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  {
   "cell_type": "code",
   "metadata": {
    "tags": [
     "plotly-renderer-setup"
    ]
   },
   "source": [
    "# Configure Plotly to render inline figures via CDN so they display\n",
    "# in the rendered Sphinx output (RTD).  Safe to ignore when running\n",
    "# locally in Jupyter.\n",
    "import plotly.io as pio\n",
    "pio.renderers.default = 'notebook_connected'\n"
   ],
   "outputs": [],
   "execution_count": null
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Example analysis of a PPG/ECG waveform using the vital_sqi package\n",
    "\n",
    "The following notebook shows an example of PPG/ECG waveform processing using the vital_sqi package. The aim of the package is to automate signal quality classification for PPG/ECG waveforms. It is achieved by computing various signal quality indices for each signal segment and using them to form a decision. \n",
    "\n",
    "## The pipeline can be briefly summarized as follows:\n",
    "1. Load dataset under analysis\n",
    "2. Preprocess and segment the dataset\n",
    "3. Compute SQI for each dataset segment\n",
    "4. Make decision for each segment"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Global Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import warnings\n",
    "import os\n",
    "import pandas as pd \n",
    "import matplotlib.pyplot as plt\n",
    "import vital_sqi\n",
    "from vital_sqi.data.signal_io import ECG_reader,PPG_reader"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Start by importing the signal via the PPG_reader function\n",
    "\n",
    "The function expects a .csv or similar data format with named columns. The column names are used to separate between data column, timestamp columns and any additional information columns.\n",
    "This returns a SignalSQI class that is compatible with other vital_sqi package functions, the main class members of interest are:\n",
    "* signals:         an ndarray of shape (m, n) where m is the number of rows and n is the number of channels of the signal\n",
    "* sqis:     an ndarray of shape (m, n) where m is the number of signal segments, n is the number of SQIs.\n",
    "* sampling_rate:   sampling rate in hertz (Hz)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from vital_sqi.pipeline.pipeline_highlevel import get_ppg_sqis\n",
    "\n",
    "file_in = os.path.abspath('test_data/ppg_smartcare.csv') #FIle input location\n",
    "sqi_dict = os.path.abspath('test_data/sqi_dict.json') #input dictionary -> which sqi features \n",
    "segments, signal_obj = get_ppg_sqis(file_in,\n",
    "                                    signal_idx=['PLETH'], \n",
    "                                    timestamp_idx=['TIMESTAMP_MS'], \n",
    "                                    sqi_dict_filename = sqi_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "signal_obj.sqis[0].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Get decision"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from vital_sqi.pipeline.pipeline_highlevel import get_qualified_ppg\n",
    "rule_dict_filename = os.path.abspath('test_data/rule_dict_test.json') #  location dictionary contain threshold\n",
    "ruleset_order = {3: 'skewness_1',\n",
    "                  2: 'entropy',\n",
    "                  1: 'perfusion'}\n",
    "file_in = os.path.abspath('test_data/ppg_smartcare.csv') #FIle input location\n",
    "sqi_dict = os.path.abspath('test_data/sqi_dict.json') #input dictionary -> which sqi features \n",
    "signal_obj = get_qualified_ppg(file_in,\n",
    "                                    signal_idx=['PLETH'], \n",
    "                                    timestamp_idx=['TIMESTAMP_MS'], \n",
    "                                    sqi_dict_filename = sqi_dict,\n",
    "                                    rule_dict_filename = rule_dict_filename,\n",
    "                                    ruleset_order=ruleset_order,\n",
    "                                    output_dir=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "signal_obj.sqis[0].describe()"
   ]
  }
 ],
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