DownTest.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%capture\n",
    "import os\n",
    "import mne\n",
    "import glob\n",
    "#\n",
    "import numpy as np\n",
    "import math\n",
    "import matplotlib.pyplot as plt\n",
    "# %matplotlib inline\n",
    "\n",
    "import copy\n",
    "import pandas as pd\n",
    "import torch \n",
    "\n",
    "from torch import nn\n",
    "from torch.utils.data import Dataset, DataLoader\n",
    "import torch.nn.functional as F\n",
    "\n",
    "from pyFiles.models import *\n",
    "from pyFiles.dataprep2_down import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "DownPath = \"C:\\\\Users\\\\TheSy\\\\Desktop\\\\dataverse_files\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pre-processing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "SANE =[ \"h01.edf\",\n",
    "        \"h02.edf\",\n",
    "        \"h03.edf\",\n",
    "        \"h04.edf\",\n",
    "        \"h05.edf\",\n",
    "        \"h06.edf\",\n",
    "        \"h07.edf\",\n",
    "        \"h08.edf\",\n",
    "        \"h09.edf\",\n",
    "        \"h10.edf\",\n",
    "        \"h11.edf\",\n",
    "        \"h12.edf\",\n",
    "        \"h13.edf\",\n",
    "        \"h14.edf\",\n",
    "        ]\n",
    "\n",
    "ABNORMAL = [\"s01.edf\",\n",
    "            \"s02.edf\",\n",
    "            \"s03.edf\",\n",
    "            \"s04.edf\",\n",
    "            \"s05.edf\",\n",
    "            \"s06.edf\",\n",
    "            \"s07.edf\",\n",
    "            \"s08.edf\",\n",
    "            \"s09.edf\",\n",
    "            \"s10.edf\",\n",
    "            \"s11.edf\",\n",
    "            \"s12.edf\",\n",
    "            \"s13.edf\",\n",
    "            \"s14.edf\",\n",
    "]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def channel_select(data, channels):\n",
    "    '''\n",
    "    Selects channels from array \n",
    "    '''\n",
    "    extracted = data.pick(channels, exclude=\"bads\")\n",
    "    return extracted\n",
    "\n",
    "def clip(data, channels,max= 500e-6):\n",
    "    def cliper(array):\n",
    "        for i in range(len(array)):\n",
    "            if abs(array[i]) > max:\n",
    "                array[i] = math.copysign(max,array[i])\n",
    "        return array\n",
    "    data.apply_function(cliper, picks=channels, channel_wise= True)\n",
    "\n",
    "def eeg_filter(data, lfreq = 1, hfreq= 70):\n",
    "    '''\n",
    "    \n",
    "    '''\n",
    "    data_copy = copy.copy(data)\n",
    "    filtered = data_copy.filter(#l_freq = lfreq,\n",
    "                                l_freq = lfreq,\n",
    "                                h_freq = hfreq,\n",
    "                                method = \"iir\",\n",
    "                                )\n",
    "    return filtered\n",
    "\n",
    "def temporal_crop(data, tin = 60, tfin = 12*60):\n",
    "    ''' \n",
    "    Cut the channels from the second \"tin\" to \"tfin\"\n",
    "    '''\n",
    "    data_copy = copy.copy(data)\n",
    "    croped = data_copy.crop(tmin = tin, tmax = min(tfin, int(data.times[-1])))\n",
    "    return croped\n",
    "\n",
    "def get_epochs(data, channels, window = 10):\n",
    "    ''' \n",
    "    window es la ventana de tiempo\n",
    "    '''\n",
    "    data_copy = copy.copy(data)\n",
    "    # Create events\n",
    "    events = mne.make_fixed_length_events(data_copy, duration = window, first_samp = True)\n",
    "    # Divide accordingly\n",
    "    picks = channels\n",
    "    epochs = mne.Epochs(raw = data_copy, events = events, picks = picks, preload = True,\n",
    "                        tmin = 0., tmax = window, baseline = None,\n",
    "                        flat = dict(eeg = 1e-6))\n",
    "    \n",
    "    epochs.drop(-1,reason = \"Unfixed duration\")\n",
    "    return epochs\n",
    "\n",
    "def downsample(epoch, freq = 100): # original 200\n",
    "    ''' \n",
    "    Downsamples the data given by a factor\n",
    "    En nuestro caso, down corresponde a (frecuencia que queremos)/(frecuencia actual)\n",
    "    '''\n",
    "    down = epoch.resample(freq, npad = \"auto\")\n",
    "    return down\n",
    "\n",
    "def normalization(epochs):\n",
    "    obj = mne.decoding.Scaler(info = epochs.info, scalings='mean')\n",
    "    values = obj.fit_transform(epochs.get_data())\n",
    "    return values\n",
    "\n",
    "def EDFprep(edf, n_channels = 19, norm = True, random = True, ):\n",
    "    '''\n",
    "    Pipeline\n",
    "    '''\n",
    "\n",
    "    #Random channel select\n",
    "    channels = edf.ch_names\n",
    "    if random:\n",
    "        ch = np.random.choice(channels[:-3], size = n_channels, replace=False)\n",
    "    else:\n",
    "        ch = channels[:n_channels]\n",
    "    \n",
    "    channel_data = channel_select(edf, ch)\n",
    "    filtered = eeg_filter(channel_data)\n",
    "    clip(filtered, ch)\n",
    "    trimmed_data = temporal_crop(filtered)\n",
    "    re_ref = trimmed_data.copy().set_eeg_reference(ref_channels=\"average\")\n",
    "    down_data = downsample(re_ref)\n",
    "    epochs = get_epochs(down_data,down_data.ch_names)\n",
    "\n",
    "    if norm:\n",
    "        norm_data = normalization(epochs)\n",
    "        norm_data = np.delete(norm_data,-1,2)\n",
    "        return norm_data \n",
    "    return epochs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def Save_win(data,loc_df, final_dir, patient_id,label, save = False):\n",
    "\n",
    "    for i, win in enumerate(data):\n",
    "        sdir = os.path.join(final_dir,f\"{patient_id}_w{i+1}.pt\")\n",
    "        loc_df.loc[len(loc_df)] = [patient_id,i+1,sdir]\n",
    "\n",
    "        win_save = torch.from_numpy(win).type(torch.FloatTensor)\n",
    "        if save:\n",
    "            torch.save((win_save, label),sdir)\n",
    "    return loc_df\n",
    "\n",
    "def Save_ch(data,loc_df, final_dir, patient_id,label, save = False):\n",
    "    \n",
    "    for i, win in enumerate(data):\n",
    "        for j, ch in enumerate(win):\n",
    "            # sdir = f\"{save_dir}/{patient_id}/{patient_id}_{session_id}_w{i+1}_ch{j+1}.pt\"\n",
    "            sdir = os.path.join(final_dir, f\"{patient_id}_w{i+1}_ch{j+1}.pt\")\n",
    "            loc_df.loc[len(loc_df)] = [patient_id,i+1,sdir]\n",
    "            \n",
    "            if save:\n",
    "                ch_save = torch.from_numpy(ch).type(torch.FloatTensor)\n",
    "                ch_save = ch_save.unsqueeze(dim = 0)\n",
    "                torch.save((ch_save, label), sdir)\n",
    "    return loc_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "def prep(path, save = False, mode = \"per_win\", save_dir = \"data\"):\n",
    "    ''' \n",
    "    Lectura de todos los edfs de cada paciente, guardado de ventanas temporales y csv de direcciones\n",
    "\n",
