
              "      <td>0.126368</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>51.944893</td>\n",
              "      <td>0.012386</td>\n",
              "      <td>0.124841</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>53.946285</td>\n",
              "      <td>0.011436</td>\n",
              "      <td>0.123123</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>55.947105</td>\n",
              "      <td>0.010394</td>\n",
              "      <td>0.121478</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>57.947807</td>\n",
              "      <td>0.009321</td>\n",
              "      <td>0.119785</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>59.948410</td>\n",
              "      <td>0.008222</td>\n",
              "      <td>0.118045</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>61.948921</td>\n",
              "      <td>0.007096</td>\n",
              "      <td>0.116262</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>63.949356</td>\n",
              "      <td>0.005946</td>\n",
              "      <td>0.114440</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>65.949905</td>\n",
              "      <td>0.004819</td>\n",
              "      <td>0.112488</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>67.950172</td>\n",
              "      <td>0.003619</td>\n",
              "      <td>0.110612</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>69.950356</td>\n",
              "      <td>0.002390</td>\n",
              "      <td>0.108725</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31</th>\n",
              "      <td>71.950470</td>\n",
              "      <td>0.001132</td>\n",
              "      <td>0.106835</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32</th>\n",
              "      <td>73.950516</td>\n",
              "      <td>-0.000150</td>\n",
              "      <td>0.104944</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33</th>\n",
              "      <td>75.950554</td>\n",
              "      <td>-0.001443</td>\n",
              "      <td>0.103031</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34</th>\n",
              "      <td>77.950424</td>\n",
              "      <td>-0.002792</td>\n",
              "      <td>0.101202</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>35</th>\n",
              "      <td>79.950203</td>\n",
              "      <td>-0.004177</td>\n",
              "      <td>0.099423</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>36</th>\n",
              "      <td>81.949898</td>\n",
              "      <td>-0.005594</td>\n",
              "      <td>0.097698</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37</th>\n",
              "      <td>83.949509</td>\n",
              "      <td>-0.007046</td>\n",
              "      <td>0.096042</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38</th>\n",
              "      <td>85.948921</td>\n",
              "      <td>-0.008562</td>\n",
              "      <td>0.094535</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>39</th>\n",
              "      <td>87.948181</td>\n",
              "      <td>-0.010122</td>\n",
              "      <td>0.093164</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>40</th>\n",
              "      <td>89.947403</td>\n",
              "      <td>-0.011636</td>\n",
              "      <td>0.091884</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         vCar    hRideF    hRideR\n",
              "0    9.893382  0.027693  0.143110\n",
              "1   11.928808  0.027240  0.143004\n",
              "2   13.963180  0.026509  0.142673\n",
              "3   15.980705  0.025901  0.142306\n",
              "4   17.988016  0.025314  0.141898\n",
              "5   19.989450  0.024715  0.141450\n",
              "6   21.987408  0.024109  0.140945\n",
              "7   23.983580  0.023514  0.140329\n",
              "8   25.978909  0.022895  0.139640\n",
              "9   27.975653  0.022405  0.138623\n",
              "10  29.970448  0.021682  0.137865\n",
              "11  31.965170  0.020926  0.137049\n",
              "12  33.959953  0.020142  0.136179\n",
              "13  35.955013  0.019341  0.135242\n",
              "14  37.950569  0.018537  0.134226\n",
              "15  39.946686  0.017729  0.133129\n",
              "16  41.944988  0.017009  0.131774\n",
              "17  43.941723  0.016164  0.130570\n",
              "18  45.941841  0.015264  0.129229\n",
              "19  47.942772  0.014333  0.127830\n",
              "20  49.943840  0.013373  0.126368\n",
              "21  51.944893  0.012386  0.124841\n",
              "22  53.946285  0.011436  0.123123\n",
              "23  55.947105  0.010394  0.121478\n",
              "24  57.947807  0.009321  0.119785\n",
              "25  59.948410  0.008222  0.118045\n",
              "26  61.948921  0.007096  0.116262\n",
              "27  63.949356  0.005946  0.114440\n",
              "28  65.949905  0.004819  0.112488\n",
              "29  67.950172  0.003619  0.110612\n",
              "30  69.950356  0.002390  0.108725\n",
