loading_worksheet_study_data.ipynb

{
  "nbformat": 4,
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  "metadata": {
    "colab": {
      "name": "loading_worksheet_study_data.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyN/Lj16fi+6lXf4A7vRLlYC",
      "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_worksheet_study_data.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",
        "import re\n",
        "from typing import List, Dict, NamedTuple, Optional\n",
        "import statistics\n",
        "import pandas as pd\n",
        "import numpy as np\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": [
        "# Setup the Example"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "INX8erWn3Cf0"
      },
      "source": [
        "Ask which worksheet to query."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DlqNRpZD3Jsk"
      },
      "source": [
        "worksheet_name_or_id = input(\"Worksheet name or ID: \")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qyrWl-PdW-ie"
      },
      "source": [
        "Load the worksheet."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2yulpUlX3Ow2"
      },
      "source": [
        "worksheet = None\n",
        "if re.match('^[0-9a-f]{32}$', worksheet_name_or_id):\n",
        "    logging.info(f'Worksheet ID provided: {worksheet_name_or_id}')\n",
        "    worksheet = await canopy.load_config(session, worksheet_name_or_id)\n",
        "else:\n",
        "    logging.info(f'Worksheet name provided: {worksheet_name_or_id}')\n",
        "    worksheet = await canopy.find_config(session, 'worksheet', worksheet_name_or_id)\n",
        "\n",
        "worksheet"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "poiA4nhv0FC9"
      },
      "source": [
        "This function does all the work of collating the data we've requested."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9CY_VLTP5Egg"
      },
      "source": [
        "sim_types_to_load = ['QuasiStaticLap', 'DynamicLap']\n",
        "\n",
        "class NameAndUnits(NamedTuple):\n",
        "    name: str\n",
        "    units: str\n",
        "\n",
        "async def load_worksheet_job_data(\n",
        "    worksheet: canopy.ConfigResult, \n",
        "    scalar_channels: Optional[List[NameAndUnits]],\n",
        "    vector_channels: Optional[List[NameAndUnits]],\n",
        "    sim_types_to_load: List[str]) -> pd.DataFrame:\n",
        "\n",
        "    if scalar_channels is None:\n",
        "        scalar_channels = []\n",
        "\n",
        "    if vector_channels is None:\n",
        "        vector_channels = []\n",
        "\n",
        "    scalar_results_requested = len(scalar_channels) > 0\n",
        "    vector_results_requested = len(vector_channels) > 0\n",
        "\n",
        "    jobs_data: List[Dict] = []\n",
        "\n",
        "    for row_index, row in enumerate(worksheet.data.rows):\n",
        "        if 'reference' in row.study:\n",
        "            study_id = row.study.reference.targetId\n",
        "            logging.info(f'Loading metadata for study ID {study_id}')\n",
        "            \n",
        "            study_metadata = await canopy.load_study(\n",
        "                session,\n",
        "                study_id,\n",
        "                include_study_sim_types=True)\n",
        "            \n",
        "            logging.info('Found sim types: %s', ','.join(study_metadata.sim_types))\n",
        "            filtered_sim_types = [sim_type for sim_type in study_metadata.sim_types if sim_type in sim_types_to_load]\n",
        "            \n",
        "            is_exploration = study_metadata.simulation_count > 1\n",
        "            logging.info(f'is_exploration: {is_exploration}')\n",
        "\n",
        "            for sim_type in filtered_sim_types:\n",
        "                logging.info(f'Loading data for {sim_type}')\n",
        "                study = await canopy.load_study(\n",
        "                    session,\n",
        "                    study_id,\n",
        "                    sim_type,\n",
        "                    channel_names=[v.name for v in vector_channels],\n",
        "                    \n",
        "                    # If the study was an exploration it is more efficient to load\n",