    "    Inputs:\n",
    "        -path\n",
    "    Output:\n",
    "        -dir_csv: csv con todos los datos de guardado de las ventanas de cada edf.    \n",
    "    \n",
    "    '''\n",
    "    folders = [\"sane\", \"abnormal\"]\n",
    "    LEN_PAT = 7\n",
    "    loc_df = pd.DataFrame(columns= [\"Patient\",\"N_Win\", \"Dir\"], )\n",
    "    save_dir = os.path.join(save_dir, mode)\n",
    "\n",
    "    if save:\n",
    "        if not os.path.exists(save_dir):\n",
    "            os.makedirs(save_dir)\n",
    "            print(\"Data directory created :D\")\n",
    "    \n",
    "\n",
    "    patient_path = glob.glob(path + \"/*.edf\" )\n",
    "    for patient in patient_path:\n",
    "\n",
    "        #Para guardar la id en el DF\n",
    "        patient_id = patient[-LEN_PAT:]\n",
    "\n",
    "        if patient_id in SANE:\n",
    "            label = torch.zeros(1)\n",
    "        if patient_id in ABNORMAL:\n",
    "            label = torch.ones(1)\n",
    "        \n",
    "        patient_id = patient_id[:-4]\n",
    "        \n",
    "        folder = folders[int(label)]\n",
    "        final_dir = os.path.join(save_dir, folder)\n",
    "\n",
    "        if save:\n",
    "            if not os.path.exists(final_dir):\n",
    "                os.makedirs(final_dir)\n",
    "\n",
    "        raw = mne.io.read_raw_edf(patient,preload=True)\n",
    "        try:\n",
    "            data = EDFprep(raw,random = False)\n",
    "        except:\n",
    "            print(f\"{patient_id}_failed\")\n",
    "            continue\n",
    "\n",
    "        if mode == \"per_win\":\n",
    "            loc_df = Save_win(data,loc_df,final_dir,patient_id,label, save)\n",
    "        elif mode == \"per_channel\":\n",
    "            loc_df = Save_ch(data,loc_df,final_dir,patient_id,label, save)\n",
    "\n",
    "    if save:\n",
    "        if mode == \"per_channel\":\n",
    "            loc_df.to_csv(\"down_prep_channels.csv\", encoding= \"utf-8\" ,index = False)\n",
    "        elif mode == \"per_win\":\n",
    "            loc_df.to_csv(\"down_prep_windows.csv\", encoding= \"utf-8\", index = False)\n",
    "                \n",
    "    return loc_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data directory created :D\n",
      "Extracting EDF parameters from C:\\Users\\TheSy\\Desktop\\dataverse_files\\h01.edf...\n",
      "EDF file detected\n",
      "Setting channel info structure...\n",
      "Creating raw.info structure...\n",
      "Reading 0 ... 231249  =      0.000 ...   924.996 secs...\n",
      "Filtering raw data in 1 contiguous segment\n",
      "Setting up band-pass filter from 1 - 70 Hz\n",
      "\n",
      "IIR filter parameters\n",
      "---------------------\n",
      "Butterworth bandpass zero-phase (two-pass forward and reverse) non-causal filter:\n",
      "- Filter order 16 (effective, after forward-backward)\n",
      "- Cutoffs at 1.00, 70.00 Hz: -6.02, -6.02 dB\n",
      "\n",
      "EEG channel type selected for re-referencing\n",
      "Applying average reference.\n",
      "Applying a custom ('EEG',) reference.\n",
      "Not setting metadata\n",
      "66 matching events found\n",
      "No baseline correction applied\n",
      "0 projection items activated\n",
      "Using data from preloaded Raw for 66 events and 1001 original time points ...\n",
      "1 bad epochs dropped\n",
      "Dropped 1 epoch: 64\n",
      "Extracting EDF parameters from C:\\Users\\TheSy\\Desktop\\dataverse_files\\h02.edf...\n",
      "EDF file detected\n",
      "Setting channel info structure...\n",
      "Creating raw.info structure...\n",
      "Reading 0 ... 227499  =      0.000 ...   909.996 secs...\n",
      "Filtering raw data in 1 contiguous segment\n",
      "Setting up band-pass filter from 1 - 70 Hz\n",
      "\n",
      "IIR filter parameters\n",
      "---------------------\n",
      "Butterworth bandpass zero-phase (two-pass forward and reverse) non-causal filter:\n",
      "- Filter order 16 (effective, after forward-backward)\n",
      "- Cutoffs at 1.00, 70.00 Hz: -6.02, -6.02 dB\n",
      "\n",
      "EEG channel type selected for re-referencing\n",
      "Applying average reference.\n",
      "Applying a custom ('EEG',) reference.\n",
      "Not setting metadata\n",
      "66 matching events found\n",
      "No baseline correction applied\n",
      "0 projection items activated\n",
      "Using data from preloaded Raw for 66 events and 1001 original time points ...\n",
      "1 bad epochs dropped\n",
      "Dropped 1 epoch: 64\n",
      "Extracting EDF parameters from C:\\Users\\TheSy\\Desktop\\dataverse_files\\h03.edf...\n",
      "EDF file detected\n",
      "Setting channel info structure...\n",
      "Creating raw.info structure...\n",
      "Reading 0 ... 227499  =      0.000 ...   909.996 secs...\n",
      "Filtering raw data in 1 contiguous segment\n",
      "Setting up band-pass filter from 1 - 70 Hz\n",
      "\n",
      "IIR filter parameters\n",
      "---------------------\n",
      "Butterworth bandpass zero-phase (two-pass forward and reverse) non-causal filter:\n",
      "- Filter order 16 (effective, after forward-backward)\n",
      "- Cutoffs at 1.00, 70.00 Hz: -6.02, -6.02 dB\n",
      "\n",
      "EEG channel type selected for re-referencing\n",
      "Applying average reference.\n",
      "Applying a custom ('EEG',) reference.\n",
      "Not setting metadata\n",
      "66 matching events found\n",
      "No baseline correction applied\n",
      "0 projection items activated\n",
      "Using data from preloaded Raw for 66 events and 1001 original time points ...\n",
      "1 bad epochs dropped\n",
      "Dropped 1 epoch: 64\n",
      "Extracting EDF parameters from C:\\Users\\TheSy\\Desktop\\dataverse_files\\h04.edf...\n",
      "EDF file detected\n",
      "Setting channel info structure...\n",
      "Creating raw.info structure...\n",
      "Reading 0 ... 231249  =      0.000 ...   924.996 secs...\n",
      "Filtering raw data in 1 contiguous segment\n",
      "Setting up band-pass filter from 1 - 70 Hz\n",
      "\n",
      "IIR filter parameters\n",
      "---------------------\n",
      "Butterworth bandpass zero-phase (two-pass forward and reverse) non-causal filter:\n",
      "- Filter order 16 (effective, after forward-backward)\n",
      "- Cutoffs at 1.00, 70.00 Hz: -6.02, -6.02 dB\n",
      "\n",
      "EEG channel type selected for re-referencing\n",
      "Applying average reference.\n",
      "Applying a custom ('EEG',) reference.\n",
      "Not setting metadata\n",
      "66 matching events found\n",
      "No baseline correction applied\n",
      "0 projection items activated\n",
      "Using data from preloaded Raw for 66 events and 1001 original time points ...\n",
      "1 bad epochs dropped\n",
      "Dropped 1 epoch: 64\n",
