{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "b7a45b11",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The autoreload extension is already loaded. To reload it, use:\n",
      "  %reload_ext autoreload\n"
     ]
    }
   ],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "d5051d3d",
   "metadata": {},
   "outputs": [],
   "source": [
    "from compute_vector_feature import *\n",
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "56752746",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(75768, 5)\n",
      "131072\n"
     ]
    },
    {
     "ename": "TypeError",
     "evalue": "slice indices must be integers or None or have an __index__ method",
     "output_type": "error",
     "traceback": [
      "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
      "\u001b[31mTypeError\u001b[39m                                 Traceback (most recent call last)",
      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[43]\u001b[39m\u001b[32m, line 12\u001b[39m\n\u001b[32m      9\u001b[39m eegdata = df.to_numpy()\n\u001b[32m     10\u001b[39m \u001b[38;5;28mprint\u001b[39m(eegdata.shape)\n\u001b[32m---> \u001b[39m\u001b[32m12\u001b[39m ret = \u001b[43mcompute_feature_vector\u001b[49m\u001b[43m(\u001b[49m\u001b[43meegdata\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mFs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m     13\u001b[39m ret\n",
      "\u001b[36mFile \u001b[39m\u001b[32m~/workspaces/git.anishalle.me/musi/compute_vector_feature.py:20\u001b[39m, in \u001b[36mcompute_feature_vector\u001b[39m\u001b[34m(eegdata, Fs)\u001b[39m\n\u001b[32m     17\u001b[39m # 1. Compute the PSD\n\u001b[32m     18\u001b[39m winSampleLength, nbCh = eegdata.shape\n\u001b[32m---> \u001b[39m\u001b[32m20\u001b[39m # Apply Hamming window\n\u001b[32m     21\u001b[39m w = np.hamming(winSampleLength)\n\u001b[32m     22\u001b[39m dataWinCentered = eegdata - np.mean(eegdata, axis=0) # Remove offset\n",
      "\u001b[31mTypeError\u001b[39m: slice indices must be integers or None or have an __index__ method"
     ]
    }
   ],
   "source": [
    "# sampling rate\n",
    "Fs = 256\n",
    "temp_file = \"data/hasini/brain_1761337390.csv\"\n",
    "\n",
    "df = pd.read_csv(temp_file)\n",
    "\n",
    "df.drop(columns = 'timestamps',inplace=True)\n",
    "\n",
    "eegdata = df.to_numpy()\n",
    "print(eegdata.shape)\n",
    "\n",
    "ret = compute_feature_vector(eegdata, Fs)\n",
    "ret\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": ".venv",
   "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": "3.12.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}