Milestone0-Preprocessing-FeaturesExtraction.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "4b5d6f92-2dda-4865-85f2-46c15b07b247",
   "metadata": {},
   "source": [
    "# Milestone 0\n",
    "\n",
    "This notebook performs the preprocessing and feature extraction of EEG, EOG y PERCLOS data.\n",
    "\n",
    "Once defined the preprocessing functions, the data from each client are obtained and generates a CSV file with the preprocessed dat from each client."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2ff95bea-78ca-4137-8994-db2d6ddcf7d6",
   "metadata": {},
   "source": [
    "# Pre-processing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "a6163faf-c962-4072-83ad-80278f6909bf",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Defaulting to user installation because normal site-packages is not writeable\n",
      "Requirement already satisfied: mne in c:\\users\\josem\\appdata\\roaming\\python\\python39\\site-packages (1.0.2)\n",
      "Requirement already satisfied: matplotlib in c:\\users\\josem\\appdata\\roaming\\python\\python39\\site-packages (from mne) (3.4.3)\n",
      "Requirement already satisfied: scipy>=1.1.0 in c:\\users\\josem\\appdata\\roaming\\python\\python39\\site-packages (from mne) (1.7.1)\n",
      "Requirement already satisfied: decorator in c:\\users\\josem\\appdata\\roaming\\python\\python39\\site-packages (from mne) (5.1.0)\n",
      "Requirement already satisfied: jinja2 in c:\\users\\josem\\appdata\\roaming\\python\\python39\\site-packages (from mne) (3.0.1)\n",
      "Requirement already satisfied: numpy>=1.15.4 in c:\\users\\josem\\appdata\\roaming\\python\\python39\\site-packages (from mne) (1.21.2)\n",
      "Requirement already satisfied: tqdm in c:\\users\\josem\\appdata\\roaming\\python\\python39\\site-packages (from mne) (4.62.3)\n",
      "Requirement already satisfied: packaging in c:\\users\\josem\\appdata\\roaming\\python\\python39\\site-packages (from mne) (21.0)\n",
      "Requirement already satisfied: pooch>=1.5 in c:\\users\\josem\\appdata\\roaming\\python\\python39\\site-packages (from mne) (1.6.0)\n",
      "Requirement already satisfied: requests>=2.19.0 in c:\\users\\josem\\appdata\\roaming\\python\\python39\\site-packages (from pooch>=1.5->mne) (2.26.0)\n",
      "Requirement already satisfied: appdirs>=1.3.0 in c:\\users\\josem\\appdata\\roaming\\python\\python39\\site-packages (from pooch>=1.5->mne) (1.4.4)\n",
      "Requirement already satisfied: pyparsing>=2.0.2 in c:\\users\\josem\\appdata\\roaming\\python\\python39\\site-packages (from packaging->mne) (2.4.7)\n",
      "Requirement already satisfied: urllib3<1.27,>=1.21.1 in c:\\users\\josem\\appdata\\roaming\\python\\python39\\site-packages (from requests>=2.19.0->pooch>=1.5->mne) (1.26.6)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in c:\\users\\josem\\appdata\\roaming\\python\\python39\\site-packages (from requests>=2.19.0->pooch>=1.5->mne) (2021.5.30)\n",
      "Requirement already satisfied: charset-normalizer~=2.0.0 in c:\\users\\josem\\appdata\\roaming\\python\\python39\\site-packages (from requests>=2.19.0->pooch>=1.5->mne) (2.0.6)\n",
      "Requirement already satisfied: idna<4,>=2.5 in c:\\users\\josem\\appdata\\roaming\\python\\python39\\site-packages (from requests>=2.19.0->pooch>=1.5->mne) (3.2)\n",
      "Requirement already satisfied: MarkupSafe>=2.0 in c:\\users\\josem\\appdata\\roaming\\python\\python39\\site-packages (from jinja2->mne) (2.0.1)\n",
      "Requirement already satisfied: pillow>=6.2.0 in c:\\users\\josem\\appdata\\roaming\\python\\python39\\site-packages (from matplotlib->mne) (8.3.2)\n",
      "Requirement already satisfied: kiwisolver>=1.0.1 in c:\\users\\josem\\appdata\\roaming\\python\\python39\\site-packages (from matplotlib->mne) (1.3.2)\n",
      "Requirement already satisfied: python-dateutil>=2.7 in c:\\users\\josem\\appdata\\roaming\\python\\python39\\site-packages (from matplotlib->mne) (2.8.2)\n",
      "Requirement already satisfied: cycler>=0.10 in c:\\users\\josem\\appdata\\roaming\\python\\python39\\site-packages (from matplotlib->mne) (0.10.0)\n",
