fitting_aero_data_to_polynomial_and_rbf.ipynb

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    "colab": {
      "name": "fitting_aero_data_to_polynomial_and_rbf.ipynb",
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      "include_colab_link": true
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  "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/fitting_aero_data_to_polynomial_and_rbf.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",
        "from numpy.matlib import repmat\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd\n",
        "import json\n",
        "from typing import Sequence, Optional, NamedTuple, Any\n",
        "import nest_asyncio\n",
        "\n",
        "logging.basicConfig(level=logging.INFO)\n",
        "np.set_printoptions(suppress=True)\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": "t2NztzxRQtGm"
      },
      "source": [
        "Later in this example we render the aero component we have generated as JSON. The `np.ndarray` type is not JSON serializable by default, so this simple encoder converts `np.ndarray` to a Python array during serialization.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fDWyk1suQcyq"
      },
      "source": [
        "class NumpyEncoder(json.JSONEncoder):\n",
        "    def default(self, obj):\n",
        "        if isinstance(obj, np.ndarray):\n",
        "            return obj.tolist()\n",
        "        return json.JSONEncoder.default(self, obj)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tnJeZcc0vHif"
      },
      "source": [
        "# Example: Fitting Aero Data to a Polynomial and RBF.\n",
        "\n",
        "Using a CSV of aero data containing the columns `hRidef`, `hRideR`, `aRollAbs`, `CLiftBodyF`, `CLiftBodyR` and `CDragBody` we will fit the data to a polynomial and then fit the residuals of the polynomial to an RBF.\n",
        "\n",
        "We will then generate a Canopy aero component from the data and save a new car to the Canopy platform containing the new aero data."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zOJAdFYTNtIH"
      },
      "source": [
        "## Create Helper Functions"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0f5HddYrN1fK"
      },
      "source": [
        "This function returns a random set indices, given a data length and maximum number of positions."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YjT25ZJENw60"
      },
      "source": [
        "def create_basis_position_indices(data_length: int, max_positions: int) -> Sequence[int]:\n",
        "    n = min(max_positions, data_length)\n",
        "    random_index_permutation = np.random.permutation(data_length)\n",
        "    basis_position_indices = random_index_permutation[0:n]\n",
        "    return basis_position_indices"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1tu4NwABOE0s"
      },
      "source": [
        "This function will do the fitting, and return a `FittedAeroData` class containing the information we need to construct the car component.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fYuVTy4hWIzz",
        "pycharm": {
          "is_executing": false
        }
      },
      "source": [
        "class FittedAeroData(NamedTuple):\n",
        "    polynomial_residuals: pd.DataFrame\n",
        "    polynomial_rbf_residuals: pd.DataFrame\n",
        "    polynomial_coefficients: np.ndarray\n",
        "    rbf_basis_weights: np.ndarray\n",
        "    input_offsets: np.ndarray \n",
        "    input_scales: np.ndarray\n",
        "    input_length_scales: np.ndarray\n",
        "    input_basis_positions: np.ndarray\n",
        "\n",
        "# input_basis_position_indices defaults to a random 200 indices.\n",
        "# input_length_scales defaults to all ones.\n",
        "def fit_aero_data(\n",
        "        data: pd.DataFrame,\n",
        "        input_names: Sequence[str],\n",
        "        output_names: Sequence[str],\n",
        "        maximum_input_basis_positions: Optional[int] = 200,\n",
        "        input_basis_position_indices: Optional[Sequence[int]] = None,\n",
        "        input_length_scales: Optional[Sequence[float]] = None) -> FittedAeroData:\n",
        "\n",
        "    data_length = len(data)\n",
