Add proportion split

run_sim_different_parcels_equal_groups.ipynb

@@ -0,0 +1,150 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "64cbe8c1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import simulation_tools as sim\n",
+    "import pandas as pd\n",
+    "import pickle"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "e913a747",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def simulation_loop(N_values, conn_df):\n",
+    "    # Loop through the values of N and run simulation with specified parameters\n",
+    "    result_dict = {\"N\": [], \"mean_sensitivity\": [], \"mean_specificity\": []}\n",
+    "    for N in N_values:\n",
+    "        mean_sens, mean_spef = sim.run_multiple_simulation(conn_df, N=N, pi=0.20, \n",
+    "                                                       d=0.3, proportion_split=0.5, q=0.1, num_sample=100)\n",
+    "        #print(f\"Simulation ran for N={N}.\")\n",
+    "        \n",
+    "        # Append results to the dictionary\n",
+    "        result_dict[\"N\"].append(N)\n",
+    "        result_dict[\"mean_sensitivity\"].append(mean_sens)\n",
+    "        result_dict[\"mean_specificity\"].append(mean_spef)\n",
+    "    \n",
+    "    return result_dict"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "da0df0de",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Path to a .csv file with connectomes in upper triangular form\n",
+    "path_conn_20parcels = \"/home/neuromod/ad_sz/data/abide/abide1and2_controls_20parcels.csv\"\n",
+    "path_conn_36parcels = \"/home/neuromod/ad_sz/data/abide/abide1and2_controls_36parcels.csv\"\n",
+    "path_conn_64parcels = \"/home/neuromod/ad_sz/data/abide/abide1and2_controls_64parcels.csv\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "ad76085c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Load control connectomes from ABIDE\n",
+    "conn_20parcels_df = pd.read_csv(path_conn_20parcels)\n",
+    "conn_36parcels_df = pd.read_csv(path_conn_36parcels)\n",
+    "conn_64parcels_df = pd.read_csv(path_conn_64parcels)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "a44f6133",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create a range of N values\n",
+    "N_values = range(300, 901, 50)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "6d54cf99",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "result_20parcels = simulation_loop(N_values, conn_20parcels_df)\n",
+    "result_36parcels = simulation_loop(N_values, conn_36parcels_df)\n",
+    "result_64parcels = simulation_loop(N_values, conn_64parcels_df)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "d08cf2e2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with open('sim_result_20parcels.pkl', 'wb') as f:\n",
+    "    pickle.dump(result_20parcels, f)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "96090de1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with open('sim_result_36parcels.pkl', 'wb') as f:\n",
+    "    pickle.dump(result_36parcels, f)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "fe070bbd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with open('sim_result_64parcels.pkl', 'wb') as f:\n",
+    "    pickle.dump(result_64parcels, f)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4d8ffe20",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "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": "3.10.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}