      "Requirement already satisfied: six in c:\\users\\josem\\appdata\\roaming\\python\\python39\\site-packages (from cycler>=0.10->matplotlib->mne) (1.15.0)\n",
      "Requirement already satisfied: colorama in c:\\users\\josem\\appdata\\roaming\\python\\python39\\site-packages (from tqdm->mne) (0.4.4)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: You are using pip version 21.2.3; however, version 22.0.4 is available.\n",
      "You should consider upgrading via the 'C:\\Program Files\\Python39\\python.exe -m pip install --upgrade pip' command.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Defaulting to user installation because normal site-packages is not writeable\n",
      "Requirement already satisfied: neurokit2 in c:\\users\\josem\\appdata\\roaming\\python\\python39\\site-packages (0.1.7)\n",
      "Requirement already satisfied: numpy in c:\\users\\josem\\appdata\\roaming\\python\\python39\\site-packages (from neurokit2) (1.21.2)\n",
      "Requirement already satisfied: scipy in c:\\users\\josem\\appdata\\roaming\\python\\python39\\site-packages (from neurokit2) (1.7.1)\n",
      "Requirement already satisfied: pandas in c:\\users\\josem\\appdata\\roaming\\python\\python39\\site-packages (from neurokit2) (1.3.4)\n",
      "Requirement already satisfied: matplotlib in c:\\users\\josem\\appdata\\roaming\\python\\python39\\site-packages (from neurokit2) (3.4.3)\n",
      "Requirement already satisfied: scikit-learn in c:\\users\\josem\\appdata\\roaming\\python\\python39\\site-packages (from neurokit2) (1.0)\n",
      "Requirement already satisfied: kiwisolver>=1.0.1 in c:\\users\\josem\\appdata\\roaming\\python\\python39\\site-packages (from matplotlib->neurokit2) (1.3.2)\n",
      "Requirement already satisfied: cycler>=0.10 in c:\\users\\josem\\appdata\\roaming\\python\\python39\\site-packages (from matplotlib->neurokit2) (0.10.0)\n",
      "Requirement already satisfied: python-dateutil>=2.7 in c:\\users\\josem\\appdata\\roaming\\python\\python39\\site-packages (from matplotlib->neurokit2) (2.8.2)\n",
      "Requirement already satisfied: pillow>=6.2.0 in c:\\users\\josem\\appdata\\roaming\\python\\python39\\site-packages (from matplotlib->neurokit2) (8.3.2)\n",
      "Requirement already satisfied: pyparsing>=2.2.1 in c:\\users\\josem\\appdata\\roaming\\python\\python39\\site-packages (from matplotlib->neurokit2) (2.4.7)\n",
      "Requirement already satisfied: six in c:\\users\\josem\\appdata\\roaming\\python\\python39\\site-packages (from cycler>=0.10->matplotlib->neurokit2) (1.15.0)\n",
      "Requirement already satisfied: pytz>=2017.3 in c:\\users\\josem\\appdata\\roaming\\python\\python39\\site-packages (from pandas->neurokit2) (2021.1)\n",
      "Requirement already satisfied: threadpoolctl>=2.0.0 in c:\\users\\josem\\appdata\\roaming\\python\\python39\\site-packages (from scikit-learn->neurokit2) (3.0.0)\n",
      "Requirement already satisfied: joblib>=0.11 in c:\\users\\josem\\appdata\\roaming\\python\\python39\\site-packages (from scikit-learn->neurokit2) (1.0.1)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: You are using pip version 21.2.3; however, version 22.0.4 is available.\n",
      "You should consider upgrading via the 'C:\\Program Files\\Python39\\python.exe -m pip install --upgrade pip' command.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Defaulting to user installation because normal site-packages is not writeable\n",
      "Requirement already satisfied: pymatreader in c:\\users\\josem\\appdata\\roaming\\python\\python39\\site-packages (0.0.30)\n",
      "Requirement already satisfied: xmltodict in c:\\users\\josem\\appdata\\roaming\\python\\python39\\site-packages (from pymatreader) (0.12.0)\n",
      "Requirement already satisfied: h5py in c:\\users\\josem\\appdata\\roaming\\python\\python39\\site-packages (from pymatreader) (3.1.0)\n",
      "Requirement already satisfied: future in c:\\users\\josem\\appdata\\roaming\\python\\python39\\site-packages (from pymatreader) (0.18.2)\n",