      "Extracting EDF parameters from C:\\Users\\TheSy\\Desktop\\dataverse_files\\h05.edf...\n",
      "EDF file detected\n",
      "Setting channel info structure...\n",
      "Creating raw.info structure...\n",
      "Reading 0 ... 236249  =      0.000 ...   944.996 secs...\n",
      "Filtering raw data in 1 contiguous segment\n",
      "Setting up band-pass filter from 1 - 70 Hz\n",
      "\n",
      "IIR filter parameters\n",
      "---------------------\n",
      "Butterworth bandpass zero-phase (two-pass forward and reverse) non-causal filter:\n",
      "- Filter order 16 (effective, after forward-backward)\n",
      "- Cutoffs at 1.00, 70.00 Hz: -6.02, -6.02 dB\n",
      "\n",
      "EEG channel type selected for re-referencing\n",
      "Applying average reference.\n",
      "Applying a custom ('EEG',) reference.\n",
      "Not setting metadata\n",
      "66 matching events found\n",
      "No baseline correction applied\n",
      "0 projection items activated\n",
      "Using data from preloaded Raw for 66 events and 1001 original time points ...\n",
      "1 bad epochs dropped\n",
      "Dropped 1 epoch: 64\n",
      "Extracting EDF parameters from C:\\Users\\TheSy\\Desktop\\dataverse_files\\h06.edf...\n",
      "EDF file detected\n",
      "Setting channel info structure...\n",
      "Creating raw.info structure...\n",
      "Reading 0 ... 232499  =      0.000 ...   929.996 secs...\n",
      "Filtering raw data in 1 contiguous segment\n",
      "Setting up band-pass filter from 1 - 70 Hz\n",
      "\n",
      "IIR filter parameters\n",
      "---------------------\n",
      "Butterworth bandpass zero-phase (two-pass forward and reverse) non-causal filter:\n",
      "- Filter order 16 (effective, after forward-backward)\n",
      "- Cutoffs at 1.00, 70.00 Hz: -6.02, -6.02 dB\n",
      "\n",
      "EEG channel type selected for re-referencing\n",
      "Applying average reference.\n",
      "Applying a custom ('EEG',) reference.\n",
      "Not setting metadata\n",
      "66 matching events found\n",
      "No baseline correction applied\n",
      "0 projection items activated\n",
      "Using data from preloaded Raw for 66 events and 1001 original time points ...\n",
      "1 bad epochs dropped\n",
      "Dropped 1 epoch: 64\n",
      "Extracting EDF parameters from C:\\Users\\TheSy\\Desktop\\dataverse_files\\h07.edf...\n",
      "EDF file detected\n",
      "Setting channel info structure...\n",
      "Creating raw.info structure...\n",
      "Reading 0 ... 227499  =      0.000 ...   909.996 secs...\n",
      "Filtering raw data in 1 contiguous segment\n",
      "Setting up band-pass filter from 1 - 70 Hz\n",
      "\n",
      "IIR filter parameters\n",
      "---------------------\n",
      "Butterworth bandpass zero-phase (two-pass forward and reverse) non-causal filter:\n",
      "- Filter order 16 (effective, after forward-backward)\n",
      "- Cutoffs at 1.00, 70.00 Hz: -6.02, -6.02 dB\n",
      "\n",
      "EEG channel type selected for re-referencing\n",
      "Applying average reference.\n",
      "Applying a custom ('EEG',) reference.\n",
      "Not setting metadata\n",
      "66 matching events found\n",
      "No baseline correction applied\n",
      "0 projection items activated\n",
      "Using data from preloaded Raw for 66 events and 1001 original time points ...\n",
      "1 bad epochs dropped\n",
      "Dropped 1 epoch: 64\n",
      "Extracting EDF parameters from C:\\Users\\TheSy\\Desktop\\dataverse_files\\h08.edf...\n",
      "EDF file detected\n",
      "Setting channel info structure...\n",
      "Creating raw.info structure...\n",
      "Reading 0 ... 227499  =      0.000 ...   909.996 secs...\n",
      "Filtering raw data in 1 contiguous segment\n",
      "Setting up band-pass filter from 1 - 70 Hz\n",
      "\n",
      "IIR filter parameters\n",
      "---------------------\n",
      "Butterworth bandpass zero-phase (two-pass forward and reverse) non-causal filter:\n",
      "- Filter order 16 (effective, after forward-backward)\n",
      "- Cutoffs at 1.00, 70.00 Hz: -6.02, -6.02 dB\n",
      "\n",
      "EEG channel type selected for re-referencing\n",
      "Applying average reference.\n",
      "Applying a custom ('EEG',) reference.\n",
      "Not setting metadata\n",
      "66 matching events found\n",
      "No baseline correction applied\n",
      "0 projection items activated\n",
      "Using data from preloaded Raw for 66 events and 1001 original time points ...\n",
      "1 bad epochs dropped\n",
      "Dropped 1 epoch: 64\n",
      "Extracting EDF parameters from C:\\Users\\TheSy\\Desktop\\dataverse_files\\h09.edf...\n",
      "EDF file detected\n",
      "Setting channel info structure...\n",
      "Creating raw.info structure...\n",
      "Reading 0 ... 226249  =      0.000 ...   904.996 secs...\n",
      "Filtering raw data in 1 contiguous segment\n",
      "Setting up band-pass filter from 1 - 70 Hz\n",
      "\n",
      "IIR filter parameters\n",
      "---------------------\n",
      "Butterworth bandpass zero-phase (two-pass forward and reverse) non-causal filter:\n",
      "- Filter order 16 (effective, after forward-backward)\n",
      "- Cutoffs at 1.00, 70.00 Hz: -6.02, -6.02 dB\n",
      "\n",
      "EEG channel type selected for re-referencing\n",
      "Applying average reference.\n",
      "Applying a custom ('EEG',) reference.\n",
      "Not setting metadata\n",
      "66 matching events found\n",
      "No baseline correction applied\n",
      "0 projection items activated\n",
      "Using data from preloaded Raw for 66 events and 1001 original time points ...\n",
      "1 bad epochs dropped\n",
      "Dropped 1 epoch: 64\n",
      "Extracting EDF parameters from C:\\Users\\TheSy\\Desktop\\dataverse_files\\h10.edf...\n",
      "EDF file detected\n",
      "Setting channel info structure...\n",
      "Creating raw.info structure...\n",
      "Reading 0 ... 278749  =      0.000 ...  1114.996 secs...\n",
      "Filtering raw data in 1 contiguous segment\n",
      "Setting up band-pass filter from 1 - 70 Hz\n",
      "\n",
      "IIR filter parameters\n",
      "---------------------\n",
      "Butterworth bandpass zero-phase (two-pass forward and reverse) non-causal filter:\n",
      "- Filter order 16 (effective, after forward-backward)\n",
      "- Cutoffs at 1.00, 70.00 Hz: -6.02, -6.02 dB\n",
      "\n",
      "EEG channel type selected for re-referencing\n",
      "Applying average reference.\n",
      "Applying a custom ('EEG',) reference.\n",
      "Not setting metadata\n",
      "66 matching events found\n",
      "No baseline correction applied\n",
      "0 projection items activated\n",
      "Using data from preloaded Raw for 66 events and 1001 original time points ...\n",
      "1 bad epochs dropped\n",
      "Dropped 1 epoch: 64\n",
      "Extracting EDF parameters from C:\\Users\\TheSy\\Desktop\\dataverse_files\\h11.edf...\n",