              "31  71.950470  0.001132  0.106835\n",
              "32  73.950516 -0.000150  0.104944\n",
              "33  75.950554 -0.001443  0.103031\n",
              "34  77.950424 -0.002792  0.101202\n",
              "35  79.950203 -0.004177  0.099423\n",
              "36  81.949898 -0.005594  0.097698\n",
              "37  83.949509 -0.007046  0.096042\n",
              "38  85.948921 -0.008562  0.094535\n",
              "39  87.948181 -0.010122  0.093164\n",
              "40  89.947403 -0.011636  0.091884"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "M6QjAzHFZhVe"
      },
      "source": [
        "We can request individual vector results in our desired units:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7yQVhrU3ZmWG",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 763
        },
        "outputId": "7a86d07d-d225-4402-ec96-8b50f3003a38"
      },
      "source": [
        "job_0.vector_as('hRideF', 'mm')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "INFO:numexpr.utils:NumExpr defaulting to 2 threads.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0     27.693409\n",
              "1     27.240007\n",
              "2     26.509382\n",
              "3     25.901253\n",
              "4     25.313660\n",
              "5     24.714855\n",
              "6     24.109306\n",
              "7     23.514183\n",
              "8     22.894676\n",
              "9     22.404600\n",
              "10    21.681618\n",
              "11    20.925753\n",
              "12    20.141588\n",
              "13    19.341364\n",
              "14    18.536528\n",
              "15    17.729074\n",
              "16    17.009451\n",
              "17    16.164230\n",
              "18    15.263780\n",
              "19    14.333287\n",
              "20    13.373140\n",
              "21    12.385751\n",
              "22    11.436469\n",
              "23    10.393733\n",
              "24     9.321388\n",
              "25     8.221671\n",
              "26     7.096004\n",
              "27     5.946103\n",
              "28     4.819232\n",
              "29     3.618631\n",
              "30     2.389540\n",
              "31     1.132143\n",
              "32    -0.150155\n",
              "33    -1.443413\n",
              "34    -2.792138\n",
              "35    -4.176954\n",
              "36    -5.594275\n",
              "37    -7.046223\n",
              "38    -8.562011\n",
              "39   -10.121965\n",
              "40   -11.635673\n",
              "Name: hRideF, dtype: float32"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0VPQ8IMuZuZX"
      },
      "source": [
        "You can also use the `vector_as` function and omit the units, which will then use the preferred units saved in your Canopy account:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f3lWWpWtZ0eJ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 745
        },
        "outputId": "4d84f6da-6e9b-4c8e-d744-19f159e54932"
      },
      "source": [
        "job_0.vector_as('hRideF')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0     0.027693\n",
              "1     0.027240\n",
              "2     0.026509\n",
              "3     0.025901\n",
              "4     0.025314\n",
              "5     0.024715\n",
              "6     0.024109\n",
              "7     0.023514\n",
              "8     0.022895\n",
              "9     0.022405\n",
              "10    0.021682\n",
              "11    0.020926\n",
              "12    0.020142\n",
              "13    0.019341\n",
              "14    0.018537\n",
              "15    0.017729\n",
              "16    0.017009\n",
              "17    0.016164\n",
              "18    0.015264\n",
              "19    0.014333\n",
              "20    0.013373\n",
              "21    0.012386\n",
              "22    0.011436\n",
              "23    0.010394\n",
              "24    0.009321\n",
              "25    0.008222\n",
              "26    0.007096\n",
              "27    0.005946\n",
              "28    0.004819\n",
              "29    0.003619\n",
              "30    0.002390\n",
              "31    0.001132\n",
              "32   -0.000150\n",
              "33   -0.001443\n",
              "34   -0.002792\n",
              "35   -0.004177\n",
              "36   -0.005594\n",
              "37   -0.007046\n",
              "38   -0.008562\n",
              "39   -0.010122\n",
              "40   -0.011636\n",
              "Name: hRideF, dtype: float32"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nu7uwKuXoRqG"
      },
      "source": [
        "We can then easily plot the results:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tlPhLurTnsbK",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "outputId": "ae2c6e4b-f98b-4fd1-8c4a-9868f45d2659"
      },
      "source": [
        "x = job_0.vector_as('vCar', 'kph')\n",
        "\n",
        "plt.figure()\n",
        "plt.xlabel('vCar (kph)')\n",
        "plt.plot(x, job_0.vector_as('hRideR', 'mm'), 'r-', label='hRideR (mm)')\n",
