{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyedflib import highlevel\n",
    "from portiloop.src.demo.utils import xdf2array\n",
    "import numpy as np\n",
    "\n",
    "filename_edf = '/home/ubuntu/portiloop-software/BSP_L22_Portiloop_EDF.edf'\n",
    "filename_xdf = '/home/ubuntu/portiloop-software/BSP_L22_Portiloop_XDF.xdf'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1147000,)"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "edf_read = highlevel.read_edf(filename_edf)\n",
    "signal_edf = edf_read[0][1, :]\n",
    "signal_edf.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_whole, columns = xdf2array(filename_xdf, 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1142166,)"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "signal_xdf = data_whole[:, columns.index(\"online_filtered_signal_portiloop\")]\n",
    "signal_xdf.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4834"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(signal_edf) - len(signal_xdf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.isin(signal_edf, signal_xdf).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-0.020828564888990615 != -0.3275071084499359\n"
     ]
    }
   ],
   "source": [
    "print(f\"{signal_edf[10000]} != {signal_xdf[10000]}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([], dtype=int64),)"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.where(signal_edf == signal_xdf[100000])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "# signal_xdf = np.concatenate([signal_xdf, np.zeros(len(signal_edf) - len(signal_xdf))])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "# signals = np.concatenate((np.expand_dims(signal_edf, 0), np.expand_dims(signal_xdf, 0)), axis = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2, 1147000)"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# signals.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# # Create and edf file with both signals:\n",
    "# channel_names = ['EDF_Data', \"XDF_Data\"]\n",
    "# signal_headers = highlevel.make_signal_headers(channel_names, sample_frequency=250)\n",
    "# headers = highlevel.make_header(patientname='L22', gender='Male')\n",
    "\n",
    "\n",
    "# highlevel.write_edf('edf_file.edf', signals, signal_headers, headers)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [],
   "source": [
    "from portiloop.src.demo.utils import OfflineSleepSpindleRealTimeStimulator\n",
    "from portiloop.src.detection import SleepSpindleRealTimeDetector\n",
    "from portiloop.src.processing import FilterPipeline\n",
    "\n",
    "\n",
    "filter = FilterPipeline(nb_channels=1, sampling_rate=250)\n",
    "detector = SleepSpindleRealTimeDetector(threshold=0.82, channel=1) # always 1 because we have only one channel\n",
    "stimulator = OfflineSleepSpindleRealTimeStimulator()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running online filtering and detection...\n"
     ]
    }
   ],
   "source": [
    "print(\"Running online filtering and detection...\")\n",
    "\n",
    "points = []\n",
    "online_activations = []\n",
    "delayed_stims = []\n",
    "\n",
    "# Go through the data\n",
    "for index, point in enumerate(signal_xdf):\n",
    "    # Filter the data\n",
    "    filtered_point = filter.filter(np.array([point]))\n",
    "\n",
    "    filtered_point = filtered_point.tolist()\n",
    "    points.append(filtered_point[0])\n",
    "    # Detect the spindles\n",
    "    result = detector.detect([[point]])\n",
    "\n",
    "    # if stimulation_phase != \"Fast\":\n",
    "    #     delayed_stim = stimulation_delayer.step_timesteps(filtered_point[0])\n",
    "    #     if delayed_stim:\n",
    "    #         delayed_stims.append(1)\n",
    "    #     else:\n",
    "    #         delayed_stims.append(0)\n",
    "\n",
    "    # Stimulate if necessary\n",
    "    stim = stimulator.stimulate(result)\n",
    "    if stim:\n",
    "        online_activations.append(1)\n",
    "    else:\n",
    "        online_activations.append(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1147000"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(online_activations)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "31"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(online_activations)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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