finding_quickest_5_seconds_to_apply_power_increase.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "finding_quickest_5_seconds_to_apply_power_increase",
      "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/finding_quickest_5_seconds_to_apply_power_increase.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 numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import matplotlib.transforms as mtransforms\n",
        "import nest_asyncio\n",
        "from typing import List, Tuple, NamedTuple\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": "kS0Sep8eul_B"
      },
      "source": [
        "**NOTE:**\n",
        " - If you would like to run a new SLS study, set `existing_study_id` to `None`. Running a new study will take several minutes.\n",
        "\n",
        " - If you would like to load an existing Dynamic Lap SLS study instead, set `existing_study_id` to the ID of the study in your account."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5sbIfMULuec5"
      },
      "source": [
        "existing_study_id = None"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PEVtj4qwn2g6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 87
        },
        "outputId": "48e971c5-8c97-43be-b75b-28a7ae940702"
      },
      "source": [
        "async def create_new_study():\n",
        "    logging.info('Loading study inputs...')\n",
        "\n",
        "    car = await canopy.load_default_config(session, 'car', 'Canopy F1 Car 2019')\n",
        "    weather = await canopy.load_default_config(session, 'weather', '25 deg, dry')\n",
        "    track = await canopy.load_default_config(session, 'track', 'Barcelona-F1')\n",
        "\n",
        "    logging.info('Submitting study...')\n",
        "\n",
        "    study_id = await canopy.create_study(\n",
        "        session,\n",
        "        'dynamicLapWithSLS',\n",
        "        'Example SLS Study',\n",
        "        [ car, weather, track ])\n",
        "\n",
        "    logging.info('Waiting for study...')\n",
        "\n",
        "    await canopy.wait_for_study(\n",
        "        session,\n",
        "        study_id,\n",
        "        timeout_seconds=900)\n",
        "    \n",
        "    return study_id\n",
        "\n",
        "if existing_study_id is not None:\n",
        "    study_id = existing_study_id\n",
        "else:\n",
        "    study_id = await create_new_study()\n",
        "\n",
        "logging.info(f'Using study {study_id}')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "INFO:root:Loading study inputs...\n",
            "INFO:root:Submitting study...\n",
            "INFO:root:Waiting for study...\n",
            "INFO:root:Using study aa739c18017f4060b0158a66b21c3e68\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tnJeZcc0vHif"
      },
      "source": [
        "# Example: Finding the quickest 5 seconds to apply a power increase.\n",
        "\n",
        "Using the `dTLap_drEnginePowerFactor` channel we will find the sLap ranges representing the optimal places to deploy 5 seconds of power increase."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4IlUCvGRuqdW"
      },
      "source": [
        "## Create Helper Functions\n",
        "\n",
        "This function will return the total time the target column is over a threshold, and the tLap and sLap ranges where that occurs:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6mrQ7HTlzItA"
      },
      "source": [
        "class SecondsOverThresholdResult(NamedTuple):\n",
        "    tLap_total: float\n",
        "    tLap_ranges: List[Tuple[float, float]]\n",
        "    sLap_ranges: List[Tuple[float, float]]\n",
        "\n",
        "def find_seconds_over_threshold(data: pd.DataFrame, column_name: str, threshold: float) -> SecondsOverThresholdResult:\n",
        "    # Make a copy\n",
        "    data = data[['tLap', 'sLap', column_name]].copy()\n",
        "\n",
        "    # Force the final value to zero, to include cases where the data finishes above the threshold.\n",
        "    data.loc[data.index[-1], column_name] = 0\n",
        "\n",
        "    # This column is True when the target column is greater than the threshold.\n",
        "    data['over_threshold'] = data[column_name] >= threshold\n",
        "\n",
        "    # Create a new DataFrame containing only the rows where \"over_threshold\" \n",
        "    # changes from False to True, or from True to False, relative to the previous row.\n",
        "    data = data.loc[(data['over_threshold'].shift() != data['over_threshold'])].copy()\n",
        "\n",
        "    # This column will be the difference between tLap on current and previous row.\n",
        "    data['tLap_diff'] = (data['tLap'] - data.shift()['tLap'])\n",
        "\n",
        "    # Create tuple columns representing the sLap and tLap ranges each row represents.\n",