      "Requirement already satisfied: numpy in c:\\users\\josem\\appdata\\roaming\\python\\python39\\site-packages (from pymatreader) (1.21.2)\n",
      "Requirement already satisfied: scipy!=1.7.0 in c:\\users\\josem\\appdata\\roaming\\python\\python39\\site-packages (from pymatreader) (1.7.1)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: You are using pip version 21.2.3; however, version 22.0.4 is available.\n",
      "You should consider upgrading via the 'C:\\Program Files\\Python39\\python.exe -m pip install --upgrade pip' command.\n"
     ]
    }
   ],
   "source": [
    "!pip install mne\n",
    "!pip install neurokit2\n",
    "!pip install pymatreader"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "76e6ca08-5a7d-4a68-8b53-ff13754dad94",
   "metadata": {},
   "outputs": [],
   "source": [
    "import mne\n",
    "\n",
    "from pymatreader import read_mat\n",
    "\n",
    "import neurokit2 as nk\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from scipy import stats"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d5197fb3-f557-4d3b-a201-d0ca1f9eb42b",
   "metadata": {
    "tags": []
   },
   "source": [
    "## PSD"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "373bbd78-5d4d-4dbb-a42c-9108b55ac3d2",
   "metadata": {
    "tags": []
   },
   "source": [
    "EEG features (PSD, DE) from five frequency bands: delta (1-4 Hz), theta (4-8 Hz), alpha (8-14 Hz), beta (14-31 Hz), and gamma (31-50 Hz)\n",
    "\n",
    "i) psd_movingAve: PSD with moving average\n",
    "\n",
    "ii) psd_LDS: PSD with linear dynamic system\n",
    "\n",
    "iii) de_movingAve: DE with moving average\n",
    "\n",
    "iv) de_LDS: DE with linear dynamic system\n",
    "\n",
    "The data format is channel\\*sample_number\\*frequency_bands (17\\*885\\*5). The first 1-5 in the first dimension 'channel' are corresponding to temporal brain areas, and the last 7-17 are corresponding to posterior brain areas."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0d61d2bd-9e9c-4438-b8a6-3a22f2c25b87",
   "metadata": {},
   "source": [
    "**Extracts PSD from each frequency band from the average of each channel.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "9b97e270-8cfa-4bc9-813e-e10856884c1c",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_psd_data(client):\n",
    "    mat_data = read_mat(\"./data/SEED-VIG/EEG_Feature_5Bands/\" + client)\n",
    "    \n",
    "    n_channels = 17\n",
    "    n_samples = 885\n",
    "    n_bands = 5\n",
    "    \n",
    "    # Calculate the average of each band along the 17 channels\n",
    "    PSD = {\n",
    "        \"psd_delta\": np.zeros(n_samples),\n",
    "        \"psd_theta\": np.zeros(n_samples),\n",
    "        \"psd_alpha\": np.zeros(n_samples),\n",
    "        \"psd_beta\": np.zeros(n_samples),\n",
    "        \"psd_gamma\": np.zeros(n_samples)\n",
    "    }\n",
    "\n",
    "    for s in range(n_samples):\n",
    "        avg_psd = np.zeros(5)\n",
    "        for b in range(n_bands):\n",
    "            for c in range(n_channels):\n",
    "                #print(mat_data[\"psd_movingAve\"][c][s][b])\n",
    "                avg_psd[b] += mat_data[\"psd_movingAve\"][c][s][b]\n",
    "\n",
    "        PSD[\"psd_delta\"][s] = avg_psd[0]\n",
    "        PSD[\"psd_theta\"][s] = avg_psd[1]\n",
    "        PSD[\"psd_alpha\"][s] = avg_psd[2]\n",
    "        PSD[\"psd_beta\"][s] = avg_psd[3]\n",
    "        PSD[\"psd_gamma\"][s] = avg_psd[4]\n",
    "    \n",
    "    psd_df = pd.DataFrame.from_dict(PSD)\n",
    "    \n",
    "    return psd_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "157a8750-27ba-41d9-9db6-4fb2f7ceb3b4",
   "metadata": {},
   "source": [
    "## EOG\n",
    "\n",
    "Extract the number of eye blinks and the signal variance for the vertical EOG channel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "77705b7b-0ce7-4a14-ba3a-32e8e2446a4b",
   "metadata": {},
   "outputs": [],
   "source": [
    "def clean_eog(data, sampling_rate=125, method='neurokit'):\n",