        "plt.plot(x, job_0.vector_as('hRideF', 'mm'), 'b-', label='hRideF (mm)')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mWWmbVeobKJG"
      },
      "source": [
        "## Loading for an entire study\n",
        "\n",
        "We can load the vector results for the entire study by passing a list of channels to the `load_study` function.\n",
        "\n",
        "We'll also load the study scalar results for context."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iYD2SiZZpL1K",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 69
        },
        "outputId": "2aca7ae8-0d0d-496c-9f47-71f9ba605e06"
      },
      "source": [
        "study = await canopy.load_study(\n",
        "    session,\n",
        "    study_id,\n",
        "    'ApexSim',\n",
        "    channel_names=[\n",
        "        'vCar',\n",
        "        'hRideF',\n",
        "        'hRideR'\n",
        "    ],\n",
        "    include_study_scalar_results=True)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "INFO:canopy.load_study_job:Loading job index 0\n",
            "INFO:canopy.load_study_job:Loading job index 1\n",
            "INFO:canopy.load_study_job:Loading job index 2\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oaNkLFOoqOjU",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 331
        },
        "outputId": "6f0dbb2f-c424-4fee-c83a-db98ab5f5138"
      },
      "source": [
        "plt.figure()\n",
        "plt.xlabel('vCar (kph)')\n",
        "plt.ylabel('hRideR (mm)')\n",
        "\n",
        "colors = plt.cm.Set1.colors\n",
        "\n",
        "for index, job in enumerate(study.jobs):\n",
        "    print(index)\n",
        "    plt.plot(\n",
        "        job.vector_as('vCar', 'kph'),\n",
        "        job.vector_as('hRideR', 'mm'),\n",
        "        label=job.document.name,\n",
        "        color=colors[index % len(colors)])\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0\n",
            "1\n",
            "2\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pMK3AHoCsHqi"
      },
      "source": [
        "If we want to understand exactly what changed for these three jobs to produce these results, we can look at the inputs in the scalar results:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f0lc_sp9r3wX",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 141
        },
        "outputId": "8832c493-78c6-45f7-d9aa-a5ac25271f8f"
      },
      "source": [
        "study.scalar_results.inputs"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>car.chassis.carRunningMass.mCar+</th>\n",
              "      <th>car.chassis.ICar+</th>\n",
              "      <th>car.chassis.hRideFSetup+</th>\n",
              "      <th>car.chassis.hRideRSetup+</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>6.319281</td>\n",
              "      <td>[4.47892127767,0.0,0.0,0.0,8.95784255534,0.0,0...</td>\n",
              "      <td>0.004119</td>\n",
              "      <td>0.017813</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>-0.325107</td>\n",
              "      <td>[-5.48765997191,0.0,0.0,0.0,-10.97531994382,0....</td>\n",
              "      <td>0.000353</td>\n",
              "      <td>0.014984</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>9.916108</td>\n",
              "      <td>[9.87416220823,0.0,0.0,0.0,19.74832441646,0.0,...</td>\n",
              "      <td>0.005481</td>\n",
              "      <td>0.012640</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   car.chassis.carRunningMass.mCar+  ... car.chassis.hRideRSetup+\n",
              "0                          6.319281  ...                 0.017813\n",
              "1                         -0.325107  ...                 0.014984\n",
              "2                          9.916108  ...                 0.012640\n",
              "\n",
              "[3 rows x 4 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0Nf_2yNunGCL"
      },
      "source": [
        "## Interactive Plots\n",
        "\n",
        "We can create an interactive plot with a library like Bokeh. You will need to view this in a notebook viewer which supports interactive plots, as it won't render on GitHub.\n",
        "\n",
        "The following code creates two stacked plots with linked X axes, so that as you pan and zoom the charts stay in sync."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gh56xVu0nQFk",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 647
        },
        "outputId": "4e668243-470e-4d93-c139-28048e6284f0"
      },
      "source": [
        "from bokeh.plotting import figure, show, output_notebook\n",
        "from bokeh.layouts import gridplot\n",
        "from bokeh.palettes import Category10\n",
        "output_notebook()\n",
        "\n",
        "p1 = figure(\n",
        "    plot_width=800,\n",
        "    plot_height=300,\n",
        "    y_axis_label='hRideF (mm)')\n",
        "\n",
        "p2 = figure(\n",
        "    plot_width=800,\n",
        "    plot_height=300,\n",
        "    x_axis_label='vCar (kph)', \n",
        "    y_axis_label='hRideR (mm)',\n",
        "    x_range=p1.x_range)\n",
        "\n",