        "                    # scalar results from the aggregated study scalar results file.\n",
        "                    include_study_scalar_results = scalar_results_requested and is_exploration,\n",
        "                    \n",
        "                    # If the study was a single job then the aggregated file doesn't exist,\n",
        "                    # so we will load the scalar results for the individual job.\n",
        "                    include_job_scalar_results = scalar_results_requested and not is_exploration)\n",
        "\n",
        "                converted_scalars: Dict[List[float]] = {}\n",
        "                if scalar_results_requested:\n",
        "                    for item in scalar_channels:\n",
        "                        if is_exploration:\n",
        "                            # When we load from the aggregated file we need to fully qualify the\n",
        "                            # scalar result channel name with the sim type, because the aggregated\n",
        "                            # file contains the results for all sim types which were run in the job.\n",
        "                            scalar_channel_name = f'{item.name}:{sim_type}'\n",
        "                            converted_scalars[item.name] = study.scalar_as(scalar_channel_name, item.units)\n",
        "                        else:\n",
        "                            # When we load from the individual job scalar results we don't need to fully\n",
        "                            # qualify the channel name, because it has only loaded the scalar results for the\n",
        "                            # sim type we requested in the load_study method.\n",
        "                            scalar_channel_name = item.name\n",
        "                            converted_scalars[item.name] = [study.jobs[0].scalar_as(scalar_channel_name, item.units)]\n",
        "\n",
        "                study_scalars_tLapTotal: List[number]\n",
        "                for job_index in range(0, study_metadata.simulation_count):\n",
        "                    \n",
        "                    # The list of jobs is only populated if required by the data we have requested.\n",
        "                    # If we don't request any vector data, and the scalar results were loaded from the\n",
        "                    # aggregated scalar results file, then it will not be populated because \n",
        "                    # loading data for individual jobs was not required.\n",
        "                    job = study.jobs[job_index] if study.jobs else None\n",
        "\n",
        "                    job_data: Dict = {\n",
        "                        'row_index': row_index,\n",
        "                        'study_id': study_id,\n",
        "                        'study_name': study.document.name,\n",
        "                        'sim_type': sim_type,\n",
        "                        'job_index': job_index,\n",
        "                        'job_name': job.document.name if job else '',\n",
        "                    }\n",
        "\n",
        "                    for item in vector_channels:\n",
        "                        data = job.vector_as(item.name, item.units)\n",
        "                        job_data[f'{item.name}_Min'] = min(data) if data is not None else np.nan\n",
        "                        job_data[f'{item.name}_Mean'] = statistics.mean(data) if data is not None else np.nan\n",
        "                        job_data[f'{item.name}_Max'] = max(data) if data is not None else np.nan\n",
        "\n",
        "                    for item in scalar_channels:\n",
        "                        scalar_data = converted_scalars[item.name]\n",
        "                        job_data[item.name] = scalar_data[job_index] if scalar_data is not None else np.nan\n",
        "\n",
        "                    jobs_data.append(job_data)\n",
        "    \n",
        "    return pd.DataFrame(jobs_data)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5qseZSqJ0Nno"
      },
      "source": [
        "# Example: Loading Worksheet Study Data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 573
        },
        "id": "IHkZZL___nPR",