    "    return nk.eog_clean(data, sampling_rate=sampling_rate, method=method)\n",
    "\n",
    "def amend_outliers_eog(data):\n",
    "    data = abs(data)\n",
    "    thresh=np.percentile(data, 99.5)\n",
    "\n",
    "    data_o = []\n",
    "    for el in data:\n",
    "        if el > thresh:\n",
    "            el = thresh\n",
    "        \n",
    "        data_o.append(el)\n",
    "    \n",
    "    return data_o\n",
    "\n",
    "def make_epochs(eog, samples_per_epoch=1000):\n",
    "    epochs = []\n",
    "    for e in range(len(eog)//samples_per_epoch):\n",
    "        epochs.append(eog[e*samples_per_epoch:(e+1)*samples_per_epoch])\n",
    "    \n",
    "    return np.array(epochs)\n",
    "\n",
    "def eog_epochs_get_features(eog_epochs, thresh):\n",
    "    eog_blinks = []\n",
    "    eog_var = []\n",
    "    \n",
    "    for e in range(len(eog_epochs)):\n",
    "        data = eog_epochs[e]\n",
    "        \n",
    "        # abs again in case remove_outliers_eog() is not used\n",
    "        blinks = mne.preprocessing.peak_finder(abs(data), thresh=thresh, extrema=1, verbose=False)\n",
    "\n",
    "        eog_blinks.append(len(blinks[0]))\n",
    "        eog_var.append(data.var())\n",
    "    \n",
    "    return np.array(eog_blinks), np.array(eog_var)\n",
    "\n",
    "def get_eog_data(client):\n",
    "    mat_data = read_mat(\"./data/SEED-VIG/Raw_Data/\" + client)\n",
    "\n",
    "    eogv = mat_data['EOG']['eog_v']*1e-6\n",
    "    eogv_clean = clean_eog(eogv)\n",
    "    eogv_clean_o = amend_outliers_eog(eogv_clean)\n",
    "\n",
    "    eog_epochs = make_epochs(eogv_clean_o, 1000)\n",
    "\n",
    "    thresh = np.percentile(eogv_clean_o, 95)\n",
    "    eog_blinks, eog_var = eog_epochs_get_features(eog_epochs, thresh)\n",
    "    \n",
    "    data = {\n",
    "        \"eog_blinks\": eog_blinks,\n",
    "        \"eog_var\": eog_var\n",
    "    }\n",
    "    \n",
    "    eog_df = pd.DataFrame.from_dict(data)\n",
    "    \n",
    "    return eog_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "dda559de-38d8-493c-a4f8-fcf3a777aecf",
   "metadata": {},
   "outputs": [],
   "source": [
    "client = \"4_20151105_noon.mat\"\n",
    "\n",
    "mat_data = read_mat(\"./data/SEED-VIG/Raw_Data/\" + client)\n",
    "\n",
    "eogv = mat_data['EOG']['eog_v']*1e-6\n",
    "eogv_clean = clean_eog(eogv)\n",
    "eogv_clean_o = amend_outliers_eog(eogv_clean)\n",
    "\n",
    "thresh = np.percentile(eogv_clean_o, 95)\n",
    "\n",
    "eog_epochs = make_epochs(eogv_clean_o, 1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "f7055318-302a-4315-8a73-47d6bfe5967e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[4.824, 5.776, 7.536, 11.48, 14.088000000000001, 18.4, 22.968]"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "global_blinks = []\n",
    "\n",
    "for e in range(3):\n",
    "    data = eog_epochs[e]\n",
    "        \n",
    "    # abs again in case remove_outliers_eog() is not used\n",
    "    blinks = mne.preprocessing.peak_finder(abs(data), thresh=thresh, extrema=1, verbose=False)\n",
    "    \n",
    "    global_blinks.append(list(np.array(blinks[0]/125)+(8*e)))\n",
    "    \n",
    "flat_global_blinks = [item for sublist in global_blinks for item in sublist]\n",
    "flat_global_blinks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "450c08ae-2b3f-41bf-9770-9d266c257b1b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "t = np.array(range(0, 3*len(eog_epochs[0])))\n",
    "\n",
    "y1 = np.array(eog_epochs[0])\n",
    "y2 = np.array(eog_epochs[1])\n",
    "y3 = np.array(eog_epochs[2])\n",
    "\n",
    "y = np.concatenate((y1,y2,y3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "3eb92172-777e-47cf-bcfa-d9a89cf8633c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,8))\n",
    "\n",
    "plt.plot(t/125, y, zorder=0, label=\"EOG Vertical\")\n",
    "\n",
    "plt.axhline(thresh, c='r', linestyle='--', zorder=0, alpha=0.5)\n",
    "\n",
    "plt.axvline(8, c='r', alpha=0.5)\n",
    "plt.axvline(16, c='r', alpha=0.5)\n",
    "\n",