        "for index, job in enumerate(study.jobs):\n",
        "    p1.line(\n",
        "        job.vector_as('vCar', 'kph'), \n",
        "        job.vector_as('hRideF', 'mm'), \n",
        "        color=Category10[10][index % 10], alpha=0.5)\n",
        "\n",
        "    p2.line(\n",
        "        job.vector_as('vCar', 'kph'), \n",
        "        job.vector_as('hRideR', 'mm'), \n",
        "        color=Category10[10][index % 10], alpha=0.5)\n",
        "\n",
        "grid = gridplot([[p1], [p2]])\n",
        "show(grid)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/javascript": [
              "\n",
              "(function(root) {\n",
              "  function now() {\n",
              "    return new Date();\n",
              "  }\n",
              "\n",
              "  var force = true;\n",
              "\n",
              "  if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n",
              "    root._bokeh_onload_callbacks = [];\n",
              "    root._bokeh_is_loading = undefined;\n",
              "  }\n",
              "\n",
              "  var JS_MIME_TYPE = 'application/javascript';\n",
              "  var HTML_MIME_TYPE = 'text/html';\n",
              "  var EXEC_MIME_TYPE = 'application/vnd.bokehjs_exec.v0+json';\n",
              "  var CLASS_NAME = 'output_bokeh rendered_html';\n",
              "\n",
              "  /**\n",
              "   * Render data to the DOM node\n",
              "   */\n",
              "  function render(props, node) {\n",
              "    var script = document.createElement(\"script\");\n",
              "    node.appendChild(script);\n",
              "  }\n",
              "\n",
              "  /**\n",
              "   * Handle when an output is cleared or removed\n",
              "   */\n",
              "  function handleClearOutput(event, handle) {\n",
              "    var cell = handle.cell;\n",
              "\n",
              "    var id = cell.output_area._bokeh_element_id;\n",
              "    var server_id = cell.output_area._bokeh_server_id;\n",
              "    // Clean up Bokeh references\n",
              "    if (id != null && id in Bokeh.index) {\n",
              "      Bokeh.index[id].model.document.clear();\n",
              "      delete Bokeh.index[id];\n",
              "    }\n",
              "\n",
              "    if (server_id !== undefined) {\n",
              "      // Clean up Bokeh references\n",
              "      var cmd = \"from bokeh.io.state import curstate; print(curstate().uuid_to_server['\" + server_id + \"'].get_sessions()[0].document.roots[0]._id)\";\n",
              "      cell.notebook.kernel.execute(cmd, {\n",
              "        iopub: {\n",
              "          output: function(msg) {\n",
              "            var id = msg.content.text.trim();\n",
              "            if (id in Bokeh.index) {\n",
              "              Bokeh.index[id].model.document.clear();\n",
              "              delete Bokeh.index[id];\n",
              "            }\n",
              "          }\n",
              "        }\n",
              "      });\n",
              "      // Destroy server and session\n",
              "      var cmd = \"import bokeh.io.notebook as ion; ion.destroy_server('\" + server_id + \"')\";\n",
              "      cell.notebook.kernel.execute(cmd);\n",
              "    }\n",
              "  }\n",
              "\n",
              "  /**\n",
              "   * Handle when a new output is added\n",
              "   */\n",
              "  function handleAddOutput(event, handle) {\n",
              "    var output_area = handle.output_area;\n",
              "    var output = handle.output;\n",
              "\n",
              "    // limit handleAddOutput to display_data with EXEC_MIME_TYPE content only\n",
              "    if ((output.output_type != \"display_data\") || (!output.data.hasOwnProperty(EXEC_MIME_TYPE))) {\n",
              "      return\n",
              "    }\n",
              "\n",
              "    var toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n",
              "\n",
              "    if (output.metadata[EXEC_MIME_TYPE][\"id\"] !== undefined) {\n",
              "      toinsert[toinsert.length - 1].firstChild.textContent = output.data[JS_MIME_TYPE];\n",
              "      // store reference to embed id on output_area\n",
              "      output_area._bokeh_element_id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n",
              "    }\n",
              "    if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n",
              "      var bk_div = document.createElement(\"div\");\n",
              "      bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n",
              "      var script_attrs = bk_div.children[0].attributes;\n",
              "      for (var i = 0; i < script_attrs.length; i++) {\n",
              "        toinsert[toinsert.length - 1].firstChild.setAttribute(script_attrs[i].name, script_attrs[i].value);\n",
              "      }\n",
              "      // store reference to server id on output_area\n",