      "EDF file detected\n",
      "Setting channel info structure...\n",
      "Creating raw.info structure...\n",
      "Reading 0 ... 228749  =      0.000 ...   914.996 secs...\n",
      "Filtering raw data in 1 contiguous segment\n",
      "Setting up band-pass filter from 1 - 70 Hz\n",
      "\n",
      "IIR filter parameters\n",
      "---------------------\n",
      "Butterworth bandpass zero-phase (two-pass forward and reverse) non-causal filter:\n",
      "- Filter order 16 (effective, after forward-backward)\n",
      "- Cutoffs at 1.00, 70.00 Hz: -6.02, -6.02 dB\n",
      "\n",
      "EEG channel type selected for re-referencing\n",
      "Applying average reference.\n",
      "Applying a custom ('EEG',) reference.\n",
      "Not setting metadata\n",
      "66 matching events found\n",
      "No baseline correction applied\n",
      "0 projection items activated\n",
      "Using data from preloaded Raw for 66 events and 1001 original time points ...\n",
      "1 bad epochs dropped\n",
      "Dropped 1 epoch: 64\n",
      "Extracting EDF parameters from C:\\Users\\TheSy\\Desktop\\dataverse_files\\h12.edf...\n",
      "EDF file detected\n",
      "Setting channel info structure...\n",
      "Creating raw.info structure...\n",
      "Reading 0 ... 224999  =      0.000 ...   899.996 secs...\n",
      "Filtering raw data in 1 contiguous segment\n",
      "Setting up band-pass filter from 1 - 70 Hz\n",
      "\n",
      "IIR filter parameters\n",
      "---------------------\n",
      "Butterworth bandpass zero-phase (two-pass forward and reverse) non-causal filter:\n",
      "- Filter order 16 (effective, after forward-backward)\n",
      "- Cutoffs at 1.00, 70.00 Hz: -6.02, -6.02 dB\n",
      "\n",
      "EEG channel type selected for re-referencing\n",
      "Applying average reference.\n",
      "Applying a custom ('EEG',) reference.\n",
      "Not setting metadata\n",
      "66 matching events found\n",
      "No baseline correction applied\n",
      "0 projection items activated\n",
      "Using data from preloaded Raw for 66 events and 1001 original time points ...\n",
      "1 bad epochs dropped\n",
      "Dropped 1 epoch: 64\n",
      "Extracting EDF parameters from C:\\Users\\TheSy\\Desktop\\dataverse_files\\h13.edf...\n",
      "EDF file detected\n",
      "Setting channel info structure...\n",
      "Creating raw.info structure...\n",
      "Reading 0 ... 241249  =      0.000 ...   964.996 secs...\n",
      "Filtering raw data in 1 contiguous segment\n",
      "Setting up band-pass filter from 1 - 70 Hz\n",
      "\n",
      "IIR filter parameters\n",
      "---------------------\n",
      "Butterworth bandpass zero-phase (two-pass forward and reverse) non-causal filter:\n",
      "- Filter order 16 (effective, after forward-backward)\n",
      "- Cutoffs at 1.00, 70.00 Hz: -6.02, -6.02 dB\n",
      "\n",
      "EEG channel type selected for re-referencing\n",
      "Applying average reference.\n",
      "Applying a custom ('EEG',) reference.\n",
      "Not setting metadata\n",
      "66 matching events found\n",
      "No baseline correction applied\n",
      "0 projection items activated\n",
      "Using data from preloaded Raw for 66 events and 1001 original time points ...\n",
      "1 bad epochs dropped\n",
      "Dropped 1 epoch: 64\n",
      "Extracting EDF parameters from C:\\Users\\TheSy\\Desktop\\dataverse_files\\h14.edf...\n",
      "EDF file detected\n",
      "Setting channel info structure...\n",
      "Creating raw.info structure...\n",
      "Reading 0 ... 216249  =      0.000 ...   864.996 secs...\n",
      "Filtering raw data in 1 contiguous segment\n",
      "Setting up band-pass filter from 1 - 70 Hz\n",
      "\n",
      "IIR filter parameters\n",
      "---------------------\n",
      "Butterworth bandpass zero-phase (two-pass forward and reverse) non-causal filter:\n",
      "- Filter order 16 (effective, after forward-backward)\n",
      "- Cutoffs at 1.00, 70.00 Hz: -6.02, -6.02 dB\n",
      "\n",
      "EEG channel type selected for re-referencing\n",
      "Applying average reference.\n",
      "Applying a custom ('EEG',) reference.\n",
      "Not setting metadata\n",
      "66 matching events found\n",
      "No baseline correction applied\n",
      "0 projection items activated\n",
      "Using data from preloaded Raw for 66 events and 1001 original time points ...\n",
      "1 bad epochs dropped\n",
      "Dropped 1 epoch: 64\n",
      "Extracting EDF parameters from C:\\Users\\TheSy\\Desktop\\dataverse_files\\s01.edf...\n",
      "EDF file detected\n",
      "Setting channel info structure...\n",
      "Creating raw.info structure...\n",
      "Reading 0 ... 211249  =      0.000 ...   844.996 secs...\n",
      "Filtering raw data in 1 contiguous segment\n",
      "Setting up band-pass filter from 1 - 70 Hz\n",
      "\n",
      "IIR filter parameters\n",
      "---------------------\n",
      "Butterworth bandpass zero-phase (two-pass forward and reverse) non-causal filter:\n",
      "- Filter order 16 (effective, after forward-backward)\n",
      "- Cutoffs at 1.00, 70.00 Hz: -6.02, -6.02 dB\n",
      "\n",
      "EEG channel type selected for re-referencing\n",
      "Applying average reference.\n",
      "Applying a custom ('EEG',) reference.\n",
      "Not setting metadata\n",
      "66 matching events found\n",
      "No baseline correction applied\n",
      "0 projection items activated\n",
      "Using data from preloaded Raw for 66 events and 1001 original time points ...\n",
      "1 bad epochs dropped\n",
      "Dropped 1 epoch: 64\n",
      "Extracting EDF parameters from C:\\Users\\TheSy\\Desktop\\dataverse_files\\s02.edf...\n",
      "EDF file detected\n",
      "Setting channel info structure...\n",
      "Creating raw.info structure...\n",
      "Reading 0 ... 286249  =      0.000 ...  1144.996 secs...\n",
      "Filtering raw data in 1 contiguous segment\n",
      "Setting up band-pass filter from 1 - 70 Hz\n",
      "\n",
      "IIR filter parameters\n",
      "---------------------\n",
      "Butterworth bandpass zero-phase (two-pass forward and reverse) non-causal filter:\n",
      "- Filter order 16 (effective, after forward-backward)\n",
      "- Cutoffs at 1.00, 70.00 Hz: -6.02, -6.02 dB\n",
      "\n",
      "EEG channel type selected for re-referencing\n",
      "Applying average reference.\n",
      "Applying a custom ('EEG',) reference.\n",
      "Not setting metadata\n",
      "66 matching events found\n",
      "No baseline correction applied\n",
      "0 projection items activated\n",
      "Using data from preloaded Raw for 66 events and 1001 original time points ...\n",
      "1 bad epochs dropped\n",
      "Dropped 1 epoch: 64\n",
      "Extracting EDF parameters from C:\\Users\\TheSy\\Desktop\\dataverse_files\\s03.edf...\n",
      "EDF file detected\n",