    "plt.scatter(flat_global_blinks, np.repeat(thresh, len(flat_global_blinks)), c='r', s=200, linewidths=5, marker='x', zorder=1, label='blink')\n",
    "\n",
    "plt.xlim(0, 24.01)\n",
    "\n",
    "size_label = 26\n",
    "plt.xlabel('Time (s)', fontweight ='bold', labelpad = 10,fontsize = size_label)\n",
    "plt.ylabel('μV', fontweight ='bold', labelpad = 10,fontsize = size_label)\n",
    "\n",
    "size_ticks = 20; plt.xticks([0,8,16,24],fontsize=size_ticks); plt.yticks(fontsize=size_ticks);\n",
    "size_legend = 20; plt.legend(loc='upper right', fontsize = size_legend)\n",
    "plt.savefig(\"./results/EOG_detection_blinks.pdf\", bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7dc107c1-5a93-47cb-8e02-ca8d7b457ed1",
   "metadata": {},
   "source": [
    "## PERCLOS\n",
    "\n",
    "We store the following data:\n",
    "- raw, interval [0 -1]\n",
    "- discretization, False = Awake; True = Drowsy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "6acbf234-5745-41cf-a7a9-2307f357a78f",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_perclos_labels(client):\n",
    "    mat_data = read_mat(\"data/SEED-VIG/perclos_labels/\" + client)\n",
    "\n",
    "    perclos = mat_data[\"perclos\"]\n",
    "    boundary = min(perclos) + (max(perclos)-min(perclos))*0.25\n",
    "    \n",
    "    # TRUE=Drowsy; False=Awake\n",
    "    y = perclos >= boundary\n",
    "    \n",
    "    data = {\n",
    "        \"y_reg\": perclos,\n",
    "        \"y_class\": y\n",
    "    }\n",
    "    \n",
    "    perclos_df = pd.DataFrame.from_dict(data)\n",
    "    \n",
    "    return perclos_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e27a7947-0e18-436f-8b2b-774e697ac10e",
   "metadata": {},
   "source": [
    "## Result\n",
    "\n",
    "We show the resulting dataframe after applying the preprocessing and feature extraction for a client of the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "9593ee81-d67a-4f33-8288-8faeaf307e80",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_client_data(client):\n",
    "    psd = get_psd_data(client)\n",
    "    \n",
    "    eog = get_eog_data(client)\n",
    "    \n",
    "    perclos = get_perclos_labels(client)\n",
    "    \n",
    "    client_data = psd.join(eog.join(perclos))\n",
    "    return client_data\n",
    "\n",
    "def client_remove_psd_outliers(client_data):\n",
    "    z = stats.zscore(client_data[['psd_delta', 'psd_theta', 'psd_alpha', 'psd_beta', 'psd_gamma']])\n",
    "    client_data_o = client_data[(z < 3).all(axis=1)]\n",
    "    \n",
    "    return client_data_o"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "8a6593fb-57bd-4565-b707-6405809f5107",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "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>psd_delta</th>\n",
       "      <th>psd_theta</th>\n",
       "      <th>psd_alpha</th>\n",
       "      <th>psd_beta</th>\n",
       "      <th>psd_gamma</th>\n",
       "      <th>eog_blinks</th>\n",
       "      <th>eog_var</th>\n",
       "      <th>y_reg</th>\n",
       "      <th>y_class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6.999198e+06</td>\n",
       "      <td>1.851912e+06</td>\n",
       "      <td>638946.782370</td>\n",
       "      <td>388136.052353</td>\n",
       "      <td>224085.539611</td>\n",
       "      <td>5</td>\n",
       "      <td>6.400181e-09</td>\n",
       "      <td>0.285217</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.336626e+06</td>\n",
       "      <td>8.455721e+05</td>\n",
       "      <td>691864.523608</td>\n",
       "      <td>302993.216781</td>\n",
       "      <td>160214.061150</td>\n",
       "      <td>4</td>\n",
       "      <td>2.549366e-09</td>\n",
       "      <td>0.318094</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6.012465e+06</td>\n",
       "      <td>1.075922e+06</td>\n",
       "      <td>506950.161982</td>\n",
       "      <td>357392.049073</td>\n",
       "      <td>174394.097226</td>\n",
       "      <td>1</td>\n",
       "      <td>1.580665e-09</td>\n",