        "    input_count = len(input_names)\n",
        "    inputs = data[input_names].to_numpy()\n",
        "    outputs = data[output_names].to_numpy()\n",
        "    \n",
        "    # Set default parameter values if not provided.\n",
        "    if input_basis_position_indices is None:\n",
        "        input_basis_position_indices = create_basis_position_indices(\n",
        "            data_length,\n",
        "            maximum_input_basis_positions)        \n",
        "\n",
        "    if input_length_scales is None:\n",
        "        input_length_scales = np.ones((input_count,1))\n",
        "    else:\n",
        "        input_length_scales = np.reshape(input_length_scales, (input_count, 1))\n",
        "\n",
        "    # Scale all the input data so it ranges between +/- 1.\n",
        "    inputs_max = inputs.max(0)\n",
        "    inputs_min = inputs.min(0)\n",
        "\n",
        "    input_offsets = -0.5 * (inputs_max + inputs_min)\n",
        "    input_scales = 0.5 * (inputs_max - inputs_min)\n",
        "\n",
        "    input_scaled = inputs + repmat(input_offsets, data_length, 1)\n",
        "    input_scaled = input_scaled / repmat(input_scales, data_length, 1)\n",
        "\n",
        "    # Now fit a simple polynomial. This consists of: \n",
        "    # 1. Cubic terms; \n",
        "    # 2. Quadratic terms. \n",
        "    # 3. All linear cross terms. \n",
        "    # 4. All linear terms. \n",
        "    # 5. A constant. \n",
        "    # It's not meant to be a perfect fit, just a passable one.\n",
        "    polynomial = np.concatenate((\n",
        "        inputs**3,\n",
        "        inputs**2),\n",
        "        axis=1)\n",
        "    \n",
        "    for i in range(0, input_count):\n",
        "        for j in range(i+1, input_count):\n",
        "            polynomial = np.concatenate((\n",
        "                polynomial,\n",
        "                (inputs[:,i] * inputs[:,j])[:, np.newaxis]),\n",
        "                axis=1)\n",
        "\n",
        "    polynomial = np.concatenate((\n",
        "        polynomial,\n",
        "        inputs, \n",
        "        (1+0*inputs[:, 0])[:, np.newaxis]),\n",
        "        axis=1)\n",
        "    \n",
        "    polynomial_coefficients_result = np.linalg.lstsq(polynomial, outputs, rcond=None)\n",
        "    polynomial_coefficients = polynomial_coefficients_result[0]\n",
        "\n",
        "    polynomial_y = polynomial @ polynomial_coefficients\n",
        "\n",
        "    # Compute the fit residuals: we're going to fit the RBF to these. \n",
        "    polynomial_residuals = outputs - polynomial_y\n",
        "\n",
        "    input_basis_positions = input_scaled[input_basis_position_indices, :]\n",
        "    input_basis_positions_length = len(input_basis_positions)\n",
        "\n",
        "    # Compute the matrix of mahalanobis distances between all basis function\n",
        "    # positions and all data points.\n",
        "    mahalanobis_distances = np.zeros((data_length, input_basis_positions_length))\n",
        "    for k in range(0,input_count):    \n",
        "        mahalanobis_distances = \\\n",
        "            mahalanobis_distances + \\\n",
        "            input_length_scales[k] * (\n",
        "                repmat(np.vstack(input_scaled[:,k]), 1, input_basis_positions_length) - \n",
        "                repmat(input_basis_positions[:, k].T, len(input_scaled), 1)\n",
        "            )**2    \n",
        "\n",
        "    # Take the exponential to yield the learning matrix.\n",
        "    rbf = np.power(np.math.e, -mahalanobis_distances)\n",
        "\n",
        "    # And compute the basis weights.\n",
        "    rbf_basis_weights_result = np.linalg.lstsq(rbf, polynomial_residuals, rcond=None)\n",
        "    rbf_basis_weights = rbf_basis_weights_result[0]\n",
        "\n",
        "    # The final estimate is just the polynomial estimate + the rbf correction.\n",
        "    polynomial_rbf_y = rbf @ rbf_basis_weights + polynomial_y\n",
        "\n",
        "    polynomial_rbf_residuals = polynomial_rbf_y - outputs\n",
        "\n",
        "    return FittedAeroData(\n",
        "        polynomial_residuals,\n",
        "        polynomial_rbf_residuals,\n",
