lab-6-escape-response-analysis.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "toc_visible": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# Save a copy in your Google Drive with your initials appended to the filename\n",
        "Do this first, then proceed."
      ],
      "metadata": {
        "id": "e0MSgSN99Aie"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Lab 6: Escape response kinematics\n",
        "\n",
        "In this lab you will be working with fish escape response data collected from videos of experiments conducted by Prof. Alberto Soto and colleagues Jenn Bullard and Dr. Katie Whitlow at Friday Harbor Labs (part of University of Washington). The fish used in this experiment is the [Pacific staghorn sculpin](https://mexican-fish.com/pacific-staghorn-sculpin/). To induce a fast-start escape response, we dropped an object into the experimental tank. This produces an acoustic and mechanical stimulus to the escape response circuit.  \n",
        "\n",
        "To gather these data, we tracked the position of the sculpin's mouth and center-of-mass. The data sets contain the following variables: \n",
        "\n",
        "`time, xMouth, yMouth, xCOM, yCOM`  "
      ],
      "metadata": {
        "id": "OYihe2C39Vbu"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Useful parameters and notes\n",
        "\n",
        "* The units of position are given in centimeters, time is in seconds.\n",
        "* The videos were recorded at 500fps, so the $\\Delta t$ between consecutive frames is 0.002 seconds (2 ms). \n",
        "* The timeseries data begin a couple of frames before the start of the escape response.\n",
        "* At the top of the CSV file, there are 3 relevant video parameters: **start stimulus, start tracking, start escape**, given as frame numbers. You will use this information to caluclate the response latency, which is the time between the stimulus start and the start of the escape response. \n",
        "\n",
        "* Conversion factor with units cm/pixels:\n",
        "`cm_px = 4.5285e-2`\n",
        "\n",
        "* Video dimensions in pixels:\n",
        "  * `vid_width = 1920`\n",
        "  *`vid_height = 1080`\n",
        "\n",
        "* The origin for these data is set to center of the image frame. We'll want to redefine a new origin to be the bottom left corner of the image. \n",
        "To do this use the following equations: \n",
        "  * `x_new = cm_px*(1920/2) + x`\n",
        "  * `y_new =cm_px*(1080/2) + y`\n",
        "\n",
        "* Position where the stimulus hits the water (cm), relative to the new origin:\n",
        "  * `stimX = cm_px*(1920/2) + 29.680`\n",
        "  * `stimY = cm_px*(1080/2) + (-15.9750)`\n"
      ],
      "metadata": {
        "id": "3gVbTl5Zd67A"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Lab 6: Tasks\n",
        "In this lab you will work with 2 different escape response sequences, they may be found in your Drive folder. \n",
        "\n",
        "For each sequence:\n",
        "  * Determine the total duration in **sec** of each sequence\n",
        "  * Calculate the response latency, units should be reported in **ms**\n",
        "  * Plot the trajectory of the fish's center-of-mass, make sure to denote the initial position of the fish on the figure\n",
        "  * Calculate the distance in **cm** between the stimulus position and the initial position of the fish.\n",
        "  * Compute and plot the following derived kinematic variables with appropriate units \n",
        "    * Heading of fish, \n",
        "$\\theta (t)$,\n",
        "computed using the vector from (xCOM, yCOM) to (xMouth, yMouth)\n",
        "    * Velocity, \n",
        "$\\dot x(t) = v(t)$\n",
        "    * Acceleration, \n",
        "$\\dot v(t) = a(t)$\n",
        "    * Rotational velocity, \n",
        "$\\dot \\theta(t)$\n",
        "  * Use the heading and rotational velocity data to calculate the timing (**ms** after escape begins) and duration (in **ms**) of Stage 1, Stage 2, and Stage 3 of the escape response. \n",
        "  * Determine whether the escape was toward the stimulus or away from the stimulus\n",
        "\n",
        "**Note:** Derivatives should be computed using the finite difference method provided"
      ],
      "metadata": {
        "id": "LoquWzmILF8O"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## What to turn in\n",