       "      <td>0.339400</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8.672925e+06</td>\n",
       "      <td>9.387626e+05</td>\n",
       "      <td>520973.650245</td>\n",
       "      <td>271533.321277</td>\n",
       "      <td>135426.910339</td>\n",
       "      <td>3</td>\n",
       "      <td>2.433784e-09</td>\n",
       "      <td>0.344124</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6.047004e+06</td>\n",
       "      <td>1.253480e+06</td>\n",
       "      <td>394803.384113</td>\n",
       "      <td>305521.950704</td>\n",
       "      <td>167811.157718</td>\n",
       "      <td>4</td>\n",
       "      <td>3.558069e-09</td>\n",
       "      <td>0.356346</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>880</th>\n",
       "      <td>7.379965e+06</td>\n",
       "      <td>1.344927e+06</td>\n",
       "      <td>525646.937208</td>\n",
       "      <td>401727.171304</td>\n",
       "      <td>353890.472318</td>\n",
       "      <td>3</td>\n",
       "      <td>3.266126e-09</td>\n",
       "      <td>0.295166</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>881</th>\n",
       "      <td>5.818732e+06</td>\n",
       "      <td>1.284721e+06</td>\n",
       "      <td>482326.967848</td>\n",
       "      <td>244935.702215</td>\n",
       "      <td>141096.239727</td>\n",
       "      <td>7</td>\n",
       "      <td>8.107354e-09</td>\n",
       "      <td>0.294018</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>882</th>\n",
       "      <td>6.101910e+06</td>\n",
       "      <td>1.336721e+06</td>\n",
       "      <td>496443.033236</td>\n",
       "      <td>251382.589212</td>\n",
       "      <td>161901.070173</td>\n",
       "      <td>7</td>\n",
       "      <td>1.000420e-08</td>\n",
       "      <td>0.292964</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>883</th>\n",
       "      <td>5.760702e+06</td>\n",
       "      <td>1.269146e+06</td>\n",
       "      <td>490467.522595</td>\n",
       "      <td>237636.280501</td>\n",
       "      <td>146994.686433</td>\n",
       "      <td>1</td>\n",
       "      <td>1.572221e-09</td>\n",
       "      <td>0.290991</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>884</th>\n",
       "      <td>5.273172e+06</td>\n",
       "      <td>1.201211e+06</td>\n",
       "      <td>451955.411775</td>\n",
       "      <td>214742.034014</td>\n",
       "      <td>124374.955697</td>\n",
       "      <td>5</td>\n",
       "      <td>6.816812e-09</td>\n",
       "      <td>0.289151</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>827 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        psd_delta     psd_theta      psd_alpha       psd_beta      psd_gamma  \\\n",
       "0    6.999198e+06  1.851912e+06  638946.782370  388136.052353  224085.539611   \n",
       "1    4.336626e+06  8.455721e+05  691864.523608  302993.216781  160214.061150   \n",
       "2    6.012465e+06  1.075922e+06  506950.161982  357392.049073  174394.097226   \n",
       "3    8.672925e+06  9.387626e+05  520973.650245  271533.321277  135426.910339   \n",
       "4    6.047004e+06  1.253480e+06  394803.384113  305521.950704  167811.157718   \n",
       "..            ...           ...            ...            ...            ...   \n",
       "880  7.379965e+06  1.344927e+06  525646.937208  401727.171304  353890.472318   \n",
       "881  5.818732e+06  1.284721e+06  482326.967848  244935.702215  141096.239727   \n",
       "882  6.101910e+06  1.336721e+06  496443.033236  251382.589212  161901.070173   \n",
       "883  5.760702e+06  1.269146e+06  490467.522595  237636.280501  146994.686433   \n",
       "884  5.273172e+06  1.201211e+06  451955.411775  214742.034014  124374.955697   \n",
       "\n",
       "     eog_blinks       eog_var     y_reg  y_class  \n",
       "0             5  6.400181e-09  0.285217    False  \n",
       "1             4  2.549366e-09  0.318094    False  \n",
       "2             1  1.580665e-09  0.339400     True  \n",
       "3             3  2.433784e-09  0.344124     True  \n",