        "        polynomial_coefficients,\n",
        "        rbf_basis_weights,\n",
        "        input_offsets,\n",
        "        input_scales,\n",
        "        input_length_scales.flatten(),\n",
        "        input_basis_positions)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3i45tJepOhvg"
      },
      "source": [
        "This function will take the output of the `fit_aero_data` function and return a component we can put on a car."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "saWZ2nKuOdfF"
      },
      "source": [
        "def get_fit_as_component(\n",
        "        fitted_aero_data: FittedAeroData,\n",
        "        input_names: Sequence[str],\n",
        "        output_names: Sequence[str]) -> Any:\n",
        "    \n",
        "    polynomial_coefficients = fitted_aero_data.polynomial_coefficients\n",
        "    input_offsets = fitted_aero_data.input_offsets\n",
        "    input_scales = fitted_aero_data.input_scales\n",
        "    input_length_scales = fitted_aero_data.input_length_scales\n",
        "    input_basis_positions = fitted_aero_data.input_basis_positions\n",
        "    rbf_basis_weights = fitted_aero_data.rbf_basis_weights\n",
        "    \n",
        "    aero_freedoms = input_names\n",
        "    polymaps = [f'Polynomial{output_name}Definition' for output_name in output_names]\n",
        "    \n",
        "    config = {}\n",
        "    for i in range(0, len(polymaps)):\n",
        "        polymap = polymaps[i]\n",
        "        maps = []\n",
        "        config[polymap] = maps\n",
        "        \n",
        "        k = 0\n",
        "        for j in range(0, len(aero_freedoms)):\n",
        "            maps.append({\n",
        "                'expression': f'{aero_freedoms[j]} * {aero_freedoms[j]} * {aero_freedoms[j]}',\n",
        "                'coefficient': polynomial_coefficients[k,i],\n",
        "            })\n",
        "            k += 1\n",
        "        \n",
        "        for j in range(0, len(aero_freedoms)):\n",
        "            maps.append({\n",
        "                'expression': f'{aero_freedoms[j]} * {aero_freedoms[j]}',\n",
        "                'coefficient': polynomial_coefficients[k,i],\n",
        "            })\n",
        "            k += 1\n",
        "            \n",
        "        for m in range(0, len(aero_freedoms)):\n",
        "            for j in range(m+1, len(aero_freedoms)):\n",
        "                maps.append({\n",
        "                    'expression': f'{aero_freedoms[m]} * {aero_freedoms[j]}',\n",
        "                    'coefficient': polynomial_coefficients[k,i],\n",
        "                })\n",
        "                k += 1\n",
        "                \n",
        "        for j in range(0, len(aero_freedoms)):\n",
        "            maps.append({\n",
        "                'expression': aero_freedoms[j],\n",
        "                'coefficient': polynomial_coefficients[k,i],\n",
        "            })\n",
        "            k += 1\n",
        "            \n",
        "        maps.append({\n",
        "            'expression': 'Const',\n",
        "            'coefficient': polynomial_coefficients[k,i],\n",
        "        })\n",
        "    \n",
        "    radial_basis_function_aero_map = {\n",
        "        'basisVariables': input_names,\n",
        "        'xOffset': input_offsets,\n",
        "        'xScaling': input_scales,\n",
        "        'xLengthScales': input_length_scales,\n",
        "        'xBasisPositions': input_basis_positions\n",
        "    }\n",
        "    \n",
        "    for i, name in enumerate([f'{output_name}BasisWeights' for output_name in output_names]):\n",
        "        radial_basis_function_aero_map[name] = rbf_basis_weights[:, i]\n",
        "        \n",
        "    config['radialBasisFunctionAeroMap'] = radial_basis_function_aero_map \n",
        "    \n",
        "    return config"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "niOCpN3TO0o_"
      },
      "source": [
        "## Load data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vcPQr5UkO73z"
      },
      "source": [