        "outputId": "de2719a7-3e01-48d9-f062-5e489a6f6ece"
      },
      "source": [
        "# This example doesn't request any vector data.\n",
        "# It will complete very quickly because all the scalar results\n",
        "# can be loaded from the single aggregated scalar results file for a study.\n",
        "await load_worksheet_job_data(\n",
        "    worksheet,\n",
        "    [\n",
        "        NameAndUnits('tLapTotal', 's'),\n",
        "        NameAndUnits('mFuelTotal', 'kg'),\n",
        "    ],\n",
        "    None,\n",
        "    [\n",
        "        'QuasiStaticLap',\n",
        "        'DynamicLap',\n",
        "    ])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "INFO:root:Loading metadata for study ID cc7a57880790493cb3cbd21410ea3d1c\n",
            "INFO:root:Found sim types: QuasiStaticLap\n",
            "INFO:root:is_exploration: True\n",
            "INFO:root:Loading data for QuasiStaticLap\n",
            "INFO:root:Loading metadata for study ID 178e2c8962c7450381bdb116729c22bd\n",
            "INFO:root:Found sim types: DynamicLap\n",
            "INFO:root:is_exploration: False\n",
            "INFO:root:Loading data for DynamicLap\n",
            "INFO:canopy.load_study_job:Loading job index 0\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>row_index</th>\n",
              "      <th>study_id</th>\n",
              "      <th>study_name</th>\n",
              "      <th>sim_type</th>\n",
              "      <th>job_index</th>\n",
              "      <th>job_name</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
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              "      <td>cc7a57880790493cb3cbd21410ea3d1c</td>\n",
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              "      <td>cc7a57880790493cb3cbd21410ea3d1c</td>\n",
              "      <td>Canopy F1 hRideF Constraint</td>\n",
              "      <td>QuasiStaticLap</td>\n",
              "      <td>2</td>\n",
              "      <td></td>\n",
              "      <td>84.7487</td>\n",
              "      <td>NaN</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0</td>\n",
              "      <td>cc7a57880790493cb3cbd21410ea3d1c</td>\n",
              "      <td>Canopy F1 hRideF Constraint</td>\n",
              "      <td>QuasiStaticLap</td>\n",
              "      <td>3</td>\n",
              "      <td></td>\n",
              "      <td>84.7487</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>cc7a57880790493cb3cbd21410ea3d1c</td>\n",
              "      <td>Canopy F1 hRideF Constraint</td>\n",
              "      <td>QuasiStaticLap</td>\n",
              "      <td>4</td>\n",
              "      <td></td>\n",
              "      <td>84.7487</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>0</td>\n",
              "      <td>cc7a57880790493cb3cbd21410ea3d1c</td>\n",
              "      <td>Canopy F1 hRideF Constraint</td>\n",
              "      <td>QuasiStaticLap</td>\n",
              "      <td>5</td>\n",
              "      <td></td>\n",
              "      <td>84.7487</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>0</td>\n",
              "      <td>cc7a57880790493cb3cbd21410ea3d1c</td>\n",
              "      <td>Canopy F1 hRideF Constraint</td>\n",
              "      <td>QuasiStaticLap</td>\n",
              "      <td>6</td>\n",
              "      <td></td>\n",
              "      <td>84.7487</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>0</td>\n",
              "      <td>cc7a57880790493cb3cbd21410ea3d1c</td>\n",
              "      <td>Canopy F1 hRideF Constraint</td>\n",
              "      <td>QuasiStaticLap</td>\n",
              "      <td>7</td>\n",
              "      <td></td>\n",
              "      <td>84.7487</td>\n",
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              "      <td>cc7a57880790493cb3cbd21410ea3d1c</td>\n",
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              "      <td>0</td>\n",
              "      <td>cc7a57880790493cb3cbd21410ea3d1c</td>\n",
              "      <td>Canopy F1 hRideF Constraint</td>\n",
              "      <td>QuasiStaticLap</td>\n",
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              "      <td>Test</td>\n",
              "      <td>DynamicLap</td>\n",