       "4             4  3.558069e-09  0.356346     True  \n",
       "..          ...           ...       ...      ...  \n",
       "880           3  3.266126e-09  0.295166    False  \n",
       "881           7  8.107354e-09  0.294018    False  \n",
       "882           7  1.000420e-08  0.292964    False  \n",
       "883           1  1.572221e-09  0.290991    False  \n",
       "884           5  6.816812e-09  0.289151    False  \n",
       "\n",
       "[827 rows x 9 columns]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "client = \"18_20150926_noon.mat\"\n",
    "client_data = get_client_data(client)\n",
    "\n",
    "client_data_o = client_remove_psd_outliers(client_data)\n",
    "\n",
    "client_data_o"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "fe1a6269-9c9e-493c-afdc-5268baa9390e",
   "metadata": {},
   "outputs": [],
   "source": [
    "client_data_o.to_csv(\"./data/test.csv\", index=False, header=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "089cd3b7-7e9b-4f08-b790-2ced7dad66a1",
   "metadata": {},
   "outputs": [
    {
     "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>psd_delta</th>\n",
       "      <th>psd_theta</th>\n",
       "      <th>psd_alpha</th>\n",
       "      <th>psd_beta</th>\n",
       "      <th>psd_gamma</th>\n",
       "      <th>eog_blinks</th>\n",
       "      <th>eog_var</th>\n",
       "      <th>y_reg</th>\n",
       "      <th>y_class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>13057090.0</td>\n",
       "      <td>1945167.0</td>\n",
       "      <td>835064.0</td>\n",
       "      <td>582382.0</td>\n",
       "      <td>503923.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.037713e-10</td>\n",
       "      <td>0.119233</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>13161134.0</td>\n",
       "      <td>1744527.0</td>\n",
       "      <td>736466.0</td>\n",
       "      <td>525029.0</td>\n",
       "      <td>423829.0</td>\n",
       "      <td>1</td>\n",
       "      <td>6.786951e-09</td>\n",
       "      <td>0.149089</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6110325.0</td>\n",
       "      <td>1538632.0</td>\n",
       "      <td>763933.0</td>\n",
       "      <td>498000.0</td>\n",
       "      <td>454346.0</td>\n",
       "      <td>2</td>\n",
       "      <td>7.330638e-09</td>\n",
       "      <td>0.177563</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6245159.0</td>\n",
       "      <td>1476258.0</td>\n",
       "      <td>642524.0</td>\n",
       "      <td>461092.0</td>\n",
       "      <td>484130.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2.657537e-09</td>\n",
       "      <td>0.194524</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>21925572.0</td>\n",
       "      <td>1996168.0</td>\n",
       "      <td>865699.0</td>\n",
       "      <td>524286.0</td>\n",
       "      <td>469602.0</td>\n",
       "      <td>2</td>\n",
       "      <td>3.379798e-09</td>\n",
       "      <td>0.220294</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>838</th>\n",
       "      <td>291581314.0</td>\n",
       "      <td>12516012.0</td>\n",
       "      <td>3184157.0</td>\n",
       "      <td>248220.0</td>\n",
       "      <td>33413.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.680135e-10</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>839</th>\n",
       "      <td>234869058.0</td>\n",
       "      <td>11851006.0</td>\n",
       "      <td>3141714.0</td>\n",
       "      <td>248768.0</td>\n",
       "      <td>33365.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.110112e-10</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>840</th>\n",
       "      <td>255784531.0</td>\n",
       "      <td>11687640.0</td>\n",
       "      <td>3077573.0</td>\n",
       "      <td>250154.0</td>\n",
       "      <td>33336.0</td>\n",
       "      <td>0</td>\n",
       "      <td>3.495151e-10</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>841</th>\n",
       "      <td>259054942.0</td>\n",
       "      <td>12256428.0</td>\n",