        "We are loading the sample data from a CSV file in our `canopy-python-examples` GitHub repository."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "66qecRbyPFUg",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 415
        },
        "outputId": "aa7b5bb3-86dd-4198-eaa6-769ec55da8e5"
      },
      "source": [
        "sample_data = pd.read_csv('https://raw.githubusercontent.com/CanopySimulations/canopy-python-examples/master/data/canopy_f1_90_percent_barc_t9_t10_rh_scan_for_aeromap_rbf_sine_wave.csv')\n",
        "sample_data"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>hRideF</th>\n",
              "      <th>hRideR</th>\n",
              "      <th>aRollAbs</th>\n",
              "      <th>CLiftBodyF</th>\n",
              "      <th>CLiftBodyR</th>\n",
              "      <th>CDragBody</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>-0.002654</td>\n",
              "      <td>0.070993</td>\n",
              "      <td>3.328467e-03</td>\n",
              "      <td>1.906163</td>\n",
              "      <td>2.034354</td>\n",
              "      <td>0.819640</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>-0.009514</td>\n",
              "      <td>0.088048</td>\n",
              "      <td>1.487898e-02</td>\n",
              "      <td>2.128253</td>\n",
              "      <td>1.943626</td>\n",
              "      <td>0.878609</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.000537</td>\n",
              "      <td>0.090153</td>\n",
              "      <td>1.613331e-02</td>\n",
              "      <td>2.016960</td>\n",
              "      <td>2.160629</td>\n",
              "      <td>0.882672</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.004207</td>\n",
              "      <td>0.084581</td>\n",
              "      <td>1.629368e-02</td>\n",
              "      <td>1.915504</td>\n",
              "      <td>2.165675</td>\n",
              "      <td>0.859981</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.002992</td>\n",
              "      <td>0.082024</td>\n",
              "      <td>1.626428e-02</td>\n",
              "      <td>1.908604</td>\n",
              "      <td>2.123501</td>\n",
              "      <td>0.850876</td>\n",
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              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>247</th>\n",
              "      <td>-0.017477</td>\n",
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              "      <td>2.315976e-03</td>\n",
              "      <td>2.191508</td>\n",
              "      <td>1.732843</td>\n",
              "      <td>0.847463</td>\n",
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              "    <tr>\n",
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              "      <td>0.099344</td>\n",
              "      <td>2.417001e-03</td>\n",
              "      <td>2.156371</td>\n",
              "      <td>2.246043</td>\n",
              "      <td>0.900522</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>249</th>\n",
              "      <td>0.009221</td>\n",
              "      <td>0.102049</td>\n",
              "      <td>1.128490e-02</td>\n",
              "      <td>1.998724</td>\n",
              "      <td>2.353952</td>\n",
              "      <td>0.921972</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>250</th>\n",
              "      <td>0.011404</td>\n",
              "      <td>0.097143</td>\n",
              "      <td>1.001568e-02</td>\n",
              "      <td>1.957492</td>\n",
              "      <td>2.357822</td>\n",
              "      <td>0.905300</td>\n",
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              "    <tr>\n",
              "      <th>251</th>\n",
              "      <td>0.008550</td>\n",
              "      <td>0.084777</td>\n",
              "      <td>3.500000e-07</td>\n",
              "      <td>1.936019</td>\n",
              "      <td>2.287405</td>\n",
              "      <td>0.848113</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>252 rows × 6 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "       hRideF    hRideR      aRollAbs  CLiftBodyF  CLiftBodyR  CDragBody\n",
              "0   -0.002654  0.070993  3.328467e-03    1.906163    2.034354   0.819640\n",
              "1   -0.009514  0.088048  1.487898e-02    2.128253    1.943626   0.878609\n",