              "      <td>0</td>\n",
              "      <td>Job</td>\n",
              "      <td>83.1475</td>\n",
              "      <td>1.43174</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    row_index                          study_id  ... tLapTotal mFuelTotal\n",
              "0           0  cc7a57880790493cb3cbd21410ea3d1c  ...   84.7487        NaN\n",
              "1           0  cc7a57880790493cb3cbd21410ea3d1c  ...   84.7487        NaN\n",
              "2           0  cc7a57880790493cb3cbd21410ea3d1c  ...   84.7487        NaN\n",
              "3           0  cc7a57880790493cb3cbd21410ea3d1c  ...   84.7487        NaN\n",
              "4           0  cc7a57880790493cb3cbd21410ea3d1c  ...   84.7487        NaN\n",
              "5           0  cc7a57880790493cb3cbd21410ea3d1c  ...   84.7487        NaN\n",
              "6           0  cc7a57880790493cb3cbd21410ea3d1c  ...   84.7487        NaN\n",
              "7           0  cc7a57880790493cb3cbd21410ea3d1c  ...   84.7487        NaN\n",
              "8           0  cc7a57880790493cb3cbd21410ea3d1c  ...   84.7487        NaN\n",
              "9           0  cc7a57880790493cb3cbd21410ea3d1c  ...   84.7487        NaN\n",
              "10          0  cc7a57880790493cb3cbd21410ea3d1c  ...   84.7487        NaN\n",
              "11          1  178e2c8962c7450381bdb116729c22bd  ...   83.1475    1.43174\n",
              "\n",
              "[12 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jaf-3jbtu6TW",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 763
        },
        "outputId": "c22ce8bd-da3d-4c07-ccf4-4baf56868dc4"
      },
      "source": [
        "# This example will be slower as vector data has to be loaded from\n",
        "# each job.\n",
        "await load_worksheet_job_data(\n",
        "    worksheet,\n",
        "    [\n",
        "        NameAndUnits('tLapTotal', 's'),\n",
        "        NameAndUnits('mFuelTotal', 'kg'),\n",
        "    ],\n",
        "    [\n",
        "        NameAndUnits('vCar', 'kph'),\n",
        "        NameAndUnits('hRideF', 'mm'),\n",
        "    ],\n",
        "    [\n",
        "        'QuasiStaticLap',\n",
        "        'DynamicLap',\n",
        "    ])\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "INFO:root:Loading metadata for study ID cc7a57880790493cb3cbd21410ea3d1c\n",
            "INFO:root:Found sim types: QuasiStaticLap\n",
            "INFO:root:is_exploration: True\n",
            "INFO:root:Loading data for QuasiStaticLap\n",
            "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",
            "INFO:canopy.load_study_job:Loading job index 3\n",
            "INFO:canopy.load_study_job:Loading job index 4\n",
            "INFO:canopy.load_study_job:Loading job index 5\n",
            "INFO:canopy.load_study_job:Loading job index 6\n",
            "INFO:canopy.load_study_job:Loading job index 7\n",
            "INFO:canopy.load_study_job:Loading job index 8\n",
            "INFO:canopy.load_study_job:Loading job index 9\n",
            "INFO:canopy.load_study_job:Loading job index 10\n",
            "INFO:root:Loading metadata for study ID 178e2c8962c7450381bdb116729c22bd\n",
            "INFO:root:Found sim types: DynamicLap\n",
            "INFO:root:is_exploration: False\n",
            "INFO:root:Loading data for DynamicLap\n",
            "INFO:canopy.load_study_job:Loading job index 0\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>row_index</th>\n",
              "      <th>study_id</th>\n",
              "      <th>study_name</th>\n",
              "      <th>sim_type</th>\n",
              "      <th>job_index</th>\n",
              "      <th>job_name</th>\n",
              "      <th>vCar_Min</th>\n",
              "      <th>vCar_Mean</th>\n",
              "      <th>vCar_Max</th>\n",
              "      <th>hRideF_Min</th>\n",
              "      <th>hRideF_Mean</th>\n",
              "      <th>hRideF_Max</th>\n",
              "      <th>tLapTotal</th>\n",
              "      <th>mFuelTotal</th>\n",
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              "  <tbody>\n",
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              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>cc7a57880790493cb3cbd21410ea3d1c</td>\n",