      "Setting channel info structure...\n",
      "Creating raw.info structure...\n",
      "Reading 0 ... 240999  =      0.000 ...   963.996 secs...\n",
      "Filtering raw data in 1 contiguous segment\n",
      "Setting up band-pass filter from 1 - 70 Hz\n",
      "\n",
      "IIR filter parameters\n",
      "---------------------\n",
      "Butterworth bandpass zero-phase (two-pass forward and reverse) non-causal filter:\n",
      "- Filter order 16 (effective, after forward-backward)\n",
      "- Cutoffs at 1.00, 70.00 Hz: -6.02, -6.02 dB\n",
      "\n",
      "EEG channel type selected for re-referencing\n",
      "Applying average reference.\n",
      "Applying a custom ('EEG',) reference.\n",
      "Not setting metadata\n",
      "66 matching events found\n",
      "No baseline correction applied\n",
      "0 projection items activated\n",
      "Using data from preloaded Raw for 66 events and 1001 original time points ...\n",
      "1 bad epochs dropped\n",
      "Dropped 1 epoch: 64\n",
      "Extracting EDF parameters from C:\\Users\\TheSy\\Desktop\\dataverse_files\\s04.edf...\n",
      "EDF file detected\n",
      "Setting channel info structure...\n",
      "Creating raw.info structure...\n",
      "Reading 0 ... 301249  =      0.000 ...  1204.996 secs...\n",
      "Filtering raw data in 1 contiguous segment\n",
      "Setting up band-pass filter from 1 - 70 Hz\n",
      "\n",
      "IIR filter parameters\n",
      "---------------------\n",
      "Butterworth bandpass zero-phase (two-pass forward and reverse) non-causal filter:\n",
      "- Filter order 16 (effective, after forward-backward)\n",
      "- Cutoffs at 1.00, 70.00 Hz: -6.02, -6.02 dB\n",
      "\n",
      "EEG channel type selected for re-referencing\n",
      "Applying average reference.\n",
      "Applying a custom ('EEG',) reference.\n",
      "Not setting metadata\n",
      "66 matching events found\n",
      "No baseline correction applied\n",
      "0 projection items activated\n",
      "Using data from preloaded Raw for 66 events and 1001 original time points ...\n",
      "1 bad epochs dropped\n",
      "Dropped 1 epoch: 64\n",
      "Extracting EDF parameters from C:\\Users\\TheSy\\Desktop\\dataverse_files\\s05.edf...\n",
      "EDF file detected\n",
      "Setting channel info structure...\n",
      "Creating raw.info structure...\n",
      "Reading 0 ... 222499  =      0.000 ...   889.996 secs...\n",
      "Filtering raw data in 1 contiguous segment\n",
      "Setting up band-pass filter from 1 - 70 Hz\n",
      "\n",
      "IIR filter parameters\n",
      "---------------------\n",
      "Butterworth bandpass zero-phase (two-pass forward and reverse) non-causal filter:\n",
      "- Filter order 16 (effective, after forward-backward)\n",
      "- Cutoffs at 1.00, 70.00 Hz: -6.02, -6.02 dB\n",
      "\n",
      "EEG channel type selected for re-referencing\n",
      "Applying average reference.\n",
      "Applying a custom ('EEG',) reference.\n",
      "Not setting metadata\n",
      "66 matching events found\n",
      "No baseline correction applied\n",
      "0 projection items activated\n",
      "Using data from preloaded Raw for 66 events and 1001 original time points ...\n",
      "1 bad epochs dropped\n",
      "Dropped 1 epoch: 64\n",
      "Extracting EDF parameters from C:\\Users\\TheSy\\Desktop\\dataverse_files\\s06.edf...\n",
      "EDF file detected\n",
      "Setting channel info structure...\n",
      "Creating raw.info structure...\n",
      "Reading 0 ... 184999  =      0.000 ...   739.996 secs...\n",
      "Filtering raw data in 1 contiguous segment\n",
      "Setting up band-pass filter from 1 - 70 Hz\n",
      "\n",
      "IIR filter parameters\n",
      "---------------------\n",
      "Butterworth bandpass zero-phase (two-pass forward and reverse) non-causal filter:\n",
      "- Filter order 16 (effective, after forward-backward)\n",
      "- Cutoffs at 1.00, 70.00 Hz: -6.02, -6.02 dB\n",
      "\n",
      "EEG channel type selected for re-referencing\n",
      "Applying average reference.\n",
      "Applying a custom ('EEG',) reference.\n",
      "Not setting metadata\n",
      "66 matching events found\n",
      "No baseline correction applied\n",
      "0 projection items activated\n",
      "Using data from preloaded Raw for 66 events and 1001 original time points ...\n",
      "1 bad epochs dropped\n",
      "Dropped 1 epoch: 64\n",
      "Extracting EDF parameters from C:\\Users\\TheSy\\Desktop\\dataverse_files\\s07.edf...\n",
      "EDF file detected\n",
      "Setting channel info structure...\n",
      "Creating raw.info structure...\n",
      "Reading 0 ... 336499  =      0.000 ...  1345.996 secs...\n",
      "Filtering raw data in 1 contiguous segment\n",
      "Setting up band-pass filter from 1 - 70 Hz\n",
      "\n",
      "IIR filter parameters\n",
      "---------------------\n",
      "Butterworth bandpass zero-phase (two-pass forward and reverse) non-causal filter:\n",
      "- Filter order 16 (effective, after forward-backward)\n",
      "- Cutoffs at 1.00, 70.00 Hz: -6.02, -6.02 dB\n",
      "\n",
      "EEG channel type selected for re-referencing\n",
      "Applying average reference.\n",
      "Applying a custom ('EEG',) reference.\n",
      "Not setting metadata\n",
      "66 matching events found\n",
      "No baseline correction applied\n",
      "0 projection items activated\n",
      "Using data from preloaded Raw for 66 events and 1001 original time points ...\n",
      "1 bad epochs dropped\n",
      "Dropped 1 epoch: 64\n",
      "Extracting EDF parameters from C:\\Users\\TheSy\\Desktop\\dataverse_files\\s08.edf...\n",
      "EDF file detected\n",
      "Setting channel info structure...\n",
      "Creating raw.info structure...\n",
      "Reading 0 ... 227749  =      0.000 ...   910.996 secs...\n",
      "Filtering raw data in 1 contiguous segment\n",
      "Setting up band-pass filter from 1 - 70 Hz\n",
      "\n",
      "IIR filter parameters\n",
      "---------------------\n",
      "Butterworth bandpass zero-phase (two-pass forward and reverse) non-causal filter:\n",
      "- Filter order 16 (effective, after forward-backward)\n",
      "- Cutoffs at 1.00, 70.00 Hz: -6.02, -6.02 dB\n",
      "\n",
      "EEG channel type selected for re-referencing\n",
      "Applying average reference.\n",
      "Applying a custom ('EEG',) reference.\n",
      "Not setting metadata\n",
      "66 matching events found\n",
      "No baseline correction applied\n",
      "0 projection items activated\n",
      "Using data from preloaded Raw for 66 events and 1001 original time points ...\n",
      "1 bad epochs dropped\n",
      "Dropped 1 epoch: 64\n",
      "Extracting EDF parameters from C:\\Users\\TheSy\\Desktop\\dataverse_files\\s09.edf...\n",
      "EDF file detected\n",
      "Setting channel info structure...\n",