              "2    0.000537  0.090153  1.613331e-02    2.016960    2.160629   0.882672\n",
              "3    0.004207  0.084581  1.629368e-02    1.915504    2.165675   0.859981\n",
              "4    0.002992  0.082024  1.626428e-02    1.908604    2.123501   0.850876\n",
              "..        ...       ...           ...         ...         ...        ...\n",
              "247 -0.017477  0.083197  2.315976e-03    2.191508    1.732843   0.847463\n",
              "248 -0.001652  0.099344  2.417001e-03    2.156371    2.246043   0.900522\n",
              "249  0.009221  0.102049  1.128490e-02    1.998724    2.353952   0.921972\n",
              "250  0.011404  0.097143  1.001568e-02    1.957492    2.357822   0.905300\n",
              "251  0.008550  0.084777  3.500000e-07    1.936019    2.287405   0.848113\n",
              "\n",
              "[252 rows x 6 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HBdj74c1RG3A"
      },
      "source": [
        "## Perform the fit"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MoEJ2TCRKbhs"
      },
      "source": [
        "input_names = ['hRideF', 'hRideR', 'aRollAbs']\n",
        "output_names = ['CLiftBodyF', 'CLiftBodyR', 'CDragBody']\n",
        "\n",
        "# We'll override the length scales to something more appropriate for this data.\n",
        "input_length_scales = [10., 10., 10.]\n",
        "\n",
        "fit_result = fit_aero_data(\n",
        "    sample_data, \n",
        "    input_names,\n",
        "    output_names,\n",
        "    input_length_scales=input_length_scales)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "r5h6Ou9CKcJE"
      },
      "source": [
        "## Plot the sorted residuals"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "pycharm": {
          "name": "#%%\n",
          "is_executing": false
        },
        "id": "GrxUa-LyWCRv",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 413
        },
        "outputId": "e55899ab-e058-4eb5-c059-1a765290707b"
      },
      "source": [
        "plt.figure(figsize=(8, 6), dpi=80)\n",
        "for r, output_name in zip(fit_result.polynomial_residuals.T, output_names):\n",
        "    plt.plot(sorted(abs(r)), '-', lw=1, label=f'{output_name} Poly Residual')\n",
        "for r, output_name in zip(fit_result.polynomial_rbf_residuals.T, output_names):\n",
        "    plt.plot(sorted(abs(r)), ',', lw=1, label=f'{output_name} Poly+RBF Residual')\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 640x480 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TW-ZcA1aRJgp"
      },
      "source": [
        "## Create the car component"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ynaamkaIPTy0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "0d70245e-97bf-42cc-b819-ba45580acd8e"
      },
      "source": [
        "aero_component = get_fit_as_component(\n",
        "    fit_result,\n",
        "    input_names,\n",
        "    output_names)\n",
        "\n",
        "print(json.dumps(aero_component, cls=NumpyEncoder, indent=2))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "{\n",
            "  \"PolynomialCLiftBodyFDefinition\": [\n",
            "    {\n",
            "      \"expression\": \"hRideF * hRideF * hRideF\",\n",
            "      \"coefficient\": 3319.3183779338992\n",
            "    },\n",
            "    {\n",
            "      \"expression\": \"hRideR * hRideR * hRideR\",\n",
            "      \"coefficient\": -22.99828253623338\n",
            "    },\n",
            "    {\n",
            "      \"expression\": \"aRollAbs * aRollAbs * aRollAbs\",\n",
            "      \"coefficient\": 102242.08054790125\n",
            "    },\n",
            "    {\n",
            "      \"expression\": \"hRideF * hRideF\",\n",
            "      \"coefficient\": 133.67416673512594\n",
            "    },\n",
            "    {\n",
            "      \"expression\": \"hRideR * hRideR\",\n",
            "      \"coefficient\": -0.8005164043273815\n",
            "    },\n",
            "    {\n",
            "      \"expression\": \"aRollAbs * aRollAbs\",\n",
            "      \"coefficient\": -1893.7483559197335\n",
            "    },\n",
            "    {\n",
            "      \"expression\": \"hRideF * hRideR\",\n",