              "      output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n",
              "    }\n",
              "  }\n",
              "\n",
              "  function register_renderer(events, OutputArea) {\n",
              "\n",
              "    function append_mime(data, metadata, element) {\n",
              "      // create a DOM node to render to\n",
              "      var toinsert = this.create_output_subarea(\n",
              "        metadata,\n",
              "        CLASS_NAME,\n",
              "        EXEC_MIME_TYPE\n",
              "      );\n",
              "      this.keyboard_manager.register_events(toinsert);\n",
              "      // Render to node\n",
              "      var props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n",
              "      render(props, toinsert[toinsert.length - 1]);\n",
              "      element.append(toinsert);\n",
              "      return toinsert\n",
              "    }\n",
              "\n",
              "    /* Handle when an output is cleared or removed */\n",
              "    events.on('clear_output.CodeCell', handleClearOutput);\n",
              "    events.on('delete.Cell', handleClearOutput);\n",
              "\n",
              "    /* Handle when a new output is added */\n",
              "    events.on('output_added.OutputArea', handleAddOutput);\n",
              "\n",
              "    /**\n",
              "     * Register the mime type and append_mime function with output_area\n",
              "     */\n",
              "    OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n",
              "      /* Is output safe? */\n",
              "      safe: true,\n",
              "      /* Index of renderer in `output_area.display_order` */\n",
              "      index: 0\n",
              "    });\n",
              "  }\n",
              "\n",
              "  // register the mime type if in Jupyter Notebook environment and previously unregistered\n",
              "  if (root.Jupyter !== undefined) {\n",
              "    var events = require('base/js/events');\n",
              "    var OutputArea = require('notebook/js/outputarea').OutputArea;\n",
              "\n",
              "    if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n",
              "      register_renderer(events, OutputArea);\n",
              "    }\n",
              "  }\n",
              "\n",
              "  \n",
              "  if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n",
              "    root._bokeh_timeout = Date.now() + 5000;\n",
              "    root._bokeh_failed_load = false;\n",
              "  }\n",
              "\n",
              "  var NB_LOAD_WARNING = {'data': {'text/html':\n",
              "     \"<div style='background-color: #fdd'>\\n\"+\n",
              "     \"<p>\\n\"+\n",
              "     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n",
              "     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n",
              "     \"</p>\\n\"+\n",
              "     \"<ul>\\n\"+\n",
              "     \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n",
              "     \"<li>use INLINE resources instead, as so:</li>\\n\"+\n",
              "     \"</ul>\\n\"+\n",
              "     \"<code>\\n\"+\n",
              "     \"from bokeh.resources import INLINE\\n\"+\n",
              "     \"output_notebook(resources=INLINE)\\n\"+\n",
              "     \"</code>\\n\"+\n",
              "     \"</div>\"}};\n",
              "\n",
              "  function display_loaded() {\n",
              "    var el = document.getElementById(null);\n",
              "    if (el != null) {\n",
              "      el.textContent = \"BokehJS is loading...\";\n",
              "    }\n",
              "    if (root.Bokeh !== undefined) {\n",
              "      if (el != null) {\n",
              "        el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n",
              "      }\n",
              "    } else if (Date.now() < root._bokeh_timeout) {\n",
              "      setTimeout(display_loaded, 100)\n",
              "    }\n",
              "  }\n",
              "\n",
              "\n",
              "  function run_callbacks() {\n",
              "    try {\n",
              "      root._bokeh_onload_callbacks.forEach(function(callback) {\n",
              "        if (callback != null)\n",
              "          callback();\n",
              "      });\n",
              "    } finally {\n",
              "      delete root._bokeh_onload_callbacks\n",
              "    }\n",
              "    console.debug(\"Bokeh: all callbacks have finished\");\n",
              "  }\n",
              "\n",
              "  function load_libs(css_urls, js_urls, callback) {\n",
              "    if (css_urls == null) css_urls = [];\n",
              "    if (js_urls == null) js_urls = [];\n",
              "\n",
              "    root._bokeh_onload_callbacks.push(callback);\n",
              "    if (root._bokeh_is_loading > 0) {\n",
              "      console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n",