      "Creating raw.info structure...\n",
      "Reading 0 ... 296249  =      0.000 ...  1184.996 secs...\n",
      "Filtering raw data in 1 contiguous segment\n",
      "Setting up band-pass filter from 1 - 70 Hz\n",
      "\n",
      "IIR filter parameters\n",
      "---------------------\n",
      "Butterworth bandpass zero-phase (two-pass forward and reverse) non-causal filter:\n",
      "- Filter order 16 (effective, after forward-backward)\n",
      "- Cutoffs at 1.00, 70.00 Hz: -6.02, -6.02 dB\n",
      "\n",
      "EEG channel type selected for re-referencing\n",
      "Applying average reference.\n",
      "Applying a custom ('EEG',) reference.\n",
      "Not setting metadata\n",
      "66 matching events found\n",
      "No baseline correction applied\n",
      "0 projection items activated\n",
      "Using data from preloaded Raw for 66 events and 1001 original time points ...\n",
      "1 bad epochs dropped\n",
      "Dropped 1 epoch: 64\n",
      "Extracting EDF parameters from C:\\Users\\TheSy\\Desktop\\dataverse_files\\s10.edf...\n",
      "EDF file detected\n",
      "Setting channel info structure...\n",
      "Creating raw.info structure...\n",
      "Reading 0 ... 212499  =      0.000 ...   849.996 secs...\n",
      "Filtering raw data in 1 contiguous segment\n",
      "Setting up band-pass filter from 1 - 70 Hz\n",
      "\n",
      "IIR filter parameters\n",
      "---------------------\n",
      "Butterworth bandpass zero-phase (two-pass forward and reverse) non-causal filter:\n",
      "- Filter order 16 (effective, after forward-backward)\n",
      "- Cutoffs at 1.00, 70.00 Hz: -6.02, -6.02 dB\n",
      "\n",
      "EEG channel type selected for re-referencing\n",
      "Applying average reference.\n",
      "Applying a custom ('EEG',) reference.\n",
      "Not setting metadata\n",
      "66 matching events found\n",
      "No baseline correction applied\n",
      "0 projection items activated\n",
      "Using data from preloaded Raw for 66 events and 1001 original time points ...\n",
      "1 bad epochs dropped\n",
      "Dropped 1 epoch: 64\n",
      "Extracting EDF parameters from C:\\Users\\TheSy\\Desktop\\dataverse_files\\s11.edf...\n",
      "EDF file detected\n",
      "Setting channel info structure...\n",
      "Creating raw.info structure...\n",
      "Reading 0 ... 339999  =      0.000 ...  1359.996 secs...\n",
      "Filtering raw data in 1 contiguous segment\n",
      "Setting up band-pass filter from 1 - 70 Hz\n",
      "\n",
      "IIR filter parameters\n",
      "---------------------\n",
      "Butterworth bandpass zero-phase (two-pass forward and reverse) non-causal filter:\n",
      "- Filter order 16 (effective, after forward-backward)\n",
      "- Cutoffs at 1.00, 70.00 Hz: -6.02, -6.02 dB\n",
      "\n",
      "EEG channel type selected for re-referencing\n",
      "Applying average reference.\n",
      "Applying a custom ('EEG',) reference.\n",
      "Not setting metadata\n",
      "66 matching events found\n",
      "No baseline correction applied\n",
      "0 projection items activated\n",
      "Using data from preloaded Raw for 66 events and 1001 original time points ...\n",
      "1 bad epochs dropped\n",
      "Dropped 1 epoch: 64\n",
      "Extracting EDF parameters from C:\\Users\\TheSy\\Desktop\\dataverse_files\\s12.edf...\n",
      "EDF file detected\n",
      "Setting channel info structure...\n",
      "Creating raw.info structure...\n",
      "Reading 0 ... 271749  =      0.000 ...  1086.996 secs...\n",
      "Filtering raw data in 1 contiguous segment\n",
      "Setting up band-pass filter from 1 - 70 Hz\n",
      "\n",
      "IIR filter parameters\n",
      "---------------------\n",
      "Butterworth bandpass zero-phase (two-pass forward and reverse) non-causal filter:\n",
      "- Filter order 16 (effective, after forward-backward)\n",
      "- Cutoffs at 1.00, 70.00 Hz: -6.02, -6.02 dB\n",
      "\n",
      "EEG channel type selected for re-referencing\n",
      "Applying average reference.\n",
      "Applying a custom ('EEG',) reference.\n",
      "Not setting metadata\n",
      "66 matching events found\n",
      "No baseline correction applied\n",
      "0 projection items activated\n",
      "Using data from preloaded Raw for 66 events and 1001 original time points ...\n",
      "1 bad epochs dropped\n",
      "Dropped 1 epoch: 64\n",
      "Extracting EDF parameters from C:\\Users\\TheSy\\Desktop\\dataverse_files\\s13.edf...\n",
      "EDF file detected\n",
      "Setting channel info structure...\n",
      "Creating raw.info structure...\n",
      "Reading 0 ... 283749  =      0.000 ...  1134.996 secs...\n",
      "Filtering raw data in 1 contiguous segment\n",
      "Setting up band-pass filter from 1 - 70 Hz\n",
      "\n",
      "IIR filter parameters\n",
      "---------------------\n",
      "Butterworth bandpass zero-phase (two-pass forward and reverse) non-causal filter:\n",
      "- Filter order 16 (effective, after forward-backward)\n",
      "- Cutoffs at 1.00, 70.00 Hz: -6.02, -6.02 dB\n",
      "\n",
      "EEG channel type selected for re-referencing\n",
      "Applying average reference.\n",
      "Applying a custom ('EEG',) reference.\n",
      "Not setting metadata\n",
      "66 matching events found\n",
      "No baseline correction applied\n",
      "0 projection items activated\n",
      "Using data from preloaded Raw for 66 events and 1001 original time points ...\n",
      "1 bad epochs dropped\n",
      "Dropped 1 epoch: 64\n",
      "Extracting EDF parameters from C:\\Users\\TheSy\\Desktop\\dataverse_files\\s14.edf...\n",
      "EDF file detected\n",
      "Setting channel info structure...\n",
      "Creating raw.info structure...\n",
      "Reading 0 ... 542499  =      0.000 ...  2169.996 secs...\n",
      "Filtering raw data in 1 contiguous segment\n",
      "Setting up band-pass filter from 1 - 70 Hz\n",
      "\n",
      "IIR filter parameters\n",
      "---------------------\n",
      "Butterworth bandpass zero-phase (two-pass forward and reverse) non-causal filter:\n",
      "- Filter order 16 (effective, after forward-backward)\n",
      "- Cutoffs at 1.00, 70.00 Hz: -6.02, -6.02 dB\n",
      "\n",
      "EEG channel type selected for re-referencing\n",
      "Applying average reference.\n",
      "Applying a custom ('EEG',) reference.\n",
      "Not setting metadata\n",
      "66 matching events found\n",
      "No baseline correction applied\n",
      "0 projection items activated\n",
      "Using data from preloaded Raw for 66 events and 1001 original time points ...\n",
      "1 bad epochs dropped\n",
      "Dropped 1 epoch: 64\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<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>Patient</th>\n",
       "      <th>N_Win</th>\n",
       "      <th>Dir</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>h01</td>\n",
