old musi stuff

main.ipynb

@@ -2,24 +2,77 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "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"
+    "import numpy as np\n",
+    "import pandas as pd"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 43,
    "id": "56752746",
    "metadata": {},
-   "outputs": [],
+   "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"
+    "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"
    ]
   }
  ],