{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "cell_marker": "\"\"\"",
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "# CFT Example"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "<div class=\"alert alert-block alert-info\">\n",
    "This example illustrates how to process data collected when conducting the <i>Cold Face Test (CFT)</i> procedure, i.e., how to compute parameters quantifying the reaction to the CFT and how to create specialized CFT plots.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "## Setup and Helper Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from pathlib import Path\n",
    "\n",
    "import re\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "from fau_colors import cmaps\n",
    "\n",
    "import biopsykit as bp\n",
    "from biopsykit.protocols import CFT\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "%matplotlib widget\n",
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "plt.close(\"all\")\n",
    "\n",
    "palette = sns.color_palette(cmaps.faculties)\n",
    "sns.set_theme(context=\"notebook\", style=\"ticks\", font=\"sans-serif\", palette=palette)\n",
    "\n",
    "plt.rcParams[\"figure.figsize\"] = (8, 4)\n",
    "plt.rcParams[\"pdf.fonttype\"] = 42\n",
    "plt.rcParams[\"mathtext.default\"] = \"regular\"\n",
    "\n",
    "palette"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "## Create CFT Object"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "The [CFT](https://biopsykit.readthedocs.io/en/latest/api/biopsykit.protocols.cft.html#biopsykit.protocols.cft.CFT) is a protocol and thus part of the Protocol API of `BioPsyKit`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "cft = CFT()\n",
    "cft"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "## Load Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "hr_dict = bp.example_data.get_mist_hr_example()\n",
    "\n",
    "# Alternatively: Use your own data\n",
    "# hr_dict = bp.io.ecg.load_hr_phase_dict(\"<path-to-heart-rate-data-dictionary>\")\n",
    "df_hr = hr_dict[\"MIST3\"]\n",
    "\n",
    "df_hr.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "## Plot Course of Heart Rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "fig, ax = plt.subplots()\n",
    "bp.signals.ecg.plotting.hr_plot(df_hr, ax=ax)\n",
    "fig.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "Note: See [plotting.hr_plot()](https://biopsykit.readthedocs.io/en/latest/api/biopsykit.signals.ecg.plotting.html#biopsykit.signals.ecg.plotting.hr_plot) for further information!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "## Compute CFT Parameter"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "Extract CFT Interval, i.e., the interval of the data where the Cold Face stimulus was actually applied (corresponds to the duration of the \"CFT\" phase specified in the [CFT](https://biopsykit.readthedocs.io/en/latest/api/biopsykit.protocols.cft.html#biopsykit.protocols.cft.CFT) object) and compute baseline heart rate (the mean heart rate during the \"Baseline\" phase specified in the [CFT](https://biopsykit.readthedocs.io/en/latest/api/biopsykit.protocols.cft.html#biopsykit.protocols.cft.CFT) object).\n",
    "\n",
    "Note: See [CFT.extract_cft_interval()](https://biopsykit.readthedocs.io/en/latest/api/biopsykit.protocols.cft.html#biopsykit.protocols.cft.CFT.extract_cft_interval) and [CFT.compute_cft_parameter()](https://biopsykit.readthedocs.io/en/latest/api/biopsykit.protocols.cft.html#biopsykit.protocols.cft.CFT.compute_cft_parameter) for further information!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "df_cft = cft.extract_cft_interval(df_hr)\n",
    "bl = cft.baseline_hr(df_hr)\n",
    "\n",
    "print(\"Baseline Heart Rate: {:.2f}\".format(bl))\n",
    "df_cft.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "cft.compute_cft_parameter(df_hr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "## Plot CFT Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    },
    "tags": [
     "nbsphinx-thumbnail"
    ]
   },
   "outputs": [],
   "source": [
    "fig, ax = plt.subplots()\n",
    "\n",
    "cft.cft_plot(data=df_hr, ax=ax)\n",
    "fig.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "Note: See [CFT.cft_plot()](https://biopsykit.readthedocs.io/en/latest/api/biopsykit.protocols.html#biopsykit.protocols.CFT.cft_plot) for further information!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": []
  }
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