              "      return null;\n",
              "    }\n",
              "    if (js_urls == null || js_urls.length === 0) {\n",
              "      run_callbacks();\n",
              "      return null;\n",
              "    }\n",
              "    console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n",
              "    root._bokeh_is_loading = css_urls.length + js_urls.length;\n",
              "\n",
              "    function on_load() {\n",
              "      root._bokeh_is_loading--;\n",
              "      if (root._bokeh_is_loading === 0) {\n",
              "        console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n",
              "        run_callbacks()\n",
              "      }\n",
              "    }\n",
              "\n",
              "    function on_error() {\n",
              "      console.error(\"failed to load \" + url);\n",
              "    }\n",
              "\n",
              "    for (var i = 0; i < css_urls.length; i++) {\n",
              "      var url = css_urls[i];\n",
              "      const element = document.createElement(\"link\");\n",
              "      element.onload = on_load;\n",
              "      element.onerror = on_error;\n",
              "      element.rel = \"stylesheet\";\n",
              "      element.type = \"text/css\";\n",
              "      element.href = url;\n",
              "      console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n",
              "      document.body.appendChild(element);\n",
              "    }\n",
              "\n",
              "    for (var i = 0; i < js_urls.length; i++) {\n",
              "      var url = js_urls[i];\n",
              "      var element = document.createElement('script');\n",
              "      element.onload = on_load;\n",
              "      element.onerror = on_error;\n",
              "      element.async = false;\n",
              "      element.src = url;\n",
              "      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
              "      document.head.appendChild(element);\n",
              "    }\n",
              "  };\n",
              "\n",
              "  function inject_raw_css(css) {\n",
              "    const element = document.createElement(\"style\");\n",
              "    element.appendChild(document.createTextNode(css));\n",
              "    document.body.appendChild(element);\n",
              "  }\n",
              "\n",
              "  \n",
              "  var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-1.4.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.4.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.4.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-gl-1.4.0.min.js\"];\n",
              "  var css_urls = [];\n",
              "  \n",
              "\n",
              "  var inline_js = [\n",
              "    function(Bokeh) {\n",
              "      Bokeh.set_log_level(\"info\");\n",
              "    },\n",
              "    function(Bokeh) {\n",
              "    \n",
              "    \n",
              "    }\n",
              "  ];\n",
              "\n",
              "  function run_inline_js() {\n",
              "    \n",
              "    if (root.Bokeh !== undefined || force === true) {\n",
              "      \n",
              "    for (var i = 0; i < inline_js.length; i++) {\n",
              "      inline_js[i].call(root, root.Bokeh);\n",
              "    }\n",
              "    } else if (Date.now() < root._bokeh_timeout) {\n",
              "      setTimeout(run_inline_js, 100);\n",
              "    } else if (!root._bokeh_failed_load) {\n",
              "      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n",
              "      root._bokeh_failed_load = true;\n",
              "    } else if (force !== true) {\n",
              "      var cell = $(document.getElementById(null)).parents('.cell').data().cell;\n",
              "      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n",
              "    }\n",
              "\n",
              "  }\n",
              "\n",
              "  if (root._bokeh_is_loading === 0) {\n",
              "    console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n",
              "    run_inline_js();\n",
              "  } else {\n",
              "    load_libs(css_urls, js_urls, function() {\n",
              "      console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n",
              "      run_inline_js();\n",
              "    });\n",
              "  }\n",
              "}(window));"
            ],