            "      \"coefficient\": -2.0291633812091634\n",
            "    },\n",
            "    {\n",
            "      \"expression\": \"hRideF * aRollAbs\",\n",
            "      \"coefficient\": -98.92374892277655\n",
            "    },\n",
            "    {\n",
            "      \"expression\": \"hRideR * aRollAbs\",\n",
            "      \"coefficient\": 4.20244733662337\n",
            "    },\n",
            "    {\n",
            "      \"expression\": \"hRideF\",\n",
            "      \"coefficient\": -10.058863853385164\n",
            "    },\n",
            "    {\n",
            "      \"expression\": \"hRideR\",\n",
            "      \"coefficient\": 9.88110105712503\n",
            "    },\n",
            "    {\n",
            "      \"expression\": \"aRollAbs\",\n",
            "      \"coefficient\": 1.7249450186507818\n",
            "    },\n",
            "    {\n",
            "      \"expression\": \"Const\",\n",
            "      \"coefficient\": 1.1918083511089996\n",
            "    }\n",
            "  ],\n",
            "  \"PolynomialCLiftBodyRDefinition\": [\n",
            "    {\n",
            "      \"expression\": \"hRideF * hRideF * hRideF\",\n",
            "      \"coefficient\": 438.0052760505142\n",
            "    },\n",
            "    {\n",
            "      \"expression\": \"hRideR * hRideR * hRideR\",\n",
            "      \"coefficient\": -1043.3784403725517\n",
            "    },\n",
            "    {\n",
            "      \"expression\": \"aRollAbs * aRollAbs * aRollAbs\",\n",
            "      \"coefficient\": 66215.93558602109\n",
            "    },\n",
            "    {\n",
            "      \"expression\": \"hRideF * hRideF\",\n",
            "      \"coefficient\": -350.3529483334503\n",
            "    },\n",
            "    {\n",
            "      \"expression\": \"hRideR * hRideR\",\n",
            "      \"coefficient\": 265.36237261620266\n",
            "    },\n",
            "    {\n",
            "      \"expression\": \"aRollAbs * aRollAbs\",\n",
            "      \"coefficient\": -1324.0030268795988\n",
            "    },\n",
            "    {\n",
            "      \"expression\": \"hRideF * hRideR\",\n",
            "      \"coefficient\": -5.063949613436339\n",
            "    },\n",
            "    {\n",
            "      \"expression\": \"hRideF * aRollAbs\",\n",
            "      \"coefficient\": -59.58108774849398\n",
            "    },\n",
            "    {\n",
            "      \"expression\": \"hRideR * aRollAbs\",\n",
            "      \"coefficient\": 31.543151748566984\n",
            "    },\n",
            "    {\n",
            "      \"expression\": \"hRideF\",\n",
            "      \"coefficient\": 17.588755279992284\n",
            "    },\n",
            "    {\n",
            "      \"expression\": \"hRideR\",\n",
            "      \"coefficient\": -15.75141849875206\n",
            "    },\n",
            "    {\n",
            "      \"expression\": \"aRollAbs\",\n",
            "      \"coefficient\": -2.3629615070949477\n",
            "    },\n",
            "    {\n",
            "      \"expression\": \"Const\",\n",
            "      \"coefficient\": 2.2429820456486596\n",
            "    }\n",
            "  ],\n",
            "  \"PolynomialCDragBodyDefinition\": [\n",
            "    {\n",
            "      \"expression\": \"hRideF * hRideF * hRideF\",\n",
            "      \"coefficient\": 756.7534620018919\n",
            "    },\n",
            "    {\n",
            "      \"expression\": \"hRideR * hRideR * hRideR\",\n",
            "      \"coefficient\": -259.1331990036359\n",
            "    },\n",
            "    {\n",
            "      \"expression\": \"aRollAbs * aRollAbs * aRollAbs\",\n",
            "      \"coefficient\": 5756.661135806554\n",
            "    },\n",
            "    {\n",
            "      \"expression\": \"hRideF * hRideF\",\n",
            "      \"coefficient\": 42.47237422297145\n",
            "    },\n",
            "    {\n",
            "      \"expression\": \"hRideR * hRideR\",\n",
            "      \"coefficient\": 95.01166680643672\n",
            "    },\n",
            "    {\n",
