{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Feature Engineering"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Morphology Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from vitalDSP.filtering.signal_filtering import SignalFiltering\n",
    "import numpy as np\n",
    "from plotly import graph_objects as go\n",
    "import plotly.io as pio\n",
    "pio.renderers.default = \"sphinx_gallery\"\n",
    "# pio.renderers.default = \"plotly_mimetype\"  # or \"plotly_mimetype\"\n",
    "# from IPython.display import display, HTML\n",
    "# display(HTML('<script src=\"https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.6/require.min.js\"></script>'))\n",
    "import os\n",
    "from vitalDSP.notebooks import load_sample_ecg_small, plot_trace\n",
    "from vitalDSP.feature_engineering.morphology_features import PhysiologicalFeatureExtractor,\\\n",
    "    PreprocessConfig\n",
    "\n",
    "fs = 128\n",
    "signal_col, date_col = load_sample_ecg_small()\n",
    "signal_col = np.array(signal_col)\n",
    "\n",
    "preprocess_config = PreprocessConfig(\n",
    "    filter_type=\"butterworth\",\n",
    "    lowcut=0.5,\n",
    "    highcut=5,\n",
    "    order=4,\n",
    "    # noise_reduction_method=\"wavelet\"\n",
    ")\n",
    "extractor = PhysiologicalFeatureExtractor(signal_col, fs=fs)\n",
    "features = extractor.extract_features(signal_type=\"ECG\", preprocess_config=preprocess_config)\n",
    "print(\"Features extracted successfully:\")\n",
    "print(features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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