        "In contrast to previous labs, rather than submitting a pdf of your Google Colab notebook, you’ll compile the results of your lab work (the tasks listed above) into a separate file. "
      ],
      "metadata": {
        "id": "bQWvchugQGgs"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Load the Dependencies and Data"
      ],
      "metadata": {
        "id": "FRGEfogfE03f"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "LnEcEVv382zM"
      },
      "outputs": [],
      "source": [
        "import scipy.io\n",
        "import numpy as np\n",
        "from matplotlib import pyplot as plt\n",
        "import pandas as pd\n",
        "\n",
        "%matplotlib inline\n",
        "\n",
        "# Scipy signal for smoothing noisy data\n",
        "from scipy import signal\n",
        "\n",
        "# Plotly for interactive plots\n",
        "import plotly.express as px\n",
        "from plotly.subplots import make_subplots\n",
        "import plotly.graph_objects as go"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Sample code to load the timeseries and video parameter data separately "
      ],
      "metadata": {
        "id": "m1YABLIoIMK-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# sample data URL\n",
        "sample_url = \"https://raw.githubusercontent.com/adsoto/Shark-Analysis/main/180815-expF-trial2.csv\""
      ],
      "metadata": {
        "id": "p_0iloPeJ5e9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Read timeseries data \n",
        "escape_1 = pd.read_csv(sample_url, skiprows=3)\n",
        "escape_1.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "IEfpnIsGE-13",
        "outputId": "8107601e-38a3-4d45-b570-5b5b280fa26f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    time     xMouth    yMouth      xCOM      yCOM\n",
              "0  1.294  24.740876 -3.865762  20.16185 -4.579476\n",
              "1  1.296  24.649917 -3.956721  20.16185 -4.579476\n",
              "2  1.298  24.695396 -3.729323  20.16185 -4.579476\n",
              "3  1.300  24.740876 -3.774803  20.16185 -4.579476\n",
              "4  1.302  24.695396 -3.956721  20.16185 -4.498423"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-9e234f02-68b6-4800-b62b-ef79d1e33e45\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>time</th>\n",
              "      <th>xMouth</th>\n",
              "      <th>yMouth</th>\n",
              "      <th>xCOM</th>\n",
              "      <th>yCOM</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1.294</td>\n",
              "      <td>24.740876</td>\n",
              "      <td>-3.865762</td>\n",
              "      <td>20.16185</td>\n",
              "      <td>-4.579476</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1.296</td>\n",
              "      <td>24.649917</td>\n",
              "      <td>-3.956721</td>\n",
              "      <td>20.16185</td>\n",
              "      <td>-4.579476</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1.298</td>\n",
              "      <td>24.695396</td>\n",
              "      <td>-3.729323</td>\n",
              "      <td>20.16185</td>\n",
              "      <td>-4.579476</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1.300</td>\n",
              "      <td>24.740876</td>\n",
              "      <td>-3.774803</td>\n",
              "      <td>20.16185</td>\n",
              "      <td>-4.579476</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1.302</td>\n",
              "      <td>24.695396</td>\n",
              "      <td>-3.956721</td>\n",
              "      <td>20.16185</td>\n",
              "      <td>-4.498423</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-9e234f02-68b6-4800-b62b-ef79d1e33e45')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-9e234f02-68b6-4800-b62b-ef79d1e33e45 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-9e234f02-68b6-4800-b62b-ef79d1e33e45');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# video parameter rows\n",
        "param_rows = [0, 1]\n",
        "\n",
        "# Read video parameters \n",
        "escape_1_params = pd.read_csv(sample_url, \n",
        "                              skiprows = lambda x: x not in param_rows, \n",
        "                              usecols=['start stimulus', 'start tracking', 'start escape'])\n",
        "\n",
        "escape_1_params"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81
