{

 "cells": [

  {

   "cell_type": "code",

   "execution_count": 5,

   "metadata": {},

   "outputs": [],

   "source": [

    "import numpy as np\n",

    "import scipy\n",

    "import pandas as pd"

   ]

  },

  {

   "cell_type": "code",

   "execution_count": 6,

   "metadata": {},

   "outputs": [],

   "source": [

    "from dataset import load_dataset, load_labels, split_data, format_labels\n",

    "from features import time_series_features, fractal_features, entropy_features, hjorth_features, freq_band_features\n",

    "import variables as v"

   ]

  },

  {

   "cell_type": "markdown",

   "metadata": {},

   "source": [

    "## Variables"

   ]

  },

  {

   "cell_type": "code",

   "execution_count": 7,

   "metadata": {},

   "outputs": [],

   "source": [

    "data_type = \"ica_filtered\"\n",

    "test_type = \"Arithmetic\""

   ]

  },

  {

   "cell_type": "markdown",

   "metadata": {},

   "source": [

    "## Load Data"

   ]

  },

  {

   "cell_type": "code",

   "execution_count": 8,

   "metadata": {},

   "outputs": [],

   "source": [

    "dataset_ = load_dataset(data_type=data_type, test_type=test_type)\n",

    "dataset = split_data(dataset_, v.SFREQ)"

   ]

  },

  {

   "cell_type": "code",

   "execution_count": 9,

   "metadata": {},

   "outputs": [],

   "source": [

    "label_ = load_labels()\n",

    "label = format_labels(label_, test_type=test_type, epochs=dataset.shape[1])"

   ]

  },

  {

   "cell_type": "markdown",

   "metadata": {},

   "source": [

    "## Compute Features"

   ]

  },

  {

   "cell_type": "code",

   "execution_count": 10,

   "metadata": {},

   "outputs": [],

   "source": [

    "# features = time_series_features(dataset)\n",

    "# freq_bands = np.array([1, 4, 8, 12, 30, 50])\n",

    "# features = freq_band_features(dataset, freq_bands)\n",

    "# features = fractal_features(dataset)\n",

    "features = hjorth_features(dataset)\n",

    "# features = entropy_features(dataset)"

   ]

  },

  {

   "cell_type": "markdown",

   "metadata": {},

   "source": [

    "## Split Data"

   ]

  },

  {

   "cell_type": "code",

   "execution_count": 11,

   "metadata": {},

   "outputs": [],

   "source": [

    "stressed_data = features[np.where(label != 0)]\n",

    "non_stressed_data = features[np.where(label == 0)]"

   ]

  },

  {

   "cell_type": "markdown",

   "metadata": {},

   "source": [

    "## Compute t-tests"

   ]

  },

  {

   "cell_type": "code",

   "execution_count": 12,

   "metadata": {},

   "outputs": [],

   "source": [

    "P = np.zeros(features.shape[1])\n",

    "for j, i in enumerate(range(features.shape[1])):\n",

    "    _, p = scipy.stats.ttest_ind(stressed_data[:, i], non_stressed_data[:, i])\n",

    "    P[j] = p"

   ]

  },

  {

   "cell_type": "code",

   "execution_count": 13,

   "metadata": {},

   "outputs": [],

   "source": [

    "pd.set_option('display.max_rows', None)\n",

    "pd.set_option('display.max_columns', None)"

   ]

  },

  {

   "cell_type": "code",

   "execution_count": 14,

   "metadata": {},

   "outputs": [

    {

     "name": "stdout",

     "output_type": "stream",

     "text": [

      "               0\n",

      "0   8.565707e-09\n",

      "1   4.199706e-01\n",

      "2   9.243714e-04\n",

      "3   2.969316e-01\n",

      "4   7.612570e-03\n",

      "5   7.449619e-01\n",

      "6   9.528565e-02\n",

      "7   8.299859e-01\n",

      "8   6.632852e-01\n",

      "9   7.414754e-03\n",

      "10  5.860924e-05\n",

      "11  2.434920e-02\n",

      "12  1.278021e-01\n",

      "13  2.940169e-05\n",

      "14  7.768960e-07\n",

      "15  1.016899e-05\n",

      "16  1.985319e-02\n",

      "17  1.070608e-01\n",

      "18  5.213117e-06\n",

      "19  1.358506e-22\n",

      "20  1.255365e-18\n",

      "21  2.182334e-06\n",

      "22  3.866509e-08\n",

      "23  1.044755e-05\n",

      "24  4.995159e-03\n",

      "25  2.483783e-01\n",

      "26  4.591010e-05\n",

      "27  5.557755e-05\n",

      "28  5.001415e-04\n",

      "29  8.098234e-01\n",

      "30  1.842379e-01\n",

      "31  2.943885e-01\n",

      "32  1.492710e-02\n",

      "33  2.327972e-03\n",

      "34  3.122502e-06\n",

      "35  4.874759e-02\n",

      "36  6.426229e-01\n",

      "37  2.972795e-02\n",

      "38  8.022454e-03\n",

      "39  8.336686e-01\n",

      "40  4.752397e-01\n",

      "41  2.231969e-02\n",

      "42  2.065300e-04\n",

      "43  1.022721e-01\n",

      "44  8.809528e-01\n",

      "45  4.398541e-06\n",

      "46  1.541289e-07\n",

      "47  1.503279e-03\n",

      "48  8.190212e-01\n",

      "49  3.946622e-01\n",

      "50  1.518190e-02\n",

      "51  3.568426e-16\n",

      "52  8.320353e-16\n",

      "53  1.328597e-03\n",

      "54  2.122381e-04\n",

      "55  4.511848e-03\n",

      "56  1.170364e-02\n",

      "57  8.785522e-01\n",

      "58  7.475689e-04\n",

      "59  5.430692e-03\n",

      "60  7.998087e-02\n",

      "61  7.635854e-02\n",

      "62  8.381844e-03\n",

      "63  8.201117e-03\n"

     ]

    }

   ],

   "source": [

    "print(pd.DataFrame(P))"

   ]

  },

  {

   "cell_type": "code",

   "execution_count": null,

   "metadata": {},

   "outputs": [],

   "source": []

  }

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