              "      <td>Canopy F1 hRideF Constraint</td>\n",
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              "      <td>0</td>\n",
              "      <td>Factorial 1</td>\n",
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              "      <td>22.595723</td>\n",
              "      <td>84.7487</td>\n",
              "      <td>NaN</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0</td>\n",
              "      <td>cc7a57880790493cb3cbd21410ea3d1c</td>\n",
              "      <td>Canopy F1 hRideF Constraint</td>\n",
              "      <td>QuasiStaticLap</td>\n",
              "      <td>1</td>\n",
              "      <td>Factorial 2</td>\n",
              "      <td>73.941054</td>\n",
              "      <td>217.658312</td>\n",
              "      <td>302.430413</td>\n",
              "      <td>-14.384037</td>\n",
              "      <td>5.663982</td>\n",
              "      <td>22.595723</td>\n",
              "      <td>84.7487</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0</td>\n",
              "      <td>cc7a57880790493cb3cbd21410ea3d1c</td>\n",
              "      <td>Canopy F1 hRideF Constraint</td>\n",
              "      <td>QuasiStaticLap</td>\n",
              "      <td>2</td>\n",
              "      <td>Factorial 3</td>\n",
              "      <td>73.941054</td>\n",
              "      <td>217.658312</td>\n",
              "      <td>302.430413</td>\n",
              "      <td>-14.384037</td>\n",
              "      <td>5.663982</td>\n",
              "      <td>22.595723</td>\n",
              "      <td>84.7487</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0</td>\n",
              "      <td>cc7a57880790493cb3cbd21410ea3d1c</td>\n",
              "      <td>Canopy F1 hRideF Constraint</td>\n",
              "      <td>QuasiStaticLap</td>\n",
              "      <td>3</td>\n",
              "      <td>Factorial 4</td>\n",
              "      <td>73.941054</td>\n",
              "      <td>217.658312</td>\n",
              "      <td>302.430413</td>\n",
              "      <td>-14.384037</td>\n",
              "      <td>5.663982</td>\n",
              "      <td>22.595723</td>\n",
              "      <td>84.7487</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>cc7a57880790493cb3cbd21410ea3d1c</td>\n",
              "      <td>Canopy F1 hRideF Constraint</td>\n",
              "      <td>QuasiStaticLap</td>\n",
              "      <td>4</td>\n",
              "      <td>Factorial 5</td>\n",
              "      <td>73.941054</td>\n",
              "      <td>217.658312</td>\n",
              "      <td>302.430413</td>\n",
              "      <td>-14.384037</td>\n",
              "      <td>5.663982</td>\n",
              "      <td>22.595723</td>\n",
              "      <td>84.7487</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>0</td>\n",
              "      <td>cc7a57880790493cb3cbd21410ea3d1c</td>\n",
              "      <td>Canopy F1 hRideF Constraint</td>\n",
              "      <td>QuasiStaticLap</td>\n",
              "      <td>5</td>\n",
              "      <td>Factorial 6</td>\n",
              "      <td>73.941054</td>\n",
              "      <td>217.658312</td>\n",
              "      <td>302.430413</td>\n",
              "      <td>-14.384037</td>\n",
              "      <td>5.663982</td>\n",
              "      <td>22.595723</td>\n",
              "      <td>84.7487</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>0</td>\n",
              "      <td>cc7a57880790493cb3cbd21410ea3d1c</td>\n",
              "      <td>Canopy F1 hRideF Constraint</td>\n",
              "      <td>QuasiStaticLap</td>\n",
              "      <td>6</td>\n",
              "      <td>Factorial 7</td>\n",
              "      <td>73.941054</td>\n",
              "      <td>217.658312</td>\n",
              "      <td>302.430413</td>\n",
              "      <td>-14.384037</td>\n",
              "      <td>5.663982</td>\n",
              "      <td>22.595723</td>\n",
              "      <td>84.7487</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>0</td>\n",
              "      <td>cc7a57880790493cb3cbd21410ea3d1c</td>\n",
              "      <td>Canopy F1 hRideF Constraint</td>\n",
              "      <td>QuasiStaticLap</td>\n",