        },
        "id": "Y-_2ny0jFrJZ",
        "outputId": "037279f0-d136-4acf-e118-3e193f900a1e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   start stimulus  start tracking  start escape\n",
              "0              13             647           648"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-766c52a3-d579-4b20-b021-d610edd73a11\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>start stimulus</th>\n",
              "      <th>start tracking</th>\n",
              "      <th>start escape</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>13</td>\n",
              "      <td>647</td>\n",
              "      <td>648</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-766c52a3-d579-4b20-b021-d610edd73a11')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-766c52a3-d579-4b20-b021-d610edd73a11 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-766c52a3-d579-4b20-b021-d610edd73a11');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Numerical differentiation: Finite differences\n",
        "Use the following function to compute the derivative of a 1D array versus time. \n",
        "\n",
        "Thanks to Martin Susanto for writing the function and test case. "
      ],
      "metadata": {
        "id": "Zh8hLbh-Qwuu"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def finite_difference(t, x):\n",
        "    '''\n",
        "    Computes the derivative using finite difference approximation.\n",
        "\n",
        "    Inputs:\n",
        "        t (1D array)\n",
        "        x (1D array)\n",
        "\n",
        "    Returns:\n",
        "        v (1D array): derivative of x with respect to t\n",
        "    '''\n",
        "\n",
        "    lx = len(x)\n",
        "    v = []\n",
        "\n",
        "    # Check for equal length\n",
        "    if x.shape[0] != t.shape[0]:\n",
        "        raise ValueError('x and t must have the same first dimension')\n",
        "    \n",
        "    if lx == 1:\n",
        "        return 0\n",
        "\n",
        "    # forward difference for 1st element\n",
        "    v += [(x[1]-x[0])/(t[1]-t[0])]\n",
        "\n",
        "    # central difference for central elements\n",
        "    for i in range(lx-1)[1:]:\n",
        "        v += [(x[i-1]-x[i+1])/(t[i-1]-t[i+1])]\n",
        "\n",
        "    # backward difference for last element\n",
        "    v += [(x[-1]-x[-2])/(t[-1]-t[-2])]\n",
        "\n",
        "    return v"
      ],
      "metadata": {
        "id": "FhFlHy84Pn5t"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Sample call to the finite_difference function\n",
        "\n",
        "The code below computes the derivate of $\\cos(t)$ and plots the result over the exact solution. "
      ],
      "metadata": {
        "id": "LtSv6ey-RmR5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "    plt.style.use('seaborn-poster')\n",
        "\n",
        "    # step size for time interval\n",
        "    h = 0.01\n",
        "\n",
        "    # define 1D time array\n",
        "    t = np.arange(0, 4*np.pi, h) \n",
        "\n",
        "    # generate a 1D array for cos(t)\n",
        "    x = np.cos(t) \n",
        "\n",
        "    # compute the derivative of x w.r.t. time\n",
        "    dot_x = finite_difference(t, x)\n",
        "\n",
        "    # exact solution; -sin(t)\n",
        "    exact_solution = -np.sin(t)\n",
        "    \n",
        "    # Plot the results\n",
        "    plt.figure(figsize = (12, 8))\n",
        "    plt.plot(t, exact_solution, \\\n",
        "            label = 'Exact solution')\n",
        "    plt.plot(t, dot_x, '--', \\\n",
        "            label = 'Finite difference approximation')\n",
        "    plt.legend()\n",
        "    plt.show(block=True)\n",
        "\n",
        "    # Compute max error between \n",
        "    # numerical derivative and exact solution\n",
        "    max_error = max(abs(exact_solution - dot_x))\n",
        "    print(\"max absolute error:\", max_error)"
      ],
      "metadata": {
        "id": "XESZgzc4Rk5T",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 513
        },
        "outputId": "b533c386-4338-47b1-e5e6-248a3f893ca1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x576 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "max absolute error: 0.004999958333473664\n"
          ]
        }
      ]
    }
  ]
}