        "    data['tLap_ranges'] = list(zip(data.shift()['tLap'], data['tLap']))\n",
        "    data['sLap_ranges'] = list(zip(data.shift()['sLap'], data['sLap']))\n",
        "    \n",
        "    # Drop the first row.\n",
        "    data.drop(data.index[0], inplace=True)\n",
        "\n",
        "    # We are interested in all rows where 'over_threshold' is False, which is\n",
        "    # where each tLap range above the threshold ends.\n",
        "    target_rows = data[data['over_threshold'] == False]\n",
        "\n",
        "    return SecondsOverThresholdResult(\n",
        "        sum(target_rows['tLap_diff']), \n",
        "        list(target_rows['tLap_ranges']), \n",
        "        list(target_rows['sLap_ranges']))\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "53py6b7euP9l"
      },
      "source": [
        "Perform some tests to check the function is working as expected.\n",
        " - The first test is for a standard case where the channel is over the threshold in the middle of the data. \n",
        " \n",
        " - The second test is for the case where the channel is over the threshold at the beginning and end of the data."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hXuzcz5CcHK2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "372d55eb-fb62-4a55-b09f-e14e542efc5c"
      },
      "source": [
        "# Test 1:\n",
        "result1 = find_seconds_over_threshold(\n",
        "    pd.DataFrame({\n",
        "        'sLap': np.arange(0, 20, 1),\n",
        "        'tLap': np.arange(0, 10, 0.5),\n",
        "        'sls': [0, 0, 0.1, 0.3, 0.5, 0.4, 0, 0, 0.2, 0.3, 0.3, 0.4, 0.5, 0.6, 0.5, 0.3, 0.1, 0, 0, 0]\n",
        "    }),\n",
        "    'sls',\n",
        "    0.35)\n",
        "assert result1 == SecondsOverThresholdResult(\n",
        "    3.0, \n",
        "    [(2.0, 3.0), (5.5, 7.5)],\n",
        "    [(4.0, 6.0), (11.0, 15.0)])\n",
        "\n",
        "# Test 2:\n",
        "result2 = find_seconds_over_threshold(\n",
        "    pd.DataFrame({\n",
        "        'sLap': np.arange(0, 20, 1),\n",
        "        'tLap': np.arange(0, 10, 0.5),\n",
        "        'sls': [0.5, 0.4, 0.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.1, 0.2, 0.3, 0.5, 0.6, 0.6]\n",
        "    }),\n",
        "    'sls',\n",
        "    0.35)\n",
        "assert result2 == SecondsOverThresholdResult(\n",
        "        2.0, \n",
        "        [(0.0, 1.0), (8.5, 9.5)], \n",
        "        [(0.0, 2.0), (17.0, 19.0)])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "INFO:numexpr.utils:NumExpr defaulting to 2 threads.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Yc1h2sHhTc52"
      },
      "source": [
        "This function will search for a thresold such that the target channel is over the threshold for close to the desired time.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fKN6jDHbaChZ"
      },
      "source": [
        "class SolveResult(NamedTuple):\n",
        "    tLap_total: float\n",
        "    tLap_ranges: List[Tuple[float, float]]\n",
        "    sLap_ranges: List[Tuple[float, float]]\n",
        "    threshold: float\n",
        "\n",
        "def solve(data: pd.DataFrame, column_name: str, target_time: float) -> SolveResult:\n",
        "    max_data = max(data[column_name])\n",
        "    min_delta = max_data / 10000\n",
        "    logging.info(f'Minimum delta={min_delta}')\n",
        "\n",
        "    lower = 0\n",
        "    upper = max_data\n",
        "\n",
        "    while lower < upper: \n",
        "        delta = (upper - lower) / 2\n",
        "        mid = lower + delta; \n",
        "        \n",
        "        result = find_seconds_over_threshold(data, column_name, mid)\n",
        "\n",
        "        logging.info(f'Found tLap_total {result.tLap_total} at {mid}, delta={delta}')\n",
        "\n",
        "        if result.tLap_total == target_time or delta <= min_delta:\n",
        "            return SolveResult(result.tLap_total, result.tLap_ranges, result.sLap_ranges, mid)\n",
        "  \n",
        "        # If the total time is below the target time, move lower.\n",
        "        elif result.tLap_total < target_time: \n",
        "            upper = mid\n",
        "  \n",
        "        # If the total time is above the target time, move higher\n",
        "        else: \n",
        "            lower = mid\n",
        "      \n",
        "    raise Error('Failed to find threshold.')\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XXqWOUkmul3a"
      },
      "source": [
        "## Load data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rrl81sPXyIgB"
      },
      "source": [