            "      \"expression\": \"aRollAbs * aRollAbs\",\n",
            "      \"coefficient\": -104.85754631669329\n",
            "    },\n",
            "    {\n",
            "      \"expression\": \"hRideF * hRideR\",\n",
            "      \"coefficient\": -0.11370556556503875\n",
            "    },\n",
            "    {\n",
            "      \"expression\": \"hRideF * aRollAbs\",\n",
            "      \"coefficient\": -15.993989390219996\n",
            "    },\n",
            "    {\n",
            "      \"expression\": \"hRideR * aRollAbs\",\n",
            "      \"coefficient\": 2.602069301015082\n",
            "    },\n",
            "    {\n",
            "      \"expression\": \"hRideF\",\n",
            "      \"coefficient\": 0.10062642694819246\n",
            "    },\n",
            "    {\n",
            "      \"expression\": \"hRideR\",\n",
            "      \"coefficient\": -7.697468776464916\n",
            "    },\n",
            "    {\n",
            "      \"expression\": \"aRollAbs\",\n",
            "      \"coefficient\": 1.2404586573762035\n",
            "    },\n",
            "    {\n",
            "      \"expression\": \"Const\",\n",
            "      \"coefficient\": 0.9742584844102851\n",
            "    }\n",
            "  ],\n",
            "  \"radialBasisFunctionAeroMap\": {\n",
            "    \"basisVariables\": [\n",
            "      \"hRideF\",\n",
            "      \"hRideR\",\n",
            "      \"aRollAbs\"\n",
            "    ],\n",
            "    \"xOffset\": [\n",
            "      -0.0016793449999999988,\n",
            "      -0.0892649815,\n",
            "      -0.008354381999999999\n",
            "    ],\n",
            "    \"xScaling\": [\n",
            "      0.025033676,\n",
            "      0.04352443650000001,\n",
            "      0.008354333\n",
            "    ],\n",
            "    \"xLengthScales\": [\n",
            "      10.0,\n",
            "      10.0,\n",
            "      10.0\n",
            "    ],\n",
            "    \"xBasisPositions\": [\n",
            "      [\n",
            "        -0.11212739990722892,\n",
            "        -0.0755254694681688,\n",
            "        -0.712340769753851\n",
            "      ],\n",
            "      [\n",
            "        -0.08506437488445559,\n",
            "        -0.5991719042703747,\n",
            "        -0.9992050831586433\n",
            "      ],\n",
            "      [\n",
            "        0.994268680316866,\n",
            "        0.653185837339904,\n",
            "        0.22167059895745117\n",
            "      ],\n",
            "      [\n",
            "        -0.09887185565555769,\n",
            "        -0.31965244397822357,\n",
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          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oBUw0_zGRPM2"
      },
      "source": [
        "## Save a new car to the platform"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "j_-7-xwDdpPy"
      },
      "source": [
        "For this example we'll load the `Canopy F1 Car 2019` and merge in the new aero data before saving it as a new car."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fqjAZ2x0RRcW",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "d6488fd6-a080-4f79-f4d2-69ac4211225f"
      },
      "source": [
        "input_car = await canopy.load_default_config(\n",
        "    session,\n",
        "    'car',\n",
        "    'Canopy F1 Car 2019')\n",
        "\n",
        "# Set these to zero as they are accounted for in the aero data we have fitted.\n",
        "input_car.data.aero.coefficientOffsets.CDragBodyUserOffset = 0\n",
        "input_car.data.aero.coefficientOffsets.CLiftBodyUserOffset = 0\n",
        "\n",
        "# Merge in the new aero component.\n",
        "input_car.data.aero = {**input_car.data.aero, **aero_component}\n",
        "\n",
        "new_config_id = await canopy.create_config(\n",
        "    session,\n",
        "    'car',\n",
        "    'Canopy F1 Car 2019 with Fitted Aero',\n",
        "    input_car.data)\n",
        "    \n",
        "new_config_id"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'9f3a312b51ac4ac9b19b9c2e12947183'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    }
  ]
}