       "      <td>1</td>\n",
       "      <td>test\\per_win\\sane\\h01_w1.pt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>h01</td>\n",
       "      <td>2</td>\n",
       "      <td>test\\per_win\\sane\\h01_w2.pt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>h01</td>\n",
       "      <td>3</td>\n",
       "      <td>test\\per_win\\sane\\h01_w3.pt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>h01</td>\n",
       "      <td>4</td>\n",
       "      <td>test\\per_win\\sane\\h01_w4.pt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>h01</td>\n",
       "      <td>5</td>\n",
       "      <td>test\\per_win\\sane\\h01_w5.pt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1787</th>\n",
       "      <td>s14</td>\n",
       "      <td>60</td>\n",
       "      <td>test\\per_win\\abnormal\\s14_w60.pt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1788</th>\n",
       "      <td>s14</td>\n",
       "      <td>61</td>\n",
       "      <td>test\\per_win\\abnormal\\s14_w61.pt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1789</th>\n",
       "      <td>s14</td>\n",
       "      <td>62</td>\n",
       "      <td>test\\per_win\\abnormal\\s14_w62.pt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1790</th>\n",
       "      <td>s14</td>\n",
       "      <td>63</td>\n",
       "      <td>test\\per_win\\abnormal\\s14_w63.pt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1791</th>\n",
       "      <td>s14</td>\n",
       "      <td>64</td>\n",
       "      <td>test\\per_win\\abnormal\\s14_w64.pt</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1792 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Patient  N_Win                               Dir\n",
       "0        h01      1       test\\per_win\\sane\\h01_w1.pt\n",
       "1        h01      2       test\\per_win\\sane\\h01_w2.pt\n",
       "2        h01      3       test\\per_win\\sane\\h01_w3.pt\n",
       "3        h01      4       test\\per_win\\sane\\h01_w4.pt\n",
       "4        h01      5       test\\per_win\\sane\\h01_w5.pt\n",
       "...      ...    ...                               ...\n",
       "1787     s14     60  test\\per_win\\abnormal\\s14_w60.pt\n",
       "1788     s14     61  test\\per_win\\abnormal\\s14_w61.pt\n",
       "1789     s14     62  test\\per_win\\abnormal\\s14_w62.pt\n",
       "1790     s14     63  test\\per_win\\abnormal\\s14_w63.pt\n",
       "1791     s14     64  test\\per_win\\abnormal\\s14_w64.pt\n",
       "\n",
       "[1792 rows x 3 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prep(DownPath, True, \"per_win\", \"test\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Datasets "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "SANE_P =[ \"h01\",\n",
    "        \"h02\",\n",
    "        \"h03\",\n",
    "        \"h04\",\n",
    "        \"h05\",\n",
    "        \"h06\",\n",
    "        \"h07\",\n",
    "        \"h08\",\n",
    "        \"h09\",\n",
    "        \"h10\",\n",
    "        \"h11\",\n",
    "        \"h12\",\n",
    "        \"h13\",\n",
    "        \"h14\",\n",
    "        ]\n",
    "\n",
    "ABNORMAL_P = [\"s01\",\n",
    "            \"s02\",\n",
    "            \"s03\",\n",
    "            \"s04\",\n",
    "            \"s05\",\n",
    "            \"s06\",\n",
    "            \"s07\",\n",
    "            \"s08\",\n",
    "            \"s09\",\n",
    "            \"s10\",\n",
    "            \"s11\",\n",
    "            \"s12\",\n",
    "            \"s13\",\n",
    "            \"s14\",\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "class CustomEEGDataset(Dataset):\n",
    "    def __init__(self, csv_file , root_dir , transform = None, ):\n",
    "\n",
    "        try:\n",
    "            self.loc_df = pd.read_csv(os.path.join(root_dir,csv_file)).drop(labels=\"Unnamed: 0\", axis = 1)\n",
    "        except:\n",
    "            self.loc_df = pd.read_csv(os.path.join(root_dir,csv_file))\n",
    "\n",
    "        self.transform = transform\n",
    "        self.root_dir = root_dir\n",
    "\n",
    "    def __len__(self,):\n",
    "        return len(self.loc_df)\n",
    "        \n",
    "    def __getitem__(self, idx):\n",
    "\n",
    "        eeg_file = os.path.join(self.root_dir,\n",
    "                                self.loc_df.iloc[idx, 2])\n",
    "        eeg = torch.load(eeg_file)\n",
    "            \n",
    "        if self.transform is not None:\n",
    "            ch = self.transform(eeg[0])\n",
    "            return ch, eeg[1]\n",
    "\n",
    "        return eeg\n",
    "    \n",
    "class DFSpliter():\n",
    "    def __init__(self, train_size= 0.8, val_size = 0.2, save = False, seed = 69, mode = \"down_ch\") -> None:\n",
    "        self.train_size = train_size\n",
    "        self.val_size = val_size\n",
    "        self.save = save\n",
    "        self.seed = seed\n",
    "        self.mode = mode\n",
    "\n",
    "    def __call__(self, csv_file, root_path):\n",
    "        try:\n",
    "            loc_df = pd.read_csv(os.path.join(root_path,csv_file)).drop(labels=\"Unnamed: 0\", axis = 1)\n",
    "        except:\n",
    "            loc_df = pd.read_csv(os.path.join(root_path,csv_file))\n",
    "        # loc_df = csv_file\n",
    "        patients = loc_df[\"Patient\"].unique()\n",
    "\n",
    "        sanes = [pat for pat in patients if pat in SANE]\n",
    "        abnormals = [pat for pat in patients if pat in ABNORMAL]\n",
    "\n",
    "\n",
    "        np.random.seed(self.seed)\n",
    "\n",
    "        np.random.shuffle(sanes)\n",
    "        np.random.shuffle(abnormals)\n",
    "\n",
    "        s_end_idx = round(len(sanes)*self.train_size)\n",
    "        a_end_idx = round(len(abnormals)*self.train_size)\n",
    "\n",
    "        train_patients = [*sanes[:s_end_idx], *abnormals[:a_end_idx]]\n",
    "        val_patients = [*sanes[s_end_idx:], *abnormals[a_end_idx:]]\n",
    "        \n",
    "        train_df = pd.DataFrame()\n",
    "        for patient in train_patients:\n",
    "            train_df = pd.concat([train_df,loc_df[loc_df[\"Patient\"] == patient]])\n",
    "\n",
    "        val_df = pd.DataFrame()\n",
    "        for patient in val_patients:\n",
    "            val_df = pd.concat([val_df,loc_df[loc_df[\"Patient\"] == patient]])\n",
    "        \n",
    "        val_df.reset_index(inplace=True, drop= True)\n",
    "        train_df.reset_index(inplace=True, drop=True)\n",
    "\n",
    "        if self.save:\n",
    "            train_df.to_csv(f\"{self.mode}_train_feats.csv\", encoding= \"utf-8\", index = False)\n",
    "            val_df.to_csv(f\"{self.mode}_val_feats.csv\", encoding=\"utf-8\", index=False)\n",
    "        print(\"CSVs creados\")\n",