              "      <td>7</td>\n",
              "      <td>Factorial 8</td>\n",
              "      <td>73.941054</td>\n",
              "      <td>217.658312</td>\n",
              "      <td>302.430413</td>\n",
              "      <td>-14.384037</td>\n",
              "      <td>5.663982</td>\n",
              "      <td>22.595723</td>\n",
              "      <td>84.7487</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>0</td>\n",
              "      <td>cc7a57880790493cb3cbd21410ea3d1c</td>\n",
              "      <td>Canopy F1 hRideF Constraint</td>\n",
              "      <td>QuasiStaticLap</td>\n",
              "      <td>8</td>\n",
              "      <td>Factorial 9</td>\n",
              "      <td>73.941054</td>\n",
              "      <td>217.658312</td>\n",
              "      <td>302.430413</td>\n",
              "      <td>-14.384037</td>\n",
              "      <td>5.663982</td>\n",
              "      <td>22.595723</td>\n",
              "      <td>84.7487</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>0</td>\n",
              "      <td>cc7a57880790493cb3cbd21410ea3d1c</td>\n",
              "      <td>Canopy F1 hRideF Constraint</td>\n",
              "      <td>QuasiStaticLap</td>\n",
              "      <td>9</td>\n",
              "      <td>Factorial 10</td>\n",
              "      <td>73.941054</td>\n",
              "      <td>217.658312</td>\n",
              "      <td>302.430413</td>\n",
              "      <td>-14.384037</td>\n",
              "      <td>5.663982</td>\n",
              "      <td>22.595723</td>\n",
              "      <td>84.7487</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>0</td>\n",
              "      <td>cc7a57880790493cb3cbd21410ea3d1c</td>\n",
              "      <td>Canopy F1 hRideF Constraint</td>\n",
              "      <td>QuasiStaticLap</td>\n",
              "      <td>10</td>\n",
              "      <td>Factorial 11</td>\n",
              "      <td>73.941054</td>\n",
              "      <td>217.658312</td>\n",
              "      <td>302.430413</td>\n",
              "      <td>-14.384037</td>\n",
              "      <td>5.663982</td>\n",
              "      <td>22.595723</td>\n",
              "      <td>84.7487</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>1</td>\n",
              "      <td>178e2c8962c7450381bdb116729c22bd</td>\n",
              "      <td>Test</td>\n",
              "      <td>DynamicLap</td>\n",
              "      <td>0</td>\n",
              "      <td>Job</td>\n",
              "      <td>72.585114</td>\n",
              "      <td>196.814677</td>\n",
              "      <td>302.282898</td>\n",
              "      <td>-12.026005</td>\n",
              "      <td>8.313860</td>\n",
              "      <td>22.539959</td>\n",
              "      <td>83.1475</td>\n",
              "      <td>1.43174</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    row_index                          study_id  ... tLapTotal mFuelTotal\n",
              "0           0  cc7a57880790493cb3cbd21410ea3d1c  ...   84.7487        NaN\n",
              "1           0  cc7a57880790493cb3cbd21410ea3d1c  ...   84.7487        NaN\n",
              "2           0  cc7a57880790493cb3cbd21410ea3d1c  ...   84.7487        NaN\n",
              "3           0  cc7a57880790493cb3cbd21410ea3d1c  ...   84.7487        NaN\n",
              "4           0  cc7a57880790493cb3cbd21410ea3d1c  ...   84.7487        NaN\n",
              "5           0  cc7a57880790493cb3cbd21410ea3d1c  ...   84.7487        NaN\n",
              "6           0  cc7a57880790493cb3cbd21410ea3d1c  ...   84.7487        NaN\n",
              "7           0  cc7a57880790493cb3cbd21410ea3d1c  ...   84.7487        NaN\n",
              "8           0  cc7a57880790493cb3cbd21410ea3d1c  ...   84.7487        NaN\n",
              "9           0  cc7a57880790493cb3cbd21410ea3d1c  ...   84.7487        NaN\n",
              "10          0  cc7a57880790493cb3cbd21410ea3d1c  ...   84.7487        NaN\n",
              "11          1  178e2c8962c7450381bdb116729c22bd  ...   83.1475    1.43174\n",
              "\n",
              "[12 rows x 14 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Sok4ksWS2Fe0"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}