add data loader ARCHIsoc

data/data_fMRI_ARCHIsoc/load_data.ipynb

+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "349c8bc6-1a18-4fe2-869c-5581df684b1c",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "47021efb-21dc-4ed2-8375-de1355344a16",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "base_dir = './'\n",
+    "\n",
+    "# time series\n",
+    "X_ts = pd.read_hdf(base_dir+'df_ts.hdf')\n",
+    "n_sample, n_ts = X_ts.shape\n",
+    "\n",
+    "# labels\n",
+    "y = np.load(base_dir+'lbl_task.npy')\n",
+    "\n",
+    "# functional connectivity\n",
+    "FC = np.load(base_dir+'FC.npy')\n",
+    "_, N, _ = X_FC.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4dd6acc5-40c8-48e2-81c6-562d518b5859",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print('shape of data frame for time series:', X_ts.shape)\n",
+    "print('shape of numpy array for FC:', FC.shape)\n",
+    "print('shape of labels:', y.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a6bc3926-de0a-4967-9b00-b66a5a64bd9f",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "type(X['dim_0'][0])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ea059511-2df8-447e-a064-ab23a2d4b6f5",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "plt.figure()\n",
+    "for i in range(6):\n",
+    "    plt.plot(X.iloc[0,i])\n",
+    "plt.xlabel('time')\n",
+    "plt.ylabel('a.u.')\n",
+    "plt.title('example trace')\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5c2b55ce-f85a-4800-aeed-e202bc193688",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plt.figure()\n",
+    "plt.imshow(X_FC[0,:,:])\n",
+    "plt.colorbar()\n",
+    "plt.xlabel('region index')\n",
+    "plt.ylabel('region index')\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "37e06d59-48b7-4d8d-8de3-c83641da7896",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# format FC data for scikit learn (sample x feature)\n",
+    "\n",
+    "ind_tri = np.tri(N,N,-1,dtype=bool)\n",
+    "print(ind_tri.sum())\n",
+    "\n",
+    "# X array for scikit learn\n",
+    "X_FC = FC[:,ind_tri]\n",
+    "print(X_FC.shape)"
+   ]
+  }
+ ],
+ "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": "3.11.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}