    "        return train_df,val_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "spliter = DFSpliter(save = True, mode= \"down_win\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CSVs creados\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(     Patient  N_Win                               Dir\n",
       " 0        h04      1       test\\per_win\\sane\\h04_w1.pt\n",
       " 1        h04      2       test\\per_win\\sane\\h04_w2.pt\n",
       " 2        h04      3       test\\per_win\\sane\\h04_w3.pt\n",
       " 3        h04      4       test\\per_win\\sane\\h04_w4.pt\n",
       " 4        h04      5       test\\per_win\\sane\\h04_w5.pt\n",
       " ...      ...    ...                               ...\n",
       " 1403     s11     60  test\\per_win\\abnormal\\s11_w60.pt\n",
       " 1404     s11     61  test\\per_win\\abnormal\\s11_w61.pt\n",
       " 1405     s11     62  test\\per_win\\abnormal\\s11_w62.pt\n",
       " 1406     s11     63  test\\per_win\\abnormal\\s11_w63.pt\n",
       " 1407     s11     64  test\\per_win\\abnormal\\s11_w64.pt\n",
       " \n",
       " [1408 rows x 3 columns],\n",
       "     Patient  N_Win                               Dir\n",
       " 0       h10      1       test\\per_win\\sane\\h10_w1.pt\n",
       " 1       h10      2       test\\per_win\\sane\\h10_w2.pt\n",
       " 2       h10      3       test\\per_win\\sane\\h10_w3.pt\n",
       " 3       h10      4       test\\per_win\\sane\\h10_w4.pt\n",
       " 4       h10      5       test\\per_win\\sane\\h10_w5.pt\n",
       " ..      ...    ...                               ...\n",
       " 379     s09     60  test\\per_win\\abnormal\\s09_w60.pt\n",
       " 380     s09     61  test\\per_win\\abnormal\\s09_w61.pt\n",
       " 381     s09     62  test\\per_win\\abnormal\\s09_w62.pt\n",
       " 382     s09     63  test\\per_win\\abnormal\\s09_w63.pt\n",
       " 383     s09     64  test\\per_win\\abnormal\\s09_w64.pt\n",
       " \n",
       " [384 rows x 3 columns])"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "spliter(\"down_prep_windows.csv\",\".\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "xd = [*SANE, *ABNORMAL]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Proyecciones"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "import umap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "reducer = umap.UMAP(n_neighbors= 200, n_components= 2, min_dist= 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_dataset = CustomEEGDataset(\"down_win_train_feats.csv\", root_dir = \"\",)\n",
    "trainloader = DataLoader(train_dataset, batch_size = 1000, shuffle = True, num_workers = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "params = [\"params\\\\multis\\\\infonce\\\\attempt2\\\\bestEncoderParamsmulti_inf.pt\",\n",
    "          \"params\\\\multis\\\\VR\\\\attempt2\\\\bestEncoderParamsmulti_VR.pt\"]\n",
    "names = [\"InfoNCE\",\n",
    "         \"VICReg\"]\n",
    "n_model = 0\n",
    "\n",
    "model = SleepStager(n_chans = 19, pretext = False)\n",
    "enc, fc = torch.load(params[n_model])\n",
    "# enc, fc = torch.load(\"LastDownmulti_inf_17.pt\")\n",
    "# enc, fc= torch.load(\"bestDownParamsmulti_inf.pt\")\n",
    "model.encoder.load_state_dict(enc)\n",
    "\n",
    "for i, batch in enumerate(trainloader):\n",
    "    eeg, label = batch\n",
    "    latent = model.encode(eeg)\n",
    "    space = reducer.fit_transform(latent.detach().numpy())\n",
    "    if i == 0: break\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SleepStager(\n",
       "  (encoder): SleepStagerChambon2018(\n",
       "    (spatial_conv): Conv2d(1, 19, kernel_size=(19, 1), stride=(1, 1))\n",
       "    (feature_extractor): Sequential(\n",
       "      (0): Conv2d(1, 16, kernel_size=(1, 50), stride=(1, 1), padding=(0, 25))\n",
       "      (1): Identity()\n",
       "      (2): ReLU()\n",
       "      (3): MaxPool2d(kernel_size=(1, 13), stride=(1, 13), padding=0, dilation=1, ceil_mode=False)\n",
       "      (4): Conv2d(16, 16, kernel_size=(1, 50), stride=(1, 1), padding=(0, 25))\n",
       "      (5): Identity()\n",
       "      (6): ReLU()\n",
       "      (7): MaxPool2d(kernel_size=(1, 13), stride=(1, 13), padding=0, dilation=1, ceil_mode=False)\n",
       "    )\n",
       "  )\n",
       "  (final): Sequential(\n",
       "    (0): Dropout(p=0.25, inplace=False)\n",
       "    (1): Linear(in_features=1824, out_features=2, bias=True)\n",
       "  )\n",
       ")"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(space[:, 0], space[:, 1],c=label, alpha=0.5, cmap='bwr',)\n",
    "plt.title(f'UMAP de espacio latente 19 ch, loss {names[n_model]}')\n",
    "plt.colorbar()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_dataset = CustomEEGDataset(\"down_ch_train_feats.csv\", root_dir = \"\",)\n",
    "trainloader = DataLoader(train_dataset, batch_size = 1000, shuffle = True, num_workers = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "params = [\"params\\\\singles\\\\infonce\\\\attempt3\\\\bestEncoderParamssingle_inf.pt\",\n",
    "          \"params\\\\singles\\\\VR\\\\attempt3\\\\bestEncoderParamssingle_vr.pt\"]\n",
    "\n",
    "names = [\"InfoNCE\",\n",
    "         \"VICReg\"]\n",
    "n_model = 0\n",
    "\n",
    "model = SleepStager(n_chans = 1, pretext = False)\n",
    "enc, fc = torch.load(params[n_model])\n",
    "model.encoder.load_state_dict(enc)\n",
    "\n",
    "for i, batch in enumerate(trainloader):\n",
    "    eeg, label = batch\n",
    "    latent = model.encode(eeg)\n",
    "    space = reducer.fit_transform(latent.detach().numpy())\n",
    "    if i == 0: break\n",
    "\n",
    "plt.scatter(space[:, 0], space[:, 1],c=label, alpha=0.5, cmap='bwr')\n",
    "plt.title(f'UMAP de espacio latente 1 ch, loss {names[n_model]}')\n",
    "plt.colorbar()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# fig = plt.figure()\n",
    "# ax = fig.add_subplot(projection='3d')\n",
    "\n",
    "# # Rotate the axes and update\n",
    "# for angle in range(0, 360*4 + 1):\n",
    "#     # Normalize the angle to the range [-180, 180] for display\n",
    "#     angle_norm = (angle + 180) % 360 - 180\n",
    "\n",
    "#     # Cycle through a full rotation of elevation, then azimuth, roll, and all\n",
    "#     elev = azim = roll = 0\n",
    "#     if angle <= 360:\n",
    "#         elev = angle_norm\n",
    "#     elif angle <= 360*2:\n",
    "#         azim = angle_norm\n",
    "#     elif angle <= 360*3:\n",
    "#         roll = angle_norm\n",
    "#     else:\n",
    "\n",
    "#         elev = azim = roll = angle_norm\n",
    "#     ax.scatter(space[:, 0], space[:, 1], space[:, 2],c=label, alpha=0.5, cmap='bwr')\n",
    "#     ax.set_title(f'UMAP de espacio latente 1ch, loss {names[n_model]}')\n",
    "#     ax.view_init(elev, azim, roll)\n",
    "#     # plt.colorbar()\n",
    "#     plt.draw()\n",
    "#     plt.pause(.001)"
   ]
  }
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