        "sls_channel_name = 'dTLap_drEnginePowerFactor'"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pCZ3edBRvjYv",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "ed83fae7-c55c-413c-cab5-fde4e495c195"
      },
      "source": [
        "job = await canopy.load_study_job(\n",
        "    session,\n",
        "    study_id,\n",
        "    sim_type='DynamicLapWithSLS',\n",
        "    channel_names=[ 'sLap', 'tLap', sls_channel_name ])\n",
        "\n",
        "if sls_channel_name not in job.vector_data.columns:\n",
        "    logging.error(f'SLS channel {sls_channel_name} was not found in study {study_id}')\n",
        "\n",
        "data = job.vector_data"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "INFO:canopy.load_study_job:Loading job index 0\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Z2q1DwKa2b9M"
      },
      "source": [
        "## Solve"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "P-DBZ0V00i5D",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 367
        },
        "outputId": "278c3e8e-6153-4cee-83a6-eca65641b5fa"
      },
      "source": [
        "result = solve(data, sls_channel_name, 5)\n",
        "logging.info(f'Found total time: {result.tLap_total}')\n",
        "logging.info(f'Found target threshold: {result.threshold}')\n",
        "logging.info(f'Found tLap ranges: {result.tLap_ranges}')\n",
        "logging.info(f'Found sLap ranges: {result.sLap_ranges}')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "INFO:root:Minimum delta=6.182984448969365e-07\n",
            "INFO:root:Found tLap_total 11.138423919677734 at 0.003091492224484682, delta=0.003091492224484682\n",
            "INFO:root:Found tLap_total 1.1487808227539062 at 0.004637238336727023, delta=0.001545746112242341\n",
            "INFO:root:Found tLap_total 4.055145263671875 at 0.0038643652806058526, delta=0.0007728730561211705\n",
            "INFO:root:Found tLap_total 5.830415725708008 at 0.0034779287525452673, delta=0.00038643652806058526\n",
            "INFO:root:Found tLap_total 4.675556182861328 at 0.00367114701657556, delta=0.00019321826403029263\n",
            "INFO:root:Found tLap_total 5.150577545166016 at 0.0035745378845604137, delta=9.660913201514632e-05\n",
            "INFO:root:Found tLap_total 5.046840667724609 at 0.003622842450567987, delta=4.830456600757316e-05\n",
            "INFO:root:Found tLap_total 4.85675048828125 at 0.0036469947335717734, delta=2.415228300378658e-05\n",
            "INFO:root:Found tLap_total 4.92724609375 at 0.00363491859206988, delta=1.207614150189329e-05\n",
            "INFO:root:Found tLap_total 5.046840667724609 at 0.0036288805213189335, delta=6.038070750946645e-06\n",
            "INFO:root:Found tLap_total 5.046840667724609 at 0.003631899556694407, delta=3.0190353754733223e-06\n",
            "INFO:root:Found tLap_total 4.982540130615234 at 0.0036334090743821434, delta=1.5095176877366612e-06\n",
            "INFO:root:Found tLap_total 5.046840667724609 at 0.003632654315538275, delta=7.547588438683306e-07\n",
            "INFO:root:Found tLap_total 4.982540130615234 at 0.0036330316949602093, delta=3.773794219341653e-07\n",
            "INFO:root:Found total time: 4.982540130615234\n",
            "INFO:root:Found target threshold: 0.0036330316949602093\n",
            "INFO:root:Found tLap ranges: [(34.44617462158203, 35.80943298339844), (41.638648986816406, 41.97238540649414), (42.19536590576172, 43.14568328857422), (77.33956146240234, 79.67478942871094)]\n",
            "INFO:root:Found sLap ranges: [(2073.521728515625, 2145.873291015625), (2464.20703125, 2479.832763671875), (2491.101318359375, 2545.229736328125), (4184.81396484375, 4343.11669921875)]\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RQGaaRJPUk01"
      },
      "source": [
        "## Plot Results"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OrmfSyzB1wQe",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 550
        },
        "outputId": "243c0d8e-7b32-471e-edbc-fdd0d9f8f3d0"
      },
      "source": [
        "fig, axs = plt.subplots(2, 1, figsize=(16, 8), dpi=80)\n",
        "\n",
        "for ax, x_name in zip(axs, ['sLap', 'tLap']):\n",
        "    x = data[x_name]\n",
        "    ax.set_title(x_name)\n",
        "    ax.plot(x, data[sls_channel_name], 'b-', lw=1, label=sls_channel_name)\n",
        "\n",
        "for ax in axs:\n",
        "    ax.axhline(result.threshold, color='green', lw=1, alpha=0.5, label='threshold')\n",
        "\n",
        "for ax, ranges in zip(axs, [result.sLap_ranges, result.tLap_ranges]):\n",
        "    for i, range in enumerate(ranges):\n",
        "        ax.axvspan(range[0], range[1], alpha=0.25, color='red', label='optimal deployment' if i == 0 else None)\n",
        "    \n",
        "axs[0].legend()\n",
        "fig.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1280x640 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}