       "      <td>3054228.0</td>\n",
       "      <td>250139.0</td>\n",
       "      <td>33420.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.641842e-10</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>842</th>\n",
       "      <td>255349652.0</td>\n",
       "      <td>12083497.0</td>\n",
       "      <td>3084827.0</td>\n",
       "      <td>257363.0</td>\n",
       "      <td>33592.0</td>\n",
       "      <td>0</td>\n",
       "      <td>4.029828e-10</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>843 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       psd_delta   psd_theta  psd_alpha  psd_beta  psd_gamma  eog_blinks  \\\n",
       "0     13057090.0   1945167.0   835064.0  582382.0   503923.0           0   \n",
       "1     13161134.0   1744527.0   736466.0  525029.0   423829.0           1   \n",
       "2      6110325.0   1538632.0   763933.0  498000.0   454346.0           2   \n",
       "3      6245159.0   1476258.0   642524.0  461092.0   484130.0           2   \n",
       "4     21925572.0   1996168.0   865699.0  524286.0   469602.0           2   \n",
       "..           ...         ...        ...       ...        ...         ...   \n",
       "838  291581314.0  12516012.0  3184157.0  248220.0    33413.0           0   \n",
       "839  234869058.0  11851006.0  3141714.0  248768.0    33365.0           0   \n",
       "840  255784531.0  11687640.0  3077573.0  250154.0    33336.0           0   \n",
       "841  259054942.0  12256428.0  3054228.0  250139.0    33420.0           0   \n",
       "842  255349652.0  12083497.0  3084827.0  257363.0    33592.0           0   \n",
       "\n",
       "          eog_var     y_reg  y_class  \n",
       "0    1.037713e-10  0.119233    False  \n",
       "1    6.786951e-09  0.149089    False  \n",
       "2    7.330638e-09  0.177563    False  \n",
       "3    2.657537e-09  0.194524    False  \n",
       "4    3.379798e-09  0.220294    False  \n",
       "..            ...       ...      ...  \n",
       "838  1.680135e-10  1.000000     True  \n",
       "839  2.110112e-10  1.000000     True  \n",
       "840  3.495151e-10  1.000000     True  \n",
       "841  1.641842e-10  1.000000     True  \n",
       "842  4.029828e-10  1.000000     True  \n",
       "\n",
       "[843 rows x 9 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_csv(\"./data/test.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2d1270fc-451c-453d-911f-da63bb2f2153",
   "metadata": {},
   "source": [
    "# Running script\n",
    "\n",
    "THis code generates the CSV files corresponding to each client, grouped as follows:\n",
    "\n",
    "- Company_1: Clients con **EVEN** IDs\n",
    "- Company_2: Clients with **ODD** IDs\n",
    "\n",
    "If a client has different data files associated, it \"stack\" them in the same CSV."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0ebcaefd-87c1-4125-acf7-5136c3677297",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from os import listdir, path\n",
    "import re\n",
    "\n",
    "for f in listdir(\"./data/SEED-VIG/Raw_Data/\"):\n",
    "    m = re.search(\"(.{1,2})_.*\", f)\n",
    "    if m:\n",
    "        num_client = int(m.group(1))\n",
    "        client = f\n",
    "        \n",
    "        client_data = get_client_data(client)\n",
    "        client_data_o = client_remove_psd_outliers(client_data)\n",
    "        \n",
    "        dest_filename = f\"./data/horizontal/{(num_client%2)+1}/client_{num_client}.csv\"\n",
    "        \n",
    "        # IF the file already exists => subject with two experiments => stack the dataframes to generate a bigger one\n",
    "        if path.exists(dest_filename):\n",
    "            existing_client_data = pd.read_csv(dest_filename)\n",
    "            \n",
    "            client_data_o = pd.concat([existing_client_data, client_data_o])\n",
    "        \n",
    "        client_data_o.to_csv(f\"./data/horizontal/{(num_client%2)+1}/client_{num_client}.csv\", index=False, header=True)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}