            "application/vnd.bokehjs_load.v0+json": "\n(function(root) {\n  function now() {\n    return new Date();\n  }\n\n  var force = true;\n\n  if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n    root._bokeh_onload_callbacks = [];\n    root._bokeh_is_loading = undefined;\n  }\n\n  \n\n  \n  if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n    root._bokeh_timeout = Date.now() + 5000;\n    root._bokeh_failed_load = false;\n  }\n\n  var NB_LOAD_WARNING = {'data': {'text/html':\n     \"<div style='background-color: #fdd'>\\n\"+\n     \"<p>\\n\"+\n     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n     \"</p>\\n\"+\n     \"<ul>\\n\"+\n     \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n     \"<li>use INLINE resources instead, as so:</li>\\n\"+\n     \"</ul>\\n\"+\n     \"<code>\\n\"+\n     \"from bokeh.resources import INLINE\\n\"+\n     \"output_notebook(resources=INLINE)\\n\"+\n     \"</code>\\n\"+\n     \"</div>\"}};\n\n  function display_loaded() {\n    var el = document.getElementById(null);\n    if (el != null) {\n      el.textContent = \"BokehJS is loading...\";\n    }\n    if (root.Bokeh !== undefined) {\n      if (el != null) {\n        el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n      }\n    } else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(display_loaded, 100)\n    }\n  }\n\n\n  function run_callbacks() {\n    try {\n      root._bokeh_onload_callbacks.forEach(function(callback) {\n        if (callback != null)\n          callback();\n      });\n    } finally {\n      delete root._bokeh_onload_callbacks\n    }\n    console.debug(\"Bokeh: all callbacks have finished\");\n  }\n\n  function load_libs(css_urls, js_urls, callback) {\n    if (css_urls == null) css_urls = [];\n    if (js_urls == null) js_urls = [];\n\n    root._bokeh_onload_callbacks.push(callback);\n    if (root._bokeh_is_loading > 0) {\n      console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n      return null;\n    }\n    if (js_urls == null || js_urls.length === 0) {\n      run_callbacks();\n      return null;\n    }\n    console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n    root._bokeh_is_loading = css_urls.length + js_urls.length;\n\n    function on_load() {\n      root._bokeh_is_loading--;\n      if (root._bokeh_is_loading === 0) {\n        console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n        run_callbacks()\n      }\n    }\n\n    function on_error() {\n      console.error(\"failed to load \" + url);\n    }\n\n    for (var i = 0; i < css_urls.length; i++) {\n      var url = css_urls[i];\n      const element = document.createElement(\"link\");\n      element.onload = on_load;\n      element.onerror = on_error;\n      element.rel = \"stylesheet\";\n      element.type = \"text/css\";\n      element.href = url;\n      console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n      document.body.appendChild(element);\n    }\n\n    for (var i = 0; i < js_urls.length; i++) {\n      var url = js_urls[i];\n      var element = document.createElement('script');\n      element.onload = on_load;\n      element.onerror = on_error;\n      element.async = false;\n      element.src = url;\n      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      document.head.appendChild(element);\n    }\n  };\n\n  function inject_raw_css(css) {\n    const element = document.createElement(\"style\");\n    element.appendChild(document.createTextNode(css));\n    document.body.appendChild(element);\n  }\n\n  \n  var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-1.4.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.4.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.4.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-gl-1.4.0.min.js\"];\n  var css_urls = [];\n  \n\n  var inline_js = [\n    function(Bokeh) {\n      Bokeh.set_log_level(\"info\");\n    },\n    function(Bokeh) {\n    \n    \n    }\n  ];\n\n  function run_inline_js() {\n    \n    if (root.Bokeh !== undefined || force === true) {\n      \n    for (var i = 0; i < inline_js.length; i++) {\n      inline_js[i].call(root, root.Bokeh);\n    }\n    } else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(run_inline_js, 100);\n    } else if (!root._bokeh_failed_load) {\n      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n      root._bokeh_failed_load = true;\n    } else if (force !== true) {\n      var cell = $(document.getElementById(null)).parents('.cell').data().cell;\n      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n    }\n\n  }\n\n  if (root._bokeh_is_loading === 0) {\n    console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n    run_inline_js();\n  } else {\n    load_libs(css_urls, js_urls, function() {\n      console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n      run_inline_js();\n    });\n  }\n}(window));"
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "\n",
              "\n",
              "\n",
              "\n",
              "\n",
              "  <div class=\"bk-root\" id=\"c3e786df-f069-4de3-a9dc-c927e17688e0\" data-root-id=\"4298\"></div>\n"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "application/javascript": [
              "(function(root) {\n",
              "  function embed_document(root) {\n",
              "    \n",
              "  var docs_json = 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              "          clearInterval(timer);\n",
              "          console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n",
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              "  }\n",
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